Bump version to v0.6.3

.changeset/README.md

@@ -1,8 +0,0 @@
-# Changesets
-
-Hello and welcome! This folder has been automatically generated by `@changesets/cli`, a build tool that works
-with multi-package repos, or single-package repos to help you version and publish your code. You can
-find the full documentation for it [in our repository](https://github.com/changesets/changesets)
-
-We have a quick list of common questions to get you started engaging with this project in
-[our documentation](https://github.com/changesets/changesets/blob/main/docs/common-questions.md)

.changeset/config.json

@@ -1,11 +0,0 @@
-{
-  "$schema": "https://unpkg.com/@changesets/config@3.1.1/schema.json",
-  "changelog": "@changesets/cli/changelog",
-  "commit": false,
-  "fixed": [],
-  "linked": [],
-  "access": "restricted",
-  "baseBranch": "main",
-  "updateInternalDependencies": "patch",
-  "ignore": []
-}

.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_cloud_services"

.github/workflows/build_package_py.yml

@@ -1,53 +0,0 @@
-name: Build Package - Python
-
-# Build package on its own without additional pip install
-
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - "py/**"
-  pull_request:
-    paths:
-      - "py/**"
-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@v5
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v7
-        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

@@ -1,34 +0,0 @@
-name: Build Package - TypeScript
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - "ts/**"
-  pull_request:
-    paths:
-      - "ts/**"
-
-jobs:
-  pre_release:
-    name: Pre Release
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout Repo
-        uses: actions/checkout@v5
-
-      - uses: pnpm/action-setup@v4
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v5
-        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/claude-code.yml

@@ -1,95 +0,0 @@
-name: Claude Code
-
-on:
-  issue_comment:
-    types: [created]
-  pull_request_review_comment:
-    types: [created]
-  issues:
-    types: [opened, assigned]
-  pull_request_review:
-    types: [submitted]
-
-jobs:
-  claude:
-    if: |
-      (github.event_name == 'issue_comment' && contains(github.event.comment.body, '@claude')) ||
-      (github.event_name == 'pull_request_review_comment' && contains(github.event.comment.body, '@claude')) ||
-      (github.event_name == 'pull_request_review' && contains(github.event.review.body, '@claude')) ||
-      (github.event_name == 'issues' && (contains(github.event.issue.body, '@claude') || contains(github.event.issue.title, '@claude')))
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      pull-requests: read
-      issues: read
-      id-token: write
-    steps:
-      - name: Check repository access
-        id: check-access
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: |
-          # Get the user who triggered the event
-          case "${{ github.event_name }}" in
-            "issue_comment")
-              USER="${{ github.event.comment.user.login }}"
-              ;;
-            "pull_request_review_comment")
-              USER="${{ github.event.comment.user.login }}"
-              ;;
-            "pull_request_review")
-              USER="${{ github.event.review.user.login }}"
-              ;;
-            "issues")
-              USER="${{ github.event.issue.user.login }}"
-              ;;
-          esac
-
-          echo "Checking repository access for user: $USER"
-
-          # Check if user has write access to the repository
-          REPO="${{ github.repository }}"
-          if gh api repos/$REPO/collaborators/$USER/permission --jq '.permission' | grep -E "(admin|write)" > /dev/null 2>&1; then
-            echo "User $USER has write access to the repository"
-            echo "authorized=true" >> $GITHUB_OUTPUT
-          else
-            echo "User $USER does not have write access to the repository"
-            echo "authorized=false" >> $GITHUB_OUTPUT
-            exit 1
-          fi
-
-      - name: Checkout repository
-        if: steps.check-access.outputs.authorized == 'true'
-        uses: actions/checkout@v5
-        with:
-          fetch-depth: 1
-
-      - name: Run Claude Code
-        if: steps.check-access.outputs.authorized == 'true'
-        id: claude
-        uses: anthropics/claude-code-action@beta
-        with:
-          anthropic_api_key: ${{ secrets.ANTHROPIC_GITHUB_API_KEY }}
-
-          # Optional: Specify model (defaults to Claude Sonnet 4, uncomment for Claude Opus 4)
-          # model: "claude-opus-4-20250514"
-
-          # Optional: Customize the trigger phrase (default: @claude)
-          # trigger_phrase: "/claude"
-
-          # Optional: Trigger when specific user is assigned to an issue
-          # assignee_trigger: "claude-bot"
-
-          # Optional: Allow Claude to run specific commands
-          # Allow bash commands to be run, for things like running tests, linting, etc.
-          allowed_tools: "Bash(rg:*),Bash(find:*),Bash(grep:*),Bash(pnpm:*),Bash(npm:*),Bash(uv:*),Bash(pip:*),Bash(pipx:*),Bash(make:*),Bash(cd:*),WebFetch"
-
-          # Optional: Add custom instructions for Claude to customize its behavior for your project
-          # custom_instructions: |
-          #   Follow our coding standards
-          #   Ensure all new code has tests
-          #   Use TypeScript for new files
-
-          # Optional: Custom environment variables for Claude
-          # claude_env: |
-          #   NODE_ENV: test

.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@v5
+        uses: actions/checkout@v3
 
       # Initializes the CodeQL tools for scanning.
       - name: Initialize CodeQL
-        uses: github/codeql-action/init@v4
+        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@v4
+        uses: github/codeql-action/analyze@v2
         with:
-          category: "/language:python"
+          category: "/language:${{matrix.language}}"

.github/workflows/lint.yml

@@ -1,4 +1,4 @@
-name: Lint
+name: Linting
 
 on:
   push:
@@ -7,7 +7,7 @@ on:
   pull_request:
 
 env:
-  UV_VERSION: "0.7.20"
+  POETRY_VERSION: "1.6.1"
 
 jobs:
   build:
@@ -18,29 +18,20 @@ jobs:
       matrix:
         python-version: ["3.9"]
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v3
         with:
           fetch-depth: ${{ github.event_name == 'pull_request' && 2 || 0 }}
-      - name: Install uv
-        uses: astral-sh/setup-uv@v7
+      - name: Set up python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
         with:
-          version: ${{ env.UV_VERSION }}
-
-      - name: Set up Python
-        run: uv python install ${{ matrix.python-version }}
-
-      - uses: pnpm/action-setup@v4
-      - name: Setup Node.js
-        uses: actions/setup-node@v5
+          python-version: ${{ matrix.python-version }}
+      - name: Install Poetry
+        uses: snok/install-poetry@v1
         with:
-          node-version-file: "ts/llama_cloud_services/.nvmrc"
-      - name: Install dependencies
-        run: pnpm install --no-frozen-lockfile
-
+          version: ${{ env.POETRY_VERSION }}
+      - name: Install pre-commit
+        shell: bash
+        run: poetry run pip install pre-commit
       - name: Run linter
         shell: bash
-        working-directory: py
-        run: uv run -- pre-commit run -a
-      # the js checks are run roundaboutly through lint-staged, and -a doesn't run it. Run them directly.
-      - run: pnpm -w --filter llama-cloud-services run lint
-      - run: pnpm -w --filter llama-cloud-services run format:check
+        run: poetry run make lint

.github/workflows/publish_release.yml

@@ -0,0 +1,75 @@
+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_cloud_services'
+    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 llama-cloud-services
+        uses: JRubics/poetry-publish@v2.1
+        with:
+          pypi_token: ${{ secrets.LLAMA_PARSE_PYPI_TOKEN }}
+          poetry_install_options: "--without dev"
+
+      - name: Build and publish llama-parse
+        uses: JRubics/poetry-publish@v2.1
+        with:
+          working_directory: "llama_parse"
+          pypi_token: ${{ secrets.LLAMA_PARSE_PYPI_TOKEN }}
+          poetry_install_options: "--without dev"
+
+      - 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/test_e2e_py.yml

@@ -1,39 +0,0 @@
-name: Test end-to-end - Python
-
-on:
-  pull_request:
-    paths:
-      - "py/**"
-
-env:
-  UV_VERSION: "0.7.20"
-  LLAMA_CLOUD_API_KEY: ${{ secrets.LLAMA_CLOUD_API_KEY }}
-
-jobs:
-  test_e2e:
-    runs-on: ubuntu-latest
-    timeout-minutes: 30
-    strategy:
-      # You can use PyPy versions in python-version.
-      # For example, pypy-2.7 and pypy-3.8
-      matrix:
-        python-version: ["3.12"]
-    steps:
-      - uses: actions/checkout@v5
-        with:
-          fetch-depth: 0
-      - name: Install uv
-        uses: astral-sh/setup-uv@v7
-        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: make e2e
-
-      - name: Remove virtual environment
-        working-directory: py
-        run: rm -rf .venv/

.github/workflows/test_py.yml

@@ -1,42 +0,0 @@
-name: Test - Python
-
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - "py/**"
-  pull_request:
-    paths:
-      - "py/**"
-
-env:
-  UV_VERSION: "0.7.20"
-
-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@v5
-        with:
-          fetch-depth: 0
-      - name: Install uv
-        uses: astral-sh/setup-uv@v7
-        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 unit_tests/ -v
-
-      - name: Remove virtual environment
-        working-directory: py
-        run: rm -rf .venv/

.github/workflows/test_ts.yml

@@ -1,39 +0,0 @@
-name: Test - TypeScript
-
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - "ts/**"
-  pull_request:
-    paths:
-      - "ts/**"
-
-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:
-  test:
-    name: Test - TypeScript
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v5
-      - uses: pnpm/action-setup@v4
-      - name: Setup Node.js
-        uses: actions/setup-node@v5
-        with:
-          node-version-file: "ts/llama_cloud_services/.nvmrc"
-      - name: Install dependencies
-        run: pnpm -r install --no-frozen-lockfile
-      - name: Build package
-        run: pnpm --filter llama-cloud-services build
-      - name: Run Tests
-        working-directory: ts/llama_cloud_services/
-        run: pnpm test
-      - name: Run e2e tests
-        working-directory: ts/e2e-tests/
-        run: pnpm test

.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: ["3.9", "3.10", "3.11", "3.12"]
+    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

.github/workflows/version_bump_and_release.yml

@@ -1,61 +0,0 @@
-name: Version Bump and Release
-
-on:
-  push:
-    branches:
-      - main
-
-concurrency: ${{ github.workflow }}-${{ github.ref }}
-
-jobs:
-  release:
-    name: Release
-    runs-on: ubuntu-latest
-    # Only run on main branch pushes
-    if: github.ref == 'refs/heads/main'
-    steps:
-      - name: Checkout Repo
-        uses: actions/checkout@v5
-
-      - uses: pnpm/action-setup@v4
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v5
-        with:
-          node-version: "22"
-          cache: "pnpm"
-
-      - name: Setup Python
-        uses: actions/setup-python@v6
-        with:
-          python-version: "3.11"
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v7
-
-      - name: Install dependencies
-        run: pnpm install
-
-      - name: Add auth token to .npmrc file
-        run: |
-          cat << EOF >> ".npmrc"
-            //registry.npmjs.org/:_authToken=$NPM_TOKEN
-          EOF
-        env:
-          NPM_TOKEN: ${{ secrets.NPM_TOKEN }}
-
-      - name: Create Release Pull Request or Publish packages
-        id: changesets
-        uses: changesets/action@v1
-        with:
-          commit: "chore: version packages"
-          title: "chore: version packages"
-          # Custom version script
-          version: pnpm -w run version
-          # Custom publish script
-          publish: pnpm -w run publish
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          NPM_TOKEN: ${{ secrets.NPM_TOKEN }}
-          UV_PUBLISH_TOKEN: ${{ secrets.PYPI_TOKEN }}
-          LLAMA_PARSE_PYPI_TOKEN: ${{ secrets.LLAMA_PARSE_PYPI_TOKEN }}

.gitignore

@@ -5,8 +5,3 @@ __pycache__/
 .idea
 .env*
 .ipynb_checkpoints*
-*_cache/
-node_modules/
-.turbo/
-dist/
-.npmrc

.pre-commit-config.yaml

@@ -15,26 +15,25 @@ repos:
       - id: end-of-file-fixer
       - id: mixed-line-ending
       - id: trailing-whitespace
-        exclude: ^ts/llama_cloud_services/src/client/
   - repo: https://github.com/charliermarsh/ruff-pre-commit
     rev: v0.1.5
 
     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|examples/extract/solar_panel_e2e_comparison.ipynb"
+        exclude: ".*poetry.lock"
   - repo: https://github.com/pre-commit/mirrors-mypy
     rev: v1.0.1
     hooks:
       - id: mypy
-        exclude: ^py/tests|^py/unit_tests|^examples
+        exclude: ^tests/
         additional_dependencies:
           [
             "types-requests",
@@ -60,19 +59,17 @@ repos:
         additional_dependencies: [black==23.10.1]
         # Using PEP 8's line length in docs prevents excess left/right scrolling
         args: [--line-length=79]
-  - repo: local
+  - repo: https://github.com/pre-commit/mirrors-prettier
+    rev: v3.0.3
     hooks:
-      - id: lint-staged
-        name: Run lint-staged for TS files
-        entry: pnpm -w exec lint-staged
-        language: system
-        pass_filenames: false
+      - id: prettier
+        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|examples|pnpm-lock.yaml)
+        exclude: ^(poetry.lock|examples)
         args:
           [
             "--ignore-words-list",
@@ -87,6 +84,6 @@ repos:
     rev: v0.23.1
     hooks:
       - id: toml-sort-fix
-        exclude: ".*uv.lock"
+        exclude: ".*poetry.lock"
 
-exclude: ^(.github/ISSUE_TEMPLATE|ts/llama_cloud_services/src/client|pnpm-lock.yaml)
+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 v0.x.x`.
-
-This tagging step can be done with `./scripts/version-bump tag`.
-
-# Typescript
-
-## Installation
-
-...
-
-## Versioning
-
-...

Makefile

@@ -0,0 +1,14 @@
+GIT_ROOT ?= $(shell git rev-parse --show-toplevel)
+
+help:	## Show all Makefile targets.
+	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}'
+
+format:	## Run code autoformatters (black).
+	pre-commit install
+	git ls-files | xargs pre-commit run black --files
+
+lint:	## Run linters: pre-commit (black, ruff, codespell) and mypy
+	pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files
+
+test:	## Run tests via pytest
+	pytest tests

README.md

@@ -9,8 +9,8 @@ This repository contains the code for hand-written SDKs and clients for interact
 This includes:
 
 - [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.).
-- [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.
+- [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 (beta/invite-only)](./extract.md) - A prebuilt agentic data extractor that can be used to transform data into a structured JSON representation.
 
