langchain-clickzetta 0.1.0

An integration package connecting ClickZetta and LangChain

LangChain ClickZetta Integration

An integration package connecting ClickZetta and LangChain.

LangChain integration for ClickZetta, providing SQL queries, vector storage, and full-text search capabilities.

Features

• SQL Queries: Natural language to SQL conversion and execution
• Vector Storage: Efficient vector storage and similarity search
• Full-text Search: Advanced text search capabilities with inverted index
• Chat History: Persistent conversation memory
• Hybrid Search: Combine vector and full-text search
• True Hybrid Store: Single table with both vector and inverted indexes (ClickZetta native)

Installation

pip install langchain-clickzetta

Quick Start

Basic Setup

from langchain_clickzetta import ClickZettaEngine

# Create engine
engine = ClickZettaEngine(
    service="your-service",
    instance="your-instance",
    workspace="your-workspace",
    schema="your-schema",
    username="your-username",
    password="your-password",
    vcluster="your-vcluster"
)

Vector Storage

from langchain_clickzetta import ClickZettaVectorStore
from langchain_community.embeddings import DashScopeEmbeddings

# Setup embeddings
embeddings = DashScopeEmbeddings(
    dashscope_api_key="your-api-key",
    model="text-embedding-v4"
)

# Create vector store
vector_store = ClickZettaVectorStore(
    engine=engine,
    embeddings=embeddings,
    table_name="my_vectors"
)

# Add documents
texts = ["Hello world", "LangChain is great"]
vector_store.add_texts(texts)

# Search
results = vector_store.similarity_search("greeting", k=2)

True Hybrid Search

from langchain_clickzetta import ClickZettaHybridStore, ClickZettaUnifiedRetriever

# Create hybrid store (single table with vector + full-text indexes)
hybrid_store = ClickZettaHybridStore(
    engine=engine,
    embeddings=embeddings,
    table_name="hybrid_docs"
)

# Add documents
hybrid_store.add_texts([
    "ClickZetta is a high-performance analytics database",
    "LangChain enables building applications with LLMs"
])

# Create unified retriever
retriever = ClickZettaUnifiedRetriever(
    hybrid_store=hybrid_store,
    search_type="hybrid",  # "vector", "fulltext", or "hybrid"
    alpha=0.5  # Balance between vector and full-text search
)

# Search with hybrid approach
results = retriever.get_relevant_documents("analytics database")

SQL Chain

from langchain_clickzetta import ClickZettaSQLChain
from langchain_community.llms import Tongyi

llm = Tongyi(dashscope_api_key="your-api-key")

sql_chain = ClickZettaSQLChain.from_engine(
    engine=engine,
    llm=llm
)

result = sql_chain.invoke({"query": "How many tables are there?"})
print(result["result"])

Documentation

For more detailed documentation, see the main repository README and examples.

Development

See CONTRIBUTING.md for development setup and guidelines.

License

This package is released under the MIT License.