fix: skip examples for codespell

.github/workflows/build_package_py.yml

@@ -6,11 +6,8 @@ on:
   push:
     branches:
       - main
-    paths:
-      - "py/**"
   pull_request:
-    paths:
-      - "py/**"
+
 env:
   UV_VERSION: "0.7.20"
 

.github/workflows/build_package_ts.yml

@@ -1,13 +1,5 @@
 name: Build Package - TypeScript
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - "ts/**"
-  pull_request:
-    paths:
-      - "ts/**"
+on: [pull_request]
 
 jobs:
   pre_release:

.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@v4
-        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/lint_ts.yml

@@ -4,11 +4,9 @@ on:
   push:
     branches:
       - main
-    paths:
-      - "ts/**"
   pull_request:
-    paths:
-      - "ts/**"
+    branches:
+      - main
 
 env:
   TURBO_TOKEN: ${{ secrets.TURBO_TOKEN }}
@@ -28,6 +26,7 @@ jobs:
         with:
           node-version-file: "ts/llama_cloud_services/.nvmrc"
       - name: Install dependencies
+        working-directory: ts/llama_cloud_services/
         run: pnpm install --no-frozen-lockfile
       - name: Run lint
         working-directory: ts/llama_cloud_services/

.github/workflows/release_ts.yml

@@ -22,12 +22,9 @@ jobs:
           node-version-file: "ts/llama_cloud_services/.nvmrc"
 
       - name: Install dependencies
+        working-directory: ts/llama_cloud_services
         run: pnpm install --no-frozen-lockfile
 
-      - name: Run Build
-        working-directory: ts/llama_cloud_services/
-        run: pnpm build
-
       - name: Build tarball
         run: |
           pnpm pack

.github/workflows/test_e2e_py.yml

@@ -1,38 +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
-    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@v4
-        with:
-          fetch-depth: 0
-      - name: Install uv
-        uses: astral-sh/setup-uv@v6
-        with:
-          version: ${{ env.UV_VERSION }}
-
-      - name: Set up Python
-        run: uv python install ${{ matrix.python-version }} && uv python pin ${{ matrix.python-version }}
-
-      - name: Run Tests
-        working-directory: py
-        run: uv run pytest unit_tests/ tests/ -v
-
-      - name: Remove virtual environment
-        working-directory: py
-        run: rm -rf .venv/

.github/workflows/test_py.yml

@@ -4,14 +4,11 @@ on:
   push:
     branches:
       - main
-    paths:
-      - "py/**"
   pull_request:
-    paths:
-      - "py/**"
 
 env:
   UV_VERSION: "0.7.20"
+  LLAMA_CLOUD_API_KEY: ${{ secrets.LLAMA_CLOUD_API_KEY }}
 
 jobs:
   test:
@@ -35,7 +32,7 @@ jobs:
 
       - name: Run Tests
         working-directory: py
-        run: uv run pytest unit_tests/ -v
+        run: uv run -- pytest tests/**/test_*.py
 
       - name: Remove virtual environment
         working-directory: py

.github/workflows/test_ts.yml

@@ -4,11 +4,9 @@ on:
   push:
     branches:
       - main
-    paths:
-      - "ts/**"
   pull_request:
-    paths:
-      - "ts/**"
+    branches:
+      - main
 
 env:
   TURBO_TOKEN: ${{ secrets.TURBO_TOKEN }}
@@ -17,8 +15,7 @@ env:
   LLAMA_CLOUD_API_KEY: ${{ secrets.LLAMA_CLOUD_API_KEY }}
 
 jobs:
-  test:
-    name: Test - TypeScript
+  lint:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
@@ -30,13 +27,8 @@ jobs:
         with:
           node-version-file: "ts/llama_cloud_services/.nvmrc"
       - name: Install dependencies
+        working-directory: ts/llama_cloud_services/
         run: pnpm install --no-frozen-lockfile
-      - name: Run Build
+      - name: Run tests
         working-directory: ts/llama_cloud_services/
-        run: pnpm 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
+        run: pnpm test --run

.pre-commit-config.yaml

@@ -15,7 +15,6 @@ 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
 
@@ -34,7 +33,7 @@ repos:
     rev: v1.0.1
     hooks:
       - id: mypy
-        exclude: ^py/tests|^py/unit_tests
+        exclude: ^py/tests/
         additional_dependencies:
           [
             "types-requests",
@@ -64,13 +63,13 @@ repos:
     rev: v3.0.3
     hooks:
       - id: prettier
-        exclude: ^(uv.lock|ts/llama_cloud_services/pnpm-lock.yaml|ts/e2e-tests)
+        exclude: uv.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: ^(uv.lock|examples|ts)
         args:
           [
             "--ignore-words-list",
@@ -87,4 +86,4 @@ repos:
       - id: toml-sort-fix
         exclude: ".*uv.lock"
 
-exclude: ^(.github/ISSUE_TEMPLATE|ts/llama_cloud_services/src/client|pnpm-lock.yaml)
+exclude: .github/ISSUE_TEMPLATE

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/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/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/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/README.md

@@ -1,9 +1,11 @@
-# LlamaCloud Services Examples - Python
+# LlamaCloud Services Examples
 
-In this folder you will find several python notebooks that contain examples regarding:
+In this folder you will find several python notebooks and two end-to-end typescript applications that contain examples regarding:
 
-- [LlamaParse](./parse/)
-- [LlamaExtract](./extract/)
-- [LlamaReport](./report/)
+- [LlamaParse - Python](./parse/)
+- [LlamaParse - TypeScript](./parse-ts/)
+- [LlamaExtract - Python](./extract/)
+- [LlamaReport - Python](./report/)
+- [LlamaCloud Index - TypeScript](./index-ts/)
 
-Follow the instructions in each notebook to get started!
+Follow the instructions of each notebook/application to get started!

examples/index-ts/README.md

@@ -40,8 +40,8 @@ A TypeScript demo application showcasing the power of **LlamaCloud Index** - a f
 1. Clone the repository:
 
 ```bash
-git clone https://github.com/run-llama/llama_cloud_services
-cd lama_cloud_services/examples-ts/index/
+git clone <repository-url>
+cd llamaparse-demo
 ```
 
 2. Install dependencies:
@@ -120,12 +120,12 @@ pnpm run lint
 
 ## License
 
-MIT License - see the [LICENSE](../../LICENSE) file for details.
+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`
+4. Run `pnpm format` and `pnpm lint`
 5. Submit a pull request

examples/index-ts/package.json

@@ -42,7 +42,7 @@
     "consola": "^3.4.2",
     "dotenv": "^17.2.1",
     "figlet": "^1.8.2",
-    "llama-cloud-services": "link:../../ts/llama_cloud_services",
+    "llama-cloud-services": "link:../../../ts/llama_cloud_services",
     "picocolors": "^1.1.1"
   }
 }

examples/parse-ts/README.md

@@ -40,8 +40,8 @@ A TypeScript demo application showcasing the power of **LlamaParse** - an intell
 1. Clone the repository:
 
 ```bash
-git clone https://github.com/run-llama/llama_cloud_services
-cd lama_cloud_services/examples-ts/parse/
+git clone <repository-url>
+cd llamaparse-demo
 ```
 
 2. Install dependencies:
@@ -113,12 +113,12 @@ pnpm run lint
 
 ## License
 
-MIT License - see the [LICENSE](../../LICENSE) file for details.
+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`
+4. Run `pnpm format` and `pnpm lint`
 5. Submit a pull request

examples/parse-ts/package.json

@@ -41,7 +41,7 @@
     "ai": "^4.3.19",
     "consola": "^3.4.2",
     "figlet": "^1.8.2",
-    "llama-cloud-services": "link:../../ts/llama_cloud_services",
+    "llama-cloud-services": "link:../../../ts/llama_cloud_services",
     "picocolors": "^1.1.1"
   }
 }

examples/parse/parsing_modes/demo_layout_agent_mode_visual_citations.ipynb

@@ -147,7 +147,7 @@
     "documents = []\n",
     "\n",
     "for i, page in enumerate(pages):\n",
-    "    # loop through items of the page\n",
+    "    # loop trough items of the page\n",
     "    for item in page[\"items\"]:\n",
     "        document = Document(\n",
     "            text=item[\"md\"], extra_info={\"bbox\": item[\"bBox\"], \"page\": i}\n",

extract.md

@@ -2,40 +2,14 @@
 
 LlamaExtract provides a simple API for extracting structured data from unstructured documents like PDFs, text files and images.
 
-## Table of Contents
-
-- [Quick Start](#quick-start)
-  - [Supported File Types](#supported-file-types)
-  - [Different Input Types](#different-input-types)
-  - [Async Extraction](#async-extraction)
-- [Core Concepts](#core-concepts)
-- [Defining Schemas](#defining-schemas)
-  - [Using Pydantic (Recommended)](#using-pydantic-recommended)
-  - [Using JSON Schema](#using-json-schema)
-  - [Important restrictions on JSON/Pydantic Schema](#important-restrictions-on-jsonpydantic-schema)
-- [Extraction Configuration](#extraction-configuration)
-  - [Configuration Options](#configuration-options)
-- [Extraction Agents (Advanced)](#extraction-agents-advanced)
-  - [Creating Agents](#creating-agents)
-  - [Agent Batch Processing](#agent-batch-processing)
-  - [Updating Agent Schemas](#updating-agent-schemas)
-  - [Managing Agents](#managing-agents)
-  - [When to Use Agents vs Direct Extraction](#when-to-use-agents-vs-direct-extraction)
-- [Installation](#installation)
-- [Tips & Best Practices](#tips--best-practices)
-- [Additional Resources](#additional-resources)
-
 ## Quick Start
 
-The simplest way to get started is to use the stateless API with the extraction configuration and the file/text to extract from:
-
 ```python
 from llama_cloud_services import LlamaExtract
-from llama_cloud import ExtractConfig, ExtractMode
 from pydantic import BaseModel, Field
 
 # Initialize client
-extractor = LlamaExtract(api_key="YOUR_API_KEY")
+extractor = LlamaExtract()
 
 
 # Define schema using Pydantic
@@ -45,97 +19,29 @@ class Resume(BaseModel):
     skills: list[str] = Field(description="Technical skills and technologies")
 
 
-# Configure extraction settings
-config = ExtractConfig(extraction_mode=ExtractMode.FAST)
+# Create extraction agent
+agent = extractor.create_agent(name="resume-parser", data_schema=Resume)
 
-# Extract data directly from document - no agent needed!
-result = extractor.extract(Resume, config, "resume.pdf")
+# Extract data from document
+result = agent.extract("resume.pdf")
 print(result.data)
 ```
 
-### Supported File Types
-
-LlamaExtract supports the following file formats:
-
-- **Documents**: PDF (.pdf), Word (.docx)
-- **Text files**: Plain text (.txt), CSV (.csv), JSON (.json), HTML (.html, .htm), Markdown (.md)
-- **Images**: PNG (.png), JPEG (.jpg, .jpeg)
-
-### Different Input Types
-
-```python
-# From file path (string or Path)
-result = extractor.extract(Resume, config, "resume.pdf")
-
-# From file handle
-with open("resume.pdf", "rb") as f:
-    result = extractor.extract(Resume, config, f)
-
-# From bytes with filename
-with open("resume.pdf", "rb") as f:
-    file_bytes = f.read()
-from llama_cloud_services.extract import SourceText
-
-result = extractor.extract(
-    Resume, config, SourceText(file=file_bytes, filename="resume.pdf")
-)
-
-# From text content
-text = "Name: John Doe\nEmail: john@example.com\nSkills: Python, AI"
-result = extractor.extract(Resume, config, SourceText(text_content=text))
-```
-
-### Async Extraction
-
-For better performance with multiple files or when integrating with async applications.
-Here `queue_extraction` will enqueue the extraction jobs and exit. Alternatively, you
-can use `aextract` to poll for the job and return the extraction results.
-
-```python
-import asyncio
-
-
-async def extract_resumes():
-    # Async extraction
-    result = await extractor.aextract(Resume, config, "resume.pdf")
-    print(result.data)
-
-    # Queue extraction jobs (returns immediately)
-    jobs = await extractor.queue_extraction(
-        Resume, config, ["resume1.pdf", "resume2.pdf"]
-    )
-    print(f"Queued {len(jobs)} extraction jobs")
-    return jobs
-
-
-# Run async function
-jobs = asyncio.run(extract_resumes())
-# Check job status
-for job in jobs:
-    status = agent.get_extraction_job(job.id).status
-    print(f"Job {job.id}: {status}")
-
-# Get results when complete
-results = [agent.get_extraction_run_for_job(job.id) for job in jobs]
-```
-
 ## Core Concepts
 
+- **Extraction Agents**: Reusable extractors configured with a specific schema and extraction settings.
 - **Data Schema**: Structure definition for the data you want to extract in the form of a JSON schema or a Pydantic model.
-- **Extraction Config**: Settings that control how extraction is performed (e.g., speed vs accuracy trade-offs).
 - **Extraction Jobs**: Asynchronous extraction tasks that can be monitored.
-- **Extraction Agents** (Advanced): Reusable extractors configured with a specific schema and extraction settings.
 
 ## Defining Schemas
 
-Schemas define the structure of data you want to extract. You can use either Pydantic models or JSON Schema:
+Schemas can be defined using either Pydantic models or JSON Schema:
 
 ### Using Pydantic (Recommended)
 
 ```python
 from pydantic import BaseModel, Field
 from typing import List, Optional
-from llama_cloud import ExtractConfig, ExtractMode
 
 
 class Experience(BaseModel):
@@ -148,11 +54,6 @@ class Experience(BaseModel):
 class Resume(BaseModel):
     name: str = Field(description="Candidate name")
     experience: List[Experience] = Field(description="Work history")
-
-
-# Use the schema for extraction
-config = ExtractConfig(extraction_mode=ExtractMode.FAST)
-result = extractor.extract(Resume, config, "resume.pdf")
 ```
 
 ### Using JSON Schema
@@ -187,9 +88,7 @@ schema = {
     },
 }
 
-# Use the schema for extraction
-config = ExtractConfig(extraction_mode=ExtractMode.FAST)
-result = extractor.extract(schema, config, "resume.pdf")
+agent = extractor.create_agent(name="resume-parser", data_schema=schema)
 ```
 
 ### Important restrictions on JSON/Pydantic Schema
@@ -209,100 +108,28 @@ be sufficient for a wide variety of use-cases.
   your extraction workflow to fit within these constraints, e.g. by extracting subset of fields
   and later merging them together.
 
-## Extraction Configuration
+## Other Extraction APIs
 
-Configure how extraction is performed using `ExtractConfig`. The schema is the most important part, but several configuration options can significantly impact the extraction process.
+### Extraction over bytes or text
+
+You can use the `SourceText` class to extract from bytes or text directly without using a file. If passing the file bytes,
+you will need to pass the filename to the `SourceText` class.
 
 ```python
-from llama_cloud import ExtractConfig, ExtractMode, ChunkMode, ExtractTarget
+with open("resume.pdf", "rb") as f:
+    file_bytes = f.read()
+result = test_agent.extract(SourceText(file=file_bytes, filename="resume.pdf"))
+```
 
-# Basic configuration
-config = ExtractConfig(
-    extraction_mode=ExtractMode.BALANCED,  # FAST, BALANCED, MULTIMODAL, PREMIUM
-    extraction_target=ExtractTarget.PER_DOC,  # PER_DOC, PER_PAGE
-    system_prompt="Focus on the most recent data",
-    page_range="1-5,10-15",  # Extract from specific pages
-)
-
-# Advanced configuration
-advanced_config = ExtractConfig(
-    extraction_mode=ExtractMode.MULTIMODAL,
-    chunk_mode=ChunkMode.PAGE,  # PAGE, SECTION
-    high_resolution_mode=True,  # Better OCR accuracy
-    invalidate_cache=False,  # Bypass cached results
-    cite_sources=True,  # Enable source citations
-    use_reasoning=True,  # Enable reasoning (not in FAST mode)
-    confidence_scores=True,  # MULTIMODAL/PREMIUM only
+```python
+result = test_agent.extract(
+    SourceText(text_content="Candidate Name: Jane Doe")
 )
 ```
 
-### Key Configuration Options
+### Batch Processing
 
-**Extraction Mode**: Controls processing quality and speed
-
-- `FAST`: Fastest processing, suitable for simple documents with no OCR
-- `BALANCED`: Good speed/accuracy tradeoff for text-rich documents
-- `MULTIMODAL`: For visually rich documents with text, tables, and images (recommended)
-- `PREMIUM`: Highest accuracy with OCR, complex table/header detection
-
-**Extraction Target**: Defines extraction scope
-
-- `PER_DOC`: Apply schema to entire document (default)
-- `PER_PAGE`: Apply schema to each page, returns array of results
-
-**Advanced Options**:
-
-- `system_prompt`: Additional system-level instructions
-- `page_range`: Specific pages to extract (e.g., "1,3,5-7,9")
-- `chunk_mode`: Document splitting strategy (`PAGE` or `SECTION`)
-- `high_resolution_mode`: Better OCR for small text (slower processing)
-
-**Extensions** (return additional metadata):
-
-- `cite_sources`: Source tracing for extracted fields
-- `use_reasoning`: Explanations for extraction decisions
-- `confidence_scores`: Quantitative confidence measures (MULTIMODAL/PREMIUM only)
-
-For complete configuration options, advanced settings, and detailed examples, see the [LlamaExtract Configuration Documentation](https://docs.cloud.llamaindex.ai/llamaextract/features/options).
-
-## Extraction Agents (Advanced)
-
-For reusable extraction workflows, you can create extraction agents that encapsulate both schema and configuration:
-
-### Creating Agents
-
-```python
-from llama_cloud_services import LlamaExtract
-from llama_cloud import ExtractConfig, ExtractMode
-from pydantic import BaseModel, Field
-
-# Initialize client
-extractor = LlamaExtract()
-
-
-# Define schema
-class Resume(BaseModel):
-    name: str = Field(description="Full name of candidate")
-    email: str = Field(description="Email address")
-    skills: list[str] = Field(description="Technical skills and technologies")
-
-
-# Configure extraction settings
-config = ExtractConfig(extraction_mode=ExtractMode.FAST)
-
-# Create extraction agent
-agent = extractor.create_agent(
-    name="resume-parser", data_schema=Resume, config=config
-)
-
-# Use the agent
-result = agent.extract("resume.pdf")
-print(result.data)
-```
-
-### Agent Batch Processing
-
-Process multiple files with an agent:
+Process multiple files asynchronously:
 
 ```python
 # Queue multiple files for extraction
@@ -317,7 +144,7 @@ for job in jobs:
 results = [agent.get_extraction_run_for_job(job.id) for job in jobs]
 ```
 
-### Updating Agent Schemas
+### Updating Schemas
 
 Schemas can be modified and updated after creation:
 
@@ -342,26 +169,10 @@ agent = extractor.get_agent(name="resume-parser")
 extractor.delete_agent(agent.id)
 ```
 
-### When to Use Agents vs Direct Extraction
-
-**Use Direct Extraction When:**
-
-- One-off extractions
-- Different schemas for different documents
-- Simple workflows
-- Getting started quickly
-
-**Use Extraction Agents When:**
-
-- Repeated extractions with the same schema
-- Team collaboration (shared, named extractors)
-- Complex workflows requiring state management
-- Production systems with consistent extraction patterns
-
 ## Installation
 
 ```bash
-pip install llama-cloud-services
+pip install llama-extract==0.1.0
 ```
 
 ## Tips & Best Practices
@@ -382,9 +193,9 @@ At the core of LlamaExtract is the schema, which defines the structure of the da
 2. **Running Extractions**:
    - Note that resetting `agent.schema` will not save the schema to the database,
      until you call `agent.save`, but it will be used for running extractions.
-   - Check extraction results for any errors. Error information is available in the `result.error` field for debugging.
-   - Consider async operations (`aextract` or `queue_extraction`) for large-scale extraction or when processing multiple files.
-   - For repeated extractions with the same schema, consider creating an extraction agent to avoid redefining the schema each time.
+   - Check job status prior to accessing results. Any extraction error should be available as
+     part of `job.error` or `extraction_run.error` fields for debugging.
+   - Consider async operations (`queue_extraction`) for large-scale extraction once you have finalized your schema.
 
