From 1ae4d2bbc70f616498d947119bd9cd1a2c8a89db Mon Sep 17 00:00:00 2001
From: Logan <logan.markewich@live.com>
Date: Thu, 6 Feb 2025 16:15:57 -0600
Subject: [PATCH] Refactor into llama-cloud-services (#597)

---
 .github/workflows/build_package.yml           |    2 +-
 .github/workflows/publish_release.yml         |   18 +-
 .pre-commit-config.yaml                       |    3 +-
 README.md                                     |  157 +-
 .../dynamic_section_retrieval.ipynb           |    4 +-
 .../dynamic_section_retrieval_img.png         |  Bin
 .../agents/demo_simple_openai_agent.ipynb     |    4 +-
 .../caltrain/caltrain_schedule_weekend.pdf    |  Bin
 .../caltrain/caltrain_text_mode.ipynb         |    2 +-
 examples/{ => parse}/data/BP_Excel.xlsx       |  Bin
 ...dia_quarterly_revenue_trend_by_market.xlsx |  Bin
 examples/{ => parse}/demo_advanced.ipynb      |    4 +-
 .../{ => parse}/demo_advanced_astradb.ipynb   |    4 +-
 .../{ => parse}/demo_advanced_weaviate.ipynb  |    2 +-
 examples/{ => parse}/demo_api.ipynb           |    0
 examples/{ => parse}/demo_astradb.ipynb       |    2 +-
 examples/{ => parse}/demo_basic.ipynb         |    4 +-
 .../demo_elasticsearch_vectordb.ipynb         |    2 +-
 examples/{ => parse}/demo_excel.ipynb         |    4 +-
 examples/{ => parse}/demo_get_charts.ipynb    |    4 +-
 examples/{ => parse}/demo_insurance.ipynb     |    2 +-
 examples/{ => parse}/demo_json.ipynb          |    4 +-
 examples/{ => parse}/demo_json_parsing.ipynb  |    4 +-
 examples/{ => parse}/demo_json_tour.ipynb     |    4 +-
 examples/{ => parse}/demo_languages.ipynb     |    6 +-
 examples/{ => parse}/demo_mongodb.ipynb       |    2 +-
 .../demo_parsing_instructions.ipynb           |    4 +-
 .../{ => parse}/demo_starter_multimodal.ipynb |    6 +-
 .../demo_starter_parse_selected_pages.ipynb   |    4 +-
 .../{ => parse}/demo_table_comparisons.ipynb  |    4 +-
 examples/{ => parse}/excel/dcf_rag.ipynb      |    4 +-
 examples/{ => parse}/excel/o1_excel_rag.ipynb |    4 +-
 .../{ => parse}/excel/references/query1.png   |  Bin
 .../{ => parse}/excel/references/query2.png   |  Bin
 .../{ => parse}/excel/references/query3.png   |  Bin
 .../{ => parse}/excel/references/query4.png   |  Bin
 .../{ => parse}/excel/references/query5.png   |  Bin
 .../excel/references/recursive_retrieval.png  |  Bin
 ...0-9e83-4b00-aebe-0d7f59ff0b5f-img_p0_1.png |  Bin
 ...fb0-9e83-4b00-aebe-0d7f59ff0b5f-page_1.jpg |  Bin
 .../json_tour_screenshots/img_p0_1.png        |  Bin
 .../json_tour_screenshots/links_page.png      |  Bin
 .../json_tour_screenshots/page_1.png          |  Bin
 .../json_tour_screenshots/page_35.png         |  Bin
 .../knowledge_graphs/kg_agent.ipynb           |    4 +-
 .../sf2023_budget_kg_screenshot.png           |  Bin
 .../{ => parse}/multimodal/claude_parse.ipynb |    4 +-
 .../{ => parse}/multimodal/gpt4o_mini.ipynb   |    4 +-
 .../multimodal/insurance_rag.ipynb            |    2 +-
 .../{ => parse}/multimodal/legal_rag.ipynb    |    2 +-
 .../{ => parse}/multimodal/llama2-p33.png     |  Bin
 .../{ => parse}/multimodal/llama3.1-p5.png    |  Bin
 .../multimodal_contextual_retrieval_rag.ipynb |    2 +-
 ...ultimodal_contextual_retrieval_rag_img.png |  Bin
 .../multimodal_rag_slide_deck.ipynb           |    2 +-
 .../multimodal_rag_slide_deck_img.png         |  Bin
 .../multimodal_report_generation.ipynb        |    2 +-
 .../multimodal_report_generation_agent.ipynb  |    2 +-
 ...multimodal_report_generation_agent_img.png |  Bin
 .../multimodal/product_manual_rag.ipynb       |    2 +-
 .../other_files/demo_ppt_basic.ipynb          |    2 +-
 .../other_files/demo_ppt_financial.ipynb      |    4 +-
 .../expense_report_document.pdf               |  Bin
 .../expense_report_document.png               |  Bin
 .../mcdonalds_receipt.png                     |  Bin
 .../parsing_instructions.ipynb                |    4 +-
 .../purchase_order_document.pdf               |  Bin
 .../purchase_order_document.png               |  Bin
 .../parsing_modes/demo_auto_mode.ipynb        |    4 +-
 .../{ => parse}/parsing_modes/diagram.jpg     |  Bin
 .../parsing_modes/mermaid_render.png          |  Bin
 examples/{ => parse}/parsing_modes/page_1.png |  Bin
 .../{ => parse}/parsing_modes/page_11.png     |  Bin
 .../{ => parse}/parsing_modes/page_14.png     |  Bin
 examples/{ => parse}/parsing_modes/page_3.png |  Bin
 .../rfp_response/generate_rfp.ipynb           |    2 +-
 .../rfp_response/generate_rfp_img.png         |  Bin
 .../2019-tesla-impact-report-short.pdf        |  Bin
 .../test_tesla_impact_report/test_gpt4o.ipynb |    2 +-
 examples/report/basic_report.ipynb            |  762 +++++
 llama_cloud_services/__init__.py              |    8 +
 llama_cloud_services/parse/__init__.py        |    3 +
 .../parse}/base.py                            |    2 +-
 .../parse}/cli/__init__.py                    |    0
 .../parse}/cli/main.py                        |    2 +-
 .../parse}/utils.py                           |    0
 llama_cloud_services/report/__init__.py       |    4 +
 llama_cloud_services/report/base.py           |  269 ++
 llama_cloud_services/report/report.py         |  527 +++
 llama_parse/README.md                         |  165 +
 llama_parse/__init__.py                       |    3 -
 llama_parse/llama_parse/__init__.py           |    3 +
 llama_parse/llama_parse/base.py               |   19 +
 llama_parse/llama_parse/cli/__init__.py       |    0
 llama_parse/llama_parse/cli/main.py           |    4 +
 llama_parse/llama_parse/utils.py              |   11 +
 llama_parse/pyproject.toml                    |   24 +
 parse.md                                      |  142 +
 poetry.lock                                   | 3040 +++++++++++++----
 pyproject.toml                                |   25 +-
 report.md                                     |  101 +
 tests/parse/__init__.py                       |    0
 .../test_llama_parse.py}                      |   44 +-
 tests/report/__init__.py                      |    0
 tests/report/test_llama_report.py             |  119 +
 ...c6-9edb-4550-83d9-5e35165846ca-page_1.jpg} |  Bin
 tests/test_files/paper.md                     |  269 ++
 .../resume/receipt/noisebridge_receipt.pdf    |  Bin 0 -> 21630 bytes
 .../receipt/noisebridge_receipt.test.json     |   37 +
 tests/test_files/resume/receipt/schema.json   |  124 +
 tests/test_files/resume/schema.json           |  181 +
 .../resume/software_architect_resume.html     |  298 ++
 .../software_architect_resume.test.json       |   94 +
 tests/test_files/slide/saas_slide.pdf         |  Bin 0 -> 128320 bytes
 tests/test_files/slide/saas_slide.test.json   |   48 +
 tests/test_files/slide/schema.json            |  122 +
 116 files changed, 5775 insertions(+), 983 deletions(-)
 rename examples/{ => parse}/advanced_rag/dynamic_section_retrieval.ipynb (99%)
 rename examples/{ => parse}/advanced_rag/dynamic_section_retrieval_img.png (100%)
 rename examples/{ => parse}/agents/demo_simple_openai_agent.ipynb (98%)
 rename examples/{ => parse}/caltrain/caltrain_schedule_weekend.pdf (100%)
 rename examples/{ => parse}/caltrain/caltrain_text_mode.ipynb (99%)
 rename examples/{ => parse}/data/BP_Excel.xlsx (100%)
 rename examples/{ => parse}/data/nvidia_quarterly_revenue_trend_by_market.xlsx (100%)
 rename examples/{ => parse}/demo_advanced.ipynb (99%)
 rename examples/{ => parse}/demo_advanced_astradb.ipynb (99%)
 rename examples/{ => parse}/demo_advanced_weaviate.ipynb (99%)
 rename examples/{ => parse}/demo_api.ipynb (100%)
 rename examples/{ => parse}/demo_astradb.ipynb (99%)
 rename examples/{ => parse}/demo_basic.ipynb (98%)
 rename examples/{ => parse}/demo_elasticsearch_vectordb.ipynb (99%)
 rename examples/{ => parse}/demo_excel.ipynb (99%)
 rename examples/{ => parse}/demo_get_charts.ipynb (99%)
 rename examples/{ => parse}/demo_insurance.ipynb (99%)
 rename examples/{ => parse}/demo_json.ipynb (99%)
 rename examples/{ => parse}/demo_json_parsing.ipynb (99%)
 rename examples/{ => parse}/demo_json_tour.ipynb (99%)
 rename examples/{ => parse}/demo_languages.ipynb (99%)
 rename examples/{ => parse}/demo_mongodb.ipynb (99%)
 rename examples/{ => parse}/demo_parsing_instructions.ipynb (99%)
 rename examples/{ => parse}/demo_starter_multimodal.ipynb (99%)
 rename examples/{ => parse}/demo_starter_parse_selected_pages.ipynb (99%)
 rename examples/{ => parse}/demo_table_comparisons.ipynb (99%)
 rename examples/{ => parse}/excel/dcf_rag.ipynb (99%)
 rename examples/{ => parse}/excel/o1_excel_rag.ipynb (99%)
 rename examples/{ => parse}/excel/references/query1.png (100%)
 rename examples/{ => parse}/excel/references/query2.png (100%)
 rename examples/{ => parse}/excel/references/query3.png (100%)
 rename examples/{ => parse}/excel/references/query4.png (100%)
 rename examples/{ => parse}/excel/references/query5.png (100%)
 rename examples/{ => parse}/excel/references/recursive_retrieval.png (100%)
 rename examples/{ => parse}/json_tour_screenshots/32778fb0-9e83-4b00-aebe-0d7f59ff0b5f-img_p0_1.png (100%)
 rename examples/{ => parse}/json_tour_screenshots/32778fb0-9e83-4b00-aebe-0d7f59ff0b5f-page_1.jpg (100%)
 rename examples/{ => parse}/json_tour_screenshots/img_p0_1.png (100%)
 rename examples/{ => parse}/json_tour_screenshots/links_page.png (100%)
 rename examples/{ => parse}/json_tour_screenshots/page_1.png (100%)
 rename examples/{ => parse}/json_tour_screenshots/page_35.png (100%)
 rename examples/{ => parse}/knowledge_graphs/kg_agent.ipynb (99%)
 rename examples/{ => parse}/knowledge_graphs/sf2023_budget_kg_screenshot.png (100%)
 rename examples/{ => parse}/multimodal/claude_parse.ipynb (99%)
 rename examples/{ => parse}/multimodal/gpt4o_mini.ipynb (99%)
 rename examples/{ => parse}/multimodal/insurance_rag.ipynb (99%)
 rename examples/{ => parse}/multimodal/legal_rag.ipynb (99%)
 rename examples/{ => parse}/multimodal/llama2-p33.png (100%)
 rename examples/{ => parse}/multimodal/llama3.1-p5.png (100%)
 rename examples/{ => parse}/multimodal/multimodal_contextual_retrieval_rag.ipynb (99%)
 rename examples/{ => parse}/multimodal/multimodal_contextual_retrieval_rag_img.png (100%)
 rename examples/{ => parse}/multimodal/multimodal_rag_slide_deck.ipynb (99%)
 rename examples/{ => parse}/multimodal/multimodal_rag_slide_deck_img.png (100%)
 rename examples/{ => parse}/multimodal/multimodal_report_generation.ipynb (99%)
 rename examples/{ => parse}/multimodal/multimodal_report_generation_agent.ipynb (99%)
 rename examples/{ => parse}/multimodal/multimodal_report_generation_agent_img.png (100%)
 rename examples/{ => parse}/multimodal/product_manual_rag.ipynb (99%)
 rename examples/{ => parse}/other_files/demo_ppt_basic.ipynb (99%)
 rename examples/{ => parse}/other_files/demo_ppt_financial.ipynb (98%)
 rename examples/{ => parse}/parsing_instructions/expense_report_document.pdf (100%)
 rename examples/{ => parse}/parsing_instructions/expense_report_document.png (100%)
 rename examples/{ => parse}/parsing_instructions/mcdonalds_receipt.png (100%)
 rename examples/{ => parse}/parsing_instructions/parsing_instructions.ipynb (99%)
 rename examples/{ => parse}/parsing_instructions/purchase_order_document.pdf (100%)
 rename examples/{ => parse}/parsing_instructions/purchase_order_document.png (100%)
 rename examples/{ => parse}/parsing_modes/demo_auto_mode.ipynb (99%)
 rename examples/{ => parse}/parsing_modes/diagram.jpg (100%)
 rename examples/{ => parse}/parsing_modes/mermaid_render.png (100%)
 rename examples/{ => parse}/parsing_modes/page_1.png (100%)
 rename examples/{ => parse}/parsing_modes/page_11.png (100%)
 rename examples/{ => parse}/parsing_modes/page_14.png (100%)
 rename examples/{ => parse}/parsing_modes/page_3.png (100%)
 rename examples/{ => parse}/report_generation/rfp_response/generate_rfp.ipynb (99%)
 rename examples/{ => parse}/report_generation/rfp_response/generate_rfp_img.png (100%)
 rename examples/{ => parse}/test_tesla_impact_report/2019-tesla-impact-report-short.pdf (100%)
 rename examples/{ => parse}/test_tesla_impact_report/test_gpt4o.ipynb (99%)
 create mode 100644 examples/report/basic_report.ipynb
 create mode 100644 llama_cloud_services/__init__.py
 create mode 100644 llama_cloud_services/parse/__init__.py
 rename {llama_parse => llama_cloud_services/parse}/base.py (99%)
 rename {llama_parse => llama_cloud_services/parse}/cli/__init__.py (100%)
 rename {llama_parse => llama_cloud_services/parse}/cli/main.py (98%)
 rename {llama_parse => llama_cloud_services/parse}/utils.py (100%)
 create mode 100644 llama_cloud_services/report/__init__.py
 create mode 100644 llama_cloud_services/report/base.py
 create mode 100644 llama_cloud_services/report/report.py
 create mode 100644 llama_parse/README.md
 delete mode 100644 llama_parse/__init__.py
 create mode 100644 llama_parse/llama_parse/__init__.py
 create mode 100644 llama_parse/llama_parse/base.py
 create mode 100644 llama_parse/llama_parse/cli/__init__.py
 create mode 100644 llama_parse/llama_parse/cli/main.py
 create mode 100644 llama_parse/llama_parse/utils.py
 create mode 100644 llama_parse/pyproject.toml
 create mode 100644 parse.md
 create mode 100644 report.md
 create mode 100644 tests/parse/__init__.py
 rename tests/{test_reader.py => parse/test_llama_parse.py} (80%)
 create mode 100644 tests/report/__init__.py
 create mode 100644 tests/report/test_llama_report.py
 rename tests/test_files/images/{2788d9f3-630d-4dbf-8663-d74408f25e62-page_1.jpg => 67b428c6-9edb-4550-83d9-5e35165846ca-page_1.jpg} (100%)
 create mode 100644 tests/test_files/paper.md
 create mode 100644 tests/test_files/resume/receipt/noisebridge_receipt.pdf
 create mode 100644 tests/test_files/resume/receipt/noisebridge_receipt.test.json
 create mode 100644 tests/test_files/resume/receipt/schema.json
 create mode 100644 tests/test_files/resume/schema.json
 create mode 100644 tests/test_files/resume/software_architect_resume.html
 create mode 100644 tests/test_files/resume/software_architect_resume.test.json
 create mode 100644 tests/test_files/slide/saas_slide.pdf
 create mode 100644 tests/test_files/slide/saas_slide.test.json
 create mode 100644 tests/test_files/slide/schema.json

diff --git a/.github/workflows/build_package.yml b/.github/workflows/build_package.yml
index acdcaec..6556395 100644
--- a/.github/workflows/build_package.yml
+++ b/.github/workflows/build_package.yml
@@ -45,4 +45,4 @@ jobs:
       - name: Test import
         shell: bash
         working-directory: ${{ vars.RUNNER_TEMP }}
-        run: python -c "import llama_parse"
+        run: python -c "import llama_cloud_services"
diff --git a/.github/workflows/publish_release.yml b/.github/workflows/publish_release.yml
index a7c5bf5..9c5eab3 100644
--- a/.github/workflows/publish_release.yml
+++ b/.github/workflows/publish_release.yml
@@ -23,16 +23,31 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ env.PYTHON_VERSION }}
+
       - name: Install Poetry
         uses: snok/install-poetry@v1
         with:
           version: ${{ env.POETRY_VERSION }}
+
       - name: Install deps
         shell: bash
         run: pip install -e .
-      - name: Build and publish to pypi
+
+      - name: Build and publish llama-cloud-services
         uses: JRubics/poetry-publish@v2.1
         with:
+          poetry_version: ${{ env.POETRY_VERSION }}
+          python_version: ${{ env.PYTHON_VERSION }}
+          working_directory: "llama_cloud_services"
+          pypi_token: ${{ secrets.LLAMA_PARSE_PYPI_TOKEN }}
+          poetry_install_options: "--without dev"
+
+      - name: Build and publish llama-parse
+        uses: JRubics/poetry-publish@v2.1
+        with:
+          poetry_version: ${{ env.POETRY_VERSION }}
+          python_version: ${{ env.PYTHON_VERSION }}
+          working_directory: "llama_parse"
           pypi_token: ${{ secrets.LLAMA_PARSE_PYPI_TOKEN }}
           poetry_install_options: "--without dev"
 
@@ -52,6 +67,7 @@ jobs:
           export PKG=$(ls dist/ | grep tar)
           set -- $PKG
           echo "name=$1" >> $GITHUB_ENV
+
       - name: Upload Release Asset (sdist) to GitHub
         id: upload-release-asset
         uses: actions/upload-release-asset@v1
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 226c646..6ee4cc5 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -33,6 +33,7 @@ repos:
     rev: v1.0.1
     hooks:
       - id: mypy
+        exclude: ^tests/
         additional_dependencies:
           [
             "types-requests",
@@ -46,7 +47,7 @@ repos:
           [
             --disallow-untyped-defs,
             --ignore-missing-imports,
-            --python-version=3.8,
+            --python-version=3.10,
           ]
   - repo: https://github.com/adamchainz/blacken-docs
     rev: 1.16.0
diff --git a/README.md b/README.md
index 68f5332..50623c0 100644
--- a/README.md
+++ b/README.md
@@ -1,158 +1,45 @@
-# LlamaParse
-
-[![PyPI - Downloads](https://img.shields.io/pypi/dm/llama-parse)](https://pypi.org/project/llama-parse/)
-[![GitHub contributors](https://img.shields.io/github/contributors/run-llama/llama_parse)](https://github.com/run-llama/llama_parse/graphs/contributors)
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/llama-cloud-services)](https://pypi.org/project/llama-cloud-services/)
+[![GitHub contributors](https://img.shields.io/github/contributors/run-llama/llama_cloud_services)](https://github.com/run-llama/llama_cloud_services/graphs/contributors)
 [![Discord](https://img.shields.io/discord/1059199217496772688)](https://discord.gg/dGcwcsnxhU)
 
-LlamaParse is a **GenAI-native document parser** that can parse complex document data for any downstream LLM use case (RAG, agents).
-
-It is really good at the following:
-
-- ✅ **Broad file type support**: Parsing a variety of unstructured file types (.pdf, .pptx, .docx, .xlsx, .html) with text, tables, visual elements, weird layouts, and more.
-- ✅ **Table recognition**: Parsing embedded tables accurately into text and semi-structured representations.
-- ✅ **Multimodal parsing and chunking**: Extracting visual elements (images/diagrams) into structured formats and return image chunks using the latest multimodal models.
-- ✅ **Custom parsing**: Input custom prompt instructions to customize the output the way you want it.
+# Llama Cloud Services
 
-LlamaParse directly integrates with [LlamaIndex](https://github.com/run-llama/llama_index).
+This repository contains the code for hand-written SDKs and clients for interacting with LlamaCloud.
 
-The free plan is up to 1000 pages a day. Paid plan is free 7k pages per week + 0.3c per additional page by default. There is a sandbox available to test the API [**https://cloud.llamaindex.ai/parse ↗**](https://cloud.llamaindex.ai/parse).
+This includes:
 
-Read below for some quickstart information, or see the [full documentation](https://docs.cloud.llamaindex.ai/).
-
-If you're a company interested in enterprise RAG solutions, and/or high volume/on-prem usage of LlamaParse, come [talk to us](https://www.llamaindex.ai/contact).
+- [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 (coming soon!)]() - A prebuilt agentic data extractor that can be used to transform data into a structured JSON representation.
 
 ## Getting Started
 
-First, login and get an api-key from [**https://cloud.llamaindex.ai/api-key ↗**](https://cloud.llamaindex.ai/api-key).
-
-Then, make sure you have the latest LlamaIndex version installed.
-
-**NOTE:** If you are upgrading from v0.9.X, we recommend following our [migration guide](https://pretty-sodium-5e0.notion.site/v0-10-0-Migration-Guide-6ede431dcb8841b09ea171e7f133bd77), as well as uninstalling your previous version first.
-
-```
-pip uninstall llama-index  # run this if upgrading from v0.9.x or older
-pip install -U llama-index --upgrade --no-cache-dir --force-reinstall
-```
-
-Lastly, install the package:
-
-`pip install llama-parse`
-
-Now you can parse your first PDF file using the command line interface. Use the command `llama-parse [file_paths]`. See the help text with `llama-parse --help`.
+Install the package:
 
 ```bash
-export LLAMA_CLOUD_API_KEY='llx-...'
-
-# output as text
-llama-parse my_file.pdf --result-type text --output-file output.txt
-
-# output as markdown
-llama-parse my_file.pdf --result-type markdown --output-file output.md
-
-# output as raw json
-llama-parse my_file.pdf --output-raw-json --output-file output.json
+pip install llama-cloud-services
 ```
 
-You can also create simple scripts:
-
-```python
-import nest_asyncio
-
-nest_asyncio.apply()
-
-from llama_parse import LlamaParse
-
-parser = LlamaParse(
-    api_key="llx-...",  # can also be set in your env as LLAMA_CLOUD_API_KEY
-    result_type="markdown",  # "markdown" and "text" are available
-    num_workers=4,  # if multiple files passed, split in `num_workers` API calls
-    verbose=True,
-    language="en",  # Optionally you can define a language, default=en
-)
-
-# sync
-documents = parser.load_data("./my_file.pdf")
-
-# sync batch
-documents = parser.load_data(["./my_file1.pdf", "./my_file2.pdf"])
+Then, get your API key from [LlamaCloud](https://cloud.llamaindex.ai/).
 
