Readme rewrite (#12615)

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

templates/elastic-query-generator/README.md

@@ -1,31 +1,86 @@
+
 # elastic-query-generator
 
-We can use LLMs to interact with Elasticsearch analytics databases in natural language.
+This template allows interacting with Elasticsearch analytics databases in natural language using LLMs. 
 
-This chain builds search queries via the Elasticsearch DSL API (filters and aggregations).
+It builds search queries via the Elasticsearch DSL API (filters and aggregations). 
 
-The Elasticsearch client must have permissions for index listing, mapping description and search queries.
+## Environment Setup
 
+Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
 
+### Installing Elasticsearch
 
-## Setup
-
-## Installing Elasticsearch
-
-There are a number of ways to run Elasticsearch.
-
-### Elastic Cloud
+There are a number of ways to run Elasticsearch. However, one recommended way is through Elastic Cloud.
 
 Create a free trial account on [Elastic Cloud](https://cloud.elastic.co/registration?utm_source=langchain&utm_content=langserve).
 
 With a deployment, update the connection string.
 
-Password and connection (elasticsearch url) can be found on the deployment console. Th 
+Password and connection (elasticsearch url) can be found on the deployment console.
 
-## Populating with data
+Note that the Elasticsearch client must have permissions for index listing, mapping description, and search queries.
+
+### Populating with data
 
 If you want to populate the DB with some example info, you can run `python ingest.py`.
 
-This will create a `customers` index.
-In the chain, we specify indexes to generate queries against, and we specify `["customers"]`.
-This is specific to setting up your Elastic index in this 
+This will create a `customers` index. In this package, we specify indexes to generate queries against, and we specify `["customers"]`. This is specific to setting up your Elastic index.
+
+## 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 elastic-query-generator
+```
+
+If you want to add this to an existing project, you can just run:
+
+```shell
+langchain app add elastic-query-generator
+```
+
+And add the following code to your `server.py` file:
+```python
+from elastic_query_generator.chain import chain as elastic_query_generator_chain
+
+add_routes(app, elastic_query_generator_chain, path="/elastic-query-generator")
+```
+
+(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/elastic-query-generator/playground](http://127.0.0.1:8000/elastic-query-generator/playground)  
+
+We can access the template from code with:
+
+```python
+from langserve.client import RemoteRunnable
+
+runnable = RemoteRunnable("http://localhost:8000/elastic-query-generator")
+```