Update main.py

main.py

@@ -5,9 +5,6 @@ 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
-from fastapi_cache import FastAPICache
-from fastapi_cache.decorator import cache
-from fastapi_cache.backends.inmemory import InMemoryBackend
 
 # Load environment variables from .env file
 #load_dotenv("keys.env")
@@ -57,12 +54,7 @@ class QueryModel(BaseModel):
     output_format: str = Query(default="Tabular Report", description="Output format for the report", enum=["Chat", "Full Text Report", "Tabular Report", "Tables only"])
     data_format: str = Query(default="Structured data", description="Type of data to extract from the internet", enum=["No presets", "Structured data", "Quantitative data"])
 
-@app.on_event("startup")
-async def startup():
-    FastAPICache.init(InMemoryBackend(), prefix="fastapi-cache")
-
 @app.post("/generate_report")
-#@cache(expire=60 * 60 * 24 * 7)  # Cache for 7 days
 async def generate_report(request: Request, query: QueryModel):
     query_str = query.topic
     description = query.description
@@ -75,16 +67,12 @@ async def generate_report(request: Request, query: QueryModel):
     
     # Combine query with user keywords
     if query.internet:
-        search_query = description #+" "+query_str
+        search_query = description
         # Search for relevant URLs
         try:
-            urls,optimized_search_query = search_brave(search_query, num_results=8)
+            urls,optimized_search_query = search_brave(search_query, num_results=4)
             # Fetch and extract content from the URLs
-            all_text_with_urls = fetch_and_extract_content(            
-                                        data_format=data_format,
-                                        urls=urls,
-                                        query=query,
-                                        num_refrences=8)
+            all_text_with_urls = fetch_and_extract_content(data_format, urls, query_str)
             # Prepare the prompt for generating the report
             additional_context = limit_tokens(str(all_text_with_urls))
             prompt = f"#### COMPLETE THE TASK: {description} #### IN THE CONTEXT OF ### CONTEXT: {query_str} USING THE #### SCRAPED DATA:{additional_context}"
@@ -98,7 +86,6 @@ async def generate_report(request: Request, query: QueryModel):
 
     md_report = together_response(prompt, model=llm_default_medium, SysPrompt=sys_prompt_output_format)
     
-    # Insert data into database (or other storage)
     if user_id != "test":    
         insert_data(user_id, query_str, description,  str(all_text_with_urls), md_report)
     references_html = dict()