InMemoryVectorStore

Setup:

Install langchain-core.

pip install -U langchain-core

Key init args — indexing params:

• embedding_function: Embeddings Embedding function to use.

Instantiate:

from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import OpenAIEmbeddings

vector_store = InMemoryVectorStore(OpenAIEmbeddings())

Add Documents:

from langchain_core.documents import Document

document_1 = Document(id="1", page_content="foo", metadata={"baz": "bar"})
document_2 = Document(id="2", page_content="thud", metadata={"bar": "baz"})
document_3 = Document(id="3", page_content="i will be deleted :(")

documents = [document_1, document_2, document_3]
vector_store.add_documents(documents=documents)

Inspect documents:

top_n = 10
for index, (id, doc) in enumerate(vector_store.store.items()):
    if index < top_n:
        # docs have keys 'id', 'vector', 'text', 'metadata'
        print(f"{id}: {doc['text']}")
    else:
        break

Delete Documents:

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

Search:

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

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

Search with filter:

def _filter_function(doc: Document) -> bool:
    return doc.metadata.get("bar") == "baz"

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

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

Search with score:

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}]")

* [SIM=0.832268] foo [{'baz': 'bar'}]

Async:

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

# 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}]")

* [SIM=0.832268] foo [{'baz': 'bar'}]

Use as Retriever:

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

[Document(id='2', metadata={'bar': 'baz'}, page_content='thud')]