 ### Hitting "The response was too long to be processed" Error
 
@@ -397,7 +208,5 @@ Another option (orthogonal to the above) is to break the document into smaller s
 
 ## Additional Resources
 
-- [Extract Documentation](https://docs.cloud.llamaindex.ai/llamaextract/getting_started) - Details on Extract features, API and examples.
-- [Example Notebook](docs/examples-py/extract/resume_screening.ipynb) - Detailed walkthrough of resume parsing
-- [Example Application with TypeScript](./examples-ts/extract/) - End-to-end examples using LlamaExtract TypeScript client.
+- [Example Notebook](examples/extract/resume_screening.ipynb) - Detailed walkthrough of resume parsing
 - [Discord Community](https://discord.com/invite/eN6D2HQ4aX) - Get help and share feedback

parse.md

@@ -97,7 +97,7 @@ for page in result.pages:
     print(page.structuredData)
 ```
 
-See more details about the result object in the [example notebook](./docs/examples-py/parse/demo_json_tour.ipynb).
+See more details about the result object in the [example notebook](./examples/parse/demo_json_tour.ipynb).
 
 ### Using with file object / bytes
 
@@ -153,10 +153,10 @@ Full documentation for `SimpleDirectoryReader` can be found on the [LlamaIndex D
 
 Several end-to-end indexing examples can be found in the examples folder
 
-- [Getting Started](docs/examples-py/parse/demo_basic.ipynb)
-- [Advanced RAG Example](docs/examples-py/parse/demo_advanced.ipynb)
-- [Raw API Usage](docs/examples-py/parse/demo_api.ipynb)
-- [Result Object Tour](docs/examples-py/parse/demo_json_tour.ipynb)
+- [Getting Started](./examples/parse/demo_basic.ipynb)
+- [Advanced RAG Example](./examples/parse/demo_advanced.ipynb)
+- [Raw API Usage](./examples/parse/demo_api.ipynb)
+- [Result Object Tour](./examples/parse/demo_json_tour.ipynb)
 
 ## Documentation
 

pnpm-workspace.yaml

@@ -1,2 +0,0 @@
-packages:
-  - "ts/**"

py/llama_cloud_services/beta/agent_data/schema.py

@@ -37,10 +37,11 @@ Example Usage:
 """
 
 from datetime import datetime
+import numbers
 from llama_cloud import ExtractRun
 from llama_cloud.types.agent_data import AgentData
 from llama_cloud.types.aggregate_group import AggregateGroup
-from pydantic import BaseModel, Field, ValidationError, model_validator, ConfigDict
+from pydantic import BaseModel, Field, ValidationError
 from typing import (
     Generic,
     List,
@@ -175,8 +176,20 @@ class TypedAgentDataItems(BaseModel, Generic[AgentDataT]):
     )
 
 
-class FieldCitation(BaseModel):
-    page: Optional[int] = Field(
+class ExtractedFieldMetadata(BaseModel):
+    """
+    Metadata for an extracted data field, such as confidence, and citation information.
+    """
+
+    confidence: Optional[float] = Field(
+        None,
+        description="The confidence score for the field, combined with parsing confidence if applicable",
+    )
+    extracted_confidence: Optional[float] = Field(
+        None,
+        description="The confidence score for the field based on the extracted text only",
+    )
+    page_number: Optional[int] = Field(
         None, description="The page number that the field occurred on"
     )
     matching_text: Optional[str] = Field(
@@ -185,32 +198,6 @@ class FieldCitation(BaseModel):
     )
 
 
-class ExtractedFieldMetadata(BaseModel):
-    """
-    Metadata for an extracted data field, such as confidence, and citation information.
-    """
-
-    reasoning: Optional[str] = Field(
-        None,
-        description="symbol for how the citation/confidence was derived: 'INFERRED FROM TEXT', 'VERBATIM EXTRACTION'",
-    )
-    confidence: Optional[float] = Field(
-        None,
-        description="The confidence score for the field, combined with parsing confidence if applicable",
-    )
-    extraction_confidence: Optional[float] = Field(
-        None,
-        description="The confidence score for the field based on the extracted text only",
-    )
-    citation: Optional[List[FieldCitation]] = Field(
-        None,
-        description="The citation for the field, including page number and matching text",
-    )
-
-    # Forbid unknown keys to avoid swallowing nested dicts
-    model_config = ConfigDict(extra="forbid")
-
-
 ExtractedFieldMetaDataDict = Dict[
     str, Union[ExtractedFieldMetadata, Dict[str, Any], list[Any]]
 ]
@@ -219,57 +206,42 @@ ExtractedFieldMetaDataDict = Dict[
 def parse_extracted_field_metadata(
     field_metadata: dict[str, Any],
 ) -> ExtractedFieldMetaDataDict:
-    return {
-        k: _parse_extracted_field_metadata_recursive(v)
-        for k, v in field_metadata.items()
-        if k not in _METADATA_FIELDS_SIBLING_TO_LEAF
-    }
-
-
-_METADATA_FIELDS_SIBLING_TO_LEAF = {"reasoning"}
-
-
-def _parse_extracted_field_metadata_recursive(
-    field_value: Any,
-    additional_fields: dict[str, Any] = {},
-) -> Union[ExtractedFieldMetadata, Dict[str, Any], list[Any]]:
     """
     Parse the extracted field metadata into a dictionary of field names to field metadata.
     """
+    result: ExtractedFieldMetaDataDict = {}
+    for field_name, field_value in field_metadata.items():
+        if isinstance(field_value, ExtractedFieldMetadata):
+            # support running this multiple times
+            result[field_name] = field_value
+        elif isinstance(field_value, dict):
+            if "confidence" in field_value or "citations" in field_value:
+                try:
+                    validated = ExtractedFieldMetadata.model_validate(field_value)
 
-    if isinstance(field_value, ExtractedFieldMetadata):
-        # support running this multiple times
-        return field_value
-    elif isinstance(field_value, dict):
-        # reasoning explicitly excluded, as it is included next to subfields, for example
-        # "dimensions.width" is a leaf, but there will still potentially be a "dimensions.reasoning"
-        indicator_fields = {"confidence", "extraction_confidence", "citation"}
-        if len(indicator_fields.intersection(field_value.keys())) > 0:
-            try:
-                merged = {**field_value, **additional_fields}
-                allowed_fields = ExtractedFieldMetadata.model_fields.keys()
-                merged = {k: v for k, v in merged.items() if k in allowed_fields}
-                validated = ExtractedFieldMetadata.model_validate(merged)
-
-                return validated
-            except ValidationError:
-                pass
-        additional_fields = {
-            k: v
-            for k, v in field_value.items()
-            if k in _METADATA_FIELDS_SIBLING_TO_LEAF
-        }
-        return {
-            k: _parse_extracted_field_metadata_recursive(v, additional_fields)
-            for k, v in field_value.items()
-            if k not in _METADATA_FIELDS_SIBLING_TO_LEAF
-        }
-    elif isinstance(field_value, list):
-        return [_parse_extracted_field_metadata_recursive(item) for item in field_value]
-    else:
-        raise ValueError(
-            f"Invalid field value: {field_value}. Expected ExtractedFieldMetadata, dict, or list"
-        )
+                    # grab the citation from the array. This is just an array for backwards compatibility.
+                    if "citations" in field_value and len(field_value["citations"]) > 0:
+                        first_citation = field_value["citations"][0]
+                        if "page_number" in first_citation and isinstance(
+                            first_citation["page_number"], numbers.Number
+                        ):
+                            validated.page_number = int(first_citation["page_number"])  # type: ignore
+                        if "matching_text" in first_citation and isinstance(
+                            first_citation["matching_text"], str
+                        ):
+                            validated.matching_text = first_citation["matching_text"]
+                    result[field_name] = validated
+                    continue
+                except ValidationError:
+                    pass
+            result[field_name] = parse_extracted_field_metadata(field_value)
+        elif isinstance(field_value, list):
+            result[field_name] = [
+                parse_extracted_field_metadata(item) for item in field_value
+            ]
+        else:
+            result[field_name] = field_value
+    return result
 
 
 class ExtractedData(BaseModel, Generic[ExtractedT]):
@@ -340,28 +312,6 @@ class ExtractedData(BaseModel, Generic[ExtractedT]):
         description="Additional metadata about the extracted data, such as errors, tokens, etc.",
     )
 
-    @model_validator(mode="before")
-    @classmethod
-    def _normalize_field_metadata_on_input(cls, value: Any) -> Any:
-        # Ensure any inbound representation (including JSON round-trips)
-        # gets normalized so nested dicts become ExtractedFieldMetadata where appropriate.
-        if (
-            isinstance(value, dict)
-            and "field_metadata" in value
-            and isinstance(value["field_metadata"], dict)
-        ):
-            try:
-                value = {
-                    **value,
-                    "field_metadata": parse_extracted_field_metadata(
-                        value["field_metadata"]
-                    ),
-                }
-            except Exception:
-                # Let pydantic surface detailed errors later rather than swallowing completely
-                pass
-        return value
-
     @classmethod
     def create(
         cls,

py/llama_cloud_services/beta/classifier/client.py

@@ -1,187 +0,0 @@
-import asyncio
-import time
-from typing import Optional
-from pydantic import BaseModel
-from llama_cloud.client import AsyncLlamaCloud
-from llama_cloud.types import (
-    ClassifierRule,
-    ClassifyJobResults,
-    ClassifyParsingConfiguration,
-    StatusEnum,
-    ClassifyJobWithStatus,
-    File,
-)
-from llama_cloud.resources.classifier.client import OMIT
-from llama_cloud_services.files.client import FileClient
-from llama_cloud_services.constants import POLLING_TIMEOUT_SECONDS
-from llama_cloud_services.utils import is_terminal_status, augment_async_errors
-from llama_index.core.async_utils import DEFAULT_NUM_WORKERS, run_jobs
-
-
-class ClassificationOutput(BaseModel):
-    file_id: str
-    classification: str
-
-
-class ClassifyClient:
-    """
-    Experimental - Client for interacting with the LlamaCloud Classifier API.
-    The Classification API is currently in beta and may change in the future without notice.
-
-    Args:
-        client: The LlamaCloud client to use.
-        project_id: The project ID to use.
-        organization_id: The organization ID to use.
-        polling_interval: The interval to poll for job completion in seconds.
-        polling_timeout: The timeout for the job to complete in seconds.
-    """
-
-    def __init__(
-        self,
-        client: AsyncLlamaCloud,
-        project_id: Optional[str] = None,
-        organization_id: Optional[str] = None,
-        polling_interval: float = 1.0,
-        polling_timeout: float = POLLING_TIMEOUT_SECONDS,
-    ):
-        self.client = client
-        self.project_id = project_id
-        self.organization_id = organization_id
-        self.polling_interval = polling_interval
-        self.file_client = FileClient(client, project_id, organization_id)
-        self.polling_timeout = polling_timeout
-
-    async def aclassify_file_ids(
-        self,
-        rules: list[ClassifierRule],
-        file_ids: list[str],
-        parsing_configuration: Optional[ClassifyParsingConfiguration] = None,
-        raise_on_error: bool = True,
-    ) -> ClassifyJobResults:
-        """
-        Classify a list of files by their IDs.
-        Note that even if a job fails, some of the files may have been classified successfully.
-        In this case, you may want to set raise_on_error to False and check the results for successful classifications.
-
-        Args:
-            rules: The rules to use for classification.
-            file_ids: The IDs of the files to classify.
-            parsing_configuration: The parsing configuration to use for classification.
-            raise_on_error: Whether to raise an error if the classification job fails.
-
-        Returns:
-            The results of the classification job.
-        """
-        classify_job = await self.client.classifier.create_classify_job(
-            rules=rules,
-            file_ids=file_ids,
-            parsing_configuration=parsing_configuration or OMIT,
-            project_id=self.project_id,
-            organization_id=self.organization_id,
-        )
-
-        classify_job_with_status = await self._wait_for_job_completion(classify_job.id)
-
-        if raise_on_error and classify_job_with_status.status == StatusEnum.ERROR:
-            raise ValueError(
-                f"Error classifying files under job ID {classify_job_with_status.id}"
-            )
-
-        results = await self.client.classifier.get_classification_job_results(
-            classify_job_with_status.id,
-            project_id=self.project_id,
-            organization_id=self.organization_id,
-        )
-
-        return results
-
-    def classify_file_ids(
-        self,
-        rules: list[ClassifierRule],
-        file_ids: list[str],
-        parsing_configuration: Optional[ClassifyParsingConfiguration] = None,
-        raise_on_error: bool = True,
-    ) -> ClassifyJobResults:
-        with augment_async_errors():
-            return asyncio.run(
-                self.aclassify_file_ids(
-                    rules, file_ids, parsing_configuration, raise_on_error
-                )
-            )
-
-    async def aclassify_file_path(
-        self,
-        rules: list[ClassifierRule],
-        file_input_path: str,
-        parsing_configuration: Optional[ClassifyParsingConfiguration] = None,
-        raise_on_error: bool = True,
-    ) -> ClassifyJobResults:
-        file = await self.file_client.upload_file(file_input_path)
-        return await self.aclassify_file_ids(
-            rules, [file.id], parsing_configuration, raise_on_error
-        )
-
-    def classify_file_path(
-        self,
-        rules: list[ClassifierRule],
-        file_input_path: str,
-        parsing_configuration: Optional[ClassifyParsingConfiguration] = None,
-        raise_on_error: bool = True,
-    ) -> ClassifyJobResults:
-        with augment_async_errors():
-            return asyncio.run(
-                self.aclassify_file_path(
-                    rules, file_input_path, parsing_configuration, raise_on_error
-                )
-            )
-
-    async def aclassify_file_paths(
-        self,
-        rules: list[ClassifierRule],
-        file_input_paths: list[str],
-        parsing_configuration: Optional[ClassifyParsingConfiguration] = None,
-        raise_on_error: bool = True,
-        workers: int = DEFAULT_NUM_WORKERS,
-        show_progress: bool = False,
-    ) -> ClassifyJobResults:
-        coroutines = [self.file_client.upload_file(path) for path in file_input_paths]
-        files: list[File] = await run_jobs(
-            coroutines,
-            show_progress=show_progress,
-            workers=workers,
-            desc="Uploading files for classification",
-        )
-        return await self.aclassify_file_ids(
-            rules, [file.id for file in files], parsing_configuration, raise_on_error
-        )
-
-    def classify_file_paths(
-        self,
-        rules: list[ClassifierRule],
-        file_input_paths: list[str],
-        parsing_configuration: Optional[ClassifyParsingConfiguration] = None,
-        raise_on_error: bool = True,
-    ) -> ClassifyJobResults:
-        with augment_async_errors():
-            return asyncio.run(
-                self.aclassify_file_paths(
-                    rules, file_input_paths, parsing_configuration, raise_on_error
-                )
-            )
-
-    async def _wait_for_job_completion(self, job_id: str) -> ClassifyJobWithStatus:
-        job = await self.client.classifier.get_classify_job(
-            job_id, project_id=self.project_id, organization_id=self.organization_id
-        )
-        start_time = time.time()
-        while not is_terminal_status(job.status):
-            polling_duration = time.time() - start_time
-            if polling_duration > self.polling_timeout:
-                raise TimeoutError(
-                    f"Job {job_id} timed out after {polling_duration} seconds"
-                )
-            await asyncio.sleep(self.polling_interval)
-            job = await self.client.classifier.get_classify_job(
-                job_id, project_id=self.project_id, organization_id=self.organization_id
-            )
-        return job

py/llama_cloud_services/constants.py

@@ -1,2 +1 @@
 EU_BASE_URL = "https://api.cloud.eu.llamaindex.ai"
-POLLING_TIMEOUT_SECONDS = 300.0

py/llama_cloud_services/extract/extract.py

@@ -1,5 +1,4 @@
 import asyncio
-import base64
 import os
 import time
 from io import BufferedIOBase, BufferedReader, BytesIO, TextIOWrapper
@@ -22,7 +21,6 @@ from llama_cloud import (
     ExtractJobCreate,
     ExtractRun,
     File,
-    FileData,
     ExtractMode,
     StatusEnum,
     ExtractTarget,
@@ -33,9 +31,9 @@ from llama_cloud.client import AsyncLlamaCloud
 from llama_cloud.core.api_error import ApiError
 from llama_cloud_services.extract.utils import (
     JSONObjectType,
+    augment_async_errors,
     ExperimentalWarning,
 )
-from llama_cloud_services.utils import augment_async_errors
 from llama_index.core.schema import BaseComponent
 from llama_index.core.async_utils import run_jobs
 from llama_index.core.bridge.pydantic import Field, PrivateAttr
@@ -49,7 +47,7 @@ SchemaInput = Union[JSONObjectType, Type[BaseModel]]
 
 DEFAULT_EXTRACT_CONFIG = ExtractConfig(
     extraction_target=ExtractTarget.PER_DOC,
-    extraction_mode=ExtractMode.MULTIMODAL,
+    extraction_mode=ExtractMode.BALANCED,
 )
 
 
@@ -64,132 +62,6 @@ def _is_retryable_error(exception: BaseException) -> bool:
     return False
 
 
-async def _validate_schema(
-    client: AsyncLlamaCloud, data_schema: SchemaInput
-) -> JSONObjectType:
-    """Convert SchemaInput to a validated JSON schema dictionary."""
-    processed_schema: JSONObjectType
-    if isinstance(data_schema, dict):
-        # TODO: if we expose a get_validated JSON schema method, we can use it here
-        processed_schema = data_schema  # type: ignore
-    elif isinstance(data_schema, type) and issubclass(data_schema, BaseModel):
-        processed_schema = data_schema.model_json_schema()
-    else:
-        raise ValueError("data_schema must be either a dictionary or a Pydantic model")
-
-    # Validate schema via API
-    validated_schema = await client.llama_extract.validate_extraction_schema(
-        data_schema=processed_schema
-    )
-    return validated_schema.data_schema
-
-
-async def _get_job_with_retry(
-    client: AsyncLlamaCloud,
-    job_id: str,
-    max_attempts: int = 5,
-    initial_wait: float = 1,
-    max_wait: float = 60,
-    jitter: float = 5,
-) -> ExtractJob:
-    """Get extraction job with retry logic."""
-    async for attempt in AsyncRetrying(
-        retry=retry_if_exception(_is_retryable_error),
-        stop=stop_after_attempt(max_attempts),
-        wait=wait_exponential_jitter(initial=initial_wait, max=max_wait, jitter=jitter),
-        reraise=True,
-    ):
-        with attempt:
-            return await client.llama_extract.get_job(job_id=job_id)
-
-
-async def _get_run_with_retry(
-    client: AsyncLlamaCloud,
-    job_id: str,
-    project_id: Optional[str] = None,
-    organization_id: Optional[str] = None,
-    max_attempts: int = 3,
-    initial_wait: float = 1,
-    max_wait: float = 20,
-    jitter: float = 3,
-) -> ExtractRun:
-    """Get extraction run with retry logic."""
-    async for attempt in AsyncRetrying(
-        retry=retry_if_exception(_is_retryable_error),
-        stop=stop_after_attempt(max_attempts),
-        wait=wait_exponential_jitter(initial=initial_wait, max=max_wait, jitter=jitter),
-        reraise=True,
-    ):
-        with attempt:
-            return await client.llama_extract.get_run_by_job_id(
-                job_id=job_id,
-                project_id=project_id,
-                organization_id=organization_id,
-            )
-
-
-async def _wait_for_job_result(
-    client: AsyncLlamaCloud,
-    job_id: str,
-    check_interval: int = 1,
-    max_timeout: int = 2000,
-    verbose: bool = False,
-    project_id: Optional[str] = None,
-    organization_id: Optional[str] = None,
-    job_retry_attempts: int = 5,
-    job_max_wait: float = 60,
-    job_jitter: float = 5,
-    run_retry_attempts: int = 3,
-    run_max_wait: float = 20,
-    run_jitter: float = 3,
-) -> Optional[ExtractRun]:
-    """Wait for and return the results of an extraction job."""
-    start = time.perf_counter()
-    poll_count = 0
-
-    while True:
-        await asyncio.sleep(check_interval)
-        poll_count += 1
-        job = await _get_job_with_retry(
-            client,
-            job_id,
-            max_attempts=job_retry_attempts,
-            max_wait=job_max_wait,
-            jitter=job_jitter,
-        )
-
-        if job.status == StatusEnum.SUCCESS:
-            return await _get_run_with_retry(
-                client,
-                job_id,
-                project_id,
-                organization_id,
-                max_attempts=run_retry_attempts,
-                max_wait=run_max_wait,
-                jitter=run_jitter,
-            )
-        elif job.status == StatusEnum.PENDING:
-            end = time.perf_counter()
-            if end - start > max_timeout:
-                raise Exception(f"Timeout while extracting the file: {job_id}")
-            if verbose and poll_count % 10 == 0:
-                print(".", end="", flush=True)
-            continue
-        else:
-            warnings.warn(
-                f"Failure in job: {job_id}, status: {job.status}, error: {job.error}"
-            )
-            return await _get_run_with_retry(
-                client,
-                job_id,
-                project_id,
-                organization_id,
-                max_attempts=run_retry_attempts,
-                max_wait=run_max_wait,
-                jitter=run_jitter,
-            )
-
-
 class SourceText:
     def __init__(
         self,
@@ -272,21 +144,6 @@ def _extraction_config_warning(config: ExtractConfig) -> None:
             "available in the `extraction_metadata` field for the extraction run.",
             ExperimentalWarning,
         )
-    if config.use_reasoning:
-        if config.extraction_mode == ExtractMode.FAST:
-            raise ValueError(
-                "`reasoning` is only supported with BALANCED, MULTIMODAL, or PREMIUM extraction modes."
