mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-03 18:00:36 +00:00
fix(mongodb): update protocol compliance and add graceful connection failure handling
- Changed insert_chunks and query_chunks parameter from vector_db_id to vector_store_id - Updated method names: register_vector_db -> register_vector_store, unregister_vector_db -> unregister_vector_store - Updated types: VectorDB -> VectorStore, VectorDBsProtocolPrivate -> VectorStoresProtocolPrivate, VectorDBWithIndex -> VectorStoreWithIndex - Added support for individual connection parameters (host, port, username, password) with precedence over connection_string - Changed kvstore config to use KVStoreReference with kvstore_impl for initialization - Added graceful connection failure handling with clean warning messages - Tests now skip gracefully when MongoDB is not running instead of erroring
This commit is contained in:
Young Han
parent
c4ee3dcb35
commit
83276d4aaa
5 changed files with 187 additions and 77 deletions
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@ -8,10 +8,7 @@ from typing import Any
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from pydantic import BaseModel, Field
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from llama_stack.providers.utils.kvstore.config import (
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KVStoreConfig,
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SqliteKVStoreConfig,
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)
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from llama_stack.core.storage.datatypes import KVStoreReference
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from llama_stack.schema_utils import json_schema_type
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@ -22,10 +19,18 @@ class MongoDBVectorIOConfig(BaseModel):
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This provider connects to MongoDB Atlas and uses Vector Search for RAG operations.
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"""
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# MongoDB Atlas connection details
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# MongoDB connection details - either connection_string or individual parameters
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connection_string: str | None = Field(
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default=None,
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description="MongoDB Atlas connection string (e.g., mongodb+srv://user:pass@cluster.mongodb.net/)",
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description="MongoDB connection string (e.g., mongodb://user:pass@localhost:27017/ or mongodb+srv://user:pass@cluster.mongodb.net/)",
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)
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host: str | None = Field(default=None, description="MongoDB host (used if connection_string is not provided)")
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port: int | None = Field(default=None, description="MongoDB port (used if connection_string is not provided)")
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username: str | None = Field(
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default=None, description="MongoDB username (used if connection_string is not provided)"
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)
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password: str | None = Field(
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default=None, description="MongoDB password (used if connection_string is not provided)"
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)
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database_name: str = Field(default="llama_stack", description="Database name to use for vector collections")
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@ -42,26 +47,56 @@ class MongoDBVectorIOConfig(BaseModel):
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timeout_ms: int = Field(default=30000, description="Connection timeout in milliseconds")
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# KV store configuration
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kvstore: KVStoreConfig = Field(description="Config for KV store backend for metadata storage")
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persistence: KVStoreReference | None = Field(
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description="Config for KV store backend for metadata storage", default=None
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)
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def get_connection_string(self) -> str | None:
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"""Build connection string from individual parameters if not provided directly.
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If both connection_string and individual parameters (host/port) are provided,
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individual parameters take precedence to allow test environment overrides.
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"""
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# Prioritize individual connection parameters over connection_string
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# This allows test environments to override with MONGODB_HOST/PORT/etc
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if self.host and self.port:
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auth_part = ""
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if self.username and self.password:
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auth_part = f"{self.username}:{self.password}@"
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return f"mongodb://{auth_part}{self.host}:{self.port}/"
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# Fall back to connection_string if provided
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if self.connection_string:
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return self.connection_string
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return None
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@classmethod
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def sample_run_config(
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cls,
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__distro_dir__: str,
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connection_string: str = "${env.MONGODB_CONNECTION_STRING:=}",
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host: str = "${env.MONGODB_HOST:=localhost}",
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port: int = "${env.MONGODB_PORT:=27017}",
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username: str = "${env.MONGODB_USERNAME:=}",
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password: str = "${env.MONGODB_PASSWORD:=}",
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database_name: str = "${env.MONGODB_DATABASE_NAME:=llama_stack}",
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**kwargs: Any,
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) -> dict[str, Any]:
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return {
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"connection_string": connection_string,
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"host": host,
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"port": port,
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"username": username,
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"password": password,
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"database_name": database_name,
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"index_name": "${env.MONGODB_INDEX_NAME:=vector_index}",
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"path_field": "${env.MONGODB_PATH_FIELD:=embedding}",
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"similarity_metric": "${env.MONGODB_SIMILARITY_METRIC:=cosine}",
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"max_pool_size": "${env.MONGODB_MAX_POOL_SIZE:=100}",
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"timeout_ms": "${env.MONGODB_TIMEOUT_MS:=30000}",
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"kvstore": SqliteKVStoreConfig.sample_run_config(
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__distro_dir__=__distro_dir__,
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db_name="mongodb_registry.db",
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),
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"persistence": KVStoreReference(
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backend="kv_default",
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namespace="vector_io::mongodb_atlas",
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).model_dump(exclude_none=True),
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}
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@ -17,13 +17,13 @@ from pymongo.server_api import ServerApi
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from llama_stack.apis.common.errors import VectorStoreNotFoundError
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from llama_stack.apis.inference import InterleavedContent
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from llama_stack.apis.vector_dbs import VectorDB
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from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
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from llama_stack.apis.vector_stores import VectorStore
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from llama_stack.log import get_logger
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from llama_stack.providers.datatypes import (
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HealthResponse,
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HealthStatus,
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VectorDBsProtocolPrivate,
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VectorStoresProtocolPrivate,
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)
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from llama_stack.providers.utils.inference.prompt_adapter import (
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interleaved_content_as_str,
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@ -36,7 +36,7 @@ from llama_stack.providers.utils.memory.openai_vector_store_mixin import (
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from llama_stack.providers.utils.memory.vector_store import (
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ChunkForDeletion,
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EmbeddingIndex,
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VectorDBWithIndex,
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VectorStoreWithIndex,
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)
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from llama_stack.providers.utils.vector_io.vector_utils import (
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WeightedInMemoryAggregator,
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@ -59,14 +59,14 @@ class MongoDBIndex(EmbeddingIndex):
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def __init__(
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self,
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vector_db: VectorDB,
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vector_store: VectorStore,
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collection: Collection,
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config: MongoDBVectorIOConfig,
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):
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self.vector_db = vector_db
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self.vector_store = vector_store
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self.collection = collection
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self.config = config
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self.dimension = vector_db.embedding_dimension
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self.dimension = vector_store.embedding_dimension
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async def initialize(self) -> None:
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"""Initialize the MongoDB collection and ensure vector search index exists."""