 ## Getting Started
 
@@ -25,47 +25,18 @@ Then, get your API key from [LlamaCloud](https://cloud.llamaindex.ai/).
 Then, you can use the services in your code:
 
 ```python
-from llama_cloud_services import (
-    LlamaParse,
-    LlamaExtract,
-    LlamaCloudIndex,
-)
+from llama_cloud_services import LlamaParse, LlamaReport, LlamaExtract
 
 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"
-)
 ```
 
 See the quickstart guides for each service for more information:
 
 - [LlamaParse](./parse.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).
-
-```python
-from llama_cloud_services import (
-    LlamaParse,
-    LlamaExtract,
-    EU_BASE_URL,
-)
-
-parser = LlamaParse(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,
-)
-```
+- [LlamaReport (beta/invite-only)](./report.md)
+- [LlamaExtract (beta/invite-only)](./extract.md)
 
 ## Documentation
 

examples-ts/README.md

@@ -1,8 +0,0 @@
-# LlamaCloud Services Examples - Python
-
-In this folder you will find several TypeScript end-to-end applications that contain examples regarding:
-
-- [LlamaParse](./parse/)
-- [LlamaCloud Index](./index/)
-
-Follow the instructions in each example folder to get started!

examples-ts/classify/.gitignore

@@ -1,21 +0,0 @@
-node_modules
-package-lock.json
-yarn.lock
-
-.DS_Store
-.cache
-.env
-.vercel
-.output
-.nitro
-/build/
-/api/
-/server/build
-/public/build# Sentry Config File
-.env.sentry-build-plugin
-/test-results/
-/playwright-report/
-/blob-report/
-/playwright/.cache/
-.tanstack
-.vscode

examples-ts/classify/.prettierignore

@@ -1,4 +0,0 @@
-**/build
-**/public
-pnpm-lock.yaml
-routeTree.gen.ts

examples-ts/classify/README.md

@@ -1,88 +0,0 @@
-# LlamaClassify Demo
-
-A TypeScript demo application showcasing the power of **LlamaClassify** - an agentic documents classification service from [LlamaCloud](https://cloud.llamaindex.ai). This demo allows you to classify financial documents among three different types (Cash flow statement, Income Statement and Balance Sheet).
-
-## Table of Contents
-
-- [Features](#features)
-- [Prerequisites](#prerequisites)
-- [Installation](#installation)
-- [Usage](#usage)
-  - [Start the Demo](#start-the-demo)
-- [How It Works](#how-it-works)
-- [Troubleshooting](#troubleshooting)
-  - [Common Issues](#common-issues)
-- [License](#license)
-- [Contributing](#contributing)
-
-## Features
-
-- 📄 **Documemt Classification**: Classify files based on well-defined rules you can customized and play around with.
-- 🤖 **Reasoning-based Actionable Insights**: Get in-depth, reasoning based insights on the document classification, accompanied by confidence scores.
-- 🎨 **Beautiful UI**: [DaisyUI](https://daisyui.com)-based interface powered by [TanStack](https://tanstack.com)
-- ⚡ **Fast Development**: Hot reload support with development mode
-- 🛠️ **TypeScript**: Full TypeScript support with strict type checking
-
-## Prerequisites
-
-- Node.js (version 22 or higher)
-- pnpm package manager
-- LlamaCloud API key
-
-## Installation
-
-1. Clone the repository:
-
-```bash
-git clone https://github.com/run-llama/llama_cloud_services
-cd lama_cloud_services/examples-ts/classify/
-```
-
-2. Install dependencies:
-
-```bash
-npm install
-```
-
-3. Set up your environment variables:
-
-```bash
-# Add your API key to your environment
-export LLAMA_CLOUD_API_KEY="your-llamacloud-api-key"
-```
-
-## Usage
-
-### Start the Demo
-
-```bash
-npm run dev
-```
-
-The application will be up and running on http://localhost:3000
-
-## How It Works
-
-1. **Document Input**: Enter the path to your document when prompted
-2. **Parsing**: LlamaClassify, based on the rules you can find [here](./src/utils/classifier.ts), processes the document and classifies it
-3. **Results**: The classification outcome, as well as the reasoning behind it and the confidence score, are displayed in the UI.
-
-## Troubleshooting
-
-### Common Issues
-
-1. **Module Resolution Errors**: Ensure you're using Node.js 22+ and have all dependencies installed
-2. **API Key Issues**: Verify your LlamaCloud API key is correctly set
-3. **File Path Errors**: Use absolute paths or ensure relative paths are correct from the project root
-
-## License
-
-MIT License - see the [LICENSE](../../LICENSE) file for details.
-
-## Contributing
-
-1. Fork the repository
-2. Create a feature branch
-3. Make your changes
-4. Run `npm run format` and `npm run lint`
-5. Submit a pull request

examples-ts/classify/package.json

@@ -1,34 +0,0 @@
-{
-  "name": "tanstack-start-example-basic",
-  "private": true,
-  "sideEffects": false,
-  "type": "module",
-  "scripts": {
-    "dev": "vite dev",
-    "build": "vite build && tsc --noEmit",
-    "start": "node .output/server/index.mjs"
-  },
-  "dependencies": {
-    "@tanstack/react-router": "^1.133.22",
-    "@tanstack/react-router-devtools": "^1.133.22",
-    "@tanstack/react-start": "^1.133.22",
-    "llama-cloud-services": "file:../../ts/llama_cloud_services",
-    "react": "^19.0.0",
-    "react-dom": "^19.0.0",
-    "tailwind-merge": "^2.6.0",
-    "zod": "^3.24.2"
-  },
-  "devDependencies": {
-    "@tailwindcss/postcss": "^4.1.15",
-    "@types/node": "^22.5.4",
-    "@types/react": "^19.0.8",
-    "@types/react-dom": "^19.0.3",
-    "@vitejs/plugin-react": "^4.6.0",
-    "daisyui": "^5.3.7",
-    "postcss": "^8.5.1",
-    "tailwindcss": "^4.1.15",
-    "typescript": "^5.7.2",
-    "vite": "^7.1.7",
-    "vite-tsconfig-paths": "^5.1.4"
-  }
-}

examples-ts/classify/postcss.config.mjs

@@ -1,5 +0,0 @@
-export default {
-  plugins: {
-    '@tailwindcss/postcss': {},
-  },
-}

examples-ts/classify/public/site.webmanifest

@@ -1,19 +0,0 @@
-{
-  "name": "",
-  "short_name": "",
-  "icons": [
-    {
-      "src": "/android-chrome-192x192.png",
-      "sizes": "192x192",
-      "type": "image/png"
-    },
-    {
-      "src": "/android-chrome-512x512.png",
-      "sizes": "512x512",
-      "type": "image/png"
-    }
-  ],
-  "theme_color": "#ffffff",
-  "background_color": "#ffffff",
-  "display": "standalone"
-}

examples-ts/classify/src/components/DefaultCatchBoundary.tsx

@@ -1,53 +0,0 @@
-import {
-  ErrorComponent,
-  Link,
-  rootRouteId,
-  useMatch,
-  useRouter,
-} from '@tanstack/react-router'
-import type { ErrorComponentProps } from '@tanstack/react-router'
-
-export function DefaultCatchBoundary({ error }: ErrorComponentProps) {
-  const router = useRouter()
-  const isRoot = useMatch({
-    strict: false,
-    select: (state) => state.id === rootRouteId,
-  })
-
-  console.error('DefaultCatchBoundary Error:', error)
-
-  return (
-    <div className="min-w-0 flex-1 p-4 flex flex-col items-center justify-center gap-6">
-      <ErrorComponent error={error} />
-      <div className="flex gap-2 items-center flex-wrap">
-        <button
-          onClick={() => {
-            router.invalidate()
-          }}
-          className={`px-2 py-1 bg-gray-600 dark:bg-gray-700 rounded-sm text-white uppercase font-extrabold`}
-        >
-          Try Again
-        </button>
-        {isRoot ? (
-          <Link
-            to="/"
-            className={`px-2 py-1 bg-gray-600 dark:bg-gray-700 rounded-sm text-white uppercase font-extrabold`}
-          >
-            Home
-          </Link>
-        ) : (
-          <Link
-            to="/"
-            className={`px-2 py-1 bg-gray-600 dark:bg-gray-700 rounded-sm text-white uppercase font-extrabold`}
-            onClick={(e) => {
-              e.preventDefault()
-              window.history.back()
-            }}
-          >
-            Go Back
-          </Link>
-        )}
-      </div>
-    </div>
-  )
-}

examples-ts/classify/src/components/NotFound.tsx

@@ -1,25 +0,0 @@
-import { Link } from '@tanstack/react-router'
-
-export function NotFound({ children }: { children?: any }) {
-  return (
-    <div className="space-y-2 p-2">
-      <div className="text-gray-600 dark:text-gray-400">
-        {children || <p>The page you are looking for does not exist.</p>}
-      </div>
-      <p className="flex items-center gap-2 flex-wrap">
-        <button
-          onClick={() => window.history.back()}
-          className="bg-emerald-500 text-white px-2 py-1 rounded-sm uppercase font-black text-sm"
-        >
-          Go back
-        </button>
-        <Link
-          to="/"
-          className="bg-cyan-600 text-white px-2 py-1 rounded-sm uppercase font-black text-sm"
-        >
-          Start Over
-        </Link>
-      </p>
-    </div>
-  )
-}

examples-ts/classify/src/routeTree.gen.ts

@@ -1,225 +0,0 @@
-/* eslint-disable */
-
-// @ts-nocheck
-
-// noinspection JSUnusedGlobalSymbols
-
-// This file was automatically generated by TanStack Router.
-// You should NOT make any changes in this file as it will be overwritten.
-// Additionally, you should also exclude this file from your linter and/or formatter to prevent it from being checked or modified.
-
-import { Route as rootRouteImport } from './routes/__root'
-import { Route as UsersRouteImport } from './routes/users'
-import { Route as IndexRouteImport } from './routes/index'
-import { Route as UsersIndexRouteImport } from './routes/users.index'
-import { Route as PostsIndexRouteImport } from './routes/posts.index'
-import { Route as UsersUserIdRouteImport } from './routes/users.$userId'
-import { Route as PostsPostIdRouteImport } from './routes/posts.$postId'
-import { Route as ApiClassifyRouteImport } from './routes/api/classify'
-import { Route as PostsPostIdDeepRouteImport } from './routes/posts_.$postId.deep'
-
-const UsersRoute = UsersRouteImport.update({
-  id: '/users',
-  path: '/users',
-  getParentRoute: () => rootRouteImport,
-} as any)
-const IndexRoute = IndexRouteImport.update({
-  id: '/',
-  path: '/',
-  getParentRoute: () => rootRouteImport,
-} as any)
-const UsersIndexRoute = UsersIndexRouteImport.update({
-  id: '/',
-  path: '/',
-  getParentRoute: () => UsersRoute,
-} as any)
-const PostsIndexRoute = PostsIndexRouteImport.update({
-  id: '/posts/',
-  path: '/posts/',
-  getParentRoute: () => rootRouteImport,
-} as any)
-const UsersUserIdRoute = UsersUserIdRouteImport.update({
-  id: '/$userId',
-  path: '/$userId',
-  getParentRoute: () => UsersRoute,
-} as any)
-const PostsPostIdRoute = PostsPostIdRouteImport.update({
-  id: '/posts/$postId',
-  path: '/posts/$postId',
-  getParentRoute: () => rootRouteImport,
-} as any)
-const ApiClassifyRoute = ApiClassifyRouteImport.update({
-  id: '/api/classify',
-  path: '/api/classify',
-  getParentRoute: () => rootRouteImport,
-} as any)
-const PostsPostIdDeepRoute = PostsPostIdDeepRouteImport.update({
-  id: '/posts_/$postId/deep',
-  path: '/posts/$postId/deep',
-  getParentRoute: () => rootRouteImport,
-} as any)
-
-export interface FileRoutesByFullPath {
-  '/': typeof IndexRoute
-  '/users': typeof UsersRouteWithChildren
-  '/api/classify': typeof ApiClassifyRoute
-  '/posts/$postId': typeof PostsPostIdRoute
-  '/users/$userId': typeof UsersUserIdRoute
-  '/posts': typeof PostsIndexRoute
-  '/users/': typeof UsersIndexRoute
-  '/posts/$postId/deep': typeof PostsPostIdDeepRoute
-}
-export interface FileRoutesByTo {
-  '/': typeof IndexRoute
-  '/api/classify': typeof ApiClassifyRoute
-  '/posts/$postId': typeof PostsPostIdRoute
-  '/users/$userId': typeof UsersUserIdRoute
-  '/posts': typeof PostsIndexRoute
-  '/users': typeof UsersIndexRoute
-  '/posts/$postId/deep': typeof PostsPostIdDeepRoute
-}
-export interface FileRoutesById {
-  __root__: typeof rootRouteImport
-  '/': typeof IndexRoute
-  '/users': typeof UsersRouteWithChildren
-  '/api/classify': typeof ApiClassifyRoute
-  '/posts/$postId': typeof PostsPostIdRoute
-  '/users/$userId': typeof UsersUserIdRoute
-  '/posts/': typeof PostsIndexRoute
-  '/users/': typeof UsersIndexRoute
-  '/posts_/$postId/deep': typeof PostsPostIdDeepRoute
-}
-export interface FileRouteTypes {
-  fileRoutesByFullPath: FileRoutesByFullPath
-  fullPaths:
-    | '/'
-    | '/users'
-    | '/api/classify'
-    | '/posts/$postId'
-    | '/users/$userId'
-    | '/posts'
-    | '/users/'
-    | '/posts/$postId/deep'
-  fileRoutesByTo: FileRoutesByTo
-  to:
-    | '/'
-    | '/api/classify'
-    | '/posts/$postId'
-    | '/users/$userId'
-    | '/posts'
-    | '/users'
-    | '/posts/$postId/deep'
-  id:
-    | '__root__'
-    | '/'
-    | '/users'
-    | '/api/classify'
-    | '/posts/$postId'
-    | '/users/$userId'
-    | '/posts/'
-    | '/users/'
-    | '/posts_/$postId/deep'
-  fileRoutesById: FileRoutesById
-}
-export interface RootRouteChildren {
-  IndexRoute: typeof IndexRoute
-  UsersRoute: typeof UsersRouteWithChildren
-  ApiClassifyRoute: typeof ApiClassifyRoute
-  PostsPostIdRoute: typeof PostsPostIdRoute
-  PostsIndexRoute: typeof PostsIndexRoute
-  PostsPostIdDeepRoute: typeof PostsPostIdDeepRoute
-}
-
-declare module '@tanstack/react-router' {
-  interface FileRoutesByPath {
-    '/users': {
-      id: '/users'
-      path: '/users'
-      fullPath: '/users'
-      preLoaderRoute: typeof UsersRouteImport
-      parentRoute: typeof rootRouteImport
-    }
-    '/': {
-      id: '/'
-      path: '/'
-      fullPath: '/'
-      preLoaderRoute: typeof IndexRouteImport
-      parentRoute: typeof rootRouteImport
-    }
-    '/users/': {
-      id: '/users/'
-      path: '/'
-      fullPath: '/users/'
-      preLoaderRoute: typeof UsersIndexRouteImport
-      parentRoute: typeof UsersRoute
-    }
-    '/posts/': {
-      id: '/posts/'
-      path: '/posts'
-      fullPath: '/posts'
-      preLoaderRoute: typeof PostsIndexRouteImport
-      parentRoute: typeof rootRouteImport
-    }
-    '/users/$userId': {
-      id: '/users/$userId'
-      path: '/$userId'
-      fullPath: '/users/$userId'
-      preLoaderRoute: typeof UsersUserIdRouteImport
-      parentRoute: typeof UsersRoute
-    }
-    '/posts/$postId': {
-      id: '/posts/$postId'
-      path: '/posts/$postId'
-      fullPath: '/posts/$postId'
-      preLoaderRoute: typeof PostsPostIdRouteImport
-      parentRoute: typeof rootRouteImport
-    }
-    '/api/classify': {
-      id: '/api/classify'
-      path: '/api/classify'
-      fullPath: '/api/classify'
-      preLoaderRoute: typeof ApiClassifyRouteImport
-      parentRoute: typeof rootRouteImport
-    }
-    '/posts_/$postId/deep': {
-      id: '/posts_/$postId/deep'
-      path: '/posts/$postId/deep'
-      fullPath: '/posts/$postId/deep'
-      preLoaderRoute: typeof PostsPostIdDeepRouteImport
-      parentRoute: typeof rootRouteImport
-    }
-  }
-}
-
-interface UsersRouteChildren {
-  UsersUserIdRoute: typeof UsersUserIdRoute
-  UsersIndexRoute: typeof UsersIndexRoute
-}
-
-const UsersRouteChildren: UsersRouteChildren = {
-  UsersUserIdRoute: UsersUserIdRoute,
-  UsersIndexRoute: UsersIndexRoute,
-}
-
-const UsersRouteWithChildren = UsersRoute._addFileChildren(UsersRouteChildren)
-
-const rootRouteChildren: RootRouteChildren = {
-  IndexRoute: IndexRoute,
-  UsersRoute: UsersRouteWithChildren,
-  ApiClassifyRoute: ApiClassifyRoute,
-  PostsPostIdRoute: PostsPostIdRoute,
-  PostsIndexRoute: PostsIndexRoute,
-  PostsPostIdDeepRoute: PostsPostIdDeepRoute,
-}
-export const routeTree = rootRouteImport
-  ._addFileChildren(rootRouteChildren)
-  ._addFileTypes<FileRouteTypes>()
-
-import type { getRouter } from './router.tsx'
-import type { createStart } from '@tanstack/react-start'
-declare module '@tanstack/react-start' {
-  interface Register {
-    ssr: true
-    router: Awaited<ReturnType<typeof getRouter>>
-  }
-}

