RedisVectorStore

from langchain_redis import RedisVectorStore
from langchain_openai import OpenAIEmbeddings
from redis import Redis

redis_client = Redis.from_url("redis://localhost:6379")

store = RedisVectorStore(
    embedding=OpenAIEmbeddings(),
    index_name="langchain-demo",
    redis_client=redis_client
)

from langchain_core.documents import Document

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

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

Delete Documents

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

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

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

from redisvl.query.filter import Tag

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

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

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

* [SIM=0.916] foo [{'baz': 'bar'}]

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

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