744 lines
29 KiB
Python
744 lines
29 KiB
Python
import concurrent
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import hashlib
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import json
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import logging
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import uuid
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import warnings
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from datetime import datetime
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from typing import Any, Dict
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import pytz
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from pydantic import ValidationError
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from mem0.configs.base import MemoryConfig, MemoryItem
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from mem0.configs.enums import MemoryType
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from mem0.configs.prompts import PROCEDURAL_MEMORY_SYSTEM_PROMPT, get_update_memory_messages
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from mem0.memory.base import MemoryBase
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from mem0.memory.setup import setup_config
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from mem0.memory.storage import SQLiteManager
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from mem0.memory.telemetry import capture_event
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from mem0.memory.utils import get_fact_retrieval_messages, parse_messages, parse_vision_messages, remove_code_blocks
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from mem0.utils.factory import EmbedderFactory, LlmFactory, VectorStoreFactory
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# Setup user config
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setup_config()
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logger = logging.getLogger(__name__)
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class Memory(MemoryBase):
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def __init__(self, config: MemoryConfig = MemoryConfig()):
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self.config = config
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self.custom_fact_extraction_prompt = self.config.custom_fact_extraction_prompt
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self.custom_update_memory_prompt = self.config.custom_update_memory_prompt
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self.embedding_model = EmbedderFactory.create(self.config.embedder.provider, self.config.embedder.config)
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self.vector_store = VectorStoreFactory.create(
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self.config.vector_store.provider, self.config.vector_store.config
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)
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self.llm = LlmFactory.create(self.config.llm.provider, self.config.llm.config)
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self.db = SQLiteManager(self.config.history_db_path)
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self.collection_name = self.config.vector_store.config.collection_name
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self.api_version = self.config.version
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self.enable_graph = False
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if self.config.graph_store.config:
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from mem0.memory.graph_memory import MemoryGraph
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self.graph = MemoryGraph(self.config)
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self.enable_graph = True
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capture_event("mem0.init", self)
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@classmethod
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def from_config(cls, config_dict: Dict[str, Any]):
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try:
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config = cls._process_config(config_dict)
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config = MemoryConfig(**config_dict)
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except ValidationError as e:
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logger.error(f"Configuration validation error: {e}")
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raise
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return cls(config)
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@staticmethod
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def _process_config(config_dict: Dict[str, Any]) -> Dict[str, Any]:
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if "graph_store" in config_dict:
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if "vector_store" not in config_dict and "embedder" in config_dict:
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config_dict["vector_store"] = {}
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config_dict["vector_store"]["config"] = {}
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config_dict["vector_store"]["config"]["embedding_model_dims"] = config_dict["embedder"]["config"][
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"embedding_dims"
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]
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try:
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return config_dict
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except ValidationError as e:
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logger.error(f"Configuration validation error: {e}")
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raise
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def add(
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self,
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messages,
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user_id=None,
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agent_id=None,
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run_id=None,
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metadata=None,
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filters=None,
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infer=True,
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memory_type=None,
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prompt=None,
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llm=None,
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):
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"""
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Create a new memory.
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Args:
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messages (str or List[Dict[str, str]]): Messages to store in the memory.
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user_id (str, optional): ID of the user creating the memory. Defaults to None.
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agent_id (str, optional): ID of the agent creating the memory. Defaults to None.
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run_id (str, optional): ID of the run creating the memory. Defaults to None.
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metadata (dict, optional): Metadata to store with the memory. Defaults to None.
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filters (dict, optional): Filters to apply to the search. Defaults to None.
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infer (bool, optional): Whether to infer the memories. Defaults to True.
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memory_type (str, optional): Type of memory to create. Defaults to None. By default, it creates the short term memories and long term (semantic and episodic) memories. Pass "procedural_memory" to create procedural memories.
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prompt (str, optional): Prompt to use for the memory creation. Defaults to None.
