langchain-visualizer
# LangChain Visualizer Adapts [Ought's ICE visualizer](https://github.com/oughtinc/ice) for use with [LangChain](https://github.com/hwchase17/langchain) so that you can view LangChain interactions with a beautiful UI.  You can now - See the full prompt text being sent with every interaction with the LLM - Tell from the coloring which parts of the prompt are hardcoded and which parts are templated substitutions - Inspect the execution flow and observe when each function goes up the stack - See the costs of each LLM call, and of the entire run, if you are using OpenAI's `text-davinci-003` model ## Quickstart Install this library: ```bash pip install langchain-visualizer ``` Note that if you're on a Linux distribution, you may need to install libyaml first: ```bash apt install -y libyaml-dev ``` Then: 1. Add `import langchain_visualizer` as **the first import** in your Python entrypoint file 2. Write an async function to visualize whichever workflow you're running 3. Call `langchain_visualizer.visualize` on that function For an example, see below instructions on reproducing the screenshot. ### Running the example screenshot To run the example you see in the screenshot, first install this library and optional dependencies: ```bash pip install langchain-visualizer google-search-results openai ``` If you haven't yet set up your [OpenAI API keys](https://openai.com/api/) or SERP API keys, you can [replay the recorded interactions](https://github.com/amosjyng/vcr-langchain) by cloning this repository and running ```bash $ pip install vcr-langchain $ OPENAI_API_KEY=dummy python tests/agents/test_langchain_getting_started.py ``` If you have set them up, you can run the following script (adapted from [LangChain docs](https://langchain.readthedocs.io/en/latest/modules/agents/getting_started.html)): ```python import langchain_visualizer import asyncio from langchain.agents import initialize_agent, load_tools from langchain.llms import OpenAI llm = OpenAI(temperature=0.7) tools = load_tools(["serpapi", "llm-math"], llm=llm) agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True) async def search_agent_demo(): return agent.run( "Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 " "power?" ) langchain_visualizer.visualize(search_agent_demo) ``` A browser window will open up, and you can actually see the agent execute happen in real-time! ### Jupyter notebook support Jupyter notebooks are now supported! To use this inside a Jupyter notebook, **make sure to import the `visualize` function from `langchain_visualizer.jupyter` instead.** Please look at [the demo notebook](/tests/demo.ipynb) to see an example of how it can be used in Jupyter. ### Visualizing embeddings If you want to also visualize documents being chunked up for embeddings, you can now do so by calling the `visualize_embeddings` function before you visualize the main chain: ```python from langchain_visualizer import visualize, visualize_embeddings async def run_chain(): ... visualize_embeddings() visualize(run_chain) ``` ## Why not just use LangChain's built-in tracer? For me personally: - I prefer the ICE UI. In particular: - I like the colored highlighting of parts of the prompt that are filled-in template variables - I like the ability to quickly inspect different LLM calls without leaving the trace page - I prefer the visualization of my agent logic to remain static when LLM calls are cached - I prefer seeing when the tool (e.g. `PythonREPL`) actually gets called, rather than just the high-level execution of the chain (e.g. `LLMMathChain`) That being said, LangChain's tracer is definitely better supported. **Please note that there is a lot of langchain functionality that I haven't gotten around to hijacking for visualization.** If there's anything you need to show up in the execution trace, please open a PR or issue. ## My other projects Please check out [VCR LangChain](https://github.com/amosjyng/vcr-langchain), a library that lets you record LLM interactions for your tests and demos!