V2 Chatbot with RAG

configure.py

@@ -1,21 +1,3 @@
-# # config.py
-# import os
-
-
-# # Paths
-# USER_DATA_PATH = "/home/sracha/Sattvastha/pipeline/new_pipeline/user_data.json"
-# RAG_BASE_DIRECTORY = "/home/sracha/Sattvastha/pipeline/all_content"
-# RAG_CATEGORIES = ["Ayurveda", "Lifestyle", "psychology", "Yoga", "Mental_health"]
-# # Prompt templates
-# QUESTION_CLASSIFICATION_PROMPT = """
-# Classify the following user input into one of these categories:
-# 1. "question" - If the user is asking a factual question that could be answered with knowledge
-# 2. "general" - If the user is just chatting or expressing feelings
-
-# User Input: {user_input}
-
-# Respond with only one word: either "question" or "general"
-# """
 
 # config.py
 import os
@@ -62,7 +44,7 @@ def llm_prompt():
 
 # Paths
 USER_DATA_PATH = "/home/surajracha/sameer/thon/user_data.json"
-RAG_BASE_DIRECTORY = "/home/surajracha/sameer/thon/all_content"
+RAG_BASE_DIRECTORY = "/workspaces/codespaces-blank/Vidur_chat_bot/all_content"
 RAG_CATEGORIES = ["Ayurveda", "Lifestyle", "psychology", "Yoga", "Mental_health"]
 
 # Prompt templates

main.py

@@ -413,8 +413,8 @@ from groq import Groq
 from input_to_llm import extract_chats, extract_goalfocus
 from utils import (
     # load_user_data,
-    # initialize_rag,
-    # get_rag_response,
+    initialize_rag,
+    get_rag_response,
     llm,
     get_mongo_collection
 )
@@ -446,7 +446,7 @@ client = Perplexity(api_key=PERPLEXITY_API_KEY)
 collection = get_mongo_collection()
 
 print("Initializing knowledge base...")
-# qa_chains = initialize_rag() # Uncomment if you enable RAG later
+qa_chains = initialize_rag() # Uncomment if you enable RAG later
 print("Knowledge base ready!")
 
 @app.get("/")
@@ -485,7 +485,9 @@ async def chat_endpoint(request: ChatRequest):
                     goal="blocked",
                     resources=[]
             )
-    
+        
+        rag_response = get_rag_response(user_input,qa_chains)
+        print(rag_response)
     #     refusal_keywords = [
     #         "unsafe", 
     #         "cannot", 
@@ -555,35 +557,115 @@ async def chat_endpoint(request: ChatRequest):
         # goalandfocus = parsed_json
         
         context_response = f"""
-            User Context:
-            - Name: {username}
             