-# async
-documents = await parser.aload_data("./my_file.pdf")
-
-# async batch
-documents = await parser.aload_data(["./my_file1.pdf", "./my_file2.pdf"])
-```
-
-## Using with file object
-
-You can parse a file object directly:
+Then, you can use the services in your code:
 
 ```python
-import nest_asyncio
-
-nest_asyncio.apply()
-
-from llama_parse import LlamaParse
+from llama_cloud_services import LlamaParse, LlamaReport
 
-parser = LlamaParse(
-    api_key="llx-...",  # can also be set in your env as LLAMA_CLOUD_API_KEY
-    result_type="markdown",  # "markdown" and "text" are available
-    num_workers=4,  # if multiple files passed, split in `num_workers` API calls
-    verbose=True,
-    language="en",  # Optionally you can define a language, default=en
-)
-
-file_name = "my_file1.pdf"
-extra_info = {"file_name": file_name}
-
-with open(f"./{file_name}", "rb") as f:
-    # must provide extra_info with file_name key with passing file object
-    documents = parser.load_data(f, extra_info=extra_info)
-
-# you can also pass file bytes directly
-with open(f"./{file_name}", "rb") as f:
-    file_bytes = f.read()
-    # must provide extra_info with file_name key with passing file bytes
-    documents = parser.load_data(file_bytes, extra_info=extra_info)
+parser = LlamaParse(api_key="YOUR_API_KEY")
+report = LlamaReport(api_key="YOUR_API_KEY")
 ```
 
-## Using with `SimpleDirectoryReader`
+See the quickstart guides for each service for more information:
 
-You can also integrate the parser as the default PDF loader in `SimpleDirectoryReader`:
-
-```python
-import nest_asyncio
-
-nest_asyncio.apply()
-
-from llama_parse import LlamaParse
-from llama_index.core import SimpleDirectoryReader
-
-parser = LlamaParse(
-    api_key="llx-...",  # can also be set in your env as LLAMA_CLOUD_API_KEY
-    result_type="markdown",  # "markdown" and "text" are available
-    verbose=True,
-)
-
-file_extractor = {".pdf": parser}
-documents = SimpleDirectoryReader(
-    "./data", file_extractor=file_extractor
-).load_data()
-```
-
-Full documentation for `SimpleDirectoryReader` can be found on the [LlamaIndex Documentation](https://docs.llamaindex.ai/en/stable/module_guides/loading/simpledirectoryreader.html).
-
-## Examples
-
-Several end-to-end indexing examples can be found in the examples folder
-
-- [Getting Started](examples/demo_basic.ipynb)
-- [Advanced RAG Example](examples/demo_advanced.ipynb)
-- [Raw API Usage](examples/demo_api.ipynb)
+- [LlamaParse](./parse.md)
+- [LlamaReport (beta/invite-only)](./report.md)
+- [LlamaExtract (coming soon!)]()
 
 ## Documentation
 
-[https://docs.cloud.llamaindex.ai/](https://docs.cloud.llamaindex.ai/)
+You can see complete SDK and API documentation for each service on [our official docs](https://docs.cloud.llamaindex.ai/).
 
 ## Terms of Service
 
@@ -160,6 +47,4 @@ See the [Terms of Service Here](./TOS.pdf).
 
 ## Get in Touch (LlamaCloud)
 
-LlamaParse is part of LlamaCloud, our e2e enterprise RAG platform that provides out-of-the-box, production-ready connectors, indexing, and retrieval over your complex data sources. We offer SaaS and VPC options.
-
-LlamaCloud is currently available via waitlist (join by [creating an account](https://cloud.llamaindex.ai/)). If you're interested in state-of-the-art quality and in centralizing your RAG efforts, come [get in touch with us](https://www.llamaindex.ai/contact).
+You can get in touch with us by following our [contact link](https://www.llamaindex.ai/contact).
diff --git a/examples/advanced_rag/dynamic_section_retrieval.ipynb b/examples/parse/advanced_rag/dynamic_section_retrieval.ipynb
similarity index 99%
rename from examples/advanced_rag/dynamic_section_retrieval.ipynb
rename to examples/parse/advanced_rag/dynamic_section_retrieval.ipynb
index 0728390..137f05f 100644
--- a/examples/advanced_rag/dynamic_section_retrieval.ipynb
+++ b/examples/parse/advanced_rag/dynamic_section_retrieval.ipynb
@@ -53,7 +53,7 @@
    "source": [
     "!pip install llama-index\n",
     "!pip install llama-index-core\n",
-    "!pip install llama-parse"
+    "!pip install llama-cloud-services"
    ]
   },
   {
@@ -190,7 +190,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser = LlamaParse(result_type=\"markdown\")"
    ]
diff --git a/examples/advanced_rag/dynamic_section_retrieval_img.png b/examples/parse/advanced_rag/dynamic_section_retrieval_img.png
similarity index 100%
rename from examples/advanced_rag/dynamic_section_retrieval_img.png
rename to examples/parse/advanced_rag/dynamic_section_retrieval_img.png
diff --git a/examples/agents/demo_simple_openai_agent.ipynb b/examples/parse/agents/demo_simple_openai_agent.ipynb
similarity index 98%
rename from examples/agents/demo_simple_openai_agent.ipynb
rename to examples/parse/agents/demo_simple_openai_agent.ipynb
index 9365899..a83aef7 100644
--- a/examples/agents/demo_simple_openai_agent.ipynb
+++ b/examples/parse/agents/demo_simple_openai_agent.ipynb
@@ -22,7 +22,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install llama-parse llama-index llama-index-postprocessor-sbert-rerank"
+    "!pip install llama-cloud-services llama-index llama-index-postprocessor-sbert-rerank"
    ]
   },
   {
@@ -82,7 +82,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser = LlamaParse(\n",
     "    result_type=\"markdown\",\n",
diff --git a/examples/caltrain/caltrain_schedule_weekend.pdf b/examples/parse/caltrain/caltrain_schedule_weekend.pdf
similarity index 100%
rename from examples/caltrain/caltrain_schedule_weekend.pdf
rename to examples/parse/caltrain/caltrain_schedule_weekend.pdf
diff --git a/examples/caltrain/caltrain_text_mode.ipynb b/examples/parse/caltrain/caltrain_text_mode.ipynb
similarity index 99%
rename from examples/caltrain/caltrain_text_mode.ipynb
rename to examples/parse/caltrain/caltrain_text_mode.ipynb
index b2b14d2..5318582 100644
--- a/examples/caltrain/caltrain_text_mode.ipynb
+++ b/examples/parse/caltrain/caltrain_text_mode.ipynb
@@ -81,7 +81,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "docs = LlamaParse(result_type=\"text\").load_data(\"./caltrain_schedule_weekend.pdf\")"
    ]
diff --git a/examples/data/BP_Excel.xlsx b/examples/parse/data/BP_Excel.xlsx
similarity index 100%
rename from examples/data/BP_Excel.xlsx
rename to examples/parse/data/BP_Excel.xlsx
diff --git a/examples/data/nvidia_quarterly_revenue_trend_by_market.xlsx b/examples/parse/data/nvidia_quarterly_revenue_trend_by_market.xlsx
similarity index 100%
rename from examples/data/nvidia_quarterly_revenue_trend_by_market.xlsx
rename to examples/parse/data/nvidia_quarterly_revenue_trend_by_market.xlsx
diff --git a/examples/demo_advanced.ipynb b/examples/parse/demo_advanced.ipynb
similarity index 99%
rename from examples/demo_advanced.ipynb
rename to examples/parse/demo_advanced.ipynb
index 28951c4..2ec497b 100644
--- a/examples/demo_advanced.ipynb
+++ b/examples/parse/demo_advanced.ipynb
@@ -26,7 +26,7 @@
     "!pip install llama-index-embeddings-openai\n",
     "!pip install llama-index-postprocessor-flag-embedding-reranker\n",
     "!pip install git+https://github.com/FlagOpen/FlagEmbedding.git\n",
-    "!pip install llama-parse"
+    "!pip install llama-cloud-services"
    ]
   },
   {
@@ -108,7 +108,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "documents = LlamaParse(result_type=\"markdown\").load_data(\"./apple_2021_10k.pdf\")"
    ]
diff --git a/examples/demo_advanced_astradb.ipynb b/examples/parse/demo_advanced_astradb.ipynb
similarity index 99%
rename from examples/demo_advanced_astradb.ipynb
rename to examples/parse/demo_advanced_astradb.ipynb
index cad609c..fbc9472 100644
--- a/examples/demo_advanced_astradb.ipynb
+++ b/examples/parse/demo_advanced_astradb.ipynb
@@ -22,7 +22,7 @@
     "%pip install llama-index-embeddings-openai\n",
     "%pip install llama-index-postprocessor-flag-embedding-reranker\n",
     "%pip install git+https://github.com/FlagOpen/FlagEmbedding.git\n",
-    "%pip install llama-parse\n",
+    "%pip install llama-cloud-services\n",
     "%pip install llama-index-vector-stores-astra-db"
    ]
   },
@@ -107,7 +107,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "documents = LlamaParse(result_type=\"markdown\").load_data(\"./uber_10q_march_2022.pdf\")"
    ]
diff --git a/examples/demo_advanced_weaviate.ipynb b/examples/parse/demo_advanced_weaviate.ipynb
similarity index 99%
rename from examples/demo_advanced_weaviate.ipynb
rename to examples/parse/demo_advanced_weaviate.ipynb
index a00bac1..ddbe4ca 100644
--- a/examples/demo_advanced_weaviate.ipynb
+++ b/examples/parse/demo_advanced_weaviate.ipynb
@@ -176,7 +176,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "documents = LlamaParse(result_type=\"markdown\").load_data(\"./uber_10q_march_2022.pdf\")"
    ]
diff --git a/examples/demo_api.ipynb b/examples/parse/demo_api.ipynb
similarity index 100%
rename from examples/demo_api.ipynb
rename to examples/parse/demo_api.ipynb
diff --git a/examples/demo_astradb.ipynb b/examples/parse/demo_astradb.ipynb
similarity index 99%
rename from examples/demo_astradb.ipynb
rename to examples/parse/demo_astradb.ipynb
index 8ae984f..9ee6a91 100644
--- a/examples/demo_astradb.ipynb
+++ b/examples/parse/demo_astradb.ipynb
@@ -130,7 +130,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "documents = LlamaParse(result_type=\"text\").load_data(file_path)"
    ]
diff --git a/examples/demo_basic.ipynb b/examples/parse/demo_basic.ipynb
similarity index 98%
rename from examples/demo_basic.ipynb
rename to examples/parse/demo_basic.ipynb
index 223827e..c9f9a39 100644
--- a/examples/demo_basic.ipynb
+++ b/examples/parse/demo_basic.ipynb
@@ -73,7 +73,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "documents = LlamaParse(result_type=\"text\").load_data(\"./attention.pdf\")"
    ]
@@ -120,7 +120,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "documents = LlamaParse(result_type=\"markdown\").load_data(\"./attention.pdf\")"
    ]
diff --git a/examples/demo_elasticsearch_vectordb.ipynb b/examples/parse/demo_elasticsearch_vectordb.ipynb
similarity index 99%
rename from examples/demo_elasticsearch_vectordb.ipynb
rename to examples/parse/demo_elasticsearch_vectordb.ipynb
index 21b255f..6efb180 100644
--- a/examples/demo_elasticsearch_vectordb.ipynb
+++ b/examples/parse/demo_elasticsearch_vectordb.ipynb
@@ -142,7 +142,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "documents = LlamaParse(result_type=\"text\").load_data(file_path)"
    ]
diff --git a/examples/demo_excel.ipynb b/examples/parse/demo_excel.ipynb
similarity index 99%
rename from examples/demo_excel.ipynb
rename to examples/parse/demo_excel.ipynb
index 44c09b7..25a04c1 100644
--- a/examples/demo_excel.ipynb
+++ b/examples/parse/demo_excel.ipynb
@@ -21,7 +21,7 @@
    "outputs": [],
    "source": [
     "%pip install llama-index\n",
-    "%pip install llama-parse"
+    "%pip install llama-cloud-services"
    ]
   },
   {
@@ -41,7 +41,7 @@
     "\n",
     "nest_asyncio.apply()\n",
     "\n",
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "api_key = \"llx-\"  # get from cloud.llamaindex.ai"
    ]
diff --git a/examples/demo_get_charts.ipynb b/examples/parse/demo_get_charts.ipynb
similarity index 99%
rename from examples/demo_get_charts.ipynb
rename to examples/parse/demo_get_charts.ipynb
index adf65ae..9704185 100644
--- a/examples/demo_get_charts.ipynb
+++ b/examples/parse/demo_get_charts.ipynb
@@ -32,7 +32,7 @@
    "outputs": [],
    "source": [
     "!pip install llama-index-core\n",
-    "!pip install llama-parse"
+    "!pip install llama-cloud-services"
    ]
   },
   {
@@ -119,7 +119,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser = LlamaParse(extract_charts=True, invalidate_cache=True)\n",
     "json_objs = parser.get_json_result(\"./agentless.pdf\")"
diff --git a/examples/demo_insurance.ipynb b/examples/parse/demo_insurance.ipynb
similarity index 99%
rename from examples/demo_insurance.ipynb
rename to examples/parse/demo_insurance.ipynb
index 6039d6a..397710f 100644
--- a/examples/demo_insurance.ipynb
+++ b/examples/parse/demo_insurance.ipynb
@@ -116,7 +116,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "documents = LlamaParse(result_type=\"markdown\").load_data(\"./policy.pdf\")"
    ]
diff --git a/examples/demo_json.ipynb b/examples/parse/demo_json.ipynb
similarity index 99%
rename from examples/demo_json.ipynb
rename to examples/parse/demo_json.ipynb
index 53daf98..0b751d5 100644
--- a/examples/demo_json.ipynb
+++ b/examples/parse/demo_json.ipynb
@@ -35,7 +35,7 @@
     "!pip install llama-index-core\n",
     "!pip install llama-index-llms-anthropic llama-index-multi-modal-llms-anthropic\n",
     "!pip install llama-index-embeddings-huggingface\n",
-    "!pip install llama-parse"
+    "!pip install llama-cloud-services"
    ]
   },
   {
@@ -129,7 +129,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser = LlamaParse(verbose=True)\n",
     "json_objs = parser.get_json_result(\"./uber_10q_march_2022.pdf\")\n",
diff --git a/examples/demo_json_parsing.ipynb b/examples/parse/demo_json_parsing.ipynb
similarity index 99%
rename from examples/demo_json_parsing.ipynb
rename to examples/parse/demo_json_parsing.ipynb
index b21afc4..782be83 100644
--- a/examples/demo_json_parsing.ipynb
+++ b/examples/parse/demo_json_parsing.ipynb
@@ -37,7 +37,7 @@
     "%pip install llama-index-core\n",
     "%pip install llama-index-llms-anthropic llama-index-multi-modal-llms-anthropic\n",
     "%pip install llama-index-embeddings-huggingface\n",
-    "%pip install llama-parse"
+    "%pip install llama-cloud-services"
    ]
   },
   {
@@ -110,7 +110,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser = LlamaParse(verbose=True)\n",
     "json_objs = parser.get_json_result(\"./uber_10q_march_2022.pdf\")\n",
diff --git a/examples/demo_json_tour.ipynb b/examples/parse/demo_json_tour.ipynb
similarity index 99%
rename from examples/demo_json_tour.ipynb
rename to examples/parse/demo_json_tour.ipynb
index 7178532..619da8a 100644
--- a/examples/demo_json_tour.ipynb
+++ b/examples/parse/demo_json_tour.ipynb
@@ -32,7 +32,7 @@
    "outputs": [],
    "source": [
     "!pip install llama-index-core\n",
-    "!pip install llama-parse"
+    "!pip install llama-cloud-services"
    ]
   },
   {
@@ -125,7 +125,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser = LlamaParse(verbose=True, premium_mode=True)\n",
     "json_objs = parser.get_json_result(\"./san_francisco_budget_2023.pdf\")"
diff --git a/examples/demo_languages.ipynb b/examples/parse/demo_languages.ipynb
similarity index 99%
rename from examples/demo_languages.ipynb
rename to examples/parse/demo_languages.ipynb
index 3c179bd..daa0512 100644
--- a/examples/demo_languages.ipynb
+++ b/examples/parse/demo_languages.ipynb
@@ -77,7 +77,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser = LlamaParse(result_type=\"text\", language=\"fr\")\n",
     "documents = parser.load_data(\"./treasury_report.pdf\")"
@@ -250,7 +250,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser = LlamaParse(result_type=\"text\", language=\"ch_sim\")\n",
     "documents = parser.load_data(\"./chinese_pdf.pdf\")"
@@ -404,7 +404,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "base_parser = LlamaParse(result_type=\"text\", language=\"en\")\n",
     "base_documents = parser.load_data(\"./chinese_pdf2.pdf\")"
diff --git a/examples/demo_mongodb.ipynb b/examples/parse/demo_mongodb.ipynb
similarity index 99%
rename from examples/demo_mongodb.ipynb
rename to examples/parse/demo_mongodb.ipynb
index 948dcc5..4a133c5 100644
--- a/examples/demo_mongodb.ipynb
+++ b/examples/parse/demo_mongodb.ipynb
@@ -69,7 +69,7 @@
     "import pymongo\n",
     "\n",
     "from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch\n",
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "from llama_index.embeddings.openai import OpenAIEmbedding\n",
     "from llama_index.core import VectorStoreIndex, StorageContext\n",
     "from llama_index.core.node_parser import SimpleNodeParser"
diff --git a/examples/demo_parsing_instructions.ipynb b/examples/parse/demo_parsing_instructions.ipynb
similarity index 99%
rename from examples/demo_parsing_instructions.ipynb
rename to examples/parse/demo_parsing_instructions.ipynb
index 804f990..b06e078 100644
--- a/examples/demo_parsing_instructions.ipynb
+++ b/examples/parse/demo_parsing_instructions.ipynb
@@ -114,7 +114,7 @@
     }
    ],
    "source": [
-    "%pip install llama-parse"
+    "%pip install llama-cloud-services"
    ]
   },
   {
@@ -169,7 +169,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_parse import LlamaParse"
+    "from llama_cloud_services import LlamaParse"
    ]
   },
   {
diff --git a/examples/demo_starter_multimodal.ipynb b/examples/parse/demo_starter_multimodal.ipynb
similarity index 99%
rename from examples/demo_starter_multimodal.ipynb
rename to examples/parse/demo_starter_multimodal.ipynb
index 4ae95c1..3f16a73 100644
--- a/examples/demo_starter_multimodal.ipynb
+++ b/examples/parse/demo_starter_multimodal.ipynb
@@ -35,7 +35,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install llama-parse"
+    "!pip install llama-cloud-services"
    ]
   },
   {
@@ -160,7 +160,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser = LlamaParse(\n",
     "    result_type=\"markdown\",\n",
@@ -199,7 +199,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser_gpt4o = LlamaParse(\n",
     "    result_type=\"markdown\",\n",
diff --git a/examples/demo_starter_parse_selected_pages.ipynb b/examples/parse/demo_starter_parse_selected_pages.ipynb
similarity index 99%
rename from examples/demo_starter_parse_selected_pages.ipynb
rename to examples/parse/demo_starter_parse_selected_pages.ipynb
index e4be503..3b54641 100644
--- a/examples/demo_starter_parse_selected_pages.ipynb
+++ b/examples/parse/demo_starter_parse_selected_pages.ipynb
@@ -31,7 +31,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install llama-parse"
+    "!pip install llama-cloud-services"
    ]
   },
   {
@@ -117,7 +117,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser = LlamaParse(target_pages=\"0,1,2\", result_type=\"markdown\")\n",
     "\n",
diff --git a/examples/demo_table_comparisons.ipynb b/examples/parse/demo_table_comparisons.ipynb
similarity index 99%
rename from examples/demo_table_comparisons.ipynb
rename to examples/parse/demo_table_comparisons.ipynb
index 7e77c01..6b3e655 100644
--- a/examples/demo_table_comparisons.ipynb
+++ b/examples/parse/demo_table_comparisons.ipynb
@@ -34,7 +34,7 @@
     "%pip install llama-index-question-gen-openai\n",
     "%pip install llama-index-postprocessor-flag-embedding-reranker\n",
     "%pip install git+https://github.com/FlagOpen/FlagEmbedding.git\n",
-    "%pip install llama-parse"
+    "%pip install llama-cloud-services"
    ]
   },
   {
@@ -109,7 +109,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "docs_2021 = LlamaParse(result_type=\"markdown\").load_data(\"./apple_2021_10k.pdf\")\n",
     "docs_2020 = LlamaParse(result_type=\"markdown\").load_data(\"./apple_2020_10k.pdf\")"
diff --git a/examples/excel/dcf_rag.ipynb b/examples/parse/excel/dcf_rag.ipynb
similarity index 99%
rename from examples/excel/dcf_rag.ipynb
rename to examples/parse/excel/dcf_rag.ipynb
index 7e32997..0962000 100644
--- a/examples/excel/dcf_rag.ipynb
+++ b/examples/parse/excel/dcf_rag.ipynb
@@ -31,7 +31,7 @@
    "outputs": [],
    "source": [
     "%pip install llama-index\n",
-    "%pip install llama-parse"
+    "%pip install llama-cloud-services"
    ]
   },
   {
@@ -53,7 +53,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "# api_key = \"llx-\"  # get from cloud.llamaindex.ai"
    ]
diff --git a/examples/excel/o1_excel_rag.ipynb b/examples/parse/excel/o1_excel_rag.ipynb
similarity index 99%
rename from examples/excel/o1_excel_rag.ipynb
rename to examples/parse/excel/o1_excel_rag.ipynb
index 04bbabc..df9c2ff 100755
--- a/examples/excel/o1_excel_rag.ipynb
+++ b/examples/parse/excel/o1_excel_rag.ipynb
@@ -37,7 +37,7 @@
    "outputs": [],
    "source": [
     "# !pip install llama-index\n",
