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skills/langgraph-master/example_rag_agent.md

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+# RAG Agent
+
+Implementation example of a RAG (Retrieval-Augmented Generation) agent with search functionality.
+
+## Complete Code
+
+```python
+from typing import Annotated, Literal
+from langgraph.graph import StateGraph, START, END, MessagesState
+from langgraph.prebuilt import ToolNode
+from langgraph.checkpoint.memory import MemorySaver
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool
+
+# 1. Define tool
+@tool
+def retrieve_documents(query: str) -> str:
+    """Retrieve relevant documents.
+
+    Args:
+        query: Search query
+    """
+    # In practice, search with vector store, etc.
+    # Using dummy data here
+    docs = [
+        "LangGraph is an agent framework.",
+        "StateGraph manages state.",
+        "You can extend agents with tools."
+    ]
+
+    return "\n".join(docs)
+
+tools = [retrieve_documents]
+
+# 2. Bind tools to LLM
+llm = ChatAnthropic(model="claude-sonnet-4-5-20250929")
+llm_with_tools = llm.bind_tools(tools)
+
+# 3. Define nodes
+def agent_node(state: MessagesState):
+    """Agent node"""
+    response = llm_with_tools.invoke(state["messages"])
+    return {"messages": [response]}
+
+def should_continue(state: MessagesState) -> Literal["tools", "end"]:
+    """Determine tool usage"""
+    last_message = state["messages"][-1]
+
+    if last_message.tool_calls:
+        return "tools"
+    return "end"
+
+# 4. Build graph
+builder = StateGraph(MessagesState)
+
+builder.add_node("agent", agent_node)
+builder.add_node("tools", ToolNode(tools))
+
+builder.add_edge(START, "agent")
+builder.add_conditional_edges(
+    "agent",
+    should_continue,
+    {
+        "tools": "tools",
+        "end": END
+    }
+)
+builder.add_edge("tools", "agent")
+
+# 5. Compile
+checkpointer = MemorySaver()
+graph = builder.compile(checkpointer=checkpointer)
+
+# 6. Execute
+config = {"configurable": {"thread_id": "rag-session-1"}}
+
+query = "What is LangGraph?"
+
+for chunk in graph.stream(
+    {"messages": [{"role": "user", "content": query}]},
+    config,
+    stream_mode="values"
+):
+    chunk["messages"][-1].pretty_print()
+```
+
+## Execution Flow
+
+```
+User Query: "What is LangGraph?"
+    ↓
+[Agent Node]
+    ↓
+LLM: "I'll search for information" + ToolCall(retrieve_documents)
+    ↓
+[Tool Node] ← Execute search
+    ↓
+ToolMessage: "LangGraph is an agent framework..."
+    ↓
+[Agent Node] ← Use search results
+    ↓
+LLM: "LangGraph is a framework for building agents..."
+    ↓
+END
+```
+
+## Extension Examples
+
+### Multiple Search Tools
+
+```python
+@tool
+def web_search(query: str) -> str:
+    """Search the web"""
+    return search_web(query)
+
+@tool
+def database_search(query: str) -> str:
+    """Search database"""
+    return search_database(query)
+
+tools = [retrieve_documents, web_search, database_search]
+```
+
+### Vector Search Implementation
+
+```python
+from langchain_community.vectorstores import FAISS
+from langchain_openai import OpenAIEmbeddings
+
+# Initialize vector store
+embeddings = OpenAIEmbeddings()
+vectorstore = FAISS.from_texts(
+    ["LangGraph is an agent framework.", ...],
+    embeddings
+)
+
+@tool
+def semantic_search(query: str) -> str:
+    """Perform semantic search"""
+    docs = vectorstore.similarity_search(query, k=3)
+    return "\n".join([doc.page_content for doc in docs])
+```
+
+### Adding Human-in-the-Loop
+
+```python
+from langgraph.types import interrupt
+
+@tool
+def sensitive_search(query: str) -> str:
+    """Search sensitive information (requires approval)"""
+    approved = interrupt({
+        "action": "sensitive_search",
+        "query": query,
+        "message": "Approve this sensitive search?"
+    })
+
+    if approved:
+        return perform_sensitive_search(query)
+    else:
+        return "Search cancelled by user"
+```
+
+## Related Pages
+
+- [02_graph_architecture_agent.md](02_graph_architecture_agent.md) - Agent pattern
+- [04_tool_integration_overview.md](04_tool_integration_overview.md) - Tool details
+- [example_basic_chatbot.md](example_basic_chatbot.md) - Basic chatbot