talk_to_db_using_rag.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.utilities import SQLDatabase\n",
    "from langchain.llms import OpenAI\n",
    "from langchain_experimental.sql import SQLDatabaseChain\n",
    "from langchain.prompts import PromptTemplate\n",
    "from langchain.prompts.chat import HumanMessagePromptTemplate\n",
    "from langchain_ollama.llms import OllamaLLM\n",
    "from langchain.schema import HumanMessage, SystemMessage\n",
    "import ollama\n",
    "from langchain.load.dump import dumps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading The LLM (Language Model)\n",
    "llm = OllamaLLM(model=\"llama3.1\", base_url=\"http://127.0.0.1:11434\")"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Install docker\n",
    "\n",
    "[Docker ubuntu](https://docs.docker.com/engine/install/ubuntu/#install-using-the-repository)\n",
    "\n",
    "## Create Database in docker container\n",
    "```\n",
    "docker run --name my-mysql -e MYSQL_ROOT_PASSWORD=my-secret-pw -e MYSQL_DATABASE=mydb -d mysql:latest\n",
    "```\n",
    "# Access the MySql shell\n",
    "```\n",
    "docker exec -it my-mysql bash\n",
    "```\n",
    "```\n",
    "mysql -u root -p\n",
    "```\n",
    "\n",
    "Enter password: my-secret-pw\n",
    "```\n",
    "USE agency_db; \n",
    "```\n",
    "![image.png](attachment:image.png)\n",
    "```\n",
    "CREATE TABLE job_details (\n",
    "  title TEXT NOT NULL,\n",
    "  company TEXT NOT NULL,\n",
    "  location TEXT NOT NULL,\n",
    "  description TEXT NOT NULL\n",
    ");\n",
    "```\n",
    "```\n",
    "INSERT INTO job_details (title, company, location, description) \n",
    "VALUES \n",
    "\n",
    "('Engineer', 'Intel', 'GDC', 'Lorem ipsum dolor sit amet, consectetur adipiscing elit,\n",
    "\n",
    " sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud \n",
    " \n",
    "exercitation ullamco laboris '),\n",
    "\n",
    "('Technitian', 'Intel', 'Concord', 'Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium,\n",
    "\n",
    " totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et');\n",
    " ```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Connection to a containerize SQL database.ResourceWarning \n",
    "host = 'localhost'\n",
    "port = '3306'\n",
    "username = 'root'\n",
    "password = 'my-secret-pw'\n",
    "database_schema = 'agency_db'\n",
    "mysql_uri = f\"mysql+pymysql://{username}:{password}@{host}:{port}/{database_schema}\"\n",
    "\n",
    "db = SQLDatabase.from_uri(mysql_uri, include_tables=['job_details'],sample_rows_in_table_info=2)\n",
    "\n",
    "db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LangChain is a framework designed to help developers build applications that leverage large language models (LLMs) like GPT in a more powerful and flexible way. \n",
    "\n",
    "It provides various components to build workflows for tasks such as document processing, chatbots, question answering, and more by connecting LLMs with external data sources, APIs, and tools. \n",
    "\n",
    "It enables users to work with not only single LLM calls but to create chains of LLM calls, allowing for more complex and nuanced behavior.\n",
    "\n",
    "Key Features of LangChain\n",
    "\n",
    "## Chains:\n",
    "\n",
    "Chains are sequences of calls to LLMs or other computations, allowing for structured workflows. \n",
    "\n",
    "For example, one chain could take a user query, process it through an LLM, and then perform a search in a database, \n",
    "\n",
    "while another could involve generating code snippets from a prompt and executing them.\n",
    "\n",
    "## Agents:\n",
    "\n",
    "Agents are a core feature in LangChain that allows an LLM to make decisions in real \n",
    "\n",
    "time by interacting with tools such as search engines, APIs, databases, or other models. \n",
    "\n",
    "For instance, an agent could decide which external tool to use based on the prompt.\n",
    "\n",
    "## Memory:\n",
    "\n",
    "Memory allows models to keep track of information across multiple interactions or steps in a chain, \n",
    "\n",
    "enabling contextual understanding.\n",
    " This is particularly useful for chatbots or long-running conversations where maintaining context is essential.\n",
    "\n",
    "\n",
    "## Connectors:\n",
    "\n",
    "LangChain provides integrations with APIs, document loaders, and other external tools \n",
    "\n",
    "(e.g., SQL databases, Google Search, and other custom APIs), making it possible to enhance LLM capabilities by providing additional data or instructions from external resources.\n",
    "\n",
    "Prompt Templates:\n",
    "\n",
    "LangChain offers an easy way to define and manage complex prompt templates, \n",
    "\n",
    "which are essential for giving LLMs the right instructions or structuring information for optimal performance.\n",
    "\n",
    "Use Cases:\n",
    "\n",
    "Question Answering: LangChain can help build apps that take in queries and produce answers from a variety of sources \n",
    "\n",
    "(e.g., documentation, websites, databases).\n",
    "\n",
    "Chatbots: With memory, you can create sophisticated conversational agents that maintain context across conversations.\n",
    "\n",
    "Code Generation: Automate the process of writing and running code by connecting an LLM to a code execution environment.\n",
    "\n",
    "Text Summarization: Generate summaries of long documents by feeding them into an LLM in a structured way.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LangChain Ecosystem\n",
    "LangChain integrates with several key components and tools, such as:\n",
