6137894008
This is a template demonstrating how to utilize Google Sensitive Data Protection in conjunction with ChatVertexAI(). Tagging you @efriis as you reviewed my last template. :) Thanks! Proof of successful execution:  --------- Co-authored-by: Erick Friis <erick@langchain.dev>
2.8 KiB
rag-google-cloud-sensitive-data-protection
This template is an application that utilizes Google Vertex AI Search, a machine learning powered search service, and PaLM 2 for Chat (chat-bison). The application uses a Retrieval chain to answer questions based on your documents.
This template is an application that utilizes Google Sensitive Data Protection, a service for detecting and redacting sensitive data in text, and PaLM 2 for Chat (chat-bison), although you can use any model.
For more context on using Sensitive Data Protection, check here.
Environment Setup
Before using this template, please ensure that you enable the DLP API and Vertex AI Search API in your Google Cloud project.
Set the following environment variables:
GOOGLE_CLOUD_PROJECT_ID- Your Google Cloud project ID.MODEL_TYPE- The model type for Vertex AI Search (e.g.chat-bison)
Usage
To use this package, you should first have the LangChain CLI installed:
pip install -U langchain-cli
To create a new LangChain project and install this as the only package, you can do:
langchain app new my-app --package rag-google-cloud-sensitive-data-protection
If you want to add this to an existing project, you can just run:
langchain app add rag-google-cloud-sensitive-data-protection
And add the following code to your server.py file:
from rag_google_cloud_sensitive_data_protection.chain import chain as rag_google_cloud_sensitive_data_protection_chain
add_routes(app, rag_google_cloud_sensitive_data_protection_chain, path="/rag-google-cloud-sensitive-data-protection")
(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. LangSmith is currently in private beta, you can sign up here. If you don't have access, you can skip this section
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
If you are inside this directory, then you can spin up a LangServe instance directly by:
langchain serve
This will start the FastAPI app with a server running locally at http://localhost:8000
We can see all templates at http://127.0.0.1:8000/docs We can access the playground at http://127.0.0.1:8000/rag-google-cloud-vertexai-search/playground
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/rag-google-cloud-sensitive-data-protection")