From ca218da615621564e27ff9003707903a26c5a4c8 Mon Sep 17 00:00:00 2001 From: Rajat Ahuja Date: Thu, 4 Dec 2025 12:49:53 -0500 Subject: [PATCH] feat: init turbopuffer and lanceDB --- ...move_embedding_columns_for_vector_store.py | 136 +++ pyproject.toml | 4 +- src/config.py | 41 +- src/crud/document.py | 123 ++- src/crud/message.py | 36 +- src/crud/representation.py | 24 +- src/crud/workspace.py | 48 + src/models.py | 41 +- src/utils/search.py | 82 +- src/vector_store/__init__.py | 194 ++++ src/vector_store/lancedb.py | 287 +++++ src/vector_store/turbopuffer.py | 285 +++++ uv.lock | 983 +++++++++++++++++- 13 files changed, 2204 insertions(+), 80 deletions(-) create mode 100644 migrations/versions/f1a2b3c4d5e6_remove_embedding_columns_for_vector_store.py create mode 100644 src/vector_store/__init__.py create mode 100644 src/vector_store/lancedb.py create mode 100644 src/vector_store/turbopuffer.py diff --git a/migrations/versions/f1a2b3c4d5e6_remove_embedding_columns_for_vector_store.py b/migrations/versions/f1a2b3c4d5e6_remove_embedding_columns_for_vector_store.py new file mode 100644 index 00000000..ab9f49b1 --- /dev/null +++ b/migrations/versions/f1a2b3c4d5e6_remove_embedding_columns_for_vector_store.py @@ -0,0 +1,136 @@ +"""remove embedding columns for vector store migration + +This migration removes the embedding columns from the message_embeddings and documents +tables as part of the migration from pgvector to external vector stores (turbopuffer/lancedb). + +Revision ID: f1a2b3c4d5e6 +Revises: baa22cad81e2 +Create Date: 2025-11-24 12:00:00.000000 + +""" + +from collections.abc import Sequence + +import sqlalchemy as sa +from alembic import op +from pgvector.sqlalchemy import Vector + +from migrations.utils import column_exists, get_schema, index_exists + +# revision identifiers, used by Alembic. +revision: str = "f1a2b3c4d5e6" +down_revision: str | None = "baa22cad81e2" +branch_labels: str | Sequence[str] | None = None +depends_on: str | Sequence[str] | None = None + +schema = get_schema() + + +def upgrade() -> None: + """Remove embedding columns and HNSW indexes from message_embeddings and documents tables. + Also add chunk_index column to message_embeddings for tracking chunked message embeddings. + """ + inspector = sa.inspect(op.get_bind()) + + # === message_embeddings table === + + # Drop HNSW index on message_embeddings.embedding if it exists + # Check for both possible index names (old naming vs new naming convention) + for index_name in [ + "ix_message_embeddings_embedding_hnsw", + "idx_message_embeddings_embedding_hnsw", + ]: + if index_exists("message_embeddings", index_name, inspector): + op.drop_index(index_name, table_name="message_embeddings", schema=schema) + + # Drop embedding column from message_embeddings if it exists + if column_exists("message_embeddings", "embedding", inspector): + op.drop_column("message_embeddings", "embedding", schema=schema) + + # Add chunk_index column to message_embeddings if it doesn't exist + # This is needed to track which chunk of a message this embedding represents + # Vector ID format: {message_public_id}_{chunk_index} + if not column_exists("message_embeddings", "chunk_index", inspector): + op.add_column( + "message_embeddings", + sa.Column( + "chunk_index", + sa.Integer(), + nullable=False, + server_default="0", + ), + schema=schema, + ) + + # === documents table === + + # Drop HNSW index on documents.embedding if it exists + # Check for both possible index names (old naming vs new naming convention) + for index_name in [ + "ix_documents_embedding_hnsw", + "idx_documents_embedding_hnsw", + ]: + if index_exists("documents", index_name, inspector): + op.drop_index(index_name, table_name="documents", schema=schema) + + # Drop embedding column from documents if it exists + if column_exists("documents", "embedding", inspector): + op.drop_column("documents", "embedding", schema=schema) + + +def downgrade() -> None: + """Restore embedding columns and HNSW indexes, remove chunk_index. + + Note: This downgrade will create empty embedding columns. The actual embeddings + would need to be restored from a backup or regenerated if rolling back this migration. + """ + + inspector = sa.inspect(op.get_bind()) + + # === documents table === + + # Add embedding column back to documents if it doesn't exist + if not column_exists("documents", "embedding", inspector): + op.add_column( + "documents", + sa.Column("embedding", Vector(1536), nullable=True), + schema=schema, + ) + + # Recreate HNSW index on documents.embedding + if not index_exists("documents", "ix_documents_embedding_hnsw", inspector): + op.execute( + f""" + CREATE INDEX ix_documents_embedding_hnsw + ON {schema}.documents + USING hnsw (embedding vector_cosine_ops) + WITH (m = 16, ef_construction = 64) + """ + ) + + # === message_embeddings table === + + # Remove chunk_index column if it exists + if column_exists("message_embeddings", "chunk_index", inspector): + op.drop_column("message_embeddings", "chunk_index", schema=schema) + + # Add embedding column back to message_embeddings if it doesn't exist + if not column_exists("message_embeddings", "embedding", inspector): + op.add_column( + "message_embeddings", + sa.Column("embedding", Vector(1536), nullable=True), + schema=schema, + ) + + # Recreate HNSW index on message_embeddings.embedding + if not index_exists( + "message_embeddings", "ix_message_embeddings_embedding_hnsw", inspector + ): + op.execute( + f""" + CREATE INDEX ix_message_embeddings_embedding_hnsw + ON {schema}.message_embeddings + USING hnsw (embedding vector_cosine_ops) + WITH (m = 16, ef_construction = 64) + """ + ) diff --git a/pyproject.toml b/pyproject.toml index 10332784..cd279cc4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,6 +35,8 @@ dependencies = [ "json-repair>=0.49.0", "redis>=6.0.0", "cashews[redis]==7.4.1", + "turbopuffer>=1.8.1", + "lancedb>=0.25.3", ] [tool.uv] dev-dependencies = [ @@ -110,7 +112,7 @@ reportUnusedCallResult = false reportCallInDefaultInitializer = false reportAny = false reportExplicitAny = false -allowedUntypedLibraries = ["langfuse"] +allowedUntypedLibraries = ["langfuse", "lancedb", "pyarrow"] reportImplicitOverride = false reportImportCycles = false diff --git a/src/config.py b/src/config.py index 492d3125..8df92c25 100644 --- a/src/config.py +++ b/src/config.py @@ -67,6 +67,7 @@ class TomlConfigSettingsSource(PydanticBaseSettingsSource): "SUMMARY": "summary", "WEBHOOK": "webhook", "DREAM": "dream", + "VECTOR_STORE": "vector_store", "": "app", # For AppSettings with no prefix } @@ -356,6 +357,38 @@ class DreamSettings(BackupLLMSettingsMixin, HonchoSettings): MAX_OUTPUT_TOKENS: Annotated[int, Field(default=2000, gt=0, le=10_000)] = 2000 +class VectorStoreSettings(HonchoSettings): + """Settings for external vector store (Turbopuffer or LanceDB).""" + + model_config = SettingsConfigDict(env_prefix="VECTOR_STORE_", extra="ignore") # pyright: ignore + + # Vector store type: "turbopuffer" or "lancedb" + TYPE: Literal["turbopuffer", "lancedb"] = "lancedb" + + # Global namespace prefix for all vector namespaces + # Namespaces follow the pattern: + # - Documents: {NAMESPACE}-{workspace}-{observer}-{observed} + # - Messages: {NAMESPACE}-{workspace}-messages + NAMESPACE: str = "honcho" + + # Turbopuffer-specific settings + TURBOPUFFER_API_KEY: str | None = None + # Turbopuffer region (e.g., "gcp-us-east4", "aws-us-east-1") + # Can also be set via TURBOPUFFER_REGION environment variable + TURBOPUFFER_REGION: str | None = None + + # LanceDB-specific settings (local embedded mode) + LANCEDB_PATH: str = "./lancedb_data" + + @model_validator(mode="after") + def _require_api_key_for_turbopuffer(self) -> "VectorStoreSettings": + if self.TYPE == "turbopuffer" and not self.TURBOPUFFER_API_KEY: + raise ValueError( + "VECTOR_STORE_TURBOPUFFER_API_KEY must be set when TYPE is 'turbopuffer'" + ) + return self + + class AppSettings(HonchoSettings): # No env_prefix for app-level settings model_config = SettingsConfigDict( # pyright: ignore @@ -397,6 +430,7 @@ class AppSettings(HonchoSettings): METRICS: MetricsSettings = Field(default_factory=MetricsSettings) CACHE: CacheSettings = Field(default_factory=CacheSettings) DREAM: DreamSettings = Field(default_factory=DreamSettings) + VECTOR_STORE: VectorStoreSettings = Field(default_factory=VectorStoreSettings) @field_validator("LOG_LEVEL") def validate_log_level(cls, v: str) -> str: @@ -409,13 +443,16 @@ class AppSettings(HonchoSettings): def propagate_namespace(self) -> "AppSettings": """Propagate top-level NAMESPACE to nested settings if not explicitly set. - After this validator runs, CACHE.NAMESPACE and METRICS.NAMESPACE are guaranteed - to exist. + After this validator runs, CACHE.NAMESPACE, METRICS.NAMESPACE, and + VECTOR_STORE.NAMESPACE are guaranteed to exist. """ if self.CACHE.NAMESPACE is None: self.CACHE.NAMESPACE = self.NAMESPACE if self.METRICS.NAMESPACE is None: self.METRICS.NAMESPACE = self.NAMESPACE + # Note: VECTOR_STORE.NAMESPACE has its own default of "honcho", + # but we propagate the top-level NAMESPACE if the user explicitly set it + # and wants consistency across all namespaced services return self diff --git a/src/crud/document.py b/src/crud/document.py index 8708515d..ae058cf9 100644 --- a/src/crud/document.py +++ b/src/crud/document.py @@ -10,7 +10,7 @@ from src import models, schemas from src.config import settings from src.embedding_client import embedding_client from src.exceptions import ValidationException -from src.utils.filter import apply_filter +from src.vector_store import VectorRecord, get_vector_store logger = getLogger(__name__) @@ -61,7 +61,7 @@ async def query_documents( query: Search query text observer: Name of the observing peer observed: Name of the observed peer - filters: Optional filters to apply + filters: Optional filters to apply at vector store level (supports: level, session_name) max_distance: Maximum cosine distance for results top_k: Number of results to return embedding: Optional pre-computed embedding for the query (avoids extra API call if possible) @@ -78,22 +78,56 @@ async def query_documents( f"Query exceeds maximum token limit of {settings.MAX_EMBEDDING_TOKENS}." ) from e + # Get vector store and namespace for this collection + vector_store = get_vector_store() + namespace = vector_store.get_document_namespace(workspace_name, observer, observed) + + # Build vector store filters + # Convert filter dict to vector store format (handles level, session_name, etc.) + vector_filters: dict[str, Any] = {} + if filters: + # Direct pass-through for simple equality filters + # The filters dict can contain: level, session_name, or other document fields + # We can push level and session_name to vector store since they're in metadata + for key in ["level", "session_name"]: + if key in filters: + vector_filters[key] = filters[key] + + # Query vector store for similar documents with filters applied + vector_results = await vector_store.query( + namespace, + embedding, + top_k=top_k, + max_distance=max_distance, + filters=vector_filters if vector_filters else None, + ) + + if not vector_results: + return [] + + # Get document IDs from vector results (vector ID = document ID for documents) + document_ids = [result.id for result in vector_results] + + # Fetch documents from database + # No additional filtering needed since vector store already applied all supported filters stmt = ( select(models.Document) .where(models.Document.workspace_name == workspace_name) .where(models.Document.observer == observer) .where(models.Document.observed == observed) + .where(models.Document.id.in_(document_ids)) ) - if max_distance is not None: - stmt = stmt.where( - models.Document.embedding.cosine_distance(embedding) < max_distance - ) - stmt = apply_filter(stmt, models.Document, filters) - stmt = stmt.limit(top_k).order_by( - models.Document.embedding.cosine_distance(embedding) - ) + result = await db.execute(stmt) - return result.scalars().all() + documents = {doc.id: doc for doc in result.scalars().all()} + + # Return documents in order of similarity (preserving vector store order) + ordered_docs: list[models.Document] = [] + for vr in vector_results: + if vr.id in documents: + ordered_docs.append(documents[vr.id]) + + return ordered_docs async def