Self-query template (#12694)

Co-authored-by: Erick Friis <erick@langchain.dev>

templates/rag-self-query/README.md

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+# rag-self-query
+
+This template performs RAG using the self-query retrieval technique. The main idea is to let an LLM convert unstructured queries into structured queries. See the [docs for more on how this works](https://python.langchain.com/docs/modules/data_connection/retrievers/self_query).
+
+## Environment Setup
+
+In this template we'll use OpenAI models and an Elasticsearch vector store, but the approach generalizes to all LLMs/ChatModels and [a number of vector stores](https://python.langchain.com/docs/integrations/retrievers/self_query/).
+
+Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
+
+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>
+```
+For local development with Docker, use:
+
+```bash
+export ES_URL = "http://localhost:9200"
+docker run -p 9200:9200 -e "discovery.type=single-node" -e "xpack.security.enabled=false" -e "xpack.security.http.ssl.enabled=false" docker.elastic.co/elasticsearch/elasticsearch:8.9.0
+```
+
+## 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-self-query
+```
+
+If you want to add this to an existing project, you can just run:
+
+```shell
+langchain app add rag-self-query
+```
+
+And add the following code to your `server.py` file:
+```python
+from rag_self_query import chain
+
+add_routes(app, chain, path="/rag-elasticsearch")
+```
+
+To populate the vector store with the sample data, from the root of the directory run:
+```bash
+python ingest.py
+```
+
+(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-self-query")
+```