| # import os | |
| # import shutil | |
| # from langchain.vectorstores import Chroma | |
| # from langchain.embeddings.openai import OpenAIEmbeddings | |
| # from langchain.docstore.document import Document | |
| # def prepare_vector_store(df): | |
| # chroma_db_path = "./chroma_db" | |
| # if os.path.exists(chroma_db_path): | |
| # shutil.rmtree(chroma_db_path) | |
| # documents = [ | |
| # Document( | |
| # page_content=( | |
| # f"Neighborhood : {row['neighborhood']}\n" | |
| # f"Price : {row['price']}\n" | |
| # f"Bedrooms : {row['bedrooms']}\n" | |
| # f"Bathrooms : {row['bathrooms']}\n" | |
| # f"House Size : {row['house_size']}\n" | |
| # f"Property Type : {row['property_type']}\n" | |
| # f"Description : {row['description']}\n" | |
| # f"Neighborhood Description: {row['neighborhood_description']}" | |
| # ), | |
| # metadata={"id": str(idx)} | |
| # ) | |
| # for idx, row in df.iterrows() | |
| # ] | |
| # embedding = OpenAIEmbeddings() | |
| # vectorstore = Chroma.from_documents(documents, embedding, persist_directory=chroma_db_path) | |
| # vectorstore.persist() | |
| # return vectorstore | |
| import os | |
| from langchain.vectorstores import FAISS | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.docstore.document import Document | |
| def prepare_vector_store(df): | |
| documents = [ | |
| Document( | |
| page_content=( | |
| f"Neighborhood : {row['neighborhood']}\n" | |
| f"Price : {row['price']}\n" | |
| f"Bedrooms : {row['bedrooms']}\n" | |
| f"Bathrooms : {row['bathrooms']}\n" | |
| f"House Size : {row['house_size']}\n" | |
| f"Property Type : {row['property_type']}\n" | |
| f"Description : {row['description']}\n" | |
| f"Neighborhood Description: {row['neighborhood_description']}" | |
| ), | |
| metadata={"id": str(idx)} | |
| ) | |
| for idx, row in df.iterrows() | |
| ] | |
| embedding = OpenAIEmbeddings() | |
| vectorstore = FAISS.from_documents(documents, embedding) | |
| return vectorstore |