create_documents( @@ -119,6 +153,8 @@ async def create_documents( Count of new documents """ honcho_documents: list[models.Document] = [] + embeddings_to_store: list[tuple[str, list[float]]] = [] # [(doc_id, embedding)] + for doc in documents: try: # for each document, if deduplicate is True, perform a process @@ -132,27 +168,59 @@ async def create_documents( continue metadata_dict = doc.metadata.model_dump(exclude_none=True) - honcho_documents.append( - models.Document( - workspace_name=workspace_name, - observer=observer, - observed=observed, - content=doc.content, - level=doc.level, - times_derived=doc.times_derived, - internal_metadata=metadata_dict, - embedding=doc.embedding, - session_name=doc.session_name, - ) + new_doc = models.Document( + workspace_name=workspace_name, + observer=observer, + observed=observed, + content=doc.content, + level=doc.level, + times_derived=doc.times_derived, + internal_metadata=metadata_dict, + session_name=doc.session_name, ) + honcho_documents.append(new_doc) + + # Track embedding for vector store (will use document's generated ID) + if doc.embedding: + embeddings_to_store.append((new_doc.id, doc.embedding)) + except Exception as e: logger.error( f"Error adding new document to {workspace_name}/{doc.session_name}/{observer}/{observed}: {e}" ) continue + try: db.add_all(honcho_documents) await db.commit() + + # Store embeddings in vector store after documents are committed + if embeddings_to_store: + vector_store = get_vector_store() + namespace = vector_store.get_document_namespace( + workspace_name, observer, observed + ) + + # Build vector records with metadata for filtering + vector_records: list[VectorRecord] = [] + doc_lookup = {doc.id: doc for doc in honcho_documents} + for doc_id, embedding in embeddings_to_store: + doc = doc_lookup[doc_id] + vector_records.append( + VectorRecord( + id=doc_id, + embedding=embedding, + metadata={ + "workspace_name": workspace_name, + "observer": observer, + "observed": observed, + "session_name": doc.session_name, + "level": doc.level, + }, + ) + ) + await vector_store.upsert_many(namespace, vector_records) + except IntegrityError as e: await db.rollback() raise ValidationException( @@ -216,8 +284,17 @@ async def is_rejected_duplicate( logger.warning( f"[DUPLICATE DETECTION] Deleting existing in favor of new. new='{doc.content}', existing='{existing_doc.content}'." ) + # Delete from database await db.delete(existing_doc) await db.flush() # Flush to make deletion visible in this transaction + + # Delete from vector store + vector_store = get_vector_store() + namespace = vector_store.get_document_namespace( + workspace_name, observer, observed + ) + await vector_store.delete(namespace, existing_doc.id) + return False # Don't reject the new document # Existing document has more information, reject the new one diff --git a/src/crud/message.py b/src/crud/message.py index c6d3476d..6a05edc0 100644 --- a/src/crud/message.py +++ b/src/crud/message.py @@ -9,6 +9,7 @@ from src import models, schemas from src.config import settings from src.embedding_client import embedding_client from src.utils.filter import apply_filter +from src.vector_store import VectorRecord, get_vector_store from .session import get_or_create_session @@ -136,25 +137,52 @@ async def create_messages( } embedding_dict = await embedding_client.batch_embed(id_resource_dict) - # Create MessageEmbedding entries for each embedded message + # Get vector store and namespace for this workspace's messages + vector_store = get_vector_store() + namespace = vector_store.get_message_namespace(workspace_name) + + # Create MessageEmbedding entries and vector records embedding_objects: list[models.MessageEmbedding] = [] + vector_records: list[VectorRecord] = [] + for message_obj in message_objects: embeddings = embedding_dict.get(message_obj.public_id, []) - for embedding in embeddings: + for chunk_index, embedding in enumerate(embeddings): + # Create MessageEmbedding record for metadata tracking embedding_obj = models.MessageEmbedding( content=message_obj.content, - embedding=embedding, message_id=message_obj.public_id, workspace_name=workspace_name, session_name=session_name, peer_name=message_obj.peer_name, + chunk_index=chunk_index, ) embedding_objects.append(embedding_obj) - # Add all embedding objects to the session + # Create vector record for external vector store + vector_id = f"{message_obj.public_id}_{chunk_index}" + vector_records.append( + VectorRecord( + id=vector_id, + embedding=embedding, + metadata={ + "message_id": message_obj.public_id, + "session_name": session_name, + "peer_name": message_obj.peer_name, + "chunk_index": chunk_index, + }, + ) + ) + + # Add all embedding metadata objects to the session if embedding_objects: db.add_all(embedding_objects) await db.commit() + + # Upsert vectors to external vector store + if vector_records: + await vector_store.upsert_many(namespace, vector_records) + except Exception: logger.exception( "Failed to generate message embeddings for %s messages in workspace %s and session %s.", diff --git a/src/crud/representation.py b/src/crud/representation.py index 14c1dd56..0c07af25 100644 --- a/src/crud/representation.py +++ b/src/crud/representation.py @@ -23,9 +23,6 @@ from src.utils.representation import ( logger = logging.getLogger(__name__) -# Fetch extra documents to ensure we have enough after filtering -FILTER_OVERSAMPLING_FACTOR = 3 - class RepresentationManager: """Unified manager for representation and document queries.""" @@ -398,30 +395,31 @@ class RepresentationManager: observed=self.observed, query=query, max_distance=max_distance, - top_k=count * FILTER_OVERSAMPLING_FACTOR, + top_k=count, filters=self._build_filter_conditions(level), ) - # Sort by creation time and return top count + # Sort by creation time docs_sorted: list[models.Document] = sorted( list(documents), key=lambda x: x.created_at, reverse=True ) - return docs_sorted[:count] + return docs_sorted def _build_filter_conditions( self, level: str | None = None, ) -> dict[str, Any]: - """Build complete filter conditions for document queries.""" - conditions: list[dict[str, Any]] = [] + """ + Build filter conditions for document queries. + + Returns a flat dict of key-value pairs for vector store filtering. + """ + filters: dict[str, Any] = {} if level: - conditions.append({"level": level}) + filters["level"] = level - if not conditions: - return {} - - return conditions[0] if len(conditions) == 1 else {"AND": conditions} + return filters # Module-level functions for backward compatibility and convenience diff --git a/src/crud/workspace.py b/src/crud/workspace.py index c872b728..f5af6ab6 100644 --- a/src/crud/workspace.py +++ b/src/crud/workspace.py @@ -11,6 +11,7 @@ from src.cache.client import cache, get_cache_namespace from src.config import settings from src.exceptions import ConflictException, ResourceNotFoundException from src.utils.filter import apply_filter +from src.vector_store import get_vector_store logger = getLogger(__name__) @@ -251,6 +252,14 @@ async def delete_workspace(db: AsyncSession, workspace_name: str) -> schemas.Wor ) ) + # Get all collections for this workspace to delete their vector namespaces + collections_result = await db.execute( + select(models.Collection).where( + models.Collection.workspace_name == workspace_name + ) + ) + collections = collections_result.scalars().all() + await db.execute( delete(models.MessageEmbedding).where( models.MessageEmbedding.workspace_name == workspace_name @@ -293,6 +302,45 @@ async def delete_workspace(db: AsyncSession, workspace_name: str) -> schemas.Wor await db.delete(honcho_workspace) await db.commit() + # Delete vector store namespaces for this workspace + vector_store = get_vector_store() + + # Delete message embeddings namespace for this workspace + message_namespace = vector_store.get_message_namespace(workspace_name) + try: + await vector_store.delete_namespace(message_namespace) + logger.debug( + "Deleted message embeddings namespace %s for workspace %s", + message_namespace, + workspace_name, + ) + except Exception as e: + logger.warning( + "Failed to delete message embeddings namespace %s: %s", + message_namespace, + e, + ) + + # Delete document embeddings namespaces for each collection + for collection in collections: + doc_namespace = vector_store.get_document_namespace( + workspace_name, collection.observer, collection.observed + ) + try: + await vector_store.delete_namespace(doc_namespace) + logger.debug( + "Deleted document namespace %s for collection %s/%s", + doc_namespace, + collection.observer, + collection.observed, + ) + except Exception as e: + logger.warning( + "Failed to delete document namespace %s: %s", + doc_namespace, + e, + ) + cache_key = workspace_cache_key(workspace_name) workspace_pattern = f"{cache_key}*" await cache.delete_match(workspace_pattern) diff --git a/src/models.py b/src/models.py index 03533fbc..58739766 100644 --- a/src/models.py +++ b/src/models.py @@ -4,7 +4,6 @@ from typing import Any, final from dotenv import load_dotenv from nanoid import generate as generate_nanoid -from pgvector.sqlalchemy import Vector from sqlalchemy import ( BigInteger, Boolean, @@ -22,7 +21,6 @@ from sqlalchemy import ( ) from sqlalchemy.dialects.postgresql import JSONB, TEXT from sqlalchemy.orm import Mapped, mapped_column, relationship -from sqlalchemy.orm.properties import MappedColumn from sqlalchemy.sql import func from typing_extensions import override @@ -273,13 +271,21 @@ class Message(Base): @final class MessageEmbedding(Base): + """ + Stores metadata for message embeddings. + + Note: The actual embedding vectors are stored in the external vector store + (Turbopuffer or LanceDB), not in PostgreSQL. This table maintains the + relationship between messages and their embeddings, along with metadata + needed for filtering and lookups. + """ + __tablename__: str = "message_embeddings" id: Mapped[int] = mapped_column( BigInteger, Identity(), primary_key=True, autoincrement=True ) content: Mapped[str] = mapped_column(TEXT) - embedding: MappedColumn[Any] = mapped_column(Vector(1536)) message_id: Mapped[str] = mapped_column( ForeignKey("messages.public_id", ondelete="CASCADE"), nullable=False, index=True ) @@ -291,6 +297,8 @@ class MessageEmbedding(Base): created_at: Mapped[datetime.datetime] = mapped_column( DateTime(timezone=True), server_default=func.now(), index=True ) + # Chunk index for messages that are split into multiple embeddings + chunk_index: Mapped[int] = mapped_column(Integer, nullable=False, default=0) __table_args__ = ( # Compound foreign key constraints @@ -302,14 +310,6 @@ class MessageEmbedding(Base): ["peer_name", "workspace_name"], ["peers.name", "peers.workspace_name"], ), - # HNSW index on embedding column for efficient similarity search - Index( - "ix_message_embeddings_embedding_hnsw", - "embedding", - postgresql_using="hnsw", - postgresql_with={"m": 16, "ef_construction": 64}, - postgresql_ops={"embedding": "vector_cosine_ops"}, - ), ) @@ -359,6 +359,14 @@ class Collection(Base): @final class Document(Base): + """ + Stores document metadata and content. + + Note: The actual embedding vectors are stored in the external vector store + (Turbopuffer or LanceDB), not in PostgreSQL. The vector ID is the document's + primary key (id field). + """ + __tablename__: str = "documents" id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=True) internal_metadata: Mapped[dict[str, Any]] = mapped_column( @@ -371,7 +379,6 @@ class Document(Base): times_derived: Mapped[int] = mapped_column( Integer, nullable=False, server_default=text("1") ) - embedding: MappedColumn[Any] = mapped_column(Vector(1536)) created_at: Mapped[datetime.datetime] = mapped_column( DateTime(timezone=True), server_default=func.now(), index=True ) @@ -412,16 +419,6 @@ class Document(Base): ["session_name", "workspace_name"], ["sessions.name", "sessions.workspace_name"], ), - # HNSW index on embedding column - Index( - "ix_documents_embedding_hnsw", - "embedding", - postgresql_using="hnsw", # HNSW index type - postgresql_with={"m": 16, "ef_construction": 64}, # HNSW parameters - postgresql_ops={ - "embedding": "vector_cosine_ops" - }, # Cosine distance operator - ), ) diff --git a/src/utils/search.py b/src/utils/search.py index a79d2cac..268c6b81 100644 --- a/src/utils/search.py +++ b/src/utils/search.py @@ -16,6 +16,7 @@ from src.config import settings from src.embedding_client import embedding_client from src.exceptions import ValidationException from src.utils.filter import apply_filter +from src.vector_store import get_vector_store T = TypeVar("T") @@ -65,17 +66,19 @@ def reciprocal_rank_fusion(*ranked_lists: list[T], k: int = 60, limit: int) -> l async def _semantic_search( db: AsyncSession, query: str, - stmt: Select[tuple[models.Message]], + workspace_name: str, limit: int, + filters: dict[str, Any] | None = None, ) -> list[models.Message]: """ - Perform semantic search using message embeddings. + Perform semantic search using external vector store for message embeddings. Args: db: Database session query: Search query - stmt: Base SQL query conditions + workspace_name: Name of the workspace to search in limit: Maximum number of results to return + filters: Optional filters to apply at vector store level (supports: session_id, peer_id) Returns: list of messages ordered by semantic similarity @@ -87,16 +90,61 @@ async def _semantic_search( f"Query exceeds maximum token limit of {settings.MAX_EMBEDDING_TOKENS}." ) from e - # Use cosine distance for semantic search on MessageEmbedding table - semantic_query = stmt.join( - models.MessageEmbedding, - models.Message.public_id == models.MessageEmbedding.message_id, - ).order_by(models.MessageEmbedding.embedding.cosine_distance(embedding_query)) + # Get vector store and namespace for this workspace's messages + vector_store = get_vector_store() + namespace = vector_store.get_message_namespace(workspace_name) - semantic_query = semantic_query.limit(limit) + # Build vector store filters from the provided filters + vector_filters: dict[str, Any] = {} + if filters: + # Map external filter keys to vector store metadata keys + if "session_id" in filters: + vector_filters["session_name"] = filters["session_id"] + if "peer_id" in filters: + vector_filters["peer_name"] = filters["peer_id"] + + # Query vector store for similar message embeddings + # Since all filters are applied at the vector store level, we don't need to oversample + vector_results = await vector_store.query( + namespace, + embedding_query, + top_k=limit, + filters=vector_filters if vector_filters else None, + ) + + if not vector_results: + return [] + + # Extract message IDs from vector results (vector ID format: {message_public_id}_{chunk_index}) + # Use dict to deduplicate while preserving order (dict keys maintain insertion order in Python 3.7+) + seen_message_ids: dict[str, None] = {} + + for result in vector_results: + # Vector ID format: {message_public_id}_{chunk_index} + parts = result.id.rsplit("_", 1) + if len(parts) >= 1: + message_id = parts[0] + if message_id not in seen_message_ids: + seen_message_ids[message_id] = None + + message_ids = list(seen_message_ids.keys()) + + # Fetch messages from database by the IDs from vector search + # No additional filtering needed since vector store already applied all filters + semantic_query = select(models.Message).where( + models.Message.public_id.in_(message_ids) + ) result = await db.execute(semantic_query) - return list(result.scalars().all()) + messages = {msg.public_id: msg for msg in result.scalars().all()} + + # Return messages in order of similarity (preserving vector store order) + ordered_messages: list[models.Message] = [] + for msg_id in message_ids: + if msg_id in messages: + ordered_messages.append(messages[msg_id]) + + return ordered_messages async def _fulltext_search( @@ -173,7 +221,7 @@ async def search( Args: db: Database session query: Search query to match against message content - filters: Optional filters to scope search + filters: Optional filters to scope search (must include workspace_id for semantic search) limit: Maximum number of results to return Returns: @@ -188,12 +236,18 @@ async def search( search_results: list[list[models.Message]] = [] - # Perform semantic search if enabled - if settings.EMBED_MESSAGES: + # Perform semantic search if enabled and we have workspace context + # workspace_id is required for semantic search to determine the vector namespace + workspace_name = filters.get("workspace_id") if filters else None + if settings.EMBED_MESSAGES and workspace_name: # Get more results for fusion semantic_limit = limit * 2 semantic_results = await _semantic_search( - db=db, query=query, stmt=stmt, limit=semantic_limit + db=db, + query=query, + workspace_name=workspace_name, + limit=semantic_limit, + filters=filters, ) search_results.append(semantic_results) diff --git a/src/vector_store/__init__.py b/src/vector_store/__init__.py new file mode 100644 index 00000000..ce6d9409 --- /dev/null +++ b/src/vector_store/__init__.py @@ -0,0 +1,194 @@ +""" +Vector store abstraction layer for Honcho. +""" + +from abc import ABC, abstractmethod +from dataclasses import dataclass, field +from typing import Any + +from src.config import settings +from src.vector_store.lancedb import LanceDBVectorStore +from src.vector_store.turbopuffer import TurbopufferVectorStore + + +@dataclass +class VectorRecord: + """A single vector record to be stored in the vector store.""" + + id: str + embedding: list[float] + metadata: dict[str, Any] = field(default_factory=dict) + + +@dataclass +class QueryResult: + """A single result from a vector query.""" + + id: str + score: float # Distance/similarity score (lower = more similar for cosine distance) + metadata: dict[str, Any] = field(default_factory=dict) + + +class VectorStore(ABC): + """ + Abstract base class for vector store implementations. + + All vector operations are namespace-scoped. Namespaces map to: + - Document embeddings: {prefix}-{workspace}-{observer}-{observed} (per collection) + - Message embeddings: {prefix}-{workspace}-messages (per workspace) + """ + + namespace_prefix: str + + def __init__(self): + """ + Initialize the vector store. + """ + self.namespace_prefix = settings.VECTOR_STORE.NAMESPACE + + # === Namespace helpers === + def get_document_namespace( + self, workspace_name: str, observer: str, observed: str + ) -> str: + """ + Get the namespace