Add src/deep_agents_from_scratch/research_tools.py

src/deep_agents_from_scratch/research_tools.py

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+"""Research tools for the deep agent.
+
+This module contains three tools used by the graph:
+
+* ``tavily_search`` – performs a web search using the Tavily API.
+* ``think_tool`` – a simple tool that decides the next action.
+* ``summarize_webpage_content`` – generates a summary of a webpage.
+
+The implementations are intentionally lightweight and rely on LangChain
+tool wrappers.
+"""
+
+from __future__ import annotations
+
+from typing import Dict, List, Any
+
+from langchain_core.messages import HumanMessage
+from langchain_core.tools import tool
+from langchain_openai import ChatOpenAI
+from langchain.prompts import PromptTemplate
+
+# Simple summarization model
+summary_llm = ChatOpenAI(model="gpt-4o-mini")
+
+# Prompt for summarization
+SUMMARIZE_PROMPT = PromptTemplate(
+    input_variables=["content"],
+    template="""
+You are a helpful assistant. Summarize the following webpage content in 3-5 sentences.
+
+Content:
+{content}
+
+Summary:
+""",
+)
+
+@tool("tavily_search")
+async def tavily_search(search_query: str) -> List[Dict[str, Any]]:
+    """Perform a web search using the Tavily API.
+
+    The function returns a list of dictionaries each containing
+    ``title``, ``url`` and ``content`` (raw HTML).
+    """
+    from tavily import TavilyClient
+    client = TavilyClient()
+    result = client.search(search_query, max_results=3, include_raw_content=True)
+    return result
+
+@tool("think_tool")
+async def think_tool(query: str) -> str:
+    """Return the next query to search.
+
+    For the demo we simply echo the query back – in a real agent this
+    could be a more sophisticated planning step.
+    """
+    return query
+
+@tool("summarize_webpage_content")
+async def summarize_webpage_content(content: str) -> Dict[str, Any]:
+    """Generate a summary for a webpage.
+
+    Returns a dict with ``filename`` and ``summary``.
+    """
+    response = await summary_llm.invoke([HumanMessage(content=SUMMARIZE_PROMPT.format(content=content))])
+    summary_text = response.content.strip()
+    return {"filename": "summary.md", "summary": summary_text}