remove poetry.lock

Remove report generation related code from llama_cloud_services
fix: make ts release beautiful again (#902 )
2026-07-20 19:47:38 -04:00 · 2025-09-05 16:26:48 -07:00 · 2025-09-05 16:25:07 -07:00 · 2025-09-05 10:41:39 -06:00 · 2025-09-05 15:48:44 +09:00 · 2025-09-04 15:39:27 -04:00
32 changed files with 3764 additions and 4171 deletions
@@ -19,8 +19,6 @@ jobs:
        uses: actions/checkout@v5

      - uses: pnpm/action-setup@v4
-        with:
-          version: 10

      - name: Setup Node.js
        uses: actions/setup-node@v4
@@ -1,4 +1,4 @@
-name: Lint - Python
+name: Lint

 on:
  push:
@@ -29,7 +29,18 @@ jobs:
      - name: Set up Python
        run: uv python install ${{ matrix.python-version }}

+      - uses: pnpm/action-setup@v4
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version-file: "ts/llama_cloud_services/.nvmrc"
+      - name: Install dependencies
+        run: pnpm install --no-frozen-lockfile
+
      - name: Run linter
        shell: bash
        working-directory: py
        run: uv run -- pre-commit run -a
+      # the js checks are run roundaboutly through lint-staged, and -a doesn't run it. Run them directly.
+      - run: pnpm -w --filter llama-cloud-services run lint
+      - run: pnpm -w --filter llama-cloud-services run format:check
@@ -1,37 +0,0 @@
-name: Lint - TypeScript
-
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - "ts/**"
-  pull_request:
-    paths:
-      - "ts/**"
-
-env:
-  TURBO_TOKEN: ${{ secrets.TURBO_TOKEN }}
-  TURBO_TEAM: ${{ vars.TURBO_TEAM }}
-  TURBO_REMOTE_ONLY: true
-
-jobs:
-  lint:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v5
-      - uses: pnpm/action-setup@v4
-        with:
-          version: 10
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version-file: "ts/llama_cloud_services/.nvmrc"
-      - name: Install dependencies
-        run: pnpm install --no-frozen-lockfile
-      - name: Run lint
-        working-directory: ts/llama_cloud_services/
-        run: pnpm run lint
-      - name: Run Prettier
-        working-directory: ts/llama_cloud_services/
-        run: pnpm run format
@@ -13,8 +13,6 @@ jobs:
        uses: actions/checkout@v5

      - uses: pnpm/action-setup@v4
-        with:
-          version: 10

      - name: Setup Node.js
        uses: actions/setup-node@v4
@@ -49,6 +47,6 @@ jobs:
        uses: ncipollo/release-action@v1
        with:
          artifacts: "ts/llama_cloud_services/llama-cloud-services*.tgz"
-          name: Release ${{ github.ref }} - LlamaCloud Services TS
-          bodyFile: "ts/llama_cloud_services/CHANGELOG.md"
+          name: Release ${{ github.ref_name }} - LlamaCloud Services TS
+          generateReleaseNotes: true
          token: ${{ secrets.GITHUB_TOKEN }}
@@ -1,4 +1,4 @@
-name: Lint - TypeScript
+name: Test - TypeScript

 on:
  push:
@@ -23,17 +23,14 @@ jobs:
    steps:
      - uses: actions/checkout@v5
      - uses: pnpm/action-setup@v4
-        with:
-          version: 10
      - name: Setup Node.js
        uses: actions/setup-node@v4
        with:
          node-version-file: "ts/llama_cloud_services/.nvmrc"
      - name: Install dependencies
-        run: pnpm install --no-frozen-lockfile
-      - name: Run Build
-        working-directory: ts/llama_cloud_services/
-        run: pnpm build
+        run: pnpm -r install --no-frozen-lockfile
+      - name: Build package
+        run: pnpm --filter llama-cloud-services build
      - name: Run Tests
        working-directory: ts/llama_cloud_services/
        run: pnpm test
@@ -60,11 +60,13 @@ repos:
        additional_dependencies: [black==23.10.1]
        # Using PEP 8's line length in docs prevents excess left/right scrolling
        args: [--line-length=79]
-  - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v3.0.3
+  - repo: local
    hooks:
-      - id: prettier
-        exclude: ^(uv.lock|ts/llama_cloud_services/pnpm-lock.yaml|ts/e2e-tests)
+      - id: lint-staged
+        name: Run lint-staged for TS files
+        entry: pnpm -w exec lint-staged
+        language: system
+        pass_filenames: false
  - repo: https://github.com/codespell-project/codespell
    rev: v2.2.6
    hooks:
@@ -18,7 +18,7 @@ versions need to be kept consistent to sidecar it with `llama_cloud_services`. B

 You can also do this with `./scripts/version-bump.py set 0.x.x` if you have `uv` installed.

-Once the change is merged, push a tag `git tag -a v0.x.x -m 0.x.x` and `git push origin 0.x.x`.
+Once the change is merged, push a tag `git tag -a v0.x.x -m 0.x.x` and `git push origin v0.x.x`.

 This tagging step can be done with `./scripts/version-bump tag`.

@@ -9,7 +9,6 @@ This repository contains the code for hand-written SDKs and clients for interact
 This includes:

 - [LlamaParse](./parse.md) - A GenAI-native document parser that can parse complex document data for any downstream LLM use case (Agents, RAG, data processing, etc.).
- [LlamaReport (beta/invite-only)](./report.md) - A prebuilt agentic report builder that can be used to build reports from a variety of data sources.
 - [LlamaExtract](./extract.md) - A prebuilt agentic data extractor that can be used to transform data into a structured JSON representation.
 - [LlamaCloud Index](./index.md) - A widely customizable and fully automated document ingestion pipeline that also serves retrieval purposes.

@@ -28,13 +27,11 @@ Then, you can use the services in your code:
 ```python
 from llama_cloud_services import (
    LlamaParse,
-    LlamaReport,
    LlamaExtract,
    LlamaCloudIndex,
 )

 parser = LlamaParse(api_key="YOUR_API_KEY")
-report = LlamaReport(api_key="YOUR_API_KEY")
 extract = LlamaExtract(api_key="YOUR_API_KEY")
 index = LlamaCloudIndex(
    "my_first_index", project_name="default", api_key="YOUR_API_KEY"
@@ -44,7 +41,6 @@ index = LlamaCloudIndex(
 See the quickstart guides for each service for more information:

 - [LlamaParse](./parse.md)
- [LlamaReport (beta/invite-only)](./report.md)
 - [LlamaExtract](./extract.md)
 - [LlamaCloud Index](./index.md)

@@ -57,13 +53,11 @@ You can also create your API key in the EU region [here](https://cloud.eu.llamai
 ```python
 from llama_cloud_services import (
    LlamaParse,
-    LlamaReport,
    LlamaExtract,
    EU_BASE_URL,
 )

 parser = LlamaParse(api_key="YOUR_API_KEY", base_url=EU_BASE_URL)
-report = LlamaReport(api_key="YOUR_API_KEY", base_url=EU_BASE_URL)
 extract = LlamaExtract(api_key="YOUR_API_KEY", base_url=EU_BASE_URL)
 index = LlamaCloudIndex(
    "my_first_index",
@@ -4,6 +4,6 @@ In this folder you will find several python notebooks that contain examples rega

 - [LlamaParse](./parse/)
 - [LlamaExtract](./extract/)
- [LlamaReport](./report/)
+- [LlamaCloudIndex](./index/)