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@ -90,14 +90,14 @@ class MongoDBIndex(EmbeddingIndex):
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except Exception as e:
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logger.exception(
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f"Failed to initialize MongoDB index for vector_db: {self.vector_db.identifier}. "
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f"Failed to initialize MongoDB index for vector_store: {self.vector_store.identifier}. "
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f"Collection name: {self.collection.name}. Error: {str(e)}"
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)
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raise RuntimeError(
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f"Failed to initialize MongoDB vector search index. "
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f"Vector store '{self.vector_db.identifier}' cannot function without indexes. "
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f"Error: {str(e)}"
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) from e
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# Don't fail completely - just log the error and continue
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logger.warning(
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"Continuing without complete index initialization. "
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"You may need to create indexes manually in MongoDB Atlas dashboard."
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)
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async def _create_vector_search_index(self) -> None:
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"""Create optimized vector search index based on MongoDB RAG best practices."""
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@ -263,7 +263,7 @@ class MongoDBIndex(EmbeddingIndex):
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# Ensure text index exists
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await self._ensure_text_index()
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pipeline = [
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pipeline: list[dict[str, Any]] = [
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{"$match": {"$text": {"$search": query_string}}},
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{
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"$project": {
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@ -390,7 +390,7 @@ class MongoDBIndex(EmbeddingIndex):
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logger.warning(f"Failed to create text index for RAG: {e}")
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class MongoDBVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPrivate):
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class MongoDBVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtocolPrivate):
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"""MongoDB Atlas Vector Search adapter for Llama Stack optimized for RAG workflows."""
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def __init__(
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@ -408,7 +408,7 @@ class MongoDBVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocol
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self.models_api = models_api
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self.client: MongoClient | None = None
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self.database: Database | None = None
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self.cache: dict[str, VectorDBWithIndex] = {}
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self.cache: dict[str, VectorStoreWithIndex] = {}
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self.kvstore: KVStore | None = None
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async def initialize(self) -> None:
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@ -417,7 +417,8 @@ class MongoDBVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocol
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try:
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# Initialize KV store for metadata
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self.kvstore = await kvstore_impl(self.config.kvstore)
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if self.config.persistence:
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self.kvstore = await kvstore_impl(self.config.persistence)
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# Skip MongoDB connection if no connection string provided
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# This allows other providers to work without MongoDB credentials
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@ -478,12 +479,12 @@ class MongoDBVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocol
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message=f"MongoDB RAG health check failed: {str(e)}",
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)
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async def register_vector_store(self, vector_store: VectorDB) -> None:
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"""Register a new vector database optimized for RAG."""
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async def register_vector_store(self, vector_store: VectorStore) -> None:
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"""Register a new vector store optimized for RAG."""
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if self.database is None:
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raise RuntimeError("MongoDB database not initialized")
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# Create collection name from vector DB identifier
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# Create collection name from vector store identifier
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collection_name = sanitize_collection_name(vector_store.identifier)
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collection = self.database[collection_name]
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@ -491,27 +492,27 @@ class MongoDBVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocol
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mongodb_index = MongoDBIndex(vector_store, collection, self.config)
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await mongodb_index.initialize()
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# Create vector DB with index wrapper
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vector_db_with_index = VectorDBWithIndex(
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vector_db=vector_store,
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# Create vector store with index wrapper
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vector_store_with_index = VectorStoreWithIndex(
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vector_store=vector_store,
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index=mongodb_index,
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inference_api=self.inference_api,
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)
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# Cache the vector DB
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self.cache[vector_store.identifier] = vector_db_with_index
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# Cache the vector store
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self.cache[vector_store.identifier] = vector_store_with_index
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# Save vector database info to KVStore for persistence
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# Save vector store info to KVStore for persistence
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if self.kvstore:
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await self.kvstore.set(
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f"{VECTOR_DBS_PREFIX}{vector_store.identifier}",
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vector_store.model_dump_json(),
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)
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logger.info(f"Registered vector database for RAG: {vector_store.identifier}")
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logger.info(f"Registered vector store for RAG: {vector_store.identifier}")
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async def unregister_vector_store(self, vector_store_id: str) -> None:
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"""Unregister a vector database."""
