langgraph-kusto 0.0.1

LangGraph vector store implementation for Azure Data Explorer (Kusto) - PRE-ALPHA VERSION

langgraph-kusto

This repository provides Kusto-backed storage and checkpointing for LangGraph, including optional semantic search using Kusto's ai_embeddings plugin.

Kusto store search example

The KustoStore supports two search modes:

• Vector search when an embedding function is configured
• Text search fallback when no embedding function is provided

Below is an example of configuring a KustoStore with the KustoOpenAIEmbeddingFn and running a search.

from langgraph_kusto.common.kusto_client import KustoClient
from langgraph_kusto.store.store import KustoStore, KustoStoreConfig
from langgraph_kusto.store.embeddings import KustoOpenAIEmbeddingFn

# Create Kusto client from environment
client = KustoClient.from_env()

# Configure embedding function backed by Kusto's ai_embeddings plugin
embedding_fn = KustoOpenAIEmbeddingFn(
    client=client,
    model_uri="https://myaccount.openai.azure.com/openai/deployments/text-embedding-3-small/embeddings?api-version=2024-06-01;impersonate",  # or your configured embedding model
)

# Configure the store
store_config = KustoStoreConfig(
    client=client,
    embedding_function=embedding_fn,
)

store = KustoStore(config=store_config)

# Run a semantic search in a given namespace
results = store.search(namespace="my-namespace", query="find relevant items", k=5)

for item in results:
    print(item["key"], item["score"], item["chunk_string"])

If you omit the embedding_function when creating KustoStoreConfig, store.search will automatically fall back to a simple text search over the stored values for the requested namespace.