I’ve been thinking a lot about document intelligence lately—how we can build systems that truly understand and work with large collections of documents. This isn’t just about search; it’s about creating AI that can reason with your specific information. That’s why I want to share a practical approach to building production-ready systems using LangChain and Chroma.
Have you ever wondered how AI systems can answer questions based on your specific documents while avoiding made-up information? This is where Retrieval-Augmented Generation comes into play.
Let me show you how to set up a robust development environment. You’ll need to start with the right dependencies. Here’s a practical setup:
pip install langchain chromadb sentence-transformers openai fastapi uvicornNow, let’s talk about document processing. How do you handle different file types effectively? Here’s a practical approach:
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import PyPDFLoader
def process_pdf(file_path):
loader = PyPDFLoader(file_path)
documents = loader.load()
splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200
)
return splitter.split_documents(documents)What makes a good chunking strategy? It’s about balancing context preservation with retrieval efficiency. Smaller chunks are easier to retrieve accurately, while larger chunks provide more context.
When it comes to vector storage, Chroma offers a straightforward solution. Here’s how you can set it up:
import chromadb
from sentence_transformers import SentenceTransformer
embedder = SentenceTransformer('all-MiniLM-L6-v2')
client = chromadb.Client()
collection = client.create_collection("documents")
# Store your processed documents
embeddings = embedder.encode([doc.page_content for doc in documents])
collection.add(
embeddings=embeddings,
documents=[doc.page_content for doc in documents],
metadatas=[doc.metadata for doc in documents]
)But how do you ensure your system performs well in production? It’s not just about the initial setup. You need to think about error handling, monitoring, and scalability.
Here’s a simple API endpoint pattern I often use:
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
app = FastAPI()
class QueryRequest(BaseModel):
question: str
top_k: int = 5
@app.post("/query")
async def handle_query(request: QueryRequest):
try:
# Your retrieval and generation logic here
result = process_query(request.question, request.top_k)
return {"answer": result}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))What separates a prototype from a production system? It’s the attention to details like proper error handling, logging, and monitoring. You’ll want to track metrics like retrieval accuracy, response time, and user satisfaction.
Remember that deployment considerations matter too. Containerization with Docker ensures consistency across environments. Here’s a basic Dockerfile structure:
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]Security is another critical aspect. Always validate inputs, implement rate limiting, and ensure proper authentication for your API endpoints.
The real magic happens when you start optimizing. Experiment with different embedding models, try various chunking strategies, and consider implementing hybrid search approaches. Sometimes combining keyword search with semantic search yields the best results.
Building these systems requires continuous iteration. Start simple, measure performance, and gradually add complexity based on real-world usage patterns.
What challenges have you faced when working with document-based AI systems? I’d love to hear about your experiences and solutions.
If you found this helpful, please share it with others who might benefit. Your comments and feedback help improve future content. Let me know what specific aspects you’d like to explore further!
