REASON FOR IMPORTING THIS THIRD-PARTY LIBRARY INTO QUERYOUS GEORGE SOURCE CODE, SETUP, AND HOW TO UPDATE

Our fork of Vanna: https://github.com/nrgmr/vanna-QG

REASON: This library was imported so the Flask web app could save the chat history to local storage, instead of cache. If cache and a database are required for chat history in the future, the Flask request with the user's info will need to be passed to the cache (which also requires importing this library to the source code). This library has been imported as a git submodule and trimmed as much as possible to not contain excess code/weight.

SETUP: Treat this library, and any git submodule, like it's own git repository because it is. Navigate to the submodule's root directory, src/vendor/vanna_qg, and run:

pip install .

UPDATING THE SUBMODULE: If Vanna updates one of the files we are using within Vendor (only related to the Flask app/web UI, we are using Vanna via pip install in the regular manner for everything else):

-Within Queryous George navigate to /src/vendor/vanna_qg

git checkout main

git fetch upstream

*if this causes an error run the following command and then fetch again:

git remote add upstream git@github.com:vanna-ai/vanna.git

git checkout upstream/main [file_to_update]

This will update [file_to_update] in your working directory.

ORIGINAL README.md FOLLOWS

GitHub	PyPI	Documentation	Gurubase

Vanna

Vanna is an MIT-licensed open-source Python RAG (Retrieval-Augmented Generation) framework for SQL generation and related functionality.

0802.mp4

How Vanna works

Vanna works in two easy steps - train a RAG "model" on your data, and then ask questions which will return SQL queries that can be set up to automatically run on your database.

1. Train a RAG "model" on your data.
2. Ask questions.

If you don't know what RAG is, don't worry -- you don't need to know how this works under the hood to use it. You just need to know that you "train" a model, which stores some metadata and then use it to "ask" questions.

See the base class for more details on how this works under the hood.

User Interfaces

These are some of the user interfaces that we've built using Vanna. You can use these as-is or as a starting point for your own custom interface.

• Jupyter Notebook
• vanna-ai/vanna-streamlit
• vanna-ai/vanna-flask
• vanna-ai/vanna-slack

Supported LLMs

• OpenAI
• Anthropic
• Gemini
• HuggingFace
• AWS Bedrock
• Ollama
• Qianwen
• Qianfan
• Zhipu

Supported VectorStores

• AzureSearch
• Opensearch
• PgVector
• PineCone
• ChromaDB
• FAISS
• Marqo
• Milvus
• Qdrant
• Weaviate

Supported Databases

• PostgreSQL
• MySQL
• PrestoDB
• Apache Hive
• ClickHouse
• Snowflake
• Oracle
• Microsoft SQL Server
• BigQuery
• SQLite
• DuckDB

Getting started

See the documentation for specifics on your desired database, LLM, etc.

If you want to get a feel for how it works after training, you can try this Colab notebook.

Install

pip install vanna

There are a number of optional packages that can be installed so see the documentation for more details.

Import

See the documentation if you're customizing the LLM or vector database.

# The import statement will vary depending on your LLM and vector database. This is an example for OpenAI + ChromaDB

from vanna.openai.openai_chat import OpenAI_Chat
from vanna.chromadb.chromadb_vector import ChromaDB_VectorStore

class MyVanna(ChromaDB_VectorStore, OpenAI_Chat):
    def __init__(self, config=None):
        ChromaDB_VectorStore.__init__(self, config=config)
        OpenAI_Chat.__init__(self, config=config)

vn = MyVanna(config={'api_key': 'sk-...', 'model': 'gpt-4-...'})

# See the documentation for other options

Training

You may or may not need to run these vn.train commands depending on your use case. See the documentation for more details.

These statements are shown to give you a feel for how it works.

Train with DDL Statements

DDL statements contain information about the table names, columns, data types, and relationships in your database.

vn.train(ddl="""
    CREATE TABLE IF NOT EXISTS my-table (
        id INT PRIMARY KEY,
        name VARCHAR(100),
        age INT
    )
""")

Train with Documentation

Sometimes you may want to add documentation about your business terminology or definitions.

vn.train(documentation="Our business defines XYZ as ...")

Train with SQL

You can also add SQL queries to your training data. This is useful if you have some queries already laying around. You can just copy and paste those from your editor to begin generating new SQL.

vn.train(sql="SELECT name, age FROM my-table WHERE name = 'John Doe'")

Asking questions

vn.ask("What are the top 10 customers by sales?")

You'll get SQL

SELECT c.c_name as customer_name,
        sum(l.l_extendedprice * (1 - l.l_discount)) as total_sales
FROM   snowflake_sample_data.tpch_sf1.lineitem l join snowflake_sample_data.tpch_sf1.orders o
        ON l.l_orderkey = o.o_orderkey join snowflake_sample_data.tpch_sf1.customer c
        ON o.o_custkey = c.c_custkey
GROUP BY customer_name
ORDER BY total_sales desc limit 10;

If you've connected to a database, you'll get the table:

	CUSTOMER_NAME	TOTAL_SALES
0	Customer#000143500	6757566.0218
1	Customer#000095257	6294115.3340
2	Customer#000087115	6184649.5176
3	Customer#000131113	6080943.8305
4	Customer#000134380	6075141.9635
5	Customer#000103834	6059770.3232
6	Customer#000069682	6057779.0348
7	Customer#000102022	6039653.6335
8	Customer#000098587	6027021.5855
9	Customer#000064660	5905659.6159

You'll also get an automated Plotly chart:

RAG vs. Fine-Tuning

RAG

• Portable across LLMs
• Easy to remove training data if any of it becomes obsolete
• Much cheaper to run than fine-tuning
• More future-proof -- if a better LLM comes out, you can just swap it out

Fine-Tuning

• Good if you need to minimize tokens in the prompt
• Slow to get started
• Expensive to train and run (generally)

Why Vanna?

1. High accuracy on complex datasets.
   • Vanna’s capabilities are tied to the training data you give it
   • More training data means better accuracy for large and complex datasets
2. Secure and private.
   • Your database contents are never sent to the LLM or the vector database
   • SQL execution happens in your local environment
3. Self learning.
   • If using via Jupyter, you can choose to "auto-train" it on the queries that were successfully executed
   • If using via other interfaces, you can have the interface prompt the user to provide feedback on the results
   • Correct question to SQL pairs are stored for future reference and make the future results more accurate
4. Supports any SQL database.
   • The package allows you to connect to any SQL database that you can otherwise connect to with Python
5. Choose your front end.
   • Most people start in a Jupyter Notebook.
   • Expose to your end users via Slackbot, web app, Streamlit app, or a custom front end.

Extending Vanna

Vanna is designed to connect to any database, LLM, and vector database. There's a VannaBase abstract base class that defines some basic functionality. The package provides implementations for use with OpenAI and ChromaDB. You can easily extend Vanna to use your own LLM or vector database. See the documentation for more details.

Vanna in 100 Seconds

vanna-in-100-seconds-480.mov

More resources

• Full Documentation
• Website
• Discord group for support