Init commit

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.python-version

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+3.13

Dockerfile

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+
+# Get a distribution that has uv already installed
+FROM ghcr.io/astral-sh/uv:python3.13-bookworm-slim
+
+# Add user - this is the user that will run the app
+# If you do not set user, the app will run as root (undesirable)
+RUN useradd -m -u 1000 user
+USER user
+
+# Set the home directory and path
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+ENV UVICORN_WS_PROTOCOL=websockets
+
+
+# Set the working directory
+WORKDIR $HOME/app
+
+# Copy the app to the container
+COPY --chown=user . $HOME/app
+
+# Install the dependencies
+# RUN uv sync --frozen
+RUN uv sync
+
+# Expose the port
+EXPOSE 7860
+
+# Run the app
+CMD ["uv", "run", "chainlit", "run", "app.py", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from typing import TypedDict, Annotated
+
+import chainlit as cl
+from dotenv import load_dotenv
+from langchain_community.tools.arxiv.tool import ArxivQueryRun
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage
+from langchain_community.tools import WikipediaQueryRun
+from langchain_community.utilities import WikipediaAPIWrapper
+from langchain_openai import ChatOpenAI
+from langgraph.graph import StateGraph, END
+from langgraph.graph.message import add_messages
+from langgraph.prebuilt import ToolNode
+from langchain.tools import tool
+
+load_dotenv()
+
+
+system_prompt = """
+You are a highly intelligent AI assistant designed to provide helpful, accurate, and well-structured responses to user queries. 
+You use Retrieval-Augmented Generation (RAG) to incorporate relevant external knowledge. Follow these rules:
+
+1. **Use Retrieved Knowledge First**: 
+   - When external documents are available, prioritize them.
+   - Summarize instead of copying text.
+   - If sources conflict, highlight differences.
+
+2. **Fallback to General Knowledge**: 
+   - If no retrieved data, use your own knowledge.
+   - If uncertain, clarify the limitation.
+
+3. **Provide Clear, Structured Responses**: 
+   - Use bullet points, step-by-step formats, and concise explanations.
+   - Provide citations where needed.
+
+4. **Maintain Context & Engagement**: 
+   - Keep track of prior conversation.
+   - If a query is unclear, ask for clarification.
+
+5. **Security & Ethics**: 
+   - Avoid harmful, illegal, or biased content.
+   - Never ask for personal information.
+"""
+
+
+class State(TypedDict):
+    messages: Annotated[list, add_messages]
+
+
+def call_model(state):
+    messages = state["messages"]
+    response = model.invoke(messages)
+    return {"messages": [response]}
+
+
+def should_continue(state):
+    last_message = state["messages"][-1]
+    if last_message.tool_calls:
+        return "action"
+    return END
+
+
+def parse_output(input_state):
+    return input_state["messages"][-1].content
+
+
+model = ChatOpenAI(model_name="gpt-4o", temperature=0, streaming=True)
+
+wiki_desc = """
+Use it only if the query is about history.
+A wrapper around Wikipedia.
+Useful for when you need to answer general questions about 
+people, places, companies, facts, historical events, or other subjects.
+Input should be a search query.
+"""
+wiki = WikipediaQueryRun(
+    description=wiki_desc,
+    api_wrapper=WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=100),
+)
+
+tav_desc = """
+Use it only if the query is about news.
+A search engine optimized for comprehensive, accurate, and trusted results.
+Useful for when you need to answer questions about current events.
+Input should be a search query.
+"""
+tav = TavilySearchResults(description=tav_desc, max_results=1)
+
+arx_desc = """
+Use it only if the query is about science.
+A wrapper around Arxiv.org
+Useful for when you need to answer questions about Physics, Mathematics, 
+Computer Science, Quantitative Biology, Quantitative Finance, Statistics, 
+Electrical Engineering, and Economics from scientific articles on arxiv.org.
+Input should be a search query.
+"""
+arx = ArxivQueryRun(description=arx_desc)
+
+
+@tool("IdiotQueryRun", return_direct=True)
+def idiot_query_run(query: str) -> str:
+    """
+    Use it only if the query is about strange or unreal things.
+    A wrapper around memes from internet.
+    Input should be a search query.
+    """
+    return "Don't believe everything you read in internet. (c)Albert Einstein"
+
+
+tool_belt = [wiki, tav, arx, idiot_query_run]
+
+model = model.bind_tools(tool_belt)
+tool_node = ToolNode(tool_belt)
+
+
+uncompiled_graph = StateGraph(State)
+uncompiled_graph.add_node("agent", call_model)
+uncompiled_graph.add_node("action", tool_node)
+uncompiled_graph.set_entry_point("agent")
+uncompiled_graph.add_conditional_edges("agent", should_continue)
+uncompiled_graph.add_edge("action", "agent")
+graph = uncompiled_graph.compile()
+
+
+@cl.on_chat_start
+async def on_chat_start():
+    msg = cl.Message(content="Hello! Please ask your questions.")
+    await msg.send()
+
+
+@cl.on_message
+async def main(message):
+    inputs = {"messages": [SystemMessage(system_prompt), HumanMessage(message.content)]}
+    messages = graph.invoke(inputs)["messages"]
+    response = messages[-1].content
+    if isinstance(messages[-2], ToolMessage):
+        response += f"\n\nThis response was generated using `{messages[-2].name}` tool"
+    msg = cl.Message(content="")
+    for i in response:
+        await msg.stream_token(i)
+    await msg.send()

chainlit.md

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+# Welcome to Chainlit! 🚀🤖
+
+Hi there, Developer! 👋 We're excited to have you on board. Chainlit is a powerful tool designed to help you prototype, debug and share applications built on top of LLMs.
+
+## Useful Links 🔗
+
+- **Documentation:** Get started with our comprehensive [Chainlit Documentation](https://docs.chainlit.io) 📚
+- **Discord Community:** Join our friendly [Chainlit Discord](https://discord.gg/k73SQ3FyUh) to ask questions, share your projects, and connect with other developers! 💬
+
+We can't wait to see what you create with Chainlit! Happy coding! 💻😊
+
+## Welcome screen
+
+To modify the welcome screen, edit the `chainlit.md` file at the root of your project. If you do not want a welcome screen, just leave this file empty.

pyproject.toml

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+[project]
+name = "05-our-first-agent-with-langgraph"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "arxiv>=2.1.3",
+    "chainlit>=2.1.0",
+    "duckduckgo-search==5.3.1b1",
+    "jupyter>=1.1.1",
+    "langchain>=0.3.15",
+    "langchain-community>=0.3.15",
+    "langchain-openai>=0.3.2",
+    "langgraph>=0.2.67",
+    "wikipedia>=1.4.0",
+]