RAG Pipelines
Retrieval-Augmented Generation pipeline design: chunking, embeddings, vector stores, retrieval strategies, and evaluation.
Pipeline Architecture
Documents → Chunk → Embed → Store → Retrieve → Generate
Chunking Strategies
| Strategy |
Best For |
Chunk Size |
| Fixed-size |
General docs |
500-1000 tokens |
| Semantic |
Long-form content |
By paragraph/section |
| Code-aware |
Source code |
By function/class |
| Recursive |
Mixed content |
1000 → 500 → 200 tokens |
from langchain.text_splitter import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
separators=["\n\n", "\n", ". ", " ", ""]
)
Embedding Models
| Model |
Dimensions |
Speed |
Quality |
text-embedding-3-small |
1536 |
Fast |
Good |
text-embedding-3-large |
3072 |
Medium |
Best |
bge-large-en |
1024 |
Fast |
Good |
e5-large-v2 |
1024 |
Fast |
Good |
Vector Stores
| Store |
Use Case |
Scaling |
| Pinecone |
Production, managed |
Auto-scales |
| Weaviate |
Production, self-hosted |
Horizontal |
| pgvector |
PostgreSQL shops |
Vertical |
| Chroma |
Prototyping, local |
Single-node |
from langchain.vectorstores import Chroma
vector_store = Chroma.from_documents(
documents=chunks,
embedding=embeddings,
persist_directory="./chroma_db"
)
Retrieval Strategies
Similarity Search
retriever = vector_store.as_retriever(search_type="similarity", k=4)
MMR (Diversity-focused)
retriever = vector_store.as_retriever(
search_type="mmr",
search_kwargs={"k": 4, "lambda_mult": 0.7}
)
Hybrid (BM25 + Semantic)
from langchain.retrievers import EnsembleRetriever
retriever = EnsembleRetriever(
retrievers=[bm25_retriever, vector_retriever],
weights=[0.3, 0.7]
)
Self-Querying
from langchain.retrievers.self_query.base import SelfQueryRetriever
retriever = SelfQueryRetriever.from_llm(
llm, vector_store, document_contents, metadata_field_info
)
Evaluation (RAGAS)
from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevance, context_precision
result = evaluate(
dataset=eval_dataset,
metrics=[faithfulness, answer_relevance, context_precision]
)
| Metric |
Target |
| Faithfulness |
≥0.8 |
| Answer Relevance |
≥0.8 |
| Context Precision |
≥0.7 |
Demo Patterns
Gradio
import gradio as gr
def answer(question):
return qa_chain.run(question)
gr.Interface(fn=answer, inputs="text", outputs="text").launch()
Streamlit
import streamlit as st
question = st.text_input("Ask a question")
if question:
st.write(qa_chain.run(question))
Best Practices
- Chunk overlap prevents context loss at boundaries
- Metadata filtering improves retrieval precision
- Hybrid retrieval balances recall and precision
- Evaluate before deploying — use RAGAS metrics
- Cache frequent queries to reduce latency
ils15