Build searches that find documents by meaning and support recommendation features using vector databases. Includes implementation patterns for storing and retrieving vectors, combining meaning with keyword matches, and tuning search speed and result quality.
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Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
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Patterns for implementing efficient similarity search in production systems.
| Metric | Formula | Best For |
|---|---|---|
| Cosine | 1 - (A·B)/(‖A‖‖B‖) | Normalized embeddings |
| Euclidean (L2) | √Σ(a-b)² | Raw embeddings |
| Dot Product | A·B | Magnitude matters |
| Manhattan (L1) | Σ | a-b |
┌─────────────────────────────────────────────────┐
│ Index Types │
├─────────────┬───────────────┬───────────────────┤
│ Flat │ HNSW │ IVF+PQ │
│ (Exact) │ (Graph-based) │ (Quantized) │
├─────────────┼───────────────┼───────────────────┤
│ O(n) search │ O(log n) │ O(√n) │
│ 100% recall │ ~95-99% │ ~90-95% │
│ Small data │ Medium-Large │ Very Large │
└─────────────┴───────────────┴───────────────────┘
from pinecone import Pinecone, ServerlessSpec
from typing import List, Dict, Optional
import hashlib
class PineconeVectorStore:
def __init__(
self,
api_key: str,
index_name: str,
dimension: int = 1536,
metric: str = "cosine"
):
self.pc = Pinecone(api_key=api_key)
# Create index if not exists
if index_name not in self.pc.list_indexes().names():
self.pc.create_index(
name=index_name,
dimension=dimension,
metric=metric,
spec=ServerlessSpec(cloud="aws", region="us-east-1")
)
self.index = self.pc.Index(index_name)
def upsert(
self,
vectors: List[Dict],
namespace: str = ""
) -> int:
"""
Upsert vectors.
vectors: [{"id": str, "values": List[float], "metadata": dict}]
"""
# Batch upsert
batch_size = 100
total = 0
for i in range(0, len(vectors), batch_size):
batch = vectors[i:i + batch_size]
self.index.upsert(vectors=batch, namespace=namespace)
total += len(batch)
return total
def search(
self,
query_vector: List[float],
top_k: int = 10,
namespace: str = "",
filter: Optional[Dict] = None,
include_metadata: bool = True
) -> List[Dict]:
"""Search for similar vectors."""
results = self.index.query(
vector=query_vector,
top_k=top_k,
namespace=namespace,
filter=filter,
include_metadata=include_metadata
)
return [
{
"id": match.id,
"score": match.score,
"metadata": match.metadata
}
for match in results.matches
]
def search_with_rerank(
self,
query: str,
query_vector: List[float],
top_k: int = 10,
rerank_top_n: int = 50,
namespace: str = ""
) -> List[Dict]:
"""Search and rerank results."""
# Over-fetch for reranking
initial_results = self.search(
query_vector,
top_k=rerank_top_n,
namespace=namespace
)
# Rerank with cross-encoder or LLM
reranked = self._rerank(query, initial_results)
return reranked[:top_k]
def _rerank(self, query: str, results: List[Dict]) -> List[Dict]:
"""Rerank results using cross-encoder."""
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
pairs = [(query, r["metadata"]["text"]) for r in results]
scores = model.predict(pairs)
for result, score in zip(results, scores):
result["rerank_score"] = float(score)
return sorted(results, key=lambda x: x["rerank_score"], reverse=True)
def delete(self, ids: List[str], namespace: str = ""):
"""Delete vectors by ID."""
self.index.delete(ids=ids, namespace=namespace)
def delete_by_filter(self, filter: Dict, namespace: str = ""):
"""Delete vectors matching filter."""
self.index.delete(filter=filter, namespace=namespace)
from qdrant_client import QdrantClient
from qdrant_client.http import models
from typing import List, Dict, Optional
class QdrantVectorStore:
def __init__(
self,
url: str = "localhost",
port: int = 6333,
collection_name: str = "documents",
vector_size: int = 1536
):
self.client = QdrantClient(url=url, port=port)
self.collection_name = collection_name
# Create collection if not exists
collections = self.client.get_collections().collections
if collection_name not in [c.name for c in collections]:
self.client.create_collection(
collection_name=collection_name,
vectors_config=models.VectorParams(
size=vector_size,
distance=models.Distance.COSINE
),
# Optional: enable quantization for memory efficiency
quantization_config=models.ScalarQuantization(
scalar=models.ScalarQuantizationConfig(
type=models.ScalarType.INT8,
quantile=0.99,
always_ram=True
)
)
)
def upsert(self, points: List[Dict]) -> int:
"""
Upsert points.
