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baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
66 lines
1.7 KiB
Markdown
66 lines
1.7 KiB
Markdown
# rag-lancedb
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This template performs RAG using LanceDB and OpenAI.
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## Environment Setup
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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 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 rag-lancedb
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```
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If you want to add this to as existing project, you can just run:
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```shell
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langchain app add rag-lancedb
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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_lancedb import chain as rag_lancedb_chain
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add_routes(app, rag_lancedb_chain, path="/rag-lancedb")
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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/rag-lancedb/playground](http://127.0.0.1:8000/rag-lancedb/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/rag-lancedb")
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``` |