# README
API for RAG (Retrieval-Augmented Generation)
This project creates API endpoints which enables users to chat with or ask questions based on supplied text/knowledge.
The application makes use of LangChain langchaingo to communicate with LLMs and facilitate RAG.
Table of Contents
Installation
To install and run the project, follow these steps:
-
Clone the repository:
git clone https://github.com/Naadborole/TextRAGApi.git cd TextRAGApi -
Start the server:
go run .
Usage
Once the server is running, you can interact with the API endpoints using tools like curl, Postman, or any HTTP client of your choice.
API Endpoints
Embed and Store Text
Endpoint: /embedAndStore
Method: POST
Description: Embed the uploaded text and store it for retrieval.
Request Body:
{
"text": "Your text data here."
}
Response:
{
"ID": "1097108e-49b0-4e65-a931-9b2a91f1d1da"
}
Query Document
Endpoint: /queryDoc
Method: POST
Description: Query the stored documents based on the input text.
Request Body:
{
"text": "Your query here."
}
Response:
{
"results": [
{
"document": "Relevant document text.",
"score": 0.95
},
...
]
}
Query Chat
Endpoint: /queryChat
Method: POST
Description: Chat with the system based on the input query, using RAG for generating responses.
Request Body:
{
"query": "Your query here."
}
Response:
{
"Generated response based on the input query."
}