{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from dotenv import load_dotenv\n",
"from langgraph.graph import START, StateGraph, MessagesState\n",
"from langgraph.prebuilt import tools_condition\n",
"from langgraph.prebuilt import ToolNode\n",
"from langchain_google_genai import ChatGoogleGenerativeAI\n",
"from langchain_groq import ChatGroq\n",
"from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings\n",
"from langchain_community.tools.tavily_search import TavilySearchResults\n",
"from langchain_community.document_loaders import WikipediaLoader\n",
"from langchain_community.document_loaders import ArxivLoader\n",
"from langchain_community.vectorstores import SupabaseVectorStore\n",
"from langchain_core.messages import SystemMessage, HumanMessage\n",
"from langchain_core.tools import tool\n",
"from langchain.tools.retriever import create_retriever_tool\n",
"from supabase.client import Client, create_client"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"load_dotenv(\"./env.local\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"@tool\n",
"def multiply(a: int, b: int) -> int:\n",
" \"\"\"Multiply two numbers.\n",
"\n",
" Args:\n",
" a: first int\n",
" b: second int\n",
" \"\"\"\n",
" return a * b\n",
"\n",
"@tool\n",
"def add(a: int, b: int) -> int:\n",
" \"\"\"Add two numbers.\n",
" \n",
" Args:\n",
" a: first int\n",
" b: second int\n",
" \"\"\"\n",
" return a + b\n",
"\n",
"@tool\n",
"def subtract(a: int, b: int) -> int:\n",
" \"\"\"Subtract two numbers.\n",
" \n",
" Args:\n",
" a: first int\n",
" b: second int\n",
" \"\"\"\n",
" return a - b\n",
"\n",
"@tool\n",
"def divide(a: int, b: int) -> int:\n",
" \"\"\"Divide two numbers.\n",
" \n",
" Args:\n",
" a: first int\n",
" b: second int\n",
" \"\"\"\n",
" if b == 0:\n",
" raise ValueError(\"Cannot divide by zero.\")\n",
" return a / b\n",
"\n",
"@tool\n",
"def modulus(a: int, b: int) -> int:\n",
" \"\"\"Get the modulus of two numbers.\n",
" \n",
" Args:\n",
" a: first int\n",
" b: second int\n",
" \"\"\"\n",
" return a % b\n",
"\n",
"@tool\n",
"def wiki_search(query: str) -> str:\n",
" \"\"\"Search Wikipedia for a query and return maximum 2 results.\n",
" \n",
" Args:\n",
" query: The search query.\"\"\"\n",
" search_docs = WikipediaLoader(query=query, load_max_docs=2).load()\n",
" formatted_search_docs = \"\\n\\n---\\n\\n\".join(\n",
" [\n",
" f'\\n{doc.page_content}\\n'\n",
" for doc in search_docs\n",
" ])\n",
" return {\"wiki_results\": formatted_search_docs}\n",
"\n",
"@tool\n",
"def web_search(query: str) -> str:\n",
" \"\"\"Search Tavily for a query and return maximum 3 results.\n",
" \n",
" Args:\n",
" query: The search query.\"\"\"\n",
" search_docs = TavilySearchResults(max_results=3).invoke(query=query)\n",
" formatted_search_docs = \"\\n\\n---\\n\\n\".join(\n",
" [\n",
" f'\\n{doc.page_content}\\n'\n",
" for doc in search_docs\n",
" ])\n",
" return {\"web_results\": formatted_search_docs}\n",
"\n",
"@tool\n",
"def arvix_search(query: str) -> str:\n",
" \"\"\"Search Arxiv for a query and return maximum 3 result.\n",
" \n",
" Args:\n",
" query: The search query.\"\"\"\n",
" search_docs = ArxivLoader(query=query, load_max_docs=3).load()\n",
" formatted_search_docs = \"\\n\\n---\\n\\n\".join(\n",
" [\n",
" f'\\n{doc.page_content[:1000]}\\n'\n",
" for doc in search_docs\n",
" ])\n",
" return {\"arvix_results\": formatted_search_docs}\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# load the system prompt from the file\n",
"with open(\"system_prompt.txt\", \"r\", encoding=\"utf-8\") as f:\n",
" system_prompt = f.read()\n",
"\n",
"# System message\n",
"sys_msg = SystemMessage(content=system_prompt)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# build a retriever\n",
"embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\") # dim=768\n",
"supabase: Client = create_client(\n",
" os.environ.get(\"SUPABASE_URL\"), \n",
" os.environ.get(\"SUPABASE_SERVICE_KEY\"))\n",
"vector_store = SupabaseVectorStore(\n",
" client=supabase,\n",
" embedding= embeddings,\n",
" table_name=\"documents\",\n",
" query_name=\"match_documents_langchain\",\n",
")\n",
"create_retriever_tool = create_retriever_tool(\n",
" retriever=vector_store.as_retriever(),\n",
" name=\"Question Search\",\n",
" description=\"A tool to retrieve similar questions from a vector store.\",\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"tools = [\n",
" multiply,\n",
" add,\n",
" subtract,\n",
" divide,\n",
" modulus,\n",
" wiki_search,\n",
" web_search,\n",
" arvix_search,\n",
"]\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# Build graph function\n",
"def build_graph(provider: str = \"groq\"):\n",
" \"\"\"Build the graph\"\"\"\n",
" # Load environment variables from .env file\n",
" if provider == \"google\":\n",
" # Google Gemini\n",
" llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\", temperature=0)\n",
" elif provider == \"groq\":\n",
" # Groq https://console.groq.com/docs/models\n",
" llm = ChatGroq(model=\"qwen-qwq-32b\", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it\n",
" elif provider == \"huggingface\":\n",
" # TODO: Add huggingface endpoint\n",
" llm = ChatHuggingFace(\n",
" llm=HuggingFaceEndpoint(\n",
" url=\"https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf\",\n",
" temperature=0,\n",
" ),\n",
" )\n",
" else:\n",
" raise ValueError(\"Invalid provider. Choose 'google', 'groq' or 'huggingface'.\")\n",
" # Bind tools to LLM\n",
" llm_with_tools = llm.bind_tools(tools)\n",
"\n",
" # Node\n",
" def assistant(state: MessagesState):\n",
" \"\"\"Assistant node\"\"\"\n",
" return {\"messages\": [llm_with_tools.invoke(state[\"messages\"])]}\n",