    "\n",
    "- Document Loaders (e.g., PDFs, HTML)\n",
    "- Databases (SQL databases, Elasticsearch)\n",
    "- External APIs (Google Search, Wikipedia)\n",
    "- LLMs (OpenAI, Anthropic, Hugging Face)\n",
    "- Execution environments (to run code or interact with operating system tools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "def retrieve_from_db(query: str) -> str:\n",
    "    db_context = db_chain(query)\n",
    "    db_context = db_context['result'].strip()\n",
    "    return db_context"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prompts contruction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate(query: str) -> str:\n",
    "    db_context = retrieve_from_db(query)\n",
    "    \n",
    "    system_message = \"\"\"You are a professional representative of an employment agency.\n",
    "        You have to answer user's queries and provide relevant information to help in their job search. \n",
    "        Example:\n",
    "        \n",
    "        Input:\n",
    "        Where are the most number of jobs for an English Teacher in Canada?\n",
    "        \n",
    "        Context:\n",
    "        The most number of jobs for an English Teacher in Canada is in the following cities:\n",
    "        1. Ontario\n",
    "        2. British Columbia\n",
    "        \n",
    "        Output:\n",
    "        The most number of jobs for an English Teacher in Canada is in Toronto and British Columbia\n",
    "        \"\"\"\n",
    "    \n",
    "    human_qry_template = HumanMessagePromptTemplate.from_template(\n",
    "        \"\"\"Input:\n",
    "        {human_input}\n",
    "        \n",
    "        Context:\n",
    "        {db_context}\n",
    "        \n",
    "        Output:\n",
    "        \"\"\"\n",
    "    )\n",
    "    messages = [\n",
    "      SystemMessage(content=system_message),\n",
    "      human_qry_template.format(human_input=query, db_context=db_context)\n",
    "    ]\n",
    "   \n",
    "    print(dumps(llm.generate(messages), pretty=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function generate interacts with the LLM and returns a response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
      "Whats the most popular job in GDC\n",
      "SQLQuery:\u001b[32;1m\u001b[1;3mQuestion: Whats the most popular job in GDC\n",
      "SQLQuery: \n",
      "SELECT `title` FROM job_details WHERE `location` = 'GDC' GROUP BY `title` ORDER BY COUNT(`title`) DESC LIMIT 5\u001b[0m\n",
      "SQLResult: \u001b[33;1m\u001b[1;3m[('Engineer',)]\u001b[0m\n",
      "Answer:\u001b[32;1m\u001b[1;3mQuestion: Whats the most popular job in GDC\n",
      "SQLQuery: SELECT `title` FROM job_details WHERE `location` = 'GDC' GROUP BY `title` ORDER BY COUNT(*) DESC LIMIT 1\u001b[0m\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Object of type SystemMessage is not JSON serializable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[165], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mWhats the most popular job in GDC\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# generate(\"Cual es el trabajo mas popular en GDC\")\u001b[39;00m\n",
      "Cell \u001b[0;32mIn[164], line 35\u001b[0m, in \u001b[0;36mgenerate\u001b[0;34m(query)\u001b[0m\n\u001b[1;32m     20\u001b[0m human_qry_template \u001b[38;5;241m=\u001b[39m HumanMessagePromptTemplate\u001b[38;5;241m.\u001b[39mfrom_template(\n\u001b[1;32m     21\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Input:\u001b[39;00m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;124;03m    {human_input}\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m     29\u001b[0m )\n\u001b[1;32m     30\u001b[0m messages \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     31\u001b[0m   SystemMessage(content\u001b[38;5;241m=\u001b[39msystem_message),\n\u001b[1;32m     32\u001b[0m   human_qry_template\u001b[38;5;241m.\u001b[39mformat(human_input\u001b[38;5;241m=\u001b[39mquery, db_context\u001b[38;5;241m=\u001b[39mdb_context)\n\u001b[1;32m     33\u001b[0m ]\n\u001b[0;32m---> 35\u001b[0m \u001b[38;5;28mprint\u001b[39m(dumps(\u001b[43mllm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m)\u001b[49m, pretty\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m))\n",
      "File \u001b[0;32m~/Documents/MNA/.venv/lib/python3.12/site-packages/langchain_core/language_models/llms.py:946\u001b[0m, in \u001b[0;36mBaseLLM.generate\u001b[0;34m(self, prompts, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[1;32m    931\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcache \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m get_llm_cache() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcache \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[1;32m    932\u001b[0m     run_managers \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    933\u001b[0m         callback_manager\u001b[38;5;241m.\u001b[39mon_llm_start(\n\u001b[1;32m    934\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_serialized,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    944\u001b[0m         )\n\u001b[1;32m    945\u001b[0m     ]\n\u001b[0;32m--> 946\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate_helper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    947\u001b[0m \u001b[43m        \u001b[49m\u001b[43mprompts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mbool\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mnew_arg_supported\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    948\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    949\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m output\n\u001b[1;32m    950\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(missing_prompts) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "File \u001b[0;32m~/Documents/MNA/.venv/lib/python3.12/site-packages/langchain_core/language_models/llms.py:789\u001b[0m, in \u001b[0;36mBaseLLM._generate_helper\u001b[0;34m(self, prompts, stop, run_managers, new_arg_supported, **kwargs)\u001b[0m\n\u001b[1;32m    787\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m run_manager \u001b[38;5;129;01min\u001b[39;00m run_managers:\n\u001b[1;32m    788\u001b[0m         run_manager\u001b[38;5;241m.