-            )
-    if config.cite_sources:
-        if config.extraction_mode in (ExtractMode.FAST, ExtractMode.BALANCED):
-            raise ValueError(
-                "`cite_sources` is only supported with MULTIMODAL or PREMIUM extraction modes."
-            )
-    if config.confidence_scores:
-        if config.extraction_mode in (ExtractMode.FAST, ExtractMode.BALANCED):
-            raise ValueError(
-                "`confidence_scores` is only supported with MULTIMODAL or PREMIUM extraction modes."
-            )
 
 
 class ExtractionAgent:
@@ -337,10 +194,22 @@ class ExtractionAgent:
 
     @data_schema.setter
     def data_schema(self, data_schema: SchemaInput) -> None:
-        # Use the shared schema processing and validation function
-        self._data_schema = self._run_in_thread(
-            _validate_schema(self._client, data_schema)
+        processed_schema: JSONObjectType
+        if isinstance(data_schema, dict):
+            # TODO: if we expose a get_validated JSON schema method, we can use it here
+            processed_schema = data_schema  # type: ignore
+        elif isinstance(data_schema, type) and issubclass(data_schema, BaseModel):
+            processed_schema = data_schema.model_json_schema()
+        else:
+            raise ValueError(
+                "data_schema must be either a dictionary or a Pydantic model"
+            )
+        validated_schema = self._run_in_thread(
+            self._client.llama_extract.validate_extraction_schema(
+                data_schema=processed_schema
+            )
         )
+        self._data_schema = validated_schema.data_schema
 
     @property
     def config(self) -> ExtractConfig:
@@ -399,9 +268,7 @@ class ExtractionAgent:
                 project_id=self._project_id, upload_file=file_contents
             )
         finally:
-            if file_contents is not None and isinstance(
-                file_contents, (BufferedReader, BytesIO)
-            ):
+            if file_contents is not None and isinstance(file_contents, BufferedReader):
                 file_contents.close()
 
     async def _upload_file(self, file_input: FileInput) -> File:
@@ -429,23 +296,60 @@ class ExtractionAgent:
 
         return await self.upload_file(source_text)
 
+    async def _get_job_with_retry(self, job_id: str) -> ExtractJob:
+        """Get job with retry logic for transient errors."""
+        async for attempt in AsyncRetrying(
+            retry=retry_if_exception(_is_retryable_error),
+            stop=stop_after_attempt(5),
+            wait=wait_exponential_jitter(initial=1, max=60, jitter=5),
+            reraise=True,
+        ):
+            with attempt:
+                return await self._client.llama_extract.get_job(job_id=job_id)
+
+    async def _get_run_with_retry(self, job_id: str) -> ExtractRun:
+        """Get extraction run with retry logic for transient errors."""
+        async for attempt in AsyncRetrying(
+            retry=retry_if_exception(_is_retryable_error),
+            stop=stop_after_attempt(3),
+            wait=wait_exponential_jitter(initial=1, max=20, jitter=3),
+            reraise=True,
+        ):
+            with attempt:
+                return await self._client.llama_extract.get_run_by_job_id(job_id=job_id)
+
     async def _wait_for_job_result(self, job_id: str) -> Optional[ExtractRun]:
         """Wait for and return the results of an extraction job."""
-        return await _wait_for_job_result(
-            client=self._client,
-            job_id=job_id,
-            check_interval=self.check_interval,
-            max_timeout=self.max_timeout,
-            verbose=self._verbose,
-            project_id=self._project_id,
-            organization_id=self._organization_id,
-            job_retry_attempts=5,
-            job_max_wait=60,
-            job_jitter=5,
-            run_retry_attempts=3,
-            run_max_wait=20,
-            run_jitter=3,
-        )
+        start = time.perf_counter()
+        tries = 0
+
+        while True:
+            await asyncio.sleep(self.check_interval)
+            tries += 1
+
+            try:
+                job = await self._get_job_with_retry(job_id)
+
+                if job.status == StatusEnum.SUCCESS:
+                    return await self._get_run_with_retry(job_id)
+                elif job.status == StatusEnum.PENDING:
+                    end = time.perf_counter()
+                    if end - start > self.max_timeout:
+                        raise Exception(f"Timeout while extracting the file: {job_id}")
+                    if self._verbose and tries % 10 == 0:
+                        print(".", end="", flush=True)
+                    continue
+                else:
+                    warnings.warn(
+                        f"Failure in job: {job_id}, status: {job.status}, error: {job.error}"
+                    )
+                    return await self._get_run_with_retry(job_id)
+
+            except Exception as e:
+                # If we get a non-retryable error or all retries are exhausted, re-raise
+                if self._verbose:
+                    print(f"\nError in job polling for {job_id}: {e}")
+                raise e
 
     def save(self) -> None:
         """Persist the extraction agent's schema and config to the database.
@@ -739,14 +643,12 @@ class LlamaExtract(BaseComponent):
             base_url = os.getenv("LLAMA_CLOUD_BASE_URL", None) or DEFAULT_BASE_URL
 
         super().__init__(
-            api_key=api_key,  # type: ignore
-            base_url=base_url,  # type: ignore
+            api_key=api_key,
+            base_url=base_url,
             check_interval=check_interval,
             max_timeout=max_timeout,
             num_workers=num_workers,
             show_progress=show_progress,
-            project_id=project_id,
-            organization_id=organization_id,
             verify=verify,
             httpx_timeout=httpx_timeout,
             verbose=verbose,
@@ -800,7 +702,7 @@ class LlamaExtract(BaseComponent):
             config = DEFAULT_EXTRACT_CONFIG
 
         if isinstance(data_schema, dict):
-            pass
+            data_schema = data_schema
         elif issubclass(data_schema, BaseModel):
             data_schema = data_schema.model_json_schema()
         else:
@@ -901,8 +803,6 @@ class LlamaExtract(BaseComponent):
                 num_workers=self.num_workers,
                 show_progress=self.show_progress,
                 verbose=self.verbose,
-                verify=self.verify,
-                httpx_timeout=self.httpx_timeout,
             )
             for agent in agents
         ]
@@ -919,245 +819,6 @@ class LlamaExtract(BaseComponent):
             )
         )
 
-    async def _wait_for_job_result(self, job_id: str) -> Optional[ExtractRun]:
-        """Wait for and return the results of an extraction job."""
-        return await _wait_for_job_result(
-            client=self._async_client,
-            job_id=job_id,
-            check_interval=self.check_interval,
-            max_timeout=self.max_timeout,
-            verbose=self.verbose,
-            project_id=self._project_id,
-            organization_id=self._organization_id,
-            job_retry_attempts=3,
-            job_max_wait=4,
-            job_jitter=5,
-            run_retry_attempts=3,
-            run_max_wait=4,
-            run_jitter=3,
-        )
-
-    def _get_mime_type(
-        self,
-        filename: Optional[str] = None,
-        file_path: Optional[Union[str, Path]] = None,
-    ) -> str:
-        """Determine MIME type for a file based on filename or path."""
-        # MIME type mappings for supported formats
-        MIME_TYPE_MAP = {
-            # Text files
-            ".txt": "text/plain",
-            ".csv": "text/csv",
-            ".json": "application/json",
-            ".html": "text/html",
-            ".htm": "text/html",
-            ".md": "text/markdown",
-            # Document files
-            ".pdf": "application/pdf",
-            ".docx": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
-            # Image files
-            ".png": "image/png",
-            ".jpg": "image/jpeg",
-            ".jpeg": "image/jpeg",
-        }
-
-        # Try to get extension from filename or file_path
-        extension = None
-        if filename:
-            extension = Path(filename).suffix.lower()
-        elif file_path:
-            extension = Path(file_path).suffix.lower()
-
-        # Check if the extension is supported
-        if extension and extension in MIME_TYPE_MAP:
-            return MIME_TYPE_MAP[extension]
-
-        # If we don't have a supported extension, provide helpful error message
-        supported_extensions = [ext[1:] for ext in MIME_TYPE_MAP.keys()]  # Remove dots
-        supported_list = ", ".join(sorted(supported_extensions))
-
-        if extension:
-            ext_without_dot = extension[1:]  # Remove the leading dot
-            raise ValueError(
-                f"Unsupported file type: '{ext_without_dot}'. "
-                f"Supported formats are: {supported_list}"
-            )
-        else:
-            raise ValueError(
-                f"Could not determine file type. Please provide a filename with one of these supported extensions: {supported_list}"
-            )
-
-    def _convert_file_to_file_data(self, file_input: FileInput) -> Union[FileData, str]:
-        """Convert FileInput to FileData or text string for stateless extraction."""
-        if isinstance(file_input, SourceText):
-            if file_input.text_content is not None:
-                return file_input.text_content
-            elif file_input.file is not None:
-                if isinstance(file_input.file, bytes):
-                    data = file_input.file
-                    filename = file_input.filename
-                elif isinstance(file_input.file, (str, Path)):
-                    with open(file_input.file, "rb") as f:
-                        data = f.read()
-                    filename = file_input.filename or str(file_input.file)
-                elif isinstance(file_input.file, (BufferedIOBase, TextIOWrapper)):
-                    if hasattr(file_input.file, "read"):
-                        content = file_input.file.read()
-                        if isinstance(content, str):
-                            data = content.encode("utf-8")
-                        else:
-                            data = content
-                    else:
-                        raise ValueError("File object must have a read method")
-                    filename = file_input.filename or getattr(
-                        file_input.file, "name", None
-                    )
-                else:
-                    raise ValueError(f"Unsupported file type: {type(file_input.file)}")
-
-                # Encode as base64
-                encoded_data = base64.b64encode(data).decode("utf-8")
-
-                # Determine mime type
-                mime_type = self._get_mime_type(filename=filename)
-
-                return FileData(data=encoded_data, mime_type=mime_type)
-            else:
-                raise ValueError("SourceText must have either text_content or file")
-
-        elif isinstance(file_input, (str, Path)):
-            with open(file_input, "rb") as f:
-                data = f.read()
-            encoded_data = base64.b64encode(data).decode("utf-8")
-            mime_type = self._get_mime_type(file_path=file_input)
-            return FileData(data=encoded_data, mime_type=mime_type)
-
-        elif isinstance(file_input, bytes):
-            # For raw bytes, we can't determine the file type, so we need to raise an error
-            raise ValueError(
-                "Cannot determine file type from raw bytes. Please use SourceText with a filename, or provide a file path."
-            )
-
-        elif isinstance(file_input, (BufferedIOBase, TextIOWrapper)):
-            if hasattr(file_input, "read"):
-                content = file_input.read()
-                if isinstance(content, str):
-                    data = content.encode("utf-8")
-                else:
-                    data = content
-                encoded_data = base64.b64encode(data).decode("utf-8")
-
-                # Try to get filename from the file object
-                filename = getattr(file_input, "name", None)
-                mime_type = self._get_mime_type(filename=filename)
-
-                return FileData(data=encoded_data, mime_type=mime_type)
-            else:
-                raise ValueError("File object must have a read method")
-
-        else:
-            raise ValueError(f"Unsupported file input type: {type(file_input)}")
-
-    async def queue_extraction(
-        self,
-        data_schema: SchemaInput,
-        config: ExtractConfig,
-        files: Union[FileInput, List[FileInput]],
-    ) -> Union[ExtractJob, List[ExtractJob]]:
-        """Queue extraction jobs using stateless extraction (no agent required).
-
-        Args:
-            data_schema: The schema defining what data to extract
-            config: The extraction configuration
-            files: File(s) to extract from
-
-        Returns:
-            ExtractJob or list of ExtractJobs
-        """
-        _extraction_config_warning(config)
-        processed_schema = await _validate_schema(self._async_client, data_schema)
-
-        if not isinstance(files, list):
-            files = [files]
-
-        jobs = []
-        for file_input in files:
-            file_data_or_text = self._convert_file_to_file_data(file_input)
-
-            if isinstance(file_data_or_text, str):
-                # It's text content
-                job = await self._async_client.llama_extract.extract_stateless(
-                    project_id=self._project_id,
-                    organization_id=self._organization_id,
-                    data_schema=processed_schema,
-                    config=config,
-                    text=file_data_or_text,
-                )
-            else:
-                # It's FileData
-                job = await self._async_client.llama_extract.extract_stateless(
-                    project_id=self._project_id,
-                    organization_id=self._organization_id,
-                    data_schema=processed_schema,
-                    config=config,
-                    file=file_data_or_text,
-                )
-            jobs.append(job)
-
-        return jobs[0] if len(jobs) == 1 else jobs
-
-    async def aextract(
-        self,
-        data_schema: SchemaInput,
-        config: ExtractConfig,
-        files: Union[FileInput, List[FileInput]],
-    ) -> Union[ExtractRun, List[ExtractRun]]:
-        """Run stateless extraction and wait for results.
-
-        Args:
-            data_schema: The schema defining what data to extract
-            config: The extraction configuration
-            files: File(s) to extract from
-
-        Returns:
-            ExtractRun or list of ExtractRuns with the extraction results
-        """
-        jobs = await self.queue_extraction(data_schema, config, files)
-
-        if isinstance(jobs, list):
-            runs = []
-            for job in jobs:
-                run = await self._wait_for_job_result(job.id)
-                if run is None:
-                    raise RuntimeError(
-                        f"Failed to get extraction result for job {job.id}"
-                    )
-                runs.append(run)
-            return runs
-        else:
-            run = await self._wait_for_job_result(jobs.id)
-            if run is None:
-                raise RuntimeError(f"Failed to get extraction result for job {jobs.id}")
-            return run
-
-    def extract(
-        self,
-        data_schema: SchemaInput,
-        config: ExtractConfig,
-        files: Union[FileInput, List[FileInput]],
-    ) -> Union[ExtractRun, List[ExtractRun]]:
-        """Run stateless extraction and wait for results (synchronous version).
-
-        Args:
-            data_schema: The schema defining what data to extract
-            config: The extraction configuration
-            files: File(s) to extract from
-
-        Returns:
-            ExtractRun or list of ExtractRuns with the extraction results
-        """
-        return self._run_in_thread(self.aextract(data_schema, config, files))
-
     def __del__(self) -> None:
         """Cleanup resources properly."""
         try:
@@ -1172,20 +833,6 @@ if __name__ == "__main__":
 
     load_dotenv()
 
-    # Example usage:
-    #
-    # # Basic usage with stateless extraction (no agent required)
-    # extractor = LlamaExtract()
-    # schema = {"name": {"type": "string"}, "email": {"type": "string"}}
-    # config = ExtractConfig(extraction_mode=ExtractMode.FAST)
-    # files = ["path/to/document.pdf"]
-    #
-    # # Queue extraction jobs
-    # jobs = extractor.queue_extraction(schema, config, files)
-    #
-    # # Or run extraction and wait for results
-    # results = extractor.extract(schema, config, files)
-
     data_dir = Path(__file__).parent.parent / "tests" / "data"
     extractor = LlamaExtract()
     try:

py/llama_cloud_services/extract/utils.py

@@ -1,4 +1,12 @@
-from typing import Any, Dict, List, Union
+from typing import Any, Dict, List, Union, Generator
+from contextlib import contextmanager
+
+# Asyncio error messages
+nest_asyncio_err = "cannot be called from a running event loop"
+nest_asyncio_msg = (
+    "The event loop is already running. "
+    "Add `import nest_asyncio; nest_asyncio.apply()` to your code to fix this issue."
+)
 
 
 def is_jupyter() -> bool:
@@ -11,6 +19,17 @@ def is_jupyter() -> bool:
         return False
 
 
+@contextmanager
+def augment_async_errors() -> Generator[None, None, None]:
+    """Context manager to add helpful information for errors due to nested event loops."""
+    try:
+        yield
+    except RuntimeError as e:
+        if nest_asyncio_err in str(e):
+            raise RuntimeError(nest_asyncio_msg)
+        raise
+
+
 JSONType = Union[Dict[str, Any], List[Any], str, int, float, bool, None]
 JSONObjectType = Dict[str, JSONType]
 

py/llama_cloud_services/files/__init__.py

@@ -1,3 +0,0 @@
-from .client import FileClient
-
-__all__ = ["FileClient"]

py/llama_cloud_services/files/client.py

@@ -1,97 +0,0 @@
-from io import BytesIO
-from typing import BinaryIO
-import os
-from llama_cloud.client import AsyncLlamaCloud
-from llama_cloud.types import File, FileCreate
-from typing import Optional
-
-
-class FileClient:
-    """
-    Higher-level client for interacting with the LlamaCloud Files API.
-    Uses presigned URLs for uploads by default.
-
-    Args:
-        client: The LlamaCloud client to use.
-        project_id: The project ID to use.
-        organization_id: The organization ID to use.
-        use_presigned_url: Whether to use presigned URLs for uploads (set to False when uploading to BYOC deployments).
-    """
-
-    def __init__(
-        self,
-        client: AsyncLlamaCloud,
-        project_id: Optional[str] = None,
-        organization_id: Optional[str] = None,
-        use_presigned_url: bool = True,
-    ):
-        self.client = client
-        self.project_id = project_id
-        self.organization_id = organization_id
-        self.use_presigned_url = use_presigned_url
-
-    async def get_file(self, file_id: str) -> File:
-        return await self.client.files.get_file(
-            file_id, project_id=self.project_id, organization_id=self.organization_id
-        )
-
-    async def read_file_content(self, file_id: str) -> bytes:
-        presigned_url = await self.client.files.read_file_content(
-            file_id,
-            project_id=self.project_id,
-            organization_id=self.organization_id,
-        )
-        httpx_client = self.client._client_wrapper.httpx_client
-        response = await httpx_client.get(presigned_url.url)
-        response.raise_for_status()
-        return response.content
-
-    async def upload_file(
-        self, file_path: str, external_file_id: Optional[str] = None
-    ) -> File:
-        external_file_id = external_file_id or file_path
-        file_size = os.path.getsize(file_path)
-        with open(file_path, "rb") as file:
-            return await self.upload_buffer(file, external_file_id, file_size)
-
-    async def upload_bytes(self, bytes: bytes, external_file_id: str) -> File:
-        return await self.upload_buffer(BytesIO(bytes), external_file_id, len(bytes))
-
-    async def upload_buffer(
-        self,
-        buffer: BinaryIO,
-        external_file_id: str,
-        file_size: int,
-    ) -> File:
-        if self.use_presigned_url:
-            if getattr(buffer, "name", None):
-                name = os.path.basename(str(getattr(buffer, "name", external_file_id)))
-            else:
-                name = external_file_id
-            presigned_url = await self.client.files.generate_presigned_url(
-                project_id=self.project_id,
-                organization_id=self.organization_id,
-                request=FileCreate(
-                    name=name,
-                    external_file_id=external_file_id,
-                    file_size=file_size,
-                ),
-            )
-            httpx_client = self.client._client_wrapper.httpx_client
-            upload_response = await httpx_client.put(
-                presigned_url.url,
-                data=buffer.read(),
-            )
-            upload_response.raise_for_status()
-            return await self.client.files.get_file(
-                presigned_url.file_id,
-                project_id=self.project_id,
-                organization_id=self.organization_id,
-            )
-        else:
-            return await self.client.files.upload_file(
-                upload_file=buffer,
-                external_file_id=external_file_id,
-                project_id=self.project_id,
-                organization_id=self.organization_id,
-            )

py/llama_cloud_services/parse/base.py

@@ -25,7 +25,7 @@ from llama_index.core.readers.base import BasePydanticReader
 from llama_index.core.readers.file.base import get_default_fs
 from llama_index.core.schema import Document
 
-from llama_cloud_services.utils import check_extra_params, check_for_updates
+from llama_cloud_services.utils import check_extra_params
 from llama_cloud_services.parse.types import JobResult
 from llama_cloud_services.parse.utils import (
     SUPPORTED_FILE_TYPES,
@@ -541,11 +541,6 @@ class LlamaParse(BasePydanticReader):
         description="Whether to use the vendor multimodal API.",
     )
 
-    check_for_updates: Optional[bool] = Field(
-        default=False,
-        description="Automatically check for Python SDK updates.",