" \n",
" def retriever(state: MessagesState):\n",
" \"\"\"Retriever node\"\"\"\n",
" similar_question = vector_store.similarity_search(state[\"messages\"][0].content)\n",
" if similar_question:\n",
" example_msg = HumanMessage(\n",
" content=f\"Here I provide a similar question and answer for reference: \\n\\n{similar_question[0].page_content}\",\n",
" )\n",
" else:\n",
" example_msg = HumanMessage(\n",
" content=\"No similar question found in the database.\"\n",
" )\n",
" return {\"messages\": [sys_msg] + state[\"messages\"] + [example_msg]}\n",
"\n",
" builder = StateGraph(MessagesState)\n",
" builder.add_node(\"retriever\", retriever)\n",
" builder.add_node(\"assistant\", assistant)\n",
" builder.add_node(\"tools\", ToolNode(tools))\n",
" builder.add_edge(START, \"retriever\")\n",
" builder.add_edge(\"retriever\", \"assistant\")\n",
" builder.add_conditional_edges(\n",
" \"assistant\",\n",
" tools_condition,\n",
" )\n",
" builder.add_edge(\"tools\", \"assistant\")\n",
"\n",
" # Compile graph\n",
" return builder.compile()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"ename": "PermissionDeniedError",
"evalue": "Error code: 403 - {'error': {'message': 'Access denied. Please check your network settings.'}}",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mPermissionDeniedError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[16]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 4\u001b[39m \u001b[38;5;66;03m# Run the graph\u001b[39;00m\n\u001b[32m 5\u001b[39m messages = [HumanMessage(content=question)]\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m messages = \u001b[43mgraph\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmessages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m m \u001b[38;5;129;01min\u001b[39;00m messages[\u001b[33m\"\u001b[39m\u001b[33mmessages\u001b[39m\u001b[33m\"\u001b[39m]:\n\u001b[32m 8\u001b[39m m.pretty_print()\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\coding\\github-mine\\agents\\huggingface_course\\Final_Assignment_Template\\.venv\\Lib\\site-packages\\langgraph\\pregel\\__init__.py:2719\u001b[39m, in \u001b[36mPregel.invoke\u001b[39m\u001b[34m(self, input, config, stream_mode, output_keys, interrupt_before, interrupt_after, checkpoint_during, debug, **kwargs)\u001b[39m\n\u001b[32m 2716\u001b[39m chunks: \u001b[38;5;28mlist\u001b[39m[Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]] = []\n\u001b[32m 2717\u001b[39m interrupts: \u001b[38;5;28mlist\u001b[39m[Interrupt] = []\n\u001b[32m-> \u001b[39m\u001b[32m2719\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2720\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 2721\u001b[39m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2722\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2723\u001b[39m \u001b[43m \u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2724\u001b[39m \u001b[43m \u001b[49m\u001b[43minterrupt_before\u001b[49m\u001b[43m=\u001b[49m\u001b[43minterrupt_before\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2725\u001b[39m \u001b[43m \u001b[49m\u001b[43minterrupt_after\u001b[49m\u001b[43m=\u001b[49m\u001b[43minterrupt_after\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2726\u001b[39m \u001b[43m \u001b[49m\u001b[43mcheckpoint_during\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcheckpoint_during\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2727\u001b[39m \u001b[43m \u001b[49m\u001b[43mdebug\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdebug\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2728\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2729\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 2730\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mvalues\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\n\u001b[32m 2731\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2732\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 2733\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;129;43;01mand\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mints\u001b[49m\u001b[43m \u001b[49m\u001b[43m:=\u001b[49m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mINTERRUPT\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\n\u001b[32m 2734\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\coding\\github-mine\\agents\\huggingface_course\\Final_Assignment_Template\\.venv\\Lib\\site-packages\\langgraph\\pregel\\__init__.py:2436\u001b[39m, in \u001b[36mPregel.stream\u001b[39m\u001b[34m(self, input, config, stream_mode, output_keys, interrupt_before, interrupt_after, checkpoint_during, debug, subgraphs)\u001b[39m\n\u001b[32m 2434\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m task \u001b[38;5;129;01min\u001b[39;00m loop.match_cached_writes():\n\u001b[32m 2435\u001b[39m loop.output_writes(task.id, task.writes, cached=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m-> \u001b[39m\u001b[32m2436\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrunner\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtick\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2437\u001b[39m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mloop\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtasks\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwrites\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2438\u001b[39m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mstep_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2439\u001b[39m \u001b[43m \u001b[49m\u001b[43mget_waiter\u001b[49m\u001b[43m=\u001b[49m\u001b[43mget_waiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2440\u001b[39m \u001b[43m \u001b[49m\u001b[43mschedule_task\u001b[49m\u001b[43m=\u001b[49m\u001b[43mloop\u001b[49m\u001b[43m.