Readme rewrite (#12615)

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

templates/rag-redis/README.md

@@ -1,18 +1,15 @@
-# Redis RAG Example
 
-Using Langserve and Redis to build a RAG search example for answering questions on financial 10k filings docs (for Nike).
+# rag-redis
 
-Relies on the sentence transformer `all-MiniLM-L6-v2` for embedding chunks of the pdf and user questions.
+This template performs RAG using Redis and OpenAI on financial 10k filings docs (for Nike). 
 
-## Running Redis
+It relies on the sentence transformer `all-MiniLM-L6-v2` for embedding chunks of the pdf and user questions.
 
-There are a number of ways to run Redis depending on your use case and scenario.
+## Environment Setup
 
-### Easiest? Redis Cloud
+Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
 
-Create a free database on [Redis Cloud](https://redis.com/try-free). *No credit card information is required*. Simply fill out the info form and select the cloud vendor of your choice and region.
-
-Once you have created an account and database, you can find the connection credentials by clicking on the database and finding the "Connect" button which will provide a few options. Below are the environment variables you need to configure to run this RAG app.
+The following Redis environment variables need to be set:
 
 ```bash
 export REDIS_HOST = <YOUR REDIS HOST>
@@ -21,29 +18,6 @@ export REDIS_USER = <YOUR REDIS USER NAME>
 export REDIS_PASSWORD = <YOUR REDIS PASSWORD>
 ```
 
-For larger use cases (greater than 30mb of data), you can certainly created a Fixed or Flexible billing subscription which can scale with your dataset size.
-
-### Redis Stack -- Local Docker
-
-For local development, you can use Docker:
-
-```bash
-docker run -p 6397:6397 -p 8001:8001 redis/redis-stack:latest
-```
-
-This will run Redis on port 6379. You can then check that it is running by visiting the RedisInsight GUI at [http://localhost:8001](http://localhost:8001).
-
-This is the connection that the application will try to use by default -- local dockerized Redis.
-
-## Data
-
-To load the financial 10k pdf (for Nike) into the vectorstore, run the following command from the root of this repository:
-
-```bash
-poetry shell
-python ingest.py
-```
-
 ## Supported Settings
 We use a variety of environment variables to configure this application
 
@@ -57,21 +31,61 @@ We use a variety of environment variables to configure this application
 | `REDIS_URL`            | Full URL for connecting to Redis  | `None`, Constructed from user, password, host, and port if not provided |
 | `INDEX_NAME`           | Name of the vector index          | "rag-redis"   |
 
+## Usage
 
+To use this package, you should first have the LangChain CLI installed:
 
-## Installation
-To create a langserve application using this template, run the following:
-```bash
-langchain app new my-langserve-app
-cd my-langserve-app
+```shell
+pip install -U "langchain-cli[serve]"
 ```
 
-Add this template:
-```bash
+To create a new LangChain project and install this as the only package, you can do:
+
+```shell
+langchain app new my-app --package rag-redis
+```
+
+If you want to add this to an existing project, you can just run:
+
+```shell
 langchain app add rag-redis
 ```
 
-Start the server:
-```bash
+And add the following code to your `server.py` file:
+```python
+from rag_redis.chain import chain as rag_redis_chain
+
+add_routes(app, rag_redis_chain, path="/rag-redis")
+```
+
+(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-redis/playground](http://127.0.0.1:8000/rag-redis/playground)  
+
+We can access the template from code with:
+
+```python
+from langserve.client import RemoteRunnable
+
+runnable = RemoteRunnable("http://localhost:8000/rag-redis")
 ```