app.py

import os
import torch.cuda
import numpy as np
import faiss
import gradio as gr
import re
from openai import OpenAI
from langchain_community.document_loaders import TextLoader, DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS 
from sentence_transformers import SentenceTransformer

class MultiAgentRAG:
    def __init__(self, embedding_model_name, openai_model_id, data_folder, api_key=None):
        self.use_gpu = torch.cuda.is_available()
        self.all_splits = self.load_documents(data_folder)
        self.embeddings = SentenceTransformer(embedding_model_name)
        self.faiss_index = self.create_faiss_index()
        self.openai_client = OpenAI(api_key=api_key or os.environ.get("OPENAI_API_KEY"))
        self.openai_model_id = openai_model_id

    def load_documents(self, folder_path):
        loader = DirectoryLoader(folder_path, loader_cls=TextLoader)
        documents = loader.load()
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=250)
        all_splits = text_splitter.split_documents(documents)
        return all_splits

    def create_faiss_index(self):
        all_texts = [split.page_content for split in self.all_splits]
        embeddings = self.embeddings.encode(all_texts, convert_to_tensor=True).cpu().numpy()
        index = faiss.IndexFlatL2(embeddings.shape[1])
        index.add(embeddings)
        try:
            gpu_resource = faiss.StandardGpuResources()
            gpu_index = faiss.index_cpu_to_gpu(gpu_resource, 0, index)
            return gpu_index
        except:
            return index

    def generate_openai_response(self, messages, max_tokens=1000):
        try:
            response = self.openai_client.chat.completions.create(
                model=self.openai_model_id,
                messages=messages,
                max_tokens=max_tokens,
                temperature=0.8,
                top_p=1.0,
                frequency_penalty=0,
                presence_penalty=0
            )
            return response.choices[0].message.content
        except:
            return "Text generation process encountered an error"

    def retrieval_agent(self, query):
        query_embedding = self.embeddings.encode(query, convert_to_tensor=True).cpu().numpy()
        distances, indices = self.faiss_index.search(np.array([query_embedding]), k=3)
        content = ""
        for idx in indices[0]:
            content += self.all_splits[idx].page_content + "\n"
        return content

    def grading_agent(self, query, retrieved_content):
        messages = [
            {"role": "system", "content": "You are an expert at evaluating relevance."},
            {"role": "user", "content": f"Query: {query}\nRetrieved Content:\n{retrieved_content}\nRate the relevance on a scale of 1-10."}
        ]
        grading_response = self.generate_openai_response(messages)
        match = re.search(r'\b([1-9]|10)\b', grading_response)
        rating = int(match.group()) if match else 5
        return rating, grading_response

    def query_rewrite_agent(self, original_query):
        messages = [
            {"role": "system", "content": "You are an expert at rewriting queries."},
            {"role": "user", "content": f"Original Query: {original_query}\nRewritten Query:"}
        ]
        return self.generate_openai_response(messages).strip()

    def generation_agent(self, query, retrieved_content):
        messages = [
            {"role": "system", "content": "You are a knowledgeable assistant."},
            {"role": "user", "content": f"Query: {query}\nSolution==>"}
        ]
        return self.generate_openai_response(messages)

    def run_multi_agent_rag(self, query):
        for _ in range(3):
            retrieved_content = self.retrieval_agent(query)
            relevance_score, grading_explanation = self.grading_agent(query, retrieved_content)
            if relevance_score >= 7:
                return self.generation_agent(query, retrieved_content), retrieved_content, grading_explanation
            query = self.query_rewrite_agent(query)
        return "Unable to find a relevant answer.", "", "Low relevance across all attempts."

    def qa_infer_gradio(self, query):
        answer, retrieved_content, grading_explanation = self.run_multi_agent_rag(query)
        return answer, f"Retrieved Content:\n{retrieved_content}\n\nGrading Explanation:\n{grading_explanation}"

def launch_interface(doc_retrieval_gen):
    css_code = """
        .gradio-container { background-color: #daccdb; }
        button { background-color: #927fc7; color: black; border: 1px solid black; padding: 10px; margin-right: 10px; font-size: 16px; font-weight: bold; }
    """
    EXAMPLES = [
        "On which devices can the VIP and CSI2 modules operate simultaneously?", 
        "I'm using Code Composer Studio 5.4.0.00091 and enabled FPv4SPD16 floating point support for CortexM4 in TDA2. However, after building the project, the .asm file shows --float_support=vfplib instead of FPv4SPD16. Why is this happening?", 
        "Could you clarify the maximum number of cameras that can be connected simultaneously to the video input ports on the TDA2x SoC?"
    ]
    interface = gr.Interface(
        fn=doc_retrieval_gen.qa_infer_gradio,
        inputs=[gr.Textbox(label="QUERY", placeholder="Enter your query here")],
        allow_flagging='never',
        examples=EXAMPLES,
        cache_examples=False,
        outputs=[gr.Textbox(label="RESPONSE"), gr.Textbox(label="RELATED QUERIES")],
        css=css_code,
        title="TI E2E FORUM Multi-Agent RAG"
    )
    interface.launch(debug=True)

if __name__ == "__main__":
    embedding_model_name = 'flax-sentence-embeddings/all_datasets_v3_MiniLM-L12'
    openai_model_id = "gpt-4-turbo"
    data_folder = 'sample_embedding_folder2'
    try:
        multi_agent_rag = MultiAgentRAG(embedding_model_name, openai_model_id, data_folder)
        launch_interface(multi_agent_rag)
    except Exception as e:
        print(f"Error initializing Multi-Agent RAG: {str(e)}")