\u001b[39mon_llm_error(e, response\u001b[38;5;241m=\u001b[39mLLMResult(generations\u001b[38;5;241m=\u001b[39m[]))\n\u001b[0;32m--> 789\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    790\u001b[0m flattened_outputs \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39mflatten()\n\u001b[1;32m    791\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m manager, flattened_output \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(run_managers, flattened_outputs):\n",
      "File \u001b[0;32m~/Documents/MNA/.venv/lib/python3.12/site-packages/langchain_core/language_models/llms.py:776\u001b[0m, in \u001b[0;36mBaseLLM._generate_helper\u001b[0;34m(self, prompts, stop, run_managers, new_arg_supported, **kwargs)\u001b[0m\n\u001b[1;32m    766\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_generate_helper\u001b[39m(\n\u001b[1;32m    767\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    768\u001b[0m     prompts: \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mstr\u001b[39m],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    772\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    773\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m LLMResult:\n\u001b[1;32m    774\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    775\u001b[0m         output \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 776\u001b[0m             \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    777\u001b[0m \u001b[43m                \u001b[49m\u001b[43mprompts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    778\u001b[0m \u001b[43m                \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    779\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;66;43;03m# TODO: support multiple run managers\u001b[39;49;00m\n\u001b[1;32m    780\u001b[0m \u001b[43m                \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_managers\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;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[1;32m    781\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    782\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    783\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[1;32m    784\u001b[0m             \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate(prompts, stop\u001b[38;5;241m=\u001b[39mstop)\n\u001b[1;32m    785\u001b[0m         )\n\u001b[1;32m    786\u001b[0m     \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[1;32m    787\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m run_manager \u001b[38;5;129;01min\u001b[39;00m run_managers:\n",
      "File \u001b[0;32m~/Documents/MNA/.venv/lib/python3.12/site-packages/langchain_ollama/llms.py:304\u001b[0m, in \u001b[0;36mOllamaLLM._generate\u001b[0;34m(self, prompts, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    302\u001b[0m generations \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    303\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m prompt \u001b[38;5;129;01min\u001b[39;00m prompts:\n\u001b[0;32m--> 304\u001b[0m     final_chunk \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stream_with_aggregation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    305\u001b[0m \u001b[43m        \u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    306\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    307\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    308\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    309\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    310\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    311\u001b[0m     generations\u001b[38;5;241m.\u001b[39mappend([final_chunk])\n\u001b[1;32m    312\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m LLMResult(generations\u001b[38;5;241m=\u001b[39mgenerations)\n",
      "File \u001b[0;32m~/Documents/MNA/.venv/lib/python3.12/site-packages/langchain_ollama/llms.py:272\u001b[0m, in \u001b[0;36mOllamaLLM._stream_with_aggregation\u001b[0;34m(self, prompt, stop, run_manager, verbose, **kwargs)\u001b[0m\n\u001b[1;32m    263\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_stream_with_aggregation\u001b[39m(\n\u001b[1;32m    264\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    265\u001b[0m     prompt: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    269\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    270\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m GenerationChunk:\n\u001b[1;32m    271\u001b[0m     final_chunk \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 272\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_resp\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[38;5;241;43m.