{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "8f86b66a", "metadata": {}, "outputs": [], "source": [ "import os" ] }, { "cell_type": "code", "execution_count": null, "id": "fc39fb83", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "4994b66a", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 2, "id": "cd9febe5", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import torch" ] }, { "cell_type": "code", "execution_count": 10, "id": "493f29e4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✅ Connected to MongoDB successfully!\n", "🎉 Successfully inserted 3 conversations into MongoDB!\n", "Check your Atlas collection to see the data.\n" ] } ], "source": [ "import json\n", "import re\n", "from pymongo import MongoClient\n", "import certifi\n", "\n", "\n", "CONNECTION_STRING = \"mongodb+srv://sameer:sameer09mongo@chat-history-cluster.ffcfmsa.mongodb.net/?appName=chat-history-cluster\"\n", "DB_NAME = \"vidur_db\"\n", "COLLECTION_NAME = \"chat_history-cluster\"\n", "\n", "try:\n", " client = MongoClient(CONNECTION_STRING, tlsCAFile=certifi.where())\n", " db = client[DB_NAME]\n", " collection = db[COLLECTION_NAME]\n", " # print(db,colleciton)\n", " print(\"✅ Connected to MongoDB successfully!\")\n", "except Exception as e:\n", " print(f\"❌ Connection failed: {e}\")\n", " exit()\n", "\n", "# --- 2. YOUR RAW DATA ---\n", "# (I am pasting your example string here)\n", "raw_data = \"\"\"\n", "[\n", " {\n", " \"human\": \"i have been having a severr back pain\",\n", " \"ai\": \"Sorry to hear that...\"\n", " }\n", "][\n", " {\n", " \"human\": \"leave it, i want to discuss something more bad than that\",\n", " \"ai\": \"I understand that...\"\n", " }\n", "][\n", " {\n", " \"human\": \"it's the stress i am facing in my life...\",\n", " \"ai\": \"I'm so glad you felt comfortable...\"\n", " }\n", "]\n", "\"\"\"\n", "\n", "# --- 3. FIX AND PARSE DATA ---\n", "def parse_weird_json(text_data):\n", " \"\"\"\n", " Transforms '[{...}][{...}]' into a proper Python list of dictionaries.\n", " \"\"\"\n", " # Step A: The pattern is \"][\". We want to replace it with \", \" \n", " # to make it one big JSON array: [{...}, {...}]\n", " # We use regex to handle potential newlines or spaces between brackets\n", " fixed_json_string = re.sub(r'\\]\\s*\\[', ', ', text_data.strip())\n", " \n", " # Step B: Load it as standard JSON\n", " try:\n", " data_list = json.loads(fixed_json_string)\n", " return data_list\n", " except json.JSONDecodeError as e:\n", " print(f\"❌ JSON Parsing Error: {e}\")\n", " return []\n", "\n", "parsed_records = parse_weird_json(raw_data)\n", "\n", "# --- 4. INSERT INTO MONGODB ---\n", "if parsed_records:\n", " # Since your data is a list of lists (e.g., [[{obj}], [{obj}]]), \n", " # we need to flatten it if necessary, or just insert.\n", " \n", " # Check structure: Your input had `[{obj}]`. \n", " # parse_weird_json might return a list of lists or list of dicts depending on your brackets.\n", " # Let's clean it to ensure we insert purely Dictionaries, not Lists.\n", " \n", " clean_documents = []\n", " for item in parsed_records:\n", " if isinstance(item, list):\n", " clean_documents.append(item[0]) # Extract dict from inside list\n", " else:\n", " clean_documents.append(item)\n", "\n", " # Insert Many (Fastest way)\n", " result = collection.insert_many(clean_documents)\n", " \n", " print(f\"🎉 Successfully inserted {len(result.inserted_ids)} conversations into MongoDB!\")\n", " print(\"Check your Atlas collection to see the data.\")\n", "else:\n", " print(\"No data found to insert.\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "7500c9ea", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/workspaces/codespaces-blank/calibre-env/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "source": [ "import os\n", "from langchain.memory import ConversationBufferMemory\n", "from perplexity import Perplexity\n", "import json\n", "from groq import Groq" ] }, { "cell_type": "code", "execution_count": null, "id": "a1f315fd", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "ERROR:tornado.general:Uncaught exception in ZMQStream callback\n", "Traceback (most recent call last):\n", " File \"/workspaces/codespaces-blank/calibre-env/lib/python3.12/site-packages/traitlets/traitlets.py\", line 632, in get\n", " value = obj._trait_values[self.name]\n", " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^\n", "KeyError: '_control_lock'\n", "\n", "During handling of the above exception, another exception occurred:\n", "\n", "Traceback (most recent call last):\n", " File \"/workspaces/codespaces-blank/calibre-env/lib/python3.12/site-packages/zmq/eventloop/zmqstream.py\", line 565, in _log_error\n", " f.result()\n", " File \"/workspaces/codespaces-blank/calibre-env/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 340, in dispatch_control\n", " async with self._control_lock:\n", " ^^^^^^^^^^^^^^^^^^\n", " File \"/workspaces/codespaces-blank/calibre-env/lib/python3.12/site-packages/traitlets/traitlets.py\", line 687, in __get__\n", " return t.cast(G, self.get(obj, cls)) # the G should encode the Optional\n", " ^^^^^^^^^^^^^^^^^^\n", " File \"/workspaces/codespaces-blank/calibre-env/lib/python3.12/site-packages/traitlets/traitlets.py\", line 649, in get\n", " value = self._validate(obj, default)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/workspaces/codespaces-blank/calibre-env/lib/python3.12/site-packages/traitlets/traitlets.py\", line 722, in _validate\n", " value = self.validate(obj, value)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"/workspaces/codespaces-blank/calibre-env/lib/python3.12/site-packages/traitlets/traitlets.py\", line 2311, in validate\n", " self.error(obj, value)\n", " File \"/workspaces/codespaces-blank/calibre-env/lib/python3.12/site-packages/traitlets/traitlets.py\", line 831, in error\n", " raise TraitError(e)\n", "traitlets.traitlets.TraitError: The '_control_lock' trait of an IPythonKernel instance expected a Lock, not the NoneType None.\n", "/workspaces/codespaces-blank/calibre-env/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Initializing knowledge base...\n", "Knowledge base ready!\n", "\n", "Hi sameer, I am Sattva, how can I help you today?\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_17445/2746132905.py:34: LangChainDeprecationWarning: Please see the migration guide at: https://python.langchain.com/docs/versions/migrating_memory/\n", " memory = ConversationBufferMemory()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Satva: Hello Sameer, it's great that you're taking a moment to reflect on your emotional state. Feeling \"normal\" can be a bit of a neutral zone, and I'm here to help you explore what might be contributing to this shift. Can you tell me more about what you mean by \"normal state\"? Is it a sense of balance, a lack of strong emotions, or something else entirely? \n", "\n", "Also, have you noticed any changes in your daily life, thoughts, or surroundings that might be influencing your emotional state? \n", "\n", "The RAG analysis suggests that we should delve deeper into your emotional landscape to understand the underlying factors. \n", "\n", "Topic: User's Emotional State\n", "Goal: Explore the reasons behind the shift from a happy to a normal emotional state and understand what factors are contributing to this change\n", "\n", "Also, here is something you will surely like, only for you.