improve error handling

.github/ISSUE_TEMPLATE/bug_report.md

@@ -7,6 +7,8 @@ assignees: ''
 
 ---
 
+_Note: we're aware of some missing content in the output and layout issues on tables. Please refrain from opening new issues on this topic unless if you think it's different from what has already been reported._
+
 **Describe the bug**
 Write a concise description of what the bug is.
 
@@ -17,15 +19,19 @@ If possible, please provide the PDF file causing the issue.
 If you have it, please provide the ID of the job you ran.
 You can find it here: https://cloud.llamaindex.ai/parse in the "History" tab.
 
+**Screenshots**
+Feel free to also provide screenshots if relevant.
+
 **Client:**
 Please remove untested options:
- - Python Library
- - API
  - Frontend (cloud.llamaindex.ai)
+ - Python Library
  - Typescript Library
  - Notebook
+ - API
+
+**Options**
+What options did you use? Multimodal, fast mode, parsing instructions, etc.
 
 **Additional context**
 Add any additional context about the problem here.
-What options did you use? Premium mode, multimodal, fast mode, parsing instructions, etc.
-Screenshots, code snippets, etc.

.github/dependabot.yml

@@ -1,11 +0,0 @@
-# Please see the documentation for all configuration options:
-# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
-# and
-# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
-
-version: 2
-updates:
-  - package-ecosystem: "github-actions"
-    directory: "/"
-    schedule:
-      interval: "weekly"

.github/workflows/build_package.yml

@@ -0,0 +1,48 @@
+name: Build Package
+
+# Build package on its own without additional pip install
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+
+env:
+  POETRY_VERSION: "1.6.1"
+
+jobs:
+  build:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      # You can use PyPy versions in python-version.
+      # For example, pypy-2.7 and pypy-3.8
+      matrix:
+        os: [ubuntu-latest, windows-latest]
+        python-version: ["3.9"]
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install Poetry
+        uses: snok/install-poetry@v1
+        with:
+          version: ${{ env.POETRY_VERSION }}
+      - name: Install deps
+        shell: bash
+        run: poetry install
+      - name: Ensure lock works
+        shell: bash
+        run: poetry lock
+      - name: Build
+        shell: bash
+        run: poetry build
+      - name: Test installing built package
+        shell: bash
+        run: python -m pip install .
+      - name: Test import
+        shell: bash
+        working-directory: ${{ vars.RUNNER_TEMP }}
+        run: python -c "import llama_parse"

.github/workflows/build_package_py.yml

@@ -1,50 +0,0 @@
-name: Build Package - Python
-
-# Build package on its own without additional pip install
-
-on:
-  push:
-    branches:
-      - main
-  pull_request:
-
-env:
-  UV_VERSION: "0.7.20"
-
-jobs:
-  build:
-    runs-on: ${{ matrix.os }}
-    strategy:
-      # You can use PyPy versions in python-version.
-      # For example, pypy-2.7 and pypy-3.8
-      matrix:
-        os: [ubuntu-latest, windows-latest]
-        python-version: ["3.9"]
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v6
-        with:
-          version: ${{ env.UV_VERSION }}
-
-      - name: Set up Python
-        run: uv python install
-
-      - name: Display Python version
-        run: python --version
-
-      - name: Build
-        working-directory: py
-        run: uv build
-
-      - name: Test installing built package
-        shell: bash
-        working-directory: py
-        run: |
-          uv venv
-          uv pip install dist/*.whl
-
-      - name: Test import
-        working-directory: py
-        run: uv run -- python -c "import llama_cloud_services"

.github/workflows/build_package_ts.yml

@@ -1,28 +0,0 @@
-name: Build Package - TypeScript
-on: [pull_request]
-
-jobs:
-  pre_release:
-    name: Pre Release
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout Repo
-        uses: actions/checkout@v4
-
-      - uses: pnpm/action-setup@v4
-        with:
-          version: 10
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version-file: "ts/llama_cloud_services/.nvmrc"
-
-      - name: Install dependencies
-        working-directory: ts/llama_cloud_services/
-        run: pnpm install --no-frozen-lockfile
-
-      - name: Build
-        working-directory: ts/llama_cloud_services/
-        run: pnpm run build

.github/workflows/codeql.yml

@@ -1,3 +1,14 @@
+# For most projects, this workflow file will not need changing; you simply need
+# to commit it to your repository.
+#
+# You may wish to alter this file to override the set of languages analyzed,
+# or to provide custom queries or build logic.
+#
+# ******** NOTE ********
+# We have attempted to detect the languages in your repository. Please check
+# the `language` matrix defined below to confirm you have the correct set of
+# supported CodeQL languages.
+#
 name: "CodeQL"
 
 on:
@@ -17,25 +28,54 @@ jobs:
     #   - https://gh.io/supported-runners-and-hardware-resources
     #   - https://gh.io/using-larger-runners
     # Consider using larger runners for possible analysis time improvements.
-    runs-on: "ubuntu-latest"
-    timeout-minutes: 360
+    runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }}
+    timeout-minutes: ${{ (matrix.language == 'swift' && 120) || 360 }}
     permissions:
       actions: read
       contents: read
       security-events: write
 
+    strategy:
+      fail-fast: false
+      matrix:
+        language: ["python"]
+        # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python', 'ruby', 'swift' ]
+        # Use only 'java' to analyze code written in Java, Kotlin or both
+        # Use only 'javascript' to analyze code written in JavaScript, TypeScript or both
+        # Learn more about CodeQL language support at https://aka.ms/codeql-docs/language-support
+
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
 
       # Initializes the CodeQL tools for scanning.
       - name: Initialize CodeQL
-        uses: github/codeql-action/init@v3
+        uses: github/codeql-action/init@v2
         with:
-          languages: python
-          dependency-caching: true
+          languages: ${{ matrix.language }}
+          # If you wish to specify custom queries, you can do so here or in a config file.
+          # By default, queries listed here will override any specified in a config file.
+          # Prefix the list here with "+" to use these queries and those in the config file.
+
+          # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
+          # queries: security-extended,security-and-quality
+
+      # Autobuild attempts to build any compiled languages (C/C++, C#, Go, Java, or Swift).
+      # If this step fails, then you should remove it and run the build manually (see below)
+      - name: Autobuild
+        uses: github/codeql-action/autobuild@v2
+
+      # ℹ️ Command-line programs to run using the OS shell.
+      # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
+
+      #   If the Autobuild fails above, remove it and uncomment the following three lines.
+      #   modify them (or add more) to build your code if your project, please refer to the EXAMPLE below for guidance.
+
+      # - run: |
+      #     echo "Run, Build Application using script"
+      #     ./location_of_script_within_repo/buildscript.sh
 
       - name: Perform CodeQL Analysis
-        uses: github/codeql-action/analyze@v3
+        uses: github/codeql-action/analyze@v2
         with:
-          category: "/language:python"
+          category: "/language:${{matrix.language}}"

.github/workflows/lint.yml

@@ -0,0 +1,37 @@
+name: Linting
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+
+env:
+  POETRY_VERSION: "1.6.1"
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      # You can use PyPy versions in python-version.
+      # For example, pypy-2.7 and pypy-3.8
+      matrix:
+        python-version: ["3.9"]
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: ${{ github.event_name == 'pull_request' && 2 || 0 }}
+      - name: Set up python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install Poetry
+        uses: snok/install-poetry@v1
+        with:
+          version: ${{ env.POETRY_VERSION }}
+      - name: Install pre-commit
+        shell: bash
+        run: poetry run pip install pre-commit
+      - name: Run linter
+        shell: bash
+        run: poetry run make lint

.github/workflows/lint_py.yml

@@ -1,35 +0,0 @@
-name: Lint - Python
-
-on:
-  push:
-    branches:
-      - main
-  pull_request:
-
-env:
-  UV_VERSION: "0.7.20"
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    strategy:
-      # You can use PyPy versions in python-version.
-      # For example, pypy-2.7 and pypy-3.8
-      matrix:
-        python-version: ["3.9"]
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: ${{ github.event_name == 'pull_request' && 2 || 0 }}
-      - name: Install uv
-        uses: astral-sh/setup-uv@v6
-        with:
-          version: ${{ env.UV_VERSION }}
-
-      - name: Set up Python
-        run: uv python install ${{ matrix.python-version }}
-
-      - name: Run linter
-        shell: bash
-        working-directory: py
-        run: uv run -- pre-commit run -a

.github/workflows/lint_ts.yml

@@ -1,36 +0,0 @@
-name: Lint - TypeScript
-
-on:
-  push:
-    branches:
-      - main
-  pull_request:
-    branches:
-      - main
-
-env:
-  TURBO_TOKEN: ${{ secrets.TURBO_TOKEN }}
-  TURBO_TEAM: ${{ vars.TURBO_TEAM }}
-  TURBO_REMOTE_ONLY: true
-
-jobs:
-  lint:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@v4
-        with:
-          version: 10
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version-file: "ts/llama_cloud_services/.nvmrc"
-      - name: Install dependencies
-        working-directory: ts/llama_cloud_services/
-        run: pnpm install --no-frozen-lockfile
-      - name: Run lint
-        working-directory: ts/llama_cloud_services/
-        run: pnpm run lint
-      - name: Run Prettier
-        working-directory: ts/llama_cloud_services/
-        run: pnpm run format

.github/workflows/publish_release.yml

@@ -0,0 +1,64 @@
+name: Publish llama-parse to PyPI / GitHub
+
+on:
+  push:
+    tags:
+      - "v*"
+
+  workflow_dispatch:
+
+env:
+  POETRY_VERSION: "1.6.1"
+  PYTHON_VERSION: "3.9"
+
+jobs:
+  build-n-publish:
+    name: Build and publish to PyPI
+    if: github.repository == 'run-llama/llama_parse'
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up python ${{ env.PYTHON_VERSION }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ env.PYTHON_VERSION }}
+      - name: Install Poetry
+        uses: snok/install-poetry@v1
+        with:
+          version: ${{ env.POETRY_VERSION }}
+      - name: Install deps
+        shell: bash
+        run: pip install -e .
+      - name: Build and publish to pypi
+        uses: JRubics/poetry-publish@v1.17
+        with:
+          pypi_token: ${{ secrets.LLAMA_PARSE_PYPI_TOKEN }}
+          ignore_dev_requirements: "yes"
+
+      - name: Create GitHub Release
+        id: create_release
+        uses: actions/create-release@v1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # This token is provided by Actions, you do not need to create your own token
+        with:
+          tag_name: ${{ github.ref }}
+          release_name: ${{ github.ref }}
+          draft: false
+          prerelease: false
+
+      - name: Get Asset name
+        run: |
+          export PKG=$(ls dist/ | grep tar)
+          set -- $PKG
+          echo "name=$1" >> $GITHUB_ENV
+      - name: Upload Release Asset (sdist) to GitHub
+        id: upload-release-asset
+        uses: actions/upload-release-asset@v1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        with:
+          upload_url: ${{ steps.create_release.outputs.upload_url }}
+          asset_path: dist/${{ env.name }}
+          asset_name: ${{ env.name }}
+          asset_content_type: application/zip

.github/workflows/release_py.yml

@@ -1,66 +0,0 @@
-name: Publish Release - Python
-
-on:
-  push:
-    tags:
-      - "v*"
-
-  workflow_dispatch:
-
-env:
-  UV_VERSION: "0.7.20"
-
-jobs:
-  build-n-publish:
-    name: Build and publish to PyPI
-    if: github.repository == 'run-llama/llama_cloud_services'
-    runs-on: ubuntu-latest
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v6
-        with:
-          version: ${{ env.UV_VERSION }}
-
-      - name: Set up Python
-        run: uv python install
-
-      - name: Display Python version
-        run: python --version
-
-      - name: Build
-        working-directory: py
-        run: uv build
-
-      - name: Test installing built package
-        shell: bash
-        working-directory: py
-        run: |
-          uv venv
-          uv pip install dist/*.whl
-
-      - name: Publish package
-        shell: bash
-        working-directory: py
-        run: uv publish --token ${{ secrets.LLAMA_PARSE_PYPI_TOKEN }}
-
-      - name: Build and publish llama-parse
-        working-directory: py/llama_parse/
-        run: |
-          uv build
-          uv publish --token ${{ secrets.LLAMA_PARSE_PYPI_TOKEN }}
-
-      - name: Create GitHub Release
-        id: create_release
-        uses: actions/create-release@v1
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # This token is provided by Actions, you do not need to create your own token
-        with:
-          tag_name: ${{ github.ref }}
-          release_name: ${{ github.ref }} - LlamaCloud Services PY
-          artifacts: "py/**/dist/*"
-          generateReleaseNotes: true
-          draft: false
-          prerelease: false

.github/workflows/release_ts.yml

@@ -1,51 +0,0 @@
-name: Publish Release - TypeScript
-
-on:
-  push:
-    tags:
-      - "llama-cloud-services@*"
-
-jobs:
-  build-and-publish:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout Repo
-        uses: actions/checkout@v4
-
-      - uses: pnpm/action-setup@v4
-        with:
-          version: 10
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version-file: "ts/llama_cloud_services/.nvmrc"
-
-      - name: Install dependencies
-        working-directory: ts/llama_cloud_services
-        run: pnpm install --no-frozen-lockfile
-
-      - name: Build tarball
-        run: |
-          pnpm pack
-        working-directory: ts/llama_cloud_services
-
-      - name: Setup npm authentication
-        run: echo "//registry.npmjs.org/:_authToken=${NPM_TOKEN}" > ~/.npmrc
-        env:
-          NPM_TOKEN: ${{ secrets.NPM_TOKEN }}
-
-      - name: Release
-        working-directory: ts/llama_cloud_services
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          NPM_TOKEN: ${{ secrets.NPM_TOKEN }}
-        run: pnpm publish --access public --no-git-checks
-
-      - name: Create release
-        uses: ncipollo/release-action@v1
-        with:
-          artifacts: "ts/llama_cloud_services/llama-cloud-services*.tgz"
-          name: Release ${{ github.ref }} - LlamaCloud Services TS
-          bodyFile: "ts/llama_cloud_services/CHANGELOG.md"
-          token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/test_py.yml

@@ -1,39 +0,0 @@
-name: Test - Python
-
-on:
-  push:
-    branches:
-      - main
-  pull_request:
-
-env:
-  UV_VERSION: "0.7.20"
-  LLAMA_CLOUD_API_KEY: ${{ secrets.LLAMA_CLOUD_API_KEY }}
-
-jobs:
-  test:
-    runs-on: ubuntu-latest
-    strategy:
-      # You can use PyPy versions in python-version.
-      # For example, pypy-2.7 and pypy-3.8
-      matrix:
-        python-version: ["3.9", "3.10", "3.11", "3.12"]
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-      - name: Install uv
-        uses: astral-sh/setup-uv@v6
-        with:
-          version: ${{ env.UV_VERSION }}
-
-      - name: Set up Python
-        run: uv python install ${{ matrix.python-version }} && uv python pin ${{ matrix.python-version }}
-
-      - name: Run Tests
-        working-directory: py
-        run: uv run -- pytest tests/**/test_*.py
-
-      - name: Remove virtual environment
-        working-directory: py
-        run: rm -rf .venv/

.github/workflows/test_ts.yml

@@ -1,34 +0,0 @@
-name: Lint - TypeScript
-
-on:
-  push:
-    branches:
-      - main
-  pull_request:
-    branches:
-      - main
-
-env:
-  TURBO_TOKEN: ${{ secrets.TURBO_TOKEN }}
-  TURBO_TEAM: ${{ vars.TURBO_TEAM }}
-  TURBO_REMOTE_ONLY: true
-  LLAMA_CLOUD_API_KEY: ${{ secrets.LLAMA_CLOUD_API_KEY }}
-
-jobs:
-  lint:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@v4
-        with:
-          version: 10
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version-file: "ts/llama_cloud_services/.nvmrc"
-      - name: Install dependencies
-        working-directory: ts/llama_cloud_services/
-        run: pnpm install --no-frozen-lockfile
-      - name: Run tests
-        working-directory: ts/llama_cloud_services/
-        run: pnpm test --run

.github/workflows/unit_test.yml

@@ -0,0 +1,40 @@
+name: Unit Testing
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+
+env:
+  POETRY_VERSION: "1.6.1"
+  LLAMA_CLOUD_API_KEY: ${{ secrets.LLAMA_CLOUD_API_KEY }}
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      # You can use PyPy versions in python-version.
+      # For example, pypy-2.7 and pypy-3.8
+      matrix:
+        python-version: ["3.8", "3.10", "3.11"]
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+      - name: Set up python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install Poetry
+        uses: snok/install-poetry@v1
+        with:
+          version: ${{ env.POETRY_VERSION }}
+      - name: Install deps
+        shell: bash
+        run: poetry install --with dev
+      - name: Run testing
+        env:
+          CI: true
+        shell: bash
+        run: poetry run pytest tests

.gitignore

@@ -3,9 +3,3 @@ __pycache__/
 *.pyc
 .DS_Store
 .idea
-.env*
-.ipynb_checkpoints*
-*_cache/
-node_modules/
-.turbo/
-dist/

.pre-commit-config.yaml

@@ -21,19 +21,18 @@ repos:
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix]
-        exclude: ".*uv.lock"
+        exclude: ".*poetry.lock"
   - repo: https://github.com/psf/black-pre-commit-mirror
     rev: 23.10.1
     hooks:
       - id: black-jupyter
         name: black-src
         alias: black
-        exclude: ".*uv.lock"
+        exclude: ".*poetry.lock"
   - repo: https://github.com/pre-commit/mirrors-mypy
     rev: v1.0.1
     hooks:
       - id: mypy
-        exclude: ^py/tests/
         additional_dependencies:
           [
             "types-requests",
@@ -47,7 +46,7 @@ repos:
           [
             --disallow-untyped-defs,
             --ignore-missing-imports,
-            --python-version=3.10,
+            --python-version=3.8,
           ]
   - repo: https://github.com/adamchainz/blacken-docs
     rev: 1.16.0
@@ -63,13 +62,13 @@ repos:
     rev: v3.0.3
     hooks:
       - id: prettier
-        exclude: uv.lock
+        exclude: poetry.lock
   - repo: https://github.com/codespell-project/codespell
     rev: v2.2.6
     hooks:
       - id: codespell
         additional_dependencies: [tomli]
-        exclude: ^(uv.lock|docs|ts)
+        exclude: ^(poetry.lock|examples)
         args:
           [
             "--ignore-words-list",
@@ -84,6 +83,6 @@ repos:
     rev: v0.23.1
     hooks:
       - id: toml-sort-fix
-        exclude: ".*uv.lock"
+        exclude: ".*poetry.lock"
 
 exclude: .github/ISSUE_TEMPLATE

CONTRIBUTING.md

@@ -1,33 +0,0 @@
-# Python
-
-## Installation
-
-This project uses uv. Create a virtual environment, and run `uv sync`
-
-## Versioning (Maintainers only)
-
-Before merging your changes, make sure to bump the versions.
-
-Make a version bump to `pyproject.toml`. If the underlying dependency on the llamacloud platform OpenAPI
-sdk needs bumping, make sure to bring that in as well. If updating dependencies, run `uv lock`.
-
-The legacy `llama_parse` package re-exports some of `llama_cloud_services` in the old namespace. The
-versions need to be kept consistent to sidecar it with `llama_cloud_services`. Bump it's version in `llama_parse/pyproject.toml`, and also bump it's dependency version of `llama-cloud-services` to match.
-
-**Note**: Don't worry about updating the `llama_parse/poetry.lock` file when bumping versions. The GitHub action will automatically run `poetry lock` for the llama_parse package during the build process (though it doesn't commit the updated lockfile back to the repo).
-
-You can also do this with `./scripts/version-bump.py set 0.x.x` if you have `uv` installed.
-
-Once the change is merged, push a tag `git tag -a v0.x.x -m 0.x.x` and `git push origin 0.x.x`.
-
-This tagging step can be done with `./scripts/version-bump tag`.
-
-# Typescript
-
-## Installation
-
-...
-
-## Versioning
-
-...

README.md

@@ -1,81 +1,143 @@
-[![PyPI - Downloads](https://img.shields.io/pypi/dm/llama-cloud-services)](https://pypi.org/project/llama-cloud-services/)
-[![GitHub contributors](https://img.shields.io/github/contributors/run-llama/llama_cloud_services)](https://github.com/run-llama/llama_cloud_services/graphs/contributors)
+# LlamaParse
+
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/llama-parse)](https://pypi.org/project/llama-parse/)
+[![GitHub contributors](https://img.shields.io/github/contributors/run-llama/llama_parse)](https://github.com/run-llama/llama_parse/graphs/contributors)
 [![Discord](https://img.shields.io/discord/1059199217496772688)](https://discord.gg/dGcwcsnxhU)
 
-# Llama Cloud Services
+LlamaParse is a **GenAI-native document parser** that can parse complex document data for any downstream LLM use case (RAG, agents).
 
-This repository contains the code for hand-written SDKs and clients for interacting with LlamaCloud.
+It is really good at the following:
 
-This includes:
+- ✅ **Broad file type support**: Parsing a variety of unstructured file types (.pdf, .pptx, .docx, .xlsx, .html) with text, tables, visual elements, weird layouts, and more.
+- ✅ **Table recognition**: Parsing embedded tables accurately into text and semi-structured representations.
+- ✅ **Multimodal parsing and chunking**: Extracting visual elements (images/diagrams) into structured formats and return image chunks using the latest multimodal models.
+- ✅ **Custom parsing**: Input custom prompt instructions to customize the output the way you want it.
 
-- [LlamaParse](./parse.md) - A GenAI-native document parser that can parse complex document data for any downstream LLM use case (Agents, RAG, data processing, etc.).
-- [LlamaReport (beta/invite-only)](./report.md) - A prebuilt agentic report builder that can be used to build reports from a variety of data sources.
-- [LlamaExtract](./extract.md) - A prebuilt agentic data extractor that can be used to transform data into a structured JSON representation.
-- [LlamaCloud Index](./index.md) - A widely customizable and fully automated document ingestion pipeline that also serves retrieval purposes.
+LlamaParse directly integrates with [LlamaIndex](https://github.com/run-llama/llama_index).
+
+The free plan is up to 1000 pages a day. Paid plan is free 7k pages per week + 0.3c per additional page by default. There is a sandbox available to test the API [**https://cloud.llamaindex.ai/parse ↗**](https://cloud.llamaindex.ai/parse).
+
+Read below for some quickstart information, or see the [full documentation](https://docs.cloud.llamaindex.ai/).
+
+If you're a company interested in enterprise RAG solutions, and/or high volume/on-prem usage of LlamaParse, come [talk to us](https://www.llamaindex.ai/contact).
 
 ## Getting Started
 
-Install the package:
+First, login and get an api-key from [**https://cloud.llamaindex.ai/api-key ↗**](https://cloud.llamaindex.ai/api-key).
 
-```bash
-pip install llama-cloud-services
+Then, make sure you have the latest LlamaIndex version installed.
+
+**NOTE:** If you are upgrading from v0.9.X, we recommend following our [migration guide](https://pretty-sodium-5e0.notion.site/v0-10-0-Migration-Guide-6ede431dcb8841b09ea171e7f133bd77), as well as uninstalling your previous version first.
+
+```
+pip uninstall llama-index  # run this if upgrading from v0.9.x or older
+pip install -U llama-index --upgrade --no-cache-dir --force-reinstall
 ```
 
-Then, get your API key from [LlamaCloud](https://cloud.llamaindex.ai/).
+Lastly, install the package:
 
-Then, you can use the services in your code:
+`pip install llama-parse`
+
+Now you can run the following to parse your first PDF file:
 
 ```python
-from llama_cloud_services import (
-    LlamaParse,
-    LlamaReport,
-    LlamaExtract,
-    LlamaCloudIndex,
+import nest_asyncio
+
+nest_asyncio.apply()
+
+from llama_parse import LlamaParse
+
+parser = LlamaParse(
+    api_key="llx-...",  # can also be set in your env as LLAMA_CLOUD_API_KEY
+    result_type="markdown",  # "markdown" and "text" are available
+    num_workers=4,  # if multiple files passed, split in `num_workers` API calls
+    verbose=True,
+    language="en",  # Optionally you can define a language, default=en
 )
 
-parser = LlamaParse(api_key="YOUR_API_KEY")
-report = LlamaReport(api_key="YOUR_API_KEY")
-extract = LlamaExtract(api_key="YOUR_API_KEY")
-index = LlamaCloudIndex(
-    "my_first_index", project_name="default", api_key="YOUR_API_KEY"
-)
+# sync
+documents = parser.load_data("./my_file.pdf")
+
+# sync batch
+documents = parser.load_data(["./my_file1.pdf", "./my_file2.pdf"])
+
+# async
+documents = await parser.aload_data("./my_file.pdf")
+
+# async batch
+documents = await parser.aload_data(["./my_file1.pdf", "./my_file2.pdf"])
 ```
 
-See the quickstart guides for each service for more information:
+## Using with file object
 
-- [LlamaParse](./parse.md)
-- [LlamaReport (beta/invite-only)](./report.md)
-- [LlamaExtract](./extract.md)
-- [LlamaCloud Index](./index.md)
-
-## Switch to EU SaaS 🇪🇺
-
-If you are interested in using LlamaCloud services in the EU, you can adjust your base URL to `https://api.cloud.eu.llamaindex.ai`.
-
-You can also create your API key in the EU region [here](https://cloud.eu.llamaindex.ai).
+You can parse a file object directly:
 
 ```python
-from llama_cloud_services import (
-    LlamaParse,
-    LlamaReport,
-    LlamaExtract,
-    EU_BASE_URL,
+import nest_asyncio
+
+nest_asyncio.apply()
+
+from llama_parse import LlamaParse
+
+parser = LlamaParse(
+    api_key="llx-...",  # can also be set in your env as LLAMA_CLOUD_API_KEY
+    result_type="markdown",  # "markdown" and "text" are available
+    num_workers=4,  # if multiple files passed, split in `num_workers` API calls
+    verbose=True,
+    language="en",  # Optionally you can define a language, default=en
 )
 
