LLM: Building a RAG System with Python, LangChain, OpenAI, and Qdrant

With the rapid adoption of large language models (LLMs) across industries, combining them with retrieval techniques to access real-time, specific information has become a necessity. This integration, known as Retrieval-Augmented Generation (RAG), allows LLMs to pull in relevant data for more accurate, context-specific responses. In this article, I’ll walk you through building a RAG system using Python, LangChain, OpenAI's API, and Qdrant, a fast and scalable vector database.

What is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation involves a two-step approach:

1. Retrieve: Documents or data are fetched from a knowledge base based on user queries.
2. Generate: The LLM processes the query along with retrieved data to generate a contextually informed response.

By pairing an LLM with retrieval, you can reduce hallucinations, tailor responses to specific knowledge bases, and improve response quality overall.

Why Choose LangChain and Qdrant?

LangChain is a Python library that simplifies the creation of LLM applications. It streamlines prompt management, chains steps, and integrates with both retrieval tools and memory structures, making it an ideal framework for RAG.

Qdrant is a vector database designed for handling large datasets with high performance, making it well-suited for RAG implementations where speed and accuracy are critical. Qdrant’s ease of integration with LangChain further simplifies our pipeline.

Step-by-Step Guide to Building a RAG System with Python, LangChain, OpenAI, and Qdrant

1. Set Up Your Environment

pip install langchain openai qdrant-client        

2. Creating Your Knowledge Base with Qdrant

To set up RAG, you need a robust knowledge base for retrieving relevant data. We’ll use Qdrant as our vector store to manage and search documents.

from qdrant_client import QdrantClient
from qdrant_client.models import PointStruct
from langchain.embeddings import OpenAIEmbeddings

# Initialize Qdrant client
qdrant_client = QdrantClient(host="localhost", port=6333)

# Create a collection in Qdrant
qdrant_client.recreate_collection(
    collection_name="document_collection",
    vector_size=1536  # Ensure this matches the embedding model
)

# Generate embeddings and store them in Qdrant
documents = ["Document 1 text", "Document 2 text", "Document 3 text"]
embedding_model = OpenAIEmbeddings(model="text-embedding-ada-002")

for idx, doc in enumerate(documents):
    embedding = embedding_model.embed_text(doc)
    point = PointStruct(id=idx, vector=embedding, payload={"text": doc})
    qdrant_client.upsert(collection_name="document_collection", points=[point])
        

3. Configuring the RAG System with LangChain

With Qdrant set up, we can now define the retrieval and generation components. LangChain allows us to use Qdrant as a retriever and connect it to an OpenAI LLM.

from langchain.vectorstores import Qdrant
from langchain.llms import OpenAI
from langchain.chains import RetrievalQAChain

# Set up vector store with Qdrant
vector_store = Qdrant(qdrant_client, "document_collection", embedding_model)

# Initialize the LLM
llm = OpenAI(model="text-davinci-003")

# Define the RAG Chain
rag_chain = RetrievalQAChain(llm=llm, retriever=vector_store.as_retriever())        

4. Testing Your RAG System

Now you’re ready to run some queries and see your RAG system in action!

query = "Explain Document 1's content in simple terms"
response = rag_chain.run(query)
print(response)        

By following this workflow, the RAG system will retrieve relevant information from the knowledge base and generate a response using the OpenAI model. The result? A highly relevant and accurate response that’s informed by your specific data.

Real-World Applications for RAG Systems

This RAG system setup can be applied in various domains:

• Customer Support: Enhance response quality with specific knowledge-based answers.
• Healthcare: Provide reliable responses by retrieving information from medical databases.
• Education: Deliver accurate answers to student queries based on curriculum documents.

Conclusion

Combining Python, LangChain, OpenAI, and Qdrant, you can build a powerful RAG system that enables your LLM to generate accurate responses with relevant background data. This architecture not only scales well but also lets you maintain and update your knowledge base effortlessly.

Ready to build your own RAG system? Feel free to reach out or leave comments on how you’re applying RAG in your projects!