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- **Description:** This is a template demonstrating how to utilize Google Vertex AI Search in conjunction with ChatVertexAI()
89 lines
3.2 KiB
Markdown
89 lines
3.2 KiB
Markdown
# rag-google-cloud-vertexai-search
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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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For more context on building RAG applications with Vertex AI Search,
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check [here](https://cloud.google.com/generative-ai-app-builder/docs/enterprise-search-introduction).
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## Environment Setup
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Before using this template, please ensure that you are authenticated with Vertex AI Search. See the authentication
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guide: [here](https://cloud.google.com/generative-ai-app-builder/docs/authentication).
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You will also need to create:
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- A search application [here](https://cloud.google.com/generative-ai-app-builder/docs/create-engine-es)
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- A data store [here](https://cloud.google.com/generative-ai-app-builder/docs/create-data-store-es)
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A suitable dataset to test this template with is the Alphabet Earnings Reports, which you can
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find [here](https://abc.xyz/investor/). The data is also available
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at `gs://cloud-samples-data/gen-app-builder/search/alphabet-investor-pdfs`.
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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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* `DATA_STORE_ID` - The ID of the data store in Vertex AI Search, which is a 36-character alphanumeric value found on
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the data store details page.
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* `MODEL_TYPE` - The model type for Vertex AI Search.
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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-vertexai-search
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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-vertexai-search
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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_vertexai_search.chain import chain as rag_google_cloud_vertexai_search_chain
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add_routes(app, rag_google_cloud_vertexai_search_chain, path="/rag-google-cloud-vertexai-search")
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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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LangSmith is currently in private beta, you can sign up [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-vertexai-search")
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```
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