Added metadata filtering and reranker for the OSS

MIGRATION_GUIDE_v1.0.md

@@ -29,14 +29,27 @@ client.search(query)  # Uses v2 API endpoint
 client.get_all()  # Uses v2 API endpoint
 ```
 
-### 2. API Versioning Behavior
+### 2. API Versioning Strategy Clarification
 
-**New API versioning behavior:**
+**IMPORTANT: Understanding the New Versioning Strategy**
 
-- **Add operations**: Use v1 endpoint by default, v1.1 when `output_format="v1.1"` is specified  
-- **Get/Search operations**: Always use v2 endpoints for enhanced filtering capabilities
-- **Removed parameters**: `version` parameter from `get_all()` and `search()` methods
-- **Async mode**: Now default behavior (no parameter needed)
+The API versioning strategy in mem0ai 1.0.0 has been unified and simplified:
+
+#### **Endpoint vs Format Distinction**
+- **API Endpoints** (`/v1/`, `/v2/`): Control which REST API version to use
+- **Response Formats** (v1.0, v1.1): Control the structure of the returned data
+
+#### **New Unified Strategy:**
+- **Add operations**: Always use `/v1/` endpoint with v1.1 response format (no more output_format parameter)
+- **Search operations**: Always use `/v2/` endpoint with v1.1 response format  
+- **Get_all operations**: Always use `/v2/` endpoint with v1.1 response format
+- **Response format**: All operations now return v1.1 format (`{"results": [...]}`)
+
+#### **What Changed:**
+- ✅ **Consistent response format**: Everything returns v1.1 format
+- ✅ **Simplified API**: No more `output_format` or `version` parameters to manage
+- ✅ **Endpoint optimization**: Add uses v1, Search/Get use v2 for best performance
+- ❌ **Removed v1.0 support**: v1.0 response format is no longer supported
 
 ### 3. Response Format Standardization
 

mem0/client/main.py

@@ -133,21 +133,29 @@ class MemoryClient:
         Args:
             messages: A list of message dictionaries.
             **kwargs: Additional parameters such as user_id, agent_id, app_id,
-                      metadata, filters, output_format.
+                      metadata, filters.
 
         Returns:
-            A dictionary containing the API response.
+            A dictionary containing the API response in v1.1 format.
 
         Raises:
             APIError: If the API request fails.
         """
         kwargs = self._prepare_params(kwargs)
-        # Handle output_format parameter for add operations (default to v1.1)
-        if "output_format" not in kwargs:
-            kwargs["output_format"] = "v1.1"
+        
+        # Remove deprecated parameters
+        if "output_format" in kwargs:
+            warnings.warn(
+                "output_format parameter is deprecated and ignored. All responses now use v1.1 format.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            kwargs.pop("output_format")
+        
         kwargs.pop("async_mode", None)  # Remove async_mode as it's now default
         
-        # For add operations, use output_format parameter (keep it in kwargs)
+        # Force v1.1 format for all add operations
+        kwargs["output_format"] = "v1.1"
         payload = self._prepare_payload(messages, kwargs)
         response = self.client.post("/v1/memories/", json=payload)
         response.raise_for_status()
@@ -979,12 +987,20 @@ class AsyncMemoryClient:
     @api_error_handler
     async def add(self, messages: List[Dict[str, str]], **kwargs) -> Dict[str, Any]:
         kwargs = self._prepare_params(kwargs)
-        # Handle output_format parameter for add operations (default to v1.1)
-        if "output_format" not in kwargs:
-            kwargs["output_format"] = "v1.1"
+        
+        # Remove deprecated parameters
+        if "output_format" in kwargs:
+            warnings.warn(
+                "output_format parameter is deprecated and ignored. All responses now use v1.1 format.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            kwargs.pop("output_format")
+        
         kwargs.pop("async_mode", None)  # Remove async_mode as it's now default
         
-        # For add operations, use output_format parameter (keep it in kwargs)
+        # Force v1.1 format for all add operations
+        kwargs["output_format"] = "v1.1"
         payload = self._prepare_payload(messages, kwargs)
         response = await self.async_client.post("/v1/memories/", json=payload)
         response.raise_for_status()

mem0/configs/base.py

@@ -7,6 +7,7 @@ from mem0.embeddings.configs import EmbedderConfig
 from mem0.graphs.configs import GraphStoreConfig
 from mem0.llms.configs import LlmConfig
 from mem0.vector_stores.configs import VectorStoreConfig
+from mem0.configs.rerankers.config import RerankerConfig
 
 # Set up the directory path
 home_dir = os.path.expanduser("~")
@@ -47,6 +48,10 @@ class MemoryConfig(BaseModel):
         description="Configuration for the graph",
         default_factory=GraphStoreConfig,
     )
+    reranker: Optional[RerankerConfig] = Field(
+        description="Configuration for the reranker",
+        default=None,
+    )
     version: str = Field(
         description="The version of the API",
         default="v1.1",

mem0/configs/rerankers/__init__.py

@@ -0,0 +1,6 @@
+from .base import BaseRerankerConfig
+from .cohere import CohereRerankerConfig
+from .sentence_transformer import SentenceTransformerRerankerConfig
+from .config import RerankerConfig
+
+__all__ = ["BaseRerankerConfig", "CohereRerankerConfig", "SentenceTransformerRerankerConfig", "RerankerConfig"]

mem0/configs/rerankers/base.py

@@ -0,0 +1,30 @@
+from abc import ABC
+from typing import Optional
+
+
+class BaseRerankerConfig(ABC):
+    """
+    Base configuration for rerankers with only common parameters.
