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"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "45831b5b",
"metadata": {},
"outputs": [],
"source": [
"supervisor_prompt = \"\"\"\n",
"You are the high-level project manager for an automated data analysis workflow. Your sole responsibility is to act as a router, directing the workflow to the correct next step based on a summary of the current state and the outcome of the last completed task.\n",
"\n",
"**Your Decision-Making Process:**\n",
"\n",
"1. **Review the `Current Workflow Status` summary.** This tells you which major milestones have been completed.\n",
"2. **Review the `Last Event` message.** This tells you what *just* happened and its outcome (e.g., a plan was created, a validation failed, a report was generated).\n",
"3. **Apply the first matching rule** from the list below to determine the next node.\n",
"\n",
"---\n",
"**ROUTING RULES (Evaluate in this exact order):**\n",
"\n",
"**1. Start of Workflow -> Create Plan**\n",
"- **IF** `Analysis Plan Generated` is 'No'.\n",
"- **THEN** The first step is always to create a preprocessing plan.\n",
"- **ACTION:** Route to `PreprocessingPlanner_node`.\n",
"\n",
"**2. Plan Exists, Not Cleaned -> Clean Data**\n",
"- **IF** `Analysis Plan Generated` is 'Yes' AND `Preprocessing Attempted` is 'No'.\n",
"- **THEN** A plan is ready for execution.\n",
"- **ACTION:** Route to `Cleaner_node`.\n",
"\n",
"**3. Validation Failed -> Re-Clean Data**\n",
"- **IF** the `Last Event` message is from the `Validation_node` AND it explicitly reports a **FAILURE**.\n",
"- **THEN** The previous cleaning attempt was incorrect. It must be re-attempted.\n",
"- **ACTION:** Route back to `Cleaner_node`.\n",
"\n",
"**4. Data Cleaned, Not Validated -> Validate Data**\n",
"- **IF** `Preprocessing Attempted` is 'Yes' AND `Validation Status` is 'Not Run'.\n",
"- **THEN** The data has been cleaned and must now be programmatically verified.\n",
"- **ACTION:** Route to `Validation_node`.\n",
"\n",
"**5. Validation Succeeded, No Report -> Generate Report**\n",
"- **IF** `Validation Status` is 'SUCCESS' AND `Report Generated` is 'No'.\n",
"- **THEN** The data is clean and verified. The next step is to generate the main strategic business report.\n",
"- **ACTION:** Route to `Reporter_node`.\n",
"\n",
"**6. Report Done, No Visuals -> Generate Visuals**\n",
"- **IF** `Report Generated` is 'Yes' AND `Visualizations Generated` is 'No'.\n",
"- **THEN** The text report is complete. Now, create the supporting data visualizations.\n",
"- **ACTION:** Route to `visualizer_node`.\n",
"\n",
"**7. All Artifacts Generated -> Finish**\n",
"- **IF** `Visualizations Generated` is 'Yes'.\n",
"- **THEN** All required artifacts (report and visuals) have been created. The mission is complete.\n",
"- **ACTION:** Route to `END`.\n",
"---\n",
"\n",
"Your final output MUST be a single, valid JSON object with \"next\" and \"reasoning\" keys. Your reasoning should be a brief, one-sentence justification based on the rule you applied.\n",
"\"\"\"\n",
"\n",
"PreprocessingPlanner_prompt = \"\"\"\n",
"You are an expert, execution-focused DataFrame analyzer. Your sole purpose is to create a specific and actionable preprocessing plan suitable for Exploratory Data Analysis (EDA).\n",
"\n",
"**CRITICAL INSTRUCTIONS:**\n",
"\n",
"1. You MUST first call the `eda_fact_sheet` tool to analyze the data's schema and quality.\n",
"2. Based on the fact sheet, you will decide on a single, precise action for each column.\n",
"3. Your action for each column **MUST** be chosen from this exact list of approved commands:\n",
" - `none` (Use this if no change is needed.)\n",
" - `convert_to_datetime` (For date-like object/string columns.)\n",
" - `convert_to_time` (For time-like object/string columns.)\n",
" - `strip_whitespace` (For object/string columns with leading/trailing spaces.)\n",
" - `fill_missing_with_mean` (Use ONLY for numeric columns.)\n",
" - `fill_missing_with_median` (Use ONLY for numeric columns.)\n",
" - `fill_missing_with_mode` (Use for numeric or categorical columns.)\n",
" - `fill_missing_with_constant: 'VALUE'` (Replace 'VALUE' with a specific constant, e.g., 'Unknown' for strings, '00:00:00' for time.)\n",
" - `drop_column` (Use ONLY if a column is constant or a useless identifier.)\n",
"\n",
"4. **SAFETY RULES:**\n",
" - Do NOT suggest any Machine Learning preparations (e.g., one-hot encoding, scaling).\n",
" - Do NOT suggest dropping rows. Handling missing values via filling is preferred.\n",
" - Your plan should only enable basic EDA (data type correction, cleaning, and imputation).\n",
"\n",
"**FINAL OUTPUT FORMATTING:**\n",
"- After analyzing the fact sheet, your final answer **MUST** be a single, valid JSON object.\n",
"- This JSON object must conform **EXACTLY** to the schema provided below.\n",
"- Do NOT output any other text, explanation, or markdown.\n",
"\n",
"**Schema:**\n",
"```json\n",
"{\n",
" \"plan\": [\n",
" {\"column\": \"<column_name>\", \"action\": \"<chosen_command_from_list>\"},\n",
" ...\n",
" ],\n",
" \"summary\": \"<A concise, one-sentence summary of the overall plan.>\",\n",
" \"details\": \"<A detailed, human-readable summary of all the actions to be taken.>\"\n",
"}\"\"\"\n",
"\n",
"\n",
"\n",
"cleaner_prompt = \"\"\"\n",
"You are a data preprocessing execution agent. Your mission is to permanently modify a CSV file based on a given plan and **any previous validation feedback provided in the prompt**.\n",
"\n",
"You must follow these steps precisely:\n",
"\n",
"1. **Analyze the Request:**\n",
" - Review the preprocessing plan.\n",
" - **CRITICAL:** Check if `Validation Feedback` is provided. If it is, your primary goal is to write Python code that **specifically fixes the reported errors** while still respecting the overall plan.\n",
"\n",
"2. **Load Data**: Read the CSV file from the provided path into a pandas DataFrame.\n",
"\n",
"3. **Apply Transformations**: Write and execute Python code to perform all required steps. You MUST write robust code to handle potential errors.\n",
" - **CRITICAL - For Date/Time Conversions**: When using `pd.to_datetime`, you MUST include the `errors='coerce'` parameter (e.g., `df['Time'] = pd.to_datetime(df['Time'], errors='coerce').dt.time`). This prevents the entire process from failing if it encounters a single invalid time format.\n",
" - **CRITICAL - For Dropping Columns**: Before attempting to drop a column, you MUST first check if it exists in the DataFrame (e.g., `if 'cash_type' in df.columns:`).\n",
"\n",
"4. **Save Changes (CRITICAL STEP)**: After all transformations are applied, you MUST save the modified DataFrame back to the original file path to make the changes permanent. Use `df.to_csv(path, index=False)`.\n",
"\n",
"5. **Format Final Output**: Your final answer MUST be a single, valid JSON object that strictly follows this schema:\n",
" ```json\n",
" {\"summary\": \"A concise, one-sentence summary of the cleaning outcome.\", \"details\": \"A detailed summary of all actions taken and confirmation of the save.\"}\n",
" ```\n",
"\"\"\"\n",
"\n",
"validation_prompt = \"\"\"\n",
"You are a hyper-vigilant and strict data validation agent. Your sole purpose is to programmatically verify that a data cleaning plan was executed correctly on a CSV file. You will use the `python_repl_ast` tool to run pandas code to check the file on disk.\n",
