Vector Search Patterns

Implement semantic similarity search for retrieval-augmented generation and intelligent querying.

Embedding Pipeline

Generating Embeddings

import httpx

async def get_embeddings(texts: list[str], model: str = "text-embedding-3-small") -> list[list[float]]:
    async with httpx.AsyncClient() as client:
        response = await client.post(
            "https://api.openai.com/v1/embeddings",
            headers={"Authorization": f"Bearer {api_key}"},
            json={"input": texts, "model": model},
        )
        data = response.json()
        return [item["embedding"] for item in data["data"]]

Chunking Strategies

Strategy	Use Case	Chunk Size
Fixed-size	Simple documents	500-1000 tokens
Sentence-based	Articles, essays	3-5 sentences
Paragraph-based	Structured docs	Natural breaks
Recursive	Mixed content	Hierarchical split
Semantic	Research papers	Topic boundaries

def chunk_text(text: str, max_tokens: int = 500, overlap: int = 50) -> list[str]:
    sentences = text.split(". ")
    chunks = []
    current = []
    current_len = 0

    for sentence in sentences:
        sentence_len = len(sentence.split())
        if current_len + sentence_len > max_tokens and current:
            chunks.append(". ".join(current) + ".")
            # Keep overlap
            overlap_start = max(0, len(current) - 2)
            current = current[overlap_start:]
            current_len = sum(len(s.split()) for s in current)
        current.append(sentence)
        current_len += sentence_len

    if current:
        chunks.append(". ".join(current) + ".")
    return chunks

Vector Stores

ChromaDB (Development / Small Scale)

import chromadb

client = chromadb.PersistentClient(path="./chroma_data")
collection = client.get_or_create_collection(
    name="documents",
    metadata={"hnsw:space": "cosine"},
)

# Ingest
collection.add(
    ids=["doc1", "doc2"],
    documents=["First document text", "Second document text"],
    metadatas=[{"source": "wiki"}, {"source": "blog"}],
)

# Query
results = collection.query(
    query_texts=["search query"],
    n_results=5,
    where={"source": "wiki"},  # Metadata filter
)

pgvector (Production / PostgreSQL)

-- Enable extension
CREATE EXTENSION IF NOT EXISTS vector;

-- Create table
CREATE TABLE documents (
    id SERIAL PRIMARY KEY,
    content TEXT NOT NULL,
    embedding vector(1536),  -- Match embedding dimension
    metadata JSONB,
    created_at TIMESTAMPTZ DEFAULT NOW()
);

-- Create index (IVFFlat for large datasets)
CREATE INDEX ON documents
    USING ivfflat (embedding vector_cosine_ops)
    WITH (lists = 100);  -- sqrt(num_rows) is a good starting point

-- Or HNSW for better recall
CREATE INDEX ON documents
    USING hnsw (embedding vector_cosine_ops)
    WITH (m = 16, ef_construction = 64);

-- Similarity search
SELECT id, content, 1 - (embedding <=> $1::vector) AS similarity
FROM documents
ORDER BY embedding <=> $1::vector
LIMIT 10;

import asyncpg

async def search(query_embedding: list[float], limit: int = 10) -> list[dict]:
    conn = await asyncpg.connect(dsn)
    rows = await conn.fetch(
        """
        SELECT id, content, 1 - (embedding <=> $1::vector) AS similarity
        FROM documents
        ORDER BY embedding <=> $1::vector
        LIMIT $2
        """,
        str(query_embedding), limit,
    )
    return [dict(row) for row in rows]

FAISS (High-Performance Local)

import faiss
import numpy as np

# Build index
dimension = 1536
index = faiss.IndexFlatIP(dimension)  # Inner product (use with normalized vectors)

# Add vectors
vectors = np.array(embeddings, dtype=np.float32)
faiss.normalize_L2(vectors)
index.add(vectors)

# Search
query = np.array([query_embedding], dtype=np.float32)
faiss.normalize_L2(query)
distances, indices = index.search(query, k=10)

Hybrid Search

Combine vector similarity with keyword search for better recall:

-- Hybrid: vector + full-text search
WITH vector_results AS (
    SELECT id, content, 1 - (embedding <=> $1::vector) AS vscore
    FROM documents
    ORDER BY embedding <=> $1::vector
    LIMIT 20
),
text_results AS (
    SELECT id, content, ts_rank(tsv, plainto_tsquery($2)) AS tscore
    FROM documents
    WHERE tsv @@ plainto_tsquery($2)
    LIMIT 20
)
SELECT
    COALESCE(v.id, t.id) AS id,
    COALESCE(v.content, t.content) AS content,
    COALESCE(v.vscore, 0) * 0.7 + COALESCE(t.tscore, 0) * 0.3 AS combined_score
FROM vector_results v
FULL OUTER JOIN text_results t ON v.id = t.id
ORDER BY combined_score DESC
LIMIT 10;

RAG Pipeline

async def rag_query(question: str) -> str:
    # 1. Embed the question
    query_embedding = (await get_embeddings([question]))[0]

    # 2. Retrieve relevant chunks
    chunks = await search(query_embedding, limit=5)

    # 3. Build context
    context = "\n\n".join(c["content"] for c in chunks)

    # 4. Generate answer with context
    response = await llm.complete(
        system="Answer based on the provided context. Cite sources.",
        messages=[{
            "role": "user",
            "content": f"Context:\n{context}\n\nQuestion: {question}",
        }],
    )
    return response

Index Selection Guide

Dataset Size	Recommended Index	Recall	Speed
< 10K	Flat (exact)	100%	Fast enough
10K - 1M	HNSW	95-99%	Very fast
1M - 100M	IVFFlat	90-95%	Fast
> 100M	IVF + PQ	80-90%	Fastest

Anti-Patterns

• Embedding entire documents — Chunk first, embed chunks
• No metadata filtering — Pre-filter with metadata before vector search
• Ignoring embedding model limits — Respect max token limits per embedding call
• Static chunking for all content — Match chunking strategy to content type
• No reranking — Use a cross-encoder reranker for top results in production
• Storing embeddings without source text — Always store the original text alongside vectors