394 lines
44 KiB
Text
394 lines
44 KiB
Text
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "5133f8fa",
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"metadata": {},
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"source": [
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"# Remote Agent Testing\n",
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"Using google genAI to test an agentic workflow with Gemini 2.5"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "62ec2147",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Imports\n",
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"import os \n",
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"from dotenv import load_dotenv\n",
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"from langchain.agents import create_agent\n",
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"from langchain.agents.middleware import dynamic_prompt, ModelRequest\n",
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"from langchain.chat_models import init_chat_model\n",
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"from langchain.tools import tool\n",
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"from langchain_chroma import Chroma\n",
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"from langchain_google_genai import GoogleGenerativeAIEmbeddings\n",
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"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
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"\n",
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"load_dotenv(os.path.join('', '..', '.env'))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6dc525a1",
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"metadata": {},
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"source": [
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"Using Gemini 2.5 via Langchain's Google Generative AI integration to test an agentic workflow."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "a401cf8a",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"model = init_chat_model(\"google_genai:gemini-2.5-flash-lite\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "aaa68979",
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"metadata": {},
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"source": [
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"Setting up embeddings model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "45805907",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"embeddings = GoogleGenerativeAIEmbeddings(model=\"models/gemini-embedding-001\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b3f90586",
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"metadata": {},
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"source": [
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"Vector store setup for data storage and retrieval"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "500f90f4",
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"metadata": {},
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"outputs": [],
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"source": [
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"vector_store = Chroma(\n",
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" collection_name=\"example_collection\",\n",
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" embedding_function=embeddings,\n",
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" persist_directory=\"./build/langchain_db\",\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "d4ff7ec0",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"6,900 pages later… *“This story is just for that one reader.”* \n",
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"*Omniscient Reader’s Viewpoint* is probably one of the most ambitious epics I’ve ever read in this genre. Regression-themed novels are already a flooded trope, but this one blows the rest out of the water purely from how many layers it stacks on top of itself and still manages to come out narratively clean. When I first got into this series (via the webtoon, like most people), the wait between weekly releases drove me up the wall,\n",
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"Total characters: 8578\n"
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]
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}
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],
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"source": [
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"import requests\n",
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"from langchain_core.documents import Document\n",
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"\n",
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"response = requests.get(\"https://viswamedha.com/api/post/a-story-for-one-reader/\")\n",
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"data = response.json()\n",
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"content = data['content']\n",
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"\n",
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"docs = [Document(page_content=content, metadata={\"source\": response.url})]\n",
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"\n",
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"assert len(docs) == 1\n",
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"print(docs[0].page_content[:500])\n",
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"print(f\"Total characters: {len(docs[0].page_content)}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "82bcfabc",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Split blog post into 13 sub-documents.\n"
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]
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}
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],
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"source": [
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"\n",
