Maintained by deepset

Integration: FAISS

Use a FAISS vector database with Haystack

Authors

deepset

GitHub Repo PyPI Package

Faiss is a project by Meta, for efficient vector search. You can use it in your Haystack pipelines with the FAISSDocumentStore

For a detailed explanation on different initialization options of the FAISSDocumentStore, please visit the Haystack Documentation and API Reference. Below are some examples of how you might use it within a Haystack Pipeline.

Installation

pip install farm-haystack[faiss]

or to install FAISSDocumentStore with GPU support, you may install:

pip install farm-haystack[faiss-gpu]

Usage

Once installed, you can start using FAISS with Haystack by initializing it:

from haystack.document_stores import FAISSDocumentStore

document_store = FAISSDocumentStore()

Writing Documents to FAISSDocumentStore

To write documents to your FAISSDocumentStore, create an indexing pipeline, or use the write_documents() function. For this step, you may make use of the available FileConverters and PreProcessors, as well as other Integrations that might help you fetch data from other resources.

Indexing Pipeline

from haystack import Pipeline
from haystack.document_stores import FAISSDocumentStore
from haystack.nodes import PDFToTextConverter, PreProcessor

document_store = FAISSDocumentStore()
converter = PDFToTextConverter()
preprocessor = PreProcessor()

indexing_pipeline = Pipeline()
indexing_pipeline.add_node(component=converter, name="PDFConverter", inputs=["File"])
indexing_pipeline.add_node(component=preprocessor, name="PreProcessor", inputs=["PDFConverter"])
indexing_pipeline.add_node(component=document_store, name="DocumentStore", inputs=["PreProcessor"])

indexing_pipeline.run(file_paths=["filename.pdf"])

Using Faiss in a Query Pipeline

Once you have documents in your FAISSDocumentStore, it’s ready to be used in any Haystack pipeline. Such as a Retrieval Augmented Generation (RAG) pipeline. Learn more about Retrievers to make use of vector search within your LLM pipelines.

from haystack import Pipeline
from haystack.document_stores import FAISSDocumentStore
from haystack.nodes import EmbeddingRetriever, PromptNode

document_store = FAISSDocumentStore()
retriever = EmbeddingRetriever(document_store = document_store,
                               embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1")
prompt_node = PromptNode(model_name_or_path = "gpt-4",
                         api_key = "YOUR_OPENAI_KEY",
                         default_prompt_template = "deepset/question-answering-with-references")

query_pipeline = Pipeline()
query_pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
query_pipeline.add_node(component=prompt_node, name="PromptNode", inputs=["Retriever"])

query_pipeline.run(query = "What is Haystack?")