"\n",
"Your mission is to validate each action from the provided analysis plan. You must follow this Validation Playbook precisely:\n",
"\n",
"**Validation Playbook:**\n",
"\n",
"1. **For \"convert to datetime\" actions:**\n",
" * **Your primary method:** Attempt to reload the entire CSV using `pd.read_csv(path, parse_dates=['column_name_here'])`.\n",
" * If this command executes without error, the validation is a **SUCCESS**.\n",
" * If it raises an exception, it's a **FAILURE**.\n",
"\n",
"2. **For \"strip whitespace\" actions:**\n",
" * Load the CSV. Check if any value in the column contains leading/trailing whitespace using `df['column'].str.strip() != df['column']`. If this finds any `True` values, it's a **FAILURE**.\n",
"\n",
"3. **For actions like \"fill missing with mean/median/mode\":**\n",
" * Load the CSV. Check for any remaining nulls using `df['column'].isnull().sum()`. If the sum is greater than `0`, it's a **FAILURE**.\n",
"\n",
"4. **For any other action:**\n",
" * You must infer the most logical programmatic test.\n",
"\n",
"**Output Requirements (CRITICAL):**\n",
"Your final answer **MUST** be a JSON object that strictly follows this schema:\n",
"```json\n",
"{\"status\": \"SUCCESS\" | \"FAILURE\", \"message\": \"Your finding\"}\"\"\"\n",
"\n",
"\n",
"Reporter_prompt = \"\"\"You are an elite business intelligence consultant, renowned for your ability to parachute into any business, analyze a raw dataset, and emerge with a strategic plan that creates significant value. Your client, a business owner, has given you a CSV file and a simple request: \"Find the single biggest opportunity hidden in this data and tell me exactly how to capitalize on it.\"\n",
"\n",
"Your mission: Perform a comprehensive, hypothesis-driven analysis of the provided data. You must autonomously identify the key metrics, dimensions, and relationships within the dataset to build a compelling business case study.\n",
"\n",
"Your Tools:\n",
"1. `eda_fact_sheet(df_path)`: Use this once at the very beginning to perform initial data reconnaissance.\n",
"2. `python_repl_ast`: This is your primary analytical tool. You will use it multiple times to explore hypotheses, segment the data, and quantify your findings.\n",
"\n",
"---\n",
"\n",
"### Your Mandated Investigative Framework:\n",
"\n",
"1. **Data Reconnaissance & Profiling:** Use `eda_fact_sheet` to get a high-level overview.\n",
"2. **Hypothesis Generation & Deep-Dive Analysis:** After your initial recon, form hypotheses about the data and use `python_repl_ast` to test them, covering:\n",
" - **Core Metric Identification:** What are the most important KPIs? (e.g., `Sales`, `Revenue`, `Profit`).\n",
" - **Dimensional Analysis:** How do the core metrics perform across different segments? (e.g., `Region`, `Product_Category`).\n",
" - **Temporal Analysis:** Are there trends over time? (e.g., year-over-year growth, seasonality).\n",
" - **Correlation & Relationship Analysis:** How do numerical features interact?\n",
" - **Segmentation & Outlier Analysis:** Can you identify distinct groups or outliers?\n",
"3. **Synthesize and Report:** After your investigation, you must weave all your quantitative findings into the mandatory JSON report format below.\n",
"\n",
"---\n",
"\n",
"### Mandatory Report Structure (To be formatted as a JSON object):\n",
"\n",
"- **Subject:** \"Strategic Deep-Dive: Unlocking Growth Opportunities in [Dataset Theme]\"\n",
"- **Executive Summary:** A brief, 3-4 sentence overview for a C-suite audience.\n",
"- **Current State of the Business:** An overview of macro trends and key segments.\n",
"- **The Core Insight:** Detail your main finding with specific, hard numbers.\n",
"- **Supporting Analysis:** Add layers with other detailed findings (e.g., temporal or behavioral patterns).\n",
"- **The Go-Forward Strategic Plan:** A multi-phase action plan.\n",
" - **Phase 1: Immediate Realignment & Proof of Concept (Next 30 Days):** Detail a strategic focus shift and an initial test.\n",
" - **Phase 2: Scale & Market Capture (Next Quarter):** Describe a full-scale rollout and operational adjustments.\n",
"- **Metrics for Success:** A list of KPIs to track the plan's success.\n",
"\n",
"---\n",
"\n",
"### **FINAL OUTPUT FORMATTING (CRITICAL):**\n",
"- After completing your entire analysis using your tools, your final answer **MUST** be a single, valid JSON object.\n",
"- This JSON object must conform **EXACTLY** to the schema of the `BusinessReport` model.\n",
"- **Do NOT output any other text, explanation, or markdown.** Your entire final response must be only the JSON object.\n",
"\"\"\"\n",
"\n",
"VISUALIZATION_PROMPT = \"\"\"\n",
"You are an expert Data Analyst serving a business owner. Your mission is to identify the **5 to 7 most impactful business insights** from the provided CSV dataset and present them as professional-quality visualizations. The goal is quality over quantity.\n",
"\n",
"**Your Mandated Workflow:**\n",
"\n",
"1. **Initial Reconnaissance:** You MUST first call the `eda_fact_sheet` tool on the provided `df_path` to understand the data's structure and content.\n",
"2. **Strategic Visualization Plan:** After analyzing the fact sheet, you must formulate a plan to create between 5 and 7 highly relevant visualizations. Do not create more than 7. Focus on visuals that directly support key business decisions (e.g., top-selling products, sales trends, customer behavior).\n",
"3. **Iterative Generation:** For each planned visualization, you MUST perform the following steps:\n",
" a. Use the `python_repl_ast` tool to write and execute Python code using `pandas`, `matplotlib`, and `seaborn`.\n",
" b. Your code MUST generate a high-quality plot with a clear, business-focused title and labeled X/Y axes.\n",
" c. You MUST save the plot as a unique `.png` file into the `visualizations/` directory using a descriptive filename.\n",
" d. You MUST use `plt.close()` after saving each plot to manage memory.\n",
"\n",
"**CRITICAL FINAL OUTPUT INSTRUCTION:**\n",
"\n",
"- After you have generated and saved your **final** planned visualization (between 5 and 7 total), your next and **ABSOLUTELY FINAL action** is to output a single, valid JSON object.\n",
"- This JSON object **MUST** conform exactly to the `VisualizationReport` schema provided. It will contain a list of objects, where each object details a visualization you created.\n",
"- **DO NOT** call the `python_repl_ast` tool again after you have finished creating your plots. Your sole focus must be on generating the final JSON report.\n",
"- **DO NOT** output any other text, markdown, or explanation. Your entire final response must be only the JSON object.\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "991afff6",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"def eda_fact_sheet_bakery(path: str, sample_size: int = 3, freq_top: int = 3):\n",
" \"\"\"\n",
" Generates a batchwise fact sheet for a dataset.\n",
"\n",
" Args:\n",
" path (str): Path to CSV or Excel dataset.\n",
" sample_size (int): Number of example values per column.\n",
" freq_top (int): Number of top frequent values per column.\n",
"\n",
" Returns:\n",
" dict: Fact sheet containing dataset-level info and column-level stats in batches.\n",
" \"\"\"\n",
" # --- Load dataset ---\n",
" if path.endswith(\".csv\"):\n",
" df = pd.read_csv(path)\n",
