add vector store but not working

app.py

@@ -17,8 +17,7 @@ SYSTEM_PROMPT_TEMPLATE = """You are an AI assistant designed to assist users wit
 Maintain clarity, conciseness, and relevance in your responses, ensuring a seamless user experience.
 Always respond in the same language as the user’s query to preserve their preferred language.
 
-The assistant must always start the conversation with:
-"Welcome to Nou Vall de Núria! How can I assist you today?"""
+If the user asks for any information about it's history, a region, its cities, activities, tourism, or surroundings, please use the function `get_documents`."""
 
 
 # print(json.dumps(oitools, indent=2))
@@ -115,7 +114,7 @@ def llm_in_loop(history, system_prompt, recursive):
         #             metadata= {"title": f"🛠️ Using tool '{name}', arguments: {json.dumps(json_arguments, ensure_ascii=False)}"},
         #             options=[{"label":"tool_calls", "value": json.dumps([{"id": "call_FthC9qRpsL5kBpwwyw6c7j4k","function": {"arguments": arguments,"name": name},"type": "function"}])}]
         #         )
-        history.append(ChatMessage(role="assistant", content=result, metadata={"title": json.dumps([{"id": "call_id", "function": {"arguments": json.dumps(arguments), "name": name}, "type": "function"}])}))
+        history.append(ChatMessage(role="assistant", content=result, metadata={"title": json.dumps([{"id": "call_id", "function": {"arguments": json.dumps(arguments, ensure_ascii=False), "name": name}, "type": "function"}], ensure_ascii=False)}))
         yield history[recursive:]
         yield from llm_in_loop(history, system_prompt, recursive - 1)
 

data/vs/index.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8f27a1cde9fae66a2551e256aa50771bcd4eb6db518f080751c43b1066e6cbb1
+size 83028

requirements.txt

@@ -1,3 +1,7 @@
 gradio==5.15.0
 openai==1.61.0
-python-dotenv==1.0.1
+python-dotenv==1.0.1
+langchain-community==0.2.1
+langchain-core==0.2.1
+faiss-cpu==1.10.0
+sentence-transformers

tools.py

@@ -5,14 +5,29 @@ import os
 # from langchain.tools import tool
 import json
 from pydantic import BaseModel, Field
-from openai import OpenAI
-from typing import Dict, Optional, Union
+from typing import Dict,  Union
 import random
 import copy
 from types import UnionType
 
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
 
-
+class VectorStore:
+    def __init__(self, embeddings_model, vectorstore):
+        embeddings = HuggingFaceEmbeddings(model_name=embeddings_model, model_kwargs={'device': 'cpu'})
+        self.vectore_store = FAISS.load_local(vectorstore, embeddings, allow_dangerous_deserialization=True)
+    
+    def get_context(self, instruction, number_of_contexts=2):
+        documentos = self.vectore_store.similarity_search_with_score(instruction, k=number_of_contexts)
+        return self._beautiful_context(documentos)
+    
+    def _beautiful_context(self, docs):
+        context = ""
+        for doc in docs:
+            context += doc[0].page_content + "\n"
+        return context
+    
 def read_json(data_path: str) -> tuple[list, dict]:
     try:
         with open(data_path, 'r', encoding="utf-8") as f:
@@ -93,6 +108,7 @@ class ToolBase(BaseModel, ABC):
 
 tools: Dict[str, ToolBase] = {}
 oitools = []
+# vector_store = VectorStore(embeddings_model="BAAI/bge-m3", vectorstore="data/vs")
 
 def tool_register(cls: BaseModel):
     oaitool = cls.to_openai_tool()
@@ -113,11 +129,11 @@ For an unforgettable stay, guests can choose from **special packages**, includin
 Whether for relaxation or adventure, **Nou Vall de Núria** promises a unique and memorable experience.""" 
 
 @tool_register
-class retrieve_hotel_info(ToolBase):
+class get_documents(ToolBase):
     """
-    Retrieves information about the hotel based on a user query.
+    Retrieves general information about a region, its cities, activities, tourism, or surrounding areas based on query.
     """
-    query: str = Field(description="The user query for retrieving hotel information.")
+    query: str = Field(description="An enhanced user query optimized for retrieving information")
 
     @classmethod
     def invoke(cls, input: Dict) -> str:
@@ -125,7 +141,8 @@ class retrieve_hotel_info(ToolBase):
         if not query:
             return "Missing required argument: query."
         
-        return "We are currently working on it. You can't use this tool right now—please try again later. Thank you for your patience!"
+        # return "We are currently working on it. You can't use this tool right now—please try again later. Thank you for your patience!"
+        # return vector_store.get_context(query)
     
 # @tool_register
 class hotel_facilities(ToolBase):