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"text_splitter = RecursiveCharacterTextSplitter(\n",
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" chunk_size=1000, \n",
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" chunk_overlap=200, \n",
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" add_start_index=True,\n",
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")\n",
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"all_splits = text_splitter.split_documents(docs)\n",
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"\n",
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"print(f\"Split blog post into {len(all_splits)} sub-documents.\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "2ee1a9ca",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['44706f38-6bd0-4e9a-8a41-d27790bdddc8', '9d2a2300-a311-4389-86b8-71eef221186c', '3970098b-f681-47bb-8c1d-6929cb67b537']\n"
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]
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}
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],
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"source": [
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"document_ids = vector_store.add_documents(documents=all_splits)\n",
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"\n",
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"print(document_ids[:3])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "a9096893",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"\n",
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"@tool(response_format=\"content_and_artifact\")\n",
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"def retrieve_context(query: str):\n",
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" \"\"\"Retrieve information to help answer a query.\"\"\"\n",
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" retrieved_docs = vector_store.similarity_search(query, k=2)\n",
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" serialized = \"\\n\\n\".join(\n",
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" (f\"Source: {doc.metadata}\\nContent: {doc.page_content}\")\n",
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" for doc in retrieved_docs\n",
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" )\n",
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" return serialized, retrieved_docs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "dff2345d",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"\n",
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"tools = [retrieve_context]\n",
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"prompt = (\n",
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" \"You have access to a tool that retrieves context from a blog post. \"\n",
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" \"Use the tool to help answer user queries.\"\n",
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")\n",
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"agent = create_agent(model, tools, system_prompt=prompt)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "aaa2fad9",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"================================\u001b[1m Human Message \u001b[0m=================================\n",
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"\n",
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"What is the significance of the second loop?\n",
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"\n",
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"Use the retrieved context to provide a detailed answer.\n",
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"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
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"Tool Calls:\n",
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" retrieve_context (b6c5ce4e-a030-47cf-8fed-f1279f022766)\n",
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" Call ID: b6c5ce4e-a030-47cf-8fed-f1279f022766\n",
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" Args:\n",
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" query: Significance of the second loop\n",
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"=================================\u001b[1m Tool Message \u001b[0m=================================\n",
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"Name: retrieve_context\n",
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"\n",
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"Source: {'start_index': 3377, 'source': 'https://viswamedha.com/api/post/a-story-for-one-reader/'}\n",
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"Content: And this is where the paradox really hits. The Great Plotter, while observing regressions and chasing a better ending, ends up **creating the very timeline** he’s been watching. In trying to fix his own story, he triggers a new one. He unknowingly causes the very events that lead to KDJ’s worldline existing in the first place. It's absolutely wild. He becomes the most influential figure in this timeline, yet completely powerless to interact with it directly (due to the constraints of Probability). All he can do is watch as KDJ lives through the story he thought he already knew.\n",
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"\n",
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"---\n",
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"\n",
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"## What is the second paradox, and where does the loop begin?\n",
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"\n",
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"Source: {'start_index': 3377, 'source': 'https://viswamedha.com/api/post/a-story-for-one-reader/'}\n",