 Follow the instructions in each notebook to get started!
@@ -43,7 +43,7 @@
   "source": [
    "from llama_cloud_services import LlamaParse\n",
    "\n",
-    "api_key = \"llx-jwAQZL8T38onyL9hKBOXyRtnuCU0Fk3z7tmDhIT3L0GEfohJ\"  # get from cloud.llamaindex.ai"
+    "api_key = \"llx-...\"  # get from cloud.llamaindex.ai"
   ]
  },
  {
@@ -75,7 +75,6 @@
    "    adaptive_long_table=True,\n",
    "    outlined_table_extraction=True,\n",
    "    output_tables_as_HTML=True,\n",
-    "    api_key=\"llx-jwAQZL8T38onyL9hKBOXyRtnuCU0Fk3z7tmDhIT3L0GEfohJ\",\n",
    ")\n",
    "\n",
    "result = await parser.aparse(\"./dcf_template.xlsx\")\n",
@@ -1,762 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Report Generation with LlamaReport\n",
-    "\n",
-    "In this notebook, we'll walk through the basic process of generating a report with LlamaReport, and highlight some of the key features of the library.\n",
-    "\n",
-    "TLDR:\n",
-    "1. Download source data to use as knowledge base for the report\n",
-    "2. Kick off report generation with a template\n",
-    "3. Get the plan and review/accept/reject suggestions\n",
-    "4. Get the final report\n",
-    "5. Review/accept/reject suggestions to edit the final report\n",
-    "6. Print the final report"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install llama-cloud-services"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Download Source Data\n",
-    "\n",
-    "Here, we download the `Attention is All You Need` paper as a PDF.\n",
-    "\n",
-    "LlamaReport currently supports up to 5 files as input, and essentially any file type that can be parsed by LlamaParse.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget \"https://arxiv.org/pdf/1706.03762.pdf\" -O \"./attention.pdf\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Kick off Report Generation\n",
-    "\n",
-    "Here, we kick off report generation with a template.\n",
-    "\n",
-    "The template can either be a string or a file path, but here we'll use a string.\n",
-    "\n",
-    "In our experiments, anything works as a template, but some general guidelines:\n",
-    "\n",
-    "- Use markdown formatting + instructions in each section to guide the report generation\n",
-    "- If using an existing file as a template, provide extra instructions to guide the report generation\n",
-    "\n",
-    "**NOTE:** Since we are in a notebook, we will use async functions and `await` throughout. Synchronous methods that work without `await` are available by just removing the `a` from the method name and removing the `await` keyword."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_cloud_services import LlamaReport\n",
-    "\n",
-    "llama_report = LlamaReport(\n",
-    "    api_key=\"llx-...\",\n",
-    ")\n",
-    "\n",
-    "report_client = await llama_report.acreate_report(\n",
-    "    name=\"my_cool_report_on_attention\",\n",
-    "    # can pass in file paths or bytes\n",
-    "    input_files=[\"./attention.pdf\"],\n",
-    "    template_text=\"\"\"\\\n",
-    "# [Some title]\\n\\n\n",
-    "## TLDR\\n\n",
-    "A quick summary of the paper.\\n\\n\n",
-    "## Details\\n\n",
-    "More details about the paper, possibly more than one section here.\\n\n",
-    "\"\"\",\n",
-    "    # optional additional instructions for the report generation\n",
-    "    # template_instructions=None,\n",
-    "    # optional file path to an existing template instead of template_text\n",
-    "    # template_file=None,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The returned `ReportClient` object is used to interact with the report generation process for this specific report."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Report(id=0a394b33-1a3e-463c-b5cb-7ff8ab827d0a, name=my_cool_report_on_attention)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(report_client)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Get the plan\n",
-    "\n",
-    "The first phases of report generation involve ingesting the source data and generating a plan.\n",
-    "\n",
-    "The plan is a list of instructions for the report generation, and can be reviewed/accepted/rejected by the user.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plan = await report_client.await_for_plan(\n",
-    "    timeout=10000,\n",
-    "    poll_interval=10,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# {title}\n",
-      "[ReportQuery(field='title', prompt='Generate a clear and concise title for this paper about the Transformer model and attention mechanisms', context='The paper discusses the Transformer architecture for sequence transduction using attention mechanisms, focusing on machine translation applications')]\n",
-      "==================\n",
-      "## TLDR\n",
-      "\n",
-      "{tldr_content}\n",
-      "[ReportQuery(field='tldr_content', prompt='Write a brief, clear summary of the key points about the Transformer model', context='Focus on the main innovations: attention mechanisms, efficiency improvements, and state-of-the-art results in machine translation')]\n",
-      "==================\n",
-      "## Details\n",
-      "\n",
-      "{details_content}\n",
-      "[ReportQuery(field='details_content', prompt='Provide detailed information about the Transformer model architecture and its applications', context='Include information about:\\n- The attention mechanism implementation\\n- Advantages over recurrent and convolutional models\\n- Performance in machine translation tasks\\n- Training efficiency improvements')]\n",
-      "==================\n"
-     ]
-    }
-   ],
-   "source": [
-    "for plan_block in plan.blocks:\n",
-    "    print(plan_block.block.template)\n",
-    "    print(plan_block.queries)\n",
-    "    print(\"==================\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the plan, we can either use it to kick off generation of the final report, or we can edit the plan and adjust it as needed.\n",
-    "\n",
-    "While we could manually edit the objects here and use `await report_client.aupdate_plan(action=\"edit\", updated_plan=plan)`, we can also use `LlamaReport` to agentically edit the plan."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "suggestions = await report_client.asuggest_edits(\n",
-    "    \"Can you split the details section into two sections?\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Justification for change: \n",
-      "I'll help you break down the details section into two distinct parts - one focusing on the architecture and another on the practical applications and performance. This will make the content more organized and easier to follow. The original block at index 2 will be replaced with these two new sections.\n",
-      "\n",
-      "Proposed changes:\n",
-      "\n",
-      "## Architecture Details\n",
-      "\n",
-      "{architecture_content}\n",
-      "\n",
-      "[ReportQuery(field='architecture_content', prompt='Describe the technical details of the Transformer model architecture', context='Focus on:\\n- Core components of the Transformer architecture\\n- Self-attention mechanism implementation\\n- Multi-head attention details\\n- Position encoding approach\\n- Feed-forward network structure')]\n",
-      "==================\n",
-      "\n",
-      "## Performance and Applications\n",
-      "\n",
-      "{applications_content}\n",
-      "\n",
-      "[ReportQuery(field='applications_content', prompt='Explain the practical applications and performance advantages of the Transformer model', context='Cover:\\n- Comparison with RNN and CNN models\\n- Machine translation results and benchmarks\\n- Training efficiency improvements\\n- Real-world applications and use cases\\n- Scalability benefits')]\n",
-      "==================\n"
-     ]
-    }
-   ],
-   "source": [
-    "for suggestion in suggestions:\n",
-    "    print(\"Justification for change:\", suggestion.justification)\n",
-    "    print(\"Proposed changes:\")\n",
-    "    for plan_block in suggestion.blocks:\n",
-    "        print(plan_block.block.template)\n",
-    "        print(plan_block.queries)\n",
-    "        print(\"==================\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This looks pretty good! We can also use the client to automatically accept and apply, or reject, these suggestions.\n",
-    "\n",
-    "This will (locally) keep track of the history of changes, so that future suggestions can be based on the previous changes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for suggestion in suggestions:\n",
-    "    await report_client.aaccept_edit(suggestion)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What effect did that have on the tracked local history? Let's see!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[EditAction(block_idx=2, old_content='## Details\\n\\n{details_content}\\n\\nField: details_content, Prompt: Provide detailed information about the Transformer model architecture and its applications, Context: Include information about:\\n- The attention mechanism implementation\\n- Advantages over recurrent and convolutional models\\n- Performance in machine translation tasks\\n- Training efficiency improvements\\nDepends on: none', new_content='\\n## Architecture Details\\n\\n{architecture_content}\\n\\n\\nField: architecture_content, Prompt: Describe the technical details of the Transformer model architecture, Context: Focus on:\\n- Core components of the Transformer architecture\\n- Self-attention mechanism implementation\\n- Multi-head attention details\\n- Position encoding approach\\n- Feed-forward network structure\\nDepends on: none', action='approved', timestamp=datetime.datetime(2025, 2, 4, 20, 59, 55, 773558)),\n",
-       " EditAction(block_idx=3, old_content='[No old content]', new_content='\\n## Performance and Applications\\n\\n{applications_content}\\n\\n\\nField: applications_content, Prompt: Explain the practical applications and performance advantages of the Transformer model, Context: Cover:\\n- Comparison with RNN and CNN models\\n- Machine translation results and benchmarks\\n- Training efficiency improvements\\n- Real-world applications and use cases\\n- Scalability benefits\\nDepends on: previous', action='approved', timestamp=datetime.datetime(2025, 2, 4, 20, 59, 55, 773687))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "report_client.edit_history"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Message(role=<MessageRole.USER: 'user'>, content='Can you split the details section into two sections?', timestamp=datetime.datetime(2025, 2, 4, 20, 59, 47, 754848)),\n",
-       " Message(role=<MessageRole.ASSISTANT: 'assistant'>, content=\"\\nI'll help you break down the details section into two distinct parts - one focusing on the architecture and another on the practical applications and performance. This will make the content more organized and easier to follow. The original block at index 2 will be replaced with these two new sections.\\n\", timestamp=datetime.datetime(2025, 2, 4, 20, 59, 55, 482070))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "report_client.chat_history"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These two items are used to provide context for future suggestions! You can always clear this, or provide your own history."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# report_client.suggest_edits(\"....\", chat_history=[{\"role\": \"user\", \"content\": \"...\"}, ...])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. Get the final report\n",
-    "\n",
-    "Now that we have a plan, we can kick off generation of the final report."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# kicks off report generation\n",
-    "await report_client.aupdate_plan(action=\"approve\")\n",
-    "\n",
-    "# waits for report generation to complete\n",
-    "report = await report_client.await_completion(\n",
-    "    timeout=10000,\n",
-    "    poll_interval=10,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# Attention Is All You Need: A Pure Attention-Based Architecture for Neural Machine Translation\n",
-      "\n",
-      "## TLDR\n",
-      "\n",
-      "The Transformer introduced a revolutionary architecture that relies entirely on attention mechanisms, eliminating the need for recurrence or convolution in sequence processing. Its key innovations include multi-head self-attention for parallel processing of input sequences, scaled dot-product attention for efficient computation, and positional encodings for sequence order awareness. The model achieved breakthrough results in machine translation (28.4 BLEU on English-to-German, 41.8 BLEU on English-to-French) while requiring significantly less training time than previous approaches, training in 3.5 days on 8 GPUs. This architecture demonstrated that attention mechanisms alone are sufficient for state-of-the-art sequence modeling, setting a new direction for natural language processing.\n",
-      "\n",
-      "\n",
-      "## Architecture Details\n",
-      "\n",
-      "The Transformer architecture represents a groundbreaking approach to sequence processing, built entirely on attention mechanisms without recurrence or convolution. Here are its key technical details:\n",
-      "\n",
-      "Core Components:\n",
-      "- Encoder-decoder architecture with stacked self-attention and point-wise feed-forward layers\n",
-      "- Each layer contains two main sub-layers: multi-head self-attention mechanism and position-wise feed-forward network\n",
-      "- Layer normalization and residual connections between sub-layers\n",
-      "- No recurrent or convolutional elements, enabling parallel processing\n",
-      "\n",
-      "Self-Attention Mechanism:\n",
-      "- Processes relationships between all positions in a sequence simultaneously\n",
-      "- Computes attention weights using queries, keys, and values derived from input representations\n",
-      "- Implements scaled dot-product attention to prevent gradient issues with large input dimensions\n",
-      "- Allows direct modeling of dependencies regardless of positional distance\n",
-      "- Uses masking in decoder to prevent leftward information flow and maintain auto-regressive property\n",
-      "\n",
-      "Multi-Head Attention:\n",
-      "- Employs multiple attention heads operating in parallel\n",
-      "- Each head processes information in different representation subspaces\n",
-      "- Three types of attention applications:\n",
-      "  1. Encoder self-attention (all positions attend to each other)\n",
-      "  2. Decoder self-attention (each position attends to previous positions)\n",
-      "  3. Encoder-decoder attention (decoder queries attend to encoder outputs)\n",
-      "- Counteracts reduced resolution from attention averaging through parallel processing\n",
-      "\n",
-      "Position-wise Feed-Forward Network:\n",
-      "- Applied identically to each position separately\n",
-      "- Consists of two linear transformations with ReLU activation\n",
-      "- Structure: FFN(x) = max(0, xW1 + b1)W2 + b2\n",
-      "- Input and output dimensionality: dmodel = 512\n",
-      "- Inner-layer dimensionality: dff = 2048\n",
-      "- Parameters vary between layers but remain constant across positions\n",
-      "\n",
-      "Position Encoding:\n",
-      "- Adds positional information to input embeddings\n",
-      "- Enables the model to consider sequential order without recurrence\n",
-      "- Implements sinusoidal position encodings to allow model to attend to relative positions\n",
-      "- Maintains constant number of operations between any two positions, unlike convolutional approaches\n",
-      "- Allows effective modeling of both local and long-range dependencies\n",
-      "\n",
-      "\n",
-      "\n",
-      "## Performance and Applications\n",
-      "\n",
-      "The Transformer model demonstrates significant performance advantages and practical applications across multiple domains:\n",
-      "\n",
-      "Performance Advantages over RNN/CNN Models:\n",
-      "- Eliminates sequential computation constraints present in RNNs, enabling superior parallelization\n",
-      "- Reduces operations needed for relating distant positions to a constant number, compared to linear/logarithmic scaling in CNNs\n",
-      "- Processes all input and output positions simultaneously through self-attention mechanisms\n",
-      "- Achieves state-of-the-art results while requiring significantly less computational resources\n",
-      "\n",
-      "Machine Translation Benchmarks:\n",
-      "- WMT 2014 English-to-German: 28.4 BLEU score, exceeding previous best results by over 2 BLEU points\n",
-      "- WMT 2014 English-to-French: 41.8 BLEU score (single-model state-of-the-art)\n",
-      "- Surpasses performance of existing model ensembles in translation tasks\n",
-      "\n",
-      "Training Efficiency:\n",
-      "- Requires only 3.5 days of training on eight GPUs for state-of-the-art performance\n",
-      "- Achieves superior results at \"a small fraction of the training costs\" compared to previous models\n",
-      "- Enables significantly faster training through parallel processing of input/output sequences\n",
-      "- Can reach production-quality performance in as little as twelve hours on modern GPU hardware\n",
-      "\n",
-      "Real-world Applications:\n",
-      "- Machine translation systems\n",
-      "- Natural language understanding tasks\n",
-      "- Reading comprehension\n",
-      "- Abstractive summarization\n",
-      "- Text entailment analysis\n",
-      "- Constituency parsing (achieving 92.7 F1 score in semi-supervised settings)\n",
-      "- Adaptable to both large and limited training data scenarios\n",
-      "\n",
-      "Scalability Benefits:\n",
-      "- Highly parallelizable architecture enables efficient scaling across multiple GPUs\n",
-      "- Constant computational complexity for relating any input/output positions\n",
-      "- Effective handling of long-range dependencies in sequences\n",
-      "- Maintains performance quality while scaling to larger datasets and model sizes\n",
-      "- Generalizes well across different tasks and domains without architectural changes\n",
-      "- Supports efficient inference and deployment in production environments\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "report_text = \"\\n\\n\".join([block.template for block in report.blocks])\n",
-    "print(report_text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5. Edit the final report\n",
-    "\n",
-    "Now that we have a report, we can edit it.\n",
-    "\n",
-    "We can use the `asuggest_edits` method to get suggestions for edits, and then use the `aaccept_edit`/`areject_edit` methods to apply them.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Justification for change: \n",
-      "I'd suggest changing \"TLDR\" to \"Executive Summary\" which is more appropriate for a professional or academic report. This term is widely used in formal documents and better reflects the nature of this concise overview section while maintaining the same function of providing a quick summary of the key points.\n",
-      "\n",
-      "Proposed changes:\n",
-      "## Executive Summary\n",
-      "\n",
-      "The Transformer introduced a revolutionary architecture that relies entirely on attention mechanisms, eliminating the need for recurrence or convolution in sequence processing. Its key innovations include multi-head self-attention for parallel processing of input sequences, scaled dot-product attention for efficient computation, and positional encodings for sequence order awareness. The model achieved breakthrough results in machine translation (28.4 BLEU on English-to-German, 41.8 BLEU on English-to-French) while requiring significantly less training time than previous approaches, training in 3.5 days on 8 GPUs. This architecture demonstrated that attention mechanisms alone are sufficient for state-of-the-art sequence modeling, setting a new direction for natural language processing.\n",
-      "==================\n"
-     ]
-    }
-   ],
-   "source": [
-    "suggestions = await report_client.asuggest_edits(\n",
-    "    \"Can you change the TLDR header to something more professional?\"\n",
-    ")\n",
-    "for suggestion in suggestions:\n",
-    "    print(\"Justification for change:\", suggestion.justification)\n",
-    "    print(\"Proposed changes:\")\n",
-    "    for block in suggestion.blocks:\n",
-    "        print(block.template)\n",
-    "        print(\"==================\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Changing to \"Executive Summary\" sounds reasonable, lets accept that!\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for suggestion in suggestions:\n",
-    "    await report_client.aaccept_edit(suggestion)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 7. Print the final report\n",
-    "\n",
-    "Now that we have a report, we can print it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# Attention Is All You Need: A Pure Attention-Based Architecture for Neural Machine Translation\n",
-      "\n",
-      "## Executive Summary\n",
-      "\n",
-      "The Transformer introduced a revolutionary architecture that relies entirely on attention mechanisms, eliminating the need for recurrence or convolution in sequence processing. Its key innovations include multi-head self-attention for parallel processing of input sequences, scaled dot-product attention for efficient computation, and positional encodings for sequence order awareness. The model achieved breakthrough results in machine translation (28.4 BLEU on English-to-German, 41.8 BLEU on English-to-French) while requiring significantly less training time than previous approaches, training in 3.5 days on 8 GPUs. This architecture demonstrated that attention mechanisms alone are sufficient for state-of-the-art sequence modeling, setting a new direction for natural language processing.\n",
-      "\n",
-      "\n",
-      "## Architecture Details\n",
-      "\n",
-      "The Transformer architecture represents a groundbreaking approach to sequence processing, built entirely on attention mechanisms without recurrence or convolution. Here are its key technical details:\n",
-      "\n",
-      "Core Components:\n",
-      "- Encoder-decoder architecture with stacked self-attention and point-wise feed-forward layers\n",
-      "- Each layer contains two main sub-layers: multi-head self-attention mechanism and position-wise feed-forward network\n",
-      "- Layer normalization and residual connections between sub-layers\n",
-      "- No recurrent or convolutional elements, enabling parallel processing\n",
-      "\n",
-      "Self-Attention Mechanism:\n",
-      "- Processes relationships between all positions in a sequence simultaneously\n",
-      "- Computes attention weights using queries, keys, and values derived from input representations\n",
-      "- Implements scaled dot-product attention to prevent gradient issues with large input dimensions\n",
-      "- Allows direct modeling of dependencies regardless of positional distance\n",
-      "- Uses masking in decoder to prevent leftward information flow and maintain auto-regressive property\n",
-      "\n",
-      "Multi-Head Attention:\n",
-      "- Employs multiple attention heads operating in parallel\n",
-      "- Each head processes information in different representation subspaces\n",
-      "- Three types of attention applications:\n",
-      "  1. Encoder self-attention (all positions attend to each other)\n",
-      "  2. Decoder self-attention (each position attends to previous positions)\n",
-      "  3. Encoder-decoder attention (decoder queries attend to encoder outputs)\n",
-      "- Counteracts reduced resolution from attention averaging through parallel processing\n",
-      "\n",
-      "Position-wise Feed-Forward Network:\n",
-      "- Applied identically to each position separately\n",
-      "- Consists of two linear transformations with ReLU activation\n",
-      "- Structure: FFN(x) = max(0, xW1 + b1)W2 + b2\n",
-      "- Input and output dimensionality: dmodel = 512\n",
-      "- Inner-layer dimensionality: dff = 2048\n",
-      "- Parameters vary between layers but remain constant across positions\n",
-      "\n",
-      "Position Encoding:\n",
-      "- Adds positional information to input embeddings\n",
-      "- Enables the model to consider sequential order without recurrence\n",
-      "- Implements sinusoidal position encodings to allow model to attend to relative positions\n",
-      "- Maintains constant number of operations between any two positions, unlike convolutional approaches\n",
-      "- Allows effective modeling of both local and long-range dependencies\n",
-      "\n",
-      "\n",
-      "\n",
-      "## Performance and Applications\n",
-      "\n",
-      "The Transformer model demonstrates significant performance advantages and practical applications across multiple domains:\n",
-      "\n",
-      "Performance Advantages over RNN/CNN Models:\n",
-      "- Eliminates sequential computation constraints present in RNNs, enabling superior parallelization\n",
-      "- Reduces operations needed for relating distant positions to a constant number, compared to linear/logarithmic scaling in CNNs\n",
-      "- Processes all input and output positions simultaneously through self-attention mechanisms\n",
-      "- Achieves state-of-the-art results while requiring significantly less computational resources\n",
-      "\n",
-      "Machine Translation Benchmarks:\n",
-      "- WMT 2014 English-to-German: 28.4 BLEU score, exceeding previous best results by over 2 BLEU points\n",
-      "- WMT 2014 English-to-French: 41.8 BLEU score (single-model state-of-the-art)\n",
-      "- Surpasses performance of existing model ensembles in translation tasks\n",
-      "\n",
-      "Training Efficiency:\n",
-      "- Requires only 3.5 days of training on eight GPUs for state-of-the-art performance\n",
-      "- Achieves superior results at \"a small fraction of the training costs\" compared to previous models\n",
-      "- Enables significantly faster training through parallel processing of input/output sequences\n",
-      "- Can reach production-quality performance in as little as twelve hours on modern GPU hardware\n",
-      "\n",
-      "Real-world Applications:\n",
-      "- Machine translation systems\n",
-      "- Natural language understanding tasks\n",
-      "- Reading comprehension\n",
-      "- Abstractive summarization\n",
-      "- Text entailment analysis\n",
-      "- Constituency parsing (achieving 92.7 F1 score in semi-supervised settings)\n",
-      "- Adaptable to both large and limited training data scenarios\n",
-      "\n",
-      "Scalability Benefits:\n",
-      "- Highly parallelizable architecture enables efficient scaling across multiple GPUs\n",
-      "- Constant computational complexity for relating any input/output positions\n",
-      "- Effective handling of long-range dependencies in sequences\n",
-      "- Maintains performance quality while scaling to larger datasets and model sizes\n",
-      "- Generalizes well across different tasks and domains without architectural changes\n",
-      "- Supports efficient inference and deployment in production environments\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "report_response = await report_client.aget()\n",
-    "report_text = \"\\n\\n\".join([block.template for block in report_response.report.blocks])\n",
-    "print(report_text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also see the sources for each block!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.99687636\n",
-      "# Abstract\n",
-      "\n",
-      "The dominant sequence transduction models are based on complex recurrent or convolutiona\n",
-      "==================\n",
-      "0.99591404\n",
-      "# 2 Background\n",
-      "\n",
-      "The goal of reducing sequential computation also forms the foundation of the Extende\n",
-      "==================\n",
-      "0.9951325\n",
-      "# 1 Introduction\n",
-      "\n",
-      "Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neu\n",
-      "==================\n",
-      "0.99442345\n",
-      "# 7 Conclusion\n",
-      "\n",
-      "In this work, we presented the Transformer, the first sequence transduction model ba\n",
-      "==================\n",
-      "0.9967649\n",
-      "# 3.2.3 Applications of Attention in our Model\n",
-      "\n",
-      "The Transformer uses multi-head attention in three d\n",
-      "==================\n",
-      "0.99533635\n",
-      "# 2 Background\n",
-      "\n",
-      "The goal of reducing sequential computation also forms the foundation of the Extende\n",
-      "==================\n",
-      "0.9935868\n",
-      "# Abstract\n",
-      "\n",
-      "The dominant sequence transduction models are based on complex recurrent or convolutiona\n",
-      "==================\n",
-      "0.98780584\n",
-      "# Outputs\n",
-      "\n",
-      "(shifted right)\n",
-      "\n",
-      "Figure 1: The Transformer - model architecture.\n",
-      "\n",
-      "The Transformer follows\n",
-      "==================\n",
-      "0.9205043\n",
-      "# 3.3 Position-wise Feed-Forward Networks\n",
-      "\n",
-      "In addition to attention sub-layers, each of the layers i\n",
-      "==================\n",
-      "0.79581684\n",
-      "# 1 Introduction\n",
-      "\n",
-      "Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neu\n",
-      "==================\n",
-      "0.9946774\n",
-      "# Abstract\n",
-      "\n",
-      "The dominant sequence transduction models are based on complex recurrent or convolutiona\n",
-      "==================\n",
-      "0.97079873\n",
-      "# 7 Conclusion\n",
-      "\n",
-      "In this work, we presented the Transformer, the first sequence transduction model ba\n",
-      "==================\n",
-      "0.9535353\n",
-      "# 6.3 English Constituency Parsing\n",
-      "\n",
-      "To evaluate if the Transformer can generalize to other tasks we \n",
-      "==================\n",
-      "0.9514138\n",
-      "# 2 Background\n",
-      "\n",
-      "The goal of reducing sequential computation also forms the foundation of the Extende\n",
-      "==================\n",
-      "0.9790758\n",
-      "# 1 Introduction\n",
-      "\n",
-      "Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neu\n",
-      "==================\n",
-      "0.92262185\n",
-      "# Outputs\n",
-      "\n",
-      "(shifted right)\n",
-      "\n",
-      "Figure 1: The Transformer - model architecture.\n",
-      "\n",
-      "The Transformer follows\n",
-      "==================\n"
-     ]
-    }
-   ],
-   "source": [
-    "for block in report_response.report.blocks:\n",
-    "    # Each block has a list of sources, which are the nodes that were used to generate the block\n",
-    "    for source in block.sources:\n",
-    "        print(source.score)\n",
-    "        print(source.node.text[:100])\n",
-    "        print(\"==================\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "llama-parse-aNC435Vv-py3.10",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
@@ -0,0 +1,19 @@
+{
+  "name": "llama-cloud-services-workspace",
+  "version": "0.0.1",
+  "description": "",
+  "private": true,
+  "keywords": [],
+  "author": "",
+  "devDependencies": {
+    "prettier": "^3.6.2",
+    "lint-staged": "^15.4.2"
+  },
+  "lint-staged": {
+    "ts/llama_cloud_services/src/**/*.{ts,tsx,js,jsx}": [
+      "pnpm --filter llama-cloud-services exec eslint --fix",
+      "pnpm --filter llama-cloud-services exec prettier --write"
+    ]
+  },
+  "packageManager": "pnpm@10.11.1+sha512.e519b9f7639869dc8d5c3c5dfef73b3f091094b0a006d7317353c72b124e80e1afd429732e28705ad6bfa1ee879c1fce46c128ccebd3192101f43dd67c667912"
+}
@@ -6,6 +6,15 @@ settings:

