🚀 feat: Add Atlas MongoDB as an option for Vector Store

README.md

@@ -79,6 +79,38 @@ The following environment variables are required to run the application:
 
 Make sure to set these environment variables before running the application. You can set them in a `.env` file or as system environment variables.
 
+### Use Atlas MongoDB as Vector Database
+
+Instead of using the default pgvector, we could use [Atlas MongoDB](https://www.mongodb.com/products/platform/atlas-vector-search) as the vector database. To do so, set the following environment variables
+
+```env
+VECTOR_DB_TYPE=atlas-mongo
+ATLAS_MONGO_DB_URI=<mongodb+srv://...>
+MONGO_VECTOR_COLLECTION=<collection name>
+```
+
+In additional, make sure the collection defined by `$MONGO_VECTOR_COLLECTION` has the following vector search index created
+
+```json
+{
+  "fields": [
+    {
+      "numDimensions": 1536,
+      "path": "embedding",
+      "similarity": "cosine",
+      "type": "vector"
+    },
+    {
+      "path": "file_id",
+      "type": "filter"
+    }
+  ]
+}
+```
+
+Follw one of the [four documented methods](https://www.mongodb.com/docs/atlas/atlas-vector-search/create-index/#procedure) to create the vector index.
+
+
 ### Cloud Installation Settings:
 
 #### AWS:

config.py

@@ -5,8 +5,11 @@
 from datetime import datetime
 
 from dotenv import find_dotenv, load_dotenv
-from langchain_community.embeddings import HuggingFaceEmbeddings, \
-    HuggingFaceHubEmbeddings, OllamaEmbeddings
+from langchain_community.embeddings import (
+    HuggingFaceEmbeddings,
+    HuggingFaceHubEmbeddings,
+    OllamaEmbeddings,
+)
 from langchain_openai import AzureOpenAIEmbeddings, OpenAIEmbeddings
 from starlette.middleware.base import BaseHTTPMiddleware
 
@@ -15,27 +18,37 @@
 load_dotenv(find_dotenv())
 
 
-def get_env_variable(var_name: str, default_value: str = None, required: bool = False) -> str:
+def get_env_variable(
+    var_name: str, default_value: str = None, required: bool = False
+) -> str:
     value = os.getenv(var_name)
     if value is None:
         if default_value is None and required:
             raise ValueError(f"Environment variable '{var_name}' not found.")
         return default_value
     return value
 
-RAG_HOST = os.getenv('RAG_HOST', '0.0.0.0')
-RAG_PORT = int(os.getenv('RAG_PORT', 8000))
+
+RAG_HOST = os.getenv("RAG_HOST", "0.0.0.0")
+RAG_PORT = int(os.getenv("RAG_PORT", 8000))
 
 RAG_UPLOAD_DIR = get_env_variable("RAG_UPLOAD_DIR", "./uploads/")
 if not os.path.exists(RAG_UPLOAD_DIR):
     os.makedirs(RAG_UPLOAD_DIR, exist_ok=True)
 
+VECTOR_DB_TYPE = get_env_variable("VECTOR_DB_TYPE", "pgvector")
 POSTGRES_DB = get_env_variable("POSTGRES_DB", "mydatabase")
 POSTGRES_USER = get_env_variable("POSTGRES_USER", "myuser")
 POSTGRES_PASSWORD = get_env_variable("POSTGRES_PASSWORD", "mypassword")
 DB_HOST = get_env_variable("DB_HOST", "db")
 DB_PORT = get_env_variable("DB_PORT", "5432")
 COLLECTION_NAME = get_env_variable("COLLECTION_NAME", "testcollection")
+ATLAS_MONGO_DB_URI = get_env_variable(
+    "ATLAS_MONGO_DB_URI", "mongodb://127.0.0.1:27018/LibreChat"
+)
+MONGO_VECTOR_COLLECTION = get_env_variable(
+    "MONGO_VECTOR_COLLECTION", "vector_collection"
+)
 
