mirror of
https://github.com/hwchase17/langchain
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97 lines
3.3 KiB
Markdown
97 lines
3.3 KiB
Markdown
# rag-google-cloud-sensitive-data-protection
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This template is an application that utilizes Google Vertex AI Search, a machine learning powered search service, and
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PaLM 2 for Chat (chat-bison). The application uses a Retrieval chain to answer questions based on your documents.
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This template is an application that utilizes Google Sensitive Data Protection, a service for detecting and redacting
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sensitive data in text, and PaLM 2 for Chat (chat-bison), although you can use any model.
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For more context on using Sensitive Data Protection,
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check [here](https://cloud.google.com/dlp/docs/sensitive-data-protection-overview).
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## Environment Setup
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Before using this template, please ensure that you enable the [DLP API](https://console.cloud.google.com/marketplace/product/google/dlp.googleapis.com)
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and [Vertex AI API](https://console.cloud.google.com/marketplace/product/google/aiplatform.googleapis.com) in your Google Cloud
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project.
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For some common environment troubleshooting steps related to Google Cloud, see the bottom
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of this readme.
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Set the following environment variables:
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* `GOOGLE_CLOUD_PROJECT_ID` - Your Google Cloud project ID.
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* `MODEL_TYPE` - The model type for Vertex AI Search (e.g. `chat-bison`)
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package rag-google-cloud-sensitive-data-protection
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add rag-google-cloud-sensitive-data-protection
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```
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And add the following code to your `server.py` file:
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```python
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from rag_google_cloud_sensitive_data_protection.chain import chain as rag_google_cloud_sensitive_data_protection_chain
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add_routes(app, rag_google_cloud_sensitive_data_protection_chain, path="/rag-google-cloud-sensitive-data-protection")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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You can sign up for LangSmith [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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We can access the playground
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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)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/rag-google-cloud-sensitive-data-protection")
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```
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```
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# Troubleshooting Google Cloud
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You can set your `gcloud` credentials with their CLI using `gcloud auth application-default login`
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You can set your `gcloud` project with the following commands
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```bash
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gcloud config set project <your project>
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gcloud auth application-default set-quota-project <your project>
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export GOOGLE_CLOUD_PROJECT_ID=<your project>
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```
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