version bump -> 0.1.108 (#2958)

Co-authored-by: Divya Gupta <divya.gupta@mongodb.com>

CONTRIBUTING.md

@@ -16,18 +16,19 @@ To make a contribution, follow these steps:
 For more details about pull requests, please read [GitHub's guides](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request).
 
 
-### 📦 Package manager
+### 📦 Development Environment
 
-We use `poetry` as our package manager. You can install poetry by following the instructions [here](https://python-poetry.org/docs/#installation).
-
-Please DO NOT use pip or conda to install the dependencies. Instead, use poetry:
+We use `hatch` for managing development environments. To set up:
 
 ```bash
-make install_all
+# Activate environment for specific Python version:
+hatch shell dev_py_3_9   # Python 3.9
+hatch shell dev_py_3_10  # Python 3.10  
+hatch shell dev_py_3_11  # Python 3.11
 
-#activate
-
-poetry shell
+# The environment will automatically install all dev dependencies
+# Run tests within the activated shell:
+make test
 ```
 
 ### 📌 Pre-commit
@@ -40,16 +41,21 @@ pre-commit install
 
 ### 🧪 Testing
 
-We use `pytest` to test our code. You can run the tests by running the following command:
+We use `pytest` to test our code across multiple Python versions. You can run tests using:
 
 ```bash
-poetry run pytest tests
-
-# or
-
+# Run tests with default Python version
 make test
+
+# Test specific Python versions:
+make test-py-3.9   # Python 3.9 environment
+make test-py-3.10  # Python 3.10 environment
+make test-py-3.11  # Python 3.11 environment
+
+# When using hatch shells, run tests with:
+make test  # After activating a shell with hatch shell test_XX
 ```
 
-Several packages have been removed from Poetry to make the package lighter. Therefore, it is recommended to run `make install_all` to install the remaining packages and ensure all tests pass. Make sure that all tests pass before submitting a pull request.
+Make sure that all tests pass across all supported Python versions before submitting a pull request.
 
-We look forward to your pull requests and can't wait to see your contributions!
+We look forward to your pull requests and can't wait to see your contributions!

Makefile

@@ -12,7 +12,7 @@ install:
 
 install_all:
 	pip install ruff==0.6.9 groq together boto3 litellm ollama chromadb weaviate weaviate-client sentence_transformers vertexai \
-	                        google-generativeai elasticsearch opensearch-py vecs pinecone pinecone-text faiss-cpu langchain-community \
+	                        google-generativeai elasticsearch opensearch-py vecs "pinecone<7.0.0" pinecone-text faiss-cpu langchain-community \
 							upstash-vector azure-search-documents langchain-memgraph langchain-neo4j rank-bm25
 
 # Format code with ruff
@@ -41,3 +41,12 @@ clean:
 
 test:
 	hatch run test
+
+test-py-3.9:
+	hatch run dev_py_3_9:test
+
+test-py-3.10:
+	hatch run dev_py_3_10:test
+
+test-py-3.11:
+	hatch run dev_py_3_11:test

docs/components/vectordbs/dbs/mongodb.mdx

@@ -0,0 +1,49 @@
+# MongoDB
+
+[MongoDB](https://www.mongodb.com/) is a versatile document database that supports vector search capabilities, allowing for efficient high-dimensional similarity searches over large datasets with robust scalability and performance.
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "mongodb",
+        "config": {
+            "db_name": "mem0-db",
+            "collection_name": "mem0-collection",
+            "user": "my-user",
+            "password": "my-password",
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="alice", metadata={"category": "movies"})
+```
+
+## Config
+
+Here are the parameters available for configuring MongoDB:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| db_name | Name of the MongoDB database | `"mem0_db"` |
+| collection_name | Name of the MongoDB collection | `"mem0_collection"` |
+| embedding_model_dims | Dimensions of the embedding vectors | `1536` |
+| user | MongoDB user for authentication | `None` |
+| password | Password for the MongoDB user | `None` |
+| host | MongoDB host | `"localhost"` |
+| port | MongoDB port | `27017` |
+
+> **Note**: `user` and `password` must either be provided together or omitted together.

docs/components/vectordbs/overview.mdx

@@ -23,6 +23,7 @@ See the list of supported vector databases below.
   <Card title="Upstash Vector" href="/components/vectordbs/dbs/upstash-vector"></Card>
   <Card title="Milvus" href="/components/vectordbs/dbs/milvus"></Card>
   <Card title="Pinecone" href="/components/vectordbs/dbs/pinecone"></Card>
+  <Card title="MongoDB" href="/components/vectordbs/dbs/mongodb"></Card>
   <Card title="Azure" href="/components/vectordbs/dbs/azure"></Card>
   <Card title="Redis" href="/components/vectordbs/dbs/redis"></Card>
   <Card title="Elasticsearch" href="/components/vectordbs/dbs/elasticsearch"></Card>