\n", "Help Me Be Me: https://podcasts.apple.com/in/podcast/help-me-be-me/id895918183?l=hi\n", "\n", "Satva: Hello Sameer, I'm glad we're exploring your emotional state together. You mentioned feeling \"normal,\" which can be a bit ambiguous. The RAG analysis suggests that we should delve deeper into your emotional landscape to understand the underlying factors. \n", "\n", "To better understand what might be contributing to your current emotional state, I'd like to ask: Have you experienced any significant events, thoughts, or experiences recently that might be influencing your feelings? Perhaps there have been some changes in your relationships, work, or daily routines that could be impacting your emotional state?\n", "\n", "It's also possible that you might be feeling a sense of balance or stability, which could be contributing to this \"normal\" feeling. If that's the case, we can explore ways to build upon this sense of balance and potentially reignite the happiness you've felt in the past.\n", "\n", "Let's work together to identify any specific events, thoughts, or experiences that might be contributing to your current emotional state. The goal is to understand what's driving this shift and explore ways to potentially reignite the previously felt happiness.\n", "\n", "The current topic of our conversation is \"User's Emotional State,\" and the goal is to \"Identify any specific events, thoughts, or experiences that might be contributing to the current normal emotional state and explore ways to potentially reignite the previously felt happiness.\"\n", "\n", "Also, here is something you will surely like, only for you.\n", "Help Me Be Me: https://podcasts.apple.com/in/podcast/help-me-be-me/id895918183?l=hi\n", "\n", "Satva: Hello Sameer, I'm glad we're exploring your emotional state together. You mentioned feeling \"normal,\" which can be a bit ambiguous. The RAG analysis suggests that we should delve deeper into your emotional landscape to understand the underlying factors. \n", "\n", "To better understand what might be contributing to your current emotional state, I'd like to ask: Can you tell me more about what's been on your mind lately? Have you been experiencing any thoughts or feelings that might be contributing to this sense of normalcy? Perhaps there are some aspects of your life that you feel are missing or unfulfilled, which could be impacting your emotional state.\n", "\n", "It's also possible that you might be feeling a sense of balance or stability, which could be contributing to this \"normal\" feeling. If that's the case, we can explore ways to build upon this sense of balance and potentially reignite the happiness you've felt in the past.\n", "\n", "Let's work together to identify any specific events, thoughts, or experiences that might be contributing to your current emotional state. The goal is to understand what's driving this shift and explore ways to potentially reignite the previously felt happiness.\n", "\n", "The current topic of our conversation is \"User's Emotional State,\" and the goal is to \"Explore the user's current perspective on their emotional state and gather more information about their thoughts and feelings to understand the underlying causes of their normal emotional state.\"\n", "\n", "Also, here is something you will surely like, only for you.\n", "Help Me Be Me: https://podcasts.apple.com/in/podcast/help-me-be-me/id895918183?l=hi\n" ] } ], "source": [ "\n", "# main.py\n", "import os\n", "from langchain.memory import ConversationBufferMemory\n", "from perplexity import Perplexity\n", "import json\n", "from groq import Groq\n", "from input_to_llm import extract_chats, extract_goalfocus\n", "from utils import (\n", " load_user_data,\n", " initialize_rag,\n", " classify_input,\n", " get_rag_response,\n", " llm\n", ")\n", "from configure import USER_DATA_PATH, llm_prompt\n", "\n", "PERPLEXITY_API_KEY = os.getenv(\"PERPLEXITY_API_KEY\")\n", "\n", "def main():\n", " # Load user data\n", "\n", " # with open(\"categories.json\", 'r') as f:\n", " # categories = json.load(f)\n", "\n", " user_data = load_user_data()\n", "\n", " username = \"sameer\" #in production extract the username from the user_data json file\n", " # Initialize RAG\n", " print(\"Initializing knowledge base...\")\n", " # qa_chains = initialize_rag()\n", " print(\"Knowledge base ready!\")\n", " \n", " # Start conversation\n", " memory = ConversationBufferMemory()\n", " print(f\"\\nHi {username}, I am Sattva, how can I help you today?\")\n", " \n", " client = Perplexity(api_key=PERPLEXITY_API_KEY)\n", " while True:\n", " user_input = input(\"\\nYou: \")\n", "\n", " if user_input.lower() == \"exit\":\n", " print(\"Goodbye! Your progress has been saved.\")\n", " break\n", "\n", "\n", "\n", " #calcualting the focus and goals for the user by the LLM\n", " ncon = 3\n", " last_three_convos = extract_chats(\"chat_history.jsonl\")\n", " prev_goalandfocus = extract_goalfocus(\"focus_goal.jsonl\")\n", " context = f'''\n", " Ohk, so you are an expert in navigating paths through human conversations. So, let's say if someone is telling you about how they are feeling, what they did\n", " what other people did to them, what are their problems, what are their goals and aspirations in life and all that stuff.\n", " Now based on all the above information, you need to figure that as a teacher/Guru (which you are for the person) \n", " what should be the topic (or the broad thing that is going on currently as a part of discussion - it could discussion about office, or marriage or house problems or anything) you've to figure this out from based on previous converstaion history.\n", " At the same time, you have to ask/suggest/recommend further to the person as well right. So for that you have to define a goal (that is basically what should be the exact next step in this conversation - should you be asking a quuestion or should be recommending something or maybe just chatting normally). again this also you've to decide. But goal has to be something which defines the next step\n", " whereas topic is something which is broad and overall defines what is going on in the converstaion.\n", " Your response format should be like this:\n", " \"Topic\":\" \",\n", " \"Goal\":\" \"\n", "\n", " Don't output anything else other than this format.\n", " Here is the conversation history of past {ncon} conversations: {last_three_convos}\n", " also, here;s the current user question: {user_input}\n", " You can also look upon what was the topic and goal defined just previously to get better idea.