RAG Retrieval

RAG & Retrieval - Semantic Search & Knowledge Augmentation

RAG, Embeddings, Vector Stores, ו-Semantic Search.

Quick Start - Simple RAG Pipeline

from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI

# 1. Chunk documents
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
chunks = splitter.split_documents(documents)

# 2. Create embeddings and store
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
vectorstore = Chroma.from_documents(chunks, embeddings, persist_directory="./db")

# 3. Create RAG chain
qa_chain = RetrievalQA.from_chain_type(
    llm=OpenAI(),
    retriever=vectorstore.as_retriever(search_kwargs={"k": 3}),
    return_source_documents=True
)

# 4. Query
result = qa_chain({"query": "What is machine learning?"})
print(result["result"])

When This Skill Activates

Use this skill when:

• Building knowledge bases or Q&A systems
• Implementing semantic search
• Reducing LLM hallucinations with grounding
• Creating chatbots with document context
• Working with embeddings and vector databases
• Evaluating RAG system quality

Core Patterns

Pattern 1: RAG Architecture

┌─────────────────────────────────────────────────────────────┐
│                    INDEXING STAGE (Offline)                  │
├─────────────────────────────────────────────────────────────┤
│   Parse  →  Chunk  →  Embed  →  Store                       │
│   (PDF)     (500t)    (384d)    (FAISS/Chroma)             │
├─────────────────────────────────────────────────────────────┤
│                    RUNTIME STAGE (Online)                    │
├─────────────────────────────────────────────────────────────┤
│   Query  →  Embed  →  Retrieve  →  Inject  →  Generate     │
│   (user)    (384d)    (top-k)      (prompt)   (LLM)        │
└─────────────────────────────────────────────────────────────┘

Pattern 2: Chunking Strategies

from langchain.text_splitter import RecursiveCharacterTextSplitter

# Fixed-size chunks
splitter_fixed = RecursiveCharacterTextSplitter(
    chunk_size=500,
    chunk_overlap=0
)

# Overlapping chunks (RECOMMENDED)
splitter_overlap = RecursiveCharacterTextSplitter(
    chunk_size=500,
    chunk_overlap=100  # 20% overlap preserves context
)

# Semantic chunking by headers
from langchain.text_splitter import MarkdownHeaderTextSplitter

headers = [
    ("#", "Header 1"),
    ("##", "Header 2"),
]
splitter_semantic = MarkdownHeaderTextSplitter(headers_to_split_on=headers)

Pattern 3: Embedding Models

# OpenAI Embeddings
from openai import OpenAI

client = OpenAI()
response = client.embeddings.create(
    model="text-embedding-3-small",
    input="Hello world"
)
embedding = response.data[0].embedding  # 1536 dimensions

# Sentence Transformers (Local, Free)
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("all-MiniLM-L6-v2")
embedding = model.encode("Hello world")  # 384 dimensions

# HuggingFace via LangChain
from langchain.embeddings import HuggingFaceEmbeddings

embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-base-en-v1.5")
vector = embeddings.embed_query("Hello world")  # 768 dimensions

Pattern 4: FAISS Vector Store

import faiss
import numpy as np

# Create index
dimension = 384
index = faiss.IndexFlatL2(dimension)  # L2 distance

# Add vectors
vectors = np.array(embeddings, dtype=np.float32)
index.add(vectors)

# Search
query_vector = np.array([query_embedding], dtype=np.float32)
distances, indices = index.search(query_vector, k=5)

# Get results
for i, (dist, idx) in enumerate(zip(distances[0], indices[0])):
    print(f"Rank {i+1}: Document {idx}, Distance: {dist:.4f}")

# Save/Load
faiss.write_index(index, "index.faiss")
index = faiss.read_index("index.faiss")

Pattern 5: ChromaDB Vector Store

import chromadb
from chromadb.utils import embedding_functions

# Create client
client = chromadb.PersistentClient(path="./chroma_db")

# Create collection with embeddings
embedding_fn = embedding_functions.SentenceTransformerEmbeddingFunction(
    model_name="all-MiniLM-L6-v2"
)

collection = client.create_collection(
    name="my_documents",
    embedding_function=embedding_fn
)

# Add documents
collection.add(
    documents=["Document 1 text", "Document 2 text"],
    metadatas=[{"source": "file1.pdf"}, {"source": "file2.pdf"}],
    ids=["doc1", "doc2"]
)

# Query
results = collection.query(
    query_texts=["What is machine learning?"],
    n_results=3,
    where={"source": "file1.pdf"}  # Metadata filtering
)

Pattern 6: Hybrid Search (BM25 + Semantic)

from langchain.retrievers import BM25Retriever, EnsembleRetriever
from langchain.vectorstores import Chroma