for document embeddings (per collection). + + Args: + workspace_name: Name of the workspace + observer: Name of the observing peer + observed: Name of the observed peer + + Returns: + Namespace string in format: {prefix}_{workspace}_{observer}_{observed} + """ + return f"{self.namespace_prefix}_{workspace_name}_{observer}_{observed}" + + def get_message_namespace(self, workspace_name: str) -> str: + """ + Get the namespace for message embeddings (per workspace). + + Args: + workspace_name: Name of the workspace + + Returns: + Namespace string in format: {prefix}_{workspace}_messages + """ + return f"{self.namespace_prefix}_{workspace_name}_messages" + + # === Core operations === + @abstractmethod + async def upsert( + self, + namespace: str, + vector: VectorRecord, + ) -> None: + """ + Upsert a single vector into the store. + + Args: + namespace: The namespace to store the vector in + id: Unique identifier for the vector + embedding: The embedding vector + metadata: Optional metadata to store with the vector + """ + pass + + @abstractmethod + async def upsert_many( + self, + namespace: str, + vectors: list[VectorRecord], + ) -> None: + """ + Upsert multiple vectors into the store. + + Args: + namespace: The namespace to store the vectors in + vectors: List of VectorRecord objects to upsert + """ + pass + + @abstractmethod + async def query( + self, + namespace: str, + embedding: list[float], + *, + top_k: int = 10, + filters: dict[str, Any] | None = None, + max_distance: float | None = None, + ) -> list[QueryResult]: + """ + Query for similar vectors. + + Args: + namespace: The namespace to query + embedding: The query embedding vector + top_k: Maximum number of results to return + filters: Optional metadata filters + max_distance: Optional maximum distance threshold (cosine distance) + + Returns: + List of QueryResult objects, ordered by similarity (most similar first) + """ + pass + + @abstractmethod + async def delete_many(self, namespace: str, ids: list[str]) -> None: + """ + Delete multiple vectors from the store. + + Args: + namespace: The namespace containing the vectors + ids: List of vector identifiers to delete + """ + pass + + @abstractmethod + async def delete_namespace(self, namespace: str) -> None: + """ + Delete an entire namespace and all its vectors. + + Args: + namespace: The namespace to delete + """ + pass + + +# Singleton instance +_vector_store_instance: VectorStore | None = None + + +def get_vector_store() -> VectorStore: + """ + Get the configured vector store instance (singleton). + + Returns: + The vector store instance based on configuration. + + Raises: + ValueError: If the configured vector store type is invalid. + """ + global _vector_store_instance + + if _vector_store_instance is not None: + return _vector_store_instance + + store_type = settings.VECTOR_STORE.TYPE + + if store_type == "turbopuffer": + _vector_store_instance = TurbopufferVectorStore() + elif store_type == "lancedb": + _vector_store_instance = LanceDBVectorStore() + else: + raise ValueError(f"Unknown vector store type: {store_type}") + + return _vector_store_instance + + +__all__ = [ + "VectorStore", + "VectorRecord", + "QueryResult", + "get_vector_store", +] diff --git a/src/vector_store/lancedb.py b/src/vector_store/lancedb.py new file mode 100644 index 00000000..a5735d79 --- /dev/null +++ b/src/vector_store/lancedb.py @@ -0,0 +1,287 @@ +""" +LanceDB vector store implementation. + +This module provides a LanceDB-based implementation of the VectorStore interface +for use in self-hosted deployments of Honcho. +""" + +import logging +from typing import Any + +import lancedb +import pyarrow as pa + +from src.config import settings + +from . import QueryResult, VectorRecord, VectorStore + +logger = logging.getLogger(__name__) + +# Schema for LanceDB tables +# id: string, vector: fixed_size_list of float32 (1536 dimensions for OpenAI embeddings) +# Additional metadata columns are added dynamically +VECTOR_DIMENSION = 1536 + + +class LanceDBVectorStore(VectorStore): + """ + LanceDB implementation of the VectorStore interface. + + Uses LanceDB's embedded mode for local vector storage. + Each namespace corresponds to a LanceDB table. + """ + + _db: lancedb.DBConnection + + def __init__(self): + """Initialize the LanceDB vector store.""" + super().__init__() + self._db = lancedb.connect(settings.VECTOR_STORE.LANCEDB_PATH) + + def _get_table(self, namespace: str) -> lancedb.table.Table | None: + """Get a table if it exists, otherwise return None.""" + if namespace in self._db.table_names(): + return self._db.open_table(namespace) + return None + + def _get_or_create_table( + self, namespace: str, sample_data: list[dict[str, Any]] | None = None + ) -> lancedb.table.Table: + """ + Get existing table or create if not exists. + + Args: + namespace: Table name (namespace) + sample_data: Optional sample data to infer schema from + + Returns: + LanceDB table + """ + if namespace in self._db.table_names(): + return self._db.open_table(namespace) + + # Create table with sample data if provided + if sample_data: + return self._db.create_table(namespace, data=sample_data) + + # Create empty table with base schema + schema = pa.schema( + [ + pa.field("id", pa.string()), + pa.field("vector", pa.list_(pa.float32(), VECTOR_DIMENSION)), + ] + ) + return self._db.create_table(namespace, schema=schema) + + def _row_to_dict(self, vector: VectorRecord) -> dict[str, Any]: + """Convert a VectorRecord to a dict for LanceDB.""" + row: dict[str, Any] = { + "id": vector.id, + "vector": vector.embedding, + } + # Add metadata fields + if vector.metadata: + row.update(vector.metadata) + return row + + async def upsert( + self, + namespace: str, + vector: VectorRecord, + ) -> None: + """ + Upsert a single vector into LanceDB. + + Args: + namespace: The namespace (table) to store the vector in + vector: VectorRecord containing id, embedding, and metadata + """ + try: + row = self._row_to_dict(vector) + table = self._get_or_create_table(namespace, sample_data=[row]) + + # Use merge_insert for upsert behavior + table.merge_insert("id").when_matched_update_all().execute([row]) + + logger.debug(f"Upserted