\u001b[49m\u001b[43maccept_push\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2441\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 2442\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# emit output\u001b[39;49;00m\n\u001b[32m 2443\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01myield from\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2444\u001b[39m \u001b[38;5;66;03m# emit output\u001b[39;00m\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\coding\\github-mine\\agents\\huggingface_course\\Final_Assignment_Template\\.venv\\Lib\\site-packages\\langgraph\\pregel\\runner.py:161\u001b[39m, in \u001b[36mPregelRunner.tick\u001b[39m\u001b[34m(self, tasks, reraise, timeout, retry_policy, get_waiter, schedule_task)\u001b[39m\n\u001b[32m 159\u001b[39m t = tasks[\u001b[32m0\u001b[39m]\n\u001b[32m 160\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m161\u001b[39m \u001b[43mrun_with_retry\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 162\u001b[39m \u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 163\u001b[39m \u001b[43m \u001b[49m\u001b[43mretry_policy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 164\u001b[39m \u001b[43m \u001b[49m\u001b[43mconfigurable\u001b[49m\u001b[43m=\u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 165\u001b[39m \u001b[43m \u001b[49m\u001b[43mCONFIG_KEY_CALL\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mpartial\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 166\u001b[39m \u001b[43m \u001b[49m\u001b[43m_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 167\u001b[39m \u001b[43m \u001b[49m\u001b[43mweakref\u001b[49m\u001b[43m.\u001b[49m\u001b[43mref\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 168\u001b[39m \u001b[43m \u001b[49m\u001b[43mretry\u001b[49m\u001b[43m=\u001b[49m\u001b[43mretry_policy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 169\u001b[39m \u001b[43m \u001b[49m\u001b[43mfutures\u001b[49m\u001b[43m=\u001b[49m\u001b[43mweakref\u001b[49m\u001b[43m.\u001b[49m\u001b[43mref\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfutures\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 170\u001b[39m \u001b[43m \u001b[49m\u001b[43mschedule_task\u001b[49m\u001b[43m=\u001b[49m\u001b[43mschedule_task\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 171\u001b[39m \u001b[43m \u001b[49m\u001b[43msubmit\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msubmit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 172\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 173\u001b[39m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 174\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 175\u001b[39m \u001b[38;5;28mself\u001b[39m.commit(t, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m 176\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\coding\\github-mine\\agents\\huggingface_course\\Final_Assignment_Template\\.venv\\Lib\\site-packages\\langgraph\\pregel\\retry.py:40\u001b[39m, in \u001b[36mrun_with_retry\u001b[39m\u001b[34m(task, retry_policy, configurable)\u001b[39m\n\u001b[32m 38\u001b[39m task.writes.clear()\n\u001b[32m 39\u001b[39m \u001b[38;5;66;03m# run the task\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m40\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtask\u001b[49m\u001b[43m.\u001b[49m\u001b[43mproc\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m.\u001b[49m\u001b[43minput\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 41\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ParentCommand \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[32m 42\u001b[39m ns: \u001b[38;5;28mstr\u001b[39m = config[CONF][CONFIG_KEY_CHECKPOINT_NS]\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\coding\\github-mine\\agents\\huggingface_course\\Final_Assignment_Template\\.venv\\Lib\\site-packages\\langgraph\\utils\\runnable.py:623\u001b[39m, in \u001b[36mRunnableSeq.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m 621\u001b[39m \u001b[38;5;66;03m# run in context\u001b[39;00m\n\u001b[32m 622\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m set_config_context(config, run) \u001b[38;5;28;01mas\u001b[39;00m context:\n\u001b[32m--> \u001b[39m\u001b[32m623\u001b[39m \u001b[38;5;28minput\u001b[39m = \u001b[43mcontext\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 624\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 625\u001b[39m \u001b[38;5;28minput\u001b[39m = step.invoke(\u001b[38;5;28minput\u001b[39m, config)\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\coding\\github-mine\\agents\\huggingface_course\\Final_Assignment_Template\\.venv\\Lib\\site-packages\\langgraph\\utils\\runnable.py:377\u001b[39m, in \u001b[36mRunnableCallable.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m 375\u001b[39m run_manager.on_chain_end(ret)\n\u001b[32m 376\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m377\u001b[39m ret = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 378\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.recurse \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(ret, Runnable):\n\u001b[32m 379\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m ret.invoke(\u001b[38;5;28minput\u001b[39m, config)\n",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[14]\u001b[39m\u001b[32m, line 27\u001b[39m, in \u001b[36mbuild_graph..assistant\u001b[39m\u001b[34m(state)\u001b[39m\n\u001b[32m 25\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34massistant\u001b[39m(state: MessagesState):\n\u001b[32m 26\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Assistant node\"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m27\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m {\u001b[33m\"\u001b[39m\u001b[33mmessages\u001b[39m\u001b[33m\"\u001b[39m: [\u001b[43mllm_with_tools\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmessages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m]}\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\coding\\github-mine\\agents\\huggingface_course\\Final_Assignment_Template\\.venv\\Lib\\site-packages\\langchain_core\\runnables\\base.py:5431\u001b[39m, in \u001b[36mRunnableBindingBase.