ic

apps/bursatil/api/agent/__init__.py

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+from .main import MayaBursatil
+
+__all__ = ["MayaBursatil"]

apps/bursatil/api/agent/main.py

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+from pathlib import Path
+from typing import Any
+
+from langchain_core.messages import AIMessageChunk
+from pydantic import BaseModel, Field
+from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
+from langchain_azure_ai.embeddings import AzureAIEmbeddingsModel
+from banortegpt.storage.azure_storage import AzureStorage
+from banortegpt.vector.qdrant import AsyncQdrant
+
+from api import context
+from api.config import config
+
+parent = Path(__file__).parent
+SYSTEM_PROMPT = (parent / "system_prompt.md").read_text()
+
+AZURE_AI_URI = "https://eastus2.api.cognitive.microsoft.com"
+
+class get_information(BaseModel):
+    """Search a private repository for information."""
+
+    question: str = Field(..., description="The user question")
+
+class MayaBursatil:
+    system_prompt = SYSTEM_PROMPT
+    generation_config = {
+        "temperature": config.model_temperature,
+    }
+    embedding_model = config.embedding_model
+    message_limit = config.message_limit
+    index = config.vector_index
+    limit = config.search_limit
+    bucket = config.storage_bucket
+
+    search = AsyncQdrant.from_config(config)
+    llm = AzureAIChatCompletionsModel(
+        endpoint=f"{AZURE_AI_URI}/openai/deployments/{config.model}",
+        credential=config.openai_api_key,
+    ).bind_tools([get_information])
+    embedder = AzureAIEmbeddingsModel(
+        endpoint=f"{AZURE_AI_URI}/openai/deployments/{config.embedding_model}",
+        credential=config.openai_api_key,
+    )
+    storage = AzureStorage.from_config(config)
+
+    def __init__(self) -> None:
+        self.tool_map = {
+            "get_information": self.get_information
+        }
+
+    def build_response(self, payloads, fallback):
+        template = "<FAQ {index}>\n\n{content}\n\n</FAQ {index}>"
+
+        filled_templates = [
+            template.format(index=idx, content=payload["content"])
+            for idx, payload in enumerate(payloads)
+        ]
+
+        filled_templates.append(f"<FALLBACK>\n{fallback}\n</FALLBACK>")
+
+        return "\n".join(filled_templates)
+
+    async def get_information(self, question: str):
+        embedding = await self.embedder.aembed_query(question)
+
+        payloads = await self.search.semantic_search(embedding=embedding, collection=self.index, limit=self.limit)
+
+        fallback_messages: dict[str, int] = {}
+
+        for payload in payloads:
+            fallback_message = payload.get("fallback_message", "None")
+            if fallback_message not in fallback_messages:
+                fallback_messages[fallback_message] = 1
+            else:
+                fallback_messages[fallback_message] += 1
+
+        fallback = max(fallback_messages, key=fallback_messages.get)  # type: ignore
+
+        tool_response = self.build_response(payloads, fallback)
+
+        return tool_response, payloads
+
+    async def get_shareable_urls(self, payloads: list):
+        reference_urls = []
+        image_urls = []
+
+        for payload in payloads:
+            if imagen := payload.get("imagen"):
+                image_url = await self.storage.get_file_url(
+                    filename=imagen,
+                    bucket=self.bucket,
+                    minute_duration=20,
+                    image=True,
+                )
+
+                if image_url:
+                    image_urls.append(image_url)
+                else:
+                    print("Image not found")
+
+        return reference_urls, image_urls
+
+    def _generation_config_overwrite(self, overwrites: dict | None) -> dict[str, Any]:
+        generation_config_copy = self.generation_config.copy()
+        if overwrites:
+            for k, v in overwrites.items():
+                generation_config_copy[k] = v
+        return generation_config_copy
+
+    async def stream(self, history, overwrites: dict | None = None):
+        generation_config = self._generation_config_overwrite(overwrites)
+
+        async for delta in self.llm.astream(input=history, **generation_config):
+            assert isinstance(delta, AIMessageChunk)
+            if call := delta.tool_call_chunks:
+                if tool_id := call[0].get("id"):
+                    context.tool_id.set(tool_id)
+                if name := call[0].get("name"):
+                    context.tool_name.set(name)
+                if args := call[0].get("args"):
+                    context.tool_buffer.set(context.tool_buffer.get() + args)
+            else:
+                if buffer := delta.content:
+                    assert isinstance(buffer, str)
+                    context.buffer.set(context.buffer.get() + buffer)
+                    yield buffer
+
+    async def generate(self, history, overwrites: dict | None = None):
+        generation_config = self._generation_config_overwrite(overwrites)
+        return await self.llm.ainvoke(input=history, **generation_config)

apps/bursatil/api/agent/system_prompt.md

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+Eres MayaBursatil, una muy amigable y símpatica asistente virtual del departamento de contraloria bursatil de Banorte.
+Tu objetivo es responder preguntas de usuarios de manera informativa y empatica.
+Para cada pregunta, utiliza la herramienta 'get_information' para obtener informacion de nuestro FAQ.
+Utiliza la informacion para responder la pregunta del usuario.
+Utiliza emojis.
+Si no puedes responder la pregunta basado en la informacion del FAQ, responde con el contenido en el FALLBACK.