docs/docs.json

@@ -137,6 +137,7 @@
                           "components/vectordbs/dbs/pgvector",
                           "components/vectordbs/dbs/milvus",
                           "components/vectordbs/dbs/pinecone",
+                          "components/vectordbs/dbs/mongodb",
                           "components/vectordbs/dbs/azure",
                           "components/vectordbs/dbs/redis",
                           "components/vectordbs/dbs/elasticsearch",

mem0/configs/vector_stores/mongodb.py

@@ -0,0 +1,42 @@
+from typing import Any, Dict, Optional, Callable, List
+
+from pydantic import BaseModel, Field, root_validator
+
+
+class MongoVectorConfig(BaseModel):
+    """Configuration for MongoDB vector database."""
+
+    db_name: str = Field("mem0_db", description="Name of the MongoDB database")
+    collection_name: str = Field("mem0", description="Name of the MongoDB collection")
+    embedding_model_dims: Optional[int] = Field(1536, description="Dimensions of the embedding vectors")
+    user: Optional[str] = Field(None, description="MongoDB user for authentication")
+    password: Optional[str] = Field(None, description="Password for the MongoDB user")
+    host: Optional[str] = Field("localhost", description="MongoDB host. Default is 'localhost'")
+    port: Optional[int] = Field(27017, description="MongoDB port. Default is 27017")
+
+    @root_validator(pre=True)
+    def check_auth_and_connection(cls, values):
+        user = values.get("user")
+        password = values.get("password")
+        if (user is None) != (password is None):
+            raise ValueError("Both 'user' and 'password' must be provided together or omitted together.")
+
+        host = values.get("host")
+        port = values.get("port")
+        if host is None:
+            raise ValueError("The 'host' must be provided.")
+        if port is None:
+            raise ValueError("The 'port' must be provided.")
+        return values
+
+    @root_validator(pre=True)
+    def validate_extra_fields(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        allowed_fields = set(cls.__fields__)
+        input_fields = set(values.keys())
+        extra_fields = input_fields - allowed_fields
+        if extra_fields:
+            raise ValueError(
+                f"Extra fields not allowed: {', '.join(extra_fields)}. "
+                f"Please provide only the following fields: {', '.join(allowed_fields)}."
+            )
+        return values

mem0/memory/main.py

@@ -28,8 +28,8 @@ from mem0.memory.utils import (
     get_fact_retrieval_messages,
     parse_messages,
     parse_vision_messages,
-    remove_code_blocks,
     process_telemetry_filters,
+    remove_code_blocks,
 )
 from mem0.utils.factory import EmbedderFactory, LlmFactory, VectorStoreFactory
 
@@ -338,10 +338,9 @@ class Memory(MemoryBase):
         except Exception as e:
             logging.error(f"Error in new_retrieved_facts: {e}")
             new_retrieved_facts = []
-
+        
         if not new_retrieved_facts:
             logger.debug("No new facts retrieved from input. Skipping memory update LLM call.")
-            return []
 
         retrieved_old_memory = []
         new_message_embeddings = {}
@@ -369,24 +368,27 @@ class Memory(MemoryBase):
             temp_uuid_mapping[str(idx)] = item["id"]
             retrieved_old_memory[idx]["id"] = str(idx)
 
-        function_calling_prompt = get_update_memory_messages(
-            retrieved_old_memory, new_retrieved_facts, self.config.custom_update_memory_prompt
-        )
-
-        try:
-            response: str = self.llm.generate_response(
-                messages=[{"role": "user", "content": function_calling_prompt}],
-                response_format={"type": "json_object"},
+        if new_retrieved_facts:
+            function_calling_prompt = get_update_memory_messages(
+                retrieved_old_memory, new_retrieved_facts, self.config.custom_update_memory_prompt
             )
-        except Exception as e:
-            logging.error(f"Error in new memory actions response: {e}")
-            response = ""
 
-        try:
-            response = remove_code_blocks(response)
-            new_memories_with_actions = json.loads(response)
-        except Exception as e:
-            logging.error(f"Invalid JSON response: {e}")
+            try:
+                response: str = self.llm.generate_response(
+                    messages=[{"role": "user", "content": function_calling_prompt}],
+                    response_format={"type": "json_object"},
+                )
+            except Exception as e:
+                logging.error(f"Error in new memory actions response: {e}")
+                response = ""
+
+            try:
+                response = remove_code_blocks(response)
+                new_memories_with_actions = json.loads(response)
+            except Exception as e:
+                logging.error(f"Invalid JSON response: {e}")
+                new_memories_with_actions = {}
+        else:
             new_memories_with_actions = {}
 
         returned_memories = []
@@ -1162,13 +1164,11 @@ class AsyncMemory(MemoryBase):
             response = remove_code_blocks(response)
             new_retrieved_facts = json.loads(response)["facts"]
         except Exception as e:
-            new_retrieved_facts = []
-
-        if not new_retrieved_facts:
-            logger.info("No new facts retrieved from input. Skipping memory update LLM call.")
-            return []
             logging.error(f"Error in new_retrieved_facts: {e}")
             new_retrieved_facts = []
+        
+        if not new_retrieved_facts:
+            logger.debug("No new facts retrieved from input. Skipping memory update LLM call.")
 
