Unstructured RAG Example

Instructions

Create a new TakeShape Project

1. Click Deploy to TakeShape.
2. Select "Create new project" from the dropdown
3. Enter a name for the new project or leave the default
4. Click "Add to TakeShape"

Add your API keys

Unstructured

Follow the directions in the TakeShape Unstructured documentation to connect Unstructured API

OpenAI

1. Create an OpenAI API Key https://platform.openai.com/api-keys with the "Models" and "Model capabilities" permissions (this example uses /v1/embeddings and /v1/chat/completions)
2. Copy/paste your API key into the service configuration dialog
3. Click "Save"

Try it out!

Once your services are connected now try out the API in the API Explorer

mutation {
  chat(input: "what shoes should I buy?")
}

query {
  getRelatedDocumentList(text: "what shoes should I buy?") {
    items {
      filename
      chunks {
        text
      }
    }
    total
  }
}

How it works

This example demonstrates how to use TakeShape's vector capabilities combined with indexing to enable the RAG use-case. A prerequisite for RAG is to populate a vector database, for this example we will use TakeShape's built-in index. The first step to preparing our data is to create the Document which extends the built-in Asset shape from TakeShape but adds chunks an array of text chunks and their corresponding vector embeddings.

sequenceDiagram
    participant TakeShape
    participant Unstructured
    participant OpenAI
    participant Index

    TakeShape->>Unstructured: 1. Assets uploaded to TakeShape are sent to Unstructured
    Unstructured->>TakeShape: 2. Unstructured parses and chunks the documents
    TakeShape->>OpenAI: 3. Create embeddings the text chunks
    OpenAI->>TakeShape: 4. OpenAI returns embeddings
    TakeShape->>Index: 5. Text chunks and embeddings in TakeShape Index

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Now that our Document data is stored in the built-in index we can perform RAG:

sequenceDiagram
    actor User
    participant TakeShape
    participant Index
    participant OpenAI

    User->>TakeShape: 1. GraphQL containing user prompt
    TakeShape->>OpenAI: 2. Create embedding of user prompt
    OpenAI->>TakeShape: 3. OpenAI returns vector
    TakeShape->>Index: 4. Use vector to search TakeShape Index
    Index->>TakeShape: 5. Return related products
    TakeShape->>OpenAI: 6. Combine related results with original prompt and send to GPT 4o
    OpenAI->>TakeShape: 7. OpenAI returns generated text
    TakeShape->>User: 8. GraphQL response

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