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163ef35dd1
Updated titles into a consistent format. Fixed links to the diagrams. Fixed typos. Note: The Templates menu in the navbar is now sorted by the file names. I'll try sorting the navbar menus by the page titles, not the page file names.
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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