 importers:

+  .:
+    devDependencies:
+      lint-staged:
+        specifier: ^15.4.2
+        version: 15.5.2
+      prettier:
+        specifier: ^3.6.2
+        version: 3.6.2
+
  ts/e2e-tests:
    devDependencies:
      '@types/node':
@@ -829,6 +838,10 @@ packages:
  ajv@8.17.1:
    resolution: {integrity: sha512-B/gBuNg5SiMTrPkC+A2+cW0RszwxYmn6VYxB/inlBStS5nx6xHIt/ehKRhIMhqusl7a8LjQoZnjCs5vhwxOQ1g==}

+  ansi-escapes@7.0.0:
+    resolution: {integrity: sha512-GdYO7a61mR0fOlAsvC9/rIHf7L96sBc6dEWzeOu+KAea5bZyQRPIpojrVoI4AXGJS/ycu/fBTdLrUkA4ODrvjw==}
+    engines: {node: '>=18'}
+
  ansi-regex@5.0.1:
    resolution: {integrity: sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ==}
    engines: {node: '>=8'}
@@ -933,6 +946,10 @@ packages:
    resolution: {integrity: sha512-ywqV+5MmyL4E7ybXgKys4DugZbX0FC6LnwrhjuykIjnK9k8OQacQ7axGKnjDXWNhns0xot3bZI5h55H8yo9cJg==}
    engines: {node: '>=6'}

+  cli-truncate@4.0.0:
+    resolution: {integrity: sha512-nPdaFdQ0h/GEigbPClz11D0v/ZJEwxmeVZGeMo3Z5StPtUTkA9o1lD6QwoirYiSDzbcwn2XcjwmCp68W1IS4TA==}
+    engines: {node: '>=18'}
+
  cliui@8.0.1:
    resolution: {integrity: sha512-BSeNnyus75C4//NQ9gQt1/csTXyo/8Sb+afLAkzAptFuMsod9HFokGNudZpi/oQV73hnVK+sR+5PVRMd+Dr7YQ==}
    engines: {node: '>=12'}
@@ -944,10 +961,17 @@ packages:
  color-name@1.1.4:
    resolution: {integrity: sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==}

+  colorette@2.0.20:
+    resolution: {integrity: sha512-IfEDxwoWIjkeXL1eXcDiow4UbKjhLdq6/EuSVR9GMN7KVH3r9gQ83e73hsz1Nd1T3ijd5xv1wcWRYO+D6kCI2w==}
+
  commander@13.0.0:
    resolution: {integrity: sha512-oPYleIY8wmTVzkvQq10AEok6YcTC4sRUBl8F9gVuwchGVUCTbl/vhLTaQqutuuySYOsu8YTgV+OxKc/8Yvx+mQ==}
    engines: {node: '>=18'}

+  commander@13.1.0:
+    resolution: {integrity: sha512-/rFeCpNJQbhSZjGVwO9RFV3xPqbnERS8MmIQzCtD/zl6gpJuV/bMLuN92oG3F7d8oDEHHRrujSXNUr8fpjntKw==}
+    engines: {node: '>=18'}
+
  commondir@1.0.1:
    resolution: {integrity: sha512-W9pAhw0ja1Edb5GVdIF1mjZw/ASI0AlShXM83UUGe2DVr5TdAPEA1OA8m/g8zWp9x6On7gqufY+FatDbC3MDQg==}

@@ -1007,6 +1031,10 @@ packages:
  emoji-regex@9.2.2:
    resolution: {integrity: sha512-L18DaJsXSUk2+42pv8mLs5jJT2hqFkFE4j21wOmgbUqsZ2hL72NsUU785g9RXgo3s0ZNgVl42TiHp3ZtOv/Vyg==}

+  environment@1.1.0:
+    resolution: {integrity: sha512-xUtoPkMggbz0MPyPiIWr1Kp4aeWJjDZ6SMvURhimjdZgsRuDplF5/s9hcgGhyXMhs+6vpnuoiZ2kFiu3FMnS8Q==}
+    engines: {node: '>=18'}
+
  es-module-lexer@1.7.0:
    resolution: {integrity: sha512-jEQoCwk8hyb2AZziIOLhDqpm5+2ww5uIE6lkO/6jcOCusfk6LhMHpXXfBLXTZ7Ydyt0j4VoUQv6uGNYbdW+kBA==}

@@ -1071,6 +1099,13 @@ packages:
    resolution: {integrity: sha512-kVscqXk4OCp68SZ0dkgEKVi6/8ij300KBWTJq32P/dYeWTSwK41WyTxalN1eRmA5Z9UU/LX9D7FWSmV9SAYx6g==}
    engines: {node: '>=0.10.0'}