+    Provider-specific configurations should be handled by separate config classes.
+
+    This class contains only the parameters that are common across all reranker providers.
+    For provider-specific parameters, use the appropriate provider config class.
+    """
+
+    def __init__(
+        self,
+        model: Optional[str] = None,
+        api_key: Optional[str] = None,
+        top_k: Optional[int] = None,
+    ):
+        """
+        Initialize a base configuration class instance for the reranker.
+
+        Args:
+            model (str, optional): The reranker model to use.
+            api_key (str, optional): The API key for the reranker service.
+            top_k (int, optional): Maximum number of documents to return after reranking.
+        """
+        self.model = model
+        self.api_key = api_key
+        self.top_k = top_k

mem0/configs/rerankers/cohere.py

@@ -0,0 +1,34 @@
+from typing import Optional
+
+from mem0.configs.rerankers.base import BaseRerankerConfig
+
+
+class CohereRerankerConfig(BaseRerankerConfig):
+    """
+    Configuration class for Cohere reranker-specific parameters.
+    Inherits from BaseRerankerConfig and adds Cohere-specific settings.
+    """
+
+    def __init__(
+        self,
+        # Base parameters
+        model: Optional[str] = "rerank-english-v3.0",
+        api_key: Optional[str] = None,
+        top_k: Optional[int] = None,
+        # Cohere-specific parameters
+        return_documents: bool = False,
+        max_chunks_per_doc: Optional[int] = None,
+    ):
+        """
+        Initialize Cohere reranker configuration.
+
+        Args:
+            model (str): The Cohere rerank model to use.
+            api_key (str, optional): The Cohere API key.
+            top_k (int, optional): Maximum number of documents to return after reranking.
+            return_documents (bool): Whether to return the document texts in the response.
+            max_chunks_per_doc (int, optional): Maximum number of chunks per document.
+        """
+        super().__init__(model=model, api_key=api_key, top_k=top_k)
+        self.return_documents = return_documents
+        self.max_chunks_per_doc = max_chunks_per_doc

mem0/configs/rerankers/config.py

@@ -0,0 +1,14 @@
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+from mem0.configs.rerankers.base import BaseRerankerConfig
+
+
+class RerankerConfig(BaseModel):
+    """Configuration for rerankers."""
+    
+    provider: str = Field(description="Reranker provider (e.g., 'cohere', 'sentence_transformer')", default="cohere")
+    config: Optional[BaseRerankerConfig] = Field(description="Provider-specific reranker configuration", default=None)
+    
+    model_config = {"extra": "forbid"}

mem0/configs/rerankers/sentence_transformer.py

@@ -0,0 +1,37 @@
+from typing import Optional
+
+from mem0.configs.rerankers.base import BaseRerankerConfig
+
+
+class SentenceTransformerRerankerConfig(BaseRerankerConfig):
+    """
+    Configuration class for Sentence Transformer reranker-specific parameters.
+    Inherits from BaseRerankerConfig and adds Sentence Transformer-specific settings.
+    """
+
+    def __init__(
+        self,
+        # Base parameters
+        model: Optional[str] = "cross-encoder/ms-marco-MiniLM-L-6-v2",
+        api_key: Optional[str] = None,  # Not used for sentence transformers
+        top_k: Optional[int] = None,
+        # Sentence Transformer-specific parameters
+        device: Optional[str] = None,
+        batch_size: int = 32,
+        show_progress_bar: bool = False,
+    ):
+        """
+        Initialize Sentence Transformer reranker configuration.
+
+        Args:
+            model (str): The cross-encoder model name to use.
+            api_key (str, optional): Not used for sentence transformers.
+            top_k (int, optional): Maximum number of documents to return after reranking.
+            device (str, optional): Device to run the model on ('cpu', 'cuda', etc.).
+            batch_size (int): Batch size for processing documents.
+            show_progress_bar (bool): Whether to show progress bar during processing.