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"Content: And this is where the paradox really hits. The Great Plotter, while observing regressions and chasing a better ending, ends up **creating the very timeline** he’s been watching. In trying to fix his own story, he triggers a new one. He unknowingly causes the very events that lead to KDJ’s worldline existing in the first place. It's absolutely wild. He becomes the most influential figure in this timeline, yet completely powerless to interact with it directly (due to the constraints of Probability). All he can do is watch as KDJ lives through the story he thought he already knew.\n",
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"\n",
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"---\n",
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"\n",
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"## What is the second paradox, and where does the loop begin?\n",
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"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
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"\n",
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"The second loop is significant because it represents a paradox where the Great Plotter, in his attempt to alter his own story and create a better ending, inadvertently becomes the catalyst for the very timeline he is observing. He ends up creating the timeline he has been watching, triggering new events, and causing the existence of KDJ's worldline. Despite being the most influential figure in this new timeline, the Great Plotter is powerless to intervene directly and can only watch as KDJ experiences the story.\n"
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]
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}
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],
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"source": [
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"query = (\n",
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" \"What is the significance of the second loop?\\n\\n\"\n",
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" \"Use the retrieved context to provide a detailed answer.\"\n",
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")\n",
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"\n",
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"for event in agent.stream(\n",
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" {\"messages\": [{\"role\": \"user\", \"content\": query}]},\n",
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" stream_mode=\"values\",\n",
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"):\n",
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" event[\"messages\"][-1].pretty_print()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "bda6d7d0",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"\n",
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"@dynamic_prompt\n",
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"def prompt_with_context(request: ModelRequest) -> str:\n",
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" \"\"\"Inject context into state messages.\"\"\"\n",
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" last_query = request.state[\"messages\"][-1].text\n",
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" retrieved_docs = vector_store.similarity_search(last_query)\n",
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"\n",
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" docs_content = \"\\n\\n\".join(doc.page_content for doc in retrieved_docs)\n",
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"\n",
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" system_message = (\n",
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" \"You are a helpful assistant. Use the following context in your response:\"\n",
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" f\"\\n\\n{docs_content}\"\n",
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" )\n",
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"\n",
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" return system_message\n",
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"\n",
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"\n",
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"agent = create_agent(model, tools=[], middleware=[prompt_with_context])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1540855c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'messages': [HumanMessage(content='What is the significance of the second loop?\\n\\n', additional_kwargs={}, response_metadata={}, id='eaca10e5-a350-4ad8-80ad-c62645b69e5a')]}\n",
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"================================\u001b[1m Human Message \u001b[0m=================================\n",
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"\n",
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"What is the significance of the second loop?\n",
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"\n",
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"\n"
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]
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},
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{
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"ename": "GoogleGenerativeAIError",
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"evalue": "Error embedding content: 500 INTERNAL. {'error': {'code': 500, 'message': 'Internal error encountered.', 'status': 'INTERNAL'}}",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mServerError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\langchain_google_genai\\embeddings.py:480\u001b[39m, in \u001b[36mGoogleGenerativeAIEmbeddings.embed_query\u001b[39m\u001b[34m(self, text, task_type, title, output_dimensionality)\u001b[39m\n\u001b[32m 479\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m480\u001b[39m result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mclient\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m.\u001b[49m\u001b[43membed_content\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 481\u001b[39m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 482\u001b[39m \u001b[43m \u001b[49m\u001b[43mcontents\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 483\u001b[39m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 484\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 485\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ClientError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