 CHUNK_SIZE = int(get_env_variable("CHUNK_SIZE", "1500"))
 CHUNK_OVERLAP = int(get_env_variable("CHUNK_OVERLAP", "100"))
@@ -62,6 +75,7 @@ def get_env_variable(var_name: str, default_value: str = None, required: bool =
     logger.setLevel(logging.INFO)
 
 if console_json:
+
     class JsonFormatter(logging.Formatter):
         def __init__(self):
             super(JsonFormatter, self).__init__()
@@ -96,7 +110,8 @@ def format(self, record):
     formatter = JsonFormatter()
 else:
     formatter = logging.Formatter(
-        '%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+        "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+    )
 
 handler = logging.StreamHandler()  # or logging.FileHandler("app.log")
 handler.setFormatter(formatter)
@@ -113,12 +128,11 @@ async def dispatch(self, request, call_next):
             logger_method = logger.debug
 
         logger_method(
-                f"Request {request.method} {request.url} - {response.status_code}",
-                extra={
-                    HTTP_REQ: {"method": request.method,
-                               "url": str(request.url)},
-                    HTTP_RES: {"status_code": response.status_code},
-                },
+            f"Request {request.method} {request.url} - {response.status_code}",
+            extra={
+                HTTP_REQ: {"method": request.method, "url": str(request.url)},
+                HTTP_RES: {"status_code": response.status_code},
+            },
         )
 
         return response
@@ -135,33 +149,38 @@ async def dispatch(self, request, call_next):
 RAG_OPENAI_PROXY = get_env_variable("RAG_OPENAI_PROXY", None)
 AZURE_OPENAI_API_KEY = get_env_variable("AZURE_OPENAI_API_KEY", "")
 RAG_AZURE_OPENAI_API_VERSION = get_env_variable("RAG_AZURE_OPENAI_API_VERSION", None)
-RAG_AZURE_OPENAI_API_KEY = get_env_variable("RAG_AZURE_OPENAI_API_KEY", AZURE_OPENAI_API_KEY)
+RAG_AZURE_OPENAI_API_KEY = get_env_variable(
+    "RAG_AZURE_OPENAI_API_KEY", AZURE_OPENAI_API_KEY
+)
 AZURE_OPENAI_ENDPOINT = get_env_variable("AZURE_OPENAI_ENDPOINT", "")
-RAG_AZURE_OPENAI_ENDPOINT = get_env_variable("RAG_AZURE_OPENAI_ENDPOINT", AZURE_OPENAI_ENDPOINT).rstrip("/")
+RAG_AZURE_OPENAI_ENDPOINT = get_env_variable(
+    "RAG_AZURE_OPENAI_ENDPOINT", AZURE_OPENAI_ENDPOINT
+).rstrip("/")
 HF_TOKEN = get_env_variable("HF_TOKEN", "")
 OLLAMA_BASE_URL = get_env_variable("OLLAMA_BASE_URL", "http://ollama:11434")
 
-
 ## Embeddings
 
+
 def init_embeddings(provider, model):
     if provider == "openai":
         return OpenAIEmbeddings(
             model=model,
             api_key=RAG_OPENAI_API_KEY,
             openai_api_base=RAG_OPENAI_BASEURL,
-            openai_proxy=RAG_OPENAI_PROXY
+            openai_proxy=RAG_OPENAI_PROXY,
         )
     elif provider == "azure":
         return AzureOpenAIEmbeddings(
             azure_deployment=model,
             api_key=RAG_AZURE_OPENAI_API_KEY,
             azure_endpoint=RAG_AZURE_OPENAI_ENDPOINT,
-            api_version=RAG_AZURE_OPENAI_API_VERSION
+            api_version=RAG_AZURE_OPENAI_API_VERSION,
         )
     elif provider == "huggingface":
-        return HuggingFaceEmbeddings(model_name=model, encode_kwargs={
-            'normalize_embeddings': True})
+        return HuggingFaceEmbeddings(
+            model_name=model, encode_kwargs={"normalize_embeddings": True}
+        )
     elif provider == "huggingfacetei":
         return HuggingFaceHubEmbeddings(model=model)
     elif provider == "ollama":
@@ -173,20 +192,20 @@ def init_embeddings(provider, model):
 EMBEDDINGS_PROVIDER = get_env_variable("EMBEDDINGS_PROVIDER", "openai").lower()
 
 if EMBEDDINGS_PROVIDER == "openai":
-    EMBEDDINGS_MODEL = get_env_variable("EMBEDDINGS_MODEL",
-                                        "text-embedding-3-small")
+    EMBEDDINGS_MODEL = get_env_variable("EMBEDDINGS_MODEL", "text-embedding-3-small")
 
 elif EMBEDDINGS_PROVIDER == "azure":
-    EMBEDDINGS_MODEL = get_env_variable("EMBEDDINGS_MODEL",
-                                        "text-embedding-3-small")
+    EMBEDDINGS_MODEL = get_env_variable("EMBEDDINGS_MODEL", "text-embedding-3-small")
 