+  eventemitter3@5.0.1:
+    resolution: {integrity: sha512-GWkBvjiSZK87ELrYOSESUYeVIc9mvLLf/nXalMOS5dYrgZq9o5OVkbZAVM06CVxYsCwH9BDZFPlQTlPA1j4ahA==}
+
+  execa@8.0.1:
+    resolution: {integrity: sha512-VyhnebXciFV2DESc+p6B+y0LjSm0krU4OgJN44qFAhBY0TJ+1V61tYD2+wHusZ6F9n5K+vl8k0sTy7PEfV4qpg==}
+    engines: {node: '>=16.17'}
+
  expect-type@1.2.2:
    resolution: {integrity: sha512-JhFGDVJ7tmDJItKhYgJCGLOWjuK9vPxiXoUFLwLDc99NlmklilbiQJwoctZtt13+xMw91MCk/REan6MWHqDjyA==}
    engines: {node: '>=12.0.0'}
@@ -1152,6 +1187,14 @@ packages:
    resolution: {integrity: sha512-vpeMIQKxczTD/0s2CdEWHcb0eeJe6TFjxb+J5xgX7hScxqrGuyjmv4c1D4A/gelKfyox0gJJwIHF+fLjeaM8kQ==}
    engines: {node: '>=18'}

+  get-east-asian-width@1.3.1:
+    resolution: {integrity: sha512-R1QfovbPsKmosqTnPoRFiJ7CF9MLRgb53ChvMZm+r4p76/+8yKDy17qLL2PKInORy2RkZZekuK0efYgmzTkXyQ==}
+    engines: {node: '>=18'}
+
+  get-stream@8.0.1:
+    resolution: {integrity: sha512-VaUJspBffn/LMCJVoMvSAdmscJyS1auj5Zulnn5UoYcY531UWmdwhRWkcGKnGU93m5HSXP9LP2usOryrBtQowA==}
+    engines: {node: '>=16'}
+
  get-tsconfig@4.10.1:
    resolution: {integrity: sha512-auHyJ4AgMz7vgS8Hp3N6HXSmlMdUyhSUrfBF16w153rxtLIEOE+HGqaBppczZvnHLqQJfiHotCYpNhl0lUROFQ==}

@@ -1198,6 +1241,10 @@ packages:
  html-escaper@2.0.2:
    resolution: {integrity: sha512-H2iMtd0I4Mt5eYiapRdIDjp+XzelXQ0tFE4JS7YFwFevXXMmOp9myNrUvCg0D6ws8iqkRPBfKHgbwig1SmlLfg==}

+  human-signals@5.0.0:
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+
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    engines: {node: '>=8'}

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+    engines: {node: '>=12'}
+
+  is-fullwidth-code-point@5.1.0:
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+    engines: {node: '>=18'}
+
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+    engines: {node: ^12.20.0 || ^14.13.1 || >=16.0.0}
+
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    engines: {node: '>= 0.8.0'}

+  lilconfig@3.1.3:
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+    engines: {node: '>=14'}
+
+  lint-staged@15.5.2:
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+    engines: {node: '>=18.12.0'}
+    hasBin: true
+
+  listr2@8.3.3:
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+    engines: {node: '>=18.0.0'}
+
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    engines: {node: '>=18'}

+  log-update@6.1.0:
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+    engines: {node: '>=18'}
+
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    engines: {node: '>=10'}

+  merge-stream@2.0.0:
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+
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    engines: {node: '>=8.6'}

+  mimic-fn@4.0.0:
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+    engines: {node: '>=12'}
+
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@@ -1414,6 +1497,10 @@ packages:
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+  npm-run-path@5.3.0:
+    resolution: {integrity: sha512-ppwTtiJZq0O/ai0z7yfudtBpWIoxM8yE6nHi1X47eFR2EWORqfbu6CnPlNsjeN683eT0qG6H/Pyf9fCcvjnnnQ==}
+    engines: {node: ^12.20.0 || ^14.13.1 || >=16.0.0}
+
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    engines: {node: ^14.16.0 || >=16.10.0}
@@ -1422,6 +1509,10 @@ packages:
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+  onetime@6.0.0:
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+    engines: {node: '>=12'}
+
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    engines: {node: '>=18'}
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    engines: {node: '>=8'}

+  path-key@4.0.0:
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+    engines: {node: '>=12'}
+
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    engines: {node: '>=12'}

+  pidtree@0.6.0:
+    resolution: {integrity: sha512-eG2dWTVw5bzqGRztnHExczNxt5VGsE6OwTeCG3fdUf9KBsZzO3R5OIIIzWR+iZA0NtZ+RDVdaoE2dK1cn6jH4g==}
+    engines: {node: '>=0.10'}
+    hasBin: true
+
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@@ -1558,6 +1658,9 @@ packages:
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    engines: {iojs: '>=1.0.0', node: '>=0.10.0'}

+  rfdc@1.4.1:
+    resolution: {integrity: sha512-q1b3N5QkRUWUl7iyylaaj3kOpIT0N2i9MqIEQXP73GVsN9cw3fdx8X63cEmWhJGi2PPCF23Ijp7ktmd39rawIA==}
+
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    engines: {node: '>=16'}
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    engines: {node: '>=18'}

+  slice-ansi@5.0.0:
+    resolution: {integrity: sha512-FC+lgizVPfie0kkhqUScwRu1O/lF6NOgJmlCgK+/LYxDCTk8sGelYaHDhFcDN+Sn3Cv+3VSa4Byeo+IMCzpMgQ==}
+    engines: {node: '>=12'}
+
+  slice-ansi@7.1.0:
+    resolution: {integrity: sha512-bSiSngZ/jWeX93BqeIAbImyTbEihizcwNjFoRUIY/T1wWQsfsm2Vw1agPKylXvQTU7iASGdHhyqRlqQzfz+Htg==}
+    engines: {node: '>=18'}
+
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    engines: {node: '>=0.10.0'}
@@ -1627,6 +1738,10 @@ packages:
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    engines: {node: '>=18'}

+  string-argv@0.3.2:
+    resolution: {integrity: sha512-aqD2Q0144Z+/RqG52NeHEkZauTAUWJO8c6yTftGJKO3Tja5tUgIfmIl6kExvhtxSDP7fXB6DvzkfMpCd/F3G+Q==}
+    engines: {node: '>=0.6.19'}
+
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    resolution: {integrity: sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==}
    engines: {node: '>=8'}
@@ -1647,6 +1762,10 @@ packages:
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    engines: {node: '>=12'}

+  strip-final-newline@3.0.0:
+    resolution: {integrity: sha512-dOESqjYr96iWYylGObzd39EuNTa5VJxyvVAEm5Jnh7KGo75V43Hk1odPQkNDyXNmUR6k+gEiDVXnjB8HJ3crXw==}
+    engines: {node: '>=12'}
+
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    resolution: {integrity: sha512-6fPc+R4ihwqP6N/aIv2f1gMH8lOVtWQHoqC4yK6oSDVVocumAsfCqjkXnqiYMhmMwS/mEHLp7Vehlt3ql6lEig==}
    engines: {node: '>=8'}
@@ -1871,6 +1990,10 @@ packages:
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    engines: {node: '>=12'}

+  wrap-ansi@9.0.0:
+    resolution: {integrity: sha512-G8ura3S+3Z2G+mkgNRq8dqaFZAuxfsxpBB8OCTGRTCtp+l/v9nbFNmCUP1BZMts3G1142MsZfn6eeUKrr4PD1Q==}
+    engines: {node: '>=18'}
+
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    engines: {node: '>=10'}
@@ -1878,6 +2001,11 @@ packages:
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+  yaml@2.8.1:
+    resolution: {integrity: sha512-lcYcMxX2PO9XMGvAJkJ3OsNMw+/7FKes7/hgerGUYWIoWu5j/+YQqcZr5JnPZWzOsEBgMbSbiSTn/dv/69Mkpw==}
+    engines: {node: '>= 14.6'}
+    hasBin: true
+
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    resolution: {integrity: sha512-tVpsJW7DdjecAiFpbIB1e3qxIQsE6NoPc5/eTdrbbIC4h0LVsWhnoa3g+m2HclBIujHzsxZ4VJVA+GUuc2/LBw==}
    engines: {node: '>=12'}
@@ -2551,6 +2679,10 @@ snapshots:
      json-schema-traverse: 1.0.0
      require-from-string: 2.0.2

+  ansi-escapes@7.0.0:
+    dependencies:
+      environment: 1.1.0
+
  ansi-regex@5.0.1: {}

  ansi-regex@6.1.0: {}
@@ -2665,6 +2797,11 @@ snapshots:

  cli-spinners@2.9.2: {}

+  cli-truncate@4.0.0:
+    dependencies:
+      slice-ansi: 5.0.0
+      string-width: 7.2.0
+
  cliui@8.0.1:
    dependencies:
      string-width: 4.2.3
@@ -2677,8 +2814,12 @@ snapshots:

  color-name@1.1.4: {}

+  colorette@2.0.20: {}
+
  commander@13.0.0: {}

+  commander@13.1.0: {}
+
  commondir@1.0.1: {}

  concat-map@0.0.1: {}
@@ -2717,6 +2858,8 @@ snapshots:

  emoji-regex@9.2.2: {}

+  environment@1.1.0: {}
+
  es-module-lexer@1.7.0: {}

  esbuild@0.21.5:
@@ -2824,6 +2967,20 @@ snapshots:

  esutils@2.0.3: {}

+  eventemitter3@5.0.1: {}
+
+  execa@8.0.1:
+    dependencies:
+      cross-spawn: 7.0.6
+      get-stream: 8.0.1
+      human-signals: 5.0.0
+      is-stream: 3.0.0
+      merge-stream: 2.0.0
+      npm-run-path: 5.3.0
+      onetime: 6.0.0
+      signal-exit: 4.1.0
+      strip-final-newline: 3.0.0
+
  expect-type@1.2.2: {}

  fast-deep-equal@3.1.3: {}
@@ -2899,6 +3056,10 @@ snapshots:

  get-east-asian-width@1.3.0: {}

+  get-east-asian-width@1.3.1: {}
+
+  get-stream@8.0.1: {}
+
  get-tsconfig@4.10.1:
    dependencies:
      resolve-pkg-maps: 1.0.0
@@ -2953,6 +3114,8 @@ snapshots:

  html-escaper@2.0.2: {}

+  human-signals@5.0.0: {}
+
  ieee754@1.2.1: {}

  ignore@5.3.2: {}
@@ -2974,6 +3137,12 @@ snapshots:

  is-fullwidth-code-point@3.0.0: {}

+  is-fullwidth-code-point@4.0.0: {}
+
+  is-fullwidth-code-point@5.1.0:
+    dependencies:
+      get-east-asian-width: 1.3.1
+
  is-glob@4.0.3:
    dependencies:
      is-extglob: 2.1.1
@@ -2990,6 +3159,8 @@ snapshots:
    dependencies:
      '@types/estree': 1.0.8

+  is-stream@3.0.0: {}
+
  is-unicode-supported@1.3.0: {}

  is-unicode-supported@2.1.0: {}
@@ -3053,6 +3224,32 @@ snapshots:
      prelude-ls: 1.2.1
      type-check: 0.4.0

+  lilconfig@3.1.3: {}
+
+  lint-staged@15.5.2:
+    dependencies:
+      chalk: 5.5.0
+      commander: 13.1.0
+      debug: 4.4.1
+      execa: 8.0.1
+      lilconfig: 3.1.3
+      listr2: 8.3.3
+      micromatch: 4.0.8
+      pidtree: 0.6.0
+      string-argv: 0.3.2
+      yaml: 2.8.1
+    transitivePeerDependencies:
+      - supports-color
+
+  listr2@8.3.3:
+    dependencies:
+      cli-truncate: 4.0.0
+      colorette: 2.0.20
+      eventemitter3: 5.0.1
+      log-update: 6.1.0
+      rfdc: 1.4.1
+      wrap-ansi: 9.0.0
+
  locate-path@6.0.0:
    dependencies:
      p-locate: 5.0.0
@@ -3066,6 +3263,14 @@ snapshots:
      chalk: 5.5.0
      is-unicode-supported: 1.3.0

+  log-update@6.1.0:
+    dependencies:
+      ansi-escapes: 7.0.0
+      cli-cursor: 5.0.0
+      slice-ansi: 7.1.0
+      strip-ansi: 7.1.0
+      wrap-ansi: 9.0.0
+
  loupe@3.2.0: {}

  lru-cache@10.4.3: {}
@@ -3086,6 +3291,8 @@ snapshots:
    dependencies:
      semver: 7.7.2

+  merge-stream@2.0.0: {}
+
  merge2@1.4.1: {}

  micromatch@4.0.8:
@@ -3093,6 +3300,8 @@ snapshots:
      braces: 3.0.3
      picomatch: 2.3.1