examples-ts/classify/src/router.tsx

@@ -1,15 +0,0 @@
-import { createRouter } from '@tanstack/react-router'
-import { routeTree } from './routeTree.gen'
-import { DefaultCatchBoundary } from './components/DefaultCatchBoundary'
-import { NotFound } from './components/NotFound'
-
-export function getRouter() {
-  const router = createRouter({
-    routeTree,
-    defaultPreload: 'intent',
-    defaultErrorComponent: DefaultCatchBoundary,
-    defaultNotFoundComponent: () => <NotFound />,
-    scrollRestoration: true,
-  })
-  return router
-}

examples-ts/classify/src/routes/__root.tsx

@@ -1,128 +0,0 @@
-/// <reference types="vite/client" />
-import {
-  HeadContent,
-  Scripts,
-  createRootRoute,
-} from '@tanstack/react-router'
-import * as React from 'react'
-import { DefaultCatchBoundary } from '~/components/DefaultCatchBoundary'
-import { NotFound } from '~/components/NotFound'
-import { seo } from '~/utils/seo'
-
-export const Route = createRootRoute({
-  head: () => ({
-    meta: [
-      {
-        charSet: 'utf-8',
-      },
-      {
-        name: 'viewport',
-        content: 'width=device-width, initial-scale=1',
-      },
-      ...seo({
-        title:
-          'Financial Documents Classification Agent',
-        description: `Classify financial documents as balance sheets, income statements and cash flow statemets. `,
-      }),
-    ],
-    links: [
-      { rel: 'stylesheet', href: "https://cdn.jsdelivr.net/npm/daisyui@5" },
-      {
-        rel: 'apple-touch-icon',
-        sizes: '180x180',
-        href: '/apple-touch-icon.png',
-      },
-      {
-        rel: 'icon',
-        type: 'image/png',
-        sizes: '32x32',
-        href: '/favicon-32x32.png',
-      },
-      {
-        rel: 'icon',
-        type: 'image/png',
-        sizes: '16x16',
-        href: '/favicon-16x16.png',
-      },
-      { rel: 'manifest', href: '/site.webmanifest', color: '#fffff' },
-      { rel: 'icon', href: '/favicon.ico' },
-    ],
-    scripts: [
-      {
-        src: '/customScript.js',
-        type: 'text/javascript',
-      },
-      {
-        src: "https://cdn.jsdelivr.net/npm/@tailwindcss/browser@4",
-        type: "text/javascript",
-      }
-    ],
-  }),
-  errorComponent: DefaultCatchBoundary,
-  notFoundComponent: () => <NotFound />,
-  shellComponent: RootDocument,
-})
-
-function RootDocument({ children }: { children: React.ReactNode }) {
-  return (
-    <html>
-      <head>
-        <HeadContent />
-      </head>
-      <body>
-        <div className="navbar bg-base-100 shadow-sm">
-          <div className="navbar-start">
-            <div className="dropdown">
-              <div tabIndex={0} role="button" className="btn btn-ghost btn-circle">
-                <svg
-                  xmlns="http://www.w3.org/2000/svg"
-                  className="h-5 w-5"
-                  fill="none"
-                  viewBox="0 0 24 24"
-                  stroke="currentColor"
-                >
-                  <path
-                    strokeLinecap="round"
-                    strokeLinejoin="round"
-                    strokeWidth="2"
-                    d="M4 6h16M4 12h16M4 18h7"
-                  />
-                </svg>
-              </div>
-              <ul
-                tabIndex={0}
-                className="menu menu-lg dropdown-content bg-base-100 rounded-box z-1 mt-3 w-80 p-2 shadow"
-              >
-                <li><a href="/">Home</a></li>
-                <li><a href="https://cloud.llamaindex.ai">Get Started with LlamaCloud</a></li>
-                <li><a href="https://developers.llamaindex.ai/python/cloud/llamaclassify/getting_started/">LlamaClassify Docs</a></li>
-              </ul>
-            </div>
-          </div>
-          <div className="navbar-center">
-            <a className="btn btn-ghost text-xl" href="/">Financial Documents Classification Agent</a>
-          </div>
-          <div className="navbar-end">
-            <a href="https://github.com/run-llama/llama_cloud_services/main/blob/examples-ts/classify">
-              <button className="btn btn-ghost btn-circle">
-                <div className="indicator">
-                  <svg
-                    xmlns="http://www.w3.org/2000/svg"
-                    className="h-10 w-10"
-                    fill="currentColor"
-                    viewBox="0 0 640 512"
-                  >
-                    <path d="M237.9 461.4C237.9 463.4 235.6 465 232.7 465C229.4 465.3 227.1 463.7 227.1 461.4C227.1 459.4 229.4 457.8 232.3 457.8C235.3 457.5 237.9 459.1 237.9 461.4zM206.8 456.9C206.1 458.9 208.1 461.2 211.1 461.8C213.7 462.8 216.7 461.8 217.3 459.8C217.9 457.8 216 455.5 213 454.6C210.4 453.9 207.5 454.9 206.8 456.9zM251 455.2C248.1 455.9 246.1 457.8 246.4 460.1C246.7 462.1 249.3 463.4 252.3 462.7C255.2 462 257.2 460.1 256.9 458.1C256.6 456.2 253.9 454.9 251 455.2zM316.8 72C178.1 72 72 177.3 72 316C72 426.9 141.8 521.8 241.5 555.2C254.3 557.5 258.8 549.6 258.8 543.1C258.8 536.9 258.5 502.7 258.5 481.7C258.5 481.7 188.5 496.7 173.8 451.9C173.8 451.9 162.4 422.8 146 415.3C146 415.3 123.1 399.6 147.6 399.9C147.6 399.9 172.5 401.9 186.2 425.7C208.1 464.3 244.8 453.2 259.1 446.6C261.4 430.6 267.9 419.5 275.1 412.9C219.2 406.7 162.8 398.6 162.8 302.4C162.8 274.9 170.4 261.1 186.4 243.5C183.8 237 175.3 210.2 189 175.6C209.9 169.1 258 202.6 258 202.6C278 197 299.5 194.1 320.8 194.1C342.1 194.1 363.6 197 383.6 202.6C383.6 202.6 431.7 169 452.6 175.6C466.3 210.3 457.8 237 455.2 243.5C471.2 261.2 481 275 481 302.4C481 398.9 422.1 406.6 366.2 412.9C375.4 420.8 383.2 435.8 383.2 459.3C383.2 493 382.9 534.7 382.9 542.9C382.9 549.4 387.5 557.3 400.2 555C500.2 521.8 568 426.9 568 316C568 177.3 455.5 72 316.8 72zM169.2 416.9C167.9 417.9 168.2 420.2 169.9 422.1C171.5 423.7 173.8 424.4 175.1 423.1C176.4 422.1 176.1 419.8 174.4 417.9C172.8 416.3 170.5 415.6 169.2 416.9zM158.4 408.8C157.7 410.1 158.7 411.7 160.7 412.7C162.3 413.7 164.3 413.4 165 412C165.7 410.7 164.7 409.1 162.7 408.1C160.7 407.5 159.1 407.8 158.4 408.8zM190.8 444.4C189.2 445.7 189.8 448.7 192.1 450.6C194.4 452.9 197.3 453.2 198.6 451.6C199.9 450.3 199.3 447.3 197.3 445.4C195.1 443.1 192.1 442.8 190.8 444.4zM179.4 429.7C177.8 430.7 177.8 433.3 179.4 435.6C181 437.9 183.7 438.9 185 437.9C186.6 436.6 186.6 434 185 431.7C183.6 429.4 181 428.4 179.4 429.7z" />
-                  </svg>
-                </div>
-              </button>
-            </a>
-          </div>
-        </div>
-        <hr />
-        {children}
-        <Scripts />
-      </body>
-    </html>
-  )
-}

examples-ts/classify/src/routes/api/classify.ts

@@ -1,45 +0,0 @@
-import { createFileRoute } from '@tanstack/react-router'
-import { classifier, classificationRules, parsingConfig } from '~/utils/classifier'
-
-export const Route = createFileRoute('/api/classify')({
-  component: RouteComponent,
-  server: {
-      handlers: {
-        POST: async ({ request }) => {
-          const body = await request.formData()
-          const fl = body.get("file") as File;
-          if (!fl) {
-                return new Response(JSON.stringify({"result": "you need to provide a file"}))
-            }
-          const buff = await fl.arrayBuffer()
-          const rawRes = await classifier.classify(
-            classificationRules,
-            parsingConfig,
-            { fileContents: [new Uint8Array(buff)] },
-          )
-          const results = rawRes.items
-          let classification = ""
-
-          for (const result of results) {
-            if ("result" in result && result.result) {
-                classification += `
-                    <div class="card bg-base-100 shadow-xl p-6 mb-4">
-                        <div class="space-y-3">
-                        <p><span class="font-semibold">📄 Document:</span> ${fl.name}</p>
-                        <p><span class="font-semibold">🏷️ Type:</span> <span class="badge badge-primary">${result.result.type}</span></p>
-                        <p><span class="font-semibold">📊 Confidence:</span> ${result.result.confidence*100}%</p>
-                        <p><span class="font-semibold">💭 Reasoning:</span> ${result.result.reasoning}</p>
-                        </div>
-                    </div>
-                `
-            }
-          }
-          return new Response(JSON.stringify({"result": classification}))
-        },
-      },
-    },
-})
-
-function RouteComponent() {
-  return
-}