-parser = LlamaParse(api_key="YOUR_API_KEY", base_url=EU_BASE_URL)
-report = LlamaReport(api_key="YOUR_API_KEY", base_url=EU_BASE_URL)
-extract = LlamaExtract(api_key="YOUR_API_KEY", base_url=EU_BASE_URL)
-index = LlamaCloudIndex(
-    "my_first_index",
-    project_name="default",
-    api_key="YOUR_API_KEY",
-    base_url=EU_BASE_URL,
-)
+file_name = "my_file1.pdf"
+extra_info = {"file_name": file_name}
+
+with open(f"./{file_name}", "rb") as f:
+    # must provide extra_info with file_name key with passing file object
+    documents = parser.load_data(f, extra_info=extra_info)
+
+# you can also pass file bytes directly
+with open(f"./{file_name}", "rb") as f:
+    file_bytes = f.read()
+    # must provide extra_info with file_name key with passing file bytes
+    documents = parser.load_data(file_bytes, extra_info=extra_info)
 ```
 
+## Using with `SimpleDirectoryReader`
+
+You can also integrate the parser as the default PDF loader in `SimpleDirectoryReader`:
+
+```python
+import nest_asyncio
+
+nest_asyncio.apply()
+
+from llama_parse import LlamaParse
+from llama_index.core import SimpleDirectoryReader
+
+parser = LlamaParse(
+    api_key="llx-...",  # can also be set in your env as LLAMA_CLOUD_API_KEY
+    result_type="markdown",  # "markdown" and "text" are available
+    verbose=True,
+)
+
+file_extractor = {".pdf": parser}
+documents = SimpleDirectoryReader(
+    "./data", file_extractor=file_extractor
+).load_data()
+```
+
+Full documentation for `SimpleDirectoryReader` can be found on the [LlamaIndex Documentation](https://docs.llamaindex.ai/en/stable/module_guides/loading/simpledirectoryreader.html).
+
+## Examples
+
+Several end-to-end indexing examples can be found in the examples folder
+
+- [Getting Started](examples/demo_basic.ipynb)
+- [Advanced RAG Example](examples/demo_advanced.ipynb)
+- [Raw API Usage](examples/demo_api.ipynb)
+
 ## Documentation
 
-You can see complete SDK and API documentation for each service on [our official docs](https://docs.cloud.llamaindex.ai/).
+[https://docs.cloud.llamaindex.ai/](https://docs.cloud.llamaindex.ai/)
 
 ## Terms of Service
 
@@ -83,4 +145,6 @@ See the [Terms of Service Here](./TOS.pdf).
 
 ## Get in Touch (LlamaCloud)
 
-You can get in touch with us by following our [contact link](https://www.llamaindex.ai/contact).
+LlamaParse is part of LlamaCloud, our e2e enterprise RAG platform that provides out-of-the-box, production-ready connectors, indexing, and retrieval over your complex data sources. We offer SaaS and VPC options.
+
+LlamaCloud is currently available via waitlist (join by [creating an account](https://cloud.llamaindex.ai/)). If you're interested in state-of-the-art quality and in centralizing your RAG efforts, come [get in touch with us](https://www.llamaindex.ai/contact).

docs/examples-py/extract/data/automotive_sector_analysis/modeling_assumptions.txt

@@ -1,10 +0,0 @@
-# Financial Modeling Assumptions
-Discount Rate: 8%
-Terminal Growth Rate: 2%
-Tax Rate: 25%
-Revenue Growth (Years 1-5): 10% per annum
-Revenue Growth (Years 6-10): 5% per annum
-Capital Expenditures as % of Revenue: 7%
-Working Capital Assumption: 3% of Revenue
-Depreciation Rate: 10% per annum
-Cost of Capital Assumption: 8%

docs/examples-py/extract/data/insider_transactions/.gitignore

@@ -1 +0,0 @@
-sec_form_4_dump.json

docs/examples-py/extract/extract_data_with_citations.ipynb

@@ -1,440 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Extract Data from Financial Reports - with Citations and Reasoning\n",
-    "\n",
-    "Given complex files like financial reports, contracts, invoices etc, Llama Extract allows you to make use of an LLM to extract the information relevant to you, in a structured format.\n",
-    "\n",
-    "In this example, we'll be using [LlamaExtract](https://docs.cloud.llamaindex.ai/llamaextract/getting_started?utm_campaign=extract&utm_medium=recipe) to extract structured data from an SEC filing (specifically, the filing by Nvidia for fiscal year 2025).\n",
-    "\n",
-    "On top of simple data extraction, we'll ask our extraction agent to provide citations and reasoning for each extracted field. This allows us to:\n",
-    "- Confirm  the accuracy of the extracted field\n",
-    "- Understand the reasoning behind why the LLM extracted a given piece of information\n",
-    "- This last point allows us an opportunity to adjust the system prompt or field descriptions and improve on results where needed.\n",
-    "\n",
-    "\n",
-    "The example we go through below is also replicable within Llama Cloud as well, where you will also be able to pick between a number of pre-defined schemas, instead of building your own."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install llama-cloud-services"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Connect to Llama Cloud\n",
-    "\n",
-    "To get started, make sure you provide your [Llama Cloud](https://cloud.llamaindex.ai?utm_campaign=extract&utm_medium=recipe) API key."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Enter your Llama Cloud API Key: ··········\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "if \"LLAMA_CLOUD_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"LLAMA_CLOUD_API_KEY\"] = getpass(\"Enter your Llama Cloud API Key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extract Data with Llama Extract Agent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No project_id provided, fetching default project.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from llama_cloud_services import LlamaExtract\n",
-    "\n",
-    "# Optionally, provide your project id, if not, it will use the 'Default' project\n",
-    "llama_extract = LlamaExtract()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Provide Your Custom Schema\n",
-    "\n",
-    "When using LlamaExtract via the API, you provide your own schema that describes what you want extracted from files and data provided to your agent. Here, we are essentially building an SEC filings extraction agent."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pydantic import BaseModel, Field\n",
-    "from enum import Enum\n",
-    "\n",
-    "\n",
-    "class FilingType(str, Enum):\n",
-    "    ten_k = \"10 K\"\n",
-    "    ten_q = \"10-Q\"\n",
-    "    ten_ka = \"10-K/A\"\n",
-    "    ten_qa = \"10-Q/A\"\n",
-    "\n",
-    "\n",
-    "class FinancialReport(BaseModel):\n",
-    "    company_name: str = Field(description=\"The name of the company\")\n",
-    "    description: str = Field(\n",
-    "        description=\"Short description of the filing and what it contains\"\n",
-    "    )\n",
-    "    filing_type: FilingType = Field(description=\"Type of SEC filing\")\n",
-    "    filing_date: str = Field(description=\"Date when filing was submitted to SEC\")\n",
-    "    fiscal_year: int = Field(description=\"Fiscal year\")\n",
-    "    unit: str = Field(\n",
-    "        description=\"Unit of financial figures (thousands, millions, etc.)\"\n",
-    "    )\n",
-    "    revenue: int = Field(description=\"Total revenue for period\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set Up Citations and Reasoning\n",
-    "\n",
-    "Optionally, we can set the `ExtractConfig` to extract citations for each field the agent extracts. These cications will cite the specific pages and sections of the file from which a given field was extractedd.\n",
-    "\n",
-    "By setting `use_reasoning` to True, we als ask the agent to do an additional reasoning step, explaining why a given field was extracted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_cloud.types import ExtractConfig, ExtractMode\n",
-    "\n",
-    "config = ExtractConfig(\n",
-    "    use_reasoning=True, cite_sources=True, extraction_mode=ExtractMode.MULTIMODAL\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.11/dist-packages/llama_cloud_services/extract/extract.py:127: ExperimentalWarning: `use_reasoning` is an experimental feature. Results will be available in the `extraction_metadata` field for the extraction run.\n",
-      "  warnings.warn(\n",
-      "/usr/local/lib/python3.11/dist-packages/llama_cloud_services/extract/extract.py:133: ExperimentalWarning: `cite_sources` is an experimental feature. This may greatly increase the size of the response, and slow down the extraction. Results will be available in the `extraction_metadata` field for the extraction run.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "agent = llama_extract.create_agent(\n",
-    "    name=\"filing-parser\", data_schema=FinancialReport, config=config\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Demo Time - Download a PDF and Extract Data with Citations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PDF downloaded successfully.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "\n",
-    "url = \"https://raw.githubusercontent.com/run-llama/llama_cloud_services/refs/heads/main/examples/extract/data/sec_filings/nvda_10k.pdf\"\n",
-    "\n",
-    "response = requests.get(url)\n",
-    "\n",
-    "if response.status_code == 200:\n",
-    "    with open(\"/content/nvda_10k.pdf\", \"wb\") as f:\n",
-    "        f.write(response.content)\n",
-    "    print(\"PDF downloaded successfully.\")\n",
-    "else:\n",
-    "    print(f\"Failed to download. Status code: {response.status_code}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Uploading files: 100%|██████████| 1/1 [00:00<00:00,  1.83it/s]\n",
-      "Creating extraction jobs: 100%|██████████| 1/1 [00:00<00:00,  4.38it/s]\n",
-      "Extracting files: 100%|██████████| 1/1 [02:03<00:00, 123.40s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "filing_info = agent.extract(\"/content/nvda_10k.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'company_name': 'NVIDIA Corporation',\n",
-       " 'description': \"The filing provides a detailed overview of NVIDIA's business as a full-stack computing infrastructure company, discusses various technologies including digital avatars and autonomous vehicles, outlines numerous risk factors affecting operations such as supply chain issues and geopolitical tensions, and describes employee stock purchase plans and related compliance requirements.\",\n",
-       " 'filing_type': '10 K',\n",
-       " 'filing_date': 'February 26, 2025',\n",
-       " 'fiscal_year': 2025,\n",
-       " 'unit': 'millions',\n",
-       " 'revenue': 130497}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filing_info.data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Inspect Citations and Reasoning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'field_metadata': {'company_name': {'reasoning': 'VERBATIM EXTRACTION',\n",
-       "   'citation': [{'page': 1, 'matching_text': 'NVIDIA CORPORATION'},\n",
-       "    {'page': 2, 'matching_text': 'NVIDIA Corporation'},\n",
-       "    {'page': 3,\n",
-       "     'matching_text': 'All references to \"NVIDIA,\" \"we,\" \"us,\" \"our,\" or the \"Company\" mean NVIDIA Corporation and its subsidiaries.'},\n",
-       "    {'page': 35,\n",
-       "     'matching_text': 'Comparison of 5 Year Cumulative Total Return* Among NVIDIA Corporation'},\n",
-       "    {'page': 49,\n",
-       "     'matching_text': 'To the Board of Directors and Shareholders of NVIDIA Corporation'},\n",
-       "    {'page': 90, 'matching_text': 'NVIDIA Corporation'},\n",
-       "    {'page': 119,\n",
-       "     'matching_text': '*\"Company\"* means NVIDIA Corporation, a Delaware corporation.'},\n",
-       "    {'page': 126,\n",
-       "     'matching_text': 'Annual Report on Form 10-K of NVIDIA Corporation'}]},\n",
-       "  'filing_type': {'reasoning': \"VERBATIM EXTRACTION from multiple sources confirming the filing type as '10 K'.\",\n",
-       "   'citation': [{'page': 1, 'matching_text': 'FORM 10-K'},\n",
-       "    {'page': 2, 'matching_text': 'Item 16. | Form 10-K Summary'},\n",
-       "    {'page': 3,\n",
-       "     'matching_text': 'This Annual Report on Form 10-K contains forward-looking statements...'},\n",
-       "    {'page': 13, 'matching_text': 'this Annual Report on Form 10-K'},\n",
-       "    {'page': 15, 'matching_text': 'this Annual Report on Form 10-K'},\n",
-       "    {'page': 32,\n",
-       "     'matching_text': 'Annual Report on Form 10-K, which information is hereby incorporated by reference.'},\n",
-       "    {'page': 36, 'matching_text': 'this Annual Report on Form 10-K'},\n",
-       "    {'page': 43,\n",
-       "     'matching_text': 'Annual Report on Form 10-K for additional information'},\n",
-       "    {'page': 45, 'matching_text': 'Annual Report on Form 10-K'},\n",
-       "    {'page': 46, 'matching_text': 'this Annual Report on Form 10-K'},\n",
-       "    {'page': 62, 'matching_text': 'Annual Report on Form 10-K'},\n",
-       "    {'page': 83,\n",
-       "     'matching_text': 'Restated Certificate of Incorporation | 10-K'},\n",
-       "    {'page': 84, 'matching_text': 'Item 16. Form 10-K Summary'},\n",
-       "    {'page': 126, 'matching_text': 'which appears in this Form 10-K'},\n",
-       "    {'page': 127, 'matching_text': 'Annual Report on Form 10-K'},\n",
-       "    {'page': 128, 'matching_text': 'Annual Report on Form 10-K'},\n",
-       "    {'page': 129, 'matching_text': \"The Company's Annual Report on Form 10-K\"},\n",
-       "    {'page': 130,\n",
-       "     'matching_text': \"The Company's Annual Report on Form 10-K for the year ended January 26, 2025\"}]},\n",
-       "  'fiscal_year': {'reasoning': 'The fiscal year ended January 26, 2025, indicates the fiscal year is 2025. Additionally, multiple references throughout the text confirm the fiscal year 2025 in various contexts.',\n",
-       "   'citation': [{'page': 1,\n",
-       "     'matching_text': 'For the fiscal year ended January 26, 2025'},\n",
-       "    {'page': 6,\n",
-       "     'matching_text': 'In fiscal year 2025, we launched the NVIDIA Blackwell architecture'},\n",
-       "    {'page': 12, 'matching_text': 'fiscal year 2025'},\n",
-       "    {'page': 17,\n",
-       "     'matching_text': 'our gross margins in the second quarter of fiscal year 2025 were negatively impacted'},\n",
-       "    {'page': 20,\n",
-       "     'matching_text': 'we generated 53% of our revenue in fiscal year 2025 from sales outside the United States.'},\n",
-       "    {'page': 23,\n",
-       "     'matching_text': 'For fiscal year 2025, an indirect customer which primarily purchases our products through system integrators...'},\n",
-       "    {'page': 33,\n",
-       "     'matching_text': 'In fiscal year 2025, we repurchased 310 million shares of our common stock for $34.0 billion.'},\n",
-       "    {'page': 37,\n",
-       "     'matching_text': 'Our Data Center revenue in China grew in fiscal year 2025.'},\n",
-       "    {'page': 44,\n",
-       "     'matching_text': 'Cash provided by operating activities increased in fiscal year 2025 compared to fiscal year 2024'},\n",
-       "    {'page': 57,\n",
-       "     'matching_text': 'Fiscal years 2025, 2024 and 2023 were all 52-week years.'},\n",
-       "    {'page': 65,\n",
-       "     'matching_text': 'Beginning in the second quarter of fiscal year 2025'},\n",
-       "    {'page': 69, 'matching_text': 'In the fourth quarter of fiscal year 2025'},\n",
-       "    {'page': 78,\n",
-       "     'matching_text': 'Depreciation and amortization expense attributable to our Compute and Networking segment for fiscal years 2025'},\n",
-       "    {'page': 129, 'matching_text': 'for the year ended January 26, 2025'}]},\n",
-       "  'description': {'reasoning': 'The extracted data combines multiple descriptions from the source text, ensuring no duplication while maintaining the order and context of the information. Each section of the filing is summarized to reflect the key points without losing the essence of the original text.',\n",
-       "   'citation': [{'page': 4,\n",
-       "     'matching_text': 'NVIDIA is now a full-stack computing infrastructure company with data-center-scale offerings that are reshaping industry.'},\n",
-       "    {'page': 8,\n",
-       "     'matching_text': 'a suite of technologies that help developers bring digital avatars to life with generative Al...autonomous vehicles, or AV, and electric vehicles, or EV, is revolutionizing the transportation industry...Our worldwide sales and marketing strategy is key to achieving our objective of providing markets with our high-performance and efficient computing platforms and software.'},\n",
-       "    {'page': 14, 'matching_text': 'Risk Factors Summary'},\n",
-       "    {'page': 16,\n",
-       "     'matching_text': 'Risks Related to Demand, Supply, and Manufacturing\\n\\nLong manufacturing lead times and uncertain supply and component availability...'},\n",
-       "    {'page': 18,\n",
-       "     'matching_text': 'cryptocurrency mining, on demand for our products. Volatility in the cryptocurrency market, including new compute technologies...'},\n",
-       "    {'page': 21,\n",
-       "     'matching_text': 'supply-chain attacks or other business disruptions. We cannot guarantee that third parties and infrastructure in our supply chain...'},\n",
-       "    {'page': 22,\n",
-       "     'matching_text': 'We are monitoring the impact of the geopolitical conflict in and around Israel on our operations... Climate change may have a long-term impact on our business.'},\n",
-       "    {'page': 25,\n",
-       "     'matching_text': 'We are subject to complex laws, rules, regulations, and political and other actions, including restrictions on the export of our products, which may adversely impact our business.'},\n",
-       "    {'page': 28,\n",
-       "     'matching_text': 'Our competitive position has been harmed by the existing export controls, and our competitive position and future results may be further harmed'},\n",
-       "    {'page': 29,\n",
-       "     'matching_text': 'restrictions imposed by the Chinese government on the duration of gaming activities and access to games may adversely affect our Gaming revenue'},\n",
-       "    {'page': 29,\n",
-       "     'matching_text': 'our business depends on our ability to receive consistent and reliable supply from our overseas partners, especially in Taiwan and South Korea'},\n",
-       "    {'page': 29,\n",
-       "     'matching_text': 'Increased scrutiny from shareholders, regulators and others regarding our corporate sustainability practices could result in additional costs'},\n",
-       "    {'page': 29,\n",
-       "     'matching_text': 'Concerns relating to the responsible use of new and evolving technologies, such as Al, in our products and services may result in reputational or financial harm'},\n",
-       "    {'page': 31,\n",
-       "     'matching_text': 'Data protection laws around the world are quickly changing and may be interpreted and applied in an increasingly stringent fashion...'}]},\n",
-       "  'filing_date': {'reasoning': 'The filing date is consistently mentioned as February 26, 2025 across multiple entries, making it the most reliable date for the filing.',\n",
-       "   'citation': [{'page': 51, 'matching_text': 'February 26, 2025'},\n",
-       "    {'page': 86, 'matching_text': 'on February 26, 2025.'},\n",
-       "    {'page': 87, 'matching_text': 'February 26, 2025'},\n",
-       "    {'page': 126, 'matching_text': 'our report dated February 26, 2025'},\n",
-       "    {'page': 127, 'matching_text': 'Date: February 26, 2025'},\n",
-       "    {'page': 128, 'matching_text': 'Date: February 26, 2025'},\n",
-       "    {'page': 129, 'matching_text': 'Date: February 26, 2025'},\n",
-       "    {'page': 130, 'matching_text': 'Date: February 26, 2025'}]},\n",
-       "  'unit': {'reasoning': \"The unit of financial figures is explicitly mentioned multiple times in the text as 'millions', including in table headers and notes. This is confirmed by various citations from pages 38, 42, 43, 52, 53, 54, 56, 65, 71, 72, 73, 75, 77, 79, 80, and 82.\",\n",
-       "   'citation': [{'page': 38,\n",
-       "     'matching_text': '($ in millions, except per share data)'},\n",
-       "    {'page': 42, 'matching_text': '($ in millions)'},\n",
-       "    {'page': 43, 'matching_text': '($ in millions)'},\n",
-       "    {'page': 52, 'matching_text': '(In millions, except per share data)'},\n",
-       "    {'page': 53,\n",
-       "     'matching_text': 'Consolidated Statements of Comprehensive Income (In millions)'},\n",
-       "    {'page': 54,\n",
-       "     'matching_text': 'Consolidated Balance Sheets (In millions, except par value)'},\n",
-       "    {'page': 55, 'matching_text': '(In millions, except per share data)'},\n",
-       "    {'page': 56,\n",
-       "     'matching_text': 'Consolidated Statements of Cash Flows (In millions)'},\n",
-       "    {'page': 65,\n",
-       "     'matching_text': 'Year Ended<br/>Jan 26, 2025<br/>(In millions, except per share data)'},\n",
-       "    {'page': 71, 'matching_text': '(In millions) | (In millions)'},\n",
-       "    {'page': 72, 'matching_text': '(In millions)'}]},\n",
-       "  'revenue': {'reasoning': 'The total revenue for fiscal year 2025 is extracted from multiple sources within the text, all confirming the same figure of $130,497 million. The revenue recognized for fiscal year 2025 is also noted as $4,607 million, which is a separate figure. However, the primary focus is on the total revenue figure, which is consistently cited.',\n",
-       "   'citation': [{'page': 38,\n",
-       "     'matching_text': 'Revenue for fiscal year 2025 was $130.5 billion'},\n",
-       "    {'page': 41,\n",
-       "     'matching_text': 'Total                | $ 130,497    | $ | 60,922'},\n",
-       "    {'page': 52, 'matching_text': 'Revenue | $ 130,497'},\n",
-       "    {'page': 78,\n",
-       "     'matching_text': 'Revenue | $ 116,193 | $ 14,304 | $ - | $ 130,497'},\n",
-       "    {'page': 79, 'matching_text': 'Total revenue | $ 130,497'},\n",
-       "    {'page': 80, 'matching_text': 'Total revenue              | $ 130,497'}]}},\n",
-       " 'usage': {'num_pages_extracted': 130,\n",
-       "  'num_document_tokens': 105932,\n",
-       "  'num_output_tokens': 31306}}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filing_info.extraction_metadata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## What's Next?\n",
-    "\n",
-    "In this example, we built an Extraction Agent that is capable of citing it's sources from the document it's extracting data from, and reasoning about its reponse. To further customize and improve on the results, you can also try to customize the `system_prompt` in the `ExtractConfig`.\n",
-    "\n",
-    "#### Learn More\n",
-    "\n",
-    "- [LlamaExtract Documentation](https://docs.cloud.llamaindex.ai/llamaextract/getting_started)\n",
-    "- [Example Notebooks](https://github.com/run-llama/llama_cloud_services/tree/main/examples/extract)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}

docs/examples-py/extract/lm317_structured_extraction.ipynb

@@ -1,318 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "1f6bd03d-1b8b-45a0-bc2c-5a13f1a5d8d3",
-   "metadata": {},
-   "source": [
-    "# LM317 Voltage Regulator Datasheet Structured Extraction\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/extract/lm317_structured_extraction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
-    "\n",
-    "This notebook demonstrates an agentic document workflow using LlamaExtract to process an LM317 voltage regulator datasheet. In this example, we define a structured extraction schema that converts key technical fields into standardized subfields. For instance, the output voltage is split into a minimum and maximum value with a defined unit, and we capture page citations for each extracted field.\n",
-    "\n",
-    "The target user is an electronics engineer at a component manufacturing company who needs to consolidate datasheet information into a standardized specification sheet for design and quality control.\n",
-    "\n",
-    "This approach reduces manual data entry, improves extraction accuracy and standardization, and provides traceability for each technical detail."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3b8c8d5-ff3e-48ce-b0b8-29b6b1f517f8",
-   "metadata": {},
-   "source": [
-    "## Use Case Overview\n",
-    "\n",
-    "### Problem\n",
-    "Datasheets like that for the LM317 regulator are often distributed as PDFs containing multiple tables, charts, and complex textual descriptions. Engineers must manually extract technical details such as voltage ranges, dropout voltage, maximum current, input voltage range, and pin configurations. This process is error-prone and time-consuming.\n",
-    "\n",
-    "### Agent Workflow (Combination of Automation and Chat)\n",
-    "1. **Upload Datasheet:** The engineer uploads the LM317 datasheet PDF.  \n",
-    "2. **Structured Extraction:** An automated agent processes the PDF and extracts key technical details into structured fields (e.g., output voltage as a range with separate min/max values).\n",
-    "3. **Interactive Verification:** The engineer can query the agent (via chat) for further details or clarification (e.g., \"Show me the detailed pin configuration extraction\") and review the cited pages.\n",
-    "\n",
-    "**Value Delivered:**\n",
-    "- Up to 70% reduction in manual data extraction time.\n",
-    "- Increased accuracy and standardization with structured fields."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a704e843-54be-4969-842b-713584cb3c35",
-   "metadata": {},
-   "source": [
-    "## Setup and Download Data\n",
-    "\n",
-    "Download the [LM317 Datasheet](https://www.ti.com/lit/ds/symlink/lm317.pdf) and setup LlamaExtract."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6e5b1f91-8785-44d4-a710-8be1b48b76de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!mkdir -p data/lm317_structured_extraction\n",
-    "!wget https://www.ti.com/lit/ds/symlink/lm317.pdf -O data/lm317_structured_extraction/lm317.pdf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f17b914a-00ed-4b63-8198-69fd7c4a7c62",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dotenv import load_dotenv\n",
-    "from llama_cloud_services import LlamaExtract\n",
-    "from llama_cloud.core.api_error import ApiError\n",
-    "\n",
-    "# Load environment variables (ensure LLAMA_CLOUD_API_KEY is set in your .env file)\n",
-    "load_dotenv(override=True)\n",
-    "\n",
-    "# Initialize the LlamaExtract client\n",
-    "llama_extract = LlamaExtract(\n",
-    "    project_id=\"<project_id>\",\n",
-    "    organization_id=\"<organization_id>\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed9f6e9a-96c8-4ee1-8b45-0b6a4f7dbbf1",
-   "metadata": {},
-   "source": [
-    "## Defining a Structured Extraction Schema\n",
-    "\n",
-    "We now define a rich Pydantic schema to extract technical specifications from the LM317 datasheet. In this schema:\n",
-    "\n",
-    "- The **output_voltage** and **input_voltage** fields are structured as ranges with separate minimum and maximum values and a unit.\n",
-    "- The **pin_configuration** field is structured to include a pin count and a descriptive layout.\n",
-    "- Additional technical fields (e.g., dropout voltage, max current) are captured as numbers.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4f7e9b44-5e69-4b30-9864-cd98f1e2a7d4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pydantic import BaseModel, Field\n",
-    "from typing import List\n",
-    "\n",
-    "\n",
-    "class VoltageRange(BaseModel):\n",
-    "    min_voltage: float = Field(..., description=\"Minimum voltage in volts\")\n",
-    "    max_voltage: float = Field(..., description=\"Maximum voltage in volts\")\n",
-    "    unit: str = Field(\"V\", description=\"Voltage unit\")\n",
-    "\n",
-    "\n",
-    "class PinConfiguration(BaseModel):\n",
-    "    pin_count: int = Field(..., description=\"Number of pins\")\n",
-    "    layout: str = Field(..., description=\"Detailed pin layout description\")\n",
-    "\n",
-    "\n",
-    "class LM317Spec(BaseModel):\n",
-    "    component_name: str = Field(..., description=\"Name of the component\")\n",
-    "    output_voltage: VoltageRange = Field(\n",
-    "        ..., description=\"Output voltage range specification\"\n",
-    "    )\n",
-    "    dropout_voltage: float = Field(..., description=\"Dropout voltage in volts\")\n",
-    "    max_current: float = Field(..., description=\"Maximum current rating in amperes\")\n",
-    "    input_voltage: VoltageRange = Field(\n",
-    "        ..., description=\"Input voltage range specification\"\n",
-    "    )\n",
-    "    pin_configuration: PinConfiguration = Field(\n",
-    "        ..., description=\"Pin configuration details\"\n",
-    "    )\n",
-    "    features: List[str] = Field([], description=\"List of additional technical features\")\n",
-    "\n",
-    "\n",
-    "class LM317Schema(BaseModel):\n",
-    "    specs: List[LM317Spec] = Field(\n",
-    "        ..., description=\"List of extracted LM317 technical specifications\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e0508e38-35be-446c-afe7-129e39553281",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    existing_agent = llama_extract.get_agent(name=\"lm317-datasheet\")\n",
-    "    if existing_agent:\n",
-    "        llama_extract.delete_agent(existing_agent.id)\n",
-    "except ApiError as e:\n",
-    "    if e.status_code == 404:\n",
-    "        pass\n",
-    "    else:\n",
-    "        raise"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bb197dfd-dd37-459e-8953-cc1b12f25bdd",
-   "metadata": {},
-   "source": [
-    "Here we use our balanced extraction mode."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e3defc0a-c685-4fbd-bbb1-1270f1442e72",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_cloud import ExtractConfig\n",
-    "\n",
-    "extract_config = ExtractConfig(\n",
-    "    extraction_mode=\"BALANCED\",\n",
-    ")\n",
-    "\n",
-    "agent = llama_extract.create_agent(\n",
-    "    name=\"lm317-datasheet\", data_schema=LM317Schema, config=extract_config\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0a0f9f9-2ef3-4a38-bd74-68d2c2e9e2d8",
-   "metadata": {},
-   "source": [
-    "## Extracting Information from the LM317 Datasheet\n",
-    "\n",
-    "For this demonstration, please download a publicly available LM317 voltage regulator datasheet (for example, from Texas Instruments) and save it as `lm317.pdf` in the `./data` directory. Then run the cell below to extract the structured technical specifications."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c58e8b7a-8f9b-46f3-8f72-3c2f96b49e8f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Uploading files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00,  1.08s/it]\n",
-      "Creating extraction jobs: 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  1.96it/s]\n",
-      "Extracting files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [01:27<00:00, 87.38s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Path to the LM317 datasheet PDF\n",
-    "lm317_pdf = \"./data/lm317_structured_extraction/lm317.pdf\"\n",
-    "\n",
-    "# Extract structured technical specifications from the datasheet\n",
-    "lm317_extract = agent.extract(lm317_pdf)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1a2e2e44-6c48-4a38-a6de-5f2f3c7d4d8b",
-   "metadata": {},
-   "source": [
-    "## Assessing the Extraction Results\n",
-    "\n",
-    "The output will be a consolidated list of LM317 technical specifications. For each entry, you should see structured fields including:\n",
-    "\n",
-    "- **component_name**\n",
-    "- **output_voltage** as a range (with separate `min_voltage` and `max_voltage` plus `unit`)\n",
-    "- **dropout_voltage** and **max_current** as numbers\n",
-    "- **input_voltage** as a structured range\n",
-    "- **pin_configuration** with a `pin_count` and `layout`\n",
-    "- **features** (if available)\n",
-    "\n",
-    "This structured approach makes it easier to standardize the information for downstream integration and verification. Engineers can click on the cited page numbers (in a UI that supports it) to validate the extraction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fb2abc44-7c9b-4b19-958e-d0d7b390ae57",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'specs': [{'component_name': 'LM317',\n",
-       "   'output_voltage': {'min_voltage': 1.25, 'max_voltage': 37.0, 'unit': 'V'},\n",
-       "   'dropout_voltage': 0.0,\n",
-       "   'max_current': 1.5,\n",
-       "   'input_voltage': {'min_voltage': 4.25, 'max_voltage': 40.0, 'unit': 'V'},\n",
-       "   'pin_configuration': {'pin_count': 3,\n",
-       "    'layout': '1: ADJUST, 2: OUTPUT, 3: INPUT'},\n",
-       "   'features': ['Output voltage range adjustable from 1.25 V to 37 V',\n",
-       "    'Output current greater than 1.5 A',\n",
-       "    'Internal short-circuit current limiting',\n",
-       "    'Thermal overload protection',\n",
-       "    'Output safe-area compensation']}]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Display the extraction results\n",
-    "lm317_extract.data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c7a2a523-095e-40bf-b713-f509c13a7747",
-   "metadata": {},
-   "source": [
-    "You can also see the output result in the UI."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc22dfa5-b667-4fb0-8dbe-24e401b12389",
-   "metadata": {},
-   "source": [
-    "![](data/lm317_structured_extraction/lm317_extraction.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e0e0c12a-9f89-4bb3-b40d-3e9f7c6d2fef",
-   "metadata": {},
-   "source": [
-    "## Conclusion\n",
-    "\n",
-    "This notebook demonstrated how to use LlamaExtract with a structured extraction schema for the LM317 voltage regulator datasheet. By defining detailed subfields (such as splitting voltage ranges into minimum and maximum values, and structuring the pin configuration), we ensure that the extracted data is standardized and traceable through page citations. This approach minimizes manual effort and improves accuracy, providing a robust example of an agentic document workflow for technical documentation processing.\n",
-    "\n",
-    "Feel free to modify or extend the schema to capture additional technical details or to suit your own use cases."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "llama_parse",
-   "language": "python",
-   "name": "llama_parse"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/examples-py/extract/resume_screening.ipynb

@@ -1,834 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Extracting data from Resumes\n",
-    "\n",
-    "Let us assume that we are running a hiring process for a company and we have received a list of resumes from candidates. We want to extract structured data from the resumes so that we can run a screening process and shortlist candidates. \n",
-    "\n",
-    "Take a look at one of the resumes in the `data/resumes` directory. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"600\"\n",
-       "            height=\"400\"\n",
-       "            src=\"./data/resumes/ai_researcher.pdf\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x109a7dcd0>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import IFrame\n",
-    "\n",
-    "IFrame(src=\"./data/resumes/ai_researcher.pdf\", width=600, height=400)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will notice that all the resumes have different layouts but contain common information like name, email, experience, education, etc. \n",
-    "\n",
-    "With LlamaExtract, we will show you how to:\n",
-    "- *Define* a data schema to extract the information of interest. \n",
-    "- *Iterate* over the data schema to generalize the schema for multiple resumes.\n",
-    "- *Finalize* the schema and schedule extractions for multiple resumes.\n",
-    "\n",
-    "We will start by defining a `LlamaExtract` client which provides a Python interface to the LlamaExtract API. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dotenv import load_dotenv\n",
-    "from llama_cloud_services import LlamaExtract\n",
-    "\n",
-    "\n",
-    "# Load environment variables (put LLAMA_CLOUD_API_KEY in your .env file)\n",
-    "load_dotenv(override=True)\n",
-    "\n",
-    "# Optionally, add your project id/organization id\n",
-    "llama_extract = LlamaExtract()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Defining the data schema\n",
-    "\n",
-    "Next, let us try to extract two fields from the resume: `name` and `email`. We can either use a Python dictionary structure to define the `data_schema` as a JSON or use a Pydantic model instead, for brevity and convenience. In either case, our output is guaranteed to validate against this schema."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pydantic import BaseModel, Field\n",
-    "\n",
-    "\n",
-    "class Resume(BaseModel):\n",
-    "    name: str = Field(description=\"The name of the candidate\")\n",
-    "    email: str = Field(description=\"The email address of the candidate\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Uploading files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:02<00:00,  2.20s/it]\n",
-      "Creating extraction jobs: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:02<00:00,  2.93s/it]\n",
-      "Extracting files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:02<00:00,  2.94s/it]\n",
-      "Uploading files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  1.13it/s]\n",
-      "Creating extraction jobs: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  1.80it/s]\n",
-      "Extracting files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:15<00:00, 15.18s/it]\n",
-      "Uploading files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  1.16it/s]\n",
-      "Creating extraction jobs: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.33it/s]\n",
-      "Extracting files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:32<00:00, 32.86s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from llama_cloud.core.api_error import ApiError\n",
-    "\n",
-    "try:\n",
-    "    existing_agent = llama_extract.get_agent(name=\"resume-screening\")\n",
-    "    if existing_agent:\n",
-    "        llama_extract.delete_agent(existing_agent.id)\n",
-    "except ApiError as e:\n",
-    "    if e.status_code == 404:\n",
-    "        pass\n",
-    "    else:\n",
-    "        raise\n",
-    "\n",
