Building RAG Systems from Scratch

Give AI Your Custom Knowledge

RAG lets AI answer questions using your documents, not just training data.

RAG Architecture

1. Ingest: Split documents into chunks
2. Embed: Convert chunks to vectors
3. Store: Save in vector database
4. Retrieve: Find relevant chunks
5. Generate: AI answers using context

Implementation Steps

1. Chunk documents:
```python
from langchain.text_splitter import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200
)
chunks = splitter.split_documents(docs)
```

2. Create embeddings:
```python
from openai import OpenAI
client = OpenAI()

embedding = client.embeddings.create(
model="text-embedding-3-small",
input=text
)
```

3. Retrieve and generate:

• Search vector DB for relevant chunks
• Pass chunks as context to LLM
• Generate answer

Optimization

• Tune chunk size
• Improve retrieval relevance
• Handle multi-hop questions
• Add metadata filtering