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llm (BaseChatModel, optional): LLM class to use for generating procedural memories. Defaults to None. Useful when user is using LangChain ChatModel.
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Returns:
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dict: A dictionary containing the result of the memory addition operation.
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result: dict of affected events with each dict has the following key:
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'memories': affected memories
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'graph': affected graph memories
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'memories' and 'graph' is a dict, each with following subkeys:
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'add': added memory
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'update': updated memory
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'delete': deleted memory
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"""
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if metadata is None:
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metadata = {}
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filters = filters or {}
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if user_id:
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filters["user_id"] = metadata["user_id"] = user_id
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if agent_id:
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filters["agent_id"] = metadata["agent_id"] = agent_id
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if run_id:
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filters["run_id"] = metadata["run_id"] = run_id
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if not any(key in filters for key in ("user_id", "agent_id", "run_id")):
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raise ValueError("One of the filters: user_id, agent_id or run_id is required!")
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if memory_type is not None and memory_type != MemoryType.PROCEDURAL.value:
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raise ValueError(
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f"Invalid 'memory_type'. Please pass {MemoryType.PROCEDURAL.value} to create procedural memories."
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)
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if isinstance(messages, str):
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messages = [{"role": "user", "content": messages}]
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if agent_id is not None and memory_type == MemoryType.PROCEDURAL.value:
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results = self._create_procedural_memory(messages, metadata=metadata, llm=llm, prompt=prompt)
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return results
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if self.config.llm.config.get("enable_vision"):
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messages = parse_vision_messages(messages, self.llm, self.config.llm.config.get("vision_details"))
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else:
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messages = parse_vision_messages(messages)
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with concurrent.futures.ThreadPoolExecutor() as executor:
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future1 = executor.submit(self._add_to_vector_store, messages, metadata, filters, infer)
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future2 = executor.submit(self._add_to_graph, messages, filters)
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concurrent.futures.wait([future1, future2])
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vector_store_result = future1.result()
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graph_result = future2.result()
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if self.api_version == "v1.0":
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warnings.warn(
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"The current add API output format is deprecated. "
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"To use the latest format, set `api_version='v1.1'`. "
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"The current format will be removed in mem0ai 1.1.0 and later versions.",
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category=DeprecationWarning,
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stacklevel=2,
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)
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return vector_store_result
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if self.enable_graph:
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return {
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"results": vector_store_result,
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"relations": graph_result,
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}
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return {"results": vector_store_result}
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def _add_to_vector_store(self, messages, metadata, filters, infer):
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if not infer:
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returned_memories = []
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for message in messages:
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if message["role"] != "system":
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message_embeddings = self.embedding_model.embed(message["content"], "add")
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memory_id = self._create_memory(message["content"], message_embeddings, metadata)
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returned_memories.append({"id": memory_id, "memory": message["content"], "event": "ADD"})
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return returned_memories
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parsed_messages = parse_messages(messages)
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if self.custom_fact_extraction_prompt:
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system_prompt = self.custom_fact_extraction_prompt
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user_prompt = f"Input:\n{parsed_messages}"
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else:
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system_prompt, user_prompt = get_fact_retrieval_messages(parsed_messages)
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response = self.llm.generate_response(
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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],
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response_format={"type": "json_object"},
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)
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try:
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response = remove_code_blocks(response)
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new_retrieved_facts = json.loads(response)["facts"]
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except Exception as e:
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logging.error(f"Error in new_retrieved_facts: {e}")
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new_retrieved_facts = []
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retrieved_old_memory = []
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new_message_embeddings = {}
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for new_mem in new_retrieved_facts:
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messages_embeddings = self.embedding_model.embed(new_mem, "add")
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new_message_embeddings[new_mem] = messages_embeddings
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existing_memories = self.vector_store.search(
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query=new_mem,