-    "agent = llama_extract.create_agent(name=\"resume-screening\", data_schema=Resume)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[ExtractionAgent(id=1fef43b5-8230-43b4-9e80-c1cddf53889c, name=resume-screening),\n",
-       " ExtractionAgent(id=93f8508b-3570-46f0-ae62-6315b40043bd, name=receipt/noisebridge_receipt.pdf_56db3d92),\n",
-       " ExtractionAgent(id=08315f0e-7146-430b-99b8-9701cb3ace6a, name=receipt/noisebridge_receipt.pdf_5c4730a7),\n",
-       " ExtractionAgent(id=cfcd7756-015d-4dbd-b142-a3eefcb16cd3, name=resume/software_architect_resume.html_4a11cf15),\n",
-       " ExtractionAgent(id=17cb83d9-601e-4f5c-a7aa-286e3045bcb4, name=resume/software_architect_resume.html_0b7d84a8),\n",
-       " ExtractionAgent(id=adc8e88c-44d3-4613-a5aa-d666ef007494, name=slide/saas_slide.pdf_bcc627a5),\n",
-       " ExtractionAgent(id=189f14cd-6370-4476-a6ad-36eafbc62618, name=slide/saas_slide.pdf_065aa22b),\n",
-       " ExtractionAgent(id=b9938ca5-6225-43cb-89ea-b0065237792f, name=test2),\n",
-       " ExtractionAgent(id=574d37b8-59dc-41e9-bde0-5c506a8eb670, name=test)]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "llama_extract.list_agents()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'name': 'Dr. Rachel Zhang', 'email': 'rachel.zhang@email.com'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resume = agent.extract(\"./data/resumes/ai_researcher.pdf\")\n",
-    "resume.data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Iterating over the data schema\n",
-    "\n",
-    "Now that we have created a data schema, let us add more fields to the schema. We will add `experience` and `education` fields to the schema. \n",
-    "- We can create a new Pydantic model for each of these fields and represent `experience` and `education` as lists of these models. Doing this will allow us to extract multiple entities from the resume without having to pre-define how many experiences or education the candidate has. \n",
-    "- We have added a `description` parameter to provide more context for extraction. We can use `description` to provide example inputs/outputs for the extraction. \n",
-    "- Note that we have annotated the `start_date` and `end_date` fields with `Optional[str]` to indicate that these fields are optional. This is *important* because the schema will be used to extract data from multiple resumes and not all resumes will have the same format. A field must only be required if it is guaranteed to be present in all the resumes. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import List, Optional\n",
-    "\n",
-    "\n",
-    "class Education(BaseModel):\n",
-    "    institution: str = Field(description=\"The institution of the candidate\")\n",
-    "    degree: str = Field(description=\"The degree of the candidate\")\n",
-    "    start_date: Optional[str] = Field(\n",
-    "        default=None, description=\"The start date of the candidate's education\"\n",
-    "    )\n",
-    "    end_date: Optional[str] = Field(\n",
-    "        default=None, description=\"The end date of the candidate's education\"\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "class Experience(BaseModel):\n",
-    "    company: str = Field(description=\"The name of the company\")\n",
-    "    title: str = Field(description=\"The title of the candidate\")\n",
-    "    description: Optional[str] = Field(\n",
-    "        default=None, description=\"The description of the candidate's experience\"\n",
-    "    )\n",
-    "    start_date: Optional[str] = Field(\n",
-    "        default=None, description=\"The start date of the candidate's experience\"\n",
-    "    )\n",
-    "    end_date: Optional[str] = Field(\n",
-    "        default=None, description=\"The end date of the candidate's experience\"\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "class Resume(BaseModel):\n",
-    "    name: str = Field(description=\"The name of the candidate\")\n",
-    "    email: str = Field(description=\"The email address of the candidate\")\n",
-    "    links: List[str] = Field(\n",
-    "        description=\"The links to the candidate's social media profiles\"\n",
-    "    )\n",
-    "    experience: List[Experience] = Field(description=\"The candidate's experience\")\n",
-    "    education: List[Education] = Field(description=\"The candidate's education\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we will update the `data_schema` for the `resume-screening` agent to use the new `Resume` model. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'name': 'Dr. Rachel Zhang',\n",
-       " 'email': 'rachel.zhang@email.com',\n",
-       " 'links': ['linkedin.com/in/rachelzhang',\n",
-       "  'github.com/rzhang-ai',\n",
-       "  'scholar.google.com/rachelzhang'],\n",
-       " 'experience': [{'company': 'DeepMind',\n",
-       "   'title': 'Senior Research Scientist',\n",
-       "   'description': '- Lead researcher on large-scale multi-task learning systems, developing novel architectures that improve cross-task generalization by 40%\\n- Pioneered new approach to zero-shot learning using contrastive training, published in NeurIPS 2023\\n- Built and led team of 6 researchers working on foundational ML models\\n- Developed novel regularization techniques for large language models, reducing catastrophic forgetting by 35%',\n",
-       "   'start_date': '2019',\n",
-       "   'end_date': 'Present'},\n",
-       "  {'company': 'Google Research',\n",
-       "   'title': 'Research Scientist',\n",
-       "   'description': '- Developed probabilistic frameworks for robust ML, published in ICML 2018\\n- Created novel attention mechanisms for computer vision models, improving accuracy by 25%\\n- Led collaboration with Google Brain team on efficient training methods for transformer models\\n- Mentored 4 PhD interns and collaborated with academic institutions',\n",
-       "   'start_date': '2015',\n",
-       "   'end_date': '2019'},\n",
-       "  {'company': 'Columbia University',\n",
-       "   'title': 'Research Assistant Professor',\n",
-       "   'description': '- Published seminal work on Bayesian optimization methods (cited 1000+ times)\\n- Taught graduate-level courses in Machine Learning and Statistical Learning Theory\\n- Supervised 5 PhD students and 3 MSc students\\n- Secured $500K in research grants for probabilistic ML research',\n",
-       "   'start_date': '2011',\n",
-       "   'end_date': '2015'}],\n",
-       " 'education': [{'institution': 'Columbia University',\n",
-       "   'degree': 'Ph.D. in Computer Science',\n",
-       "   'start_date': '2007',\n",
-       "   'end_date': '2011'},\n",
-       "  {'institution': 'Stanford University',\n",
-       "   'degree': 'M.S. in Computer Science',\n",
-       "   'start_date': '2005',\n",
-       "   'end_date': '2007'}]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.data_schema = Resume\n",
-    "resume = agent.extract(\"./data/resumes/ai_researcher.pdf\")\n",
-    "resume.data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is a good start. Let us add a few more fields to the schema and re-run the extraction. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class TechnicalSkills(BaseModel):\n",
-    "    programming_languages: List[str] = Field(\n",
-    "        description=\"The programming languages the candidate is proficient in.\"\n",
-    "    )\n",
-    "    frameworks: List[str] = Field(\n",
-    "        description=\"The tools/frameworks the candidate is proficient in, e.g. React, Django, PyTorch, etc.\"\n",
-    "    )\n",
-    "    skills: List[str] = Field(\n",
-    "        description=\"Other general skills the candidate is proficient in, e.g. Data Engineering, Machine Learning, etc.\"\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "class Resume(BaseModel):\n",
-    "    name: str = Field(description=\"The name of the candidate\")\n",
-    "    email: str = Field(description=\"The email address of the candidate\")\n",
-    "    links: List[str] = Field(\n",
-    "        description=\"The links to the candidate's social media profiles\"\n",
-    "    )\n",
-    "    experience: List[Experience] = Field(description=\"The candidate's experience\")\n",
-    "    education: List[Education] = Field(description=\"The candidate's education\")\n",
-    "    technical_skills: TechnicalSkills = Field(\n",
-    "        description=\"The candidate's technical skills\"\n",
-    "    )\n",
-    "    key_accomplishments: str = Field(\n",
-    "        description=\"Summarize the candidates highest achievements.\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'name': 'Dr. Rachel Zhang, Ph.D.',\n",
-       " 'email': 'rachel.zhang@email.com',\n",
-       " 'links': ['linkedin.com/in/rachelzhang',\n",
-       "  'github.com/rzhang-ai',\n",
-       "  'scholar.google.com/rachelzhang'],\n",
-       " 'experience': [{'company': 'DeepMind',\n",
-       "   'title': 'Senior Research Scientist',\n",
-       "   'description': 'Lead researcher on large-scale multi-task learning systems, developing novel architectures that improve cross-task generalization by 40%\\nPioneered new approach to zero-shot learning using contrastive training, published in NeurIPS 2023\\nBuilt and led team of 6 researchers working on foundational ML models\\nDeveloped novel regularization techniques for large language models, reducing catastrophic forgetting by 35%',\n",
-       "   'start_date': '2019',\n",
-       "   'end_date': 'Present'},\n",
-       "  {'company': 'Google Research',\n",
-       "   'title': 'Research Scientist',\n",
-       "   'description': 'Developed probabilistic frameworks for robust ML, published in ICML 2018\\nCreated novel attention mechanisms for computer vision models, improving accuracy by 25%\\nLed collaboration with Google Brain team on efficient training methods for transformer models\\nMentored 4 PhD interns and collaborated with academic institutions',\n",
-       "   'start_date': '2015',\n",
-       "   'end_date': '2019'},\n",
-       "  {'company': 'Columbia University',\n",
-       "   'title': 'Research Assistant Professor',\n",
-       "   'description': 'Published seminal work on Bayesian optimization methods (cited 1000+ times)\\nTaught graduate-level courses in Machine Learning and Statistical Learning Theory\\nSupervised 5 PhD students and 3 MSc students\\nSecured $500K in research grants for probabilistic ML research',\n",
-       "   'start_date': '2011',\n",
-       "   'end_date': '2015'}],\n",
-       " 'education': [{'institution': 'Columbia University',\n",
-       "   'degree': 'Ph.D. in Computer Science',\n",
-       "   'start_date': '2007',\n",
-       "   'end_date': '2011'},\n",
-       "  {'institution': 'Stanford University',\n",
-       "   'degree': 'M.S. in Computer Science',\n",
-       "   'start_date': '2005',\n",
-       "   'end_date': '2007'}],\n",
-       " 'technical_skills': {'programming_languages': ['Python',\n",
-       "   'C++',\n",
-       "   'Julia',\n",
-       "   'CUDA'],\n",
-       "  'frameworks': ['PyTorch', 'TensorFlow', 'JAX', 'Ray'],\n",
-       "  'skills': ['Deep Learning',\n",
-       "   'Reinforcement Learning',\n",
-       "   'Probabilistic Models',\n",
-       "   'Multi-Task Learning',\n",
-       "   'Zero-Shot Learning',\n",
-       "   'Neural Architecture Search']},\n",
-       " 'key_accomplishments': 'AI researcher with 12+ years of experience spanning classical machine learning, deep learning, and probabilistic modeling. Led groundbreaking research in reinforcement learning, generative models, and multi-task learning. Published 25+ papers in top-tier conferences (NeurIPS, ICML, ICLR). Strong track record of transitioning theoretical advances into practical applications in both academic and industrial settings.'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.data_schema = Resume\n",
-    "resume = agent.extract(\"./data/resumes/ai_researcher.pdf\")\n",
-    "resume.data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Finalizing the schema\n",
-    "\n",
-    "This is great! We have extracted a lot of key information from the resume that is well-typed and can be used downstream for further processing. Until now, this data is ephemeral and will be lost if we close the session. Let us save the state of our extraction and use it to extract data from multiple resumes. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent.save()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'type': 'object',\n",
-       " 'required': ['name',\n",
-       "  'email',\n",
-       "  'links',\n",
-       "  'experience',\n",
-       "  'education',\n",
-       "  'technical_skills',\n",
-       "  'key_accomplishments'],\n",
-       " 'properties': {'name': {'type': 'string',\n",
-       "   'description': 'The name of the candidate'},\n",
-       "  'email': {'type': 'string',\n",
-       "   'description': 'The email address of the candidate'},\n",
-       "  'links': {'type': 'array',\n",
-       "   'items': {'type': 'string'},\n",
-       "   'description': \"The links to the candidate's social media profiles\"},\n",
-       "  'education': {'type': 'array',\n",
-       "   'items': {'type': 'object',\n",
-       "    'required': ['institution', 'degree', 'start_date', 'end_date'],\n",
-       "    'properties': {'degree': {'type': 'string',\n",
-       "      'description': 'The degree of the candidate'},\n",
-       "     'end_date': {'anyOf': [{'type': 'string'}, {'type': 'null'}],\n",
-       "      'description': \"The end date of the candidate's education\"},\n",
-       "     'start_date': {'anyOf': [{'type': 'string'}, {'type': 'null'}],\n",
-       "      'description': \"The start date of the candidate's education\"},\n",
-       "     'institution': {'type': 'string',\n",
-       "      'description': 'The institution of the candidate'}},\n",
-       "    'additionalProperties': False},\n",
-       "   'description': \"The candidate's education\"},\n",
-       "  'experience': {'type': 'array',\n",
-       "   'items': {'type': 'object',\n",
-       "    'required': ['company', 'title', 'description', 'start_date', 'end_date'],\n",
-       "    'properties': {'title': {'type': 'string',\n",
-       "      'description': 'The title of the candidate'},\n",
-       "     'company': {'type': 'string', 'description': 'The name of the company'},\n",
-       "     'end_date': {'anyOf': [{'type': 'string'}, {'type': 'null'}],\n",
-       "      'description': \"The end date of the candidate's experience\"},\n",
-       "     'start_date': {'anyOf': [{'type': 'string'}, {'type': 'null'}],\n",
-       "      'description': \"The start date of the candidate's experience\"},\n",
-       "     'description': {'anyOf': [{'type': 'string'}, {'type': 'null'}],\n",
-       "      'description': \"The description of the candidate's experience\"}},\n",
-       "    'additionalProperties': False},\n",
-       "   'description': \"The candidate's experience\"},\n",
-       "  'technical_skills': {'type': 'object',\n",
-       "   'required': ['programming_languages', 'frameworks', 'skills'],\n",
-       "   'properties': {'skills': {'type': 'array',\n",
-       "     'items': {'type': 'string'},\n",
-       "     'description': 'Other general skills the candidate is proficient in, e.g. Data Engineering, Machine Learning, etc.'},\n",
-       "    'frameworks': {'type': 'array',\n",
-       "     'items': {'type': 'string'},\n",
-       "     'description': 'The tools/frameworks the candidate is proficient in, e.g. React, Django, PyTorch, etc.'},\n",
-       "    'programming_languages': {'type': 'array',\n",
-       "     'items': {'type': 'string'},\n",
-       "     'description': 'The programming languages the candidate is proficient in.'}},\n",
-       "   'description': \"The candidate's technical skills\",\n",
-       "   'additionalProperties': False},\n",
-       "  'key_accomplishments': {'type': 'string',\n",
-       "   'description': 'Summarize the candidates highest achievements.'}},\n",
-       " 'additionalProperties': False}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent = llama_extract.get_agent(\"resume-screening\")\n",
-    "agent.data_schema  # Latest schema should be returned"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Queueing extractions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For multiple resumes, we can use the `queue_extraction` method to run extractions asynchronously. This is ideal for processing batch extraction jobs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Uploading files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:01<00:00,  2.13it/s]\n",
-      "Creating extraction jobs: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00,  5.83it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "\n",
-    "# All resumes in the data/resumes directory\n",
-    "resumes = []\n",
-    "\n",
-    "with os.scandir(\"./data/resumes\") as entries:\n",
-    "    for entry in entries:\n",
-    "        if entry.is_file():\n",
-    "            resumes.append(entry.path)\n",
-    "\n",
-    "jobs = await agent.queue_extraction(resumes)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To get the latest status of the extractions for any `job_id`, we can use the `get_extraction_job` method. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<StatusEnum.PENDING: 'PENDING'>,\n",
-       " <StatusEnum.PENDING: 'PENDING'>,\n",
-       " <StatusEnum.PENDING: 'PENDING'>]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[agent.get_extraction_job(job_id=job.id).status for job in jobs]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We notice that all extraction runs are in a PENDING state. We can check back again to see if the extractions have completed. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<StatusEnum.SUCCESS: 'SUCCESS'>,\n",
-       " <StatusEnum.SUCCESS: 'SUCCESS'>,\n",
-       " <StatusEnum.SUCCESS: 'SUCCESS'>]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[agent.get_extraction_job(job_id=job.id).status for job in jobs]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Retrieving results\n",
-    "\n",
-    "Let us now retrieve the results of the extractions. If the status of the extraction is `SUCCESS`, we can retrieve the data from the `data` field. In case there are errors (status = `ERROR`), we can retrieve the error message from the `error` field. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = []\n",
-    "for job in jobs:\n",
-    "    extract_run = agent.get_extraction_run_for_job(job.id)\n",
-    "    if extract_run.status == \"SUCCESS\":\n",
-    "        results.append(extract_run.data)\n",
-    "    else:\n",
-    "        print(f\"Extraction status for job {job.id}: {extract_run.status}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'name': 'Dr. Rachel Zhang, Ph.D.',\n",
-       " 'email': 'rachel.zhang@email.com',\n",
-       " 'links': ['linkedin.com/in/rachelzhang',\n",
-       "  'github.com/rzhang-ai',\n",
-       "  'scholar.google.com/rachelzhang'],\n",
-       " 'education': [{'degree': 'Ph.D. in Computer Science',\n",
-       "   'end_date': '2011',\n",
-       "   'start_date': '2007',\n",
-       "   'institution': 'Columbia University'},\n",
-       "  {'degree': 'M.S. in Computer Science',\n",
-       "   'end_date': '2007',\n",
-       "   'start_date': '2005',\n",
-       "   'institution': 'Stanford University'}],\n",
-       " 'experience': [{'title': 'Senior Research Scientist',\n",
-       "   'company': 'DeepMind',\n",
-       "   'end_date': None,\n",
-       "   'start_date': '2019',\n",
-       "   'description': '- Lead researcher on large-scale multi-task learning systems, developing novel architectures that improve cross-task generalization by 40%\\n- Pioneered new approach to zero-shot learning using contrastive training, published in NeurIPS 2023\\n- Built and led team of 6 researchers working on foundational ML models\\n- Developed novel regularization techniques for large language models, reducing catastrophic forgetting by 35%'},\n",
-       "  {'title': 'Research Scientist',\n",
-       "   'company': 'Google Research',\n",
-       "   'end_date': '2019',\n",
-       "   'start_date': '2015',\n",
-       "   'description': '- Developed probabilistic frameworks for robust ML, published in ICML 2018\\n- Created novel attention mechanisms for computer vision models, improving accuracy by 25%\\n- Led collaboration with Google Brain team on efficient training methods for transformer models\\n- Mentored 4 PhD interns and collaborated with academic institutions'},\n",
-       "  {'title': 'Research Assistant Professor',\n",
-       "   'company': 'Columbia University',\n",
-       "   'end_date': '2015',\n",
-       "   'start_date': '2011',\n",
-       "   'description': '- Published seminal work on Bayesian optimization methods (cited 1000+ times)\\n- Taught graduate-level courses in Machine Learning and Statistical Learning Theory\\n- Supervised 5 PhD students and 3 MSc students\\n- Secured $500K in research grants for probabilistic ML research'}],\n",
-       " 'technical_skills': {'skills': ['Deep Learning',\n",
-       "   'Reinforcement Learning',\n",
-       "   'Probabilistic Models',\n",
-       "   'Multi-Task Learning',\n",
-       "   'Zero-Shot Learning',\n",
-       "   'Neural Architecture Search'],\n",
-       "  'frameworks': ['PyTorch', 'TensorFlow', 'JAX', 'Ray'],\n",
-       "  'programming_languages': ['Python', 'C++', 'Julia', 'CUDA']},\n",
-       " 'key_accomplishments': 'AI researcher with 12+ years of experience spanning classical machine learning, deep learning, and probabilistic modeling. Led groundbreaking research in reinforcement learning, generative models, and multi-task learning. Published 25+ papers in top-tier conferences (NeurIPS, ICML, ICLR). Strong track record of transitioning theoretical advances into practical applications in both academic and industrial settings.'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'name': 'Alex Park',\n",
-       " 'email': 'alex park@email.com',\n",
-       " 'links': ['linkedin.com/in/alexpark'],\n",
-       " 'education': [{'degree': 'M.S. Computer Science',\n",
-       "   'end_date': None,\n",
-       "   'start_date': None,\n",
-       "   'institution': 'University of California, Berkeley'},\n",
-       "  {'degree': 'B.S. Computer Science',\n",
-       "   'end_date': None,\n",
-       "   'start_date': None,\n",
-       "   'institution': 'University of California, Berkeley'}],\n",
-       " 'experience': [{'title': 'Senior Machine Learning Engineer',\n",
-       "   'company': 'SearchTech AI',\n",
-       "   'end_date': None,\n",
-       "   'start_date': None,\n",
-       "   'description': 'Led development of next-generation learning-to-rank system using BER\\nArchitected and deployed real-time personalization system processing 10\\nIncreasing CTR by 15%\\nImproving search relevance by 24% (NDCG@10)'},\n",
-       "  {'title': '',\n",
-       "   'company': 'Commerce Corp',\n",
-       "   'end_date': None,\n",
-       "   'start_date': None,\n",
-       "   'description': 'Developed semantic search system using transformer models and approximate nearest neighbors, reducing null search results by 35%'},\n",
-       "  {'title': 'Machine Learning Engineer',\n",
-       "   'company': 'Tech Solutions Inc',\n",
-       "   'end_date': None,\n",
-       "   'start_date': None,\n",
-       "   'description': 'Implemented query understanding pipeline'},\n",
-       "  {'title': 'Software Engineer',\n",
-       "   'company': '',\n",
-       "   'end_date': None,\n",
-       "   'start_date': None,\n",
-       "   'description': 'Built data pipelines and Flasticsearch'}],\n",
-       " 'technical_skills': {'skills': ['Elasticsearch',\n",
-       "   'Solr',\n",
-       "   'Lucene',\n",
-       "   'Python',\n",
-       "   'SQL',\n",
-       "   'Java',\n",
-       "   'Scala',\n",
-       "   'Shell Scripting'],\n",
-       "  'frameworks': ['PyTorch',\n",
-       "   'TensorFlow',\n",
-       "   'Scikit-learn',\n",
-       "   'BERT',\n",
-       "   'Word2Vec',\n",
-       "   'FastAI',\n",
-       "   'BM25',\n",
-       "   'FAISS',\n",
-       "   'Docker',\n",
-       "   'Kubernetes'],\n",
-       "  'programming_languages': []},\n",
-       " 'key_accomplishments': 'Machine Learning Engineer with 5 years of experience building and deploying large-scale search and relevance systems: Specialized in developing personalized search algorithms, learning-to-rank models; and recommendation systems. Strong track record of improving search relevance metrics and user engagement through ML-driven solutions:'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'name': 'Sarah Chen',\n",
-       " 'email': 'sarah.chen@email.com',\n",
-       " 'links': [],\n",
-       " 'education': [{'degree': 'Master of Science in Computer Science',\n",
-       "   'end_date': '2013',\n",
-       "   'start_date': None,\n",
-       "   'institution': 'Stanford University'},\n",
-       "  {'degree': 'Bachelor of Science in Computer Engineering',\n",
-       "   'end_date': '2011',\n",
-       "   'start_date': None,\n",
-       "   'institution': 'University of California, Berkeley'}],\n",
-       " 'experience': [{'title': 'Senior Software Architect',\n",
-       "   'company': 'TechCorp Solutions',\n",
-       "   'end_date': None,\n",
-       "   'start_date': '2020',\n",
-       "   'description': '- Led architectural design and implementation of a cloud-native platform serving 2M+ users\\n- Established architectural guidelines and best practices adopted across 12 development teams\\n- Reduced system latency by 40% through implementation of event-driven architecture\\n- Mentored 15+ senior developers in cloud-native development practices'},\n",
-       "  {'title': 'Lead Software Engineer',\n",
-       "   'company': 'DataFlow Systems',\n",
-       "   'end_date': '2020',\n",
-       "   'start_date': '2016',\n",
-       "   'description': '- Architected and led development of distributed data processing platform handling 5TB daily\\n- Designed microservices architecture reducing deployment time by 65%\\n- Led migration of legacy monolith to cloud-native architecture\\n- Managed team of 8 engineers across 3 international locations'},\n",
-       "  {'title': 'Senior Software Engineer',\n",
-       "   'company': 'InnovateTech',\n",
-       "   'end_date': '2016',\n",
-       "   'start_date': '2013',\n",
-       "   'description': '- Developed high-performance trading platform processing 100K transactions per second\\n- Implemented real-time analytics engine reducing processing latency by 75%\\n- Led adoption of container orchestration reducing deployment costs by 35%'}],\n",
-       " 'technical_skills': {'skills': ['Architecture & Design',\n",
-       "   'Microservices',\n",
-       "   'Event-Driven Architecture',\n",
-       "   'Domain-Driven Design',\n",
-       "   'REST APIs',\n",
-       "   'Cloud Platforms'],\n",
-       "  'frameworks': ['AWS (Advanced)', 'Azure', 'Google Cloud Platform'],\n",
-       "  'programming_languages': ['Java', 'Python', 'Go', 'JavaScript/TypeScript']},\n",
-       " 'key_accomplishments': '- Co-inventor on three patents for distributed systems architecture\\n- Published paper on \"Scalable Microservices Architecture\" at IEEE Cloud Computing Conference 2022\\n- Keynote Speaker, CloudCon 2023: \"Future of Cloud-Native Architecture\"\\n- Regular presenter at local tech meetups and conferences'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results[2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Congratulations! You now have an agent that can extract structured data from resumes. \n",
-    "- You can now use this agent to extract data from more resumes and use the extracted data for further processing. \n",
-    "- To update the schema, you can simply update the `data_schema` attribute of the agent and re-run the extraction. \n",
-    "- You can also use the `save` method to save the state of the agent and persist changes to the schema for future use. \n",
-    "\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/examples-py/extract/solar_panel_e2e_comparison.ipynb

@@ -1,450 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "00f6713b-2a32-4f8f-80e5-9a7d9b6e3b90",
-   "metadata": {},
-   "source": [
-    "# Solar Panel Datasheet Comparison Workflow\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/extract/solar_panel_e2e_comparison.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
-    "\n",
-    "\n",
-    "This notebook demonstrates an end‑to‑end agentic workflow using LlamaExtract and the LlamaIndex event‑driven workflow framework. In this workflow, we:\n",
-    "\n",
-    "1. **Extract** structured technical specifications from a solar panel datasheet (e.g. a PDF downloaded from a vendor).\n",
-    "2. **Load** design requirements (provided as a text blob) for a lab‑grade solar panel.\n",
-    "3. **Generate** a detailed comparison report by triggering an event that injects both the extracted data and the requirements into an LLM prompt.\n",
-    "\n",
-    "The workflow is designed for renewable energy engineers who need to quickly validate that a solar panel meets specific design criteria.\n",
-    "\n",
-    "The following notebook uses the event‑driven syntax (with custom events, steps, and a workflow class) adapted from the technical datasheet and contract review examples."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "36d8e34e-ed98-46ac-b744-1642f6e253d5",
-   "metadata": {},
-   "source": [
-    "## Setup and Load Data\n",
-    "\n",
-    "We download the [Honey M TSM-DE08M.08(II) datasheet](https://static.trinasolar.com/sites/default/files/EU_Datasheet_HoneyM_DE08M.08%28II%29_2021_A.pdf) as a PDF.\n",
-    "\n",
-    "**NOTE**: The design requirements are already stored in `data/solar_panel_e2e_comparison/design_reqs.txt`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1de7b1b3-c285-492c-8b2e-b37974b4fc63",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--2025-04-01 14:47:56--  https://static.trinasolar.com/sites/default/files/EU_Datasheet_HoneyM_DE08M.08%28II%29_2021_A.pdf\n",
-      "Resolving static.trinasolar.com (static.trinasolar.com)... 47.246.23.232, 47.246.23.234, 47.246.23.227, ...\n",
-      "Connecting to static.trinasolar.com (static.trinasolar.com)|47.246.23.232|:443... connected.\n",
-      "WARNING: cannot verify static.trinasolar.com's certificate, issued by ‘CN=DigiCert Global G2 TLS RSA SHA256 2020 CA1,O=DigiCert Inc,C=US’:\n",
-      "  Unable to locally verify the issuer's authority.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 1888183 (1.8M) [application/pdf]\n",
-      "Saving to: ‘data/solar_panel_e2e_comparison/datasheet.pdf’\n",
-      "\n",
-      "data/solar_panel_e2 100%[===================>]   1.80M  7.47MB/s    in 0.2s    \n",
-      "\n",
-      "2025-04-01 14:47:56 (7.47 MB/s) - ‘data/solar_panel_e2e_comparison/datasheet.pdf’ saved [1888183/1888183]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "!wget https://static.trinasolar.com/sites/default/files/EU_Datasheet_HoneyM_DE08M.08%28II%29_2021_A.pdf -O data/solar_panel_e2e_comparison/datasheet.pdf --no-check-certificate"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "89d2f4c9-f785-424d-a409-3381796c457c",
-   "metadata": {},
-   "source": [
-    "## Define the Structured Extraction Schema\n",
-    "\n",
-    "We define a new, rich schema called `SolarPanelSchema` to capture key technical details from the datasheet. This schema includes:\n",
-    "\n",
-    "- **PowerRange:** Structured as minimum and maximum power output (in Watts).\n",
-    "- **SolarPanelSpec:** Includes module name, power output range, maximum efficiency, certifications, and a mapping of page citations.\n",
-    "\n",
-    "This schema replaces the earlier LM317 schema and will be used when creating our extraction agent."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bfb40d48-36e0-4b1c-97a1-32a1704c582b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pydantic import BaseModel, Field\n",
-    "from typing import List\n",
-    "\n",
-    "\n",
-    "class PowerRange(BaseModel):\n",
-    "    min_power: float = Field(..., description=\"Minimum power output in Watts\")\n",
-    "    max_power: float = Field(..., description=\"Maximum power output in Watts\")\n",
-    "    unit: str = Field(\"W\", description=\"Power unit\")\n",
-    "\n",
-    "\n",
-    "class SolarPanelSpec(BaseModel):\n",
-    "    module_name: str = Field(..., description=\"Name or model of the solar panel module\")\n",
-    "    power_output: PowerRange = Field(..., description=\"Power output range\")\n",
-    "    maximum_efficiency: float = Field(\n",
-    "        ..., description=\"Maximum module efficiency in percentage\"\n",
-    "    )\n",
-    "    temperature_coefficient: float = Field(\n",
-    "        ..., description=\"Temperature coefficient in %/°C\"\n",
-    "    )\n",
-    "    certifications: List[str] = Field([], description=\"List of certifications\")\n",
-    "    page_citations: dict = Field(\n",
-    "        ..., description=\"Mapping of each extracted field to its page numbers\"\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "class SolarPanelSchema(BaseModel):\n",
-    "    specs: List[SolarPanelSpec] = Field(\n",
-    "        ..., description=\"List of extracted solar panel specifications\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "19dc309e-7cec-43c1-8f6c-72e14df58f8f",
-   "metadata": {},
-   "source": [
-    "## Initialize Extraction Agent\n",
-    "\n",
-    "Here we initialize our extraction agent that will be responsible for extracting the schema from the solar panel datasheet."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c9d9f4a2-2e14-493d-8a7e-d01159d38b8f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dotenv import load_dotenv\n",
-    "from llama_cloud_services import LlamaExtract\n",
-    "from llama_cloud.core.api_error import ApiError\n",
-    "from llama_cloud import ExtractConfig\n",
-    "\n",
-    "# Initialize the LlamaExtract client\n",
-    "llama_extract = LlamaExtract(\n",
-    "    project_id=\"2fef999e-1073-40e6-aeb3-1f3c0e64d99b\",\n",
-    "    organization_id=\"43b88c8f-e488-46f6-9013-698e3d2e374a\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ec0eb2a7-6e02-45da-a6af-227e2f7c81f2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    existing_agent = llama_extract.get_agent(name=\"solar-panel-datasheet\")\n",
-    "    if existing_agent:\n",
-    "        llama_extract.delete_agent(existing_agent.id)\n",
-    "except ApiError as e:\n",
-    "    if e.status_code == 404:\n",
-    "        pass\n",
-    "    else:\n",
-    "        raise\n",
-    "\n",
-    "extract_config = ExtractConfig(\n",
-    "    extraction_mode=\"BALANCED\",\n",
-    ")\n",
-    "\n",
-    "agent = llama_extract.create_agent(\n",
-    "    name=\"solar-panel-datasheet\", data_schema=SolarPanelSchema, config=extract_config\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b4d7bb60-0456-4a2d-8d48-14f9bb3e71d2",
-   "metadata": {},
-   "source": [
-    "## Workflow Overview\n",
-    "\n",
-    "The workflow consists of four main steps:\n",
-    "\n",
-    "1. **parse_datasheet:** Reads the solar panel datasheet (PDF) and converts its content into text (with page citations).\n",
-    "2. **load_requirements:** Loads the design requirements (as a text blob) that will be injected into the prompt.\n",
-    "3. **generate_comparison_report:** Constructs a prompt using the extracted datasheet content and design requirements and triggers the LLM to generate a comparison report.\n",
-    "4. **output_result:** Logs and returns the final report as the workflow’s result.\n",
-    "\n",
-    "Each step is implemented as an asynchronous function decorated with `@step`, and the workflow is built by subclassing `Workflow`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7c482e3a-66b4-4e1b-8d2d-9a9c6b3967f3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_index.core.workflow import (\n",
-    "    Event,\n",
-    "    StartEvent,\n",
-    "    StopEvent,\n",
-    "    Context,\n",
-    "    Workflow,\n",
-    "    step,\n",
-    ")\n",
-    "from llama_index.llms.openai import OpenAI\n",
-    "from llama_index.core.prompts import ChatPromptTemplate\n",
-    "from llama_cloud_services import LlamaExtract\n",
-    "from llama_cloud.core.api_error import ApiError\n",
-    "from pydantic import BaseModel, Field\n",
-    "from typing import List\n",
-    "\n",
-    "\n",
-    "# Define output schema for the comparison report (for reference)\n",
-    "class ComparisonReportOutput(BaseModel):\n",
-    "    component_name: str = Field(\n",
-    "        ..., description=\"The name of the component being evaluated.\"\n",
-    "    )\n",
-    "    meets_requirements: bool = Field(\n",
-    "        ...,\n",
-    "        description=\"Overall indicator of whether the component meets the design criteria.\",\n",
-    "    )\n",
-    "    summary: str = Field(..., description=\"A brief summary of the evaluation results.\")\n",
-    "    details: dict = Field(\n",
-    "        ..., description=\"Detailed comparisons for each key parameter.\"\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "# Define custom events\n",
-    "\n",
-    "\n",
-    "class DatasheetParseEvent(Event):\n",
-    "    datasheet_content: dict\n",
-    "\n",
-    "\n",
-    "class RequirementsLoadEvent(Event):\n",
-    "    requirements_text: str\n",
-    "\n",
-    "\n",
-    "class ComparisonReportEvent(Event):\n",
-    "    report: ComparisonReportOutput\n",
-    "\n",
-    "\n",
-    "class LogEvent(Event):\n",
-    "    msg: str\n",
-    "    delta: bool = False\n",
-    "\n",
-    "\n",
-    "# For our demonstration, we assume that LlamaExtract is used to parse the datasheet into text.\n",
-    "# We'll also use OpenAI (via LlamaIndex) as our LLM for generating the report.\n",
-    "\n",
-    "llm = OpenAI(model=\"gpt-4o\")  # or your preferred model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "67a0c391-c7f5-4b93-8d6b-9e31b2d7a817",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class SolarPanelComparisonWorkflow(Workflow):\n",
-    "    \"\"\"\n",
-    "    Workflow to extract data from a solar panel datasheet and generate a comparison report\n",
-    "    against provided design requirements.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    def __init__(self, agent: LlamaExtract, requirements_path: str, **kwargs):\n",
-    "        super().__init__(**kwargs)\n",
-    "        self.agent = agent\n",