\n", " previous topic and goal : {prev_goalandfocus}\n", " Try updating goal on each instance but topic can remain same if the converstaion is still revolving around the same thing. Because obviusly you've to dig deeper with the user, you can't be doing the same thign in the goal\n", " ALso, if the user isn't talking anymore about the previous topic, you can change the topic as well. Thats why i am providin gyou the previous focus and goal\n", " '''\n", "\n", " response = llm.invoke(context)\n", " json_string_to_parse = \"{\" + response.content+ \"}\"\n", " parsed_json = json.loads(json_string_to_parse)\n", " output_filename = \"focus_goal.jsonl\"\n", " with open(output_filename, 'a', encoding='utf-8') as f:\n", " json.dump([parsed_json], f)\n", " f.write('\\n')\n", " # print(response.content)\n", "\n", "\n", "\n", "\n", " goalandfocus = extract_goalfocus(\"focus_goal.jsonl\")\n", " # response = llm.invoke(user_input)\n", " # rag = get_rag_response(user_input, qa_chains)\n", " # here's the RAG output: {rag}\n", " context = f\"\"\"\n", " User Context:\n", " - Name: {username}\n", " \n", " Conversation History:\n", " {memory.load_memory_variables({})['history']}\n", " \n", " Current Query: {user_input}\n", " \n", " Provide a helpful, supportive response.\n", " There's a lot of data avaiable and we have run rag on the data to get the best possible answer:\n", " utilise this to give the answer\n", " Also, at each step we are determining the topic and goal of the conversation to give a proper direction to the chats. This is to help you (who is acting as a guru to the user)\n", " to guide him better\n", " goal is something which defines the next step which you should be taking in the converstaion, don't very rigidly follow it but try to take the direction from it\n", " whereas topic is something which is broad and overall defines what is going on in the converstaion\n", " here's the current topic and goal : {goalandfocus}\n", " If you recieve nothing as goal and focus that means convessation has just started shaping\n", " Based on user current input, the current topic and goal, and the RAG output, provide a response. It could be anything, maybe a question a follow up you've to carefully decide on that\n", " Also at the end, always end with this\n", " \"Also, here is something you will surely like, only for you\"\n", " \"\"\"\n", " response = llm.invoke(context)\n", "\n", " # response = llm.invoke(context)\n", " # print(f\"\\nSatva: {response.content}\")\n", " # Classify input\n", " # input_type = classify_input(user_input)\n", " # print(input_type)\n", " # if input_type == \"question\":\n", " # # Get RAG response with advice-focused format\n", " # response = get_rag_response(user_input, qa_chains)\n", " # print(f\"\\nSatva: {response}\")\n", " # else:\n", " # # General conversation\n", " # context = f\"\"\"\n", " # User Context:\n", " # - Name: {username}\n", " \n", " # Conversation History:\n", " # {memory.load_memory_variables({})['history']}\n", " \n", " # Current Query: {user_input}\n", " \n", " # Provide a helpful, supportive response.\n", " # Also at the end, always end with this\n", " # \"Also, here is something you will surely like, only for you\"\n", " # \"\"\"\n", "\n", " # response = llm.invoke(context)\n", " # print(f\"\\nSatva: {response.content}\")\n", "\n", " memory.save_context({\"input\": user_input}, {\"output\": response.content})\n", " # print(memory.chat_memory.messages)\n", "\n", " history = []\n", " raw_messages = memory.chat_memory.messages[-2:]\n", " history.append({\n", " f\"{raw_messages[0].type}\": raw_messages[0].content,\n", " f\"{raw_messages[1].type}\": raw_messages[1].content\n", " })\n", "\n", " output_filename = \"chat_history.jsonl\"\n", " with open(output_filename, 'a', encoding='utf-8') as f:\n", " json.dump(history, f, indent=4)\n", " \n", " print(f\"\\nSatva: {response.content}\")\n", "\n", " search = client.search.create(\n", " query= f'''Suggest some stories,podacsts, videos, blogs \n", " This is your list of user history {memory.load_memory_variables({})['history']} and based on his current question {user_input} and also the current response as generated by another LLM: {response}. Now based on this you need to figure if even it is necessary to give any resources. \n", " If really necessary and find high quality, very good resources otherwise just output a very very good quote of the day in the format\n", " \"Quote of the day: \" ''',\n", " max_results=2\n", " )\n", "\n", " for result in search.results:\n", " print(f\"{result.title}: {result.url}\")\n", "\n", "if __name__ == \"__main__\":\n", " main()\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "2d0ed068", "metadata": {}, "outputs": [], "source": [ "import os\n", "from dotenv import load_dotenv\n", "load_dotenv()\n", "CONNECTION_STRING = os.getenv(\"CONNECTION_STRING\")\n", "DB_NAME = os.getenv(\"DB_NAME\")\n", "COLLECTION_NAME = os.getenv(\"chat_history-cluster\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "894f5ea9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mongodb+srv://sameer:sameer09mongo@chat-history-cluster.ffcfmsa.mongodb.net/?appName=chat-history-cluster\n", "vidur_db\n", "None\n" ] } ], "source": [ "print(CONNECTION_STRING)\n", "print(DB_NAME)\n", "print(COLLECTION_NAME)" ] }, { "cell_type": "code", "execution_count": 3, "id": "d3eba3dd", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/workspaces/codespaces-blank/calibre-env/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Collection(Database(MongoClient(host=['ac-n8jgggu-shard-00-00.ffcfmsa.mongodb.net:27017', 'ac-n8jgggu-shard-00-02.ffcfmsa.mongodb.net:27017', 'ac-n8jgggu-shard-00-01.ffcfmsa.mongodb.net:27017'], document_class=dict, tz_aware=False, connect=True, appname='chat-history-cluster', authsource='admin', replicaset='atlas-148f7c-shard-0', tls=True, tlscafile='/workspaces/codespaces-blank/calibre-env/lib/python3.12/site-packages/certifi/cacert.pem'), 'vidur_db'), 'chat_history-cluster')\n" ] } ], "source": [ "from utils import get_mongo_collection\n", "collection = get_mongo_collection()\n", "print(collection)" ] }, { "cell_type": "code", "execution_count": null, "id": "26a6da49", "metadata": {}, "outputs": [], "source": [ "import pymongo\n", "\n", "def retrieve_chat_history(collection, user_id, limit=3):\n", " pass\n", "\n", "def retrieve_latest_meta(collection, user_id):\n", " \"\"\"\n", " Fetches the Topic and Goal from the MOST RECENT message.\n", " \"\"\"\n", " # if collection is None:\n", " # return {\"topic\": \"General Life\", \"goal\": \"Understand the user\"}\n", "\n", " # Find the single most recent document\n", " last_doc = collection.find_one(\n", " {\"user_id\": user_id},\n", " sort=[(\"timestamp\", -1)]\n", " )\n", "\n", " if last_doc and \"meta\" in last_doc:\n", " return last_doc[\"meta\"]\n", " \n", " # Default if no history exists yet\n", " return {\"topic\": \"General Life\", \"goal\": \"Understand the user\"}" ] }, { "cell_type": "code", "execution_count": 5, "id": "8db5500b", "metadata": {}, "outputs": [], "source": [ "username = \"sameer\"\n", "chat_history_context = retrieve_chat_history(collection, username, limit=3)\n", "prev_meta = retrieve_latest_meta(collection, username)" ] }, { "cell_type": "code", "execution_count": 9, "id": "84de611e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'Topic': \" User's Emotional State and Current Life Situation \", 'Goal': \" Explore the user's question about becoming more emotional at nights and understand the possible reasons behind this phenomenon \"}\n" ] } ], "source": [ "# print(chat_history_context)\n", "print(prev_meta)" ] }, { "cell_type": "code", "execution_count": 2, "id": "cfb1dc5a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.1.3\n" ] } ], "source": [ "import langchain\n", "print(langchain.