         retrieved_old_memory = []
         new_message_embeddings = {}
@@ -1200,31 +1200,25 @@ class AsyncMemory(MemoryBase):
             temp_uuid_mapping[str(idx)] = item["id"]
             retrieved_old_memory[idx]["id"] = str(idx)
 
-        function_calling_prompt = get_update_memory_messages(
-            retrieved_old_memory, new_retrieved_facts, self.config.custom_update_memory_prompt
-        )
-        try:
-            response = await asyncio.to_thread(
-                self.llm.generate_response,
-                messages=[{"role": "user", "content": function_calling_prompt}],
-                response_format={"type": "json_object"},
+        if new_retrieved_facts:
+            function_calling_prompt = get_update_memory_messages(
+                retrieved_old_memory, new_retrieved_facts, self.config.custom_update_memory_prompt
             )
-        except Exception as e:
-            response = ""
-            logging.error(f"Error in new memory actions response: {e}")
-            response = ""
-        try:
-            response = remove_code_blocks(response)
-            new_memories_with_actions = json.loads(response)
-        except Exception as e:
-            new_memories_with_actions = {}
-
-        if not new_memories_with_actions:
-            logger.info("No new facts retrieved from input (async). Skipping memory update LLM call.")
-            return []
-
-            logging.error(f"Invalid JSON response: {e}")
-            new_memories_with_actions = {}
+            try:
+                response = await asyncio.to_thread(
+                    self.llm.generate_response,
+                    messages=[{"role": "user", "content": function_calling_prompt}],
+                    response_format={"type": "json_object"},
+                )
+            except Exception as e:
+                logging.error(f"Error in new memory actions response: {e}")
+                response = ""
+            try:
+                response = remove_code_blocks(response)
+                new_memories_with_actions = json.loads(response)
+            except Exception as e:
+                logging.error(f"Invalid JSON response: {e}")
+                new_memories_with_actions = {}
 
         returned_memories = []
         try:

mem0/utils/factory.py

@@ -79,6 +79,7 @@ class VectorStoreFactory:
         "upstash_vector": "mem0.vector_stores.upstash_vector.UpstashVector",
         "azure_ai_search": "mem0.vector_stores.azure_ai_search.AzureAISearch",
         "pinecone": "mem0.vector_stores.pinecone.PineconeDB",
+        "mongodb": "mem0.vector_stores.mongodb.MongoDB",
         "redis": "mem0.vector_stores.redis.RedisDB",
         "elasticsearch": "mem0.vector_stores.elasticsearch.ElasticsearchDB",
         "vertex_ai_vector_search": "mem0.vector_stores.vertex_ai_vector_search.GoogleMatchingEngine",

mem0/vector_stores/configs.py

@@ -15,6 +15,7 @@ class VectorStoreConfig(BaseModel):
         "chroma": "ChromaDbConfig",
         "pgvector": "PGVectorConfig",
         "pinecone": "PineconeConfig",
+        "mongodb": "MongoDBConfig",
         "milvus": "MilvusDBConfig",
         "upstash_vector": "UpstashVectorConfig",
         "azure_ai_search": "AzureAISearchConfig",

