304 lines
7.8 KiB
Plaintext
304 lines
7.8 KiB
Plaintext
---
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title: Overview
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description: 'Enhance your memory system with graph-based knowledge representation and retrieval'
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---
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Mem0 now supports **Graph Memory**.
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With Graph Memory, users can now create and utilize complex relationships between pieces of information, allowing for more nuanced and context-aware responses.
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This integration enables users to leverage the strengths of both vector-based and graph-based approaches, resulting in more accurate and comprehensive information retrieval and generation.
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Try Graph Memory on Google Colab.
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<a target="_blank" href="https://colab.research.google.com/drive/1PfIGVHnliIlG2v8cx0g45TF0US-jRPZ1?usp=sharing">
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
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</a>
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<iframe
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width="100%"
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height="400"
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src="https://www.youtube.com/embed/u_ZAqNNVtXA"
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title="YouTube video player"
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frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
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allowfullscreen
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></iframe>
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## Initialize Graph Memory
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To initialize Graph Memory you'll need to set up your configuration with graph store providers.
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Currently, we support Neo4j as a graph store provider. You can setup [Neo4j](https://neo4j.com/) locally or use the hosted [Neo4j AuraDB](https://neo4j.com/product/auradb/).
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Moreover, you also need to set the version to `v1.1` (*prior versions are not supported*).
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User can also customize the LLM for Graph Memory from the [Supported LLM list](https://docs.mem0.ai/components/llms/overview) with three levels of configuration:
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1. **Main Configuration**: If `llm` is set in the main config, it will be used for all graph operations.
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2. **Graph Store Configuration**: If `llm` is set in the graph_store config, it will override the main config `llm` and be used specifically for graph operations.
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3. **Default Configuration**: If no custom LLM is set, the default LLM (`gpt-4o-2024-08-06`) will be used for all graph operations.
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Here's how you can do it:
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<CodeGroup>
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```python Basic
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from mem0 import Memory
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config = {
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"graph_store": {
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"provider": "neo4j",
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"config": {
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"url": "neo4j+s://xxx",
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"username": "neo4j",
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"password": "xxx"
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}
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},
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"version": "v1.1"
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}
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m = Memory.from_config(config_dict=config)
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```
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```python Advanced (Custom LLM)
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from mem0 import Memory
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config = {
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"llm": {
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"provider": "openai",
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"config": {
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"model": "gpt-4o",
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"temperature": 0.2,
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"max_tokens": 1500,
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}
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},
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"graph_store": {
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"provider": "neo4j",
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"config": {
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"url": "neo4j+s://xxx",
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"username": "neo4j",
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"password": "xxx"
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},
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"llm" : {
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"provider": "openai",
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"config": {
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"model": "gpt-4o-mini",
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"temperature": 0.0,
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}
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}
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},
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"version": "v1.1"
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}
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m = Memory.from_config(config_dict=config)
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```
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</CodeGroup>
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## Graph Operations
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The Mem0's graph supports the following operations:
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### Add Memories
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<CodeGroup>
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```python Code
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m.add("I like pizza", user_id="alice")
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```
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```json Output
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{'message': 'ok'}
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```
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</CodeGroup>
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### Get all memories
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<CodeGroup>
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```python Code
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m.get_all(user_id="alice")
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```
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```json Output
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{
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'memories': [
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{
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'id': 'de69f426-0350-4101-9d0e-5055e34976a5',
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'memory': 'Likes pizza',
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'hash': '92128989705eef03ce31c462e198b47d',
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'metadata': None,
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'created_at': '2024-08-20T14:09:27.588719-07:00',
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'updated_at': None,
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'user_id': 'alice'
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}
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],
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'entities': [
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{
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'source': 'alice',
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'relationship': 'likes',
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'target': 'pizza'
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}
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]
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}
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```
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</CodeGroup>
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### Search Memories
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<CodeGroup>
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```python Code
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m.search("tell me my name.", user_id="alice")
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```
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```json Output
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{
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'memories': [
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{
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'id': 'de69f426-0350-4101-9d0e-5055e34976a5',
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'memory': 'Likes pizza',
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'hash': '92128989705eef03ce31c462e198b47d',
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'metadata': None,
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'created_at': '2024-08-20T14:09:27.588719-07:00',
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'updated_at': None,
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'user_id': 'alice'
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}
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],
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'entities': [
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{
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'source': 'alice',
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'relationship': 'likes',
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'target': 'pizza'
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}
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]
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}
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```
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</CodeGroup>
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### Delete all Memories
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```python
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m.delete_all(user_id="alice")
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```
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# Example Usage
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Here's an example of how to use Mem0's graph operations:
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1. First, we'll add some memories for a user named Alice.
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2. Then, we'll visualize how the graph evolves as we add more memories.
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3. You'll see how entities and relationships are automatically extracted and connected in the graph.
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### Add Memories
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Below are the steps to add memories and visualize the graph:
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<Steps>
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<Step title="Add memory 'I like going to hikes'">
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```python
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m.add("I like going to hikes", user_id="alice123")
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```
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</Step>
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<Step title="Add memory 'I love to play badminton'">
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```python
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m.add("I love to play badminton", user_id="alice123")
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```
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</Step>
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<Step title="Add memory 'I hate playing badminton'">
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```python
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m.add("I hate playing badminton", user_id="alice123")
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```
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</Step>
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<Step title="Add memory 'My friend name is john and john has a dog named tommy'">
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```python
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m.add("My friend name is john and john has a dog named tommy", user_id="alice123")
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```
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</Step>
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<Step title="Add memory 'My name is Alice'">
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```python
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m.add("My name is Alice", user_id="alice123")
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```
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</Step>
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<Step title="Add memory 'John loves to hike and Harry loves to hike as well'">
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```python
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m.add("John loves to hike and Harry loves to hike as well", user_id="alice123")
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```
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</Step>
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<Step title="Add memory 'My friend peter is the spiderman'">
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```python
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m.add("My friend peter is the spiderman", user_id="alice123")
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```
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</Step>
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</Steps>
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### Search Memories
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<CodeGroup>
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```python Code
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m.search("What is my name?", user_id="alice123")
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```
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```json Output
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{
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'memories': [...],
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'entities': [
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{'source': 'alice123', 'relation': 'dislikes_playing','destination': 'badminton'},
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{'source': 'alice123', 'relation': 'friend', 'destination': 'peter'},
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{'source': 'alice123', 'relation': 'friend', 'destination': 'john'},
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{'source': 'alice123', 'relation': 'has_name', 'destination': 'alice'},
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{'source': 'alice123', 'relation': 'likes', 'destination': 'hiking'}
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]
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}
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```
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</CodeGroup>
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Below graph visualization shows what nodes and relationships are fetched from the graph for the provided query.
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<CodeGroup>
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```python Code
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m.search("Who is spiderman?", user_id="alice123")
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```
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```json Output
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{
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'memories': [...],
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'entities': [
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{'source': 'peter', 'relation': 'identity','destination': 'spiderman'}
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]
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}
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```
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</CodeGroup>
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> **Note:** The Graph Memory implementation is not standalone. You will be adding/retrieving memories to the vector store and the graph store simultaneously.
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If you want to use a managed version of Mem0, please check out [Mem0](https://app.mem0.ai). If you have any questions, please feel free to reach out to us using one of the following methods:
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<Snippet file="get-help.mdx" /> |