Update README with filter options README.md

Author: abrookins

README.md

@@ -7,16 +7,18 @@ A Redis-powered memory server built for AI agents and applications. It manages b
 - **Short-Term Memory**
   - Storage for messages, token count, context, and metadata for a session
   - Automatically and recursively summarizes conversations
-  - Token limit management based on specific model capabilities
+  - Client model-aware token limit management (adapts to the context window of the client's LLM)
+  - Supports all major OpenAI and Anthropic models
 
 - **Long-Term Memory**
   - Storage for long-term memories across sessions
-  - Semantic search to retrieve memories, with filters such as topic, entity, etc.
+  - Semantic search to retrieve memories with advanced filtering system
+  - Filter by session, namespace, topics, entities, timestamps, and more
+  - Supports both exact match and semantic similarity search
   - Automatic topic modeling for stored memories with BERTopic
   - Automatic Entity Recognition using BERT
 
 - **Other Features**
-  - Support for OpenAI and Anthropic model providers
   - Namespace support for session and long-term memory isolation
   - Both a REST interface and MCP server
 
@@ -56,6 +58,11 @@ The following endpoints are available:
 - **GET /sessions/{session_id}/memory**
   Retrieves conversation memory for a session, including messages and
   summarized older messages.
+  _Query Parameters:_
+  - `namespace` (string, optional): The namespace to use for the session
+  - `window_size` (int, optional): Number of messages to include in the response (default from config)
+  - `model_name` (string, optional): The client's LLM model name to determine appropriate context window size
+  - `context_window_max` (int, optional): Direct specification of max context window tokens (overrides model_name)
 
 - **POST /sessions/{session_id}/memory**
   Adds messages (and optional context) to a session's memory.
@@ -81,6 +88,40 @@ The following endpoints are available:
   }
   ```
 
+- **POST /long-term-memory/search**
+  Performs semantic search on long-term memories with advanced filtering options.
+  _Request Body Example:_
+  ```json
+  {
+    "text": "Search query text",
+    "limit": 10,
+    "offset": 0,
+    "session_id": {"eq": "session-123"},
+    "namespace": {"eq": "default"},
+    "topics": {"any": ["AI", "Machine Learning"]},
+    "entities": {"all": ["OpenAI", "Claude"]},
+    "created_at": {"gte": 1672527600, "lte": 1704063599},
+    "last_accessed": {"gt": 1704063600},
+    "user_id": {"eq": "user-456"}
+  }
+  ```
+
+  _Filter options:_
+  - Tag filters (session_id, namespace, topics, entities, user_id):
+    - `eq`: Equals this value
+    - `ne`: Not equals this value
+    - `any`: Contains any of these values
+    - `all`: Contains all of these values
+
+  - Numeric filters (created_at, last_accessed):
+    - `gt`: Greater than
+    - `lt`: Less than
+    - `gte`: Greater than or equal
+    - `lte`: Less than or equal
+    - `eq`: Equals
+    - `ne`: Not equals
+    - `between`: Between two values
+
 ## MCP Server Interface
 Agent Memory Server offers an MCP (Model Context Protocol) server interface powered by FastMCP, providing tool-based long-term memory management:
 

agent_memory_server/api.py

@@ -1,7 +1,10 @@
+from typing import Literal
+
 from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException
 
 from agent_memory_server import long_term_memory, messages
 from agent_memory_server.config import settings
+from agent_memory_server.llms import get_model_config
 from agent_memory_server.logging import get_logger
 from agent_memory_server.models import (
     AckResponse,
@@ -18,6 +21,32 @@
 
 logger = get_logger(__name__)
 
+ModelNameLiteral = Literal[
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-16k",
+    "gpt-4",
+    "gpt-4-32k",
+    "gpt-4o",
+    "gpt-4o-mini",
+    "o1",
+    "o1-mini",
+    "o3-mini",
+    "text-embedding-ada-002",
+    "text-embedding-3-small",
+    "text-embedding-3-large",
+    "claude-3-opus-20240229",
+    "claude-3-sonnet-20240229",
+    "claude-3-haiku-20240307",
+    "claude-3-5-sonnet-20240620",
+    "claude-3-7-sonnet-20250219",
+    "claude-3-5-sonnet-20241022",
+    "claude-3-5-haiku-20241022",
+    "claude-3-7-sonnet-latest",
+    "claude-3-5-sonnet-latest",
+    "claude-3-5-haiku-latest",
+    "claude-3-opus-latest",
+]
+
 router = APIRouter()
 
 
@@ -54,6 +83,8 @@ async def get_session_memory(
     session_id: str,
     namespace: str | None = None,
     window_size: int = settings.window_size,
+    model_name: ModelNameLiteral | None = None,
+    context_window_max: int | None = None,
 ):
     """
     Get memory for a session.
@@ -62,18 +93,31 @@ async def get_session_memory(
 
     Args:
         session_id: The session ID
-        window_size: The number of messages to include in the response
         namespace: The namespace to use for the session
+        window_size: The number of messages to include in the response
+        model_name: The client's LLM model name (will determine context window size if provided)
+        context_window_max: Direct specification of the context window max tokens (overrides model_name)
 
     Returns:
         Conversation history and context
     """
     redis = get_redis_conn()
 
+    # If context_window_max is explicitly provided, use that
+    if context_window_max is not None:
+        effective_window_size = min(window_size, context_window_max)
+    # If model_name is provided, get its max_tokens from our config
+    elif model_name is not None:
+        model_config = get_model_config(model_name)
+        effective_window_size = min(window_size, model_config.max_tokens)
+    # Otherwise use the default window_size
+    else:
+        effective_window_size = window_size
+
     session = await messages.get_session_memory(
         redis=redis,
         session_id=session_id,
-        window_size=window_size,
+        window_size=effective_window_size,
         namespace=namespace,
     )
     if not session: