# rag-google-cloud-vertexai-search 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. For more context on building RAG applications with Vertex AI Search, check [here](https://cloud.google.com/generative-ai-app-builder/docs/enterprise-search-introduction). ## Environment Setup Before using this template, please ensure that you are authenticated with Vertex AI Search. See the authentication guide: [here](https://cloud.google.com/generative-ai-app-builder/docs/authentication). You will also need to create: - A search application [here](https://cloud.google.com/generative-ai-app-builder/docs/create-engine-es) - A data store [here](https://cloud.google.com/generative-ai-app-builder/docs/create-data-store-es) A suitable dataset to test this template with is the Alphabet Earnings Reports, which you can find [here](https://abc.xyz/investor/). The data is also available at `gs://cloud-samples-data/gen-app-builder/search/alphabet-investor-pdfs`. Set the following environment variables: * `GOOGLE_CLOUD_PROJECT_ID` - Your Google Cloud project ID. * `DATA_STORE_ID` - The ID of the data store in Vertex AI Search, which is a 36-character alphanumeric value found on the data store details page. * `MODEL_TYPE` - The model type for Vertex AI Search. ## 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-vertexai-search ``` If you want to add this to an existing project, you can just run: ```shell langchain app add rag-google-cloud-vertexai-search ``` And add the following code to your `server.py` file: ```python from rag_google_cloud_vertexai_search.chain import chain as rag_google_cloud_vertexai_search_chain add_routes(app, rag_google_cloud_vertexai_search_chain, path="/rag-google-cloud-vertexai-search") ``` (Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith [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= export LANGCHAIN_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-vertexai-search") ```