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vectors=messages_embeddings,
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limit=5,
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filters=filters,
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)
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for mem in existing_memories:
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retrieved_old_memory.append({"id": mem.id, "text": mem.payload["data"]})
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unique_data = {}
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for item in retrieved_old_memory:
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unique_data[item["id"]] = item
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retrieved_old_memory = list(unique_data.values())
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logging.info(f"Total existing memories: {len(retrieved_old_memory)}")
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# mapping UUIDs with integers for handling UUID hallucinations
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temp_uuid_mapping = {}
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for idx, item in enumerate(retrieved_old_memory):
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temp_uuid_mapping[str(idx)] = item["id"]
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retrieved_old_memory[idx]["id"] = str(idx)
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function_calling_prompt = get_update_memory_messages(
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retrieved_old_memory, new_retrieved_facts, self.custom_update_memory_prompt
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)
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try:
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new_memories_with_actions = self.llm.generate_response(
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messages=[{"role": "user", "content": function_calling_prompt}],
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response_format={"type": "json_object"},
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)
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except Exception as e:
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logging.error(f"Error in new_memories_with_actions: {e}")
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new_memories_with_actions = []
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try:
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new_memories_with_actions = remove_code_blocks(new_memories_with_actions)
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new_memories_with_actions = json.loads(new_memories_with_actions)
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except Exception as e:
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logging.error(f"Invalid JSON response: {e}")
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new_memories_with_actions = []
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returned_memories = []
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try:
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for resp in new_memories_with_actions.get("memory", []):
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logging.info(resp)
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try:
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if not resp.get("text"):
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logging.info("Skipping memory entry because of empty `text` field.")
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continue
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elif resp.get("event") == "ADD":
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memory_id = self._create_memory(
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data=resp.get("text"), existing_embeddings=new_message_embeddings, metadata=metadata
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)
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returned_memories.append(
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{
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"id": memory_id,
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"memory": resp.get("text"),
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"event": resp.get("event"),
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}
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)
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elif resp.get("event") == "UPDATE":
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self._update_memory(
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memory_id=temp_uuid_mapping[resp["id"]],
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data=resp.get("text"),
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existing_embeddings=new_message_embeddings,
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metadata=metadata,
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)
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returned_memories.append(
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{
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"id": temp_uuid_mapping[resp.get("id")],
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"memory": resp.get("text"),
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"event": resp.get("event"),
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"previous_memory": resp.get("old_memory"),
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}
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)
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elif resp.get("event") == "DELETE":
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self._delete_memory(memory_id=temp_uuid_mapping[resp.get("id")])
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returned_memories.append(
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{
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"id": temp_uuid_mapping[resp.get("id")],
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"memory": resp.get("text"),
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"event": resp.get("event"),
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}
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)
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elif resp.get("event") == "NONE":
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logging.info("NOOP for Memory.")
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except Exception as e:
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logging.error(f"Error in new_memories_with_actions: {e}")
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except Exception as e:
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logging.error(f"Error in new_memories_with_actions: {e}")
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capture_event("mem0.add", self, {"version": self.api_version, "keys": list(filters.keys())})
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return returned_memories
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def _add_to_graph(self, messages, filters):
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added_entities = []
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if self.enable_graph:
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if filters.get("user_id") is None:
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filters["user_id"] = "user"
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data = "\n".join([msg["content"] for msg in messages if "content" in msg and msg["role"] != "system"])
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added_entities = self.graph.add(data, filters)
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return added_entities
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def get(self, memory_id):
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"""
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Retrieve a memory by ID.
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Args:
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memory_id (str): ID of the memory to retrieve.
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Returns:
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dict: Retrieved memory.