-    )
-
     @model_validator(mode="before")
     @classmethod
     def warn_extra_params(cls, data: Dict[str, Any]) -> Dict[str, Any]:
@@ -601,16 +596,6 @@ class LlamaParse(BasePydanticReader):
 
         return self._aclient
 
-    _update_checked = False
-
-    async def _check_for_updates(self) -> None:
-        if self.check_for_updates and not self._update_checked:
-            try:
-                await check_for_updates(self.aclient, quiet=False)
-                self._update_checked = True
-            except (ValueError, httpx.HTTPStatusError):
-                pass
-
     @asynccontextmanager
     async def client_context(self) -> AsyncGenerator[httpx.AsyncClient, None]:
         """Create a context for the HTTPX client."""
@@ -660,7 +645,6 @@ class LlamaParse(BasePydanticReader):
         fs: Optional[AbstractFileSystem] = None,
         partition_target_pages: Optional[str] = None,
     ) -> str:
-        await self._check_for_updates()
         files = None
         file_handle = None
         input_url = file_input if self._is_input_url(file_input) else None
@@ -1550,7 +1534,6 @@ class LlamaParse(BasePydanticReader):
         self, json_result: List[dict], download_path: str, asset_key: str
     ) -> List[dict]:
         """Download assets (images or charts) from the parsed result."""
-        await self._check_for_updates()
         # Make the download path
         if not os.path.exists(download_path):
             os.makedirs(download_path)
@@ -1659,7 +1642,6 @@ class LlamaParse(BasePydanticReader):
         self, json_result: List[dict], download_path: str
     ) -> List[dict]:
         """Download xlsx from the parsed result."""
-        await self._check_for_updates()
         # make the download path
         if not os.path.exists(download_path):
             os.makedirs(download_path)

py/llama_cloud_services/parse/types.py

@@ -52,10 +52,6 @@ class PageItem(BaseModel):
     bBox: Optional[BBox] = Field(
         default=None, description="The bounding box of the item."
     )
-    html: Optional[str] = Field(
-        default=None,
-        description="The HTML-formatted content of the item. Only applicable for table items when output_tables_as_HTML=True.",
-    )
 
 
 class ImageItem(BaseModel):
@@ -112,7 +108,7 @@ class ChartItem(BaseModel):
 class Page(BaseModel):
     """A page of the document."""
 
-    page: int = Field(default=0, description="The page number.")
+    page: int = Field(description="The page number.")
     text: Optional[str] = Field(default=None, description="The text of the page.")
     md: Optional[str] = Field(default=None, description="The markdown of the page.")
     images: List[ImageItem] = Field(
@@ -153,12 +149,6 @@ class Page(BaseModel):
     noTextContent: bool = Field(
         default=False, description="Whether the page has no text content."
     )
-    isAudioTranscript: bool = Field(
-        default=False, description="Whether the page is an audio transcript."
-    )
-    durationInSeconds: Optional[float] = Field(
-        default=None, description="The duration of the audio transcript in seconds."
-    )
 
 
 class JobResult(BaseModel):

py/llama_cloud_services/utils.py

@@ -1,21 +1,7 @@
-import os
-import importlib.metadata
-from contextlib import contextmanager
-from typing import Generator
 import difflib
-from llama_cloud.types import StatusEnum
-import httpx
-import packaging.version
 from pydantic import BaseModel
 from typing import Any, Dict, List, Tuple, Type
 
-# Asyncio error messages
-nest_asyncio_err = "cannot be called from a running event loop"
-nest_asyncio_msg = (
-    "The event loop is already running. "
-    "Add `import nest_asyncio; nest_asyncio.apply()` to your code to fix this issue."
-)
-
 
 def check_extra_params(
     model_cls: Type[BaseModel], data: Dict[str, Any]
@@ -41,66 +27,3 @@ def check_extra_params(
                 )
 
     return extra_params, suggestions
-
-
-def is_terminal_status(status: StatusEnum) -> bool:
-    """
-    Check if a status is terminal, i.e. the job is done and no more updates are expected.
-    Note: this must be updated if the status enum is updated.
-
-    Args:
-        status: The status to check
-
-    Returns:
-        True if the status is terminal, False otherwise
-    """
-    return status in {
-        StatusEnum.SUCCESS,
-        StatusEnum.ERROR,
-        StatusEnum.CANCELLED,
-        StatusEnum.PARTIAL_SUCCESS,
-    }
-
-
-async def check_for_updates(client: httpx.AsyncClient, quiet: bool = True) -> bool:
-    """Check if an SDK update is available.
-
-    Args:
-        client: HTTPX client to use.
-        quiet: If False, update availability will also be printed to stdout.
-
-    Returns: True if an update is available.
-
-    Raises:
-        ValueError: Failed to get a valid release version from PyPI.
-    """
-    package_name = "llama-cloud-services"
-    r = await client.get(f"https://pypi.org/pypi/{package_name}/json")
-    version = r.json().get("info", {}).get("version", "")
-    if not version:
-        raise ValueError("Failed to fetch package info from PyPI")
-    latest = packaging.version.parse(version)
-    current = packaging.version.parse(importlib.metadata.version(package_name))
-    if current < latest:
-        if not quiet:
-            msg = [
-                f"\u26A0\uFE0F {package_name} is out of date",
-                f"Current version: {current} | Latest: {latest}",
-                "To upgrade: pip install -U --force-reinstall llama-cloud-services",
-            ]
-            print(os.linesep.join(msg))
-        return True
-    elif not quiet:
-        print(f"{package_name} is up to date")
-    return False
-
-
-@contextmanager
-def augment_async_errors() -> Generator[None, None, None]:
-    """Context manager to add helpful information for errors due to nested event loops."""
-    try:
-        yield
-    except RuntimeError as e:
-        if nest_asyncio_err in str(e):
-            raise RuntimeError(nest_asyncio_msg)
-        raise

py/llama_parse/pyproject.toml

@@ -11,13 +11,13 @@ dev = [
 
 [project]
 name = "llama-parse"
-version = "0.6.58"
+version = "0.6.53"
 description = "Parse files into RAG-Optimized formats."
 authors = [{name = "Logan Markewich", email = "logan@llamaindex.ai"}]
 requires-python = ">=3.9,<4.0"
 readme = "README.md"
 license = "MIT"
-dependencies = ["llama-cloud-services>=0.6.58"]
+dependencies = ["llama-cloud-services>=0.6.53"]
 
 [project.scripts]
 llama-parse = "llama_parse.cli.main:parse"

py/pyproject.toml

@@ -12,13 +12,12 @@ dev = [
   "deepdiff>=8.1.1,<9",
   "ipython>=8.12.3,<9",
   "jupyter>=1.1.1,<2",
-  "mypy>=1.14.1,<2",
-  "pydantic-settings>=2.10.1"
+  "mypy>=1.14.1,<2"
 ]
 
 [project]
 name = "llama-cloud-services"
-version = "0.6.58"
+version = "0.6.53"
 description = "Tailored SDK clients for LlamaCloud services."
 authors = [{name = "Logan Markewich", email = "logan@runllama.ai"}]
 requires-python = ">=3.9,<4.0"
@@ -26,14 +25,13 @@ readme = "README.md"
 license = "MIT"
 dependencies = [
   "llama-index-core>=0.12.0",
-  "llama-cloud==0.1.37",
+  "llama-cloud==0.1.35",
   "pydantic>=2.8,!=2.10",
   "click>=8.1.7,<9",
   "python-dotenv>=1.0.1,<2",
   "eval-type-backport>=0.2.0,<0.3 ; python_version < '3.10'",
   "platformdirs>=4.3.7,<5",
-  "tenacity>=8.5.0, <10.0",
-  "packaging>=25.0"
+  "tenacity>=8.5.0, <10.0"
 ]
 
 [project.scripts]

py/pytest.ini

@@ -1,2 +0,0 @@
-[pytest]
-asyncio_mode = auto

py/tests/beta/agent_data/test_agent_data_client.py

@@ -37,7 +37,7 @@ LLAMA_CLOUD_BASE_URL = os.getenv("LLAMA_CLOUD_BASE_URL")
 LLAMA_DEPLOY_DEPLOYMENT_NAME = os.getenv("LLAMA_DEPLOY_DEPLOYMENT_NAME")
 
 
-class ExampleData(BaseModel):
+class TestData(BaseModel):
     """Simple test data model for agent data testing"""
 
     name: str
@@ -66,13 +66,13 @@ async def test_agent_data_crud_operations():
     # Create agent data client with unique collection name
     agent_data_client = AsyncAgentDataClient(
         client=client,
-        type=ExampleData,
+        type=TestData,
         collection=f"test-collection-{test_id[:8]}",
         agent_url_id=LLAMA_DEPLOY_DEPLOYMENT_NAME,
     )
 
     # Create test data
-    test_data = ExampleData(name="test-item", test_id=test_id, value=42)
+    test_data = TestData(name="test-item", test_id=test_id, value=42)
 
     created_item = None
     try:
@@ -107,7 +107,7 @@ async def test_agent_data_crud_operations():
         assert aggregate_results.items[0].count == 1
 
         # Test UPDATE
-        updated_data = ExampleData(name="updated-item", test_id=test_id, value=84)
+        updated_data = TestData(name="updated-item", test_id=test_id, value=84)
         updated_item = await agent_data_client.update_item(
             created_item.id, updated_data
         )

py/tests/beta/agent_data/test_agent_data_schema.py

@@ -1,18 +1,15 @@
 from datetime import datetime
-import json
-from pathlib import Path
-from typing import Any, Dict, Optional
+from typing import Any, Dict
 
 import pytest
 from llama_cloud import ExtractRun, File
 from llama_cloud.types.agent_data import AgentData
 from llama_cloud.types.aggregate_group import AggregateGroup
-from pydantic import BaseModel, Field, ValidationError
+from pydantic import BaseModel, ValidationError
 
 from llama_cloud_services.beta.agent_data.schema import (
     ExtractedData,
     ExtractedFieldMetadata,
-    FieldCitation,
     InvalidExtractionData,
     TypedAgentData,
     TypedAggregateGroup,
@@ -84,12 +81,8 @@ def test_extracted_data_create_method():
 
     # Test with custom values using ExtractedFieldMetadata
     field_metadata = {
-        "name": ExtractedFieldMetadata(
-            confidence=0.99, citation=[FieldCitation(page=1)]
-        ),
-        "age": ExtractedFieldMetadata(
-            confidence=0.85, citation=[FieldCitation(page=1)]
-        ),
+        "name": ExtractedFieldMetadata(confidence=0.99, page_number=1),
+        "age": ExtractedFieldMetadata(confidence=0.85, page_number=1),
     }
     extracted_custom = ExtractedData.create(
         person, status="accepted", field_metadata=field_metadata
@@ -237,20 +230,20 @@ def test_parse_extracted_field_metadata():
     raw_metadata = {
         "name": {
             "confidence": 0.95,
-            "citation": [{"page": 1, "matching_text": "John Smith"}],
+            "citations": [{"page_number": 1, "matching_text": "John Smith"}],
         },
         "age": {
             "confidence": 0.87,
-            "citation": [
+            "citations": [
                 {
-                    "page": 2.0,  # Float page number
+                    "page_number": 2.0,  # Float page number
                     "matching_text": "25 years old",
                 }
             ],
         },
         "email": {
             "confidence": 0.92,
-            "citation": [],  # Empty citations
+            "citations": [],  # Empty citations
         },
     }
 
@@ -261,132 +254,20 @@ def test_parse_extracted_field_metadata():
     # name should have parsed citation data
     assert isinstance(result["name"], ExtractedFieldMetadata)
     assert result["name"].confidence == 0.95
-    assert result["name"].citation == [
-        FieldCitation(page=1, matching_text="John Smith")
-    ]
+    assert result["name"].page_number == 1
+    assert result["name"].matching_text == "John Smith"
 
     # age should handle float page number
     assert isinstance(result["age"], ExtractedFieldMetadata)
     assert result["age"].confidence == 0.87
-    assert result["age"].citation == [
-        FieldCitation(page=2, matching_text="25 years old")
-    ]
+    assert result["age"].page_number == 2  # Should be converted to int
+    assert result["age"].matching_text == "25 years old"
 
     # email should handle empty citations
     assert isinstance(result["email"], ExtractedFieldMetadata)
     assert result["email"].confidence == 0.92
 
 
-def test_parse_extracted_field_metadata_complex():
-    """Test parse_extracted_field_metadata with new citation format and reasoning field."""
-    raw_metadata = {
-        "title": {
-            "reasoning": "Combined key parametrics and construction from the datasheet for a structured title.",
-            "citation": [
-                {
-                    "page": 1,
-                    "matching_text": "PHE844/F844, Film, Metallized Polypropylene, Safety, 0.47 uF",
-                }
-            ],
-            "extraction_confidence": 0.9470628580889779,
-            "confidence": 0.9470628580889779,
-        },
-        "manufacturer": {
-            "reasoning": "VERBATIM EXTRACTION",
-            "citation": [{"page": 1, "matching_text": "YAGEO KEMET"}],
-            "extraction_confidence": 0.9997446550976602,
-            "confidence": 0.9997446550976602,
-        },
-        "features": [
-            {
-                "reasoning": "VERBATIM EXTRACTION",
-                "citation": [
-                    {"page": 1, "matching_text": "Features</td><td>EMI Safety"}
-                ],
-                "extraction_confidence": 0.9999308195540074,
-                "confidence": 0.9999308195540074,
-            },
-            {
-                "reasoning": "VERBATIM EXTRACTION",
-                "citation": [
-                    {"page": 1, "matching_text": "THB Performance</td><td>Yes"}
-                ],
-                "extraction_confidence": 0.8642493886452225,
-                "confidence": 0.8642493886452225,
-            },
-        ],
-        "dimensions": {
-            "length": {
-                "citation": [{"page": 1, "matching_text": "L</td><td>41mm MAX"}],
-                "extraction_confidence": 0.8986941382802304,
-                "confidence": 0.8986941382802304,
-            },
-            "width": {
-                "citation": [{"page": 1, "matching_text": "T</td><td>13mm MAX"}],
-                "extraction_confidence": 0.9999377974447091,
-                "confidence": 0.9999377974447091,
-            },
-            "reasoning": "VERBATIM EXTRACTION",
-        },
-    }
-
-    result = parse_extracted_field_metadata(raw_metadata)
-    assert result == {
-        "title": ExtractedFieldMetadata(
-            reasoning="Combined key parametrics and construction from the datasheet for a structured title.",
-            confidence=0.9470628580889779,
-            extraction_confidence=0.9470628580889779,
-            citation=[
-                FieldCitation(
-                    page=1,
-                    matching_text="PHE844/F844, Film, Metallized Polypropylene, Safety, 0.47 uF",
-                )
-            ],
-        ),
-        "manufacturer": ExtractedFieldMetadata(
-            reasoning="VERBATIM EXTRACTION",
-            confidence=0.9997446550976602,
-            extraction_confidence=0.9997446550976602,
-            citation=[FieldCitation(page=1, matching_text="YAGEO KEMET")],
-        ),
-        "features": [
-            ExtractedFieldMetadata(
-                reasoning="VERBATIM EXTRACTION",
-                confidence=0.9999308195540074,
-                extraction_confidence=0.9999308195540074,
-                citation=[
-                    FieldCitation(
-                        page=1,
-                        matching_text="Features</td><td>EMI Safety",
-                    )
-                ],
-            ),
-            ExtractedFieldMetadata(
-                reasoning="VERBATIM EXTRACTION",
-                confidence=0.8642493886452225,
-                extraction_confidence=0.8642493886452225,
-                citation=[
-                    FieldCitation(page=1, matching_text="THB Performance</td><td>Yes")
-                ],
-            ),
-        ],
-        "dimensions": {
-            "length": ExtractedFieldMetadata(
-                reasoning="VERBATIM EXTRACTION",
-                confidence=0.8986941382802304,
-                extraction_confidence=0.8986941382802304,
-                citation=[FieldCitation(page=1, matching_text="L</td><td>41mm MAX")],
-            ),
-            "width": ExtractedFieldMetadata(
-                reasoning="VERBATIM EXTRACTION",
-                confidence=0.9999377974447091,
-                extraction_confidence=0.9999377974447091,
-                citation=[FieldCitation(page=1, matching_text="T</td><td>13mm MAX")],
-            ),
-        },
-    }
-
-
 def create_file(
     id: str = "file-456",
     name: str = "resume.pdf",
@@ -409,12 +290,12 @@ def create_extract_run(
     extraction_metadata: Dict[str, Any] = {
         "name": {
             "confidence": 0.95,
-            "citation": [{"page": 1, "matching_text": "John Doe"}],
+            "citations": [{"page_number": 1, "matching_text": "John Doe"}],
         },
         "age": {"confidence": 0.87},
         "email": {
             "confidence": 0.92,
-            "citation": [{"page": 1, "matching_text": "john@example.com"}],
+            "citations": [{"page_number": 1, "matching_text": "john@example.com"}],
         },
     },
     data_schema: Dict[str, Any] = {},
@@ -465,9 +346,8 @@ def test_extracted_data_from_extraction_result_success():
     # Verify field metadata was parsed
     assert isinstance(extracted.field_metadata["name"], ExtractedFieldMetadata)
     assert extracted.field_metadata["name"].confidence == 0.95
-    assert extracted.field_metadata["name"].citation == [
-        FieldCitation(page=1, matching_text="John Doe")
-    ]
+    assert extracted.field_metadata["name"].page_number == 1
+    assert extracted.field_metadata["name"].matching_text == "John Doe"
 
     # Verify overall confidence was calculated
     expected_confidence = (0.95 + 0.87 + 0.92) / 3
@@ -539,54 +419,3 @@ def test_extracted_data_from_extraction_result_invalid_data():
     assert isinstance(invalid_data.field_metadata["name"], ExtractedFieldMetadata)
     assert invalid_data.field_metadata["name"].confidence == 0.9
     assert invalid_data.overall_confidence == 0.9
-
-
-class Dimensions(BaseModel):
-    length: Optional[str] = Field(
-        None, description="Length in mm (Size, Longest Side, L)"
-    )
-    width: Optional[str] = Field(
-        None, description="Width in mm (Breadth, Side Width, W)"
-    )
-    height: Optional[str] = Field(
-        None, description="Height in mm (Thickness, Vertical Size, H)"
-    )
-    diameter: Optional[str] = Field(
-        None,
-        description="Diameter in mm (for radial or cylindrical types) (Outer Diameter, dt, OD, D, d<sub>t</sub>)",
-    )
-    lead_spacing: Optional[str] = Field(
-        None, description="Lead spacing in mm (Pin Pitch, Terminal Gap, LS)"
-    )
-
-
-class Capacitor(BaseModel):
-    dimensions: Optional[Dimensions] = None
-
-
-def test_full_parse_nested_dimensions():
-    with open(Path(__file__).parent.parent.parent / "data" / "capacitor.json") as f:
-        data = json.load(f)
-    result = ExtractedData.from_extraction_result(ExtractRun.parse_obj(data), Capacitor)
-    expected = {
-        "dimensions": {
-            "diameter": ExtractedFieldMetadata(
-                reasoning="VERBATIM EXTRACTION",
-                confidence=1.0,
-                extraction_confidence=1.0,
-            ),
-            "lead_spacing": ExtractedFieldMetadata(
-                reasoning="VERBATIM EXTRACTION",
-                confidence=0.9999999031936799,
-                extraction_confidence=0.9999999031936799,
-            ),
-            "length": ExtractedFieldMetadata(
-                reasoning="VERBATIM EXTRACTION",
-                confidence=0.9999968039036192,
-                extraction_confidence=0.9999968039036192,
-            ),
-        }
-    }
-    assert result.field_metadata == expected
-    parsed = ExtractedData.model_validate_json(result.model_dump_json())
-    assert parsed.field_metadata == expected

py/tests/classifier/test_client.py

@@ -1,212 +0,0 @@
-import os
-import pytest
-from llama_cloud.client import AsyncLlamaCloud
-from llama_cloud.types import Project, ClassifierRule, ClassifyJobResults
-from llama_cloud_services.beta.classifier.client import ClassifyClient
-from llama_cloud_services.files.client import FileClient
-from llama_cloud.errors.unprocessable_entity_error import UnprocessableEntityError
-from tests.conftest import EndToEndTestSettings
-import nest_asyncio
-
-# Skip all tests if API key is not set
-pytestmark = pytest.mark.skipif(
-    not os.getenv("LLAMA_CLOUD_API_KEY"), reason="LLAMA_CLOUD_API_KEY not set"
-)
-
-
-@pytest.fixture
-def async_llama_cloud_client(
-    e2e_test_settings: EndToEndTestSettings,
-) -> AsyncLlamaCloud:
-    return AsyncLlamaCloud(
-        token=e2e_test_settings.LLAMA_CLOUD_API_KEY.get_secret_value(),
-        base_url=e2e_test_settings.LLAMA_CLOUD_BASE_URL,
-    )
-
-
-@pytest.fixture
-async def project(
-    async_llama_cloud_client: AsyncLlamaCloud, e2e_test_settings: EndToEndTestSettings
-) -> Project:
-    projects = await async_llama_cloud_client.projects.list_projects(
-        project_name=e2e_test_settings.LLAMA_CLOUD_PROJECT_NAME,
-        organization_id=e2e_test_settings.LLAMA_CLOUD_ORGANIZATION_ID,
-    )
-    assert len(projects) == 1
-    return projects[0]
-
-
-@pytest.fixture
-def classify_client(
-    async_llama_cloud_client: AsyncLlamaCloud, project: Project
-) -> ClassifyClient:
-    return ClassifyClient(
-        async_llama_cloud_client,
-        project_id=project.id,
-        organization_id=project.organization_id,
-        polling_interval=1,
-    )
-
-
-@pytest.fixture
-def file_client(
-    async_llama_cloud_client: AsyncLlamaCloud, project: Project
-) -> FileClient:
-    return FileClient(
-        async_llama_cloud_client,
-        project_id=project.id,
-        organization_id=project.organization_id,
-        use_presigned_url=False,
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def apply_nest_asyncio():
-    nest_asyncio.apply()
-
-
-@pytest.fixture
-def simple_pdf_file_path() -> str:
-    return "tests/test_files/index/Simple PDF Slides.pdf"
-
-
-@pytest.fixture
-def research_paper_path() -> str:
-    return "tests/test_files/attention_is_all_you_need_chart.pdf"
-
-
-@pytest.fixture
-def classification_rules() -> list[ClassifierRule]:
-    return [
-        ClassifierRule(
-            type="number",
-            description="Documents with numbers",
-        ),
-        ClassifierRule(
-            type="research_paper",
-            description="Research papers",
-        ),
-    ]
-
-
-parameterize_sync_and_async = pytest.mark.parametrize("sync", [True, False])
-
-
-@parameterize_sync_and_async
-@pytest.mark.asyncio
-async def test_classify_file_ids(
-    classify_client: ClassifyClient,
-    file_client: FileClient,
-    simple_pdf_file_path: str,
-    research_paper_path: str,
-    classification_rules: list[ClassifierRule],
