| from typing import Callable, List | |
| def create_vector_store( | |
| docs: List, | |
| metric: str = 'cos', | |
| top_k: int = 4 | |
| ) -> Callable: | |
| from langchain.vectorstores import FAISS | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| embeddings = OpenAIEmbeddings() | |
| # Embed your documents and combine with the raw text in a pseudo db. | |
| # Note: This will make an API call to OpenAI | |
| docsearch = FAISS.from_documents(docs, embeddings) | |
| # Retriver object | |
| retriever = docsearch.as_retriever() | |
| # Retriver configs | |
| retriever.search_kwargs['distance_metric'] = metric | |
| retriever.search_kwargs['k'] = top_k | |
| return retriever |