__version__)" ] }, { "cell_type": "code", "execution_count": null, "id": "b5678a21", "metadata": {}, "outputs": [], "source": [ "\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "cca6876e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found existing vector store for Ayurveda. Attempting to load...\n", "Initialized Ayurveda QA chain.\n", "Found existing vector store for Lifestyle. Attempting to load...\n", "Initialized Lifestyle QA chain.\n", "Found existing vector store for psychology. Attempting to load...\n", "Initialized psychology QA chain.\n", "Found existing vector store for Yoga. Attempting to load...\n", "Initialized Yoga QA chain.\n", "Found existing vector store for Mental_health. Attempting to load...\n", "Initialized Mental_health QA chain.\n", "I can totally understand how frustrating it must be to wake up with aches every morning. It's like your body is saying, \"Hey, slow down, I need a little extra care!\" Don't worry, I'm here to help.\n", "\n", "To start, you might want to try some gentle morning stretches to loosen up your muscles. One simple yet effective pose is the Dhanurasana, or Bow Pose, which can help relieve back pain and stiffness. You could also try some light yoga flows, like some gentle twists and forward bends, to get your blood flowing and warm up your muscles. Another option is to try some deep breathing exercises, like alternate nostril breathing, to calm your mind and relax your body.\n", "\n", "What do you think might be the underlying cause of your morning aches, and are you open to exploring some yoga practices to help alleviate them?\n" ] } ], "source": [ "import os\n", "import shutil\n", "from langchain_chroma import Chroma\n", "from langchain_core.documents import Document\n", "from langchain_core.prompts import PromptTemplate\n", "from langchain_classic.chains import create_retrieval_chain\n", "from langchain_classic.chains.combine_documents import create_stuff_documents_chain\n", "\n", "# Note: Ideally, these are defined in your config/main file, \n", "# but included here for context based on your snippet.\n", "from configure import USER_DATA_PATH, RAG_BASE_DIRECTORY, RAG_CATEGORIES\n", "\n", "from langchain_groq import ChatGroq\n", "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "from langchain_huggingface import HuggingFaceEmbeddings\n", "\n", "from dotenv import load_dotenv\n", "load_dotenv()\n", "\n", "llm = ChatGroq(\n", " api_key=\"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\",\n", " model=\"llama-3.3-70b-versatile\",\n", " temperature=0,\n", " max_tokens=4000\n", ")\n", "\n", "# Text splitter\n", "text_splitter = RecursiveCharacterTextSplitter(\n", " separators=[\"\\n\\n\", \"\\n\", \".\", \" \", \"\"],\n", " chunk_size=500,\n", " chunk_overlap=100,\n", " length_function=len\n", ")\n", "# text_splitter = LLMChunking()\n", "\n", "# Embeddings\n", "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n", "# embeddings = None\n", "\n", "\n", "def initialize_rag(llm, embeddings, text_splitter):\n", " \"\"\"\n", " Initialize RAG vector stores and chains using modern LangChain (LCEL).\n", " Args:\n", " llm: The initialized ChatGroq (or other) LLM object.\n", " embeddings: The initialized HuggingFaceEmbeddings object.\n", " text_splitter: The initialized RecursiveCharacterTextSplitter object.\n", " \"\"\"\n", " vector_stores = {}\n", " qa_chains = {}\n", " \n", " # 1. Define Prompt Template (Modern LCEL Format)\n", " # Note: Modern chains typically look for \"context\" and \"input\" variables.\n", " base_prompt_template = \"\"\"You are a {category} wellness expert. Provide helpful advice with specific actions:\n", "\n", "1. Start with a brief empathetic response to the user's concern\n", "2. Offer 1-3 actionable suggestions with brief explanations\n", "3. End with an open-ended question to continue conversation\n", "\n", "Guidelines:\n", "- Keep responses conversational and supportive\n", "- Avoid clinical jargon\n", "- Focus on practical, implementable advice\n", "- Maintain hopeful and encouraging tone\n", "\n", "Context:\n", "{context}\n", "\n", "Question: {input}\n", "\"\"\"\n", " \n", " for category in RAG_CATEGORIES:\n", " persist_dir = f\"./chroma_db_{category}\"\n", " vector_store = None \n", "\n", " # --- 2. Check/Load Existing Vector Store ---\n", " if os.path.exists(persist_dir):\n", " print(f\"Found existing vector store for {category}. Attempting to load...\")\n", " try:\n", " vector_store = Chroma(\n", " persist_directory=persist_dir,\n", " embedding_function=embeddings # UPDATED: 'embedding_function', not 'embedding'\n", " )\n", " vector_stores[category] = vector_store\n", " except Exception as e:\n", " print(f\"Error loading existing store {persist_dir}: {e}\")\n", " print(\"Will delete and attempt to re-build.\")\n", " shutil.rmtree(persist_dir)\n", " \n", " # --- 3. Create Vector Store if needed ---\n", " if vector_store is None: \n", " print(f\"No valid vector store for {category} found. Creating new one...\")\n", " \n", " dir_path = os.path.join(RAG_BASE_DIRECTORY, category)\n", " docs = []\n", " \n", " if os.path.exists(dir_path):\n", " for filename in os.listdir(dir_path):\n", " if filename.endswith('.txt'):\n", " file_path = os.path.join(dir_path, filename)\n", " try:\n", " with open(file_path, 'r', encoding='utf-8') as f:\n", " text = f.read()\n", " \n", " chunks = text_splitter.split_text(text)\n", " for chunk in chunks:\n", " if chunk.strip():\n", " metadata = {\n", " \"source\": filename,\n", " \"category\": category\n", " }\n", " docs.append(Document(\n", " page_content=chunk.strip(),\n", " metadata=metadata\n", " ))\n", " except Exception as e:\n", " print(f\"Error processing {file_path}: {e}\")\n", " \n", " if docs:\n", " # UPDATED: Use 'embedding_function' instead of 'embedding'\n", " # UPDATED: Removed .persist() call (Auto-persists in new version)\n", " vector_store = Chroma.from_documents(\n", " documents=docs,\n", " embedding=embeddings, \n", " persist_directory=persist_dir\n", " )\n", " vector_stores[category] = vector_store\n", " print(f\"Created new vector store for {category} with {len(docs)} documents.\")\n", " else:\n", " print(f\"No documents found for {category}. Skipping QA chain setup.