Faiss Vector Store

Creating a Faiss Index

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

import faiss
# dimensions of text-ada-embedding-002
d = 1536 
faiss_index = faiss.IndexFlatL2(d)

Load documents, build the VectorStoreIndex

from llama_index import SimpleDirectoryReader, load_index_from_storage, VectorStoreIndex, StorageContext
from llama_index.vector_stores.faiss import FaissVectorStore
from IPython.display import Markdown, display

# load documents
documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()

vector_store = FaissVectorStore(faiss_index=faiss_index)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

# save index to disk
index.storage_context.persist()

# load index from disk
vector_store = FaissVectorStore.from_persist_dir('./storage')
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = load_index_from_storage(storage_context=storage_context)

Query Index

# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")

display(Markdown(f"<b>{response}</b>"))

# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do after his time at Y Combinator?")

display(Markdown(f"<b>{response}</b>"))