Add async support (#2492)
Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>
This commit is contained in:
Dev Khant
co-authored by
Deshraj Yadav
Deshraj Yadav
parent
44f2490667
commit
616313b8b5
@@ -73,6 +73,7 @@
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"group": "Features",
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"icon": "wrench",
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"pages": [
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"open-source/features/async-memory",
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"features/openai_compatibility",
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"features/custom-fact-extraction-prompt",
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"features/custom-update-memory-prompt",
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@@ -0,0 +1,169 @@
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---
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title: Async Memory
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description: 'Asynchronous memory for Mem0'
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icon: "bolt"
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iconType: "solid"
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---
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## AsyncMemory
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The `AsyncMemory` class is a direct asynchronous interface to Mem0's in-process memory operations. Unlike the memory, which interacts with an API, `AsyncMemory` works directly with the underlying storage systems. This makes it ideal for applications where you want to embed Mem0 directly into your codebase.
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### Initialization
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To use `AsyncMemory`, import it from the `mem0.memory` module:
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```python Python
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import asyncio
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from mem0 import AsyncMemory
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# Initialize with default configuration
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memory = AsyncMemory()
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# Or initialize with custom configuration
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from mem0.configs.base import MemoryConfig
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custom_config = MemoryConfig(
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# Your custom configuration here
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)
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memory = AsyncMemory(config=custom_config)
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```
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### Key Features
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1. **Non-blocking Operations** - All memory operations use `asyncio` to avoid blocking the event loop
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2. **Concurrent Processing** - Parallel execution of vector store and graph operations
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3. **Efficient Resource Utilization** - Better handling of I/O bound operations
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4. **Compatible with Async Frameworks** - Seamless integration with FastAPI, aiohttp, and other async frameworks
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### Methods
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All methods in `AsyncMemory` have the same parameters as the synchronous `Memory` class but are designed to be used with `async/await`.
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#### Create memories
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Add a new memory asynchronously:
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```python Python
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await memory.add(
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messages=[
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{"role": "user", "content": "I'm travelling to SF"},
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{"role": "assistant", "content": "That's great to hear!"}
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],
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user_id="alice"
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)
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```
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#### Retrieve memories
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Retrieve memories related to a query:
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```python Python
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await memory.search(
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query="Where am I travelling?",
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user_id="alice"
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)
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```
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#### List memories
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List all memories for a `user_id`, `agent_id`, or `run_id`:
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```python Python
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await memory.get_all(user_id="alice")
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```
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#### Get specific memory
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Retrieve a specific memory by its ID:
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```python Python
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await memory.get(memory_id="memory-id-here")
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```
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#### Update memory
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Update an existing memory by ID:
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```python Python
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await memory.update(
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memory_id="memory-id-here",
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data="I'm travelling to Seattle"
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)
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```
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#### Delete memory
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Delete a specific memory by ID:
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```python Python
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await memory.delete(memory_id="memory-id-here")
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```
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#### Delete all memories
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Delete all memories for a specific user, agent, or run:
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```python Python
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await memory.delete_all(user_id="alice")
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```
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Note: At least one filter (user_id, agent_id, or run_id) is required when using delete_all.
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#### Memory History
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Get the history of changes for a specific memory:
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```python Python
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await memory.history(memory_id="memory-id-here")
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```
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### Example: Concurrent Usage with Other APIs
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`AsyncMemory` can be effectively combined with other async operations. Here's an example showing how to use it alongside OpenAI API calls in separate threads:
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```python Python
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import asyncio
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from openai import AsyncOpenAI
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from mem0 import AsyncMemory
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async_openai_client = AsyncOpenAI()
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async_memory = AsyncMemory()
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async def chat_with_memories(message: str, user_id: str = "default_user") -> str:
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# Retrieve relevant memories
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search_result = await async_memory.search(query=message, user_id=user_id, limit=3)
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relevant_memories = search_result["results"]
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memories_str = "\n".join(f"- {entry['memory']}" for entry in relevant_memories)
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# Generate Assistant response
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system_prompt = f"You are a helpful AI. Answer the question based on query and memories.\nUser Memories:\n{memories_str}"
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messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": message}]
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response = await async_openai_client.chat.completions.create(model="gpt-4o-mini", messages=messages)
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assistant_response = response.choices[0].message.content
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# Create new memories from the conversation
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messages.append({"role": "assistant", "content": assistant_response})
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await async_memory.add(messages, user_id=user_id)
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return assistant_response
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async def async_main():
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print("Chat with AI (type 'exit' to quit)")
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while True:
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user_input = input("You: ").strip()
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if user_input.lower() == 'exit':
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print("Goodbye!")
