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| Updated Multi-agent RAG-based LLM Model | |
| import os | |
| import multiprocessing | |
| import concurrent.futures | |
| from langchain.document_loaders import TextLoader, DirectoryLoader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.vectorstores import FAISS | |
| from sentence_transformers import SentenceTransformer | |
| import faiss | |
| import torch | |
| import numpy as np | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig | |
| from datetime import datetime | |
| import json | |
| import gradio as gr | |
| import re | |
| from threading import Thread | |
| class Agent: | |
| def __init__(self, name, role, doc_retrieval_gen, tokenizer): | |
| self.name = name | |
| self.role = role | |
| self.doc_retrieval_gen = doc_retrieval_gen | |
| self.tokenizer = tokenizer | |
| def generate_response(self, query, context): | |
| if self.role == "Information Retrieval": | |
| return self.retriever_logic(query, context) | |
| elif self.role == "Content Analysis": | |
| return self.analyzer_logic(query, context) | |
| elif self.role == "Response Generation": | |
| return self.generator_logic(query, context) | |
| elif self.role == "Task Coordination": | |
| return self.coordinator_logic(query, context) | |
| def retriever_logic(self, query, all_splits): | |
| query_embedding = self.doc_retrieval_gen.embeddings.encode(query, convert_to_tensor=True).cpu().numpy() | |
| distances, indices = self.doc_retrieval_gen.gpu_index.search(np.array([query_embedding]), k=3) | |
| relevant_docs = [all_splits[i] for i in indices[0] if distances[0][i] <= 1] | |
| return relevant_docs | |
| def analyzer_logic(self, query, relevant_docs): | |
| analysis_prompt = f"Analyze the following documents in relation to the query: '{query}'\n\nDocuments:\n" | |
| for doc in relevant_docs: | |
| analysis_prompt += f"- {doc.page_content}\n" | |
| analysis_prompt += "\nProvide a concise analysis of the key points relevant to the query." | |
| input_ids = self.tokenizer.encode(analysis_prompt, return_tensors="pt").to(self.doc_retrieval_gen.model.device) | |
| analysis = self.doc_retrieval_gen.model.generate(input_ids, max_length=200, num_return_sequences=1) | |
| return self.tokenizer.decode(analysis[0], skip_special_tokens=True) | |
| def generator_logic(self, query, analyzed_content): | |
| generation_prompt = f"Based on the following analysis, generate a comprehensive answer to the query: '{query}'\n\nAnalysis:\n{analyzed_content}\n\nGenerate a detailed response:" | |
| input_ids = self.tokenizer.encode(generation_prompt, return_tensors="pt").to(self.doc_retrieval_gen.model.device) | |
| response = self.doc_retrieval_gen.model.generate(input_ids, max_length=300, num_return_sequences=1) | |
| return self.tokenizer.decode(response[0], skip_special_tokens=True) | |
| def coordinator_logic(self, query, final_response): | |
| coordination_prompt = f"As a coordinator, review and refine the following response to the query: '{query}'\n\nResponse:\n{final_response}\n\nProvide a final, polished answer:" | |
| input_ids = self.tokenizer.encode(coordination_prompt, return_tensors="pt").to(self.doc_retrieval_gen.model.device) | |
| coordinated_response = self.doc_retrieval_gen.model.generate(input_ids, max_length=350, num_return_sequences=1) | |
| return self.tokenizer.decode(coordinated_response[0], skip_special_tokens=True) | |
| class DocumentRetrievalAndGeneration: | |
| def __init__(self, embedding_model_name, lm_model_id, data_folder): | |
| self.all_splits = self.load_documents(data_folder) | |
| self.embeddings = SentenceTransformer(embedding_model_name) | |
| self.gpu_index = self.create_faiss_index() | |
| self.tokenizer, self.model = self.initialize_llm(lm_model_id) | |
| self.agents = self.initialize_agents() | |
| def initialize_agents(self): | |
| agents = [ | |
| Agent("Retriever", "Information Retrieval", self, self.tokenizer), | |
| Agent("Analyzer", "Content Analysis", self, self.tokenizer), | |
| Agent("Generator", "Response Generation", self, self.tokenizer), | |
| Agent("Coordinator", "Task Coordination", self, self.tokenizer) | |
| ] | |
| return agents | |
| 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) | |
| print('Length of documents:', len(documents)) | |
| print("LEN of all_splits", len(all_splits)) | |
| for i in range(3): | |
| print(all_splits[i].page_content) | |
| 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) | |
| gpu_resource = faiss.StandardGpuResources() | |
| gpu_index = faiss.index_cpu_to_gpu(gpu_resource, 0, index) | |
| return gpu_index | |
| def initialize_llm(self, model_id): | |
| quantization_config = BitsAndBytesConfig( | |
| load_in_4bit=True, | |
| bnb_4bit_use_double_quant=True, | |
| bnb_4bit_quant_type="nf4", | |
| bnb_4bit_compute_dtype=torch.bfloat16 | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.bfloat16, | |
| device_map="auto", | |
| quantization_config=quantization_config | |
| ) | |
| return tokenizer, model | |
| def coordinate_agents(self, query): | |
| coordinator = next(agent for agent in self.agents if agent.name == "Coordinator") | |
