Update agent.py
Browse files
agent.py
CHANGED
@@ -1,213 +1,266 @@
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"""LangGraph Agent"""
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import os
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from dotenv import load_dotenv
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from
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from
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from langchain_google_genai import ChatGoogleGenerativeAI
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from langchain_groq import ChatGroq
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from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
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from langchain_community.tools.tavily_search import TavilySearchResults
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from langchain_community.document_loaders import WikipediaLoader
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from langchain_community.document_loaders import ArxivLoader
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from langchain_community.
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from langchain_core.messages import SystemMessage, HumanMessage
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from
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from
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from
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load_dotenv()
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@tool
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def
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"""
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Args:
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a: first int
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b: second int
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"""
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return a
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@tool
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def
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"""
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Args:
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a: first int
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b: second int
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"""
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return a
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@tool
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def
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"""
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Args:
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a: first int
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b: second int
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"""
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return a
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@tool
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def divide(a: int, b: int) -> int:
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"""
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Args:
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a: first int
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b: second int
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"""
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if b == 0:
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raise ValueError("
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return a / b
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@tool
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def
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"""
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Args:
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a: first int
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b: second int
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"""
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return a % b
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@tool
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def wiki_search(query: str) -> str:
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"""
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Args:
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query:
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[
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f'<
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for doc in search_docs
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])
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return {"wiki_results": formatted_search_docs}
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@tool
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def
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"""
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Args:
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query:
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[
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f'<
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for doc in search_docs
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])
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return {"
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@tool
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def
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"""
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Args:
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query:
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[
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f'<
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for doc in search_docs
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])
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return {"
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# load the system prompt from the file
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with open("system_prompt.txt", "r", encoding="utf-8") as f:
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system_prompt = f.read()
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# System message
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sys_msg = SystemMessage(content=system_prompt)
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create_retriever_tool = create_retriever_tool(
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retriever=vector_store.as_retriever(),
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name="Question Search",
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description="A tool to retrieve similar questions from a vector store.",
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)
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tools = [
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multiply,
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add,
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divide,
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wiki_search,
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web_search,
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arvix_search,
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]
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#
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temperature=0,
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),
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)
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else:
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raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
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# Bind tools to LLM
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llm_with_tools = llm.bind_tools(tools)
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#
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def assistant(state: MessagesState):
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"""Assistant node"""
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return {"messages": [llm_with_tools.invoke(state["messages"])]}
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def retriever(state: MessagesState):
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"""Retriever node"""
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similar_question = vector_store.similarity_search(state["messages"][0].content)
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example_msg = HumanMessage(
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content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
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)
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return {"messages": [sys_msg] + state["messages"] + [example_msg]}
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builder = StateGraph(MessagesState)
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builder.add_node("retriever", retriever)
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builder.add_node("assistant", assistant)
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builder.add_node("tools", ToolNode(tools))
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builder.add_edge(START, "
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builder.
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builder.add_conditional_edges(
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"assistant",
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tools_condition,
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)
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builder.add_edge("tools", "assistant")
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# Compile graph
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return builder.compile()
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if __name__ == "__main__":
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from dotenv import load_dotenv
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from langchain_openai import ChatOpenAI
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from langchain_core.tools import tool
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from langchain_community.document_loaders import WikipediaLoader
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from langchain_community.document_loaders import ArxivLoader
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from langchain_community.tools.tavily_search import TavilySearchResults
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from langchain_tavily import TavilyExtract
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from youtube_transcript_api import YouTubeTranscriptApi
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from langchain_core.messages import SystemMessage, HumanMessage
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from langgraph.graph import START, StateGraph, MessagesState
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from langgraph.prebuilt import ToolNode
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from langgraph.prebuilt import tools_condition
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import base64
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import httpx
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load_dotenv()
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@tool
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def add(a: int, b: int) -> int:
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"""
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Add b to a.
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Args:
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a: first int number
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b: second int number
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"""
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return a + b
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@tool
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def substract(a: int, b: int) -> int:
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"""
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Subtract b from a.
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Args:
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a: first int number
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b: second int number
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"""
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return a - b
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@tool
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def multiply(a: int, b: int) -> int:
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"""
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Multiply a by b.
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Args:
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a: first int number
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b: second int number
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"""
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return a * b
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@tool
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def divide(a: int, b: int) -> int:
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"""
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Divide a by b.
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Args:
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a: first int number
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b: second int number
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"""
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if b == 0:
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raise ValueError("Can't divide by zero.")
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return a / b
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@tool
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def mod(a: int, b: int) -> int:
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"""
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Remainder of a devided by b.
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Args:
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a: first int number
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b: second int number
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"""
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return a % b
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@tool
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def wiki_search(query: str) -> str:
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"""
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Search Wikipedia.
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Args:
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query: what to search for
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"""
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search_docs = WikipediaLoader(query=query, load_max_docs=3).load()
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formatted_search_docs = "".join(
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[
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f'<START source="{doc.metadata["source"]}">{doc.page_content[:1000]}<END>'
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for doc in search_docs
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])
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return {"wiki_results": formatted_search_docs}
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@tool
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def arvix_search(query: str) -> str:
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"""
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Search arXiv which is online archive of preprint and postprint manuscripts
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for different fields of science.
