Backend-Only Advanced RAG with Multi-Step Self-Correction

This HTML document describes a backend-only implementation of a Retrieval-Augmented Generation (RAG) system featuring an advanced Multi-Step Self-Correction mechanism using Python, LangChain, OpenAI, and ChromaDB.

Overview

The goal of this project is to demonstrate how to build a RAG pipeline where the language model iteratively evaluates and refines its own answers based on retrieved knowledge, leading to potentially more accurate and higher-quality responses.

Prerequisites

• Python 3.7+
• pip package installer
• An OpenAI API key

Project Files

# Backend-Only Advanced RAG with Multi-Step Self-Correction

This project implements a backend-only Retrieval-Augmented Generation (RAG) system with an advanced Multi-Step Self-Correction mechanism using Python, LangChain, OpenAI, and ChromaDB.

## Overview

The goal of this project is to demonstrate how to build a RAG pipeline where the language model iteratively evaluates and refines its own answers based on retrieved knowledge, leading to potentially more accurate and higher-quality responses.

## Prerequisites

- Python 3.7+
- pip package installer

## Setup

1.  **Clone the repository** (if you've been following along, you're already in the right directory structure).
2.  **Create a virtual environment:**
    ```bash
    python -m venv venv
    source venv/bin/activate  # On macOS/Linux
    # venv\Scripts\activate   # On Windows
    ```
3.  **Install dependencies:**
    ```bash
    pip install -r requirements.txt
    ```
4.  **Create a `.env` file** in the project root and add your OpenAI API key:
    ```
    OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
    ```
    Replace `"YOUR_OPENAI_API_KEY"` with your actual OpenAI API key.
5.  **Create a `knowledge_base` directory** in the project root.
6.  **Place your knowledge base documents** (e.g., `.txt`, `.pdf`) inside the `knowledge_base` directory.

## Usage

### 1. Ingesting the Knowledge Base

Run the `knowledge_ingestor.py` script to load, chunk, embed, and store your knowledge base in ChromaDB:

```bash
python knowledge_ingestor.py
```

This will create a `chroma_db` directory in your project root containing the persisted vector database.

### 2\. Running the RAG Engine with Multi-Step Self-Correction

Execute the `rag_engine.py` script to test the RAG pipeline with multi-step self-correction:

```bash
python rag_engine.py
```

The script will load the vector database, create the RAG chain with the self-correction mechanism, and run a sample query. The output will show the initial answer and the subsequent revisions made during the self-correction steps, along with the final corrected answer and the source documents used.

You can modify the `query` variable in the `if __name__ == "__main__":` block of `rag_engine.py` to test with different questions. You can also adjust the `num_steps` parameter in the `create_multi_step_self_correction_chain` function to control the number of self-correction iterations.

## Code Structure

  - `knowledge_base/`: Directory to store your knowledge base documents.
  - `rag_engine.py`: Contains the core RAG logic with the multi-step self-correction mechanism.
  - `knowledge_ingestor.py`: Script to load and process the knowledge base and create the vector store.
  - `requirements.txt`: Lists the Python dependencies for the project.
  - `.env`: Stores environment variables, such as the OpenAI API key.
  - `chroma_db/`: Directory where the Chroma vector database is persisted.

## Further Enhancements

As outlined in the code and previous discussions, potential enhancements include:

  - More sophisticated self-correction prompting.
  - Implementing more intelligent stopping conditions for the self-correction loop.
  - Integrating fact-verification models.
  - Implementing logging.
  - Adding error handling.
  - Building an API endpoint (using Flask or FastAPI) to interact with the RAG engine.

