---
title: RAG
---

<Tip>
Looking for an agent to handle this for you? Check out [DocAgent](/docs/user-guide/reference-agents/docagent), which simplifies the RAG process by streamlining data retrieval and generation-making it faster and easier for you.
</Tip>

Retrieval-Augmented Generation is a technique to improve LLM-generated responses by providing additional knowledge. This typically involves gathering the information and injecting it into an agent's system message for use by their LLM.

There are a number of ways to incorporate RAG into your AG2 workflow and agents:
- Use an AG2 reference agent, [DocAgent](/docs/user-guide/reference-agents/docagent)
- Add RAG capabilities to an agent
- Incorporate context into an agent's system message (manually and automatically)

## 1. DocAgent

Use AG2's reference agent, [DocAgent](/docs/user-guide/reference-agents/docagent), built specifically for RAG. It will take the hassle out of loading, parsing, storing, and querying documents/web pages.

## 2. Add RAG capabilities to an agent

AG2 allows you to add *capabilities* to agents and an example of a capability is RAG using a graph database.

It only takes two steps to do this:
1. Create the capability
2. Add it to the agent

See the notebooks associated with the capabilities below for walk-throughs.

#### RAG Capability: Neo4j GraphRAG
Based on AG2's base GraphRAG capability, this Neo4j GraphRAG capability allows the embedding and querying of information with a Neo4j graph database.

See the [Using Neo4j's graph database with AG2 agents for Q&A notebook](/docs/use-cases/notebooks/notebooks/agentchat_graph_rag_neo4j).

#### RAG Capability: FalkorDB GraphRAG
Also based on AG2's base GraphRAG capability, this capability uses a FalkorDB GraphRAG database.

See the [Trip planning with a FalkorDB GraphRAG agent using a Swarm notebook](/docs/use-cases/notebooks/notebooks/agentchat_swarm_graphrag_trip_planner).

<Tip>
If you need a capability for a different GraphRAG database, consider building a capability similar to these using our [GraphRagCapability](/docs/api-reference/autogen/agentchat/contrib/graph_rag/graph_rag_capability/GraphRagCapability) base class.
</Tip>

## 3. Incorporating context into an Agent's system message

ConversableAgent has a number of hooks that get run before an agent replies. You can utilise the `update_agent_state` hook to run a function that updates your agent's system message with some context before it goes to the LLM.

Within the function use the [ConversableAgent.update_system_message](/docs/api-reference/autogen/ConversableAgent#update-system-message) method to update the system message.

Let’s walk through a simple example where we take a list of files from the current directory, include it in an agent’s system message, and ask an LLM to analyze or explain the files.

We start with our imports, LLM configuration, and the system message template, which we'll inject the file listing in to.
```python
import os
from typing import Any

from autogen import ConversableAgent

config_list = {"api_type": "openai", "model": "gpt-5", "api_key": os.environ["OPENAI_API_KEY"]}

base_system_message = "You are a helpful agent, answering questions about the files in a directory:\n{filelisting}"
```

Here's the function we'll attach to the hook, it gets all files in the current directory and updates the associated agent's system message accordingly.

```python
def give_agent_file_listing(agent: ConversableAgent, messages: list[dict[str, Any]]) -> None:
    # Get the list of files in the current directory
    files = os.listdir()

    # Put them in a string
    files_str = "\n".join(files)

    # Use the system message template and update the agent's system message to include the file listing
    agent.update_system_message(base_system_message.format(filelisting=files_str))
```

Now we create the agent and attach the hook.

```python
files_agent = ConversableAgent(
    name="files_agent",
    system_message="""You are a helpful agent, answering questions about the files in a directory.""",
    llm_config=config_list,
    )

files_agent.register_hook(
    hookable_method="update_agent_state",
    hook=give_agent_file_listing,
    )
```

Finally we create a human-in-the-loop agent and ask our `files_agent` about the files.

```python
human = ConversableAgent(
    name="human",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=1,
    )

human.initiate_chat(
    recipient=files_agent,
    message="Tell me about the files in my directory.",
    max_turns=1,
    )
```

And we can see the LLM now knows about the files and directories in the current folder and is able to provide some information about them.

```console
human (to files_agent):

Tell me about the files in my directory.

--------------------------------------------------------------------------------

>>>>>>>> USING AUTO REPLY...
files_agent (to human):

Certainly! Here's a brief explanation of each file and directory in your list:

1. **.ssh**: Contains configuration files and keys related to the SSH protocol, which are used to securely log into remote systems.

2. **.dotnet**: This directory is related to .NET and contains files and settings for .NET core and related development tools.

3. **.config**: A directory commonly used to store user-level configuration files for applications.

4. **.cache**: This directory is typically used to store cached data, which can improve the performance of the applications.

5. **ag2**: A directory for the open-source AgentOS, AG2 :)

These explanations are based on typical setups and usages; your specific use cases might vary. If you need more details on any item or it's a custom entry, checking the contents or configuration might be necessary for precise information.

--------------------------------------------------------------------------------
```