\u001b[39;49m\u001b[43m_create_generate_stream\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m    273\u001b[0m \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[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mstream_resp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m    274\u001b[0m \u001b[43m            \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mGenerationChunk\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    275\u001b[0m \u001b[43m                \u001b[49m\u001b[43mtext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_resp\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mresponse\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;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;124;43m\"\u001b[39;49m\u001b[38;5;124;43mresponse\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_resp\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    276\u001b[0m \u001b[43m                \u001b[49m\u001b[43mgeneration_info\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    277\u001b[0m \u001b[43m                    \u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mstream_resp\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[43mstream_resp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdone\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;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;28;43;01mTrue\u001b[39;49;00m\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\n\u001b[1;32m    278\u001b[0m \u001b[43m                \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    279\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/MNA/.venv/lib/python3.12/site-packages/langchain_ollama/llms.py:222\u001b[0m, in \u001b[0;36mOllamaLLM._create_generate_stream\u001b[0;34m(self, prompt, stop, **kwargs)\u001b[0m\n\u001b[1;32m    219\u001b[0m         params[key] \u001b[38;5;241m=\u001b[39m kwargs[key]\n\u001b[1;32m    221\u001b[0m params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptions\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstop\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m stop\n\u001b[0;32m--> 222\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_client\u001b[38;5;241m.\u001b[39mgenerate(\n\u001b[1;32m    223\u001b[0m     model\u001b[38;5;241m=\u001b[39mparams[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m    224\u001b[0m     prompt\u001b[38;5;241m=\u001b[39mprompt,\n\u001b[1;32m    225\u001b[0m     stream\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    226\u001b[0m     options\u001b[38;5;241m=\u001b[39mOptions(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptions\u001b[39m\u001b[38;5;124m\"\u001b[39m]),\n\u001b[1;32m    227\u001b[0m     keep_alive\u001b[38;5;241m=\u001b[39mparams[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkeep_alive\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m    228\u001b[0m     \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39mparams[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformat\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m    229\u001b[0m )\n",
      "File \u001b[0;32m~/Documents/MNA/.venv/lib/python3.12/site-packages/ollama/_client.py:80\u001b[0m, in \u001b[0;36mClient._stream\u001b[0;34m(self, method, url, **kwargs)\u001b[0m\n\u001b[1;32m     79\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_stream\u001b[39m(\u001b[38;5;28mself\u001b[39m, method: \u001b[38;5;28mstr\u001b[39m, url: \u001b[38;5;28mstr\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Mapping[\u001b[38;5;28mstr\u001b[39m, Any]]:\n\u001b[0;32m---> 80\u001b[0m \u001b[43m  \u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mas\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m     81\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mtry\u001b[39;49;00m\u001b[43m:\u001b[49m\n\u001b[1;32m     82\u001b[0m \u001b[43m      \u001b[49m\u001b[43mr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/lib/python3.12/contextlib.py:137\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 137\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    138\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m    139\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerator didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt yield\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/Documents/MNA/.venv/lib/python3.12/site-packages/httpx/_client.py:867\u001b[0m, in \u001b[0;36mClient.stream\u001b[0;34m(self, method, url, content, data, files, json, params, headers, cookies, auth, follow_redirects, timeout, extensions)\u001b[0m\n\u001b[1;32m    839\u001b[0m \u001b[38;5;129m@contextmanager\u001b[39m\n\u001b[1;32m    840\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstream\u001b[39m(\n\u001b[1;32m    841\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    855\u001b[0m     extensions: RequestExtensions \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    856\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m typing\u001b[38;5;241m.\u001b[39mIterator[Response]:\n\u001b[1;32m    857\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    858\u001b[0m \u001b[38;5;124;03m    Alternative to `httpx.request()` that streams the response body\u001b[39;00m\n\u001b[1;32m    859\u001b[0m \u001b[38;5;124;03m    instead of loading it into memory at once.