When loading the index from load_local, it should still be an IndexFlatIP. dosubotbotaddedⱭ: embeddingsRelated to text embedding models moduleⱭ: vector storeRelated to vector store module🤖:bugRelated to a bug, vulnerability, unexpected error with an existing featurelabelsDec 1, 2023...
@lru_cache(CACHED_VS_NUM) def load_vector_store(vs_path, embeddings): return MyFAISS.load_local(vs_path, embeddings) 可以看到最后返回的是一个vector_store的FAISS(VectorStore)类 @classmethoddef load_local( cls, folder_path: str, embeddings: Embeddings, index_name: str = "index") -> FAISS...
@lru_cache(CACHED_VS_NUM) def load_vector_store(vs_path, embeddings): return MyFAISS.load_local(vs_path, embeddings) 可以看到最后返回的是一个vector_store的FAISS(VectorStore)类 @classmethoddef load_local( cls, folder_path: str, embeddings: Embeddings, index_name: str = "index") -> FAISS...
db.save_local("faiss_index") new_db=FAISS.load_local("faiss_index",embeddings) docs=new_db.similarity_search(query) docs[0] Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass...
In this example,arg1andarg2are the additional arguments that you want to pass to theload_qa_chainfunction. You should replacevalue1andvalue2with the actual values for these arguments. Please note that the actual arguments that you can pass to theload_qa_chainfunction depend on the specific cha...
Step 2: Load your Local Document You need to load your local document using Langchain’s TextLoader class. You can use any text format, such as PDF, HTML, or plain text, as long as it is readable by Langchain. For example, you can load a PDF document us...
Here is an example image in the dataset: When you run this code, you will first be asked to authenticate if you have not already signed in to Roboflow via the command line. You only need to run this code once to download your dataset, so it does not need to be part of your main ...
The module creates the indexes and requests them just fine, but it doesn't seem to be saving the index file to the path that i specify. I suspect that it's saving it to the local memory, which means that i would have to load each book and create it's indexes every time. ...
From what I understand, you want to merge two FAISS indexes to consolidate your database without converting documents to embeddings again. There have been multiple users who reported the same problem and suggested potential solutions. One user made a local modification to the library code, while ...
Example Code from dotenv import load_dotenv from langchain_community.vectorstores.faiss import FAISS from azure import azure_embeddings load_dotenv() if __name__ == '__main__': db = FAISS.load_local("faiss_index", azure_embeddings) retriever = db.as_retriever() previously saved index with...