| # Step 1: Information Retrieval | |
| retriever = next(agent for agent in self.agents if agent.name == "Retriever") | |
| relevant_docs = retriever.generate_response(query, self.all_splits) | |
| # Step 2: Content Analysis | |
| analyzer = next(agent for agent in self.agents if agent.name == "Analyzer") | |
| analyzed_content = analyzer.generate_response(query, relevant_docs) | |
| # Step 3: Response Generation | |
| generator = next(agent for agent in self.agents if agent.name == "Generator") | |
| final_response = generator.generate_response(query, analyzed_content) | |
| # Step 4: Coordination and Refinement | |
| coordinated_response = coordinator.generate_response(query, final_response) | |
| return coordinated_response, "\n".join([doc.page_content for doc in relevant_docs]) | |
| def query_and_generate_response(self, query): | |
| return self.coordinate_agents(query) | |
| def generate_response_with_timeout(self, input_ids, max_new_tokens=1000): | |
| try: | |
| streamer = TextIteratorStreamer(self.tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True) | |
| generate_kwargs = dict( | |
| input_ids=input_ids, | |
| max_new_tokens=max_new_tokens, | |
| do_sample=True, | |
| top_p=1.0, | |
| top_k=20, | |
| temperature=0.8, | |
| repetition_penalty=1.2, | |
| eos_token_id=[128001, 128008, 128009], | |
| streamer=streamer, | |
| ) | |
| thread = Thread(target=self.model.generate, kwargs=generate_kwargs) | |
| thread.start() | |
| generated_text = "" | |
| for new_text in streamer: | |
| generated_text += new_text | |
| return generated_text | |
| except Exception as e: | |
| print(f"Error in generate_response_with_timeout: {str(e)}") | |
| return "Text generation process encountered an error" | |
| def query_and_generate_response(self, query): | |
| similarityThreshold = 1 | |
| query_embedding = self.embeddings.encode(query, convert_to_tensor=True).cpu().numpy() | |
| distances, indices = self.gpu_index.search(np.array([query_embedding]), k=3) | |
| print("Distance", distances, "indices", indices) | |
| content = "" | |
| filtered_results = [] | |
| for idx, distance in zip(indices[0], distances[0]): | |
| if distance <= similarityThreshold: | |
| filtered_results.append(idx) | |
| for i in filtered_results: | |
| print(self.all_splits[i].page_content) | |
| content += "-" * 50 + "\n" | |
| content += self.all_splits[idx].page_content + "\n" | |
| print("CHUNK", idx) | |
| print("Distance:", distance) | |
| print("indices:", indices) | |
| print(self.all_splits[idx].page_content) | |
| print("############################") | |
| conversation = [ | |
| {"role": "system", "content": "You are a knowledgeable assistant with access to a comprehensive database."}, | |
| {"role": "user", "content": f""" | |
| I need you to answer my question and provide related information in a specific format. | |
| I have provided five relatable json files {content}, choose the most suitable chunks for answering the query. | |
| RETURN ONLY SOLUTION without additional comments, sign-offs, retrived chunks, refrence to any Ticket or extra phrases. Be direct and to the point. | |
| IF THERE IS NO ANSWER RELATABLE IN RETRIEVED CHUNKS, RETURN "NO SOLUTION AVAILABLE". | |
| DO NOT GIVE REFRENCE TO ANY CHUNKS OR TICKETS,BE ON POINT. | |
| Here's my question: | |
| Query: {query} | |
| Solution==> | |
| """} | |
| ] | |
| #Include a final answer without additional comments, sign-offs, or extra phrases. Be direct and to the point. | |
| input_ids = self.tokenizer.apply_chat_template(conversation, return_tensors="pt").to(self.model.device) | |
| start_time = datetime.now() | |
| generated_response = self.generate_response_with_timeout(input_ids) | |
| elapsed_time = datetime.now() - start_time | |
| print("Generated response:", generated_response) | |
| print("Time elapsed:", elapsed_time) | |
| print("Device in use:", self.model.device) | |
| solution_text = generated_response.strip() | |
| if "Solution:" in solution_text: | |
| solution_text = solution_text.split("Solution:", 1)[1].strip() | |
| # Post-processing to remove "assistant" prefix | |
| solution_text = re.sub(r'^assistant\s*', '', solution_text, flags=re.IGNORECASE) | |
| solution_text = solution_text.strip() | |
| return solution_text, content | |
| def qa_infer_gradio(self, query): | |
| response, related_queries = self.query_and_generate_response(query) | |
| return response, related_queries | |
| if __name__ == "__main__": | |
| embedding_model_name = 'flax-sentence-embeddings/all_datasets_v3_MiniLM-L12' | |
| lm_model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct" | |
| data_folder = 'sample_embedding_folder2' | |
| doc_retrieval_gen = DocumentRetrievalAndGeneration(embedding_model_name, lm_model_id, data_folder) | |
| def launch_interface(): | |
| 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, considering it supports up to 10 multiplexed input ports and includes 3 dedicated video input modules?" | |
| ] | |
| 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" | |
| ) | |
| interface.launch(debug=True) | |
| launch_interface() |