chatdbt

What is this?

chatdbt is an openai-based dbt documentation robot. You can use natural language to describe your data query requirements to the robot, and chatdbt will help you select the dbt model you need, or generate sql responses based on these dbt models to meet your needs. Of course, you need to set up your dbt documentation for chatdbt in advance.

Quick Install

pip install chatdbt

package extras:

• nomic: use nomic/atlas as vector storage backend
• pgvector: use pgvector as vector storage backend

Internals

Chatdbt uses openai's text-embedding-ada-002 model interface to embed your dbt documentation and save the vectors to the vector storage you provide. When you make an inquiry to chatdbt, it retrieves the models and metrics (todo😊) that are semantically similar to your question. Based on the returned content and your question, it uses openai gpt-3.5-turbo model to provide appropriate answers. Similar to langchain or llama_index.

How does chatdbt integrate with my dbt doc, and where is my embedding data stored?

There are several interfaces within chatdbt:

• VectorStorage is responsible for storing embedding vectors. Currently supporting:

  • atlas

    Set up your api_key and project_name to use Nomic Atlas for storing and retrieving the vector data.

  • pgvector

    Set up your connect_string and table_name to use pgvector for storing and retrieving the vector data.

• DBTDocResolver is responsible for providing dbt manifest and catalog data. Currently supporting:

  • localfs

    Set up manifest_json_path and manifest_json_path, and chatdbt will read the dbt manifest and catalog from the local file system.

• TikTokenProvider is responsible for estimating the number of tokens consumed by OpenAI. Currently supporting:

  • tiktoken_http_server

    Set up a tiktoken-http-server api_base(example: http://localhost:8080) to use tiktoken-http-server for estimating the number of tokens consumed by OpenAI.

You can also implement the above interfaces yourself and integrate them into your own system.

Quick Start

You can initialize a chatdbt instance manually:

your_pgvector_connect_string = "postgresql+psycopg://postgres:foobar@localhost:5432/chatdbt"
your_pgvector_table_name = "chatdbt"
your_manifest_json_path = "data/manifest.json"
your_catalog_json_path = "data/catalog.json"
your_openai_key = "sk-foobar"

import os

os.environ["OPENAI_API_KEY"] = your_openai_key

from chatdbt import ChatBot
from chatdbt.vector_storage.pgvector import PGVectorStorage
from chatdbt.dbt_doc_resolver.localfs import LocalfsDBTDocResolver


vector_storage = PGVectorStorage(connect_string=your_pgvector_connect_string, table_name=your_pgvector_table_name)
dbt_doc_resolver = LocalfsDBTDocResolver(manifest_json_path=your_manifest_json_path, catalog_json_path=your_catalog_json_path)

bot = ChatBot(doc_resolver=dbt_doc_resolver, vector_storage=vector_storage, tiktoken_provider=None)

bot.suggest_table("query the number of users who have purchased a product")

bot.suggest_sql("query the number of users who have purchased a product")

or initialize a chatdbt instance with environment variables:

import os

os.environ["CHATDBT_I18N"] = "zh-cn"
os.environ["CHATDBT_VECTOR_STORAGE_TYPE"] = "pgvector"
os.environ[
    "CHATDBT_VECTOR_STORAGE_CONFIG_CONNECT_STRING"
] = your_pgvector_connect_string
os.environ["CHATDBT_VECTOR_STORAGE_CONFIG_TABLE_NAME"] = your_pgvector_table_name

os.environ["CHATDBT_DBT_DOC_RESOLVER_TYPE"] = "localfs"
os.environ["CHATDBT_DBT_DOC_RESOLVER_CONFIG_MANIFEST_JSON_PATH"] = your_manifest_json_path
os.environ["CHATDBT_DBT_DOC_RESOLVER_CONFIG_CATALOG_JSON_PATH"] = your_catalog_json_path

os.environ["OPENAI_API_KEY"] = your_openai_key

import chatdbt

chatdbt.suggest_table("query the number of users who have purchased a product")

chatdbt.suggest_sql("query the number of users who have purchased a product")