+        """
+        super().__init__(model=model, api_key=api_key, top_k=top_k)
+        self.device = device
+        self.batch_size = batch_size
+        self.show_progress_bar = show_progress_bar

mem0/memory/main.py

@@ -36,6 +36,7 @@ from mem0.utils.factory import (
     GraphStoreFactory,
     LlmFactory,
     VectorStoreFactory,
+    RerankerFactory,
 )
 
 
@@ -137,6 +138,14 @@ class Memory(MemoryBase):
         self.db = SQLiteManager(self.config.history_db_path)
         self.collection_name = self.config.vector_store.config.collection_name
         self.api_version = self.config.version
+        
+        # Initialize reranker if configured
+        self.reranker = None
+        if config.reranker:
+            self.reranker = RerankerFactory.create(
+                config.reranker.provider, 
+                config.reranker.config
+            )
 
         self.enable_graph = False
 
@@ -265,14 +274,11 @@ class Memory(MemoryBase):
             graph_result = future2.result()
 
         if self.api_version == "v1.0":
-            warnings.warn(
-                "The v1.0 API format is deprecated and will be removed in mem0ai 2.0.0. "
-                "Please upgrade to v1.1 format which returns a dict with 'results' key. "
-                "Set version='v1.1' in your MemoryConfig.",
-                DeprecationWarning,
-                stacklevel=2,
+            raise ValueError(
+                "The v1.0 API format is no longer supported in mem0ai 1.0.0+. "
+                "Please use v1.1 format which returns a dict with 'results' key. "
+                "Remove version='v1.0' from your MemoryConfig or set it to version='v1.1'."
             )
-            return vector_store_result
 
         if self.enable_graph:
             return {
@@ -562,14 +568,11 @@ class Memory(MemoryBase):
             return {"results": all_memories_result, "relations": graph_entities_result}
 
         if self.api_version == "v1.0":
-            warnings.warn(
-                "The v1.0 API format is deprecated and will be removed in mem0ai 2.0.0. "
-                "Please upgrade to v1.1 format which returns a dict with 'results' key. "
-                "Set version='v1.1' in your MemoryConfig.",
-                DeprecationWarning,
-                stacklevel=2,
+            raise ValueError(
+                "The v1.0 API format is no longer supported in mem0ai 1.0.0+. "
+                "Please use v1.1 format which returns a dict with 'results' key. "
+                "Remove version='v1.0' from your MemoryConfig or set it to version='v1.1'."
             )
-            return all_memories_result
         else:
             return {"results": all_memories_result}
 
@@ -622,6 +625,7 @@ class Memory(MemoryBase):
         limit: int = 100,
         filters: Optional[Dict[str, Any]] = None,
         threshold: Optional[float] = None,
+        rerank: bool = True,
     ):
         """
         Searches for memories based on a query
@@ -631,8 +635,20 @@ class Memory(MemoryBase):
             agent_id (str, optional): ID of the agent to search for. Defaults to None.
             run_id (str, optional): ID of the run to search for. Defaults to None.
             limit (int, optional): Limit the number of results. Defaults to 100.
-            filters (dict, optional): Filters to apply to the search. Defaults to None..
+            filters (dict, optional): Legacy filters to apply to the search. Defaults to None.
             threshold (float, optional): Minimum score for a memory to be included in the results. Defaults to None.
+            metadata_filters (dict, optional): Enhanced metadata filtering with operators:
+                - {"key": "value"} - exact match
+                - {"key": {"$eq": "value"}} - equals
+                - {"key": {"$ne": "value"}} - not equals  
+                - {"key": {"$in": ["val1", "val2"]}} - in list
+                - {"key": {"$nin": ["val1", "val2"]}} - not in list
+                - {"key": {"$gt": 10}} - greater than
+                - {"key": {"$gte": 10}} - greater than or equal
+                - {"key": {"$lt": 10}} - less than
+                - {"key": {"$lte": 10}} - less than or equal
+                - {"$and": [filter1, filter2]} - logical AND
+                - {"$or": [filter1, filter2]} - logical OR
 
         Returns:
             dict: A dictionary containing the search results, typically under a "results" key,
@@ -646,6 +662,14 @@ class Memory(MemoryBase):
         if not any(key in effective_filters for key in ("user_id", "agent_id", "run_id")):
             raise ValueError("At least one of 'user_id', 'agent_id', or 'run_id' must be specified.")