-    "# !pip install llama-parse"
+    "# !pip install llama-cloud-services"
    ]
   },
   {
@@ -59,7 +59,7 @@
     "from llama_index.core import VectorStoreIndex\n",
     "from IPython.display import Image, Markdown\n",
     "\n",
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "from llama_index.core.node_parser import MarkdownElementNodeParser"
    ]
diff --git a/examples/excel/references/query1.png b/examples/parse/excel/references/query1.png
similarity index 100%
rename from examples/excel/references/query1.png
rename to examples/parse/excel/references/query1.png
diff --git a/examples/excel/references/query2.png b/examples/parse/excel/references/query2.png
similarity index 100%
rename from examples/excel/references/query2.png
rename to examples/parse/excel/references/query2.png
diff --git a/examples/excel/references/query3.png b/examples/parse/excel/references/query3.png
similarity index 100%
rename from examples/excel/references/query3.png
rename to examples/parse/excel/references/query3.png
diff --git a/examples/excel/references/query4.png b/examples/parse/excel/references/query4.png
similarity index 100%
rename from examples/excel/references/query4.png
rename to examples/parse/excel/references/query4.png
diff --git a/examples/excel/references/query5.png b/examples/parse/excel/references/query5.png
similarity index 100%
rename from examples/excel/references/query5.png
rename to examples/parse/excel/references/query5.png
diff --git a/examples/excel/references/recursive_retrieval.png b/examples/parse/excel/references/recursive_retrieval.png
similarity index 100%
rename from examples/excel/references/recursive_retrieval.png
rename to examples/parse/excel/references/recursive_retrieval.png
diff --git a/examples/json_tour_screenshots/32778fb0-9e83-4b00-aebe-0d7f59ff0b5f-img_p0_1.png b/examples/parse/json_tour_screenshots/32778fb0-9e83-4b00-aebe-0d7f59ff0b5f-img_p0_1.png
similarity index 100%
rename from examples/json_tour_screenshots/32778fb0-9e83-4b00-aebe-0d7f59ff0b5f-img_p0_1.png
rename to examples/parse/json_tour_screenshots/32778fb0-9e83-4b00-aebe-0d7f59ff0b5f-img_p0_1.png
diff --git a/examples/json_tour_screenshots/32778fb0-9e83-4b00-aebe-0d7f59ff0b5f-page_1.jpg b/examples/parse/json_tour_screenshots/32778fb0-9e83-4b00-aebe-0d7f59ff0b5f-page_1.jpg
similarity index 100%
rename from examples/json_tour_screenshots/32778fb0-9e83-4b00-aebe-0d7f59ff0b5f-page_1.jpg
rename to examples/parse/json_tour_screenshots/32778fb0-9e83-4b00-aebe-0d7f59ff0b5f-page_1.jpg
diff --git a/examples/json_tour_screenshots/img_p0_1.png b/examples/parse/json_tour_screenshots/img_p0_1.png
similarity index 100%
rename from examples/json_tour_screenshots/img_p0_1.png
rename to examples/parse/json_tour_screenshots/img_p0_1.png
diff --git a/examples/json_tour_screenshots/links_page.png b/examples/parse/json_tour_screenshots/links_page.png
similarity index 100%
rename from examples/json_tour_screenshots/links_page.png
rename to examples/parse/json_tour_screenshots/links_page.png
diff --git a/examples/json_tour_screenshots/page_1.png b/examples/parse/json_tour_screenshots/page_1.png
similarity index 100%
rename from examples/json_tour_screenshots/page_1.png
rename to examples/parse/json_tour_screenshots/page_1.png
diff --git a/examples/json_tour_screenshots/page_35.png b/examples/parse/json_tour_screenshots/page_35.png
similarity index 100%
rename from examples/json_tour_screenshots/page_35.png
rename to examples/parse/json_tour_screenshots/page_35.png
diff --git a/examples/knowledge_graphs/kg_agent.ipynb b/examples/parse/knowledge_graphs/kg_agent.ipynb
similarity index 99%
rename from examples/knowledge_graphs/kg_agent.ipynb
rename to examples/parse/knowledge_graphs/kg_agent.ipynb
index 1460d9e..fe4b99a 100644
--- a/examples/knowledge_graphs/kg_agent.ipynb
+++ b/examples/parse/knowledge_graphs/kg_agent.ipynb
@@ -33,7 +33,7 @@
     "!pip install llama-index-postprocessor-flag-embedding-reranker\n",
     "!pip install git+https://github.com/FlagOpen/FlagEmbedding.git\n",
     "!pip install llama-index-graph-stores-neo4j\n",
-    "!pip install llama-parse"
+    "!pip install llama-cloud-services"
    ]
   },
   {
@@ -125,7 +125,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "docs = LlamaParse(result_type=\"text\").load_data(\"./data/budget_2023.pdf\")"
    ]
diff --git a/examples/knowledge_graphs/sf2023_budget_kg_screenshot.png b/examples/parse/knowledge_graphs/sf2023_budget_kg_screenshot.png
similarity index 100%
rename from examples/knowledge_graphs/sf2023_budget_kg_screenshot.png
rename to examples/parse/knowledge_graphs/sf2023_budget_kg_screenshot.png
diff --git a/examples/multimodal/claude_parse.ipynb b/examples/parse/multimodal/claude_parse.ipynb
similarity index 99%
rename from examples/multimodal/claude_parse.ipynb
rename to examples/parse/multimodal/claude_parse.ipynb
index db438e8..1923de6 100644
--- a/examples/multimodal/claude_parse.ipynb
+++ b/examples/parse/multimodal/claude_parse.ipynb
@@ -141,7 +141,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser = LlamaParse(\n",
     "    result_type=\"markdown\",\n",
@@ -205,7 +205,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser_gpt4o = LlamaParse(\n",
     "    result_type=\"markdown\",\n",
diff --git a/examples/multimodal/gpt4o_mini.ipynb b/examples/parse/multimodal/gpt4o_mini.ipynb
similarity index 99%
rename from examples/multimodal/gpt4o_mini.ipynb
rename to examples/parse/multimodal/gpt4o_mini.ipynb
index 6f13e6d..40b3da9 100644
--- a/examples/multimodal/gpt4o_mini.ipynb
+++ b/examples/parse/multimodal/gpt4o_mini.ipynb
@@ -118,7 +118,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser = LlamaParse(\n",
     "    result_type=\"markdown\",\n",
@@ -181,7 +181,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser_gpt4o = LlamaParse(\n",
     "    result_type=\"markdown\",\n",
diff --git a/examples/multimodal/insurance_rag.ipynb b/examples/parse/multimodal/insurance_rag.ipynb
similarity index 99%
rename from examples/multimodal/insurance_rag.ipynb
rename to examples/parse/multimodal/insurance_rag.ipynb
index 593680a..6fa02ba 100644
--- a/examples/multimodal/insurance_rag.ipynb
+++ b/examples/parse/multimodal/insurance_rag.ipynb
@@ -99,7 +99,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser = LlamaParse(\n",
     "    result_type=\"markdown\",\n",
diff --git a/examples/multimodal/legal_rag.ipynb b/examples/parse/multimodal/legal_rag.ipynb
similarity index 99%
rename from examples/multimodal/legal_rag.ipynb
rename to examples/parse/multimodal/legal_rag.ipynb
index 57e65b3..4cca21b 100644
--- a/examples/multimodal/legal_rag.ipynb
+++ b/examples/parse/multimodal/legal_rag.ipynb
@@ -102,7 +102,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser = LlamaParse(\n",
     "    result_type=\"markdown\",\n",
diff --git a/examples/multimodal/llama2-p33.png b/examples/parse/multimodal/llama2-p33.png
similarity index 100%
rename from examples/multimodal/llama2-p33.png
rename to examples/parse/multimodal/llama2-p33.png
diff --git a/examples/multimodal/llama3.1-p5.png b/examples/parse/multimodal/llama3.1-p5.png
similarity index 100%
rename from examples/multimodal/llama3.1-p5.png
rename to examples/parse/multimodal/llama3.1-p5.png
diff --git a/examples/multimodal/multimodal_contextual_retrieval_rag.ipynb b/examples/parse/multimodal/multimodal_contextual_retrieval_rag.ipynb
similarity index 99%
rename from examples/multimodal/multimodal_contextual_retrieval_rag.ipynb
rename to examples/parse/multimodal/multimodal_contextual_retrieval_rag.ipynb
index 34e7281..5905915 100644
--- a/examples/multimodal/multimodal_contextual_retrieval_rag.ipynb
+++ b/examples/parse/multimodal/multimodal_contextual_retrieval_rag.ipynb
@@ -169,7 +169,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "\n",
     "parser = LlamaParse(\n",
diff --git a/examples/multimodal/multimodal_contextual_retrieval_rag_img.png b/examples/parse/multimodal/multimodal_contextual_retrieval_rag_img.png
similarity index 100%
rename from examples/multimodal/multimodal_contextual_retrieval_rag_img.png
rename to examples/parse/multimodal/multimodal_contextual_retrieval_rag_img.png
diff --git a/examples/multimodal/multimodal_rag_slide_deck.ipynb b/examples/parse/multimodal/multimodal_rag_slide_deck.ipynb
similarity index 99%
rename from examples/multimodal/multimodal_rag_slide_deck.ipynb
rename to examples/parse/multimodal/multimodal_rag_slide_deck.ipynb
index cbc7470..a10d3a9 100644
--- a/examples/multimodal/multimodal_rag_slide_deck.ipynb
+++ b/examples/parse/multimodal/multimodal_rag_slide_deck.ipynb
@@ -153,7 +153,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "\n",
     "parser_text = LlamaParse(result_type=\"text\")\n",
diff --git a/examples/multimodal/multimodal_rag_slide_deck_img.png b/examples/parse/multimodal/multimodal_rag_slide_deck_img.png
similarity index 100%
rename from examples/multimodal/multimodal_rag_slide_deck_img.png
rename to examples/parse/multimodal/multimodal_rag_slide_deck_img.png
diff --git a/examples/multimodal/multimodal_report_generation.ipynb b/examples/parse/multimodal/multimodal_report_generation.ipynb
similarity index 99%
rename from examples/multimodal/multimodal_report_generation.ipynb
rename to examples/parse/multimodal/multimodal_report_generation.ipynb
index a789bf4..a404572 100644
--- a/examples/multimodal/multimodal_report_generation.ipynb
+++ b/examples/parse/multimodal/multimodal_report_generation.ipynb
@@ -143,7 +143,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser = LlamaParse(\n",
     "    result_type=\"markdown\",\n",
diff --git a/examples/multimodal/multimodal_report_generation_agent.ipynb b/examples/parse/multimodal/multimodal_report_generation_agent.ipynb
similarity index 99%
rename from examples/multimodal/multimodal_report_generation_agent.ipynb
rename to examples/parse/multimodal/multimodal_report_generation_agent.ipynb
index 1225815..53d0edd 100644
--- a/examples/multimodal/multimodal_report_generation_agent.ipynb
+++ b/examples/parse/multimodal/multimodal_report_generation_agent.ipynb
@@ -172,7 +172,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser = LlamaParse(\n",
     "    result_type=\"markdown\",\n",
diff --git a/examples/multimodal/multimodal_report_generation_agent_img.png b/examples/parse/multimodal/multimodal_report_generation_agent_img.png
similarity index 100%
rename from examples/multimodal/multimodal_report_generation_agent_img.png
rename to examples/parse/multimodal/multimodal_report_generation_agent_img.png
diff --git a/examples/multimodal/product_manual_rag.ipynb b/examples/parse/multimodal/product_manual_rag.ipynb
similarity index 99%
rename from examples/multimodal/product_manual_rag.ipynb
rename to examples/parse/multimodal/product_manual_rag.ipynb
index bab4c87..c9a37e2 100644
--- a/examples/multimodal/product_manual_rag.ipynb
+++ b/examples/parse/multimodal/product_manual_rag.ipynb
@@ -104,7 +104,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser = LlamaParse(\n",
     "    result_type=\"markdown\",\n",
diff --git a/examples/other_files/demo_ppt_basic.ipynb b/examples/parse/other_files/demo_ppt_basic.ipynb
similarity index 99%
rename from examples/other_files/demo_ppt_basic.ipynb
rename to examples/parse/other_files/demo_ppt_basic.ipynb
index 3573a50..e91f78c 100644
--- a/examples/other_files/demo_ppt_basic.ipynb
+++ b/examples/parse/other_files/demo_ppt_basic.ipynb
@@ -25,7 +25,7 @@
     "\n",
     "nest_asyncio.apply()\n",
     "\n",
-    "from llama_parse import LlamaParse"
+    "from llama_cloud_services import LlamaParse"
    ]
   },
   {
diff --git a/examples/other_files/demo_ppt_financial.ipynb b/examples/parse/other_files/demo_ppt_financial.ipynb
similarity index 98%
rename from examples/other_files/demo_ppt_financial.ipynb
rename to examples/parse/other_files/demo_ppt_financial.ipynb
index abffef5..6bc56f4 100644
--- a/examples/other_files/demo_ppt_financial.ipynb
+++ b/examples/parse/other_files/demo_ppt_financial.ipynb
@@ -27,7 +27,7 @@
    "outputs": [],
    "source": [
     "%pip install llama-index\n",
-    "%pip install llama-parse\n",
+    "%pip install llama-cloud-services\n",
     "%pip install torch transformers python-pptx Pillow"
    ]
   },
@@ -85,7 +85,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_parse import LlamaParse"
+    "from llama_cloud_services import LlamaParse"
    ]
   },
   {
diff --git a/examples/parsing_instructions/expense_report_document.pdf b/examples/parse/parsing_instructions/expense_report_document.pdf
similarity index 100%
rename from examples/parsing_instructions/expense_report_document.pdf
rename to examples/parse/parsing_instructions/expense_report_document.pdf
diff --git a/examples/parsing_instructions/expense_report_document.png b/examples/parse/parsing_instructions/expense_report_document.png
similarity index 100%
rename from examples/parsing_instructions/expense_report_document.png
rename to examples/parse/parsing_instructions/expense_report_document.png
diff --git a/examples/parsing_instructions/mcdonalds_receipt.png b/examples/parse/parsing_instructions/mcdonalds_receipt.png
similarity index 100%
rename from examples/parsing_instructions/mcdonalds_receipt.png
rename to examples/parse/parsing_instructions/mcdonalds_receipt.png
diff --git a/examples/parsing_instructions/parsing_instructions.ipynb b/examples/parse/parsing_instructions/parsing_instructions.ipynb
similarity index 99%
rename from examples/parsing_instructions/parsing_instructions.ipynb
rename to examples/parse/parsing_instructions/parsing_instructions.ipynb
index 96f8ff7..6fb7223 100644
--- a/examples/parsing_instructions/parsing_instructions.ipynb
+++ b/examples/parse/parsing_instructions/parsing_instructions.ipynb
@@ -45,7 +45,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install llama-parse"
+    "!pip install llama-cloud-services"
    ]
   },
   {
@@ -100,7 +100,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "vanilaParsing = LlamaParse(result_type=\"markdown\").load_data(\"./mcdonalds_receipt.png\")"
    ]
diff --git a/examples/parsing_instructions/purchase_order_document.pdf b/examples/parse/parsing_instructions/purchase_order_document.pdf
similarity index 100%
rename from examples/parsing_instructions/purchase_order_document.pdf
rename to examples/parse/parsing_instructions/purchase_order_document.pdf
diff --git a/examples/parsing_instructions/purchase_order_document.png b/examples/parse/parsing_instructions/purchase_order_document.png
similarity index 100%
rename from examples/parsing_instructions/purchase_order_document.png
rename to examples/parse/parsing_instructions/purchase_order_document.png
diff --git a/examples/parsing_modes/demo_auto_mode.ipynb b/examples/parse/parsing_modes/demo_auto_mode.ipynb
similarity index 99%
rename from examples/parsing_modes/demo_auto_mode.ipynb
rename to examples/parse/parsing_modes/demo_auto_mode.ipynb
index adfa35d..85e9764 100644
--- a/examples/parsing_modes/demo_auto_mode.ipynb
+++ b/examples/parse/parsing_modes/demo_auto_mode.ipynb
@@ -22,7 +22,7 @@
     "!pip install llama-index\n",
     "!pip install llama-index-core\n",
     "!pip install llama-index-embeddings-openai llama-index-llms-openai\n",
-    "!pip install llama-parse"
+    "!pip install llama-cloud-services"
    ]
   },
   {
@@ -111,7 +111,7 @@
     }
    ],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "file_path = \"data/nova_technical_report.pdf\"\n",
     "\n",
diff --git a/examples/parsing_modes/diagram.jpg b/examples/parse/parsing_modes/diagram.jpg
similarity index 100%
rename from examples/parsing_modes/diagram.jpg
rename to examples/parse/parsing_modes/diagram.jpg
diff --git a/examples/parsing_modes/mermaid_render.png b/examples/parse/parsing_modes/mermaid_render.png
similarity index 100%
rename from examples/parsing_modes/mermaid_render.png
rename to examples/parse/parsing_modes/mermaid_render.png
diff --git a/examples/parsing_modes/page_1.png b/examples/parse/parsing_modes/page_1.png
similarity index 100%
rename from examples/parsing_modes/page_1.png
rename to examples/parse/parsing_modes/page_1.png
diff --git a/examples/parsing_modes/page_11.png b/examples/parse/parsing_modes/page_11.png
similarity index 100%
rename from examples/parsing_modes/page_11.png
rename to examples/parse/parsing_modes/page_11.png
diff --git a/examples/parsing_modes/page_14.png b/examples/parse/parsing_modes/page_14.png
similarity index 100%
rename from examples/parsing_modes/page_14.png
rename to examples/parse/parsing_modes/page_14.png
diff --git a/examples/parsing_modes/page_3.png b/examples/parse/parsing_modes/page_3.png
similarity index 100%
rename from examples/parsing_modes/page_3.png
rename to examples/parse/parsing_modes/page_3.png
diff --git a/examples/report_generation/rfp_response/generate_rfp.ipynb b/examples/parse/report_generation/rfp_response/generate_rfp.ipynb
similarity index 99%
rename from examples/report_generation/rfp_response/generate_rfp.ipynb
rename to examples/parse/report_generation/rfp_response/generate_rfp.ipynb
index 15dc0c4..38c8d9c 100644
--- a/examples/report_generation/rfp_response/generate_rfp.ipynb
+++ b/examples/parse/report_generation/rfp_response/generate_rfp.ipynb
@@ -95,7 +95,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "# use our multimodal models for extractions\n",
     "parser = LlamaParse(\n",
diff --git a/examples/report_generation/rfp_response/generate_rfp_img.png b/examples/parse/report_generation/rfp_response/generate_rfp_img.png
similarity index 100%
rename from examples/report_generation/rfp_response/generate_rfp_img.png
rename to examples/parse/report_generation/rfp_response/generate_rfp_img.png
diff --git a/examples/test_tesla_impact_report/2019-tesla-impact-report-short.pdf b/examples/parse/test_tesla_impact_report/2019-tesla-impact-report-short.pdf
similarity index 100%
rename from examples/test_tesla_impact_report/2019-tesla-impact-report-short.pdf
rename to examples/parse/test_tesla_impact_report/2019-tesla-impact-report-short.pdf
diff --git a/examples/test_tesla_impact_report/test_gpt4o.ipynb b/examples/parse/test_tesla_impact_report/test_gpt4o.ipynb
similarity index 99%
rename from examples/test_tesla_impact_report/test_gpt4o.ipynb
rename to examples/parse/test_tesla_impact_report/test_gpt4o.ipynb
index 9c30f14..2fd3d44 100644
--- a/examples/test_tesla_impact_report/test_gpt4o.ipynb
+++ b/examples/parse/test_tesla_impact_report/test_gpt4o.ipynb
@@ -107,7 +107,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_parse import LlamaParse\n",
+    "from llama_cloud_services import LlamaParse\n",
     "\n",
     "parser_gpt4o = LlamaParse(\n",
     "    result_type=\"markdown\",\n",
diff --git a/examples/report/basic_report.ipynb b/examples/report/basic_report.ipynb
new file mode 100644
index 0000000..7858318
--- /dev/null
+++ b/examples/report/basic_report.ipynb
@@ -0,0 +1,762 @@
+{
+ "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
+}
diff --git a/llama_cloud_services/__init__.py b/llama_cloud_services/__init__.py
new file mode 100644
index 0000000..1761407
--- /dev/null
+++ b/llama_cloud_services/__init__.py
@@ -0,0 +1,8 @@
+from llama_cloud_services.parse import LlamaParse
+from llama_cloud_services.report import ReportClient, LlamaReport
+
+__all__ = [
+    "LlamaParse",
+    "ReportClient",
+    "LlamaReport",
+]
diff --git a/llama_cloud_services/parse/__init__.py b/llama_cloud_services/parse/__init__.py
new file mode 100644
index 0000000..4499d16
--- /dev/null
+++ b/llama_cloud_services/parse/__init__.py
@@ -0,0 +1,3 @@
+from llama_cloud_services.parse.base import LlamaParse, ResultType
+
+__all__ = ["LlamaParse", "ResultType"]
diff --git a/llama_parse/base.py b/llama_cloud_services/parse/base.py
similarity index 99%
rename from llama_parse/base.py
rename to llama_cloud_services/parse/base.py
index 3e19260..52f4c79 100644
--- a/llama_parse/base.py
+++ b/llama_cloud_services/parse/base.py
@@ -18,7 +18,7 @@
 from llama_index.core.readers.file.base import get_default_fs
 from llama_index.core.schema import Document
 
