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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) See the installation instruction

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Setup

# Pip install necessary package
pip install -U  falkordb
pip install -U  tiktoken
pip install -U  langchain langchain_huggingface

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Credentials

We want to use HuggingFace so we have to get the HuggingFace 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"

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Initialization

from langchain_community.vectorstores.falkordb_vector import FalkorDBVector
from langchain_core.documents import Document
from langchain_huggingface import 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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Manage vector store

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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"])

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

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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)

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Delete items from vector store

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

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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.

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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'}]

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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')]

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API reference

For detailed documentation of all FalkorDBVector features and configurations head to the API reference: python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.falkordb_vector.FalkorDBVector.html
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