# research-assistant This template implements a version of [GPT Researcher](https://github.com/assafelovic/gpt-researcher) that you can use as a starting point for a research agent. ## Environment Setup The default template relies on ChatOpenAI and DuckDuckGo, so you will need the following environment variable: - `OPENAI_API_KEY` And to use the Tavily LLM-optimized search engine, you will need: - `TAVILY_API_KEY` ## Usage To use this package, you should first have the LangChain CLI installed: ```shell pip install -U langchain-cli ``` To create a new LangChain project and install this as the only package, you can do: ```shell langchain app new my-app --package research-assistant ``` If you want to add this to an existing project, you can just run: ```shell langchain app add research-assistant ``` And add the following code to your `server.py` file: ```python from research_assistant import chain as research_assistant_chain add_routes(app, research_assistant_chain, path="/research-assistant") ``` (Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. LangSmith is currently in private beta, you can sign up [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/research-assistant/playground](http://127.0.0.1:8000/research-assistant/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/research-assistant") ```