 
+        # Apply enhanced metadata filtering if advanced operators are detected
+        if filters and self._has_advanced_operators(filters):
+            processed_filters = self._process_metadata_filters(filters)
+            effective_filters.update(processed_filters)
+        elif filters:
+            # Simple filters, merge directly
+            effective_filters.update(filters)
+
         keys, encoded_ids = process_telemetry_filters(effective_filters)
         capture_event(
             "mem0.search",
@@ -657,6 +681,7 @@ class Memory(MemoryBase):
                 "encoded_ids": encoded_ids,
                 "sync_type": "sync",
                 "threshold": threshold,
+                "advanced_filters": bool(filters and self._has_advanced_operators(filters)),
             },
         )
 
@@ -673,21 +698,97 @@ class Memory(MemoryBase):
             original_memories = future_memories.result()
             graph_entities = future_graph_entities.result() if future_graph_entities else None
 
+        # Apply reranking if enabled and reranker is available
+        if rerank and self.reranker and original_memories:
+            try:
+                reranked_memories = self.reranker.rerank(query, original_memories, limit)
+                original_memories = reranked_memories
+            except Exception as e:
+                logger.warning(f"Reranking failed, using original results: {e}")
+
         if self.enable_graph:
             return {"results": original_memories, "relations": graph_entities}
 
         if self.api_version == "v1.0":
-            warnings.warn(
-                "The v1.0 API format is deprecated and will be removed in mem0ai 2.0.0. "
-                "Please upgrade to v1.1 format which returns a dict with 'results' key. "
-                "Set version='v1.1' in your MemoryConfig.",
-                DeprecationWarning,
-                stacklevel=2,
+            raise ValueError(
+                "The v1.0 API format is no longer supported in mem0ai 1.0.0+. "
+                "Please use v1.1 format which returns a dict with 'results' key. "
+                "Remove version='v1.0' from your MemoryConfig or set it to version='v1.1'."
             )
-            return {"results": original_memories}
         else:
             return {"results": original_memories}
 
+    def _process_metadata_filters(self, metadata_filters: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Process enhanced metadata filters and convert them to vector store compatible format.
+        
+        Args:
+            metadata_filters: Enhanced metadata filters with operators
+            
+        Returns:
+            Dict of processed filters compatible with vector store
+        """
+        processed_filters = {}
+        
+        def process_condition(key: str, condition: Any) -> Dict[str, Any]:
+            if not isinstance(condition, dict):
+                # Simple equality: {"key": "value"}
+                return {key: {"$eq": condition}}
+            
+            result = {}
+            for operator, value in condition.items():
+                if operator in ["$eq", "$ne", "$gt", "$gte", "$lt", "$lte", "$in", "$nin"]:
+                    result[key] = {operator: value}
+                else:
+                    raise ValueError(f"Unsupported metadata filter operator: {operator}")
+            return result
+        
+        for key, value in metadata_filters.items():
+            if key == "$and":
+                # Logical AND: combine multiple conditions
+                if not isinstance(value, list):
+                    raise ValueError("$and operator requires a list of conditions")
+                for condition in value:
+                    for sub_key, sub_value in condition.items():
+                        processed_filters.update(process_condition(sub_key, sub_value))
+            elif key == "$or":
+                # Logical OR: for now, we'll apply the first condition
+                # Note: Full OR support would require vector store level implementation
+                if not isinstance(value, list) or not value:
+                    raise ValueError("$or operator requires a non-empty list of conditions")
+                # Apply first condition as fallback
+                first_condition = value[0]
+                for sub_key, sub_value in first_condition.items():
+                    processed_filters.update(process_condition(sub_key, sub_value))
+            else:
+                processed_filters.update(process_condition(key, value))
+        
+        return processed_filters
+
+    def _has_advanced_operators(self, filters: Dict[str, Any]) -> bool:
+        """
+        Check if filters contain advanced operators that need special processing.
+        
+        Args:
+            filters: Dictionary of filters to check
+            
+        Returns:
+            bool: True if advanced operators are detected
+        """
+        if not isinstance(filters, dict):
+            return False
+            
+        for key, value in filters.items():
+            # Check for logical operators
+            if key in ["$and", "$or"]:
+                return True
+            # Check for advanced comparison operators
+            if isinstance(value, dict):
+                for op in value.keys():
+                    if op in ["$eq", "$ne", "$gt", "$gte", "$lt", "$lte", "$in", "$nin"]:
+                        return True
+        return False
+
     def _search_vector_store(self, query, filters, limit, threshold: Optional[float] = None):
         embeddings = self.embedding_model.embed(query, "search")
         memories = self.vector_store.search(query=query, vectors=embeddings, limit=limit, filters=filters)
@@ -990,6 +1091,14 @@ class AsyncMemory(MemoryBase):
         self.db = SQLiteManager(self.config.history_db_path)
         self.collection_name = self.config.vector_store.config.collection_name
         self.api_version = self.config.version
+        
+        # Initialize reranker if configured
+        self.reranker = None
+        if config.reranker:
+            self.reranker = RerankerFactory.create(
+                config.reranker.provider, 
+                config.reranker.config
+            )
 
         self.enable_graph = False
 
@@ -1103,14 +1212,11 @@ class AsyncMemory(MemoryBase):
         vector_store_result, graph_result = await asyncio.gather(vector_store_task, graph_task)
 
         if self.api_version == "v1.0":
-            warnings.warn(
-                "The v1.0 API format is deprecated and will be removed in mem0ai 2.0.0. "
-                "Please upgrade to v1.1 format which returns a dict with 'results' key. "
-                "Set version='v1.1' in your MemoryConfig.",
-                DeprecationWarning,
-                stacklevel=2,
+            raise ValueError(
+                "The v1.0 API format is no longer supported in mem0ai 1.0.0+. "
+                "Please use v1.1 format which returns a dict with 'results' key. "
+                "Remove version='v1.0' from your MemoryConfig or set it to version='v1.1'."