+  mimic-fn@4.0.0: {}
+
  mimic-function@5.0.1: {}

  minimatch@3.1.2:
@@ -3139,6 +3348,10 @@ snapshots:

  node-fetch-native@1.6.7: {}

+  npm-run-path@5.3.0:
+    dependencies:
+      path-key: 4.0.0
+
  nypm@0.5.4:
    dependencies:
      citty: 0.1.6
@@ -3150,6 +3363,10 @@ snapshots:

  ohash@1.1.6: {}

+  onetime@6.0.0:
+    dependencies:
+      mimic-fn: 4.0.0
+
  onetime@7.0.0:
    dependencies:
      mimic-function: 5.0.1
@@ -3199,6 +3416,8 @@ snapshots:

  path-key@3.1.1: {}

+  path-key@4.0.0: {}
+
  path-parse@1.0.7: {}

  path-scurry@1.11.1:
@@ -3220,6 +3439,8 @@ snapshots:

  picomatch@4.0.3: {}

+  pidtree@0.6.0: {}
+
  pkg-types@1.3.1:
    dependencies:
      confbox: 0.1.8
@@ -3272,6 +3493,8 @@ snapshots:

  reusify@1.1.0: {}

+  rfdc@1.4.1: {}
+
  rollup-plugin-dts@6.2.1(rollup@4.46.2)(typescript@5.9.2):
    dependencies:
      magic-string: 0.30.17
@@ -3342,6 +3565,16 @@ snapshots:
      mrmime: 2.0.1
      totalist: 3.0.1

+  slice-ansi@5.0.0:
+    dependencies:
+      ansi-styles: 6.2.1
+      is-fullwidth-code-point: 4.0.0
+
+  slice-ansi@7.1.0:
+    dependencies:
+      ansi-styles: 6.2.1
+      is-fullwidth-code-point: 5.1.0
+
  source-map-js@1.2.1: {}

  source-map@0.6.1: {}
@@ -3352,6 +3585,8 @@ snapshots:

  stdin-discarder@0.2.2: {}

+  string-argv@0.3.2: {}
+
  string-width@4.2.3:
    dependencies:
      emoji-regex: 8.0.0
@@ -3378,6 +3613,8 @@ snapshots:
    dependencies:
      ansi-regex: 6.1.0

+  strip-final-newline@3.0.0: {}
+
  strip-json-comments@3.1.1: {}

  strtok3@10.3.4:
@@ -3584,10 +3821,18 @@ snapshots:
      string-width: 5.1.2
      strip-ansi: 7.1.0

+  wrap-ansi@9.0.0:
+    dependencies:
+      ansi-styles: 6.2.1
+      string-width: 7.2.0
+      strip-ansi: 7.1.0
+
  y18n@5.0.8: {}

  yallist@4.0.0: {}

+  yaml@2.8.1: {}
+
  yargs-parser@21.1.1: {}

  yargs@17.7.2:
@@ -9,7 +9,6 @@ This repository contains the code for hand-written SDKs and clients for interact
 This includes:

 - [LlamaParse](../parse.md) - A GenAI-native document parser that can parse complex document data for any downstream LLM use case (Agents, RAG, data processing, etc.).
- [LlamaReport (beta/invite-only)](../report.md) - A prebuilt agentic report builder that can be used to build reports from a variety of data sources.
 - [LlamaExtract](../extract.md) - A prebuilt agentic data extractor that can be used to transform data into a structured JSON representation.
 - [LlamaCloud Index](../index.md) - A widely customizable and fully automated document ingestion pipeline that also serves retrieval purposes.

@@ -28,14 +27,12 @@ Then, you can use the services in your code:
 ```python
 from llama_cloud_services import (
    LlamaParse,
-    LlamaReport,
    LlamaExtract,
    LlamaCloudIndex,
 )
-from llama_cloud_services import LlamaParse, LlamaReport, LlamaExtract
+from llama_cloud_services import LlamaParse, LlamaExtract

 parser = LlamaParse(api_key="YOUR_API_KEY")
-report = LlamaReport(api_key="YOUR_API_KEY")
 extract = LlamaExtract(api_key="YOUR_API_KEY")
 index = LlamaCloudIndex(
    "my_first_index", project_name="default", api_key="YOUR_API_KEY"
@@ -45,7 +42,6 @@ index = LlamaCloudIndex(
 See the quickstart guides for each service for more information:

 - [LlamaParse](../parse.md)
- [LlamaReport (beta/invite-only)](../report.md)
 - [LlamaExtract](../extract.md)
 - [LlamaCloud Index](../index.md)

@@ -58,13 +54,11 @@ You can also create your API key in the EU region [here](https://cloud.eu.llamai
 ```python
 from llama_cloud_services import (
    LlamaParse,
-    LlamaReport,
    LlamaExtract,
    EU_BASE_URL,
 )

 parser = LlamaParse(api_key="YOUR_API_KEY", base_url=EU_BASE_URL)
-report = LlamaReport(api_key="YOUR_API_KEY", base_url=EU_BASE_URL)
 extract = LlamaExtract(api_key="YOUR_API_KEY", base_url=EU_BASE_URL)
 index = LlamaCloudIndex(
    "my_first_index",
@@ -1,6 +1,5 @@
 from llama_cloud_services.parse import LlamaParse
-from llama_cloud_services.report import ReportClient, LlamaReport
-from llama_cloud_services.extract import LlamaExtract, ExtractionAgent
+from llama_cloud_services.extract import LlamaExtract, ExtractionAgent, SourceText
 from llama_cloud_services.constants import EU_BASE_URL
 from llama_cloud_services.index import (
    LlamaCloudCompositeRetriever,
@@ -10,10 +9,9 @@ from llama_cloud_services.index import (

 __all__ = [
    "LlamaParse",
-    "ReportClient",
-    "LlamaReport",
    "LlamaExtract",
    "ExtractionAgent",
+    "SourceText",
    "EU_BASE_URL",
    "LlamaCloudIndex",
    "LlamaCloudRetriever",
@@ -159,6 +159,9 @@ class Page(BaseModel):
    durationInSeconds: Optional[float] = Field(
        default=None, description="The duration of the audio transcript in seconds."
    )
+    slideSpeakerNotes: Optional[str] = Field(
+        default=None, description="The speaker notes for the slide."
+    )