vector {vector.id} to namespace {namespace}") + except Exception: + logger.exception( + f"Failed to upsert vector {vector.id} to namespace {namespace}" + ) + raise + + async def upsert_many( + self, + namespace: str, + vectors: list[VectorRecord], + ) -> None: + """ + Upsert multiple vectors into LanceDB. + + Args: + namespace: The namespace (table) to store the vectors in + vectors: List of VectorRecord objects to upsert + """ + if not vectors: + return + + try: + rows = [self._row_to_dict(v) for v in vectors] + table = self._get_or_create_table(namespace, sample_data=rows) + + # Use merge_insert for upsert behavior + table.merge_insert("id").when_matched_update_all().execute(rows) + + logger.debug(f"Upserted {len(vectors)} vectors to namespace {namespace}") + except Exception: + logger.exception( + f"Failed to upsert {len(vectors)} vectors to namespace {namespace}" + ) + raise + + async def query( + self, + namespace: str, + embedding: list[float], + *, + top_k: int = 10, + filters: dict[str, Any] | None = None, + max_distance: float | None = None, + ) -> list[QueryResult]: + """ + Query for similar vectors in LanceDB. + + Args: + namespace: The namespace (table) to query + embedding: The query embedding vector + top_k: Maximum number of results to return + filters: Optional metadata filters + max_distance: Optional maximum distance threshold (cosine distance) + + Returns: + List of QueryResult objects, ordered by similarity (most similar first) + """ + table = self._get_table(namespace) + if table is None: + logger.debug(f"Table {namespace} does not exist, returning empty results") + return [] + + try: + # Build query (LanceDB types are incomplete, so type checker reports false positive) + query = table.search(embedding).distance_type("cosine").limit(top_k) # pyright: ignore[reportAttributeAccessIssue] + + # Apply filters if provided + if filters: + where_clause = self._build_where_clause(filters) + if where_clause: + query = query.where(where_clause) + + # Execute query + results = query.to_list() + + # Convert to QueryResult objects + query_results: list[QueryResult] = [] + for row in results: + dist = float(row.get("_distance", 0.0)) + + # Filter by max_distance if specified + if max_distance is not None and dist > max_distance: + continue + + # Extract metadata (everything except id, vector, _distance) + metadata: dict[str, Any] = { + k: v + for k, v in row.items() + if k not in ("id", "vector", "_distance") + } + + query_results.append( + QueryResult( + id=str(row["id"]), + score=dist, + metadata=metadata, + ) + ) + + logger.debug( + f"Query returned {len(query_results)} results from namespace {namespace}" + ) + return query_results + + except Exception: + logger.exception(f"Failed to query namespace {namespace}") + raise + + def _build_where_clause(self, filters: dict[str, Any]) -> str | None: + """ + Convert a filter dict to SQL WHERE clause syntax. + + Args: + filters: Dictionary of attribute -> value filters + + Returns: + SQL WHERE clause string or None if no filters + """ + if not filters: + return None + + conditions: list[str] = [] + for key, value in filters.items(): + # Handle string values with proper quoting + if isinstance(value, str): + # Escape single quotes in the value + escaped_value = value.replace("'", "''") + conditions.append(f"{key} = '{escaped_value}'") + elif isinstance(value, bool): + conditions.append(f"{key} = {str(value).lower()}") + elif value is None: + conditions.append(f"{key} IS NULL") + else: + conditions.append(f"{key} = {value}") + + return " AND ".join(conditions) if conditions else None + + async def delete_many(self, namespace: str, ids: list[str]) -> None: + """ + Delete multiple vectors from LanceDB. + + Args: + namespace: The namespace (table) containing the vectors + ids: List of vector identifiers to delete + """ + if not ids: + return + + table = self._get_table(namespace) + if table is None: + logger.debug(f"Table {namespace} does not exist, nothing to delete") + return + + try: + # Build IN clause with properly escaped IDs + escaped_ids = [f"'{id.replace(chr(39), chr(39) + chr(39))}'" for id in ids] + in_clause = ", ".join(escaped_ids) + table.delete(f"id IN ({in_clause})") + logger.debug(f"Deleted {len(ids)} vectors from namespace {namespace}") + except Exception: + logger.exception( + f"Failed to delete {len(ids)} vectors from namespace {namespace}" + ) + raise + + async def delete_namespace(self, namespace: str) -> None: + """ + Delete an entire namespace (table) and all its vectors from LanceDB. + + Args: + namespace: The namespace (table) to delete + """ + try: + if namespace in self._db.table_names(): + self._db.drop_table(namespace) + logger.debug(f"Deleted namespace {namespace}") + else: + logger.debug(f"Namespace {namespace} does not exist, nothing to delete") + except Exception: + logger.exception(f"Failed to delete namespace {namespace}") + raise diff --git a/src/vector_store/turbopuffer.py b/src/vector_store/turbopuffer.py new file mode 100644 index 00000000..30a05884 --- /dev/null +++ b/src/vector_store/turbopuffer.py @@ -0,0 +1,285 @@ +""" +Turbopuffer vector store implementation. + +This module provides a Turbopuffer-based implementation of the VectorStore interface +for use in managed deployments of Honcho. +""" + +import logging +from collections.abc import Sequence +from typing import Any, Literal + +from turbopuffer import Turbopuffer +from turbopuffer.lib.namespace import Namespace +from turbopuffer.types import Filter + +from src.config import settings + +from . import QueryResult, VectorRecord, VectorStore + +logger = logging.getLogger(__name__) + +# Type alias for Turbopuffer's equality filter format +EqFilter = tuple[str, Literal["Eq"], Any] +AndFilter = tuple[Literal["And"], Sequence[Filter]] + +DISTANCE_METRIC = "cosine_distance" + + +class TurbopufferVectorStore(VectorStore): + """ + Turbopuffer implementation of the VectorStore interface. + + Uses Turbopuffer's Python SDK for vector operations. + Each namespace corresponds to either: + - A document collection: {prefix}-{workspace}-{observer}-{observed} + - A workspace's message embeddings: {prefix}-{workspace}-messages + """ + + tpuf: Turbopuffer + + def __init__(self): + """ + Initialize the Turbopuffer vector store. + """ + super().