examples-ts/classify/src/routes/index.tsx

@@ -1,99 +0,0 @@
-import { createFileRoute } from '@tanstack/react-router'
-import { useRef, useState } from 'react'
-
-export const Route = createFileRoute('/')({
-  component: Home,
-})
-
-function Home() {
-  const [file, setFile] = useState<null | File>(null)
-  const fileInputRef = useRef<HTMLInputElement>(null)
-  const [reply, setReply] = useState<null | string>(null)
-  const [loading, setLoading] = useState<boolean>(false)
-  const handleFileChange = (event: React.ChangeEvent<HTMLInputElement>) => {
-    const selectedFile = event.target.files?.[0]
-    if (selectedFile) {
-      setFile(selectedFile)
-    }
-  }
-  const handleClearFile = () => {
-    if (file) {
-      setFile(null)
-    }
-    if (fileInputRef.current) {
-      fileInputRef.current.value = ''
-    }
-    if (reply) {
-      setReply(null)
-    }
-  }
-
-  const handleClassify = async () => {
-    if (!file) return
-
-    if (reply) {
-      setReply(null)
-    }
-    setLoading(true)
-    try {
-      const formData = new FormData()
-      formData.append('file', file)
-
-      const res = await fetch('/api/classify', {
-        method: 'POST',
-        body: formData,
-      })
-
-      const data = await res.json()
-      setReply(data.result)
-    } catch (error) {
-      console.error('Error:', error)
-    } finally {
-      setLoading(false)
-    }
-  }
-
-  return (
-    <div className="flex flex-col justify-center items-center gap-y-8">
-        <br />
-        <h1 className="text-xl font-bold text-gray-700">AI-Powered finacial document classification</h1>
-        <h2 className="text-lg font-semibold text-gray-500">Need help sorting out the financial documents jungle? Let our classification agent handle it!</h2>
-        <fieldset className="fieldset bg-base-100 border-base-300 rounded-box w-200 border p-4">
-            <legend className="fieldset-legend text-lg">Upload your financial document here</legend>
-            <label className="label flex justify-center">
-                <input type="file" className="file-input" onChange={handleFileChange} accept='application/pdf' ref={fileInputRef} />
-            </label>
-        </fieldset>
-        {file && (
-          <div className="flex flex-col justify-center items-center gap-y-8">
-          <p className="text-sm text-gray-600">Selected file: {file.name}</p>
-          <div className='grid grid-cols-2 gap-x-6'>
-            <button
-                type="button"
-                className='btn bg-gray-500 text-white shadow-lg hover:bg-gray-600 hover:shadow-xl rounded'
-                onClick={handleClassify}
-            >
-            Classify
-            </button>
-            <button
-                  onClick={handleClearFile}
-                  type="button"
-                  className="px-4 py-2 bg-red-300 text-black rounded hover:bg-red-400 hover:shadow-xl shadow-lg"
-              >
-              Clear
-            </button>
-          </div>
-          </div>
-        )}
-        {loading && (
-          <span className="loading loading-spinner text-primary"></span>
-        )}
-        {reply && (
-          <div
-            className="max-w-2xl w-full"
-            dangerouslySetInnerHTML={{ __html: reply }}
-          />
-        )}
-    </div>
-  )
-}

examples-ts/classify/src/utils/classifier.ts

@@ -1,23 +0,0 @@
-import { LlamaClassify, ClassifierRule, ClassifyParsingConfiguration } from "llama-cloud-services"
-
-export const classifier = new LlamaClassify(process.env.LLAMA_CLOUD_API_KEY);
-
-export const classificationRules: ClassifierRule[] = [
-    {
-        description: "Shows a company's assets, liabilities, and shareholders' equity at a specific point in time, providing a snapshot of financial position.",
-        type: "balance_sheet"
-    },
-    {
-        description: "Reports cash inflows and outflows from operating, investing, and financing activities, highlighting liquidity and cash management.",
-        type: "cash_flow_statement"
-    },
-    {
-        description: "Summarizes revenues, expenses, and profits over a period, indicating financial performance and profitability.",
-        type: "income_statement"
-    },
-];
-
-export const parsingConfig: ClassifyParsingConfiguration = {
-    lang: "en",
-    max_pages: 20,
-}

examples-ts/classify/src/utils/seo.ts

@@ -1,33 +0,0 @@
-export const seo = ({
-  title,
-  description,
-  keywords,
-  image,
-}: {
-  title: string
-  description?: string
-  image?: string
-  keywords?: string
-}) => {
-  const tags = [
-    { title },
-    { name: 'description', content: description },
-    { name: 'keywords', content: keywords },
-    { name: 'twitter:title', content: title },
-    { name: 'twitter:description', content: description },
-    { name: 'twitter:creator', content: '@tannerlinsley' },
-    { name: 'twitter:site', content: '@tannerlinsley' },
-    { name: 'og:type', content: 'website' },
-    { name: 'og:title', content: title },
-    { name: 'og:description', content: description },
-    ...(image
-      ? [
-          { name: 'twitter:image', content: image },
-          { name: 'twitter:card', content: 'summary_large_image' },
-          { name: 'og:image', content: image },
-        ]
-      : []),
-  ]
-
-  return tags
-}

examples-ts/classify/tsconfig.json

@@ -1,22 +0,0 @@
-{
-  "include": ["**/*.ts", "**/*.tsx"],
-  "compilerOptions": {
-    "strict": true,
-    "esModuleInterop": true,
-    "jsx": "react-jsx",
-    "module": "ESNext",
-    "moduleResolution": "Bundler",
-    "lib": ["DOM", "DOM.Iterable", "ES2022"],
-    "isolatedModules": true,
-    "resolveJsonModule": true,
-    "skipLibCheck": true,
-    "target": "ES2022",
-    "allowJs": true,
-    "forceConsistentCasingInFileNames": true,
-    "baseUrl": ".",
-    "paths": {
-      "~/*": ["./src/*"]
-    },
-    "noEmit": true
-  }
-}

examples-ts/classify/vite.config.ts

@@ -1,19 +0,0 @@
-import { tanstackStart } from '@tanstack/react-start/plugin/vite'
-import { defineConfig } from 'vite'
-import tsConfigPaths from 'vite-tsconfig-paths'
-import viteReact from '@vitejs/plugin-react'
-
-export default defineConfig({
-  server: {
-    port: 3000,
-  },
-  plugins: [
-    tsConfigPaths({
-      projects: ['./tsconfig.json'],
-    }),
-    tanstackStart({
-      srcDirectory: 'src',
-    }),
-    viteReact(),
-  ],
-})

examples-ts/extract/README.md

@@ -1,122 +0,0 @@
-# LlamaExtract Demo
-
-A TypeScript demo application showcasing the power of **LlamaExract** - a structured data extraction agentic service from [LlamaCloud](https://cloud.llamaindex.ai). This demo allows you to extract structured information from scientific papers and get them into a nice markdown format.
-
-## Table of Contents
-
-- [Features](#features)
-- [Prerequisites](#prerequisites)
-- [Installation](#installation)
-- [Usage](#usage)
-  - [Start the Demo](#start-the-demo)
-  - [Development Mode](#development-mode)
-  - [Build the Project](#build-the-project)
-  - [Code Quality](#code-quality)
-  - [Quick Commands Reference](#quick-commands-reference)
-- [How It Works](#how-it-works)
-- [API Dependencies](#api-dependencies)
-- [Troubleshooting](#troubleshooting)
-  - [Common Issues](#common-issues)
-- [License](#license)
-- [Contributing](#contributing)
-
-## Features
-
-- 📄 **Structured Data Extraction**: Extract data from your files effortlessly, and structure them the way you want!
-- 🤖 **Markdown Rendering**: Generate markdown directly from your extracted data
-- 🎨 **Beautiful CLI**: Styled console interface with colors and ASCII art
-- ⚡ **Fast Development**: Hot reload support with watch mode
-- 🛠️ **TypeScript**: Full TypeScript support with strict type checking
-
-## Prerequisites
-
-- Node.js (version 18 or higher)
-- pnpm package manager
-- LlamaCloud API key
-
-## Installation
-
-1. Clone the repository:
-
-```bash
-git clone https://github.com/run-llama/llama_cloud_services
-cd lama_cloud_services/examples-ts/extract/
-```
-
-2. Install dependencies:
-
-```bash
-npm install
-```
-
-3. Set up your environment variables:
-
-```bash
-# Add your API key to your environment
-export LLAMA_CLOUD_API_KEY="your-llamacloud-api-key"
-```
-
-## Usage
-
-### Start the Demo
-
-```bash
-npm run start
-```
-
-The application will display a welcome screen and prompt you to enter the path to a document you'd like to process.
-
-### Development Mode
-
-For development with hot reload:
-
-```bash
-npm run dev
-```
-
-### Build the Project
-
-```bash
-npm run build
-```
-
-### Code Quality
-
-Format code:
-
-```bash
-npm run format
-```
-
-Lint code:
-
-```bash
-npm run lint
-```
-
-## How It Works
-
-1. **Document Input**: Enter the path to your document when prompted
-2. **Parsing**: LlamaExtract, based on the schema you can find [here](./src/schema.ts), processes the document and extracts structured data
-3. **Markdown Rendering**: The extracted content is rendered into beautiful markdown
-4. **Results**: View the results directly in your terminal
-
-## Troubleshooting
-
-### Common Issues
-
-1. **Module Resolution Errors**: Ensure you're using Node.js 18+ and have all dependencies installed
-2. **API Key Issues**: Verify your LlamaCloud API key is correctly set
-3. **File Path Errors**: Use absolute paths or ensure relative paths are correct from the project root
-
-## License
-
-MIT License - see the [LICENSE](../../LICENSE) file for details.
-
-## Contributing
-
-1. Fork the repository
-2. Create a feature branch
-3. Make your changes
-4. Run `npm run format` and `npm run lint`
-5. Submit a pull request

examples-ts/extract/eslint.config.mts

@@ -1,14 +0,0 @@
-import js from "@eslint/js";
-import globals from "globals";
-import tseslint from "typescript-eslint";
-import { defineConfig } from "eslint/config";
-
-export default defineConfig([
-  {
-    files: ["**/*.{js,mjs,cjs,ts,mts,cts}"],
-    plugins: { js },
-    extends: ["js/recommended"],
-    languageOptions: { globals: globals.browser },
-  },
-  tseslint.configs.recommended,
-]);

examples-ts/extract/package.json

@@ -1,37 +0,0 @@
-{
-  "name": "llama-extract-demo",
-  "version": "0.1.0",
-  "description": "Demo for LlamaExtract in TypeScript",
-  "main": "index.js",
-  "scripts": {
-    "test": "echo \"There are no tests\"",
-    "start": "npm exec tsx src/index.ts",
-    "lint": "eslint ./src/",
-    "format": "prettier --write ./src/",
-    "build": "tsc",
-    "dev": "npm exec tsx --watch src/index.ts"
-  },
-  "author": "LlamaIndex",
-  "license": "MIT",
-  "dependencies": {
-    "cli-markdown": "^3.5.1",
-    "consola": "^3.4.2",
-    "figlet": "^1.8.2",
-    "llama-cloud-services": "file:../../ts/llama_cloud_services",
-    "marked": "^15.0.12",
-    "marked-terminal": "^7.3.0",
-    "picocolors": "^1.1.1"
-  },
-  "devDependencies": {
-    "@eslint/js": "^9.32.0",
-    "@types/figlet": "^1.7.0",
-    "@types/marked-terminal": "^6.1.1",
-    "@types/node": "^24.2.0",
-    "eslint": "^9.32.0",
-    "globals": "^16.3.0",
-    "jiti": "^2.5.1",
-    "prettier": "^3.6.2",
-    "typescript": "^5.9.2",
-    "typescript-eslint": "^8.39.0"
-  }
-}

examples-ts/extract/src/index.ts

@@ -1,47 +0,0 @@
-import { LlamaExtract, ExtractConfig } from "llama-cloud-services";
-import cliMarkdown from "cli-markdown";
-import { logger } from "./logger";
-import pc from "picocolors";
-import { consoleInput, renderLogo } from "./utils";
-import { dataSchema } from "./schema";
-import { renderMarkdown, ResearchData } from "./markdown";
-
-export async function main(): Promise<number> {
-  const extractClient = new LlamaExtract(
-    process.env.LLAMA_CLOUD_API_KEY!,
-    "https://api.cloud.llamaindex.ai",
-  );
-  await renderLogo();
-  logger.log(
-    `Welcome to ${pc.bold(
-      pc.magentaBright("LlamaExtract Demo✨"),
-    )}, our demo for ${pc.bold(pc.green("LlamaExtract"))}, a ${pc.bold(
-      pc.cyan("LlamaCloud☁️"),
-    )}  (https://cloud.llamaindex.ai) product!.\nIn this demo we are going to try extracting relevant information  ${pc.bold(
-      pc.yellowBright("from scientific papers"),
-    )}. Type the path to the paper you would like to process below👇\nIf you wish to exit, just type ${pc.bold(
-      pc.gray("quit"),
-    )}.\n`,
-  );
-  while (true) {
-    const userInput = await consoleInput();
-    if (userInput.toLowerCase() == "quit") {
-      break;
-    }
-    try {
-      const generatedData = await extractClient.extract(
-        dataSchema,
-        {} as ExtractConfig,
-        userInput,
-      );
-      const research = renderMarkdown(generatedData?.data as ResearchData); // Added await here
-      logger.log(`${pc.bold(pc.cyan("Extracted information:✨"))}:\n`);
-      logger.log(cliMarkdown(research));
-    } catch (error) {
-      logger.error(`Error processing file: ${error}`);
-    }
-  }
-  return 0;
-}
-
-main().catch(console.error);

examples-ts/extract/src/logger.ts

@@ -1,8 +0,0 @@
-import { createConsola } from "consola";
-import type { ConsolaInstance } from "consola";
-
-export const logger: ConsolaInstance = createConsola({
-  formatOptions: {
-    date: false,
-  },
-});