 class JobResult(BaseModel):
@@ -1,4 +0,0 @@
-from llama_cloud_services.report.report import ReportClient
-from llama_cloud_services.report.base import LlamaReport
-
-__all__ = ["ReportClient", "LlamaReport"]
@@ -1,269 +0,0 @@
-import asyncio
-import httpx
-import os
-import io
-from concurrent.futures import ThreadPoolExecutor
-from typing import Optional, List, Union, Any, Coroutine, TypeVar
-from urllib.parse import urljoin
-
-from llama_cloud.types import ReportMetadata
-from llama_cloud_services.report.report import ReportClient
-
-T = TypeVar("T")
-
-
-class LlamaReport:
-    """Client for managing reports and general report operations."""
-
-    def __init__(
-        self,
-        api_key: Optional[str] = None,
-        project_id: Optional[str] = None,
-        organization_id: Optional[str] = None,
-        base_url: Optional[str] = None,
-        timeout: Optional[int] = None,
-        async_httpx_client: Optional[httpx.AsyncClient] = None,
-    ):
-        self.api_key = api_key or os.getenv("LLAMA_CLOUD_API_KEY", None)
-        if not self.api_key:
-            raise ValueError("No API key provided.")
-
-        self.base_url = base_url or os.getenv(
-            "LLAMA_CLOUD_BASE_URL", "https://api.cloud.llamaindex.ai"
-        )
-        self.timeout = timeout or 60
-
-        # Initialize HTTP clients
-        self._aclient = async_httpx_client or httpx.AsyncClient(timeout=self.timeout)
-
-        # Set auth headers
-        self.headers = {
-            "Authorization": f"Bearer {self.api_key}",
-        }
-
-        self.organization_id = organization_id
-        self.project_id = project_id
-        self._client_params = {
-            "timeout": self._aclient.timeout,
-            "headers": self._aclient.headers,
-            "base_url": self._aclient.base_url,
-            "auth": self._aclient.auth,
-            "event_hooks": self._aclient.event_hooks,
-            "cookies": self._aclient.cookies,
-            "max_redirects": self._aclient.max_redirects,
-            "params": self._aclient.params,
-            "trust_env": self._aclient.trust_env,
-        }
-        self._thread_pool = ThreadPoolExecutor(
-            max_workers=min(10, (os.cpu_count() or 1) + 4)
-        )
-
-    @property
-    def aclient(self) -> httpx.AsyncClient:
-        if self._aclient is None:
-            self._aclient = httpx.AsyncClient(**self._client_params)
-        return self._aclient
-
-    def _run_sync(self, coro: Coroutine[Any, Any, T]) -> T:
-        """Run coroutine in a separate thread to avoid event loop issues"""
-
-        # force a new client for this thread/event loop
-        original_client = self._aclient
-        self._aclient = None
-
-        def run_coro() -> T:
-            async def wrapped_coro() -> T:
-                return await coro
-
-            return asyncio.run(wrapped_coro())
-
-        result = self._thread_pool.submit(run_coro).result()
-
-        # restore the original client
-        self._aclient = original_client
-
-        return result
-
-    async def _get_default_project(self) -> str:
-        response = await self.aclient.get(
-            urljoin(str(self.base_url), "/api/v1/projects"), headers=self.headers
-        )
-        response.raise_for_status()
-        projects = response.json()
-        default_project = [p for p in projects if p.get("is_default")]
-        return default_project[0]["id"]
-
-    async def _build_url(
-        self, endpoint: str, extra_params: Optional[List[str]] = None
-    ) -> str:
-        """Helper method to build URLs with common query parameters."""
-        url = urljoin(str(self.base_url), endpoint)
-
-        if not self.project_id:
-            self.project_id = await self._get_default_project()
-
-        query_params = []
-        if self.organization_id:
-            query_params.append(f"organization_id={self.organization_id}")
-        if self.project_id:
-            query_params.append(f"project_id={self.project_id}")
-        if extra_params:
-            query_params.extend([p for p in extra_params if p is not None])
-
-        if query_params:
-            url += "?" + "&".join(query_params)
-
-        return url
-
-    async def acreate_report(
-        self,
-        name: str,
-        template_instructions: Optional[str] = None,
-        template_text: Optional[str] = None,
-        template_file: Optional[Union[str, tuple[str, bytes]]] = None,
-        input_files: Optional[List[Union[str, tuple[str, bytes]]]] = None,
-        existing_retriever_id: Optional[str] = None,
-    ) -> ReportClient:
-        """Create a new report asynchronously."""
-        url = await self._build_url("/api/v1/reports/")
-        open_files: List[io.BufferedReader] = []
-
-        data = {"name": name}
-        if template_instructions:
-            data["template_instructions"] = template_instructions
-        if template_text:
-            data["template_text"] = template_text
-        if existing_retriever_id:
-            data["existing_retriever_id"] = str(existing_retriever_id)
-
-        files: List[tuple[str, io.BufferedReader | bytes]] = []
-        if template_file:
-            if isinstance(template_file, str):
-                open_files.append(open(template_file, "rb"))
-                files.append(("template_file", open_files[-1]))
-            else:
-                files.append(("template_file", template_file[1]))
-
-        if input_files:
-            for f in input_files:
-                if isinstance(f, str):
-                    open_files.append(open(f, "rb"))
-                    files.append(("files", open_files[-1]))
-                else:
-                    files.append(("files", f[1]))
-
-        response = await self.aclient.post(
-            url, headers=self.headers, data=data, files=files
-        )
-        try:
-            response.raise_for_status()
-            report_id = response.json()["id"]
-            return ReportClient(report_id, name, self)
-        except httpx.HTTPStatusError as e:
-            raise ValueError(
-                f"Failed to create report: {e.response.text}\nError Code: {e.response.status_code}"
-            )
-        finally:
-            for open_file in open_files:
-                open_file.close()
-
-    def create_report(
-        self,
-        name: str,
-        template_instructions: Optional[str] = None,
-        template_text: Optional[str] = None,
-        template_file: Optional[Union[str, tuple[str, bytes]]] = None,
-        input_files: Optional[List[Union[str, tuple[str, bytes]]]] = None,
-        existing_retriever_id: Optional[str] = None,
-    ) -> ReportClient:
-        """Create a new report."""
-        return self._run_sync(
-            self.acreate_report(
-                name=name,
-                template_instructions=template_instructions,
-                template_text=template_text,
-                template_file=template_file,
-                input_files=input_files,
-                existing_retriever_id=existing_retriever_id,
-            )
-        )
-
-    async def alist_reports(
-        self, state: Optional[str] = None, limit: int = 100, offset: int = 0
-    ) -> List[ReportClient]:
-        """List all reports asynchronously."""
-        params = []
-        if state:
-            params.append(f"state={state}")
-        if limit:
-            params.append(f"limit={limit}")
-        if offset:
-            params.append(f"offset={offset}")
-
-        url = await self._build_url(
-            "/api/v1/reports/list",
-            extra_params=params,
-        )
-
-        response = await self.aclient.get(url, headers=self.headers)
-        response.raise_for_status()
-        data = response.json()
-
-        return [
-            ReportClient(r["report_id"], r["name"], self)
-            for r in data["report_responses"]
-        ]
-
-    def list_reports(
-        self, state: Optional[str] = None, limit: int = 100, offset: int = 0
-    ) -> List[ReportClient]:
-        """Synchronous wrapper for listing reports."""
-        return self._run_sync(self.alist_reports(state, limit, offset))
-
-    async def aget_report(self, report_id: str) -> ReportClient:
-        """Get a Report instance for working with a specific report."""
-        url = await self._build_url(f"/api/v1/reports/{report_id}")
-
-        response = await self.aclient.get(url, headers=self.headers)
-        response.raise_for_status()
-        data = response.json()
-
-        return ReportClient(data["report_id"], data["name"], self)
-
-    def get_report(self, report_id: str) -> ReportClient:
-        """Synchronous wrapper for getting a report."""
-        return self._run_sync(self.aget_report(report_id))
-
-    async def aget_report_metadata(self, report_id: str) -> ReportMetadata:
-        """Get metadata for a specific report asynchronously.
-
-        Returns:
-            dict containing:
-            - id: Report ID
-            - name: Report name
-            - state: Current report state
-            - report_metadata: Additional metadata
-            - template_file: Name of template file if used
-            - template_instructions: Template instructions if provided
-            - input_files: List of input file names
-        """
-        url = await self._build_url(f"/api/v1/reports/{report_id}/metadata")
-
-        response = await self.aclient.get(url, headers=self.headers)
-        response.raise_for_status()
-        return ReportMetadata(**response.json())
-
-    def get_report_metadata(self, report_id: str) -> ReportMetadata:
-        """Synchronous wrapper for getting report metadata."""
-        return self._run_sync(self.aget_report_metadata(report_id))
-
-    async def adelete_report(self, report_id: str) -> None:
-        """Delete a specific report asynchronously."""
-        url = await self._build_url(f"/api/v1/reports/{report_id}")
-
-        response = await self.aclient.delete(url, headers=self.headers)
-        response.raise_for_status()
-
-    def delete_report(self, report_id: str) -> None:
-        """Synchronous wrapper for deleting a report."""
-        return self._run_sync(self.adelete_report(report_id))
@@ -1,527 +0,0 @@
-import asyncio
-import httpx
-import time
-from typing import Optional, List, Literal, Union, TYPE_CHECKING
-from dataclasses import dataclass
-from datetime import datetime
-from enum import Enum
-
-from llama_cloud.types import (
-    ReportEventItemEventData_Progress,
-    ReportMetadata,
-    EditSuggestion,
-    ReportResponse,
-    ReportPlan,
-    ReportBlock,
-    ReportPlanBlock,
-    Report,
-)
-
-if TYPE_CHECKING:
-    from llama_cloud_services.report.base import LlamaReport
-
-
-class MessageRole(str, Enum):
-    USER = "user"
-    ASSISTANT = "assistant"
-
-
-@dataclass
-class Message:
-    role: MessageRole
-    content: str
-    timestamp: datetime
-
-
-@dataclass
-class EditAction:
-    block_idx: int
-    old_content: str
-    new_content: Optional[str]
-    action: Literal["approved", "rejected"]
-    timestamp: datetime
-
-
-DEFAULT_POLL_INTERVAL = 5
-DEFAULT_TIMEOUT = 600
-
-
-class ReportClient:
-    """Client for operations on a specific report."""
-
-    def __init__(self, report_id: str, name: str, parent_client: "LlamaReport"):
-        self.report_id = report_id
-        self.name = name
-        self._client = parent_client
-        self._headers = parent_client.headers
-        self._run_sync = parent_client._run_sync
-        self._build_url = parent_client._build_url
-        self.chat_history: List[Message] = []
-        self.edit_history: List[EditAction] = []
-
-    @property
-    def aclient(self) -> httpx.AsyncClient:
-        return self._client.aclient
-
-    def __str__(self) -> str:
-        return f"Report(id={self.report_id}, name={self.name})"
-
-    def __repr__(self) -> str:
-        return f"Report(id={self.report_id}, name={self.name})"
-
-    def _get_block_content(self, block: Union[ReportBlock, ReportPlanBlock]) -> str:
-        if isinstance(block, ReportBlock):
-            return block.template
-        elif isinstance(block, ReportPlanBlock):
-            return block.block.template
-        else:
-            raise ValueError(f"Invalid block type: {type(block)}")
-
-    def _get_block_idx(self, block: Union[ReportBlock, ReportPlanBlock]) -> int:
-        if isinstance(block, ReportBlock):
-            return block.idx
-        elif isinstance(block, ReportPlanBlock):
-            return block.block.idx
-        else:
-            raise ValueError(f"Invalid block type: {type(block)}")
-
-    async def aget(self, version: Optional[int] = None) -> ReportResponse:
-        """Get this report's details asynchronously."""
-        extra_params = []
-        if version is not None:
-            extra_params.append(f"version={version}")
-
-        url = await self._build_url(f"/api/v1/reports/{self.report_id}", extra_params)
-
-        response = await self.aclient.get(url, headers=self._headers)
-        response.raise_for_status()
-        return ReportResponse(**response.json())
-
-    def get(self, version: Optional[int] = None) -> ReportResponse:
-        """Synchronous wrapper for getting this report's details."""
-        return self._run_sync(self.aget(version))
-
-    async def aupdate_report(self, updated_report: Report) -> ReportResponse:
-        """Update this report's content asynchronously."""
-        url = await self._build_url(f"/api/v1/reports/{self.report_id}")
-        response = await self.aclient.patch(
-            url, headers=self._headers, json={"content": updated_report.dict()}
-        )
-        response.raise_for_status()
-        return ReportResponse(**response.json())
-
-    def update_report(self, updated_report: Report) -> ReportResponse:
-        """Synchronous wrapper for updating this report's content."""
-        return self._run_sync(self.aupdate_report(updated_report))
-
-    async def aupdate_plan(
-        self,
-        action: Literal["approve", "reject", "edit"],
-        updated_plan: Optional[ReportPlan] = None,
-    ) -> ReportResponse:
-        """Update this report's plan asynchronously."""
-        if action == "edit" and not updated_plan:
-            raise ValueError("updated_plan is required when action is 'edit'")
-
-        url = await self._build_url(
-            f"/api/v1/reports/{self.report_id}/plan", [f"action={action}"]
-        )
-
-        data = None
-        if updated_plan is not None:
-            plan_dict = updated_plan.dict()
-            plan_dict.pop("generated_at", None)
-            data = plan_dict
-
-        if updated_plan is None and action == "edit":
-            raise ValueError("updated_plan is required when action is 'edit'")
-
-        response = await self.aclient.patch(url, headers=self._headers, json=data)
-        response.raise_for_status()
-        return ReportResponse(**response.json())
-
-    def update_plan(
-        self,
-        action: Literal["approve", "reject", "edit"],
-        updated_plan: Optional[ReportPlan] = None,
-    ) -> ReportResponse:
-        """Synchronous wrapper for updating this report's plan."""
-        return self._run_sync(self.aupdate_plan(action, updated_plan))
-
-    async def asuggest_edits(
-        self,
-        user_query: str,
-        auto_history: bool = True,
-        chat_history: Optional[List[dict]] = None,
-    ) -> List[EditSuggestion]:
-        """Get AI suggestions for edits to this report asynchronously.
-
-        Args:
-            user_query: The user's request/question about what to edit
-            auto_history: Whether to automatically add the user's message to the chat history
-            chat_history:
-                A list of chat messages to include in the chat history.
-                The format being a list of dictionaries with "role" and "content" keys.
-        """
-        # Add user message to history
-        self.chat_history.append(
-            Message(role=MessageRole.USER, content=user_query, timestamp=datetime.now())
-        )
-
-        # Format chat history with edit summaries
-        chat_history_dicts = []
-        for msg in self.chat_history[:-1]:  # Exclude current message
-            content = msg.content
-            if msg.role == MessageRole.USER:
-                # Add edit summary for user messages
-                edit_summary = self._get_edit_summary_after_message(msg.timestamp)
-                if edit_summary:
-                    content = f"{content}\n\nActions taken:\n{edit_summary}"
-
-            chat_history_dicts.append({"role": msg.role.value, "content": content})
-
-        # decide whether to include chat history or not
-        if chat_history:
-            chat_history_dicts = chat_history
-        elif auto_history:
-            chat_history_dicts = chat_history_dicts
-        else:
-            chat_history_dicts = []
-
-        # Make the API call
-        url = await self._build_url(f"/api/v1/reports/{self.report_id}/suggest_edits")
-        data = {"user_query": user_query, "chat_history": chat_history_dicts}
-
-        response = await self.aclient.post(url, headers=self._headers, json=data)
-        response.raise_for_status()
-        suggestions = response.json()
-        suggestions = [EditSuggestion(**suggestion) for suggestion in suggestions]
-
-        # Add assistant response to history
-        if suggestions:
-            for suggestion in suggestions:
-                self.chat_history.append(
-                    Message(
-                        role=MessageRole.ASSISTANT,
-                        content=suggestion.justification,
-                        timestamp=datetime.now(),
-                    )
-                )
-
-        return suggestions
-
-    def suggest_edits(
-        self,
-        user_query: str,
-        auto_history: bool = True,
-        chat_history: Optional[List[dict]] = None,
-    ) -> List[EditSuggestion]:
-        """Synchronous wrapper for getting edit suggestions."""
-        return self._run_sync(
-            self.asuggest_edits(user_query, auto_history, chat_history)
-        )
-
-    async def await_completion(
-        self, timeout: int = DEFAULT_TIMEOUT, poll_interval: int = DEFAULT_POLL_INTERVAL
-    ) -> Report:
-        """Wait for this report to complete processing."""
-        start_time = time.time()
-        while True:
-            report_response = await self.aget()
-            status = report_response.status
-
-            if status == "completed":
-                return report_response.report
-            elif status == "error":
-                events = await self.aget_events()
-                raise ValueError(f"Report entered error state: {events[-1].msg}")
-            elif time.time() - start_time > timeout:
-                raise TimeoutError(f"Report did not complete within {timeout} seconds")
-
-            await asyncio.sleep(poll_interval)
-
-    def wait_for_completion(
-        self, timeout: int = DEFAULT_TIMEOUT, poll_interval: int = DEFAULT_POLL_INTERVAL
-    ) -> Report:
-        """Synchronous wrapper for awaiting report completion."""
-        return self._run_sync(self.await_completion(timeout, poll_interval))
-
-    async def await_for_plan(
-        self, timeout: int = DEFAULT_TIMEOUT, poll_interval: int = DEFAULT_POLL_INTERVAL
-    ) -> ReportPlan:
-        """Wait for this report's plan to be ready for review."""
-        start_time = time.time()
-        while True:
-            report_metadata = await self.aget_metadata()
-            state = report_metadata.state
-
-            if state == "waiting_approval":
-                report_response = await self.aget()
-                return report_response.plan
-            elif state == "error":
-                events = await self.aget_events()
-                raise ValueError(f"Report entered error state: {events[-1].msg}")
-            elif time.time() - start_time > timeout:
-                raise TimeoutError(f"Plan was not ready within {timeout} seconds")
-
-            await asyncio.sleep(poll_interval)
-
-    def wait_for_plan(
-        self, timeout: int = DEFAULT_TIMEOUT, poll_interval: int = DEFAULT_POLL_INTERVAL
-    ) -> ReportPlan:
-        """Synchronous wrapper for awaiting plan readiness."""
-        return self._run_sync(self.await_for_plan(timeout, poll_interval))
-
-    async def aget_metadata(self) -> ReportMetadata:
-        """Get this report's metadata asynchronously."""
-        return await self._client.aget_report_metadata(self.report_id)
-
-    def get_metadata(self) -> ReportMetadata:
-        """Synchronous wrapper for getting this report's metadata."""
-        return self._run_sync(self.aget_metadata())
-
-    async def adelete(self) -> None:
-        """Delete this report asynchronously."""
-        return await self._client.adelete_report(self.report_id)
-
-    def delete(self) -> None:
-        """Synchronous wrapper for deleting this report."""
-        return self._run_sync(self.adelete())
-
-    async def aaccept_edit(self, suggestion: EditSuggestion) -> None:
-        """Accept a suggested edit.
-
-        Args:
-            suggestion: The EditSuggestion to accept, typically from suggest_edits()
-        """
-        if len(suggestion.blocks) == 0:
-            return
-
-        # Determine if we're editing a plan or report based on first block type
-        is_plan_edit = isinstance(suggestion.blocks[0], ReportPlanBlock)
-
-        # Get current content
-        report_response = await self.aget()
-        current_blocks = (
-            report_response.plan.blocks
-            if is_plan_edit
-            else report_response.report.blocks
-        )
-
-        # Track the edit
-        new_blocks = []
-        for edit_block in suggestion.blocks:
-            # Find matching block in current content
-            old_block = next(
-                (
-                    b
-                    for b in current_blocks
-                    if self._get_block_idx(b) == self._get_block_idx(edit_block)
-                ),
-                None,
-            )
-
-            old_content = (
-                self._get_block_content(old_block) if old_block else "[No old content]"
-            )
-            new_content = self._get_block_content(edit_block)
-
-            if is_plan_edit:
-                new_queries_str = "\n".join(
-                    [
-                        f"Field: {q.field}, Prompt: {q.prompt}, Context: {q.context}"
-                        for q in edit_block.queries
-                    ]
-                )
-                new_dependency_str = (
-                    f"Depends on: {edit_block.dependency}"
-                    if edit_block.dependency
-                    else ""
-                )
-                new_content += f"\n\n{new_queries_str}\n{new_dependency_str}"
-
-                if old_block:
-                    old_queries_str = "\n".join(
-                        [
-                            f"Field: {q.field}, Prompt: {q.prompt}, Context: {q.context}"
-                            for q in old_block.queries
-                        ]
-                    )
-                    old_dependency_str = (
-                        f"Depends on: {old_block.dependency}"
-                        if old_block.dependency
-                        else ""
-                    )
-                    old_content += f"\n\n{old_queries_str}\n{old_dependency_str}"
-
-            self.edit_history.append(
-                EditAction(
-                    block_idx=self._get_block_idx(edit_block),
-                    old_content=old_content,
-                    new_content=new_content,
-                    action="approved",
-                    timestamp=datetime.now(),
-                )
-            )
-
-            # Create updated block
-            if is_plan_edit:
-                new_blocks.append(
-                    ReportPlanBlock(
-                        block=ReportBlock(
-                            idx=edit_block.block.idx,
-                            template=self._get_block_content(edit_block),
-                            sources=edit_block.block.sources,
-                        ),
-                        queries=edit_block.queries,
-                        dependency=edit_block.dependency,
-                    )
-                )
-            else:
-                new_blocks.append(
-                    ReportBlock(
-                        idx=edit_block.idx,
-                        template=self._get_block_content(edit_block),
-                        sources=edit_block.sources,
-                    )
-                )
-
-        if new_blocks:
-            if is_plan_edit:
-                # Update plan in place
-                plan = report_response.plan
-
-                # Replace edited blocks and add new ones
-                for new_block in new_blocks:
-                    block_idx = self._get_block_idx(new_block)
-                    existing_block_idx = next(
-                        (
-                            i
-                            for i, b in enumerate(plan.blocks)
-                            if b.block.idx == block_idx
-                        ),
-                        None,
-                    )
-
-                    if existing_block_idx is not None:
-                        # Replace existing block
-                        plan.blocks[existing_block_idx] = new_block
-                    else:
-                        # Add new block to end
-                        plan.blocks.append(new_block)
-
-                await self.aupdate_plan("edit", plan)
-            else:
-                # Update report in place
-                report = report_response.report
-
-                # Replace edited blocks and add new ones
-                for new_block in new_blocks:
-                    block_idx = self._get_block_idx(new_block)
-                    existing_block_idx = next(
-                        (i for i, b in enumerate(report.blocks) if b.idx == block_idx),
-                        None,
-                    )
-
-                    if existing_block_idx is not None:
-                        # Replace existing block
-                        report.blocks[existing_block_idx] = new_block
-                    else:
-                        # Add new block to end
-                        report.blocks.append(new_block)
-
-                await self.aupdate_report(report)
-
-    def accept_edit(self, suggestion: EditSuggestion) -> None:
-        """Synchronous wrapper for accepting an edit."""
-        return self._run_sync(self.aaccept_edit(suggestion))
-
-    async def areject_edit(self, suggestion: EditSuggestion) -> None:
-        """Reject a suggested edit.
-
-        Args:
-            suggestion: The EditSuggestion to reject, typically from suggest_edits()
-        """
-        # Track the rejections
-        for edit_block in suggestion.blocks:
-            self.edit_history.append(
-                EditAction(
-                    block_idx=self._get_block_idx(edit_block),
-                    old_content=self._get_block_content(edit_block),
-                    new_content=None,
-                    action="rejected",
-                    timestamp=datetime.now(),
-                )
-            )
-
-    def reject_edit(self, suggestion: EditSuggestion) -> None:
-        """Synchronous wrapper for rejecting an edit."""
-        return self._run_sync(self.areject_edit(suggestion))
-
-    def _get_edit_summary_after_message(
-        self, message_timestamp: datetime
-    ) -> Optional[str]:
-        """Get a summary of edits that occurred after a specific message."""
-        relevant_edits = [
-            edit for edit in self.edit_history if edit.timestamp > message_timestamp
-        ]
-
-        if not relevant_edits:
-            return None
-
-        approved = [edit for edit in relevant_edits if edit.action == "approved"]
-        rejected = [edit for edit in relevant_edits if edit.action == "rejected"]
-
-        summary = []
-
-        if approved:
-            summary.append("Approved edits:")
-            for edit in approved:
-                summary.append(
-                    f'Block {edit.block_idx}: "{edit.old_content}" -> "{edit.new_content}"'
-                )
-
-        if rejected:
-            if approved:  # Add spacing if we had approved edits
-                summary.append("")
-            summary.append("Rejected edits:")
-            for edit in rejected:
-                summary.append(f'Block {edit.block_idx}: "{edit.old_content}"')
-
-        return "\n".join(summary)
-
-    async def aget_events(
-        self, last_sequence: Optional[int] = None
-    ) -> List[ReportEventItemEventData_Progress]:
-        """Get all events for this report asynchronously.
-
-        Args:
-            last_sequence: If provided, only get events after this sequence number
-
-        Returns:
-            List of ReportEvent objects
-        """
-        extra_params = []
-        if last_sequence is not None:
-            extra_params.append(f"last_sequence={last_sequence}")
-
-        url = await self._build_url(
-            f"/api/v1/reports/{self.report_id}/events", extra_params
-        )
-
-        response = await self.aclient.get(url, headers=self._headers)
-        response.raise_for_status()
-        progress_events = []
-        for event in response.json():
-            if event["event_type"] == "progress":
-                progress_events.append(
-                    ReportEventItemEventData_Progress(**event["event_data"])
-                )
-
-        return progress_events
-
-    def get_events(
-        self, last_sequence: Optional[int] = None
-    ) -> List[ReportEventItemEventData_Progress]:
-        """Synchronous wrapper for getting report events."""
-        return self._run_sync(self.aget_events(last_sequence))
@@ -11,13 +11,13 @@ dev = [