-    "        # Load design requirements from file as a text blob\n",
-    "        with open(requirements_path, \"r\") as f:\n",
-    "            self.requirements_text = f.read()\n",
-    "\n",
-    "    @step\n",
-    "    async def parse_datasheet(\n",
-    "        self, ctx: Context, ev: StartEvent\n",
-    "    ) -> DatasheetParseEvent:\n",
-    "        # datasheet_path is provided in the StartEvent\n",
-    "        datasheet_path = (\n",
-    "            ev.datasheet_path\n",
-    "        )  # e.g., \"./data/solar_panel_comparison/datasheet.pdf\"\n",
-    "        extraction_result = await self.agent.aextract(datasheet_path)\n",
-    "        datasheet_dict = (\n",
-    "            extraction_result.data\n",
-    "        )  # assumed to be a string with page citations\n",
-    "        await ctx.set(\"datasheet_content\", datasheet_dict)\n",
-    "        ctx.write_event_to_stream(LogEvent(msg=\"Datasheet parsed successfully.\"))\n",
-    "        return DatasheetParseEvent(datasheet_content=datasheet_dict)\n",
-    "\n",
-    "    @step\n",
-    "    async def load_requirements(\n",
-    "        self, ctx: Context, ev: DatasheetParseEvent\n",
-    "    ) -> RequirementsLoadEvent:\n",
-    "        # Use the pre-loaded requirements text from __init__\n",
-    "        req_text = self.requirements_text\n",
-    "        ctx.write_event_to_stream(LogEvent(msg=\"Design requirements loaded.\"))\n",
-    "        return RequirementsLoadEvent(requirements_text=req_text)\n",
-    "\n",
-    "    @step\n",
-    "    async def generate_comparison_report(\n",
-    "        self, ctx: Context, ev: RequirementsLoadEvent\n",
-    "    ) -> StopEvent:\n",
-    "        # Build a prompt that injects both the extracted datasheet content and the design requirements\n",
-    "        datasheet_content = await ctx.get(\"datasheet_content\")\n",
-    "        prompt_str = \"\"\"\n",
-    "You are an expert renewable energy engineer.\n",
-    "\n",
-    "Compare the following solar panel datasheet information with the design requirements.\n",
-    "\n",
-    "Design Requirements:\n",
-    "{requirements_text}\n",
-    "\n",
-    "Extracted Datasheet Information:\n",
-    "{datasheet_content}\n",
-    "\n",
-    "Generate a detailed comparison report in JSON format with the following schema:\n",
-    "  - component_name: string\n",
-    "  - meets_requirements: boolean\n",
-    "  - summary: string\n",
-    "  - details: dictionary of comparisons for each parameter\n",
-    "\n",
-    "For each parameter (Maximum Power, Open-Circuit Voltage, Short-Circuit Current, Efficiency, Temperature Coefficient),\n",
-    "indicate PASS or FAIL and provide brief explanations and recommendations.\n",
-    "\"\"\"\n",
-    "\n",
-    "        # extract from contract\n",
-    "        prompt = ChatPromptTemplate.from_messages([(\"user\", prompt_str)])\n",
-    "\n",
-    "        # Call the LLM to generate the report using the prompt\n",
-    "        report_output = await llm.astructured_predict(\n",
-    "            ComparisonReportOutput,\n",
-    "            prompt,\n",
-    "            requirements_text=ev.requirements_text,\n",
-    "            datasheet_content=str(datasheet_content),\n",
-    "        )\n",
-    "        ctx.write_event_to_stream(LogEvent(msg=\"Comparison report generated.\"))\n",
-    "        return StopEvent(\n",
-    "            result={\"report\": report_output, \"datasheet_content\": datasheet_content}\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d205f532-1a11-4a48-b5a8-87a7f85e9ce7",
-   "metadata": {},
-   "source": [
-    "## Running the Workflow\n",
-    "\n",
-    "Below, we instantiate and run the workflow. We inject the design requirements as a text blob (no custom code to load) and pass the path to the solar panel datasheet (the HoneyM datasheet from Trina).\n",
-    "\n",
-    "The design requirements are:\n",
-    "\n",
-    "```\n",
-    "Solar Panel Design Requirements:\n",
-    "- Power Output Range: ≥ 350 W\n",
-    "- Maximum Efficiency: ≥ 18%\n",
-    "- Certifications: Must include IEC61215 and UL1703\n",
-    "```\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6b24fa61-a2f5-4ebb-84eb-1c9b48683b1b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import nest_asyncio\n",
-    "\n",
-    "nest_asyncio.apply()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "be3ebad5-1f70-4671-a2ec-17bf9e4d788f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Path to design requirements file (e.g., a text file with design criteria for solar panels)\n",
-    "requirements_path = \"./data/solar_panel_e2e_comparison/design_reqs.txt\"\n",
-    "\n",
-    "# Instantiate the workflow\n",
-    "workflow = SolarPanelComparisonWorkflow(\n",
-    "    agent=agent, requirements_path=requirements_path, verbose=True, timeout=120\n",
-    ")\n",
-    "\n",
-    "# Run the workflow; pass the datasheet path in the StartEvent\n",
-    "result = await workflow.run(\n",
-    "    datasheet_path=\"./data/solar_panel_e2e_comparison/datasheet.pdf\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e1e61f1e-8701-4acc-8f99-cc89d8aae535",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "********Final Comparison Report:********\n",
-      "\n",
-      "{\n",
-      "    \"component_name\": \"TSM-DE08M.08(II)\",\n",
-      "    \"meets_requirements\": true,\n",
-      "    \"summary\": \"The solar panel TSM-DE08M.08(II) meets all the design requirements, making it a suitable choice for the intended application.\",\n",
-      "    \"details\": {\n",
-      "        \"Maximum Power Output\": \"PASS - The panel's power output ranges from 360 W to 385 W, exceeding the minimum requirement of 350 W.\",\n",
-      "        \"Open-Circuit Voltage\": \"PASS - The datasheet does not specify Voc, but the panel meets other critical requirements. Verification of Voc is recommended.\",\n",
-      "        \"Short-Circuit Current\": \"PASS - The datasheet does not specify Isc, but the panel meets other critical requirements. Verification of Isc is recommended.\",\n",
-      "        \"Efficiency\": \"PASS - The panel's efficiency is 21.0%, which is above the required 18%.\",\n",
-      "        \"Temperature Coefficient\": \"PASS - The temperature coefficient is -0.34%/°C, which is better than the maximum allowable -0.5%/°C.\"\n",
-      "    }\n",
-      "}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"\\n********Final Comparison Report:********\\n\")\n",
-    "print(result[\"report\"].model_dump_json(indent=4))\n",
-    "# print(\"\\n********Datasheet Content:********\\n\", result[\"datasheet_content\"])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "llama_parse",
-   "language": "python",
-   "name": "llama_parse"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/examples-py/parse/demo_advanced.ipynb

@@ -1,618 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Advanced RAG with LlamaParse\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/demo_advanced.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
-    "\n",
-    "This notebook is a complete walkthrough for using LlamaParse with advanced indexing/retrieval techniques in LlamaIndex over the Apple 10K Filing. \n",
-    "\n",
-    "This allows us to ask sophisticated questions that aren't possible with \"naive\" parsing/indexing techniques with existing models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install llama-index llama-cloud-services"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget \"https://s2.q4cdn.com/470004039/files/doc_financials/2021/q4/_10-K-2021-(As-Filed).pdf\" -O apple_2021_10k.pdf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Some OpenAI and LlamaParse details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "# API access to llama-cloud\n",
-    "os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"llx-...\"\n",
-    "\n",
-    "# Using OpenAI API for embeddings/llms\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-...\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_index.llms.openai import OpenAI\n",
-    "from llama_index.embeddings.openai import OpenAIEmbedding\n",
-    "from llama_index.core import Settings\n",
-    "\n",
-    "embed_model = OpenAIEmbedding(model_name=\"text-embedding-3-small\")\n",
-    "llm = OpenAI(model=\"gpt-4o-mini\")\n",
-    "\n",
-    "Settings.llm = llm\n",
-    "Settings.embed_model = embed_model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Using brand new `LlamaParse` PDF reader for PDF Parsing\n",
-    "\n",
-    "We also compare three different retrieval/query engine strategies:\n",
-    "1. Baseline using default parsing from `SimpleDirectoryReader`\n",
-    "2. Using raw markdown text as nodes for building index and apply simple query engine for generating the results;\n",
-    "3. Using markdown + page screenshots to help retrieve the proper nodes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Started parsing the file under job_id e403a457-1721-4093-82bf-4a316d2d637a\n"
-     ]
-    }
-   ],
-   "source": [
-    "from llama_cloud_services import LlamaParse\n",
-    "\n",
-    "result = await LlamaParse(take_screenshot=True).aparse(\"./apple_2021_10k.pdf\")\n",
-    "\n",
-    "markdown_nodes = await result.aget_markdown_nodes(split_by_page=True)\n",
-    "screenshot_image_nodes = await result.aget_image_nodes(\n",
-    "    include_screenshot_images=True,\n",
-    "    include_object_images=False,\n",
-    "    image_download_dir=\"./images\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_index.core import SimpleDirectoryReader\n",
-    "\n",
-    "baseline_documents = SimpleDirectoryReader(\n",
-    "    input_files=[\"apple_2021_10k.pdf\"]\n",
-    ").load_data()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup Baseline Index\n",
-    "\n",
-    "For comparison, we setup a naive RAG pipeline with default parsing and standard chunking, indexing, retrieval."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_index.core import VectorStoreIndex\n",
-    "\n",
-    "baseline_index = VectorStoreIndex.from_documents(baseline_documents)\n",
-    "baseline_query_engine = baseline_index.as_query_engine(similarity_top_k=3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup our LlamaParse Indexes\n",
-    "\n",
-    "Using both the markdown and screenshot images, we can build two different indexes.\n",
-    "\n",
-    "1. An index over just the markdown documents\n",
-    "2. A custom index that uses the markdown + screenshot images to help with response quality."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_index.core import VectorStoreIndex\n",
-    "\n",
-    "markdown_index = VectorStoreIndex(nodes=markdown_nodes)\n",
-    "markdown_query_engine = markdown_index.as_query_engine(similarity_top_k=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_index.core.indices import MultiModalVectorStoreIndex\n",
-    "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
-    "from llama_index.core import Settings\n",
-    "\n",
-    "# could also use other API-based multimodal models like voyageai or jinaai\n",
-    "# Note: this may take quite a while if running on CPU!\n",
-    "image_embed_model = HuggingFaceEmbedding(\n",
-    "    model_name=\"llamaindex/vdr-2b-multi-v1\",\n",
-    "    embed_batch_size=2,\n",
-    "    trust_remote_code=True,\n",
-    "    cache_folder=\"./hf_cache_2\",\n",
-    "    device=\"cpu\",  # set to \"cuda\" if you have a GPU or remove to auto-detect\n",
-    ")\n",
-    "\n",
-    "multi_modal_index = MultiModalVectorStoreIndex(\n",
-    "    nodes=[*markdown_nodes, *screenshot_image_nodes],\n",
-    "    embed_model=Settings.embed_model,\n",
-    "    image_embed_model=image_embed_model,\n",
-    "    show_progress=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Below, we will create a custom query engine that does a few things\n",
-    "1. Retrieves both image nodes and text nodes\n",
-    "2. Combines them into two lists -- one where images and texts come from the same page, and one where we have texts alone\n",
-    "3. Use a Jinja-based `RichPromptTemplate` to format the retrieved content automatically into a list of multimodal chat messages\n",
-    "4. Send our messages to the LLM and return a result\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_index.core.async_utils import asyncio_run\n",
-    "from llama_index.core.llms import LLM\n",
-    "from llama_index.core.query_engine import CustomQueryEngine\n",
-    "from llama_index.core.prompts import RichPromptTemplate\n",
-    "from llama_index.core.response import Response\n",
-    "from llama_index.core.schema import NodeWithScore\n",
-    "from llama_index.core import Settings\n",
-    "\n",
-    "TEXT_IMAGE_PROMPT_TEMPLATE = RichPromptTemplate(\n",
-    "    \"\"\"\n",
-    "<context>\n",
-    "Here is some retrieved content from a knowledge base:\n",
-    "{% for image_path, text in images_and_texts %}\n",
-    "<page>\n",
-    "<text>{{ text }}</text>\n",
-    "<image>{{ image_path | image }}</image>\n",
-    "</page>\n",
-    "{% endfor %}\n",
-    "{% for text in texts %}\n",
-    "<page>\n",
-    "<text>{{ text }}</text>\n",
-    "</page>\n",
-    "{% endfor %}\n",
-    "</context>\n",
-    "\n",
-    "Using the context, answer the following question:\n",
-    "<query>{{ query_str }}</query>\n",
-    "\"\"\"\n",
-    ")\n",
-    "\n",
-    "\n",
-    "class SimpleMultiModalQueryEngine(CustomQueryEngine):\n",
-    "    def __init__(\n",
-    "        self,\n",
-    "        index: MultiModalVectorStoreIndex,\n",
-    "        image_top_k: int = 4,\n",
-    "        text_top_k: int = 4,\n",
-    "        llm: LLM | None = None,\n",
-    "        **kwargs\n",
-    "    ):\n",
-    "        super().__init__(**kwargs)\n",
-    "        self._retriever = index.as_retriever(\n",
-    "            similarity_top_k=text_top_k, image_similarity_top_k=image_top_k\n",
-    "        )\n",
-    "        self._llm = llm or Settings.llm\n",
-    "\n",
-    "    def _match_images_and_texts(\n",
-    "        self, text_results: list[NodeWithScore], image_results: list[NodeWithScore]\n",
-    "    ) -> tuple[list[NodeWithScore], list[NodeWithScore]]:\n",
-    "        # combine results, prioritize images and texts\n",
-    "        # if both an image and matching text was retrieved, that is a strong indicator\n",
-    "        images_and_texts = []\n",
-    "        text_keys = {\n",
-    "            (x.metadata[\"page_number\"], x.metadata[\"file_name\"]): x\n",
-    "            for x in text_results\n",
-    "        }\n",
-    "        for image_result in image_results:\n",
-    "            key = (\n",
-    "                image_result.metadata[\"page_number\"],\n",
-    "                image_result.metadata[\"file_name\"],\n",
-    "            )\n",
-    "            # add matching text to results if available\n",
-    "            if key in text_keys:\n",
-    "                text_result = text_keys[key]\n",
-    "                images_and_texts.append(\n",
-    "                    (image_result.node.image_path, text_result.node.text)\n",
-    "                )\n",
-    "\n",
-    "                # remove from list\n",
-    "                text_keys.pop(key)\n",
-    "\n",
-    "        # get the remaining texts as a fallback\n",
-    "        texts = [result.node.text for result in text_keys.values()]\n",
-    "\n",
-    "        return images_and_texts, texts\n",
-    "\n",
-    "    def custom_query(self, query_str: str) -> Response:\n",
-    "        # wrap the async method to avoid code duplication\n",
-    "        # asyncio_run is a slightly safer asyncio.run() call\n",
-    "        return asyncio_run(self.acustom_query(query_str))\n",
-    "\n",
-    "    async def acustom_query(self, query_str: str) -> Response:\n",
-    "        text_results = await self._retriever.atext_retrieve(query_str)\n",
-    "        image_results = await self._retriever.atext_to_image_retrieve(query_str)\n",
-    "\n",
-    "        images_and_texts, texts = self._match_images_and_texts(\n",
-    "            text_results, image_results\n",
-    "        )\n",
-    "        messages = TEXT_IMAGE_PROMPT_TEMPLATE.format_messages(\n",
-    "            images_and_texts=images_and_texts, texts=texts, query_str=str(query_str)\n",
-    "        )\n",
-    "\n",
-    "        response = await self._llm.achat(messages)\n",
-    "\n",
-    "        return Response(\n",
-    "            response.message.content, source_nodes=[*text_results, *image_results]\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "multimodal_query_engine = SimpleMultiModalQueryEngine(\n",
-    "    index=multi_modal_index,\n",
-    "    image_top_k=3,\n",
-    "    text_top_k=3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Try out the Query Engines and Compare!\n",
-    "\n",
-    "Now with our three query engines assembled, we can compare each approach with a rough \"vibes-based\" evaluation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "***********Baseline Query Engine***********\n",
-      "The total fair value of marketable securities in 2020 was $190,516 million.\n",
-      "\n",
-      "***********Markdown Query Engine***********\n",
-      "The total fair value of marketable securities in 2020 was $191,830 million.\n",
-      "\n",
-      "***********MultiModal Query Engine***********\n",
-      "The total fair value of marketable securities in 2020 was $191,830 million.\n"
-     ]
-    }
-   ],
-   "source": [
-    "query = \"What were the total fair value of marketable securities in 2020\"\n",
-    "\n",
-    "response_1 = await baseline_query_engine.aquery(query)\n",
-    "print(\"\\n***********Baseline Query Engine***********\")\n",
-    "print(response_1)\n",
-    "\n",
-    "response_2 = await markdown_query_engine.aquery(query)\n",
-    "print(\"\\n***********Markdown Query Engine***********\")\n",
-    "print(response_2)\n",
-    "\n",
-    "response_3 = await multimodal_query_engine.aquery(query)\n",
-    "print(\"\\n***********MultiModal Query Engine***********\")\n",
-    "print(response_3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we can see, the multimodal and markdown query engines are able to retrieve the correct content, while the default query engine struggles to find the correct total value."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also inspect the source nodes, and see the pages that were retrieved. Here is the correct page for the total fair value of marketable securities in 2020:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'images/page_41.jpg'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "response_3.source_nodes[4].node.image_path"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Lets try a few more queries to see how the query engines perform."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "***********Baseline Query Engine***********\n",
-      "The effective interest rates for the debt issuances in 2021 were as follows:\n",
-      "\n",
-      "- Floating-rate notes: 0.48% – 0.63%\n",
-      "- Fixed-rate notes: 0.03% – 4.78% for maturities from 2022 to 2060\n",
-      "- Fixed-rate notes issued in the second quarter: 0.75% – 2.81% for maturities from 2026 to 2061\n",
-      "- Fixed-rate notes issued in the fourth quarter: 1.43% – 2.86% for maturities from 2028 to 2061\n",
-      "\n",
-      "***********Markdown Query Engine***********\n",
-      "The effective interest rates for the debt issuances in 2021 were as follows:\n",
-      "\n",
-      "- Floating-rate notes: 0.48% – 0.63%\n",
-      "- Fixed-rate notes: 0.03% – 4.78% for the 0.000% – 4.650% notes, 0.75% – 2.81% for the 0.700% – 2.800% notes, and 1.43% – 2.86% for the 1.400% – 2.850% notes.\n",
-      "\n",
-      "***********MultiModal Query Engine***********\n",
-      "The effective interest rates of all debt issuances in 2021 were as follows:\n",
-      "\n",
-      "1. **Floating-rate notes**: 0.48% – 0.63%\n",
-      "2. **Fixed-rate 0.000% – 4.650% notes**: 0.03% – 4.78%\n",
-      "3. **Fixed-rate 0.700% – 2.800% notes**: 0.75% – 2.81%\n",
-      "4. **Fixed-rate 1.400% – 2.850% notes**: 1.43% – 2.86%\n"
-     ]
-    }
-   ],
-   "source": [
-    "query = \"What were the effective interest rates of all debt issuances in 2021\"\n",
-    "\n",
-    "response_1 = await baseline_query_engine.aquery(query)\n",
-    "print(\"\\n***********Baseline Query Engine***********\")\n",
-    "print(response_1)\n",
-    "\n",
-    "response_2 = await markdown_query_engine.aquery(query)\n",
-    "print(\"\\n***********Markdown Query Engine***********\")\n",
-    "print(response_2)\n",
-    "\n",
-    "response_3 = await multimodal_query_engine.aquery(query)\n",
-    "print(\"\\n***********MultiModal Query Engine***********\")\n",
-    "print(response_3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "***********Baseline Query Engine***********\n",
-      "The federal deferred tax amounts for the years 2019 to 2021 are as follows (in millions):\n",
-      "\n",
-      "- **2019**: $(2,939)\n",
-      "- **2020**: $(3,619)\n",
-      "- **2021**: $(7,176)\n",
-      "\n",
-      "These figures represent the deferred tax expense for each respective year.\n",
-      "\n",
-      "***********Markdown Query Engine***********\n",
-      "As of September 25, 2021, the total deferred tax assets and liabilities for the years 2021 and 2020 are as follows:\n",
-      "\n",
-      "**Deferred Tax Assets:**\n",
-      "- 2021: $25,176 million\n",
-      "- 2020: $19,336 million\n",
-      "\n",
-      "**Deferred Tax Liabilities:**\n",
-      "- 2021: $7,200 million\n",
-      "- 2020: $10,138 million\n",
-      "\n",
-      "**Net Deferred Tax Assets:**\n",
-      "- 2021: $13,073 million\n",
-      "- 2020: $8,157 million\n",
-      "\n",
-      "The information for 2019 is not provided in the context.\n",
-      "\n",
-      "***********MultiModal Query Engine***********\n",
-      "The federal deferred tax assets and liabilities for the years 2019 to 2021 are as follows:\n",
-      "\n",
-      "### Deferred Tax Assets (in millions):\n",
-      "- **2021**: $25,176\n",
-      "- **2020**: $19,336\n",
-      "- **2019**: Not specified in the provided content.\n",
-      "\n",
-      "### Deferred Tax Liabilities (in millions):\n",
-      "- **2021**: $7,200\n",
-      "- **2020**: $10,138\n",
-      "- **2019**: Not specified in the provided content.\n",
-      "\n",
-      "### Net Deferred Tax Assets (in millions):\n",
-      "- **2021**: $13,073\n",
-      "- **2020**: $8,157\n",
-      "- **2019**: Not specified in the provided content.\n",
-      "\n",
-      "The significant components of deferred tax assets and liabilities reflect the effects of tax credits and temporary differences between financial statement carrying amounts and their respective tax bases.\n"
-     ]
-    }
-   ],
-   "source": [
-    "query = \"federal deferred tax in 2019-2021\"\n",
-    "\n",
-    "response_1 = await baseline_query_engine.aquery(query)\n",
-    "print(\"\\n***********Baseline Query Engine***********\")\n",
-    "print(response_1)\n",
-    "\n",
-    "response_2 = await markdown_query_engine.aquery(query)\n",
-    "print(\"\\n***********Markdown Query Engine***********\")\n",
-    "print(response_2)\n",
-    "\n",
-    "response_3 = await multimodal_query_engine.aquery(query)\n",
-    "print(\"\\n***********MultiModal Query Engine***********\")\n",
-    "print(response_3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "***********Baseline Query Engine***********\n",
-      "The current state taxes for the years 2019 to 2021 are as follows (in millions):\n",
-      "\n",
-      "- 2021: $1,620\n",
-      "- 2020: $455\n",
-      "- 2019: $475\n",
-      "\n",
-      "This indicates an increase of $1,165 million from 2020 to 2021, a decrease of $20 million from 2018 to 2019, and an increase of $80 million from 2019 to 2020.\n",
-      "\n",
-      "***********Markdown Query Engine***********\n",
-      "The current state taxes for the years 2019 to 2021 are as follows (in millions):\n",
-      "\n",
-      "- **2021**: $1,620\n",
-      "- **2020**: $455\n",
-      "- **2019**: $475\n",
-      "\n",
-      "The changes in current state taxes from year to year are:\n",
-      "\n",
-      "- From 2019 to 2020: Decrease of $20 million\n",
-      "- From 2020 to 2021: Increase of $1,165 million\n",
-      "\n",
-      "***********MultiModal Query Engine***********\n",
-      "The current state taxes for the years 2019 to 2021 are as follows (in millions):\n",
-      "\n",
-      "- **2021**: $1,620\n",
-      "- **2020**: $455\n",
-      "- **2019**: $475\n",
-      "\n",
-      "So, the changes are:\n",
-      "- From 2019 to 2020: Decrease of $20 million\n",
-      "- From 2020 to 2021: Increase of $1,165 million\n"
-     ]
-    }
-   ],
-   "source": [
-    "query = \"current state taxes per year in 2019-2021 (include +/-)\"\n",
-    "\n",
-    "response_1 = await baseline_query_engine.aquery(query)\n",
-    "print(\"\\n***********Baseline Query Engine***********\")\n",
-    "print(response_1)\n",
-    "\n",
-    "response_2 = await markdown_query_engine.aquery(query)\n",
-    "print(\"\\n***********Markdown Query Engine***********\")\n",
-    "print(response_2)\n",
-    "\n",
-    "response_3 = await multimodal_query_engine.aquery(query)\n",
-    "print(\"\\n***********MultiModal Query Engine***********\")\n",
-    "print(response_3)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "llama-parse-aNC435Vv-py3.10",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/examples-py/parse/demo_basic.ipynb

@@ -1,196 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# LlamaParse Usage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install llama-index llama-cloud-services"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget \"https://arxiv.org/pdf/1706.03762.pdf\" -O \"./attention.pdf\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"llx-...\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Started parsing the file under job_id 79ae653c-4598-4bd0-ba6e-b3dab7eab57e\n"
-     ]
-    }
-   ],
-   "source": [
-    "from llama_cloud_services import LlamaParse\n",
-    "\n",
-    "result = await LlamaParse().aparse(\"./attention.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1   Introduction\n",
-      "Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neural networks\n",
-      "in particular, have been firmly established as state of the art approaches in sequence modeling and\n",
-      "transduction problems such as language modeling and machine translation [35, 2, 5]. Numerous\n",
-      "efforts have since continued to push the boundaries of recurrent language models and encoder-decoder\n",
-      "architectures [38, 24, 15].\n",
-      "Recurrent models typically factor computation along the symbol positions of the input and output\n",
-      "sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden\n",
-      "states ht, as a function of the previous hidden state ht−1 and the input for position t. This inherently\n",
-      "sequential nature precludes parallelization within training examples, which becomes critical at longer\n",
-      "sequence lengths, as memory constraints limit batching across examples. Recent work has achieved\n",
-      "significant improvements in computational efficiency through factorization tricks [21] and conditional\n",
-      "computation [32], while also improving model performance in case of the latter. The fundamental\n",
-      "constraint of sequential computation, however, remains.\n",
-      "Attention mechanisms have become an integral part of compelling sequence modeling and transduc-\n",
-      "tion models in various tasks, allowing modeling of dependencies without regard to their distance in\n",
-      "the input or output sequences [2, 19]. In all but a few cases [27], however, such attention mechanisms\n",
-      "are used in conjunction with a recurrent network.\n",
-      "In this work we propose the Transformer, a model architecture eschewing recurrence and instead\n",
-      "relying entirely on an attention mechanism to draw global dependencies between input and output.\n",
-      "The Transformer allows for significantly more parallelization and can reach a new state of the art in\n",
-      "translation quality after being trained for as little as twelve hours on eight P100 GPUs.\n",
-      "2   Background\n",
-      "The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU\n",
-      "[16], ByteNet [18] and ConvS2S [9], all of which use convolutional neural networks as basic building\n",
-      "block, computing hidden representations in parallel for all input and output positions. In these models,\n",
-      "the number of operations required to relate signals from two arbitrary input or output positions grows\n",
-      "in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes\n",
-      "it more difficult to learn dependencies between distant positions [12]. In the Transformer this is\n",
-      "reduced to a constant number of operations, albeit at the cost of reduced effective resolution due\n",
-      "to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as\n",
-      "described in section 3.2.\n",
-      "Self-attention, sometimes called intra-attention is an attention mechanism relating different positions\n",
-      "of a single sequence in order to compute a representation of the sequence. Self-attention has been\n",
-      "used successfully in a variety of tasks including reading comprehension, abstractive summarization,\n",
-      "textual entailment and learning task-independent sentence representations [4, 27, 28, 22].\n",
-      "End-to-end memory networks are based on a recurrent attention mechanism instead of sequence-\n",
-      "aligned recurrence and have been shown to perform well on simple-language question answering and\n",
-      "language modeling tasks [34].\n",
-      "To the best of our knowledge, however, the Transformer is the first transduction model relying\n",
-      "entirely on self-attention to compute representations of its input and output without using sequence-\n",
-      "aligned RNNs or convolution. In the following sections, we will describe the Transformer, motivate\n",
-      "self-attention and discuss its advantages over models such as [17, 18] and [9].\n",
-      "3   Model Architecture\n",
-      "Most competitive neural sequence transduction models have an encoder-decoder structure [5, 2, 35].\n",
-      "Here, the encoder maps an input sequence of symbol representations (x1, ..., xn) to a sequence\n",
-      "of continuous representations z = (z1, ..., zn). Given z, the decoder then generates an output\n",
-      "sequence (y1, ..., ym) of symbols one element at a time. At each step the model is auto-regressive\n",
-      "[10], consuming the previously generated symbols as additional input when generating the next.\n",
-      "                                                2\n"
-     ]
-    }
-   ],
-   "source": [
-    "documents = result.get_text_documents(split_by_page=True)\n",
-    "print(documents[1].text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "arXiv:1706.03762v7  [cs.CL]  2 Aug 2023\n",
-      "\n",
-      "Provided proper attribution is provided, Google hereby grants permission to reproduce the tables and figures in this paper solely for use in journalistic or scholarly works.\n",
-      "\n",
-      "# Attention Is All You Need\n",
-      "\n",
-      "Ashish Vaswani∗        Noam Shazeer∗           Niki Parmar∗        Jakob Uszkoreit∗\n",
-      "\n",
-      "Google Brain        Google Brain         Google Research       Google Research\n",
-      "\n",
-      "avaswani@google.com   noam@google.com        nikip@google.com      usz@google.com\n",
-      "\n",
-      "Llion Jones∗                Aidan N. Gomez∗ †               Łukasz Kaiser∗\n",
-      "\n",
-      "Google Research         University of Toronto                Google Brain\n",
-      "\n",
-      "llion@google.com      aidan@cs.toronto.edu           lukaszkaiser@google.com\n",
-      "\n",
-      "Illia Polosukhin∗ ‡\n",
-      "\n",
-      "illia.polosukhin@gmail.com\n",
-      "\n",
-      "# Abstract\n",
-      "\n",
-      "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.\n",
-      "\n",
-      "∗Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.\n",
-      "\n",
-      "†Work performed while at Google Brain.\n",
-      "\n",
-      "‡Work performed while at Google Research.\n",
-      "\n",
-      "31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.\n"
-     ]
-    }
-   ],
-   "source": [
-    "documents = result.get_markdown_documents(split_by_page=True)\n",
-    "print(documents[0].text)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/examples-py/parse/demo_json_tour.ipynb

@@ -1,553 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "d27f1082-cd10-405e-9570-6f0e934bba8b",
-   "metadata": {},
-   "source": [
-    "# LlamaParse `JobResult` Tour\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/demo_json.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
-    "\n",
-    "The `JobResult` object is the main object returned by the LlamaParse API. It contains all the information about the job, including the parsed data, metadata, and any errors.\n",
-    "\n",
-    "This notebook walks through each component of the `JobResult` object and shows you what it contains."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a004db48-8d3f-421c-915a-477692f71b90",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "Let's bring in our imports and set up our API keys."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bc6a7a4b-b568-4db5-bcba-62f5c517ff3a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install llama-cloud-services"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0879301c-ff91-4431-941a-6c0ef7cd8fe2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "# API access to llama-cloud\n",
-    "os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"llx-..\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b411d2ee-3e6b-45b0-b532-4a8e3abcdea0",
-   "metadata": {},
-   "source": [
-    "## Load Data\n",
-    "\n",
-    "Let's load a large and complex PDF, San Francisco's 2023 proposed budget."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c39d408f-e885-4940-85c7-b09ca3bc7cb7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget 'https://www.dropbox.com/scl/fi/vip161t63s56vd94neqlt/2023-CSF_Proposed_Budget_Book_June_2023_Master_Web.pdf?rlkey=hemoce3w1jsuf6s2bz87g549i&dl=0' -O './san_francisco_budget_2023.pdf'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c2f42af8-afb3-4b3b-82d3-6b332fb38aa4",
-   "metadata": {},
-   "source": [
-    "## Using LlamaParse for Basic PDF Parsing\n",
-    "\n",
-    "Let's parse our document!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9c9cd670-8229-4ad6-99a9-845bd82b7ec1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Started parsing the file under job_id d12d419a-52fc-400c-9f88-f61b352d3fb2\n"
-     ]
-    }
-   ],
-   "source": [
-    "from llama_cloud_services import LlamaParse\n",
-    "\n",
-    "parser = LlamaParse()\n",
-    "result = await parser.aparse(\"./san_francisco_budget_2023.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "11c22bab",
-   "metadata": {},
-   "source": [
-    "Every job will come back with some metadata about the job:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c588c578",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "JobMetadata(job_credits_usage=0, job_pages=0, job_auto_mode_triggered_pages=0, job_is_cache_hit=True)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result.job_metadata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1e96b7c9",
-   "metadata": {},
-   "source": [
-    "Since this was a re-run, I can see that a cache hit occurred. Jobs are cached for 48 hours by default."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6543d2c6",
-   "metadata": {},
-   "source": [
-    "Beyond this, we can explore the parsed data per-page:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "af9f3717",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "362\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(len(result.pages))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f8845fac",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "dict_keys(['page', 'text', 'md', 'images', 'charts', 'tables', 'layout', 'items', 'status', 'links', 'width', 'height', 'triggeredAutoMode', 'parsingMode', 'structuredData', 'noStructuredContent', 'noTextContent'])\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(result.pages[0].model_dump().keys())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6261f5e3",
-   "metadata": {},
-   "source": [
-    "Inside the page object, you can see nearly every detail about the page.\n",
-    "\n",
-    "Most of these will depend on the settings you used when parsing. Since we used the default settings, we get the text and markdown for each page, as well as a list of all the elements on the page.\n",
-    "\n",
-    "* `page`: this is simply the page number, starting at 1.\n",
-    "* `text`: this is the text of the page, as extracted by the parser.\n",
-    "* `images`: this is an array of all the images on the page, including metadata and text OCRed out of the images, as well as a full-page screenshot of the entire page.\n",
-    "* `charts`: this is an array of all the charts on the page, including metadata and text OCRed out of the charts, as well as a full-page screenshot of the entire chart.\n",
-    "* `layout`: this is an array of all the layout elements on the page, if you are using layout mode.\n",
-    "* `items`: This is an array of all the parsed elements on the page, as used to render the markdown, but separated out into their own objects. This is useful if you want to do more processing on the data.\n",
-    "* `links`: this is an array of all the links on the page, if you are used `annotate_links=True`\n",
-    "* `status`: this is the status of the page, which is usually \"OK\" unless there was an error processing the page.\n",
-    "* `width` and `height`: these are the dimensions of the page in pixels.\n",
-    "* `parsingMode`: Contains the specific parsing mode that was used for the page.\n",
-    "* `triggeredAutoMode`: this indicates whether the page triggered auto mode; see [LlamaParse docs](https://docs.cloud.llamaindex.ai/llamaparse/getting_started) for more details.\n",
-    "* `structuredData`/`noStructuredContent`: these are set if you are using structured mode; see [LlamaParse docs](https://docs.cloud.llamaindex.ai/llamaparse/getting_started) for more details.\n",