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    865\u001b[0m \u001b[38;5;124;03m    [0]: /quickstart#streaming-responses\u001b[39;00m\n\u001b[1;32m    866\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 867\u001b[0m     request \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    868\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    869\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    870\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcontent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    871\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    872\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfiles\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfiles\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    873\u001b[0m \u001b[43m        \u001b[49m\u001b[43mjson\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjson\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    874\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcookies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcookies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m        \u001b[49m\u001b[43mextensions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextensions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    880\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(\n\u001b[1;32m    881\u001b[0m         request\u001b[38;5;241m=\u001b[39mrequest,\n\u001b[1;32m    882\u001b[0m         auth\u001b[38;5;241m=\u001b[39mauth,\n\u001b[1;32m    883\u001b[0m         follow_redirects\u001b[38;5;241m=\u001b[39mfollow_redirects,\n\u001b[1;32m    884\u001b[0m         stream\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    885\u001b[0m     )\n\u001b[1;32m    886\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[0;32m~/Documents/MNA/.venv/lib/python3.12/site-packages/httpx/_client.py:358\u001b[0m, in \u001b[0;36mBaseClient.build_request\u001b[0;34m(self, method, url, content, data, files, json, params, headers, cookies, timeout, extensions)\u001b[0m\n\u001b[1;32m    352\u001b[0m     timeout \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    353\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout\n\u001b[1;32m    354\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(timeout, UseClientDefault)\n\u001b[1;32m    355\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m Timeout(timeout)\n\u001b[1;32m    356\u001b[0m     )\n\u001b[1;32m    357\u001b[0m     extensions \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mextensions, timeout\u001b[38;5;241m=\u001b[39mtimeout\u001b[38;5;241m.\u001b[39mas_dict())\n\u001b[0;32m--> 358\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mRequest\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    359\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    360\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    361\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcontent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    362\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    363\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiles\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfiles\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    364\u001b[0m \u001b[43m    \u001b[49m\u001b[43mjson\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjson\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    365\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    366\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    367\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcookies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcookies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    368\u001b[0m \u001b[43m    \u001b[49m\u001b[43mextensions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextensions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/MNA/.venv/lib/python3.12/site-packages/httpx/_models.py:342\u001b[0m, in \u001b[0;36mRequest.__init__\u001b[0;34m(self, method, url, params, headers, cookies, content, data, files, json, stream, extensions)\u001b[0m\n\u001b[1;32m    340\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m stream \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    341\u001b[0m     content_type: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mheaders\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent-type\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 342\u001b[0m     headers, stream \u001b[38;5;241m=\u001b[39m \u001b[43mencode_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    343\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcontent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    344\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    345\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfiles\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfiles\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    346\u001b[0m \u001b[43m        \u001b[49m\u001b[43mjson\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjson\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    347\u001b[0m \u001b[43m        \u001b[49m\u001b[43mboundary\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mget_multipart_boundary_from_content_type\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    348\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcontent_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontent_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    349\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcontent_type\u001b[49m\n\u001b[1;32m    350\u001b[0m \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\n\u001b[1;32m    351\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    352\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    353\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare(headers)\n\u001b[1;32m    354\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstream \u001b[38;5;241m=\u001b[39m stream\n",
