Feature: baidu vector db integration (mem0ai#2929)

Author: WayneCao

Makefile

@@ -13,7 +13,7 @@ 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<7.0.0" pinecone-text faiss-cpu langchain-community \
-							upstash-vector azure-search-documents langchain-memgraph langchain-neo4j rank-bm25
+							upstash-vector azure-search-documents langchain-memgraph langchain-neo4j rank-bm25 pymochow
 
 # Format code with ruff
 format:

docs/components/vectordbs/dbs/baidu.mdx

@@ -0,0 +1,67 @@
+---
+title: Baidu VectorDB (Mochow)
+---
+
+[Baidu VectorDB](https://cloud.baidu.com/doc/VDB/index.html) is an enterprise-level distributed vector database service developed by Baidu Intelligent Cloud. It is powered by Baidu's proprietary "Mochow" vector database kernel, providing high performance, availability, and security for vector search.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+config = {
+    "vector_store": {
+        "provider": "baidu",
+        "config": {
+            "endpoint": "http://your-mochow-endpoint:8287",
+            "account": "root",
+            "api_key": "your-api-key",
+            "database_name": "mem0",
+            "table_name": "mem0_table",
+            "embedding_model_dims": 1536,
+            "metric_type": "COSINE"
+        }
+    }
+}
+
+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 movie? 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 available parameters for the `mochow` config:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `endpoint` | Endpoint URL for your Baidu VectorDB instance | Required |
+| `account` | Baidu VectorDB account name | `root` |
+| `api_key` | API key for accessing Baidu VectorDB | Required |
+| `database_name` | Name of the database | `mem0` |
+| `table_name` | Name of the table | `mem0_table` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `metric_type` | Distance metric for similarity search | `L2` |
+
+### Distance Metrics
+
+The following distance metrics are supported:
+
+- `L2`: Euclidean distance (default)
+- `IP`: Inner product
+- `COSINE`: Cosine similarity
+
+### Index Configuration
+
+The vector index is automatically configured with the following HNSW parameters:
+
+- `m`: 16 (number of connections per element)
+- `efconstruction`: 200 (size of the dynamic candidate list)
+- `auto_build`: true (automatically build index)
+- `auto_build_index_policy`: Incremental build with 10000 rows increment

docs/docs.json

@@ -146,7 +146,8 @@
                           "components/vectordbs/dbs/vertex_ai",
                           "components/vectordbs/dbs/weaviate",
                           "components/vectordbs/dbs/faiss",
-                          "components/vectordbs/dbs/langchain"
+                          "components/vectordbs/dbs/langchain",
+                          "components/vectordbs/dbs/baidu"
                         ]
                       }
                     ]

mem0/configs/vector_stores/baidu.py

@@ -0,0 +1,30 @@
+from enum import Enum
+from typing import Any, Dict
+
+from pydantic import BaseModel, Field, model_validator
+
+
+class BaiduDBConfig(BaseModel):
+    endpoint: str = Field("http://localhost:8287", description="Endpoint URL for Baidu VectorDB")
+    account: str = Field("root", description="Account for Baidu VectorDB")
+    api_key: str = Field(None, description="API Key for Baidu VectorDB")
+    database_name: str = Field("mem0", description="Name of the database")
+    table_name: str = Field("mem0", description="Name of the table")
+    embedding_model_dims: int = Field(1536, description="Dimensions of the embedding model")
+    metric_type: str = Field("L2", description="Metric type for similarity search")
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_extra_fields(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        allowed_fields = set(cls.model_fields.keys())
+        input_fields = set(values.keys())
+        extra_fields = input_fields - allowed_fields
+        if extra_fields:
+            raise ValueError(
+                f"Extra fields not allowed: {', '.join(extra_fields)}. Please input only the following fields: {', '.join(allowed_fields)}"
+            )
+        return values
+
+    model_config = {
+        "arbitrary_types_allowed": True,
+    }