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"""
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capture_event("mem0.get", self, {"memory_id": memory_id})
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memory = self.vector_store.get(vector_id=memory_id)
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if not memory:
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return None
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filters = {key: memory.payload[key] for key in ["user_id", "agent_id", "run_id"] if memory.payload.get(key)}
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# Prepare base memory item
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memory_item = MemoryItem(
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id=memory.id,
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memory=memory.payload["data"],
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hash=memory.payload.get("hash"),
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created_at=memory.payload.get("created_at"),
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updated_at=memory.payload.get("updated_at"),
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).model_dump(exclude={"score"})
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# Add metadata if there are additional keys
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excluded_keys = {"user_id", "agent_id", "run_id", "hash", "data", "created_at", "updated_at", "id"}
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additional_metadata = {k: v for k, v in memory.payload.items() if k not in excluded_keys}
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if additional_metadata:
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memory_item["metadata"] = additional_metadata
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result = {**memory_item, **filters}
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return result
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def get_all(self, user_id=None, agent_id=None, run_id=None, limit=100):
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"""
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List all memories.
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Returns:
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list: List of all memories.
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"""
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filters = {}
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if user_id:
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filters["user_id"] = user_id
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if agent_id:
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filters["agent_id"] = agent_id
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if run_id:
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filters["run_id"] = run_id
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capture_event("mem0.get_all", self, {"limit": limit, "keys": list(filters.keys())})
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with concurrent.futures.ThreadPoolExecutor() as executor:
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future_memories = executor.submit(self._get_all_from_vector_store, filters, limit)
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future_graph_entities = executor.submit(self.graph.get_all, filters, limit) if self.enable_graph else None
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concurrent.futures.wait(
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[future_memories, future_graph_entities] if future_graph_entities else [future_memories]
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)
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all_memories = future_memories.result()
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graph_entities = future_graph_entities.result() if future_graph_entities else None
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if self.enable_graph:
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return {"results": all_memories, "relations": graph_entities}
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if self.api_version == "v1.0":
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warnings.warn(
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"The current get_all API output format is deprecated. "
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"To use the latest format, set `api_version='v1.1'`. "
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"The current format will be removed in mem0ai 1.1.0 and later versions.",
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category=DeprecationWarning,
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stacklevel=2,
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)
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return all_memories
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else:
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return {"results": all_memories}
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def _get_all_from_vector_store(self, filters, limit):
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memories = self.vector_store.list(filters=filters, limit=limit)
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excluded_keys = {
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"user_id",
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"agent_id",
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"run_id",
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"hash",
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"data",
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"created_at",
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"updated_at",
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"id",
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}
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all_memories = [
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{
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**MemoryItem(
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id=mem.id,
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memory=mem.payload["data"],
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hash=mem.payload.get("hash"),
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created_at=mem.payload.get("created_at"),
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updated_at=mem.payload.get("updated_at"),
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).model_dump(exclude={"score"}),
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**{key: mem.payload[key] for key in ["user_id", "agent_id", "run_id"] if key in mem.payload},
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**(
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{"metadata": {k: v for k, v in mem.payload.items() if k not in excluded_keys}}
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if any(k for k in mem.payload if k not in excluded_keys)
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else {}
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),
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}
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for mem in memories[0]
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]
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return all_memories
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def search(self, query, user_id=None, agent_id=None, run_id=None, limit=100, filters=None):
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"""
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Search for memories.
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Args:
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query (str): Query to search for.
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user_id (str, optional): ID of the user to search for. Defaults to None.
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agent_id (str, optional): ID of the agent to search for. Defaults to None.
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run_id (str, optional): ID of the run to search for. Defaults to None.
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limit (int, optional): Limit the number of results. Defaults to 100.
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filters (dict, optional): Filters to apply to the search. Defaults to None.
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Returns:
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list: List of search results.
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"""
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filters = filters or {}
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if user_id:
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filters["user_id"] = user_id
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if agent_id:
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filters["agent_id"] = agent_id
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if run_id:
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filters["run_id"] = run_id
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if not any(key in filters for key in ("user_id", "agent_id", "run_id")):
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raise ValueError("One of the filters: user_id, agent_id or run_id is required!")
