chat_with_memory
Enhance AI responses by automatically retrieving and incorporating relevant stored memories to provide contextual answers to user queries.
Instructions
Chat with memory-enhanced responses.
Use this for questions where stored memories might provide context. The response will incorporate relevant memories automatically.
Args: query: User's question or message top_k: Number of memories to use as context (default: 5)
Returns: AI response enhanced with relevant memories
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| top_k | No |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Implementation Reference
- src/memovault/api/mcp.py:94-113 (handler)MCP tool handler for chat_with_memory - decorated with @self.mcp.tool() and calls the core chat method
@self.mcp.tool() async def chat_with_memory(query: str, top_k: int = 5) -> str: """Chat with memory-enhanced responses. Use this for questions where stored memories might provide context. The response will incorporate relevant memories automatically. Args: query: User's question or message top_k: Number of memories to use as context (default: 5) Returns: AI response enhanced with relevant memories """ try: response = self.vault.chat(query, top_k=top_k) return response except Exception as e: logger.error(f"Error in chat: {e}") return f"Error generating response: {str(e)}" - src/memovault/core/memovault.py:182-238 (handler)Core implementation of chat logic - searches for relevant memories, builds context into system prompt, generates LLM response, and maintains chat history
def chat( self, query: str, top_k: int = 5, system_prompt: str | None = None, include_history: bool = True, ) -> str: """Chat with memory-enhanced responses. Searches for relevant memories and uses them as context for the LLM. Args: query: User's query. top_k: Number of memories to include as context. system_prompt: Optional custom system prompt (can include {memories_section}). include_history: Whether to include chat history. Returns: Assistant's response. Example: >>> response = mem.chat("What language should I use for my backend?") >>> print(response) """ # Search for relevant memories memories = self._cube.search(query, top_k) # Build memories section if memories: memory_lines = [f"- {mem.memory}" for mem in memories] memories_section = "## Relevant Memories:\n" + "\n".join(memory_lines) else: memories_section = "" # Build system prompt if system_prompt: system_content = system_prompt.format(memories_section=memories_section) else: system_content = CHAT_SYSTEM_PROMPT.format(memories_section=memories_section) # Build messages messages = [{"role": "system", "content": system_content}] if include_history: messages.extend(self._chat_history.get_messages()) messages.append({"role": "user", "content": query}) # Generate response response = self._llm.generate(messages) # Update chat history self._chat_history.add_user_message(query) self._chat_history.add_assistant_message(response) logger.debug(f"Chat response generated with {len(memories)} memories as context") return response - src/memovault/utils/prompts.py:5-10 (helper)System prompt template used by the chat function - includes placeholder for memories_section to inject relevant memories
CHAT_SYSTEM_PROMPT = """You are a knowledgeable and helpful AI assistant with access to personal memories. You have stored memories that help you provide personalized responses. Use these memories to understand the user's context, preferences, and past interactions. Reference memories naturally when relevant, but don't explicitly mention having a memory system. {memories_section}""" - src/memovault/api/mcp.py:32-33 (registration)Tool registration setup call in __init__ that triggers registration of chat_with_memory and other tools
self._setup_tools()