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163ef35dd1
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.
80 lines
2.5 KiB
Markdown
80 lines
2.5 KiB
Markdown
# RAG - Google Cloud Matching Engine
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This template performs RAG using [Google Cloud Vertex Matching Engine](https://cloud.google.com/blog/products/ai-machine-learning/vertex-matching-engine-blazing-fast-and-massively-scalable-nearest-neighbor-search).
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It utilizes 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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You can sign up for LangSmith [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`. |