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m 5424\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m 5425\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minvoke\u001b[39m(\n\u001b[32m 5426\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 5429\u001b[39m **kwargs: Optional[Any],\n\u001b[32m 5430\u001b[39m ) -> Output:\n\u001b[32m-> \u001b[39m\u001b[32m5431\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mbound\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 5432\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 5433\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_merge_configs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5434\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43m{\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5435\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\coding\\github-mine\\agents\\huggingface_course\\Final_Assignment_Template\\.venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:372\u001b[39m, in \u001b[36mBaseChatModel.invoke\u001b[39m\u001b[34m(self, input, config, stop, **kwargs)\u001b[39m\n\u001b[32m 360\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m 361\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minvoke\u001b[39m(\n\u001b[32m 362\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 367\u001b[39m **kwargs: Any,\n\u001b[32m 368\u001b[39m ) -> BaseMessage:\n\u001b[32m 369\u001b[39m config = ensure_config(config)\n\u001b[32m 370\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m cast(\n\u001b[32m 371\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mChatGeneration\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m--> \u001b[39m\u001b[32m372\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgenerate_prompt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 373\u001b[39m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_convert_input\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 374\u001b[39m \u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 375\u001b[39m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcallbacks\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 376\u001b[39m \u001b[43m \u001b[49m\u001b[43mtags\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtags\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 377\u001b[39m \u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmetadata\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 378\u001b[39m \u001b[43m \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mrun_name\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 379\u001b[39m \u001b[43m \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mrun_id\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 380\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 381\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m.generations[\u001b[32m0\u001b[39m][\u001b[32m0\u001b[39m],\n\u001b[32m 382\u001b[39m ).message\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\coding\\github-mine\\agents\\huggingface_course\\Final_Assignment_Template\\.venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:957\u001b[39m, in \u001b[36mBaseChatModel.generate_prompt\u001b[39m\u001b[34m(self, prompts, stop, callbacks, **kwargs)\u001b[39m\n\u001b[32m 948\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m 949\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mgenerate_prompt\u001b[39m(\n\u001b[32m 950\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 954\u001b[39m **kwargs: Any,\n\u001b[32m 955\u001b[39m ) -> LLMResult:\n\u001b[32m 956\u001b[39m prompt_messages = [p.to_messages() \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m prompts]\n\u001b[32m--> \u001b[39m\u001b[32m957\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt_messages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\coding\\github-mine\\agents\\huggingface_course\\Final_Assignment_Template\\.venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:776\u001b[39m, in \u001b[36mBaseChatModel.generate\u001b[39m\u001b[34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[39m\n\u001b[32m 773\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i, m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(input_messages):\n\u001b[32m 774\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 775\u001b[39m results.append(\n\u001b[32m--> \u001b[39m\u001b[32m776\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_generate_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 777\u001b[39m \u001b[43m \u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 778\u001b[39m \u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 779\u001b[39m \u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 780\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 781\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 782\u001b[39m )\n\u001b[32m 783\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 