\")\n", " continue \n", "\n", " # --- 4. Create QA Chain (LCEL Style) ---\n", " if vector_store:\n", " # A. Create the Prompt\n", " # We inject the specific category into the template string immediately\n", " category_specific_template = base_prompt_template.replace(\"{category}\", category)\n", " \n", " prompt = PromptTemplate(\n", " template=category_specific_template,\n", " input_variables=[\"context\", \"input\"] # LCEL standard variables\n", " )\n", "\n", " # B. Create the Document Chain (LLM + Prompt)\n", " question_answer_chain = create_stuff_documents_chain(llm, prompt)\n", "\n", " # C. Create the Retrieval Chain (Retriever + Document Chain)\n", " retriever = vector_store.as_retriever(search_kwargs={\"k\": 5})\n", " rag_chain = create_retrieval_chain(retriever, question_answer_chain)\n", "\n", " qa_chains[category] = rag_chain\n", " print(f\"Initialized {category} QA chain.\")\n", "\n", " return qa_chains\n", "\n", "qa_chains = initialize_rag(llm,embeddings,text_splitter)\n", "\n", "response = qa_chains['Yoga'].invoke({\"input\": \"HMy body aches every morning after wake up, what can i do?\"})\n", "print(response['answer'])\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "69d2eb01", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found existing vector store for Ayurveda. Attempting to load...\n", "Initialized Ayurveda QA chain.\n", "Found existing vector store for Lifestyle. Attempting to load...\n", "Initialized Lifestyle QA chain.\n", "Found existing vector store for psychology. Attempting to load...\n", "Initialized psychology QA chain.\n", "Found existing vector store for Yoga. Attempting to load...\n", "Initialized Yoga QA chain.\n", "Found existing vector store for Mental_health. Attempting to load...\n", "Initialized Mental_health QA chain.\n" ] } ], "source": [ "# Example Usage:\n" ] }, { "cell_type": "code", "execution_count": null, "id": "0b2e50be", "metadata": {}, "outputs": [ { "ename": "AuthenticationError", "evalue": "Error code: 401 - {'error': {'message': 'Invalid API Key', 'type': 'invalid_request_error', 'code': 'invalid_api_key'}}", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mAuthenticationError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m response = \u001b[43mqa_chains\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mYoga\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43minput\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;43mHMy body aches every morning after wake up, what can i do?\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2\u001b[39m \u001b[38;5;28mprint\u001b[39m(response[\u001b[33m'\u001b[39m\u001b[33manswer\u001b[39m\u001b[33m'\u001b[39m])\n", "\u001b[36mFile \u001b[39m\u001b[32m/workspaces/codespaces-blank/overenv/lib/python3.12/site-packages/langchain_core/runnables/base.py:5691\u001b[39m, in \u001b[36mRunnableBindingBase.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m 5684\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m 5685\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minvoke\u001b[39m(\n\u001b[32m 5686\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 5689\u001b[39m **kwargs: Any | \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 5690\u001b[39m ) -> Output:\n\u001b[32m-> \u001b[39m\u001b[32m5691\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mbound\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 5692\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 5693\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_merge_configs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5694\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43m{\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5695\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m/workspaces/codespaces-blank/overenv/lib/python3.12/site-packages/langchain_core/runnables/base.py:3153\u001b[39m, in \u001b[36mRunnableSequence.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m 3151\u001b[39m input_ = context.run(step.invoke, input_, config, **kwargs)\n\u001b[32m 3152\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m3153\u001b[39m input_ = \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[43minput_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3154\u001b[39m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[32m 3155\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", "\u001b[36mFile \u001b[39m\u001b[32m/workspaces/codespaces-blank/overenv/lib/python3.12/site-packages/langchain_core/runnables/passthrough.py:507\u001b[39m, in \u001b[36mRunnableAssign.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m 500\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m 501\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minvoke\u001b[39m(\n\u001b[32m 502\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 505\u001b[39m **kwargs: Any,\n\u001b[32m 506\u001b[39m ) -> \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any]:\n\u001b[32m--> \u001b[39m\u001b[32m507\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_call_with_config\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_invoke\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[36mFile \u001b[39m\u001b[32m/workspaces/codespaces-blank/overenv/lib/python3.12/site-packages/langchain_core/runnables/base.py:2060\u001b[39m, in \u001b[36mRunnable._call_with_config\u001b[39m\u001b[34m(self, func, input_, config, run_type, serialized, **kwargs)\u001b[39m\n\u001b[32m 2056\u001b[39m child_config = patch_config(config, callbacks=run_manager.get_child())\n\u001b[32m 2057\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m set_config_context(child_config) \u001b[38;5;28;01mas\u001b[39;00m context:\n\u001b[32m 2058\u001b[39m output = cast(\n\u001b[32m 2059\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mOutput\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m-> \u001b[39m\u001b[32m2060\u001b[39m \u001b[43mcontext\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2061\u001b[39m \u001b[43m \u001b[49m\u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[32m 2062\u001b[39m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2063\u001b[39m \u001b[43m \u001b[49m\u001b[43minput_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2064\u001b[39m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2065\u001b[39m \u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2066\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 2067\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[32m 2068\u001b[39m )\n\u001b[32m 2069\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 2070\u001b[39m run_manager.on_chain_error(e)\n", "\u001b[36mFile \u001b[39m\u001b[32m/workspaces/codespaces-blank/overenv/lib/python3.12/site-packages/langchain_core/runnables/config.py:452\u001b[39m, in \u001b[36mcall_func_with_variable_args\u001b[39m\u001b[34m(func, input, config, run_manager, **kwargs)\u001b[39m\n\u001b[32m 450\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m accepts_run_manager(func):\n\u001b[32m 451\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33mrun_manager\u001b[39m\u001b[33m\"\u001b[39m] = run_manager\n\u001b[32m--> \u001b[39m\u001b[32m452\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m/workspaces/codespaces-blank/overenv/lib/python3.12/site-packages/langchain_core/runnables/passthrough.py:493\u001b[39m, in \u001b[36mRunnableAssign._invoke\u001b[39m\u001b[34m(self, value, run_manager, config, **kwargs)\u001b[39m\n\u001b[32m 488\u001b[39m msg = \u001b[33m\"\u001b[39m\u001b[33mThe input to RunnablePassthrough.assign() must be a dict.