Update Vertex template

templates/rag-matching-engine/README.md

@@ -1 +1,81 @@
+
 # rag-matching-engine
+
+This template performs RAG using Google Cloud Platform's Vertex AI with the matching engine.
+
+It will utilize a previously created index to retrieve relevant documents or contexts based on user-provided questions. 
+
+## Environment Setup
+
+An index should be created before running the code. 
+
+The process to create this index can be found [here](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/language/use-cases/document-qa/question_answering_documents_langchain_matching_engine.ipynb).
+
+Environment variables for Vertex should be set:
+```
+PROJECT_ID
+ME_REGION
+GCS_BUCKET
+ME_INDEX_ID
+ME_ENDPOINT_ID
+```
+
+## Usage
+
+To use this package, you should first have the LangChain CLI installed:
+
+```shell
+pip install -U "langchain-cli[serve]"
+```
+
+To create a new LangChain project and install this as the only package, you can do:
+
+```shell
+langchain app new my-app --package rag-matching-engine
+```
+
+If you want to add this to an existing project, you can just run:
+
+```shell
+langchain app add rag-matching-engine
+```
+
+And add the following code to your `server.py` file:
+```python
+from rag_semi_structured import chain as rag_matching_engine_chain
+
+add_routes(app, rag_matching_engine_chain, path="/rag-matching-engine")
+```
+
+(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 is 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-matching-engine/playground](http://127.0.0.1:8000/rag-matching-engine/playground)  
+
+We can access the template from code with:
+
+```python
+from langserve.client import RemoteRunnable
+
+runnable = RemoteRunnable("http://localhost:8000/rag-matching-engine")
+```
+
+For more details on how to connect to the template, refer to the Jupyter notebook `rag_matching_engine`.

templates/rag-matching-engine/pyproject.toml

@@ -1,8 +1,8 @@
 [tool.poetry]
-name = "rag_matching_engine"
+name = "rag-matching-engine"
 version = "0.0.1"
 description = ""
-authors = []
+authors = ["Leonid Kuligin"]
 readme = "README.md"
 
 [tool.poetry.dependencies]
@@ -16,7 +16,7 @@ fastapi = "^0.104.0"
 sse-starlette = "^1.6.5"
 
 [tool.langserve]
-export_module = "rag_matching_engine.chain"
+export_module = "rag_matching_engine"
 export_attr = "chain"
 
 [build-system]

templates/rag-matching-engine/rag_matching_engine/__init__.py

@@ -0,0 +1,3 @@
+from rag_matching_engine.chain import chain
+
+__all__ = ["chain"]