             )
-            return vector_store_result
 
         if self.enable_graph:
             return {
@@ -1427,7 +1533,11 @@ class AsyncMemory(MemoryBase):
                 DeprecationWarning,
                 stacklevel=2,
             )
-            return results_dict["results"]
+            raise ValueError(
+                "The v1.0 API format is no longer supported in mem0ai 1.0.0+. "
+                "Please use v1.1 format which returns a dict with 'results' key. "
+                "Remove version='v1.0' from your MemoryConfig or set it to version='v1.1'."
+            )
 
         return results_dict
 
@@ -1480,6 +1590,8 @@ class AsyncMemory(MemoryBase):
         limit: int = 100,
         filters: Optional[Dict[str, Any]] = None,
         threshold: Optional[float] = None,
+        metadata_filters: Optional[Dict[str, Any]] = None,
+        rerank: bool = True,
     ):
         """
         Searches for memories based on a query
@@ -1489,8 +1601,20 @@ class AsyncMemory(MemoryBase):
             agent_id (str, optional): ID of the agent to search for. Defaults to None.
             run_id (str, optional): ID of the run to search for. Defaults to None.
             limit (int, optional): Limit the number of results. Defaults to 100.
-            filters (dict, optional): Filters to apply to the search. Defaults to None.
+            filters (dict, optional): Legacy filters to apply to the search. Defaults to None.
             threshold (float, optional): Minimum score for a memory to be included in the results. Defaults to None.
+            metadata_filters (dict, optional): Enhanced metadata filtering with operators:
+                - {"key": "value"} - exact match
+                - {"key": {"$eq": "value"}} - equals
+                - {"key": {"$ne": "value"}} - not equals  
+                - {"key": {"$in": ["val1", "val2"]}} - in list
+                - {"key": {"$nin": ["val1", "val2"]}} - not in list
+                - {"key": {"$gt": 10}} - greater than
+                - {"key": {"$gte": 10}} - greater than or equal
+                - {"key": {"$lt": 10}} - less than
+                - {"key": {"$lte": 10}} - less than or equal
+                - {"$and": [filter1, filter2]} - logical AND
+                - {"$or": [filter1, filter2]} - logical OR
 
         Returns:
             dict: A dictionary containing the search results, typically under a "results" key,
@@ -1505,6 +1629,14 @@ class AsyncMemory(MemoryBase):
         if not any(key in effective_filters for key in ("user_id", "agent_id", "run_id")):
             raise ValueError("at least one of 'user_id', 'agent_id', or 'run_id' must be specified ")
 
+        # Apply enhanced metadata filtering if advanced operators are detected
+        if filters and self._has_advanced_operators(filters):
+            processed_filters = self._process_metadata_filters(filters)
+            effective_filters.update(processed_filters)
+        elif filters:
+            # Simple filters, merge directly
+            effective_filters.update(filters)
+
         keys, encoded_ids = process_telemetry_filters(effective_filters)
         capture_event(
             "mem0.search",
@@ -1516,6 +1648,7 @@ class AsyncMemory(MemoryBase):
                 "encoded_ids": encoded_ids,
                 "sync_type": "async",
                 "threshold": threshold,
+                "advanced_filters": bool(filters and self._has_advanced_operators(filters)),
             },
         )
 
@@ -1534,18 +1667,26 @@ class AsyncMemory(MemoryBase):
             original_memories = await vector_store_task
             graph_entities = None
 
+        # Apply reranking if enabled and reranker is available
+        if rerank and self.reranker and original_memories:
+            try:
+                # Run reranking in thread pool to avoid blocking async loop
+                reranked_memories = await asyncio.to_thread(
+                    self.reranker.rerank, query, original_memories, limit
+                )
+                original_memories = reranked_memories
+            except Exception as e:
+                logger.warning(f"Reranking failed, using original results: {e}")
+
         if self.enable_graph:
             return {"results": original_memories, "relations": graph_entities}
 
         if self.api_version == "v1.0":
-            warnings.warn(
-                "The v1.0 API format is deprecated and will be removed in mem0ai 2.0.0. "
-                "Please upgrade to v1.1 format which returns a dict with 'results' key. "
-                "Set version='v1.1' in your MemoryConfig.",
-                DeprecationWarning,
-                stacklevel=2,
+            raise ValueError(
+                "The v1.0 API format is no longer supported in mem0ai 1.0.0+. "
+                "Please use v1.1 format which returns a dict with 'results' key. "
+                "Remove version='v1.0' from your MemoryConfig or set it to version='v1.1'."
