Skip to content

Latest commit

 

History

History
104 lines (72 loc) · 3.15 KB

README.md

File metadata and controls

104 lines (72 loc) · 3.15 KB
Raw

Eidolon S3 Rag Recipe

In this recipe we have created a RAG chatbot powered by documents living in s3.

The documents are parsed and embedded on the fly which is valuable if you have a small body of data that may change frequently. To process a large body of data, you will want to set up an ingestion pipeline.

Core Concepts

  • Multi-agent communication
  • Sub-component customization
  • Dynamic embedding management

Agents

Conversational Agent

The user facing copilot. Ask this agent questions and it use the llm to provide answers while reaching out to the S3 Search Agent as needed for relevant documents as needed assistance of the repo search agent.

S3 Search Agent

Handles loading, embedding, and re-embedding documents ensuring they are up-to-date.

Translates queries into a vector search query and returns the top results.

Directory Structure

  • resources: This directory contains additional resources for the project. An example agent is provided for reference.
  • components: This directory is where any custom code should be placed.

Running the Server in Docker

First you need to clone the project and navigate to the project directory:

git clone https://github.com/eidolon-ai/eidolon-s3-rag.git
cd agent-machine

Then run the server using docker, use the following command:

make docker-serve

The first time you run this command, you may be prompted to enter credentials that the machine needs to run (ie, OpenAI API Key).

This command will download the dependencies required to run your agent machine and start the Eidolon http server in "dev-mode".

If the server starts successfully, you should see the following output:

Starting Server...
INFO:     Started server process [34623]
INFO:     Waiting for application startup.
INFO - Building machine 'local_dev'
...
INFO - Server Started in 1.50s

Running the server in K8s

Prerequisites

WARNING: This will work for local k8s environments only. See Readme.md in the k8s directory if you are using this against a cloud based k8s environment.

To use kubernetes for local development, you will need to have the following installed:

Installation

If you are using Minikube, run the following commands before any make commands:

alias kubectl="minikube kubectl --"
eval $(minikube docker-env)

Make sure your kubernetes environment is set up properly and install the Eidolon k8s operator.

make k8s-operator

This will install the Eidolon operator in your k8s cluster. This only needs to be done once.

Next install the Eidolon resources. This will create an Eidolon machine and an Eidolon agent in your cluster, start them, and tail the logs:

make k8s-serve

If the server starts successfully, you should see the following output:

Deployment is ready. Tailing logs from new pods...
INFO:     Started server process [1]
INFO:     Waiting for application startup.
INFO - Building machine 'local-dev'
INFO - Starting agent 'hello-world'
INFO - Server Started in 0.86s