mem0/vector_stores/mongodb.py

@@ -0,0 +1,299 @@
+import logging
+from typing import List, Optional, Dict, Any, Callable
+
+from pydantic import BaseModel
+
+try:
+    from pymongo import MongoClient
+    from pymongo.operations import SearchIndexModel
+    from pymongo.errors import PyMongoError
+except ImportError:
+    raise ImportError("The 'pymongo' library is required. Please install it using 'pip install pymongo'.")
+
+from mem0.vector_stores.base import VectorStoreBase
+
+logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO)
+
+
+class OutputData(BaseModel):
+    id: Optional[str]
+    score: Optional[float]
+    payload: Optional[dict]
+
+
+class MongoVector(VectorStoreBase):
+    VECTOR_TYPE = "knnVector"
+    SIMILARITY_METRIC = "cosine"
+
+    def __init__(
+        self,
+        db_name: str,
+        collection_name: str,
+        embedding_model_dims: int,
+        mongo_uri: str
+    ):
+        """
+        Initialize the MongoDB vector store with vector search capabilities.
+
+        Args:
+            db_name (str): Database name
+            collection_name (str): Collection name
+            embedding_model_dims (int): Dimension of the embedding vector
+            mongo_uri (str): MongoDB connection URI
+        """
+        self.collection_name = collection_name
+        self.embedding_model_dims = embedding_model_dims
+        self.db_name = db_name
+
+        self.client = MongoClient(
+            mongo_uri
+        )
+        self.db = self.client[db_name]
+        self.collection = self.create_col()
+
+    def create_col(self):
+        """Create new collection with vector search index."""
+        try:
+            database = self.client[self.db_name]
+            collection_names = database.list_collection_names()
+            if self.collection_name not in collection_names:
+                logger.info(f"Collection '{self.collection_name}' does not exist. Creating it now.")
+                collection = database[self.collection_name]
+                # Insert and remove a placeholder document to create the collection
+                collection.insert_one({"_id": 0, "placeholder": True})
+                collection.delete_one({"_id": 0})
+                logger.info(f"Collection '{self.collection_name}' created successfully.")
+            else:
+                collection = database[self.collection_name]
+
+            self.index_name = f"{self.collection_name}_vector_index"
+            found_indexes = list(collection.list_search_indexes(name=self.index_name))
+            if found_indexes:
+                logger.info(f"Search index '{self.index_name}' already exists in collection '{self.collection_name}'.")
+            else:
+                search_index_model = SearchIndexModel(
+                    name=self.index_name,
+                    definition={
+                        "mappings": {
+                            "dynamic": False,
+                            "fields": {
+                                "embedding": {
+                                    "type": self.VECTOR_TYPE,
+                                    "dimensions": self.embedding_model_dims,
+                                    "similarity": self.SIMILARITY_METRIC,
+                                }
+                            },
+                        }
+                    },
+                )
+                collection.create_search_index(search_index_model)
+                logger.info(
+                    f"Search index '{self.index_name}' created successfully for collection '{self.collection_name}'."
+                )
+            return collection
+        except PyMongoError as e:
+            logger.error(f"Error creating collection and search index: {e}")
+            return None
+
+    def insert(
+        self, vectors: List[List[float]], payloads: Optional[List[Dict]] = None, ids: Optional[List[str]] = None
+    ) -> None:
+        """
+        Insert vectors into the collection.
+
+        Args:
+            vectors (List[List[float]]): List of vectors to insert.
+            payloads (List[Dict], optional): List of payloads corresponding to vectors.
+            ids (List[str], optional): List of IDs corresponding to vectors.
+        """
+        logger.info(f"Inserting {len(vectors)} vectors into collection '{self.collection_name}'.")
+
+        data = []
+        for vector, payload, _id in zip(vectors, payloads or [{}] * len(vectors), ids or [None] * len(vectors)):
+            document = {"_id": _id, "embedding": vector, "payload": payload}
+            data.append(document)
+        try:
+            self.collection.insert_many(data)
+            logger.info(f"Inserted {len(data)} documents into '{self.collection_name}'.")
+        except PyMongoError as e:
+            logger.error(f"Error inserting data: {e}")
+
+    def search(self, query: str, query_vector: List[float], limit=5, filters: Optional[Dict] = None) -> List[OutputData]:
+        """
+        Search for similar vectors using the vector search index.
+
+        Args:
+            query (str): Query string
+            query_vector (List[float]): Query vector.
+            limit (int, optional): Number of results to return. Defaults to 5.
+            filters (Dict, optional): Filters to apply to the search.
+
+        Returns:
+            List[OutputData]: Search results.
+        """
+
+        found_indexes = list(self.collection.list_search_indexes(name=self.index_name))
+        if not found_indexes:
+            logger.error(f"Index '{self.index_name}' does not exist.")
+            return []
+
+        results = []
+        try:
+            collection = self.client[self.db_name][self.collection_name]
+            pipeline = [
+                {
+                    "$vectorSearch": {
+                        "index": self.index_name,
+                        "limit": limit,
+                        "numCandidates": limit,
+                        "queryVector": query_vector,
+                        "path": "embedding",
+                    }
+                },
+                {"$set": {"score": {"$meta": "vectorSearchScore"}}},
+                {"$project": {"embedding": 0}},
+            ]
+            results = list(collection.aggregate(pipeline))
+            logger.info(f"Vector search completed. Found {len(results)} documents.")
+        except Exception as e:
+            logger.error(f"Error during vector search for query {query}: {e}")
+            return []
+
+        output = [OutputData(id=str(doc["_id"]), score=doc.get("score"), payload=doc.get("payload")) for doc in results]
+        return output
+
+    def delete(self, vector_id: str) -> None:
+        """
+        Delete a vector by ID.
+
+        Args:
+            vector_id (str): ID of the vector to delete.
+        """
+        try:
+            result = self.collection.delete_one({"_id": vector_id})
+            if result.deleted_count > 0:
+                logger.info(f"Deleted document with ID '{vector_id}'.")
+            else:
+                logger.warning(f"No document found with ID '{vector_id}' to delete.")
+        except PyMongoError as e:
+            logger.error(f"Error deleting document: {e}")
+
+    def update(self, vector_id: str, vector: Optional[List[float]] = None, payload: Optional[Dict] = None) -> None:
+        """
+        Update a vector and its payload.
+
+        Args:
+            vector_id (str): ID of the vector to update.
+            vector (List[float], optional): Updated vector.
+            payload (Dict, optional): Updated payload.
+        """
+        update_fields = {}
+        if vector is not None:
+            update_fields["embedding"] = vector
+        if payload is not None:
+            update_fields["payload"] = payload
+
+        if update_fields:
+            try:
+                result = self.collection.update_one({"_id": vector_id}, {"$set": update_fields})
+                if result.matched_count > 0:
+                    logger.info(f"Updated document with ID '{vector_id}'.")
+                else:
+                    logger.warning(f"No document found with ID '{vector_id}' to update.")
+            except PyMongoError as e:
+                logger.error(f"Error updating document: {e}")
+
+    def get(self, vector_id: str) -> Optional[OutputData]:
+        """
+        Retrieve a vector by ID.
+
+        Args:
+            vector_id (str): ID of the vector to retrieve.
+
+        Returns:
+            Optional[OutputData]: Retrieved vector or None if not found.
+        """
+        try:
+            doc = self.collection.find_one({"_id": vector_id})
+            if doc:
+                logger.info(f"Retrieved document with ID '{vector_id}'.")
+                return OutputData(id=str(doc["_id"]), score=None, payload=doc.get("payload"))
+            else:
+                logger.warning(f"Document with ID '{vector_id}' not found.")
+                return None
+        except PyMongoError as e:
+            logger.error(f"Error retrieving document: {e}")
+            return None
+
+    def list_cols(self) -> List[str]:
+        """
+        List all collections in the database.
+
+        Returns:
+            List[str]: List of collection names.
+        """
+        try:
+            collections = self.db.list_collection_names()
+            logger.info(f"Listing collections in database '{self.db_name}': {collections}")
+            return collections
+        except PyMongoError as e:
+            logger.error(f"Error listing collections: {e}")
+            return []
+
+    def delete_col(self) -> None:
+        """Delete the collection."""
+        try:
+            self.collection.drop()
+            logger.info(f"Deleted collection '{self.collection_name}'.")
+        except PyMongoError as e:
+            logger.error(f"Error deleting collection: {e}")
+
+    def col_info(self) -> Dict[str, Any]:
+        """
+        Get information about the collection.
+
+        Returns:
+            Dict[str, Any]: Collection information.
+        """
+        try:
+            stats = self.db.command("collstats", self.collection_name)
+            info = {"name": self.collection_name, "count": stats.get("count"), "size": stats.get("size")}
+            logger.info(f"Collection info: {info}")
+            return info
+        except PyMongoError as e:
+            logger.error(f"Error getting collection info: {e}")
+            return {}
+
+    def list(self, filters: Optional[Dict] = None, limit: int = 100) -> List[OutputData]:
+        """
+        List vectors in the collection.
+
+        Args:
+            filters (Dict, optional): Filters to apply to the list.
+            limit (int, optional): Number of vectors to return.
+
+        Returns:
+            List[OutputData]: List of vectors.
+        """
+        try:
+            query = filters or {}
+            cursor = self.collection.find(query).limit(limit)
+            results = [OutputData(id=str(doc["_id"]), score=None, payload=doc.get("payload")) for doc in cursor]
+            logger.info(f"Retrieved {len(results)} documents from collection '{self.collection_name}'.")
+            return results
+        except PyMongoError as e:
+            logger.error(f"Error listing documents: {e}")
+            return []
+    
+    def reset(self):
+        """Reset the index by deleting and recreating it."""
+        logger.warning(f"Resetting index {self.collection_name}...")
+        self.delete_col()
+        self.collection = self.create_col(self.collection_name)
+
+    def __del__(self) -> None:
+        """Close the database connection when the object is deleted."""
+        if hasattr(self, "client"):
+            self.client.close()
+            logger.info("MongoClient connection closed.")