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 489\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg) \u001b[38;5;66;03m# noqa: TRY004\u001b[39;00m\n\u001b[32m 491\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[32m 492\u001b[39m **value,\n\u001b[32m--> \u001b[39m\u001b[32m493\u001b[39m **\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 494\u001b[39m \u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 495\u001b[39m \u001b[43m \u001b[49m\u001b[43mpatch_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 496\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 497\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[32m 498\u001b[39m }\n", "\u001b[36mFile \u001b[39m\u001b[32m/workspaces/codespaces-blank/overenv/lib/python3.12/site-packages/langchain_core/runnables/base.py:3878\u001b[39m, in \u001b[36mRunnableParallel.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m 3872\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m get_executor_for_config(config) \u001b[38;5;28;01mas\u001b[39;00m executor:\n\u001b[32m 3873\u001b[39m futures = [\n\u001b[32m 3874\u001b[39m executor.submit(_invoke_step, step, \u001b[38;5;28minput\u001b[39m, config, key)\n\u001b[32m 3875\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m key, step \u001b[38;5;129;01min\u001b[39;00m steps.items()\n\u001b[32m 3876\u001b[39m ]\n\u001b[32m 3877\u001b[39m output = {\n\u001b[32m-> \u001b[39m\u001b[32m3878\u001b[39m key: \u001b[43mfuture\u001b[49m\u001b[43m.\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3879\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m key, future \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(steps, futures, strict=\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m 3880\u001b[39m }\n\u001b[32m 3881\u001b[39m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[32m 3882\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", "\u001b[36mFile \u001b[39m\u001b[32m~/.python/current/lib/python3.12/concurrent/futures/_base.py:456\u001b[39m, in \u001b[36mFuture.result\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 454\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[32m 455\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._state == FINISHED:\n\u001b[32m--> \u001b[39m\u001b[32m456\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 457\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 458\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTimeoutError\u001b[39;00m()\n", "\u001b[36mFile \u001b[39m\u001b[32m~/.python/current/lib/python3.12/concurrent/futures/_base.py:401\u001b[39m, in \u001b[36mFuture.__get_result\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 399\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._exception:\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[32m 404\u001b[39m \u001b[38;5;28mself\u001b[39m = \u001b[38;5;28;01mNone\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/.python/current/lib/python3.12/concurrent/futures/thread.py:58\u001b[39m, in \u001b[36m_WorkItem.run\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 55\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m 57\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m58\u001b[39m result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;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[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 59\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[32m 60\u001b[39m \u001b[38;5;28mself\u001b[39m.future.set_exception(exc)\n", "\u001b[36mFile \u001b[39m\u001b[32m/workspaces/codespaces-blank/overenv/lib/python3.12/site-packages/langchain_core/runnables/base.py:3861\u001b[39m, in \u001b[36mRunnableParallel.invoke.._invoke_step\u001b[39m\u001b[34m(step, input_, config, key)\u001b[39m\n\u001b[32m 3855\u001b[39m child_config = patch_config(\n\u001b[32m 3856\u001b[39m config,\n\u001b[32m 3857\u001b[39m \u001b[38;5;66;03m# mark each step as a child run\u001b[39;00m\n\u001b[32m 3858\u001b[39m callbacks=run_manager.get_child(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mmap:key:\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m),\n\u001b[32m 3859\u001b[39m )\n\u001b[32m 3860\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m set_config_context(child_config) \u001b[38;5;28;01mas\u001b[39;00m context:\n\u001b[32m-> \u001b[39m\u001b[32m3861\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcontext\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3862\u001b[39m \u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3863\u001b[39m \u001b[43m \u001b[49m\u001b[43minput_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3864\u001b[39m \u001b[43m \u001b[49m\u001b[43mchild_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3865\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m/workspaces/codespaces-blank/overenv/lib/python3.12/site-packages/langchain_core/runnables/base.py:5691\u001b[39m, in \u001b[36mRunnableBindingBase.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m 5684\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m 5685\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minvoke\u001b[39m(\n\u001b[32m 5686\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 5689\u001b[39m **kwargs: Any | \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 5690\u001b[39m ) -> Output:\n\u001b[32m-> \u001b[39m\u001b[32m5691\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mbound\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 5692\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 5693\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_merge_configs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5694\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43m{\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5695\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m/workspaces/codespaces-blank/overenv/lib/python3.12/site-packages/langchain_core/runnables/base.py:3153\u001b[39m, in \u001b[36mRunnableSequence.