Unleashing the Power of Vector SQL with Langchain and MyScale

By GptWriter

484 words

Introduction: Revolutionizing Data Retrieval in LLM Applications

In the ever-evolving landscape of large language models (LLMs), the ability to efficiently retrieve and process data is paramount. This is where the integration of vector databases like MyScale and advanced retrieval frameworks such as Langchain come into play. Today, I’m excited to dive into how MyScale’s vector SQL capabilities can supercharge your LLM applications, making them more scalable and versatile.

Vector SQL: The Game-Changer for LLM Apps

Why MyScale Matters

MyScale is not just any database; it’s an integrated vector database that speaks SQL fluently. With MyScale, you can leverage various data types and functions for filters, which is a significant advantage when scaling your data or expanding your system’s applications. It’s designed to work seamlessly with Langchain, a framework that connects LLMs with databases and other services to create intelligent applications.

Setting Up the Environment

Before we can harness the power of MyScale with Langchain, we need to set up our environment. This involves installing necessary packages:

!pip3 install clickhouse-sqlalchemy InstructorEmbedding sentence_transformers openai langchain-experimental

Next, we establish a connection to the MyScale database and set up our OpenAI API key:

from os import environ
import getpass
from sqlalchemy import create_engine, MetaData

MYSCALE_HOST = "msc-4a9e710a.us-east-1.aws.staging.myscale.cloud"
MYSCALE_PORT = 443
MYSCALE_USER = "chatdata"
MYSCALE_PASSWORD = "myscale_rocks"
OPENAI_API_KEY = getpass.getpass("OpenAI API Key:")

engine = create_engine(
    f"clickhouse://{MYSCALE_USER}:{MYSCALE_PASSWORD}@{MYSCALE_HOST}:{MYSCALE_PORT}/default?protocol=https"
)
metadata = MetaData(bind=engine)
environ["OPENAI_API_KEY"] = OPENAI_API_KEY

Embedding Magic with HuggingFace

To parse the SQL output, we use embeddings from HuggingFace:

from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain_experimental.sql.vector_sql import VectorSQLOutputParser

output_parser = VectorSQLOutputParser.from_embeddings(
    model=HuggingFaceInstructEmbeddings(
        model_name="hkunlp/instructor-xl", model_kwargs={"device": "cpu"}
    )
)

SQL Database as Retriever

Crafting the Vector SQL Chain

With the setup complete, we can now create a Vector SQL Database Chain:

from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.utilities.sql_database import SQLDatabase
from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain
from langchain_experimental.sql.prompt import MYSCALE_PROMPT

chain = VectorSQLDatabaseChain(
    llm_chain=LLMChain(
        llm=OpenAI(openai_api_key=OPENAI_API_KEY, temperature=0),
        prompt=MYSCALE_PROMPT,
    ),
    top_k=10,
    return_direct=True,
    sql_cmd_parser=output_parser,
    database=SQLDatabase(engine, None, metadata),
)

Retrieving and Displaying Results

Finally, we can execute our chain to retrieve relevant documents:

import pandas as pd

pd.DataFrame(
    chain.run(
        "Please give me 10 papers to ask what is PageRank?",
        callbacks=[StdOutCallbackHandler()],
    )
)

Conclusion: Embrace the Vector SQL Revolution

The integration of MyScale’s vector SQL capabilities with Langchain represents a significant leap forward for developers working with LLMs. By utilizing vector stores and advanced retrieval techniques, we can build applications that are not only smarter but also more efficient and scalable.

Take the Next Step

If you’re looking to enhance your LLM applications, consider exploring the synergy between MyScale and Langchain. With the code snippets and setup guide provided, you’re well on your way to unlocking the full potential of vector SQL in your projects. Remember, the future of data retrieval is here, and it’s vectorized!

Now, it’s your turn to take action. Start by setting up your environment, experiment with the code, and watch as your LLM applications transform with the power of vector SQL. Happy coding!