feat: adding mongodb vector_io module

updated mongodb sample run config

README.md

@@ -50,6 +50,7 @@ Here is a list of the various API providers and available distributions that can
 |        NVIDIA NIM        | Hosted and Single Node |            |       ✅       |            |            |               |
 |          Chroma          |      Single Node       |            |               |     ✅      |            |               |
 |        PG Vector         |      Single Node       |            |               |     ✅      |            |               |
+|      MongoDB Atlas       |      Hosted            |            |               |     ✅      |            |               |
 |    PyTorch ExecuTorch    |     On-device iOS      |     ✅      |       ✅       |            |            |               |
 |           vLLM           | Hosted and Single Node |            |       ✅       |            |            |               |
 |          OpenAI          |         Hosted         |            |       ✅       |            |            |               |

docs/source/providers/vector_io/mongodb.md

@@ -0,0 +1,35 @@
+---
+orphan: true
+---
+# MongoDB Atlas
+
+[MongoDB Atlas](https://www.mongodb.com/atlas) is a cloud database service that can be used as a vector store provider for Llama Stack. It supports vector search capabilities through its Atlas Vector Search feature, allowing you to store and query vectors within your MongoDB database.
+
+## Features
+MongoDB Atlas Vector Search supports:
+- Store embeddings and their metadata
+- Vector search with multiple algorithms (cosine similarity, euclidean distance, dot product)
+- Hybrid search (combining vector and keyword search)
+- Metadata filtering
+- Scalable vector indexing
+- Managed cloud infrastructure
+
+## Usage
+
+To use MongoDB Atlas in your Llama Stack project, follow these steps:
+
+1. Create a MongoDB Atlas account and cluster.
+2. Configure your Atlas cluster to enable Vector Search.
+3. Configure your Llama Stack project to use MongoDB Atlas.
+4. Start storing and querying vectors.
+
+## Installation
+
+You can install the MongoDB Python driver using pip:
+
+```bash
+pip install pymongo
+```
+
+## Documentation
+See [MongoDB Atlas Vector Search documentation](https://www.mongodb.com/docs/atlas/atlas-vector-search/) for more details about vector search capabilities in MongoDB Atlas.

llama_stack/providers/registry/vector_io.py

@@ -110,6 +110,16 @@ def available_providers() -> List[ProviderSpec]:
             ),
             api_dependencies=[Api.inference],
         ),
+        remote_provider_spec(
+            Api.vector_io,
+            AdapterSpec(
+                adapter_type="mongodb",
+                pip_packages=["pymongo"],
+                module="llama_stack.providers.remote.vector_io.mongodb",
+                config_class="llama_stack.providers.remote.vector_io.mongodb.MongoDBVectorIOConfig",
+            ),
+            api_dependencies=[Api.inference],
+        ),
         remote_provider_spec(
             Api.vector_io,
             AdapterSpec(

llama_stack/providers/remote/vector_io/mongodb/__init__.py

@@ -0,0 +1,19 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Dict
+
+from llama_stack.providers.datatypes import Api, ProviderSpec
+
+from .config import MongoDBVectorIOConfig
+
+
+async def get_adapter_impl(config: MongoDBVectorIOConfig, deps: Dict[Api, ProviderSpec]):
+    from .mongodb import MongoDBVectorIOAdapter      
+
+    impl = MongoDBVectorIOAdapter(config, deps[Api.inference])
+    await impl.initialize()
+    return impl

llama_stack/providers/remote/vector_io/mongodb/config.py

@@ -4,26 +4,23 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Optional, List
 
 from pydantic import BaseModel, Field
 
 
 class MongoDBVectorIOConfig(BaseModel):
-    conncetion_str: str
+    connection_str: str = Field(None, description="Connection string for the MongoDB Atlas collection")
-    namespace: str = Field(None, description="Namespace of the MongoDB collection")
+    namespace: str = Field(None, description="Namespace i.e. db_name.collection_name of the MongoDB Atlas collection")
-    index_name: Optional[str] = Field("default", description="Name of the index in the MongoDB collection") 
+    index_name: Optional[str] = Field("default", description="Name of the index in the MongoDB Atlas collection") 
-    filter_fields: Optional[str] = Field(None, description="Fields to filter the MongoDB collection")
+    filter_fields: Optional[List[str]] = Field([], description="Fields to filter along side vector search in MongoDB Atlas collection")
-    embedding_field: Optional[str] = Field("embeddings", description="Field name for the embeddings in the MongoDB collection")
+    embeddings_key: Optional[str] = Field("embeddings", description="Field name for the embeddings in the MongoDB Atlas collection")
-    text_field: Optional[str] = Field("text", description="Field name for the text in the MongoDB collection")
+    text_field: Optional[str] = Field("text", description="Field name for the text in the MongoDB Atlas collection")
+
 