" elif path.endswith(\".xlsx\") or path.endswith(\".xls\"):\n",
" df = pd.read_excel(path)\n",
" else:\n",
" raise ValueError(\"Unsupported file format. Use CSV or Excel.\")\n",
"\n",
" n_rows, n_columns = df.shape\n",
"\n",
" fact_sheet = {\n",
" \"n_rows\": n_rows,\n",
" \"n_columns\": n_columns,\n",
" \"batches\": []\n",
" }\n",
"\n",
" # --- Determine batch splits ---\n",
" batch_size = 15\n",
" batch_splits = [df.columns[i:i+batch_size].tolist() for i in range(0, n_columns, batch_size)]\n",
"\n",
" # --- Process each batch ---\n",
" for batch_cols in batch_splits:\n",
" batch_profile = {\"columns\": {}}\n",
" for col in batch_cols:\n",
" series = df[col]\n",
" col_profile = {}\n",
"\n",
" total = len(series)\n",
"\n",
" # Core stats\n",
" col_profile[\"dtype\"] = str(series.dtype)\n",
" col_profile[\"null_percent\"] = round(float(series.isna().sum() / total * 100), 2)\n",
" col_profile[\"unique_percent\"] = round(float(series.nunique(dropna=True) / total * 100), 2)\n",
"\n",
" # Example values\n",
" try:\n",
" col_profile[\"examples\"] = series.dropna().sample(\n",
" min(sample_size, series.dropna().shape[0]),\n",
" random_state=42\n",
" ).tolist()\n",
" except ValueError:\n",
" col_profile[\"examples\"] = []\n",
"\n",
" # Top frequent values\n",
" if not series.isna().all():\n",
" top_freq = series.value_counts(dropna=True).head(freq_top)\n",
" col_profile[\"top_values\"] = {str(k): int(v) for k, v in top_freq.to_dict().items()}\n",
"\n",
" # Numeric columns\n",
" if pd.api.types.is_numeric_dtype(series):\n",
" col_profile.update({\n",
" \"min\": float(series.min(skipna=True)),\n",
" \"max\": float(series.max(skipna=True)),\n",
" \"mean\": float(series.mean(skipna=True)),\n",
" \"std\": float(series.std(skipna=True))\n",
" })\n",
" if series.std(skipna=True) > 0:\n",
" z_scores = ((series - series.mean(skipna=True)) / series.std(skipna=True)).abs()\n",
" col_profile[\"has_outliers\"] = bool((z_scores > 3).any())\n",
" else:\n",
" col_profile[\"has_outliers\"] = False\n",
"\n",
" # Datetime columns\n",
" elif pd.api.types.is_datetime64_any_dtype(series):\n",
" if not series.dropna().empty:\n",
" col_profile[\"min_date\"] = str(series.min())\n",
" col_profile[\"max_date\"] = str(series.max())\n",
"\n",
" # Text/categorical columns\n",
" elif pd.api.types.is_object_dtype(series):\n",
" lengths = series.dropna().astype(str).map(len)\n",
" if not lengths.empty:\n",
" col_profile[\"avg_length\"] = float(lengths.mean())\n",
" col_profile[\"max_length\"] = int(lengths.max())\n",
" unusual = series.dropna().astype(str).str.contains(r\"[^a-zA-Z0-9\\s]\", regex=True).sum()\n",
" col_profile[\"unusual_char_percent\"] = round(float(unusual / total * 100), 2)\n",
"\n",
" # Flags\n",
" if series.nunique(dropna=True) == total:\n",
" col_profile[\"is_identifier\"] = True\n",
" elif series.nunique(dropna=True) <= 1:\n",
" col_profile[\"is_constant\"] = True\n",
"\n",
" batch_profile[\"columns\"][col] = col_profile\n",
"\n",
" fact_sheet[\"batches\"].append(batch_profile)\n",
"\n",
" return fact_sheet\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "e10c0ea4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'n_rows': 234005,\n",
" 'n_columns': 7,\n",
" 'batches': [{'columns': {'Unnamed: 0': {'dtype': 'int64',\n",
" 'null_percent': 0.0,\n",
" 'unique_percent': 100.0,\n",
" 'examples': [254689, 147840, 321961],\n",
" 'top_values': {'0': 1, '1': 1, '4': 1},\n",
" 'min': 0.0,\n",
" 'max': 511395.0,\n",
" 'mean': 255205.03585393474,\n",
" 'std': 147501.62599208727,\n",
" 'has_outliers': False,\n",
" 'is_identifier': True},\n",
" 'date': {'dtype': 'object',\n",
" 'null_percent': 0.0,\n",
" 'unique_percent': 0.26,\n",
" 'examples': ['2021-11-04', '2021-07-19', '2022-03-07'],\n",
" 'top_values': {'2022-08-14': 997, '2021-08-15': 961, '2022-07-17': 941},\n",
" 'avg_length': 10.0,\n",
" 'max_length': 10,\n",
" 'unusual_char_percent': 100.0},\n",
" 'time': {'dtype': 'object',\n",
" 'null_percent': 0.0,\n",
" 'unique_percent': 0.29,\n",
" 'examples': ['12:37', '19:14', '16:20'],\n",
" 'top_values': {'11:43': 859, '11:46': 850, '12:11': 850},\n",
" 'avg_length': 5.0,\n",
" 'max_length': 5,\n",
" 'unusual_char_percent': 100.0},\n",
" 'ticket_number': {'dtype': 'float64',\n",
" 'null_percent': 0.0,\n",
" 'unique_percent': 58.31,\n",
" 'examples': [218841.0, 190030.0, 237182.0],\n",
" 'top_values': {'225766.0': 13, '202116.0': 12, '155163.0': 12},\n",
" 'min': 150040.0,\n",
" 'max': 288913.0,\n",
" 'mean': 219201.25873806115,\n",
" 'std': 40053.22389631496,\n",
" 'has_outliers': False},\n",
" 'article': {'dtype': 'object',\n",
" 'null_percent': 0.0,\n",
" 'unique_percent': 0.06,\n",
" 'examples': ['MOISSON', 'COUPE', 'BAGUETTE'],\n",
" 'top_values': {'TRADITIONAL BAGUETTE': 67689,\n",
" 'COUPE': 20470,\n",
" 'BAGUETTE': 15292},\n",
" 'avg_length': 12.91998461571334,\n",
" 'max_length': 24,\n",
" 'unusual_char_percent': 0.26},\n",
" 'Quantity': {'dtype': 'float64',\n",
" 'null_percent': 0.0,\n",
" 'unique_percent': 0.02,\n",
" 'examples': [1.0, 1.0, 1.0],\n",
" 'top_values': {'1.0': 156712, '2.0': 49367, '3.0': 13677},\n",
" 'min': -200.0,\n",
" 'max': 200.0,\n",
" 'mean': 1.538377385098609,\n",
" 'std': 1.2896033140596856,\n",
" 'has_outliers': True},\n",
" 'unit_price': {'dtype': 'object',\n",
" 'null_percent': 0.0,\n",
" 'unique_percent': 0.05,\n",
" 'examples': ['2,00 €', '0,15 €', '0,95 €'],\n",
" 'top_values': {'1,20 €': 49080, '0,15 €': 20471, '1,30 €': 19778},\n",
" 'avg_length': 6.006734898826948,\n",
" 'max_length': 7,\n",
" 'unusual_char_percent': 100.0}}}]}"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eda_fact_sheet_bakery(r\"D:\\Code Assistant\\Bakery sales.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "d79b1adf",
"metadata": {},
"outputs": [],
"source": [
"def eda_fact_sheet_bakery(sample_size: int = 3, freq_top: int = 3):\n",
" \"\"\"\n",
" Returns a fact sheet for the 'Bakery sales.csv' dataset.\n",
" \n",
" Args:\n",
" sample_size (int): Number of random sample values to show per column.\n",
" freq_top (int): Number of top frequent values to display.\n",
" \"\"\"\n",
" path = r\"D:\\Code Assistant\\Bakery sales.csv\" # dataset path\n",
"\n",
" # --- load dataset ---\n",
" if path.endswith(\".csv\"):\n",
" df = pd.read_csv(path)\n",
" elif path.endswith(\".xlsx\") or path.endswith(\".xls\"):\n",
" df = pd.read_excel(path)\n",
" else:\n",
" raise ValueError(\"Unsupported file format. Use CSV or Excel.\")\n",
"\n",
" fact_sheet = {\n",
" \"n_rows\": len(df),\n",
" \"n_columns\": len(df.columns),\n",
" \"batches\": []\n",
" }\n",
"\n",
" # --- decide split logic ---\n",
" n_cols = len(df.columns)\n",
" if n_cols <= 15:\n",
" batch_splits = [df.columns.tolist()]\n",
" elif n_cols <= 30:\n",
" mid = n_cols // 2\n",
" batch_splits = [df.columns[:mid].tolist(), df.columns[mid:].tolist()]\n",
" else:\n",
" batch_splits = [df.columns[i:i+15].tolist() for i in range(0, n_cols, 15)]\n",
"\n",
" # --- process each batch ---\n",
" for batch_cols in batch_splits:\n",
" batch_profile = {\"columns\": {}}\n",
" for col in batch_cols:\n",
" series = df[col]\n",
" col_profile = {}\n",
" total = len(series)\n",
"\n",
" # Core stats\n",
" col_profile[\"dtype\"] = str(series.dtype)\n",
" col_profile[\"null_percent\"] = round((series.isna().sum() / total) * 100, 2)\n",
" col_profile[\"unique_percent\"] = round((series.nunique(dropna=True) / total) * 100, 2)\n",
"\n",
" # Example values\n",
