https://github.com/takeshape/unstructured-rag-example

https://github.com/takeshape/unstructured-rag-example

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• Host: GitHub
• URL: https://github.com/takeshape/unstructured-rag-example
• Owner: takeshape
• Created: 2024-11-14T17:46:52.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-18T17:49:24.000Z (2 months ago)
• Last Synced: 2024-11-18T18:51:42.552Z (2 months ago)
• Size: 2.93 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Unstructured RAG Example



## Instructions

### Create a new TakeShape Project

1. Click [Deploy to TakeShape](https://app.takeshape.io/add-pattern?repo=https://github.com/takeshape/unstructured-rag-example).

1. Select "Create new project" from the dropdown

1. Enter a name for the new project or leave the default

1. Click "Add to TakeShape"

### Add your API keys

#### Unstructured 

Follow the directions in the [TakeShape Unstructured documentation](https://app.takeshape.io/docs/services/providers/unstructured) 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

```graphql

mutation {

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

}

```

```graphql

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.

```mermaid

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

```

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

```mermaid

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

```