examples-ts/extract/src/markdown.ts

@@ -1,172 +0,0 @@
-type Author = {
-  name: string;
-  affiliation?: string;
-  email?: string;
-};
-
-type Methodology = {
-  approach?: string;
-  participants?: string;
-  methods?: string[];
-};
-
-type Result = {
-  finding?: string;
-  significance?: string;
-  supportingData?: string;
-};
-
-type Reference = {
-  title: string;
-  authors: string;
-  year?: string;
-  relevance?: string;
-};
-
-type Discussion = {
-  implications?: string[];
-  limitations?: string[];
-  futureWork?: string[];
-};
-
-type Publication = {
-  journal?: string;
-  year: string;
-  doi?: string;
-  url?: string;
-};
-
-export type ResearchData = {
-  title: string;
-  authors: Author[];
-  abstract: string;
-  keywords?: string[];
-  mainFindings: string[];
-  methodology?: Methodology;
-  results?: Result[];
-  discussion?: Discussion;
-  references?: Reference[];
-  publication?: Publication;
-};
-
-export function renderMarkdown(data: ResearchData): string {
-  const {
-    title,
-    authors,
-    abstract,
-    keywords,
-    mainFindings,
-    methodology,
-    results,
-    discussion,
-    references,
-    publication,
-  } = data;
-
-  const md: string[] = [];
-
-  md.push(`# ${title}\n`);
-
-  // Authors
-  md.push(`## Authors`);
-  md.push(
-    authors
-      .map(
-        (author) =>
-          `- **${author.name}**${
-            author.affiliation ? `, *${author.affiliation}*` : ""
-          }${author.email ? ` (${author.email})` : ""}`,
-      )
-      .join("\n"),
-  );
-
-  // Abstract
-  md.push(`\n## Abstract\n${abstract}`);
-
-  // Keywords
-  if (keywords && keywords.length > 0) {
-    md.push(`\n## Keywords\n${keywords.map((k) => `- ${k}`).join("\n")}`);
-  }
-
-  // Main Findings
-  md.push(
-    `\n## Main Findings\n${mainFindings.map((f) => `- ${f}`).join("\n")}`,
-  );
-
-  // Methodology
-  if (methodology) {
-    md.push(`\n## Methodology`);
-    if (methodology.approach) md.push(`**Approach:** ${methodology.approach}`);
-    if (methodology.participants)
-      md.push(`**Participants:** ${methodology.participants}`);
-    if (methodology.methods?.length) {
-      md.push(
-        `**Methods:**\n${methodology.methods.map((m) => `- ${m}`).join("\n")}`,
-      );
-    }
-  }
-
-  // Results
-  if (results?.length) {
-    md.push(`\n## Results`);
-    results.forEach((result, i) => {
-      md.push(`\n### Result ${i + 1}`);
-      if (result.finding) md.push(`- **Finding:** ${result.finding}`);
-      if (result.significance)
-        md.push(`- **Significance:** ${result.significance}`);
-      if (result.supportingData)
-        md.push(`- **Supporting Data:** ${result.supportingData}`);
-    });
-  }
-
-  // Discussion
-  if (discussion) {
-    md.push(`\n## Discussion`);
-    if (discussion.implications?.length) {
-      md.push(
-        `### Implications\n${discussion.implications
-          .map((d) => `- ${d}`)
-          .join("\n")}`,
-      );
-    }
-    if (discussion.limitations?.length) {
-      md.push(
-        `### Limitations\n${discussion.limitations
-          .map((d) => `- ${d}`)
-          .join("\n")}`,
-      );
-    }
-    if (discussion.futureWork?.length) {
-      md.push(
-        `### Future Work\n${discussion.futureWork
-          .map((d) => `- ${d}`)
-          .join("\n")}`,
-      );
-    }
-  }
-
-  // References
-  if (references?.length) {
-    md.push(`\n## References`);
-    references.forEach((ref, i) => {
-      md.push(
-        `\n**[${i + 1}]** ${ref.title} — *${ref.authors}*${
-          ref.year ? ` (${ref.year})` : ""
-        }`,
-      );
-      if (ref.relevance) md.push(`> ${ref.relevance}`);
-    });
-  }
-
-  // Publication Info
-  if (publication) {
-    md.push(`\n## Publication`);
-    if (publication.journal) md.push(`- **Journal:** ${publication.journal}`);
-    if (publication.year) md.push(`- **Year:** ${publication.year}`);
-    if (publication.doi) md.push(`- **DOI:** ${publication.doi}`);
-    if (publication.url)
-      md.push(`- **URL:** [${publication.url}](${publication.url})`);
-  }
-
-  return md.join("\n");
-}

examples-ts/extract/src/schema.ts

@@ -1,169 +0,0 @@
-export const dataSchema = {
-  type: "object",
-  required: ["title", "authors", "abstract", "mainFindings"],
-  properties: {
-    title: {
-      type: "string",
-      description: "The full title of the research paper",
-    },
-    authors: {
-      type: "array",
-      description: "List of all authors of the paper",
-      items: {
-        type: "object",
-        properties: {
-          name: {
-            type: "string",
-            description: "Full name of the author",
-          },
-          affiliation: {
-            type: "string",
-            description:
-              "Institution or organization the author is affiliated with",
-          },
-          email: {
-            type: "string",
-            description: "Contact email of the author if provided",
-          },
-        },
-      },
-    },
-    abstract: {
-      type: "string",
-      description: "Complete abstract or summary of the paper",
-    },
-    keywords: {
-      type: "array",
-      description:
-        "Key terms and phrases that describe the paper's main topics",
-      items: {
-        type: "string",
-      },
-    },
-    mainFindings: {
-      type: "array",
-      description: "Key findings, conclusions, or contributions of the paper",
-      items: {
-        type: "string",
-      },
-    },
-    methodology: {
-      type: "object",
-      description: "Research methods and approaches used",
-      properties: {
-        approach: {
-          type: "string",
-          description: "Overall research approach or study design",
-        },
-        participants: {
-          type: "string",
-          description: "Description of study participants or data sources",
-        },
-        methods: {
-          type: "array",
-          description: "Specific methods, techniques, or tools used",
-          items: {
-            type: "string",
-          },
-        },
-      },
-    },
-    results: {
-      type: "array",
-      description: "Main results and outcomes of the research",
-      items: {
-        type: "object",
-        properties: {
-          finding: {
-            type: "string",
-            description: "Description of the specific result or finding",
-          },
-          significance: {
-            type: "string",
-            description:
-              "Statistical significance or importance of the finding",
-          },
-          supportingData: {
-            type: "string",
-            description: "Relevant statistics, measurements, or data points",
-          },
-        },
-      },
-    },
-    discussion: {
-      type: "object",
-      properties: {
-        implications: {
-          type: "array",
-          description: "Theoretical or practical implications of the findings",
-          items: {
-            type: "string",
-          },
-        },
-        limitations: {
-          type: "array",
-          description: "Study limitations or constraints",
-          items: {
-            type: "string",
-          },
-        },
-        futureWork: {
-          type: "array",
-          description: "Suggested future research directions",
-          items: {
-            type: "string",
-          },
-        },
-      },
-    },
-    references: {
-      type: "array",
-      description:
-        "Key papers cited that are crucial to understanding this work",
-      items: {
-        type: "object",
-        properties: {
-          title: {
-            type: "string",
-            description: "Title of the cited paper",
-          },
-          authors: {
-            type: "string",
-            description: "Authors of the cited paper",
-          },
-          year: {
-            type: "string",
-            description: "Publication year",
-          },
-          relevance: {
-            type: "string",
-            description: "Why this reference is important to the current paper",
-          },
-        },
-        required: ["title", "authors"],
-      },
-    },
-    publication: {
-      type: "object",
-      properties: {
-        journal: {
-          type: "string",
-          description: "Name of the journal or conference",
-        },
-        year: {
-          type: "string",
-          description: "Year of publication",
-        },
-        doi: {
-          type: "string",
-          description: "Digital Object Identifier (DOI) of the paper",
-        },
-        url: {
-          type: "string",
-          description: "URL where the paper can be accessed",
-        },
-      },
-      required: ["year"],
-    },
-  },
-};

examples-ts/extract/src/types/cli-markdown.d.ts

@@ -1,4 +0,0 @@
-declare module "cli-markdown" {
-  function cliMarkdown(input: string): string;
-  export default cliMarkdown;
-}

examples-ts/extract/src/utils.ts

@@ -1,33 +0,0 @@
-import * as readline from "readline/promises";
-import figlet from "figlet";
-import pc from "picocolors";
-
-export async function renderLogo(): Promise<void> {
-  const logoText = figlet.textSync("Extract Demo", {
-    font: "ANSI Shadow",
-    horizontalLayout: "default",
-    verticalLayout: "default",
-    width: 100,
-    whitespaceBreak: true,
-  });
-
-  // Add some styling with picocolors
-  const styledLogo = pc.bold(pc.redBright(logoText));
-
-  // Add some padding/margin
-  console.log("\n");
-  console.log(styledLogo);
-  console.log(pc.gray("─".repeat(60)));
-  console.log("\n");
-}
-
-export async function consoleInput(): Promise<string> {
-  const rl = readline.createInterface({
-    input: process.stdin,
-    output: process.stdout,
-  });
-
-  const answer = await rl.question("Path to your file: ");
-  rl.close();
-  return answer;
-}

examples-ts/index/README.md

@@ -1,131 +0,0 @@
-# LlamaCloud Index Demo
-
-A TypeScript demo application showcasing the power of **LlamaCloud Index** - a fully automated document ingestion and retrieval serviced offered within [LlamaCloud](https://cloud.llamaindex.ai). This demo allows you to ask questions, retrieve relevant contextual information and generate AI-powered responses using OpenAI's GPT models.
-
-## Table of Contents
-
-- [Features](#features)
-- [Prerequisites](#prerequisites)
-- [Installation](#installation)
-- [Usage](#usage)
-  - [Start the Demo](#start-the-demo)
-  - [Development Mode](#development-mode)
-  - [Build the Project](#build-the-project)
-  - [Code Quality](#code-quality)
-  - [Quick Commands Reference](#quick-commands-reference)
-- [How It Works](#how-it-works)
-- [API Dependencies](#api-dependencies)
-- [Troubleshooting](#troubleshooting)
-  - [Common Issues](#common-issues)
-- [License](#license)
-- [Contributing](#contributing)
-
-## Features
-
-- 🤖 **RAG**: Simple-yet-effective Retrieval Augmented Generation pipeline built on top of LlamaCloud Index and OpenAI
-- 🎨 **Beautiful CLI**: Styled console interface with colors and ASCII art
-- ⚡ **Fast Development**: Hot reload support with watch mode
-- 🛠️ **TypeScript**: Full TypeScript support with strict type checking
-
-## Prerequisites
-
-- Node.js (version 18 or higher)
-- pnpm package manager
-- OpenAI API key
-- LlamaCloud API key
-- An existing LlamaCloud Index pipeline
-
-## Installation
-
-1. Clone the repository:
-
-```bash
-git clone https://github.com/run-llama/llama_cloud_services
-cd lama_cloud_services/examples-ts/index/
-```
-
-2. Install dependencies:
-
-```bash
-pnpm install
-```
-
-3. Set up your environment variables:
-
-```bash
-export OPENAI_API_KEY="your-openai-api-key"
-export LLAMA_CLOUD_API_KEY="your-llamacloud-api-key"
-export PIPELINE_NAME="your-pipeline-name"
-```
-
-4. Or write them into a `.env` file:
-
-```env
-OPENAI_API_KEY="your-openai-api-key"
-LLAMA_CLOUD_API_KEY="your-llamacloud-api-key"
-PIPELINE_NAME="your-pipeline-name"
-```
-
-## Usage
-
-### Start the Demo
-
-```bash
-pnpm run start
-```
-
-The application will display a welcome screen and prompt you to start chatting!
-
-### Development Mode
-
-For development with hot reload:
-
-```bash
-pnpm run dev
-```
-
-### Build the Project
-
-```bash
-pnpm run build
-```
-
-### Code Quality
-
-Format code:
-
-```bash
-pnpm run format
-```
-
-Lint code:
-
-```bash
-pnpm run lint
-```
-
-## How It Works
-
-1. **Message Input**: Enter a message
-2. **Retrieval**: Several nodes are retrieved from the LlamaCloud index you specified
-3. **AI Response Generation**: The retrieved information is passed on to the AI model, along with its relevance score, and a reply to your original message is generated starting from that.
-4. **Results**: View the AI-generated summary in your terminal
-
-## Troubleshooting
-
-### Common Issues
-
-1. **Module Resolution Errors**: Ensure you're using Node.js 18+ and have all dependencies installed
-2. **API Key Issues**: Verify your OpenAI and LlamaCloud API keys are correctly set
-
-## License
-
-MIT License - see the [LICENSE](../../LICENSE) file for details.
-
-## Contributing
-
-1. Fork the repository
-2. Create a feature branch
-3. Make your changes
-4. Run `pnpm run format` and `pnpm run lint`
-5. Submit a pull request

examples-ts/index/eslint.config.mts

@@ -1,15 +0,0 @@
-import js from "@eslint/js";
-import globals from "globals";
-import tseslint from "typescript-eslint";
-import { defineConfig } from "eslint/config";
-
-export default defineConfig([
-  {
-    files: ["**/*.{js,mjs,cjs,ts,mts,cts}"],
-    plugins: { js },
-    extends: ["js/recommended"],
-    languageOptions: { globals: globals.browser },
-  },
-  { files: ["**/*.js"], languageOptions: { sourceType: "script" } },
-  tseslint.configs.recommended,
-]);

examples-ts/index/package.json

@@ -1,48 +0,0 @@
-{
-  "name": "llama-chat",
-  "version": "0.1.0",
-  "description": "Demo for LlamaCloud Index in TypeScript",
-  "type": "module",
-  "main": "index.js",
-  "scripts": {
-    "test": "echo \"There are no tests\"",
-    "start": "pnpm exec tsx src/index.ts",
-    "lint": "eslint ./src/",
-    "format": "prettier --write ./src/",
-    "build": "tsc",
-    "dev": "pnpm exec tsx --watch src/index.ts"
-  },
-  "keywords": [
-    "ai",
-    "rag",
-    "retrieval",
-    "pipeline",
-    "llms",
-    "chatbot"
-  ],
-  "author": "LlamaIndex",
-  "license": "MIT",
-  "packageManager": "pnpm@10.12.4",
-  "devDependencies": {
-    "@eslint/js": "^9.32.0",
-    "@types/figlet": "^1.7.0",
-    "@types/node": "^24.1.0",
-    "@typescript-eslint/eslint-plugin": "^8.38.0",
-    "@typescript-eslint/parser": "^8.38.0",
-    "eslint": "^9.32.0",
-    "globals": "^16.3.0",
-    "jiti": "^2.5.1",
-    "prettier": "^3.6.2",
-    "typescript": "^5.8.3",
-    "typescript-eslint": "^8.38.0"
-  },
-  "dependencies": {
-    "@ai-sdk/openai": "^1.3.23",
-    "ai": "^4.3.19",
-    "consola": "^3.4.2",
-    "dotenv": "^17.2.1",
-    "figlet": "^1.8.2",
-    "llama-cloud-services": "link:../../ts/llama_cloud_services",
-    "picocolors": "^1.1.1"
-  }
-}