     @classmethod
-    def sample_config(cls) -> Dict[str, Any]:
+    def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]:
         return {
             "connection_str": "{env.MONGODB_CONNECTION_STR}",
             "namespace": "{env.MONGODB_NAMESPACE}",
-            "index_name": "{env.MONGODB_INDEX_NAME}",
+        }
-            "filter_fields": "{env.MONGODB_FILTER_FIELDS}",
-            "embedding_field": "{env.MONGODB_EMBEDDING_FIELD}",
-            "text_field": "{env.MONGODB_TEXT_FIELD}",
-        }

llama_stack/providers/remote/vector_io/mongodb/mongodb.py

@@ -1,5 +1,3 @@
-import pymongo
-
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # All rights reserved.
 #
@@ -11,8 +9,8 @@ import logging
 from typing import Any, Dict, List, Optional, Union
 from urllib.parse import urlparse
 
-from pymongo import MongoClient,
+from pymongo import MongoClient
-from pymongo.operations import UpdateOne, InsertOne, DeleteOne, DeleteMany, SearchIndexModel
+from pymongo.operations import InsertOne, SearchIndexModel, UpdateOne
 import certifi
 from numpy.typing import NDArray
 
@@ -25,19 +23,24 @@ from llama_stack.providers.utils.memory.vector_store import (
     EmbeddingIndex,
     VectorDBWithIndex,
 )
+  
+from .config import MongoDBVectorIOConfig
 
-from .config import MongoDBAtlasVectorIOConfig
 from time import sleep
 
 log = logging.getLogger(__name__)
 CHUNK_ID_KEY = "_chunk_id"
 
+
 class MongoDBAtlasIndex(EmbeddingIndex):
-    def __init__(self, client: MongoClient, namespace: str, embeddings_key: str, index_name: str):
+
+    def __init__(self, client: MongoClient, namespace: str, embeddings_key: str, embedding_dimension: str, index_name: str, filter_fields: List[str]):
         self.client = client
         self.namespace = namespace
         self.embeddings_key = embeddings_key
         self.index_name = index_name
+        self.filter_fields = filter_fields
+        self.embedding_dimension = embedding_dimension
 
     def _get_index_config(self, collection, index_name):
         idxs = list(collection.list_search_indexes())
@@ -45,14 +48,39 @@ class MongoDBAtlasIndex(EmbeddingIndex):
             if ele["name"] == index_name:
                 return ele
 
+    def _get_search_index_model(self):
+        index_fields = [
+            {
+                "path": self.embeddings_key,
+                "type": "vector",
+                        "numDimensions": self.embedding_dimension,
+                        "similarity": "cosine"
+            }
+        ]
+
+        if len(self.filter_fields) > 0:
+            for filter_field in self.filter_fields:
+                index_fields.append(
+                    {
+                        "path": filter_field,
+                        "type": "filter"
+                    }
+                )
+
+        return SearchIndexModel(
+            name=self.index_name,
+            type="vectorSearch",
+            definition={
+                "fields": index_fields
+            }
+        )
+
     def _check_n_create_index(self):
         client = self.client
-        db,collection = self.namespace.split(".")
+        db, collection = self.namespace.split(".")
         collection = client[db][collection]
         index_name = self.index_name
-        print(">>>>>>>>Index name: ", index_name, "<<<<<<<<<<")
         idx = self._get_index_config(collection, index_name)
-        print(idx)
         if not idx:
             log.info("Creating search index ...")
             search_index_model = self._get_search_index_model()
@@ -60,10 +88,10 @@ class MongoDBAtlasIndex(EmbeddingIndex):
             while True:
                 idx = self._get_index_config(collection, index_name)
                 if idx and idx["queryable"]:
-                    print("Search index created successfully.")
+                    log.info("Search index created successfully.")
                     break
                 else:
-                    print("Waiting for search index to be created ...")
+                    log.info("Waiting for search index to be created ...")
                     sleep(5)
         else:
             log.info("Search index already exists.")
@@ -77,46 +105,53 @@ class MongoDBAtlasIndex(EmbeddingIndex):
         operations = []
         for i, (chunk, embedding) in enumerate(zip(chunks, embeddings)):
             chunk_id = f"{chunk.metadata['document_id']}:chunk-{i}"
+
             operations.append(
-                InsertOne(
+                UpdateOne(
+                    {CHUNK_ID_KEY: chunk_id},
                     {
-                        CHUNK_ID_KEY: chunk_id,
+                        "$set": {
-                        "chunk_content": chunk.model_dump_json(),
+                            CHUNK_ID_KEY: chunk_id,
-                        self.embeddings_key: embedding.tolist(),
+                            "chunk_content": json.loads(chunk.model_dump_json()),
-                    }
+                            self.embeddings_key: embedding.tolist(),
+                        }
+                    },
+                    upsert=True,
                 )
             )
 
         # Perform the bulk operations
-        db,collection_name = self.namespace.split(".")
+        db, collection_name = self.namespace.split(".")
         collection = self.client[db][collection_name]
         collection.bulk_write(operations)
-
+        print(f"Added {len(chunks)} chunks to the collection")
-    async def query(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
-        
         # Create a search index model
+        print("Creating search index ...")
         self._check_n_create_index()
 