-    sync: bool,
-):
-    """Test classifying files by their IDs"""
-    # Upload test files first to get their IDs
-    pdf_file = await file_client.upload_file(simple_pdf_file_path)
-    research_paper_file = await file_client.upload_file(research_paper_path)
-
-    # Classify the uploaded files
-    if sync:
-        results = classify_client.classify_file_ids(
-            rules=classification_rules, file_ids=[pdf_file.id, research_paper_file.id]
-        )
-    else:
-        results = await classify_client.aclassify_file_ids(
-            rules=classification_rules, file_ids=[pdf_file.id, research_paper_file.id]
-        )
-
-    assert isinstance(results, ClassifyJobResults)
-    assert len(results.items) == 2
-
-    file_id_to_expected_type = {
-        pdf_file.id: "number",
-        research_paper_file.id: "research_paper",
-    }
-    # Verify each file got classified
-    for item in results.items:
-        expected_type = file_id_to_expected_type[item.file_id]
-        assert item.result.type == expected_type
-
-
-@parameterize_sync_and_async
-@pytest.mark.asyncio
-async def test_classify_file_path(
-    classify_client: ClassifyClient,
-    simple_pdf_file_path: str,
-    classification_rules: list[ClassifierRule],
-    sync: bool,
-):
-    """Test classifying a single file by path"""
-    # Classify the file
-    if sync:
-        results = classify_client.classify_file_path(
-            rules=classification_rules, file_input_path=simple_pdf_file_path
-        )
-    else:
-        results = await classify_client.aclassify_file_path(
-            rules=classification_rules, file_input_path=simple_pdf_file_path
-        )
-
-    assert isinstance(results, ClassifyJobResults)
-    assert len(results.items) == 1
-
-    # Verify the file got classified
-    item = results.items[0]
-    assert item.result.type == "number"
-
-
-@parameterize_sync_and_async
-@pytest.mark.asyncio
-async def test_classify_file_paths(
-    classify_client: ClassifyClient,
-    file_client: FileClient,
-    simple_pdf_file_path: str,
-    research_paper_path: str,
-    classification_rules: list[ClassifierRule],
-    sync: bool,
-):
-    """Test classifying multiple files by paths"""
-    # Classify all test files
-    if sync:
-        results = classify_client.classify_file_paths(
-            rules=classification_rules,
-            file_input_paths=[simple_pdf_file_path, research_paper_path],
-        )
-    else:
-        results = await classify_client.aclassify_file_paths(
-            rules=classification_rules,
-            file_input_paths=[simple_pdf_file_path, research_paper_path],
-        )
-
-    assert isinstance(results, ClassifyJobResults)
-    assert len(results.items) == 2
-
-    file_name_to_expected_type = {
-        os.path.basename(simple_pdf_file_path): "number",
-        os.path.basename(research_paper_path): "research_paper",
-    }
-    # Verify each file got classified
-    for item in results.items:
-        file = await file_client.get_file(item.file_id)
-        expected_type = file_name_to_expected_type[file.name]
-        assert item.result.type == expected_type
-
-
-@parameterize_sync_and_async
-@pytest.mark.asyncio
-async def test_classify_empty_file_list(
-    classify_client: ClassifyClient,
-    classification_rules: list[ClassifierRule],
-    sync: bool,
-):
-    """Test classifying an empty list of files"""
-    # This should throw an error
-    with pytest.raises(UnprocessableEntityError):
-        if sync:
-            classify_client.classify_file_ids(rules=classification_rules, file_ids=[])
-        else:
-            await classify_client.aclassify_file_ids(
-                rules=classification_rules, file_ids=[]
-            )

py/tests/conftest.py

@@ -1,26 +0,0 @@
-import pytest
-from llama_index.core.constants import DEFAULT_BASE_URL
-from pydantic import Field, SecretStr
-from pydantic_settings import BaseSettings
-
-
-class EndToEndTestSettings(BaseSettings):
-    LLAMA_CLOUD_BASE_URL: str = Field(
-        description="The base URL of the LlamaCloud API", default=DEFAULT_BASE_URL
-    )
-    LLAMA_CLOUD_API_KEY: SecretStr = Field(
-        description="The API key for the LlamaCloud API"
-    )
-    LLAMA_CLOUD_ORGANIZATION_ID: str | None = Field(
-        description="The organization ID for the LlamaCloud API",
-        default=None,
-    )
-    LLAMA_CLOUD_PROJECT_NAME: str = Field(
-        description="The project name for the LlamaCloud API",
-        default="framework_integration_test",
-    )
-
-
-@pytest.fixture
-def e2e_test_settings() -> EndToEndTestSettings:
-    return EndToEndTestSettings()

py/tests/extract/conftest.py

@@ -6,12 +6,6 @@ from llama_cloud_services.extract import LlamaExtract
 _TEST_AGENTS_TO_CLEANUP: List[str] = []
 
 
-def pytest_configure(config):
-    """Register custom markers for extract tests."""
-    config.addinivalue_line("markers", "agent_name: custom agent name for test")
-    config.addinivalue_line("markers", "agent_schema: custom agent schema for test")
-
-
 def pytest_sessionfinish(session, exitstatus):
     """Hook that runs after all tests complete - cleanup agents here"""
     print(

py/tests/extract/test_benchmark.py

@@ -23,9 +23,8 @@ LLAMA_CLOUD_API_KEY = os.getenv("LLAMA_CLOUD_API_KEY")
 LLAMA_CLOUD_BASE_URL = os.getenv("LLAMA_CLOUD_BASE_URL")
 LLAMA_CLOUD_PROJECT_ID = os.getenv("LLAMA_CLOUD_PROJECT_ID")
 
-BenchmarkTestCase = namedtuple(
-    "BenchmarkTestCase",
-    ["name", "schema_path", "config", "input_file", "expected_output"],
+TestCase = namedtuple(
+    "TestCase", ["name", "schema_path", "config", "input_file", "expected_output"]
 )
 
 
@@ -33,7 +32,7 @@ def get_test_cases():
     """Get all test cases from TEST_DIR.
 
     Returns:
-        List[BenchmarkTestCase]: List of test cases
+        List[TestCase]: List of test cases
     """
     test_cases = []
 
@@ -74,7 +73,7 @@ def get_test_cases():
             test_name = f"{data_type}/{os.path.basename(input_file)}"
             for setting in settings:
                 test_cases.append(
-                    BenchmarkTestCase(
+                    TestCase(
                         name=test_name,
                         schema_path=schema_path,
                         input_file=input_file,
@@ -101,7 +100,7 @@ def extractor():
 
 
 @pytest.fixture
-def extraction_agent(test_case: BenchmarkTestCase, extractor: LlamaExtract):
+def extraction_agent(test_case: TestCase, extractor: LlamaExtract):
     """Fixture to create and cleanup extraction agent for each test."""
     # Create unique name with random UUID (important for CI to avoid conflicts)
     unique_id = uuid.uuid4().hex[:8]
@@ -125,13 +124,13 @@ def extraction_agent(test_case: BenchmarkTestCase, extractor: LlamaExtract):
 
 
 @pytest.mark.skipif(
-    "CI" in os.environ or not LLAMA_CLOUD_API_KEY,
-    reason="LLAMA_CLOUD_API_KEY not set or CI environment not suitable for benchmarking",
+    "CI" in os.environ,
+    reason="CI environment is not suitable for benchmarking",
 )
 @pytest.mark.parametrize("test_case", get_test_cases(), ids=lambda x: x.name)
 @pytest.mark.asyncio(loop_scope="session")
 async def test_extraction(
-    test_case: BenchmarkTestCase, extraction_agent: ExtractionAgent
+    test_case: TestCase, extraction_agent: ExtractionAgent
 ) -> None:
     start = perf_counter()
     result = await extraction_agent._run_extraction_test(

py/tests/extract/test_extract_api.py

@@ -4,7 +4,6 @@ from pathlib import Path
 from pydantic import BaseModel
 
 from llama_cloud_services.extract import LlamaExtract, ExtractionAgent, SourceText
-from llama_cloud.types import ExtractConfig, ExtractMode, ExtractRun
 from tests.extract.util import load_test_dotenv
 from .conftest import register_agent_for_cleanup
 
@@ -22,7 +21,7 @@ pytestmark = pytest.mark.skipif(
 
 
 # Test data
-class ExampleSchema(BaseModel):
+class TestSchema(BaseModel):
     title: str
     summary: str
 
@@ -108,7 +107,7 @@ class TestLlamaExtract:
         assert isinstance(test_agent, ExtractionAgent)
 
     @pytest.mark.agent_name("test-pydantic-schema-agent")
-    @pytest.mark.agent_schema((ExampleSchema,))
+    @pytest.mark.agent_schema((TestSchema,))
     def test_create_agent_with_pydantic_schema(self, test_agent):
         assert isinstance(test_agent, ExtractionAgent)
 
@@ -223,189 +222,7 @@ class TestExtractionAgent:
 
     def test_delete_extraction_run(self, test_agent: ExtractionAgent):
         assert test_agent.list_extraction_runs().total == 0
-        run: ExtractRun = test_agent.extract(TEST_PDF)
+        run = test_agent.extract(TEST_PDF)
         test_agent.delete_extraction_run(run.id)
         runs = test_agent.list_extraction_runs()
         assert runs.total == 0
-
-
-@pytest.mark.skipif(
-    "CI" in os.environ, reason="Test locally; functionality is mostly duplicated."
-)
-class TestStatelessExtraction:
-    """Tests for stateless extraction methods that don't require creating an agent."""
-
-    @pytest.fixture
-    def test_config(self):
-        return ExtractConfig(extraction_mode=ExtractMode.FAST)
-
-    @pytest.fixture
-    def test_schema_dict(self):
-        return {
-            "type": "object",
-            "properties": {
-                "title": {"type": "string"},
-                "summary": {"type": "string"},
-            },
-        }
-
-    @pytest.mark.asyncio
-    async def test_aextract_single_file(
-        self, llama_extract, test_schema_dict, test_config
-    ):
-        """Test async stateless extraction with a single file."""
-        result = await llama_extract.aextract(test_schema_dict, test_config, TEST_PDF)
-        assert result.status == "SUCCESS"
-        assert result.data is not None
-        assert isinstance(result.data, dict)
-        assert "title" in result.data
-        assert "summary" in result.data
-
-    def test_extract_single_file(self, llama_extract, test_schema_dict, test_config):
-        """Test synchronous stateless extraction with a single file."""
-        result = llama_extract.extract(test_schema_dict, test_config, TEST_PDF)
-        assert result.status == "SUCCESS"
-        assert result.data is not None
-        assert isinstance(result.data, dict)
-        assert "title" in result.data
-        assert "summary" in result.data
-
-    def test_extract_from_bytes_with_source_text(
-        self, llama_extract, test_schema_dict, test_config
-    ):
-        """Test stateless extraction from bytes using SourceText with filename."""
-        with open(TEST_PDF, "rb") as f:
-            file_bytes = f.read()
-        source_text = SourceText(file=file_bytes, filename=TEST_PDF.name)
-        result = llama_extract.extract(test_schema_dict, test_config, source_text)
-        assert result.status == "SUCCESS"
-        assert result.data is not None
-        assert isinstance(result.data, dict)
-        assert "title" in result.data
-        assert "summary" in result.data
-
-    def test_extract_from_source_text_with_file(
-        self, llama_extract, test_schema_dict, test_config
-    ):
-        """Test stateless extraction from SourceText with file."""
-        source_text = SourceText(file=TEST_PDF, filename=TEST_PDF.name)
-        result = llama_extract.extract(test_schema_dict, test_config, source_text)
-        assert result.status == "SUCCESS"
-        assert result.data is not None
-        assert isinstance(result.data, dict)
-        assert "title" in result.data
-        assert "summary" in result.data
-
-    def test_extract_from_buffered_io(
-        self, llama_extract, test_schema_dict, test_config
-    ):
-        """Test stateless extraction from BufferedIO file handle."""
-        with open(TEST_PDF, "rb") as file_handle:
-            result = llama_extract.extract(test_schema_dict, test_config, file_handle)
-            assert result.status == "SUCCESS"
-            assert result.data is not None
-            assert isinstance(result.data, dict)
-            assert "title" in result.data
-            assert "summary" in result.data
-
-    def test_extract_from_source_text_with_text_content(
-        self, llama_extract, test_schema_dict, test_config
-    ):
-        """Test stateless extraction from SourceText with text content."""
-        TEST_TEXT = """
-        # Llamas
-        Llamas are social animals and live with others as a herd. Their wool is soft and
-        contains only a small amount of lanolin. Llamas can learn simple tasks after a
-        few repetitions. When using a pack, they can carry about 25 to 30% of their body
-        weight for 8 to 13 km (5–8 miles). The name llama was adopted by European settlers
-        from native Peruvians.
-        """
-        source_text = SourceText(text_content=TEST_TEXT)
-        result = llama_extract.extract(test_schema_dict, test_config, source_text)
-        assert result.status == "SUCCESS"
-        assert result.data is not None
-        assert isinstance(result.data, dict)
-        assert "title" in result.data
-        assert "summary" in result.data
-
-    @pytest.mark.asyncio
-    async def test_queue_extraction_single_file(
-        self, llama_extract, test_schema_dict, test_config
-    ):
-        """Test queuing extraction job without waiting for completion."""
-        job = await llama_extract.queue_extraction(
-            test_schema_dict, test_config, TEST_PDF
-        )
-        assert hasattr(job, "id")
-        assert hasattr(job, "status")
-
-    @pytest.mark.asyncio
-    async def test_extract_multiple_files(
-        self, llama_extract, test_schema_dict, test_config
-    ):
-        """Test stateless extraction with multiple files."""
-        files = [TEST_PDF, TEST_PDF]  # Using same file twice for testing
-        results = await llama_extract.aextract(test_schema_dict, test_config, files)
-
-        assert isinstance(results, list)
-        assert len(results) == 2
-
-        for result in results:
-            assert result.status == "SUCCESS"
-            assert result.data is not None
-            assert isinstance(result.data, dict)
-            assert "title" in result.data
-            assert "summary" in result.data
-
-    def test_extract_with_pydantic_schema(self, llama_extract, test_config):
-        """Test stateless extraction with Pydantic schema."""
-        result = llama_extract.extract(ExampleSchema, test_config, TEST_PDF)
-        assert result.status == "SUCCESS"
-        assert result.data is not None
-        assert isinstance(result.data, dict)
-        assert "title" in result.data
-        assert "summary" in result.data
-
-    def test_extract_from_raw_bytes_raises_error(
-        self, llama_extract, test_schema_dict, test_config
-    ):
-        """Test that raw bytes without filename raises an error."""
-        with open(TEST_PDF, "rb") as f:
-            file_bytes = f.read()
-
-        with pytest.raises(
-            ValueError, match="Cannot determine file type from raw bytes"
-        ):
-            llama_extract.extract(test_schema_dict, test_config, file_bytes)
-
-    def test_mime_type_detection(self, llama_extract):
-        """Test that MIME types are correctly detected for various file types."""
-        # Test PDF
-        assert llama_extract._get_mime_type(filename="test.pdf") == "application/pdf"
-
-        # Test DOCX
-        assert (
-            llama_extract._get_mime_type(filename="test.docx")
-            == "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
-        )
-
-        # Test text files
-        assert llama_extract._get_mime_type(filename="test.txt") == "text/plain"
-        assert llama_extract._get_mime_type(filename="test.csv") == "text/csv"
-        assert llama_extract._get_mime_type(filename="test.json") == "application/json"
-
-        # Test image files
-        assert llama_extract._get_mime_type(filename="test.png") == "image/png"
-        assert llama_extract._get_mime_type(filename="test.jpg") == "image/jpeg"
-
-        # Test file path
-        from pathlib import Path
-
-        assert (
-            llama_extract._get_mime_type(file_path=Path("test.pdf"))
-            == "application/pdf"
-        )
-
-        # Test unsupported file type
-        with pytest.raises(ValueError, match="Unsupported file type: 'xyz'"):
-            llama_extract._get_mime_type(filename="test.xyz")

py/tests/extract/test_extract_e2e.py

@@ -19,9 +19,8 @@ LLAMA_CLOUD_API_KEY = os.getenv("LLAMA_CLOUD_API_KEY")
 LLAMA_CLOUD_BASE_URL = os.getenv("LLAMA_CLOUD_BASE_URL")
 LLAMA_CLOUD_PROJECT_ID = os.getenv("LLAMA_CLOUD_PROJECT_ID")
 
-ExtractionTestCase = namedtuple(
-    "ExtractionTestCase",
-    ["name", "schema_path", "config", "input_file", "expected_output"],
+TestCase = namedtuple(
+    "TestCase", ["name", "schema_path", "config", "input_file", "expected_output"]
 )
 
 
@@ -29,7 +28,7 @@ def get_test_cases():
     """Get all test cases from TEST_DIR.
 
     Returns:
-        List[ExtractionTestCase]: List of test cases
+        List[TestCase]: List of test cases
     """
     test_cases = []
 
@@ -70,7 +69,7 @@ def get_test_cases():
             test_name = f"{data_type}/{os.path.basename(input_file)}"
             for setting in settings:
                 test_cases.append(
-                    ExtractionTestCase(
+                    TestCase(
                         name=test_name,
                         schema_path=schema_path,
                         input_file=input_file,
@@ -97,7 +96,7 @@ def extractor():
 
 
 @pytest.fixture
-def extraction_agent(test_case: ExtractionTestCase, extractor: LlamaExtract):
+def extraction_agent(test_case: TestCase, extractor: LlamaExtract):
     """Fixture to create and cleanup extraction agent for each test."""
     # Create unique name with random UUID (important for CI to avoid conflicts)
     unique_id = uuid.uuid4().hex[:8]
@@ -129,9 +128,7 @@ def extraction_agent(test_case: ExtractionTestCase, extractor: LlamaExtract):
     reason="LLAMA_CLOUD_API_KEY not set",
 )
 @pytest.mark.parametrize("test_case", get_test_cases(), ids=lambda x: x.name)
-def test_extraction(
-    test_case: ExtractionTestCase, extraction_agent: ExtractionAgent
-) -> None:
+def test_extraction(test_case: TestCase, extraction_agent: ExtractionAgent) -> None:
     result = extraction_agent.extract(test_case.input_file).data  # type: ignore
     with open(test_case.expected_output, "r") as f:
         expected = json.load(f)

py/tests/index/test_index.py

@@ -16,6 +16,7 @@ from llama_cloud import (
 from llama_cloud.client import LlamaCloud
 from llama_index.core.bridge.pydantic import BaseModel
 from llama_index.core.constants import DEFAULT_BASE_URL
+from llama_index.core.indices.managed.base import BaseManagedIndex
 from llama_index.core.schema import Document, ImageNode
 from llama_cloud_services.index import (
     LlamaCloudIndex,
@@ -90,6 +91,16 @@ def _setup_index_with_file(
     return pipeline
 
 
+def test_class():
+    names_of_base_classes = [b.__name__ for b in LlamaCloudIndex.__mro__]
+    assert BaseManagedIndex.__name__ in names_of_base_classes
+
+
+def test_conflicting_index_identifiers():
+    with pytest.raises(ValueError):
+        LlamaCloudIndex(name="test", pipeline_id="test", index_id="test")
+
+
 @pytest.mark.skipif(
     not base_url or not api_key, reason="No platform base url or api key set"
 )
@@ -351,9 +362,6 @@ async def test_page_figure_retrieval(index_name: str, local_figures_file: str):
     not base_url or not api_key, reason="No platform base url or api key set"
 )
 @pytest.mark.asyncio
-@pytest.mark.skip(
-    reason="Consistently failing with 'Server disconnected without sending a response'"
-)
 async def test_composite_retriever(index_name: str):
     """Test the LlamaCloudCompositeRetriever with multiple indices."""
     # Create first index with documents

py/tests/parse/test_llama_parse.py

@@ -202,12 +202,3 @@ async def test_get_result(markdown_parser: LlamaParse) -> None:
     result = await markdown_parser.aget_result(expected.job_id)
     assert result.job_id == expected.job_id
     assert len(result.pages) == len(expected.pages)
-
-
-@pytest.mark.asyncio
-async def test_parse_audio() -> None:
-    parser = LlamaParse()
-    filepath = "tests/test_files/hello_world.m4a"
-
-    result = await parser.aparse(filepath)
-    assert result.job_id is not None

py/tests/test_files_client.py

@@ -1,147 +0,0 @@
-import os
-import pytest
-from io import BytesIO
-from llama_cloud.client import AsyncLlamaCloud
-from llama_cloud.types import Project, File
-from llama_cloud_services.files.client import FileClient
-from tests.conftest import EndToEndTestSettings
-
-
-@pytest.fixture
-async def llama_cloud_client(
-    e2e_test_settings: EndToEndTestSettings,
-) -> AsyncLlamaCloud:
-    return AsyncLlamaCloud(
-        token=e2e_test_settings.LLAMA_CLOUD_API_KEY.get_secret_value(),
-        base_url=e2e_test_settings.LLAMA_CLOUD_BASE_URL,
-    )
-
-
-@pytest.fixture
-async def project(
-    llama_cloud_client: AsyncLlamaCloud, e2e_test_settings: EndToEndTestSettings
-) -> Project:
-    projects = await llama_cloud_client.projects.list_projects(
-        project_name=e2e_test_settings.LLAMA_CLOUD_PROJECT_NAME,
-        organization_id=e2e_test_settings.LLAMA_CLOUD_ORGANIZATION_ID,
-    )
-    assert len(projects) == 1
-    return projects[0]
-
-
-@pytest.fixture
-async def file_client(
-    llama_cloud_client: AsyncLlamaCloud, project: Project, use_presigned_url: bool
-) -> FileClient:
-    return FileClient(
-        llama_cloud_client,
-        project_id=project.id,
-        organization_id=project.organization_id,
-        use_presigned_url=use_presigned_url,
-    )
-
-
-@pytest.fixture
-def test_file() -> str:
-    return "tests/test_files/index/Simple PDF Slides.pdf"
-
-
-parametrize_use_presigned_url = pytest.mark.parametrize(
-    "use_presigned_url", [True, False]
-)
-
-
-@parametrize_use_presigned_url
-@pytest.mark.asyncio
-async def test_upload_file_from_path(file_client: FileClient, test_file: str):
-    """Test uploading a file from file path"""
-    external_file_id = f"test_upload_path_{os.getpid()}"
-    uploaded_file = await file_client.upload_file(test_file, external_file_id)
-
-    assert isinstance(uploaded_file, File)
-    expected_name = os.path.basename(test_file)
-    assert uploaded_file.name == expected_name
-    assert uploaded_file.external_file_id == external_file_id
-
-
-@parametrize_use_presigned_url
-@pytest.mark.asyncio
-async def test_upload_bytes(
-    file_client: FileClient, test_file: str, use_presigned_url: bool
-):
-    """Test uploading a file from bytes"""
-    # Read file as bytes
-    with open(test_file, "rb") as f:
-        file_bytes = f.read()
-
-    external_file_id = f"test_upload_bytes_{os.getpid()}"
-    uploaded_file = await file_client.upload_bytes(file_bytes, external_file_id)