|
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"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\google\\genai\\models.py:4179\u001b[39m, in \u001b[36mModels.embed_content\u001b[39m\u001b[34m(self, model, contents, config)\u001b[39m\n\u001b[32m 4177\u001b[39m request_dict = _common.encode_unserializable_types(request_dict)\n\u001b[32m-> \u001b[39m\u001b[32m4179\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_api_client\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 4180\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mpost\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrequest_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhttp_options\u001b[49m\n\u001b[32m 4181\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4183\u001b[39m response_dict = {} \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m response.body \u001b[38;5;28;01melse\u001b[39;00m json.loads(response.body)\n",
|
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"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\google\\genai\\_api_client.py:1386\u001b[39m, in \u001b[36mBaseApiClient.request\u001b[39m\u001b[34m(self, http_method, path, request_dict, http_options)\u001b[39m\n\u001b[32m 1383\u001b[39m http_request = \u001b[38;5;28mself\u001b[39m._build_request(\n\u001b[32m 1384\u001b[39m http_method, path, request_dict, http_options\n\u001b[32m 1385\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1386\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhttp_request\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhttp_options\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 1387\u001b[39m response_body = (\n\u001b[32m 1388\u001b[39m response.response_stream[\u001b[32m0\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m response.response_stream \u001b[38;5;28;01melse\u001b[39;00m \u001b[33m'\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m 1389\u001b[39m )\n",
|
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"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\google\\genai\\_api_client.py:1222\u001b[39m, in \u001b[36mBaseApiClient._request\u001b[39m\u001b[34m(self, http_request, http_options, stream)\u001b[39m\n\u001b[32m 1220\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m retry(\u001b[38;5;28mself\u001b[39m._request_once, http_request, stream) \u001b[38;5;66;03m# type: ignore[no-any-return]\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1222\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_retry\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_request_once\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhttp_request\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m)\u001b[49m\n",
|
|||
|
|
"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\tenacity\\__init__.py:477\u001b[39m, in \u001b[36mRetrying.__call__\u001b[39m\u001b[34m(self, fn, *args, **kwargs)\u001b[39m\n\u001b[32m 476\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m477\u001b[39m do = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43miter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mretry_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43mretry_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 478\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(do, DoAttempt):\n",
|
|||
|
|
"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\tenacity\\__init__.py:378\u001b[39m, in \u001b[36mBaseRetrying.iter\u001b[39m\u001b[34m(self, retry_state)\u001b[39m\n\u001b[32m 377\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m action \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.iter_state.actions:\n\u001b[32m--> \u001b[39m\u001b[32m378\u001b[39m result = \u001b[43maction\u001b[49m\u001b[43m(\u001b[49m\u001b[43mretry_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 379\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
|
|||
|
|
"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\tenacity\\__init__.py:420\u001b[39m, in \u001b[36mBaseRetrying._post_stop_check_actions.<locals>.exc_check\u001b[39m\u001b[34m(rs)\u001b[39m\n\u001b[32m 419\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.reraise:\n\u001b[32m--> \u001b[39m\u001b[32m420\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[43mretry_exc\u001b[49m\u001b[43m.\u001b[49m\u001b[43mreraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 421\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m retry_exc \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mfut\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mexception\u001b[39;00m()\n",
|
|||
|
|
"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\tenacity\\__init__.py:187\u001b[39m, in \u001b[36mRetryError.reraise\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 186\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.last_attempt.failed:\n\u001b[32m--> \u001b[39m\u001b[32m187\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mlast_attempt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 188\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
|
|||
|
|
"\u001b[36mFile \u001b[39m\u001b[32mC:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.13_3.13.2544.0_x64__qbz5n2kfra8p0\\Lib\\concurrent\\futures\\_base.py:449\u001b[39m, in \u001b[36mFuture.result\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 448\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._state == FINISHED:\n\u001b[32m--> \u001b[39m\u001b[32m449\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 451\u001b[39m \u001b[38;5;28mself\u001b[39m._condition.wait(timeout)\n",
|
|||
|
|
