ggggggggggg

my_tools.py

@@ -1,88 +1,48 @@
-import os, math, wikipedia, asyncio
-import google.generativeai as genai
-from typing import List, Generator
-from llama_index.core.llms import LLMMetadata
-
-from llama_index.core.llms import (
-    LLM,
-    ChatMessage,
-    ChatResponse,
-    CompletionResponse,
-    CompletionResponseGen,
-)
-from llama_index.core.agent import ReActAgent
-from llama_index.core.tools import FunctionTool
+import os
+import math
+import pandas as pd
 from duckduckgo_search import DDGS
+import wikipedia
+from llama_index.core.tools import FunctionTool
+from llama_index.core.agent import ReActAgent
+from llama_index.core.llms.base import ChatMessage, LLMMetadata, LLM
+from llama_index.core.callbacks import CallbackManager, LlamaDebugHandler
+import google.generativeai as genai
 
-# ── Configurar clave de Google Gemini ──
-genai.configure(api_key=os.getenv("GEMINI_API_KEY", ""))
-
+# --- Gemini LLM personalizado ---
 class GeminiLLM(LLM):
-    """Implementación mínima de LLM que satisface todos los métodos abstractos."""
+    def __init__(self, model="models/gemini-1.5-flash-latest"):
+        genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
+        self.model = genai.GenerativeModel(model)
+
+    def chat(self, messages: list[ChatMessage], **kwargs):
+        prompt = "\n".join([
+            ("User: " if m.role == "user" else "Assistant: ") + m.content
+            for m in messages
+        ]) + "\nAssistant:"
+        resp = self.model.generate_content(prompt)
+        return ChatMessage(role="assistant", content=resp.text)
 
-    def __init__(self, model: str = "models/gemini-1.5-flash-latest"):
-        self._model = genai.GenerativeModel(model)
-        self._model_name = model
-
-    # ---------- metadata ----------
     @property
     def metadata(self) -> LLMMetadata:
         return LLMMetadata(
-            context_window=32768,
-            num_output=2048,
+            context_window=8192,
+            num_output=1024,
             is_chat_model=True,
-            model_name=self._model_name
+            is_function_calling_model=False,
+            model_name="gemini-1.5-flash-latest"
         )
 
-    # ---------- chat ----------
-    def chat(self, messages: List[ChatMessage], **kwargs) -> ChatResponse:
-        prompt = "\n".join(
-            [("User: " if m.role == "user" else "Assistant: ") + m.content for m in messages]
-        ) + "\nAssistant:"
-        resp = self._model.generate_content(prompt)
-        return ChatResponse(
-            message=ChatMessage(role="assistant", content=resp.text),
-            raw=resp
-        )
-
-    def stream_chat(self, messages: List[ChatMessage], **kwargs) -> Generator[ChatResponse, None, None]:
-        yield self.chat(messages, **kwargs)
-
-    async def achat(self, messages: List[ChatMessage], **kwargs) -> ChatResponse:
-        return self.chat(messages, **kwargs)
-
-    async def astream_chat(self, messages: List[ChatMessage], **kwargs):
-        yield self.chat(messages, **kwargs)
-
-    # ---------- complete ----------
-    def complete(self, prompt: str, **kwargs) -> CompletionResponse:
-        resp = self._model.generate_content(prompt)
-        return CompletionResponse(text=resp.text, raw=resp)
-
-    def stream_complete(self, prompt: str, **kwargs) -> CompletionResponseGen:
-        yield self.complete(prompt, **kwargs)
-
-    async def acomplete(self, prompt: str, **kwargs) -> CompletionResponse:
-        return self.complete(prompt, **kwargs)
-
-    async def astream_complete(self, prompt: str, **kwargs):
-        yield self.complete(prompt, **kwargs)
+    @property
+    def callback_manager(self):
+        return CallbackManager([LlamaDebugHandler()])
 
-# ── Instanciar el LLM ──
 llm = GeminiLLM()
 
-# ── Herramientas auxiliares ──
+# --- Herramientas ---
 def buscar_web(query: str) -> str:
     with DDGS() as ddgs:
-        results = list(
-            ddgs.text(
-                query,
-                region="es-es",
-                safesearch="moderate",
-                timelimit="y",
-                max_results=3,
-            )
-        )
+        results = list(ddgs.text(query, region='es-es', safesearch='moderate', timelimit='y', max_results=3))
         if results:
             return "\n".join([f"{r['title']}: {r['body']}" for r in results])
     return "No se encontraron resultados."
@@ -90,7 +50,7 @@ def buscar_web(query: str) -> str:
 search_tool = FunctionTool.from_defaults(
     fn=buscar_web,
     name="web_search",
-    description="Busca en la web utilizando DuckDuckGo.",
+    description="Busca en la web utilizando DuckDuckGo."
 )
 
 def get_wikipedia_summary(query: str) -> str:
@@ -102,7 +62,7 @@ def get_wikipedia_summary(query: str) -> str:
 wikipedia_tool = FunctionTool.from_defaults(
     fn=get_wikipedia_summary,
     name="wikipedia_lookup",
-    description="Obtiene un resumen breve de un tema en Wikipedia.",
+    description="Busca un resumen breve de un tema en Wikipedia."
 )
 
 def calcular_expresion(expr: str) -> str:
@@ -115,17 +75,18 @@ def calcular_expresion(expr: str) -> str:
 calculator_tool = FunctionTool.from_defaults(
     fn=calcular_expresion,
     name="calculadora",
-    description="Resuelve expresiones matemáticas (trigonometría, log, potencias, etc.).",
+    description="Resuelve expresiones matemáticas, incluyendo funciones trigonométricas, logaritmos, potencias, etc."
 )
 
-# ── Agente ReAct con herramientas ──
+# --- Agente ---
 alfred_agent = ReActAgent.from_tools(
     tools=[search_tool, wikipedia_tool, calculator_tool],
     llm=llm,
-    verbose=False,
+    verbose=False
 )
 
-def basic_agent_response(question: str) -> str:
-    """Usa el agente para responder una pregunta."""
-    return str(alfred_agent.query(question))
+def basic_agent_response(question):
+    response = alfred_agent.query(question)
+    return str(response)
+