-from llama_parse.utils import (
+from llama_cloud_services.parse.utils import (
     SUPPORTED_FILE_TYPES,
     ResultType,
     nest_asyncio_err,
diff --git a/llama_parse/cli/__init__.py b/llama_cloud_services/parse/cli/__init__.py
similarity index 100%
rename from llama_parse/cli/__init__.py
rename to llama_cloud_services/parse/cli/__init__.py
diff --git a/llama_parse/cli/main.py b/llama_cloud_services/parse/cli/main.py
similarity index 98%
rename from llama_parse/cli/main.py
rename to llama_cloud_services/parse/cli/main.py
index 616c901..43de8bf 100644
--- a/llama_parse/cli/main.py
+++ b/llama_cloud_services/parse/cli/main.py
@@ -5,7 +5,7 @@
 from pydantic.fields import FieldInfo
 from typing import Any, Callable, List
 
-from llama_parse.base import LlamaParse
+from llama_cloud_services.parse.base import LlamaParse
 
 
 def pydantic_field_to_click_option(name: str, field: FieldInfo) -> click.Option:
diff --git a/llama_parse/utils.py b/llama_cloud_services/parse/utils.py
similarity index 100%
rename from llama_parse/utils.py
rename to llama_cloud_services/parse/utils.py
diff --git a/llama_cloud_services/report/__init__.py b/llama_cloud_services/report/__init__.py
new file mode 100644
index 0000000..4f2fe1d
--- /dev/null
+++ b/llama_cloud_services/report/__init__.py
@@ -0,0 +1,4 @@
+from llama_cloud_services.report.report import ReportClient
+from llama_cloud_services.report.base import LlamaReport
+
+__all__ = ["ReportClient", "LlamaReport"]
diff --git a/llama_cloud_services/report/base.py b/llama_cloud_services/report/base.py
new file mode 100644
index 0000000..13d5c94
--- /dev/null
+++ b/llama_cloud_services/report/base.py
@@ -0,0 +1,269 @@
+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))
diff --git a/llama_cloud_services/report/report.py b/llama_cloud_services/report/report.py
new file mode 100644
index 0000000..5ec4b4a
--- /dev/null
+++ b/llama_cloud_services/report/report.py
@@ -0,0 +1,527 @@
+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))
diff --git a/llama_parse/README.md b/llama_parse/README.md
new file mode 100644
index 0000000..68f5332
--- /dev/null
+++ b/llama_parse/README.md
@@ -0,0 +1,165 @@
+# LlamaParse
+
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/llama-parse)](https://pypi.org/project/llama-parse/)
+[![GitHub contributors](https://img.shields.io/github/contributors/run-llama/llama_parse)](https://github.com/run-llama/llama_parse/graphs/contributors)
+[![Discord](https://img.shields.io/discord/1059199217496772688)](https://discord.gg/dGcwcsnxhU)
+
+LlamaParse is a **GenAI-native document parser** that can parse complex document data for any downstream LLM use case (RAG, agents).
+
+It is really good at the following:
+
+- ✅ **Broad file type support**: Parsing a variety of unstructured file types (.pdf, .pptx, .docx, .xlsx, .html) with text, tables, visual elements, weird layouts, and more.
+- ✅ **Table recognition**: Parsing embedded tables accurately into text and semi-structured representations.
+- ✅ **Multimodal parsing and chunking**: Extracting visual elements (images/diagrams) into structured formats and return image chunks using the latest multimodal models.
+- ✅ **Custom parsing**: Input custom prompt instructions to customize the output the way you want it.
+
+LlamaParse directly integrates with [LlamaIndex](https://github.com/run-llama/llama_index).
+
+The free plan is up to 1000 pages a day. Paid plan is free 7k pages per week + 0.3c per additional page by default. There is a sandbox available to test the API [**https://cloud.llamaindex.ai/parse ↗**](https://cloud.llamaindex.ai/parse).
+
+Read below for some quickstart information, or see the [full documentation](https://docs.cloud.llamaindex.ai/).
+
+If you're a company interested in enterprise RAG solutions, and/or high volume/on-prem usage of LlamaParse, come [talk to us](https://www.llamaindex.ai/contact).
+
+## Getting Started
+
+First, login and get an api-key from [**https://cloud.llamaindex.ai/api-key ↗**](https://cloud.llamaindex.ai/api-key).
+
+Then, make sure you have the latest LlamaIndex version installed.
+
+**NOTE:** If you are upgrading from v0.9.X, we recommend following our [migration guide](https://pretty-sodium-5e0.notion.site/v0-10-0-Migration-Guide-6ede431dcb8841b09ea171e7f133bd77), as well as uninstalling your previous version first.
+
+```
+pip uninstall llama-index  # run this if upgrading from v0.9.x or older
+pip install -U llama-index --upgrade --no-cache-dir --force-reinstall
+```
+
+Lastly, install the package:
+
+`pip install llama-parse`
+
+Now you can parse your first PDF file using the command line interface. Use the command `llama-parse [file_paths]`. See the help text with `llama-parse --help`.
+
+```bash
+export LLAMA_CLOUD_API_KEY='llx-...'
+
+# output as text
+llama-parse my_file.pdf --result-type text --output-file output.txt
+
+# output as markdown
+llama-parse my_file.pdf --result-type markdown --output-file output.md
+
+# output as raw json
+llama-parse my_file.pdf --output-raw-json --output-file output.json
+```
+
+You can also create simple scripts:
+
+```python
+import nest_asyncio
+
+nest_asyncio.apply()
+
+from llama_parse import LlamaParse
+
+parser = LlamaParse(
+    api_key="llx-...",  # can also be set in your env as LLAMA_CLOUD_API_KEY
+    result_type="markdown",  # "markdown" and "text" are available
+    num_workers=4,  # if multiple files passed, split in `num_workers` API calls
+    verbose=True,
+    language="en",  # Optionally you can define a language, default=en
+)
+
+# sync
+documents = parser.load_data("./my_file.pdf")
+
+# sync batch
+documents = parser.load_data(["./my_file1.pdf", "./my_file2.pdf"])
+
+# async
+documents = await parser.aload_data("./my_file.pdf")
+
+# async batch
+documents = await parser.aload_data(["./my_file1.pdf", "./my_file2.pdf"])
+```
+
+## Using with file object
+
+You can parse a file object directly:
+
+```python
+import nest_asyncio
+
+nest_asyncio.apply()
+
+from llama_parse import LlamaParse
+
+parser = LlamaParse(
+    api_key="llx-...",  # can also be set in your env as LLAMA_CLOUD_API_KEY
+    result_type="markdown",  # "markdown" and "text" are available
+    num_workers=4,  # if multiple files passed, split in `num_workers` API calls
+    verbose=True,
+    language="en",  # Optionally you can define a language, default=en
+)
+
+file_name = "my_file1.pdf"
+extra_info = {"file_name": file_name}
+
+with open(f"./{file_name}", "rb") as f:
+    # must provide extra_info with file_name key with passing file object
+    documents = parser.load_data(f, extra_info=extra_info)
+
+# you can also pass file bytes directly
+with open(f"./{file_name}", "rb") as f:
+    file_bytes = f.read()
+    # must provide extra_info with file_name key with passing file bytes
+    documents = parser.load_data(file_bytes, extra_info=extra_info)
+```
+
+## Using with `SimpleDirectoryReader`
+
+You can also integrate the parser as the default PDF loader in `SimpleDirectoryReader`:
+
+```python
+import nest_asyncio
+
+nest_asyncio.apply()
+
+from llama_parse import LlamaParse
+from llama_index.core import SimpleDirectoryReader
+
+parser = LlamaParse(
+    api_key="llx-...",  # can also be set in your env as LLAMA_CLOUD_API_KEY
+    result_type="markdown",  # "markdown" and "text" are available
+    verbose=True,
+)
+
+file_extractor = {".pdf": parser}
+documents = SimpleDirectoryReader(
+    "./data", file_extractor=file_extractor
+).load_data()
+```
+
+Full documentation for `SimpleDirectoryReader` can be found on the [LlamaIndex Documentation](https://docs.llamaindex.ai/en/stable/module_guides/loading/simpledirectoryreader.html).
+
+## Examples
+
+Several end-to-end indexing examples can be found in the examples folder
+
+- [Getting Started](examples/demo_basic.ipynb)
+- [Advanced RAG Example](examples/demo_advanced.ipynb)
+- [Raw API Usage](examples/demo_api.ipynb)
+
+## Documentation
+
+[https://docs.cloud.llamaindex.ai/](https://docs.cloud.llamaindex.ai/)
+
+## Terms of Service
+
+See the [Terms of Service Here](./TOS.pdf).
+
+## Get in Touch (LlamaCloud)
+
+LlamaParse is part of LlamaCloud, our e2e enterprise RAG platform that provides out-of-the-box, production-ready connectors, indexing, and retrieval over your complex data sources. We offer SaaS and VPC options.
+
+LlamaCloud is currently available via waitlist (join by [creating an account](https://cloud.llamaindex.ai/)). If you're interested in state-of-the-art quality and in centralizing your RAG efforts, come [get in touch with us](https://www.llamaindex.ai/contact).
diff --git a/llama_parse/__init__.py b/llama_parse/__init__.py
deleted file mode 100644
index d62b759..0000000
--- a/llama_parse/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from llama_parse.base import LlamaParse, ResultType
-
-__all__ = ["LlamaParse", "ResultType"]
diff --git a/llama_parse/llama_parse/__init__.py b/llama_parse/llama_parse/__init__.py
new file mode 100644
index 0000000..bf34cb5
--- /dev/null
+++ b/llama_parse/llama_parse/__init__.py
@@ -0,0 +1,3 @@
+from llama_cloud_services.parse import LlamaParse, ResultType
+
+__all__ = ["LlamaParse", "ResultType"]
diff --git a/llama_parse/llama_parse/base.py b/llama_parse/llama_parse/base.py
new file mode 100644
index 0000000..1d63880
--- /dev/null
+++ b/llama_parse/llama_parse/base.py
@@ -0,0 +1,19 @@
+from llama_cloud_services.parse.base import (
+    LlamaParse,
+    ResultType,
+    FileInput,
+    _DEFAULT_SEPARATOR,
+    JOB_RESULT_URL,
+    JOB_STATUS_ROUTE,
+    JOB_UPLOAD_ROUTE,
+)
+
+__all__ = [
+    "LlamaParse",
+    "ResultType",
+    "FileInput",
+    "_DEFAULT_SEPARATOR",
+    "JOB_RESULT_URL",
+    "JOB_STATUS_ROUTE",
+    "JOB_UPLOAD_ROUTE",
+]
diff --git a/llama_parse/llama_parse/cli/__init__.py b/llama_parse/llama_parse/cli/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/llama_parse/llama_parse/cli/main.py b/llama_parse/llama_parse/cli/main.py
new file mode 100644
index 0000000..11d7847
--- /dev/null
+++ b/llama_parse/llama_parse/cli/main.py
@@ -0,0 +1,4 @@
+from llama_cloud_services.parse.cli.main import parse
+
+if __name__ == "__main__":
+    parse()
diff --git a/llama_parse/llama_parse/utils.py b/llama_parse/llama_parse/utils.py
new file mode 100644
index 0000000..39b0165
--- /dev/null
+++ b/llama_parse/llama_parse/utils.py
@@ -0,0 +1,11 @@
+from llama_cloud_services.parse.utils import (
+    SUPPORTED_FILE_TYPES,
+    Language,
+    ResultType,
+)
+
+__all__ = [
+    "SUPPORTED_FILE_TYPES",
+    "Language",
+    "ResultType",
+]
diff --git a/llama_parse/pyproject.toml b/llama_parse/pyproject.toml
new file mode 100644
index 0000000..4463f07
--- /dev/null
+++ b/llama_parse/pyproject.toml
@@ -0,0 +1,24 @@
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "llama-parse"
+version = "0.5.21"
+description = "Parse files into RAG-Optimized formats."
+authors = ["Logan Markewich <logan@llamaindex.ai>"]
+license = "MIT"
+readme = "README.md"
+packages = [{include = "llama_parse"}]
+
+[tool.poetry.dependencies]
+python = ">=3.9,<4.0"
+llama-cloud-services = "*"
+
+[tool.poetry.group.dev.dependencies]
+pytest = "^8.0.0"
+pytest-asyncio = "*"
+ipykernel = "^6.29.0"
+
+[tool.poetry.scripts]
+llama-parse = "llama_parse.cli.main:parse"
diff --git a/parse.md b/parse.md
new file mode 100644
index 0000000..b4d276e
--- /dev/null
+++ b/parse.md
@@ -0,0 +1,142 @@
+# LlamaParse
+
+LlamaParse is a **GenAI-native document parser** that can parse complex document data for any downstream LLM use case (RAG, agents).
+
+It is really good at the following:
+
+- ✅ **Broad file type support**: Parsing a variety of unstructured file types (.pdf, .pptx, .docx, .xlsx, .html) with text, tables, visual elements, weird layouts, and more.
+- ✅ **Table recognition**: Parsing embedded tables accurately into text and semi-structured representations.
+- ✅ **Multimodal parsing and chunking**: Extracting visual elements (images/diagrams) into structured formats and return image chunks using the latest multimodal models.
+- ✅ **Custom parsing**: Input custom prompt instructions to customize the output the way you want it.
+
+LlamaParse directly integrates with [LlamaIndex](https://github.com/run-llama/llama_index).
+
+The free plan is up to 1000 pages a day. Paid plan is free 7k pages per week + 0.3c per additional page by default. There is a sandbox available to test the API [**https://cloud.llamaindex.ai/parse ↗**](https://cloud.llamaindex.ai/parse).
+
+Read below for some quickstart information, or see the [full documentation](https://docs.cloud.llamaindex.ai/).
+
+If you're a company interested in enterprise RAG solutions, and/or high volume/on-prem usage of LlamaParse, come [talk to us](https://www.llamaindex.ai/contact).
+
+## Getting Started
+
+First, login and get an api-key from [**https://cloud.llamaindex.ai/api-key ↗**](https://cloud.llamaindex.ai/api-key).
+
+Then, install the package:
+
+`pip install llama-cloud-services`
+
+Now you can parse your first PDF file using the command line interface. Use the command `llama-parse [file_paths]`. See the help text with `llama-parse --help`.
+
+```bash
+export LLAMA_CLOUD_API_KEY='llx-...'
+
+# output as text
+llama-parse my_file.pdf --result-type text --output-file output.txt
+
+# output as markdown
+llama-parse my_file.pdf --result-type markdown --output-file output.md
+
+# output as raw json
+llama-parse my_file.pdf --output-raw-json --output-file output.json
+```
+
+You can also create simple scripts:
+
+```python
+import nest_asyncio
+
+nest_asyncio.apply()
+
+from llama_cloud_services import LlamaParse
+
+parser = LlamaParse(
+    api_key="llx-...",  # can also be set in your env as LLAMA_CLOUD_API_KEY
+    result_type="markdown",  # "markdown" and "text" are available
+    num_workers=4,  # if multiple files passed, split in `num_workers` API calls
+    verbose=True,
+    language="en",  # Optionally you can define a language, default=en
+)
+
+# sync
+documents = parser.load_data("./my_file.pdf")
+
+# sync batch
+documents = parser.load_data(["./my_file1.pdf", "./my_file2.pdf"])
+
+# async
+documents = await parser.aload_data("./my_file.pdf")
+
+# async batch
+documents = await parser.aload_data(["./my_file1.pdf", "./my_file2.pdf"])
+```
+
+## Using with file object
+
+You can parse a file object directly:
+
+```python
+import nest_asyncio
+
+nest_asyncio.apply()
+
+from llama_cloud_services import LlamaParse
+
+parser = LlamaParse(
+    api_key="llx-...",  # can also be set in your env as LLAMA_CLOUD_API_KEY
+    result_type="markdown",  # "markdown" and "text" are available
+    num_workers=4,  # if multiple files passed, split in `num_workers` API calls
+    verbose=True,
+    language="en",  # Optionally you can define a language, default=en
+)
+
+file_name = "my_file1.pdf"
+extra_info = {"file_name": file_name}
+
+with open(f"./{file_name}", "rb") as f:
+    # must provide extra_info with file_name key with passing file object
+    documents = parser.load_data(f, extra_info=extra_info)
+
+# you can also pass file bytes directly
+with open(f"./{file_name}", "rb") as f:
+    file_bytes = f.read()
+    # must provide extra_info with file_name key with passing file bytes
+    documents = parser.load_data(file_bytes, extra_info=extra_info)
+```
+
+## Using with `SimpleDirectoryReader`
+
+You can also integrate the parser as the default PDF loader in `SimpleDirectoryReader`:
+
+```python
+import nest_asyncio
+
+nest_asyncio.apply()
+
+from llama_cloud_services import LlamaParse
+from llama_index.core import SimpleDirectoryReader
+
+parser = LlamaParse(
+    api_key="llx-...",  # can also be set in your env as LLAMA_CLOUD_API_KEY
+    result_type="markdown",  # "markdown" and "text" are available
+    verbose=True,
+)
+
+file_extractor = {".pdf": parser}
+documents = SimpleDirectoryReader(
+    "./data", file_extractor=file_extractor
+).load_data()
+```
+
+Full documentation for `SimpleDirectoryReader` can be found on the [LlamaIndex Documentation](https://docs.llamaindex.ai/en/stable/module_guides/loading/simpledirectoryreader.html).
+
+## Examples
+
+Several end-to-end indexing examples can be found in the examples folder
+
+- [Getting Started](examples/parse/demo_basic.ipynb)
+- [Advanced RAG Example](examples/parse/demo_advanced.ipynb)
+- [Raw API Usage](examples/parse/demo_api.ipynb)
+
+## Documentation
+
+[https://docs.cloud.llamaindex.ai/](https://docs.cloud.llamaindex.ai/)
diff --git a/poetry.lock b/poetry.lock
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 [[package]]
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@@ -136,24 +136,24 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.6.2.post1"
+version = "4.8.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"},
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+    {file = "anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a"},
+    {file = "anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a"},
 ]
 
 [package.dependencies]
 exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
 idna = ">=2.8"
 sniffio = ">=1.1"
-typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
-doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
+doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -167,6 +167,82 @@ files = [
     {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
 ]
 
+[[package]]
+name = "argon2-cffi"
+version = "23.1.0"
+description = "Argon2 for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "argon2_cffi-23.1.0-py3-none-any.whl", hash = "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea"},
+    {file = "argon2_cffi-23.1.0.tar.gz", hash = "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08"},
+]
+
+[package.dependencies]
+argon2-cffi-bindings = "*"
+
+[package.extras]
+dev = ["argon2-cffi[tests,typing]", "tox (>4)"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-copybutton", "sphinx-notfound-page"]
+tests = ["hypothesis", "pytest"]
+typing = ["mypy"]
+
+[[package]]
+name = "argon2-cffi-bindings"
+version = "21.2.0"
+description = "Low-level CFFI bindings for Argon2"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "argon2-cffi-bindings-21.2.0.tar.gz", hash = "sha256:bb89ceffa6c791807d1305ceb77dbfacc5aa499891d2c55661c6459651fc39e3"},
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+    {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-win_amd64.whl", hash = "sha256:b2ef1c30440dbbcba7a5dc3e319408b59676e2e039e2ae11a8775ecf482b192f"},
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+    {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:3b9ef65804859d335dc6b31582cad2c5166f0c3e7975f324d9ffaa34ee7e6583"},
+    {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d4966ef5848d820776f5f562a7d45fdd70c2f330c961d0d745b784034bd9f48d"},
+    {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:20ef543a89dee4db46a1a6e206cd015360e5a75822f76df533845c3cbaf72670"},
+    {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ed2937d286e2ad0cc79a7087d3c272832865f779430e0cc2b4f3718d3159b0cb"},
+    {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:5e00316dabdaea0b2dd82d141cc66889ced0cdcbfa599e8b471cf22c620c329a"},
+]
+
+[package.dependencies]
+cffi = ">=1.0.1"
+
+[package.extras]
+dev = ["cogapp", "pre-commit", "pytest", "wheel"]
+tests = ["pytest"]
+
+[[package]]
+name = "arrow"
+version = "1.3.0"
+description = "Better dates & times for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "arrow-1.3.0-py3-none-any.whl", hash = "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80"},
+    {file = "arrow-1.3.0.tar.gz", hash = "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85"},
+]
+
+[package.dependencies]
+python-dateutil = ">=2.7.0"
+types-python-dateutil = ">=2.8.10"
+
+[package.extras]
+doc = ["doc8", "sphinx (>=7.0.0)", "sphinx-autobuild", "sphinx-autodoc-typehints", "sphinx_rtd_theme (>=1.3.0)"]
+test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "pytz (==2021.1)", "simplejson (==3.*)"]
+
 [[package]]
 name = "asttokens"
 version = "3.0.0"
@@ -182,6 +258,20 @@ files = [
 astroid = ["astroid (>=2,<4)"]
 test = ["astroid (>=2,<4)", "pytest", "pytest-cov", "pytest-xdist"]
 
+[[package]]
+name = "async-lru"
+version = "2.0.4"
+description = "Simple LRU cache for asyncio"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "async-lru-2.0.4.tar.gz", hash = "sha256:b8a59a5df60805ff63220b2a0c5b5393da5521b113cd5465a44eb037d81a5627"},
+    {file = "async_lru-2.0.4-py3-none-any.whl", hash = "sha256:ff02944ce3c288c5be660c42dbcca0742b32c3b279d6dceda655190240b99224"},
+]
+
+[package.dependencies]
+typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
+
 [[package]]
 name = "async-timeout"
 version = "5.0.1"
@@ -195,32 +285,120 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "24.2.0"
+version = "25.1.0"
 description = "Classes Without Boilerplate"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"},
-    {file = "attrs-24.2.0.tar.gz", hash = "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346"},
+    {file = "attrs-25.1.0-py3-none-any.whl", hash = "sha256:c75a69e28a550a7e93789579c22aa26b0f5b83b75dc4e08fe092980051e1090a"},
+    {file = "attrs-25.1.0.tar.gz", hash = "sha256:1c97078a80c814273a76b2a298a932eb681c87415c11dee0a6921de7f1b02c3e"},
 ]
 
 [package.extras]
 benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
-dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
 tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
+[[package]]
+name = "autoevals"
+version = "0.0.114"
+description = "Universal library for evaluating AI models"
+optional = false
+python-versions = ">=3.8.0"
+files = [
+    {file = "autoevals-0.0.114-py3-none-any.whl", hash = "sha256:b638cb7a863888177bb8554c345daba6337894e0de4d48b619608c404cf09fa2"},
+    {file = "autoevals-0.0.114.tar.gz", hash = "sha256:c1a8dbe67bcf6a2e74562e2242f82a68ece9e756eb7be4846a7167de5f8f4446"},
+]
+
+[package.dependencies]
+braintrust_core = "0.0.57"
+chevron = "*"
+jsonschema = "*"
+levenshtein = "*"
+pyyaml = "*"
+
+[package.extras]
+all = ["IPython", "black", "build", "flake8", "flake8-isort", "isort (==5.12.0)", "numpy", "pre-commit", "pydoc-markdown", "pytest", "respx", "scipy", "twine"]
+dev = ["IPython", "black", "build", "flake8", "flake8-isort", "isort (==5.12.0)", "pre-commit", "pytest", "respx", "twine"]
+doc = ["pydoc-markdown"]
+scipy = ["numpy", "scipy"]
+
+[[package]]
+name = "babel"
+version = "2.16.0"
+description = "Internationalization utilities"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "babel-2.16.0-py3-none-any.whl", hash = "sha256:368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b"},
+    {file = "babel-2.16.0.tar.gz", hash = "sha256:d1f3554ca26605fe173f3de0c65f750f5a42f924499bf134de6423582298e316"},
+]
+
+[package.extras]
+dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
+
+[[package]]
+name = "beautifulsoup4"
+version = "4.12.3"
+description = "Screen-scraping library"
+optional = false
+python-versions = ">=3.6.0"
+files = [
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+    {file = "beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051"},
+]
+
+[package.dependencies]
+soupsieve = ">1.2"
+
+[package.extras]
+cchardet = ["cchardet"]
+chardet = ["chardet"]
+charset-normalizer = ["charset-normalizer"]
+html5lib = ["html5lib"]
+lxml = ["lxml"]
+
+[[package]]
+name = "bleach"
+version = "6.2.0"
+description = "An easy safelist-based HTML-sanitizing tool."
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "bleach-6.2.0-py3-none-any.whl", hash = "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e"},
+    {file = "bleach-6.2.0.tar.gz", hash = "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f"},
+]
+
+[package.dependencies]
+tinycss2 = {version = ">=1.1.0,<1.5", optional = true, markers = "extra == \"css\""}
+webencodings = "*"
+
+[package.extras]
+css = ["tinycss2 (>=1.1.0,<1.5)"]
+
+[[package]]
+name = "braintrust-core"
+version = "0.0.57"
+description = "Shared core dependencies for Braintrust packages"
+optional = false
+python-versions = ">=3.8.0"
+files = [
+    {file = "braintrust_core-0.0.57-py3-none-any.whl", hash = "sha256:c127ee9b9f6acdfadf4dc380867fa3ed7a99389ccae094c92abb4a906323c325"},
+    {file = "braintrust_core-0.0.57.tar.gz", hash = "sha256:617f9d3e288c3fb020172d10592d4b7fe7410f9dee9f29cdbc4d7e0d1e8e58e3"},
+]
+
 [[package]]
 name = "certifi"
-version = "2024.8.30"
+version = "2024.12.14"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"},
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+    {file = "certifi-2024.12.14-py3-none-any.whl", hash = "sha256:1275f7a45be9464efc1173084eaa30f866fe2e47d389406136d332ed4967ec56"},
+    {file = "certifi-2024.12.14.tar.gz", hash = "sha256:b650d30f370c2b724812bee08008be0c4163b163ddaec3f2546c1caf65f191db"},
 ]
 
 [[package]]
@@ -303,128 +481,137 @@ files = [
 pycparser = "*"
 
 [[package]]
-name = "charset-normalizer"
+name = "cfgv"
 version = "3.4.0"
+description = "Validate configuration and produce human readable error messages."
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560"},
+]
+
+[[package]]
+name = "charset-normalizer"
+version = "3.4.1"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
-python-versions = ">=3.7.0"
-files = [
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+]
+
+[[package]]
+name = "chevron"
+version = "0.14.0"
+description = "Mustache templating language renderer"
+optional = false
+python-versions = "*"
+files = [
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 ]
 
 [[package]]
 name = "click"
-version = "8.1.7"
+version = "8.1.8"
 description = "Composable command line interface toolkit"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -475,37 +662,37 @@ typing-inspect = ">=0.4.0,<1"
 
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+    {file = "debugpy-1.8.12.tar.gz", hash = "sha256:646530b04f45c830ceae8e491ca1c9320a2d2f0efea3141487c82130aba70dce"},
 ]
 
 [[package]]
@@ -519,22 +706,51 @@ files = [
     {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
 ]
 
+[[package]]
+name = "deepdiff"
+version = "8.1.1"
+description = "Deep Difference and Search of any Python object/data. Recreate objects by adding adding deltas to each other."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "deepdiff-8.1.1-py3-none-any.whl", hash = "sha256:b0231fa3afb0f7184e82535f2b4a36636442ed21e94a0cf3aaa7982157e7ebca"},
+    {file = "deepdiff-8.1.1.tar.gz", hash = "sha256:dd7bc7d5c8b51b5b90f01b0e2fe23c801fd8b4c6a7ee7e31c5a3c3663fcc7ceb"},
+]
+
+[package.dependencies]
+orderly-set = ">=5.2.3,<6"
+
+[package.extras]
+cli = ["click (==8.1.7)", "pyyaml (==6.0.2)"]
+optimize = ["orjson"]
+
+[[package]]
+name = "defusedxml"
+version = "0.7.1"
+description = "XML bomb protection for Python stdlib modules"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+files = [
+    {file = "defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61"},
+    {file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"},
+]
+
 [[package]]
 name = "deprecated"
-version = "1.2.15"
+version = "1.2.18"
 description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
 files = [
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+    {file = "Deprecated-1.2.18-py2.py3-none-any.whl", hash = "sha256:bd5011788200372a32418f888e326a09ff80d0214bd961147cfed01b5c018eec"},
+    {file = "deprecated-1.2.18.tar.gz", hash = "sha256:422b6f6d859da6f2ef57857761bfb392480502a64c3028ca9bbe86085d72115d"},
 ]
 
 [package.dependencies]
 wrapt = ">=1.10,<2"
 
 [package.extras]
-dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "jinja2 (>=3.0.3,<3.1.0)", "setuptools", "sphinx (<2)", "tox"]
+dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "setuptools", "tox"]
 
