mirror of
https://github.com/hwchase17/langchain
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ad502e8d50
Thank you for contributing to LangChain! **Description:** update to the Vectara / Langchain integration to integrate new Vectara capabilities: - Full RAG implemented as a Runnable with as_rag() - Vectara chat supported with as_chat() - Both support streaming response - Updated documentation and example notebook to reflect all the changes - Updated Vectara templates **Twitter handle:** ofermend **Add tests and docs**: no new tests or docs, but updated both existing tests and existing docs
73 lines
1.9 KiB
Markdown
73 lines
1.9 KiB
Markdown
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# rag-vectara-multiquery
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This template performs multiquery RAG with vectara.
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## Environment Setup
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Set the `OPENAI_API_KEY` environment variable to access the OpenAI models for the multi-query processing.
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Also, ensure the following environment variables are set:
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* `VECTARA_CUSTOMER_ID`
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* `VECTARA_CORPUS_ID`
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* `VECTARA_API_KEY`
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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-vectara-multiquery
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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-vectara-multiquery
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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_vectara import chain as rag_vectara_chain
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add_routes(app, rag_vectara_chain, path="/rag-vectara-multiquery")
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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 "vectara-demo"
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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-vectara-multiquery/playground](http://127.0.0.1:8000/rag-vectara-multiquery/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-vectara-multiquery")
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```
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