784\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\coding\\github-mine\\agents\\huggingface_course\\Final_Assignment_Template\\.venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:1022\u001b[39m, in \u001b[36mBaseChatModel._generate_with_cache\u001b[39m\u001b[34m(self, messages, stop, run_manager, **kwargs)\u001b[39m\n\u001b[32m 1020\u001b[39m result = generate_from_stream(\u001b[38;5;28miter\u001b[39m(chunks))\n\u001b[32m 1021\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m inspect.signature(\u001b[38;5;28mself\u001b[39m._generate).parameters.get(\u001b[33m\"\u001b[39m\u001b[33mrun_manager\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m-> \u001b[39m\u001b[32m1022\u001b[39m result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_generate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1023\u001b[39m \u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[32m 1024\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1025\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1026\u001b[39m result = \u001b[38;5;28mself\u001b[39m._generate(messages, stop=stop, **kwargs)\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\coding\\github-mine\\agents\\huggingface_course\\Final_Assignment_Template\\.venv\\Lib\\site-packages\\langchain_groq\\chat_models.py:498\u001b[39m, in \u001b[36mChatGroq._generate\u001b[39m\u001b[34m(self, messages, stop, run_manager, **kwargs)\u001b[39m\n\u001b[32m 493\u001b[39m message_dicts, params = \u001b[38;5;28mself\u001b[39m._create_message_dicts(messages, stop)\n\u001b[32m 494\u001b[39m params = {\n\u001b[32m 495\u001b[39m **params,\n\u001b[32m 496\u001b[39m **kwargs,\n\u001b[32m 497\u001b[39m }\n\u001b[32m--> \u001b[39m\u001b[32m498\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mclient\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmessage_dicts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 499\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._create_chat_result(response)\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\coding\\github-mine\\agents\\huggingface_course\\Final_Assignment_Template\\.venv\\Lib\\site-packages\\groq\\resources\\chat\\completions.py:368\u001b[39m, in \u001b[36mCompletions.create\u001b[39m\u001b[34m(self, messages, model, exclude_domains, frequency_penalty, function_call, functions, include_domains, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, n, parallel_tool_calls, presence_penalty, reasoning_effort, reasoning_format, response_format, search_settings, seed, service_tier, stop, store, stream, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[39m\n\u001b[32m 181\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcreate\u001b[39m(\n\u001b[32m 182\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 183\u001b[39m *,\n\u001b[32m (...)\u001b[39m\u001b[32m 229\u001b[39m timeout: \u001b[38;5;28mfloat\u001b[39m | httpx.Timeout | \u001b[38;5;28;01mNone\u001b[39;00m | NotGiven = NOT_GIVEN,\n\u001b[32m 230\u001b[39m ) -> ChatCompletion | Stream[ChatCompletionChunk]:\n\u001b[32m 231\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 232\u001b[39m \u001b[33;03m Creates a model response for the given chat conversation.\u001b[39;00m\n\u001b[32m 233\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 366\u001b[39m \u001b[33;03m timeout: Override the client-level default timeout for this request, in seconds\u001b[39;00m\n\u001b[32m 367\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m368\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 369\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m/openai/v1/chat/completions\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 370\u001b[39m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 371\u001b[39m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 372\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmessages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 373\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmodel\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 374\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mexclude_domains\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude_domains\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 375\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfrequency_penalty\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrequency_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 376\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfunction_call\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunction_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 377\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfunctions\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunctions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 378\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43minclude_domains\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43minclude_domains\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 379\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlogit_bias\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogit_bias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 380\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlogprobs\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 381\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmax_completion_tokens\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_completion_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 382\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmax_tokens\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 