Update app.py

app.py

@@ -339,18 +339,18 @@ def web_search_and_extract_threading(
 @app.get("/api/adv_web_search")
 async def adv_web_search(
     q: str,
-    model: str = "gpt-3.5",
-    max_results: int = 3,
+    model: str = "llama3-8b",
+    max_results: int = 5,
     timelimit: Optional[str] = None,
     safesearch: str = "moderate",
     region: str = "wt-wt",
     backend: str = "html",
     max_chars: int = 6000,
-    system_prompt: str = "You are Most Advanced and Powerful Ai chatbot, User ask you questions and you have to answer that, You are also provided with Google Search Results, To increase your accuracy and providing real time data. Your task is to answer in best way to user.",
+    system_prompt: str = "You are an advanced AI chatbot. Provide the best answer to the user based on Google search results.",
     proxy: Optional[str] = None
 ):
     """
-    Combines web search, web extraction, and LLM chat for advanced search.
+    Combines web search, web extraction, and FastAI chat for advanced search.
     """
     try:
         with WEBS(proxy=proxy) as webs:
@@ -368,19 +368,17 @@ async def adv_web_search(
                 if result['text']:
                     extracted_text += f"## Content from: {result['link']}\n\n{result['text']}\n\n"
 
-        # 3. Construct the prompt for the LLM
-        llm_prompt = f"Query by user: {q} , Answer the query asked by user in detail. Now, You are provided with Google Search Results, To increase your accuracy and providing real time data. SEarch Result: {extracted_text}"
-
-        # 4. Get the LLM's response using LLM class (similar to /api/llm)
-        messages = [{"role": "user", "content": llm_prompt}]
-        if system_prompt:
-            messages.insert(0, {"role": "system", "content": system_prompt})
+        # 3. Construct the prompt for FastAI
+        ai_prompt = f"Query by user: {q} . Answer the query asked by user in detail. SEarch Result: {extracted_text}"
 
-        llm = LLM(model=model)
-        llm_response = llm.chat(messages=messages)
+        # 4. Get the FastAI's response using FastAI service
+        try:
+            response = await asyncio.to_thread(FastAI(model=model, system_prompt=system_prompt).get_response, ai_prompt)
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=f"Error during FastAI request: {e}")
 
         # 5. Return the results
-        return JSONResponse(content=jsonable_encoder({ "llm_response": llm_response }))
+        return JSONResponse(content={"response": response})
 
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"Error during advanced search: {e}")