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break
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response = await chat_with_memories(user_input)
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print(f"AI: {response}")
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def main():
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asyncio.run(async_main())
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if __name__ == "__main__":
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main()
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```
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If you have any questions or need further assistance, please don't hesitate to reach out:
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<Snippet file="get-help.mdx" />
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@@ -28,6 +28,16 @@ from mem0 import Memory
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os.environ["OPENAI_API_KEY"] = "your-api-key"
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m = Memory()
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```
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</Tab>
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<Tab title="Async">
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```python
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import os
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from mem0 import AsyncMemory
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os.environ["OPENAI_API_KEY"] = "your-api-key"
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m = AsyncMemory()
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```
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</Tab>
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<Tab title="Advanced">
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+1
-1
@@ -3,4 +3,4 @@ import importlib.metadata
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__version__ = importlib.metadata.version("mem0ai")
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from mem0.client.main import AsyncMemoryClient, MemoryClient # noqa
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from mem0.memory.main import Memory # noqa
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from mem0.memory.main import Memory, AsyncMemory # noqa
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+774
-3
@@ -1,3 +1,4 @@
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import asyncio
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import concurrent
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import hashlib
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import json
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@@ -196,8 +197,8 @@ class Memory(MemoryBase):
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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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if self.config.custom_fact_extraction_prompt:
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system_prompt = self.config.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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@@ -243,7 +244,7 @@ class Memory(MemoryBase):
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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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retrieved_old_memory, new_retrieved_facts, self.config.custom_update_memory_prompt
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)
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try:
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@@ -755,3 +756,773 @@ class Memory(MemoryBase):
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def chat(self, query):
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raise NotImplementedError("Chat function not implemented yet.")
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class AsyncMemory(MemoryBase):
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def __init__(self, config: MemoryConfig = MemoryConfig()):
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self.config = config
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self.embedding_model = EmbedderFactory.create(
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self.config.embedder.provider,
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self.config.embedder.config,
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self.config.vector_store.config,
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)
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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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async 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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async 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 asynchronously.
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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 = await 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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# Run vector store and graph operations concurrently
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vector_store_task = asyncio.create_task(self._add_to_vector_store(messages, metadata, filters, infer))
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graph_task = asyncio.create_task(self._add_to_graph(messages, filters))
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vector_store_result, graph_result = await asyncio.gather(vector_store_task, graph_task)
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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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async 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 = await asyncio.to_thread(self.embedding_model.embed, message["content"], "add")
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memory_id = await 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.config.custom_fact_extraction_prompt:
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system_prompt = self.config.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 = await asyncio.to_thread(
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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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# Process all facts concurrently
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async def process_fact(new_mem):
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messages_embeddings = await asyncio.to_thread(self.embedding_model.embed, new_mem, "add")
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new_message_embeddings[new_mem] = messages_embeddings
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existing_memories = await asyncio.to_thread(
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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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return [(mem.id, mem.payload["data"]) for mem in existing_memories]
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fact_tasks = [process_fact(fact) for fact in new_retrieved_facts]
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fact_results = await asyncio.gather(*fact_tasks)
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# Flatten results and build retrieved_old_memory
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for result in fact_results:
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for mem_id, mem_data in result:
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retrieved_old_memory.append({"id": mem_id, "text": mem_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.config.custom_update_memory_prompt
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)
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try:
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new_memories_with_actions = await asyncio.to_thread(
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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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memory_tasks = []
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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"):
|
||||
logging.info("Skipping memory entry because of empty `text` field.")
