mirror of
https://github.com/hwchase17/langchain
synced 2024-11-18 09:25:54 +00:00
97 lines
3.4 KiB
Markdown
97 lines
3.4 KiB
Markdown
# 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](https://cloud.google.com/dlp/docs/sensitive-data-protection-overview).
|
|
|
|
## Environment Setup
|
|
|
|
Before using this template, please ensure that you enable the [DLP API](https://console.cloud.google.com/marketplace/product/google/dlp.googleapis.com)
|
|
and [Vertex AI API](https://console.cloud.google.com/marketplace/product/google/aiplatform.googleapis.com) in your Google Cloud
|
|
project.
|
|
|
|
For some common environment troubleshooting steps related to Google Cloud, see the bottom
|
|
of this readme.
|
|
|
|
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:
|
|
|
|
```shell
|
|
pip install -U langchain-cli
|
|
```
|
|
|
|
To create a new LangChain project and install this as the only package, you can do:
|
|
|
|
```shell
|
|
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:
|
|
|
|
```shell
|
|
langchain app add rag-google-cloud-sensitive-data-protection
|
|
```
|
|
|
|
And add the following code to your `server.py` file:
|
|
|
|
```python
|
|
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](https://smith.langchain.com/).
|
|
If you don't have access, you can skip this section
|
|
|
|
```shell
|
|
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:
|
|
|
|
```shell
|
|
langchain serve
|
|
```
|
|
|
|
This will start the FastAPI app with a server running locally at
|
|
[http://localhost:8000](http://localhost:8000)
|
|
|
|
We can see all templates at [http://127.0.0.1:8000/docs](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](http://127.0.0.1:8000/rag-google-cloud-vertexai-search/playground)
|
|
|
|
We can access the template from code with:
|
|
|
|
```python
|
|
from langserve.client import RemoteRunnable
|
|
|
|
runnable = RemoteRunnable("http://localhost:8000/rag-google-cloud-sensitive-data-protection")
|
|
```
|
|
```
|
|
|
|
# Troubleshooting Google Cloud
|
|
|
|
You can set your `gcloud` credentials with their CLI using `gcloud auth application-default login`
|
|
|
|
You can set your `gcloud` project with the following commands
|
|
```bash
|
|
gcloud config set project <your project>
|
|
gcloud auth application-default set-quota-project <your project>
|
|
export GOOGLE_CLOUD_PROJECT_ID=<your project>
|
|
```
|