 [[package]]
 name = "dirtyjson"
@@ -547,15 +763,26 @@ files = [
     {file = "dirtyjson-1.0.8.tar.gz", hash = "sha256:90ca4a18f3ff30ce849d100dcf4a003953c79d3a2348ef056f1d9c22231a25fd"},
 ]
 
+[[package]]
+name = "distlib"
+version = "0.3.9"
+description = "Distribution utilities"
+optional = false
+python-versions = "*"
+files = [
+    {file = "distlib-0.3.9-py2.py3-none-any.whl", hash = "sha256:47f8c22fd27c27e25a65601af709b38e4f0a45ea4fc2e710f65755fa8caaaf87"},
+    {file = "distlib-0.3.9.tar.gz", hash = "sha256:a60f20dea646b8a33f3e7772f74dc0b2d0772d2837ee1342a00645c81edf9403"},
+]
+
 [[package]]
 name = "eval-type-backport"
-version = "0.2.0"
+version = "0.2.2"
 description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "eval_type_backport-0.2.2-py3-none-any.whl", hash = "sha256:cb6ad7c393517f476f96d456d0412ea80f0a8cf96f6892834cd9340149111b0a"},
+    {file = "eval_type_backport-0.2.2.tar.gz", hash = "sha256:f0576b4cf01ebb5bd358d02314d31846af5e07678387486e2c798af0e7d849c1"},
 ]
 
 [package.extras]
@@ -577,18 +804,48 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "executing"
-version = "2.1.0"
+version = "2.2.0"
 description = "Get the currently executing AST node of a frame, and other information"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "executing-2.1.0-py2.py3-none-any.whl", hash = "sha256:8d63781349375b5ebccc3142f4b30350c0cd9c79f921cde38be2be4637e98eaf"},
-    {file = "executing-2.1.0.tar.gz", hash = "sha256:8ea27ddd260da8150fa5a708269c4a10e76161e2496ec3e587da9e3c0fe4b9ab"},
+    {file = "executing-2.2.0-py2.py3-none-any.whl", hash = "sha256:11387150cad388d62750327a53d3339fad4888b39a6fe233c3afbb54ecffd3aa"},
+    {file = "executing-2.2.0.tar.gz", hash = "sha256:5d108c028108fe2551d1a7b2e8b713341e2cb4fc0aa7dcf966fa4327a5226755"},
 ]
 
 [package.extras]
 tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"]
 
+[[package]]
+name = "fastjsonschema"
+version = "2.21.1"
+description = "Fastest Python implementation of JSON schema"
+optional = false
+python-versions = "*"
+files = [
+    {file = "fastjsonschema-2.21.1-py3-none-any.whl", hash = "sha256:c9e5b7e908310918cf494a434eeb31384dd84a98b57a30bcb1f535015b554667"},
+    {file = "fastjsonschema-2.21.1.tar.gz", hash = "sha256:794d4f0a58f848961ba16af7b9c85a3e88cd360df008c59aac6fc5ae9323b5d4"},
+]
+
+[package.extras]
+devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"]
+
+[[package]]
+name = "filelock"
+version = "3.17.0"
+description = "A platform independent file lock."
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "filelock-3.17.0-py3-none-any.whl", hash = "sha256:533dc2f7ba78dc2f0f531fc6c4940addf7b70a481e269a5a3b93be94ffbe8338"},
+    {file = "filelock-3.17.0.tar.gz", hash = "sha256:ee4e77401ef576ebb38cd7f13b9b28893194acc20a8e68e18730ba9c0e54660e"},
+]
+
+[package.extras]
+docs = ["furo (>=2024.8.6)", "sphinx (>=8.1.3)", "sphinx-autodoc-typehints (>=3)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.6.10)", "diff-cover (>=9.2.1)", "pytest (>=8.3.4)", "pytest-asyncio (>=0.25.2)", "pytest-cov (>=6)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.28.1)"]
+typing = ["typing-extensions (>=4.12.2)"]
+
 [[package]]
 name = "filetype"
 version = "1.2.0"
@@ -600,6 +857,17 @@ files = [
     {file = "filetype-1.2.0.tar.gz", hash = "sha256:66b56cd6474bf41d8c54660347d37afcc3f7d1970648de365c102ef77548aadb"},
 ]
 
+[[package]]
+name = "fqdn"
+version = "1.5.1"
+description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers"
+optional = false
+python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4"
+files = [
+    {file = "fqdn-1.5.1-py3-none-any.whl", hash = "sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014"},
+    {file = "fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f"},
+]
+
 [[package]]
 name = "frozenlist"
 version = "1.5.0"
@@ -703,13 +971,13 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2024.10.0"
+version = "2024.12.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2024.10.0-py3-none-any.whl", hash = "sha256:03b9a6785766a4de40368b88906366755e2819e758b83705c88cd7cb5fe81871"},
-    {file = "fsspec-2024.10.0.tar.gz", hash = "sha256:eda2d8a4116d4f2429db8550f2457da57279247dd930bb12f821b58391359493"},
+    {file = "fsspec-2024.12.0-py3-none-any.whl", hash = "sha256:b520aed47ad9804237ff878b504267a3b0b441e97508bd6d2d8774e3db85cee2"},
+    {file = "fsspec-2024.12.0.tar.gz", hash = "sha256:670700c977ed2fb51e0d9f9253177ed20cbde4a3e5c0283cc5385b5870c8533f"},
 ]
 
 [package.extras]
@@ -860,13 +1128,13 @@ trio = ["trio (>=0.22.0,<1.0)"]
 
 [[package]]
 name = "httpx"
-version = "0.28.0"
+version = "0.28.1"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "httpx-0.28.0-py3-none-any.whl", hash = "sha256:dc0b419a0cfeb6e8b34e85167c0da2671206f5095f1baa9663d23bcfd6b535fc"},
-    {file = "httpx-0.28.0.tar.gz", hash = "sha256:0858d3bab51ba7e386637f22a61d8ccddaeec5f3fe4209da3a6168dbb91573e0"},
+    {file = "httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad"},
+    {file = "httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc"},
 ]
 
 [package.dependencies]
@@ -882,6 +1150,20 @@ http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "identify"
+version = "2.6.6"
+description = "File identification library for Python"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "identify-2.6.6-py2.py3-none-any.whl", hash = "sha256:cbd1810bce79f8b671ecb20f53ee0ae8e86ae84b557de31d89709dc2a48ba881"},
+    {file = "identify-2.6.6.tar.gz", hash = "sha256:7bec12768ed44ea4761efb47806f0a41f86e7c0a5fdf5950d4648c90eca7e251"},
+]
+
+[package.extras]
+license = ["ukkonen"]
+
 [[package]]
 name = "idna"
 version = "3.10"
@@ -898,13 +1180,13 @@ all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2
 
 [[package]]
 name = "importlib-metadata"
-version = "8.5.0"
+version = "8.6.1"
 description = "Read metadata from Python packages"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "importlib_metadata-8.5.0-py3-none-any.whl", hash = "sha256:45e54197d28b7a7f1559e60b95e7c567032b602131fbd588f1497f47880aa68b"},
-    {file = "importlib_metadata-8.5.0.tar.gz", hash = "sha256:71522656f0abace1d072b9e5481a48f07c138e00f079c38c8f883823f9c26bd7"},
+    {file = "importlib_metadata-8.6.1-py3-none-any.whl", hash = "sha256:02a89390c1e15fdfdc0d7c6b25cb3e62650d0494005c97d6f148bf5b9787525e"},
+    {file = "importlib_metadata-8.6.1.tar.gz", hash = "sha256:310b41d755445d74569f993ccfc22838295d9fe005425094fad953d7f15c8580"},
 ]
 
 [package.dependencies]
@@ -916,7 +1198,7 @@ cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 enabler = ["pytest-enabler (>=2.2)"]
 perf = ["ipython"]
-test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
+test = ["flufl.flake8", "importlib_resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
 type = ["pytest-mypy"]
 
 [[package]]
@@ -1000,6 +1282,41 @@ qtconsole = ["qtconsole"]
 test = ["pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath"]
 test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath", "trio"]
 
+[[package]]
+name = "ipywidgets"
+version = "8.1.5"
+description = "Jupyter interactive widgets"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "ipywidgets-8.1.5-py3-none-any.whl", hash = "sha256:3290f526f87ae6e77655555baba4f36681c555b8bdbbff430b70e52c34c86245"},
+    {file = "ipywidgets-8.1.5.tar.gz", hash = "sha256:870e43b1a35656a80c18c9503bbf2d16802db1cb487eec6fab27d683381dde17"},
+]
+
+[package.dependencies]
+comm = ">=0.1.3"
+ipython = ">=6.1.0"
+jupyterlab-widgets = ">=3.0.12,<3.1.0"
+traitlets = ">=4.3.1"
+widgetsnbextension = ">=4.0.12,<4.1.0"
+
+[package.extras]
+test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
+
+[[package]]
+name = "isoduration"
+version = "20.11.0"
+description = "Operations with ISO 8601 durations"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042"},
+    {file = "isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9"},
+]
+
+[package.dependencies]
+arrow = ">=0.15.0"
+
 [[package]]
 name = "jedi"
 version = "0.19.2"
@@ -1019,6 +1336,23 @@ docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alab
 qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
 testing = ["Django", "attrs", "colorama", "docopt", "pytest (<9.0.0)"]
 
+[[package]]
+name = "jinja2"
+version = "3.1.5"
+description = "A very fast and expressive template engine."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "jinja2-3.1.5-py3-none-any.whl", hash = "sha256:aba0f4dc9ed8013c424088f68a5c226f7d6097ed89b246d7749c2ec4175c6adb"},
+    {file = "jinja2-3.1.5.tar.gz", hash = "sha256:8fefff8dc3034e27bb80d67c671eb8a9bc424c0ef4c0826edbff304cceff43bb"},
+]
+
+[package.dependencies]
+MarkupSafe = ">=2.0"
+
+[package.extras]
+i18n = ["Babel (>=2.7)"]
+
 [[package]]
 name = "joblib"
 version = "1.4.2"
@@ -1030,6 +1364,93 @@ files = [
     {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
 ]
 
+[[package]]
+name = "json5"
+version = "0.10.0"
+description = "A Python implementation of the JSON5 data format."
+optional = false
+python-versions = ">=3.8.0"
+files = [
+    {file = "json5-0.10.0-py3-none-any.whl", hash = "sha256:19b23410220a7271e8377f81ba8aacba2fdd56947fbb137ee5977cbe1f5e8dfa"},
+    {file = "json5-0.10.0.tar.gz", hash = "sha256:e66941c8f0a02026943c52c2eb34ebeb2a6f819a0be05920a6f5243cd30fd559"},
+]
+
+[package.extras]
+dev = ["build (==1.2.2.post1)", "coverage (==7.5.3)", "mypy (==1.13.0)", "pip (==24.3.1)", "pylint (==3.2.3)", "ruff (==0.7.3)", "twine (==5.1.1)", "uv (==0.5.1)"]
+
+[[package]]
+name = "jsonpointer"
+version = "3.0.0"
+description = "Identify specific nodes in a JSON document (RFC 6901)"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"},
+    {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
+]
+
+[[package]]
+name = "jsonschema"
+version = "4.23.0"
+description = "An implementation of JSON Schema validation for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jsonschema-4.23.0-py3-none-any.whl", hash = "sha256:fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566"},
+    {file = "jsonschema-4.23.0.tar.gz", hash = "sha256:d71497fef26351a33265337fa77ffeb82423f3ea21283cd9467bb03999266bc4"},
+]
+
+[package.dependencies]
+attrs = ">=22.2.0"
+fqdn = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+idna = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+isoduration = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+jsonpointer = {version = ">1.13", optional = true, markers = "extra == \"format-nongpl\""}
+jsonschema-specifications = ">=2023.03.6"
+referencing = ">=0.28.4"
+rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""}
+rpds-py = ">=0.7.1"
+uri-template = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+webcolors = {version = ">=24.6.0", optional = true, markers = "extra == \"format-nongpl\""}
+
+[package.extras]
+format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=24.6.0)"]
+
+[[package]]
+name = "jsonschema-specifications"
+version = "2024.10.1"
+description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "jsonschema_specifications-2024.10.1-py3-none-any.whl", hash = "sha256:a09a0680616357d9a0ecf05c12ad234479f549239d0f5b55f3deea67475da9bf"},
+    {file = "jsonschema_specifications-2024.10.1.tar.gz", hash = "sha256:0f38b83639958ce1152d02a7f062902c41c8fd20d558b0c34344292d417ae272"},
+]
+
+[package.dependencies]
+referencing = ">=0.31.0"
+
+[[package]]
+name = "jupyter"
+version = "1.1.1"
+description = "Jupyter metapackage. Install all the Jupyter components in one go."
+optional = false
+python-versions = "*"
+files = [
+    {file = "jupyter-1.1.1-py2.py3-none-any.whl", hash = "sha256:7a59533c22af65439b24bbe60373a4e95af8f16ac65a6c00820ad378e3f7cc83"},
+    {file = "jupyter-1.1.1.tar.gz", hash = "sha256:d55467bceabdea49d7e3624af7e33d59c37fff53ed3a350e1ac957bed731de7a"},
+]
+
+[package.dependencies]
+ipykernel = "*"
+ipywidgets = "*"
+jupyter-console = "*"
+jupyterlab = "*"
+nbconvert = "*"
+notebook = "*"
+
 [[package]]
 name = "jupyter-client"
 version = "8.6.3"
@@ -1053,6 +1474,30 @@ traitlets = ">=5.3"
 docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
 test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest (<8.2.0)", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
 
+[[package]]
+name = "jupyter-console"
+version = "6.6.3"
+description = "Jupyter terminal console"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "jupyter_console-6.6.3-py3-none-any.whl", hash = "sha256:309d33409fcc92ffdad25f0bcdf9a4a9daa61b6f341177570fdac03de5352485"},
+    {file = "jupyter_console-6.6.3.tar.gz", hash = "sha256:566a4bf31c87adbfadf22cdf846e3069b59a71ed5da71d6ba4d8aaad14a53539"},
+]
+
+[package.dependencies]
+ipykernel = ">=6.14"
+ipython = "*"
+jupyter-client = ">=7.0.0"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+prompt-toolkit = ">=3.0.30"
+pygments = "*"
+pyzmq = ">=17"
+traitlets = ">=5.4"
+
+[package.extras]
+test = ["flaky", "pexpect", "pytest"]
+
 [[package]]
 name = "jupyter-core"
 version = "5.7.2"
@@ -1073,15 +1518,309 @@ traitlets = ">=5.3"
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
 test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"]
 
+[[package]]
+name = "jupyter-events"
+version = "0.11.0"
+description = "Jupyter Event System library"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "jupyter_events-0.11.0-py3-none-any.whl", hash = "sha256:36399b41ce1ca45fe8b8271067d6a140ffa54cec4028e95491c93b78a855cacf"},
+    {file = "jupyter_events-0.11.0.tar.gz", hash = "sha256:c0bc56a37aac29c1fbc3bcfbddb8c8c49533f9cf11f1c4e6adadba936574ab90"},
+]
+
+[package.dependencies]
+jsonschema = {version = ">=4.18.0", extras = ["format-nongpl"]}
+python-json-logger = ">=2.0.4"
+pyyaml = ">=5.3"
+referencing = "*"
+rfc3339-validator = "*"
+rfc3986-validator = ">=0.1.1"
+traitlets = ">=5.3"
+
+[package.extras]
+cli = ["click", "rich"]
+docs = ["jupyterlite-sphinx", "myst-parser", "pydata-sphinx-theme (>=0.16)", "sphinx (>=8)", "sphinxcontrib-spelling"]
+test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "pytest-console-scripts", "rich"]
+
+[[package]]
+name = "jupyter-lsp"
+version = "2.2.5"
+description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter-lsp-2.2.5.tar.gz", hash = "sha256:793147a05ad446f809fd53ef1cd19a9f5256fd0a2d6b7ce943a982cb4f545001"},
+    {file = "jupyter_lsp-2.2.5-py3-none-any.whl", hash = "sha256:45fbddbd505f3fbfb0b6cb2f1bc5e15e83ab7c79cd6e89416b248cb3c00c11da"},
+]
+
+[package.dependencies]
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+jupyter-server = ">=1.1.2"
+
+[[package]]
+name = "jupyter-server"
+version = "2.15.0"
+description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "jupyter_server-2.15.0-py3-none-any.whl", hash = "sha256:872d989becf83517012ee669f09604aa4a28097c0bd90b2f424310156c2cdae3"},
+    {file = "jupyter_server-2.15.0.tar.gz", hash = "sha256:9d446b8697b4f7337a1b7cdcac40778babdd93ba614b6d68ab1c0c918f1c4084"},
+]
+
+[package.dependencies]
+anyio = ">=3.1.0"
+argon2-cffi = ">=21.1"
+jinja2 = ">=3.0.3"
+jupyter-client = ">=7.4.4"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+jupyter-events = ">=0.11.0"
+jupyter-server-terminals = ">=0.4.4"
+nbconvert = ">=6.4.4"
+nbformat = ">=5.3.0"
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+
 [[package]]
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-version = "3.23.1"
+version = "3.26.0"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
 python-versions = ">=3.9"
 files = [
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 [package.dependencies]
@@ -1125,7 +1934,7 @@ packaging = ">=17.0"
 
 [package.extras]
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 tests = ["pytest", "simplejson"]
 
 [[package]]
@@ -1142,6 +1951,20 @@ files = [
 [package.dependencies]
 traitlets = "*"
 
+[[package]]
+name = "mistune"
+version = "3.1.1"
+description = "A sane and fast Markdown parser with useful plugins and renderers"
+optional = false
+python-versions = ">=3.8"
+files = [
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+[package.dependencies]
+typing-extensions = {version = "*", markers = "python_version < \"3.11\""}
+
 [[package]]
 name = "multidict"
 version = "6.1.0"
@@ -1244,19 +2067,158 @@ files = [
 ]
 
 [package.dependencies]
-typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.11\""}
+typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.11\""}
+
+[[package]]
+name = "mypy"
+version = "1.14.1"
+description = "Optional static typing for Python"
+optional = false
+python-versions = ">=3.8"
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+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
+typing_extensions = ">=4.6.0"
+
+[package.extras]
+dmypy = ["psutil (>=4.0)"]
+faster-cache = ["orjson"]
+install-types = ["pip"]
+mypyc = ["setuptools (>=50)"]
+reports = ["lxml"]
+
+[[package]]
+name = "mypy-extensions"
+version = "1.0.0"
+description = "Type system extensions for programs checked with the mypy type checker."
+optional = false
+python-versions = ">=3.5"
+files = [
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+]
+
+[[package]]
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+version = "0.10.2"
+description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
+optional = false
+python-versions = ">=3.9.0"
+files = [
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+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+nbformat = ">=5.1"
+traitlets = ">=5.4"
+
+[package.extras]
+dev = ["pre-commit"]
+docs = ["autodoc-traits", "flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "mock", "moto", "myst-parser", "nbconvert (>=7.1.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling", "testpath", "xmltodict"]
+test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.1.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
 
 [[package]]
-name = "mypy-extensions"
-version = "1.0.0"
-description = "Type system extensions for programs checked with the mypy type checker."
+name = "nbconvert"
+version = "7.16.6"
+description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.8"
 files = [
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+    {file = "nbconvert-7.16.6.tar.gz", hash = "sha256:576a7e37c6480da7b8465eefa66c17844243816ce1ccc372633c6b71c3c0f582"},
+]
+
+[package.dependencies]
+beautifulsoup4 = "*"
+bleach = {version = "!=5.0.0", extras = ["css"]}
+defusedxml = "*"
+importlib-metadata = {version = ">=3.6", markers = "python_version < \"3.10\""}
+jinja2 = ">=3.0"
+jupyter-core = ">=4.7"
+jupyterlab-pygments = "*"
+markupsafe = ">=2.0"
+mistune = ">=2.0.3,<4"
+nbclient = ">=0.5.0"
+nbformat = ">=5.7"
+packaging = "*"
+pandocfilters = ">=1.4.1"
+pygments = ">=2.4.1"
+traitlets = ">=5.1"
+
+[package.extras]
+all = ["flaky", "ipykernel", "ipython", "ipywidgets (>=7.5)", "myst-parser", "nbsphinx (>=0.2.12)", "playwright", "pydata-sphinx-theme", "pyqtwebengine (>=5.15)", "pytest (>=7)", "sphinx (==5.0.2)", "sphinxcontrib-spelling", "tornado (>=6.1)"]
+docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sphinx-theme", "sphinx (==5.0.2)", "sphinxcontrib-spelling"]
+qtpdf = ["pyqtwebengine (>=5.15)"]
+qtpng = ["pyqtwebengine (>=5.15)"]
+serve = ["tornado (>=6.1)"]
+test = ["flaky", "ipykernel", "ipywidgets (>=7.5)", "pytest (>=7)"]
+webpdf = ["playwright"]
+
+[[package]]
+name = "nbformat"
+version = "5.10.4"
+description = "The Jupyter Notebook format"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b"},
+    {file = "nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a"},
 ]
 
+[package.dependencies]
+fastjsonschema = ">=2.15"
+jsonschema = ">=2.6"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+traitlets = ">=5.1"
+
+[package.extras]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
+test = ["pep440", "pre-commit", "pytest", "testpath"]
+
 [[package]]
 name = "nest-asyncio"
 version = "1.6.0"
@@ -1311,6 +2273,57 @@ plot = ["matplotlib"]
 tgrep = ["pyparsing"]
 twitter = ["twython"]
 
+[[package]]
+name = "nodeenv"
+version = "1.9.1"
+description = "Node.js virtual environment builder"
+optional = false
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 fpx = ["olefile"]
 mic = ["olefile"]
-tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
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 xmp = ["defusedxml"]
 
@@ -1528,15 +2570,47 @@ files = [
 dev = ["pre-commit", "tox"]
 testing = ["pytest", "pytest-benchmark"]
 
+[[package]]
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+description = "A framework for managing and maintaining multi-language pre-commit hooks."
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+python-versions = ">=3.8"
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+
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+identify = ">=1.0.0"
+nodeenv = ">=0.11.1"
+pyyaml = ">=5.1"
+virtualenv = ">=20.10.0"
+
+[[package]]
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+description = "Python client for the Prometheus monitoring system."
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+python-versions = ">=3.8"
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+
+[package.extras]
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+
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.48"
+version = "3.0.50"
 description = "Library for building powerful interactive command lines in Python"
 optional = false
-python-versions = ">=3.7.0"
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 ]
 
 [package.dependencies]
@@ -1635,32 +2709,32 @@ files = [
 
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+version = "6.1.1"
 description = "Cross-platform lib for process and system monitoring in Python."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
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 [[package]]
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 [[package]]
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+    {file = "SQLAlchemy-2.0.37-cp39-cp39-win_amd64.whl", hash = "sha256:4b12885dc85a2ab2b7d00995bac6d967bffa8594123b02ed21e8eb2205a7584b"},
+    {file = "SQLAlchemy-2.0.37-py3-none-any.whl", hash = "sha256:a8998bf9f8658bd3839cbc44ddbe982955641863da0c1efe5b00c1ab4f5c16b1"},
+    {file = "sqlalchemy-2.0.37.tar.gz", hash = "sha256:12b28d99a9c14eaf4055810df1001557176716de0167b91026e648e65229bffb"},
+]
+
+[package.dependencies]
+greenlet = {version = "!=0.4.17", optional = true, markers = "python_version < \"3.14\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\") or extra == \"asyncio\""}
 typing-extensions = ">=4.6.0"
 