## Disclaimer

This is a demonstration project and may require further development for production use. Ensure you manage your OpenAI API key securely.

import os
from dotenv import load_dotenv
from langchain.vectorstores import Chroma
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate

load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")
embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
llm = OpenAI(openai_api_key=openai_api_key)
DB_PATH = "chroma_db"

def load_vectorstore(embeddings, db_path):
    vectordb = Chroma(persist_directory=db_path, embedding_function=embeddings)
    retriever = vectordb.as_retriever()
    return retriever

def create_rag_chain(llm, retriever):
    rag_chain = RetrievalQA.from_llm(llm=llm, retriever=retriever, return_source_documents=True)
    return rag_chain

# Prompt for self-correction (iterative)
SELF_CORRECTION_PROMPT = PromptTemplate(
    template="""You are a helpful AI assistant. You have provided the following answer to the user's question:
{current_answer}

Here are the source documents that were used to generate the answer:
{source_documents}

The user's original question was:
{question}

Critically evaluate the current answer based on the source documents and the question.
Identify any inaccuracies, missing information, areas for improvement in clarity or completeness.

Provide a revised answer, incorporating the feedback from your evaluation. If the current answer is accurate and complete, you can indicate that no further revision is needed by saying "NO FURTHER REVISION NEEDED".

Revised Answer:""",
    input_variables=["current_answer", "source_documents", "question"]
)

def create_multi_step_self_correction_chain(llm, rag_chain, self_correction_prompt, num_steps=3):
    def self_correct_multi_step(query):
        rag_result = rag_chain({"query": query})
        current_answer = rag_result["result"]
        source_documents = rag_result["source_documents"]

        formatted_sources = "\n".join([f"Source {i+1}: {doc.page_content[:200]}..." for i, doc in enumerate(source_documents)])

        for step in range(num_steps):
            correction_prompt = self_correction_prompt.format(
                current_answer=current_answer,
                source_documents=formatted_sources,
                question=query
            )

            revised_answer = llm(correction_prompt)
            print(f"\nSelf-Correction Step {step + 1}:")
            print(f"Current Answer: {current_answer}")
            print(f"Revised Answer: {revised_answer}")

            if "NO FURTHER REVISION NEEDED" in revised_answer.upper():
                return {"corrected_answer": current_answer, "source_documents": source_documents, "iterations": step + 1}
            current_answer = revised_answer

        return {"corrected_answer": current_answer, "source_documents": source_documents, "iterations": num_steps}

    return self_correct_multi_step

if __name__ == "__main__":
    retriever = load_vectorstore(embeddings, DB_PATH)
    rag_chain = create_rag_chain(llm, retriever)
    multi_step_correction_engine = create_multi_step_self_correction_chain(llm, rag_chain, SELF_CORRECTION_PROMPT, num_steps=3)