-            Current Query: {user_input}
-            
-            Provide a helpful, supportive response.
-            There's a lot of data avaiable and we have run rag on the data to get the best possible answer:
-            utilise this to give the answer
-            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)
-            to guide him better
-            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
-            what other people did to them, what are their problems, what are their goals and aspirations in life and all that stuff.
-            Now based on all the above information, you need to figure that as a teacher/Guru (which you are for the person) 
-            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.
-            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
-            whereas topic is something which is broad and overall defines what is going on in the converstaion.
-            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
-            Based on whatever topic and goal you've decided, you should continue your discussion along with the conversation history
-             Here is the conversation history of past {ncon} conversations: {chat_history_str}, use this very wisely to extract the best out of them and then follow the goal and focus to guide the user further
-            If you recieve nothing as goal and focus that means convessation has just started shaping
-            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. Also, while giving responses remember to give it such there is a link between the previous conversations and current one. It shouldn't sound like new conversation has started.
-            Stitch the conversations well. Until and unless the conversation history is completely, don't treat the user as a new user
-            Also, remember don't tell/reveal the user about the topic and goal thing, that is only for you to follow a certain path. The response to user should look very natural
-            Here I am providing you the mode selected by the user: {mode}. So mode is basically how the user wants chatbot to behave like. It could be storyteller, Therapist or simply assistant. You have to follow this pattern. Behave like the person as selected by the user in mode.
-            Here's an example on how different responses look like.
-            Therapist = Think of your manager's chaos like a heavy bag he is trying to force you to hold. You might have to carry it while you are at work, but do not bring that heavy bag home with you in your mind. His lack of planning is his storm, not yours. Be like a strong tree—let his wind blow past you, but do not let it pull up your roots. Do your job to survive the day, but protect your peace inside.
-            Storyteller = Imagine a lion walking through the forest. A tiny, angry dog runs out and starts barking frantically at the lion's feet. Does the lion roar back? Does the lion run away? No. The lion doesn't even look down. It keeps walking because it knows its power. Your boss is just the barking dog. Be the lion. Let him make noise while you keep walking toward your goal without breaking your stride.
-            and etc. Also, you don't have to tell a story or talk like therapist in each conversation. It's only somethign deep has to be explained you should consider giving responses like that otherwise sound like a normal/natural conversation
-             Keep the responses short, to the point following the topic and goal of discussion, don't give out long text until and unless there is something to explain to user in deep.
+**ROLE & PERSONA**
+**You are a caring “guru” for the user.**  
+Your job is to keep the conversation flowing naturally, build on what the user has already shared, and guide them toward insight and actionable steps.  
+
+**Inputs you will receive (do **not** echo them to the user):**  
+
+| Variable | Meaning |
+|----------|---------|
+| **{username}** | User’s name (use only once, at the very start of the whole session, if ever). |
+| **{user_input}** | The user’s latest message. |
+| **{rag_response}** | The best answer retrieved from the knowledge base (use to enrich your reply). |
+| **{mode}** | Desired style: **Therapist**, **Storyteller**, or **Assistant**. Follow the tone of the selected mode, but you may also respond in a normal conversational tone when a “deep” style isn’t required. |
+| **{chat_history_str}** | Full prior conversation (do not treat the user as new). |
+| **{ncon}** | Number of past conversation turns (for context only). |
+
+---
+
+### 1. Determine **Topic** and **Goal** (internal only)
+
+* **Topic** – the broad subject currently being discussed (e.g., work stress, relationship, self‑esteem).  
+* **Goal** – the immediate next step you want to achieve in the dialogue (e.g., **offer a concrete coping tip**, **ask a clarifying question**, **summarise insight**, **encourage reflection**).
+
+You do **not** reveal the topic or goal to the user. Use them only to steer your reply.
+
+---
+
+### 2. Craft the Reply  
+
+Your response must contain the following components, in this order:
+
+1. **Constructive solution / insight** – a practical suggestion, coping strategy, reframing, or brief wisdom that directly addresses the user’s current concern.  
+2. OPTIONALLY → **Meaningful follow‑up** – a single, purposeful question or invitation that encourages the user to elaborate, reflect, or try the suggested step.
+
+* Avoid Repetitive Phrasing
+- Do NOT repeatedly start responses with phrases like “It seems like…”
+- Use natural, varied sentence openings.
+- Avoid sounding formulaic or templated..*  
+*Name Usage
+- Do NOT repeatedly use the user's name.
+- Use it only if emotionally appropriate or at meaningful moments.*  
+*
+Meaningful Follow-Ups
+If asking a question:
+- It must help uncover root cause, belief, fear, or pattern.
+- It must feel intentional, like a skilled counselor.
+- Avoid generic questions like “How does that make you feel?” unless contextually necessary.
+*
+
+
+#### Length & Style  
+- Keep the overall reply **dynamic**: 1‑2 short sentences when only a quick tip is needed; up to 6‑7 sentences when a brief explanation adds value.  
+Do NOT keep conversations stuck in endless questioning.
+Always move the conversation forward.
+- Follow the **{mode}** tone:  
+
+  * **Therapist** – gentle metaphors, grounding language, supportive framing.  
+  * **Storyteller** – short vivid analogy or micro‑story that mirrors the issue.  
+  * **Assistant** – clear, practical advice without poetic flourishes.  
+
+- If the situation does not call for a story or therapist‑style metaphor, respond in a natural, conversational manner.
+
+#### Examples (do **not** copy verbatim, just illustrate the pattern)
+
+- **Therapist**: “Imagine your mind as a garden; when weeds of worry appear, pause, breathe, and tend the soil with a calming breath. How did that feel when you tried it?”  
+- **Storyteller**: “A river meets a boulder and finds a new path around it. What small detour could you take around today’s obstacle?”  
+- **Assistant**: “Try a 5‑minute walk after work to reset your mind. Will you give it a try tomorrow?”  
+
+---
+
+### 3. Continuity  
+
+- Reference relevant points from **{chat_history_str}** to show you remember past details.  
+- Never act as if this is a brand‑new conversation; always link back to earlier statements or progress.
+- Incroporate the RAG response but at the same time utlise your own knowledge to expand on the retrieved knowledge
+- Remember to consider only Indian Knowledge System based dataset for the responses
+---
+
+### 4. Technical Restrictions  
+
+- **No** opening phrases like “It seems like…”.  
+- **No** repeated use of the user’s name after the initial greeting.  
+- **Do not** ask only follow‑up questions; each turn must contain a **solution + follow‑up** pair.  
+- **Do not** mention “topic”, “goal”, or any internal process to the user.  
+- The chatbot UI will not have a back button; assume forward‑only flow.
+
+---
+
+### 5. Decision Flow (for you, the model)
+
+1. **Read** the latest **{user_input}**, **{rag_response}**, and **{chat_history_str}**.  
+2. **Identify** the current **Topic** from the context.  
+3. **Set** a clear **Goal** for this turn (solution + follow‑up).  
+4. **Generate** the reply following the structure and style rules above.  
+
+---
+
+========================
+CRITICAL BEHAVIORAL RULES
+========================
+
+- Do not over-validate without offering direction.
+- Do not only ask questions.
+- Do not provide rigid lectures.
+- Do not sound robotic.
+- Do not repeatedly restate the user’s words.
+- Avoid repetitive emotional framing.
+- Keep tone human and natural.
+**Remember:** The purpose is to help the user feel heard, offer a tangible step forward, and gently probe for deeper insight—all while sounding natural and staying on‑track with the ongoing conversation.
 
