# RAG - Timescale - conversation 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. It passes both a conversation history and retrieved documents into an LLM for synthesis. ## Environment Setup 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. To load the sample dataset, set `LOAD_SAMPLE_DATA=1`. To load your own dataset see the section below. Set the `OPENAI_API_KEY` environment variable to access the OpenAI models. ## Usage To use this package, you should first have the LangChain CLI installed: ```shell pip install -U "langchain-cli[serve]" ``` To create a new LangChain project and install this as the only package, you can do: ```shell langchain app new my-app --package rag-timescale-conversation ``` If you want to add this to an existing project, you can just run: ```shell langchain app add rag-timescale-conversation ``` And add the following code to your `server.py` file: ```python from rag_timescale_conversation import chain as rag_timescale_conversation_chain add_routes(app, rag_timescale_conversation_chain, path="/rag-timescale_conversation") ``` (Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith [here](https://smith.langchain.com/). If you don't have access, you can skip this section ```shell export LANGCHAIN_TRACING_V2=true export LANGCHAIN_API_KEY= export LANGCHAIN_PROJECT= # if not specified, defaults to "default" ``` If you are inside this directory, then you can spin up a LangServe instance directly by: ```shell langchain serve ``` This will start the FastAPI app with a server is running locally at [http://localhost:8000](http://localhost:8000) We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) 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) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/rag-timescale-conversation") ``` See the `rag_conversation.ipynb` notebook for example usage. ## Loading your own dataset To load your own dataset you will have to create a `load_dataset` function. You can see an example, in the `load_ts_git_dataset` function defined in the `load_sample_dataset.py` file. You can then run this as a standalone function (e.g. in a bash script) or add it to chain.py (but then you should run it just once).