# BM25 (keyword-based)
bm25_retriever = BM25Retriever.from_documents(documents)
bm25_retriever.k = 5

# Vector retriever (semantic)
vectorstore = Chroma.from_documents(documents, embeddings)
vector_retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

# Combine
ensemble_retriever = EnsembleRetriever(
    retrievers=[bm25_retriever, vector_retriever],
    weights=[0.5, 0.5]  # Equal weights
)

# Use
docs = ensemble_retriever.get_relevant_documents("my query")

Pattern 7: Re-ranking with Cross-Encoder

from sentence_transformers import CrossEncoder

# Bi-Encoder: Fast initial retrieval (top-20)
# Cross-Encoder: Accurate re-ranking (top-5)

# Initial retrieval
initial_results = vectorstore.similarity_search(query, k=20)

# Re-rank with cross-encoder
reranker = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")
pairs = [[query, doc.page_content] for doc in initial_results]
scores = reranker.predict(pairs)

# Sort by score
reranked = sorted(zip(scores, initial_results), reverse=True)
top_5 = [doc for score, doc in reranked[:5]]

Pattern 8: RAG Evaluation (RAGAS)

from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy, context_precision

# Prepare evaluation data
eval_data = {
    "question": ["What is ML?", "What is DL?"],
    "answer": ["ML is...", "DL is..."],
    "contexts": [["ML context 1", "ML context 2"], ["DL context"]],
    "ground_truth": ["ML ground truth", "DL ground truth"]
}

# Evaluate
results = evaluate(
    dataset=eval_data,
    metrics=[faithfulness, answer_relevancy, context_precision]
)

print(results)

Reference Navigation

For detailed content, see:

• Embeddings Guide: reference/embeddings_guide.md - OpenAI, Gemini, Sentence-Transformers
• Vector Stores: reference/vector_stores.md - FAISS, ChromaDB, Pinecone
• RAG Architectures: reference/rag_architectures.md - Variants, Memory systems
• RAG Memory: reference/rag_memory.md - Conversational, Multi-turn
• RAG Evaluation: reference/rag_evaluation.md - RAGAS, LLM-as-Judge
• Hybrid Search: reference/hybrid_search.md - BM25 + Semantic

Common Mistakes to Avoid

1. Chunks Too Small

# WRONG: Information gets fragmented
splitter = RecursiveCharacterTextSplitter(chunk_size=100)

# CORRECT: Reasonable chunk size
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)

2. No Overlap Between Chunks

# WRONG: Context lost at boundaries
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)

# CORRECT: Overlap preserves context
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)

3. Mixing Embedding Models

# WRONG: Different models produce incompatible embeddings
index_embeddings = model_a.encode(documents)
query_embedding = model_b.encode(query)  # Different model!

# CORRECT: Same model for indexing and querying
embeddings_model = SentenceTransformer("all-MiniLM-L6-v2")
index_embeddings = embeddings_model.encode(documents)
query_embedding = embeddings_model.encode(query)

4. Not Validating Retrieval Before LLM

# WRONG: Debugging LLM when retrieval is the problem
# Always check what's being retrieved first!

# CORRECT: Validate retrieval separately
docs = retriever.get_relevant_documents(query)
for i, doc in enumerate(docs):
    print(f"Doc {i}: {doc.page_content[:200]}")
# Then check if these are the right documents

5. Ignoring Metadata Filtering

# WRONG: Retrieve from all documents
results = vectorstore.similarity_search(query, k=5)

# CORRECT: Filter by metadata when relevant
results = vectorstore.similarity_search(
    query, k=5,
    filter={"document_type": "policy", "year": 2024}
)

Teaching Mode

When explaining RAG:

RAG Intuition

Without RAG:
"What's our refund policy?" → LLM guesses (may hallucinate!)

With RAG:
"What's our refund policy?"
  → Search company documents
  → Find: "Refunds within 30 days with receipt"
  → LLM answers: "Our refund policy allows returns within 30 days..."

RAG = "Open book exam" for LLMs

Embedding Space Visual

Similar documents are CLOSE in embedding space:

    ● "machine learning tutorial"
        ↘
          ● "ML course content"
            ↘
              ● "deep learning basics"


    ● "cooking recipes"
    (far away - different topic)

Retrieval Pipeline

Query: "How do I train a neural network?"
            ↓
       [Embed Query]
            ↓
       [Search Vector DB]
            ↓
    Top 3 relevant chunks:
    1. "Training neural networks involves..."
    2. "Use backpropagation to update..."
    3. "Choose optimizer like Adam..."
            ↓
       [Inject into prompt]
            ↓
    "Using the following context: [chunks]
     Answer: How do I train a neural network?"
            ↓
       [LLM generates grounded answer]