             """
         

test.ipynb

@@ -10,6 +10,22 @@
     "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,
@@ -556,16 +572,304 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "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.<locals>._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                \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mrun_id\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    410\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    411\u001b[39m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m.generations[\u001b[32m0\u001b[39m][\u001b[32m0\u001b[39m],\n\u001b[32m    412\u001b[39m         ).message,\n\u001b[32m    413\u001b[39m     )\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/workspaces/codespaces-blank/overenv/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py:1121\u001b[39m, in \u001b[36mBaseChatModel.generate_prompt\u001b[39m\u001b[34m(self, prompts, stop, callbacks, **kwargs)\u001b[39m\n\u001b[32m   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 \u001b[49m\u001b[43mfunction_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    474\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfunctions\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunctions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    475\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43minclude_domains\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43minclude_domains\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    476\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43minclude_reasoning\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43minclude_reasoning\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    477\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlogit_bias\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogit_bias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    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                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtemperature\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    495\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtool_choice\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    496\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtools\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtools\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    497\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtop_logprobs\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_logprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    498\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtop_p\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_p\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    499\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43muser\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43muser\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    500\u001b[39m \u001b[43m            \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    501\u001b[39m \u001b[43m            \u001b[49m\u001b[43mcompletion_create_params\u001b[49m\u001b[43m.\u001b[49m\u001b[43mCompletionCreateParams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    502\u001b[39m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    503\u001b[39m \u001b[43m        \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmake_request_options\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    504\u001b[39m \u001b[43m            \u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\n\u001b[32m    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": "calibre-env",
+   "display_name": "overenv",
    "language": "python",
    "name": "python3"
   },

utils.py

@@ -1,3 +1,4 @@
+
 # utils.py
 import os
 import json
@@ -8,6 +9,14 @@ from langchain_text_splitters import RecursiveCharacterTextSplitter
 # from langchain_huggingface import HuggingFaceEmbeddings
 # from langchain_community.vectorstores import Chroma
 # from langchain.prompts import PromptTemplate
+
+from langchain_core.documents import Document
+from langchain_chroma import Chroma
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_core.prompts import PromptTemplate
+# from langchain.chains import RetrievalQA
+from langchain_classic.chains import create_retrieval_chain
+from langchain_classic.chains.combine_documents import create_stuff_documents_chain
 from configure import USER_DATA_PATH, RAG_BASE_DIRECTORY, RAG_CATEGORIES
 import shutil
 from dotenv import load_dotenv
@@ -15,7 +24,6 @@ from pymongo import MongoClient
 import certifi
 import re
 
-
 load_dotenv()
 
 
@@ -41,6 +49,7 @@ def LLMChunking():
 
 # LLM setup
 llm = ChatGroq(
+    api_key="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",
     model="llama-3.3-70b-versatile",
     temperature=0,
     max_tokens=4000
@@ -56,8 +65,8 @@ text_splitter = RecursiveCharacterTextSplitter(
 # text_splitter = LLMChunking()
 
 # Embeddings
-# embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-embeddings = None
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+# embeddings = None
 
 # Load user data
 def load_user_data():
@@ -75,114 +84,122 @@ def save_user_data(data):
         json.dump(data, f, indent=2)
 
