RAG System Development 2025: Complete Guide to LLM Integration + Vector Database Setup

1. Document Ingestion Pipeline

• • Document Loading: PDFs, Word docs, web pages, databases
• • Chunking: Split documents into semantic chunks (500-1000 tokens)
• • Metadata Extraction: Tags, dates, authors, categories
• • Embedding Generation: Convert text to vector representations
• • Vector Storage: Index embeddings in vector database

2. Query Pipeline

• • Query Processing: Parse and enhance user question
• • Embedding: Convert query to vector
• • Retrieval: Find similar chunks in vector DB
• • Re-ranking: Order results by relevance
• • Context Assembly: Build prompt with retrieved context
• • Generation: LLM generates response

Popular Embedding Models 2025

• • OpenAI text-embedding-3-large: Best overall accuracy, 3072 dimensions
• • Cohere embed-v3: Great for multilingual, competitive pricing
• • Voyage AI: Excellent for code and technical docs
• • BGE (BAAI): Open source, self-hostable
• • E5: Microsoft's multilingual option

Advanced Retrieval Techniques

• Hybrid Search: Combine vector search with keyword (BM25)
• Re-ranking: Use cross-encoder models for result refinement
• Query Expansion: Generate multiple query variants
• Contextual Compression: Extract only relevant parts
• Multi-query: Break complex questions into sub-queries

Basic RAG Pipeline with LangChain

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA

# Initialize components
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
vectorstore = Pinecone.from_existing_index("my-index", embeddings)
llm = ChatOpenAI(model="gpt-4-turbo", temperature=0)

# Create RAG chain
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever(search_kwargs={"k": 5}),
    return_source_documents=True
)

# Query
result = qa_chain.invoke({"query": "What is our refund policy?"})
print(result["result"])