 [package.extras]
@@ -2371,19 +3791,40 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
 [[package]]
 name = "tenacity"
-version = "8.5.0"
+version = "9.0.0"
 description = "Retry code until it succeeds"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "tenacity-8.5.0-py3-none-any.whl", hash = "sha256:b594c2a5945830c267ce6b79a166228323ed52718f30302c1359836112346687"},
-    {file = "tenacity-8.5.0.tar.gz", hash = "sha256:8bc6c0c8a09b31e6cad13c47afbed1a567518250a9a171418582ed8d9c20ca78"},
+    {file = "tenacity-9.0.0-py3-none-any.whl", hash = "sha256:93de0c98785b27fcf659856aa9f54bfbd399e29969b0621bc7f762bd441b4539"},
+    {file = "tenacity-9.0.0.tar.gz", hash = "sha256:807f37ca97d62aa361264d497b0e31e92b8027044942bfa756160d908320d73b"},
 ]
 
 [package.extras]
 doc = ["reno", "sphinx"]
 test = ["pytest", "tornado (>=4.5)", "typeguard"]
 
+[[package]]
+name = "terminado"
+version = "0.18.1"
+description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "terminado-0.18.1.tar.gz", hash = "sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e"},
+]
+
+[package.dependencies]
+ptyprocess = {version = "*", markers = "os_name != \"nt\""}
+pywinpty = {version = ">=1.1.0", markers = "os_name == \"nt\""}
+tornado = ">=6.1.0"
+
+[package.extras]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
+test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"]
+typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
+
 [[package]]
 name = "tiktoken"
 version = "0.8.0"
@@ -2399,7 +3840,6 @@ files = [
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     {file = "tiktoken-0.8.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9fb0e352d1dbe15aba082883058b3cce9e48d33101bdaac1eccf66424feb5b47"},
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@@ -2431,6 +3871,24 @@ requests = ">=2.26.0"
 [package.extras]
 blobfile = ["blobfile (>=2)"]
 
+[[package]]
+name = "tinycss2"
+version = "1.4.0"
+description = "A tiny CSS parser"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "tinycss2-1.4.0-py3-none-any.whl", hash = "sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289"},
+    {file = "tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7"},
+]
+
+[package.dependencies]
+webencodings = ">=0.4"
+
+[package.extras]
+doc = ["sphinx", "sphinx_rtd_theme"]
+test = ["pytest", "ruff"]
+
 [[package]]
 name = "tomli"
 version = "2.2.1"
@@ -2528,6 +3986,17 @@ files = [
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
 test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
 
+[[package]]
+name = "types-python-dateutil"
+version = "2.9.0.20241206"
+description = "Typing stubs for python-dateutil"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "types_python_dateutil-2.9.0.20241206.tar.gz", hash = "sha256:18f493414c26ffba692a72369fea7a154c502646301ebfe3d56a04b3767284cb"},
+]
+
 [[package]]
 name = "typing-extensions"
 version = "4.12.2"
@@ -2554,15 +4023,29 @@ files = [
 mypy-extensions = ">=0.3.0"
 typing-extensions = ">=3.7.4"
 
+[[package]]
+name = "uri-template"
+version = "1.3.0"
+description = "RFC 6570 URI Template Processor"
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363"},
+]
+
+[package.extras]
+dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake8-commas", "flake8-comprehensions", "flake8-continuation", "flake8-datetimez", "flake8-docstrings", "flake8-import-order", "flake8-literal", "flake8-modern-annotations", "flake8-noqa", "flake8-pyproject", "flake8-requirements", "flake8-typechecking-import", "flake8-use-fstring", "mypy", "pep8-naming", "types-PyYAML"]
+
 [[package]]
 name = "urllib3"
-version = "2.2.3"
+version = "2.3.0"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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+    {file = "urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df"},
+    {file = "urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d"},
 ]
 
 [package.extras]
@@ -2571,6 +4054,26 @@ h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "virtualenv"
+version = "20.29.1"
+description = "Virtual Python Environment builder"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "virtualenv-20.29.1-py3-none-any.whl", hash = "sha256:4e4cb403c0b0da39e13b46b1b2476e505cb0046b25f242bee80f62bf990b2779"},
+    {file = "virtualenv-20.29.1.tar.gz", hash = "sha256:b8b8970138d32fb606192cb97f6cd4bb644fa486be9308fb9b63f81091b5dc35"},
+]
+
+[package.dependencies]
+distlib = ">=0.3.7,<1"
+filelock = ">=3.12.2,<4"
+platformdirs = ">=3.9.1,<5"
+
+[package.extras]
+docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2,!=7.3)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
+test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"]
+
 [[package]]
 name = "wcwidth"
 version = "0.2.13"
@@ -2582,78 +4085,141 @@ files = [
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 ]
 
+[[package]]
+name = "webcolors"
+version = "24.11.1"
+description = "A library for working with the color formats defined by HTML and CSS."
+optional = false
+python-versions = ">=3.9"
+files = [
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+]
+
+[[package]]
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+version = "0.5.1"
+description = "Character encoding aliases for legacy web content"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[[package]]
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+version = "1.8.0"
+description = "WebSocket client for Python with low level API options"
+optional = false
+python-versions = ">=3.8"
+files = [
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+optional = ["python-socks", "wsaccel"]
+test = ["websockets"]
+
+[[package]]
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+version = "4.0.13"
+description = "Jupyter interactive widgets for Jupyter Notebook"
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "wrapt-1.17.2-cp313-cp313t-win32.whl", hash = "sha256:13e6afb7fe71fe7485a4550a8844cc9ffbe263c0f1a1eea569bc7091d4898555"},
+    {file = "wrapt-1.17.2-cp313-cp313t-win_amd64.whl", hash = "sha256:eaf675418ed6b3b31c7a989fd007fa7c3be66ce14e5c3b27336383604c9da85c"},
+    {file = "wrapt-1.17.2-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:5c803c401ea1c1c18de70a06a6f79fcc9c5acfc79133e9869e730ad7f8ad8ef9"},
+    {file = "wrapt-1.17.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:f917c1180fdb8623c2b75a99192f4025e412597c50b2ac870f156de8fb101119"},
+    {file = "wrapt-1.17.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:ecc840861360ba9d176d413a5489b9a0aff6d6303d7e733e2c4623cfa26904a6"},
+    {file = "wrapt-1.17.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bb87745b2e6dc56361bfde481d5a378dc314b252a98d7dd19a651a3fa58f24a9"},
+    {file = "wrapt-1.17.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:58455b79ec2661c3600e65c0a716955adc2410f7383755d537584b0de41b1d8a"},
+    {file = "wrapt-1.17.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b4e42a40a5e164cbfdb7b386c966a588b1047558a990981ace551ed7e12ca9c2"},
+    {file = "wrapt-1.17.2-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:91bd7d1773e64019f9288b7a5101f3ae50d3d8e6b1de7edee9c2ccc1d32f0c0a"},
+    {file = "wrapt-1.17.2-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:bb90fb8bda722a1b9d48ac1e6c38f923ea757b3baf8ebd0c82e09c5c1a0e7a04"},
+    {file = "wrapt-1.17.2-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:08e7ce672e35efa54c5024936e559469436f8b8096253404faeb54d2a878416f"},
+    {file = "wrapt-1.17.2-cp38-cp38-win32.whl", hash = "sha256:410a92fefd2e0e10d26210e1dfb4a876ddaf8439ef60d6434f21ef8d87efc5b7"},
+    {file = "wrapt-1.17.2-cp38-cp38-win_amd64.whl", hash = "sha256:95c658736ec15602da0ed73f312d410117723914a5c91a14ee4cdd72f1d790b3"},
+    {file = "wrapt-1.17.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:99039fa9e6306880572915728d7f6c24a86ec57b0a83f6b2491e1d8ab0235b9a"},
+    {file = "wrapt-1.17.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:2696993ee1eebd20b8e4ee4356483c4cb696066ddc24bd70bcbb80fa56ff9061"},
+    {file = "wrapt-1.17.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:612dff5db80beef9e649c6d803a8d50c409082f1fedc9dbcdfde2983b2025b82"},
+    {file = "wrapt-1.17.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:62c2caa1585c82b3f7a7ab56afef7b3602021d6da34fbc1cf234ff139fed3cd9"},
+    {file = "wrapt-1.17.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c958bcfd59bacc2d0249dcfe575e71da54f9dcf4a8bdf89c4cb9a68a1170d73f"},
+    {file = "wrapt-1.17.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc78a84e2dfbc27afe4b2bd7c80c8db9bca75cc5b85df52bfe634596a1da846b"},
+    {file = "wrapt-1.17.2-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:ba0f0eb61ef00ea10e00eb53a9129501f52385c44853dbd6c4ad3f403603083f"},
+    {file = "wrapt-1.17.2-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:1e1fe0e6ab7775fd842bc39e86f6dcfc4507ab0ffe206093e76d61cde37225c8"},
+    {file = "wrapt-1.17.2-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:c86563182421896d73858e08e1db93afdd2b947a70064b813d515d66549e15f9"},
+    {file = "wrapt-1.17.2-cp39-cp39-win32.whl", hash = "sha256:f393cda562f79828f38a819f4788641ac7c4085f30f1ce1a68672baa686482bb"},
+    {file = "wrapt-1.17.2-cp39-cp39-win_amd64.whl", hash = "sha256:36ccae62f64235cf8ddb682073a60519426fdd4725524ae38874adf72b5f2aeb"},
+    {file = "wrapt-1.17.2-py3-none-any.whl", hash = "sha256:b18f2d1533a71f069c7f82d524a52599053d4c7166e9dd374ae2136b7f40f7c8"},
+    {file = "wrapt-1.17.2.tar.gz", hash = "sha256:41388e9d4d1522446fe79d3213196bd9e3b301a336965b9e27ca2788ebd122f3"},
 ]
 
 [[package]]
@@ -2774,4 +4340,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "207c79ab108e95d56f58b860b5f69ab2fc2f47473111d880d96f255e39c079b1"
+content-hash = "3230a58d7e93e7223172920e604ba610e2d14b916fe43ab620f5a75839aeb149"
diff --git a/pyproject.toml b/pyproject.toml
index cf470da..87919f1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -2,25 +2,38 @@
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
 
+[tool.mypy]
+files = ["llama_cloud_services"]
+python_version = "3.10"
+
 [tool.poetry]
-name = "llama-parse"
-version = "0.5.20"
-description = "Parse files into RAG-Optimized formats."
-authors = ["Logan Markewich <logan@llamaindex.ai>"]
+name = "llama-cloud-services"
+version = "0.1.0"
+description = "Tailored SDK clients for LlamaCloud services."
+authors = ["Logan Markewich <logan@runllama.ai>"]
 license = "MIT"
 readme = "README.md"
-packages = [{include = "llama_parse"}]
+packages = [{include = "llama_cloud_services"}]
 
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
 llama-index-core = ">=0.11.0"
+llama-cloud = "^0.1.11"
 pydantic = "!=2.10"
 click = "^8.1.7"
+python-dotenv = "^1.0.1"
+eval-type-backport = {python = "<3.10", version = "^0.2.0"}
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.0.0"
 pytest-asyncio = "*"
 ipykernel = "^6.29.0"
+pre-commit = "3.2.0"
+autoevals = "^0.0.114"
+deepdiff = "^8.1.1"
+ipython = "^8.12.3"
+jupyter = "^1.1.1"
+mypy = "^1.14.1"
 
 [tool.poetry.scripts]
-llama-parse = "llama_parse.cli.main:parse"
+llama-parse = "llama_cloud_services.parse.cli.main:parse"
diff --git a/report.md b/report.md
new file mode 100644
index 0000000..efb8947
--- /dev/null
+++ b/report.md
@@ -0,0 +1,101 @@
+# 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.
diff --git a/tests/parse/__init__.py b/tests/parse/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/test_reader.py b/tests/parse/test_llama_parse.py
similarity index 80%
rename from tests/test_reader.py
rename to tests/parse/test_llama_parse.py
index 3a07b05..45947b8 100644
--- a/tests/test_reader.py
+++ b/tests/parse/test_llama_parse.py
@@ -4,7 +4,7 @@
 from fsspec.implementations.local import LocalFileSystem
 from httpx import AsyncClient
 
-from llama_parse import LlamaParse
+from llama_cloud_services.parse import LlamaParse
 
 
 @pytest.mark.skipif(
@@ -14,9 +14,7 @@
 def test_simple_page_text() -> None:
     parser = LlamaParse(result_type="text")
 
-    filepath = os.path.join(
-        os.path.dirname(__file__), "test_files/attention_is_all_you_need.pdf"
-    )
+    filepath = "tests/test_files/attention_is_all_you_need.pdf"
     result = parser.load_data(filepath)
     assert len(result) == 1
     assert len(result[0].text) > 0
@@ -30,9 +28,7 @@ def markdown_parser() -> LlamaParse:
 
 
 def test_simple_page_markdown(markdown_parser: LlamaParse) -> None:
-    filepath = os.path.join(
-        os.path.dirname(__file__), "test_files/attention_is_all_you_need.pdf"
-    )
+    filepath = "tests/test_files/attention_is_all_you_need.pdf"
     result = markdown_parser.load_data(filepath)
     assert len(result) == 1
     assert len(result[0].text) > 0
@@ -41,9 +37,7 @@ def test_simple_page_markdown(markdown_parser: LlamaParse) -> None:
 def test_simple_page_markdown_bytes(markdown_parser: LlamaParse) -> None:
     markdown_parser = LlamaParse(result_type="markdown", ignore_errors=False)
 
-    filepath = os.path.join(
-        os.path.dirname(__file__), "test_files/attention_is_all_you_need.pdf"
-    )
+    filepath = "tests/test_files/attention_is_all_you_need.pdf"
     with open(filepath, "rb") as f:
         file_bytes = f.read()
     # client must provide extra_info with file_name
@@ -59,9 +53,7 @@ def test_simple_page_markdown_bytes(markdown_parser: LlamaParse) -> None:
 def test_simple_page_markdown_buffer(markdown_parser: LlamaParse) -> None:
     markdown_parser = LlamaParse(result_type="markdown", ignore_errors=False)
 
-    filepath = os.path.join(
-        os.path.dirname(__file__), "test_files/attention_is_all_you_need.pdf"
-    )
+    filepath = "tests/test_files/attention_is_all_you_need.pdf"
     with open(filepath, "rb") as f:
         # client must provide extra_info with file_name
         with pytest.raises(ValueError):
@@ -81,9 +73,7 @@ def test_simple_page_markdown_buffer(markdown_parser: LlamaParse) -> None:
 async def test_simple_page_with_custom_fs() -> None:
     parser = LlamaParse(result_type="markdown")
     fs = LocalFileSystem()
-    filepath = os.path.join(
-        os.path.dirname(__file__), "test_files/attention_is_all_you_need.pdf"
-    )
+    filepath = "tests/test_files/attention_is_all_you_need.pdf"
     result = await parser.aload_data(filepath, fs=fs)
     assert len(result) == 1
 
@@ -96,9 +86,7 @@ async def test_simple_page_with_custom_fs() -> None:
 async def test_simple_page_progress_workers() -> None:
     parser = LlamaParse(result_type="markdown", show_progress=True, verbose=True)
 
-    filepath = os.path.join(
-        os.path.dirname(__file__), "test_files/attention_is_all_you_need.pdf"
-    )
+    filepath = "tests/test_files/attention_is_all_you_need.pdf"
     result = await parser.aload_data([filepath, filepath])
     assert len(result) == 2
     assert len(result[0].text) > 0
@@ -107,9 +95,7 @@ async def test_simple_page_progress_workers() -> None:
         result_type="markdown", show_progress=True, num_workers=2, verbose=True
     )
 
-    filepath = os.path.join(
-        os.path.dirname(__file__), "test_files/attention_is_all_you_need.pdf"
-    )
+    filepath = "tests/test_files/attention_is_all_you_need.pdf"
     result = await parser.aload_data([filepath, filepath])
     assert len(result) == 2
     assert len(result[0].text) > 0
@@ -123,9 +109,7 @@ async def test_simple_page_progress_workers() -> None:
 async def test_custom_client() -> None:
     custom_client = AsyncClient(verify=False, timeout=10)
     parser = LlamaParse(result_type="markdown", custom_client=custom_client)
-    filepath = os.path.join(
-        os.path.dirname(__file__), "test_files/attention_is_all_you_need.pdf"
-    )
+    filepath = "tests/test_files/attention_is_all_you_need.pdf"
     result = await parser.aload_data(filepath)
     assert len(result) == 1
     assert len(result[0].text) > 0
@@ -166,9 +150,7 @@ async def test_input_url_with_website_input() -> None:
 @pytest.mark.asyncio
 async def test_mixing_input_types() -> None:
     parser = LlamaParse(result_type="markdown")
-    filepath = os.path.join(
-        os.path.dirname(__file__), "test_files/attention_is_all_you_need.pdf"
-    )
+    filepath = "tests/test_files/attention_is_all_you_need.pdf"
     input_url = "https://cdn-blog.novoresume.com/articles/google-docs-resume-templates/basic-google-docs-resume.png"
     result = await parser.aload_data([filepath, input_url])
 
@@ -182,15 +164,13 @@ async def test_mixing_input_types() -> None:
 @pytest.mark.asyncio
 async def test_download_images() -> None:
     parser = LlamaParse(result_type="markdown", take_screenshot=True)
-    filepath = os.path.join(
-        os.path.dirname(__file__), "test_files/attention_is_all_you_need.pdf"
-    )
+    filepath = "tests/test_files/attention_is_all_you_need.pdf"
     json_result = await parser.aget_json([filepath])
 
     assert len(json_result) == 1
     assert len(json_result[0]["pages"][0]["images"]) > 0
 
-    download_path = os.path.join(os.path.dirname(__file__), "test_files/images")
+    download_path = "tests/test_files/images"
     shutil.rmtree(download_path, ignore_errors=True)
 