" try:\n",
" col_profile[\"examples\"] = series.dropna().sample(\n",
" min(sample_size, series.dropna().shape[0]),\n",
" random_state=42\n",
" ).tolist()\n",
" except ValueError:\n",
" col_profile[\"examples\"] = []\n",
"\n",
" # Top frequent values\n",
" if not series.isna().all():\n",
" top_freq = series.value_counts(dropna=True).head(freq_top)\n",
" col_profile[\"top_values\"] = top_freq.to_dict()\n",
"\n",
" # Numeric columns\n",
" if pd.api.types.is_numeric_dtype(series):\n",
" col_profile.update({\n",
" \"min\": float(series.min(skipna=True)),\n",
" \"max\": float(series.max(skipna=True)),\n",
" \"mean\": float(series.mean(skipna=True)),\n",
" \"std\": float(series.std(skipna=True))\n",
" })\n",
" if series.std(skipna=True) > 0:\n",
" z_scores = ((series - series.mean(skipna=True)) / series.std(skipna=True)).abs()\n",
" col_profile[\"has_outliers\"] = bool((z_scores > 3).any())\n",
" else:\n",
" col_profile[\"has_outliers\"] = False\n",
"\n",
" # Datetime columns\n",
" elif pd.api.types.is_datetime64_any_dtype(series):\n",
" if not series.dropna().empty:\n",
" col_profile[\"min_date\"] = str(series.min())\n",
" col_profile[\"max_date\"] = str(series.max())\n",
"\n",
" # Text/categorical columns\n",
" elif pd.api.types.is_object_dtype(series):\n",
" lengths = series.dropna().astype(str).map(len)\n",
" if not lengths.empty:\n",
" col_profile[\"avg_length\"] = float(lengths.mean())\n",
" col_profile[\"max_length\"] = int(lengths.max())\n",
" unusual = series.dropna().astype(str).str.contains(r\"[^a-zA-Z0-9\\s]\", regex=True).sum()\n",
" col_profile[\"unusual_char_percent\"] = round((unusual / total) * 100, 2)\n",
"\n",
" # Flags\n",
" if series.nunique(dropna=True) == total:\n",
" col_profile[\"is_identifier\"] = True\n",
" elif series.nunique(dropna=True) <= 1:\n",
" col_profile[\"is_constant\"] = True\n",
"\n",
" batch_profile[\"columns\"][col] = col_profile\n",
"\n",
" fact_sheet[\"batches\"].append(batch_profile)\n",
"\n",
" return fact_sheet\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2bb01335",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 18,
"id": "155309ff",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'n_rows': 234005, 'n_columns': 7, 'batches': [{'columns': {'Unnamed: 0': {'dtype': 'int64', 'null_percent': np.float64(0.0), 'unique_percent': 100.0, 'examples': [254689, 147840, 321961], 'top_values': {0: 1, 1: 1, 4: 1}, 'min': 0.0, 'max': 511395.0, 'mean': 255205.03585393474, 'std': 147501.62599208727, 'has_outliers': False, 'is_identifier': True}, 'date': {'dtype': 'object', 'null_percent': np.float64(0.0), 'unique_percent': 0.26, 'examples': ['2021-11-04', '2021-07-19', '2022-03-07'], 'top_values': {'2022-08-14': 997, '2021-08-15': 961, '2022-07-17': 941}, 'avg_length': 10.0, 'max_length': 10, 'unusual_char_percent': np.float64(100.0)}, 'time': {'dtype': 'object', 'null_percent': np.float64(0.0), 'unique_percent': 0.29, 'examples': ['12:37', '19:14', '16:20'], 'top_values': {'11:43': 859, '11:46': 850, '12:11': 850}, 'avg_length': 5.0, 'max_length': 5, 'unusual_char_percent': np.float64(100.0)}, 'ticket_number': {'dtype': 'float64', 'null_percent': np.float64(0.0), 'unique_percent': 58.31, 'examples': [218841.0, 190030.0, 237182.0], 'top_values': {225766.0: 13, 202116.0: 12, 155163.0: 12}, 'min': 150040.0, 'max': 288913.0, 'mean': 219201.25873806115, 'std': 40053.22389631496, 'has_outliers': False}, 'article': {'dtype': 'object', 'null_percent': np.float64(0.0), 'unique_percent': 0.06, 'examples': ['MOISSON', 'COUPE', 'BAGUETTE'], 'top_values': {'TRADITIONAL BAGUETTE': 67689, 'COUPE': 20470, 'BAGUETTE': 15292}, 'avg_length': 12.91998461571334, 'max_length': 24, 'unusual_char_percent': np.float64(0.26)}, 'Quantity': {'dtype': 'float64', 'null_percent': np.float64(0.0), 'unique_percent': 0.02, 'examples': [1.0, 1.0, 1.0], 'top_values': {1.0: 156712, 2.0: 49367, 3.0: 13677}, 'min': -200.0, 'max': 200.0, 'mean': 1.538377385098609, 'std': 1.2896033140596856, 'has_outliers': True}, 'unit_price': {'dtype': 'object', 'null_percent': np.float64(0.0), 'unique_percent': 0.05, 'examples': ['2,00 €', '0,15 €', '0,95 €'], 'top_values': {'1,20 €': 49080, '0,15 €': 20471, '1,30 €': 19778}, 'avg_length': 6.006734898826948, 'max_length': 7, 'unusual_char_percent': np.float64(100.0)}}}]}\n"
]
}
],
"source": [
"fact_sheet = eda_fact_sheet_bakery()\n",
"print(fact_sheet)\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a2a8602d",
"metadata": {},
"outputs": [],
"source": [
"from langchain_groq import ChatGroq\n",
"llm = ChatGroq(temperature=0, model_name=\"openai/gpt-oss-120b\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "82232a14",
"metadata": {},
"outputs": [],
"source": [
"# Import relevant functionality\n",
"from langchain.chat_models import init_chat_model\n",
"from langchain_tavily import TavilySearch\n",
"from langgraph.checkpoint.memory import MemorySaver\n",
"from langgraph.prebuilt import create_react_agent\n",
"\n",
"# Create the agent\n",
"memory = MemorySaver()\n",
"model = llm\n",
"search = TavilySearch(max_results=2)\n",
"tools = [search]\n",
"agent_executor = create_react_agent(model, tools, checkpointer=memory)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "cb1a3fe4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"================================\u001b[1m Human Message \u001b[0m=================================\n",
"\n",
"Hi, I'm Bob and I live in SF.\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\n",
"Hi Bob! 👋 Great to meet you. How can I help you today?\n"
]
}
],
"source": [
"# Use the agent\n",
"config = {\"configurable\": {\"thread_id\": \"abc123\"}}\n",
"\n",
"input_message = {\n",
" \"role\": \"user\",\n",
" \"content\": \"Hi, I'm Bob and I live in SF.\",\n",
"}\n",
"for step in agent_executor.stream(\n",
" {\"messages\": [input_message]}, config, stream_mode=\"values\"\n",
"):\n",
" step[\"messages\"][-1].pretty_print()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "6d1b9914",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"================================\u001b[1m Human Message \u001b[0m=================================\n",
"\n",
"What's my name and where do i live?\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\n",
"You introduced yourself as **Bob**, and you mentioned that you live in **San Francisco (SF)**.\n"
]
}
],
"source": [
"input_message = {\n",
" \"role\": \"user\",\n",
" \"content\": \"What's my name and where do i live?\",\n",
"}\n",
"\n",
"for step in agent_executor.stream(\n",
" {\"messages\": [input_message]}, config, stream_mode=\"values\"\n",
"):\n",
" step[\"messages\"][-1].pretty_print()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "a1c44f76",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"if not os.environ.get(\"GROQ_API_KEY\"):\n",
" os.environ[\"GROQ_API_KEY\"] = getpass.getpass(\"Enter API key for Groq: \")\n",
"\n",
"from langchain.chat_models import init_chat_model\n",
"\n",
"model = init_chat_model(\"openai/gpt-oss-120b\", model_provider=\"groq\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "f3b5e365",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Hello! How can I help you today?'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = \"Hi!\"\n",
"response = model.invoke([{\"role\": \"user\", \"content\": query}])\n",
