Update my_tools.py

my_tools.py

@@ -6,17 +6,15 @@ import time
 import asyncio
 import subprocess
 import pandas as pd
-from io import StringIO
 from duckduckgo_search import DDGS
 import wikipedia
+from pydantic import Field
+import google.generativeai as genai
+
 from llama_index.core.llms import ChatMessage, LLMMetadata, LLM, CompletionResponse
 from llama_index.core.tools import FunctionTool
 from llama_index.core.agent import ReActAgent
-from llama_index.indices.object_index import ObjectIndex
-from llama_index.indices.vector_store import VectorStoreIndex
 from llama_index.core.callbacks.llama_debug import LlamaDebugHandler
-from pydantic import Field
-import google.generativeai as genai
 
 # -------------------------------------------------------------------
 # 1) GeminiLLM personalizado
@@ -128,8 +126,8 @@ def buscar_web(query: str, max_attempts: int = 2) -> str:
                     return "\n".join(f"Título: {r['title']}\nCuerpo: {r['body']}" for r in results)
             return "No se encontraron resultados."
         except Exception as e:
-            if i < max_attempts-1:
-                time.sleep(5*(i+1))
+            if i < max_attempts - 1:
+                time.sleep(5 * (i + 1))
             else:
                 return f"Error buscar_web tras {max_attempts} intentos: {e}"
 
@@ -140,7 +138,7 @@ def analyze_table(table_md: str, question: str) -> str:
     try:
         lines = [l.strip() for l in table_md.strip().splitlines() if l.strip()]
         content = [l for l in lines if not set(l) <= set("|- ")]
-        rows = [ [c.strip() for c in r.split("|")[1:-1]] for r in content ]
+        rows = [[c.strip() for c in r.split("|")[1:-1]] for r in content]
         df = pd.DataFrame(rows[1:], columns=rows[0])
         if "no conmut" in question.lower():
             S = df.columns.tolist()
@@ -150,7 +148,7 @@ def analyze_table(table_md: str, question: str) -> str:
                     a = df.loc[df[rows[0][0]]==x, y].values[0]
                     b = df.loc[df[rows[0][0]]==y, x].values[0]
                     if a != b:
-                        counter.update([x,y])
+                        counter.update([x, y])
             return ", ".join(sorted(counter)) or "No hay contraejemplos"
         return df.to_csv(index=False)
     except Exception as e:
@@ -158,8 +156,10 @@ def analyze_table(table_md: str, question: str) -> str:
 
 def execute_code(code: str) -> str:
     try:
-        res = subprocess.run(["python","- << 'EOF'\n" + code + "\nEOF"], shell=True,
-                             capture_output=True, text=True, timeout=5)
+        res = subprocess.run(
+            ["python", "-c", code],
+            capture_output=True, text=True, timeout=5
+        )
         if res.stderr:
             return f"Error código: {res.stderr.strip()}"
         return res.stdout.strip() or "(sin salida)"
@@ -169,42 +169,39 @@ def execute_code(code: str) -> str:
 def no_tool_solution(query: str) -> str:
     return "Procedo con conocimiento interno."
 
-# -------------------------------------------------------------------
-# 3) FunctionTools
-# -------------------------------------------------------------------
-search_tool   = FunctionTool.from_defaults(fn=buscar_web, name="web_search", description="Búsqueda DDG (3 resultados).")
-reverse_tool  = FunctionTool.from_defaults(fn=reverse_text, name="reverse_text", description="Invierte texto.")
-table_tool    = FunctionTool.from_defaults(fn=analyze_table, name="analyze_table", description="Parses Markdown tables y responde conmutatividad.")
-code_tool     = FunctionTool.from_defaults(fn=execute_code, name="execute_code", description="Ejecuta Python para cálculos.")
-fallback_tool = FunctionTool.from_defaults(fn=no_tool_solution, name="no_tool_solution", description="Fallback: conocimiento interno.")
 
 # -------------------------------------------------------------------
-# 4) ObjectIndex + Retriever
+# 3) Encapsular como FunctionTool
 # -------------------------------------------------------------------
-all_tools      = [search_tool, reverse_tool, table_tool, code_tool, fallback_tool]
-obj_index      = ObjectIndex.from_objects(all_tools, index_cls=VectorStoreIndex)
-tool_retriever = obj_index.as_retriever(similarity_top_k=3)
+search_tool   = FunctionTool.from_defaults(fn=buscar_web,     name="web_search",       description="Búsqueda DDG (3 resultados).")
+reverse_tool  = FunctionTool.from_defaults(fn=reverse_text,   name="reverse_text",     description="Invierte texto.")
+table_tool    = FunctionTool.from_defaults(fn=analyze_table, name="analyze_table",    description="Parses Markdown tables y responde conmutatividad.")
+code_tool     = FunctionTool.from_defaults(fn=execute_code,  name="execute_code",     description="Ejecuta Python para cálculos.")
+fallback_tool = FunctionTool.from_defaults(fn=no_tool_solution,name="no_tool_solution", description="Fallback: conocimiento interno.")
+
+all_tools = [search_tool, reverse_tool, table_tool, code_tool, fallback_tool]
 
 # -------------------------------------------------------------------
-# 5) Prompt de sistema
+# 4) Prompt de sistema
 # -------------------------------------------------------------------
 system_prompt = (
     "Eres Alfred, un agente ReAct paso a paso.\n"
     "1) Analiza la pregunta.\n"
-    "2) Decide la herramienta adecuada.\n"
-    "3) Usa web_search, reverse_text, analyze_table, execute_code o no_tool_solution.\n"
-    "4) Si no puedes procesar audio/video, indícalo.\n"
-    "5) Responde claramente.\n"
+    "2) Usa la herramienta adecuada.\n"
+    "3) Si es posible, responde con web_search, reverse_text, analyze_table o execute_code.\n"
+    "4) Si nada aplica, usa no_tool_solution.\n"
+    "5) Si la pregunta requiere audio/video, informa que no puedes procesarlos.\n"
+    "6) Responde claro al usuario.\n"
     "Herramientas: {tool_descriptions}"
 )
 
 # -------------------------------------------------------------------
-# 6) Inicializar agente
+# 5) Inicializar agente con GeminiLLM
 # -------------------------------------------------------------------
 llm = GeminiLLM(model_name="models/gemini-1.5-flash-latest", temperature=0.0)
 
 alfred_agent = ReActAgent.from_tools(
-    tool_retriever=tool_retriever,
+    tools=all_tools,
     llm=llm,
     system_prompt=system_prompt,
     verbose=True,
@@ -212,7 +209,7 @@ alfred_agent = ReActAgent.from_tools(
 )
 
 # -------------------------------------------------------------------
-# 7) Función pública
+# 6) Función pública
 # -------------------------------------------------------------------
 def basic_agent_response(question: str) -> str:
     try:
@@ -220,5 +217,3 @@ def basic_agent_response(question: str) -> str:
         return resp.response or "No se generó respuesta."
     except Exception as e:
         return f"Error crítico del agente: {e}"
-
-