"\u001b[36mFile \u001b[39m\u001b[32mC:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.13_3.13.2544.0_x64__qbz5n2kfra8p0\\Lib\\concurrent\\futures\\_base.py:401\u001b[39m, in \u001b[36mFuture.__get_result\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 400\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m401\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m._exception\n\u001b[32m 402\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 403\u001b[39m \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n",
|
|||
|
|
"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\tenacity\\__init__.py:480\u001b[39m, in \u001b[36mRetrying.__call__\u001b[39m\u001b[34m(self, fn, *args, **kwargs)\u001b[39m\n\u001b[32m 479\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m480\u001b[39m result = \u001b[43mfn\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 481\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m: \u001b[38;5;66;03m# noqa: B902\u001b[39;00m\n",
|
|||
|
|
"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\google\\genai\\_api_client.py:1199\u001b[39m, in \u001b[36mBaseApiClient._request_once\u001b[39m\u001b[34m(self, http_request, stream)\u001b[39m\n\u001b[32m 1192\u001b[39m response = \u001b[38;5;28mself\u001b[39m._httpx_client.request(\n\u001b[32m 1193\u001b[39m method=http_request.method,\n\u001b[32m 1194\u001b[39m url=http_request.url,\n\u001b[32m (...)\u001b[39m\u001b[32m 1197\u001b[39m timeout=http_request.timeout,\n\u001b[32m 1198\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1199\u001b[39m \u001b[43merrors\u001b[49m\u001b[43m.\u001b[49m\u001b[43mAPIError\u001b[49m\u001b[43m.\u001b[49m\u001b[43mraise_for_response\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1200\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m HttpResponse(\n\u001b[32m 1201\u001b[39m response.headers, response \u001b[38;5;28;01mif\u001b[39;00m stream \u001b[38;5;28;01melse\u001b[39;00m [response.text]\n\u001b[32m 1202\u001b[39m )\n",
|
|||
|
|
"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\google\\genai\\errors.py:121\u001b[39m, in \u001b[36mAPIError.raise_for_response\u001b[39m\u001b[34m(cls, response)\u001b[39m\n\u001b[32m 119\u001b[39m response_json = response.body_segments[\u001b[32m0\u001b[39m].get(\u001b[33m'\u001b[39m\u001b[33merror\u001b[39m\u001b[33m'\u001b[39m, {})\n\u001b[32m--> \u001b[39m\u001b[32m121\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mraise_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstatus_code\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_json\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m)\u001b[49m\n",
|
|||
|
|
"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\google\\genai\\errors.py:148\u001b[39m, in \u001b[36mAPIError.raise_error\u001b[39m\u001b[34m(cls, status_code, response_json, response)\u001b[39m\n\u001b[32m 147\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[32m500\u001b[39m <= status_code < \u001b[32m600\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m148\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m ServerError(status_code, response_json, response)\n\u001b[32m 149\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n",
|
|||
|
|
"\u001b[31mServerError\u001b[39m: 500 INTERNAL. {'error': {'code': 500, 'message': 'Internal error encountered.', 'status': 'INTERNAL'}}",
|
|||
|
|
"\nThe above exception was the direct cause of the following exception:\n",
|
|||
|
|
"\u001b[31mGoogleGenerativeAIError\u001b[39m Traceback (most recent call last)",
|
|||
|
|
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[13]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43magent\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2\u001b[39m \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[43m[\u001b[49m\u001b[43m{\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mrole\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\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[33;43m\"\u001b[39;49m\u001b[33;43mcontent\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[43m}\u001b[49m\u001b[43m]\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream_mode\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 4\u001b[39m \u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mimport\u001b[39;49;00m\u001b[38;5;250;43m \u001b[39;49m\u001b[34;43;01mpprint\u001b[39;49;00m\n\u001b[32m 6\u001b[39m \u001b[43m \u001b[49m\u001b[43mpprint\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpprint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# inspect the event structure\u001b[39;49;00m\n",
|
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"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\langgraph\\pregel\\main.py:2646\u001b[39m, in \u001b[36mPregel.stream\u001b[39m\u001b[34m(self, input, config, context, stream_mode, print_mode, output_keys, interrupt_before, interrupt_after, durability, subgraphs, debug, **kwargs)\u001b[39m\n\u001b[32m 2644\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 2645\u001b[39m loop.output_writes(task.id, task.writes, cached=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m-> \u001b[39m\u001b[32m2646\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 2647\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 2648\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 2649\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 2650\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 2651\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 2652\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# emit output\u001b[39;49;00m\n\u001b[32m 2653\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01myield from\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m_output\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2654\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprint_mode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msubgraphs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mqueue\u001b[49m\u001b[43m.\u001b[49m\u001b[43mEmpty\u001b[49m\n\u001b[32m 2655\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2656\u001b[39m loop.after_tick()\n",