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capture_event(
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"mem0.search",
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self,
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{"limit": limit, "version": self.api_version, "keys": list(filters.keys())},
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)
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with concurrent.futures.ThreadPoolExecutor() as executor:
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future_memories = executor.submit(self._search_vector_store, query, filters, limit)
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future_graph_entities = (
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executor.submit(self.graph.search, query, filters, limit) if self.enable_graph else None
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)
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concurrent.futures.wait(
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[future_memories, future_graph_entities] if future_graph_entities else [future_memories]
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)
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original_memories = future_memories.result()
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graph_entities = future_graph_entities.result() if future_graph_entities else None
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if self.enable_graph:
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return {"results": original_memories, "relations": graph_entities}
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|
|
if self.api_version == "v1.0":
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|
warnings.warn(
|
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"The current get_all API output format is deprecated. "
|
|
"To use the latest format, set `api_version='v1.1'`. "
|
|
"The current format will be removed in mem0ai 1.1.0 and later versions.",
|
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category=DeprecationWarning,
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stacklevel=2,
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)
|
|
return original_memories
|
|
else:
|
|
return {"results": original_memories}
|
|
|
|
def _search_vector_store(self, query, filters, limit):
|
|
embeddings = self.embedding_model.embed(query, "search")
|
|
memories = self.vector_store.search(query=query, vectors=embeddings, limit=limit, filters=filters)
|
|
|
|
excluded_keys = {
|
|
"user_id",
|
|
"agent_id",
|
|
"run_id",
|
|
"hash",
|
|
"data",
|
|
"created_at",
|
|
"updated_at",
|
|
"id",
|
|
}
|
|
|
|
original_memories = [
|
|
{
|
|
**MemoryItem(
|
|
id=mem.id,
|
|
memory=mem.payload["data"],
|
|
hash=mem.payload.get("hash"),
|
|
created_at=mem.payload.get("created_at"),
|
|
updated_at=mem.payload.get("updated_at"),
|
|
score=mem.score,
|
|
).model_dump(),
|
|
**{key: mem.payload[key] for key in ["user_id", "agent_id", "run_id"] if key in mem.payload},
|
|
**(
|
|
{"metadata": {k: v for k, v in mem.payload.items() if k not in excluded_keys}}
|
|
if any(k for k in mem.payload if k not in excluded_keys)
|
|
else {}
|
|
),
|
|
}
|
|
for mem in memories
|
|
]
|
|
|
|
return original_memories
|
|
|
|
def update(self, memory_id, data):
|
|
"""
|
|
Update a memory by ID.
|
|
|
|
Args:
|
|
memory_id (str): ID of the memory to update.
|
|
data (dict): Data to update the memory with.
|
|
|
|
Returns:
|
|
dict: Updated memory.
|
|
"""
|
|
capture_event("mem0.update", self, {"memory_id": memory_id})
|
|
|
|
existing_embeddings = {data: self.embedding_model.embed(data, "update")}
|
|
|
|
self._update_memory(memory_id, data, existing_embeddings)
|
|
return {"message": "Memory updated successfully!"}
|
|
|
|
def delete(self, memory_id):
|
|
"""
|
|
Delete a memory by ID.
|
|
|
|
Args:
|
|
memory_id (str): ID of the memory to delete.
|
|
"""
|
|
capture_event("mem0.delete", self, {"memory_id": memory_id})
|
|
self._delete_memory(memory_id)
|
|
return {"message": "Memory deleted successfully!"}
|
|
|
|
def delete_all(self, user_id=None, agent_id=None, run_id=None):
|
|
"""
|
|
Delete all memories.
|
|
|
|
Args:
|
|
user_id (str, optional): ID of the user to delete memories for. Defaults to None.
|
|
agent_id (str, optional): ID of the agent to delete memories for. Defaults to None.
|
|
run_id (str, optional): ID of the run to delete memories for. Defaults to None.