points: [{"id": str/int, "vector": List[float], "payload": dict}]
"""
qdrant_points = [
models.PointStruct(
id=p["id"],
vector=p["vector"],
payload=p.get("payload", {})
)
for p in points
]
self.client.upsert(
collection_name=self.collection_name,
points=qdrant_points
)
return len(points)
def search(
self,
query_vector: List[float],
limit: int = 10,
filter: Optional[models.Filter] = None,
score_threshold: Optional[float] = None
) -> List[Dict]:
"""Search for similar vectors."""
results = self.client.search(
collection_name=self.collection_name,
query_vector=query_vector,
limit=limit,
query_filter=filter,
score_threshold=score_threshold
)
return [
{
"id": r.id,
"score": r.score,
"payload": r.payload
}
for r in results
]
def search_with_filter(
self,
query_vector: List[float],
must_conditions: List[Dict] = None,
should_conditions: List[Dict] = None,
must_not_conditions: List[Dict] = None,
limit: int = 10
) -> List[Dict]:
"""Search with complex filters."""
conditions = []
if must_conditions:
conditions.extend([
models.FieldCondition(
key=c["key"],
match=models.MatchValue(value=c["value"])
)
for c in must_conditions
])
filter = models.Filter(must=conditions) if conditions else None
return self.search(query_vector, limit=limit, filter=filter)
def search_with_sparse(
self,
dense_vector: List[float],
sparse_vector: Dict[int, float],
limit: int = 10,
dense_weight: float = 0.7
) -> List[Dict]:
"""Hybrid search with dense and sparse vectors."""
# Requires collection with named vectors
results = self.client.search(
collection_name=self.collection_name,
query_vector=models.NamedVector(
name="dense",
vector=dense_vector
),
limit=limit
)
return [{"id": r.id, "score": r.score, "payload": r.payload} for r in results]
import asyncpg
from typing import List, Dict, Optional
import numpy as np
class PgVectorStore:
def __init__(self, connection_string: str):
self.connection_string = connection_string
async def init(self):
"""Initialize connection pool and extension."""
self.pool = await asyncpg.create_pool(self.connection_string)
async with self.pool.acquire() as conn:
# Enable extension
await conn.execute("CREATE EXTENSION IF NOT EXISTS vector")
# Create table
await conn.execute("""
CREATE TABLE IF NOT EXISTS documents (
id TEXT PRIMARY KEY,
content TEXT,
metadata JSONB,
embedding vector(1536)
)
""")
# Create index (HNSW for better performance)
await conn.execute("""
CREATE INDEX IF NOT EXISTS documents_embedding_idx
ON documents
USING hnsw (embedding vector_cosine_ops)
WITH (m = 16, ef_construction = 64)
""")
async def upsert(self, documents: List[Dict]):
"""Upsert documents with embeddings."""
async with self.pool.acquire() as conn:
await conn.executemany(
"""
INSERT INTO documents (id, content, metadata, embedding)
VALUES ($1, $2, $3, $4)
ON CONFLICT (id) DO UPDATE SET
content = EXCLUDED.content,
metadata = EXCLUDED.metadata,
embedding = EXCLUDED.embedding
""",
[
(
doc["id"],
doc["content"],
doc.get("metadata", {}),
np.array(doc["embedding"]).tolist()
)
for doc in documents
]
)
async def search(
self,
query_embedding: List[float],
limit: int = 10,
filter_metadata: Optional[Dict] = None
) -> List[Dict]:
"""Search for similar documents."""