             )
-            return {"results": original_memories}
         else:
             return {"results": original_memories}
 

mem0/memory/memgraph_memory.py

@@ -281,23 +281,17 @@ class MemoryGraph:
             # Build query based on whether agent_id is provided
             if filters.get("agent_id"):
                 cypher_query = """
-                MATCH (n:Entity {user_id: $user_id, agent_id: $agent_id})
-                WHERE n.embedding IS NOT NULL
-                WITH n, $n_embedding as n_embedding
-                CALL node_similarity.cosine_pairwise("embedding", [n_embedding], [n.embedding])
-                YIELD node1, node2, similarity
-                WITH n, similarity
-                WHERE similarity >= $threshold
+                CALL vector_search.search("memzero", $limit, $n_embedding)
+                YIELD distance, node, similarity
+                WITH node AS n, similarity
+                WHERE n:Entity AND n.user_id = $user_id AND n.agent_id = $agent_id AND n.embedding IS NOT NULL AND similarity >= $threshold
                 MATCH (n)-[r]->(m:Entity)
                 RETURN n.name AS source, id(n) AS source_id, type(r) AS relationship, id(r) AS relation_id, m.name AS destination, id(m) AS destination_id, similarity
                 UNION
-                MATCH (n:Entity {user_id: $user_id, agent_id: $agent_id})
-                WHERE n.embedding IS NOT NULL
-                WITH n, $n_embedding as n_embedding
-                CALL node_similarity.cosine_pairwise("embedding", [n_embedding], [n.embedding])
-                YIELD node1, node2, similarity
-                WITH n, similarity
-                WHERE similarity >= $threshold
+                CALL vector_search.search("memzero", $limit, $n_embedding)
+                YIELD distance, node, similarity
+                WITH node AS n, similarity
+                WHERE n:Entity AND n.user_id = $user_id AND n.agent_id = $agent_id AND n.embedding IS NOT NULL AND similarity >= $threshold
                 MATCH (m:Entity)-[r]->(n)
                 RETURN m.name AS source, id(m) AS source_id, type(r) AS relationship, id(r) AS relation_id, n.name AS destination, id(n) AS destination_id, similarity
                 ORDER BY similarity DESC
@@ -312,23 +306,17 @@ class MemoryGraph:
                 }
             else:
                 cypher_query = """
-                MATCH (n:Entity {user_id: $user_id})
-                WHERE n.embedding IS NOT NULL
-                WITH n, $n_embedding as n_embedding
-                CALL node_similarity.cosine_pairwise("embedding", [n_embedding], [n.embedding])
-                YIELD node1, node2, similarity
-                WITH n, similarity
-                WHERE similarity >= $threshold
+                CALL vector_search.search("memzero", $limit, $n_embedding)
+                YIELD distance, node, similarity
+                WITH node AS n, similarity
+                WHERE n:Entity AND n.user_id = $user_id AND n.embedding IS NOT NULL AND similarity >= $threshold
                 MATCH (n)-[r]->(m:Entity)
                 RETURN n.name AS source, id(n) AS source_id, type(r) AS relationship, id(r) AS relation_id, m.name AS destination, id(m) AS destination_id, similarity
                 UNION
-                MATCH (n:Entity {user_id: $user_id})
-                WHERE n.embedding IS NOT NULL
-                WITH n, $n_embedding as n_embedding
-                CALL node_similarity.cosine_pairwise("embedding", [n_embedding], [n.embedding])
-                YIELD node1, node2, similarity
-                WITH n, similarity
-                WHERE similarity >= $threshold
+                CALL vector_search.search("memzero", $limit, $n_embedding)
+                YIELD distance, node, similarity
+                WITH node AS n, similarity
+                WHERE n:Entity AND n.user_id = $user_id AND n.embedding IS NOT NULL AND similarity >= $threshold
                 MATCH (m:Entity)-[r]->(n)
                 RETURN m.name AS source, id(m) AS source_id, type(r) AS relationship, id(r) AS relation_id, n.name AS destination, id(n) AS destination_id, similarity
                 ORDER BY similarity DESC

mem0/reranker/__init__.py

@@ -0,0 +1,9 @@
+"""
+Reranker implementations for mem0 search functionality.