+    async def query(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
         # Perform a query
-        db,collection_name = self.namespace.split(".")
+        db, collection_name = self.namespace.split(".")
         collection = self.client[db][collection_name]
 
         # Create vector search query
-        vs_query = {"$vectorSearch": 
+        vs_query = {"$vectorSearch":
-            {
+                    {
-                "index": "vector_index",
+                        "index": self.index_name,
-                "path": self.embeddings_key,
+                        "path": self.embeddings_key,
-                "queryVector": embedding.tolist(),
+                        "queryVector": embedding.tolist(),
-                "numCandidates": k,
+                        "numCandidates": k,
-                "limit": k,
+                        "limit": k,
-            }
+                    }
-        }
+                    }
         # Add a field to store the score
         score_add_field_query = {
             "$addFields": {
                 "score": {"$meta": "vectorSearchScore"}
             }
         }
+        if score_threshold is None:
+            score_threshold = 0.01
         # Filter the results based on the score threshold
         filter_query = {
             "$match": {
@@ -141,60 +176,90 @@ class MongoDBAtlasIndex(EmbeddingIndex):
         chunks = []
         scores = []
         for result in results:
+            content = result["chunk_content"]
             chunk = Chunk(
-                metadata={"document_id": result[CHUNK_ID_KEY]},
+                metadata=content["metadata"],
-                content=json.loads(result["chunk_content"]),
+                content=content["content"],
             )
             chunks.append(chunk)
             scores.append(result["score"])
 
         return QueryChunksResponse(chunks=chunks, scores=scores)
-    
 
-class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
+    async def delete(self):
-    def __init__(self, config: MongoDBAtlasVectorIOConfig, inference_api: Api.inference):
+        db, _ = self.namespace.split(".")
+        self.client.drop_database(db)
+
+
+class MongoDBVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
+    def __init__(self, config: MongoDBVectorIOConfig, inference_api: Api.inference):
         self.config = config
         self.inference_api = inference_api
-    
+        self.cache = {}
 
     async def initialize(self) -> None:
         self.client = MongoClient(
-            self.config.uri,
+            self.config.connection_str,
             tlsCAFile=certifi.where(),
         )
-        self.cache = {}
-        pass
 
     async def shutdown(self) -> None:
-        self.client.close()
+        if not self.client:
-        pass
+            self.client.close()
 
-    async def register_vector_db( self, vector_db: VectorDB) -> None:
+    async def register_vector_db(self, vector_db: VectorDB) -> None:
-        index = VectorDBWithIndex(
+        index=MongoDBAtlasIndex(
+                client=self.client,
+                namespace=self.config.namespace,
+                embeddings_key=self.config.embeddings_key,
+                embedding_dimension=vector_db.embedding_dimension,
+                index_name=self.config.index_name,
+                filter_fields=self.config.filter_fields,
+            )
+        self.cache[vector_db.identifier] = VectorDBWithIndex(
             vector_db=vector_db,
+            index=index,
+            inference_api=self.inference_api,
+        )
+
+    async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> VectorDBWithIndex:
+        if vector_db_id in self.cache:
+            return self.cache[vector_db_id]
+        vector_db = await self.vector_db_store.get_vector_db(vector_db_id)
+        self.cache[vector_db_id] = VectorDBWithIndex(
+            vector_db=vector_db_id,
             index=MongoDBAtlasIndex(
                 client=self.client,
                 namespace=self.config.namespace,
                 embeddings_key=self.config.embeddings_key,
+                embedding_dimension=vector_db.embedding_dimension,
                 index_name=self.config.index_name,
+                filter_fields=self.config.filter_fields,
             ),
+            inference_api=self.inference_api,
         )
-        self.cache[vector_db] = index
+        return self.cache[vector_db_id]
-        pass
 
-    async def insert_chunks(self, vector_db_id, chunks, ttl_seconds = None):
+    async def unregister_vector_db(self, vector_db_id: str) -> None:
-        index = self.cache[vector_db_id].index
+        await self.cache[vector_db_id].index.delete()
+        del self.cache[vector_db_id]
+
+    async def insert_chunks(self,
+                            vector_db_id: str,
+                            chunks: List[Chunk],
+                            ttl_seconds: Optional[int] = None,
+                            ) -> None:
+        index = await self._get_and_cache_vector_db_index(vector_db_id)
         if not index:
             raise ValueError(f"Vector DB {vector_db_id} not found")
         await index.insert_chunks(chunks)
-    
 
-    async def query_chunks(self, vector_db_id, query, params = None):
+    async def query_chunks(self,
-        index = self.cache[vector_db_id].index
+                           vector_db_id: str,
+                           query: InterleavedContent,
+                           params: Optional[Dict[str, Any]] = None,
+                           ) -> QueryChunksResponse:
+        index = await self._get_and_cache_vector_db_index(vector_db_id)
         if not index:
             raise ValueError(f"Vector DB {vector_db_id} not found")
         return await index.query_chunks(query, params)
-    
-    
-
-