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llama_stack/providers/remote/vector_io/pgvector/pgvector.py
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@ -4,6 +4,7 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import heapq
from typing import Any
import psycopg2
@ -23,6 +24,9 @@ from llama_stack.apis.vector_io import (
)
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
from llama_stack.providers.utils.inference.prompt_adapter import (
interleaved_content_as_str,
)
from llama_stack.providers.utils.kvstore import kvstore_impl
from llama_stack.providers.utils.kvstore.api import KVStore
from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
@ -31,6 +35,7 @@ from llama_stack.providers.utils.memory.vector_store import (
EmbeddingIndex,
VectorDBWithIndex,
)
from llama_stack.providers.utils.vector_io.vector_utils import WeightedInMemoryAggregator, sanitize_collection_name
from .config import PGVectorVectorIOConfig
@ -72,25 +77,63 @@ def load_models(cur, cls):
class PGVectorIndex(EmbeddingIndex):
def __init__(self, vector_db: VectorDB, dimension: int, conn, kvstore: KVStore | None = None):
self.conn = conn
with conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
# Sanitize the table name by replacing hyphens with underscores
# SQL doesn't allow hyphens in table names, and vector_db.identifier may contain hyphens
# when created with patterns like "test-vector-db-{uuid4()}"
sanitized_identifier = vector_db.identifier.replace("-", "_")
self.table_name = f"vector_store_{sanitized_identifier}"
self.kvstore = kvstore
# reference: https://github.com/pgvector/pgvector?tab=readme-ov-file#querying
PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION: dict[str, str] = {
"L2": "<->",
"L1": "<+>",
"COSINE": "<=>",
"INNER_PRODUCT": "<#>",
"HAMMING": "<~>",
"JACCARD": "<%>",
}
cur.execute(
f"""
CREATE TABLE IF NOT EXISTS {self.table_name} (
id TEXT PRIMARY KEY,
document JSONB,
embedding vector({dimension})
def __init__(
self,
vector_db: VectorDB,
dimension: int,
conn: psycopg2.extensions.connection,
kvstore: KVStore | None = None,
distance_metric: str = "COSINE",
):
self.vector_db = vector_db
self.dimension = dimension
self.conn = conn
self.kvstore = kvstore
self.check_distance_metric_availability(distance_metric)
self.distance_metric = distance_metric
self.table_name = None
async def initialize(self) -> None:
try:
with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
# Sanitize the table name by replacing hyphens with underscores
# SQL doesn't allow hyphens in table names, and vector_db.identifier may contain hyphens
# when created with patterns like "test-vector-db-{uuid4()}"
sanitized_identifier = sanitize_collection_name(self.vector_db.identifier)
self.table_name = f"vs_{sanitized_identifier}"
cur.execute(
f"""
CREATE TABLE IF NOT EXISTS {self.table_name} (
id TEXT PRIMARY KEY,
document JSONB,
embedding vector({self.dimension}),
content_text TEXT,
tokenized_content TSVECTOR
)
"""
)
"""
)
# Create GIN index for full-text search performance
cur.execute(
f"""
CREATE INDEX IF NOT EXISTS {self.table_name}_content_gin_idx
ON {self.table_name} USING GIN(tokenized_content)
"""
)
except Exception as e:
log.exception(f"Error creating PGVectorIndex for vector_db: {self.vector_db.identifier}")
raise RuntimeError(f"Error creating PGVectorIndex for vector_db: {self.vector_db.identifier}") from e
async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray):
assert len(chunks) == len(embeddings), (
@ -99,29 +142,49 @@ class PGVectorIndex(EmbeddingIndex):
values = []
for i, chunk in enumerate(chunks):
content_text = interleaved_content_as_str(chunk.content)
values.append(
(
f"{chunk.chunk_id}",
Json(chunk.model_dump()),
embeddings[i].tolist(),
content_text,
content_text, # Pass content_text twice - once for content_text column, once for to_tsvector function. Eg. to_tsvector(content_text) = tokenized_content
)
)
query = sql.SQL(
f"""
INSERT INTO {self.table_name} (id, document, embedding)
INSERT INTO {self.table_name} (id, document, embedding, content_text, tokenized_content)
VALUES %s
ON CONFLICT (id) DO UPDATE SET embedding = EXCLUDED.embedding, document = EXCLUDED.document
ON CONFLICT (id) DO UPDATE SET
embedding = EXCLUDED.embedding,
document = EXCLUDED.document,
content_text = EXCLUDED.content_text,
tokenized_content = EXCLUDED.tokenized_content
"""
)
with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
execute_values(cur, query, values, template="(%s, %s, %s::vector)")
execute_values(cur, query, values, template="(%s, %s, %s::vector, %s, to_tsvector('english', %s))")
async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
"""
Performs vector similarity search using PostgreSQL's search function. Default distance metric is COSINE.
Args:
embedding: The query embedding vector
k: Number of results to return
score_threshold: Minimum similarity score threshold
Returns:
QueryChunksResponse with combined results
"""
pgvector_search_function = self.get_pgvector_search_function()
with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
cur.execute(
f"""
SELECT document, embedding <-> %s::vector AS distance
SELECT document, embedding {pgvector_search_function} %s::vector AS distance
FROM {self.table_name}
ORDER BY distance
LIMIT %s
@ -147,7 +210,40 @@ class PGVectorIndex(EmbeddingIndex):
k: int,
score_threshold: float,
) -> QueryChunksResponse:
raise NotImplementedError("Keyword search is not supported in PGVector")
"""
Performs keyword-based search using PostgreSQL's full-text search with ts_rank scoring.
