from fastapi import FastAPI, HTTPException, Request, Query from pydantic import BaseModel from typing import List, Dict, Any from helper_functions_api import md_to_html, search_brave, fetch_and_extract_content, limit_tokens, together_response, insert_data import os from dotenv import load_dotenv, find_dotenv # Load environment variables from .env file # load_dotenv("keys.env") app = FastAPI() TOGETHER_API_KEY = os.getenv('TOGETHER_API_KEY') BRAVE_API_KEY = os.getenv('BRAVE_API_KEY') GROQ_API_KEY = os.getenv("GROQ_API_KEY") HELICON_API_KEY = os.getenv("HELICON_API_KEY") SUPABASE_USER = os.environ['SUPABASE_USER'] SUPABASE_PASSWORD = os.environ['SUPABASE_PASSWORD'] llm_default_small = "llama3-8b-8192" llm_default_medium = "llama3-70b-8192" SysPromptJson = "You are now in the role of an expert AI who can extract structured information from user request. Both key and value pairs must be in double quotes. You must respond ONLY with a valid JSON file. Do not add any additional comments." SysPromptList = "You are now in the role of an expert AI who can extract structured information from user request. All elements must be in double quotes. You must respond ONLY with a valid python List. Do not add any additional comments." SysPromptDefault = "You are an expert AI, complete the given task. Do not add any additional comments." SysPromptMd = "You are an expert AI who can create a structured report using information provided in the context from user request.The report should be in markdown format consists of markdown tables structured into subtopics. Do not add any additional comments." class Query(BaseModel): query: str = Query(default="market research", description="input query to generate Report") description: str = Query(default="", description="additional context for report") user_id: str = Query(default="", description="unique user id") user_name: str = Query(default="", description="user name") @app.post("/generate_report") async def generate_report(request: Request, query: Query): query_str = query.query description = query.description user_id = query.user_id # Combine query with user keywords search_query = query_str # Search for relevant URLs urls = search_brave(search_query, num_results=4) # Fetch and extract content from the URLs all_text_with_urls = fetch_and_extract_content(urls, query_str) # Prepare the prompt for generating the report additional_context = limit_tokens(str(all_text_with_urls)) prompt = f"#### ADDITIONAL CONTEXT:{additional_context} #### CREATE A DETAILED REPORT FOR THE QUERY:{query_str} #### IN THE CONTEXT OF ### CONTEXT: {description}" md_report = together_response(prompt, model=llm_default_medium, SysPrompt=SysPromptMd) # Insert data into database (or other storage) insert_data(user_id, query_str, description, str(all_text_with_urls), md_report) references_html = dict() for text, url in all_text_with_urls: references_html[url] = str(md_to_html(text)) # Return the generated report return { "report": md_to_html(md_report), "references": references_html } app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], )