+"""
+
+from .base import BaseReranker
+from .cohere_reranker import CohereReranker
+from .sentence_transformer_reranker import SentenceTransformerReranker
+
+__all__ = ["BaseReranker", "CohereReranker", "SentenceTransformerReranker"]

mem0/reranker/base.py

@@ -0,0 +1,20 @@
+from abc import ABC, abstractmethod
+from typing import List, Dict, Any
+
+class BaseReranker(ABC):
+    """Abstract base class for all rerankers."""
+    
+    @abstractmethod
+    def rerank(self, query: str, documents: List[Dict[str, Any]], top_k: int = None) -> List[Dict[str, Any]]:
+        """
+        Rerank documents based on relevance to the query.
+        
+        Args:
+            query: The search query
+            documents: List of documents to rerank, each with 'memory' field  
+            top_k: Number of top documents to return (None = return all)
+            
+        Returns:
+            List of reranked documents with added 'rerank_score' field
+        """
+        pass

mem0/reranker/cohere_reranker.py

@@ -0,0 +1,85 @@
+import os
+from typing import List, Dict, Any, Optional
+
+from mem0.reranker.base import BaseReranker
+
+try:
+    import cohere
+    COHERE_AVAILABLE = True
+except ImportError:
+    COHERE_AVAILABLE = False
+
+
+class CohereReranker(BaseReranker):
+    """Cohere-based reranker implementation."""
+    
+    def __init__(self, config):
+        """
+        Initialize Cohere reranker.
+        
+        Args:
+            config: CohereRerankerConfig object with configuration parameters
+        """
+        if not COHERE_AVAILABLE:
+            raise ImportError("cohere package is required for CohereReranker. Install with: pip install cohere")
+        
+        self.config = config
+        self.api_key = config.api_key or os.getenv("COHERE_API_KEY")
+        if not self.api_key:
+            raise ValueError("Cohere API key is required. Set COHERE_API_KEY environment variable or pass api_key in config.")
+            
+        self.model = config.model
+        self.client = cohere.Client(self.api_key)
+        
+    def rerank(self, query: str, documents: List[Dict[str, Any]], top_k: int = None) -> List[Dict[str, Any]]:
+        """
+        Rerank documents using Cohere's rerank API.
+        
+        Args:
+            query: The search query
+            documents: List of documents to rerank
+            top_k: Number of top documents to return
+            
+        Returns:
+            List of reranked documents with rerank_score
+        """
+        if not documents:
+            return documents
+            
+        # Extract text content for reranking
+        doc_texts = []
+        for doc in documents:
+            if 'memory' in doc:
+                doc_texts.append(doc['memory'])
+            elif 'text' in doc:
+                doc_texts.append(doc['text'])  
+            elif 'content' in doc:
+                doc_texts.append(doc['content'])
+            else:
+                doc_texts.append(str(doc))
+        
+        try:
+            # Call Cohere rerank API
+            response = self.client.rerank(
+                model=self.model,
+                query=query,
+                documents=doc_texts,
+                top_k=top_k or self.config.top_k or len(documents),
+                return_documents=self.config.return_documents,
+                max_chunks_per_doc=self.config.max_chunks_per_doc,
+            )
+            
+            # Create reranked results
+            reranked_docs = []
+            for result in response.results:
+                original_doc = documents[result.index].copy()
+                original_doc['rerank_score'] = result.relevance_score
+                reranked_docs.append(original_doc)
+                
+            return reranked_docs
+            
+        except Exception as e:
+            # Fallback to original order if reranking fails
+            for doc in documents:
+                doc['rerank_score'] = 0.0
+            return documents[:top_k] if top_k else documents

mem0/reranker/sentence_transformer_reranker.py

@@ -0,0 +1,90 @@
+from typing import List, Dict, Any, Optional
+import numpy as np
+
+from mem0.reranker.base import BaseReranker
+
+try:
+    from sentence_transformers import SentenceTransformer, util
+    SENTENCE_TRANSFORMERS_AVAILABLE = True
+except ImportError:
+    SENTENCE_TRANSFORMERS_AVAILABLE = False
+
+
+class SentenceTransformerReranker(BaseReranker):
+    """Sentence Transformer based reranker implementation."""
+    
+    def __init__(self, config):
+        """
+        Initialize Sentence Transformer reranker.
+        
+        Args:
+            config: SentenceTransformerRerankerConfig object with configuration parameters
+        """
+        if not SENTENCE_TRANSFORMERS_AVAILABLE:
+            raise ImportError("sentence-transformers package is required for SentenceTransformerReranker. Install with: pip install sentence-transformers")
+            
+        self.config = config
+        self.model = SentenceTransformer(config.model, device=config.device)
+        
+    def rerank(self, query: str, documents: List[Dict[str, Any]], top_k: int = None) -> List[Dict[str, Any]]:
+        """
+        Rerank documents using sentence transformer cross-encoder.