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"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\langgraph\\pregel\\_runner.py:167\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 165\u001b[39m t = tasks[\u001b[32m0\u001b[39m]\n\u001b[32m 166\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m167\u001b[39m \u001b[43mrun_with_retry\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 168\u001b[39m \u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 169\u001b[39m \u001b[43m \u001b[49m\u001b[43mretry_policy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 170\u001b[39m \u001b[43m \u001b[49m\u001b[43mconfigurable\u001b[49m\u001b[43m=\u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 171\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 172\u001b[39m \u001b[43m \u001b[49m\u001b[43m_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 173\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 174\u001b[39m \u001b[43m \u001b[49m\u001b[43mretry_policy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mretry_policy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 175\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 176\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 177\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 178\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 179\u001b[39m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 180\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 181\u001b[39m \u001b[38;5;28mself\u001b[39m.commit(t, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m 182\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",
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"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\langgraph\\pregel\\_retry.py:42\u001b[39m, in \u001b[36mrun_with_retry\u001b[39m\u001b[34m(task, retry_policy, configurable)\u001b[39m\n\u001b[32m 40\u001b[39m task.writes.clear()\n\u001b[32m 41\u001b[39m \u001b[38;5;66;03m# run the task\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m42\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 43\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ParentCommand \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[32m 44\u001b[39m ns: \u001b[38;5;28mstr\u001b[39m = config[CONF][CONFIG_KEY_CHECKPOINT_NS]\n",
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"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\langgraph\\_internal\\_runnable.py:656\u001b[39m, in \u001b[36mRunnableSeq.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m 654\u001b[39m \u001b[38;5;66;03m# run in context\u001b[39;00m\n\u001b[32m 655\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[32m656\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 657\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 658\u001b[39m \u001b[38;5;28minput\u001b[39m = step.invoke(\u001b[38;5;28minput\u001b[39m, config)\n",
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"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\langgraph\\_internal\\_runnable.py:400\u001b[39m, in \u001b[36mRunnableCallable.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m 398\u001b[39m run_manager.on_chain_end(ret)\n\u001b[32m 399\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m400\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 401\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 402\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m ret.invoke(\u001b[38;5;28minput\u001b[39m, config)\n",
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"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\langchain\\agents\\factory.py:1144\u001b[39m, in \u001b[36mcreate_agent.<locals>.model_node\u001b[39m\u001b[34m(state, runtime)\u001b[39m\n\u001b[32m 1141\u001b[39m response = _execute_model_sync(request)\n\u001b[32m 1142\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1143\u001b[39m \u001b[38;5;66;03m# Call composed handler with base handler\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1144\u001b[39m response = \u001b[43mwrap_model_call_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_execute_model_sync\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1146\u001b[39m \u001b[38;5;66;03m# Extract state updates from ModelResponse\u001b[39;00m\n\u001b[32m 1147\u001b[39m state_updates = {\u001b[33m\"\u001b[39m\u001b[33mmessages\u001b[39m\u001b[33m\"\u001b[39m: response.result}\n",
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"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\langchain\\agents\\factory.py:146\u001b[39m, in \u001b[36m_chain_model_call_handlers.<locals>.normalized_single\u001b[39m\u001b[34m(request, handler)\u001b[39m\n\u001b[32m 142\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mnormalized_single\u001b[39m(\n\u001b[32m 143\u001b[39m request: ModelRequest,\n\u001b[32m 144\u001b[39m handler: Callable[[ModelRequest], ModelResponse],\n\u001b[32m 145\u001b[39m ) -> ModelResponse:\n\u001b[32m--> \u001b[39m\u001b[32m146\u001b[39m result = \u001b[43msingle_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhandler\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 147\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _normalize_to_model_response(result)\n",