mem0/vector_stores/opensearch.py

@@ -83,19 +83,19 @@ class OpenSearchDB(VectorStoreBase):
             },
         }
 
+        logger.warning(f"Creating index {name}, it might take 1-2 minutes...")
         if not self.client.indices.exists(index=name):
             self.client.indices.create(index=name, body=index_settings)
-            logger.info(f"Created index {name}")
 
             # Wait for index to be ready
-            max_retries = 60  # 60 seconds timeout
+            max_retries = 180 # 3 minutes timeout
             retry_count = 0
             while retry_count < max_retries:
                 try:
                     # Check if index is ready by attempting a simple search
                     self.client.search(index=name, body={"query": {"match_all": {}}})
-                    logger.info(f"Index {name} is ready")
                     time.sleep(1)
+                    logger.info(f"Index {name} is ready")
                     return
                 except Exception:
                     retry_count += 1

poetry.lock

@@ -2200,4 +2200,4 @@ graph = ["langchain-neo4j", "neo4j", "rank-bm25"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.9,<4.0"
-content-hash = "07f2aee9c596c2d2470df085b92551b7b7e3c19cabe61ae5bee7505395601417"
+content-hash = "07f2aee9c596c2d2470df085b92551b7b7e3c19cabe61ae5bee7505395601417"

