FAISS

Setup:

Install langchain_community and faiss-cpu python packages.

.. code-block:: bash

pip install -qU langchain_community faiss-cpu

Key init args — indexing params: embedding_function: Embeddings Embedding function to use.

Key init args — client params: index: Any FAISS index to use. docstore: Docstore Docstore to use. index_to_docstore_id: Dict[int, str] Mapping of index to docstore id.

Instantiate:

.. code-block:: python

import faiss from langchain_community.vectorstores import FAISS from langchain_community.docstore.in_memory import InMemoryDocstore from langchain_openai import OpenAIEmbeddings

index = faiss.IndexFlatL2(len(OpenAIEmbeddings().embed_query("hello world")))

vector_store = FAISS( embedding_function=OpenAIEmbeddings(), index=index, docstore= InMemoryDocstore(), index_to_docstore_id={} )

Add Documents:

.. code-block:: python

from langchain_core.documents import Document

document_1 = Document(page_content="foo", metadata={"baz": "bar"}) document_2 = Document(page_content="thud", metadata={"bar": "baz"}) document_3 = Document(page_content="i will be deleted :(")

documents = [document_1, document_2, document_3] ids = ["1", "2", "3"] vector_store.add_documents(documents=documents, ids=ids)

Delete Documents:

.. code-block:: python

vector_store.delete(ids=["3"])

Search:

.. code-block:: python

results = vector_store.similarity_search(query="thud",k=1)
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

.. code-block:: python

* thud [{'bar': 'baz'}]

Search with filter:

.. code-block:: python

results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"})
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

.. code-block:: python

* thud [{'bar': 'baz'}]

Search with score:

.. code-block:: python

results = vector_store.similarity_search_with_score(query="qux",k=1)
for doc, score in results:
    print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

.. code-block:: python

* [SIM=0.335304] foo [{'baz': 'bar'}]

Async:

.. code-block:: python

# add documents
# await vector_store.aadd_documents(documents=documents, ids=ids)

# delete documents
# await vector_store.adelete(ids=["3"])

# search
# results = vector_store.asimilarity_search(query="thud",k=1)

# search with score
results = await vector_store.asimilarity_search_with_score(query="qux",k=1)
for doc,score in results:
    print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

.. code-block:: python

* [SIM=0.335304] foo [{'baz': 'bar'}]

Use as Retriever:

.. code-block:: python

retriever = vector_store.as_retriever(
    search_type="mmr",
    search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5},
)
retriever.invoke("thud")

.. code-block:: python

[Document(metadata={'bar': 'baz'}, page_content='thud')]