     await parser.aget_images(json_result, download_path)
diff --git a/tests/report/__init__.py b/tests/report/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/report/test_llama_report.py b/tests/report/test_llama_report.py
new file mode 100644
index 0000000..9d85d9d
--- /dev/null
+++ b/tests/report/test_llama_report.py
@@ -0,0 +1,119 @@
+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"), 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
+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
+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(
+        "Make the text 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
diff --git a/tests/test_files/images/2788d9f3-630d-4dbf-8663-d74408f25e62-page_1.jpg b/tests/test_files/images/67b428c6-9edb-4550-83d9-5e35165846ca-page_1.jpg
similarity index 100%
rename from tests/test_files/images/2788d9f3-630d-4dbf-8663-d74408f25e62-page_1.jpg
rename to tests/test_files/images/67b428c6-9edb-4550-83d9-5e35165846ca-page_1.jpg
diff --git a/tests/test_files/paper.md b/tests/test_files/paper.md
new file mode 100644
index 0000000..c14704f
--- /dev/null
+++ b/tests/test_files/paper.md
@@ -0,0 +1,269 @@
+# HEALTH-PARIKSHA: Assessing RAG Models for Health Chatbots in
+
+Varun Gumma♠ Anandhita Raghunath\*♢ Mohit Jain†♠ Sunayana Sitaram†♠
+
+♠Microsoft Corporation ♢University of Washington
+
+varun230999@gmail.com, sunayana.sitaram@microsoft.com
+
+# Abstract
+
+Assessing the capabilities and limitations of large language models (LLMs) has garnered significant interest, yet the evaluation of multiple models in real-world scenarios remains rare. Multilingual evaluation often relies on translated benchmarks, which typically do not capture linguistic and cultural nuances present in the source language. This study provides an extensive assessment of 24 LLMs on real world data collected from Indian patients interacting with a medical chatbot in Indian English and 4 other Indic languages. We employ a uniform Retrieval Augmented Generation framework to generate responses, which are evaluated using both automated techniques and human evaluators on four specific metrics relevant to our application. We find that models vary significantly in their performance and that instruction tuned Indic models do not always perform well on Indic language queries. Further, we empirically show that factual correctness is generally lower for responses to Indic queries compared to English queries. Finally, our qualitative work shows that code-mixed and culturally relevant queries in our dataset pose challenges to evaluated models.
+
+# 1 Introduction
+
+Large Language Models (LLMs) have demonstrated impressive proficiency across various domains. Nonetheless, their full spectrum of capabilities and limitations remains unclear, resulting in unpredictable performance on certain tasks. Additionally, there is now a wide selection of LLMs available. Therefore, evaluation has become crucial for comprehending the internal mechanisms of LLMs and for comparing them against each other.
+
+Despite the importance of evaluation, significant challenges still persist. Many widely-used benchmarks for assessing LLMs are contaminated (Ahuja et al., 2024; Oren et al., 2024; Xu et al., 2024), meaning that they often appear in LLM training data. Some of these benchmarks were originally created for conventional Natural Language Processing tasks and may not fully represent current practical applications of LLMs (Conneau et al., 2018; Pan et al., 2017). Recently, there has been growing interest in assessing LLMs within multilingual and multicultural contexts (Ahuja et al., 2023, 2024; Faisal et al., 2024; Watts et al., 2024; Chiu et al., 2024). Traditionally, these benchmarks were developed by translating English versions into various languages. However, due to the loss of linguistic and cultural context during translation, new benchmarks specific to different languages and cultures are now being created. However, such benchmarks are few in number, and several of the older ones are contaminated in training data (Ahuja et al., 2024; Oren et al., 2024). Thus, there is a need for new benchmarks that can test the abilities of models in real-world multilingual settings.
+
+LLMs are employed in various fields, including critical areas like healthcare. Jin et al. (2024) translate an English healthcare dataset into Spanish, Chinese, and Hindi, and demonstrate that performance declines in these languages compared to English. This highlights the necessity of examining LLMs more thoroughly in multilingual contexts for these important uses.
+
+In this study, we conduct the first comprehensive assessment of multilingual models within a real-world healthcare context. We evaluate responses from 24 multilingual and Indic models using 750 questions posed by users of a health chatbot in five languages (Indian English and four Indic languages). All the models being evaluated function within the same RAG framework, and their outputs are compared to doctor-verified ground truth responses. We evaluate LLM responses on four metrics curated for our application, including factual correctness, semantic similarity, coherence,
+
+- Work done during an internship at Microsoft
+
+† Equal Advising
+and conciseness and present leaderboards for each metric, as well as an overall leaderboard. We use human evaluation and automated methods (LLMs-as-a-judge) to compute these metrics by comparing LLM responses with ground-truth reference responses or assessing the responses in a reference-free manner.
+
+Our results suggest that models vary significantly in their performance, with some smaller models outperforming larger ones. Factual Correctness is generally lower for non-English queries compared to English queries. We observe that instruction-tuned Indic models do not always perform well on Indic language queries. Our dataset contains several instances of code-mixed and culturally-relevant queries, which models sometimes struggle to answer. The contributions of our work are as follows:
+
+- We evaluate 24 models (proprietary as well as open weights) in a healthcare setting using queries provided by patients using a medical chatbot. This guarantees that our dataset is not contaminated in the training data of any of the models we evaluate.
+- We curate a dataset of queries from multilingual users that spans multiple languages. The queries feature language typical of multilingual communities, such as code-switching, which is rarely found in translated datasets, making ours a more realistic dataset for model evaluation.
+- We evaluate several models in an identical RAG setting, making it possible to compare models in a fair manner. The RAG setting is a popular configuration that numerous models are being deployed in for real-world applications.
+- We establish relevant metrics for our application and determine an overall combined metric by consulting domain experts - doctors working on the medical chatbot project.
+- We perform assessments (with and without ground truth references) using LLM-as-a-judge and conduct human evaluations on a subset of the models and data to confirm the validity of the LLM assessment.
+
+# 2 Related Works
+
+# Healthcare Chatbots in India
+
+Within the Indian context, the literature has documented great diversity in health seeking and health communication behaviors based on gender (Das et al., 2018), varying educational status, poor functional literacy, cultural context (Islary, 2018), stigmas (Wang et al.) etc. This diversity in behavior may translate to people’s use of medical chatbots, which are increasingly reaching hundreds of Indian patients at the margins of the healthcare system (Mishra et al., 2023). These bots solicit personal health information directly from patients in their native Indic languages or in Indic English. For example, (Ramjee et al., 2024) find that their CataractBot deployed in Bangalore, India yields patient questions on topics such as surgery, preoperative preparation, diet, exercise, discharge, medication, pain management, etc. Mishra et al. (2023) find that Indian people share “deeply personal questions and concerns about sexual and reproductive health” with their chatbot SnehAI. Yadav et al. (2019) find that queries to chatbots are “embedded deeply into a communities myths and existing belief systems” while (Xiao et al., 2023) note that patients have difficulties finding health information at an appropriate level for them to comprehend. Therefore, LLMs powering medical chatbots in India and other Low and Middle Income Countries are challenged to respond lucidly to medical questions that are asked in ways that may be hyperlocal to patient context. Few works have documented how LLMs react to this linguistic diversity in the medical domain. Our work begins to bridge this gap.
+
+# Multilingual and RAG evaluation
+
+Several previous studies have conducted in-depth evaluation of Multilingual capabilities of LLMs by evaluating across standard tasks (Srivastava et al., 2022; Liang et al., 2023; Ahuja et al., 2023, 2024; Asia et al., 2024; Lai et al., 2023; Robinson et al., 2023), with a common finding that current LLMs only have a limited multilingual capacity. Other works (Watts et al., 2024; Leong et al., 2023) include evaluating LLMs on creative and generative tasks. Salemi and Zamani (2024) state that evaluating RAG models require a joint evaluating of the retrieval and generated output. Recent works such as Chen et al. (2024); Chirkova et al. (2024) benchmark LLMs as RAG models in bilingual and multilingual setups. Lastly, several tools and benchmarks have also been built for automatic evaluation of RAG.
+even in medical domains (Es et al., 2024; Tang and Yang, 2024; Xiong et al., 2024a,b), and we refer the readers to Yu et al. (2024) for such a comprehensive list and survey.
+
+# LLM-based Evaluators
+
+With the advent of large-scale instruction following capabilities in LLMs, automatic evaluations with the help of these models is being preferred (Kim et al., 2024a,b; Liu et al., 2024; Shen et al., 2023; Kocmi and Federmann, 2023). However, it has been shown that it is optimal to assess these evaluations in tandem with human annotations as LLMs can provide inflated scores (Hada et al., 2024b,a; Watts et al., 2024). Other works (Zheng et al., 2023; Watts et al., 2024) have employed GPT-4 alongside human evaluators to leaderboards to assess other LLMs. Ning et al. (2024) proposed an innovative approach using LLMs for peer review, where models evaluate each other’s outputs. However, a recent study by Doddapani et al. (2024) highlighted the limitations of LLM-based evaluators, revealing their inability to reliably detect subtle drops in input quality during evaluations, raising concerns about their precision and dependability for fine-grained assessments. In this work, we use LLM-based evaluators both with and without ground-truth references and also use human evaluation to validate LLM-based evaluation.
+
+# 3 Methodology
+
+In this study, we leveraged a dataset collected from a deployed medical chatbot. Here, we provide an overview of the question dataset, the knowledge base employed for answering those questions, the process for generating responses, and the evaluation framework.
+
+# 3.1 Data
+
+The real-world test data was collected by our collaborators as part of an ongoing research effort that designed and deployed a medical chatbot, hereafter referred to as HEALTHBOT, to patients scheduled for cataract surgery at a large hospital in urban India. An Ethics approval was obtained from our institution prior to conducting this work, and once enrolled in the study and consent was obtained, both the patient and their accompanying family member or attendant were instructed on how to use HEALTHBOT on WhatsApp. Through this instructional phase, they were informed that questions could be asked by voice or by text, in one of 5 languages - English, Hindi, Kannada, Tamil, Telugu. The workflow of chatting with HEALTHBOT was as follows: Patients sent questions through the WhatsApp interface to HEALTHBOT. Their questions were transcribed automatically (using a speech recognition system) and translated (using an off-the-shelf translator) into English if needed, after which GPT-4 was used to produce an initial response by performing RAG on the documents in the knowledge base (KB, see below). This initial response was passed to doctors who reviewed, validated, and if needed, edited the answer. The doctor approved answer is henceforth referred to as the ground truth (GT) response associated with the patient query.
+
+Our evaluation dataset was curated from this data by including all questions sent to HEALTHBOT along with their associated GT response. Exclusion criteria removed exact duplicate questions, those with personally identifying information, and those not relevant to health. Additionally, for this work, we only consider questions to which the GPT-4 answer was directly approved by the expert as the “correct and complete answer" without additional editing on the doctors’ part. The final dataset contained 749 question and GT answer pairs that were sent in to HEALTHBOT between December 2023 to June 2024. In the pool, 666 questions were in English, 19 in Hindi, 27 in Tamil, 14 in Telugu, and 23 in Kannada. Note that, queries written in the script of a specific language were classified as belonging to that language. For code-mixed and Romanized queries, we determined whether they were English or non-English based on the matrix language of the query.
+
+The evaluation dataset consists of queries that (1) have misspelled English words, (2) are code-mixed, (3) represent non-native English, (4) are relevant to the patient’s cultural context and (5) are specific to the patient’s condition. We provide some examples of each of these categories.
+
+Examples of misspelled queries include questions such as “How long should saving not be done after surgery?” where the patient intended to ask about shaving, and “Sarjere is don mam?” which the attendant used to inquire about the patient’s discharge status. Instances of code mixing can be seen in phrases like “Agar operation ke baad pain ho raha hai, to kya karna hai?” meaning “If there is pain after the surgery, what should I do?” in Hindi-English (Hinglish). Other examples include “Can I eat before the kanna operation?” where
+“kanna” means eye in Tamil, and “kanna operation” is a well understood, common way of referring to cataract surgery, and “In how many days can a patient take Karwat?” where “Karwat” means turning over in sleep in Hindi.
+
+# 3.3 Models
+
+Indian English was used in a majority of the English queries, making the phrasing of questions different from what they would be with native English speech. Examples are as follows - “Because I have diabetes sugar problem I am worried much”, “Why to eat light meal only? What comes under light meal?” and “Is the patient should be in dark room after surgery?” Taking a shower was commonly referred to as “taking a bath”, and eye glasses were commonly referred to as “goggles”, “spex” or “spectacles”.
+
+Culturally-relevant questions were also many in number, for example questions about specific foods were asked like “Can he take chapati, Puri etc on the day of surgery?” and “Can I eat non veg after surgery?” (“non-veg” is a term used in Indian English to denote eating meat). Questions about yoga were asked, like “How long after the surgery should the Valsalva maneuver be avoided?” and “Are there any specific yoga poses I can do?”. The notion of a patient’s native place or village was brought up in queries such as “If a person gets operated here and then goes to his native place and if some problem occurs what shall he do?” or “Can she travel by car with AC for 100 kms?”.
+
+# 3.4 Response Generation
+
+We chose 24 models including proprietary multilingual models, as well as Open-weights multilingual and Indic language models for our evaluation. A full list of models can be found in Table 1.
+
+We use the standard Retrieval-Augmented-Generation (RAG) strategy to elicit responses from all the models. Each model is asked to respond to the given query by extracting the appropriate pieces of text from the knowledge-base chunks. During prompting, we segregate the chunks into RAWCHUNKS and KBUPDATECHUNKS symbolizing the data from the standard sources, and the KB updates. Then model is explicitly instructed to prioritize the information from the most latest sources, i.e. the KBUPDATECHUNKS (if they are available). The exact prompt used for generation is provided in Appendix X. Note that each model gets the same RAWCHUNKS and KBUPDATECHUNKS, which are also the same that are given to the GPT-4 model in the HEALTHBOT, based on which the GT responses are verified.
+
+# 3.5 Response Evaluation
+
+We used both human and automated evaluation to evaluate the performance of models in the setup described above. GPT-4o3 was employed as an LLM evaluator. We prompted the model separately to judge each metric, as Hada et al. (2024b,a) show that individual calls reduce interaction and influence among and their evaluations.
+
+# 3.5.1 LLM Evaluation
+
+In consultation with domain experts working on the HEALTHBOT, we curated metrics that are relevant for our application. We limit ourselves to 3 classes (Good - 2, Medium - 1, Bad - 0) for each metric, as a larger number of classes could hurt interpretability and lower LLM-evaluator performance. The prompt used for each of our metrics are available in Appendix A.2, and a general overview is provided below.
+
+1 https://www.trychroma.com
+
+2 https://platform.openai.com/docs/guides/embeddings/embedding-models
+
+3 https://openai.com/index/hello-gpt-4o/
+
+# Models
+
+- GPT-4
+- GPT-4o
+- microsoft/Phi-3.5-MoE-instruct
+- CohereForAI/c4ai-command-r-plus-08-2024
+- Qwen/Qwen2.5-72B-Instruct
+- CohereForAI/aya-23-35B
+- mistralai/Mistral-Large-Instruct-2407
+- google/gemma-2-27b-it
+- meta-llama/Meta-Llama-3.1-70B-Instruct
+- GenVRadmin/llama38bGenZ_Vikas-Merged
+- GenVRadmin/AryaBhatta-GemmaOrca-Merged
+- GenVRadmin/AryaBhatta-GemmaUltra-Merged
+- GenVRadmin/AryaBhatta-GemmaGenZ-Vikas-Merged
+- Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0
+- ai4bharat/Airavata
+- Cognitive-Lab/LLama3-Gaja-Hindi-8B-v0.1
+- BhabhaAI/Gajendra-v0.1
+- manishiitg/open-aditi-hi-v4
+- abhinand/tamil-llama-7b-instruct-v0.2
+- abhinand/telugu-llama-7b-instruct-v0.1
+- Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct
+- Tensoic/Kan-Llama-7B-SFT-v0.5
+- Cognitive-Lab/Ambari-7B-Instruct-v0.2
+- GenVRadmin/Llamavaad
+
+# Languages Availability
+
+| All    | Proprietary  |
+| ------ | ------------ |
+| All    | Proprietary  |
+| All    | Open-weights |
+| All    | Open-weights |
+| All    | Open-weights |
+| All    | Open-weights |
+| All    | Open-weights |
+| All    | Open-weights |
+| All    | Indic        |
+| All    | Indic        |
+| All    | Indic        |
+| All    | Indic        |
+| All    | Indic        |
+| En, Hi | Indic        |
+| En, Hi | Indic        |
+| En, Hi | Indic        |
+| En, Hi | Indic        |
+| En, Ta | Indic        |
+| En, Te | Indic        |
+| En, Te | Indic        |
+| En, Ka | Indic        |
+| En, Ka | Indic        |
+| En, Hi | Indic        |
+
+Table 1: List of models tested. “En” for English, “Hi” for Hindi, “Ka” for Kannada, “Ta” for Tamil, “Te” for Telugu, and “All" refers to all the aforementioned languages. All Indic models are open-weights.
+
+# Metrics
+
+- FACTUAL CORRECTNESS (FC): As Doddapa-neni et al. (2024) had shown that LLM-based evaluators fail to identify subtle factual inaccuracies, we curate a separate metric to double-check facts like dates, numbers, procedure and medicine names.
+- SEMANTIC SIMILARITY (SS): Similarly, we formulate another metric to specifically analyse if both the prediction and the ground-truth response convey the same information semantically, especially when they are in different languages.
+- COHERENCE (COH): This metric evaluates if the model was able to stitch together appropriate pieces of information from the three data chunks provided to yield a coherent response.
+- CONCISENESS (CON): Since the knowledge base chunks extracted and provided to the model can be quite large, with important facts embedded at different positions, we build this metric to assess the ability of the model to extract and compress all these bits of information relevant to the query into a crisp response.
+
+# 3.5.2 Human Evaluation
+
+Following previous works (Hada et al., 2024b,a; Watts et al., 2024), we augment the LLM evaluation with human evaluation and draw correlations between the LLM evaluator and human evaluation for a subset of the models (PHI-3.5-MOE-INSTRUCT, MISTRAL-LARGE-INSTRUCT-2407, GPT-4O, META-LLAMA-3.1-70B-INSTRUCT, INDIC-GEMMA-7B-FINETUNED-SFT-NAVARASA-2.0). These models were selected based on results from early automated evaluations, covering a range of scores and representing models of interest.
+
+The human annotators were employed by
+KARYA, a data annotation company and were all native speakers of Indian languages that we evaluated. We selected a sample of 100 queries from English, and all the queries from Indic languages for annotation, yielding a total of 183 queries. Each instance was annotated by one annotator for SEMANTIC SIMILARITY between the model’s response and the GT response provided by the doctor. The annotations began with a briefing about the task and each of them was given a sample test task, and were provided some guidance based on their difficulties and mistakes. Finally, the annotators were asked to evaluate the model response based on the metric4, query, and ground-truth response on a scale of 0 to 2, similar to the LLM-evaluator.
+
+# 4 Results
+
+In this section, we present the outcomes of both the LLM and human evaluations. We begin by examining the average scores across all our metrics including the combined metric for English queries, followed by results for queries in other languages. Next, we examine the ranking of models based on the human and LLM-evaluator, details of which can be found in the Appendix A.1 and find it to be consistently higher than 0.7 on average across all languages and models. This shows the reliability of our LLM-based evaluation for SEMANTIC SIMILARITY which uses the GT response as a reference.
+
+# 4.1 LLM evaluator results
+
+We see from Table 2 that for English, the best performing models is the QWEN2.5-72B-INSTRUCT model across all metrics. Note that it is expected that GPT-4 performs well, as the ground truth responses are based on responses generated by GPT-4. The PHI-3.5-MOE-INSTRUCT model also performs well on all metrics, followed by MISTRAL-LARGE-INSTRUCT-2407 and OPEN-ADITI-HI-V4, which is the only Indic model that performs near the top even for English queries. Surprisingly, the META-LLAMA-3.1-70B-INSTRUCT model performs worse than expected on this task, frequently regurgitating the entire prompt that was provided. In general, all models get higher scores on conciseness and many models do well on coherence.
+
+# 4.2 Comparison of human and LLM evaluators
+
+We perform human evaluation on five models on the SEMANTIC SIMILARITY (SS) task and compare human and LLM evaluation by inspecting the ranking of the models in Appendix A.3. We find that for all languages except Telugu, we get identical rankings of all models. Additionally, we also measure the Percentage Agreement (PA) between the human and LLM-evaluator.
+
+| Model                                                                                                                             | English                                                                                                                           | English                                                                                                                           |     |
+| --------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------- | --- | --- |
+|                                                                                                                                   | gpt-4o                                                                                                                            | Indic-gemma-7b-finetuned-sft-Navarasa-2.0                                                                                         |
+| Mistral-Large-Instruct-2407                                                                                                       | 0.0                                                                                                                               | 0.1                                                                                                                               |
+| Meta-Llama-3.1-70B-Instruct                                                                                                       | 0.2                                                                                                                               | 0.3                                                                                                                               |
+| Phi-3.5-MoE-instruct                                                                                                              | 0.4                                                                                                                               | 0.5                                                                                                                               |
+| Figure 1: Percentage Agreement between human and LLM-evaluators for English. The red line indicates the average PA across models. | Figure 1: Percentage Agreement between human and LLM-evaluators for English. The red line indicates the average PA across models. | Figure 1: Percentage Agreement between human and LLM-evaluators for English. The red line indicates the average PA across models. |     |     |
+
+# 4.3 Qualitative Analysis
+
+One of the authors of the paper performed a qualitative analysis of responses from the evaluated LLMs on 100 selected patient questions. The questions were chosen to cover a range of medical topics and languages. Thematic analysis involved (1) initial familiarization with the queries and associated LLM responses, (2) theme identification where 5 themes were generated and (3) thematic coding where the generated themes were applied to the 100 question-answer pairs. We briefly summarize these results below.
+
+# Table 2: Metric-wise scores for English. The Proprietary, Open-Weights and Indic models are highlighted appropriately. All Indic models are open-weights.
+
+| Model                                     | AGG  | COH  | CON  | FC   | SS   |
+| ----------------------------------------- | ---- | ---- | ---- | ---- | ---- |
+| QWEN2.5-72B-INSTRUCT                      | 1.46 | 1.86 | 1.96 | 1.62 | 1.43 |
+| GPT-4                                     | 1.40 | 1.71 | 1.95 | 1.56 | 1.36 |
+| PHI-3.5-MOE-INSTRUCT                      | 1.29 | 1.65 | 1.93 | 1.43 | 1.22 |
+| MISTRAL-LARGE-INSTRUCT-2407               | 1.29 | 1.60 | 1.95 | 1.42 | 1.24 |
+| OPEN-ADITI-HI-V4                          | 1.27 | 1.69 | 1.85 | 1.37 | 1.22 |
+| LLAMAVAAD                                 | 1.16 | 1.34 | 0.97 | 1.36 | 1.20 |
+| ARYABHATTA-GEMMAGENZ-VIKAS-MERGED         | 1.12 | 1.48 | 1.65 | 1.22 | 1.07 |
+| KAN-LLAMA-7B-SFT-V0.5                     | 1.01 | 1.39 | 1.64 | 1.07 | 0.97 |
+| GEMMA-2-27B-IT                            | 1.00 | 1.28 | 1.88 | 1.07 | 0.91 |
+| ARYABHATTA-GEMMAORCA-MERGED               | 0.97 | 1.32 | 1.62 | 1.03 | 0.92 |
+| LLAMA3-GAJA-HINDI-8B-V0.1                 | 0.91 | 0.63 | 1.65 | 1.09 | 0.98 |
+| GPT-4O                                    | 0.91 | 1.08 | 1.78 | 0.98 | 0.87 |
+| AYA-23-35B                                | 0.91 | 1.09 | 1.65 | 1.00 | 0.83 |
+| GAJENDRA-V0.1                             | 0.88 | 1.21 | 1.38 | 0.93 | 0.85 |
+| C4AI-COMMAND-R-PLUS-08-2024               | 0.82 | 1.15 | 1.48 | 0.85 | 0.74 |
+| TAMIL-LLAMA-7BINSTRUCT-V0.2               | -    | 0.81 | 1.13 | 1.50 | 0.83 |
+| AIRAVATA                                  | 0.80 | 1.03 | 1.38 | 0.85 | 0.78 |
+| AMBARI-7B-INSTRUCTV0.2                    | -    | 0.73 | 0.86 | 1.11 | 0.76 |
+| META-LLAMA-3.1-70B-INSTRUCT               | 0.65 | 0.55 | 1.12 | 0.77 | 0.67 |
+| TELUGU-LLAMA2-7B-V0-INSTRUCT              | 0.51 | 0.60 | 1.12 | 0.53 | 0.53 |
+| LLAMA38BGENZ_VIKAS-MERGED                 | 0.51 | 0.52 | 1.09 | 0.55 | 0.53 |
+| INDIC-GEMMA-7B-FINETUNED-SFT-NAVARASA-2.0 | 0.35 | 0.32 | 0.53 | 0.40 | 0.39 |
+| ARYABHATTA-GEMMAULTRA-MERGED              | 0.32 | 0.38 | 1.19 | 0.31 | 0.27 |
+| TELUGU-LLAMA-7B-INSTRUCTV0.1              | -    | 0.04 | 0.00 | 0.58 | 0.03 |
+
+(1) misspelling of English words, (2) code-mixing, (3) non-native English, (4) relevance to cultural context and (5) specificity to the patient’s condition.
+
+For queries that involve misspellings (such as “saving” and “sarjere” mentioned in Section 3.1), many evaluated LLM were not able to come up with an appropriate response. For the query with the word “saving", responses varied from “The patient should not be saved for more than 15 days after the surgery” to “Saving should not be done after surgery” to “You should not strain to pass motion for 15 days after the surgery. If you are constipated, it is recommended to consult the doctor”. All of these responses deviate from the GPT-4 generated GT, which said “You can have a shave after the cataract surgery. However, you should avoid having a head bath or shampoo for 15 days post-surgery.”
+
+In cases of code mixing and Indian English, LLMs were more robust in their responses than to misspellings. The term “Kanna operation” was well understood by most models, and Hinglish queries were responded to in a manner that mirrored the GT answer. However, less common terms were not understood when mixed with English. The query with the word “Karwat” as mentioned in 3.1 received responses ranging from “you can start cooking after 1 week” to “I’m sorry, but I cannot provide an answer to your question. The information you are seeking is not relevant to cataract surgery or the guidelines for post-operative care” to “be careful when children get near you”. Most of the evaluated LLMs understood the use of “sugar” in reference to diabetes, as well as sentences following different syntax than would be common in native English.
+
+The responses for culturally-relevant questions varied greatly between evaluated LLMs. For example, to the question on appropriateness of chapati and puri on the day of surgery, some LLMs approved, saying “Yes, he can take chapati, Puri etc on the day of cataract surgery” while others were against this, saying “You should have a light meal before the surgery. Avoid heavy or oily foods like chapati and Puri on the day of your cataract surgery.”
+surgery. It’s best to stick to easily digestible foods. If you have any specific dietary concerns, please discuss them with your healthcare team. Questions relating to returning to a “native place” were met with refusals by around half of the evaluated LLMs.
+
+Questions that were specific to the patient’s condition were also responded to in a diverse manner by the evaluated LLMs. For example, the query “Can aztolet20 (atorvastatin and clopidogrel) tablet be taken post surgery” had the GT response “I do not know the answer to your question. If this needs to be answered by a doctor, please schedule a consultation” as there was no mention of this medication in the KB. However, some LLMs approved its use, responding “Yes, you can take the aztolet20 (atorvastatin and clopidogrel) tablet post cataract surgery. However, it is important to follow your doctor’s prescription and instructions” while others responded with mentions of medication that was referred to in the KB, “If you are referring to the IMOL tablet, it is usually taken when you experience pain. However, for other medications, please share the name so I can provide a more accurate answer. Always remember to follow your doctor’s prescription.” Around half refused to answer the question, mirroring the GT.
+
+# 5 Discussion
+
+In this study, we evaluated 24 models on healthcare-related queries in the RAG setting. Our findings revealed many insights which we share below:
+
+# Difference in model scores
+
+We find that the models that we evaluate vary widely in their scores. This indicates that not all models are suitable for use in the healthcare setting, and we find that some models perform worse than expected. For example, GPT-4O and META-LLAMA-3.1-70B-INSTRUCT perform worse than smaller models on this task.
+
+# English vs. Multilingual Queries
+
+Although the number of non-English queries is small, we find that some Indic models perform better on English queries than non-English queries. We also observe that the Factual Correctness score is lower for non-English queries than English queries on average, indicating that models find it difficult to answer non-English queries accurately. This may be due to the cultural and linguistic nuances present in our queries.
+
+# Multilingual vs. Indic models
+
+We evaluate several models that are specifically fine-tuned on Indic languages and on Indic data and observe that they do not always perform well on non-English queries. This could be because several instruction tuned models are tuned on synthetic instruction data which is usually a translation of English instruction data. A notable exception is the AYA-23-35B model, that contains manually created instruction tuning data for different languages and performs well for Hindi. Additionally, several multilingual instruction tuning datasets have short instructions, which may not be suitable for complex RAG settings, which typically have longer prompts and large chunks of data.
+
+# Human vs. LLM-based evaluation
+
+We conduct human evaluation on a subset of models and data points and observe strong alignment with the LLM evaluator overall, especially regarding the final ranking of the models. However, for certain models like MISTRAL-LARGE-INSTRUCT-2407 (for Telugu) and META-LLAMA-3.1-70B-INSTRUCT (for other languages), the agreement is low. It is important to note that we use LLM-evaluators both with and without references, and assess human agreement for SEMANTIC SIMILARITY which uses ground truth references. This suggests that LLM-evaluators should be used cautiously in a multilingual context, and we plan to broaden human evaluation to include more metrics in future work.
+
+# Evaluation in controlled settings with uncontaminated datasets
+
+We evaluate 24 models in an identical setting, leading to a fair comparison between models. Our dataset is curated based on questions from users of an application and is not contaminated in the training dataset of any of the models we evaluate, lending credibility to the results and insights we gather.
+
+# Locally-grounded, non-translated datasets
+
+Our dataset includes various instances of code-switching, Indian English colloquialisms, and culturally specific questions which cannot be obtained by translating datasets, particularly with automated translations. While models were able to handle code-switching to a certain extent, responses varied greatly to culturally-relevant questions. This underscores the importance of collecting datasets from target populations while building models or systems for real-world use.
+
+# 6 Limitations
+
+Our work is subject to several limitations.
+
+- Because our dataset is derived from actual users of a healthcare bot, we couldn’t regulate the ratio of English to non-English queries. Consequently, the volume of non-English queries in our dataset is significantly lower than that of English queries, meaning the results on non-English queries should not be considered definitive. Similarly, since the HEALTHBOT is available only in four Indian languages, we also could not evaluate on languages beyond these. The scope of our HEALTHBOT setting is currently confined to queries from patients at one hospital in India, resulting in less varied data. We intend to expand this study as HEALTHBOT extends its reach to other parts of the country.
+- While we evaluated numerous models in this work, some were excluded from this study for various reasons, such as ease of access. We aim to incorporate more models in future research.
+- Research has indicated that LLM-based evaluators tend to prefer their own responses. In our evaluations, we use GPT-4O, and there may be a bias leading to higher scores for the GPT-4O model and other models within the GPT family. Although not investigated in prior research, it is also conceivable that models fine-tuned with synthetic data generated by GPT-4O might receive elevated scores. We urge readers to keep these in mind while interpreting the scores. In future work, we plan to use multiple LLM-evaluators to obtain more robust results.
+- Finally, our human evaluation was limited to a subset of models and data, and a single metric due to time and budget constraints. In future work, we plan to incorporate more human evaluation, as well as qualitative analysis of the results.
+
+# 7 Ethical Considerations
+
+We use the framework by Bender and Friedman (2018) to discuss the ethical considerations for our work.
+
+# 8 Acknowledgements
+
+We thank Aditya Yadavalli, Vivek Seshadri, the Operations team and Annotators from KARYA for the streamlined annotation process. We also extend our gratitude to Bhuvan Sachdeva for helping us with the HEALTHBOT deployment, data collection and organization process.
diff --git a/tests/test_files/resume/receipt/noisebridge_receipt.pdf b/tests/test_files/resume/receipt/noisebridge_receipt.pdf
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HcmV?d00001