__init__() + + # Configure Turbopuffer client + api_key = settings.VECTOR_STORE.TURBOPUFFER_API_KEY + if not api_key: + raise ValueError( + "VECTOR_STORE_TURBOPUFFER_API_KEY must be set for Turbopuffer vector store" + ) + + # Initialize the Turbopuffer client + # Region can be configured via VECTOR_STORE_TURBOPUFFER_REGION or TURBOPUFFER_REGION env var + region = settings.VECTOR_STORE.TURBOPUFFER_REGION or "gcp-us-east4" + self.tpuf = Turbopuffer(api_key=api_key, region=region) + + def _get_namespace(self, namespace: str) -> Namespace: + """Get a Turbopuffer namespace object.""" + return self.tpuf.namespace(namespace) + + async def upsert( + self, + namespace: str, + vector: VectorRecord, + ) -> None: + """ + Upsert a single vector into Turbopuffer. + + Args: + namespace: The namespace to store the vector in + id: Unique identifier for the vector + embedding: The embedding vector + metadata: Optional metadata to store with the vector + """ + ns = self._get_namespace(namespace) + attributes = vector.metadata or {} + + try: + # Build row data + row: dict[str, Any] = { + "id": vector.id, + "vector": vector.embedding, + **attributes, + } + + ns.write( + upsert_rows=[row], + distance_metric=DISTANCE_METRIC, + ) + except Exception: + logger.exception( + f"Failed to upsert vector {vector.id} to namespace {namespace}" + ) + raise + + async def upsert_many( + self, + namespace: str, + vectors: list[VectorRecord], + ) -> None: + """ + Upsert multiple vectors into Turbopuffer. + + Args: + namespace: The namespace to store the vectors in + vectors: List of VectorRecord objects to upsert + """ + if not vectors: + return + + ns = self._get_namespace(namespace) + + rows: list[dict[str, Any]] = [ + { + "id": v.id, + "vector": v.embedding, + **v.metadata, + } + for v in vectors + ] + + try: + ns.write( + upsert_rows=rows, + distance_metric=DISTANCE_METRIC, + ) + except Exception: + logger.exception( + f"Failed to upsert {len(vectors)} vectors to namespace {namespace}" + ) + raise + + async def query( + self, + namespace: str, + embedding: list[float], + *, + top_k: int = 10, + filters: dict[str, Any] | None = None, + max_distance: float | None = None, + ) -> list[QueryResult]: + """ + Query for similar vectors in Turbopuffer. + + Args: + namespace: The namespace to query + embedding: The query embedding vector + top_k: Maximum number of results to return + filters: Optional metadata filters + max_distance: Optional maximum distance threshold (cosine distance) + + Returns: + List of QueryResult objects, ordered by similarity (most similar first) + """ + ns = self._get_namespace(namespace) + + try: + # Build filter conditions for Turbopuffer + filter_condition = self._build_filters(filters) if filters else None + + # Query using rank_by for vector similarity + # rank_by must be a tuple: (attribute, "ANN", vector) + rank_by: tuple[str, Literal["ANN"], Sequence[float]] = ( + "vector", + "ANN", + embedding, + ) + + # Only pass filters if we have them (avoid passing None) + query_kwargs: dict[str, Any] = { + "rank_by": rank_by, + "top_k": top_k, + "distance_metric": DISTANCE_METRIC, + "include_attributes": True, + } + if filter_condition is not None: + query_kwargs["filters"] = filter_condition + + response = ns.query(**query_kwargs) + + query_results: list[QueryResult] = [] + for row in response.rows or []: + # Distance is accessed via row["$dist"] + dist: float = float(row["$dist"]) if "$dist" in row else 0.0 + # Filter by max_distance if specified + if max_distance is not None and dist > max_distance: + continue + + # Extract attributes from model_extra (excludes id, vector, $dist) + row_metadata: dict[str, Any] = {} + if row.model_extra: + # Filter out internal fields like $dist + row_metadata = { + k: v + for k, v in row.model_extra.items() + if not k.startswith("$") + } + + query_results.append( + QueryResult( + id=str(row.id), + score=dist, + metadata=row_metadata, + ) + ) + + logger.debug( + f"Query returned {len(query_results)} results from namespace {namespace}" + ) + return query_results + + except Exception: + logger.exception(f"Failed to query namespace {namespace}") + raise + + def _build_filters(self, filters: dict[str, Any]) -> Filter | None: + """ + Convert a filter dict to Turbopuffer filter format. + + Turbopuffer uses tuples like (attribute, "Eq", value) for filters, + and ("And", [filters]) for combining multiple filters. + + Args: + filters: Dictionary of attribute -> value filters + + Returns: + Turbopuffer Filter or None if no filters + """ + if not filters: + return None + + filter_list: list[EqFilter] = [] + for key, value in filters.items(): + # Simple equality filter using "Eq" operator + eq_filter: EqFilter = (key, "Eq", value) + filter_list.append(eq_filter) + + if not filter_list: + return None + + if len(filter_list) == 1: + return filter_list[0] + + # Combine multiple filters with AND + and_filter: AndFilter = ("And", filter_list) + return and_filter + + async def delete_many(self, namespace: str, ids: list[str]) -> None: + """ + Delete multiple vectors from Turbopuffer. + + Args: + namespace: The namespace containing the vectors + ids: List of vector identifiers to delete + """ + if not ids: + return + + ns = self._get_namespace(namespace) + + try: + ns.write(deletes=ids) + except Exception: + logger.exception( + f"Failed to delete {len(ids)} vectors from namespace {namespace}" + ) + raise + + async def delete_namespace(self, namespace: str) -> None: + """ + Delete an entire namespace and all its vectors from Turbopuffer. + + Args: + namespace: The namespace to delete + """ + ns = self._get_namespace(namespace) + + try: + ns.delete_all() + logger.debug(f"Deleted all vectors from namespace {namespace}") + except Exception: + logger.exception(f"Failed to delete namespace {namespace}") + raise diff --git a/uv.lock b/uv.lock index 97dca4f1..1c2b85ad 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.10" 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