mirror of
https://github.com/hwchase17/langchain
synced 2024-11-18 09:25:54 +00:00
81 lines
2.4 KiB
Markdown
81 lines
2.4 KiB
Markdown
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# rag-matching-engine
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This template performs RAG using Google Cloud Platform's Vertex AI with the matching engine.
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It will utilize a previously created index to retrieve relevant documents or contexts based on user-provided questions.
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## Environment Setup
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An index should be created before running the code.
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The process to create this index can be found [here](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/language/use-cases/document-qa/question_answering_documents_langchain_matching_engine.ipynb).
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Environment variables for Vertex should be set:
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```
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PROJECT_ID
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ME_REGION
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GCS_BUCKET
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ME_INDEX_ID
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ME_ENDPOINT_ID
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```
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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-matching-engine
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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 rag-matching-engine
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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_matching_engine import chain as rag_matching_engine_chain
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add_routes(app, rag_matching_engine_chain, path="/rag-matching-engine")
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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-matching-engine/playground](http://127.0.0.1:8000/rag-matching-engine/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-matching-engine")
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```
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For more details on how to connect to the template, refer to the Jupyter notebook `rag_matching_engine`. |