import json import os from dotenv import load_dotenv import yaml from together import Together from langchain.llms.together import Together as TogetherLLM from langchain.prompts import PromptTemplate from langchain.schema.runnable import RunnablePassthrough from langchain.schema.output_parser import StrOutputParser from pinecone import Pinecone from typing import List, Dict import cohere load_dotenv() API_FILE_PATH = r"API.yml" COURSES_FILE_PATH = r"courses.json" # Global list to store conversation history conversation_history: List[Dict[str, str]] = [] def load_api_keys(api_file_path): """Loads API keys from a YAML file.""" with open(api_file_path, 'r') as f: api_keys = yaml.safe_load(f) return api_keys def generate_query_embedding(query, together_api_key): """Generates embedding for the user query.""" client = Together(api_key=together_api_key) response = client.embeddings.create( model="WhereIsAI/UAE-Large-V1", input=query ) return response.data[0].embedding def initialize_pinecone(pinecone_api_key): """Initializes Pinecone with API key.""" return Pinecone(api_key=pinecone_api_key) def pinecone_similarity_search(pinecone_instance, index_name, query_embedding, top_k=10): """Performs a similarity search in Pinecone and increase top k for reranking.""" try: index = pinecone_instance.Index(index_name) results = index.query(vector=query_embedding, top_k=top_k, include_metadata=True) if not results.matches: return None return results except Exception as e: print(f"Error during similarity search: {e}") return None def create_prompt_template(): """Creates a prompt template for LLM.""" template = """You are a helpful AI assistant that provides information on courses. Based on the following context, conversation history, and new user query, suggest relevant courses and explain why they might be useful, or respond accordingly if the user query is unrelated. If no relevant courses are found, please indicate that. Conversation History: {conversation_history} Context: {context} User Query: {query} Response: Let me help you find relevant courses based on your query. """ return PromptTemplate(template=template, input_variables=["context", "query", "conversation_history"]) def initialize_llm(together_api_key): """Initializes Together LLM.""" return TogetherLLM( model="mistralai/Mixtral-8x7B-Instruct-v0.1", together_api_key=together_api_key, temperature=0, max_tokens=250 ) def create_chain(llm, prompt): """Creates a chain using the new RunnableSequence approach.""" chain = ( {"context": RunnablePassthrough(), "query": RunnablePassthrough(), "conversation_history": RunnablePassthrough()} | prompt | llm | StrOutputParser() ) return chain def initialize_cohere_client(cohere_api_key): """Initializes the Cohere client.""" return cohere.ClientV2(api_key=cohere_api_key) def rerank_results(cohere_client, query, documents, top_n=3): """Reranks documents using Cohere.""" try: results = cohere_client.rerank( query=query, documents=documents, top_n=top_n, model="rerank-english-v3.0", ) return results except Exception as e: print(f"Error reranking results: {e}") return None def generate_llm_response(chain, query, retrieved_data, history, cohere_client): """Generates an LLM response based on context and conversation history.""" try: if not retrieved_data or not retrieved_data.matches: return "I couldn't find any relevant courses matching your query. Please try a different search term." # Prepare documents for reranking documents = [] for match in retrieved_data.matches: metadata = match.metadata if metadata: documents.append( { "text" :f"Title: {metadata.get('title', 'No title')}\nDescription: {metadata.get('text', 'No description')}\nLink: {metadata.get('course_link', 'No link')}" } ) if not documents: return "I found some matches but couldn't extract course information. Please try again." # Rerank the documents reranked_results = rerank_results(cohere_client, query, documents) if not reranked_results: return "I couldn't rerank the results, please try again." # Prepare context from reranked results context_parts = [] for result in reranked_results.results: context_parts.append(documents[result.index]["text"]) context = "\n\n".join(context_parts) # Format conversation history formatted_history = "\n".join(f"User: {item['user']}\nAssistant: {item['assistant']}" for item in history) if history else "No previous conversation." response = chain.invoke({"context": context, "query": query, "conversation_history":formatted_history}) return response except Exception as e: print(f"Error generating response: {e}") return "I encountered an error while generating the response. Please try again." def check_context_similarity(query_embedding, previous_query_embedding, threshold=0.7): """Checks if the new query is related to the previous one.""" if not previous_query_embedding: return False # First query, no previous embedding to compare from numpy import dot from numpy.linalg import norm cos_sim = dot(query_embedding, previous_query_embedding) / (norm(query_embedding) * norm(previous_query_embedding)) return cos_sim > threshold def main(): global conversation_history previous_query_embedding = None try: api_keys = load_api_keys(API_FILE_PATH) together_api_key = api_keys["together_ai_api_key"] pinecone_api_key = api_keys["pinecone_api_key"] index_name = api_keys["pinecone_index_name"] cohere_api_key = api_keys["cohere_api_key"] print("Initializing services...") # Initialize Pinecone pinecone_instance = initialize_pinecone(pinecone_api_key) # Initialize Together LLM llm = initialize_llm(together_api_key) # Initialize Cohere client cohere_client = initialize_cohere_client(cohere_api_key) prompt = create_prompt_template() # Create chain chain = create_chain(llm, prompt) print("Ready to process queries!") while True: user_query = input("\nEnter your query (or 'quit' to exit): ").strip() if user_query.lower() == 'quit': break if not user_query: print("Please enter a valid query.") continue try: print("Generating query embedding...") query_embedding = generate_query_embedding(user_query, together_api_key) # Check context similarity if previous_query_embedding and check_context_similarity(query_embedding, previous_query_embedding): print("Continuing the previous conversation...") else: print("Starting a new conversation...") conversation_history = [] # Clear history for a new conversation print("Searching for relevant courses...") pinecone_results = pinecone_similarity_search( pinecone_instance, index_name, query_embedding ) print("Generating response...") llm_response = generate_llm_response(chain, user_query, pinecone_results, conversation_history, cohere_client) print("\nResponse:") print(llm_response) print("\n" + "="*50) # Update conversation history conversation_history.append({"user": user_query, "assistant": llm_response}) previous_query_embedding = query_embedding # Save for next turn except Exception as e: print(f"Error processing query: {e}") print("Please try again with a different query.") except Exception as e: print(f"An error occurred during initialization: {str(e)}") if __name__ == "__main__": main()