383\u001b[39m \u001b[43m 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\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mreasoning_effort\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mreasoning_effort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 388\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mreasoning_format\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mreasoning_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 389\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mresponse_format\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 390\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43msearch_settings\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msearch_settings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 391\u001b[39m \u001b[43m 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\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstream\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 396\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtemperature\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 397\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtool_choice\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 398\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtools\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtools\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 399\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtop_logprobs\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_logprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 400\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtop_p\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_p\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 401\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43muser\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43muser\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 402\u001b[39m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 403\u001b[39m \u001b[43m \u001b[49m\u001b[43mcompletion_create_params\u001b[49m\u001b[43m.\u001b[49m\u001b[43mCompletionCreateParams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 404\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 405\u001b[39m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmake_request_options\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 406\u001b[39m \u001b[43m \u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\n\u001b[32m 407\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 408\u001b[39m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m=\u001b[49m\u001b[43mChatCompletion\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 409\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 410\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatCompletionChunk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 411\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\coding\\github-mine\\agents\\huggingface_course\\Final_Assignment_Template\\.venv\\Lib\\site-packages\\groq\\_base_client.py:1225\u001b[39m, in \u001b[36mSyncAPIClient.post\u001b[39m\u001b[34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[39m\n\u001b[32m 1211\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpost\u001b[39m(\n\u001b[32m 1212\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 1213\u001b[39m path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 1220\u001b[39m stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 1221\u001b[39m ) -> ResponseT | _StreamT:\n\u001b[32m 1222\u001b[39m opts = FinalRequestOptions.construct(\n\u001b[32m 1223\u001b[39m method=\u001b[33m\"\u001b[39m\u001b[33mpost\u001b[39m\u001b[33m\"\u001b[39m, url=path, json_data=body, files=to_httpx_files(files), **options\n\u001b[32m 1224\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1225\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\coding\\github-mine\\agents\\huggingface_course\\Final_Assignment_Template\\.venv\\Lib\\site-packages\\groq\\_base_client.py:1034\u001b[39m, in \u001b[36mSyncAPIClient.request\u001b[39m\u001b[34m(self, cast_to, options, stream, stream_cls)\u001b[39m\n\u001b[32m 1031\u001b[39m err.response.read()\n\u001b[32m 1033\u001b[39m log.debug(\u001b[33m\"\u001b[39m\u001b[33mRe-raising status error\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m1034\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m._make_status_error_from_response(err.response) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1036\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m 1038\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m response \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[33m\"\u001b[39m\u001b[33mcould not resolve response (should never happen)\u001b[39m\u001b[33m\"\u001b[39m\n",
"\u001b[31mPermissionDeniedError\u001b[39m: Error code: 403 - {'error': {'message': 'Access denied. Please check your network settings.'}}",
"During task with name 'assistant' and id 'ea13c7cc-564f-cc7e-d2d0-497d7d7645ef'"
]
}
],
"source": [
"question = \"When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?\"\n",
"# Build the graph\n",
"graph = build_graph(provider=\"groq\")\n",
"# Run the graph\n",
"messages = [HumanMessage(content=question)]\n",
"messages = graph.invoke({\"messages\": messages})\n",
"for m in messages[\"messages\"]:\n",
" m.pretty_print()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
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"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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