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"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\langchain\\agents\\middleware\\types.py:1656\u001b[39m, in \u001b[36mdynamic_prompt.<locals>.decorator.<locals>.wrapped\u001b[39m\u001b[34m(_self, request, handler)\u001b[39m\n\u001b[32m 1651\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mwrapped\u001b[39m(\n\u001b[32m 1652\u001b[39m _self: AgentMiddleware[StateT, ContextT],\n\u001b[32m 1653\u001b[39m request: ModelRequest,\n\u001b[32m 1654\u001b[39m handler: Callable[[ModelRequest], ModelResponse],\n\u001b[32m 1655\u001b[39m ) -> ModelCallResult:\n\u001b[32m-> \u001b[39m\u001b[32m1656\u001b[39m prompt = \u001b[43mcast\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mCallable[[ModelRequest], SystemMessage | str]\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1657\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(prompt, SystemMessage):\n\u001b[32m 1658\u001b[39m request = request.override(system_message=prompt)\n",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 5\u001b[39m, in \u001b[36mprompt_with_context\u001b[39m\u001b[34m(request)\u001b[39m\n\u001b[32m 3\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Inject context into state messages.\"\"\"\u001b[39;00m\n\u001b[32m 4\u001b[39m last_query = request.state[\u001b[33m\"\u001b[39m\u001b[33mmessages\u001b[39m\u001b[33m\"\u001b[39m][-\u001b[32m1\u001b[39m].text\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m retrieved_docs = \u001b[43mvector_store\u001b[49m\u001b[43m.\u001b[49m\u001b[43msimilarity_search\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlast_query\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 7\u001b[39m docs_content = \u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m.join(doc.page_content \u001b[38;5;28;01mfor\u001b[39;00m doc \u001b[38;5;129;01min\u001b[39;00m retrieved_docs)\n\u001b[32m 9\u001b[39m system_message = (\n\u001b[32m 10\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mYou are a helpful assistant. Use the following context in your response:\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 11\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mdocs_content\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 12\u001b[39m )\n",
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"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\langchain_chroma\\vectorstores.py:748\u001b[39m, in \u001b[36mChroma.similarity_search\u001b[39m\u001b[34m(self, query, k, filter, **kwargs)\u001b[39m\n\u001b[32m 730\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msimilarity_search\u001b[39m(\n\u001b[32m 731\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 732\u001b[39m query: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 735\u001b[39m **kwargs: Any,\n\u001b[32m 736\u001b[39m ) -> \u001b[38;5;28mlist\u001b[39m[Document]:\n\u001b[32m 737\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Run similarity search with Chroma.\u001b[39;00m\n\u001b[32m 738\u001b[39m \n\u001b[32m 739\u001b[39m \u001b[33;03m Args:\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 746\u001b[39m \u001b[33;03m List of documents most similar to the query text.\u001b[39;00m\n\u001b[32m 747\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m748\u001b[39m docs_and_scores = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msimilarity_search_with_score\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 749\u001b[39m \u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 750\u001b[39m \u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 751\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mfilter\u001b[39;49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mfilter\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 752\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 753\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 754\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m [doc \u001b[38;5;28;01mfor\u001b[39;00m doc, _ \u001b[38;5;129;01min\u001b[39;00m docs_and_scores]\n",
|
|||
|
|
"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\langchain_chroma\\vectorstores.py:848\u001b[39m, in \u001b[36mChroma.similarity_search_with_score\u001b[39m\u001b[34m(self, query, k, filter, where_document, **kwargs)\u001b[39m\n\u001b[32m 840\u001b[39m results = \u001b[38;5;28mself\u001b[39m.__query_collection(\n\u001b[32m 841\u001b[39m query_texts=[query],\n\u001b[32m 842\u001b[39m n_results=k,\n\u001b[32m (...)\u001b[39m\u001b[32m 845\u001b[39m **kwargs,\n\u001b[32m 846\u001b[39m )\n\u001b[32m 847\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m848\u001b[39m query_embedding = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_embedding_function\u001b[49m\u001b[43m.\u001b[49m\u001b[43membed_query\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 849\u001b[39m results = \u001b[38;5;28mself\u001b[39m.__query_collection(\n\u001b[32m 850\u001b[39m query_embeddings=[query_embedding],\n\u001b[32m 851\u001b[39m n_results=k,\n\u001b[32m (...)\u001b[39m\u001b[32m 854\u001b[39m **kwargs,\n\u001b[32m 855\u001b[39m )\n\u001b[32m 857\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _results_to_docs_and_scores(results)\n",
|
|||
|
|
"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\langchain_google_genai\\embeddings.py:490\u001b[39m, in \u001b[36mGoogleGenerativeAIEmbeddings.embed_query\u001b[39m\u001b[34m(self, text, task_type, title, output_dimensionality)\u001b[39m\n\u001b[32m 488\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 e:\n\u001b[32m 489\u001b[39m msg = \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mError embedding content: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m490\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m GoogleGenerativeAIError(msg) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01me\u001b[39;00m\n\u001b[32m 492\u001b[39m \u001b[38;5;66;03m# Single text returns single embedding\u001b[39;00m\n\u001b[32m 493\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(result.embeddings[\u001b[32m0\u001b[39m].values)\n",
|
|||
|
|
"\u001b[31mGoogleGenerativeAIError\u001b[39m: Error embedding content: 500 INTERNAL. {'error': {'code': 500, 'message': 'Internal error encountered.', 'status': 'INTERNAL'}}",
|
|||
|
|
"During task with name 'model' and id '2df4c75f-65ba-cd3e-b448-0ed95a7614f8'"
|
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|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"query = \"What is the significance of the second loop?\\n\\n\"\n",
|
|||
|
|
"for step in agent.stream(\n",
|
|||
|
|
" {\"messages\": [{\"role\": \"user\", \"content\": query}]},\n",
|
|||
|
|
" stream_mode=\"values\",\n",
|
|||
|
|
"):\n",
|
|||
|
|
" step[\"messages\"][-1].pretty_print()"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": ".venv",
|
|||
|
|
"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": "3.13.9"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 5
|
|||
|
|
}
|