implementation details or a sample RAG pipeline using LangChain

 

RAG pipeline using LangChain + FAISS + OpenAI GPT-4

Here's a sample RAG pipeline using LangChain + FAISS + OpenAI GPT-4 for document-based retrieval and generation.

RAG Pipeline Implementation (LangChain + FAISS + OpenAI)

1. Install Dependencies

First, install the required Python libraries:

Bash

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pip install langchain openai faiss-cpu chromadb pypdf tiktoken

2. Set Up Environment Variables

You'll need an OpenAI API key to use GPT-4. Set it up in your environment:

python

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import os

os.environ["OPENAI_API_KEY"] = "your_openai_api_key"

3. Load and Chunk Documents

We will load PDF/text documents and break them into smaller chunks for efficient retrieval.

python

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from langchain.document_loaders import PyPDFLoader, TextLoader

from langchain.text_splitter import RecursiveCharacterTextSplitter

# Load Documents

loader = PyPDFLoader("example.pdf")  # Change to TextLoader("example.txt") for text files

documents = loader.load()

# Split Documents into Chunks

text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)

docs = text_splitter.split_documents(documents)

print(f"Total document chunks: {len(docs)}")

4. Convert Text to Embeddings and Store in FAISS

We use OpenAI's embedded model (text-embedding-ada-002) and store the vectors in FAISS.

python

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from langchain.vectorstores import FAISS

from langchain.embeddings.openai import OpenAIEmbeddings

 

Create Embeddings

embeddings = OpenAIEmbeddings()

 

# Store embeddings in FAISS

vector_store = FAISS.from_documents(docs, embeddings)

 

# Save FAISS Index

vector_store.save_local("faiss_index")

 

5. Load FAISS and Perform Retrieval

We will reload the FAISS index and perform a similarity search.

python

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# Load FAISS index

vector_store = FAISS.load_local("faiss_index", embeddings)

 

# Retrieve Top 3 Similar Documents

query = "What is RAG AI?"

retrieved_docs = vector_store.similarity_search(query, k=3)

 

for i, doc in enumerate(retrieved_docs):

    print(f"\nDocument {i+1}:\n{doc.page_content[:300]}...")

 

6. Use GPT-4 for Augmented Generation

We now pass the retrieved documents as context to GPT-4 for a final response.

python

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from langchain.chat_models import ChatOpenAI

from langchain.chains import RetrievalQA

 

# Initialize OpenAI GPT-4 Model

llm = ChatOpenAI(model_name="gpt-4", temperature=0)

 

# Create RAG Pipeline

qa_chain = RetrievalQA.from_chain_type(

    llm=llm, chain_type="stuff", retriever=vector_store.as_retriever()

)

 

# Ask a Question

response = qa_chain.run("Explain RAG in simple terms")

print("\nAI Response:\n", response)

Vedio Link:https://youtu.be/jgqe9dMeacQ?si=q5MuPxFqH3Sr3ZPB

How This Works

1. Load documents (PDFs, text, etc.).

2.    Split into chunks for better retrieval.

3. Convert chunks to embedded systems using OpenAI.

4. Store & index Embeddings using FAISS.

5. Retrieve Relevant Chunks using Similarity Search.

6. Feed retrieved data to GPT-4 for context-aware generation.

 Vedio Link: https://youtu.be/44MsptcNzp4?si=fJR-Q4l0U3Sb6fFW

Next Steps & Optimizations

·      Use ChromaDB/Pinecone instead of FAISS for scalable cloud-based retrieval.

·      Implement hybrid search (combining BM25 + dense retrieval).

·      Fine-tune a custom LLM for domain-specific applications.

·      Streamline for Real-time Retrieval (Fetching fresh web data).