Readme rewrite (#12615)

Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

templates/rag-elasticsearch/README.md

@@ -1,59 +1,89 @@
-# Elasticsearch RAG Example
 
-Using Langserve and ElasticSearch to build a RAG search example for answering questions on workplace documents.
+# rag-elasticsearch
 
-Relies on sentence transformer `MiniLM-L6-v2` for embedding passages and questions.
+This template performs RAG using ElasticSearch.
 
-## Running Elasticsearch
+It relies on sentence transformer `MiniLM-L6-v2` for embedding passages and questions.
 
-There are a number of ways to run Elasticsearch.
+## Environment Setup
 
-### Elastic Cloud
+Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
 
-Create a free trial account on [Elastic Cloud](https://cloud.elastic.co/registration?utm_source=langchain&utm_content=langserve).
-
-Once you have created an account, you can create a deployment. With a deployment, you can use these environment variables to connect to your Elasticsearch instance:
+To connect to your Elasticsearch instance, use the following environment variables:
 
 ```bash
 export ELASTIC_CLOUD_ID = <ClOUD_ID>
 export ELASTIC_USERNAME = <ClOUD_USERNAME>
 export ELASTIC_PASSWORD = <ClOUD_PASSWORD>
 ```
-
-### Docker
-
-For local development, you can use Docker:
-
-```bash
-docker run -p 9200:9200 \
-  -e "discovery.type=single-node" \
-  -e "xpack.security.enabled=false" \
-  -e "xpack.security.http.ssl.enabled=false" \
-  -e "xpack.license.self_generated.type=trial" \
-  docker.elastic.co/elasticsearch/elasticsearch:8.10.0
-```
-
-This will run Elasticsearch on port 9200. You can then check that it is running by visiting [http://localhost:9200](http://localhost:9200).
-
-With a deployment, you can use these environment variables to connect to your Elasticsearch instance:
+For local development with Docker, use:
 
 ```bash
 export ES_URL = "http://localhost:9200"
 ```
 
-## Documents
+## Usage
 
-To load fictional workplace documents, run the following command from the root of this repository:
+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-elasticsearch
+```
+
+If you want to add this to an existing project, you can just run:
+
+```shell
+langchain app add rag-elasticsearch
+```
+
+And add the following code to your `server.py` file:
+```python
+from rag_elasticsearch import chain as rag_elasticsearch_chain
+
+add_routes(app, rag_elasticsearch_chain, path="/rag-elasticsearch")
+```
+
+(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-elasticsearch/playground](http://127.0.0.1:8000/rag-elasticsearch/playground)  
+
+We can access the template from code with:
+
+```python
+from langserve.client import RemoteRunnable
+
+runnable = RemoteRunnable("http://localhost:8000/rag-elasticsearch")
+```
+
+For loading the fictional workplace documents, run the following command from the root of this repository:
 
 ```bash
 python ./data/load_documents.py
 ```
 
-However, you can choose from a large number of document loaders [here](https://python.langchain.com/docs/integrations/document_loaders).
-
-## Installation
-
-```bash
-# from inside your LangServe instance
-poe add rag-elasticsearch
-```
+However, you can choose from a large number of document loaders [here](https://python.langchain.com/docs/integrations/document_loaders).  

templates/rag-elasticsearch/pyproject.toml

@@ -15,7 +15,7 @@ jq = "^1.6.0"
 tiktoken = "^0.5.1"
 
 [tool.langserve]
-export_module = "rag-elasticsearch"
+export_module = "rag_elasticsearch"
 export_attr = "chain"