      "File \u001b[0;32m~/Documents/MNA/.venv/lib/python3.12/site-packages/httpx/_content.py:214\u001b[0m, in \u001b[0;36mencode_request\u001b[0;34m(content, data, files, json, boundary)\u001b[0m\n\u001b[1;32m    212\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m encode_urlencoded_data(data)\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m json \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 214\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mencode_json\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjson\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    216\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {}, ByteStream(\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/Documents/MNA/.venv/lib/python3.12/site-packages/httpx/_content.py:177\u001b[0m, in \u001b[0;36mencode_json\u001b[0;34m(json)\u001b[0m\n\u001b[1;32m    176\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mencode_json\u001b[39m(json: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mtuple\u001b[39m[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mstr\u001b[39m], ByteStream]:\n\u001b[0;32m--> 177\u001b[0m     body \u001b[38;5;241m=\u001b[39m \u001b[43mjson_dumps\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjson\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mencode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    178\u001b[0m     content_length \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mlen\u001b[39m(body))\n\u001b[1;32m    179\u001b[0m     content_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapplication/json\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "File \u001b[0;32m/usr/lib/python3.12/json/__init__.py:231\u001b[0m, in \u001b[0;36mdumps\u001b[0;34m(obj, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)\u001b[0m\n\u001b[1;32m    226\u001b[0m \u001b[38;5;66;03m# cached encoder\u001b[39;00m\n\u001b[1;32m    227\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;129;01mnot\u001b[39;00m skipkeys \u001b[38;5;129;01mand\u001b[39;00m ensure_ascii \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m    228\u001b[0m     check_circular \u001b[38;5;129;01mand\u001b[39;00m allow_nan \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m    229\u001b[0m     \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m indent \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m separators \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m    230\u001b[0m     default \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m sort_keys \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kw):\n\u001b[0;32m--> 231\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_default_encoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    232\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    233\u001b[0m     \u001b[38;5;28mcls\u001b[39m \u001b[38;5;241m=\u001b[39m JSONEncoder\n",
      "File \u001b[0;32m/usr/lib/python3.12/json/encoder.py:200\u001b[0m, in \u001b[0;36mJSONEncoder.encode\u001b[0;34m(self, o)\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m encode_basestring(o)\n\u001b[1;32m    197\u001b[0m \u001b[38;5;66;03m# This doesn't pass the iterator directly to ''.join() because the\u001b[39;00m\n\u001b[1;32m    198\u001b[0m \u001b[38;5;66;03m# exceptions aren't as detailed.  The list call should be roughly\u001b[39;00m\n\u001b[1;32m    199\u001b[0m \u001b[38;5;66;03m# equivalent to the PySequence_Fast that ''.join() would do.\u001b[39;00m\n\u001b[0;32m--> 200\u001b[0m chunks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterencode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_one_shot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    201\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(chunks, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n\u001b[1;32m    202\u001b[0m     chunks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(chunks)\n",
      "File \u001b[0;32m/usr/lib/python3.12/json/encoder.py:258\u001b[0m, in \u001b[0;36mJSONEncoder.iterencode\u001b[0;34m(self, o, _one_shot)\u001b[0m\n\u001b[1;32m    253\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    254\u001b[0m     _iterencode \u001b[38;5;241m=\u001b[39m _make_iterencode(\n\u001b[1;32m    255\u001b[0m         markers, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefault, _encoder, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindent, floatstr,\n\u001b[1;32m    256\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkey_separator, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitem_separator, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msort_keys,\n\u001b[1;32m    257\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mskipkeys, _one_shot)\n\u001b[0;32m--> 258\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_iterencode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/lib/python3.12/json/encoder.py:180\u001b[0m, in \u001b[0;36mJSONEncoder.default\u001b[0;34m(self, o)\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdefault\u001b[39m(\u001b[38;5;28mself\u001b[39m, o):\n\u001b[1;32m    162\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Implement this method in a subclass such that it returns\u001b[39;00m\n\u001b[1;32m    163\u001b[0m \u001b[38;5;124;03m    a serializable object for ``o``, or calls the base implementation\u001b[39;00m\n\u001b[1;32m    164\u001b[0m \u001b[38;5;124;03m    (to raise a ``TypeError``).\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    178\u001b[0m \n\u001b[1;32m    179\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 180\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mObject of type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mo\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    181\u001b[0m                     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mis not JSON serializable\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mTypeError\u001b[0m: Object of type SystemMessage is not JSON serializable"
     ]
    }
   ],
   "source": [
    "generate(\"Whats the most popular job in GDC\")\n",
    "# generate(\"Cual es el trabajo mas popular en GDC\")"
   ]
  }
 ],
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