|
|
"""
|
|
filters = {}
|
|
if user_id:
|
|
filters["user_id"] = user_id
|
|
if agent_id:
|
|
filters["agent_id"] = agent_id
|
|
if run_id:
|
|
filters["run_id"] = run_id
|
|
|
|
if not filters:
|
|
raise ValueError(
|
|
"At least one filter is required to delete all memories. If you want to delete all memories, use the `reset()` method."
|
|
)
|
|
|
|
capture_event("mem0.delete_all", self, {"keys": list(filters.keys())})
|
|
memories = self.vector_store.list(filters=filters)[0]
|
|
for memory in memories:
|
|
self._delete_memory(memory.id)
|
|
|
|
logger.info(f"Deleted {len(memories)} memories")
|
|
|
|
if self.enable_graph:
|
|
self.graph.delete_all(filters)
|
|
|
|
return {"message": "Memories deleted successfully!"}
|
|
|
|
def history(self, memory_id):
|
|
"""
|
|
Get the history of changes for a memory by ID.
|
|
|
|
Args:
|
|
memory_id (str): ID of the memory to get history for.
|
|
|
|
Returns:
|
|
list: List of changes for the memory.
|
|
"""
|
|
capture_event("mem0.history", self, {"memory_id": memory_id})
|
|
return self.db.get_history(memory_id)
|
|
|
|
def _create_memory(self, data, existing_embeddings, metadata=None):
|
|
logging.debug(f"Creating memory with {data=}")
|
|
if data in existing_embeddings:
|
|
embeddings = existing_embeddings[data]
|
|
else:
|
|
embeddings = self.embedding_model.embed(data, memory_action="add")
|
|
memory_id = str(uuid.uuid4())
|
|
metadata = metadata or {}
|
|
metadata["data"] = data
|
|
metadata["hash"] = hashlib.md5(data.encode()).hexdigest()
|
|
metadata["created_at"] = datetime.now(pytz.timezone("US/Pacific")).isoformat()
|
|
|
|
self.vector_store.insert(
|
|
vectors=[embeddings],
|
|
ids=[memory_id],
|
|
payloads=[metadata],
|
|
)
|
|
self.db.add_history(memory_id, None, data, "ADD", created_at=metadata["created_at"])
|
|
capture_event("mem0._create_memory", self, {"memory_id": memory_id})
|
|
return memory_id
|
|
|
|
def _create_procedural_memory(self, messages, metadata=None, llm=None, prompt=None):
|
|
"""
|
|
Create a procedural memory
|
|
|
|
Args:
|
|
messages (list): List of messages to create a procedural memory from.
|
|
metadata (dict): Metadata to create a procedural memory from.
|
|
llm (BaseChatModel, optional): LLM class to use for generating procedural memories. Defaults to None. Useful when user is using LangChain ChatModel.
|
|
prompt (str, optional): Prompt to use for the procedural memory creation. Defaults to None.
|
|
"""
|
|
try:
|
|
from langchain_core.messages.utils import convert_to_messages # type: ignore
|
|
except Exception:
|
|
logger.error(
|
|
"Import error while loading langchain-core. Please install 'langchain-core' to use procedural memory."
|
|
)
|
|
raise
|
|
|
|
logger.info("Creating procedural memory")
|
|
|
|
parsed_messages = [
|
|
{"role": "system", "content": prompt or PROCEDURAL_MEMORY_SYSTEM_PROMPT},
|
|
*messages,
|
|
{"role": "user", "content": "Create procedural memory of the above conversation."},
|
|
]
|
|
|
|
try:
|
|
if llm is not None:
|
|
parsed_messages = convert_to_messages(parsed_messages)
|
|
response = llm.invoke(input=parsed_messages)
|
|
procedural_memory = response.content
|
|
else:
|
|
procedural_memory = self.llm.generate_response(messages=parsed_messages)
|
|
except Exception as e:
|
|
logger.error(f"Error generating procedural memory summary: {e}")
|
|
raise
|
|
|
|
if metadata is None:
|
|
raise ValueError("Metadata cannot be done for procedural memory.")