examples-ts/index/src/index.ts

@@ -1,48 +0,0 @@
-import { LlamaCloudIndex } from "llama-cloud-services";
-import { logger } from "./logger";
-import pc from "picocolors";
-import {
-  consoleInput,
-  retrievalAugmentedGeneration,
-  renderLogo,
-} from "./utils";
-import dotenv from "dotenv";
-
-dotenv.config();
-
-export async function main(): Promise<number> {
-  const index = new LlamaCloudIndex({
-    name: process.env.PIPELINE_NAME as string,
-    projectName: "Default",
-    apiKey: process.env.LLAMA_CLOUD_API_KEY, // can provide API-key in the constructor or in the env
-  });
-  const retriever = index.asRetriever({
-    similarityTopK: 5,
-  });
-  await renderLogo();
-  logger.log(
-    `Welcome to ${pc.bold(
-      pc.magentaBright("✨LlamaChat✨"),
-    )}, our demo for ${pc.bold(pc.green("Index🦙"))}, a ${pc.bold(
-      pc.cyan("LlamaCloud☁️"),
-    )}  (https://cloud.llamaindex.ai) product!.\nType a question below, and you will get an answer!👇\nIf you wish to exit, just type ${pc.bold(
-      pc.gray("quit"),
-    )}.\n`,
-  );
-  while (true) {
-    const userInput = await consoleInput();
-    if (userInput.toLowerCase() == "quit") {
-      break;
-    }
-    try {
-      const nodes = await retriever.retrieve(userInput);
-      const summary = await retrievalAugmentedGeneration(nodes, userInput);
-      logger.log(`${pc.bold(pc.magentaBright("LlamaChat✨:"))}\n${summary}`);
-    } catch (error) {
-      logger.error(`Error processing your request: ${error}`);
-    }
-  }
-  return 0;
-}
-
-main().catch(console.error);

examples-ts/index/src/logger.ts

@@ -1,8 +0,0 @@
-import { createConsola } from "consola";
-import type { ConsolaInstance } from "consola";
-
-export const logger: ConsolaInstance = createConsola({
-  formatOptions: {
-    date: false,
-  },
-});

examples-ts/index/src/utils.ts

@@ -1,56 +0,0 @@
-import { generateText } from "ai";
-import { openai } from "@ai-sdk/openai";
-import { NodeWithScore, MetadataMode } from "llamaindex";
-import * as readline from "readline/promises";
-import figlet from "figlet";
-import pc from "picocolors";
-
-export async function renderLogo(): Promise<void> {
-  const logoText = figlet.textSync("LlamaChat", {
-    font: "ANSI Shadow",
-    horizontalLayout: "default",
-    verticalLayout: "default",
-    width: 100,
-    whitespaceBreak: true,
-  });
-
-  // Add some styling with picocolors
-  const styledLogo = pc.bold(pc.yellowBright(logoText));
-
-  // Add some padding/margin
-  console.log("\n");
-  console.log(styledLogo);
-  console.log(pc.gray("─".repeat(60)));
-  console.log("\n");
-}
-
-export async function consoleInput(): Promise<string> {
-  const rl = readline.createInterface({
-    input: process.stdin,
-    output: process.stdout,
-  });
-
-  const answer = await rl.question(pc.cyanBright("You✨:"));
-  rl.close();
-  return answer;
-}
-
-export async function retrievalAugmentedGeneration(
-  nodes: NodeWithScore[],
-  prompt: string,
-): Promise<string> {
-  let mainText: string = "";
-
-  for (const node of nodes) {
-    mainText += `\t{information: '${node.node.getContent(
-      MetadataMode.ALL,
-    )}', relevanceScore: '${node.score ?? "no score"}'}\n`;
-  }
-
-  const { text } = await generateText({
-    model: openai("gpt-4.1"),
-    prompt: `[\n${mainText}\n]\n\nBased on the information you are given and on the relevance score of that (where -1 means no score available), answer to this user prompt: '${prompt}'`,
-  });
-
-  return text;
-}

examples-ts/index/tsconfig.json

@@ -1,22 +0,0 @@
-{
-  "compilerOptions": {
-    "target": "ES2022",
-    "module": "ES2022",
-    "lib": ["ES2022"],
-    "outDir": "./dist",
-    "rootDir": "./src",
-    "strict": true,
-    "esModuleInterop": true,
-    "skipLibCheck": true,
-    "forceConsistentCasingInFileNames": true,
-    "declaration": true,
-    "declarationMap": true,
-    "sourceMap": true,
-    "types": ["node"],
-    "moduleResolution": "bundler",
-    "allowSyntheticDefaultImports": true,
-    "resolveJsonModule": true
-  },
-  "include": ["src/**/*"],
-  "exclude": ["node_modules", "dist"]
-}

examples-ts/parse/README.md

@@ -1,124 +0,0 @@
-# LlamaParse Demo
-
-A TypeScript demo application showcasing the power of **LlamaParse** - an intelligent document parsing service from [LlamaCloud](https://cloud.llamaindex.ai). This demo allows you to parse various document formats and generate AI-powered summaries using OpenAI's GPT models.
-
-## Table of Contents
-
-- [Features](#features)
-- [Prerequisites](#prerequisites)
-- [Installation](#installation)
-- [Usage](#usage)
-  - [Start the Demo](#start-the-demo)
-  - [Development Mode](#development-mode)
-  - [Build the Project](#build-the-project)
-  - [Code Quality](#code-quality)
-  - [Quick Commands Reference](#quick-commands-reference)
-- [How It Works](#how-it-works)
-- [API Dependencies](#api-dependencies)
-- [Troubleshooting](#troubleshooting)
-  - [Common Issues](#common-issues)
-- [License](#license)
-- [Contributing](#contributing)
-
-## Features
-
-- 📄 **Document Parsing**: Parse PDFs, Word docs, and other formats using LlamaParse
-- 🤖 **AI Summaries**: Generate intelligent summaries using OpenAI GPT-4
-- 🎨 **Beautiful CLI**: Styled console interface with colors and ASCII art
-- ⚡ **Fast Development**: Hot reload support with watch mode
-- 🛠️ **TypeScript**: Full TypeScript support with strict type checking
-
-## Prerequisites
-
-- Node.js (version 18 or higher)
-- pnpm package manager
-- OpenAI API key
-- LlamaCloud API key
-
-## Installation
-
-1. Clone the repository:
-
-```bash
-git clone https://github.com/run-llama/llama_cloud_services
-cd lama_cloud_services/examples-ts/parse/
-```
-
-2. Install dependencies:
-
-```bash
-pnpm install
-```
-
-3. Set up your environment variables:
-
-```bash
-# Add your API keys to your environment
-export OPENAI_API_KEY="your-openai-api-key"
-export LLAMA_CLOUD_API_KEY="your-llamacloud-api-key"
-```
-
-## Usage
-
-### Start the Demo
-
-```bash
-pnpm run start
-```
-
-The application will display a welcome screen and prompt you to enter the path to a document you'd like to process.
-
-### Development Mode
-
-For development with hot reload:
-
-```bash
-pnpm run dev
-```
-
-### Build the Project
-
-```bash
-pnpm run build
-```
-
-### Code Quality
-
-Format code:
-
-```bash
-pnpm run format
-```
-
-Lint code:
-
-```bash
-pnpm run lint
-```
-
-## How It Works
-
-1. **Document Input**: Enter the path to your document when prompted
-2. **Parsing**: LlamaParse processes the document and extracts structured content
-3. **AI Summary**: The extracted content is sent to OpenAI GPT-4 for summarization
-4. **Results**: View the AI-generated summary in your terminal
-
-## Troubleshooting
-
-### Common Issues
-
-1. **Module Resolution Errors**: Ensure you're using Node.js 18+ and have all dependencies installed
-2. **API Key Issues**: Verify your OpenAI and LlamaCloud API keys are correctly set
-3. **File Path Errors**: Use absolute paths or ensure relative paths are correct from the project root
-
-## License
-
-MIT License - see the [LICENSE](../../LICENSE) file for details.
-
-## Contributing
-
-1. Fork the repository
-2. Create a feature branch
-3. Make your changes
-4. Run `pnpm run format` and `pnpm run lint`
-5. Submit a pull request

examples-ts/parse/eslint.config.mts

@@ -1,15 +0,0 @@
-import js from "@eslint/js";
-import globals from "globals";
-import tseslint from "typescript-eslint";
-import { defineConfig } from "eslint/config";
-
-export default defineConfig([
-  {
-    files: ["**/*.{js,mjs,cjs,ts,mts,cts}"],
-    plugins: { js },
-    extends: ["js/recommended"],
-    languageOptions: { globals: globals.browser },
-  },
-  { files: ["**/*.js"], languageOptions: { sourceType: "script" } },
-  tseslint.configs.recommended,
-]);

examples-ts/parse/package.json

@@ -1,47 +0,0 @@
-{
-  "name": "llamaparse-demo",
-  "version": "0.1.0",
-  "description": "Demo for LlamaParse in TypeScript",
-  "type": "module",
-  "main": "index.js",
-  "scripts": {
-    "test": "echo \"There are no tests\"",
-    "start": "pnpm exec tsx src/index.ts",
-    "lint": "eslint ./src/",
-    "format": "prettier --write ./src/",
-    "build": "tsc",
-    "dev": "pnpm exec tsx --watch src/index.ts"
-  },
-  "keywords": [
-    "ai",
-    "ocr",
-    "parsing",
-    "intelligent-document-processing",
-    "pdf",
-    "llms"
-  ],
-  "author": "LlamaIndex",
-  "license": "MIT",
-  "packageManager": "pnpm@10.12.4",
-  "devDependencies": {
-    "@eslint/js": "^9.32.0",
-    "@types/figlet": "^1.7.0",
-    "@types/node": "^24.1.0",
-    "@typescript-eslint/eslint-plugin": "^8.38.0",
-    "@typescript-eslint/parser": "^8.38.0",
-    "eslint": "^9.32.0",
-    "globals": "^16.3.0",
-    "jiti": "^2.5.1",
-    "prettier": "^3.6.2",
-    "typescript": "^5.8.3",
-    "typescript-eslint": "^8.38.0"
-  },
-  "dependencies": {
-    "@ai-sdk/openai": "^1.3.23",
-    "ai": "^4.3.19",
-    "consola": "^3.4.2",
-    "figlet": "^1.8.2",
-    "llama-cloud-services": "link:../../ts/llama_cloud_services",
-    "picocolors": "^1.1.1"
-  }
-}

examples-ts/parse/src/index.ts

@@ -1,34 +0,0 @@
-import { LlamaParseReader } from "llama-cloud-services";
-import { logger } from "./logger";
-import pc from "picocolors";
-import { consoleInput, generateSummary, renderLogo } from "./utils";
-
-export async function main(): Promise<number> {
-  const reader = new LlamaParseReader({ resultType: "markdown" });
-  await renderLogo();
-  logger.log(
-    `Welcome to ${pc.bold(
-      pc.magentaBright("✨LlamaParse Demo✨"),
-    )}, our demo for ${pc.bold(pc.green("LlamaParse🦙"))}, a ${pc.bold(
-      pc.cyan("LlamaCloud☁️"),
-    )}  (https://cloud.llamaindex.ai) product!.\nType the path to the document you would like to process below👇\nIf you wish to exit, just type ${pc.bold(
-      pc.gray("quit"),
-    )}.\n`,
-  );
-  while (true) {
-    const userInput = await consoleInput();
-    if (userInput.toLowerCase() == "quit") {
-      break;
-    }
-    try {
-      const documents = await reader.loadData(userInput);
-      const summary = await generateSummary(documents); // Added await here
-      logger.log(`${pc.bold(pc.cyan("AI-generated summary✨"))}:\n${summary}`);
-    } catch (error) {
-      logger.error(`Error processing file: ${error}`);
-    }
-  }
-  return 0;
-}
-
-main().catch(console.error);

examples-ts/parse/src/logger.ts

@@ -1,8 +0,0 @@
-import { createConsola } from "consola";
-import type { ConsolaInstance } from "consola";
-
-export const logger: ConsolaInstance = createConsola({
-  formatOptions: {
-    date: false,
-  },
-});

examples-ts/parse/src/utils.ts

@@ -1,51 +0,0 @@
-import { generateText } from "ai";
-import { openai } from "@ai-sdk/openai";
-import { Document } from "llamaindex";
-import * as readline from "readline/promises";
-import figlet from "figlet";
-import pc from "picocolors";
-
-export async function renderLogo(): Promise<void> {
-  const logoText = figlet.textSync("LlamaParse Demo", {
-    font: "ANSI Shadow",
-    horizontalLayout: "default",
-    verticalLayout: "default",
-    width: 100,
-    whitespaceBreak: true,
-  });
-
-  // Add some styling with picocolors
-  const styledLogo = pc.bold(pc.magentaBright(logoText));
-
-  // Add some padding/margin
-  console.log("\n");
-  console.log(styledLogo);
-  console.log(pc.gray("─".repeat(60)));
-  console.log("\n");
-}
-
-export async function consoleInput(): Promise<string> {
-  const rl = readline.createInterface({
-    input: process.stdin,
-    output: process.stdout,
-  });
-
-  const answer = await rl.question("Path to your file: ");
-  rl.close();
-  return answer;
-}
-
-export async function generateSummary(documents: Document[]): Promise<string> {
-  let mainText: string = "";
-
-  for (const document of documents) {
-    mainText += `${document.text}\n\n---\n\n`;
-  }
-
-  const { text } = await generateText({
-    model: openai("gpt-4.1"),
-    prompt: `</chat>\n\t<text>${mainText}</text>\n\t<instructions>Could you please generate a summary of the given text?</instructions>\n</chat>`,
-  });
-
-  return text;
-}

examples-ts/parse/tsconfig.json

@@ -1,22 +0,0 @@
-{
-  "compilerOptions": {
-    "target": "ES2022",
-    "module": "ES2022",
-    "lib": ["ES2022"],
-    "outDir": "./dist",
-    "rootDir": "./src",
-    "strict": true,
-    "esModuleInterop": true,
-    "skipLibCheck": true,
-    "forceConsistentCasingInFileNames": true,
-    "declaration": true,
-    "declarationMap": true,
-    "sourceMap": true,
-    "types": ["node"],
-    "moduleResolution": "bundler",
-    "allowSyntheticDefaultImports": true,
-    "resolveJsonModule": true
-  },
-  "include": ["src/**/*"],
-  "exclude": ["node_modules", "dist"]
-}

examples/README.md

@@ -1,9 +0,0 @@
-# LlamaCloud Services Examples - Python
-
-In this folder you will find several python notebooks that contain examples regarding:
-
-- [LlamaParse](./parse/)
-- [LlamaExtract](./extract/)
-- [LlamaCloudIndex](./index/)
-
-Follow the instructions in each notebook to get started!