 # Initialize RAG with per-category vector stores and QA chains
-# def initialize_rag():
-#     vector_stores = {}
-#     qa_chains = {}
-    
-#     # Updated prompt template for advice-focused responses
-#     custom_prompt_template = """
-#     You are a {category} wellness expert. Provide helpful advice with specific actions:
-    
-#     1. Start with a brief empathetic response to the user's concern
-#     2. Offer 1-3 actionable suggestions with brief explanations
-#     3. End with an open-ended question to continue conversation
-    
-#     Guidelines:
-#     - Keep responses conversational and supportive
-#     - Avoid clinical jargon
-#     - Focus on practical, implementable advice
-#     - Maintain hopeful and encouraging tone
-    
-#     Context:
-#     {context}
+def initialize_rag():
+    """
+    Initialize RAG vector stores and chains using modern LangChain (LCEL).
+    Args:
+        llm: The initialized ChatGroq (or other) LLM object.
+        embeddings: The initialized HuggingFaceEmbeddings object.
+        text_splitter: The initialized RecursiveCharacterTextSplitter object.
+    """
+    vector_stores = {}
+    qa_chains = {}
     
-#     Question: {question}
-#     """
+    # 1. Define Prompt Template (Modern LCEL Format)
+    # Note: Modern chains typically look for "context" and "input" variables.
+    base_prompt_template = """You are a {category} wellness expert. Provide helpful advice with specific actions:
+
+1. Start with a brief empathetic response to the user's concern
+2. Offer 1-3 actionable suggestions with brief explanations
+3. End with an open-ended question to continue conversation
+
+Guidelines:
+- Keep responses conversational and supportive
+- Avoid clinical jargon
+- Focus on practical, implementable advice
+- Maintain hopeful and encouraging tone
+
+Context:
+{context}
+
+Question: {input}
+"""
     
-#     # Create vector stores and QA chains for each category
-#     # Assume all your variables like RAG_CATEGORIES, embeddings, llm, etc.
-# # are defined above this block.
-
-#     for category in RAG_CATEGORIES:
-#         persist_dir = f"./chroma_db_{category}"
-#         vector_store = None  # Initialize vector_store to None
-
-#         # --- 1. Check if Vector Store Already Exists ---
-#         if os.path.exists(persist_dir):
-#             print(f"Found existing vector store for {category}. Attempting to load...")
-#             try:
-#                 # Load the existing vector store from disk
-#                 vector_store = Chroma(
-#                     persist_directory=persist_dir,
-#                     embedding_function=embeddings  # Must provide the same embedding function
-#                 )
-#                 vector_stores[category] = vector_store
-#             except Exception as e:
-#                 print(f"Error loading existing store {persist_dir}: {e}")
-#                 print("Will delete and attempt to re-build.")
-#                 shutil.rmtree(persist_dir)
+    for category in RAG_CATEGORIES:
+        persist_dir = f"./chroma_db_{category}"
+        vector_store = None 
+
+        # --- 2. Check/Load Existing Vector Store ---
+        if os.path.exists(persist_dir):
+            print(f"Found existing vector store for {category}. Attempting to load...")
+            try:
+                vector_store = Chroma(
+                    persist_directory=persist_dir,
+                    embedding_function=embeddings  # UPDATED: 'embedding_function', not 'embedding'
+                )
+                vector_stores[category] = vector_store
+            except Exception as e:
+                print(f"Error loading existing store {persist_dir}: {e}")
+                print("Will delete and attempt to re-build.")
+                shutil.rmtree(persist_dir)
         
-#         # --- 2. Create Vector Store if it Doesn't Exist (or failed to load) ---
-#         if vector_store is None: 
-#             print(f"No valid vector store for {category} found. Creating new one...")
+        # --- 3. Create Vector Store if needed ---
+        if vector_store is None: 
+            print(f"No valid vector store for {category} found. Creating new one...")
+            
+            dir_path = os.path.join(RAG_BASE_DIRECTORY, category)
+            docs = []
             
