Update app.py

app.py

@@ -214,42 +214,62 @@ async def web_search_and_extract(
 
 @app.get("/api/adv_web_search")
 async def adv_web_search(
-    model: str,
-    message: str,
+    q: str,
+    model: str = "gpt-3.5",
+    max_results: int = 3,  
+    timelimit: Optional[str] = None,
+    safesearch: str = "moderate",
+    region: str = "wt-wt",
+    backend: str = "api",
+    max_chars: int = 6000,  
+    system_prompt: str = Query(None, description="Optional custom system prompt")
 ):
-    """Interact with a specified large language model with an optional system prompt."""
+    """
+    Combines web search, web extraction, and LLM chat for advanced search.
+    """
     try:
         with WEBS() as webs:
-            # Perform WEBS search
-            search_results = webs.text(keywords=message, backend="html", max_results=3)
-            # Extract text from each result's link
-            extracted_results = []
+            # 1. Perform the web search
+            search_results = webs.text(keywords=q, region=region, 
+                                     safesearch=safesearch,
+                                     timelimit=timelimit, backend=backend, 
+                                     max_results=max_results)
+
+            # 2. Extract text from top search result URLs 
+            extracted_text = ""
             for result in search_results:
                 if 'href' in result:
                     link = result['href']
                     try:
-                        response = requests.get(link, headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0"})
+                        response = requests.get(link, headers={"User-Agent": "Mozilla/5.0"})
                         response.raise_for_status()
                         visible_text = extract_text_from_webpage(response.text)
-                        if len(visible_text) > 15000:
-                            visible_text = visible_text[:15000] + "..."
-                        extracted_results.append({"link": link, "text": visible_text})
+                        if len(visible_text) > max_chars:
+                            visible_text = visible_text[:max_chars] + "..."
+                        extracted_text += f"## Content from: {link}\n\n{visible_text}\n\n"
                     except requests.exceptions.RequestException as e:
                         print(f"Error fetching or processing {link}: {e}")
-                        extracted_results.append({"link": link, "text": None})
                 else:
-                    extracted_results.append({"link": None, "text": None})
-        message += f"Answer tis question in detail {message}. Now, You are provided with Google Search Results, To increase your accuracy and providing real time data. {extracted_results} "
-        
-        messages = [{"role": "user", "content": message}]
-        messages.insert(0, {"role": "system", "content": "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."}) 
-        llm = LLM(model=model) 
-        response = llm.chat(messages=messages)
-        return JSONResponse(content={response})
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error during LLM chat: {e}")
+                   pass
+
+        # 3. Construct the prompt for the LLM
+        llm_prompt = f"Query by user: {q} , Answerthe query asked by user using these search results, Which contains real time data, Try to give best response:  {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})
 
+        llm = LLM(model=model)
+        llm_response = llm.chat(messages=messages)
 
+        # 5. Return the results
+        return JSONResponse(content=jsonable_encoder({ "llm_response": llm_response }))
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error during advanced search: {e}")
+
+        
 @app.get("/api/website_summarizer")
 async def website_summarizer(url: str):
     """Summarizes the content of a given URL using a chat model."""