mirror of
https://github.com/hwchase17/langchain
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73 lines
2.4 KiB
Markdown
73 lines
2.4 KiB
Markdown
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# retrieval-agent-fireworks
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This package uses open source models hosted on FireworksAI to do retrieval using an agent architecture. By default, this does retrieval over Arxiv.
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We will use `Mixtral8x7b-instruct-v0.1`, which is shown in this blog to yield reasonable
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results with function calling even though it is not fine tuned for this task: https://huggingface.co/blog/open-source-llms-as-agents
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## Environment Setup
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There are various great ways to run OSS models. We will use FireworksAI as an easy way to run the models. See [here](https://python.langchain.com/docs/integrations/providers/fireworks) for more information.
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Set the `FIREWORKS_API_KEY` environment variable to access Fireworks.
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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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 as the only package, you can do:
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```shell
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langchain app new my-app --package retrieval-agent-fireworks
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add retrieval-agent-fireworks
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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 retrieval_agent_fireworks import chain as retrieval_agent_fireworks_chain
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add_routes(app, retrieval_agent_fireworks_chain, path="/retrieval-agent-fireworks")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/).
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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 is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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We can access the playground at [http://127.0.0.1:8000/retrieval-agent-fireworks/playground](http://127.0.0.1:8000/retrieval-agent-fireworks/playground)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/retrieval-agent-fireworks")
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```
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