diff --git a/tests/test_files/resume/receipt/noisebridge_receipt.test.json b/tests/test_files/resume/receipt/noisebridge_receipt.test.json
new file mode 100644
index 0000000..ea27257
--- /dev/null
+++ b/tests/test_files/resume/receipt/noisebridge_receipt.test.json
@@ -0,0 +1,37 @@
+{
+  "receiptNumber": "27215058",
+  "invoiceNumber": "87B37C90152",
+  "datePaid": "2024-07-19",
+  "paymentMethod": {
+    "type": "visa",
+    "lastFourDigits": "7267"
+  },
+  "merchant": {
+    "name": "Noisebridge",
+    "address": {
+      "street": "272 Capp St",
+      "city": "San Francisco",
+      "state": "California",
+      "postalCode": "94110",
+      "country": "United States"
+    },
+    "phone": "1 6507017829",
+    "email": "treasurer+stripe@noisebridge.net"
+  },
+  "billTo": "noisebridge@seldo.com",
+  "items": [
+    {
+      "description": "$10 / month",
+      "quantity": 1,
+      "unitPrice": 10.0,
+      "amount": 10.0,
+      "period": {
+        "start": "2024-07-19",
+        "end": "2024-08-19"
+      }
+    }
+  ],
+  "subtotal": 10.0,
+  "total": 10.0,
+  "amountPaid": 10.0
+}
diff --git a/tests/test_files/resume/receipt/schema.json b/tests/test_files/resume/receipt/schema.json
new file mode 100644
index 0000000..d26c2de
--- /dev/null
+++ b/tests/test_files/resume/receipt/schema.json
@@ -0,0 +1,124 @@
+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "type": "object",
+  "required": ["receiptNumber", "datePaid", "total", "items"],
+  "properties": {
+    "receiptNumber": {
+      "type": "string"
+    },
+    "invoiceNumber": {
+      "type": "string"
+    },
+    "datePaid": {
+      "type": "string",
+      "format": "date"
+    },
+    "paymentMethod": {
+      "type": "object",
+      "properties": {
+        "type": {
+          "type": "string",
+          "enum": ["visa", "mastercard", "amex", "cash", "other"]
+        },
+        "lastFourDigits": {
+          "type": "string",
+          "pattern": "^[0-9]{4}$"
+        }
+      }
+    },
+    "merchant": {
+      "type": "object",
+      "properties": {
+        "name": {
+          "type": "string"
+        },
+        "address": {
+          "type": "object",
+          "properties": {
+            "street": {
+              "type": "string"
+            },
+            "city": {
+              "type": "string"
+            },
+            "state": {
+              "type": "string"
+            },
+            "postalCode": {
+              "type": "string"
+            },
+            "country": {
+              "type": "string"
+            }
+          }
+        },
+        "phone": {
+          "type": "string"
+        },
+        "email": {
+          "type": "string",
+          "format": "email"
+        }
+      }
+    },
+    "billTo": {
+      "type": "string",
+      "format": "email"
+    },
+    "items": {
+      "type": "array",
+      "items": {
+        "type": "object",
+        "required": [
+          "description",
+          "quantity",
+          "unitPrice",
+          "amount",
+          "period"
+        ],
+        "properties": {
+          "description": {
+            "type": "string"
+          },
+          "quantity": {
+            "type": "integer",
+            "minimum": 1
+          },
+          "unitPrice": {
+            "type": "number",
+            "minimum": 0
+          },
+          "amount": {
+            "type": "number",
+            "minimum": 0
+          },
+          "period": {
+            "type": "object",
+            "properties": {
+              "start": {
+                "type": "string",
+                "format": "date"
+              },
+              "end": {
+                "type": "string",
+                "format": "date"
+              }
+            }
+          }
+        }
+      }
+    },
+    "subtotal": {
+      "type": "number",
+      "minimum": 0
+    },
+    "total": {
+      "type": "number",
+      "minimum": 0
+    },
+    "amountPaid": {
+      "type": "number",
+      "minimum": 0
+    }
+  }
+}
diff --git a/tests/test_files/resume/schema.json b/tests/test_files/resume/schema.json
new file mode 100644
index 0000000..2a4afb3
--- /dev/null
+++ b/tests/test_files/resume/schema.json
@@ -0,0 +1,181 @@
+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "title": "Resume Schema",
+  "type": "object",
+  "required": ["basics", "skills", "experience"],
+  "properties": {
+    "basics": {
+      "type": "object",
+      "required": ["name", "email"],
+      "properties": {
+        "name": {
+          "type": "string"
+        },
+        "email": {
+          "type": "string",
+          "format": "email"
+        },
+        "phone": {
+          "type": "string"
+        },
+        "location": {
+          "type": "object",
+          "properties": {
+            "city": {
+              "type": "string"
+            },
+            "region": {
+              "type": "string"
+            },
+            "country": {
+              "type": "string"
+            }
+          }
+        },
+        "profiles": {
+          "type": "array",
+          "items": {
+            "type": "object",
+            "properties": {
+              "network": {
+                "type": "string"
+              },
+              "url": {
+                "type": "string",
+                "format": "uri"
+              }
+            }
+          }
+        },
+        "summary": {
+          "type": "string"
+        }
+      }
+    },
+    "skills": {
+      "type": "array",
+      "items": {
+        "type": "object",
+        "properties": {
+          "category": {
+            "type": "string"
+          },
+          "keywords": {
+            "type": "array",
+            "items": {
+              "type": "string"
+            }
+          },
+          "level": {
+            "type": "string",
+            "enum": ["beginner", "intermediate", "advanced", "expert"]
+          }
+        }
+      }
+    },
+    "experience": {
+      "type": "array",
+      "items": {
+        "type": "object",
+        "required": ["company", "position", "startDate"],
+        "properties": {
+          "company": {
+            "type": "string"
+          },
+          "position": {
+            "type": "string"
+          },
+          "startDate": {
+            "type": "string",
+            "format": "date"
+          },
+          "endDate": {
+            "type": "string",
+            "format": "date"
+          },
+          "highlights": {
+            "type": "array",
+            "items": {
+              "type": "string"
+            }
+          },
+          "technologies": {
+            "type": "array",
+            "items": {
+              "type": "string"
+            }
+          }
+        }
+      }
+    },
+    "education": {
+      "type": "array",
+      "items": {
+        "type": "object",
+        "required": ["institution", "degree"],
+        "properties": {
+          "institution": {
+            "type": "string"
+          },
+          "degree": {
+            "type": "string"
+          },
+          "field": {
+            "type": "string"
+          },
+          "graduationDate": {
+            "type": "string",
+            "format": "date"
+          },
+          "gpa": {
+            "type": "number"
+          }
+        }
+      }
+    },
+    "certifications": {
+      "type": "array",
+      "items": {
+        "type": "object",
+        "properties": {
+          "name": {
+            "type": "string"
+          },
+          "issuer": {
+            "type": "string"
+          },
+          "date": {
+            "type": "string",
+            "format": "date"
+          },
+          "validUntil": {
+            "type": "string",
+            "format": "date"
+          }
+        }
+      }
+    },
+    "publications": {
+      "type": "array",
+      "items": {
+        "type": "object",
+        "properties": {
+          "title": {
+            "type": "string"
+          },
+          "publisher": {
+            "type": "string"
+          },
+          "date": {
+            "type": "string",
+            "format": "date"
+          },
+          "url": {
+            "type": "string",
+            "format": "uri"
+          }
+        }
+      }
+    }
+  }
+}
diff --git a/tests/test_files/resume/software_architect_resume.html b/tests/test_files/resume/software_architect_resume.html
new file mode 100644
index 0000000..88c25f8
--- /dev/null
+++ b/tests/test_files/resume/software_architect_resume.html
@@ -0,0 +1,298 @@
+<!doctype html>
+<html>
+  <head>
+    <style>
+      body {
+        font-family: "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif;
+        margin: 0;
+        padding: 0;
+        background: #fff;
+        color: #333;
+        line-height: 1.6;
+      }
+
+      .container {
+        display: flex;
+        max-width: 1200px;
+        margin: 0 auto;
+        box-shadow: 0 0 20px rgba(0, 0, 0, 0.1);
+        min-height: 100vh;
+      }
+
+      .sidebar {
+        background: #2c3e50;
+        color: white;
+        padding: 2rem;
+        width: 300px;
+      }
+
+      .main-content {
+        padding: 2rem;
+        flex: 1;
+      }
+
+      .profile-name {
+        font-size: 2.5rem;
+        margin: 0;
+        color: #2c3e50;
+        border-bottom: 3px solid #3498db;
+        padding-bottom: 0.5rem;
+      }
+
+      .profile-title {
+        font-size: 1.5rem;
+        color: #7f8c8d;
+        margin: 0.5rem 0 2rem 0;
+      }
+
+      .contact-info {
+        margin-bottom: 2rem;
+      }
+
+      .section-title {
+        font-size: 1.2rem;
+        text-transform: uppercase;
+        color: #3498db;
+        margin-bottom: 1rem;
+        letter-spacing: 1px;
+      }
+
+      .sidebar .section-title {
+        color: white;
+        border-bottom: 2px solid #3498db;
+        padding-bottom: 0.5rem;
+      }
+
+      .skill-category {
+        margin-bottom: 1rem;
+      }
+
+      .skill-list {
+        list-style: none;
+        padding: 0;
+        margin: 0;
+      }
+
+      .skill-list li {
+        margin-bottom: 0.5rem;
+        font-size: 0.9rem;
+      }
+
+      .experience-item {
+        margin-bottom: 2rem;
+      }
+
+      .company-name {
+        font-weight: bold;
+        color: #2c3e50;
+        font-size: 1.1rem;
+      }
+
+      .job-title {
+        color: #3498db;
+        font-weight: bold;
+      }
+
+      .date {
+        color: #7f8c8d;
+        font-size: 0.9rem;
+      }
+
+      .achievements {
+        list-style: disc;
+        padding-left: 1.2rem;
+        margin-top: 0.5rem;
+      }
+
+      .contact-info a {
+        color: white;
+        text-decoration: none;
+      }
+
+      .education-item {
+        margin-bottom: 1rem;
+      }
+    </style>
+  </head>
+
+  <body>
+    <div class="container">
+      <div class="sidebar">
+        <div class="contact-info">
+          <h2 class="section-title">Contact</h2>
+          <p>sarah.chen@email.com</p>
+          <p>(555) 123-4567</p>
+          <p>San Francisco, CA</p>
+          <p><a href="#">LinkedIn Profile</a></p>
+        </div>
+
+        <div class="skills-section">
+          <h2 class="section-title">Technical Skills</h2>
+
+          <div class="skill-category">
+            <h3>Architecture & Design</h3>
+            <ul class="skill-list">
+              <li>Microservices</li>
+              <li>Event-Driven Architecture</li>
+              <li>Domain-Driven Design</li>
+              <li>REST APIs</li>
+            </ul>
+          </div>
+
+          <div class="skill-category">
+            <h3>Cloud Platforms</h3>
+            <ul class="skill-list">
+              <li>AWS (Advanced)</li>
+              <li>Azure</li>
+              <li>Google Cloud Platform</li>
+            </ul>
+          </div>
+
+          <div class="skill-category">
+            <h3>Programming</h3>
+            <ul class="skill-list">
+              <li>Java</li>
+              <li>Python</li>
+              <li>Go</li>
+              <li>JavaScript/TypeScript</li>
+            </ul>
+          </div>
+
+          <div class="skill-category">
+            <h3>Certifications</h3>
+            <ul class="skill-list">
+              <li>AWS Solutions Architect - Professional</li>
+              <li>Google Cloud Architect</li>
+              <li>Certified Kubernetes Administrator</li>
+            </ul>
+          </div>
+        </div>
+      </div>
+
+      <div class="main-content">
+        <h1 class="profile-name">Sarah Chen</h1>
+        <div class="profile-title">Senior Software Architect</div>
+
+        <div class="section">
+          <h2 class="section-title">Professional Summary</h2>
+          <p>
+            Innovative Software Architect with over 12 years of experience
+            designing and implementing large-scale distributed systems. Proven
+            track record of leading technical teams and delivering robust
+            enterprise solutions. Expert in cloud architecture, microservices,
+            and emerging technologies with a focus on scalable, maintainable
+            systems.
+          </p>
+        </div>
+
+        <div class="section">
+          <h2 class="section-title">Professional Experience</h2>
+
+          <div class="experience-item">
+            <div class="company-name">TechCorp Solutions</div>
+            <div class="job-title">Senior Software Architect</div>
+            <div class="date">2020 - Present</div>
+            <ul class="achievements">
+              <li>
+                Led architectural design and implementation of a cloud-native
+                platform serving 2M+ users
+              </li>
+              <li>
+                Established architectural guidelines and best practices adopted
+                across 12 development teams
+              </li>
+              <li>
+                Reduced system latency by 40% through implementation of
+                event-driven architecture
+              </li>
+              <li>
+                Mentored 15+ senior developers in cloud-native development
+                practices
+              </li>
+            </ul>
+          </div>
+
+          <div class="experience-item">
+            <div class="company-name">DataFlow Systems</div>
+            <div class="job-title">Lead Software Engineer</div>
+            <div class="date">2016 - 2020</div>
+            <ul class="achievements">
+              <li>
+                Architected and led development of distributed data processing
+                platform handling 5TB daily
+              </li>
+              <li>
+                Designed microservices architecture reducing deployment time by
+                65%
+              </li>
+              <li>
+                Led migration of legacy monolith to cloud-native architecture
+              </li>
+              <li>
+                Managed team of 8 engineers across 3 international locations
+              </li>
+            </ul>
+          </div>
+
+          <div class="experience-item">
+            <div class="company-name">InnovateTech</div>
+            <div class="job-title">Senior Software Engineer</div>
+            <div class="date">2013 - 2016</div>
+            <ul class="achievements">
+              <li>
+                Developed high-performance trading platform processing 100K
+                transactions per second
+              </li>
+              <li>
+                Implemented real-time analytics engine reducing processing
+                latency by 75%
+              </li>
+              <li>
+                Led adoption of container orchestration reducing deployment
+                costs by 35%
+              </li>
+            </ul>
+          </div>
+        </div>
+
+        <div class="section">
+          <h2 class="section-title">Education</h2>
+
+          <div class="education-item">
+            <div class="company-name">Stanford University</div>
+            <div class="job-title">Master of Science in Computer Science</div>
+            <div class="date">2013</div>
+            <p>Focus: Distributed Systems and Machine Learning</p>
+          </div>
+
+          <div class="education-item">
+            <div class="company-name">University of California, Berkeley</div>
+            <div class="job-title">
+              Bachelor of Science in Computer Engineering
+            </div>
+            <div class="date">2011</div>
+            <p>Magna Cum Laude</p>
+          </div>
+        </div>
+
+        <div class="section">
+          <h2 class="section-title">Patents & Speaking</h2>
+          <ul class="achievements">
+            <li>
+              Co-inventor on three patents for distributed systems architecture
+            </li>
+            <li>
+              Published paper on "Scalable Microservices Architecture" at IEEE
+              Cloud Computing Conference 2022
+            </li>
+            <li>
+              Keynote Speaker, CloudCon 2023: "Future of Cloud-Native
+              Architecture"
+            </li>
+            <li>Regular presenter at local tech meetups and conferences</li>
+          </ul>
+        </div>
+      </div>
+    </div>
+  </body>
+</html>
diff --git a/tests/test_files/resume/software_architect_resume.test.json b/tests/test_files/resume/software_architect_resume.test.json
new file mode 100644
index 0000000..68b5083
--- /dev/null
+++ b/tests/test_files/resume/software_architect_resume.test.json
@@ -0,0 +1,94 @@
+{
+  "basics": {
+    "name": "Sarah Chen",
+    "email": "san.francisco@email.com",
+    "phone": "(555) 123-4567",
+    "location": {
+      "city": "San Francisco",
+      "region": "CA",
+      "country": "USA"
+    }
+  },
+  "skills": [
+    {
+      "category": "Architecture & Design",
+      "keywords": [
+        "Microservices",
+        "Event-Driven Architecture",
+        "Domain-Driven Design",
+        "REST APIs"
+      ]
+    },
+    {
+      "category": "Cloud Platforms",
+      "keywords": ["AWS", "Azure", "Google Cloud Platform"]
+    },
+    {
+      "category": "Programming Languages",
+      "keywords": ["Java", "Python", "Go", "JavaScript", "TypeScript"]
+    }
+  ],
+  "experience": [
+    {
+      "company": "TechCorp Solutions",
+      "position": "Senior Software Architect",
+      "startDate": "2020-01-01",
+      "endDate": "2024-01-10"
+    },
+    {
+      "company": "DataFlow Systems",
+      "position": "Lead Software Engineer",
+      "startDate": "2016-01-01",
+      "endDate": "2019-12-31",
+      "technologies": [
+        "Distributed Systems",
+        "Microservices",
+        "Cloud Migration"
+      ]
+    },
+    {
+      "company": "InnovateTech",
+      "position": "Senior Software Engineer",
+      "startDate": "2013-01-01",
+      "endDate": "2015-12-31",
+      "technologies": [
+        "High-performance Computing",
+        "Real-time Analytics",
+        "Container Orchestration"
+      ]
+    }
+  ],
+  "education": [
+    {
+      "institution": "Stanford University",
+      "degree": "Master of Science",
+      "field": "Computer Science",
+      "graduationDate": "2013-01-01",
+      "specialization": "Distributed Systems and Machine Learning"
+    },
+    {
+      "institution": "University of California, Berkeley",
+      "degree": "Bachelor of Science",
+      "field": "Computer Engineering",
+      "graduationDate": "2011-01-01"
+    }
+  ],
+  "certifications": [
+    {
+      "name": "AWS Solutions Architect - Professional"
+    },
+    {
+      "name": "Google Cloud Architect"
+    },
+    {
+      "name": "Certified Kubernetes Administrator"
+    }
+  ],
+  "publications": [
+    {
+      "title": "Scalable Microservices Architecture",
+      "publisher": "IEEE Cloud Computing Conference",
+      "date": "2022-01-01"
+    }
+  ]
+}
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diff --git a/tests/test_files/slide/saas_slide.test.json b/tests/test_files/slide/saas_slide.test.json
new file mode 100644
index 0000000..1b3702d
--- /dev/null
+++ b/tests/test_files/slide/saas_slide.test.json
@@ -0,0 +1,48 @@
+{
+  "companyInfo": {
+    "name": "CloudFlow Analytics",
+    "fundingStage": "Series A",
+    "foundedYear": null,
+    "industry": null,
+    "location": null
+  },
+  "financialMetrics": {
+    "mrr": {
+      "value": 580000,
+      "currency": "USD",
+      "growthRate": 27
+    },
+    "grossMargin": 88
+  },
+  "growthMetrics": {
+    "customers": {
+      "total": 1247,
+      "growth": 142,
+      "enterprisePercent": null
+    },
+    "nrr": 147
+  },
+  "marketMetrics": {
+    "tam": 50000000000,
+    "sam": null,
+    "marketShare": null,
+    "competitors": null
+  },
+  "differentiators": [
+    {
+      "claim": "Processing Speed",
+      "metric": "5x faster",
+      "comparisonTarget": "competitors"
+    },
+    {
+      "claim": "ML Accuracy",
+      "metric": "99.9%",
+      "comparisonTarget": null
+    },
+    {
+      "claim": "Market Potential",
+      "metric": "80%",
+      "comparisonTarget": "Fortune 500"
+    }
+  ]
+}
diff --git a/tests/test_files/slide/schema.json b/tests/test_files/slide/schema.json
new file mode 100644
index 0000000..411e276
--- /dev/null
+++ b/tests/test_files/slide/schema.json
@@ -0,0 +1,122 @@
+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "type": "object",
+  "required": ["companyInfo", "financialMetrics", "growthMetrics"],
+  "properties": {
+    "companyInfo": {
+      "type": "object",
+      "required": ["name", "fundingStage"],
+      "properties": {
+        "name": {
+          "type": "string"
+        },
+        "fundingStage": {
+          "type": "string",
+          "enum": ["Pre-seed", "Seed", "Series A", "Series B", "Series C+"]
+        },
+        "foundedYear": {
+          "anyOf": [
+            {
+              "type": "integer"
+            },
+            {
+              "type": "null"
+            }
+          ]
+        },
+        "industry": {
+          "anyOf": [
+            {
+              "type": "string"
+            },
+            {
+              "type": "null"
+            }
+          ]
+        },
+        "location": {
+          "anyOf": [
+            {
+              "type": "string"
+            },
+            {
+              "type": "null"
+            }
+          ]
+        }
+      }
+    },
+    "financialMetrics": {
+      "type": "object",
+      "required": ["mrr", "growthRate"],
+      "properties": {
+        "mrr": {
+          "type": "object",
+          "description": "Monthly Recurring Revenue",
+          "required": ["value", "currency", "growthRate"],
+          "properties": {
+            "value": {
+              "type": "number"
+            },
+            "currency": {
+              "type": "string"
+            },
+            "growthRate": {
+              "type": "number"
+            }
+          }
+        },
+        "grossMargin": {
+          "type": "number"
+        }
+      }
+    },
+    "growthMetrics": {
+      "type": "object",
+      "required": ["customers", "nrr"],
+      "properties": {
+        "customers": {
+          "type": "object",
+          "required": ["total", "growth"],
+          "properties": {
+            "total": {
+              "type": "integer"
+            },
+            "growth": {
+              "type": "number"
+            }
+          }
+        },
+        "nrr": {
+          "description": "Net Revenue Retention",
+          "type": "number"
+        }
+      }
+    },
+    "differentiators": {
+      "type": "array",
+      "items": {
+        "type": "object",
+        "required": ["claim", "metric"],
+        "properties": {
+          "claim": {
+            "type": "string"
+          },
+          "metric": {
+            "type": "string"
+          },
+          "comparisonTarget": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ]
+          }
+        }
+      }
+    }
+  }
+}