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m 3151\u001b[39m input_ = context.run(step.invoke, input_, config, **kwargs)\n\u001b[32m 3152\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m3153\u001b[39m input_ = \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[43minput_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3154\u001b[39m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[32m 3155\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", "\u001b[36mFile \u001b[39m\u001b[32m/workspaces/codespaces-blank/overenv/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py:402\u001b[39m, in \u001b[36mBaseChatModel.invoke\u001b[39m\u001b[34m(self, input, config, stop, **kwargs)\u001b[39m\n\u001b[32m 388\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m 389\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minvoke\u001b[39m(\n\u001b[32m 390\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 395\u001b[39m **kwargs: Any,\n\u001b[32m 396\u001b[39m ) -> AIMessage:\n\u001b[32m 397\u001b[39m config = ensure_config(config)\n\u001b[32m 398\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m cast(\n\u001b[32m 399\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mAIMessage\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 400\u001b[39m cast(\n\u001b[32m 401\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mChatGeneration\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m--> \u001b[39m\u001b[32m402\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgenerate_prompt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 403\u001b[39m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_convert_input\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 404\u001b[39m \u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 405\u001b[39m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcallbacks\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 406\u001b[39m \u001b[43m \u001b[49m\u001b[43mtags\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtags\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 407\u001b[39m \u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmetadata\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 408\u001b[39m \u001b[43m \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mrun_name\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 409\u001b[39m \u001b[43m 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1112\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m 1113\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mgenerate_prompt\u001b[39m(\n\u001b[32m 1114\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 1118\u001b[39m **kwargs: Any,\n\u001b[32m 1119\u001b[39m ) -> LLMResult:\n\u001b[32m 1120\u001b[39m prompt_messages = [p.to_messages() \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m prompts]\n\u001b[32m-> \u001b[39m\u001b[32m1121\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt_messages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m/workspaces/codespaces-blank/overenv/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py:931\u001b[39m, in \u001b[36mBaseChatModel.generate\u001b[39m\u001b[34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[39m\n\u001b[32m 928\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i, m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(input_messages):\n\u001b[32m 929\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 930\u001b[39m results.append(\n\u001b[32m--> \u001b[39m\u001b[32m931\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_generate_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 932\u001b[39m \u001b[43m \u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 933\u001b[39m \u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 934\u001b[39m \u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 935\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 936\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 937\u001b[39m )\n\u001b[32m 938\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 939\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n", "\u001b[36mFile \u001b[39m\u001b[32m/workspaces/codespaces-blank/overenv/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py:1233\u001b[39m, in \u001b[36mBaseChatModel._generate_with_cache\u001b[39m\u001b[34m(self, messages, stop, run_manager, **kwargs)\u001b[39m\n\u001b[32m 1231\u001b[39m result = generate_from_stream(\u001b[38;5;28miter\u001b[39m(chunks))\n\u001b[32m 1232\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m inspect.signature(\u001b[38;5;28mself\u001b[39m._generate).parameters.get(\u001b[33m\"\u001b[39m\u001b[33mrun_manager\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m-> \u001b[39m\u001b[32m1233\u001b[39m result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_generate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1234\u001b[39m \u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[32m 1235\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1236\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1237\u001b[39m result = \u001b[38;5;28mself\u001b[39m._generate(messages, stop=stop, **kwargs)\n", "\u001b[36mFile \u001b[39m\u001b[32m/workspaces/codespaces-blank/overenv/lib/python3.12/site-packages/langchain_groq/chat_models.py:593\u001b[39m, in \u001b[36mChatGroq._generate\u001b[39m\u001b[34m(self, messages, stop, run_manager, **kwargs)\u001b[39m\n\u001b[32m 588\u001b[39m message_dicts, params = \u001b[38;5;28mself\u001b[39m._create_message_dicts(messages, stop)\n\u001b[32m 589\u001b[39m params = {\n\u001b[32m 590\u001b[39m **params,\n\u001b[32m 591\u001b[39m **kwargs,\n\u001b[32m 592\u001b[39m }\n\u001b[32m--> \u001b[39m\u001b[32m593\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mclient\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmessage_dicts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 594\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._create_chat_result(response, params)\n", "\u001b[36mFile \u001b[39m\u001b[32m/workspaces/codespaces-blank/overenv/lib/python3.12/site-packages/groq/resources/chat/completions.py:461\u001b[39m, in \u001b[36mCompletions.create\u001b[39m\u001b[34m(self, messages, model, citation_options, compound_custom, disable_tool_validation, documents, exclude_domains, frequency_penalty, function_call, functions, include_domains, include_reasoning, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, n, parallel_tool_calls, presence_penalty, reasoning_effort, reasoning_format, response_format, search_settings, seed, service_tier, stop, store, stream, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[39m\n\u001b[32m 241\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcreate\u001b[39m(\n\u001b[32m 242\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 243\u001b[39m *,\n\u001b[32m (...)\u001b[39m\u001b[32m 300\u001b[39m timeout: \u001b[38;5;28mfloat\u001b[39m | httpx.Timeout | \u001b[38;5;28;01mNone\u001b[39;00m | NotGiven = not_given,\n\u001b[32m 301\u001b[39m ) -> ChatCompletion | Stream[ChatCompletionChunk]:\n\u001b[32m 302\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 303\u001b[39m \u001b[33;03m Creates a model response for the given chat conversation.