-
-    assert isinstance(uploaded_file, File)
-    expected_name = external_file_id if use_presigned_url else "upload"
-    assert uploaded_file.name == expected_name
-    assert uploaded_file.external_file_id == external_file_id
-
-
-@parametrize_use_presigned_url
-@pytest.mark.asyncio
-async def test_upload_buffer(
-    file_client: FileClient, test_file: str, use_presigned_url: bool
-):
-    """Test uploading a file from buffer"""
-    # Read file as bytes and create buffer
-    with open(test_file, "rb") as f:
-        file_bytes = f.read()
-
-    buffer = BytesIO(file_bytes)
-    file_size = len(file_bytes)
-    external_file_id = f"test_upload_buffer_{os.getpid()}"
-
-    uploaded_file = await file_client.upload_buffer(buffer, external_file_id, file_size)
-
-    assert isinstance(uploaded_file, File)
-    expected_name = external_file_id if use_presigned_url else "upload"
-    assert uploaded_file.name == expected_name
-    assert uploaded_file.external_file_id == external_file_id
-
-
-@parametrize_use_presigned_url
-@pytest.mark.asyncio
-async def test_get_file(file_client: FileClient, test_file: str):
-    """Test retrieving a file by ID"""
-    # Upload a file first
-    external_file_id = f"test_get_file_{os.getpid()}"
-    uploaded_file = await file_client.upload_file(test_file, external_file_id)
-
-    # Retrieve the file by ID
-    retrieved_file = await file_client.get_file(uploaded_file.id)
-
-    assert isinstance(retrieved_file, File)
-    assert retrieved_file == uploaded_file
-
-
-@parametrize_use_presigned_url
-@pytest.mark.asyncio
-async def test_upload_with_default_external_id(file_client: FileClient, test_file: str):
-    """Test uploading file with default external_file_id"""
-    # Upload file without specifying external_file_id
-    uploaded_file = await file_client.upload_file(test_file)
-
-    assert isinstance(uploaded_file, File)
-    assert uploaded_file.name == os.path.basename(test_file)
-    assert uploaded_file.external_file_id == test_file
-
-
-@parametrize_use_presigned_url
-@pytest.mark.asyncio
-async def test_read_file_content(file_client: FileClient, test_file: str):
-    """Test reading a file content"""
-    # Upload a file first
-    external_file_id = f"test_read_file_content_{os.getpid()}"
-    uploaded_file = await file_client.upload_file(test_file, external_file_id)
-
-    # Read the file content
-    file_content = await file_client.read_file_content(uploaded_file.id)
-    with open(test_file, "rb") as f:
-        expected_file_content = f.read()
-    assert file_content == expected_file_content

py/tests/test_utils.py

@@ -1,9 +1,5 @@
-from unittest.mock import Mock
-
-import httpx
-import pytest
 from pydantic import BaseModel
-from llama_cloud_services.utils import check_extra_params, check_for_updates
+from llama_cloud_services.utils import check_extra_params
 
 
 class MyModel(BaseModel):
@@ -68,30 +64,3 @@ def test_check_extra_params_completely_invalid():
     for suggestion in suggestions:
         assert "check the documentation or update the package" in suggestion
         assert "Did you mean" not in suggestion
-
-
-@pytest.mark.asyncio
-async def test_check_for_updates(capsys: pytest.CaptureFixture):
-    """Test update checker."""
-
-    mock_response = Mock()
-    mock_client = Mock(spec=httpx.AsyncClient)
-    mock_client.get.return_value = mock_response
-
-    mock_response.json.return_value = {"info": {"version": "0.0.0"}}
-    assert not await check_for_updates(mock_client)
-    out, err = capsys.readouterr()
-    assert not out and not err
-
-    assert not await check_for_updates(mock_client, quiet=False)
-    out, _ = capsys.readouterr()
-    assert "up to date" in out
-
-    mock_response.json.return_value = {"info": {"version": "999.0.0"}}
-    assert await check_for_updates(mock_client)
-    out, err = capsys.readouterr()
-    assert not out and not err
-
-    assert await check_for_updates(mock_client, quiet=False)
-    out, _ = capsys.readouterr()
-    assert "out of date" in out

py/unit_tests/data/capacitor.json

@@ -1,116 +0,0 @@
-{
-  "id": "de058dda-6ca7-4eea-a426-da802f84f971",
-  "created_at": "2025-08-13T15:45:39.286921Z",
-  "updated_at": "2025-08-13T15:47:04.878069Z",
-  "extraction_agent_id": "e834e99f-1f35-4748-b82f-03de4bd07ca6",
-  "data_schema": {
-    "additionalProperties": false,
-    "properties": {
-      "dimensions": {
-        "anyOf": [
-          {
-            "additionalProperties": false,
-            "properties": {
-              "length": {
-                "anyOf": [{ "type": "string" }, { "type": "null" }],
-                "description": "Length in mm (Size, Longest Side, L)"
-              },
-              "width": {
-                "anyOf": [{ "type": "string" }, { "type": "null" }],
-                "description": "Width in mm (Breadth, Side Width, W)"
-              },
-              "height": {
-                "anyOf": [{ "type": "string" }, { "type": "null" }],
-                "description": "Height in mm (Thickness, Vertical Size, H)"
-              },
-              "diameter": {
-                "anyOf": [{ "type": "string" }, { "type": "null" }],
-                "description": "Diameter in mm (for radial or cylindrical types) (Outer Diameter, OD, D)"
-              },
-              "lead_spacing": {
-                "anyOf": [{ "type": "string" }, { "type": "null" }],
-                "description": "Lead spacing in mm (Pin Pitch, Terminal Gap, LS)"
-              }
-            },
-            "required": [
-              "length",
-              "width",
-              "height",
-              "diameter",
-              "lead_spacing"
-            ],
-            "type": "object"
-          },
-          { "type": "null" }
-        ]
-      }
-    },
-    "required": ["dimensions"],
-    "type": "object"
-  },
-  "config": {
-    "priority": null,
-    "extraction_target": "PER_DOC",
-    "extraction_mode": "PREMIUM",
-    "multimodal_fast_mode": false,
-    "system_prompt": "",
-    "use_reasoning": true,
-    "cite_sources": false,
-    "confidence_scores": true,
-    "chunk_mode": "PAGE",
-    "high_resolution_mode": false,
-    "invalidate_cache": false,
-    "page_range": null
-  },
-  "file": {
-    "id": "9233cc2b-00e3-4ddd-b426-b8b59357d4cb",
-    "created_at": "2025-08-12T18:31:54.269440Z",
-    "updated_at": "2025-08-13T15:45:39.064906Z",
-    "name": "document (2).pdf.txt",
-    "external_file_id": "document (2).pdf.txt",
-    "file_size": 2562,
-    "file_type": "txt",
-    "project_id": "77bdc79f-fb69-49ae-a783-fcc573eec7ce",
-    "last_modified_at": "2025-08-13T15:45:39Z",
-    "resource_info": {
-      "file_size": 2562,
-      "last_modified_at": "2025-08-13T15:45:39"
-    },
-    "permission_info": null,
-    "data_source_id": null
-  },
-  "status": "SUCCESS",
-  "error": null,
-  "job_id": "5bb8a583-366c-416c-ba55-4f5724fef9a9",
-  "data": {
-    "dimensions": {
-      "length": "82 mm",
-      "width": null,
-      "height": null,
-      "diameter": "35 mm",
-      "lead_spacing": "6.0 mm"
-    }
-  },
-  "extraction_metadata": {
-    "field_metadata": {
-      "dimensions": {
-        "length": {
-          "extraction_confidence": 0.9999968039036192,
-          "confidence": 0.9999968039036192
-        },
-        "diameter": { "extraction_confidence": 1.0, "confidence": 1.0 },
-        "lead_spacing": {
-          "extraction_confidence": 0.9999999031936799,
-          "confidence": 0.9999999031936799
-        },
-        "reasoning": "VERBATIM EXTRACTION"
-      }
-    },
-    "usage": {
-      "num_pages_extracted": 2,
-      "num_document_tokens": 1034,
-      "num_output_tokens": 3440
-    }
-  },
-  "from_ui": false
-}

py/unit_tests/index/test_unit_index.py

@@ -1,16 +0,0 @@
-import pytest
-
-from llama_index.core.indices.managed.base import BaseManagedIndex
-from llama_cloud_services.index import (
-    LlamaCloudIndex,
-)
-
-
-def test_class():
-    names_of_base_classes = [b.__name__ for b in LlamaCloudIndex.__mro__]
-    assert BaseManagedIndex.__name__ in names_of_base_classes
-
-
-def test_conflicting_index_identifiers():
-    with pytest.raises(ValueError):
-        LlamaCloudIndex(name="test", pipeline_id="test", index_id="test")

py/uv.lock

@@ -734,7 +734,7 @@ name = "exceptiongroup"
 version = "1.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/0b/9f/a65090624ecf468cdca03533906e7c69ed7588582240cfe7cc9e770b50eb/exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88", size = 29749, upload-time = "2025-05-10T17:42:51.123Z" }
 wheels = [
@@ -1573,21 +1573,21 @@ wheels = [
 
 [[package]]
 name = "llama-cloud"
-version = "0.1.37"
+version = "0.1.35"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "certifi" },
     { name = "httpx" },
     { name = "pydantic" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/9c/dd/c4f2516523778a0d4b284fa4b66a0136afdd59694f105102838cf793e675/llama_cloud-0.1.37.tar.gz", hash = "sha256:b6d62e7386d1aa85905b7e3f7c19a40694be54c1596668bceaa456cd84ede666", size = 108707, upload-time = "2025-08-04T22:00:37.18Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/9b/72/816e6e900448e1b4a8137d90e65876b296c5264a23db6ae888bd3e6660ba/llama_cloud-0.1.35.tar.gz", hash = "sha256:200349d5d57424d7461f304cdb1355a58eea3e6ca1e6b0d75c66b2e937216983", size = 106403, upload-time = "2025-07-28T17:22:06.41Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/59/ca/a7f874b041d2566f000ecc88bf1b76819a8bdbbaea85036ca6905ae3f0f7/llama_cloud-0.1.37-py3-none-any.whl", hash = "sha256:3109ec74575f53311ec4957ca8aea2d6e30556a43d24c6316ceab91c4fed6ab7", size = 314244, upload-time = "2025-08-04T22:00:35.696Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/d2/8d18a021ab757cea231428404f21fe3186bf1ebaac3f57a73c379483fd3f/llama_cloud-0.1.35-py3-none-any.whl", hash = "sha256:b7abab4423118e6f638d2f326749e7a07c6426543bea6da99b623c715b22af71", size = 303280, upload-time = "2025-07-28T17:22:04.946Z" },
 ]
 
 [[package]]
 name = "llama-cloud-services"
-version = "0.6.58"
+version = "0.6.53"
 source = { editable = "." }
 dependencies = [
     { name = "click", version = "8.1.8", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
@@ -1595,7 +1595,6 @@ dependencies = [
     { name = "eval-type-backport", marker = "python_full_version < '3.10'" },
     { name = "llama-cloud" },
     { name = "llama-index-core" },
-    { name = "packaging" },
     { name = "platformdirs" },
     { name = "pydantic" },
     { name = "python-dotenv" },
@@ -1612,7 +1611,6 @@ dev = [
     { name = "jupyter" },
     { name = "mypy" },
     { name = "pre-commit" },
-    { name = "pydantic-settings" },
     { name = "pytest" },
     { name = "pytest-asyncio" },
 ]
@@ -1621,9 +1619,8 @@ dev = [
 requires-dist = [
     { name = "click", specifier = ">=8.1.7,<9" },
     { name = "eval-type-backport", marker = "python_full_version < '3.10'", specifier = ">=0.2.0,<0.3" },
-    { name = "llama-cloud", specifier = "==0.1.37" },
+    { name = "llama-cloud", specifier = "==0.1.35" },
     { name = "llama-index-core", specifier = ">=0.12.0" },
-    { name = "packaging", specifier = ">=25.0" },
     { name = "platformdirs", specifier = ">=4.3.7,<5" },
     { name = "pydantic", specifier = ">=2.8,!=2.10" },
     { name = "python-dotenv", specifier = ">=1.0.1,<2" },
@@ -1639,7 +1636,6 @@ dev = [
     { name = "jupyter", specifier = ">=1.1.1,<2" },
     { name = "mypy", specifier = ">=1.14.1,<2" },
     { name = "pre-commit", specifier = "==3.2.0" },
-    { name = "pydantic-settings", specifier = ">=2.10.1" },
     { name = "pytest", specifier = ">=8.0.0,<9" },
     { name = "pytest-asyncio" },
 ]
@@ -2857,20 +2853,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d4/29/3cade8a924a61f60ccfa10842f75eb12787e1440e2b8660ceffeb26685e7/pydantic_core-2.33.2-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:2807668ba86cb38c6817ad9bc66215ab8584d1d304030ce4f0887336f28a5e27", size = 2066661, upload-time = "2025-04-23T18:33:49.995Z" },
 ]
 
-[[package]]
-name = "pydantic-settings"
-version = "2.10.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "pydantic" },
-    { name = "python-dotenv" },
-    { name = "typing-inspection" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/68/85/1ea668bbab3c50071ca613c6ab30047fb36ab0da1b92fa8f17bbc38fd36c/pydantic_settings-2.10.1.tar.gz", hash = "sha256:06f0062169818d0f5524420a360d632d5857b83cffd4d42fe29597807a1614ee", size = 172583, upload-time = "2025-06-24T13:26:46.841Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/58/f0/427018098906416f580e3cf1366d3b1abfb408a0652e9f31600c24a1903c/pydantic_settings-2.10.1-py3-none-any.whl", hash = "sha256:a60952460b99cf661dc25c29c0ef171721f98bfcb52ef8d9ea4c943d7c8cc796", size = 45235, upload-time = "2025-06-24T13:26:45.485Z" },
-]
-
 [[package]]
 name = "pygments"
 version = "2.19.2"

scripts/version-bump.py

@@ -13,26 +13,17 @@ from pathlib import Path
 def get_current_versions() -> tuple[str, str, str]:
     """Get current versions from both pyproject.toml files."""
     # Read main pyproject.toml
-    main_content = Path("py/pyproject.toml").read_text()
+    main_content = Path("pyproject.toml").read_text()
     main_doc = tomlkit.parse(main_content)
-    main_version = main_doc["project"]["version"]
+    main_version = main_doc["tool"]["poetry"]["version"]
 
     # Read llama_parse/pyproject.toml
-    llama_parse_content = Path("py/llama_parse/pyproject.toml").read_text()
+    llama_parse_content = Path("llama_parse/pyproject.toml").read_text()
     llama_parse_doc = tomlkit.parse(llama_parse_content)
-    llama_parse_version = llama_parse_doc["project"]["version"]
-    # Find llama-cloud-services dependency in the dependencies list
-    dependency_version = None
-    for dep in llama_parse_doc["project"]["dependencies"]:
-        if isinstance(dep, str) and dep.startswith("llama-cloud-services"):
-            dependency_version = (
-                dep.split("==")[1]
-                if "==" in dep
-                else dep.split(">=")[1]
-                if ">=" in dep
-                else None
-            )
-            break
+    llama_parse_version = llama_parse_doc["tool"]["poetry"]["version"]
+    dependency_version = llama_parse_doc["tool"]["poetry"]["dependencies"][
+        "llama-cloud-services"
+    ]
 
     return str(main_version), str(llama_parse_version), str(dependency_version)
 
@@ -62,22 +53,19 @@ def validate_versions(
 def set_version(version: str) -> None:
     """Set version across all pyproject.toml files using tomlkit to preserve formatting."""
     # Update main pyproject.toml
-    main_content = Path("py/pyproject.toml").read_text()
+    main_content = Path("pyproject.toml").read_text()
     main_doc = tomlkit.parse(main_content)
-    main_doc["project"]["version"] = version
-    Path("py/pyproject.toml").write_text(tomlkit.dumps(main_doc))
+    main_doc["tool"]["poetry"]["version"] = version
+    Path("pyproject.toml").write_text(tomlkit.dumps(main_doc))
 
     # Update llama_parse/pyproject.toml
-    llama_parse_content = Path("py/llama_parse/pyproject.toml").read_text()
+    llama_parse_content = Path("llama_parse/pyproject.toml").read_text()
     llama_parse_doc = tomlkit.parse(llama_parse_content)
-    llama_parse_doc["project"]["version"] = version
-    for dep_index, dep in enumerate(llama_parse_doc["project"]["dependencies"]):
-        if isinstance(dep, str) and dep.startswith("llama-cloud-services"):
-            llama_parse_doc["project"]["dependencies"][
-                dep_index
-            ] = f"llama-cloud-services>={version}"
-            break
-    Path("py/llama_parse/pyproject.toml").write_text(tomlkit.dumps(llama_parse_doc))
+    llama_parse_doc["tool"]["poetry"]["version"] = version
+    llama_parse_doc["tool"]["poetry"]["dependencies"][
+        "llama-cloud-services"
+    ] = f">={version}"
+    Path("llama_parse/pyproject.toml").write_text(tomlkit.dumps(llama_parse_doc))
 
     click.echo(f"Updated all versions to {version}")
 
@@ -90,7 +78,7 @@ def get_current_branch() -> str:
     return result.stdout.strip()
 
 
-def create_if_not_exists(version: str) -> None:
+def create_and_push_tag(version: str) -> None:
     """Create a git tag and push it."""
     current_branch = get_current_branch()
     if current_branch != "main":
@@ -100,26 +88,12 @@ def create_if_not_exists(version: str) -> None:
         sys.exit(1)
 
     tag_name = f"v{version}"
-    if not tag_exists(version):
-        # Create tag
-        subprocess.run(["git", "tag", tag_name], check=True)
-        click.echo(f"Created tag {tag_name}")
-    else:
-        click.echo(f"Tag {tag_name} already exists")
 
+    # Create tag
+    subprocess.run(["git", "tag", tag_name], check=True)
+    click.echo(f"Created tag {tag_name}")
 
-def tag_exists(version: str) -> bool:
-    """Check if a git tag exists."""
-    tag_name = f"v{version}"
-    result = subprocess.run(
-        ["git", "tag", "-l", tag_name], capture_output=True, text=True, check=True
-    )
-    return tag_name in result.stdout.strip()
-
-
-def push_tag(version: str) -> None:
-    """Push a git tag."""
-    tag_name = f"v{version}"
+    # Push tag
     subprocess.run(["git", "push", "origin", tag_name], check=True)
     click.echo(f"Pushed tag {tag_name}")
 
@@ -160,20 +134,13 @@ def set(version: str) -> None:
 @click.option(
     "--version", help="Version to tag (uses current version if not specified)"
 )
-@click.option(
-    "--push",
-    is_flag=True,
-    help="Push the tag to the remote repository",
-)
-def tag(version: str | None = None, push: bool = False) -> None:
+def tag(version: str | None = None) -> None:
     """Create and push a git tag for the current version."""
     if not version:
         main_version, _, _ = get_current_versions()
         version = main_version
 
-    create_if_not_exists(version)
-    if push:
-        push_tag(version)
+    create_and_push_tag(version)
 
 
 if __name__ == "__main__":

ts/e2e-tests/package.json

@@ -1,23 +0,0 @@
-{
-  "name": "@llamaindex/llama-cloud-services-e2e-tests",
-  "private": true,
-  "version": "0.0.1",
-  "type": "module",
-  "scripts": {
-    "test": "pnpm run test:node16 && pnpm run test:nodenext && pnpm run test:bundler",
-    "build": "pnpm run build:node16 && pnpm run build:nodenext && pnpm run build:bundler",
-    "build:node16": "tsc -p src/tsconfig.node16.json --outDir dist/node16",
-    "test:node16": "pnpm run build:node16 && node --test dist/node16/index.e2e.js",
-    "build:nodenext": "tsc -p src/tsconfig.nodenext.json --outDir dist/nodenext",
-    "test:nodenext": "pnpm run build:nodenext && node --test dist/nodenext/index.e2e.js",
-    "build:bundler": "tsc -p src/tsconfig.bundler.json --outDir dist/bundler",
-    "test:bundler": "pnpm run build:bundler && node --test dist/bundler/index.e2e.js",
-    "build:node": "tsc -p src/tsconfig.node.json --outDir dist/node",
-    "test:node": "pnpm run build:node && node --test dist/node/index.e2e.js"
-  },
-  "devDependencies": {
-    "@types/node": "^24.0.13",
-    "llama-cloud-services": "workspace:*",
-    "typescript": "^5.9.2"
-  }
-}

ts/e2e-tests/src/index.e2e.ts

@@ -1,38 +0,0 @@
-import { ok } from "node:assert";
-import { test } from "node:test";
-
-import { LlamaCloudIndex } from "llama-cloud-services";
-import { LlamaParseReader } from "llama-cloud-services";
-
-test("LlamaIndex module resolution test", async (t) => {
-  await t.test("works with ES module", () => {
-    const index = new LlamaCloudIndex({
-      name: "test-index",
-      projectName: "Default",
-    });
-    const reader = new LlamaParseReader({
-      resultType: "markdown",
-      verbose: false,
-    });
-    ok(index !== undefined);
-    ok(reader !== undefined);
-  });
-
-  await t.test("works with dynamic imports", async () => {
-    const mod = await import("llama-cloud-services"); // simulates commonjs
-    ok(mod !== undefined);
-    const index = new mod.LlamaCloudIndex({
-      name: "test-index",
-      projectName: "Default",
-    });
-    ok(index !== undefined);
-  });
-
-  await t.test("all imports work", () => {
-    const allImports = [
-      LlamaCloudIndex,
-    ];
-
-    ok(allImports.filter(Boolean).length === allImports.length);
-  });
-});

ts/e2e-tests/src/tsconfig.bundler.json

@@ -1,14 +0,0 @@
-{
-  "compilerOptions": {
-    "module": "esnext",
-    "moduleResolution": "bundler",
-    "target": "esnext",
-    "outDir": "dist",
-    "rootDir": ".",
-    "strict": true,
-    "esModuleInterop": true,
-    "skipLibCheck": true
-  },
-  "include": ["**/*.ts"],
-  "exclude": ["node_modules", "dist"]
-}

ts/e2e-tests/src/tsconfig.node.json

@@ -1,14 +0,0 @@
-{
-  "compilerOptions": {
-    "module": "esnext",
-    "moduleResolution": "node",
-    "target": "esnext",
-    "outDir": "dist",
-    "rootDir": ".",
-    "strict": true,
-    "esModuleInterop": true,
-    "skipLibCheck": true
-  },
-  "include": ["**/*.ts"],
-  "exclude": ["node_modules", "dist"]
-}

ts/e2e-tests/src/tsconfig.node16.json

@@ -1,14 +0,0 @@
-{
-  "compilerOptions": {
-    "module": "node16",
-    "moduleResolution": "node16",
-    "target": "esnext",
-    "outDir": "dist",
-    "rootDir": ".",
-    "strict": true,
-    "esModuleInterop": true,
-    "skipLibCheck": true
-  },
-  "include": ["**/*.ts"],
-  "exclude": ["node_modules", "dist"]
-}

ts/e2e-tests/src/tsconfig.nodenext.json

@@ -1,14 +0,0 @@
-{
-  "compilerOptions": {
-    "module": "nodenext",
-    "moduleResolution": "nodenext",
-    "target": "esnext",
-    "outDir": "dist",
-    "rootDir": ".",
-    "strict": true,
-    "esModuleInterop": true,
-    "skipLibCheck": true
-  },
-  "include": ["**/*.ts"],
-  "exclude": ["node_modules", "dist"]
-}

ts/e2e-tests/tsconfig.json

@@ -1,34 +0,0 @@
-{
-  "compilerOptions": {
-    "target": "ESNext",
-    "module": "NodeNext",
-    "moduleResolution": "NodeNext",
-    "verbatimModuleSyntax": true,
-    "esModuleInterop": true,
-    "forceConsistentCasingInFileNames": true,
-    "noUncheckedIndexedAccess": true,
-    "strictNullChecks": true,
-    "exactOptionalPropertyTypes": true,
-    "strict": true,
-    "skipLibCheck": true,
-    "rootDir": "./src",
-    "outDir": "./dist/type",
-    "tsBuildInfoFile": "./dist/.tsbuildinfo",
-    "incremental": true,
-    "lib": [
-      "ES2022",
-      "DOM",
-      "DOM.Iterable",
-      "DOM.AsyncIterable"
-    ],
-    "types": [
-      "node"
-    ]
-  },
-  "include": [
-    "./src"
-  ],
-  "exclude": [
-    "node_modules"
-  ]
-}

ts/llama_cloud_services/extract/package.json

@@ -1,8 +0,0 @@
-{
-  "type": "module",
-  "main": "./dist/index.cjs",
-  "module": "./dist/index.js",
-  "types": "./dist/index.d.ts",
-  "exports": "./dist/index.js",
-  "private": true
-}

ts/llama_cloud_services/openapi.json

@@ -12855,72 +12855,6 @@
         }
       }
     },
