Update main.py

main.py

@@ -281,6 +281,53 @@ async def news_assistant(query: NewsQueryModel, api_key: str = Depends(verify_ap
 #meta-llama/llama-3-70b-instruct  google/gemini-pro-1.5
     return StreamingResponse(process_response(), media_type="text/event-stream")
 
+class SearchQueryModel(BaseModel):
+    query: str = Field(..., description="Search query")
+    model_id: ModelID = Field(
+        default="meta-llama/llama-3-70b-instruct",
+        description="ID of the model to use for response generation"
+    )
+    class Config:
+        schema_extra = {
+            "example": {
+                "query": "What are the latest advancements in quantum computing?",
+                "model_id": "meta-llama/llama-3-70b-instruct"
+            }
+        }
+
+def analyze_search_results(query):
+    search_data = internet_search(query, type="web")
+    
+    if not search_data:
+        return "Failed to fetch search data.", []
+
+    # Prepare the prompt for the AI
+    prompt = generate_search_prompt(query, search_data)
+
+    messages = [
+        {"role": "system", "content": SEARCH_ASSISTANT_PROMPT},
+        {"role": "user", "content": prompt}
+    ]
+
+    return messages
+
+@app.post("/search-assistant")
+async def search_assistant(query: SearchQueryModel, api_key: str = Depends(verify_api_key)):
+    """
+    Search assistant endpoint that provides summaries and analysis of web search results based on user queries.
+    Requires API Key authentication via X-API-Key header.
+    """
+    messages = analyze_search_results(query.query)
+    
+    if not messages:
+        raise HTTPException(status_code=500, detail="Failed to fetch search data")
+        
+    def process_response():
+        for content in chat_with_llama_stream(messages, model=query.model_id):
+            yield content
+
+    return StreamingResponse(process_response(), media_type="text/event-stream")
+
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=7860)