forked from innovacion/Mayacontigo
108 lines
4.0 KiB
Python
108 lines
4.0 KiB
Python
from pathlib import Path
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from typing import Any
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from langchain_core.messages import AIMessageChunk
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from pydantic import BaseModel, Field
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from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
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from langchain_azure_ai.embeddings import AzureAIEmbeddingsModel
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from banortegpt.vector.qdrant import AsyncQdrant
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from api import context
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from api.config import config
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parent = Path(__file__).parent
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SYSTEM_PROMPT = (parent / "system_prompt.md").read_text()
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AZURE_AI_URI = "https://eastus2.api.cognitive.microsoft.com"
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class get_information(BaseModel):
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"""Search a private repository for information."""
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question: str = Field(..., description="The user question")
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class Agent:
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system_prompt = SYSTEM_PROMPT
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generation_config = {
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"temperature": config.model_temperature,
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}
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embedding_model = config.embedding_model
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message_limit = config.message_limit
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index = config.vector_index
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limit = config.search_limit
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search = AsyncQdrant.from_config(config)
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llm = AzureAIChatCompletionsModel(
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endpoint=f"{AZURE_AI_URI}/openai/deployments/{config.model}",
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credential=config.openai_api_key,
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).bind_tools([get_information])
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embedder = AzureAIEmbeddingsModel(
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endpoint=f"{AZURE_AI_URI}/openai/deployments/{config.embedding_model}",
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credential=config.openai_api_key,
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)
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def __init__(self) -> None:
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self.tool_map = {
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"get_information": self.get_information
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}
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def build_response(self, payloads, fallback):
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template = "<FAQ {index}>\n\n{content}\n\n</FAQ {index}>"
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filled_templates = [
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template.format(index=idx, content=payload["content"])
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for idx, payload in enumerate(payloads)
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]
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filled_templates.append(f"<FALLBACK>\n{fallback}\n</FALLBACK>")
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return "\n".join(filled_templates)
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async def get_information(self, question: str):
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embedding = await self.embedder.aembed_query(question)
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payloads = await self.search.semantic_search(
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embedding=embedding,
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collection=self.index,
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limit=self.limit,
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)
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fallback_messages = {}
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images = []
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for idx, payload in enumerate(payloads):
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fallback_message = payload.get("fallback_message", "None")
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fallback_messages[fallback_message] = fallback_messages.get(fallback_message, 0) + 1
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# Solo extraer imágenes del primer payload
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if idx == 0 and "images" in payload:
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images.extend(payload["images"])
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fallback = max(fallback_messages, key=fallback_messages.get) # type: ignore
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response = self.build_response(payloads, fallback)
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return str(response), images[:3] # Limitar a 3 imágenes máximo
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def _generation_config_overwrite(self, overwrites: dict | None) -> dict[str, Any]:
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if not overwrites:
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return self.generation_config.copy()
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return {**self.generation_config, **overwrites}
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async def stream(self, history, overwrites: dict | None = None):
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generation_config = self._generation_config_overwrite(overwrites)
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async for delta in self.llm.astream(input=history, **generation_config):
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assert isinstance(delta, AIMessageChunk)
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if call := delta.tool_call_chunks:
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if tool_id := call[0].get("id"):
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context.tool_id.set(tool_id)
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if name := call[0].get("name"):
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context.tool_name.set(name)
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if args := call[0].get("args"):
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context.tool_buffer.set(context.tool_buffer.get() + args)
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elif delta.content:
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assert isinstance(delta.content, str)
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context.buffer.set(context.buffer.get() + delta.content)
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yield delta.content
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async def generate(self, history, overwrites: dict | None = None):
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generation_config = self._generation_config_overwrite(overwrites)
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return await self.llm.ainvoke(input=history, **generation_config) |