examples/batch/parse/.gitignore

@@ -1 +0,0 @@
-sample_files/

examples/batch/parse/batch_parse_directory.ipynb

@@ -1,807 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "cell-0",
-   "metadata": {},
-   "source": [
-    "# Batch Parse with LlamaCloud Directories\n",
-    "\n",
-    "This notebook demonstrates how to use LlamaCloud's batch processing API to parse multiple files in a directory. The workflow includes:\n",
-    "\n",
-    "1. **Creating a Directory** - Set up a directory to organize your files\n",
-    "2. **Uploading Files** - Upload multiple files to the directory\n",
-    "3. **Starting a Batch Parse Job** - Kick off batch processing on all files\n",
-    "4. **Monitoring Progress** - Check the status and view results\n",
-    "\n",
-    "This is useful when you need to parse many documents at once, as the batch API handles the orchestration and provides progress tracking."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-1",
-   "metadata": {},
-   "source": [
-    "## Setup and Installation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install llama-cloud python-dotenv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from dotenv import load_dotenv\n",
-    "import httpx\n",
-    "\n",
-    "# Load environment variables\n",
-    "load_dotenv()\n",
-    "\n",
-    "# Set your API key\n",
-    "LLAMA_CLOUD_API_KEY = os.environ.get(\"LLAMA_CLOUD_API_KEY\", \"llx-...\")\n",
-    "\n",
-    "# Optional: Set base URL (defaults to https://api.cloud.llamaindex.ai if not set)\n",
-    "LLAMA_CLOUD_BASE_URL = os.environ.get(\n",
-    "    \"LLAMA_CLOUD_BASE_URL\", \"https://api.cloud.llamaindex.ai\"\n",
-    ")\n",
-    "\n",
-    "# Optional: Set project_id if you have one, otherwise it will use your default project\n",
-    "PROJECT_ID = os.environ.get(\"LLAMA_CLOUD_PROJECT_ID\", None)\n",
-    "\n",
-    "print(\"✅ API key configured\")\n",
-    "print(f\"   Base URL: {LLAMA_CLOUD_BASE_URL}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-4",
-   "metadata": {},
-   "source": [
-    "## Setup HTTP Client\n",
-    "\n",
-    "Since the current version of the llama-cloud SDK has some issues with the beta endpoints, we'll use direct HTTP requests with httpx for reliability."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create HTTP client with authentication\n",
-    "headers = {\n",
-    "    \"Authorization\": f\"Bearer {LLAMA_CLOUD_API_KEY}\",\n",
-    "}\n",
-    "\n",
-    "print(\"✅ HTTP client configured\")\n",
-    "print(f\"   Using base URL: {LLAMA_CLOUD_BASE_URL}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-6",
-   "metadata": {},
-   "source": [
-    "## Step 1: Create a Directory\n",
-    "\n",
-    "First, we'll create a directory to organize our files. Directories help you group related files together for batch processing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "\n",
-    "# Create a directory with a timestamp in the name\n",
-    "timestamp = datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
-    "directory_name = f\"batch-parse-demo-{timestamp}\"\n",
-    "\n",
-    "# Create directory using HTTP request\n",
-    "response = httpx.post(\n",
-    "    f\"{LLAMA_CLOUD_BASE_URL}/api/v1/beta/directories\",\n",
-    "    headers=headers,\n",
-    "    params={\"project_id\": PROJECT_ID},\n",
-    "    json={\n",
-    "        \"name\": directory_name,\n",
-    "        \"description\": \"Demo directory for batch parse example\",\n",
-    "    },\n",
-    "    timeout=60.0,\n",
-    ")\n",
-    "\n",
-    "if response.status_code in [200, 201]:\n",
-    "    directory = response.json()\n",
-    "    directory_id = directory[\"id\"]\n",
-    "    project_id = directory[\"project_id\"]\n",
-    "\n",
-    "    print(f\"✅ Created directory: {directory['name']}\")\n",
-    "    print(f\"   Directory ID: {directory_id}\")\n",
-    "    print(f\"   Project ID: {project_id}\")\n",
-    "else:\n",
-    "    raise Exception(\n",
-    "        f\"Failed to create directory: {response.status_code} - {response.text}\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-8",
-   "metadata": {},
-   "source": [
-    "## Step 2: Upload Files to the Directory\n",
-    "\n",
-    "Now we'll upload some files to our directory. For this demo, we'll download some sample PDFs and upload them.\n",
-    "\n",
-    "You can replace these with your own files."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a directory for sample files\n",
-    "import requests\n",
-    "\n",
-    "os.makedirs(\"sample_files\", exist_ok=True)\n",
-    "\n",
-    "# Sample documents to download\n",
-    "sample_docs = {\n",
-    "    \"attention.pdf\": \"https://arxiv.org/pdf/1706.03762.pdf\",\n",
-    "    \"bert.pdf\": \"https://arxiv.org/pdf/1810.04805.pdf\",\n",
-    "}\n",
-    "\n",
-    "# Download sample documents\n",
-    "for filename, url in sample_docs.items():\n",
-    "    filepath = f\"sample_files/{filename}\"\n",
-    "    if not os.path.exists(filepath):\n",
-    "        print(f\"📥 Downloading {filename}...\")\n",
-    "        response = requests.get(url)\n",
-    "        if response.status_code == 200:\n",
-    "            with open(filepath, \"wb\") as f:\n",
-    "                f.write(response.content)\n",
-    "            print(f\"   ✅ Downloaded {filename}\")\n",
-    "        else:\n",
-    "            print(f\"   ❌ Failed to download {filename}\")\n",
-    "    else:\n",
-    "        print(f\"📁 {filename} already exists\")\n",
-    "\n",
-    "print(\"\\n✅ Sample files ready!\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-10",
-   "metadata": {},
-   "source": [
-    "### Upload Files to Directory\n",
-    "\n",
-    "Now let's upload the files to our directory using the `upload_file_to_directory` endpoint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-11",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "uploaded_files = []\n",
-    "\n",
-    "# Workaround: Use direct HTTP requests instead of SDK due to SDK bug\n",
-    "import httpx\n",
-    "\n",
-    "for filename in os.listdir(\"sample_files\"):\n",
-    "    if filename.endswith(\".pdf\"):\n",
-    "        filepath = f\"sample_files/{filename}\"\n",
-    "\n",
-    "        print(f\"📤 Uploading {filename}...\")\n",
-    "\n",
-    "        # Upload file using direct HTTP request (SDK has a bug with file uploads)\n",
-    "        with open(filepath, \"rb\") as f:\n",
-    "            # Prepare the multipart form data correctly\n",
-    "            files = {\"upload_file\": (filename, f, \"application/pdf\")}\n",
-    "\n",
-    "            # Make the request directly\n",
-    "            response = httpx.post(\n",
-    "                f\"{LLAMA_CLOUD_BASE_URL}/api/v1/beta/directories/{directory_id}/files/upload\",\n",
-    "                params={\"project_id\": project_id},\n",
-    "                files=files,\n",
-    "                headers={\"Authorization\": f\"Bearer {LLAMA_CLOUD_API_KEY}\"},\n",
-    "                timeout=60.0,\n",
-    "            )\n",
-    "\n",
-    "            if response.status_code in [200, 201]:\n",
-    "                directory_file = response.json()\n",
-    "                uploaded_files.append(directory_file)\n",
-    "                print(f\"   ✅ Uploaded: {directory_file.get('display_name')}\")\n",
-    "                print(f\"      File ID: {directory_file.get('id')}\")\n",
-    "            else:\n",
-    "                print(f\"   ❌ Upload failed: {response.status_code}\")\n",
-    "                print(f\"      Error: {response.text[:200]}\")\n",
-    "\n",
-    "print(f\"\\n✅ Uploaded {len(uploaded_files)} files to directory\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-12",
-   "metadata": {},
-   "source": [
-    "## Step 3: Create a Batch Parse Job\n",
-    "\n",
-    "Now that we have files in our directory, let's create a batch parse job to process them all at once.\n",
-    "\n",
-    "The batch processing API uses the same configuration as LlamaParse."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-13",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure the parse job\n",
-    "# This configuration will apply to all files in the directory\n",
-    "job_config = {\n",
-    "    \"job_name\": \"parse_raw_file_job\",  # Must match the JobNames enum value\n",
-    "    \"partitions\": {},\n",
-    "    \"parameters\": {\n",
-    "        \"type\": \"parse\",\n",
-    "        \"lang\": \"en\",\n",
-    "        \"fast_mode\": True,\n",
-    "    },\n",
-    "}\n",
-    "\n",
-    "print(\"✅ Job configuration created\")\n",
-    "print(f\"   Language: {job_config['parameters']['lang']}\")\n",
-    "print(f\"   Fast mode: {job_config['parameters']['fast_mode']}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-14",
-   "metadata": {},
-   "source": [
-    "### Submit the Batch Job\n",
-    "\n",
-    "Now let's submit the batch job to process all files in the directory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-15",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f\"🚀 Submitting batch parse job for directory: {directory_id}\")\n",
-    "print(f\"   Processing {len(uploaded_files)} files...\\n\")\n",
-    "\n",
-    "# Submit batch job using HTTP request\n",
-    "response = httpx.post(\n",
-    "    f\"{LLAMA_CLOUD_BASE_URL}/api/v1/beta/batch-processing\",\n",
-    "    headers=headers,\n",
-    "    params={\"project_id\": project_id},\n",
-    "    json={\n",
-    "        \"directory_id\": directory_id,\n",
-    "        \"job_config\": job_config,\n",
-    "        \"page_size\": 100,  # Number of files to fetch per batch\n",
-    "        \"continue_as_new_threshold\": 10,  # Workflow continuation threshold\n",
-    "    },\n",
-    "    timeout=60.0,\n",
-    ")\n",
-    "\n",
-    "if response.status_code in [200, 201]:\n",
-    "    batch_job = response.json()\n",
-    "    batch_job_id = batch_job[\"id\"]\n",
-    "\n",
-    "    print(\"✅ Batch job submitted successfully!\")\n",
-    "    print(f\"   Batch Job ID: {batch_job_id}\")\n",
-    "    print(f\"   Workflow ID: {batch_job.get('workflow_id')}\")\n",
-    "    print(f\"   Status: {batch_job.get('status')}\")\n",
-    "    print(f\"   Total Items: {batch_job.get('total_items')}\")\n",
-    "else:\n",
-    "    raise Exception(\n",
-    "        f\"Failed to create batch job: {response.status_code} - {response.text}\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-16",
-   "metadata": {},
-   "source": [
-    "## Step 4: Monitor Job Progress\n",
-    "\n",
-    "Now let's monitor the batch job progress. We'll poll the status endpoint to see how the job is progressing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-17",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "\n",
-    "\n",
-    "def print_job_status(status_data):\n",
-    "    \"\"\"Helper function to print job status in a readable format.\"\"\"\n",
-    "    job = status_data[\"job\"]\n",
-    "    progress_pct = status_data[\"progress_percentage\"]\n",
-    "\n",
-    "    print(f\"\\n{'='*60}\")\n",
-    "    print(f\"Job Status: {job['status']}\")\n",
-    "    print(f\"{'='*60}\")\n",
-    "    print(f\"Total Items: {job['total_items']}\")\n",
-    "    print(f\"Completed: {job['processed_items']}\")\n",
-    "    print(f\"Failed: {job['failed_items']}\")\n",
-    "    print(f\"Skipped: {job['skipped_items']}\")\n",
-    "    print(f\"Progress: {progress_pct:.1f}%\")\n",
-    "\n",
-    "    if job.get(\"completed_at\"):\n",
-    "        print(f\"Completed At: {job['completed_at']}\")\n",
-    "    elif job.get(\"started_at\"):\n",
-    "        print(f\"Started At: {job['started_at']}\")\n",
-    "\n",
-    "    print(f\"{'='*60}\")\n",
-    "\n",
-    "\n",
-    "# Poll for status updates\n",
-    "print(\"🔄 Monitoring batch job progress...\")\n",
-    "print(\n",
-    "    \"Note: It may take a few seconds for the workflow to initialize and count files.\\n\"\n",
-    ")\n",
-    "\n",
-    "max_polls = 60  # Maximum number of status checks (increased for longer jobs)\n",
-    "poll_interval = 10  # Seconds between checks\n",
-    "\n",
-    "for i in range(max_polls):\n",
-    "    response = httpx.get(\n",
-    "        f\"{LLAMA_CLOUD_BASE_URL}/api/v1/beta/batch-processing/{batch_job_id}\",\n",
-    "        headers=headers,\n",
-    "        params={\"project_id\": project_id},\n",
-    "        timeout=60.0,\n",
-    "    )\n",
-    "\n",
-    "    if response.status_code == 200:\n",
-    "        status_data = response.json()\n",
-    "        print_job_status(status_data)\n",
-    "\n",
-    "        # Check if job is complete\n",
-    "        job_status = status_data[\"job\"][\"status\"]\n",
-    "        if job_status in [\"completed\", \"failed\", \"cancelled\"]:\n",
-    "            print(f\"\\n✅ Job finished with status: {job_status}\")\n",
-    "            break\n",
-    "\n",
-    "        if i < max_polls - 1:\n",
-    "            print(f\"\\n⏳ Waiting {poll_interval} seconds before next check...\")\n",
-    "            time.sleep(poll_interval)\n",
-    "    else:\n",
-    "        print(f\"Error getting status: {response.status_code} - {response.text}\")\n",
-    "        break\n",
-    "else:\n",
-    "    print(f\"\\n⚠️  Reached maximum polling attempts. Job may still be running.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-18",
-   "metadata": {},
-   "source": [
-    "## Step 5: View Job Items\n",
-    "\n",
-    "Let's look at the individual items in the batch job to see which files were processed successfully."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-19",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get all items in the batch job\n",