 [project]
 name = "llama-parse"
-version = "0.6.62"
+version = "0.6.63"
 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>=0.6.62"]
+dependencies = ["llama-cloud-services>=0.6.63"]

 [project.scripts]
 llama-parse = "llama_parse.cli.main:parse"
@@ -19,7 +19,7 @@ dev = [

 [project]
 name = "llama-cloud-services"
-version = "0.6.62"
+version = "0.6.63"
 description = "Tailored SDK clients for LlamaCloud services."
 authors = [{name = "Logan Markewich", email = "logan@runllama.ai"}]
 requires-python = ">=3.9,<4.0"
@@ -1,129 +0,0 @@
-import os
-import pytest
-import uuid
-from typing import AsyncGenerator
-from pytest_asyncio import fixture as async_fixture
-from llama_cloud_services.report import LlamaReport, ReportClient
-
-# Skip tests if no API key is set
-pytestmark = pytest.mark.skipif(
-    not os.getenv("LLAMA_CLOUD_API_KEY") or os.getenv("CI") == "true",
-    reason="No API key provided",
-)
-
-
-@async_fixture(scope="function")
-async def client() -> AsyncGenerator[LlamaReport, None]:
-    """Create a LlamaReport client."""
-    client = LlamaReport()
-    reports_before = await client.alist_reports()
-    reports_before_ids = [r.report_id for r in reports_before]
-    try:
-        yield client
-    finally:
-        # clean up reports
-        try:
-            reports_after = await client.alist_reports()
-            reports_after_ids = [r.report_id for r in reports_after]
-            for report_id in reports_before_ids:
-                if report_id not in reports_after_ids:
-                    await client.adelete_report(report_id)
-        except Exception:
-            pass
-        finally:
-            await client.aclient.aclose()
-
-
-@pytest.fixture(scope="function")
-def unique_name() -> str:
-    """Generate a unique report name."""
-    return f"test-report-{uuid.uuid4()}"
-
-
-@async_fixture(scope="function")
-async def report(
-    client: LlamaReport, unique_name: str
-) -> AsyncGenerator[ReportClient, None]:
-    """Create a report."""
-    report = await client.acreate_report(
-        name=unique_name,
-        template_text=(
-            "# [Some title]\n\n"
-            " ## TLDR\n"
-            "A quick summary of the paper.\n\n"
-            "## Details\n"
-            "More details about the paper, possible more than one section here.\n"
-        ),
-        input_files=["tests/test_files/paper.md"],
-    )
-    try:
-        yield report
-    finally:
-        await report.adelete()
-
-
-@pytest.mark.asyncio
-@pytest.mark.xfail(
-    condition=lambda: os.getenv("CI"),
-    reason="Backend db issues; needs to be fixed.",
-)
-async def test_create_and_delete_report(
-    client: LlamaReport, report: ReportClient
-) -> None:
-    """Test basic report creation and deletion."""
-    # Verify the report exists
-    metadata = await report.aget_metadata()
-    assert metadata.name == report.name
-
-    # Test listing reports
-    reports = await client.alist_reports()
-    assert any(r.report_id == report.report_id for r in reports)
-
-    # Test getting report by ID
-    fetched_report = await client.aget_report(report.report_id)
-    assert fetched_report.report_id == report.report_id
-    assert fetched_report.name == report.name
-
-
-@pytest.mark.asyncio
-@pytest.mark.xfail(
-    condition=lambda: os.getenv("CI"),
-    reason="Report plan sometimes times out",
-    raises=TimeoutError,
-)
-async def test_report_plan_workflow(report: ReportClient) -> None:
-    """Test the report planning workflow."""
-    # Wait for the plan
-    plan = await report.await_for_plan()
-    assert plan is not None
-
-    # Approve the plan
-    response = await report.aupdate_plan(action="approve")
-    assert response is not None
-
-    # Wait for completion
-    completed_report = await report.await_completion()
-    assert len(completed_report.blocks) > 0
-
-    # Get edit suggestions
-    suggestions = await report.asuggest_edits(
-        "TLDR section header more formal.", auto_history=True
-    )
-    assert len(suggestions) > 0
-
-    # Test accepting an edit
-    await report.aaccept_edit(suggestions[0])
-
-    # Get more suggestions and test rejecting
-    more_suggestions = await report.asuggest_edits(
-        "Add a section about machine learning.", auto_history=True
-    )
-    assert len(more_suggestions) > 0
-    await report.areject_edit(more_suggestions[0])
-
-    # Verify chat history is maintained
-    assert len(report.chat_history) >= 4  # 2 user messages + 2 assistant responses
-
-    # get events
-    events = await report.aget_events()
-    assert len(events) > 0
@@ -1,101 +0,0 @@
-# LlamaReport (beta/invite-only)
-
-LlamaReport is a prebuilt agentic report builder that can be used to build reports from a variety of data sources.
-
-The python SDK for interacting with the LlamaReport API. The SDK provides two main classes:
-
- `LlamaReport`: For managing reports (create, list, delete)
- `ReportClient`: For working with a specific report (editing, approving, etc.)
-
-## Quickstart
-
-```bash
-pip install llama-cloud-services
-```
-
-```python
-from llama_cloud_services import LlamaReport
-
-# Initialize the client
-client = LlamaReport(
-    api_key="your-api-key",
-    # Optional: Specify project_id, organization_id, async_httpx_client
-)
-
-# Create a new report
-report = client.create_report(
-    "My Report",
-    # must have one of template_text or template_instructions
-    template_text="Your template text",
-    template_instructions="Instructions for the template",
-    # must have one of input_files or retriever_id
-    input_files=["data1.pdf", "data2.pdf"],
-    retriever_id="retriever-id",
-)
-```
-
-## Working with Reports
-
-The typical workflow for a report involves:
-
-1. Creating the report
-2. Waiting for and approving the plan
-3. Waiting for report generation
-4. Making edits to the report
-
-Here's a complete example:
-
-```python
-# Create a report
-report = client.create_report(
-    "Quarterly Analysis", input_files=["q1_data.pdf", "q2_data.pdf"]
-)
-
-# Wait for the plan to be ready
-plan = report.wait_for_plan()
-
-# Option 1: Directly approve the plan
-report.update_plan(action="approve")
-
-# Option 2: Suggest and review edits to the plan
-suggestions = report.suggest_edits(
-    "Can you add a section about market trends?"
-)
-for suggestion in suggestions:
-    print(suggestion)
-
-    # Accept or reject the suggestion
-    if input("Accept? (y/n): ").lower() == "y":
-        report.accept_edit(suggestion)
-    else:
-        report.reject_edit(suggestion)
-
-# Wait for the report to complete
-report = report.wait_for_completion()
-
-# Make edits to the final report
-suggestions = report.suggest_edits("Make the executive summary more concise")
-
-# Review and accept/reject suggestions as above
-...
-```
-
-### Getting the Final Report
-
-Once you are satisfied with the report, you can get the final report object and use the content as you see fit.
-
-Here's an example of printing out the final report:
-
-```python
-report = report.get()
-report_text = "\n\n".join([block.template for block in report.blocks])
-
-print(report_text)
-```
-
-## Additional Features
-
- **Async Support**: All methods have async counterparts: `create_report` -> `acreate_report`, `wait_for_plan` -> `await_for_plan`, etc.
- **Automatic Chat History**: The SDK automatically keeps track of chat history for each suggestion, unless you specify `auto_history=False` in `suggest_edits`.
- **Custom HTTP Client**: You can provide your own `httpx.AsyncClient` to the `LlamaReport` class.
- **Project and Organization IDs**: You can specify `project_id` and `organization_id` to use a specific project or organization.
@@ -8,10 +8,11 @@ import subprocess
 import sys
 import tomlkit
 from pathlib import Path
+import json


