API Documentation

This API provides functionality to generate embeddings for documents, perform similarity searches, and retrieve search results using FastAPI.

Installation

Install the required dependencies using pip and the requirements.txt file.

pip install -r requirements.txt

API Routes

Hello World

• Route: /
• Method: GET
• Description: Returns a greeting message.
• Example:
  • Request: GET /
  • Response:

    {
        "Hello": "World"
    }

Generate Embeddings

• Route: /embed
• Method: POST
• Description: Generates embeddings for documents and returns the collection name and extracted text.
• Parameters:
  • docName (string): Name of the document.
  • group (string): Group name for the collection.
  • userid (string): User ID associated with the document.
  • filetype (string): Type of file (url or file).
  • url (string): URL of the document or file.
• Example:
  • Request: POST /embed

    {
        "docName": "Document 1",
        "group": "Group 1",
        "userid": "User 1",
        "filetype": "url",
        "url": "https://example.com/"
    }

  • Response:

    {
        "collection_name": "Group 1",
        "extracted_text": "This is the extracted text from the document."
    }

Perform Search

• Route: /qsearch
• Method: POST
• Description: Performs a similarity search based on the provided query and returns the search results.
• Parameters:
  • query (string): Search query.
  • collection_name (string): Name of the collection to search in.
  • filter_dict (object): Filter dictionary for the search.
  • k (integer): Number of search results to retrieve.
  • with_source (boolean): Whether to include source documents in the search results.
• Example:
  • Request: POST /qsearch

    {
        "query": "example query",
        "collection_name": "Group 1",
        "filter_dict": {},
        "k": 10,
        "with_source": true
    }

  • Response:

    {
        "result": "Search results"
    }

Starting the Gunicorn Server

To start the Gunicorn server with the API, use the following command:

gunicorn -w 4 -k uvicorn.workers.UvicornWorker app:app --timeout 600

• -w 4 specifies the number of worker processes (adjust as needed).
• -k uvicorn.workers.UvicornWorker specifies the worker class to use.
• main:app specifies the location of the FastAPI application instance.

Make sure to install the necessary dependencies and provide the required environment variables (openai_api_key and cohere_api_key) before starting the server.

Please note that you may need to modify the server configuration and other deployment details based on your specific requirements and environment.

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