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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.
79 lines
2.9 KiB
Markdown
79 lines
2.9 KiB
Markdown
# 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). |