query = """
SELECT id, content, metadata,
1 - (embedding <=> $1::vector) as similarity
FROM documents
"""
params = [query_embedding]
if filter_metadata:
conditions = []
for key, value in filter_metadata.items():
params.append(value)
conditions.append(f"metadata->>'{key}' = ${len(params)}")
query += " WHERE " + " AND ".join(conditions)
query += f" ORDER BY embedding <=> $1::vector LIMIT ${len(params) + 1}"
params.append(limit)
async with self.pool.acquire() as conn:
rows = await conn.fetch(query, *params)
return [
{
"id": row["id"],
"content": row["content"],
"metadata": row["metadata"],
"score": row["similarity"]
}
for row in rows
]
async def hybrid_search(
self,
query_embedding: List[float],
query_text: str,
limit: int = 10,
vector_weight: float = 0.5
) -> List[Dict]:
"""Hybrid search combining vector and full-text."""
async with self.pool.acquire() as conn:
rows = await conn.fetch(
"""
WITH vector_results AS (
SELECT id, content, metadata,
1 - (embedding <=> $1::vector) as vector_score
FROM documents
ORDER BY embedding <=> $1::vector
LIMIT $3 * 2
),
text_results AS (
SELECT id, content, metadata,
ts_rank(to_tsvector('english', content),
plainto_tsquery('english', $2)) as text_score
FROM documents
WHERE to_tsvector('english', content) @@ plainto_tsquery('english', $2)
LIMIT $3 * 2
)
SELECT
COALESCE(v.id, t.id) as id,
COALESCE(v.content, t.content) as content,
COALESCE(v.metadata, t.metadata) as metadata,
COALESCE(v.vector_score, 0) * $4 +
COALESCE(t.text_score, 0) * (1 - $4) as combined_score
FROM vector_results v
FULL OUTER JOIN text_results t ON v.id = t.id
ORDER BY combined_score DESC
LIMIT $3
""",
query_embedding, query_text, limit, vector_weight
)
return [dict(row) for row in rows]
import weaviate
from weaviate.util import generate_uuid5
from typing import List, Dict, Optional
class WeaviateVectorStore:
def __init__(
self,
url: str = "http://localhost:8080",
class_name: str = "Document"
):
self.client = weaviate.Client(url=url)
self.class_name = class_name
self._ensure_schema()
def _ensure_schema(self):
"""Create schema if not exists."""
schema = {
"class": self.class_name,
"vectorizer": "none", # We provide vectors
"properties": [
{"name": "content", "dataType": ["text"]},
{"name": "source", "dataType": ["string"]},
{"name": "chunk_id", "dataType": ["int"]}
]
}
if not self.client.schema.exists(self.class_name):
self.client.schema.create_class(schema)
def upsert(self, documents: List[Dict]):
"""Batch upsert documents."""
with self.client.batch as batch:
batch.batch_size = 100
for doc in documents:
batch.add_data_object(
data_object={
"content": doc["content"],
"source": doc.get("source", ""),
"chunk_id": doc.get("chunk_id", 0)
},
class_name=self.class_name,
uuid=generate_uuid5(doc["id"]),
vector=doc["embedding"]
)
def search(
self,
query_vector: List[float],
limit: int = 10,
where_filter: Optional[Dict] = None
) -> List[Dict]:
"""Vector search."""
query = (
self.client.query
.get(self.class_name, ["content", "source", "chunk_id"])
.with_near_vector({"vector": query_vector})
.with_limit(limit)
.with_additional(["distance", "id"])
)
if where_filter:
query = query.with_where(where_filter)
results = query.do()
return [
{
"id": item["_additional"]["id"],
"content": item["content"],
"source": item["source"],
"score": 1 - item["_additional"]["distance"]
}
for item in results["data"]["Get"][self.class_name]
]
def hybrid_search(
self,
query: str,
query_vector: List[float],
limit: int = 10,
alpha: float = 0.5 # 0 = keyword, 1 = vector
) -> List[Dict]:
"""Hybrid search combining BM25 and vector."""
results = (
self.client.query
.get(self.class_name, ["content", "source"])
.with_hybrid(query=query, vector=query_vector, alpha=alpha)
.with_limit(limit)
.with_additional(["score"])
.do()
)
return [
{
"content": item["content"],
"source": item["source"],
"score": item["_additional"]["score"]
}
for item in results["data"]["Get"][self.class_name]
]
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