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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import dotenv\n",
"dotenv.load_dotenv(dotenv.find_dotenv())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"from typing import Annotated, List\n",
"from typing_extensions import TypedDict\n",
"from langgraph.checkpoint.memory import MemorySaver\n",
"from langchain_core.messages import ToolMessage\n",
"from langgraph.graph import StateGraph, START, END\n",
"from langgraph.graph.message import add_messages\n",
"from langgraph.prebuilt import ToolNode, tools_condition\n",
"\n",
"\n",
"# Define a State class, that each node in the graph will need\n",
"class State(TypedDict):\n",
" # Messages have the type \"list\". The `add_messages` function\n",
" # in the annotation defines how this state key should be updated\n",
" # (in this case, it appends messages to the list, rather than overwriting them)\n",
" messages: Annotated[list, add_messages]\n",
"\n",
"# Initialize the graph as a stategraph:\n",
"graph_builder = StateGraph(State)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def create_llm(use_model):\n",
" # Create the language model\n",
" if use_model == 'gpt-4o-mini':\n",
" from langchain_openai import ChatOpenAI\n",
" print(f'As llm, using OpenAI model: {use_model}')\n",
" llm = ChatOpenAI(\n",
" model_name=\"gpt-4o-mini\",\n",
" temperature=0)\n",
" elif use_model == 'zephyr-7b-alpha':\n",
" from langchain_huggingface import HuggingFaceEndpoint\n",
" print(f'As llm, using HF-Endpint: {use_model}')\n",
" llm = HuggingFaceEndpoint(\n",
" repo_id=f\"huggingfaceh4/{use_model}\",\n",
" temperature=0.1,\n",
" max_new_tokens=512\n",
" )\n",
" return llm"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Define tools to bind to llm\n",
"def create_wiki_tool(verbose):\n",
" print('Creating wiki tool')\n",
" # Let's define a wikipedia-lookup tool \n",
" from langchain_community.tools import WikipediaQueryRun\n",
" from langchain_community.utilities import WikipediaAPIWrapper\n",
"\n",
" api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=5000)\n",
" tool_wiki = WikipediaQueryRun(api_wrapper=api_wrapper)\n",
" if verbose:\n",
" test_search = \"quantum mechanics\"\n",
" print(f\"Testing wiki tool, with search key: {test_search}\")\n",
" response = tool_wiki.run({\"query\": test_search})\n",
" print(f\"Response: {response}\")\n",
" return tool_wiki\n",
"\n",
"def get_tools(verbose):\n",
" print('Gathering tools')\n",
" tool_wiki = create_wiki_tool(verbose=verbose) \n",
" tools = [tool_wiki]\n",
" return tools"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def chatbot(state: State):\n",
" tools = get_tools(verbose=False)\n",
" llm = create_llm(use_model='gpt-4o-mini')\n",
" # llm = create_llm(use_model='zephyr-7b-alpha')\n",
" llm_with_tools = llm.bind_tools(tools)\n",
" return {\"messages\": [llm_with_tools.invoke(state[\"messages\"])]}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Simpler versions of ToolNode and tools_condition\n",
"if False:\n",
" class BasicToolNode:\n",
" \"\"\"A node that runs the tools requested in the last AIMessage.\"\"\"\n",
"\n",
" def __init__(self, tools: list) -> None:\n",
" self.tools_by_name = {tool.name: tool for tool in tools}\n",
"\n",
" def __call__(self, inputs: dict):\n",
" if messages := inputs.get(\"messages\", []):\n",
" message = messages[-1]\n",
" else:\n",
" raise ValueError(\"No message found in input\")\n",
" outputs = []\n",
" for tool_call in message.tool_calls:\n",
" tool_result = self.tools_by_name[tool_call[\"name\"]].invoke(\n",
" tool_call[\"args\"]\n",
" )\n",
" outputs.append(\n",
" ToolMessage(\n",
" content=json.dumps(tool_result),\n",
" name=tool_call[\"name\"],\n",
" tool_call_id=tool_call[\"id\"],\n",
" )\n",
" )\n",
" return {\"messages\": outputs}\n",
"\n",
" def basic_tools_conditions(state: State):\n",
" \"\"\"\n",
" Use in the conditional_edge to route to the ToolNode if the last message\n",
" has tool calls. Otherwise, route to the end.\n",
" \"\"\"\n",
" if isinstance(state, list):\n",
" ai_message = state[-1]\n",
" elif messages := state.get(\"messages\", []):\n",
" ai_message = messages[-1]\n",
" else:\n",
" raise ValueError(f\"No messages found in input state to tool_edge: {state}\")\n",
" print('route_tools: {ai_message}')\n",
" if hasattr(ai_message, \"tool_calls\") and len(ai_message.tool_calls) > 0:\n",
" routing_decision = \"tools\"\n",
" else:\n",
" routing_decision = END\n",
" return routing_decision\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Define nodes:\n",
"graph_builder.add_node(\"chatbot\", chatbot)\n",
"# tool_node = BasicToolNode(tools=get_tools(verbose=False))\n",
"tool_node = ToolNode(tools=get_tools(verbose=False))\n",
"graph_builder.add_node(\"tools\", tool_node)\n",
"\n",
"# Define edges:\n",
"# Entry Point\n",
"graph_builder.add_edge(START, \"chatbot\")\n",
"# Conditional Edge between the chatbot and the tool node\n",
"# The `route_tools` function returns \"tools\" if the chatbot asks to use a tool, and \"END\" if\n",
"# it is fine directly responding. This conditional routing defines the main agent loop.\n",
"graph_builder.add_conditional_edges(\n",
" \"chatbot\",\n",
" # basic_tools_condition,\n",
" tools_condition\n",
" # The following dictionary lets you tell the graph to interpret the condition's outputs as a specific node\n",
" # It defaults to the identity function, but if you want to use a node named something else apart from \"tools\",\n",
" # You can update the value of the dictionary to something else e.g., \"tools\": \"my_tools\"\n",
" # {\"tools\": \"tools\", END: END},\n",
")\n",
"# Edge between the tool node and the chatbot\n",
"# Any time a tool is called, we return to the chatbot to decide the next step\n",
"graph_builder.add_edge(\"tools\", \"chatbot\")\n",
"\n",
"memory = MemorySaver()\n",
"graph = graph_builder.compile(checkpointer=memory)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import Image, display\n",
"\n",
"try:\n",
" display(Image(graph.get_graph().draw_mermaid_png()))\n",
"except Exception:\n",
" # This requires some extra dependencies and is optional\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"config = {\"configurable\": {\"thread_id\": \"1\"}}\n",
"\n",
"def stream_graph_updates(user_input: str):\n",
" events= graph.stream(\n",
" {\"messages\": [(\"user\", user_input)]},\n",
" config,\n",
" stream_mode=\"values\"\n",
" )\n",
" for event in events:\n",
" event[\"messages\"][-1].pretty_print()\n",
" #for value in event.values():\n",
" # print(\"Assistant:\", value[\"messages\"][-1].content)\n",
"\n",
"\n",
"while True:\n",
" try:\n",
" user_input = input(\"User: \")\n",
" if user_input.lower() in [\"quit\", \"exit\", \"q\"]:\n",
" print(\"Goodbye!\")\n",
" break\n",
" snapshot = graph.get_state(config)\n",
" print(f'Current state: {snapshot}')\n",
" stream_graph_updates(user_input)\n",
" except Exception as e:\n",
" # fallback if input() is not available\n",
" raise Exception(f'An error occured: {e}')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "langchain_311",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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