-    "/api/v1/extraction/run": {
-      "post": {
-        "tags": ["LlamaExtract"],
-        "summary": "Extract Stateless",
-        "description": "Stateless extraction endpoint that uses a default extraction agent in the user's default project. Requires data_schema, config, and either file_id, text, or base64 encoded file data.",
-        "operationId": "extract_stateless_api_v1_extraction_run_post",
-        "security": [
-          {
-            "HTTPBearer": []
-          },
-          {
-            "HTTPBearer": []
-          }
-        ],
-        "parameters": [
-          {
-            "name": "session",
-            "in": "cookie",
-            "required": false,
-            "schema": {
-              "anyOf": [
-                {
-                  "type": "string"
-                },
-                {
-                  "type": "null"
-                }
-              ],
-              "title": "Session"
-            }
-          }
-        ],
-        "requestBody": {
-          "required": true,
-          "content": {
-            "application/json": {
-              "schema": {
-                "$ref": "#/components/schemas/StatelessExtractionRequest"
-              }
-            }
-          }
-        },
-        "responses": {
-          "200": {
-            "description": "Successful Response",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/ExtractJob"
-                }
-              }
-            }
-          },
-          "422": {
-            "description": "Validation Error",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/HTTPValidationError"
-                }
-              }
-            }
-          }
-        }
-      }
-    },
     "/api/v1/extraction/jobs": {
       "get": {
         "tags": ["LlamaExtract"],
@@ -35549,64 +35483,6 @@
         "title": "WebhookConfiguration",
         "description": "Allows the user to configure webhook options for notifications and callbacks."
       },
-      "StatelessExtractionRequest": {
-        "type": "object",
-        "required": ["data_schema"],
-        "properties": {
-          "data_schema": {
-            "anyOf": [
-              {
-                "additionalProperties": {
-                  "anyOf": [
-                    {
-                      "additionalProperties": true,
-                      "type": "object"
-                    },
-                    {
-                      "items": {},
-                      "type": "array"
-                    },
-                    {
-                      "type": "string"
-                    },
-                    {
-                      "type": "integer"
-                    },
-                    {
-                      "type": "number"
-                    },
-                    {
-                      "type": "boolean"
-                    },
-                    {
-                      "type": "null"
-                    }
-                  ]
-                },
-                "type": "object"
-              },
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ]
-          },
-          "config": {
-            "$ref": "#/components/schemas/ExtractConfig",
-            "description": "The configuration parameters for the extraction agent."
-          },
-          "file_id": {
-            "type": "string",
-            "format": "uuid",
-            "title": "File Id",
-            "description": "ID of an uploaded file to extract from"
-          }
-        },
-        "title": "StatelessExtractionRequest",
-        "description": "Request body for stateless extraction. Must include either file_id, text, or base64."
-      },
       "llama_index__core__base__llms__types__ChatMessage": {
         "properties": {
           "role": {

ts/llama_cloud_services/package.json

@@ -1,6 +1,6 @@
 {
   "name": "llama-cloud-services",
-  "version": "0.3.3",
+  "version": "0.1.0",
   "type": "module",
   "license": "MIT",
   "scripts": {
@@ -9,7 +9,7 @@
     "dev": "bunchee --watch",
     "lint": "eslint src/ --ignore-pattern client/*.ts --no-warn-ignored",
     "format": "prettier --write ./src/",
-    "test": "vitest run --testTimeout=60000",
+    "test": "vitest",
     "test:watch": "vitest --watch",
     "test:ui": "vitest --ui",
     "test:coverage": "vitest --coverage"
@@ -20,8 +20,7 @@
     "./api",
     "./reader",
     "./parse",
-    "./beta/agent",
-    "./extract"
+    "./beta/agent"
   ],
   "exports": {
     "./openapi.json": "./openapi.json",
@@ -69,17 +68,6 @@
       },
       "default": "./parse/dist/index.js"
     },
-    "./extract": {
-      "require": {
-        "types": "./extract/dist/index.d.cts",
-        "default": "./extract/dist/index.cjs"
-      },
-      "import": {
-        "types": "./extract/dist/index.d.ts",
-        "default": "./extract/dist/index.js"
-      },
-      "default": "./extract/dist/index.js"
-    },
     ".": {
       "require": {
         "types": "./dist/index.d.cts",
@@ -101,12 +89,12 @@
     "@eslint/js": "^9.32.0",
     "@hey-api/client-fetch": "^0.10.1",
     "@hey-api/openapi-ts": "^0.67.5",
-    "@llamaindex/core": "^0.6.19",
+    "@llama-flow/core": "^0.4.1",
+    "@llamaindex/core": "^0.6.18",
     "@llamaindex/env": "^0.1.30",
-    "@llamaindex/workflow-core": "^0.4.1",
-    "@types/node": "^20.19.9",
     "@typescript-eslint/eslint-plugin": "^8.38.0",
     "@typescript-eslint/parser": "^8.38.0",
+    "@types/node": "^20.0.0",
     "@vitest/coverage-v8": "^2.0.0",
     "@vitest/ui": "^2.0.0",
     "bunchee": "^6.5.4",
@@ -119,13 +107,12 @@
     "vitest": "^2.0.0"
   },
   "peerDependencies": {
-    "@llamaindex/core": "^0.6.19",
+    "@llama-flow/core": "^0.4.1",
+    "@llamaindex/core": "^0.6.18",
     "@llamaindex/env": "^0.1.30",
-    "@llamaindex/workflow-core": "^0.4.1"
+    "llamaindex": "^0.11.23"
   },
   "dependencies": {
-    "ajv": "^8.17.1",
-    "file-type": "^21.0.0",
     "p-retry": "^6.2.1",
     "zod": "^3.25.76"
   },

ts/llama_cloud_services/src/LlamaCloudIndex.ts

@@ -1,10 +1,8 @@
 import type { BaseNodePostprocessor } from "@llamaindex/core/postprocessor";
-import {
-  RetrieverQueryEngine,
-  type BaseQueryEngine,
-} from "@llamaindex/core/query-engine";
+import type { BaseQueryEngine } from "@llamaindex/core/query-engine";
 import type { BaseSynthesizer } from "@llamaindex/core/response-synthesizers";
 import type { Document } from "@llamaindex/core/schema";
+import { RetrieverQueryEngine } from "llamaindex/engines";
 import type { CloudRetrieveParams } from "./LlamaCloudRetriever.js";
 import { LlamaCloudRetriever } from "./LlamaCloudRetriever.js";
 import type { CloudConstructorParams } from "./type.js";
@@ -27,14 +25,9 @@ import {
 } from "./api";
 import type { BaseRetriever } from "@llamaindex/core/retriever";
 import { getEnv } from "@llamaindex/env";
-import { createQueryEngineTool, type QueryToolParams } from "./query-tool.js";
-import type { BaseTool } from "@llamaindex/core/llms";
-
-type QueryEngineParams = {
-  responseSynthesizer?: BaseSynthesizer;
-  preFilters?: unknown;
-  nodePostprocessors?: BaseNodePostprocessor[];
-} & CloudRetrieveParams;
+import type { QueryToolParams } from "llamaindex/indices";
+import { Settings } from "llamaindex";
+import { QueryEngineTool } from "llamaindex/tools";
 
 export class LlamaCloudIndex {
   params: CloudConstructorParams;
@@ -45,7 +38,7 @@ export class LlamaCloudIndex {
   }
 
   private async waitForPipelineIngestion(
-    verbose = false,
+    verbose = Settings.debug,
     raiseOnError = false,
   ): Promise<void> {
     const pipelineId = await this.getPipelineId();
@@ -90,7 +83,7 @@ export class LlamaCloudIndex {
 
   private async waitForDocumentIngestion(
     docIds: string[],
-    verbose = false,
+    verbose = Settings.debug,
     raiseOnError = false,
   ): Promise<void> {
     const pipelineId = await this.getPipelineId();
@@ -263,7 +256,13 @@ export class LlamaCloudIndex {
     return new LlamaCloudRetriever({ ...this.params, ...params });
   }
 
-  asQueryEngine(params?: QueryEngineParams): BaseQueryEngine {
+  asQueryEngine(
+    params?: {
+      responseSynthesizer?: BaseSynthesizer;
+      preFilters?: unknown;
+      nodePostprocessors?: BaseNodePostprocessor[];
+    } & CloudRetrieveParams,
+  ): BaseQueryEngine {
     const retriever = new LlamaCloudRetriever({
       ...this.params,
       ...params,
@@ -275,15 +274,19 @@ export class LlamaCloudIndex {
     );
   }
 
-  asQueryTool(params: QueryEngineParams & QueryToolParams): BaseTool {
-    return createQueryEngineTool({
+  asQueryTool(params: QueryToolParams): QueryEngineTool {
+    if (params.options) {
+      params.retriever = this.asRetriever(params.options);
+    }
+
+    return new QueryEngineTool({
       queryEngine: this.asQueryEngine(params),
       metadata: params?.metadata,
       includeSourceNodes: params?.includeSourceNodes ?? false,
     });
   }
 
-  queryTool(params: QueryEngineParams & QueryToolParams) {
+  queryTool(params: QueryToolParams): QueryEngineTool {
     return this.asQueryTool(params);
   }
 

ts/llama_cloud_services/src/LlamaExtract.ts

@@ -1,225 +0,0 @@
-import { createClient, createConfig, type Client } from "@hey-api/client-fetch";
-import { File } from "buffer";
-import * as extract from "./extract";
-import type { ExtractAgent, ExtractConfig } from "./extract";
-import { getEnv } from "@llamaindex/env";
-import type { ExtractResult } from "./type";
-
-const URLS = {
-  us: "https://api.cloud.llamaindex.ai",
-  eu: "https://api.cloud.eu.llamaindex.ai",
-  "us-staging": "https://api.staging.llamaindex.ai",
-} as const;
-
-function getUrl(baseUrl: string | undefined, region: string | undefined) {
-  if (typeof baseUrl != "undefined") {
-    return baseUrl;
-  }
-  if (typeof region === "undefined") {
-    return URLS["us"];
-  } else if (region === "us" || region === "eu" || region === "us-staging") {
-    return URLS[region];
-  } else {
-    throw new Error(`Unsupported region: ${region}`);
-  }
-}
-
-export class LlamaExtractAgent {
-  private agent: ExtractAgent;
-  private client: Client;
-  id: string;
-  name: string;
-  dataSchema: {
-    [key: string]:
-      | string
-      | number
-      | boolean
-      | {
-          [key: string]: unknown;
-        }
-      | unknown[]
-      | null;
-  };
-
-  constructor(agent: ExtractAgent, client: Client) {
-    this.agent = agent;
-    this.client = client;
-    this.id = agent.id;
-    this.name = agent.name;
-    this.dataSchema = agent.data_schema;
-  }
-
-  async extract(
-    filePath: string | undefined = undefined,
-    fileContent:
-      | Buffer<ArrayBufferLike>
-      | Uint8Array<ArrayBuffer>
-      | string
-      | File
-      | undefined = undefined,
-    fileName: string | undefined = undefined,
-    project_id: string | null = null,
-    organization_id: string | null = null,
-    fromUi: boolean | undefined = undefined,
-    pollingInterval: number = 1,
-    maxPollingIterations: number = 1800,
-    maxRetriesOnError: number = 10,
-    retryInterval: number = 0.5,
-  ): Promise<ExtractResult | undefined> {
-    return await extract.extract(
-      this.agent.id,
-      filePath,
-      fileContent,
-      fileName,
-      project_id,
-      organization_id,
-      this.client,
-      fromUi,
-      pollingInterval,
-      maxPollingIterations,
-      maxRetriesOnError,
-      retryInterval,
-    );
-  }
-}
-
-export class LlamaExtract {
-  private client: Client;
-
-  constructor(
-    apiKey: string | undefined = undefined,
-    baseUrl: string | undefined = undefined,
-    region: string | undefined = undefined,
-  ) {
-    const key = apiKey ?? getEnv("LLAMA_CLOUD_API_KEY");
-    if (typeof key === "undefined") {
-      throw new Error(
-        "No API key provided and no API key found in environment. Please pass the API key or set `LLAMA_CLOUD_API_KEY` as an environment variable.",
-      );
-    }
-    const url = getUrl(baseUrl, region);
-    this.client = createClient(
-      createConfig({
-        baseUrl: url,
-        headers: {
-          Authorization: `Bearer ${key}`,
-        },
-      }),
-    );
-  }
-
-  async createAgent(
-    name: string,
-    dataSchema:
-      | {
-          [key: string]:
-            | { [key: string]: unknown }
-            | Array<unknown>
-            | string
-            | number
-            | number
-            | boolean
-            | null;
-        }
-      | string,
-    config: ExtractConfig | undefined = undefined,
-    project_id: string | null = null,
-    organization_id: string | null = null,
-    maxRetriesOnError: number = 10,
-    retryInterval: number = 0.5,
-  ): Promise<LlamaExtractAgent | undefined> {
-    const agent = await extract.createAgent(
-      name,
-      dataSchema,
-      config,
-      project_id,
-      organization_id,
-      this.client,
-      maxRetriesOnError,
-      retryInterval,
-    );
-    if (typeof agent != "undefined") {
-      return new LlamaExtractAgent(agent, this.client);
-    }
-  }
-
-  async getAgent(
-    name: string | undefined = undefined,
-    id: string | undefined = undefined,
-    project_id: string | null = null,
-    organization_id: string | null = null,
-    maxRetriesOnError: number = 10,
-    retryInterval: number = 0.5,
-  ): Promise<LlamaExtractAgent | undefined> {
-    const agent = await extract.getAgent(
-      id,
-      name,
-      project_id,
-      organization_id,
-      this.client,
-      maxRetriesOnError,
-      retryInterval,
-    );
-    if (typeof agent != "undefined") {
-      return new LlamaExtractAgent(agent, this.client);
-    }
-  }
-
-  async deleteAgent(
-    id: string,
-    maxRetriesOnError: number = 10,
-    retryInterval: number = 500,
-  ): Promise<boolean | undefined> {
-    return await extract.deleteAgent(
-      id,
-      this.client,
-      maxRetriesOnError,
-      retryInterval,
-    );
-  }
-
-  async extract(
-    dataSchema:
-      | {
-          [key: string]:
-            | { [key: string]: unknown }
-            | Array<unknown>
-            | string
-            | number
-            | number
-            | boolean
-            | null;
-        }
-      | string,
-    config: ExtractConfig | undefined = undefined,
-    filePath: string | undefined = undefined,
-    fileContent:
-      | Buffer<ArrayBufferLike>
-      | Uint8Array<ArrayBuffer>
-      | string
-      | File
-      | undefined = undefined,
-    fileName: string | undefined = undefined,
-    project_id: string | null = null,
-    organization_id: string | null = null,
-    pollingInterval: number = 1,
-    maxPollingIterations: number = 1800,
-    maxRetriesOnError: number = 10,
-    retryInterval: number = 0.5,
-  ): Promise<ExtractResult | undefined> {
-    return await extract.extractStateless(
-      dataSchema,
-      config,
-      filePath,
-      fileContent,
-      fileName,
-      project_id,
-      organization_id,
-      this.client,
-      pollingInterval,
-      maxPollingIterations,
-      maxRetriesOnError,
-      retryInterval,
-    );
-  }
-}

ts/llama_cloud_services/src/beta/agent/client.ts

@@ -261,7 +261,8 @@ export class AgentClient<T = unknown> {
   }
 }
 
-export interface AgentDataClientOptions {
+// eslint-disable-next-line  @typescript-eslint/no-unused-vars
+export interface AgentDataClientOptions<T = unknown> {
   /** API key for the client */
   apiKey?: string;
   /** Base URL for the client */

ts/llama_cloud_services/src/beta/agent/index.ts

@@ -4,8 +4,6 @@ export type {
   AggregateAgentDataOptions,
   ComparisonOperator,
   ExtractedData,
-  ExtractedFieldMetadata,
-  ExtractedFieldMetadataDict,
   FilterOperation,
   SearchAgentDataOptions,
   StatusType,

ts/llama_cloud_services/src/beta/agent/types.ts

@@ -28,35 +28,6 @@ export type ComparisonOperator =
  */
 export type FilterOperation = RawFilterOperation;
 
-/**
- * Metadata for an extracted field, including confidence and citation information
- */
-export interface ExtractedFieldMetadata {
-  /** The reasoning for the confidence score */
-  reasoning?: string;
-  /** The confidence score for the field, combined with parsing confidence if applicable */
-  confidence?: number;
-  /** The confidence score for the field based on the extracted text only */
-  extraction_confidence?: number;
-  citation: FieldCitation[];
-}
-
-export interface FieldCitation {
-  /** The page number that the field occurred on */
-  page?: number;
-  /** The original text this field's value was derived from */
-  matching_text?: string;
-}
-
-/**
- * Dictionary mapping field names to their metadata
- * Values can be ExtractedFieldMetadata objects, nested dictionaries, or arrays
- */
-export type ExtractedFieldMetadataDict = Record<
-  string,
-  ExtractedFieldMetadata | Record<string, unknown> | unknown[]
->;
-
 /**
  * Base extracted data interface
  */
@@ -64,13 +35,11 @@ export interface ExtractedData<T = unknown> {
   /** The original data that was extracted from the document. For tracking changes. Should not be updated. */
   original_data: T;
   /** The latest state of the data. Will differ if data has been updated. */
-  data: T;
+  data?: T;
   /** The status of the extracted data. Prefer to use the StatusType values, but any string is allowed. */
   status: StatusType | string;
-  /** The overall confidence score for the extracted data. */
-  overall_confidence?: number;
-  /** Page links, and perhaps eventually bounding boxes, for individual fields in the extracted data. */
-  field_metadata?: ExtractedFieldMetadataDict;
+  /** Confidence scores, if any, for each primitive field in the original_data data. */
+  confidence?: Record<string, unknown>;
   /** The ID of the file that was used to extract the data. */
   file_id?: string;
   /** The name of the file that was used to extract the data. */

ts/llama_cloud_services/src/client/schemas.gen.ts

@@ -18644,66 +18644,6 @@ export const WebhookConfigurationSchema = {
     "Allows the user to configure webhook options for notifications and callbacks.",
 } as const;
 
-export const StatelessExtractionRequestSchema = {
-  type: "object",
-  required: ["data_schema"],
-  properties: {
-    data_schema: {
-      anyOf: [
-        {
-          additionalProperties: {
-            anyOf: [
-              {
-                additionalProperties: true,
-                type: "object",
-              },
-              {
-                items: {},
-                type: "array",
-              },
-              {
-                type: "string",
-              },
-              {
-                type: "integer",
-              },
-              {
-                type: "number",
-              },
-              {
-                type: "boolean",
-              },
-              {
-                type: "null",
-              },
-            ],
-          },
-          type: "object",
-        },
-        {
-          type: "string",
-        },
-        {
-          type: "null",
-        },
-      ],
-    },
-    config: {
-      $ref: "#/components/schemas/ExtractConfig",
-      description: "The configuration parameters for the extraction agent.",
-    },
-    file_id: {
-      type: "string",
-      format: "uuid",
-      title: "File Id",
-      description: "ID of an uploaded file to extract from",
-    },
-  },
-  title: "StatelessExtractionRequest",
-  description:
-    "Request body for stateless extraction. Must include either file_id, text, or base64.",
-} as const;
-
 export const llama_index__core__base__llms__types__ChatMessageSchema = {
   properties: {
     role: {

ts/llama_cloud_services/src/client/sdk.gen.ts

@@ -452,9 +452,6 @@ import type {
   UpdateExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdPutData,
   UpdateExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdPutResponse,
   UpdateExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdPutError,
-  ExtractStatelessApiV1ExtractionRunPostData,
-  ExtractStatelessApiV1ExtractionRunPostResponse,
-  ExtractStatelessApiV1ExtractionRunPostError,
   ListJobsApiV1ExtractionJobsGetData,
   ListJobsApiV1ExtractionJobsGetResponse,
   ListJobsApiV1ExtractionJobsGetError,
@@ -5704,39 +5701,6 @@ export const updateExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgent
     });
   };
 
-/**
- * Extract Stateless
- * Stateless extraction endpoint that uses a default extraction agent in the user's default project. Requires data_schema, config, and either file_id, text, or base64 encoded file data.
- */
-export const extractStatelessApiV1ExtractionRunPost = <
-  ThrowOnError extends boolean = false,
->(
-  options: Options<ExtractStatelessApiV1ExtractionRunPostData, ThrowOnError>,
-) => {
-  return (options.client ?? _heyApiClient).post<
-    ExtractStatelessApiV1ExtractionRunPostResponse,
-    ExtractStatelessApiV1ExtractionRunPostError,
-    ThrowOnError
-  >({
-    security: [
-      {
-        scheme: "bearer",
-        type: "http",
-      },
-      {
-        scheme: "bearer",
-        type: "http",
-      },
-    ],
-    url: "/api/v1/extraction/run",
-    ...options,
-    headers: {
-      "Content-Type": "application/json",
-      ...options?.headers,
-    },
-  });
-};
-
 /**
  * List Jobs
  */

ts/llama_cloud_services/src/client/types.gen.ts

@@ -8272,35 +8272,6 @@ export type WebhookConfiguration = {
   > | null;
 };
 
-/**
- * Request body for stateless extraction. Must include either file_id, text, or base64.
- */
-export type StatelessExtractionRequest = {
-  data_schema:
-    | {
-        [key: string]:
-          | {
-              [key: string]: unknown;
-            }
-          | Array<unknown>
-          | string
-          | number
-          | number
-          | boolean
-          | null;
-      }
-    | string
-    | null;
-  /**
-   * The configuration parameters for the extraction agent.
-   */
-  config?: ExtractConfig;
-  /**
-   * ID of an uploaded file to extract from
-   */
-  file_id?: string;
-};
-
 /**
  * Chat message.
  */
@@ -13107,33 +13078,6 @@ export type UpdateExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentI
 export type UpdateExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdPutResponse =
   UpdateExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdPutResponses[keyof UpdateExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdPutResponses];
 
-export type ExtractStatelessApiV1ExtractionRunPostData = {
-  body: StatelessExtractionRequest;
-  path?: never;
-  query?: never;
-  url: "/api/v1/extraction/run";
-};
-
-export type ExtractStatelessApiV1ExtractionRunPostErrors = {
-  /**
-   * Validation Error
-   */
-  422: HttpValidationError;
-};
-
-export type ExtractStatelessApiV1ExtractionRunPostError =
-  ExtractStatelessApiV1ExtractionRunPostErrors[keyof ExtractStatelessApiV1ExtractionRunPostErrors];
-
-export type ExtractStatelessApiV1ExtractionRunPostResponses = {