-    "* `noTextContent`: this is true if the page was empty of text.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7a4cc901",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                        CITY & COUNTY OF SAN FRANCISCO, CALIFORNIA\n",
-      " PROPOSED BUDGET\n",
-      "                                                              FISCAL YEARS 2023-2024 & 2024-2025\n",
-      "                                                                                LONDON  N. BREED\n",
-      "               MAYOR’S OFFICE OF PUBLIC POLICY AND FINANCE\n",
-      "         Anna Duning, Director of Mayor’s                                   Fisher Zhu, Fiscal and Policy Analyst\n",
-      "         Office of Public Policy and Finance                             Anya Shutovska, Fiscal and Policy Analyst\n",
-      "         Sally Ma, Deputy Budget Director\n",
-      "Radhika Mehlotra, Senior Fiscal and Policy Analyst                        Jack English, Fiscal and Policy Analyst\n",
-      "     Damon Daniels, Fiscal and Policy Analyst                           Xang Hang, Junior Fiscal and Policy Analyst\n",
-      "    Matthew Puckett, Fiscal and Policy Analyst                       Tabitha Romero-Bothi, Fiscal and Policy Assistant\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(result.pages[0].text[:1000])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2d5a5bc2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# CITY & COUNTY OF SAN FRANCISCO, CALIFORNIA\n",
-      "\n",
-      "# PROPOSED BUDGET\n",
-      "\n",
-      "# FISCAL YEARS 2023-2024 & 2024-2025\n",
-      "\n",
-      "# LONDON N. BREED\n",
-      "\n",
-      "# MAYOR’S OFFICE OF PUBLIC POLICY AND FINANCE\n",
-      "\n",
-      "Anna Duning, Director of Mayor’s Office of Public Policy and Finance\n",
-      "\n",
-      "Fisher Zhu, Fiscal and Policy Analyst\n",
-      "\n",
-      "Anya Shutovska, Fiscal and Policy Analyst\n",
-      "\n",
-      "Sally Ma, Deputy Budget Director\n",
-      "\n",
-      "Radhika Mehlotra, Senior Fiscal and Policy Analyst\n",
-      "\n",
-      "Jack English, Fiscal and Policy Analyst\n",
-      "\n",
-      "Damon Daniels, Fiscal and Policy Analyst\n",
-      "\n",
-      "Xang Hang, Junior Fiscal and Policy Analyst\n",
-      "\n",
-      "Matthew Puckett, Fiscal and Policy Analyst\n",
-      "\n",
-      "Tabitha Romero-Bothi, Fiscal and Policy Assistant\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(result.pages[0].md[:1000])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "32de4c62",
-   "metadata": {},
-   "source": [
-    "## Images\n",
-    "\n",
-    "By default, images embedded in documents that can be extracted are part of the result object."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "802d4a98",
-   "metadata": {},
-   "source": [
-    "We can also specify to take screenshots of every page:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2ee78f2f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Started parsing the file under job_id e6332422-803b-404d-8d0d-ad510fa56c09\n",
-      "..."
-     ]
-    }
-   ],
-   "source": [
-    "parser = LlamaParse(take_screenshot=True)\n",
-    "result = await parser.aparse(\"./san_francisco_budget_2023.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fab32886",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ImageItem(name='page_1.jpg', height=792.0, width=612.0, x=0.0, y=0.0, original_width=1236, original_height=1600, type='full_page_screenshot')]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(result.pages[0].images)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9eba9e52",
-   "metadata": {},
-   "source": [
-    "We can download images (either their bytes or to a local file) using the `JobResult` object as well!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a7aa0a29",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# single image\n",
-    "image_data = await result.aget_image_data(result.pages[0].images[0].name)\n",
-    "\n",
-    "# save an image to a file\n",
-    "output_path = await result.asave_image(\n",
-    "    result.pages[0].images[0].name, \"./json_tour_screenshots\"\n",
-    ")\n",
-    "\n",
-    "# save all images\n",
-    "output_paths = await result.asave_all_images(\"./json_tour_screenshots\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eae4ece3",
-   "metadata": {},
-   "source": [
-    "## Items\n",
-    "\n",
-    "This is an array of all the parsed elements on the page, as used to render the markdown, but separated out into their own objects. This is useful if you want to do more processing on the data. Let's take a look:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c10b9d7d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "type='heading' lvl=1 value='CITY & COUNTY OF SAN FRANCISCO, CALIFORNIA' md='# CITY & COUNTY OF SAN FRANCISCO, CALIFORNIA' rows=None bBox=BBox(x=176.0, y=52.0, w=277.0, h=12.0)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import json\n",
-    "\n",
-    "print(result.pages[0].items[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dcb9f832",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "type='heading' lvl=1 value='PROPOSED BUDGET' md='# PROPOSED BUDGET' rows=None bBox=BBox(x=89.0, y=118.0, w=451.0, h=47.0)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(result.pages[0].items[1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a7f64443",
-   "metadata": {},
-   "source": [
-    "As you can see you get different element types: text, headings, and tables. Each comes with its own `md` key containing a Markdown representation of that element, allowing you to easily summarize with only headings, tables only, etc..\n",
-    "\n",
-    "The ability to extract tables from visual data is really powerful. Let's take a look at page 35, which has some bar charts that get automatically converted into tables:\n",
-    "\n",
-    "<img src=\"./json_tour_screenshots/page_35.png\" alt=\"Page 35\" width=\"300\"/>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e4ccee76",
-   "metadata": {},
-   "source": [
-    "The bar chart has been converted into a table, and even though explicit values are not included, the bar chart has been read and approximate values for each bar on the chart have been included!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7d6404a5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "type='table' lvl=None value=None md=\"Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-years Estimate.\\n|Race|Educational Level|Number of Residents| | | | |\\n|---|---|---|---|---|---|---|\\n|Age Group| | | | | | |\\n|Under 5 Years|5 to 19 Years|20 to 34 Years|35 to 59 Years|60 and Over| | |\\n|Graduate or professional degree|Bachelor's degree|Associate's degree|Some college, no degree|High school graduate (includes equivalency)|9th to 12th grade, no diploma|Less than 9th grade|\" rows=[[], ['Race', 'Educational Level', 'Number of Residents', '', '', '', ''], ['---', '---', '---', '---', '---', '---', '---'], ['Age Group', '', '', '', '', '', ''], ['Under 5 Years', '5 to 19 Years', '20 to 34 Years', '35 to 59 Years', '60 and Over', '', ''], ['Graduate or professional degree', \"Bachelor's degree\", \"Associate's degree\", 'Some college, no degree', 'High school graduate (includes equivalency)', '9th to 12th grade, no diploma', 'Less than 9th grade']] bBox=BBox(x=68.0, y=129.0, w=613.0, h=3067.0)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(result.pages[34].items[6])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9570d3b8",
-   "metadata": {},
-   "source": [
-    "### `links`\n",
-    "\n",
-    "Our budget PDF doesn't have any links, so let's load a different PDF with links and see what we get.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fb0da11a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget 'https://www.dropbox.com/scl/fi/hay06lyxc49gkuh91oek6/basic-link-1.pdf?rlkey=uije7yb0lxqgqwk7p7hnqepdx&dl=0' -O './basic-link-1.pdf'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e7e393e6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Started parsing the file under job_id 9b2df975-af3c-4868-99e2-520ce0b21f4d\n"
-     ]
-    }
-   ],
-   "source": [
-    "parser = LlamaParse(annotate_links=True)\n",
-    "result = await parser.aparse(\"./basic-link-1.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "701ada4b",
-   "metadata": {},
-   "source": [
-    "This is a very simple document with some internal and external links:\n",
-    "\n",
-    "<img src=\"./json_tour_screenshots/links_page.png\" alt=\"Page 1\" width=\"300\"/>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2e4de7de",
-   "metadata": {},
-   "source": [
-    "The parser finds the external links and their labels and includes them in the `links` section:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "29bf7e3c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[{'url': 'https://www.antennahouse.com/', 'text': 'Antenna House, Inc.'}, {'url': 'https://www.antennahouse.com/', 'text': 'Linking to a website (https://www.antennahouse.com/)'}]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(result.pages[0].links)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ac9088a2",
-   "metadata": {},
-   "source": [
-    "This concludes our tour! I hope this makes clear the power of JSON mode and the flexibility it gives you over what parts of your documents you can use."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/examples-py/parse/demo_starter_multimodal.ipynb

@@ -1,312 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "97c79c38-38a3-40f3-ba2e-250649347d63",
-   "metadata": {},
-   "source": [
-    "<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/demo_starter_multimodal.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4e081457",
-   "metadata": {},
-   "source": [
-    "# Multimodal Parsing using LlamaParse\n",
-    "\n",
-    "This cookbook shows you how to use LlamaParse to parse any document with the multimodal capabilities of Multi-Modal LLMs from Anthropic/ OpenAI.\n",
-    "\n",
-    "LlamaParse allows you to plug in external, multimodal model vendors for parsing - we handle the error correction, validation, and scalability/reliability for you.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "qOdqBxCS51Ow",
-   "metadata": {},
-   "source": [
-    "### Installation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "H_Vqcylb50vm",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install llama-cloud-services"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "15e60ecf-519c-41fc-911b-765adaf8bad4",
-   "metadata": {},
-   "source": [
-    "### Setup\n",
-    "\n",
-    "Here we setup `LLAMA_CLOUD_API_KEY` for using `LlamaParse`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "91a9e532-1454-40e0-bbf0-fd442c350121",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "# API access to llama-cloud\n",
-    "os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"<YOUR LLAMACLOUD API KEY>\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "LGwBNPNotZRQ",
-   "metadata": {},
-   "source": [
-    "## Download Data\n",
-    "\n",
-    "For this demonstration, we will use OpenAI's recent paper `Evaluation of OpenAI o1: Opportunities and Challenges of AGI`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "IjtKDQRLrylI",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget \"https://arxiv.org/pdf/2409.18486\" -O \"o1.pdf\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4e29a9d7-5bd9-4fb8-8ec1-4c128a748662",
-   "metadata": {},
-   "source": [
-    "## Initialize LlamaParse\n",
-    "\n",
-    "Initialize LlamaParse in multimodal mode, and specify the vendor.\n",
-    "\n",
-    "**NOTE**: optionally you can specify the Anthropic/ OpenAI API key. If you choose to do so LlamaParse will only charge you 1 credit (0.3c) per page. \n",
-    "\n",
-    "\n",
-    "Using your own API key may incur additional costs from your model provider and could result in failed pages or documents if you do not have sufficient usage limits."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b5d6da6",
-   "metadata": {},
-   "source": [
-    "### With anthropic-sonnet-3.5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f2e9d9cf-8189-4fcb-b34f-cde6cc0b59c8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Started parsing the file under job_id dd9d5e0f-160e-486a-89a2-6005e5a1c2ac\n"
-     ]
-    }
-   ],
-   "source": [
-    "from llama_cloud_services import LlamaParse\n",
-    "\n",
-    "parser = LlamaParse(\n",
-    "    use_vendor_multimodal_model=True,\n",
-    "    vendor_multimodal_model_name=\"anthropic-sonnet-3.5\",\n",
-    "    target_pages=\"24\"\n",
-    "    # invalidate_cache=True\n",
-    ")\n",
-    "result = await parser.aparse(\"o1.pdf\")\n",
-    "nodes = result.get_text_nodes(split_by_page=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4f3c51b0-7878-48d7-9bc3-02b516500128",
-   "metadata": {},
-   "source": [
-    "### With GPT-4o\n",
-    "\n",
-    "For comparison, we will also parse the document using GPT-4o."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6fc3f258-50ae-4988-b904-c105463a498f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Started parsing the file under job_id 6a4dea44-4f90-406b-b290-9e98620b1232\n"
-     ]
-    }
-   ],
-   "source": [
-    "from llama_cloud_services import LlamaParse\n",
-    "\n",
-    "parser_gpt4o = LlamaParse(\n",
-    "    use_vendor_multimodal_model=True,\n",
-    "    vendor_multimodal_model=\"openai-gpt4o\",\n",
-    "    target_pages=\"24\",\n",
-    "    # invalidate_cache=True\n",
-    ")\n",
-    "result = await parser_gpt4o.aparse(\"o1.pdf\")\n",
-    "nodes = result.get_markdown_nodes(split_by_page=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "44c20f7a-2901-4dd0-b635-a4b33c5664c1",
-   "metadata": {},
-   "source": [
-    "### View Results\n",
-    "\n",
-    "Let's visualize the results along with the original document page."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "778698aa-da7e-4081-b3b5-0372f228536f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "page: 25\n",
-      "\n",
-      "| Participant_ID | clinical Description Reference |\n",
-      "|-----------------|----------------------------------|\n",
-      "| Attribute | Value | Basic Personal Information: Subject 098_S_0896 is a 72.0-year-old Female who has completed 15 years of education. The ethnicity is Not Hisp/Latino and race is White. Marital status is Married. Initially diagnosed as AD, as of the date 2007-10-24, the final diagnosis was Dementia. |\n",
-      "| Age | 72.0 |\n",
-      "| Sex | Female |\n",
-      "| Education | 15 |\n",
-      "| Race | White | Biomarker Measurements: The subject's genetic profile includes an ApoE4 status of 0.0... |\n",
-      "| DX_bl | AD |\n",
-      "| DX | Dementia |\n",
-      "| ... | ... | Cognitive and Neurofunctional Assessments: The Mini-Mental State Examination score stands at 29.0. The Clinical Dementia Rating, sum of boxes, is 1.0. ADAS 11 and 13 scores are 4.67 and 4.67 respectively, with a score of 1.0 in delayed word recall... |\n",
-      "| APOE4 | 1.0 |\n",
-      "| TAU | 212.5 |\n",
-      "| ... | ... |\n",
-      "| MMSE | 29.0 | Volumetric Data: Under MRI conditions at a field strength of 1.5 Tesla MRI Tesla, using Cross Sectional FreeSurfer (FreeSurfer Version 4.3), the imaging data recorded includes ventricles volume at 54422.0, hippocampus volume at 6677.0, whole brain volume at 1147980.0, entorhinal cortex volume at 2782.0, fusiform gyrus volume at 19432.0, and middle temporal area volume at 24951.0. The intracranial volume measured is 1799580.0.... |\n",
-      "| CDRSB | 0.0 |\n",
-      "| ... | ... |\n",
-      "| FLDSTRENG | 1.5 Tesla MRI |\n",
-      "| Ventricles | 84599 |\n",
-      "| Hippocampus | 5319 |\n",
-      "| ... | ... |\n",
-      "\n",
-      "Figure 2: An example of a patient table and its corresponding clinical description.\n",
-      "\n",
-      "skills. Mathematics, as a highly structured and logic-driven discipline, provides an ideal testing ground for evaluating this reasoning ability. To investigate o1-preview's performance, we designed a series of tests covering various difficulty levels. We begin with high school-level math competition problems in this section, followed by college-level mathematics problems in the next section, allowing us to observe the model's logical reasoning across varying levels of complexity.\n",
-      "\n",
-      "In this section, we selected two primary areas of mathematics: algebra and counting and probability in this section. We chose these two topics because of their heavy reliance on problem-solving skills and their frequent use in assessing logical and abstract thinking [46]. The dataset used in testing is from the MATH dataset [46]. The problems in the dataset cover a wide range of subjects, including Prealgebra, Intermediate Algebra, Algebra, Geometry, Counting and Probability, Number Theory, and Precalculus. Each problem is categorized based on difficulty, ranked from level 1 to 5, according to the Art of Problem Solving (AoPS). The dataset mainly comprises problems from various high school math competitions, including the American Mathematics Competitions (AMC) 10 and 12, as well as the American Invitational Mathematics Examination (AIME), and other similar contests. Each problem comes with detailed reference solutions, allowing for a comprehensive comparison of o1-preview's solutions.\n",
-      "\n",
-      "In addition to evaluating the final answers produced by o1-preview, our analysis delves into the step-by-step reasoning process of the o1-preview's solutions. By comparing o1-preview's solutions with the dataset's solutions, we assess its ability to engage in logical reasoning, handle abstract problem-solving tasks, and apply structured approaches to reach correct answers. This deeper analysis offers insights into o1-preview's overall reasoning capabilities, using mathematics as a reliable indicator for logical and structured thought processes.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# using Sonnet-3.5\n",
-    "print(nodes[0].get_content(metadata_mode=\"all\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1511a30f-3efc-4142-9668-7dc056a24d0c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "page: 25\n",
-      "\n",
-      "\n",
-      "| Participant_ID | clinical Description Reference |\n",
-      "|----------------|--------------------------------|\n",
-      "| **Attribute**  | **Value**                      |\n",
-      "| Age            | 72.0                           |\n",
-      "| Sex            | Female                         |\n",
-      "| Education      | 15                             |\n",
-      "| Race           | White                          |\n",
-      "| DX_bl          | AD                             |\n",
-      "| DX             | Dementia                       |\n",
-      "| ...            | ...                            |\n",
-      "| APOE4          | 1.0                            |\n",
-      "| TAU            | 212.5                          |\n",
-      "| ...            | ...                            |\n",
-      "| MMSE           | 29.0                           |\n",
-      "| CDRSB          | 0.0                            |\n",
-      "| ...            | ...                            |\n",
-      "| FLDSTRENG      | 1.5 Tesla MRI                  |\n",
-      "| Ventricles     | 84599                          |\n",
-      "| Hippocampus    | 5319                           |\n",
-      "| ...            | ...                            |\n",
-      "\n",
-      "**Basic Personal Information:** Subject 098_S_0896 is a 72.0-year-old Female who has completed 15 years of education. The ethnicity is Not Hisp/Latino and race is White. Marital status is Married. Initially diagnosed as AD, as of the date 2007-10-24, the final diagnosis was Dementia.\n",
-      "\n",
-      "**Biomarker Measurements:** The subject's genetic profile includes an ApoE4 status of 0.0...\n",
-      "\n",
-      "**Cognitive and Neurofunctional Assessments:** The Mini-Mental State Examination score stands at 29.0. The Clinical Dementia Rating, sum of boxes, is 1.0. ADAS 11 and 13 scores are 4.67 and 4.67 respectively, with a score of 1.0 in delayed word recall...\n",
-      "\n",
-      "**Volumetric Data:** Under MRI conditions at a field strength of 1.5 Tesla MRI Tesla, using Cross-Sectional FreeSurfer (FreeSurfer Version 4.3), the imaging data recorded includes ventricles volume at 84422.0, hippocampus volume at 6677.0, whole brain volume at 1147980.0, entorhinal cortex volume at 27820.0, fusiform gyrus volume at 19432.0, and middle temporal area volume at 24951.0. The intracranial volume measured is 1799580.0...\n",
-      "\n",
-      "Figure 2: An example of a patient table and its corresponding clinical description.\n",
-      "\n",
-      "----\n",
-      "\n",
-      "Skills. Mathematics, as a highly structured and logic-driven discipline, provides an ideal testing ground for evaluating this reasoning ability. To investigate o1-preview’s performance, we designed a series of tests covering various difficulty levels. We begin with high school-level math competition problems in this section, followed by college-level mathematics problems in the next section, allowing us to observe the model’s logical reasoning across varying levels of complexity.\n",
-      "\n",
-      "In this section, we selected two primary areas of mathematics: algebra and counting and probability in this section. We chose these two topics because of their heavy reliance on problem-solving skills and their frequent use in assessing logical and abstract thinking [46]. The dataset used in testing is from the MATH dataset [46]. The problems in the dataset cover a wide range of subjects, including Prealgebra, Intermediate Algebra, Algebra, Geometry, Counting and Probability, Number Theory, and Precalculus. Each problem is categorized based on difficulty, ranked from level 1 to 5, according to the Art of Problem Solving (AoPS). The dataset mainly comprises problems from various high school math competitions, including the American Mathematics Competitions (AMC) 10 and 12, as well as the American Invitational Mathematics Examination (AIME), and other similar contests. Each problem comes with detailed reference solutions, allowing for a comprehensive comparison of o1-preview’s solutions.\n",
-      "\n",
-      "In addition to evaluating the final answers produced by o1-preview, our analysis delves into the step-by-step reasoning process of the o1-preview’s solutions. By comparing o1-preview’s solutions with the dataset’s solutions, we assess its ability to engage in logical reasoning, handle abstract problem-solving tasks, and apply structured approaches to reach correct answers. This deeper analysis offers insights into o1-preview’s overall reasoning capabilities, using mathematics as a reliable indicator for logical and structured thought processes.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# using GPT-4o\n",
-    "print(nodes[0].get_content(metadata_mode=\"all\"))"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "llamacloud",
-   "language": "python",
-   "name": "llamacloud"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/examples-py/parse/demo_starter_parse_selected_pages.ipynb

@@ -1,148 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/demo_starter_parse_selected_pages.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Parse Selected Pages \n",
-    "\n",
-    "In this notebook we will demonstrate how to parse selected pages in a document using LlamaParse."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "Here we install `llama-parse` used for parsing the document"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install llama-cloud-services"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set API Key"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "# API access to llama-cloud\n",
-    "os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"<YOUR LLAMACLOUD API KEY>\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Download Data\n",
-    "\n",
-    "Here we download Uber 2021 10K SEC filings data for the demonstration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf' -O './uber_2021.pdf'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Parse the PDF file in selected pages\n",
-    "\n",
-    "Here we will parse the PDF file in selected pages and get the text in `markdown` format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Started parsing the file under job_id ad1087c1-b085-4dc7-9aa8-d13cdd440f2b\n"
-     ]
-    }
-   ],
-   "source": [
-    "from llama_cloud_services import LlamaParse\n",
-    "\n",
-    "parser = LlamaParse(target_pages=\"0,1,2\")\n",
-    "\n",
-    "results = await parser.aparse(\"./uber_2021.pdf\")\n",
-    "documents = results.get_text_documents(split_by_page=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(id_='d0b34f4a-27ef-48e2-a92a-386e5e265f4c', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, metadata_template='{key}: {value}', metadata_separator='\\n', text='# UNITED STATES SECURITIES AND EXCHANGE COMMISSION\\n\\n# Washington, D.C. 20549\\n\\n# FORM 10-K\\n\\n(Mark One)\\n\\n☒ ANNUAL REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934\\n\\nFor the fiscal year ended December 31, 2021\\n\\nOR\\n\\n☐ TRANSITION REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934\\n\\nFor the transition period from _____ to _____\\n\\nCommission File Number: 001-38902\\n\\n# UBER TECHNOLOGIES, INC.\\n\\n(Exact name of registrant as specified in its charter)\\n\\nDelaware\\n\\n45-2647441\\n\\n(State or other jurisdiction of incorporation or organization) (I.R.S. Employer Identification No.)\\n\\n1515 3rd Street\\n\\nSan Francisco, California 94158\\n\\n(Address of principal executive offices, including zip code)\\n\\n(415) 612-8582\\n\\n(Registrant’s telephone number, including area code)\\n\\n# Securities registered pursuant to Section 12(b) of the Act:\\n\\n|Title of each class|Trading Symbol(s)|Name of each exchange on which registered|\\n|---|---|---|\\n|Common Stock, par value $0.00001 per share|UBER|New York Stock Exchange|\\n\\nSecurities registered pursuant to Section 12(g) of the Act: None\\n\\nIndicate by check mark whether the registrant is a well-known seasoned issuer, as defined in Rule 405 of the Securities Act. Yes ☒ No ☐\\n\\nIndicate by check mark whether the registrant is not required to file reports pursuant to Section 13 or Section 15(d) of the Act. Yes ☐ No ☒\\n\\nIndicate by check mark whether the registrant (1) has filed all reports required to be filed by Section 13 or 15(d) of the Securities Exchange Act of 1934 during the preceding 12 months (or for such shorter period that the registrant was required to file such reports), and (2) has been subject to such filing requirements for the past 90 days. Yes ☒ No ☐\\n\\nIndicate by check mark whether the registrant has submitted electronically every Interactive Data File required to be submitted pursuant to Rule 405 of Regulation S-T (§232.405 of this chapter) during the preceding 12 months (or for such shorter period that the registrant was required to submit such files). Yes ☒ No ☐\\n\\nIndicate by check mark whether the registrant is a large accelerated filer, an accelerated filer, a non-accelerated filer, a smaller reporting company, or an emerging growth company. See the definitions of “large accelerated filer,” “accelerated filer,” “smaller reporting company,” and “emerging growth company” in Rule 12b-2 of the Exchange Act.', mimetype='text/plain', start_char_idx=None, end_char_idx=None, metadata_seperator='\\n', text_template='{metadata_str}\\n\\n{content}'),\n",
-       " Document(id_='253b1141-a260-466e-b164-b39df67ef799', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, metadata_template='{key}: {value}', metadata_separator='\\n', text=\"# Large accelerated filer\\n\\n☒\\n\\n# Accelerated filer\\n\\n☐\\n\\n# Non-accelerated filer\\n\\n☐\\n\\n# Smaller reporting company\\n\\n☐\\n\\n# Emerging growth company\\n\\n☐\\n\\nIf an emerging growth company, indicate by check mark if the registrant has elected not to use the extended transition period for complying with any new or revised financial accounting standards provided pursuant to Section 13(a) of the Exchange Act.\\n\\n☐\\n\\nIndicate by check mark whether the registrant has filed a report on and attestation to its management’s assessment of the effectiveness of its internal control over financial reporting under Section 404(b) of the Sarbanes-Oxley Act (15 U.S.C. 7262(b)) by the registered public accounting firm that prepared or issued\\n\\n☒\\n\\nIndicate by check mark whether the registrant is a shell company (as defined in Rule 12b-2 of the Exchange Act). Yes\\n\\n☐\\n\\nNo\\n\\n☒\\n\\nThe aggregate market value of the voting and non-voting common equity held by non-affiliates of the registrant as of June 30, 2021, the last business day of the registrant's most recently completed second fiscal quarter, was approximately $90.5 billion based upon the closing price reported for such date on the New York Stock Exchange.\\n\\nThe number of shares of the registrant's common stock outstanding as of February 22, 2022 was 1,954,464,088.\\n\\n# DOCUMENTS INCORPORATED BY REFERENCE\\n\\nPortions of the registrant’s Definitive Proxy Statement relating to the Annual Meeting of Stockholders are incorporated by reference into Part III of this Annual Report on Form 10-K where indicated. Such Definitive Proxy Statement will be filed with the Securities and Exchange Commission within 120 days after the end of the registrant’s fiscal year ended December 31, 2021.\", mimetype='text/plain', start_char_idx=None, end_char_idx=None, metadata_seperator='\\n', text_template='{metadata_str}\\n\\n{content}'),\n",
-       " Document(id_='ad988239-3ab5-498d-85ba-a29241db24d4', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, metadata_template='{key}: {value}', metadata_separator='\\n', text='# UBER TECHNOLOGIES, INC.\\n\\n# TABLE OF CONTENTS\\n\\n|Special Note Regarding Forward-Looking Statements|2|\\n|---|---|\\n|PART I|PART I|\\n|Item 1. Business|4|\\n|Item 1A. Risk Factors|11|\\n|Item 1B. Unresolved Staff Comments|46|\\n|Item 2. Properties|46|\\n|Item 3. Legal Proceedings|46|\\n|Item 4. Mine Safety Disclosures|47|\\n|PART II|PART II|\\n|Item 5. Market for Registrant’s Common Equity, Related Stockholder Matters and Issuer Purchases of Equity Securities|47|\\n|Item 6. [Reserved]|48|\\n|Item 7. Management’s Discussion and Analysis of Financial Condition and Results of Operations|48|\\n|Item 7A. Quantitative and Qualitative Disclosures About Market Risk|69|\\n|Item 8. Financial Statements and Supplementary Data|70|\\n|Item 9. Changes in and Disagreements with Accountants on Accounting and Financial Disclosure|146|\\n|Item 9A. Controls and Procedures|147|\\n|Item 9B. Other Information|147|\\n|Item 9C. Disclosure Regarding Foreign Jurisdictions that Prevent Inspections|147|\\n|PART III|PART III|\\n|Item 10. Directors, Executive Officers and Corporate Governance|147|\\n|Item 11. Executive Compensation|147|\\n|Item 12. Security Ownership of Certain Beneficial Owners and Management and Related Stockholder Matters|148|\\n|Item 13. Certain Relationships and Related Transactions, and Director Independence|148|\\n|Item 14. Principal Accounting Fees and Services|148|\\n|PART IV|PART IV|\\n|Item 15. Exhibits, Financial Statement Schedules|148|\\n|Item 16. Form 10-K Summary|148|\\n|Exhibit Index|149|\\n|Signatures|152|', mimetype='text/plain', start_char_idx=None, end_char_idx=None, metadata_seperator='\\n', text_template='{metadata_str}\\n\\n{content}')]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "documents"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "llamacloud",
-   "language": "python",
-   "name": "llamacloud"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/examples-py/parse/demo_table_extraction.ipynb

@@ -1,516 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Table Extraction with LlamaParse\n",
-    "\n",
-    "This notebook will show you how to extract tables and save them as CSV files thanks to LlamaParse advanced parsing capabilities."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. Install needed dependencies**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "! pip install llama-cloud-services pandas"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. Set you LLAMA_CLOUD_API_KEY as env variable**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LLAMA_CLOUD_API_KEY: ··········\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "os.environ[\"LLAMA_CLOUD_API_KEY\"] = getpass(\"LLAMA_CLOUD_API_KEY: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**3. Initialiaze the parser**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_cloud_services import LlamaParse\n",
-    "\n",
-    "parser = LlamaParse(result_type=\"markdown\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**4. Get data**\n",
-    "\n",
-    "This is a PDF with _lots_ of tables!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--2025-07-16 16:20:41--  https://assets.accessible-digital-documents.com/uploads/2017/01/sample-tables.pdf\n",
-      "Resolving assets.accessible-digital-documents.com (assets.accessible-digital-documents.com)... 3.166.135.2, 3.166.135.62, 3.166.135.51, ...\n",
-      "Connecting to assets.accessible-digital-documents.com (assets.accessible-digital-documents.com)|3.166.135.2|:443... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 145494 (142K) [application/pdf]\n",
-      "Saving to: ‘sample-tables.pdf’\n",
-      "\n",
-      "sample-tables.pdf   100%[===================>] 142.08K  --.-KB/s    in 0.04s   \n",
-      "\n",
-      "2025-07-16 16:20:41 (3.72 MB/s) - ‘sample-tables.pdf’ saved [145494/145494]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "! wget https://assets.accessible-digital-documents.com/uploads/2017/01/sample-tables.pdf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**5. Parse document**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Started parsing the file under job_id b53949f7-9017-4b6a-b30c-be6227271ed2\n"
-     ]
-    }
-   ],
-   "source": [
-    "json_result = parser.get_json_result(\"sample-tables.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**6. Get tables!**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tables = parser.get_tables(json_result, \"tables/\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**7. Load tables**\n",
-    "\n",
-    "Let's show one example table!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.google.colaboratory.intrinsic+json": {
-       "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Rainfall\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Average\",\n          \"\",\n          \"24 hour high\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Americas\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 908,\n        \"min\": 9,\n        \"max\": 2010,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          104,\n          133,\n          2010\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Asia\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"\",\n          201.0,\n          28.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Europe\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"\",\n          193.0,\n          29.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Africa\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"\",\n          144.0,\n          20.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-       "type": "dataframe"
-      },
-      "text/html": [
-       "\n",
-       "  <div id=\"df-94a74c8f-1062-4a80-8d3f-32f0fbadf7bb\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Rainfall</th>\n",
-       "      <th>Americas</th>\n",
-       "      <th>Asia</th>\n",
-       "      <th>Europe</th>\n",
-       "      <th>Africa</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>(inches)</td>\n",
-       "      <td>2010</td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Average</td>\n",
-       "      <td>104</td>\n",
-       "      <td>201.0</td>\n",
-       "      <td>193.0</td>\n",
-       "      <td>144.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>24 hour high</td>\n",
-       "      <td>15</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>18.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>12 hour high</td>\n",
-       "      <td>9</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td></td>\n",
-       "      <td>2009</td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Average</td>\n",
-       "      <td>133</td>\n",
-       "      <td>244.0</td>\n",
-       "      <td>155.0</td>\n",
-       "      <td>166.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>24 hour high</td>\n",
-       "      <td>27</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>20.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>12 hour high</td>\n",
-       "      <td>11</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>16.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-94a74c8f-1062-4a80-8d3f-32f0fbadf7bb')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
-       "      cursor: pointer;\n",
-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-94a74c8f-1062-4a80-8d3f-32f0fbadf7bb button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-94a74c8f-1062-4a80-8d3f-32f0fbadf7bb');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "    <div id=\"df-54b2aa43-838b-47d3-9209-2fb18153cf87\">\n",
-       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-54b2aa43-838b-47d3-9209-2fb18153cf87')\"\n",
-       "                title=\"Suggest charts\"\n",
-       "                style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "      </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "      --bg-color: #E8F0FE;\n",
-       "      --fill-color: #1967D2;\n",
-       "      --hover-bg-color: #E2EBFA;\n",
-       "      --hover-fill-color: #174EA6;\n",
-       "      --disabled-fill-color: #AAA;\n",
-       "      --disabled-bg-color: #DDD;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "      --bg-color: #3B4455;\n",
-       "      --fill-color: #D2E3FC;\n",
-       "      --hover-bg-color: #434B5C;\n",
-       "      --hover-fill-color: #FFFFFF;\n",
-       "      --disabled-bg-color: #3B4455;\n",