"response.text()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "46488eea",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"Bakery sales.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "c8e23c5e",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from langchain_groq import ChatGroq\n",
"from langchain.agents import AgentExecutor, create_tool_calling_agent\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.tools import tool\n",
"from pydantic import BaseModel, Field\n",
"import sys\n",
"from io import StringIO\n",
"\n",
"class PythonInputs(BaseModel):\n",
" query: str = Field(description=\"A valid python command to run.\")\n",
"\n",
"# 3. Create the tool using the @tool decorator. This is the gold standard.\n",
"@tool(args_schema=PythonInputs)\n",
"def python_repl_ast(query: str) -> str:\n",
" \"\"\"\n",
" Runs a Python command and returns the result.\n",
" The command has access to a pandas DataFrame named `df`.\n",
" \"\"\"\n",
" # Create a restricted namespace for the execution to see the DataFrame\n",
" local_namespace = {\"df\": df}\n",
" global_namespace = {}\n",
"\n",
" # Capture stdout to return print() statements\n",
" old_stdout = sys.stdout\n",
" sys.stdout = captured_output = StringIO()\n",
"\n",
" try:\n",
" # Execute the code\n",
" exec(query, global_namespace, local_namespace)\n",
" \n",
" # Get the output\n",
" output = captured_output.getvalue()\n",
" if output:\n",
" return output\n",
" else:\n",
" # If nothing was printed, try to eval the last line for a result\n",
" try:\n",
" # Split the query into lines and eval the last non-empty one\n",
" lines = [line for line in query.strip().split('\\n') if line.strip()]\n",
" if lines:\n",
" last_line_result = eval(lines[-1], global_namespace, local_namespace)\n",
" return str(last_line_result)\n",
" else:\n",
" return \"Executed successfully, but no output was produced.\"\n",
" except Exception:\n",
" return \"Executed successfully, but no output was produced.\"\n",
"\n",
" except Exception as e:\n",
" return f\"Execution failed with error: {e}\"\n",
" finally:\n",
" # Restore stdout\n",
" sys.stdout = old_stdout\n",
"\n",
"tools = [python_repl_ast]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "fff3d56a",
"metadata": {},
"outputs": [],
"source": [
"model_with_tools = model.bind_tools(tools)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "21f60152",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "'str' object is not callable",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mTypeError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[22]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m query = \u001b[33m\"\u001b[39m\u001b[33mGive the overview of the dataframe which the tool has access and the preprocessing steps needed to perform EDA on .\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 2\u001b[39m response = model_with_tools.invoke([{\u001b[33m\"\u001b[39m\u001b[33mrole\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33muser\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mcontent\u001b[39m\u001b[33m\"\u001b[39m: query}])\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mMessage content: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[43mresponse\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m)\n",
"\u001b[31mTypeError\u001b[39m: 'str' object is not callable"
]
}
],
"source": [
"query = \"Give the overview of the dataframe which the tool has access and the preprocessing steps needed to perform EDA on .\"\n",
"response = model_with_tools.invoke([{\"role\": \"user\", \"content\": query}])\n",
"\n",
"print(f\"Message content: {response.content()}\\n\")\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "3584b90f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tool calls: [{'name': 'python_repl_ast', 'args': {'query': \"import pandas as pd\\nprint('Shape:', df.shape)\\nprint('\\\\nColumns:', df.columns.tolist())\\nprint('\\\\nHead:')\\nprint(df.head())\\nprint('\\\\nInfo:')\\nprint(df.info())\\nprint('\\\\nDescribe:')\\nprint(df.describe(include='all'))\"}, 'id': 'fc_4d928c8c-d74b-47db-b093-c5dac571064e', 'type': 'tool_call'}]\n"
]
}
],
"source": [
"print(f\"Tool calls: {response.tool_calls}\")"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "266e239b",
"metadata": {},
"outputs": [
{
"ename": "BadRequestError",
"evalue": "Error code: 400 - {'error': {'message': \"'messages' : minimum number of items is 1\", 'type': 'invalid_request_error'}}",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mBadRequestError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[27]\u001b[39m\u001b[32m, line 51\u001b[39m\n\u001b[32m 47\u001b[39m \u001b[38;5;66;03m# -------------------------\u001b[39;00m\n\u001b[32m 48\u001b[39m \u001b[38;5;66;03m# 5. Run query\u001b[39;00m\n\u001b[32m 49\u001b[39m \u001b[38;5;66;03m# -------------------------\u001b[39;00m\n\u001b[32m 50\u001b[39m query = \u001b[33m\"\u001b[39m\u001b[33mGive the overview of the dataframe `df` and the preprocessing steps needed for EDA.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m51\u001b[39m response = \u001b[43magent_executor\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 52\u001b[39m \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;43mGive the overview of the dataframe `df` and the preprocessing steps needed for EDA.\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 53\u001b[39m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m=\u001b[49m\u001b[43m{\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mconfigurable\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mthread_id\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;43mpreprocess-session-1\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m}\u001b[49m\n\u001b[32m 54\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m 57\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m=== FINAL OUTPUT ===\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 58\u001b[39m \u001b[38;5;28mprint\u001b[39m(response[\u001b[33m\"\u001b[39m\u001b[33moutput\u001b[39m\u001b[33m\"\u001b[39m])\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langgraph\\pregel\\main.py:3026\u001b[39m, in \u001b[36mPregel.invoke\u001b[39m\u001b[34m(self, input, config, context, stream_mode, print_mode, output_keys, interrupt_before, interrupt_after, durability, **kwargs)\u001b[39m\n\u001b[32m 3023\u001b[39m chunks: \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any] | Any] = []\n\u001b[32m 3024\u001b[39m interrupts: \u001b[38;5;28mlist\u001b[39m[Interrupt] = []\n\u001b[32m-> \u001b[39m\u001b[32m3026\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3027\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 3028\u001b[39m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3029\u001b[39m \u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3030\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mupdates\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;43mvalues\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[32m 3031\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mvalues\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\n\u001b[32m 3032\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3033\u001b[39m \u001b[43m \u001b[49m\u001b[43mprint_mode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprint_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3034\u001b[39m \u001b[43m \u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3035\u001b[39m \u001b[43m \u001b[49m\u001b[43minterrupt_before\u001b[49m\u001b[43m=\u001b[49m\u001b[43minterrupt_before\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3036\u001b[39m \u001b[43m \u001b[49m\u001b[43minterrupt_after\u001b[49m\u001b[43m=\u001b[49m\u001b[43minterrupt_after\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3037\u001b[39m \u001b[43m \u001b[49m\u001b[43mdurability\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdurability\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3038\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 3039\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 3040\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mvalues\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\n\u001b[32m 3041\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m2\u001b[39;49m\u001b[43m:\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langgraph\\pregel\\main.py:2647\u001b[39m, in \u001b[36mPregel.stream\u001b[39m\u001b[34m(self, input, config, context, stream_mode, print_mode, output_keys, interrupt_before, interrupt_after, durability, subgraphs, debug, **kwargs)\u001b[39m\n\u001b[32m 2645\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m task \u001b[38;5;129;01min\u001b[39;00m loop.match_cached_writes():\n\u001b[32m 2646\u001b[39m loop.output_writes(task.id, task.writes, cached=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m-> \u001b[39m\u001b[32m2647\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrunner\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtick\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2648\u001b[39m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mloop\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtasks\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwrites\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2649\u001b[39m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mstep_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2650\u001b[39m \u001b[43m \u001b[49m\u001b[43mget_waiter\u001b[49m\u001b[43m=\u001b[49m\u001b[43mget_waiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2651\u001b[39m \u001b[43m \u001b[49m\u001b[43mschedule_task\u001b[49m\u001b[43m=\u001b[49m\u001b[43mloop\u001b[49m\u001b[43m.\u001b[49m\u001b[43maccept_push\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2652\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 2653\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# emit output\u001b[39;49;00m\n\u001b[32m 2654\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01myield from\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m_output\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2655\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream_mode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprint_mode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msubgraphs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mqueue\u001b[49m\u001b[43m.\u001b[49m\u001b[43mEmpty\u001b[49m\n\u001b[32m 2656\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2657\u001b[39m loop.after_tick()\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langgraph\\pregel\\_runner.py:162\u001b[39m, in \u001b[36mPregelRunner.tick\u001b[39m\u001b[34m(self, tasks, reraise, timeout, retry_policy, get_waiter, schedule_task)\u001b[39m\n\u001b[32m 160\u001b[39m t = tasks[\u001b[32m0\u001b[39m]\n\u001b[32m 161\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m162\u001b[39m \u001b[43mrun_with_retry\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 163\u001b[39m \u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 164\u001b[39m \u001b[43m \u001b[49m\u001b[43mretry_policy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 165\u001b[39m \u001b[43m \u001b[49m\u001b[43mconfigurable\u001b[49m\u001b[43m=\u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 166\u001b[39m \u001b[43m \u001b[49m\u001b[43mCONFIG_KEY_CALL\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mpartial\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 167\u001b[39m \u001b[43m \u001b[49m\u001b[43m_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 168\u001b[39m \u001b[43m \u001b[49m\u001b[43mweakref\u001b[49m\u001b[43m.\u001b[49m\u001b[43mref\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 169\u001b[39m \u001b[43m \u001b[49m\u001b[43mretry_policy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mretry_policy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 170\u001b[39m \u001b[43m \u001b[49m\u001b[43mfutures\u001b[49m\u001b[43m=\u001b[49m\u001b[43mweakref\u001b[49m\u001b[43m.\u001b[49m\u001b[43mref\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfutures\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 171\u001b[39m \u001b[43m \u001b[49m\u001b[43mschedule_task\u001b[49m\u001b[43m=\u001b[49m\u001b[43mschedule_task\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 172\u001b[39m \u001b[43m \u001b[49m\u001b[43msubmit\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msubmit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 173\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 174\u001b[39m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 175\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 176\u001b[39m \u001b[38;5;28mself\u001b[39m.commit(t, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m 177\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langgraph\\pregel\\_retry.py:42\u001b[39m, in \u001b[36mrun_with_retry\u001b[39m\u001b[34m(task, retry_policy, configurable)\u001b[39m\n\u001b[32m 40\u001b[39m task.writes.clear()\n\u001b[32m 41\u001b[39m \u001b[38;5;66;03m# run the task\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m42\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtask\u001b[49m\u001b[43m.\u001b[49m\u001b[43mproc\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m.\u001b[49m\u001b[43minput\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 43\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ParentCommand \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[32m 44\u001b[39m ns: \u001b[38;5;28mstr\u001b[39m = config[CONF][CONFIG_KEY_CHECKPOINT_NS]\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langgraph\\_internal\\_runnable.py:657\u001b[39m, in \u001b[36mRunnableSeq.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m 655\u001b[39m \u001b[38;5;66;03m# run in context\u001b[39;00m\n\u001b[32m 656\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m set_config_context(config, run) \u001b[38;5;28;01mas\u001b[39;00m context:\n\u001b[32m--> \u001b[39m\u001b[32m657\u001b[39m \u001b[38;5;28minput\u001b[39m = \u001b[43mcontext\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 658\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 659\u001b[39m \u001b[38;5;28minput\u001b[39m = step.invoke(\u001b[38;5;28minput\u001b[39m, config)\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langgraph\\_internal\\_runnable.py:394\u001b[39m, in \u001b[36mRunnableCallable.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m 392\u001b[39m \u001b[38;5;66;03m# run in context\u001b[39;00m\n\u001b[32m 393\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m set_config_context(child_config, run) \u001b[38;5;28;01mas\u001b[39;00m context:\n\u001b[32m--> \u001b[39m\u001b[32m394\u001b[39m ret = \u001b[43mcontext\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 395\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 396\u001b[39m run_manager.on_chain_error(e)\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langgraph\\prebuilt\\chat_agent_executor.py:627\u001b[39m, in \u001b[36mcreate_react_agent.