From 2e9e4a88db70acd57d9ccfa09754d5f7f15ff2b1 Mon Sep 17 00:00:00 2001 From: Joshua Carroll Date: Thu, 8 Aug 2024 09:46:15 -0700 Subject: [PATCH] Add hosted app reference to README --- README.md | 29 +++++++++++++++++++---------- agent/tools.py | 13 ++++++++----- media/app_screenshot.png | Bin 0 -> 150189 bytes 3 files changed, 27 insertions(+), 15 deletions(-) create mode 100644 media/app_screenshot.png diff --git a/README.md b/README.md index 3229a6d..9cdb756 100644 --- a/README.md +++ b/README.md @@ -2,25 +2,24 @@ This repository provides a blueprint and full toolkit for a LangGraph-based agent service architecture. It includes a [LangGraph](https://langchain-ai.github.io/langgraph/) agent, a [FastAPI](https://fastapi.tiangolo.com/) service to serve it, a client to interact with the service, and a [Streamlit](https://streamlit.io/) app that uses the client to provide a chat interface. -## Purpose +This project offers a template for you to easily build and run your own agents using the LangGraph framework. It demonstrates a complete setup from agent definition to user interface, making it easier to get started with LangGraph-based projects by providing a full, robust toolkit. -The purpose of this project is to offer a template for developers to easily build and run their own agents using the LangGraph framework. It demonstrates a complete setup from agent definition to user interface, making it easier to get started with LangGraph-based projects by providing a full, robust kit. +## Overview -## Structure +### [Try the app!](https://agent-service-toolkit.streamlit.app/) + +[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://agent-service-toolkit.streamlit.app/) + + + -The repository is structured as follows: -- `agent/agent.py`: Defines the LangGraph agent -- `schema/__init__.py`: Defines the service schema -- `service/service.py`: FastAPI service to serve the agent -- `client/__init__.py`: Client to interact with the agent service -- `streamlit_app.py`: Streamlit app providing a chat interface ### Architecture Diagram -## Key Features +### Key Features 1. **LangGraph Agent**: A customizable agent built using the LangGraph framework. 2. **FastAPI Service**: Serves the agent with both streaming and non-streaming endpoints. @@ -31,6 +30,16 @@ The repository is structured as follows: 7. **Feedback Mechanism**: Includes a star-based feedback system integrated with LangSmith. 8. **Docker Support**: Includes Dockerfiles and a docker compose file for easy development and deployment. +### Key Files + +The repository is structured as follows: + +- `agent/agent.py`: Defines the LangGraph agent +- `schema/__init__.py`: Defines the service schema +- `service/service.py`: FastAPI service to serve the agent +- `client/__init__.py`: Client to interact with the agent service +- `streamlit_app.py`: Streamlit app providing a chat interface + ## Why LangGraph? AI agents are increasingly being built with more explicitly structured and tightly controlled [Compound AI Systems](https://bair.berkeley.edu/blog/2024/02/18/compound-ai-systems/), with careful attention to the [cognitive architecture](https://blog.langchain.dev/what-is-a-cognitive-architecture/). At the time of this repo's creation, LangGraph seems like the most advanced open source framework for building such systems, with a high degree of control as well as support for features like concurrent execution, cycles in the graph, streaming results, built-in observability, and the rich ecosystem around LangChain. diff --git a/agent/tools.py b/agent/tools.py index ec8d7da..635c95e 100644 --- a/agent/tools.py +++ b/agent/tools.py @@ -1,16 +1,16 @@ +import inspect import math import numexpr import re -from langchain_core.tools import tool +from langchain_core.tools import tool, BaseTool from langchain_community.tools import DuckDuckGoSearchResults, ArxivQueryRun -web_search = DuckDuckGoSearchResults() +web_search = DuckDuckGoSearchResults(name="WebSearch") # Kinda busted since it doesn't return links -arxiv_search = ArxivQueryRun() +arxiv_search = ArxivQueryRun(name="ArxivSearch") -@tool -def calculator(expression: str) -> str: +def calculator_func(expression: str) -> str: """Calculates a math expression using numexpr. Useful for when you need to answer questions about math using numexpr. @@ -39,3 +39,6 @@ def calculator(expression: str) -> str: f'calculator("{expression}") raised error: {e}.' 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