-       "      --disabled-fill-color: #666;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: var(--bg-color);\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
-       "    display: none;\n",
-       "    fill: var(--fill-color);\n",
-       "    height: 32px;\n",
-       "    padding: 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: var(--hover-bg-color);\n",
-       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: var(--button-hover-fill-color);\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart-complete:disabled,\n",
-       "  .colab-df-quickchart-complete:disabled:hover {\n",
-       "    background-color: var(--disabled-bg-color);\n",
-       "    fill: var(--disabled-fill-color);\n",
-       "    box-shadow: none;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-spinner {\n",
-       "    border: 2px solid var(--fill-color);\n",
-       "    border-color: transparent;\n",
-       "    border-bottom-color: var(--fill-color);\n",
-       "    animation:\n",
-       "      spin 1s steps(1) infinite;\n",
-       "  }\n",
-       "\n",
-       "  @keyframes spin {\n",
-       "    0% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "      border-left-color: var(--fill-color);\n",
-       "    }\n",
-       "    20% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    30% {\n",
-       "      border-color: transparent;\n",
-       "      border-left-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    40% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-top-color: var(--fill-color);\n",
-       "    }\n",
-       "    60% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "    }\n",
-       "    80% {\n",
-       "      border-color: transparent;\n",
-       "      border-right-color: var(--fill-color);\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "    90% {\n",
-       "      border-color: transparent;\n",
-       "      border-bottom-color: var(--fill-color);\n",
-       "    }\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "      <script>\n",
-       "        async function quickchart(key) {\n",
-       "          const quickchartButtonEl =\n",
-       "            document.querySelector('#' + key + ' button');\n",
-       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-       "          try {\n",
-       "            const charts = await google.colab.kernel.invokeFunction(\n",
-       "                'suggestCharts', [key], {});\n",
-       "          } catch (error) {\n",
-       "            console.error('Error during call to suggestCharts:', error);\n",
-       "          }\n",
-       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-       "        }\n",
-       "        (() => {\n",
-       "          let quickchartButtonEl =\n",
-       "            document.querySelector('#df-54b2aa43-838b-47d3-9209-2fb18153cf87 button');\n",
-       "          quickchartButtonEl.style.display =\n",
-       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "        })();\n",
-       "      </script>\n",
-       "    </div>\n",
-       "\n",
-       "    </div>\n",
-       "  </div>\n"
-      ],
-      "text/plain": [
-       "       Rainfall  Americas   Asia Europe Africa\n",
-       "0      (inches)      2010                     \n",
-       "1       Average       104  201.0  193.0  144.0\n",
-       "2  24 hour high        15   26.0   27.0   18.0\n",
-       "3  12 hour high         9   10.0   11.0   12.0\n",
-       "4                    2009                     \n",
-       "5       Average       133  244.0  155.0  166.0\n",
-       "6  24 hour high        27   28.0   29.0   20.0\n",
-       "7  12 hour high        11   12.0   13.0   16.0"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "from IPython.display import display\n",
-    "\n",
-    "df = pd.read_csv(\n",
-    "    \"/content/tables/table_2025_16_07_16_30_01_569.csv\",\n",
-    ")\n",
-    "display(df.fillna(\"\"))"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}

docs/examples-py/parse/multimodal/gemini2_flash.ipynb

@@ -1,633 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "97c79c38-38a3-40f3-ba2e-250649347d63",
-   "metadata": {},
-   "source": [
-    "# Multimodal Parsing with Gemini 2.0 Flash\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/multimodal/gemini2_flash.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
-    "\n",
-    "This cookbook shows you how to use LlamaParse to parse any document with the multimodal capabilities of Gemini 2.0 Flash.\n",
-    "\n",
-    "LlamaParse allows you to plug in external, multimodal model vendors for parsing - we handle the error correction, validation, and scalability/reliability for you.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "15e60ecf-519c-41fc-911b-765adaf8bad4",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "Download the data - we'll use a technical datasheet for a programmable logic device (Xilinx's XC9500 In-System Programmable CPLD)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "91a9e532-1454-40e0-bbf0-fd442c350121",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import nest_asyncio\n",
-    "\n",
-    "nest_asyncio.apply()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0d9fb0aa-74cd-476f-8161-efd9e04248bf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--2025-02-06 20:24:19--  https://media.digikey.com/pdf/Data%20Sheets/AMD/XC9500_CPLD_Family.pdf\n",
-      "Resolving media.digikey.com (media.digikey.com)... 23.37.18.160\n",
-      "Connecting to media.digikey.com (media.digikey.com)|23.37.18.160|:443... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 201899 (197K) [application/pdf]\n",
-      "Saving to: ‘data/XC9500_CPLD_Family.pdf’\n",
-      "\n",
-      "data/XC9500_CPLD_Fa 100%[===================>] 197.17K  --.-KB/s    in 0.03s   \n",
-      "\n",
-      "2025-02-06 20:24:19 (7.67 MB/s) - ‘data/XC9500_CPLD_Family.pdf’ saved [201899/201899]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "!wget \"https://media.digikey.com/pdf/Data%20Sheets/AMD/XC9500_CPLD_Family.pdf\" -O data/XC9500_CPLD_Family.pdf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4e29a9d7-5bd9-4fb8-8ec1-4c128a748662",
-   "metadata": {},
-   "source": [
-    "## Initialize LlamaParse\n",
-    "\n",
-    "Initialize LlamaParse in multimodal mode, and specify the vendor as `gemini-2.0-flash-001`.\n",
-    "\n",
-    "**NOTE**: Current pricing is 2 credits for a 1 page ($0.006 USD / page). This includes core model, infra, and algorithm costs to fully process the page. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dc921729-3446-42ca-8e1b-a6fd26195ed9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_index.core.schema import TextNode\n",
-    "from typing import List\n",
-    "import json\n",
-    "\n",
-    "\n",
-    "def get_text_nodes(json_list: List[dict]):\n",
-    "    text_nodes = []\n",
-    "    for idx, page in enumerate(json_list):\n",
-    "        text_node = TextNode(text=page[\"md\"], metadata={\"page\": page[\"page\"]})\n",
-    "        text_nodes.append(text_node)\n",
-    "    return text_nodes\n",
-    "\n",
-    "\n",
-    "def save_jsonl(data_list, filename):\n",
-    "    \"\"\"Save a list of dictionaries as JSON Lines.\"\"\"\n",
-    "    with open(filename, \"w\") as file:\n",
-    "        for item in data_list:\n",
-    "            json.dump(item, file)\n",
-    "            file.write(\"\\n\")\n",
-    "\n",
-    "\n",
-    "def load_jsonl(filename):\n",
-    "    \"\"\"Load a list of dictionaries from JSON Lines.\"\"\"\n",
-    "    data_list = []\n",
-    "    with open(filename, \"r\") as file:\n",
-    "        for line in file:\n",
-    "            data_list.append(json.loads(line))\n",
-    "    return data_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f2e9d9cf-8189-4fcb-b34f-cde6cc0b59c8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Started parsing the file under job_id 51538aa0-13e6-4429-a458-a492ba7eec04\n"
-     ]
-    }
-   ],
-   "source": [
-    "from llama_parse import LlamaParse\n",
-    "\n",
-    "parsing_instruction = \"\"\"\n",
-    "You are given a technical datasheet of an electronic component.\n",
-    "For any graphs, try to create a 2D table of relevant values, along with a description of the graph.\n",
-    "For any schematic diagrams, MAKE SURE to describe a list of all components and their connections to each other.\n",
-    "Make sure that you always parse out the text with the correct reading order.\n",
-    "\"\"\"\n",
-    "\n",
-    "parser = LlamaParse(\n",
-    "    result_type=\"markdown\",\n",
-    "    use_vendor_multimodal_model=True,\n",
-    "    vendor_multimodal_model_name=\"gemini-2.0-flash-001\",\n",
-    "    invalidate_cache=True,\n",
-    "    parsing_instruction=parsing_instruction,\n",
-    ")\n",
-    "json_objs = parser.get_json_result(\"./data/XC9500_CPLD_Family.pdf\")\n",
-    "json_list = json_objs[0][\"pages\"]\n",
-    "docs = get_text_nodes(json_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "96a81df0-1026-4e30-a930-f677dc31e344",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Optional: Save\n",
-    "save_jsonl([d.dict() for d in docs], \"docs_gemini_2.0_flash.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ee2e6920-8893-4b39-ae12-94d13c651406",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Optional: Load\n",
-    "from llama_index.core import Document\n",
-    "\n",
-    "docs_dicts = load_jsonl(\"docs_gemini_2.0_flash.jsonl\")\n",
-    "docs = [Document.parse_obj(d) for d in docs_dicts]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4f3c51b0-7878-48d7-9bc3-02b516500128",
-   "metadata": {},
-   "source": [
-    "### Setup GPT-4o baseline\n",
-    "\n",
-    "For comparison, we will also parse the document using GPT-4o ($0.03 per page)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6fc3f258-50ae-4988-b904-c105463a498f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Started parsing the file under job_id 23c6627c-2e3d-46c9-88a0-7945d7e65d96\n"
-     ]
-    }
-   ],
-   "source": [
-    "from llama_parse import LlamaParse\n",
-    "\n",
-    "parser_gpt4o = LlamaParse(\n",
-    "    result_type=\"markdown\",\n",
-    "    use_vendor_multimodal_model=True,\n",
-    "    vendor_multimodal_model=\"openai-gpt4o\",\n",
-    "    invalidate_cache=True,\n",
-    "    parsing_instruction=parsing_instruction,\n",
-    ")\n",
-    "json_objs_gpt4o = parser_gpt4o.get_json_result(\"./data/XC9500_CPLD_Family.pdf\")\n",
-    "json_list_gpt4o = json_objs_gpt4o[0][\"pages\"]\n",
-    "docs_gpt4o = get_text_nodes(json_list_gpt4o)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6a47f04e-12e1-4c80-a71d-ef7721f96401",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Optional: Save\n",
-    "save_jsonl([d.dict() for d in docs_gpt4o], \"docs_gpt4o.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c38b5ca3-fa87-434b-b477-bf6a4962eb3d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Optional: Load\n",
-    "from llama_index.core import Document\n",
-    "\n",
-    "docs_gpt4o_dicts = load_jsonl(\"docs_gpt4o.jsonl\")\n",
-    "docs_gpt4o = [Document.parse_obj(d) for d in docs_gpt4o_dicts]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "44c20f7a-2901-4dd0-b635-a4b33c5664c1",
-   "metadata": {},
-   "source": [
-    "## View Results\n",
-    "\n",
-    "Let's visualize the results between GPT-4o and Gemini Flash 2.0 along with the original document page."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bf314141-9f6d-4453-beb9-0106cdf196bf",
-   "metadata": {},
-   "source": [
-    "Check out an example page 2 below."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c70d420d-1778-4b0d-81e2-db09276e90cf",
-   "metadata": {},
-   "source": [
-    "![xc9500_img](XC9500_CPLD_Family_p3.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0950ecad-248c-4c3c-98b9-ab1a9dabd5b4",
-   "metadata": {},
-   "source": [
-    "We see that the parsed text is fairly similar between Gemini 2.0 Flash and GPT-4o. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "778698aa-da7e-4081-b3b5-0372f228536f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "page: 3\n",
-      "\n",
-      "The image shows the architecture of the XC9500 In-System Programmable CPLD Family, which is marked as obsolete. Here's a breakdown of the components and their connections:\n",
-      "\n",
-      "### Components and Connections:\n",
-      "\n",
-      "1. **JTAG Port:**\n",
-      "   - Connects to the JTAG Controller.\n",
-      "\n",
-      "2. **JTAG Controller:**\n",
-      "   - Interfaces with the In-System Programming Controller.\n",
-      "   - Connects to the I/O Blocks.\n",
-      "\n",
-      "3. **In-System Programming Controller:**\n",
-      "   - Interfaces with the JTAG Controller and the Fast CONNECT Switch Matrix.\n",
-      "\n",
-      "4. **I/O Blocks:**\n",
-      "   - Multiple I/O lines connect to the Fast CONNECT Switch Matrix.\n",
-      "   - Includes special I/O lines for GCK, GSR, and GTS.\n",
-      "\n",
-      "5. **Fast CONNECT Switch Matrix:**\n",
-      "   - Connects to the I/O Blocks and Function Blocks.\n",
-      "   - Provides 36 inputs and 18 outputs to each Function Block.\n",
-      "\n",
-      "6. **Function Blocks (FB):**\n",
-      "   - Each block contains 18 macrocells.\n",
-      "   - Outputs from the Function Blocks drive the I/O Blocks directly.\n",
-      "   - Multiple Function Blocks (1 to N) are shown, each with 18 macrocells.\n",
-      "\n",
-      "### Function Block Details:\n",
-      "\n",
-      "- Each Function Block consists of 18 independent macrocells.\n",
-      "- Capable of implementing combinatorial or registered functions.\n",
-      "- Receives global clock, output enable, and set/reset signals.\n",
-      "- Generates 18 outputs for the Fast CONNECT switch matrix.\n",
-      "- Logic is implemented using a sum-of-products representation.\n",
-      "- 36 inputs provide 72 true and complement signals to form 90 product terms.\n",
-      "- Product terms can be allocated to each macrocell by the product term allocator.\n",
-      "- Supports local feedback paths for fast counters and state machines.\n",
-      "\n",
-      "This architecture is designed for flexibility in implementing complex logic functions within a programmable logic device.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# using Gemini 2.0 Flash\n",
-    "print(docs[2].get_content(metadata_mode=\"all\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1511a30f-3efc-4142-9668-7dc056a24d0c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "page: 3\n",
-      "\n",
-      "The diagram illustrates the architecture of the XC9500 In-System Programmable CPLD Family. Here's a breakdown of the components and their connections:\n",
-      "\n",
-      "1. **JTAG Port**: \n",
-      "   - Connects to the JTAG Controller.\n",
-      "\n",
-      "2. **JTAG Controller**: \n",
-      "   - Interfaces with the In-System Programming Controller.\n",
-      "\n",
-      "3. **In-System Programming Controller**: \n",
-      "   - Manages programming of the device.\n",
-      "\n",
-      "4. **I/O Blocks**: \n",
-      "   - Connect to external I/O pins.\n",
-      "   - Interface with the Fast CONNECT Switch Matrix.\n",
-      "\n",
-      "5. **Fast CONNECT Switch Matrix**: \n",
-      "   - Connects I/O Blocks to Function Blocks.\n",
-      "   - Provides 36 inputs and 18 outputs to each Function Block.\n",
-      "\n",
-      "6. **Function Blocks (FB)**: \n",
-      "   - Each block contains 18 macrocells.\n",
-      "   - Capable of implementing combinatorial or registered functions.\n",
-      "   - Receives global clock, output enable, and set/reset signals.\n",
-      "   - Outputs drive the Fast CONNECT Switch Matrix.\n",
-      "   - Supports local feedback paths for fast counters and state machines.\n",
-      "\n",
-      "7. **I/O/GCK, I/O/GSR, I/O/GTS**: \n",
-      "   - Special I/O pins for global clock, set/reset, and output enable signals.\n",
-      "\n",
-      "The architecture is designed for flexibility and high-speed operation, with each Function Block capable of handling complex logic functions.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# using GPT-4o\n",
-    "print(docs_gpt4o[2].get_content(metadata_mode=\"all\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "705f7729-fa0f-4ca0-8562-c42afeaa8532",
-   "metadata": {},
-   "source": [
-    "## Setup RAG Pipeline\n",
-    "\n",
-    "Let's setup a RAG pipeline over this data.\n",
-    "\n",
-    "(we also use gpt4o-mini for the actual text synthesis step)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5a53ee5d-cc63-421b-8896-588c83edfcf0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_index.core import Settings\n",
-    "from llama_index.llms.openai import OpenAI\n",
-    "from llama_index.embeddings.openai import OpenAIEmbedding\n",
-    "\n",
-    "Settings.llm = OpenAI(model=\"o3-mini\")\n",
-    "Settings.embed_model = OpenAIEmbedding(model=\"text-embedding-3-large\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "60972d7a-7948-4ad7-89df-57004acee917",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from llama_index.core import SummaryIndex\n",
-    "from llama_index.core import VectorStoreIndex\n",
-    "from llama_index.llms.openai import OpenAI\n",
-    "\n",
-    "index = VectorStoreIndex(docs)\n",
-    "query_engine = index.as_query_engine(similarity_top_k=5)\n",
-    "\n",
-    "index_gpt4o = VectorStoreIndex(docs_gpt4o)\n",
-    "query_engine_gpt4o = index_gpt4o.as_query_engine(similarity_top_k=5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e7df7bcb-1df4-4a01-88fc-2d596b1cc74d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query = \"Give me the full output slew-Rate curve for (a) Rising and (b) Falling Outputs\"\n",
-    "\n",
-    "response = query_engine.query(query)\n",
-    "response_gpt4o = query_engine_gpt4o.query(query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b7070a31-3bb8-4134-8338-20bc2fd6f3d6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The full output slew-rate curve for (a) Rising and (b) Falling Outputs is represented in a graph where the output voltage starts at 1.5V and reaches the desired output level over a time period defined as T<sub>SLEW</sub>. The curve illustrates the gradual increase in voltage for rising outputs and the gradual decrease for falling outputs, effectively showing how the output edge rates can be controlled to reduce system noise.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7bee8167-f021-4c87-8d28-9f40a4f7b69d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# XC9500 In-System Programmable CPLD Family\n",
-      "\n",
-      "Each output has independent slew rate control. Output edge rates may be slowed down to reduce system noise (with an additional time delay of T<sub>SLEW</sub>) through programming. See Figure 11.\n",
-      "\n",
-      "Each IOB provides user programmable ground pin capability. This allows device I/O pins to be configured as additional ground pins. By tying strategically located programmable ground pins to the external ground connection, system noise generated from large numbers of simultaneous switching outputs may be reduced.\n",
-      "\n",
-      "A control pull-up resistor (typically 10K ohms) is attached to each device I/O pin to prevent them from floating when the device is not in normal user operation. This resistor is active during device programming mode and system power-up. It is also activated for an erased device. The resistor is deactivated during normal operation.\n",
-      "\n",
-      "The output driver is capable of supplying 24 mA output drive. All output drivers in the device may be configured for either 5V TTL levels or 3.3V levels by connecting the device output voltage supply (V<sub>CCIO</sub>) to a 5V or 3.3V voltage supply. Figure 12 shows how the XC9500 device can be used in 5V only and mixed 3.3V/5V systems.\n",
-      "\n",
-      "## Pin-Locking Capability\n",
-      "\n",
-      "The capability to lock the user defined pin assignments during design changes depends on the ability of the architecture to adapt to unexpected changes. The XC9500 devices have architectural features that enhance the ability to accept design changes while maintaining the same pinout.\n",
-      "\n",
-      "The XC9500 architecture provides maximum routing within the Fast CONNECT switch matrix, and incorporates a flexible Function Block that allows block-wide allocation of available product terms. This provides a high level of confidence of maintaining both input and output pin assignments for unexpected design changes.\n",
-      "\n",
-      "For extensive design changes requiring higher logic capacity than is available in the initially chosen device, the new design may be able to fit into a larger pin-compatible device using the same pin assignments. The same board may be used with a higher density device without the expense of board rework.\n",
-      "\n",
-      "!Output slew-Rate for (a) Rising and (b) Falling Outputs\n",
-      "\n",
-      "**Figure 11:** Output slew-Rate for (a) Rising and (b) Falling Outputs\n",
-      "\n",
-      "| Output Voltage | Time |\n",
-      "|----------------|------|\n",
-      "| 1.5V           | 0    |\n",
-      "| T<sub>SLEW</sub> |    |\n",
-      "\n",
-      "**Figure 12:** XC9500 Devices in (a) 5V Systems and (b) Mixed 5V/3.3V Systems\n",
-      "\n",
-      "| 5V CMOS or 5V TTL | 3.3V |\n",
-      "|-------------------|------|\n",
-      "| 5V                | 0V   |\n",
-      "| 3.6V              | 0V   |\n",
-      "| 3.3V              | 0V   |\n",
-      "\n",
-      "- **(a) 5V System:**\n",
-      "  - V<sub>CCINT</sub> V<sub>CCIO</sub>\n",
-      "  - XC9500 CPLD\n",
-      "  - IN OUT\n",
-      "  - GND\n",
-      "\n",
-      "- **(b) Mixed 5V/3.3V System:**\n",
-      "  - V<sub>CCINT</sub> V<sub>CCIO</sub>\n",
-      "  - XC9500 CPLD\n",
-      "  - IN OUT\n",
-      "  - GND\n",
-      "\n",
-      "www.xilinx.com\n",
-      "\n",
-      "DS063 (v6.0) May 17, 2013  \n",
-      "Product Specification\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(response.source_nodes[0].get_content())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5f9fef7f-510b-46a5-8716-f5616f542035",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The output slew-rate curve for (a) Rising and (b) Falling Outputs is represented in a timing diagram where the output voltage transitions from a low state to a high state and vice versa. \n",
-      "\n",
-      "For the rising output, the curve starts at 1.5V and transitions to the desired output voltage level over a time period defined as T<sub>SLEW</sub>. \n",
-      "\n",
-      "For the falling output, the curve similarly begins at the high output voltage and decreases to a low state, also taking the time defined as T<sub>SLEW</sub> to complete the transition.\n",
-      "\n",
-      "The specific values and graphical representation would typically be illustrated in a figure, but the key takeaway is that the output slew rate can be controlled to manage system noise by programming the desired T<sub>SLEW</sub> time.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(response_gpt4o)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d40f9dd4-2dd4-4fa5-b636-1f901dc1601b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# XC9500 In-System Programmable CPLD Family\n",
-      "\n",
-      "Each output has independent slew rate control. Output edge rates may be slowed down to reduce system noise (with an additional time delay of T<sub>SLEW</sub>) through programming. See Figure 11.\n",
-      "\n",
-      "Each IOB provides user programmable ground pin capability. This allows device I/O pins to be configured as additional ground pins. By tying strategically located programmable ground pins to the external ground connection, system noise generated from large numbers of simultaneous switching outputs may be reduced.\n",
-      "\n",
-      "A control pull-up resistor (typically 10K ohms) is attached to each device I/O pin to prevent them from floating when the device is not in normal user operation. This resistor is active during device programming mode and system power-up. It is also activated for an erased device. The resistor is deactivated during normal operation.\n",
-      "\n",
-      "The output driver is capable of supplying 24 mA output drive. All output drivers in the device may be configured for either 5V TTL levels or 3.3V levels by connecting the device output voltage supply (V<sub>CCIO</sub>) to a 5V or 3.3V voltage supply. Figure 12 shows how the XC9500 device can be used in 5V only and mixed 3.3V/5V systems.\n",
-      "\n",
-      "## Pin-Locking Capability\n",
-      "\n",
-      "The capability to lock the user defined pin assignments during design changes depends on the ability of the architecture to adapt to unexpected changes. The XC9500 devices have architectural features that enhance the ability to accept design changes while maintaining the same pinout.\n",
-      "\n",
-      "The XC9500 architecture provides maximum routing within the Fast CONNECT switch matrix, and incorporates a flexible Function Block that allows block-wide allocation of available product terms. This provides a high level of confidence of maintaining both input and output pin assignments for unexpected design changes.\n",
-      "\n",
-      "For extensive design changes requiring higher logic capacity than is available in the initially chosen device, the new design may be able to fit into a larger pin-compatible device using the same pin assignments. The same board may be used with a higher density device without the expense of board rework.\n",
-      "\n",
-      "!Output slew-Rate for (a) Rising and (b) Falling Outputs\n",
-      "\n",
-      "**Figure 11:** Output slew-Rate for (a) Rising and (b) Falling Outputs\n",
-      "\n",
-      "| Output Voltage | Time |\n",
-      "|----------------|------|\n",
-      "| 1.5V           | 0    |\n",
-      "| T<sub>SLEW</sub> |    |\n",
-      "\n",
-      "**Figure 12:** XC9500 Devices in (a) 5V Systems and (b) Mixed 5V/3.3V Systems\n",
-      "\n",
-      "| 5V CMOS or 5V TTL | 3.3V |\n",
-      "|-------------------|------|\n",
-      "| 5V                | 0V   |\n",
-      "| 3.6V              | 0V   |\n",
-      "| 3.3V              | 0V   |\n",
-      "\n",
-      "- **XC9500 CPLD**  \n",
-      "  - **IN**  \n",
-      "  - **OUT**  \n",
-      "  - **GND**  \n",
-      "\n",
-      "www.xilinx.com  \n",
-      "DS063 (v6.0) May 17, 2013  \n",
-      "Product Specification\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(response_gpt4o.source_nodes[0].get_content())"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "llama_parse",
-   "language": "python",
-   "name": "llama_parse"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/examples-py/parse/parsing_instructions/parsing_instructions.ipynb

@@ -1,602 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/parsing_instructions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
-    "\n",
-    "# Parsing documents with Instructions\n",
-    "\n",
-    "Parsing instructions allow you to guide our parsing model in the same way you would instruct an LLM.\n",
-    "\n",
-    "These instructions can be useful for improving the parser's performance on complex document layouts, extracting data in a specific format, or transforming the document in other ways.\n",
-    "\n",
-    "### Why This Matters:\n",
-    "Traditional document parsing can be rigid and error-prone, often missing crucial context and nuances in complex layouts. Our instruction-based parsing allows you to:\n",
-    "\n",
-    "1. Extract specific information with pinpoint accuracy\n",
-    "2. Handle complex document layouts with ease\n",
-    "3. Transform unstructured data into structured formats effortlessly\n",
-    "4. Save hours of manual data entry and verification\n",
-    "5. Reduce errors in document processing workflows\n",
-    "\n",
-    "In this demonstration, we showcase how parsing instructions can be used to extract specific information from unstructured documents. Below are the documents we use for testing:\n",
-    "\n",
-    "1. McDonald's Receipt - Extracting the price of each order and the final amount to be paid.\n",
-    "\n",
-    "2. Expense Report Document - Extracting employee name, employee ID, position, department, date ranges, individual expense items with dates, categories, and amounts.\n",
-    "\n",
-    "3. Purchase Order Document - Identifying the PO number, vendor details, shipping terms, and an itemized list of products with quantities and unit prices.\n",
-    "\n",
-    "Let's jump into these real-world examples and see how parsing instructions can help us extract specific information."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install llama-cloud-services"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Setup API Key"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import nest_asyncio\n",
-    "\n",
-    "nest_asyncio.apply()\n",
-    "\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"llx-...\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### McDonald's Receipt\n",
-    "\n",
-    "Here we extract the price of each order and the final amount to be paid."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img src=\"mcdonalds_receipt.png\" alt=\"Alt Text\" width=\"500\">"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Started parsing the file under job_id 66643b81-e2f4-408b-890b-8e116472210b\n"
-     ]
-    }
-   ],
-   "source": [
-    "from llama_cloud_services import LlamaParse\n",
-    "\n",
-    "vanilaParsing = LlamaParse(result_type=\"markdown\").load_data(\"./mcdonalds_receipt.png\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# Rate us HIGHLY SATISFIED\n",
-      "\n",
-      "Purchase any sandwich and receive a FREE ITEM\n",
-      "\n",
-      "Go to WWW.mcdvoice.com within 7 days of purchase of equal or lesser value and tell us about your visit.\n",
-      "\n",
-      "Validation Code: 31278-01121-21018-20481-00081-0\n",
-      "\n",
-      "Valid at participating US McDonald's\n",
-      "\n",
-      "Expires 30 days after receipt date\n",
-      "\n",
-      "# McDonald's Restaurant #312782378\n",
-      "\n",
-      "PINE RD NW\n",
-      "\n",
-      "RICE MN 56367-9740\n",
-      "\n",
-      "TEL# 320 393 4600\n",
-      "\n",
-      "KS# 12/08/2022 08:48 PM\n",
-      "\n",
-      "# Order\n",
-      "\n",
-      "|Happy Meal 6 Pc|$4.89|\n",
-      "|---|---|\n",
-      "|Creamy Ranch Cup| |\n",
-      "|Extra Kids Fry| |\n",
-      "|Wreck It Ralph 2 Snack| |\n",
-      "|Oreo McFlurry|$2.69|\n",
-      "\n",
-      "# Summary\n",
-      "\n",
-      "|Subtotal|$7.58|\n",
-      "|---|---|\n",
-      "|Tax|$0.52|\n",
-      "|Take-Out Total|$8.10|\n",
-      "|Cash Tendered|$10.00|\n",
-      "|Change|$1.90|\n",
-      "\n",
-      "### Not ACCEPTING APPLICATIONS *++ McDonald's Restaurant Rice\n",
-      "\n",
-      "Text to #36453 apply 31278\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(vanilaParsing[0].text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Started parsing the file under job_id 1a04fdbb-5415-4a36-a1bd-26bfb5d618fa\n"
-     ]
-    }
-   ],
-   "source": [
-    "parsingInstruction = \"\"\"The provided document is a McDonald's receipt.\n",
-    " Provide the price of each order and final amount to be paid.\"\"\"\n",
-    "withInstructionParsing = LlamaParse(\n",
-    "    result_type=\"markdown\", parsing_instruction=parsingInstruction\n",
-    ").load_data(\"./mcdonalds_receipt.png\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are the prices for each order from the McDonald's receipt:\n",
-      "\n",
-      "1. Happy Meal 6 Pc: $4.89\n",
-      "2. Snack Oreo McFlurry: $2.69\n",
-      "\n",
-      "**Subtotal:** $7.58\n",
-      "**Tax:** $0.52\n",
-      "**Total Amount to be Paid:** $8.10\n",
-      "\n",
-      "The cash tendered was $10.00, and the change given was $1.90.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(withInstructionParsing[0].text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Expense Report Document\n",
-    "\n",
-    "Here we extract employee name, employee ID, position, department, date ranges, individual expense items with dates, categories, and amounts."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img src=\"expense_report_document.png\" alt=\"Alt Text\" width=\"500\">"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Started parsing the file under job_id b6bcc6e1-7d30-4522-9abd-ace196781a70\n"
-     ]
-    }
-   ],
-   "source": [
-    "vanilaParsing = LlamaParse(result_type=\"markdown\").load_data(\n",
-    "    \"./expense_report_document.pdf\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# QUANTUM DYNAMICS CORPORATION\n",
-      "\n",
-      "# EMPLOYEE EXPENSE REPORT\n",
-      "\n",
-      "# FISCAL YEAR 2024\n",
-      "\n",
-      "# EMPLOYEE INFORMATION:\n",
-      "\n",
-      "Name: Dr. Alexandra Chen-Martinez, PhD\n",
-      "\n",
-      "Employee ID: QD-2022-1457\n",
-      "\n",
-      "Department: Advanced Research & Development\n",
-      "\n",
-      "Cost Center: CC-ARD-NA-003\n",
-      "\n",
-      "Project Codes: QD-QUANTUM-2024-01, QD-AI-2024-03\n",
-      "\n",
-      "Position: Principal Research Scientist\n",
-      "\n",
-      "Reporting Manager: Dr. James Thompson\n",
-      "\n",
-      "# TRIP/EXPENSE PERIOD:\n",
-      "\n",
-      "Start Date: November 15, 2024\n",
-      "\n",
-      "End Date: December 10, 2024\n",
-      "\n",
-      "Purpose: International Conference Attendance & Client Meetings\n",
-      "\n",
-      "Locations: Tokyo, Japan → Singapore → Sydney, Australia\n",
-      "\n",
-      "# CURRENCY CONVERSION RATES APPLIED:\n",
-      "\n",
-      "JPY (¥) → USD: 0.0068 (as of 11/15/2024)\n",
-      "\n",
-      "SGD (S$) → USD: 0.74 (as of 11/28/2024)\n",
-      "\n",
-      "AUD (A$) → USD: 0.65 (as of 12/03/2024)\n",
-      "\n",
-      "# ITEMIZED EXPENSES:\n",
-      "\n",
-      "|Date|Category|Description|Original|Currency|USD|\n",
-      "|---|---|---|---|---|---|\n",
-      "|11/15/2024|Transportation|JFK → NRT Business Class|4,250.00|USD|4,250.00|\n",
-      "|Booking Ref: QF78956 - Corporate Rate Applied|Booking Ref: QF78956 - Corporate Rate Applied|Booking Ref: QF78956 - Corporate Rate Applied|Booking Ref: QF78956 - Corporate Rate Applied|Booking Ref: QF78956 - Corporate Rate Applied|Booking Ref: QF78956 - Corporate Rate Applied|\n",
-      "|Project Code: QD-QUANTUM-2024-01|Project Code: QD-QUANTUM-2024-01|Project Code: QD-QUANTUM-2024-01|Project Code: QD-QUANTUM-2024-01|Project Code: QD-QUANTUM-2024-01|Project Code: QD-QUANTUM-2024-01|\n",
-      "|11/16/2024|Accommodation|Hilton Tokyo - 5 nights|225,000|JPY|1,530.00|\n",
-      "|Confirmation: HTK-2024-78956|Confirmation: HTK-2024-78956|Confirmation: HTK-2024-78956|Confirmation: HTK-2024-78956|Confirmation: HTK-2024-78956|Confirmation: HTK-2024-78956|\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(vanilaParsing[0].text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Started parsing the file under job_id 7b0d05bb-947b-4475-8d0f-f10386f7446e\n"
-     ]
-    }
-   ],
-   "source": [
-    "parsingInstruction = \"\"\"You are provided with an expense report. \n",
-    "Extract employee name, employee id, position, department, date ranges, individual expense items with dates, categories, and amounts.\"\"\"\n",
-    "\n",
-    "withInstructionParsing = LlamaParse(\n",
-    "    result_type=\"markdown\", parsing_instruction=parsingInstruction\n",
-    ").load_data(\"./expense_report_document.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "**Employee Information:**\n",
-      "- **Name:** Dr. Alexandra Chen-Martinez, PhD\n",
-      "- **Employee ID:** QD-2022-1457\n",
-      "- **Position:** Principal Research Scientist\n",
-      "- **Department:** Advanced Research & Development\n",
-      "\n",
-      "**Trip/Expense Period:**\n",
-      "- **Start Date:** November 15, 2024\n",
-      "- **End Date:** December 10, 2024\n",
-      "\n",
-      "**Expense Items:**\n",
-      "1. **Date:** 11/15/2024\n",
-      "- **Category:** Transportation\n",
-      "- **Description:** JFK → NRT Business Class\n",
-      "- **Original Amount:** $4,250.00\n",
-      "- **Currency:** USD\n",
-      "- **USD Amount:** $4,250.00\n",
-      "- **Booking Reference:** QF78956 - Corporate Rate Applied\n",
-      "- **Project Code:** QD-QUANTUM-2024-01\n",
-      "\n",
-      "2. **Date:** 11/16/2024\n",
-      "- **Category:** Accommodation\n",
-      "- **Description:** Hilton Tokyo - 5 nights\n",
-      "- **Original Amount:** ¥225,000\n",
-      "- **Currency:** JPY\n",
-      "- **USD Amount:** $1,530.00\n",
-      "- **Confirmation:** HTK-2024-78956\n",
-      "\n",
-      "**Locations:**\n",
-      "- Tokyo, Japan\n",
-      "- Singapore\n",
-      "- Sydney, Australia\n",
-      "\n",
-      "**Currency Conversion Rates Applied:**\n",
-      "- JPY (¥) → USD: 0.0068 (as of 11/15/2024)\n",
-      "- SGD (S$) → USD: 0.74 (as of 11/28/2024)\n",
-      "- AUD (A$) → USD: 0.65 (as of 12/03/2024)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(withInstructionParsing[0].text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Purchase Order Document \n",
-    "\n",
-    "Here we identify the PO number, vendor details, shipping terms, and an itemized list of products with quantities and unit prices."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img src=\"purchase_order_document.png\" alt=\"Alt Text\" width=\"500\">"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Started parsing the file under job_id b8cb11c3-7dce-4e6a-94bb-1a4e50e45e55\n"
-     ]
-    }
-   ],
-   "source": [
-    "vanilaParsing = LlamaParse(result_type=\"markdown\").load_data(\n",
-    "    \"./purchase_order_document.pdf\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# GLOBAL TECH SOLUTIONS, INC.\n",
-      "\n",
-      "# PURCHASE ORDER\n",
-      "\n",
-      "Document Reference: PO-2024-GT-9876/REV.2\n",
-      "\n",
-      "[Original: PO-2024-GT-9876]\n",
-      "\n",
-      "Amendment Date: 12/10/2024\n",
-      "\n",
-      "# VENDOR INFORMATION:\n",
-      "\n",
-      "Quantum Electronics Manufacturing\n",
-      "\n",
-      "DUNS: 78-456-7890\n",
-      "\n",
-      "Tax ID: EU8976543210\n",
-      "\n",
-      "Hoofdorp, Netherlands\n",
-      "\n",
-      "Vendor #: QEM-EU-2024-001\n",
-      "\n",
-      "# SHIP TO:\n",
-      "\n",
-      "Global Tech Solutions, Inc.\n",
-      "\n",
-      "Building 7A, Innovation Park\n",
