ic

2025-10-13 18:16:25 +00:00
parent 739f087cef
commit 325f1ef439
415 changed files with 46870 additions and 0 deletions
--- a/apps/ChatEgresos/api/server.py
+++ b/apps/ChatEgresos/api/server.py
@@ -0,0 +1,112 @@
+import uuid
+import time
+from contextlib import asynccontextmanager
+
+from fastapi import FastAPI
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+from langfuse import Langfuse
+
+from api import services
+from api.agent import Agent
+from api.config import config
+
+# Configurar Langfuse
+langfuse = Langfuse(
+    public_key="pk-lf-49cb04b3-0c7d-475b-8105-ad8b8749ecdd",
+    secret_key="sk-lf-e02fa322-c709-4d80-bef2-9cb279846a0c", 
+    host="https://ailogger.azurewebsites.net"
+)
+
+@asynccontextmanager
+async def lifespan(_: FastAPI):
+    await config.init_mongo_db()
+    yield
+
+
+app = FastAPI(lifespan=lifespan)
+agent = Agent()
+
+
+@app.post("/api/v1/conversation")
+async def create_conversation():
+    conversation_id = uuid.uuid4()
+    await services.create_conversation(conversation_id, agent.system_prompt)
+    return {"conversation_id": conversation_id}
+
+
+class Message(BaseModel):
+    conversation_id: uuid.UUID
+    prompt: str
+
+@app.post("/api/v1/message")
+async def send(message: Message):
+    # Crear trace principal
+    trace = langfuse.trace(
+        name="chat_message",
+        session_id=str(message.conversation_id),
+        input={
+            "prompt": message.prompt,
+            "conversation_id": str(message.conversation_id)
+        }
+    )
+    
+    def b64_sse(func):
+        async def wrapper(*args, **kwargs):
+            response_parts = []
+            start_time = time.time()
+            
+            async for chunk in func(*args, **kwargs):
+                if chunk.type == "text" and chunk.content:
+                    response_parts.append(str(chunk.content))
+                
+                content = chunk.model_dump_json()
+                data = f"data: {content}\n\n"
+                yield data
+            
+            end_time = time.time()
+            latency_ms = round((end_time - start_time) * 1000)  
+            full_response = "".join(response_parts)
+            
+            
+            input_tokens = len(message.prompt.split()) * 1.3 
+            output_tokens = len(full_response.split()) * 1.3
+            total_tokens = int(input_tokens + output_tokens)
+            
+            
+            cost_per_1k_input = 0.03  
+            cost_per_1k_output = 0.06  
+            total_cost = (input_tokens/1000 * cost_per_1k_input) + (output_tokens/1000 * cost_per_1k_output)
+            
+            
+            trace.update(
+                output={"response": full_response},
+                usage={
+                    "input": int(input_tokens),
+                    "output": int(output_tokens), 
+                    "total": total_tokens,
+                    "unit": "TOKENS"
+                }
+            )
+            
+            
+            langfuse.score(
+                trace_id=trace.id,
+                name="latency",
+                value=latency_ms,
+                comment=f"Response time: {latency_ms}ms"
+            )
+            
+            
+            langfuse.score(
+                trace_id=trace.id,
+                name="cost",
+                value=round(total_cost, 4),
+                comment=f"Estimated cost: ${round(total_cost, 4)}"
+            )
+
+        return wrapper
+
+    sse_stream = b64_sse(services.stream)
+    generator = sse_stream(agent, message.prompt, message.conversation_id)
+    return StreamingResponse(generator, media_type="text/event-stream")