Tableau

# pip install langchain-openai

# pip install langgraph

# pip install langchain-tableau --upgrade

Requirement already satisfied: regex>=2022.1.18 in /Users/joe.constantino/.pyenv/versions/3.12.2/lib/python3.12/site-packages (from tiktoken<1,>=0.7->langchain-openai->langchain-tableau) (2024.11.6)
Requirement already satisfied: httpcore==1.* in /Users/joe.constantino/.pyenv/versions/3.12.2/lib/python3.12/site-packages (from httpx>=0.25.2->langgraph-sdk<0.2.0,>=0.1.42->langgraph->langchain-tableau) (1.0.7)
Requirement already satisfied: h11<0.15,>=0.13 in /Users/joe.constantino/.pyenv/versions/3.12.2/lib/python3.12/site-packages (from httpcore==1.*->httpx>=0.25.2->langgraph-sdk<0.2.0,>=0.1.42->langgraph->langchain-tableau) (0.14.0)

# langchain package imports
from langchain_openai import ChatOpenAI

# langchain_tableau and langgraph imports
from langchain_tableau.tools.simple_datasource_qa import initialize_simple_datasource_qa
from langchain.agents import create_agent

import os

from dotenv import load_dotenv

load_dotenv()

tableau_server = "https://stage-dataplane2.tableau.sfdc-shbmgi.svc.sfdcfc.net/"  # replace with your Tableau server name
tableau_site = "vizqldataservicestage02"  # replace with your Tableau site
tableau_jwt_client_id = os.getenv(
    "TABLEAU_JWT_CLIENT_ID"
)  # a JWT client ID (obtained through Tableau's admin UI)
tableau_jwt_secret_id = os.getenv(
    "TABLEAU_JWT_SECRET_ID"
)  # a JWT secret ID (obtained through Tableau's admin UI)
tableau_jwt_secret = os.getenv(
    "TABLEAU_JWT_SECRET"
)  # a JWT secret ID (obtained through Tableau's admin UI)
tableau_api_version = "3.21"  # the current Tableau REST API Version
tableau_user = "joe.constantino@salesforce.com"  # enter the username querying the target Tableau Data Source

# For this cookbook we are connecting to the Superstore dataset that comes by default with every Tableau server
datasource_luid = (
    "0965e61b-a072-43cf-994c-8c6cf526940d"  # the target data source for this Tool
)
model_provider = "openai"  # the name of the model provider you are using for your Agent
# Add variables to control LLM models for the Agent and Tools
os.environ["OPENAI_API_KEY"]  # set an your model API key as an environment variable
tooling_llm_model = "gpt-4o-mini"

# Initialize simple_datasource_qa for querying Tableau Datasources through VDS
analyze_datasource = initialize_simple_datasource_qa(
    domain=tableau_server,
    site=tableau_site,
    jwt_client_id=tableau_jwt_client_id,
    jwt_secret_id=tableau_jwt_secret_id,
    jwt_secret=tableau_jwt_secret,
    tableau_api_version=tableau_api_version,
    tableau_user=tableau_user,
    datasource_luid=datasource_luid,
    tooling_llm_model=tooling_llm_model,
    model_provider=model_provider,
)

# load the List of Tools to be used by the Agent. In this case we will just load our data source Q&A tool.
tools = [analyze_datasource]

from IPython.display import Markdown, display

model = ChatOpenAI(model="gpt-4o", temperature=0)

tableauAgent = create_agent(model, tools)

# Run the agent
messages = tableauAgent.invoke(
    {
        "messages": [
            (
                "human",
                "what's going on with table sales?",
            )
        ]
    }
)
messages
# display(Markdown(messages['messages'][3].content)) #display a nicely formatted answer for successful generations