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "mem0ai"
-version = "0.1.107rc2"
+version = "0.1.108"
 description = "Long-term memory for AI Agents"
 authors = [
     { name = "Mem0", email = "founders@mem0.ai" }
@@ -26,6 +26,32 @@ graph = [
     "neo4j>=5.23.1",
     "rank-bm25>=0.2.2",
 ]
+vector_stores = [
+    "vecs>=0.4.0",
+    "chromadb>=0.4.24",
+    "weaviate-client>=4.4.0",
+    "pinecone<7.0.0",
+    "pinecone-text>=0.1.1",
+    "faiss-cpu>=1.7.4",
+    "upstash-vector>=0.1.0",
+    "azure-search-documents>=11.4.0b8",
+]
+llms = [
+    "groq>=0.3.0",
+    "together>=0.2.10",
+    "litellm>=0.1.0",
+    "ollama>=0.1.0",
+    "vertexai>=0.1.0",
+    "google-generativeai>=0.3.0",
+]
+extras = [
+    "boto3>=1.34.0",
+    "langchain-community>=0.0.0",
+    "sentence-transformers>=2.2.2",
+    "elasticsearch>=8.0.0",
+    "opensearch-py>=2.0.0",
+    "langchain-memgraph>=0.1.0",
+]
 test = [
     "pytest>=8.2.2",
     "pytest-mock>=3.14.0",
@@ -53,6 +79,36 @@ only-include = ["mem0"]
 [tool.hatch.build.targets.wheel.shared-data]
 "README.md" = "README.md"
 
+[tool.hatch.envs.dev_py_3_9]
+python = "3.9"
+features = [
+  "test",
+  "graph",
+  "vector_stores",
+  "llms",
+  "extras",
+]
+
+[tool.hatch.envs.dev_py_3_10]
+python = "3.10"
+features = [
+  "test",
+  "graph",
+  "vector_stores",
+  "llms",
+  "extras",
+]
+
+[tool.hatch.envs.dev_py_3_11]
+python = "3.11"
+features = [
+  "test",
+  "graph",
+  "vector_stores",
+  "llms",
+  "extras",
+]
+
 [tool.hatch.envs.default.scripts]
 format = [
     "ruff format",

tests/memory/test_main.py

@@ -40,10 +40,12 @@ class TestAddToVectorStoreErrors:
 
         return memory
 
-    def test_empty_llm_response_fact_extraction(self, mock_memory, caplog):
+    def test_empty_llm_response_fact_extraction(self, mocker, mock_memory, caplog):
         """Test empty response from LLM during fact extraction"""
         # Setup
         mock_memory.llm.generate_response.return_value = ""
+        mock_capture_event = mocker.MagicMock()
+        mocker.patch("mem0.memory.main.capture_event", mock_capture_event)
 
         # Execute
         with caplog.at_level(logging.ERROR):
@@ -52,9 +54,10 @@ class TestAddToVectorStoreErrors:
             )
 
         # Verify
-        assert mock_memory.llm.generate_response.call_count == 2
+        assert mock_memory.llm.generate_response.call_count == 1
         assert result == []  # Should return empty list when no memories processed
         assert "Error in new_retrieved_facts" in caplog.text
+        assert mock_capture_event.call_count == 1
 
     def test_empty_llm_response_memory_actions(self, mock_memory, caplog):
         """Test empty response from LLM during memory actions"""
@@ -94,25 +97,31 @@ class TestAsyncAddToVectorStoreErrors:
         """Test empty response in AsyncMemory._add_to_vector_store"""
         mocker.patch("mem0.utils.factory.EmbedderFactory.create", return_value=MagicMock())
         mock_async_memory.llm.generate_response.return_value = ""
+        mock_capture_event = mocker.MagicMock()
+        mocker.patch("mem0.memory.main.capture_event", mock_capture_event)
 
         with caplog.at_level(logging.ERROR):
             result = await mock_async_memory._add_to_vector_store(
-                messages=[{"role": "user", "content": "test"}], metadata={}, filters={}, infer=True
+                messages=[{"role": "user", "content": "test"}], metadata={}, effective_filters={}, infer=True
             )
-
+        assert mock_async_memory.llm.generate_response.call_count == 1
         assert result == []
         assert "Error in new_retrieved_facts" in caplog.text
+        assert mock_capture_event.call_count == 1
 
     @pytest.mark.asyncio
     async def test_async_empty_llm_response_memory_actions(self, mock_async_memory, caplog, mocker):
         """Test empty response in AsyncMemory._add_to_vector_store"""
         mocker.patch("mem0.utils.factory.EmbedderFactory.create", return_value=MagicMock())
         mock_async_memory.llm.generate_response.side_effect = ['{"facts": ["test fact"]}', ""]
+        mock_capture_event = mocker.MagicMock()
+        mocker.patch("mem0.memory.main.capture_event", mock_capture_event)
 
         with caplog.at_level(logging.ERROR):
             result = await mock_async_memory._add_to_vector_store(
-                messages=[{"role": "user", "content": "test"}], metadata={}, filters={}, infer=True
+                messages=[{"role": "user", "content": "test"}], metadata={}, effective_filters={}, infer=True
             )
 
         assert result == []
         assert "Invalid JSON response" in caplog.text
+        assert mock_capture_event.call_count == 1