|
|
|
|
metadata["memory_type"] = MemoryType.PROCEDURAL.value
|
|
# Generate embeddings for the summary
|
|
embeddings = self.embedding_model.embed(procedural_memory, memory_action="add")
|
|
# Create the memory
|
|
memory_id = self._create_memory(procedural_memory, {procedural_memory: embeddings}, metadata=metadata)
|
|
capture_event("mem0._create_procedural_memory", self, {"memory_id": memory_id})
|
|
|
|
# Return results in the same format as add()
|
|
result = {"results": [{"id": memory_id, "memory": procedural_memory, "event": "ADD"}]}
|
|
|
|
return result
|
|
|
|
def _update_memory(self, memory_id, data, existing_embeddings, metadata=None):
|
|
logger.info(f"Updating memory with {data=}")
|
|
|
|
try:
|
|
existing_memory = self.vector_store.get(vector_id=memory_id)
|
|
except Exception:
|
|
raise ValueError(f"Error getting memory with ID {memory_id}. Please provide a valid 'memory_id'")
|
|
prev_value = existing_memory.payload.get("data")
|
|
|
|
new_metadata = metadata or {}
|
|
new_metadata["data"] = data
|
|
new_metadata["hash"] = hashlib.md5(data.encode()).hexdigest()
|
|
new_metadata["created_at"] = existing_memory.payload.get("created_at")
|
|
new_metadata["updated_at"] = datetime.now(pytz.timezone("US/Pacific")).isoformat()
|
|
|
|
if "user_id" in existing_memory.payload:
|
|
new_metadata["user_id"] = existing_memory.payload["user_id"]
|
|
if "agent_id" in existing_memory.payload:
|
|
new_metadata["agent_id"] = existing_memory.payload["agent_id"]
|
|
if "run_id" in existing_memory.payload:
|
|
new_metadata["run_id"] = existing_memory.payload["run_id"]
|
|
|
|
if data in existing_embeddings:
|
|
embeddings = existing_embeddings[data]
|
|
else:
|
|
embeddings = self.embedding_model.embed(data, "update")
|
|
self.vector_store.update(
|
|
vector_id=memory_id,
|
|
vector=embeddings,
|
|
payload=new_metadata,
|
|
)
|
|
logger.info(f"Updating memory with ID {memory_id=} with {data=}")
|
|
self.db.add_history(
|
|
memory_id,
|
|
prev_value,
|
|
data,
|
|
"UPDATE",
|
|
created_at=new_metadata["created_at"],
|
|
updated_at=new_metadata["updated_at"],
|
|
)
|
|
capture_event("mem0._update_memory", self, {"memory_id": memory_id})
|
|
return memory_id
|
|
|
|
def _delete_memory(self, memory_id):
|
|
logging.info(f"Deleting memory with {memory_id=}")
|
|
existing_memory = self.vector_store.get(vector_id=memory_id)
|
|
prev_value = existing_memory.payload["data"]
|
|
self.vector_store.delete(vector_id=memory_id)
|
|
self.db.add_history(memory_id, prev_value, None, "DELETE", is_deleted=1)
|
|
capture_event("mem0._delete_memory", self, {"memory_id": memory_id})
|
|
return memory_id
|
|
|
|
def reset(self):
|
|
"""
|
|
Reset the memory store.
|
|
"""
|
|
logger.warning("Resetting all memories")
|
|
self.vector_store.delete_col()
|
|
self.vector_store = VectorStoreFactory.create(
|
|
self.config.vector_store.provider, self.config.vector_store.config
|
|
)
|
|
self.db.reset()
|
|
capture_event("mem0.reset", self)
|
|
|
|
def chat(self, query):
|
|
raise NotImplementedError("Chat function not implemented yet.")
|