Faiss
Facebook AI Similarity Search (FAISS) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also includes supporting code for evaluation and parameter tuning.
SeeThe FAISS Library paper.
You can find the FAISS documentation atthis page.
This notebook shows how to use functionality related to theFAISS vector database. It will show functionality specific to this integration. After going through, it may be useful to explorerelevant use-case pages to learn how to use this vectorstore as part of a larger chain.
Setup
The integration lives in thelangchain-community package. We also need to install thefaiss package itself. We can install these with:
Note that you can also installfaiss-gpu if you want to use the GPU enabled version
pip install-qU langchain-community faiss-cpu
If you want to get best in-class automated tracing of your model calls you can also set yourLangSmith API key by uncommenting below:
# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass()
Initialization
pip install -qU langchain-openai
import getpass
import os
ifnot os.environ.get("OPENAI_API_KEY"):
os.environ["OPENAI_API_KEY"]= getpass.getpass("Enter API key for OpenAI: ")
from langchain_openaiimport OpenAIEmbeddings
embeddings= OpenAIEmbeddings(model="text-embedding-3-large")
import faiss
from langchain_community.docstore.in_memoryimport InMemoryDocstore
from langchain_community.vectorstoresimport FAISS
index= faiss.IndexFlatL2(len(embeddings.embed_query("hello world")))
vector_store= FAISS(
embedding_function=embeddings,
index=index,
docstore=InMemoryDocstore(),
index_to_docstore_id={},
)
Manage vector store
Add items to vector store
from uuidimport uuid4
from langchain_core.documentsimport Document
document_1= Document(
page_content="I had chocolate chip pancakes and scrambled eggs for breakfast this morning.",
metadata={"source":"tweet"},
)
document_2= Document(
page_content="The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.",
metadata={"source":"news"},
)
document_3= Document(
page_content="Building an exciting new project with LangChain - come check it out!",
metadata={"source":"tweet"},
)
document_4= Document(
page_content="Robbers broke into the city bank and stole $1 million in cash.",
metadata={"source":"news"},
)
document_5= Document(
page_content="Wow! That was an amazing movie. I can't wait to see it again.",
metadata={"source":"tweet"},
)
document_6= Document(
page_content="Is the new iPhone worth the price? Read this review to find out.",
metadata={"source":"website"},
)
document_7= Document(
page_content="The top 10 soccer players in the world right now.",
metadata={"source":"website"},
)
document_8= Document(
page_content="LangGraph is the best framework for building stateful, agentic applications!",
metadata={"source":"tweet"},
)
document_9= Document(
page_content="The stock market is down 500 points today due to fears of a recession.",
metadata={"source":"news"},
)
document_10= Document(
page_content="I have a bad feeling I am going to get deleted :(",
metadata={"source":"tweet"},
)
documents=[
document_1,
document_2,
document_3,
document_4,
document_5,
document_6,
document_7,
document_8,
document_9,
document_10,
]
uuids=[str(uuid4())for _inrange(len(documents))]
vector_store.add_documents(documents=documents, ids=uuids)
['22f5ce99-cd6f-4e0c-8dab-664128307c72',
'dc3f061b-5f88-4fa1-a966-413550c51891',
'd33d890b-baad-47f7-b7c1-175f5f7b4e59',
'6e6c01d2-6020-4a7b-95da-ef43d43f01b5',
'e677223d-ad75-4c1a-bef6-b5912bd1de03',
'47e2a168-6462-4ed2-b1d9-d9edfd7391d6',
'1e4d66d6-e155-4891-9212-f7be97f36c6a',
'c0663096-e1a5-4665-b245-1c2e6c4fb653',
'8297474a-7f7c-4006-9865-398c1781b1bc',
'44e4be03-0a8d-4316-b3c4-f35f4bb2b532']
Delete items from vector store
vector_store.delete(ids=[uuids[-1]])
True
Query vector store
Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.
Query directly
Similarity search
Performing a simple similarity search with filtering on metadata can be done as follows:
results= vector_store.similarity_search(
"LangChain provides abstractions to make working with LLMs easy",
k=2,
filter={"source":"tweet"},
)
for resin results:
print(f"*{res.page_content} [{res.metadata}]")
* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]
* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]
SomeMongoDB query and projection operators are supported for more advanced metadata filtering. The current list of supported operators are as follows:
$eq(equals)$neq(not equals)$gt(greater than)$lt(less than)$gte(greater than or equal)$lte(less than or equal)$in(membership in list)$nin(not in list)$and(all conditions must match)$or(any condition must match)$not(negation of condition)
Performing the same above similarity search with advanced metadata filtering can be done as follows:
results= vector_store.similarity_search(
"LangChain provides abstractions to make working with LLMs easy",
k=2,
filter={"source":{"$eq":"tweet"}},
)
for resin results:
print(f"*{res.page_content} [{res.metadata}]")
* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]
* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]
Similarity search with score
You can also search with score:
results= vector_store.similarity_search_with_score(
"Will it be hot tomorrow?", k=1,filter={"source":"news"}
)
for res, scorein results:
print(f"* [SIM={score:3f}]{res.page_content} [{res.metadata}]")
* [SIM=0.893688] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news'}]
Other search methods
There are a variety of other ways to search a FAISS vector store. For a complete list of those methods, please refer to theAPI Reference
Query by turning into retriever
You can also transform the vector store into a retriever for easier usage in your chains.
retriever= vector_store.as_retriever(search_type="mmr", search_kwargs={"k":1})
retriever.invoke("Stealing from the bank is a crime",filter={"source":"news"})
[Document(metadata={'source': 'news'}, page_content='Robbers broke into the city bank and stole $1 million in cash.')]
Usage for retrieval-augmented generation
For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:
Saving and loading
You can also save and load a FAISS index. This is useful so you don't have to recreate it everytime you use it.
vector_store.save_local("faiss_index")
new_vector_store= FAISS.load_local(
"faiss_index", embeddings, allow_dangerous_deserialization=True
)
docs= new_vector_store.similarity_search("qux")
docs[0]
Document(metadata={'source': 'tweet'}, page_content='Building an exciting new project with LangChain - come check it out!')
Merging
You can also merge two FAISS vectorstores
db1= FAISS.from_texts(["foo"], embeddings)
db2= FAISS.from_texts(["bar"], embeddings)
db1.docstore._dict
{'b752e805-350e-4cf5-ba54-0883d46a3a44': Document(page_content='foo')}
db2.docstore._dict
{'08192d92-746d-4cd1-b681-bdfba411f459': Document(page_content='bar')}
db1.merge_from(db2)
db1.docstore._dict
{'b752e805-350e-4cf5-ba54-0883d46a3a44': Document(page_content='foo'),
'08192d92-746d-4cd1-b681-bdfba411f459': Document(page_content='bar')}
API reference
For detailed documentation of allFAISS vector store features and configurations head to the API reference:https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html
Related
- Vector storeconceptual guide
- Vector storehow-to guides