Add Upstash Vector Index support

docs/components/vectordbs/config.mdx

@@ -6,7 +6,7 @@ Config in mem0 is a dictionary that specifies the settings for your vector datab
 
 The config is defined as a Python dictionary with two main keys:
 - `vector_store`: Specifies the vector database provider and its configuration
-  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus")
+  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus", "upstash_vector")
   - `config`: A nested dictionary containing provider-specific settings
 
 ## How to Use Config

docs/components/vectordbs/dbs/upstash-vector.mdx

@@ -0,0 +1,41 @@
+[Upstash Vector](https://upstash.com/docs/vector) is a serverless vector database designed for working with vector embeddings. 
+
+<Note>
+    Currently, Mem0 does not support for Upstash Vector embedding models.
+    So you can use Upstash Vector only as a vector database provider.
+</Note>
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+os.environ["UPSTASH_VECTOR_REST_URL"] = "..."
+os.environ["UPSTASH_VECTOR_REST_TOKEN"] = "..."
+
+config = {
+    "vector_store": {
+        "provider": "upstash_vector",
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+### Config
+
+Here are the parameters available for configuring Upstash Vector:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `url` | URL for the Upstash Vector index | `None` |
+| `token` | Token for the Upstash Vector index | `None` |
+| `client` | An `upstash_vector.Index` instance | `None` |
+| `namespace` | The default namespace used | `None` |
+
+<Note>
+    When `url` and `token` are not provided, the `UPSTASH_VECTOR_REST_URL` and `UPSTASH_VECTOR_REST_TOKEN` environment variables are used.
+</Note>

docs/components/vectordbs/overview.mdx

@@ -12,6 +12,7 @@ See the list of supported vector databases below.
   <Card title="Qdrant" href="/components/vectordbs/dbs/qdrant"></Card>
   <Card title="Chroma" href="/components/vectordbs/dbs/chroma"></Card>
   <Card title="Pgvector" href="/components/vectordbs/dbs/pgvector"></Card>
+  <Card title="Upstash Vector" href="/components/vectordbs/dbs/upstash-vector"></Card>
 </CardGroup>
 
 ## Usage

docs/mint.json

@@ -109,7 +109,8 @@
                 "components/vectordbs/dbs/chroma",
                 "components/vectordbs/dbs/pgvector",
                 "components/vectordbs/dbs/qdrant",
-                "components/vectordbs/dbs/milvus"
+                "components/vectordbs/dbs/milvus",
+                "components/vectordbs/dbs/upstash-vector"
               ]
             }
           ]

mem0/client/main.py

@@ -56,12 +56,12 @@ class MemoryClient:
     """
 
     def __init__(
-            self,
-            api_key: Optional[str] = None,
-            host: Optional[str] = None,
-            organization: Optional[str] = None,
-            project: Optional[str] = None
-        ):
+        self,
+        api_key: Optional[str] = None,
+        host: Optional[str] = None,
+        organization: Optional[str] = None,
+        project: Optional[str] = None,
+    ):
         """Initialize the MemoryClient.
 
         Args:
@@ -271,9 +271,7 @@ def delete_users(self) -> Dict[str, str]:
         params = {"org_name": self.organization, "project_name": self.project}
         entities = self.users()
         for entity in entities["results"]:
-            response = self.client.delete(
-                f"/v1/entities/{entity['type']}/{entity['id']}/", params=params
-            )
+            response = self.client.delete(f"/v1/entities/{entity['type']}/{entity['id']}/", params=params)
             response.raise_for_status()
 
         capture_client_event("client.delete_users", self)

mem0/configs/vector_stores/upstash_vector.py

@@ -0,0 +1,30 @@
+import os
+from typing import Any, ClassVar, Dict, Optional
+
+from pydantic import BaseModel, Field, model_validator
+
+
+class UpstashVectorConfig(BaseModel):
+    from upstash_vector import Index
+
+    Index: ClassVar[type] = Index
+
+    url: Optional[str] = Field(None, description="URL for Upstash Vector index")
+    token: Optional[str] = Field(None, description="Token for Upstash Vector index")
+    client: Optional[Index] = Field(None, description="Existing `upstash_vector.Index` client instance")
+    namespace: Optional[str] = Field(None, description="Default namespace for the index")
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_credentials_or_client(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        client = values.get("client")
+        url = values.get("url") or os.environ.get("UPSTASH_VECTOR_REST_URL")
+        token = values.get("token") or os.environ.get("UPSTASH_VECTOR_REST_TOKEN")
+
+        if not client and not (url and token):
+            raise ValueError("Either a client or URL and token must be provided.")
+        return values
+
+    model_config = {
+        "arbitrary_types_allowed": True,
+    }

mem0/embeddings/gemini.py

@@ -1,5 +1,6 @@
 import os
 from typing import Optional
+
 import google.generativeai as genai
 
 from mem0.configs.embeddings.base import BaseEmbedderConfig
@@ -27,4 +28,4 @@ def embed(self, text):
         """
         text = text.replace("\n", " ")
         response = genai.embed_content(model=self.config.model, content=text)
-        return response['embedding']
+        return response["embedding"]