 elif EMBEDDINGS_PROVIDER == "huggingface":
-    EMBEDDINGS_MODEL = get_env_variable("EMBEDDINGS_MODEL",
-                                        "sentence-transformers/all-MiniLM-L6-v2")
+    EMBEDDINGS_MODEL = get_env_variable(
+        "EMBEDDINGS_MODEL", "sentence-transformers/all-MiniLM-L6-v2"
+    )
 
 elif EMBEDDINGS_PROVIDER == "huggingfacetei":
-    EMBEDDINGS_MODEL = get_env_variable("EMBEDDINGS_MODEL",
-                                        "http://huggingfacetei:3000")
+    EMBEDDINGS_MODEL = get_env_variable(
+        "EMBEDDINGS_MODEL", "http://huggingfacetei:3000"
+    )
 
 elif EMBEDDINGS_PROVIDER == "ollama":
     EMBEDDINGS_MODEL = get_env_variable("EMBEDDINGS_MODEL", "nomic-embed-text")
@@ -197,14 +216,25 @@ def init_embeddings(provider, model):
 
 logger.info(f"Initialized embeddings of type: {type(embeddings)}")
 
-## Vector store
-
-vector_store = get_vector_store(
+# Vector store
+if VECTOR_DB_TYPE == "pgvector":
+    vector_store = get_vector_store(
         connection_string=CONNECTION_STRING,
         embeddings=embeddings,
         collection_name=COLLECTION_NAME,
         mode="async",
-)
+    )
+elif VECTOR_DB_TYPE == "atlas-mongo":
+    # atlas-mongo vector:
+    vector_store = get_vector_store(
+        connection_string=ATLAS_MONGO_DB_URI,
+        embeddings=embeddings,
+        collection_name=MONGO_VECTOR_COLLECTION,
+        mode="atlas-mongo",
+    )
+else:
+    raise ValueError(f"Unsupported vector store type: {VECTOR_DB_TYPE}")
+
 retriever = vector_store.as_retriever()
 
 known_source_ext = [

main.py

@@ -60,14 +60,16 @@
     # RAG_EMBEDDING_MODEL,
     # RAG_EMBEDDING_MODEL_DEVICE_TYPE,
     # RAG_TEMPLATE,
+    VECTOR_DB_TYPE,
 )
 
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     # Startup logic goes here
-    await PSQLDatabase.get_pool()  # Initialize the pool
-    await ensure_custom_id_index_on_embedding()
+    if VECTOR_DB_TYPE == "pgvector":
+        await PSQLDatabase.get_pool()  # Initialize the pool
+        await ensure_custom_id_index_on_embedding()
 
     yield
 
@@ -105,7 +107,10 @@ async def get_all_ids():
 
 
 def isHealthOK():
-    return pg_health_check()
+    if VECTOR_DB_TYPE == "pgvector":
+        return pg_health_check()
+    else:
+        return True
 
 
 @app.get("/health")
@@ -137,7 +142,7 @@ async def get_documents_by_ids(ids: list[str] = Query(...)):
 
 
 @app.delete("/documents")
-async def delete_documents(ids: list[str]):
+async def delete_documents(ids: list[str] = Query(...)):
     try:
         if isinstance(vector_store, AsyncPgVector):
             existing_ids = await vector_store.get_all_ids()
@@ -497,11 +502,11 @@ async def query_embeddings_by_file_ids(body: QueryMultipleBody):
                 vector_store.similarity_search_with_score_by_vector,
                 embedding,
                 k=body.k,
-                filter={"custom_id": {"$in": body.file_ids}},
+                filter={"file_id": {"$in": body.file_ids}},
             )
         else:
             documents = vector_store.similarity_search_with_score_by_vector(
-                embedding, k=body.k, filter={"custom_id": {"$in": body.file_ids}}
+                embedding, k=body.k, filter={"file_id": {"$in": body.file_ids}}
             )
 
         return documents

requirements.lite.txt

@@ -22,3 +22,5 @@ python-multipart==0.0.9
 aiofiles==23.2.1
 rapidocr-onnxruntime==1.3.17
 opencv-python-headless==4.9.0.80
+pymongo==4.6.3
+langchain-mongodb==0.1.3

requirements.txt

@@ -23,3 +23,5 @@ sentence_transformers==2.5.1
 aiofiles==23.2.1
 rapidocr-onnxruntime==1.3.17
 opencv-python-headless==4.9.0.80
+pymongo==4.6.3
+langchain-mongodb==0.1.3