Args:
query_string: The text query for keyword search
k: Number of results to return
score_threshold: Minimum similarity score threshold
Returns:
QueryChunksResponse with combined results
"""
with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
# Use plainto_tsquery to handle user input safely and ts_rank for relevance scoring
cur.execute(
f"""
SELECT document, ts_rank(tokenized_content, plainto_tsquery('english', %s)) AS score
FROM {self.table_name}
WHERE tokenized_content @@ plainto_tsquery('english', %s)
ORDER BY score DESC
LIMIT %s
""",
(query_string, query_string, k),
)
results = cur.fetchall()
chunks = []
scores = []
for doc, score in results:
if score < score_threshold:
continue
chunks.append(Chunk(**doc))
scores.append(float(score))
return QueryChunksResponse(chunks=chunks, scores=scores)
async def query_hybrid(
self,
@ -158,7 +254,59 @@ class PGVectorIndex(EmbeddingIndex):
reranker_type: str,
reranker_params: dict[str, Any] | None = None,
) -> QueryChunksResponse:
raise NotImplementedError("Hybrid search is not supported in PGVector")
"""
Hybrid search combining vector similarity and keyword search using configurable reranking.
Args:
embedding: The query embedding vector
query_string: The text query for keyword search
k: Number of results to return
score_threshold: Minimum similarity score threshold
reranker_type: Type of reranker to use ("rrf" or "weighted")
reranker_params: Parameters for the reranker
Returns:
QueryChunksResponse with combined results
"""
if reranker_params is None:
reranker_params = {}
# Get results from both search methods
vector_response = await self.query_vector(embedding, k, score_threshold)
keyword_response = await self.query_keyword(query_string, k, score_threshold)
# Convert responses to score dictionaries using chunk_id
vector_scores = {
chunk.chunk_id: score for chunk, score in zip(vector_response.chunks, vector_response.scores, strict=False)
}
keyword_scores = {
chunk.chunk_id: score
for chunk, score in zip(keyword_response.chunks, keyword_response.scores, strict=False)
}
# Combine scores using the reranking utility
combined_scores = WeightedInMemoryAggregator.combine_search_results(
vector_scores, keyword_scores, reranker_type, reranker_params
)
# Efficient top-k selection because it only tracks the k best candidates it's seen so far
top_k_items = heapq.nlargest(k, combined_scores.items(), key=lambda x: x[1])
# Filter by score threshold
filtered_items = [(doc_id, score) for doc_id, score in top_k_items if score >= score_threshold]
# Create a map of chunk_id to chunk for both responses
chunk_map = {c.chunk_id: c for c in vector_response.chunks + keyword_response.chunks}
# Use the map to look up chunks by their IDs
chunks = []
scores = []
for doc_id, score in filtered_items:
if doc_id in chunk_map:
chunks.append(chunk_map[doc_id])
scores.append(score)
return QueryChunksResponse(chunks=chunks, scores=scores)
async def delete(self):
with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
@ -170,6 +318,25 @@ class PGVectorIndex(EmbeddingIndex):
with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
cur.execute(f"DELETE FROM {self.table_name} WHERE id = ANY(%s)", (chunk_ids,))
def get_pgvector_search_function(self) -> str:
return self.PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION[self.distance_metric]
def check_distance_metric_availability(self, distance_metric: str) -> None:
"""Check if the distance metric is supported by PGVector.
Args:
distance_metric: The distance metric to check
Raises:
ValueError: If the distance metric is not supported
"""
if distance_metric not in self.PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION:
supported_metrics = list(self.PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION.keys())
raise ValueError(
f"Distance metric '{distance_metric}' is not supported by PGVector. "
f"Supported metrics are: {', '.join(supported_metrics)}"
)
class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPrivate):
def __init__(
@ -185,8 +352,8 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco
self.files_api = files_api
self.kvstore: KVStore | None = None
self.vector_db_store = None
self.openai_vector_store: dict[str, dict[str, Any]] = {}
self.metadatadata_collection_name = "openai_vector_stores_metadata"
self.openai_vector_stores: dict[str, dict[str, Any]] = {}
self.metadata_collection_name = "openai_vector_stores_metadata"
async def initialize(self) -> None:
log.info(f"Initializing PGVector memory adapter with config: {self.config}")
@ -233,9 +400,13 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco
upsert_models(self.conn, [(vector_db.identifier, vector_db)])
# Create and cache the PGVector index table for the vector DB
pgvector_index = PGVectorIndex(
vector_db=vector_db, dimension=vector_db.embedding_dimension, conn=self.conn, kvstore=self.kvstore
)
await pgvector_index.initialize()
index = VectorDBWithIndex(
vector_db,
index=PGVectorIndex(vector_db, vector_db.embedding_dimension, self.conn, kvstore=self.kvstore),
index=pgvector_index,
inference_api=self.inference_api,
)
self.cache[vector_db.identifier] = index
@ -272,8 +443,15 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco
if vector_db_id in self.cache:
return self.cache[vector_db_id]
if self.vector_db_store is None:
raise VectorStoreNotFoundError(vector_db_id)
vector_db = await self.vector_db_store.get_vector_db(vector_db_id)
if not vector_db:
raise VectorStoreNotFoundError(vector_db_id)
index = PGVectorIndex(vector_db, vector_db.embedding_dimension, self.conn)
await index.initialize()
self.cache[vector_db_id] = VectorDBWithIndex(vector_db, index, self.inference_api)
return self.cache[vector_db_id]