Building a Vector Search Pipeline

45 min

End-to-End Vector Search

Now let's put it all together: document loading, chunking, embedding, and searching. This is the foundation of every RAG system.

The Complete Pipeline

pythonA minimal but complete RAG pipeline

# Complete RAG Pipeline with ChromaDB
import chromadb
from openai import OpenAI

client = OpenAI()
chroma = chromadb.Client()

# 1. Create a collection (like a table for vectors)
collection = chroma.create_collection(name="my_docs")

# 2. Add documents (ChromaDB handles embeddings!)
documents = [
    "The quick brown fox jumps over the lazy dog",
    "Machine learning is a subset of artificial intelligence",
    "Python is a popular programming language for data science",
    "Neural networks are inspired by the human brain",
]

collection.add(
    documents=documents,
    ids=[f"doc_{i}" for i in range(len(documents))]
)

# 3. Query the collection
results = collection.query(
    query_texts=["What is AI?"],
    n_results=2
)

print("Most relevant documents:")
for doc in results['documents'][0]:
    print(f"  - {doc}")

⚠️In production, use a persistent vector store like Pinecone, Weaviate, or Qdrant. ChromaDB is great for prototyping.

Improving Retrieval Quality

**Techniques for better retrieval:**

1. **Hybrid Search:** Combine vector + keyword search 2. **Re-ranking:** Use a second model to score relevance 3. **Query Expansion:** Rephrase queries multiple ways 4. **Metadata Filtering:** Filter by date, source, category 5. **Parent-Child Chunking:** Retrieve chunks, return parent docs

Vector Search Check

Question 1 of 2

What is cosine similarity used for in RAG?

PreviousUnderstanding Embeddings

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