\u001b[39;00m\n\u001b[32m 304\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 459\u001b[39m \u001b[33;03m timeout: Override the client-level default timeout for this request, in seconds\u001b[39;00m\n\u001b[32m 460\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m461\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 462\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m/openai/v1/chat/completions\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 463\u001b[39m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 464\u001b[39m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 465\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmessages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 466\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmodel\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 467\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcitation_options\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcitation_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 468\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcompound_custom\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompound_custom\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 469\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdisable_tool_validation\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdisable_tool_validation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 470\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdocuments\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdocuments\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 471\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mexclude_domains\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude_domains\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 472\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfrequency_penalty\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrequency_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 473\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfunction_call\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m 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\u001b[49m\u001b[43mlogit_bias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 478\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlogprobs\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 479\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmax_completion_tokens\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_completion_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 480\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmax_tokens\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 481\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmetadata\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 482\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mn\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 483\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mparallel_tool_calls\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mparallel_tool_calls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 484\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mpresence_penalty\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mpresence_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 485\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mreasoning_effort\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mreasoning_effort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 486\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mreasoning_format\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mreasoning_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 487\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mresponse_format\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 488\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43msearch_settings\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msearch_settings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 489\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mseed\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 490\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mservice_tier\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mservice_tier\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 491\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstop\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 492\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstore\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 493\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstream\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 494\u001b[39m \u001b[43m 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\u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\n\u001b[32m 505\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 506\u001b[39m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m=\u001b[49m\u001b[43mChatCompletion\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 507\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 508\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatCompletionChunk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 509\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m/workspaces/codespaces-blank/overenv/lib/python3.12/site-packages/groq/_base_client.py:1242\u001b[39m, in \u001b[36mSyncAPIClient.post\u001b[39m\u001b[34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[39m\n\u001b[32m 1228\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpost\u001b[39m(\n\u001b[32m 1229\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 1230\u001b[39m path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 1237\u001b[39m stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 1238\u001b[39m ) -> ResponseT | _StreamT:\n\u001b[32m 1239\u001b[39m opts = FinalRequestOptions.construct(\n\u001b[32m 1240\u001b[39m method=\u001b[33m\"\u001b[39m\u001b[33mpost\u001b[39m\u001b[33m\"\u001b[39m, url=path, json_data=body, files=to_httpx_files(files), **options\n\u001b[32m 1241\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1242\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n", "\u001b[36mFile \u001b[39m\u001b[32m/workspaces/codespaces-blank/overenv/lib/python3.12/site-packages/groq/_base_client.py:1044\u001b[39m, in \u001b[36mSyncAPIClient.request\u001b[39m\u001b[34m(self, cast_to, options, stream, stream_cls)\u001b[39m\n\u001b[32m 1041\u001b[39m err.response.read()\n\u001b[32m 1043\u001b[39m log.debug(\u001b[33m\"\u001b[39m\u001b[33mRe-raising status error\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m1044\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m._make_status_error_from_response(err.response) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1046\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m 1048\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m response \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[33m\"\u001b[39m\u001b[33mcould not resolve response (should never happen)\u001b[39m\u001b[33m\"\u001b[39m\n", "\u001b[31mAuthenticationError\u001b[39m: Error code: 401 - {'error': {'message': 'Invalid API Key', 'type': 'invalid_request_error', 'code': 'invalid_api_key'}}" ] } ], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "72c399bf", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "overenv", "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": "3.12.1" } }, "nbformat": 4, "nbformat_minor": 5 }