FalkorDBVectorStore

FalkorDB is an open-source graph database with integrated support for vector similarity search

it supports:

approximate nearest neighbor search
Euclidean similarity & Cosine Similarity
Hybrid search combining vector and keyword searches

This notebook shows how to use the FalkorDB vector index (FalkorDB)

Setup

# Pip install necessary package
%pip install --upgrade  falkordb
%pip install --upgrade  tiktoken
%pip install --upgrade  langchain langchain_huggingface

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Note: you may need to restart the kernel to use updated packages.

Credentials

We want to use OpenAIEmbeddings so we have to get the OpenAI API Key

import getpass
import os

if "HUGGINGFACE_API_KEY" not in os.environ:
    os.environ["HUGGINGFACE_API_KEY"] = getpass.getpass("HuggingFace API Key:")

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

Initialization

from langchain_community.vectorstores.falkordb_vector import FalkorDBVector
from langchain_core.documents import Document
from langchain_huggingface import HuggingFaceEmbeddings

API Reference:Document | HuggingFaceEmbeddings

You can use FalkorDBVector locally with docker. See installation instruction

host = 'localhost'
port = 6379

Or you can use FalkorDBVector with FalkorDB Cloud

# E.g
# host = "r-6jissuruar.instance-zwb082gpf.hc-v8noonp0c.europe-west1.gcp.f2e0a955bb84.cloud"
# port = 62471
# username = "falkordb" # SET ON FALKORDB CLOUD
# password = "password" # SET ON FALKORDB CLOUD

vector_store = FalkorDBVector(host=host, port=port, embedding=HuggingFaceEmbeddings())

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/home/atary/langchain/.venv/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1617: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be deprecated in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884
  warnings.warn(

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Manage vector store

Add items to vector store

from langchain_core.documents import Document

document_1 = Document(
    page_content="foo",
    metadata={"source": "https://example.com"}
)

document_2 = Document(
    page_content="bar",
    metadata={"source": "https://example.com"}
)

document_3 = Document(
    page_content="baz",
    metadata={"source": "https://example.com"}
)

documents = [document_1, document_2, document_3]

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

API Reference:Document

['1', '2', '3']

Update items in vector store

updated_document = Document(
    page_content="qux",
    metadata={"source": "https://another-example.com"}
)

vector_store.update_documents(document_id="1",document=updated_document)

Delete items from vector store

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

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

Performing a simple similarity search can be done as follows:

results = vector_store.similarity_search(query="thud",k=1,filter={"source":"https://another-example.com"})
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

* qux [{'text': 'qux', 'id': '1', 'source': 'https://another-example.com'}]

If you want to execute a similarity search and receive the corresponding scores you can run:

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

* [SIM=0.000001] bar [{'text': 'bar', 'id': '2', 'source': 'https://example.com'}]

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("thud")

[Document(metadata={'text': 'qux', 'id': '1', 'source': 'https://another-example.com'}, page_content='qux')]

Usage for retrieval-augmented generation

For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:

API reference

For detailed documentation of all FalkorDB__VectorStore features and configurations head to the API reference: https://api.python.langchain.com/en/latest/vectorstores/__module_name.vectorstores.__FalkorDB__VectorStore.html

Vector store conceptual guide
Vector store how-to guides

FalkorDBVectorStore

Setup

Credentials

Initialization

Manage vector store

Add items to vector store

Update items in vector store

Delete items from vector store

Query vector store

Query directly

Query by turning into retriever

Usage for retrieval-augmented generation

API reference

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You can also leave detailed feedback on GitHub.

Setup​

Credentials​

Initialization​

Manage vector store​

Add items to vector store​

Update items in vector store​

Delete items from vector store​

Query vector store​

Query directly​

Query by turning into retriever​

Usage for retrieval-augmented generation​

API reference​

Related​

Was this page helpful?

You can also leave detailed feedback on GitHub.

Setup

Credentials

Initialization

Manage vector store

Add items to vector store

Update items in vector store

Delete items from vector store

Query vector store

Query directly

Query by turning into retriever

Usage for retrieval-augmented generation

API reference

Related