mirror of
https://github.com/hwchase17/langchain
synced 2024-11-20 03:25:56 +00:00
71 lines
2.2 KiB
Markdown
71 lines
2.2 KiB
Markdown
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# rag-conversation
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This template is used for [conversational](https://python.langchain.com/docs/expression_language/cookbook/retrieval#conversational-retrieval-chain) [retrieval](https://python.langchain.com/docs/use_cases/question_answering/), which is one of the most popular LLM use-cases.
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It passes both a conversation history and retrieved documents into an LLM for synthesis.
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## Environment Setup
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This template uses Pinecone as a vectorstore and requires that `PINECONE_API_KEY`, `PINECONE_ENVIRONMENT`, and `PINECONE_INDEX` are set.
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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-conversation
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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-conversation
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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_conversation import chain as rag_conversation_chain
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add_routes(app, rag_conversation_chain, path="/rag-conversation")
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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-conversation/playground](http://127.0.0.1:8000/rag-conversation/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-conversation")
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```
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