docs: Fix incorrect link and command for generating API reference (#1124)

added MongoDB vector io

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

@@ -0,0 +1,29 @@
+# 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 Any, Dict, Optional
+
+from pydantic import BaseModel, Field
+
+
+class MongoDBVectorIOConfig(BaseModel):
+    conncetion_str: str
+    namespace: str = Field(None, description="Namespace of the MongoDB collection")
+    index_name: Optional[str] = Field("default", description="Name of the index in the MongoDB collection") 
+    filter_fields: Optional[str] = Field(None, description="Fields to filter the MongoDB collection")
+    embedding_field: Optional[str] = Field("embeddings", description="Field name for the embeddings in the MongoDB collection")
+    text_field: Optional[str] = Field("text", description="Field name for the text in the MongoDB collection")
+
+    @classmethod
+    def sample_config(cls) -> 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_atlas.py

@@ -0,0 +1,200 @@
+import pymongo
+
+# 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.
+import asyncio
+import json
+import logging
+from typing import Any, Dict, List, Optional, Union
+from urllib.parse import urlparse
+
+from pymongo import MongoClient,
+from pymongo.operations import UpdateOne, InsertOne, DeleteOne, DeleteMany, SearchIndexModel
+import certifi
+from numpy.typing import NDArray
+
+from llama_stack.apis.inference import InterleavedContent
+from llama_stack.apis.vector_dbs import VectorDB
+from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
+
+from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
+from llama_stack.providers.utils.memory.vector_store import (
+    EmbeddingIndex,
+    VectorDBWithIndex,
+)
+
+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):
+        self.client = client
+        self.namespace = namespace
+        self.embeddings_key = embeddings_key
+        self.index_name = index_name
+
+    def _get_index_config(self, collection, index_name):
+        idxs = list(collection.list_search_indexes())
+        for ele in idxs:
+            if ele["name"] == index_name:
+                return ele
+
+    def _check_n_create_index(self):
+        client = self.client
+        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()
+            collection.create_search_index(search_index_model)
+            while True:
+                idx = self._get_index_config(collection, index_name)
+                if idx and idx["queryable"]:
+                    print("Search index created successfully.")
+                    break
+                else:
+                    print("Waiting for search index to be created ...")
+                    sleep(5)
+        else:
+            log.info("Search index already exists.")
+
+    async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray):
+        assert len(chunks) == len(embeddings), (
+            f"Chunk length {len(chunks)} does not match embedding length {len(embeddings)}"
+        )
+
+        # Create a list of operations to perform in bulk
+        operations = []
+        for i, (chunk, embedding) in enumerate(zip(chunks, embeddings)):
+            chunk_id = f"{chunk.metadata['document_id']}:chunk-{i}"
+            operations.append(
+                InsertOne(
+                    {
+                        CHUNK_ID_KEY: chunk_id,
+                        "chunk_content": chunk.model_dump_json(),
+                        self.embeddings_key: embedding.tolist(),
+                    }
+                )
+            )
+
+        # Perform the bulk operations
+        db,collection_name = self.namespace.split(".")
+        collection = self.client[db][collection_name]
+        collection.bulk_write(operations)
+
+    async def query(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
+        
+        # Create a search index model
+        self._check_n_create_index()
+
+        # Perform a query
+        db,collection_name = self.namespace.split(".")
+        collection = self.client[db][collection_name]
+
+        # Create vector search query
+        vs_query = {"$vectorSearch": 
+            {
+                "index": "vector_index",
+                "path": self.embeddings_key,
+                "queryVector": embedding.tolist(),
+                "numCandidates": k,
+                "limit": k,
+            }
+        }
+        # Add a field to store the score
+        score_add_field_query = {
+            "$addFields": {
+                "score": {"$meta": "vectorSearchScore"}
+            }
+        }
+        # Filter the results based on the score threshold
+        filter_query = {
+            "$match": {
+                "score": {"$gt": score_threshold}
+            }
+        }
+
+        project_query = {
+            "$project": {
+                CHUNK_ID_KEY: 1,
+                "chunk_content": 1,
+                "score": 1,
+                "_id": 0,
+            }
+        }
+
+        query = [vs_query, score_add_field_query, filter_query, project_query]
+
+        results = collection.aggregate(query)
+
+        # Create the response
+        chunks = []
+        scores = []
+        for result in results:
+            chunk = Chunk(
+                metadata={"document_id": result[CHUNK_ID_KEY]},
+                content=json.loads(result["chunk_content"]),
+            )
+            chunks.append(chunk)
+            scores.append(result["score"])
+
+        return QueryChunksResponse(chunks=chunks, scores=scores)
+    
+
+class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
+    def __init__(self, config: MongoDBAtlasVectorIOConfig, inference_api: Api.inference):
+        self.config = config
+        self.inference_api = inference_api
+    
+
+    async def initialize(self) -> None:
+        self.client = MongoClient(
+            self.config.uri,
+            tlsCAFile=certifi.where(),
+        )
+        self.cache = {}
+        pass
+
+    async def shutdown(self) -> None:
+        self.client.close()
+        pass
+
+    async def register_vector_db( self, vector_db: VectorDB) -> None:
+        index = VectorDBWithIndex(
+            vector_db=vector_db,
+            index=MongoDBAtlasIndex(
+                client=self.client,
+                namespace=self.config.namespace,
+                embeddings_key=self.config.embeddings_key,
+                index_name=self.config.index_name,
+            ),
+        )
+        self.cache[vector_db] = index
+        pass
+
+    async def insert_chunks(self, vector_db_id, chunks, ttl_seconds = None):
+        index = self.cache[vector_db_id].index
+        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):
+        index = self.cache[vector_db_id].index
+        if not index:
+            raise ValueError(f"Vector DB {vector_db_id} not found")
+        return await index.query_chunks(query, params)
+    
+    
+
+