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Args:
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query: what to search for
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"""
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search_docs = ArxivLoader(query=query, load_max_docs=3).load()
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formatted_search_docs = "".join(
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[
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f'<START source="{doc.metadata["source"]}">{doc.page_content[:1000]}<END>'
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for doc in search_docs
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])
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return {"arvix_results": formatted_search_docs}
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@tool
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def web_search(query: str) -> str:
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"""
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Search WEB.
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Args:
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query: what to search for
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"""
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search_docs = TavilySearchResults(max_results=3, include_answer=True).invoke({"query": query})
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formatted_search_docs = "".join(
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[
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f'<START source="{doc["url"]}">{doc["content"][:1000]}<END>'
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for doc in search_docs
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])
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return {"web_results": formatted_search_docs}
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@tool
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def open_web_page(url: str) -> str:
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"""
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Open web page and get its content.
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Args:
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url: web page url in ""
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"""
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search_docs = TavilyExtract().invoke({"urls": [url]})
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formatted_search_docs = f'<START source="{search_docs["results"][0]["url"]}">{search_docs["results"][0]["raw_content"][:1000]}<END>'
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return {"web_page_content": formatted_search_docs}
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@tool
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def youtube_transcript(url: str) -> str:
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"""
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Get transcript of YouTube video.
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Args:
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url: YouTube video url in ""
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"""
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video_id = url.partition("https://www.youtube.com/watch?v=")[2]
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transcript = YouTubeTranscriptApi.get_transcript(video_id)
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transcript_text = " ".join([item["text"] for item in transcript])
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return {"youtube_transcript": transcript_text}
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tools = [
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add,
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substract,
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multiply,
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divide,
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mod,
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wiki_search,
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arvix_search,
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web_search,
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open_web_page,
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youtube_transcript,
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]
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# System prompt
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system_prompt = f"""
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You are a general AI assistant. I will ask you a question.
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First, provide a step-by-step explanation of your reasoning to arrive at the answer.
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Then, respond with your final answer in a single line, formatted as follows: "FINAL ANSWER: [YOUR FINAL ANSWER]".
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[YOUR FINAL ANSWER] should be a number, a string, or a comma-separated list of numbers and/or strings, depending on the question.
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If the answer is a number, do not use commas or units (e.g., $, %) unless specified.
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If the answer is a string, do not use articles or abbreviations (e.g., for cities), and write digits in plain text unless specified.
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If the answer is a comma-separated list, apply the above rules for each element based on whether it is a number or a string.
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"""
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system_message = SystemMessage(content=system_prompt)
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# Build graph
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def build_graph():
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"""Build LangGrapth graph of agent."""
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# Language model and tools
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llm = ChatOpenAI(
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model="gpt-4.1",
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temperature=0,
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max_retries=2
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)
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llm_with_tools = llm.bind_tools(tools, strict=True)
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# Nodes
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def assistant(state: MessagesState):
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"""Assistant node."""
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return {"messages": [llm_with_tools.invoke([system_message] + state["messages"])]}
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# Graph
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builder = StateGraph(MessagesState)
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builder.add_node("assistant", assistant)
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builder.add_node("tools", ToolNode(tools))
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builder.add_edge(START, "assistant")
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builder.add_conditional_edges("assistant", tools_condition)
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builder.add_edge("tools", "assistant")
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# Compile graph
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return builder.compile()
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# Testing and solving particular tasks
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if __name__ == "__main__":
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agent = build_graph()
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question = """
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Review the chess position provided in the image. It is black's turn.
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Provide the correct next move for black which guarantees a win.
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Please provide your response in algebraic notation.
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"""
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content_urls = {
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"image": "https://agents-course-unit4-scoring.hf.space/files/cca530fc-4052-43b2-b130-b30968d8aa44",
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"audio": None
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}
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# Define user message and add all the content
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content = [
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{
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"type": "text",
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"text": question
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}
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]
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if content_urls["image"]:
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image_data = base64.b64encode(httpx.get(content_urls["image"]).content).decode("utf-8")
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content.append(
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{
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"type": "image",
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"source_type": "base64",
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"data": image_data,
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"mime_type": "image/jpeg"
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}
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)
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if content_urls["audio"]:
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audio_data = base64.b64encode(httpx.get(content_urls["audio"]).content).decode("utf-8")
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content.append(
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{
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"type": "audio",
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"source_type": "base64",
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"data": audio_data,
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"mime_type": "audio/wav"
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}
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)
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messages = {
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"role": "user",
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"content": content
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}
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253 |
+
# Run agent on the question
|
254 |
+
messages = agent.invoke({"messages": messages})
|
255 |
+
for message in messages["messages"]:
|
256 |
+
message.pretty_print()
|
257 |
+
|
258 |
+
answer = messages["messages"][-1].content
|
259 |
+
index = answer.find("FINAL ANSWER: ")
|
260 |
+
|
261 |
+
print("\n")
|
262 |
+
print("="*30)
|
263 |
+
if index == -1:
|
264 |
+
print(answer)
|
265 |
+
print(answer[index+14:])
|
266 |
+
print("="*30)
|