> ## Documentation Index
> Fetch the complete documentation index at: https://unstructured-53-docs-243-plugins.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Notebooks

> Notebooks contain complete working sample code for end-to-end solutions.

<CardGroup cols={2}>
  <Card title="Unstructured Workflow Endpoint Quickstart" href="https://colab.research.google.com/drive/13f5C9WtUvIPjwJzxyOR3pNJ9K9vnF4ww">
    <br />

    Build an end-to-end workflow in Unstructured programmatically by using the Unstructured Workflow Endpoint.

    <br />

    `Unstructured API` `Workflows` `S3`

    <br />
  </Card>

  <Card title="RAG with Databricks Vector Search with Context from Multiple Sources" href="https://colab.research.google.com/drive/1ZsrqYVhBAqsr6L98xlVjTZltMzNi_P3o?usp=sharing">
    <br />

    Build RAG with Databricks Vector Search with context preprocessed from multiple sources by Unstructured.

    <br />

    `Databricks` `Introductory notebook`

    <br />
  </Card>

  <Card title="Agentic RAG with Hugging Face smolagents vs Vanilla RAG" href="https://colab.research.google.com/drive/1hG3dPgd8wjrO9wSD0K0Feo7EY1iXqrEN?usp=sharing">
    <br />

    Build Agentic RAG with `smolagents` library and compare the results with Vanilla RAG in pure Python

    <br />

    `GPT-4o` `smolagents` `Agents` `DataStax` `S3` `Advanced notebook`

    <br />
  </Card>

  <Card title="LLama3.2 RAG evaluation on unstructured text" href="https://colab.research.google.com/drive/14y3QOmhetk6NvJfT3HO4Tw6-Ra-LIDA9?usp=sharing">
    <br />

    Evaluate Llama3.2 for your RAG system with Unstructured, GPT-4o, Ragas, and LangChain

    <br />

    `GPT-4o` `Ragas` `LangChain` `Llama3.2` `Pinecone` `S3` `Advanced notebook`

    <br />
  </Card>

  <Card title="Multimodal RAG: Enhancing RAG outputs with image results" href="https://colab.research.google.com/drive/1gBI67HKyepmpAzf0T5yWwBwIqVXK1Ea_?usp=sharing">
    <br />

    Process a file in S3 with Unstructured and return images in your RAG output

    <br />

    `S3` `FAISS` `GPT-4o-mini` `Advanced notebook`

    <br />
  </Card>

  <Card title="Quantitative Reasoning with tables inside PDFs" href="https://app.hex.tech/5e6b6e24-dead-4d3b-b9da-a9a7ad587b96/hex/cb595d8a-6eac-4e19-96ed-b1540e5c031c/draft/logic">
    <br />

    From Pixels to Insights: Seamlessly Extracting and Visualizing Table Data with Unstructured and Hex

    <br />

    `Unstructured API` `Hex` `Advanced notebook`

    <br />
  </Card>

  <Card title="PII removal with GLiNER in unstructured data ETL" href="https://colab.research.google.com/drive/1HwOMnGjrNbcHZ1vlhaAG0MSDBcwQfexF?usp=sharing">
    <br />

    Remove Personally Identifiable Information (PII) as a part of unstructured data preprocessing.

    <br />

    `Unstructured API`  `PII` `GLiNER` `Advanced notebook`

    <br />
  </Card>

  <Card title="Custom metadata extraction and self-querying retrieval" href="https://colab.research.google.com/drive/1v2SKPEmCr0p2AFyckrYvC3heYKyKff_t?usp=sharing">
    <br />

    Extract custom metadata, and enable metadata pre-filtering in your RAG.

    <br />

    `Unstructured API` `MongoDB`  `Metadata` `Advanced notebook`

    <br />
  </Card>

  <Card title="Selecting an embedding model for custom data" href="https://colab.research.google.com/drive/132oXSGSOyzZ7GO9pJhRKlvwY4F-i9Pm6?usp=sharing">
    <br />

    End-to-end data processing pipeline using Unstructured Serverless API.

    <br />

    `Unstructured API` `Hugging Face` `Advanced notebook`

    <br />
  </Card>

  <Card title="RAG with PDFs, LangChain and Llama 3" href="https://colab.research.google.com/drive/1BJYYyrPVe0_9EGyXqeNyzmVZDrCRZwsg?usp=sharing">
    <br />

    A RAG system with the Llama 3 model from Hugging Face.