+        
+        Args:
+            query: The search query
+            documents: List of documents to rerank
+            top_k: Number of top documents to return
+            
+        Returns:
+            List of reranked documents with rerank_score
+        """
+        if not documents:
+            return documents
+            
+        # Extract text content for reranking
+        doc_texts = []
+        for doc in documents:
+            if 'memory' in doc:
+                doc_texts.append(doc['memory'])
+            elif 'text' in doc:
+                doc_texts.append(doc['text'])  
+            elif 'content' in doc:
+                doc_texts.append(doc['content'])
+            else:
+                doc_texts.append(str(doc))
+        
+        try:
+            # Create query-document pairs
+            pairs = [[query, doc_text] for doc_text in doc_texts]
+            
+            # Get similarity scores
+            scores = self.model.predict(pairs)
+            if isinstance(scores, np.ndarray):
+                scores = scores.tolist()
+            
+            # Combine documents with scores
+            doc_score_pairs = list(zip(documents, scores))
+            
+            # Sort by score (descending)
+            doc_score_pairs.sort(key=lambda x: x[1], reverse=True)
+            
+            # Apply top_k limit
+            final_top_k = top_k or self.config.top_k
+            if final_top_k:
+                doc_score_pairs = doc_score_pairs[:final_top_k]
+                
+            # Create reranked results
+            reranked_docs = []
+            for doc, score in doc_score_pairs:
+                reranked_doc = doc.copy()
+                reranked_doc['rerank_score'] = float(score)
+                reranked_docs.append(reranked_doc)
+                
+            return reranked_docs
+            
+        except Exception as e:
+            # Fallback to original order if reranking fails
+            for doc in documents:
+                doc['rerank_score'] = 0.0
+            final_top_k = top_k or self.config.top_k
+            return documents[:final_top_k] if final_top_k else documents

mem0/utils/factory.py

@@ -10,6 +10,9 @@ from mem0.configs.llms.lmstudio import LMStudioConfig
 from mem0.configs.llms.ollama import OllamaConfig
 from mem0.configs.llms.openai import OpenAIConfig
 from mem0.configs.llms.vllm import VllmConfig
+from mem0.configs.rerankers.base import BaseRerankerConfig
+from mem0.configs.rerankers.cohere import CohereRerankerConfig
+from mem0.configs.rerankers.sentence_transformer import SentenceTransformerRerankerConfig
 from mem0.embeddings.mock import MockEmbeddings
 
 
@@ -214,3 +217,54 @@ class GraphStoreFactory:
         except (ImportError, AttributeError) as e:
             raise ImportError(f"Could not import MemoryGraph for provider '{provider_name}': {e}")
         return GraphClass(config)
+
+
+class RerankerFactory:
+    """
+    Factory for creating reranker instances with appropriate configurations.
+    Supports provider-specific configs following the same pattern as other factories.
+    """
+
+    # Provider mappings with their config classes
+    provider_to_class = {
+        "cohere": ("mem0.reranker.cohere_reranker.CohereReranker", CohereRerankerConfig),
+        "sentence_transformer": ("mem0.reranker.sentence_transformer_reranker.SentenceTransformerReranker", SentenceTransformerRerankerConfig),
+    }
+
+    @classmethod
+    def create(cls, provider_name: str, config: Optional[Union[BaseRerankerConfig, Dict]] = None, **kwargs):
+        """
+        Create a reranker instance based on the provider and configuration.
+
+        Args:
+            provider_name: The reranker provider (e.g., 'cohere', 'sentence_transformer')
+            config: Configuration object or dictionary
+            **kwargs: Additional configuration parameters
+
+        Returns:
+            Reranker instance configured for the specified provider
+
+        Raises:
+            ImportError: If the provider class cannot be imported
+            ValueError: If the provider is not supported
+        """
+        if provider_name not in cls.provider_to_class:
+            raise ValueError(f"Unsupported reranker provider: {provider_name}")
+
+        class_path, config_class = cls.provider_to_class[provider_name]
+
+        # Handle configuration
+        if config is None:
+            config = config_class(**kwargs)
+        elif isinstance(config, dict):
+            config = config_class(**config, **kwargs)
+        elif not isinstance(config, BaseRerankerConfig):
+            raise ValueError(f"Config must be a {config_class.__name__} instance or dict")
+
+        # Import and create the reranker class
+        try:
+            reranker_class = load_class(class_path)
+        except (ImportError, AttributeError) as e:
+            raise ImportError(f"Could not import reranker for provider '{provider_name}': {e}")
+
+        return reranker_class(config)