mem0/embeddings/huggingface.py

@@ -26,4 +26,4 @@ def embed(self, text):
         Returns:
             list: The embedding vector.
         """
-        return self.model.encode(text, convert_to_numpy = True).tolist()
+        return self.model.encode(text, convert_to_numpy=True).tolist()

mem0/llms/openai.py

@@ -18,7 +18,9 @@ def __init__(self, config: Optional[BaseLlmConfig] = None):
         if os.environ.get("OPENROUTER_API_KEY"):  # Use OpenRouter
             self.client = OpenAI(
                 api_key=os.environ.get("OPENROUTER_API_KEY"),
-                base_url=self.config.openrouter_base_url or os.getenv("OPENROUTER_API_BASE") or "https://openrouter.ai/api/v1",
+                base_url=self.config.openrouter_base_url
+                or os.getenv("OPENROUTER_API_BASE")
+                or "https://openrouter.ai/api/v1",
             )
         else:
             api_key = self.config.api_key or os.getenv("OPENAI_API_KEY")

mem0/memory/main.py

@@ -4,7 +4,6 @@
 import logging
 import uuid
 import warnings
-from collections import defaultdict
 from datetime import datetime
 from typing import Any, Dict
 
@@ -37,7 +36,12 @@ def __init__(self, config: MemoryConfig = MemoryConfig()):
         )
         self.llm = LlmFactory.create(self.config.llm.provider, self.config.llm.config)
         self.db = SQLiteManager(self.config.history_db_path)
-        self.collection_name = self.config.vector_store.config.collection_name
+
+        if hasattr(self.config.vector_store.config, "collection_name"):
+            self.collection_name = self.config.vector_store.config.collection_name
+        else:
+            self.collection_name = None
+
         self.version = self.config.version
 
         self.enable_graph = False
@@ -195,7 +199,12 @@ def _add_to_vector_store(self, messages, metadata, filters):
                             }
                         )
                     elif resp["event"] == "UPDATE":
-                        self._update_memory(memory_id=resp["id"], data=resp["text"], existing_embeddings=new_message_embeddings, metadata=metadata)
+                        self._update_memory(
+                            memory_id=resp["id"],
+                            data=resp["text"],
+                            existing_embeddings=new_message_embeddings,
+                            metadata=metadata,
+                        )
                         returned_memories.append(
                             {
                                 "id": resp["id"],
@@ -304,10 +313,14 @@ def get_all(self, user_id=None, agent_id=None, run_id=None, limit=100):
         with concurrent.futures.ThreadPoolExecutor() as executor:
             future_memories = executor.submit(self._get_all_from_vector_store, filters, limit)
             future_graph_entities = (
-                executor.submit(self.graph.get_all, filters, limit) if self.version == "v1.1" and self.enable_graph else None
+                executor.submit(self.graph.get_all, filters, limit)
+                if self.version == "v1.1" and self.enable_graph
+                else None
             )
 
-            concurrent.futures.wait([future_memories, future_graph_entities] if future_graph_entities else [future_memories])
+            concurrent.futures.wait(
+                [future_memories, future_graph_entities] if future_graph_entities else [future_memories]
+            )
 
             all_memories = future_memories.result()
             graph_entities = future_graph_entities.result() if future_graph_entities else None
@@ -399,7 +412,9 @@ def search(self, query, user_id=None, agent_id=None, run_id=None, limit=100, fil
                 else None
             )
 
-            concurrent.futures.wait([future_memories, future_graph_entities] if future_graph_entities else [future_memories])
+            concurrent.futures.wait(
+                [future_memories, future_graph_entities] if future_graph_entities else [future_memories]
+            )
 
             original_memories = future_memories.result()
             graph_entities = future_graph_entities.result() if future_graph_entities else None

mem0/utils/factory.py

@@ -62,6 +62,7 @@ class VectorStoreFactory:
         "chroma": "mem0.vector_stores.chroma.ChromaDB",
         "pgvector": "mem0.vector_stores.pgvector.PGVector",
         "milvus": "mem0.vector_stores.milvus.MilvusDB",
+        "upstash_vector": "mem0.vector_stores.upstash_vector.UpstashVector",
     }
 
     @classmethod

mem0/vector_stores/configs.py

@@ -5,7 +5,7 @@
 
 class VectorStoreConfig(BaseModel):
     provider: str = Field(
-        description="Provider of the vector store (e.g., 'qdrant', 'chroma')",
+        description="Provider of the vector store (e.g., 'qdrant', 'chroma', 'upstash_vector')",
         default="qdrant",
     )
     config: Optional[Dict] = Field(description="Configuration for the specific vector store", default=None)
@@ -15,6 +15,7 @@ class VectorStoreConfig(BaseModel):
         "chroma": "ChromaDbConfig",
         "pgvector": "PGVectorConfig",
         "milvus": "MilvusDBConfig",
+        "upstash_vector": "UpstashVectorConfig",
     }
 
     @model_validator(mode="after")