-  /**
-   * Successful Response
-   */
-  200: ExtractJob;
-};
-
-export type ExtractStatelessApiV1ExtractionRunPostResponse =
-  ExtractStatelessApiV1ExtractionRunPostResponses[keyof ExtractStatelessApiV1ExtractionRunPostResponses];
-
 export type ListJobsApiV1ExtractionJobsGetData = {
   body?: never;
   path?: never;

ts/llama_cloud_services/src/client/zod.gen.ts

@@ -3472,12 +3472,6 @@ export const zUserOrganizationRoleCreate = z.object({
   role_id: z.string().uuid(),
 });
 
-export const zStatelessExtractionRequest = z.object({
-  data_schema: z.union([z.object({}), z.string(), z.null()]),
-  config: zExtractConfig.optional(),
-  file_id: z.string().uuid().optional(),
-});
-
 export const zListKeysApiV1ApiKeysGetResponse = z.array(zApiKey);
 
 export const zGenerateKeyApiV1ApiKeysPostResponse = zApiKey;
@@ -3835,8 +3829,6 @@ export const zGetExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentId
 export const zUpdateExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdPutResponse =
   zExtractAgent;
 
-export const zExtractStatelessApiV1ExtractionRunPostResponse = zExtractJob;
-
 export const zListJobsApiV1ExtractionJobsGetResponse = z.array(zExtractJob);
 
 export const zRunJobApiV1ExtractionJobsPostResponse = zExtractJob;

ts/llama_cloud_services/src/events.ts

@@ -1,5 +1,5 @@
-import { workflowEvent } from "@llamaindex/workflow-core";
-import { zodEvent } from "@llamaindex/workflow-core/util/zod";
+import { workflowEvent } from "@llama-flow/core";
+import { zodEvent } from "@llama-flow/core/util/zod";
 import { z } from "zod";
 import { parseFormSchema } from "./schema";
 

ts/llama_cloud_services/src/extract.ts

@@ -1,651 +0,0 @@
-import { emitWarning } from "process";
-import fs from "fs/promises";
-import { Blob } from "buffer";
-import * as path from "path";
-import type { ExtractResult } from "./type";
-import { randomUUID } from "@llamaindex/env";
-import { File } from "buffer";
-import {
-  type Options,
-  type ExtractAgentCreate,
-  type ExtractConfig,
-  type ExtractJobCreate,
-  type ExtractAgent,
-  type ExtractJob,
-  type CreateExtractionAgentApiV1ExtractionExtractionAgentsPostData,
-  type GetExtractionAgentByNameApiV1ExtractionExtractionAgentsByNameNameGetData,
-  type GetExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdGetData,
-  type RunJobApiV1ExtractionJobsPostData,
-  type GetJobApiV1ExtractionJobsJobIdGetData,
-  type GetJobResultApiV1ExtractionJobsJobIdResultGetData,
-  StatusEnum,
-  type UploadFileApiV1FilesPostData,
-  type StatelessExtractionRequest,
-  type ExtractStatelessApiV1ExtractionRunPostData,
-  type DeleteExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdDeleteData,
-  createExtractionAgentApiV1ExtractionExtractionAgentsPost,
-  getExtractionAgentByNameApiV1ExtractionExtractionAgentsByNameNameGet,
-  getExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdGet,
-  runJobApiV1ExtractionJobsPost,
-  getJobApiV1ExtractionJobsJobIdGet,
-  getJobResultApiV1ExtractionJobsJobIdResultGet,
-  uploadFileApiV1FilesPost,
-  extractStatelessApiV1ExtractionRunPost,
-  deleteExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdDelete,
-} from "./api";
-import type { Client } from "@hey-api/client-fetch";
-import { sleep } from "./utils";
-import { fileTypeFromBuffer } from "file-type";
-
-type BodyUploadFileApiV1FilesPost = {
-  upload_file: Blob | File;
-};
-
-export async function createAgent(
-  name: string,
-  dataSchema:
-    | {
-        [key: string]:
-          | { [key: string]: unknown }
-          | Array<unknown>
-          | string
-          | number
-          | number
-          | boolean
-          | null;
-      }
-    | string,
-  config: ExtractConfig = {} as ExtractConfig,
-  project_id: string | null = null,
-  organization_id: string | null = null,
-  client: Client | undefined = undefined,
-  maxRetriesOnError: number = 10,
-  retryInterval: number = 0.5,
-): Promise<ExtractAgent | undefined> {
-  const agentData = {
-    name: name,
-    data_schema: dataSchema,
-    config: config,
-  } as ExtractAgentCreate;
-  const agentDataCreation = {
-    body: agentData,
-    query: { project_id: project_id, organization_id: organization_id },
-  } as CreateExtractionAgentApiV1ExtractionExtractionAgentsPostData;
-  const options =
-    agentDataCreation as Options<CreateExtractionAgentApiV1ExtractionExtractionAgentsPostData>;
-  if (typeof client != "undefined") {
-    options.client = client;
-  }
-  let retries: number = 0;
-  while (true) {
-    if (retries > maxRetriesOnError) {
-      throw new Error(
-        "Error while creating the agent: Exceeded maximum number of retries, the API keeps returning errors.",
-      );
-    }
-    const response =
-      await createExtractionAgentApiV1ExtractionExtractionAgentsPost(options);
-    if (!response.response.ok) {
-      if ("error" in response) {
-        console.log(
-          `An error occurred while creating the extraction agent.\nDetails:\n\n${JSON.stringify(
-            response.error,
-          )}\n\nRetrying...`,
-        );
-      }
-      retries++;
-      await sleep(retryInterval * 1000);
-    } else {
-      return response.data as ExtractAgent;
-    }
-  }
-}
-
-export async function getAgent(
-  id: string | undefined = undefined,
-  name: string | undefined = undefined,
-  project_id: string | null = null,
-  organization_id: string | null = null,
-  client: Client | undefined = undefined,
-  maxRetriesOnError: number = 10,
-  retryInterval: number = 0.5,
-): Promise<ExtractAgent | undefined> {
-  if (typeof id === "undefined" && typeof name === "undefined") {
-    throw new Error("One of `id` and `string` must be passed.");
-  } else if (typeof id != "undefined" && typeof name != "undefined") {
-    emitWarning("You passed both `id` and `name`, using only id...");
-    const data = {
-      path: { extraction_agent_id: id },
-    } as GetExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdGetData;
-    const options =
-      data as Options<GetExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdGetData>;
-    if (typeof client != "undefined") {
-      options.client = client;
-    }
-    let retries: number = 0;
-    while (true) {
-      if (retries > maxRetriesOnError) {
-        throw new Error(
-          "Error while getting the agent: Exceeded maximum number of retries, the API keeps returning errors.",
-        );
-      }
-      const response =
-        await getExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdGet(
-          options,
-        );
-      if (!response.response.ok) {
-        if ("error" in response) {
-          console.log(
-            `An error occurred while getting the extraction agent by ID.\nDetails:\n\n${JSON.stringify(
-              response.error,
-            )}\n\nRetrying...`,
-          );
-        }
-        retries++;
-        await sleep(retryInterval * 1000);
-      } else {
-        return response.data as ExtractAgent;
-      }
-    }
-  } else if (typeof name != "undefined" && typeof id === "undefined") {
-    const data = {
-      path: { name: name },
-      query: { organization_id: organization_id, project_id: project_id },
-    } as GetExtractionAgentByNameApiV1ExtractionExtractionAgentsByNameNameGetData;
-    const options =
-      data as Options<GetExtractionAgentByNameApiV1ExtractionExtractionAgentsByNameNameGetData>;
-    if (typeof client != "undefined") {
-      options.client = client;
-    }
-    let retries: number = 0;
-    while (true) {
-      if (retries > maxRetriesOnError) {
-        throw new Error(
-          "Error while getting the agent: Exceeded maximum number of retries, the API keeps returning errors.",
-        );
-      }
-      const response =
-        await getExtractionAgentByNameApiV1ExtractionExtractionAgentsByNameNameGet(
-          options,
-        );
-      if (!response.response.ok) {
-        if ("error" in response) {
-          console.log(
-            `An error occurred while getting the extraction agent by name.\nDetails:\n\n${JSON.stringify(
-              response.error,
-            )}\n\nRetrying...`,
-          );
-        }
-        retries++;
-        await sleep(retryInterval * 1000);
-      } else {
-        return response.data as ExtractAgent;
-      }
-    }
-  } else {
-    const data = {
-      path: { extraction_agent_id: id },
-    } as GetExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdGetData;
-    const options =
-      data as Options<GetExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdGetData>;
-    if (typeof client != "undefined") {
-      options.client = client;
-    }
-    let retries: number = 0;
-    while (true) {
-      if (retries > maxRetriesOnError) {
-        throw new Error(
-          "Error while getting the agent: Exceeded maximum number of retries, the API keeps returning errors.",
-        );
-      }
-      const response =
-        await getExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdGet(
-          options,
-        );
-      if (!response.response.ok) {
-        if (!response.response.ok) {
-          if ("error" in response) {
-            console.log(
-              `An error occurred while getting the extraction agent by ID.\nDetails:\n\n${JSON.stringify(
-                response.error,
-              )}\n\nRetrying...`,
-            );
-          }
-          retries++;
-          await sleep(retryInterval * 1000);
-        }
-      } else {
-        return response.data as ExtractAgent;
-      }
-    }
-  }
-}
-
-function textToFile(text: string, fileName: string | null = null) {
-  return new File(
-    [text],
-    fileName ?? "uploadedFile_" + randomUUID().replaceAll("-", "_") + ".txt",
-  );
-}
-
-async function uploadFile(
-  filePath: string | undefined = undefined,
-  fileContent:
-    | Buffer<ArrayBufferLike>
-    | File
-    | Uint8Array<ArrayBuffer>
-    | string
-    | undefined = undefined,
-  fileName: string | undefined = undefined,
-  project_id: string | null = null,
-  organization_id: string | null = null,
-  client: Client | undefined = undefined,
-  maxRetriesOnError: number = 10,
-  retryInterval: number = 0.5,
-): Promise<string | undefined> {
-  let file: File | undefined = undefined;
-  if (typeof filePath === "undefined" && typeof fileContent === "undefined") {
-    throw new Error(
-      "One between filePath and fileContent needs to be provided",
-    );
-  } else if (typeof filePath != "undefined") {
-    const buffer = await fs.readFile(filePath);
-    const actualFileName = fileName ?? path.basename(filePath);
-    const uint8Array = new Uint8Array(buffer);
-    file = new File([uint8Array], actualFileName);
-  } else if (typeof fileContent != "undefined") {
-    if (fileContent instanceof File) {
-      file = fileContent;
-    } else if (fileContent instanceof Buffer) {
-      const fileType = await fileTypeFromBuffer(fileContent);
-      const ext = fileType?.ext ?? "pdf";
-      const uint8Array = new Uint8Array(fileContent);
-      file = new File(
-        [uint8Array],
-        fileName ??
-          "uploadedFile_" + randomUUID().replaceAll("-", "_") + "." + ext,
-      );
-    } else if (fileContent instanceof Uint8Array) {
-      const fileType = await fileTypeFromBuffer(fileContent);
-      const ext = fileType?.ext ?? "pdf";
-      file = new File(
-        [fileContent],
-        fileName ??
-          "uploadedFile_" + randomUUID().replaceAll("-", "_") + "." + ext,
-      );
-    } else if (typeof fileContent === "string") {
-      file = textToFile(fileContent, fileName);
-    } else {
-      throw new Error("Unsupported fileContent type");
-    }
-  }
-  const fileToUpload = {
-    upload_file: file,
-  } as BodyUploadFileApiV1FilesPost;
-  const uploadData = {
-    body: fileToUpload,
-    query: { organization_id: organization_id, project_id: project_id },
-  } as UploadFileApiV1FilesPostData;
-  const uploadOptions = uploadData as Options<UploadFileApiV1FilesPostData>;
-  if (typeof client != "undefined") {
-    uploadOptions.client = client;
-  }
-  let retries: number = 0;
-  while (true) {
-    if (retries > maxRetriesOnError) {
-      throw new Error(
-        "Error while processing your file: Exceeded maximum number of retries, the API keeps returning errors.",
-      );
-    }
-    const uploadResponse = await uploadFileApiV1FilesPost(uploadOptions);
-    let fileId: string | undefined = undefined;
-    if (!uploadResponse.response.ok) {
-      retries++;
-      await sleep(retryInterval * 1000);
-    }
-    if (typeof uploadResponse.data != "undefined") {
-      fileId = uploadResponse.data.id as string;
-      return fileId;
-    }
-  }
-}
-
-async function createExtractJob(
-  options:
-    | Options<RunJobApiV1ExtractionJobsPostData>
-    | Options<ExtractStatelessApiV1ExtractionRunPostData>,
-  stateless: boolean = false,
-  maxRetriesOnError: number = 10,
-  retryInterval: number = 0.5,
-): Promise<string | undefined> {
-  let retries: number = 0;
-  while (true) {
-    if (retries > maxRetriesOnError) {
-      throw new Error(
-        "Error while creating the extraction job: Exceeded maximum number of retries, the API keeps returning errors.",
-      );
-    }
-    let response:
-      | {
-          data: ExtractJob | undefined;
-          request: Request;
-          response: Response;
-        }
-      | undefined = undefined;
-    if (!stateless) {
-      response = (await runJobApiV1ExtractionJobsPost(
-        options as Options<RunJobApiV1ExtractionJobsPostData>,
-      )) as {
-        data: ExtractJob | undefined;
-        request: Request;
-        response: Response;
-      };
-    } else {
-      response = (await extractStatelessApiV1ExtractionRunPost(
-        options as Options<ExtractStatelessApiV1ExtractionRunPostData>,
-      )) as {
-        data: ExtractJob | undefined;
-        request: Request;
-        response: Response;
-      };
-    }
-    if (!response.response.ok) {
-      if ("error" in response) {
-        console.log(
-          "An error occurred: ",
-          JSON.stringify(response.error),
-          "\nRetrying...",
-        );
-      }
-      retries++;
-      await sleep(retryInterval * 1000);
-    }
-    if (typeof response.data != "undefined") {
-      const jobStatus = response.data.status as StatusEnum;
-      if (jobStatus == "CANCELLED") {
-        retries++;
-        await sleep(retryInterval * 1000);
-      } else if (jobStatus == "ERROR") {
-        retries++;
-        await sleep(retryInterval * 1000);
-      } else {
-        return response.data.id as string;
-      }
-    }
-  }
-}
-
-async function pollForJobCompletion(
-  jobId: string,
-  interval: number = 1,
-  maxIterations: number = 1800,
-  client: Client | undefined = undefined,
-): Promise<boolean> {
-  let status: StatusEnum | undefined = undefined;
-  const jobData = {
-    path: { job_id: jobId },
-  } as GetJobApiV1ExtractionJobsJobIdGetData;
-  const jobOptions = jobData as Options<GetJobApiV1ExtractionJobsJobIdGetData>;
-  if (typeof client != "undefined") {
-    jobOptions.client = client;
-  }
-  let numIterations: number = 0;
-  while (true) {
-    if (numIterations > maxIterations) {
-      return false;
-    }
-    const response = await getJobApiV1ExtractionJobsJobIdGet(jobOptions);
-    if (!response.response.ok) {
-      numIterations++;
-    }
-    if (typeof response.data != "undefined") {
-      status = response.data.status as StatusEnum;
-      if (status == StatusEnum.CANCELLED || status == StatusEnum.ERROR) {
-        throw new Error("There was an error extracting data from your file.");
-      } else if (status == StatusEnum.SUCCESS) {
-        return true;
-      } else {
-        numIterations++;
-        await sleep(interval * 1000);
-      }
-    }
-  }
-}
-
-async function getJobResult(
-  jobId: string,
-  client: Client | undefined = undefined,
-  project_id: string | null = null,
-  organization_id: string | null = null,
-  maxRetriesOnError: number = 10,
-  retryInterval: number = 0.5,
-): Promise<ExtractResult | undefined> {
-  const jobData = {
-    path: { job_id: jobId },
-    query: { organization_id: organization_id, project_id: project_id },
-  } as GetJobResultApiV1ExtractionJobsJobIdResultGetData;
-  const jobOptions =
-    jobData as Options<GetJobResultApiV1ExtractionJobsJobIdResultGetData>;
-  if (typeof client != "undefined") {
-    jobOptions.client = client;
-  }
-  let retries: number = 0;
-  while (true) {
-    if (retries > maxRetriesOnError) {
-      throw new Error(
-        "Error while getting the result of the extraction job: Exceeded maximum number of retries, the API keeps returning errors.",
-      );
-    }
-    const response =
-      await getJobResultApiV1ExtractionJobsJobIdResultGet(jobOptions);
-    if (!response.response.ok) {
-      if ("error" in response) {
-        console.log(
-          "An error occurred: ",
-          JSON.stringify(response.error),
-          "\nRetrying...",
-        );
-      }
-      retries++;
-      await sleep(retryInterval * 1000);
-    }
-    if (typeof response.data != "undefined") {
-      return {
-        data: response.data.data,
-        extractionMetadata: response.data.extraction_metadata,
-      } as ExtractResult;
-    }
-  }
-}
-
-export async function extract(
-  agentId: string,
-  filePath: string | undefined = undefined,
-  fileContent:
-    | Buffer<ArrayBufferLike>
-    | File
-    | Uint8Array<ArrayBuffer>
-    | string
-    | undefined = undefined,
-  fileName: string | undefined = undefined,
-  project_id: string | null = null,
-  organization_id: string | null = null,
-  client: Client | undefined = undefined,
-  fromUi: boolean | undefined = undefined,
-  pollingInterval: number = 1,
-  maxPollingIterations: number = 1800,
-  maxRetriesOnError: number = 10,
-  retryInterval: number = 0.5,
-): Promise<ExtractResult | undefined> {
-  const fileId = (await uploadFile(
-    filePath,
-    fileContent,
-    fileName,
-    project_id,
-    organization_id,
-    client,
-    maxRetriesOnError,
-    retryInterval,
-  )) as string;
-  const extractJobCreate = {
-    extraction_agent_id: agentId,
-    file_id: fileId,
-  } as ExtractJobCreate;
-  const extractData = {
-    body: extractJobCreate,
-    query: { from_ui: fromUi },
-  } as RunJobApiV1ExtractionJobsPostData;
-  const extractOptions =
-    extractData as Options<RunJobApiV1ExtractionJobsPostData>;
-  if (typeof client != "undefined") {
-    extractOptions.client = client;
-  }
-  const jobId = (await createExtractJob(
-    extractOptions,
-    false,
-    maxRetriesOnError,
-    retryInterval,
-  )) as string;
-  const success = await pollForJobCompletion(
-    jobId,
-    pollingInterval,
-    maxPollingIterations,
-    client,
-  );
-  if (!success) {
-    throw new Error("Your job is taking longer than 10 minutes, timing out...");
-  } else {
-    return (await getJobResult(
-      jobId,
-      client,
-      project_id,
-      organization_id,
-      maxRetriesOnError,
-      retryInterval,
-    )) as ExtractResult;
-  }
-}
-
-export async function extractStateless(
-  dataSchema:
-    | {
-        [key: string]:
-          | { [key: string]: unknown }
-          | Array<unknown>
-          | string
-          | number
-          | number
-          | boolean
-          | null;
-      }
-    | string,
-  config: ExtractConfig = {} as ExtractConfig,
-  filePath: string | undefined = undefined,
-  fileContent:
-    | Buffer<ArrayBufferLike>
-    | File
-    | Uint8Array<ArrayBuffer>
-    | string
-    | undefined = undefined,
-  fileName: string | undefined = undefined,
-  project_id: string | null = null,
-  organization_id: string | null = null,
-  client: Client | undefined = undefined,
-  pollingInterval: number = 1,
-  maxPollingIterations: number = 1800,
-  maxRetriesOnError: number = 10,
-  retryInterval: number = 0.5,
-): Promise<ExtractResult | undefined> {
-  const fileId = (await uploadFile(
-    filePath,
-    fileContent,
-    fileName,
-    project_id,
-    organization_id,
-    client,
-    maxRetriesOnError,
-    retryInterval,
-  )) as string;
-  const extractStatetelessCreate = {
-    data_schema: dataSchema,
-    file_id: fileId,
-    config: config,
-  } as StatelessExtractionRequest;
-  const extractStatetelessData = {
-    body: extractStatetelessCreate,
-  } as ExtractStatelessApiV1ExtractionRunPostData;
-  const extractOptions =
-    extractStatetelessData as Options<ExtractStatelessApiV1ExtractionRunPostData>;
-  if (typeof client != "undefined") {
-    extractOptions.client = client;
-  }
-  const jobId = (await createExtractJob(
-    extractOptions,
-    true,
-    maxRetriesOnError,
-    retryInterval,
-  )) as string;
-  const success = await pollForJobCompletion(
-    jobId,
-    pollingInterval,
-    maxPollingIterations,
-    client,
-  );
-  if (!success) {
-    throw new Error("Your job is taking longer than 10 minutes, timing out...");
-  } else {
-    return (await getJobResult(
-      jobId,
-      client,
-      project_id,
-      organization_id,
-      maxRetriesOnError,
-      retryInterval,
-    )) as ExtractResult;
-  }
-}
-
-export async function deleteAgent(
-  id: string,
-  client: Client | undefined = undefined,
-  maxRetriesOnError: number = 10,
-  retryInterval: number = 0.5,
-): Promise<boolean | undefined> {
-  const deleteData = {
-    path: { extraction_agent_id: id },
-  } as DeleteExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdDeleteData;
-  const deleteOptions =
-    deleteData as Options<DeleteExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdDeleteData>;
-  if (typeof client != "undefined") {
-    deleteOptions.client = client;
-  }
-  let retries: number = 0;
-  while (true) {
-    if (retries > maxRetriesOnError) {
-      throw new Error(
-        "Maximum number of attempts for deleting agent " +
-          id +
-          " reached, but the API continues to return errors.",
-      );
-    }
-    const response =
-      await deleteExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdDelete(
-        deleteOptions,
-      );
-    if (!response.response.ok) {
-      if ("error" in response) {
-        console.log(
-          `An error occurred while deleting the agent: ${JSON.stringify(
-            response.error,
-          )}\nRetrying...`,
-        );
-      }
-      retries++;
-      await sleep(retryInterval * 1000);
-    } else {
-      return true;
-    }
-  }
-}
-
-export { type ExtractAgent, type ExtractConfig };

ts/llama_cloud_services/src/index.ts

@@ -6,5 +6,3 @@ export {
 } from "./LlamaCloudRetriever.js";
 export type { CloudConstructorParams } from "./type.js";
 export { LlamaParseReader } from "./reader.js";
-export { LlamaExtract, LlamaExtractAgent } from "./LlamaExtract.js";
-export type { ExtractConfig } from "./extract.js";