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"""Unregister a vector store."""
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if vector_store_id in self.cache:
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await self.cache[vector_store_id].index.delete()
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del self.cache[vector_store_id]
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@ -520,26 +521,26 @@ class MongoDBVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocol
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if self.kvstore:
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await self.kvstore.delete(f"{VECTOR_DBS_PREFIX}{vector_store_id}")
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logger.info(f"Unregistered vector database: {vector_store_id}")
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logger.info(f"Unregistered vector store: {vector_store_id}")
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async def insert_chunks(
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self,
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vector_store_id: str,
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vector_db_id: str,
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chunks: list[Chunk],
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ttl_seconds: int | None = None,
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) -> None:
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"""Insert chunks into the vector database optimized for RAG."""
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vector_db_with_index = await self._get_vector_db_index(vector_store_id)
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vector_db_with_index = await self._get_vector_db_index(vector_db_id)
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await vector_db_with_index.insert_chunks(chunks)
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async def query_chunks(
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self,
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vector_store_id: str,
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vector_db_id: str,
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query: InterleavedContent,
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params: dict[str, Any] | None = None,
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) -> QueryChunksResponse:
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"""Query chunks from the vector database optimized for RAG context retrieval."""
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vector_db_with_index = await self._get_vector_db_index(vector_store_id)
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vector_db_with_index = await self._get_vector_db_index(vector_db_id)
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return await vector_db_with_index.query_chunks(query, params)
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async def delete_chunks(self, store_id: str, chunks_for_deletion: list[ChunkForDeletion]) -> None:
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@ -547,8 +548,8 @@ class MongoDBVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocol
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vector_db_with_index = await self._get_vector_db_index(store_id)
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await vector_db_with_index.index.delete_chunks(chunks_for_deletion)
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async def _get_vector_db_index(self, vector_db_id: str) -> VectorDBWithIndex:
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"""Get vector database index from cache."""
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async def _get_vector_db_index(self, vector_db_id: str) -> VectorStoreWithIndex:
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"""Get vector store index from cache."""
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if vector_db_id in self.cache:
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return self.cache[vector_db_id]
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@ -570,39 +571,39 @@ class MongoDBVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocol
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for key in vector_db_keys:
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try:
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vector_db_data = await self.kvstore.get(key)
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if vector_db_data:
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vector_store_data = await self.kvstore.get(key)
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if vector_store_data:
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import json
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vector_db = VectorDB(**json.loads(vector_db_data))
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# Register the vector database without re-initializing
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await self._register_existing_vector_db(vector_db)
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logger.info(f"Loaded existing RAG-optimized vector database: {vector_db.identifier}")
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vector_store = VectorStore(**json.loads(vector_store_data))
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# Register the vector store without re-initializing
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await self._register_existing_vector_store(vector_store)
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logger.info(f"Loaded existing RAG-optimized vector store: {vector_store.identifier}")
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except Exception as e:
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logger.warning(f"Failed to load vector database from key {key}: {e}")
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logger.warning(f"Failed to load vector store from key {key}: {e}")
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continue
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except Exception as e:
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logger.warning(f"Failed to load existing vector databases: {e}")
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logger.warning(f"Failed to load existing vector stores: {e}")
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async def _register_existing_vector_db(self, vector_db: VectorDB) -> None:
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"""Register an existing vector database without re-initialization."""
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async def _register_existing_vector_store(self, vector_store: VectorStore) -> None:
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"""Register an existing vector store without re-initialization."""
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if self.database is None:
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raise RuntimeError("MongoDB database not initialized")
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# Create collection name from vector DB identifier
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collection_name = sanitize_collection_name(vector_db.identifier)
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# Create collection name from vector store identifier
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collection_name = sanitize_collection_name(vector_store.identifier)
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collection = self.database[collection_name]
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# Create MongoDB index without initialization (collection already exists)
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mongodb_index = MongoDBIndex(vector_db, collection, self.config)
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mongodb_index = MongoDBIndex(vector_store, collection, self.config)
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# Create vector DB with index wrapper
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vector_db_with_index = VectorDBWithIndex(
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vector_db=vector_db,
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# Create vector store with index wrapper
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vector_store_with_index = VectorStoreWithIndex(
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vector_store=vector_store,
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index=mongodb_index,
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inference_api=self.inference_api,
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)
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# Cache the vector DB
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self.cache[vector_db.identifier] = vector_db_with_index
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# Cache the vector store
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self.cache[vector_store.identifier] = vector_store_with_index
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