|
||||
continue
|
||||
elif resp.get("event") == "ADD":
|
||||
task = asyncio.create_task(self._create_memory(
|
||||
data=resp.get("text"),
|
||||
existing_embeddings=new_message_embeddings,
|
||||
metadata=metadata
|
||||
))
|
||||
memory_tasks.append((task, resp, "ADD", None))
|
||||
elif resp.get("event") == "UPDATE":
|
||||
task = asyncio.create_task(self._update_memory(
|
||||
memory_id=temp_uuid_mapping[resp["id"]],
|
||||
data=resp.get("text"),
|
||||
existing_embeddings=new_message_embeddings,
|
||||
metadata=metadata,
|
||||
))
|
||||
memory_tasks.append((task, resp, "UPDATE", temp_uuid_mapping[resp["id"]]))
|
||||
elif resp.get("event") == "DELETE":
|
||||
task = asyncio.create_task(self._delete_memory(
|
||||
memory_id=temp_uuid_mapping[resp.get("id")]
|
||||
))
|
||||
memory_tasks.append((task, resp, "DELETE", temp_uuid_mapping[resp["id"]]))
|
||||
elif resp.get("event") == "NONE":
|
||||
logging.info("NOOP for Memory.")
|
||||
except Exception as e:
|
||||
logging.error(f"Error in new_memories_with_actions: {e}")
|
||||
|
||||
# Wait for all memory operations to complete
|
||||
for task, resp, event_type, mem_id in memory_tasks:
|
||||
try:
|
||||
result_id = await task
|
||||
if event_type == "ADD":
|
||||
returned_memories.append({
|
||||
"id": result_id,
|
||||
"memory": resp.get("text"),
|
||||
"event": resp.get("event"),
|
||||
})
|
||||
elif event_type == "UPDATE":
|
||||
returned_memories.append({
|
||||
"id": mem_id,
|
||||
"memory": resp.get("text"),
|
||||
"event": resp.get("event"),
|
||||
"previous_memory": resp.get("old_memory"),
|
||||
})
|
||||
elif event_type == "DELETE":
|
||||
returned_memories.append({
|
||||
"id": mem_id,
|
||||
"memory": resp.get("text"),
|
||||
"event": resp.get("event"),
|
||||
})
|
||||
except Exception as e:
|
||||
logging.error(f"Error processing memory task: {e}")
|
||||
|
||||
except Exception as e:
|
||||
logging.error(f"Error in new_memories_with_actions: {e}")
|
||||
|
||||
capture_event("mem0.add", self, {"version": self.api_version, "keys": list(filters.keys())})
|
||||
|
||||
return returned_memories
|
||||
|
||||
async def _add_to_graph(self, messages, filters):
|
||||
added_entities = []
|
||||
if self.enable_graph:
|
||||
if filters.get("user_id") is None:
|
||||
filters["user_id"] = "user"
|
||||
|
||||
data = "\n".join([msg["content"] for msg in messages if "content" in msg and msg["role"] != "system"])
|
||||
added_entities = await asyncio.to_thread(self.graph.add, data, filters)
|
||||
|
||||
return added_entities
|
||||
|
||||
async def get(self, memory_id):
|
||||
"""
|
||||
Retrieve a memory by ID asynchronously.
|
||||
|
||||
Args:
|
||||
memory_id (str): ID of the memory to retrieve.
|
||||
|
||||
Returns:
|
||||
dict: Retrieved memory.
|
||||
"""
|
||||
capture_event("mem0.get", self, {"memory_id": memory_id})
|
||||
memory = await asyncio.to_thread(self.vector_store.get, vector_id=memory_id)
|
||||
if not memory:
|
||||
return None
|
||||
|
||||
filters = {key: memory.payload[key] for key in ["user_id", "agent_id", "run_id"] if memory.payload.get(key)}
|
||||
|
||||
# Prepare base memory item
|
||||
memory_item = MemoryItem(
|
||||
id=memory.id,
|
||||
memory=memory.payload["data"],
|
||||
hash=memory.payload.get("hash"),
|
||||
created_at=memory.payload.get("created_at"),
|
||||
updated_at=memory.payload.get("updated_at"),
|
||||
).model_dump(exclude={"score"})
|
||||
|
||||
# Add metadata if there are additional keys
|
||||
excluded_keys = {"user_id", "agent_id", "run_id", "hash", "data", "created_at", "updated_at", "id"}
|
||||
additional_metadata = {k: v for k, v in memory.payload.items() if k not in excluded_keys}
|
||||
if additional_metadata:
|
||||
memory_item["metadata"] = additional_metadata
|
||||
|
||||
result = {**memory_item, **filters}
|
||||
|
||||
return result
|
||||
|
||||
async def get_all(self, user_id=None, agent_id=None, run_id=None, limit=100):
|
||||
"""
|
||||
List all memories asynchronously.
