RAG Agent

This RAG Agent uses  Claude 2.1 LLM model with ReAct prompting, optimizing dynamic reasoning and action planning.

ReAct

What is ReAct prompting?

ReAct is a prompting framework where LLMs are used to generate both reasoning traces and task-specific actions in an interleaved manner.

Generating reasoning traces allow the model to induce, track, and update action plans, and even handle exceptions. 

How it Works?

ReAct is a general paradigm that combines reasoning and acting with LLMs. ReAct prompts LLMs to generate verbal reasoning traces and actions for a task. This allows the system to perform dynamic reasoning to create, maintain, and adjust plans for acting into the reasoning.

How to Use this RAG Agent? 

Using Clarifai SDK

Export your PAT as an environment variable. Then, import and initialize the API Client.

Find your PAT in your security settings.

export CLARIFAI_PAT={your personal access token}

Upload your Data 

Upload you data in the Clarifai App to chat with RAG Agent.

from clarifai.rag import RAG

rag_agent = RAG.setup(
app_url= "https://clarifai.com/clarifai/rag-template",)
rag_agent.upload(folder_path = "/app/files", chunk_size= 512, chunk_overlap= 50)

folder_path  Folder path to the documents.

chunk_size: Chunk size for splitting the document.

chunk_overlap: The token overlap of each chunk when splitting.

Chat with the your Data  using RAG Agent

from clarifai.rag import RAG

rag_agent = RAG(workflow_url='https://clarifai.com/clarifai/rag-template/workflows/rag-agent-claude2-1-React-few-shot')
rag_agent.chat(messages=[{"role":"human", "content":"What is Clarifai"}])

Using Workflow

To utilize RAG Agents, you can input a query through the Blue Plus Try your own Input button. This incorporates all the documents in the Inputs as external data in RAG.