ic

apps/bursatil/api/services/generate_response.py

@@ -0,0 +1,89 @@
+import json
+from typing import Any
+from uuid import UUID
+
+from langfuse.decorators import langfuse_context, observe
+from pydantic import BaseModel
+
+from api import context as ctx
+from api.agent import MayaBursatil
+from banortegpt.database.mongo_memory import crud
+
+
+class Response(BaseModel):
+    content: str
+    urls: list[str]
+
+
+@observe(capture_input=False, capture_output=False)
+async def generate(
+    agent: MayaBursatil,
+    prompt: str,
+    conversation_id: UUID,
+) -> Response:
+    conversation = await crud.get_conversation(conversation_id)
+
+    if conversation is None:
+        raise ValueError(f"Conversation with id {conversation_id} not found")
+
+    conversation.add(role="user", content=prompt)
+
+    response = await agent.generate(conversation.to_openai_format(agent.message_limit))
+
+    reference_urls, image_urls = [], []
+
+    if call := response.tool_calls:
+        if id := call[0].id:
+            ctx.tool_id.set(id)
+        if name := call[0].function.name:
+            ctx.tool_name.set(name)
+        ctx.tool_buffer.set(call[0].function.arguments)
+    else:
+        ctx.buffer.set(response.content)
+
+    buffer = ctx.buffer.get()
+    tool_buffer = ctx.tool_buffer.get()
+    tool_id = ctx.tool_id.get()
+    tool_name = ctx.tool_name.get()
+
+    if tool_id is not None:
+        # Si tool_buffer es un string JSON, lo convertimos a diccionario
+        if isinstance(tool_buffer, str):
+            try:
+                tool_args = json.loads(tool_buffer)
+            except json.JSONDecodeError:
+                tool_args = {"question": tool_buffer}
+        else:
+            tool_args = tool_buffer
+
+        response, payloads = await agent.tool_map[tool_name](**tool_args)  # type: ignore
+
+        tool_call: dict[str, Any] = agent.llm.build_tool_call(
+            tool_id, tool_name, tool_buffer
+        )
+        tool_call_id: dict[str, Any] = agent.llm.build_tool_call_id(tool_id)
+
+        conversation.add("assistant", **tool_call)
+        conversation.add("tool", content=response, **tool_call_id)
+
+        response = await agent.generate(
+            conversation.to_openai_format(agent.message_limit), {"tools": None}
+        )
+        ctx.buffer.set(response.content)
+
+        reference_urls, image_urls = await agent.get_shareable_urls(payloads)  # type: ignore
+
+    buffer = ctx.buffer.get()
+    if buffer is None:
+        raise ValueError("No buffer found")
+
+    conversation.add(role="assistant", content=buffer)
+
+    langfuse_context.update_current_trace(
+        name=str(conversation_id),
+        session_id=str(conversation_id),
+        input=prompt,
+        output=buffer,
+    )
+
+    return Response(content=buffer, urls=reference_urls + image_urls)