|
||||
|
||||
Returns:
|
||||
list: List of all memories.
|
||||
"""
|
||||
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
|
||||
|
||||
capture_event("mem0.get_all", self, {"limit": limit, "keys": list(filters.keys())})
|
||||
|
||||
# Run vector store and graph operations concurrently
|
||||
vector_store_task = asyncio.create_task(self._get_all_from_vector_store(filters, limit))
|
||||
|
||||
if self.enable_graph:
|
||||
graph_task = asyncio.create_task(asyncio.to_thread(self.graph.get_all, filters, limit))
|
||||
all_memories, graph_entities = await asyncio.gather(vector_store_task, graph_task)
|
||||
else:
|
||||
all_memories = await vector_store_task
|
||||
graph_entities = None
|
||||
|
||||
if self.enable_graph:
|
||||
return {"results": all_memories, "relations": graph_entities}
|
||||
|
||||
if self.api_version == "v1.0":
|
||||
warnings.warn(
|
||||
"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.",
|
||||
category=DeprecationWarning,
|
||||
stacklevel=2,
|
||||
)
|
||||
return all_memories
|
||||
else:
|
||||
return {"results": all_memories}
|
||||
|
||||
async def _get_all_from_vector_store(self, filters, limit):
|
||||
memories = await asyncio.to_thread(self.vector_store.list, filters=filters, limit=limit)
|
||||
|
||||
excluded_keys = {
|
||||
"user_id",
|
||||
"agent_id",
|
||||
"run_id",
|
||||
"hash",
|
||||
"data",
|
||||
"created_at",
|
||||
"updated_at",
|
||||
"id",
|
||||
}
|
||||
all_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"),
|
||||
).model_dump(exclude={"score"}),
|
||||
**{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[0]
|
||||
]
|
||||
return all_memories
|
||||
|
||||
async def search(self, query, user_id=None, agent_id=None, run_id=None, limit=100, filters=None):
|
||||
"""
|
||||
Search for memories asynchronously.
|
||||
|
||||
Args:
|
||||
query (str): Query to search for.
|
||||
user_id (str, optional): ID of the user to search for. Defaults to None.
|
||||
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.
|
||||
|
||||
Returns:
|
||||
list: List of search results.
|
||||
"""
|
||||
filters = filters or {}
|
||||
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 any(key in filters for key in ("user_id", "agent_id", "run_id")):
|
||||
raise ValueError("One of the filters: user_id, agent_id or run_id is required!")
|
||||
|
||||
capture_event(
|
||||
"mem0.search",
|
||||
self,
|
||||
{"limit": limit, "version": self.api_version, "keys": list(filters.keys())},
|
||||
)
|
||||
|
||||
# Run vector store and graph operations concurrently
|
||||
vector_store_task = asyncio.create_task(self._search_vector_store(query, filters, limit))
|
||||
|
||||
if self.enable_graph:
|
||||
graph_task = asyncio.create_task(asyncio.to_thread(self.graph.search, query, filters, limit))
|
||||
original_memories, graph_entities = await asyncio.gather(vector_store_task, graph_task)
|
||||
else:
|
||||
original_memories = await vector_store_task
|
||||
graph_entities = None
|
||||
|
||||
if self.enable_graph:
|
||||
return {"results": original_memories, "relations": graph_entities}
|
||||
|
||||
if self.api_version == "v1.0":
|
||||
warnings.warn(
|
||||
"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.",
|
||||
category=DeprecationWarning,
|
||||
stacklevel=2,
|
||||
)
|
||||
return original_memories
|
||||
else:
|
||||
return {"results": original_memories}
|
||||
|
||||
async def _search_vector_store(self, query, filters, limit):
|
||||
embeddings = await asyncio.to_thread(self.embedding_model.embed, query, "search")
|
||||
memories = await asyncio.to_thread(
|
||||
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
|
||||
|
||||
async def update(self, memory_id, data):
|
||||
"""
|
||||
Update a memory by ID asynchronously.
|
||||
|
||||
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})
|
||||
|
||||
embeddings = await asyncio.to_thread(self.embedding_model.embed, data, "update")
|
||||
existing_embeddings = {data: embeddings}
|
||||
|
||||
await self._update_memory(memory_id, data, existing_embeddings)
|
||||
return {"message": "Memory updated successfully!"}
|
||||
|
||||
async def delete(self, memory_id):
|
||||
"""
|
||||
Delete a memory by ID asynchronously.
