revert keywordearch to static

src/chatbot.py

@@ -1,7 +1,6 @@
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_community.llms.huggingface_hub import HuggingFaceHub
 from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
 
 from src.vectordatabase import RAG, get_vectorstore
 import pandas as pd
@@ -12,8 +11,6 @@ import pandas as pd
 
 # Define important variables 
 embeddings = HuggingFaceEmbeddings(model_name="paraphrase-multilingual-MiniLM-L12-v2") # Remove embedding input parameter from functions?
-db_all = FAISS.load_local(folder_path="./src/FAISS", index_name="speeches_1949_09_12",
-                                            embeddings=embeddings, allow_dangerous_deserialization=True)
 llm = HuggingFaceHub(
     # ToDo: Try different models here
     repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
@@ -112,7 +109,7 @@ def chatbot(message, history, db_inputs, prompt_language, llm=llm):
     return response  
 
 
-def keyword_search(query, db=db_all, n=10, embeddings=embeddings, method='ss', party_filter='All'):
+def keyword_search(query, n=10, embeddings=embeddings, method='ss', party_filter='All'):
     """
     Retrieve speech contents based on keywords using a specified method.
 
@@ -154,7 +151,7 @@ def keyword_search(query, db=db_all, n=10, embeddings=embeddings, method='ss', p
     - The `party_filter` parameter is a filter for retrieving speech contents by party affiliation. Specify 'All' to retrieve 
       speeches from all parties (default is 'All').
     """
-
+    db = get_vectorstore(inputs="All", embeddings=embeddings)
     query_embedding = embeddings.embed_query(query)
 
     # Maximal Marginal Relevance