-    "response = httpx.get(\n",
-    "    f\"{LLAMA_CLOUD_BASE_URL}/api/v1/beta/batch-processing/{batch_job_id}/items\",\n",
-    "    headers=headers,\n",
-    "    params={\"project_id\": project_id, \"limit\": 100},\n",
-    "    timeout=60.0,\n",
-    ")\n",
-    "\n",
-    "if response.status_code == 200:\n",
-    "    items_response = response.json()\n",
-    "\n",
-    "    print(f\"\\n📋 Batch Job Items ({items_response['total_size']} total)\")\n",
-    "    print(f\"{'='*80}\\n\")\n",
-    "\n",
-    "    for item in items_response[\"items\"]:\n",
-    "        status_emoji = (\n",
-    "            \"✅\"\n",
-    "            if item[\"status\"] == \"completed\"\n",
-    "            else \"❌\"\n",
-    "            if item[\"status\"] == \"failed\"\n",
-    "            else \"⏳\"\n",
-    "        )\n",
-    "        print(f\"{status_emoji} {item['item_name']}\")\n",
-    "        print(f\"   Status: {item['status']}\")\n",
-    "        print(f\"   Item ID: {item['item_id']}\")\n",
-    "\n",
-    "        if item.get(\"error_message\"):\n",
-    "            print(f\"   Error: {item['error_message']}\")\n",
-    "\n",
-    "        print()\n",
-    "else:\n",
-    "    print(f\"Error listing items: {response.status_code} - {response.text}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-20",
-   "metadata": {},
-   "source": [
-    "## Step 6: Retrieve Processing Results\n",
-    "\n",
-    "For each completed file, we can retrieve the processing results to see where the parsed output is stored."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-21",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get processing results for a specific item\n",
-    "if items_response[\"items\"]:\n",
-    "    first_item = items_response[\"items\"][0]\n",
-    "\n",
-    "    print(f\"\\n🔍 Processing results for: {first_item['item_name']}\")\n",
-    "    print(f\"{'='*80}\\n\")\n",
-    "\n",
-    "    response = httpx.get(\n",
-    "        f\"{LLAMA_CLOUD_BASE_URL}/api/v1/beta/batch-processing/items/{first_item['item_id']}/processing-results\",\n",
-    "        headers=headers,\n",
-    "        params={\"project_id\": project_id},\n",
-    "        timeout=60.0,\n",
-    "    )\n",
-    "\n",
-    "    if response.status_code == 200:\n",
-    "        results = response.json()\n",
-    "\n",
-    "        print(f\"Item: {results['item_name']}\")\n",
-    "        print(f\"Total processing runs: {len(results['processing_results'])}\\n\")\n",
-    "\n",
-    "        for i, result in enumerate(results[\"processing_results\"], 1):\n",
-    "            print(f\"Run {i}:\")\n",
-    "            print(f\"  Job Type: {result['job_type']}\")\n",
-    "            print(f\"  Processed At: {result['processed_at']}\")\n",
-    "            print(f\"  Parameters Hash: {result['parameters_hash']}\")\n",
-    "\n",
-    "            if result.get(\"output_s3_path\"):\n",
-    "                print(f\"  Output S3 Path: {result['output_s3_path']}\")\n",
-    "\n",
-    "            if result.get(\"output_metadata\"):\n",
-    "                print(f\"  Output Metadata: {result['output_metadata']}\")\n",
-    "\n",
-    "            print()\n",
-    "    else:\n",
-    "        print(f\"Error getting results: {response.status_code} - {response.text}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-22",
-   "metadata": {},
-   "source": [
-    "## Optional: List All Batch Jobs\n",
-    "\n",
-    "You can also list all batch jobs in your project to see the history of batch processing operations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-23",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# List all parse jobs in the project\n",
-    "response = httpx.get(\n",
-    "    f\"{LLAMA_CLOUD_BASE_URL}/api/v1/beta/batch-processing\",\n",
-    "    headers=headers,\n",
-    "    params={\"project_id\": project_id, \"job_type\": \"parse\", \"limit\": 10},\n",
-    "    timeout=60.0,\n",
-    ")\n",
-    "\n",
-    "if response.status_code == 200:\n",
-    "    jobs_response = response.json()\n",
-    "\n",
-    "    print(f\"\\n📊 Recent Batch Parse Jobs ({jobs_response['total_size']} total)\")\n",
-    "    print(f\"{'='*80}\\n\")\n",
-    "\n",
-    "    for job in jobs_response[\"items\"]:\n",
-    "        status_emoji = (\n",
-    "            \"✅\"\n",
-    "            if job[\"status\"] == \"completed\"\n",
-    "            else \"❌\"\n",
-    "            if job[\"status\"] == \"failed\"\n",
-    "            else \"⏳\"\n",
-    "        )\n",
-    "        print(f\"{status_emoji} Job ID: {job['id']}\")\n",
-    "        print(f\"   Status: {job['status']}\")\n",
-    "        print(f\"   Directory: {job['directory_id']}\")\n",
-    "        print(f\"   Total Items: {job['total_items']}\")\n",
-    "        print(f\"   Completed: {job['processed_items']}\")\n",
-    "        print(f\"   Created: {job['created_at']}\")\n",
-    "        print()\n",
-    "else:\n",
-    "    print(f\"Error listing jobs: {response.status_code} - {response.text}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "uug7591rkq",
-   "metadata": {},
-   "source": [
-    "## Step 7: Retrieve Parsed Text Results\n",
-    "\n",
-    "Once the batch job is complete, each BatchJobItem will have a `job_id` field that maps to a parse job ID. We can use this ID with the standard parse client methods to fetch the actual parsed text results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "vpp0vxtc0y",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get all completed items and their job IDs\n",
-    "completed_items = [\n",
-    "    item for item in items_response[\"items\"] if item[\"status\"] == \"completed\"\n",
-    "]\n",
-    "\n",
-    "print(f\"📄 Found {len(completed_items)} completed items\\n\")\n",
-    "print(f\"{'='*80}\\n\")\n",
-    "\n",
-    "# Display the job_id for each completed item\n",
-    "for item in completed_items:\n",
-    "    print(f\"📝 {item['item_name']}\")\n",
-    "    print(f\"   Item ID: {item['item_id']}\")\n",
-    "    print(f\"   Parse Job ID: {item['job_id']}\")\n",
-    "    print()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4gck6hwpnl6",
-   "metadata": {},
-   "source": [
-    "### Fetch Parsed Text for a Specific Document\n",
-    "\n",
-    "Now let's use the `job_id` to retrieve the actual parsed text content using the parse client methods."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "g191kvgxxvk",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the parsed text for the first completed item\n",
-    "if completed_items:\n",
-    "    first_completed = completed_items[0]\n",
-    "\n",
-    "    print(f\"📖 Retrieving parsed text for: {first_completed['item_name']}\")\n",
-    "    print(f\"   Using Parse Job ID: {first_completed['job_id']}\\n\")\n",
-    "    print(f\"{'='*80}\\n\")\n",
-    "\n",
-    "    # Use the job_id to fetch the parse result\n",
-    "    response = httpx.get(\n",
-    "        f\"{LLAMA_CLOUD_BASE_URL}/api/v1/parsing/job/{first_completed['job_id']}/result/text\",\n",
-    "        headers=headers,\n",
-    "        params={\"project_id\": project_id},\n",
-    "        timeout=60.0,\n",
-    "    )\n",
-    "\n",
-    "    if response.status_code == 200:\n",
-    "        parse_result = response.text\n",
-    "\n",
-    "        print(f\"✅ Retrieved parsed text ({len(parse_result)} characters)\\n\")\n",
-    "\n",
-    "        # Display first 1000 characters as a preview\n",
-    "        print(\"Preview (first 1000 characters):\")\n",
-    "        print(\"-\" * 80)\n",
-    "        print(parse_result[:1000])\n",
-    "        print(\"-\" * 80)\n",
-    "\n",
-    "        if len(parse_result) > 1000:\n",
-    "            print(f\"\\n... and {len(parse_result) - 1000} more characters\")\n",
-    "    else:\n",
-    "        print(\n",
-    "            f\"Error retrieving parse result: {response.status_code} - {response.text}\"\n",
-    "        )\n",
-    "else:\n",
-    "    print(\"⚠️  No completed items found to retrieve results from\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2olccb4l8fj",
-   "metadata": {},
-   "source": [
-    "### Retrieve Parsed Results in Other Formats\n",
-    "\n",
-    "You can also retrieve the parsed results in JSON or Markdown format using different client methods."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "lcqsfxiw0sr",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if completed_items:\n",
-    "    first_completed = completed_items[0]\n",
-    "\n",
-    "    print(\n",
-    "        f\"📋 Retrieving parse results in different formats for: {first_completed['item_name']}\\n\"\n",
-    "    )\n",
-    "\n",
-    "    # Get as JSON (includes structured data with pages, images, etc.)\n",
-    "    print(\"1️⃣ Retrieving as JSON...\")\n",
-    "    response = httpx.get(\n",
-    "        f\"{LLAMA_CLOUD_BASE_URL}/api/v1/parsing/job/{first_completed['job_id']}/result/json\",\n",
-    "        headers=headers,\n",
-    "        params={\"project_id\": project_id},\n",
-    "        timeout=60.0,\n",
-    "    )\n",
-    "\n",
-    "    if response.status_code == 200:\n",
-    "        json_result = response.json()\n",
-    "        print(f\"   ✅ JSON result with {len(json_result['pages'])} pages\")\n",
-    "        print(f\"      Keys: {list(json_result.keys())}\\n\")\n",
-    "    else:\n",
-    "        print(f\"   Error: {response.status_code}\\n\")\n",
-    "\n",
-    "    # Get as Markdown\n",
-    "    print(\"2️⃣ Retrieving as Markdown...\")\n",
-    "    response = httpx.get(\n",
-    "        f\"{LLAMA_CLOUD_BASE_URL}/api/v1/parsing/job/{first_completed['job_id']}/result/markdown\",\n",
-    "        headers=headers,\n",
-    "        params={\"project_id\": project_id},\n",
-    "        timeout=60.0,\n",
-    "    )\n",
-    "\n",
-    "    if response.status_code == 200:\n",
-    "        markdown_result = response.text\n",
-    "        print(f\"   ✅ Markdown result ({len(markdown_result)} characters)\\n\")\n",
-    "\n",
-    "        # Display markdown preview\n",
-    "        print(\"Markdown Preview (first 500 characters):\")\n",
-    "        print(\"-\" * 80)\n",
-    "        print(markdown_result[:500])\n",
-    "        print(\"-\" * 80)\n",
-    "\n",
-    "        if len(markdown_result) > 500:\n",
-    "            print(f\"\\n... and {len(markdown_result) - 500} more characters\")\n",
-    "    else:\n",
-    "        print(f\"   Error: {response.status_code}\")\n",
-    "else:\n",
-    "    print(\"⚠️  No completed items found to retrieve results from\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "lr61wqkfq3",
-   "metadata": {},
-   "source": [
-    "### Batch Process All Parsed Results\n",
-    "\n",
-    "You can also loop through all completed items to retrieve and process all the parsed results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "kltydf9xzkl",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Process all completed items\n",
-    "print(f\"🔄 Processing all {len(completed_items)} completed items...\\n\")\n",
-    "print(f\"{'='*80}\\n\")\n",
-    "\n",
-    "all_results = {}\n",
-    "\n",
-    "for item in completed_items:\n",
-    "    print(f\"📄 Processing: {item['item_name']}\")\n",
-    "    print(f\"   Parse Job ID: {item['job_id']}\")\n",
-    "\n",
-    "    try:\n",
-    "        # Retrieve the parsed text for this item\n",
-    "        response = httpx.get(\n",
-    "            f\"{LLAMA_CLOUD_BASE_URL}/api/v1/parsing/job/{item['job_id']}/result/text\",\n",
-    "            headers=headers,\n",
-    "            params={\"project_id\": project_id},\n",
-    "            timeout=60.0,\n",
-    "        )\n",
-    "\n",
-    "        if response.status_code == 200:\n",
-    "            parsed_text = response.text\n",
-    "\n",
-    "            all_results[item[\"item_name\"]] = {\n",
-    "                \"job_id\": item[\"job_id\"],\n",
-    "                \"text\": parsed_text,\n",
-    "                \"length\": len(parsed_text),\n",
-    "            }\n",
-    "\n",
-    "            print(f\"   ✅ Retrieved {len(parsed_text)} characters\")\n",
-    "        else:\n",
-    "            all_results[item[\"item_name\"]] = {\n",
-    "                \"job_id\": item[\"job_id\"],\n",
-    "                \"error\": f\"HTTP {response.status_code}\",\n",
-    "            }\n",
-    "            print(f\"   ❌ Error: HTTP {response.status_code}\")\n",
-    "\n",
-    "    except Exception as e:\n",
-    "        print(f\"   ❌ Error: {str(e)}\")\n",
-    "        all_results[item[\"item_name\"]] = {\"job_id\": item[\"job_id\"], \"error\": str(e)}\n",
-    "\n",
-    "    print()\n",
-    "\n",
-    "print(f\"{'='*80}\")\n",
-    "print(f\"\\n✅ Processed {len(all_results)} items\")\n",
-    "print(f\"\\nSummary:\")\n",
-    "for name, result in all_results.items():\n",
-    "    if \"error\" in result:\n",
-    "        print(f\"  ❌ {name}: Error - {result['error']}\")\n",
-    "    else:\n",
-    "        print(f\"  ✅ {name}: {result['length']:,} characters\")"
-   ]
-  }
- ],
- "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
-}

examples/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%

examples/extract/data/insider_transactions/.gitignore

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

examples/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
-}

examples/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
-}

examples/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\"])"
-   ]
-  }
- ],
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