|
||||
|
||||
Args:
|
||||
memory_id (str): ID of the memory to delete.
|
||||
"""
|
||||
capture_event("mem0.delete", self, {"memory_id": memory_id})
|
||||
await self._delete_memory(memory_id)
|
||||
return {"message": "Memory deleted successfully!"}
|
||||
|
||||
async def delete_all(self, user_id=None, agent_id=None, run_id=None):
|
||||
"""
|
||||
Delete all memories asynchronously.
|
||||
|
||||
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 = await asyncio.to_thread(self.vector_store.list, filters=filters)
|
||||
|
||||
delete_tasks = []
|
||||
for memory in memories[0]:
|
||||
delete_tasks.append(self._delete_memory(memory.id))
|
||||
|
||||
await asyncio.gather(*delete_tasks)
|
||||
|
||||
logger.info(f"Deleted {len(memories[0])} memories")
|
||||
|
||||
if self.enable_graph:
|
||||
await asyncio.to_thread(self.graph.delete_all, filters)
|
||||
|
||||
return {"message": "Memories deleted successfully!"}
|
||||
|
||||
async def history(self, memory_id):
|
||||
"""
|
||||
Get the history of changes for a memory by ID asynchronously.
|
||||
|
||||
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 await asyncio.to_thread(self.db.get_history, memory_id)
|
||||
|
||||
async 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 = await asyncio.to_thread(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()
|
||||
|
||||
await asyncio.to_thread(
|
||||
self.vector_store.insert,
|
||||
vectors=[embeddings],
|
||||
ids=[memory_id],
|
||||
payloads=[metadata],
|
||||
)
|
||||
|
||||
await asyncio.to_thread(
|
||||
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
|
||||
|
||||
async def _create_procedural_memory(self, messages, metadata=None, llm=None, prompt=None):
|
||||
"""
|
||||
Create a procedural memory asynchronously
|
||||
|
||||
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 = await asyncio.to_thread(llm.invoke, input=parsed_messages)
|
||||
procedural_memory = response.content
|
||||
else:
|
||||
procedural_memory = await asyncio.to_thread(
|
||||
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 = await asyncio.to_thread(self.embedding_model.embed, procedural_memory, memory_action="add")
|
||||
# Create the memory
|
||||
memory_id = await 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
|
||||
|
||||
async def _update_memory(self, memory_id, data, existing_embeddings, metadata=None):
|
||||
logger.info(f"Updating memory with {data=}")
|
||||
|
||||
try:
|
||||
existing_memory = await asyncio.to_thread(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 = await asyncio.to_thread(self.embedding_model.embed, data, "update")
|
||||
|
||||
await asyncio.to_thread(
|
||||
self.vector_store.update,
|
||||
vector_id=memory_id,
|
||||
vector=embeddings,
|
||||
payload=new_metadata,
|
||||
)
|
||||
|
||||
logger.info(f"Updating memory with ID {memory_id=} with {data=}")
|
||||
|
||||
await asyncio.to_thread(
|
||||
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
|
||||
|
||||
async def _delete_memory(self, memory_id):
|
||||
logging.info(f"Deleting memory with {memory_id=}")
|
||||
existing_memory = await asyncio.to_thread(self.vector_store.get, vector_id=memory_id)
|
||||
prev_value = existing_memory.payload["data"]
|
||||
|
||||
await asyncio.to_thread(self.vector_store.delete, vector_id=memory_id)
|
||||
await asyncio.to_thread(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
|
||||
|
||||
async def reset(self):
|
||||
"""
|
||||
Reset the memory store asynchronously.
|
||||
"""
|
||||
logger.warning("Resetting all memories")
|
||||
await asyncio.to_thread(self.vector_store.delete_col)
|
||||
self.vector_store = VectorStoreFactory.create(
|
||||
self.config.vector_store.provider, self.config.vector_store.config
|
||||
)
|
||||
await asyncio.to_thread(self.db.reset)
|
||||
capture_event("mem0.reset", self)
|
||||
|
||||
async def chat(self, query):
|
||||
raise NotImplementedError("Chat function not implemented yet.")
|
||||
|
||||
Reference in New Issue
Block a user