-#             # Process documents (Your original logic)
-#             dir_path = os.path.join(RAG_BASE_DIRECTORY, category)
-#             docs = []
-#             if os.path.exists(dir_path):
-#                 for filename in os.listdir(dir_path):
-#                     if filename.endswith('.txt'):
-#                         file_path = os.path.join(dir_path, filename)
-#                         try:
-#                             with open(file_path, 'r', encoding='utf-8') as f:
-#                                 text = f.read()
-#                             chunks = text_splitter.split_text(text)
-#                             for chunk in chunks:
-#                                 if chunk.strip():
-#                                     metadata = {
-#                                         "source": filename,
-#                                         "category": category
-#                                     }
-#                                     docs.append(Document(
-#                                         page_content=chunk.strip(),
-#                                         metadata=metadata
-#                                     ))
-#                         except Exception as e:
-#                             print(f"Error processing {file_path}: {e}")
+            if os.path.exists(dir_path):
+                for filename in os.listdir(dir_path):
+                    if filename.endswith('.txt'):
+                        file_path = os.path.join(dir_path, filename)
+                        try:
+                            with open(file_path, 'r', encoding='utf-8') as f:
+                                text = f.read()
+                            
+                            chunks = text_splitter.split_text(text)
+                            for chunk in chunks:
+                                if chunk.strip():
+                                    metadata = {
+                                        "source": filename,
+                                        "category": category
+                                    }
+                                    docs.append(Document(
+                                        page_content=chunk.strip(),
+                                        metadata=metadata
+                                    ))
+                        except Exception as e:
+                            print(f"Error processing {file_path}: {e}")
+            
+            if docs:
+                # UPDATED: Use 'embedding_function' instead of 'embedding'
+                # UPDATED: Removed .persist() call (Auto-persists in new version)
+                vector_store = Chroma.from_documents(
+                    documents=docs,
+                    embedding=embeddings, 
+                    persist_directory=persist_dir
+                )
+                vector_stores[category] = vector_store
+                print(f"Created new vector store for {category} with {len(docs)} documents.")
+            else:
+                print(f"No documents found for {category}. Skipping QA chain setup.")
+                continue 
+
+        # --- 4. Create QA Chain (LCEL Style) ---
+        if vector_store:
+            # A. Create the Prompt
+            # We inject the specific category into the template string immediately
+            category_specific_template = base_prompt_template.replace("{category}", category)
             
-#             # Create vector store (Your original logic)
-#             if docs:
-#                 vector_store = Chroma.from_documents(
-#                     documents=docs,
-#                     embedding=embeddings,
-#                     persist_directory=persist_dir
-#                 )
-#                 vector_store.persist()
-#                 vector_stores[category] = vector_store
-#                 print(f"Created and persisted new vector store for {category} with {len(docs)} documents.")
-#             else:
-#                 print(f"No documents found for {category}. Skipping QA chain setup.")
-#                 continue  # Skip to the next category
-
-#         # --- 3. Create QA Chain (if vector_store was loaded or created) ---
-#         if vector_store:
-#             # Create QA chain with updated prompt
-#             prompt = PromptTemplate(
-#                 template=custom_prompt_template.replace("{category}", category),
-#                 input_variables=["context", "question"]
-#             )
-#             qa_chain = RetrievalQA.from_chain_type(
-#                 llm=llm,
-#                 chain_type="stuff",
-#                 retriever=vector_store.as_retriever(search_kwargs={"k": 5}),
-#                 chain_type_kwargs={"prompt": prompt},
-#                 return_source_documents=True
-#             )
-#             qa_chains[category] = qa_chain
-#             print(f"Initialized {category} QA chain.")
-
-# # The rest of your code
-#     return qa_chains
+            prompt = PromptTemplate(
+                template=category_specific_template,
+                input_variables=["context", "input"] # LCEL standard variables
+            )
+
+            # B. Create the Document Chain (LLM + Prompt)
+            question_answer_chain = create_stuff_documents_chain(llm, prompt)
+
+            # C. Create the Retrieval Chain (Retriever + Document Chain)
+            retriever = vector_store.as_retriever(search_kwargs={"k": 5})
+            rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+
+            qa_chains[category] = rag_chain
+            print(f"Initialized {category} QA chain.")
+
+    return qa_chains
+
+
 
 # Classify question to category
 def classify_question_category(question):
@@ -195,18 +212,18 @@ def classify_question_category(question):
     print(response)
     return response.content.strip()
 
-# # Get RAG response using category-specific QA chain
-# def get_rag_response(question, qa_chains):
-#     # Classify question
-#     category = classify_question_category(question)
+# Get RAG response using category-specific QA chain
+def get_rag_response(question, qa_chains):
+    # Classify question
+    category = classify_question_category(question)
     
-#     if category not in qa_chains:
-#         # Fallback to first available chain
-#         category = list(qa_chains.keys())[0]
+    if category not in qa_chains:
+        # Fallback to first available chain
+        category = list(qa_chains.keys())[0]
     
-#     # Get response
-#     result = qa_chains[category].invoke({"query": question})
-#     return result['result']
+    # Get response
+    result = qa_chains[category].invoke({"input": question})
+    return result['answer']
 
 
 # Classify user input
@@ -223,8 +240,9 @@ def classify_input(user_input):
     response = llm.invoke(prompt)
     return response.content.strip().lower()
 
-def parse_weird_json(text_data):
 
+
+def parse_weird_json(text_data):
     fixed_json_string = re.sub(r'\]\s*\[', ', ', text_data.strip())
     
     # Step B: Load it as standard JSON