tests/test_main.py

@@ -19,13 +19,14 @@ def mock_openai():
 def memory_instance():
     with (
         patch("mem0.utils.factory.EmbedderFactory") as mock_embedder,
-        patch("mem0.utils.factory.VectorStoreFactory") as mock_vector_store,
+        patch("mem0.memory.main.VectorStoreFactory") as mock_vector_store,
         patch("mem0.utils.factory.LlmFactory") as mock_llm,
         patch("mem0.memory.telemetry.capture_event"),
         patch("mem0.memory.graph_memory.MemoryGraph"),
     ):
         mock_embedder.create.return_value = Mock()
         mock_vector_store.create.return_value = Mock()
+        mock_vector_store.create.return_value.search.return_value = []
         mock_llm.create.return_value = Mock()
 
         config = MemoryConfig(version="v1.1")
@@ -37,13 +38,14 @@ def memory_instance():
 def memory_custom_instance():
     with (
         patch("mem0.utils.factory.EmbedderFactory") as mock_embedder,
-        patch("mem0.utils.factory.VectorStoreFactory") as mock_vector_store,
+        patch("mem0.memory.main.VectorStoreFactory") as mock_vector_store,
         patch("mem0.utils.factory.LlmFactory") as mock_llm,
         patch("mem0.memory.telemetry.capture_event"),
         patch("mem0.memory.graph_memory.MemoryGraph"),
     ):
         mock_embedder.create.return_value = Mock()
         mock_vector_store.create.return_value = Mock()
+        mock_vector_store.create.return_value.search.return_value = []
         mock_llm.create.return_value = Mock()
 
         config = MemoryConfig(
@@ -250,7 +252,11 @@ def test_get_all(memory_instance, version, enable_graph, expected_result):
 
 def test_custom_prompts(memory_custom_instance):
     messages = [{"role": "user", "content": "Test message"}]
+    from mem0.embeddings.mock import MockEmbeddings
     memory_custom_instance.llm.generate_response = Mock()
+    memory_custom_instance.llm.generate_response.return_value = '{"facts": ["fact1", "fact2"]}'
+    memory_custom_instance.embedding_model = MockEmbeddings()
+    
 
     with patch("mem0.memory.main.parse_messages", return_value="Test message") as mock_parse_messages:
         with patch(
@@ -273,7 +279,7 @@ def test_custom_prompts(memory_custom_instance):
             ## custom update memory prompt
             ##
             mock_get_update_memory_messages.assert_called_once_with(
-                [], [], memory_custom_instance.config.custom_update_memory_prompt
+                [], ["fact1", "fact2"], memory_custom_instance.config.custom_update_memory_prompt
             )
 