<locals>.call_model\u001b[39m\u001b[34m(state, runtime, config)\u001b[39m\n\u001b[32m 625\u001b[39m response = cast(AIMessage, dynamic_model.invoke(model_input, config)) \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[32m 626\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m627\u001b[39m response = cast(AIMessage, \u001b[43mstatic_model\u001b[49m\u001b[43m.\u001b[49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_input\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;66;03m# type: ignore[union-attr]\u001b[39;00m\n\u001b[32m 629\u001b[39m \u001b[38;5;66;03m# add agent name to the AIMessage\u001b[39;00m\n\u001b[32m 630\u001b[39m response.name = name\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langchain_core\\runnables\\base.py:3245\u001b[39m, in \u001b[36mRunnableSequence.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m 3243\u001b[39m input_ = context.run(step.invoke, input_, config, **kwargs)\n\u001b[32m 3244\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m3245\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 3246\u001b[39m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[32m 3247\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[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langchain_core\\runnables\\base.py:5710\u001b[39m, in \u001b[36mRunnableBindingBase.invoke\u001b[39m\u001b[34m(self, input, config, **kwargs)\u001b[39m\n\u001b[32m 5703\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m 5704\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minvoke\u001b[39m(\n\u001b[32m 5705\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 5708\u001b[39m **kwargs: Optional[Any],\n\u001b[32m 5709\u001b[39m ) -> Output:\n\u001b[32m-> \u001b[39m\u001b[32m5710\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 5711\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 5712\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 5713\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 5714\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:395\u001b[39m, in \u001b[36mBaseChatModel.invoke\u001b[39m\u001b[34m(self, input, config, stop, **kwargs)\u001b[39m\n\u001b[32m 383\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m 384\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minvoke\u001b[39m(\n\u001b[32m 385\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 390\u001b[39m **kwargs: Any,\n\u001b[32m 391\u001b[39m ) -> BaseMessage:\n\u001b[32m 392\u001b[39m config = ensure_config(config)\n\u001b[32m 393\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m cast(\n\u001b[32m 394\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mChatGeneration\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m--> \u001b[39m\u001b[32m395\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 396\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 397\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 398\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 399\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 400\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 401\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 402\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 403\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 404\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m.generations[\u001b[32m0\u001b[39m][\u001b[32m0\u001b[39m],\n\u001b[32m 405\u001b[39m ).message\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:1023\u001b[39m, in \u001b[36mBaseChatModel.generate_prompt\u001b[39m\u001b[34m(self, prompts, stop, callbacks, **kwargs)\u001b[39m\n\u001b[32m 1014\u001b[39m \u001b[38;5;129m@override\u001b[39m\n\u001b[32m 1015\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mgenerate_prompt\u001b[39m(\n\u001b[32m 1016\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 1020\u001b[39m **kwargs: Any,\n\u001b[32m 1021\u001b[39m ) -> LLMResult:\n\u001b[32m 1022\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[32m1023\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt_messages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:840\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 837\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 838\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 839\u001b[39m results.append(\n\u001b[32m--> \u001b[39m\u001b[32m840\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 841\u001b[39m \u001b[43m \u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 842\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 843\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 844\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 845\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 846\u001b[39m )\n\u001b[32m 847\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 848\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:1089\u001b[39m, in \u001b[36mBaseChatModel._generate_with_cache\u001b[39m\u001b[34m(self, messages, stop, run_manager, **kwargs)\u001b[39m\n\u001b[32m 1087\u001b[39m result = generate_from_stream(\u001b[38;5;28miter\u001b[39m(chunks))\n\u001b[32m 1088\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[32m1089\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 1090\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 1091\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1092\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1093\u001b[39m result = \u001b[38;5;28mself\u001b[39m._generate(messages, stop=stop, **kwargs)\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\site-packages\\langchain_groq\\chat_models.py:533\u001b[39m, in \u001b[36mChatGroq._generate\u001b[39m\u001b[34m(self, messages, stop, run_manager, **kwargs)\u001b[39m\n\u001b[32m 528\u001b[39m message_dicts, params = \u001b[38;5;28mself\u001b[39m._create_message_dicts(messages, stop)\n\u001b[32m 529\u001b[39m params = {\n\u001b[32m 530\u001b[39m **params,\n\u001b[32m 531\u001b[39m **kwargs,\n\u001b[32m 532\u001b[39m }\n\u001b[32m--> \u001b[39m\u001b[32m533\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 534\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[32md:\\Code Assistant\\venv\\Lib\\site-packages\\groq\\resources\\chat\\completions.py:448\u001b[39m, in \u001b[36mCompletions.create\u001b[39m\u001b[34m(self, messages, model, compound_custom, 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 238\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcreate\u001b[39m(\n\u001b[32m 239\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 240\u001b[39m *,\n\u001b[32m (...)\u001b[39m\u001b[32m 295\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 296\u001b[39m ) -> ChatCompletion | Stream[ChatCompletionChunk]:\n\u001b[32m 297\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 298\u001b[39m \u001b[33;03m Creates a model response for the given chat conversation.