Template for Ollama + Multi-query retriever (#14092)

templates/rag-ollama-multi-query/README.md

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+
+# rag-ollama-multi-query
+
+This template performs RAG using Ollama and OpenAI with a multi-query retriever. 
+
+The multi-query retriever is an example of query transformation, generating multiple queries from different perspectives based on the user's input query. 
+
+For each query, it retrieves a set of relevant documents and takes the unique union across all queries for answer synthesis.
+
+We use a private, local LLM for the narrow task of query generation to avoid excessive calls to a larger LLM API.
+
+See an example trace for Ollama LLM performing the query expansion [here](https://smith.langchain.com/public/8017d04d-2045-4089-b47f-f2d66393a999/r).
+
+But we use OpenAI for the more challenging task of answer syntesis (full trace example [here](https://smith.langchain.com/public/ec75793b-645b-498d-b855-e8d85e1f6738/r)).
+
+## Environment Setup
+
+To set up the environment, you need to download Ollama. 
+
+Follow the instructions [here](https://python.langchain.com/docs/integrations/chat/ollama). 
+
+You can choose the desired LLM with Ollama. 
+
+This template uses `zephyr`, which can be accessed using `ollama pull zephyr`.
+
+There are many other options available [here](https://ollama.ai/library).
+
+Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
+
+## Usage
+
+To use this package, you should first install the LangChain CLI:
+
+```shell
+pip install -U langchain-cli
+```
+
+To create a new LangChain project and install this package, do:
+
+```shell
+langchain app new my-app --package rag-ollama-multi-query
+```
+
+To add this package to an existing project, run:
+
+```shell
+langchain app add rag-ollama-multi-query
+```
+
+And add the following code to your `server.py` file:
+
+```python
+from rag_ollama_multi_query import chain as rag_ollama_multi_query_chain
+
+add_routes(app, rag_ollama_multi_query_chain, path="/rag-ollama-multi-query")
+```
+
+(Optional) Now, let's 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 running locally at [http://localhost:8000](http://localhost:8000)
+
+You can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
+You can access the playground at [http://127.0.0.1:8000/rag-ollama-multi-query/playground](http://127.0.0.1:8000/rag-ollama-multi-query/playground)
+
+To access the template from code, use:
+
+```python
+from langserve.client import RemoteRunnable
+
+runnable = RemoteRunnable("http://localhost:8000/rag-ollama-multi-query")
+```