Update Docs (#3520)
This commit is contained in:
Deshraj Yadav
@@ -5,15 +5,13 @@ icon: "list-check"
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iconType: "solid"
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---
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Graph Memory is a powerful feature that allows users to create and utilize complex relationships between pieces of information.
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Graph Memory is a powerful feature that allows you to create and utilize complex relationships between pieces of information.
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## Graph Memory supports the following features:
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## Graph Memory Features
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### Using Custom Prompts
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Users can specify a custom prompt that will be used to extract specific entities from the given input text.
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This allows for more targeted and relevant information extraction based on the user's needs.
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Here's an example of how to specify a custom prompt:
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You can specify a custom prompt that will be used to extract specific entities from the given input text. This allows for more targeted and relevant information extraction based on your needs. Here's an example of how to specify a custom prompt:
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<CodeGroup>
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```python Python
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@@ -5,13 +5,7 @@ icon: "info"
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iconType: "solid"
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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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<Note>
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NodeSDK now supports Graph Memory. 🎉
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</Note>
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Mem0 now supports **Graph Memory**. With Graph Memory, you can create and utilize complex relationships between pieces of information, allowing for more nuanced and context-aware responses. This integration enables you 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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## Installation
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@@ -47,17 +41,16 @@ allowfullscreen
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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
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store providers. Currently, we support [Neo4j](#initialize-neo4j), [Memgraph](#initialize-memgraph), [Neptune Analytics](#initialize-neptune-analytics), [Neptune DB Cluster](#initialize-neptune-db),and [Kuzu](#initialize-kuzu) as graph store providers.
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To initialize Graph Memory you'll need to set up your configuration with graph store providers. Currently, we support [Neo4j](#initialize-neo4j), [Memgraph](#initialize-memgraph), [Neptune Analytics](#initialize-neptune-analytics), [Neptune DB Cluster](#initialize-neptune-db),and [Kuzu](#initialize-kuzu) as graph store providers.
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### Initialize Neo4j
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You can setup [Neo4j](https://neo4j.com/) locally or use the hosted [Neo4j AuraDB](https://neo4j.com/product/auradb/).
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<Note>If you are using Neo4j locally, then you need to install [APOC plugins](https://neo4j.com/labs/apoc/4.1/installation/).</Note>
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<Note>If you are using Neo4j locally, you need to install [APOC plugins](https://neo4j.com/labs/apoc/4.1/installation/).</Note>
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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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You 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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@@ -176,13 +169,11 @@ Run Memgraph with Docker:
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docker run -p 7687:7687 memgraph/memgraph-mage:latest --schema-info-enabled=True
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```
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The `--schema-info-enabled` flag is set to `True` for more performant schema
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generation.
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The `--schema-info-enabled` flag is set to `True` for more performant schema generation.
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Additional information can be found on [Memgraph
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documentation](https://memgraph.com/docs).
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Additional information can be found in the [Memgraph documentation](https://memgraph.com/docs).
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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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You 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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@@ -234,6 +225,7 @@ m = Memory.from_config(config_dict=config)
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Note: You can use Neptune Analytics as part of an Amazon tech stack [Setup AWS Bedrock, AOSS, and Neptune](https://docs.mem0.ai/examples/aws_example#aws-bedrock-and-aoss)
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Create an instance of Amazon Neptune Analytics in your AWS account following the [AWS documentation](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/get-started.html).
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- Public connectivity is not enabled by default, and if accessing from outside a VPC, it needs to be enabled.
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- Once the Amazon Neptune Analytics instance is available, you will need the graph-identifier to connect.
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- The Neptune Analytics instance must be created using the same vector dimensions as the embedding model creates. See: [Vector indexing in Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/vector-index.html).
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@@ -329,8 +321,7 @@ Troubleshooting:
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### Initialize Kuzu
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[Kuzu](https://kuzudb.com) is a fully local in-process graph database system that runs openCypher queries.
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Kuzu comes embedded into the Python package and there is no additional setup required.
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[Kuzu](https://kuzudb.com) is a fully local in-process graph database system that runs openCypher queries. Kuzu comes embedded into the Python package and there is no additional setup required.
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Kuzu needs a path to a file where it will store the graph database. For example:
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@@ -347,8 +338,7 @@ config = {
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```
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</CodeGroup>
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Kuzu can also store its database in memory. Note that in this mode, all stored memories will be lost
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after the program has finished executing.
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Kuzu can also store its database in memory. Note that in this mode, all stored memories will be lost after the program has finished executing.
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<CodeGroup>
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```python Python
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@@ -374,6 +364,7 @@ m = Memory.from_config(config_dict=config)
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</CodeGroup>
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## Graph Operations
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Mem0's graph memory supports the following operations:
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### Add Memories
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@@ -529,7 +520,8 @@ memory.deleteAll({ userId: "alice", agentId: "food-assistant" });
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```
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</CodeGroup>
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# Example Usage
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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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@@ -679,7 +671,7 @@ memory.search("What is my name?", { userId: "alice123" });
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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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The graph visualization below shows what nodes and relationships are fetched from the graph for the provided query.
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@@ -708,14 +700,13 @@ memory.search("Who is spiderman?", { userId: "alice123" });
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## Using Multiple Agents with Graph Memory
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When working with multiple agents and sessions, you can use the `agent_id` and `run_id` parameters to organize memories by user, agent, and run context. This allows you to:
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When working with multiple agents and sessions, you can use the "agent_id" and "run_id" parameters to organize memories by user, agent, and run context. This allows you to:
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1. Create agent-specific knowledge graphs
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2. Share common knowledge between agents
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3. Isolate sensitive or specialized information to specific agents
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4. Track conversation sessions and runs separately
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5. Maintain context across different execution contexts
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1. Create agent-specific knowledge graphs.
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2. Share common knowledge between agents.
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3. Isolate sensitive or specialized information to specific agents.
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4. Track conversation sessions and runs separately.
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5. Maintain context across different execution contexts.
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### Example: Multi-Agent Setup
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