    query = "What is the capital of France and where is the Eiffel Tower located? Also, when was the tower built?"
    result = multi_step_correction_engine(query)
    print(f"\n\nQuestion: {query}")
    print(f"Final Corrected Answer: {result['corrected_answer']}")
    print(f"Number of Self-Correction Iterations: {result['iterations']}")
    print(f"\nSource Documents:")
    for doc in result['source_documents']:
        print(f"- {doc.page_content[:100]}...")
```

import os
from dotenv import load_dotenv
from langchain.document_loaders import DirectoryLoader, TextLoader  # Add more loaders as needed
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma

load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")
embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)

# Define the directory containing your knowledge base documents
KNOWLEDGE_BASE_PATH = "knowledge_base"
DB_PATH = "chroma_db"

def load_documents(path):
    loader = DirectoryLoader(path, glob="**/*.txt", loader_cls=TextLoader)  # Example for text files
    # Add more loaders for other file types (PDFLoader, etc.)
    documents = loader.load()
    return documents

def chunk_documents(documents, chunk_size=1000, chunk_overlap=100):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
    chunks = text_splitter.split_documents(documents)
    return chunks

def create_vectorstore(chunks, embeddings, db_path):
    vectordb = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_path)
    vectordb.persist()
    return vectordb

if __name__ == "__main__":
    documents = load_documents(KNOWLEDGE_BASE_PATH)
    chunks = chunk_documents(documents)
    vectordb = create_vectorstore(chunks, embeddings, DB_PATH)
    print(f"Vector database created and persisted at {DB_PATH}")

langchain
openai
chromadb
tiktoken
python-dotenv

OPENAI_API_KEY="YOUR_OPENAI_API_KEY"

Usage Instructions

Follow the instructions in the README.md section above to set up and run the project.

Further Enhancements

As outlined in the README.md, consider exploring the suggested enhancements to further improve the system.

Exposing Advanced RAG with Multi-Step Self-Correction as an API

This article outlines the steps to expose a backend-only Retrieval-Augmented Generation (RAG) system, enhanced with a Multi-Step Self-Correction mechanism, as a RESTful API using Python and FastAPI.

Introduction

Building an intelligent chatbot often requires making its advanced functionalities accessible to other applications, such as a frontend user interface. By exposing our sophisticated RAG engine as an API, we can easily integrate its capabilities into various platforms.

Technology Stack

• Python: The programming language used for the backend logic.
• FastAPI: A modern, high-performance web framework for building APIs with Python.
• Uvicorn: An ASGI server for running FastAPI applications.
• LangChain: The framework used for implementing the RAG pipeline.
• OpenAI: For the large language model and embeddings.
• ChromaDB: The vector database used for storing and retrieving knowledge.
• python-dotenv: For managing environment variables.

Step-by-Step Implementation

1. Installation

First, ensure you have FastAPI and Uvicorn installed:

pip install fastapi uvicorn
    

2. Create `api.py`

Create a Python file named `api.py` with the following content:

import os
from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from rag_engine import load_vectorstore, create_rag_chain, create_multi_step_self_correction_chain, SELF_CORRECTION_PROMPT
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import OpenAI

load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")
embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
llm = OpenAI(openai_api_key=openai_api_key)
DB_PATH = "chroma_db"

app = FastAPI()

# Load the vector store and RAG engine when the app starts
try:
    retriever = load_vectorstore(embeddings, DB_PATH)
    rag_chain = create_rag_chain(llm, retriever)
    self_correction_engine = create_multi_step_self_correction_chain(llm, rag_chain, SELF_CORRECTION_PROMPT, num_steps=3)
except Exception as e:
    raise RuntimeError(f"Error initializing RAG engine: {e}")

class QueryRequest(BaseModel):
    query: str

class QueryResponse(BaseModel):
    corrected_answer: str
    source_documents: list
    iterations: int

@app.post("/query/", response_model=QueryResponse)
async def query_rag_engine(request: QueryRequest):
    try:
        result = self_correction_engine(request.query)
        return QueryResponse(
            corrected_answer=result["corrected_answer"],
            source_documents=[{"content": doc.page_content, "metadata": doc.metadata} for doc in result["source_documents"]],
            iterations=result["iterations"]
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)
    

3. Running the API

Open your terminal in the project directory and execute:

python api.py
    

This will start the Uvicorn server, typically accessible at http://0.0.0.0:8000.

4. Testing the API

You can now send POST requests to the /query/ endpoint with a JSON body containing the query. Here’s an example using curl:

curl -X POST -H "Content-Type: application/json" -d '{"query": "What are the key features of LangChain?"}' http://0.0.0.0:8000/query/
    

The API will respond with a JSON object containing the corrected_answer, source_documents, and iterations.

Key Considerations for Production

• Asynchronous Operations: FastAPI’s asynchronous nature helps in handling concurrent requests efficiently.
• Data Validation: Pydantic models ensure the request and response data adhere to the defined structures.
• Error Handling: Implement robust error handling to provide informative feedback to clients.
• Security: Consider implementing authentication and authorization mechanisms.
• Rate Limiting: Protect your API from abuse by implementing rate limiting.
• Logging and Monitoring: Set up logging to track API usage and monitor performance.
• Scalability: Design your API with scalability in mind, especially for high-traffic scenarios.

Conclusion

Exposing your advanced RAG engine as an API using FastAPI provides a flexible and efficient way to integrate its powerful question-answering capabilities into various applications. By considering production-level concerns, you can build a robust and reliable service.