mirror of
https://github.com/hwchase17/langchain
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80 lines
2.9 KiB
Markdown
80 lines
2.9 KiB
Markdown
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# rag-timescale-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 Timescale Vector as a vectorstore and requires that `TIMESCALES_SERVICE_URL`. Signup for a 90-day trial [here](https://console.cloud.timescale.com/signup?utm_campaign=vectorlaunch&utm_source=langchain&utm_medium=referral) if you don't yet have an account.
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To load the sample dataset, set `LOAD_SAMPLE_DATA=1`. To load your own dataset see the section below.
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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[serve]"
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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-timescale-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-timescale-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_timescale_conversation import chain as rag_timescale_conversation_chain
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add_routes(app, rag_timescale_conversation_chain, path="/rag-timescale_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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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-timescale-conversation/playground](http://127.0.0.1:8000/rag-timescale-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-timescale-conversation")
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```
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See the `rag_conversation.ipynb` notebook for example usage.
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## Loading your own dataset
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To load your own dataset you will have to create a `load_dataset` function. You can see an example, in the
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`load_ts_git_dataset` function defined in the `load_sample_dataset.py` file. You can then run this as a
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standalone function (e.g. in a bash script) or add it to chain.py (but then you should run it just once). |