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Updated titles into a consistent format. Fixed links to the diagrams. Fixed typos. Note: The Templates menu in the navbar is now sorted by the file names. I'll try sorting the navbar menus by the page titles, not the page file names.
82 lines
2.9 KiB
Markdown
82 lines
2.9 KiB
Markdown
# RAG - Ollama - multi-query
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This template performs RAG using `Ollama` and `OpenAI` with a multi-query retriever.
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The `multi-query retriever` is an example of query transformation, generating multiple queries from different perspectives based on the user's input query.
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For each query, it retrieves a set of relevant documents and takes the unique union across all queries for answer synthesis.
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We use a private, local LLM for the narrow task of query generation to avoid excessive calls to a larger LLM API.
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See an example trace for Ollama LLM performing the query expansion [here](https://smith.langchain.com/public/8017d04d-2045-4089-b47f-f2d66393a999/r).
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But we use OpenAI for the more challenging task of answer synthesis (full trace example [here](https://smith.langchain.com/public/ec75793b-645b-498d-b855-e8d85e1f6738/r)).
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## Environment Setup
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To set up the environment, you need to download Ollama.
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Follow the instructions [here](https://python.langchain.com/docs/integrations/chat/ollama).
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You can choose the desired LLM with Ollama.
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This template uses `zephyr`, which can be accessed using `ollama pull zephyr`.
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There are many other options available [here](https://ollama.ai/library).
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Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
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## Usage
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To use this package, you should first install the LangChain CLI:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this package, do:
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```shell
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langchain app new my-app --package rag-ollama-multi-query
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```
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To add this package to an existing project, run:
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```shell
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langchain app add rag-ollama-multi-query
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```
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And add the following code to your `server.py` file:
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```python
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from rag_ollama_multi_query import chain as rag_ollama_multi_query_chain
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add_routes(app, rag_ollama_multi_query_chain, path="/rag-ollama-multi-query")
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```
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(Optional) Now, let's configure LangSmith. LangSmith will help us trace, monitor, and debug LangChain applications. You can sign up for LangSmith [here](https://smith.langchain.com/). If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server running locally at [http://localhost:8000](http://localhost:8000)
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You can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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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)
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To access the template from code, use:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/rag-ollama-multi-query")
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``` |