-def get_current_versions() -> tuple[str, str, str]:
-    """Get current versions from both pyproject.toml files."""
+def get_current_versions() -> tuple[str, str, str, str | None]:
+    """Get current versions from both pyproject.toml files and TS package.json."""
    # Read main pyproject.toml
    main_content = Path("py/pyproject.toml").read_text()
    main_doc = tomlkit.parse(main_content)
@@ -34,11 +35,21 @@ def get_current_versions() -> tuple[str, str, str]:
            )
            break

-    return str(main_version), str(llama_parse_version), str(dependency_version)
+    # Read TypeScript package.json version via helper
+    ts_version: str = get_ts_version()
+
+    return (
+        str(main_version),
+        str(llama_parse_version),
+        str(dependency_version),
+        str(ts_version) if ts_version is not None else None,
+    )


 def validate_versions(
-    main_version: str, llama_parse_version: str, dependency_version: str
+    main_version: str,
+    llama_parse_version: str,
+    dependency_version: str,
 ) -> list[str]:
    """Validate that versions are consistent and return warnings."""
    warnings = []
@@ -60,7 +71,7 @@ def validate_versions(


 def set_version(version: str) -> None:
-    """Set version across all pyproject.toml files using tomlkit to preserve formatting."""
+    """Set version across Python projects (no TS change)."""
    # Update main pyproject.toml
    main_content = Path("py/pyproject.toml").read_text()
    main_doc = tomlkit.parse(main_content)
@@ -79,7 +90,26 @@ def set_version(version: str) -> None:
            break
    Path("py/llama_parse/pyproject.toml").write_text(tomlkit.dumps(llama_parse_doc))

-    click.echo(f"Updated all versions to {version}")
+    click.echo(f"Updated Python versions to {version}")
+
+
+def get_ts_version() -> str:
+    """Read TypeScript package.json version (if present)."""
+    ts_package_path = Path("ts/llama_cloud_services/package.json")
+    package_data = json.loads(ts_package_path.read_text())
+    data = package_data.get("version")
+    if data is None:
+        raise RuntimeError("TypeScript package.json version not found")
+    return data
+
+
+def set_ts_version(version: str) -> None:
+    """Set TypeScript package.json version only."""
+    ts_package_path = Path("ts/llama_cloud_services/package.json")
+    package_data = json.loads(ts_package_path.read_text())
+    package_data["version"] = version
+    ts_package_path.write_text(json.dumps(package_data, indent=2) + "\n")
+    click.echo(f"Updated TypeScript package.json version to {version}")


 def get_current_branch() -> str:
@@ -99,7 +129,7 @@ def create_if_not_exists(version: str) -> None:
        )
        sys.exit(1)

-    tag_name = f"v{version}"
+    tag_name = f"v{version}" if version[0].isdigit() else version
    if not tag_exists(version):
        # Create tag
        subprocess.run(["git", "tag", tag_name], check=True)
@@ -133,12 +163,18 @@ def cli() -> None:
@cli.command()
 def get() -> None:
    """Get current versions and show validation warnings."""
-    main_version, llama_parse_version, dependency_version = get_current_versions()
+    (
+        main_version,
+        llama_parse_version,
+        dependency_version,
+        ts_version,
+    ) = get_current_versions()

    click.echo("Current versions:")
    click.echo(f"  llama-cloud-services: {main_version}")
    click.echo(f"  llama-parse: {llama_parse_version}")
    click.echo(f"  dependency reference: {dependency_version}")
+    click.echo(f"  typescript package: {ts_version}")

    warnings = validate_versions(main_version, llama_parse_version, dependency_version)
    if warnings:
@@ -151,9 +187,15 @@ def get() -> None:

@cli.command()
@click.argument("version")
-def set(version: str) -> None:
-    """Set version across all pyproject.toml files."""
-    set_version(version)
+@click.option("--js", is_flag=True, help="Update TypeScript package.json only")
+def set(version: str, js: bool) -> None:
+    """Set version for Python, TypeScript, or both (default: Python only)."""
+
+    if js:
+        set_ts_version(version)
+        return
+    else:
+        set_version(version)


@cli.command()
@@ -165,11 +207,16 @@ def set(version: str) -> None:
    is_flag=True,
    help="Push the tag to the remote repository",
 )
-def tag(version: str | None = None, push: bool = False) -> None:
+@click.option(
+    "--js",
+    is_flag=True,
+    help="tag TypeScript package.json only",
+)
+def tag(version: str | None = None, push: bool = False, js: bool = False) -> None:
    """Create and push a git tag for the current version."""
    if not version:
-        main_version, _, _ = get_current_versions()
-        version = main_version
+        main_version, _, _, js_version = get_current_versions()
+        version = f"llama-cloud-services@{js_version}" if js else main_version

    create_if_not_exists(version)
    if push:
@@ -1,6 +1,6 @@
 {
  "name": "llama-cloud-services",
-  "version": "0.3.3",
+  "version": "0.3.4",
  "type": "module",
  "license": "MIT",
  "scripts": {
@@ -9,6 +9,7 @@
    "dev": "bunchee --watch",
    "lint": "eslint src/ --ignore-pattern client/*.ts --no-warn-ignored",
    "format": "prettier --write ./src/",
+    "format:check": "prettier --check ./src/",
    "test": "vitest run --testTimeout=60000",
    "test:watch": "vitest --watch",
    "test:ui": "vitest --ui",
@@ -1,5 +1,5 @@
-import { createClient, createConfig } from "@hey-api/client-fetch";
-import { getEnv } from "@llamaindex/env";
+import { createClient } from "@hey-api/client-fetch";
+import { client as defaultClient } from "../../api";
 import {
  aggregateAgentDataApiV1BetaAgentDataAggregatePost,
  createAgentDataApiV1BetaAgentDataPost,
@@ -24,36 +24,19 @@ import type {
 */
 export class AgentClient<T = unknown> {
  private client: ReturnType<typeof createClient>;
-  private baseUrl: string;
-  private headers: Record<string, string>;
  private collection: string;
  private agentUrlId: string;

  constructor({
-    apiKey = getEnv("LLAMA_CLOUD_API_KEY"),
-    baseUrl = "https://api.cloud.llamaindex.ai/",
+    client = defaultClient,
    collection = "default",
    agentUrlId = "_public",
  }: {
-    apiKey?: string;
-    baseUrl?: string;
+    client?: ReturnType<typeof createClient>;
    collection?: string;
    agentUrlId?: string;
  }) {
-    this.baseUrl = baseUrl;
-
-    this.headers = {
-      "X-SDK-Name": "llamaindex-ts",
-      ...(apiKey && { Authorization: `Bearer ${apiKey}` }),
-    };
-
-    this.client = createClient(
-      createConfig({
-        baseUrl: this.baseUrl,
-        headers: this.headers,
-      }),
-    );
-
+    this.client = client;
    this.collection = collection;
    this.agentUrlId = agentUrlId;
  }
@@ -281,15 +264,13 @@ export interface AgentDataClientOptions {
 * @returns A new AgentClient instance
 */
 export function createAgentDataClient<T = unknown>({
-  apiKey,
-  baseUrl,
+  client = defaultClient,
  windowUrl,
  env,
  agentUrlId,
  collection = "default",
 }: {
-  apiKey?: string;
-  baseUrl?: string;
+  client?: ReturnType<typeof createClient>;
  windowUrl?: string;
  env?: Record<string, string>;
  agentUrlId?: string;
@@ -321,9 +302,8 @@ export function createAgentDataClient<T = unknown>({
  }

  return new AgentClient({
-    ...(apiKey && { apiKey }),
-    ...(baseUrl && { baseUrl }),
    ...(agentUrlId && { agentUrlId }),
    collection,
+    client,
  });
 }
@@ -194,7 +194,7 @@ export class LlamaParseReader extends FileReader {
      ? this.language
      : [this.language];
    this.stdout =
-      params.stdout ?? typeof process !== "undefined"
+      (params.stdout ?? typeof process !== "undefined")
        ? process!.stdout
        : undefined;
    const apiKey = params.apiKey ?? getEnv("LLAMA_CLOUD_API_KEY");
@@ -0,0 +1,80 @@
+import { describe, it, expect, beforeEach, afterEach, vi } from "vitest";
+import { listProjectsApiV1ProjectsGet, client } from "../src/api.js";
+
+describe("Global client configuration", () => {
+  const originalFetch = globalThis.fetch;
+
+  beforeEach(() => {
+    vi.restoreAllMocks();
+  });
+
+  afterEach(() => {
+    globalThis.fetch = originalFetch;
+  });
+
+  it("adds X-SDK-Name header from global client config", async () => {
+    const fetchSpy = vi
+      .spyOn(globalThis, "fetch")
+      .mockImplementation(async (input, init) => {
+        // Validate the header is present on the outgoing request
+        let headers: Headers;
+        if (input && typeof input === "object" && "headers" in (input as any)) {
+          headers = (input as Request).headers;
+        } else {
+          headers = new Headers((init && init.headers) || {});
+        }
+        expect(headers.get("X-SDK-Name")).toBe("llamaindex-ts");
+
+        return new Response(JSON.stringify([]), {
+          status: 200,
+          headers: { "Content-Type": "application/json" },
+        });
+      });
+
+    // Trigger any request via the generated SDK (imported through src/api.ts)
+    await listProjectsApiV1ProjectsGet({ throwOnError: false });
+
+    expect(fetchSpy).toHaveBeenCalledOnce();
+  });
+
+  it("respects additional custom headers set via setConfig", async () => {
+    const prevConfig = client.getConfig();
+    try {
+      client.setConfig({
+        ...prevConfig,
+        headers: {
+          ...(prevConfig.headers || {}),
+          "X-Custom-Header": "custom-value",
+        },
+      });
+
+      const fetchSpy = vi
+        .spyOn(globalThis, "fetch")
+        .mockImplementation(async (input, init) => {
+          let headers: Headers;
+          if (
+            input &&
+            typeof input === "object" &&
+            "headers" in (input as any)
+          ) {
+            headers = (input as Request).headers;
+          } else {
+            headers = new Headers((init && init.headers) || {});
+          }
+          expect(headers.get("X-SDK-Name")).toBe("llamaindex-ts");
+          expect(headers.get("X-Custom-Header")).toBe("custom-value");
+
+          return new Response(JSON.stringify([]), {
+            status: 200,
+            headers: { "Content-Type": "application/json" },
+          });
+        });
+
+      await listProjectsApiV1ProjectsGet({ throwOnError: false });
+      expect(fetchSpy).toHaveBeenCalledOnce();
+    } finally {
+      // Restore original configuration to avoid test cross-talk
+      client.setConfig(prevConfig);
+    }
+  });
+});
Author	SHA1	Message	Date
Neeraj Pradhan	a953225a15	remove poetry.lock	2025-09-05 16:26:48 -07:00
Neeraj Pradhan	bf110ed2cf	Remove report generation related code from llama_cloud_services	2025-09-05 16:25:07 -07:00
Clelia (Astra) Bertelli	196ab827f5	fix: make ts release beautiful again (#902 )	2025-09-05 10:41:39 -06:00
Peter Rowlands (변기호)	ba4cb4d5e9	parse: expose page.slideSpeakerNotes (#889 )	2025-09-05 15:48:44 +09:00
Adrian Lyjak	58d883b825	fix: "v" prefix being added to js versions (#898 )	2025-09-04 15:39:27 -04:00
Adrian Lyjak	5fc5ebfc6c	client unification (#895 ) read from the shared client	2025-09-04 14:12:28 -04:00
Adrian Lyjak	fe3e20fd53	Update version script, and unify the linting so that prettier is more consistent (#897 ) Add version script, and unify the linting so that prettier is more consistent	2025-09-04 14:09:27 -04:00
Jerry Liu	e7e59459ab	getting started LlamaCloudIndex notebook (#891 )	2025-09-02 14:52:39 -06:00
Logan Markewich	f4d7c84e19	remove stale param	2025-09-02 13:37:16 -06:00
Yannis Panagis	9050a346e4	Added "SourceText" to __init__.py (#892 )	2025-09-02 13:28:24 -06:00
Sourabh Desai	9690ccf4ea	Fix tag push command in CONTRIBUTING.md (#894 ) seems to be missing one little `v`	2025-09-02 10:49:14 -07:00
Sourabh Desai	97745f0f1c	version bump to 0.6.63 (#893 )	2025-09-02 10:36:51 -07:00