             memory_custom_instance.llm.generate_response.assert_any_call(

tests/vector_stores/test_mongodb.py

@@ -0,0 +1,176 @@
+import time
+import pytest
+from unittest.mock import MagicMock, patch
+from mem0.vector_stores.mongodb import MongoVector
+from pymongo.operations import SearchIndexModel
+
+@pytest.fixture
+@patch("mem0.vector_stores.mongodb.MongoClient")
+def mongo_vector_fixture(mock_mongo_client):
+    mock_client = mock_mongo_client.return_value
+    mock_db = mock_client["test_db"]
+    mock_collection = mock_db["test_collection"]
+    mock_collection.list_search_indexes.return_value = []
+    mock_collection.aggregate.return_value = []
+    mock_collection.find_one.return_value = None
+    mock_collection.find.return_value = []
+    mock_db.list_collection_names.return_value = []
+
+    mongo_vector = MongoVector(
+        db_name="test_db",
+        collection_name="test_collection",
+        embedding_model_dims=1536,
+        user="username",
+        password="password",
+    )
+    return mongo_vector, mock_collection, mock_db
+
+def test_initalize_create_col(mongo_vector_fixture):
+    mongo_vector, mock_collection, mock_db = mongo_vector_fixture
+    assert mongo_vector.collection_name == "test_collection"
+    assert mongo_vector.embedding_model_dims == 1536
+    assert mongo_vector.db_name == "test_db"
+
+    # Verify create_col being called
+    mock_db.list_collection_names.assert_called_once()
+    mock_collection.insert_one.assert_called_once_with({"_id": 0, "placeholder": True})
+    mock_collection.delete_one.assert_called_once_with({"_id": 0})
+    assert mongo_vector.index_name == "test_collection_vector_index"
+    mock_collection.list_search_indexes.assert_called_once_with(name="test_collection_vector_index")
+    mock_collection.create_search_index.assert_called_once()
+    args, _ = mock_collection.create_search_index.call_args
+    search_index_model = args[0].document
+    assert search_index_model == {
+        "name": "test_collection_vector_index",
+        "definition": {
+            "mappings": {
+                "dynamic": False,
+                "fields": {
+                    "embedding": {
+                        "type": "knnVector",
+                        "d": 1536,
+                        "similarity": "cosine",
+                    }
+                }
+            }
+        }
+    }
+    assert mongo_vector.collection == mock_collection
+
+def test_insert(mongo_vector_fixture):
+    mongo_vector, mock_collection, _ = mongo_vector_fixture
+    vectors = [[0.1] * 1536, [0.2] * 1536]
+    payloads = [{"name": "vector1"}, {"name": "vector2"}]
+    ids = ["id1", "id2"]
+
+    mongo_vector.insert(vectors, payloads, ids)
+    expected_records=[
+        ({"_id": ids[0], "embedding": vectors[0], "payload": payloads[0]}),
+        ({"_id": ids[1], "embedding": vectors[1], "payload": payloads[1]})
+    ]
+    mock_collection.insert_many.assert_called_once_with(expected_records)
+
+def test_search(mongo_vector_fixture):
+    mongo_vector, mock_collection, _ = mongo_vector_fixture
+    query_vector = [0.1] * 1536
+    mock_collection.aggregate.return_value = [
+        {"_id": "id1", "score": 0.9, "payload": {"key": "value1"}},
+        {"_id": "id2", "score": 0.8, "payload": {"key": "value2"}},
+    ]
+    mock_collection.list_search_indexes.return_value = ["test_collection_vector_index"]
+
+    results = mongo_vector.search("query_str", query_vector, limit=2)
+    mock_collection.list_search_indexes.assert_called_with(name="test_collection_vector_index")
+    mock_collection.aggregate.assert_called_once_with([
+        {
+            "$vectorSearch": {
+                "index": "test_collection_vector_index",
+                "limit": 2,
+                "numCandidates": 2,
+                "queryVector": query_vector,
+                "path": "embedding",
+            },
+        },
+        {"$set": {"score": {"$meta": "vectorSearchScore"}}},
+        {"$project": {"embedding": 0}},
+    ])
+    assert len(results) == 2
+    assert results[0].id == "id1"
+    assert results[0].score == 0.9
+    assert results[1].id == "id2"
+    assert results[1].score == 0.8
+
+def test_delete(mongo_vector_fixture):
+    mongo_vector, mock_collection, _ = mongo_vector_fixture
+    mock_delete_result = MagicMock()
+    mock_delete_result.deleted_count = 1
+    mock_collection.delete_one.return_value = mock_delete_result
+
+    mongo_vector.delete("id1")
+    mock_collection.delete_one.assert_called_with({"_id": "id1"})
+
+def test_update(mongo_vector_fixture):
+    mongo_vector, mock_collection, _ = mongo_vector_fixture
+    mock_update_result = MagicMock()
+    mock_update_result.matched_count = 1
+    mock_collection.update_one.return_value = mock_update_result
+    idValue = "id1"
+    vectorValue = [0.2] * 1536
+    payloadValue = {"key": "updated"}
+
+    mongo_vector.update(idValue, vector=vectorValue, payload=payloadValue)
+    mock_collection.update_one.assert_called_once_with(
+        {"_id": idValue},
+        {"$set": {"embedding": vectorValue, "payload": payloadValue}},
+    )
+
+def test_get(mongo_vector_fixture):
+    mongo_vector, mock_collection, _ = mongo_vector_fixture
+    mock_collection.find_one.return_value = {"_id": "id1", "payload": {"key": "value1"}}
+
+    result = mongo_vector.get("id1")
+    assert result is not None
+    assert result.id == "id1"
+    assert result.payload == {"key": "value1"}
+
+def test_list_cols(mongo_vector_fixture):
+    mongo_vector, _, mock_db = mongo_vector_fixture
+    mock_db.list_collection_names.return_value = ["col1", "col2"]
+
+    collections = mongo_vector.list_cols()
+    assert collections == ["col1", "col2"]
+
+def test_delete_col(mongo_vector_fixture):
+    mongo_vector, mock_collection, _ = mongo_vector_fixture
+
+    mongo_vector.delete_col()
+    mock_collection.drop.assert_called_once()
+
+def test_col_info(mongo_vector_fixture):
+    mongo_vector, _, mock_db = mongo_vector_fixture
+    mock_db.command.return_value = {"count": 10, "size": 1024}
+
+    info = mongo_vector.col_info()
+    mock_db.command.assert_called_once_with("collstats", "test_collection")
+    assert info["name"] == "test_collection"
+    assert info["count"] == 10
+    assert info["size"] == 1024
+
+def test_list(mongo_vector_fixture):
+    mongo_vector, mock_collection, _ = mongo_vector_fixture
+    mock_cursor = MagicMock()
+    mock_cursor.limit.return_value = [
+        {"_id": "id1", "payload": {"key": "value1"}},
+        {"_id": "id2", "payload": {"key": "value2"}},
+    ]
+    mock_collection.find.return_value = mock_cursor
+
+    query_filters = {"_id": {"$in": ["id1", "id2"]}}
+    results = mongo_vector.list(filters=query_filters, limit=2)
+    mock_collection.find.assert_called_once_with(query_filters)
+    mock_cursor.limit.assert_called_once_with(2)
+    assert len(results) == 2
+    assert results[0].id == "id1"
+    assert results[0].payload == {"key": "value1"}
+    assert results[1].id == "id2"
+    assert results[1].payload == {"key": "value2"}