    <br />

    `Unstructured API`  `🤗 Hugging Face` `LangChain` `Llama 3` `Introductory notebook`
  </Card>

  <Card title="Unstructured data ETL from S3 to SingleStore DB" href="https://colab.research.google.com/drive/1Krvn5XlYNERQe7DNIXKEz3AmESJdABLF?usp=sharing">
    <br />

    Learn to ingest, partition, chunk, embed and load data from an S3 bucket into SingleStore DB.

    <br />

    `Unstructured API`  `SingleStoreDB` `AWS S3` `Introductory notebook`
  </Card>

  <Card title="Google Drive to DataStax Astra DB" href="https://colab.research.google.com/drive/1Img_qGCTKavImbz7dlRtxg8mNUjiWjJT?usp=sharing">
    <br />

    Embed your Google Drive Docs in an Astra Vector Database with Unstructured Serverless API

    <br />

    `Unstructured API` `Google` `DataStax` `Introductory notebook`

    <br />
  </Card>

  <Card title="Weaviate RAG quickstart" href="https://colab.research.google.com/drive/1lYejikVtbPraWvh9CtxBazJuXKuJ38wu?usp=sharing">
    <br />

    Embed your local documents in an Weaviate Vector Database with Unstructured Serverless API

    <br />

    `Unstructured API` `OpenAI` `Weaviate` `Introductory notebook`

    <br />
  </Card>

  <Card title="Preprocess PDFs in AWS S3, load into Elasticsearch" href="https://colab.research.google.com/drive/1axADo7T_dMkeOWnZ5C4dKve16-wrtQuV?usp=sharing">
    <br />

    Ingest PDF documents from an S3 bucket, transform them into a normalized JSON with Unstructured Serverless API, chunk, embed and load into Elasticsearch.

    <br />

    `Unstructured API` `AWS S3` `Elasticsearch` `Introductory notebook`

    <br />
  </Card>

  <Card title="Preprocess documents in Google Drive, load into Databricks Volume" href="https://colab.research.google.com/drive/1gVd03geFUD_OTROMuhjVAHYQvgVbViq7?usp=sharing">
    <br />

    Preprocess documents from a Google Drive Unstructured Serverless API and load them into Databricks Volume.

    <br />

    `Unstructured API` `Google Drive` `Databricks` `Introductory notebook`

    <br />
  </Card>

  <Card title="Source references in RAG responses" href="https://colab.research.google.com/drive/1Lc8eq8P87JjzUhbYb33_c7h7njsWb-hn?usp=sharing">
    <br />

    Add document source references to RAG responses based on documents metadata.

    <br />

    `Unstructured API` `RAG` `LangChain` `Intermediate notebook`

    <br />
  </Card>

  <Card title="Query processed PDF with HuggingChat" href="https://colab.research.google.com/drive/1rNVVX5qo7vyBwR7wTa-zS6lDMkKpTei0?usp=sharing">
    <br />

    Send a PDF to Unstructured for processing, and send a subset of the returned PDF's processed text to [HuggingChat](https://huggingface.co/chat/) for chatbot-style querying.

    <br />

    `Unstructured API`  `🤗 Hugging Face` `🤗 HuggingChat` `Introductory notebook`
  </Card>

  <Card title="Llama 3 Local RAG with emails" href="https://colab.research.google.com/drive/1ieDJ4LoxARrHFqxXWif8Lv8e8aZTgmtH?usp=sharing">
    <br />

    Build a local RAG app for your emails with Unstructured, LangChain and Ollama.

    <br />

    `Unstructured API` `LangChain` `Ollama` `Llama 3` `Introductory notebook`
  </Card>

  <Card title="Building RAG With PowerPoint presentations" href="https://colab.research.google.com/drive/1l_e7CyqfBUxBBDc6E6XIKtKV-8YWvmRX?usp=sharing">
    <br />

    A RAG solution that is based on PowerPoint files.

    <br />

    `Unstructured API`  `🤗 Hugging Face` `LangChain` `Llama 3` `Introductory notebook`
  </Card>

  <Card title="Synthetic test dataset generation" href="https://colab.research.google.com/drive/1VvOauC46xXeZrhh8nlTyv77yvoroMQjr?usp=sharing">
    <br />

    Build a Synthetic Test Dataset for your RAG system in 5 easy steps

    <br />

    `Unstructured API` `GPT-4o` `Ragas` `LangChain` `Advanced notebook`

    <br />
  </Card>
</CardGroup>