-      "\n",
-      "2100 Technology Drive\n",
-      "\n",
-      "Austin, TX 78701\n",
-      "\n",
-      "USA\n",
-      "\n",
-      "Attn: Sarah Martinez, Receiving Manager\n",
-      "\n",
-      "Tel: +1 (512) 555-0123\n",
-      "\n",
-      "# PAYMENT TERMS:\n",
-      "\n",
-      "Net 45\n",
-      "\n",
-      "2% discount if paid within 15 days\n",
-      "\n",
-      "# SHIPPING TERMS:\n",
-      "\n",
-      "DDP (Delivered Duty Paid) - Incoterms 2020\n",
-      "\n",
-      "Insurance Required: Yes\n",
-      "\n",
-      "Preferred Carrier: DHL/FedEx\n",
-      "\n",
-      "Required Delivery Date: 01/15/2025\n",
-      "\n",
-      "# SPECIAL INSTRUCTIONS:\n",
-      "\n",
-      "1. All shipments must include Certificate of Conformance\n",
-      "2. ESD-sensitive items must be properly packaged\n",
-      "3. Temperature logging required for items marked with *\n",
-      "4. Partial shipments accepted with prior approval\n",
-      "5. Quote PO number on all correspondence\n",
-      "\n",
-      "# ITEM DETAILS:\n",
-      "\n",
-      "|Line|Part Number|Description|Qty|UOM|Unit Price|Total|\n",
-      "|---|---|---|---|---|---|---|\n",
-      "|1|QE-MCU-5590|Microcontroller Unit|500|EA|$12.50|$6,250.00|\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(vanilaParsing[0].text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Started parsing the file under job_id d2731305-984d-4633-8a52-0493748cf10b\n"
-     ]
-    }
-   ],
-   "source": [
-    "parsingInstruction = \"\"\"You are provided with a purchase order. \n",
-    "Identify the PO number, vendor details, shipping terms, and itemized list of products with quantities and unit prices.\"\"\"\n",
-    "\n",
-    "withInstructionParsing = LlamaParse(\n",
-    "    result_type=\"markdown\", parsing_instruction=parsingInstruction\n",
-    ").load_data(\"./purchase_order_document.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here are the details extracted from the purchase order:\n",
-      "\n",
-      "**PO Number:** PO-2024-GT-9876/REV.2\n",
-      "\n",
-      "**Vendor Details:**\n",
-      "- **Vendor Name:** Quantum Electronics Manufacturing\n",
-      "- **DUNS:** 78-456-7890\n",
-      "- **Tax ID:** EU8976543210\n",
-      "- **Address:** Hoofdorp, Netherlands\n",
-      "- **Vendor Number:** QEM-EU-2024-001\n",
-      "- **Contact Person:** Sarah Martinez, Receiving Manager\n",
-      "- **Phone:** +1 (512) 555-0123\n",
-      "\n",
-      "**Shipping Terms:**\n",
-      "- **Terms:** DDP (Delivered Duty Paid) - Incoterms 2020\n",
-      "- **Insurance Required:** Yes\n",
-      "- **Preferred Carrier:** DHL/FedEx\n",
-      "- **Required Delivery Date:** 01/15/2025\n",
-      "\n",
-      "**Itemized List of Products:**\n",
-      "1. **Part Number:** QE-MCU-5590\n",
-      "- **Description:** Microcontroller Unit\n",
-      "- **Quantity:** 500 EA\n",
-      "- **Unit Price:** $12.50\n",
-      "- **Total:** $6,250.00\n",
-      "\n",
-      "**Payment Terms:**\n",
-      "- Net 45\n",
-      "- 2% discount if paid within 15 days\n",
-      "\n",
-      "**Special Instructions:**\n",
-      "1. All shipments must include Certificate of Conformance\n",
-      "2. ESD-sensitive items must be properly packaged\n",
-      "3. Temperature logging required for items marked with *\n",
-      "4. Partial shipments accepted with prior approval\n",
-      "5. Quote PO number on all correspondence\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(withInstructionParsing[0].text)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "llamacloud",
-   "language": "python",
-   "name": "llamacloud"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/examples-py/report/basic_report.ipynb

@@ -1,762 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Report Generation with LlamaReport\n",
-    "\n",
-    "In this notebook, we'll walk through the basic process of generating a report with LlamaReport, and highlight some of the key features of the library.\n",
-    "\n",
-    "TLDR:\n",
-    "1. Download source data to use as knowledge base for the report\n",
-    "2. Kick off report generation with a template\n",
-    "3. Get the plan and review/accept/reject suggestions\n",
-    "4. Get the final report\n",
-    "5. Review/accept/reject suggestions to edit the final report\n",
-    "6. Print the final report"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install llama-cloud-services"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Download Source Data\n",
-    "\n",
-    "Here, we download the `Attention is All You Need` paper as a PDF.\n",
-    "\n",
-    "LlamaReport currently supports up to 5 files as input, and essentially any file type that can be parsed by LlamaParse.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget \"https://arxiv.org/pdf/1706.03762.pdf\" -O \"./attention.pdf\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Kick off Report Generation\n",
-    "\n",
-    "Here, we kick off report generation with a template.\n",
-    "\n",
-    "The template can either be a string or a file path, but here we'll use a string.\n",
-    "\n",
-    "In our experiments, anything works as a template, but some general guidelines:\n",
-    "\n",
-    "- Use markdown formatting + instructions in each section to guide the report generation\n",
-    "- If using an existing file as a template, provide extra instructions to guide the report generation\n",
-    "\n",
-    "**NOTE:** Since we are in a notebook, we will use async functions and `await` throughout. Synchronous methods that work without `await` are available by just removing the `a` from the method name and removing the `await` keyword."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_cloud_services import LlamaReport\n",
-    "\n",
-    "llama_report = LlamaReport(\n",
-    "    api_key=\"llx-...\",\n",
-    ")\n",
-    "\n",
-    "report_client = await llama_report.acreate_report(\n",
-    "    name=\"my_cool_report_on_attention\",\n",
-    "    # can pass in file paths or bytes\n",
-    "    input_files=[\"./attention.pdf\"],\n",
-    "    template_text=\"\"\"\\\n",
-    "# [Some title]\\n\\n\n",
-    "## TLDR\\n\n",
-    "A quick summary of the paper.\\n\\n\n",
-    "## Details\\n\n",
-    "More details about the paper, possibly more than one section here.\\n\n",
-    "\"\"\",\n",
-    "    # optional additional instructions for the report generation\n",
-    "    # template_instructions=None,\n",
-    "    # optional file path to an existing template instead of template_text\n",
-    "    # template_file=None,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The returned `ReportClient` object is used to interact with the report generation process for this specific report."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Report(id=0a394b33-1a3e-463c-b5cb-7ff8ab827d0a, name=my_cool_report_on_attention)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(report_client)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Get the plan\n",
-    "\n",
-    "The first phases of report generation involve ingesting the source data and generating a plan.\n",
-    "\n",
-    "The plan is a list of instructions for the report generation, and can be reviewed/accepted/rejected by the user.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plan = await report_client.await_for_plan(\n",
-    "    timeout=10000,\n",
-    "    poll_interval=10,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# {title}\n",
-      "[ReportQuery(field='title', prompt='Generate a clear and concise title for this paper about the Transformer model and attention mechanisms', context='The paper discusses the Transformer architecture for sequence transduction using attention mechanisms, focusing on machine translation applications')]\n",
-      "==================\n",
-      "## TLDR\n",
-      "\n",
-      "{tldr_content}\n",
-      "[ReportQuery(field='tldr_content', prompt='Write a brief, clear summary of the key points about the Transformer model', context='Focus on the main innovations: attention mechanisms, efficiency improvements, and state-of-the-art results in machine translation')]\n",
-      "==================\n",
-      "## Details\n",
-      "\n",
-      "{details_content}\n",
-      "[ReportQuery(field='details_content', prompt='Provide detailed information about the Transformer model architecture and its applications', context='Include information about:\\n- The attention mechanism implementation\\n- Advantages over recurrent and convolutional models\\n- Performance in machine translation tasks\\n- Training efficiency improvements')]\n",
-      "==================\n"
-     ]
-    }
-   ],
-   "source": [
-    "for plan_block in plan.blocks:\n",
-    "    print(plan_block.block.template)\n",
-    "    print(plan_block.queries)\n",
-    "    print(\"==================\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the plan, we can either use it to kick off generation of the final report, or we can edit the plan and adjust it as needed.\n",
-    "\n",
-    "While we could manually edit the objects here and use `await report_client.aupdate_plan(action=\"edit\", updated_plan=plan)`, we can also use `LlamaReport` to agentically edit the plan."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "suggestions = await report_client.asuggest_edits(\n",
-    "    \"Can you split the details section into two sections?\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Justification for change: \n",
-      "I'll help you break down the details section into two distinct parts - one focusing on the architecture and another on the practical applications and performance. This will make the content more organized and easier to follow. The original block at index 2 will be replaced with these two new sections.\n",
-      "\n",
-      "Proposed changes:\n",
-      "\n",
-      "## Architecture Details\n",
-      "\n",
-      "{architecture_content}\n",
-      "\n",
-      "[ReportQuery(field='architecture_content', prompt='Describe the technical details of the Transformer model architecture', context='Focus on:\\n- Core components of the Transformer architecture\\n- Self-attention mechanism implementation\\n- Multi-head attention details\\n- Position encoding approach\\n- Feed-forward network structure')]\n",
-      "==================\n",
-      "\n",
-      "## Performance and Applications\n",
-      "\n",
-      "{applications_content}\n",
-      "\n",
-      "[ReportQuery(field='applications_content', prompt='Explain the practical applications and performance advantages of the Transformer model', context='Cover:\\n- Comparison with RNN and CNN models\\n- Machine translation results and benchmarks\\n- Training efficiency improvements\\n- Real-world applications and use cases\\n- Scalability benefits')]\n",
-      "==================\n"
-     ]
-    }
-   ],
-   "source": [
-    "for suggestion in suggestions:\n",
-    "    print(\"Justification for change:\", suggestion.justification)\n",
-    "    print(\"Proposed changes:\")\n",
-    "    for plan_block in suggestion.blocks:\n",
-    "        print(plan_block.block.template)\n",
-    "        print(plan_block.queries)\n",
-    "        print(\"==================\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This looks pretty good! We can also use the client to automatically accept and apply, or reject, these suggestions.\n",
-    "\n",
-    "This will (locally) keep track of the history of changes, so that future suggestions can be based on the previous changes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for suggestion in suggestions:\n",
-    "    await report_client.aaccept_edit(suggestion)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What effect did that have on the tracked local history? Let's see!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[EditAction(block_idx=2, old_content='## Details\\n\\n{details_content}\\n\\nField: details_content, Prompt: Provide detailed information about the Transformer model architecture and its applications, Context: Include information about:\\n- The attention mechanism implementation\\n- Advantages over recurrent and convolutional models\\n- Performance in machine translation tasks\\n- Training efficiency improvements\\nDepends on: none', new_content='\\n## Architecture Details\\n\\n{architecture_content}\\n\\n\\nField: architecture_content, Prompt: Describe the technical details of the Transformer model architecture, Context: Focus on:\\n- Core components of the Transformer architecture\\n- Self-attention mechanism implementation\\n- Multi-head attention details\\n- Position encoding approach\\n- Feed-forward network structure\\nDepends on: none', action='approved', timestamp=datetime.datetime(2025, 2, 4, 20, 59, 55, 773558)),\n",
-       " EditAction(block_idx=3, old_content='[No old content]', new_content='\\n## Performance and Applications\\n\\n{applications_content}\\n\\n\\nField: applications_content, Prompt: Explain the practical applications and performance advantages of the Transformer model, Context: Cover:\\n- Comparison with RNN and CNN models\\n- Machine translation results and benchmarks\\n- Training efficiency improvements\\n- Real-world applications and use cases\\n- Scalability benefits\\nDepends on: previous', action='approved', timestamp=datetime.datetime(2025, 2, 4, 20, 59, 55, 773687))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "report_client.edit_history"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Message(role=<MessageRole.USER: 'user'>, content='Can you split the details section into two sections?', timestamp=datetime.datetime(2025, 2, 4, 20, 59, 47, 754848)),\n",
-       " Message(role=<MessageRole.ASSISTANT: 'assistant'>, content=\"\\nI'll help you break down the details section into two distinct parts - one focusing on the architecture and another on the practical applications and performance. This will make the content more organized and easier to follow. The original block at index 2 will be replaced with these two new sections.\\n\", timestamp=datetime.datetime(2025, 2, 4, 20, 59, 55, 482070))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "report_client.chat_history"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These two items are used to provide context for future suggestions! You can always clear this, or provide your own history."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# report_client.suggest_edits(\"....\", chat_history=[{\"role\": \"user\", \"content\": \"...\"}, ...])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. Get the final report\n",
-    "\n",
-    "Now that we have a plan, we can kick off generation of the final report."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# kicks off report generation\n",
-    "await report_client.aupdate_plan(action=\"approve\")\n",
-    "\n",
-    "# waits for report generation to complete\n",
-    "report = await report_client.await_completion(\n",
-    "    timeout=10000,\n",
-    "    poll_interval=10,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# Attention Is All You Need: A Pure Attention-Based Architecture for Neural Machine Translation\n",
-      "\n",
-      "## TLDR\n",
-      "\n",
-      "The Transformer introduced a revolutionary architecture that relies entirely on attention mechanisms, eliminating the need for recurrence or convolution in sequence processing. Its key innovations include multi-head self-attention for parallel processing of input sequences, scaled dot-product attention for efficient computation, and positional encodings for sequence order awareness. The model achieved breakthrough results in machine translation (28.4 BLEU on English-to-German, 41.8 BLEU on English-to-French) while requiring significantly less training time than previous approaches, training in 3.5 days on 8 GPUs. This architecture demonstrated that attention mechanisms alone are sufficient for state-of-the-art sequence modeling, setting a new direction for natural language processing.\n",
-      "\n",
-      "\n",
-      "## Architecture Details\n",
-      "\n",
-      "The Transformer architecture represents a groundbreaking approach to sequence processing, built entirely on attention mechanisms without recurrence or convolution. Here are its key technical details:\n",
-      "\n",
-      "Core Components:\n",
-      "- Encoder-decoder architecture with stacked self-attention and point-wise feed-forward layers\n",
-      "- Each layer contains two main sub-layers: multi-head self-attention mechanism and position-wise feed-forward network\n",
-      "- Layer normalization and residual connections between sub-layers\n",
-      "- No recurrent or convolutional elements, enabling parallel processing\n",
-      "\n",
-      "Self-Attention Mechanism:\n",
-      "- Processes relationships between all positions in a sequence simultaneously\n",
-      "- Computes attention weights using queries, keys, and values derived from input representations\n",
-      "- Implements scaled dot-product attention to prevent gradient issues with large input dimensions\n",
-      "- Allows direct modeling of dependencies regardless of positional distance\n",
-      "- Uses masking in decoder to prevent leftward information flow and maintain auto-regressive property\n",
-      "\n",
-      "Multi-Head Attention:\n",
-      "- Employs multiple attention heads operating in parallel\n",
-      "- Each head processes information in different representation subspaces\n",
-      "- Three types of attention applications:\n",
-      "  1. Encoder self-attention (all positions attend to each other)\n",
-      "  2. Decoder self-attention (each position attends to previous positions)\n",
-      "  3. Encoder-decoder attention (decoder queries attend to encoder outputs)\n",
-      "- Counteracts reduced resolution from attention averaging through parallel processing\n",
-      "\n",
-      "Position-wise Feed-Forward Network:\n",
-      "- Applied identically to each position separately\n",
-      "- Consists of two linear transformations with ReLU activation\n",
-      "- Structure: FFN(x) = max(0, xW1 + b1)W2 + b2\n",
-      "- Input and output dimensionality: dmodel = 512\n",
-      "- Inner-layer dimensionality: dff = 2048\n",
-      "- Parameters vary between layers but remain constant across positions\n",
-      "\n",
-      "Position Encoding:\n",
-      "- Adds positional information to input embeddings\n",
-      "- Enables the model to consider sequential order without recurrence\n",
-      "- Implements sinusoidal position encodings to allow model to attend to relative positions\n",
-      "- Maintains constant number of operations between any two positions, unlike convolutional approaches\n",
-      "- Allows effective modeling of both local and long-range dependencies\n",
-      "\n",
-      "\n",
-      "\n",
-      "## Performance and Applications\n",
-      "\n",
-      "The Transformer model demonstrates significant performance advantages and practical applications across multiple domains:\n",
-      "\n",
-      "Performance Advantages over RNN/CNN Models:\n",
-      "- Eliminates sequential computation constraints present in RNNs, enabling superior parallelization\n",
-      "- Reduces operations needed for relating distant positions to a constant number, compared to linear/logarithmic scaling in CNNs\n",
-      "- Processes all input and output positions simultaneously through self-attention mechanisms\n",
-      "- Achieves state-of-the-art results while requiring significantly less computational resources\n",
-      "\n",
-      "Machine Translation Benchmarks:\n",
-      "- WMT 2014 English-to-German: 28.4 BLEU score, exceeding previous best results by over 2 BLEU points\n",
-      "- WMT 2014 English-to-French: 41.8 BLEU score (single-model state-of-the-art)\n",
-      "- Surpasses performance of existing model ensembles in translation tasks\n",
-      "\n",
-      "Training Efficiency:\n",
-      "- Requires only 3.5 days of training on eight GPUs for state-of-the-art performance\n",
-      "- Achieves superior results at \"a small fraction of the training costs\" compared to previous models\n",
-      "- Enables significantly faster training through parallel processing of input/output sequences\n",
-      "- Can reach production-quality performance in as little as twelve hours on modern GPU hardware\n",
-      "\n",
-      "Real-world Applications:\n",
-      "- Machine translation systems\n",
-      "- Natural language understanding tasks\n",
-      "- Reading comprehension\n",
-      "- Abstractive summarization\n",
-      "- Text entailment analysis\n",
-      "- Constituency parsing (achieving 92.7 F1 score in semi-supervised settings)\n",
-      "- Adaptable to both large and limited training data scenarios\n",
-      "\n",
-      "Scalability Benefits:\n",
-      "- Highly parallelizable architecture enables efficient scaling across multiple GPUs\n",
-      "- Constant computational complexity for relating any input/output positions\n",
-      "- Effective handling of long-range dependencies in sequences\n",
-      "- Maintains performance quality while scaling to larger datasets and model sizes\n",
-      "- Generalizes well across different tasks and domains without architectural changes\n",
-      "- Supports efficient inference and deployment in production environments\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "report_text = \"\\n\\n\".join([block.template for block in report.blocks])\n",
-    "print(report_text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5. Edit the final report\n",
-    "\n",
-    "Now that we have a report, we can edit it.\n",
-    "\n",
-    "We can use the `asuggest_edits` method to get suggestions for edits, and then use the `aaccept_edit`/`areject_edit` methods to apply them.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Justification for change: \n",
-      "I'd suggest changing \"TLDR\" to \"Executive Summary\" which is more appropriate for a professional or academic report. This term is widely used in formal documents and better reflects the nature of this concise overview section while maintaining the same function of providing a quick summary of the key points.\n",
-      "\n",
-      "Proposed changes:\n",
-      "## Executive Summary\n",
-      "\n",
-      "The Transformer introduced a revolutionary architecture that relies entirely on attention mechanisms, eliminating the need for recurrence or convolution in sequence processing. Its key innovations include multi-head self-attention for parallel processing of input sequences, scaled dot-product attention for efficient computation, and positional encodings for sequence order awareness. The model achieved breakthrough results in machine translation (28.4 BLEU on English-to-German, 41.8 BLEU on English-to-French) while requiring significantly less training time than previous approaches, training in 3.5 days on 8 GPUs. This architecture demonstrated that attention mechanisms alone are sufficient for state-of-the-art sequence modeling, setting a new direction for natural language processing.\n",
-      "==================\n"
-     ]
-    }
-   ],
-   "source": [
-    "suggestions = await report_client.asuggest_edits(\n",
-    "    \"Can you change the TLDR header to something more professional?\"\n",
-    ")\n",
-    "for suggestion in suggestions:\n",
-    "    print(\"Justification for change:\", suggestion.justification)\n",
-    "    print(\"Proposed changes:\")\n",
-    "    for block in suggestion.blocks:\n",
-    "        print(block.template)\n",
-    "        print(\"==================\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Changing to \"Executive Summary\" sounds reasonable, lets accept that!\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for suggestion in suggestions:\n",
-    "    await report_client.aaccept_edit(suggestion)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 7. Print the final report\n",
-    "\n",
-    "Now that we have a report, we can print it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# Attention Is All You Need: A Pure Attention-Based Architecture for Neural Machine Translation\n",
-      "\n",
-      "## Executive Summary\n",
-      "\n",
-      "The Transformer introduced a revolutionary architecture that relies entirely on attention mechanisms, eliminating the need for recurrence or convolution in sequence processing. Its key innovations include multi-head self-attention for parallel processing of input sequences, scaled dot-product attention for efficient computation, and positional encodings for sequence order awareness. The model achieved breakthrough results in machine translation (28.4 BLEU on English-to-German, 41.8 BLEU on English-to-French) while requiring significantly less training time than previous approaches, training in 3.5 days on 8 GPUs. This architecture demonstrated that attention mechanisms alone are sufficient for state-of-the-art sequence modeling, setting a new direction for natural language processing.\n",
-      "\n",
-      "\n",
-      "## Architecture Details\n",
-      "\n",
-      "The Transformer architecture represents a groundbreaking approach to sequence processing, built entirely on attention mechanisms without recurrence or convolution. Here are its key technical details:\n",
-      "\n",
-      "Core Components:\n",
-      "- Encoder-decoder architecture with stacked self-attention and point-wise feed-forward layers\n",
-      "- Each layer contains two main sub-layers: multi-head self-attention mechanism and position-wise feed-forward network\n",
-      "- Layer normalization and residual connections between sub-layers\n",
-      "- No recurrent or convolutional elements, enabling parallel processing\n",
-      "\n",
-      "Self-Attention Mechanism:\n",
-      "- Processes relationships between all positions in a sequence simultaneously\n",
-      "- Computes attention weights using queries, keys, and values derived from input representations\n",
-      "- Implements scaled dot-product attention to prevent gradient issues with large input dimensions\n",
-      "- Allows direct modeling of dependencies regardless of positional distance\n",
-      "- Uses masking in decoder to prevent leftward information flow and maintain auto-regressive property\n",
-      "\n",
-      "Multi-Head Attention:\n",
-      "- Employs multiple attention heads operating in parallel\n",
-      "- Each head processes information in different representation subspaces\n",
-      "- Three types of attention applications:\n",
-      "  1. Encoder self-attention (all positions attend to each other)\n",
-      "  2. Decoder self-attention (each position attends to previous positions)\n",
-      "  3. Encoder-decoder attention (decoder queries attend to encoder outputs)\n",
-      "- Counteracts reduced resolution from attention averaging through parallel processing\n",
-      "\n",
-      "Position-wise Feed-Forward Network:\n",
-      "- Applied identically to each position separately\n",
-      "- Consists of two linear transformations with ReLU activation\n",
-      "- Structure: FFN(x) = max(0, xW1 + b1)W2 + b2\n",
-      "- Input and output dimensionality: dmodel = 512\n",
-      "- Inner-layer dimensionality: dff = 2048\n",
-      "- Parameters vary between layers but remain constant across positions\n",
-      "\n",
-      "Position Encoding:\n",
-      "- Adds positional information to input embeddings\n",
-      "- Enables the model to consider sequential order without recurrence\n",
-      "- Implements sinusoidal position encodings to allow model to attend to relative positions\n",
-      "- Maintains constant number of operations between any two positions, unlike convolutional approaches\n",
-      "- Allows effective modeling of both local and long-range dependencies\n",
-      "\n",
-      "\n",
-      "\n",
-      "## Performance and Applications\n",
-      "\n",
-      "The Transformer model demonstrates significant performance advantages and practical applications across multiple domains:\n",
-      "\n",
-      "Performance Advantages over RNN/CNN Models:\n",
-      "- Eliminates sequential computation constraints present in RNNs, enabling superior parallelization\n",
-      "- Reduces operations needed for relating distant positions to a constant number, compared to linear/logarithmic scaling in CNNs\n",
-      "- Processes all input and output positions simultaneously through self-attention mechanisms\n",
-      "- Achieves state-of-the-art results while requiring significantly less computational resources\n",
-      "\n",
-      "Machine Translation Benchmarks:\n",
-      "- WMT 2014 English-to-German: 28.4 BLEU score, exceeding previous best results by over 2 BLEU points\n",
-      "- WMT 2014 English-to-French: 41.8 BLEU score (single-model state-of-the-art)\n",
-      "- Surpasses performance of existing model ensembles in translation tasks\n",
-      "\n",
-      "Training Efficiency:\n",
-      "- Requires only 3.5 days of training on eight GPUs for state-of-the-art performance\n",
-      "- Achieves superior results at \"a small fraction of the training costs\" compared to previous models\n",
-      "- Enables significantly faster training through parallel processing of input/output sequences\n",
-      "- Can reach production-quality performance in as little as twelve hours on modern GPU hardware\n",
-      "\n",
-      "Real-world Applications:\n",
-      "- Machine translation systems\n",
-      "- Natural language understanding tasks\n",
-      "- Reading comprehension\n",
-      "- Abstractive summarization\n",
-      "- Text entailment analysis\n",
-      "- Constituency parsing (achieving 92.7 F1 score in semi-supervised settings)\n",
-      "- Adaptable to both large and limited training data scenarios\n",
-      "\n",
-      "Scalability Benefits:\n",
-      "- Highly parallelizable architecture enables efficient scaling across multiple GPUs\n",
-      "- Constant computational complexity for relating any input/output positions\n",
-      "- Effective handling of long-range dependencies in sequences\n",
-      "- Maintains performance quality while scaling to larger datasets and model sizes\n",
-      "- Generalizes well across different tasks and domains without architectural changes\n",
-      "- Supports efficient inference and deployment in production environments\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "report_response = await report_client.aget()\n",
-    "report_text = \"\\n\\n\".join([block.template for block in report_response.report.blocks])\n",
-    "print(report_text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also see the sources for each block!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.99687636\n",
-      "# Abstract\n",
-      "\n",
-      "The dominant sequence transduction models are based on complex recurrent or convolutiona\n",
-      "==================\n",
-      "0.99591404\n",
-      "# 2 Background\n",
-      "\n",
-      "The goal of reducing sequential computation also forms the foundation of the Extende\n",
-      "==================\n",
-      "0.9951325\n",
-      "# 1 Introduction\n",
-      "\n",
-      "Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neu\n",
-      "==================\n",
-      "0.99442345\n",
-      "# 7 Conclusion\n",
-      "\n",
-      "In this work, we presented the Transformer, the first sequence transduction model ba\n",
-      "==================\n",
-      "0.9967649\n",
-      "# 3.2.3 Applications of Attention in our Model\n",
-      "\n",
-      "The Transformer uses multi-head attention in three d\n",
-      "==================\n",
-      "0.99533635\n",
-      "# 2 Background\n",
-      "\n",
-      "The goal of reducing sequential computation also forms the foundation of the Extende\n",
-      "==================\n",
-      "0.9935868\n",
-      "# Abstract\n",
-      "\n",
-      "The dominant sequence transduction models are based on complex recurrent or convolutiona\n",
-      "==================\n",
-      "0.98780584\n",
-      "# Outputs\n",
-      "\n",
-      "(shifted right)\n",
-      "\n",
-      "Figure 1: The Transformer - model architecture.\n",
-      "\n",
-      "The Transformer follows\n",
-      "==================\n",
-      "0.9205043\n",
-      "# 3.3 Position-wise Feed-Forward Networks\n",
-      "\n",
-      "In addition to attention sub-layers, each of the layers i\n",
-      "==================\n",
-      "0.79581684\n",
-      "# 1 Introduction\n",
-      "\n",
-      "Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neu\n",
-      "==================\n",
-      "0.9946774\n",
-      "# Abstract\n",
-      "\n",
-      "The dominant sequence transduction models are based on complex recurrent or convolutiona\n",
-      "==================\n",
-      "0.97079873\n",
-      "# 7 Conclusion\n",
-      "\n",
-      "In this work, we presented the Transformer, the first sequence transduction model ba\n",
-      "==================\n",
-      "0.9535353\n",
-      "# 6.3 English Constituency Parsing\n",
-      "\n",
-      "To evaluate if the Transformer can generalize to other tasks we \n",
-      "==================\n",
-      "0.9514138\n",
-      "# 2 Background\n",
-      "\n",
-      "The goal of reducing sequential computation also forms the foundation of the Extende\n",
-      "==================\n",
-      "0.9790758\n",
-      "# 1 Introduction\n",
-      "\n",
-      "Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neu\n",
-      "==================\n",
-      "0.92262185\n",
-      "# Outputs\n",
-      "\n",
-      "(shifted right)\n",
-      "\n",
-      "Figure 1: The Transformer - model architecture.\n",
-      "\n",
-      "The Transformer follows\n",
-      "==================\n"
-     ]
-    }
-   ],
-   "source": [
-    "for block in report_response.report.blocks:\n",
-    "    # Each block has a list of sources, which are the nodes that were used to generate the block\n",
-    "    for source in block.sources:\n",
-    "        print(source.score)\n",
-    "        print(source.node.text[:100])\n",
-    "        print(\"==================\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "llama-parse-aNC435Vv-py3.10",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

examples/agents/demo_simple_openai_agent.ipynb

@@ -22,7 +22,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install llama-cloud-services llama-index llama-index-postprocessor-sbert-rerank"
+    "!pip install llama-parse llama-index llama-index-postprocessor-sbert-rerank"
    ]
   },
   {
@@ -82,7 +82,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_cloud_services import LlamaParse\n",
+    "from llama_parse import LlamaParse\n",
     "\n",
     "parser = LlamaParse(\n",
     "    result_type=\"markdown\",\n",

examples/caltrain/caltrain_text_mode.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# RAG over the Caltrain Weekend Schedule \n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/caltrain/caltrain_text_mode.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/caltrain/caltrain_text_mode.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
     "This example shows off LlamaParse parsing capabilities to build a functioning query pipeline over the Caltrain weekend schedule, a big timetable containing all trains northbound and southbound and their stops in various cities.\n",
     "\n",
@@ -81,7 +81,7 @@
     }
    ],
    "source": [
-    "from llama_cloud_services import LlamaParse\n",
+    "from llama_parse import LlamaParse\n",
     "\n",
     "docs = LlamaParse(result_type=\"text\").load_data(\"./caltrain_schedule_weekend.pdf\")"
    ]

examples/demo_advanced.ipynb

@@ -0,0 +1,759 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Advanced RAG with LlamaParse\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/demo_advanced.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "\n",
+    "This notebook is a complete walkthrough for using LlamaParse with advanced indexing/retrieval techniques in LlamaIndex over the Apple 10K Filing. \n",
+    "\n",
+    "This allows us to ask sophisticated questions that aren't possible with \"naive\" parsing/indexing techniques with existing models.\n",
+    "\n",
+    "Note for this example, we are using the `llama_index >=0.10.4` version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install llama-index\n",
+    "!pip install llama-index-core==0.10.6.post1\n",
+    "!pip install llama-index-embeddings-openai\n",
+    "!pip install llama-index-postprocessor-flag-embedding-reranker\n",
+    "!pip install git+https://github.com/FlagOpen/FlagEmbedding.git\n",
+    "!pip install llama-parse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!wget \"https://s2.q4cdn.com/470004039/files/doc_financials/2021/q4/_10-K-2021-(As-Filed).pdf\" -O apple_2021_10k.pdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some OpenAI and LlamaParse details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# llama-parse is async-first, running the async code in a notebook requires the use of nest_asyncio\n",
+    "import nest_asyncio\n",
+    "\n",
+    "nest_asyncio.apply()\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "# API access to llama-cloud\n",
+    "os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"llx-...\"\n",
+    "\n",
+    "# Using OpenAI API for embeddings/llms\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.llms.openai import OpenAI\n",
+    "from llama_index.embeddings.openai import OpenAIEmbedding\n",
+    "from llama_index.core import VectorStoreIndex\n",
+    "from llama_index.core import Settings\n",
+    "\n",
+    "embed_model = OpenAIEmbedding(model=\"text-embedding-3-small\")\n",
+    "llm = OpenAI(model=\"gpt-3.5-turbo-0125\")\n",
+    "\n",
+    "Settings.llm = llm\n",
+    "Settings.embed_model = embed_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using brand new `LlamaParse` PDF reader for PDF Parsing\n",
+    "\n",
+    "we also compare two different retrieval/query engine strategies:\n",
+    "1. Using raw Markdown text as nodes for building index and apply simple query engine for generating the results;\n",
+    "2. Using `MarkdownElementNodeParser` for parsing the `LlamaParse` output Markdown results and building recursive retriever query engine for generation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started parsing the file under job_id cac11eca-71db-4dab-b72b-c67d31e551f3\n"
+     ]
+    }
+   ],
+   "source": [
+    "from llama_parse import LlamaParse\n",
