Tutorial: Turn Any LLM into an Expert Assistant with Federated RAG – Part 1
TL;DR: LLMs often fail on domain-specific questions, not from lack of capability, but from missing access to expert data. RAG extends their reach with external context, but only for data one has access to, while much of it is locked behind privacy and IP walls. In this tutorial, we build Federated RAG from scratch and […] The post Tutorial: Turn Any LLM into an Expert Assistant with Federated RAG – Part 1 appeared first on OpenMined .
LLMs, or large language models, have revolutionized the way we interact with artificial intelligence. They excel at answering open-domain questions, such as "Who wrote Romeo and Juliet?" or "What is the capital of France?" However, their performance on domain-specific questions leaves much to be desired. A doctor seeking information on drug interactions, a lawyer looking for legal precedents, or a patient comparing insurance claims often receives vague or incorrect answers. This isn't because the models lack the capability; it's because they lack access to the specialized, expert data that exists in these domains.
RAG, or Retrieval-Augmented Generation, is a technique that extends the reach of LLMs by providing them with external context from a knowledge source. This context allows the models to generate more accurate and relevant responses. However, RAG has a significant limitation: it only accesses data that the user has direct access to. Much of the valuable, domain-specific data is locked behind privacy and intellectual property (IP) walls, making it inaccessible to most users.
This is where Federated RAG comes in. Federated learning is a decentralized approach to machine learning where models are trained across multiple decentralized devices or servers holding local data, without exchanging the data itself. Federated RAG extends this concept to enable LLMs to tap into privately held knowledge across a network of data sources, all while maintaining privacy and IP protections.
In this tutorial, we will build Federated RAG from scratch and demonstrate how to run it across a live network of data sources. We'll show you how to combine insights from different domains and integrate them into an LLM to create an expert assistant. The goal is to unlock much more knowledge by tapping into data owned by others in a federated way, without ever seeing or exposing it.
To get started, we'll use the Syft library, an open-source toolkit for decentralized machine learning. Syft allows us to create a network of data sources and an LLM that can access and combine insights from these sources.
First, let's import the necessary components from Syft Hub. We'll create a client object that will connect to our network of data sources and an LLM.
```python
from syft_hub import Client
cl = Client()
```
Next, we'll choose our data sources. For this example, we'll use three sources: Hacker News top stories, arXiv articles, and trending GitHub repositories. These sources represent different domains, and we'll use them to create a more robust and versatile expert assistant.
```python
hacker_news_source = cl.load_service("demo@openmined.org/hackernews-top-stories")
arxiv_source = cl.load_service("demo@openmined.org/arxiv-articles")
github_source = cl.load_service("demo@openmined.org/github-trending")
```
Now, let's select an LLM to combine the insights from our data sources. For this tutorial, we'll use Claude, an open-source LLM based on the OpenAI GPT-3.5 model.
```python
claude_llm = cl.load_service("aggregator@openmined.org/claude-3.5-sonnet")
```
With our data sources and LLM ready, we can now create a Federated RAG pipeline. This pipeline will retrieve relevant information from our data sources and pass it to the LLM for generation.
```python
fedrag_pipeline = cl.pipeline(
data_sources=[hacker_news_source, arxiv_source, github_source],
synthesizer=claude_llm
)
```
To test our pipeline, we'll ask a question and see how it performs. Let's inquire about methods to improve context in LLM agents.
```python
query = "What methods can help improve context in LLM agents?"
result = fedrag_pipeline.run(
messages=[{"role": "user", "content": query}]
)
print(result)
```
The output of this query will be a response generated by the LLM, informed by the context retrieved from our data sources. Since our data sources cover diverse domains, the LLM can provide a well-rounded answer, leveraging the collective knowledge from Hacker News, arXiv, and GitHub.
This tutorial is just the beginning. Federated RAG has the potential to revolutionize the way LLMs access and utilize specialized knowledge. By building a network of data sources and integrating them into an LLM, we can create expert assistants that are capable of answering domain-specific questions with accuracy and confidence.
As we continue to explore the possibilities of Federated RAG, we'll delve deeper into the technical aspects of setting up a federated network, expanding the range of data sources, and optimizing the performance of the LLM. We'll also examine real-world use cases where Federated RAG can make a significant impact, such as in healthcare, legal, and financial domains.
In conclusion, the key challenge for LLMs is not their capability but their access to expert data. Federated RAG offers a solution by enabling LLMs to tap into privately held knowledge across a network of data sources. By leveraging decentralized learning and combining insights from diverse domains, we can create expert assistants that provide accurate and relevant answers to domain-specific questions. The future of AI lies in unlocking the full potential of knowledge, and Federated RAG is a crucial step in that direction.
