Create main.py

main.py

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+import httpx
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel, Field
+from typing import List
+
+app = FastAPI(
+    title="Perplexity-like API",
+    description="An API that uses web search to answer questions with citations.",
+    version="1.0.0"
+)
+
+# --- API Configuration ---
+TYPEGPT_API_URL = "https://api.typegpt.net/v1/chat/completions"
+TYPEGPT_API_KEY = "sk-oPdaZC7n1JlDq0sJ5NSSyHe7sYaeAXeEuj0wX4Lk8hlOGPF8"
+SEARCH_API_URL = "https://searchapi.snapzion.com/search"
+
+
+# --- System Prompt ---
+# This prompt guides the AI to behave like a factual research assistant.
+SYSTEM_PROMPT = """
+You are an expert AI research assistant. Your primary goal is to provide accurate, comprehensive, and helpful answers based ONLY on the provided search results.
+
+Instructions:
+1.  Carefully analyze the user's query and the provided search results.
+2.  Synthesize an answer directly from the information found in the search results.
+3.  For every statement or piece of information you provide, you MUST cite the corresponding search result number in the format `[<number>]`.
+4.  If multiple sources support a statement, you can cite them like `[1, 2]`.
+5.  If the search results do not contain enough information to answer the query, you must explicitly state that you could not find the information in the provided context.
+6.  Do not use any prior knowledge or information outside of the provided search results.
+7.  Structure your response in a clear and easy-to-read format. Start with a direct answer, followed by a more detailed explanation.
+"""
+
+# --- Pydantic Models for API Request/Response ---
+
+class ChatMessage(BaseModel):
+    role: str
+    content: str
+
+class ChatCompletionRequest(BaseModel):
+    messages: List[ChatMessage] = Field(..., example=[{"role": "user", "content": "What are the benefits of learning Python?"}])
+    model: str = "gpt-4.1-mini" # Model is fixed but included for compatibility
+
+class Choice(BaseModel):
+    message: ChatMessage
+
+class ChatCompletionResponse(BaseModel):
+    choices: List[Choice]
+    search_results: List[dict]
+
+
+# --- API Endpoint ---
+
+@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
+async def chat_completions(request: ChatCompletionRequest):
+    """
+    Takes a user's chat history, performs a web search based on the latest query,
+    and uses the TypeGPT model to generate a factual, cited response.
+    """
+    if not request.messages or request.messages[-1].role != "user":
+        raise HTTPException(status_code=400, detail="Invalid request. The last message must be from the 'user'.")
+
+    user_query = request.messages[-1].content
+
+    async with httpx.AsyncClient(timeout=30.0) as client:
+        # 1. Perform a web search
+        try:
+            search_params = {"keywords": user_query}
+            search_response = await client.get(SEARCH_API_URL, params=search_params)
+            search_response.raise_for_status()
+            search_results = search_response.json()
+        except httpx.RequestError as e:
+            raise HTTPException(status_code=502, detail=f"Error calling the search API: {e}")
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=f"Failed to process search results: {e}")
+
+        # 2. Format search results into a context for the language model
+        context = ""
+        for i, result in enumerate(search_results[:7]):  # Use top 7 results for richer context
+            context += f"Source [{i+1}]:\nTitle: {result.get('title', 'N/A')}\nSnippet: {result.get('snippet', '')}\nURL: {result.get('url', 'N/A')}\n\n"
+
+        # 3. Construct the prompt for the language model
+        final_prompt = f"""
+        **Search Results:**
+        {context}
+
+        **User Query:** "{user_query}"
+
+        Please provide a comprehensive answer based on the search results above, following all instructions.
+        """
+
+        # 4. Get the response from the TypeGPT language model
+        try:
+            headers = {
+                "Authorization": f"Bearer {TYPEGPT_API_KEY}",
+                "Content-Type": "application/json"
+            }
+            # The payload now includes the system prompt and the user prompt with context
+            payload = {
+                "model": "gpt-4.1-mini",
+                "messages": [
+                    {"role": "system", "content": SYSTEM_PROMPT},
+                    {"role": "user", "content": final_prompt}
+                ]
+            }
+            llm_response = await client.post(TYPEGPT_API_URL, headers=headers, json=payload)
+            llm_response.raise_for_status()
+            llm_data = llm_response.json()
+            answer_content = llm_data['choices'][0]['message']['content']
+        except httpx.RequestError as e:
+            raise HTTPException(status_code=502, detail=f"Error calling language model API: {e}")
+        except (KeyError, IndexError) as e:
+            raise HTTPException(status_code=500, detail=f"Invalid response structure from language model API: {e}")
+
+    # 5. Format the final response
+    response_message = ChatMessage(role="assistant", content=answer_content)
+    response_choice = Choice(message=response_message)
+    
+    return ChatCompletionResponse(choices=[response_choice], search_results=search_results)
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)