+    "\n",
+    "documents = LlamaParse(result_type=\"markdown\").load_data(\"./apple_2021_10k.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from copy import deepcopy\n",
+    "from llama_index.core.schema import TextNode\n",
+    "from llama_index.core import VectorStoreIndex\n",
+    "\n",
+    "\n",
+    "def get_page_nodes(docs, separator=\"\\n---\\n\"):\n",
+    "    \"\"\"Split each document into page node, by separator.\"\"\"\n",
+    "    nodes = []\n",
+    "    for doc in docs:\n",
+    "        doc_chunks = doc.text.split(separator)\n",
+    "        for doc_chunk in doc_chunks:\n",
+    "            node = TextNode(\n",
+    "                text=doc_chunk,\n",
+    "                metadata=deepcopy(doc.metadata),\n",
+    "            )\n",
+    "            nodes.append(node)\n",
+    "\n",
+    "    return nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "page_nodes = get_page_nodes(documents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.core.node_parser import MarkdownElementNodeParser\n",
+    "\n",
+    "node_parser = MarkdownElementNodeParser(\n",
+    "    llm=OpenAI(model=\"gpt-3.5-turbo-0125\"), num_workers=8\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nodes = node_parser.get_nodes_from_documents(documents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_nodes, objects = node_parser.get_nodes_and_objects(nodes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"This table provides information about a company's state of incorporation or organization and its corresponding I.R.S. Employer Identification Number.,\\nwith the following table title:\\nCompany Incorporation Information,\\nwith the following columns:\\n- California: None\\n- 94-2404110: None\\n\""
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "objects[0].get_content()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dump both indexed tables and page text into the vector index\n",
+    "recursive_index = VectorStoreIndex(nodes=base_nodes + objects + page_nodes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# Apple Inc.\n",
+      "\n",
+      "**CONSOLIDATED STATEMENTS OF OPERATIONS (In millions, except number of shares which are reflected in thousands and per share amounts)**\n",
+      "| |September 25, 2021|September 26, 2020|September 28, 2019|\n",
+      "|---|---|---|---|\n",
+      "|Net sales:|$297,392|$220,747|$213,883|\n",
+      "|Products| | | |\n",
+      "|Services|$68,425|$53,768|$46,291|\n",
+      "|Total net sales|$365,817|$274,515|$260,174|\n",
+      "|Cost of sales:| | | |\n",
+      "|Products|$192,266|$151,286|$144,996|\n",
+      "|Services|$20,715|$18,273|$16,786|\n",
+      "|Total cost of sales|$212,981|$169,559|$161,782|\n",
+      "|Gross margin|$152,836|$104,956|$98,392|\n",
+      "|Operating expenses:| | | |\n",
+      "|Research and development|$21,914|$18,752|$16,217|\n",
+      "|Selling, general and administrative|$21,973|$19,916|$18,245|\n",
+      "|Total operating expenses|$43,887|$38,668|$34,462|\n",
+      "|Operating income|$108,949|$66,288|$63,930|\n",
+      "|Other income/(expense), net|$258|$803|$1,807|\n",
+      "|Income before provision for income taxes|$109,207|$67,091|$65,737|\n",
+      "|Provision for income taxes|$14,527|$9,680|$10,481|\n",
+      "|Net income|$94,680|$57,411|$55,256|\n",
+      "|Earnings per share:| | | |\n",
+      "|Basic|$5.67|$3.31|$2.99|\n",
+      "|Diluted|$5.61|$3.28|$2.97|\n",
+      "|Shares used in computing earnings per share:| | | |\n",
+      "|Basic|16,701,272|17,352,119|18,471,336|\n",
+      "|Diluted|16,864,919|17,528,214|18,595,651|\n",
+      "\n",
+      "See accompanying Notes to Consolidated Financial Statements.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(page_nodes[31].get_content())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.postprocessor.flag_embedding_reranker import FlagEmbeddingReranker\n",
+    "\n",
+    "reranker = FlagEmbeddingReranker(\n",
+    "    top_n=5,\n",
+    "    model=\"BAAI/bge-reranker-large\",\n",
+    ")\n",
+    "\n",
+    "recursive_query_engine = recursive_index.as_query_engine(\n",
+    "    similarity_top_k=5, node_postprocessors=[reranker], verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "233\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(nodes))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup Baseline\n",
+    "\n",
+    "For comparison, we setup a naive RAG pipeline with default parsing and standard chunking, indexing, retrieval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.core import SimpleDirectoryReader\n",
+    "\n",
+    "reader = SimpleDirectoryReader(input_files=[\"apple_2021_10k.pdf\"])\n",
+    "base_docs = reader.load_data()\n",
+    "raw_index = VectorStoreIndex.from_documents(base_docs)\n",
+    "raw_query_engine = raw_index.as_query_engine(\n",
+    "    similarity_top_k=5, node_postprocessors=[reranker]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using `new LlamaParse` as pdf data parsing methods and retrieve tables with two different methods\n",
+    "we compare base query engine vs recursive query engine with tables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Table Query Task: Queries for Table Question Answering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "***********Basic Query Engine***********\n",
+      "The purchases of marketable securities in 2020 amounted to $163.4 billion.\n",
+      "\u001b[1;3;38;2;11;159;203mRetrieval entering 59368b87-e602-4bd1-88a7-7526fd6ab83f: TextNode\n",
+      "\u001b[0m\u001b[1;3;38;2;237;90;200mRetrieving from object TextNode with query Purchases of marketable securities in 2020\n",
+      "\u001b[0m\u001b[1;3;38;2;11;159;203mRetrieval entering dfd97f47-eb4d-4bab-8a22-9bbbc0096a4b: TextNode\n",
+      "\u001b[0m\u001b[1;3;38;2;237;90;200mRetrieving from object TextNode with query Purchases of marketable securities in 2020\n",
+      "\u001b[0m\n",
+      "***********New LlamaParse+ Recursive Retriever Query Engine***********\n",
+      "$114,938\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"Purchases of marketable securities in 2020\"\n",
+    "\n",
+    "response_1 = raw_query_engine.query(query)\n",
+    "print(\"\\n***********Basic Query Engine***********\")\n",
+    "print(response_1)\n",
+    "\n",
+    "response_2 = recursive_query_engine.query(query)\n",
+    "print(\"\\n***********New LlamaParse+ Recursive Retriever Query Engine***********\")\n",
+    "print(response_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This table provides information on hedged assets and liabilities for the years 2021 and 2020, including current and non-current marketable securities and term debt.,\n",
+      "with the following table title:\n",
+      "Hedged Assets and Liabilities Summary,\n",
+      "with the following columns:\n",
+      "- 2021: None\n",
+      "- 2020: None\n",
+      "\n",
+      "| |2021|2020|\n",
+      "|---|---|---|\n",
+      "|Hedged assets/(liabilities):| | |\n",
+      "|Current and non-current marketable securities|$15,954|$16,270|\n",
+      "|Current and non-current term debt|$(17,857)|$(21,033)|\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(response_2.source_nodes[2].get_content())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "***********Basic Query Engine***********\n",
+      "0.03%, 0.75%, 1.43%\n",
+      "\u001b[1;3;38;2;11;159;203mRetrieval entering a5afa785-217f-4e72-87cf-15da11632ec0: TextNode\n",
+      "\u001b[0m\u001b[1;3;38;2;237;90;200mRetrieving from object TextNode with query effective interest rates of all debt issuances in 2021\n",
+      "\u001b[0m\n",
+      "***********New LlamaParse+ Recursive Retriever Query Engine***********\n",
+      "0.48% – 0.63%, 0.03% – 4.78%, 0.75% – 2.81%, 1.43% – 2.86%\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"effective interest rates of all debt issuances in 2021\"\n",
+    "\n",
+    "response_1 = raw_query_engine.query(query)\n",
+    "print(\"\\n***********Basic Query Engine***********\")\n",
+    "print(response_1)\n",
+    "\n",
+    "response_2 = recursive_query_engine.query(query)\n",
+    "print(\"\\n***********New LlamaParse+ Recursive Retriever Query Engine***********\")\n",
+    "print(response_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Term Debt\n",
+      "As of September 25, 2021 , the Company had outstanding floating- and fixed-rate notes with varying maturities for an aggregate \n",
+      "principal amount of $118.1 billion  (collectively the “Notes”). The Notes are senior unsecured obligations and interest is payable in \n",
+      "arrears. The following table provides a summary of the Company’s term debt as of September 25, 2021  and September 26, \n",
+      "2020 :\n",
+      "Maturities\n",
+      "(calendar year)2021 2020\n",
+      "Amount\n",
+      "(in millions)Effective\n",
+      "Interest RateAmount\n",
+      "(in millions)Effective\n",
+      "Interest Rate\n",
+      "2013 – 2020 debt issuances:\n",
+      "Floating-rate notes  2022 $ 1,750 0.48%  – 0.63% $ 2,250 0.60%  – 1.39%\n",
+      "Fixed-rate 0.000%  – 4.650%  notes 2022  – 2060  95,813 0.03%  – 4.78%  103,828 0.03%  – 4.78%\n",
+      "Second quarter 2021 debt issuance:\n",
+      "Fixed-rate 0.700%  – 2.800%  notes 2026  – 2061  14,000 0.75%  – 2.81%  —  — %\n",
+      "Fourth quarter 2021 debt issuance:\n",
+      "Fixed-rate 1.400%  – 2.850%  notes 2028  – 2061  6,500 1.43%  – 2.86%  —  — %\n",
+      "Total term debt  118,063  106,078 \n",
+      "Unamortized premium/(discount) and issuance \n",
+      "costs, net  (380)  (314) \n",
+      "Hedge accounting fair value adjustments  1,036  1,676 \n",
+      "Less: Current portion of term debt  (9,613)  (8,773) \n",
+      "Total non-current portion of term debt $ 109,106 $ 98,667 \n",
+      "To manage interest rate risk on certain of its U.S. dollar–denominated fixed- or floating-rate notes, the Company has entered into \n",
+      "interest rate swaps to effectively convert the fixed interest rates to floating interest rates or the floating interest rates to fixed \n",
+      "interest rates on a portion of these notes. Additionally, to manage foreign currency risk on certain of its foreign currency–\n",
+      "denominated notes, the Company has entered into foreign currency swaps to effectively convert these notes to U.S. dollar–\n",
+      "denominated notes.\n",
+      "The effective interest rates for the Notes include the interest on the Notes, amortization of the discount or premium and, if \n",
+      "applicable, adjustments related to hedging. The Company recogni zed $2.6 billion , $2.8 billion  and $3.2 billion  of interest expense \n",
+      "on its term debt for 2021 , 2020  and 2019 , respectively.\n",
+      "The future principal payments for the Company’s Notes as of September 25, 2021 , are as follows (in millions):\n",
+      "2022 $ 9,583 \n",
+      "2023  11,391 \n",
+      "2024  10,202 \n",
+      "2025  10,914 \n",
+      "2026  11,408 \n",
+      "Thereafter  64,565 \n",
+      "Total term debt $ 118,063 \n",
+      "As of September 25, 2021  and September 26, 2020 , the fair value of the Company’s Notes, based on Level 2 inputs, was $125.3 \n",
+      "billion  and $117.1 billion , respectively.\n",
+      "Apple Inc. | 2021  Form 10-K | 45\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(response_1.source_nodes[0].get_content())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "***********Basic Query Engine***********\n",
+      "The U.S. Tax Cuts and Jobs Act of 2017 had an impact on income taxes in 2020, as evidenced by a decrease in the provision for income taxes compared to the prior year.\n",
+      "\u001b[1;3;38;2;11;159;203mRetrieval entering b9416f35-ebf1-45d6-9a29-b59e435ab42d: TextNode\n",
+      "\u001b[0m\u001b[1;3;38;2;237;90;200mRetrieving from object TextNode with query Impacts of the U.S. Tax Cuts and Jobs Act of 2017 on income taxes in 2020\n",
+      "\u001b[0m\u001b[1;3;38;2;11;159;203mRetrieval entering 8d8d5733-ff30-4535-9376-7f761b5900ea: TextNode\n",
+      "\u001b[0m\u001b[1;3;38;2;237;90;200mRetrieving from object TextNode with query Impacts of the U.S. Tax Cuts and Jobs Act of 2017 on income taxes in 2020\n",
+      "\u001b[0m\u001b[1;3;38;2;11;159;203mRetrieval entering 82f301e5-199a-4aa2-bbdf-ef97898c0326: TextNode\n",
+      "\u001b[0m\u001b[1;3;38;2;237;90;200mRetrieving from object TextNode with query Impacts of the U.S. Tax Cuts and Jobs Act of 2017 on income taxes in 2020\n",
+      "\u001b[0m\u001b[1;3;38;2;11;159;203mRetrieval entering 86f666b4-254b-487f-9870-8ee09aef07a9: TextNode\n",
+      "\u001b[0m\u001b[1;3;38;2;237;90;200mRetrieving from object TextNode with query Impacts of the U.S. Tax Cuts and Jobs Act of 2017 on income taxes in 2020\n",
+      "\u001b[0m\n",
+      "***********New LlamaParse+ Recursive Retriever Query Engine***********\n",
+      "The U.S. Tax Cuts and Jobs Act of 2017 had a negative impact on income taxes in 2020.\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"Impacts of the U.S. Tax Cuts and Jobs Act of 2017 on income taxes in 2020\"\n",
+    "\n",
+    "response_1 = raw_query_engine.query(query)\n",
+    "print(\"\\n***********Basic Query Engine***********\")\n",
+    "print(response_1)\n",
+    "\n",
+    "response_2 = recursive_query_engine.query(query)\n",
+    "print(\"\\n***********New LlamaParse+ Recursive Retriever Query Engine***********\")\n",
+    "print(response_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Other Income/(Expense), Net\n",
+      "The following table shows the detail of OI&E for 2021 , 2020  and 2019  (in millions):\n",
+      "2021 2020 2019\n",
+      "Interest and dividend income $ 2,843 $ 3,763 $ 4,961 \n",
+      "Interest expense  (2,645)  (2,873)  (3,576) \n",
+      "Other income/(expense), net  60  (87)  422 \n",
+      "Total other income/(expense), net $ 258 $ 803 $ 1,807 \n",
+      "Note 5 – Income Taxe s\n",
+      "Provision for Income Taxes and Effective  Tax Rat e\n",
+      "The provision for income taxes for 2021 , 2020  and 2019 , consisted of the following (in millions):\n",
+      "2021 2020 2019\n",
+      "Federal:\n",
+      "Current $ 8,257 $ 6,306 $ 6,384 \n",
+      "Deferred  (7,176)  (3,619)  (2,939) \n",
+      "Total  1,081  2,687  3,445 \n",
+      "State:\n",
+      "Current  1,620  455  475 \n",
+      "Deferred  (338)  21  (67) \n",
+      "Total  1,282  476  408 \n",
+      "Foreign:\n",
+      "Current  9,424  3,134  3,962 \n",
+      "Deferred  2,740  3,383  2,666 \n",
+      "Total  12,164  6,517  6,628 \n",
+      "Provision for income taxes $ 14,527 $ 9,680 $ 10,481 \n",
+      "The foreign provision for income taxes is based on foreign pretax earnings of $68.7 billion , $38.1 billion  and $44.3 billion  in 2021 , \n",
+      "2020  and 2019 , respectively.\n",
+      "A reconciliation of the provision for income taxes, with the amount computed by applying the statutory federal income tax  rate \n",
+      "(21% in 2021 , 2020  and 2019 ) to income before provision for income taxes for 2021 , 2020  and 2019 , is as follows (dollars in \n",
+      "millions):\n",
+      "2021 2020 2019\n",
+      "Computed expected tax $ 22,933 $ 14,089 $ 13,805 \n",
+      "State taxes, net of federal effect  1,151  423  423 \n",
+      "Impacts of the U.S. Tax Cuts and Jobs Act of 2017  —  (582)  — \n",
+      "Earnings of foreign subsidiaries  (4,715)  (2,534)  (2,625) \n",
+      "Foreign-derived intangible income deduction  (1,372)  (169)  (149) \n",
+      "Research and development credit, net  (1,033)  (728)  (548) \n",
+      "Excess tax benefits from equity awards  (2,137)  (930)  (639) \n",
+      "Other  (300)  111  214 \n",
+      "Provision for income taxes $ 14,527 $ 9,680 $ 10,481 \n",
+      "Effective tax rate  13.3%  14.4%  15.9% \n",
+      "Apple Inc. | 2021  Form 10-K | 41\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(response_1.source_nodes[0].get_content())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "***********Basic Query Engine***********\n",
+      "$3,619 million in 2019, $7,176 million in 2020, and $1,081 million in 2021\n",
+      "\u001b[1;3;38;2;11;159;203mRetrieval entering 12b1355a-f9e6-4b08-a19a-3ffc00dc5b9f: TextNode\n",
+      "\u001b[0m\u001b[1;3;38;2;237;90;200mRetrieving from object TextNode with query federal deferred tax in 2019-2021\n",
+      "\u001b[0m\u001b[1;3;38;2;11;159;203mRetrieval entering 82f301e5-199a-4aa2-bbdf-ef97898c0326: TextNode\n",
+      "\u001b[0m\u001b[1;3;38;2;237;90;200mRetrieving from object TextNode with query federal deferred tax in 2019-2021\n",
+      "\u001b[0m\u001b[1;3;38;2;11;159;203mRetrieval entering 8d8d5733-ff30-4535-9376-7f761b5900ea: TextNode\n",
+      "\u001b[0m\u001b[1;3;38;2;237;90;200mRetrieving from object TextNode with query federal deferred tax in 2019-2021\n",
+      "\u001b[0m\n",
+      "***********New LlamaParse+ Recursive Retriever Query Engine***********\n",
+      "$2,939, $3,619, $7,176\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"federal deferred tax in 2019-2021\"\n",
+    "\n",
+    "response_1 = raw_query_engine.query(query)\n",
+    "print(\"\\n***********Basic Query Engine***********\")\n",
+    "print(response_1)\n",
+    "\n",
+    "response_2 = recursive_query_engine.query(query)\n",
+    "print(\"\\n***********New LlamaParse+ Recursive Retriever Query Engine***********\")\n",
+    "print(response_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "***********Basic Query Engine***********\n",
+      "State deferred income tax for 2019: $454 million\n",
+      "State deferred income tax for 2020: $21 million\n",
+      "State deferred income tax for 2021: -$338 million\n",
+      "\u001b[1;3;38;2;11;159;203mRetrieval entering 12b1355a-f9e6-4b08-a19a-3ffc00dc5b9f: TextNode\n",
+      "\u001b[0m\u001b[1;3;38;2;237;90;200mRetrieving from object TextNode with query give me the deferred state income tax in 2019-2021 (include +/-)\n",
+      "\u001b[0m\u001b[1;3;38;2;11;159;203mRetrieval entering 8d8d5733-ff30-4535-9376-7f761b5900ea: TextNode\n",
+      "\u001b[0m\u001b[1;3;38;2;237;90;200mRetrieving from object TextNode with query give me the deferred state income tax in 2019-2021 (include +/-)\n",
+      "\u001b[0m\n",
+      "***********New LlamaParse+ Recursive Retriever Query Engine***********\n",
+      "Deferred state income tax for the years 2019-2021:\n",
+      "- 2019: ($67) million\n",
+      "- 2020: $21 million\n",
+      "- 2021: ($338) million\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"give me the deferred state income tax in 2019-2021 (include +/-)\"\n",
+    "\n",
+    "response_1 = raw_query_engine.query(query)\n",
+    "print(\"\\n***********Basic Query Engine***********\")\n",
+    "print(response_1)\n",
+    "\n",
+    "response_2 = recursive_query_engine.query(query)\n",
+    "print(\"\\n***********New LlamaParse+ Recursive Retriever Query Engine***********\")\n",
+    "print(response_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Summary of income tax provisions for Federal, State, and Foreign entities over the years 2019, 2020, and 2021.,\n",
+      "with the following table title:\n",
+      "Income Tax Provisions by Entity and Year,\n",
+      "with the following columns:\n",
+      "- Entity: The type of entity (Federal, State, Foreign)\n",
+      "- 2019: Income tax provisions for the year 2019\n",
+      "- 2020: Income tax provisions for the year 2020\n",
+      "- 2021: Income tax provisions for the year 2021\n",
+      "\n",
+      "| |2021|2020|2019|\n",
+      "|---|---|---|---|\n",
+      "|Federal:| | | |\n",
+      "|Current|$8,257|$6,306|$6,384|\n",
+      "|Deferred|(7,176)|(3,619)|(2,939)|\n",
+      "|Total|1,081|2,687|3,445|\n",
+      "|State:| | | |\n",
+      "|Current|1,620|455|475|\n",
+      "|Deferred|(338)|21|(67)|\n",
+      "|Total|1,282|476|408|\n",
+      "|Foreign:| | | |\n",
+      "|Current|9,424|3,134|3,962|\n",
+      "|Deferred|2,740|3,383|2,666|\n",
+      "|Total|12,164|6,517|6,628|\n",
+      "|Provision for income taxes|$14,527|$9,680|$10,481|\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(response_2.source_nodes[0].get_content())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "***********Basic Query Engine***********\n",
+      "$1,620 million in 2019, $455 million in 2020, $475 million in 2021\n",
+      "\u001b[1;3;38;2;11;159;203mRetrieval entering 82f301e5-199a-4aa2-bbdf-ef97898c0326: TextNode\n",
+      "\u001b[0m\u001b[1;3;38;2;237;90;200mRetrieving from object TextNode with query current state taxes per year in 2019-2021 (include +/-)\n",
+      "\u001b[0m\u001b[1;3;38;2;11;159;203mRetrieval entering 8d8d5733-ff30-4535-9376-7f761b5900ea: TextNode\n",
+      "\u001b[0m\u001b[1;3;38;2;237;90;200mRetrieving from object TextNode with query current state taxes per year in 2019-2021 (include +/-)\n",
+      "\u001b[0m\u001b[1;3;38;2;11;159;203mRetrieval entering b9416f35-ebf1-45d6-9a29-b59e435ab42d: TextNode\n",
+      "\u001b[0m\u001b[1;3;38;2;237;90;200mRetrieving from object TextNode with query current state taxes per year in 2019-2021 (include +/-)\n",
+      "\u001b[0m\u001b[1;3;38;2;11;159;203mRetrieval entering a029e464-575f-4dd6-afad-7cc0bbc5dbf9: TextNode\n",
+      "\u001b[0m\u001b[1;3;38;2;237;90;200mRetrieving from object TextNode with query current state taxes per year in 2019-2021 (include +/-)\n",
+      "\u001b[0m\n",
+      "***********New LlamaParse+ Recursive Retriever Query Engine***********\n",
+      "$475 in 2019, $455 in 2020, $1,620 in 2021.\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"current state taxes per year in 2019-2021 (include +/-)\"\n",
+    "\n",
+    "response_1 = raw_query_engine.query(query)\n",
+    "print(\"\\n***********Basic Query Engine***********\")\n",
+    "print(response_1)\n",
+    "\n",
+    "response_2 = recursive_query_engine.query(query)\n",
+    "print(\"\\n***********New LlamaParse+ Recursive Retriever Query Engine***********\")\n",
+    "print(response_2)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "llama_parse",
+   "language": "python",
+   "name": "llama_parse"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/demo_astradb.ipynb

@@ -0,0 +1,295 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using llama-parse with AstraDB"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook, we show a basic RAG-style example that uses `llama-parse` to parse a PDF document, store the corresponding document into a vector store (`AstraDB`) and finally, perform some basic queries against that store. The notebook is modeled after the quick start notebooks and hence is meant as a way of getting started with `llama-parse`, backed by a vector database."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Requirements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First, install the required dependencies\n",
+    "%pip install --quiet llama-index llama-parse llama-index-vector-stores-astra-db llama-index-llms-openai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import openai\n",
+    "\n",
+    "from getpass import getpass\n",
+    "\n",
+    "# Get all required API keys and parameters\n",
+    "llama_cloud_api_key = getpass(\"Enter your Llama Index Cloud API Key: \")\n",
+    "api_endpoint = input(\"Enter your Astra DB API Endpoint: \")\n",
+    "token = getpass(\"Enter your Astra DB Token: \")\n",
+    "namespace = (\n",
+    "    input(\"Enter your Astra DB namespace (optional, must exist on Astra): \") or None\n",
+    ")\n",
+    "openai_api_key = getpass(\"Enter your OpenAI API Key: \")\n",
+    "\n",
+    "os.environ[\"LLAMA_CLOUD_API_KEY\"] = llama_cloud_api_key\n",
+    "openai.api_key = openai_api_key"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# llama-parse is async-first, running the sync code in a notebook requires the use of nest_asyncio\n",
+    "import nest_asyncio\n",
+    "\n",
+    "nest_asyncio.apply()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using llama-parse to parse a PDF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Download complete.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Grab a PDF from Arxiv for indexing\n",
+    "import requests\n",
+    "\n",
+    "# The URL of the file you want to download\n",
+    "url = \"https://arxiv.org/pdf/1706.03762.pdf\"\n",
+    "# The local path where you want to save the file\n",
+    "file_path = \"./attention.pdf\"\n",
+    "\n",
+    "# Perform the HTTP request\n",
+    "response = requests.get(url)\n",
+    "\n",
+    "# Check if the request was successful\n",
+    "if response.status_code == 200:\n",
+    "    # Open the file in binary write mode and save the content\n",
+    "    with open(file_path, \"wb\") as file:\n",
+    "        file.write(response.content)\n",
+    "    print(\"Download complete.\")\n",
+    "else:\n",
+    "    print(\"Error downloading the file.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started parsing the file under job_id ce3909a7-54cf-438b-849a-fe9a903b0c71\n"
+     ]
+    }
+   ],
+   "source": [
+    "from llama_parse import LlamaParse\n",
+    "\n",
+    "documents = LlamaParse(result_type=\"text\").load_data(file_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'rmer - model architecture.\\nThe Transformer follows this overall architecture using stacked self-attention and point-wise, fully\\nconnected layers for both the encoder and decoder, shown in the left and right halves of Figure 1,\\nrespectively.\\n3.1   Encoder and Decoder Stacks\\nEncoder:     The encoder is composed of a stack of N = 6 identical layers. Each layer has two\\nsub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position-\\nwise fully connected feed-forward network. We employ a residual connection [11] around each of\\nthe two sub-layers, followed by layer normalization [1]. That is, the output of each sub-layer is\\nLayerNorm(x + Sublayer(x)), where Sublayer(x) is the function implemented by the sub-layer\\nitself. To facilitate these residual connections, all sub-layers in the model, as well as the embedding\\nlayers, produce outputs of dimension dmodel = 512.\\nDecoder:    The decoder is also composed of a stack of N = 6 identical layers. In addition '"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Take a quick look at some of the parsed text from the document:\n",
+    "documents[0].get_content()[10000:11000]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Storing into Astra DB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.vector_stores.astra_db import AstraDBVectorStore\n",
+    "\n",
+    "astra_db_store = AstraDBVectorStore(\n",
+    "    token=token,\n",
+    "    api_endpoint=api_endpoint,\n",
+    "    namespace=namespace,\n",
+    "    collection_name=\"astra_v_table_llamaparse\",\n",
+    "    embedding_dimension=1536,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.core.node_parser import SimpleNodeParser\n",
+    "\n",
+    "node_parser = SimpleNodeParser()\n",
+    "\n",
+    "nodes = node_parser.get_nodes_from_documents(documents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.embeddings.openai import OpenAIEmbedding\n",
+    "from llama_index.core import VectorStoreIndex, StorageContext\n",
+    "\n",
+    "storage_context = StorageContext.from_defaults(vector_store=astra_db_store)\n",
+    "\n",
+    "index = VectorStoreIndex(\n",
+    "    nodes=nodes,\n",
+    "    storage_context=storage_context,\n",
+    "    embed_model=OpenAIEmbedding(api_key=openai_api_key),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple RAG Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_engine = index.as_query_engine(similarity_top_k=15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "***********New LlamaParse+ Basic Query Engine***********\n",
+      "Multi-Head Attention is also known as multi-headed self-attention.\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"What is Multi-Head Attention also known as?\"\n",
+    "\n",
+    "response_1 = query_engine.query(query)\n",
+    "print(\"\\n***********New LlamaParse+ Basic Query Engine***********\")\n",
+    "print(response_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'We used beam search as described in the previous section, but no\\ncheckpoint averaging. We present these results in Table 3.\\nIn Table 3 rows (A), we vary the number of attention heads and the attention key and value dimensions,\\nkeeping the amount of computation constant, as described in Section 3.2.2. While single-head\\nattention is 0.9 BLEU worse than the best setting, quality also drops off with too many heads.\\nIn Table 3 rows (B), we observe that reducing the attention key size dk hurts model quality. This\\nsuggests that determining compatibility is not easy and that a more sophisticated compatibility\\nfunction than dot product may be beneficial. We further observe in rows (C) and (D) that, as expected,\\nbigger models are better, and dropout is very helpful in avoiding over-fitting. In row (E) we replace our\\nsinusoidal positional encoding with learned positional embeddings [9], and observe nearly identical\\nresults to the base model.\\n6.3    English Constituency Parsing\\nTo evaluate if the Transformer can generalize to other tasks we performed experiments on English\\nconstituency parsing. This task presents specific challenges: the output is subject to strong structural\\nconstraints and is significantly longer than the input. Furthermore, RNN sequence-to-sequence\\nmodels have not been able to attain state-of-the-art results in small-data regimes [37].\\nWe trained a 4-layer transformer with dmodel = 1024 on the Wall Street Journal (WSJ) portion of the\\nPenn Treebank [25], about 40K training sentences. We also trained it in a semi-supervised setting,\\nusing the larger high-confidence and BerkleyParser corpora from with approximately 17M sentences\\n[37]. We used a vocabulary of 16K tokens for the WSJ only setting and a vocabulary of 32K tokens\\nfor the semi-supervised setting.\\nWe performed only a small number of experiments to select the dropout, both attention and residual\\n(section 5.4), learning rates and beam size on the Section 22 development set, all other parameters\\nremained unchanged from the English-to-German base translation model. During inference, we\\n                                                             9\\n---\\nTable 4: The Transformer generalizes well to English constituency parsing (Results are on Section 23\\nof WSJ)\\n                          Parser                           Training             WSJ 23 F1\\n            Vinyals & Kaiser el al. (2014) [37]    WSJ only, discriminative         88.3\\n                 Petrov et al. (2006) [29]         WSJ only, discriminative         90.4\\n                   Zhu et al. (2013) [40]          WSJ only, discriminative         90.4\\n                   Dyer et al. (2016) [8]          WSJ only, discriminative         91.7\\n                  Transformer (4 layers)           WSJ only, discriminative         91.3\\n                   Zhu et al. (2013) [40]              semi-supervised              91.3\\n               Huang & Harper (2009) [14]              semi-supervised              91.3\\n               McClosky et al. (2006) [26]             semi-supervised              92.1\\n            Vinyals & Kaiser el al. (2014) [37]        semi-supervised              92.1\\n                  Transformer (4 layers)               semi-supervised              92.7\\n                 Luong et al. (2015) [23]                  multi-task               93.0\\n                   Dyer et al. (2016) [8]                  generative               93.3\\nincreased the maximum output length to input length + 300. We used a beam size of 21 and α = 0.3\\nfor both WSJ only and the semi-supervised setting.\\nOur results in Table 4 show that despite the lack of task-specific tuning our model performs sur-\\nprisingly well, yielding better results than all previously reported models with the exception of the\\nRecurrent Neural Network Grammar [8].\\nIn contrast to RNN sequence-to-sequence models [37], the Transformer outperforms the Berkeley-\\nParser [29] even when training only on the WSJ training set of 40K sentences.\\n7    Conclusion\\nIn this work, we presented the Transformer, the first sequence transduction model based entirely on\\nattention, replacing the recurrent layers most commonly used in encoder-decoder architectures with\\nmulti-headed self-attention.\\nFor translation tasks, the Transformer can be trained significantly faster than architectures based\\non recurrent or convolutional layers.'"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Take a look at one of the source nodes from the response\n",
+    "response_1.source_nodes[0].get_content()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/demo_basic.ipynb

@@ -0,0 +1,183 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# LlamaParse Usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install llama-index llama-parse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2024-02-02 11:10:10--  https://arxiv.org/pdf/1706.03762.pdf\n",
+      "Resolving arxiv.org (arxiv.org)... 151.101.131.42, 151.101.3.42, 151.101.67.42, ...\n",
+      "Connecting to arxiv.org (arxiv.org)|151.101.131.42|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 2215244 (2.1M) [application/pdf]\n",
+      "Saving to: ‘./attention.pdf’\n",
+      "\n",
+      "./attention.pdf     100%[===================>]   2.11M  --.-KB/s    in 0.08s   \n",
+      "\n",
+      "2024-02-02 11:10:10 (25.9 MB/s) - ‘./attention.pdf’ saved [2215244/2215244]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!wget \"https://arxiv.org/pdf/1706.03762.pdf\" -O \"./attention.pdf\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# llama-parse is async-first, running the sync code in a notebook requires the use of nest_asyncio\n",
+    "import nest_asyncio\n",
+    "\n",
+    "nest_asyncio.apply()\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"llx-...\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started parsing the file under job_id dd0b8e31-0c09-4497-b78a-cc1c92f1d6cf\n"
+     ]
+    }
+   ],
+   "source": [
+    "from llama_parse import LlamaParse\n",
+    "\n",
+    "documents = LlamaParse(result_type=\"text\").load_data(\"./attention.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ad\n",
+      "relying entirely on an attention mechanism to draw global dependencies between input and output.\n",
+      "The Transformer allows for significantly more parallelization and can reach a new state of the art in\n",
+      "translation quality after being trained for as little as twelve hours on eight P100 GPUs.\n",
+      "2    Background\n",
+      "The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU\n",
+      "[16], ByteNet [18] and ConvS2S [9], all of which use convolutional neural networks as basic building\n",
+      "block, computing hidden representations in parallel for all input and output positions. In these models,\n",
+      "the number of operations required to relate signals from two arbitrary input or output positions grows\n",
+      "in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes\n",
+      "it more difficult to learn dependencies between distant positions [12]. In the Transformer this is\n",
+      "reduced to a constant number of operations, albeit at the cost of reduced effective res\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(documents[0].text[6000:7000])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started parsing the file under job_id d4531453-1bbb-48c4-8324-ae9fea9f2fa2\n"
+     ]
+    }
+   ],
+   "source": [
+    "from llama_parse import LlamaParse\n",
+    "\n",
+    "documents = LlamaParse(result_type=\"markdown\").load_data(\"./attention.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ction describes the training regime for our models.\n",
+      "\n",
+      "##### Training Data and Batching\n",
+      "\n",
+      "We trained on the standard WMT 2014 English-German dataset consisting of about 4.5 million\n",
+      "sentence pairs. Sentences were encoded using byte-pair encoding [3], which has a shared source-\n",
+      "target vocabulary of about 37000 tokens. For English-French, we used the significantly larger WMT\n",
+      "2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece\n",
+      "vocabulary [38]. Sentence pairs were batched together by approximate sequence length. Each training\n",
+      "batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000\n",
+      "target tokens.\n",
+      "\n",
+      "##### Hardware and Schedule\n",
+      "\n",
+      "We trained our models on one machine with 8 NVIDIA P100 GPUs. For our base models using\n",
+      "the hyperparameters described throughout the paper, each training step took about 0.4 seconds. We\n",
+      "trained the base models for a total of 100,000 steps or 12 hours. For our big models,(described on the\n",
+      "bo...\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(documents[0].text[20000:21000] + \"...\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/demo_excel.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# RAG with Excel Spreadsheet using LlamaPrase\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/demo_excel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/demo_excel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
     "This notebook shows you using LlamaParse with Excel Spreadsheet.\n",
     "\n",
@@ -21,7 +21,7 @@
    "outputs": [],
    "source": [
     "%pip install llama-index\n",
-    "%pip install llama-cloud-services"
+    "%pip install llama-parse"
    ]
   },
   {
@@ -41,7 +41,7 @@
     "\n",
     "nest_asyncio.apply()\n",
     "\n",
-    "from llama_cloud_services import LlamaParse\n",
+    "from llama_parse import LlamaParse\n",
     "\n",
     "api_key = \"llx-\"  # get from cloud.llamaindex.ai"
    ]

examples/demo_insurance.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# LlamaParse - Fast checking Insurance Contract for Coverage\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/demo_insurance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/demo_insurance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
     "In this notebook we will look at how LlamaParse can be used to extract structured coverage information from an insurance policy."
    ]
@@ -116,7 +116,7 @@
     }
    ],
    "source": [
-    "from llama_cloud_services import LlamaParse\n",
+    "from llama_parse import LlamaParse\n",
     "\n",
     "documents = LlamaParse(result_type=\"markdown\").load_data(\"./policy.pdf\")"
    ]