\u001b[39;00m\n\u001b[32m 299\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 446\u001b[39m \u001b[33;03m timeout: Override the client-level default timeout for this request, in seconds\u001b[39;00m\n\u001b[32m 447\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m448\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 449\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 450\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 451\u001b[39m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 452\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 453\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 454\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 455\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 456\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 457\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 458\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 459\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 460\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 461\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 462\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 463\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 464\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 465\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 466\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 467\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 468\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 469\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 470\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 471\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 472\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 473\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 474\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 475\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 476\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 477\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 478\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 479\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 480\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 481\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 482\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 483\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 484\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 485\u001b[39m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 486\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 487\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 488\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 489\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 490\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 491\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 492\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 493\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 494\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32md:\\Code Assistant\\venv\\Lib\\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[32md:\\Code Assistant\\venv\\Lib\\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[31mBadRequestError\u001b[39m: Error code: 400 - {'error': {'message': \"'messages' : minimum number of items is 1\", 'type': 'invalid_request_error'}}",
"During task with name 'agent' and id '86cec74b-8de0-333f-a9bc-70cb56e8a6a1'"
]
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"from langchain.chat_models import init_chat_model\n",
"from langchain_core.tools import tool\n",
"from langgraph.checkpoint.memory import MemorySaver\n",
"from langgraph.prebuilt import create_react_agent\n",
"\n",
"# -------------------------\n",
"# 1. Dummy DataFrame\n",
"# -------------------------\n",
"data = {\n",
" \"age\": [25, 30, None, 40, 35],\n",
" \"income\": [50000, 60000, 55000, None, 65000],\n",
" \"gender\": [\"M\", \"F\", \"F\", \"M\", None]\n",
"}\n",
"df = pd.DataFrame(data)\n",
"\n",
"# -------------------------\n",
"# 2. Define a Python tool\n",
"# -------------------------\n",
"@tool\n",
"def python_repl_ast(query: str) -> str:\n",
" \"\"\"Run Python code with access to a pandas DataFrame `df`.\"\"\"\n",
" local_namespace = {\"df\": df}\n",
" try:\n",
" exec(query, {}, local_namespace)\n",
" # Capture last expression if exists\n",
" result = local_namespace.get(\"result\", \"Executed successfully.\")\n",
" return str(result)\n",
" except Exception as e:\n",
" return f\"Error: {e}\"\n",
"\n",
"# -------------------------\n",
"# 3. Init LLM\n",
"# -------------------------\n",
"# pick a free model wrapper you have (e.g. Ollama, HuggingFace, or OpenAI key)\n",
"# here I’ll assume you’re using an OpenAI-compatible model\n",
"llm = ChatGroq(temperature=0, model_name=\"openai/gpt-oss-120b\")\n",
"\n",
"# -------------------------\n",
"# 4. Create ReAct agent\n",
"# -------------------------\n",
"memory = MemorySaver()\n",
"tools = [python_repl_ast]\n",
"agent_executor = create_react_agent(llm, tools, checkpointer=memory)\n",
"\n",
"# -------------------------\n",
"# 5. Run query\n",
"# -------------------------\n",
"query = \"Give the overview of the dataframe `df` and the preprocessing steps needed for EDA.\"\n",
"response = agent_executor.invoke(\n",
" {\"input\": \"Give the overview of the dataframe `df` and the preprocessing steps needed for EDA.\"},\n",
" config={\"configurable\": {\"thread_id\": \"preprocess-session-1\"}}\n",
")\n",
"\n",
"\n",
"print(\"\\n=== FINAL OUTPUT ===\")\n",
"print(response[\"output\"])\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "01f78bd7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--- Running Query ---\n",
"User Query: Give me a summary of the dataframe, including the data types and null values.\n",
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"An error occurred: Tool choice is none, but model called a tool\n",
"--- Running Query ---\n",
"User Query: What is the total revenue from all sales?\n",
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"An error occurred: Tool choice is none, but model called a tool\n",
"--- Running Query ---\n",
"User Query: Which product had the highest quantity sold?\n",
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"An error occurred: Tool choice is none, but model called a tool\n",
"--- Running Query ---\n",
"User Query: What was the average unit price for products in the 'Electronics' category?\n",
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"An error occurred: Tool choice is none, but model called a tool\n"
]
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent\n",
"from langchain_groq import ChatGroq\n",
"\n",
"# --- Configuration ---\n",
"# 1. Get your Groq API key from https://console.groq.com/keys\n",
"# 2. Set it as an environment variable\n",
"os.environ[\"GROQ_API_KEY\"] = \"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\"\n",
"\n",
"# --- Create a Sample DataFrame ---\n",
"data = {\n",
" 'product_name': ['Laptop', 'Mouse', 'Keyboard', 'Monitor', 'Webcam'],\n",
" 'category': ['Electronics', 'Electronics', 'Electronics', 'Electronics', 'Accessories'],\n",
" 'quantity_sold': [120, 350, 275, 150, 400],\n",
" 'unit_price': [1200, 25, 75, 300, 50],\n",
" 'sale_date': pd.to_datetime(['2023-01-15', '2023-01-20', '2023-02-10', '2023-02-12', '2023-03-05'])\n",
"}\n",
"df = pd.DataFrame(data)\n",
"df['total_revenue'] = df['quantity_sold'] * df['unit_price']\n",
"\n",
"\n",
"# --- Initialize the Groq LLM ---\n",
"# Using a model like Llama3 8b is a good choice for speed and capability\n",
"# You can also use other models available on Groq, like \"mixtral-8x7b-32768\"\n",
"llm = ChatGroq(temperature=0, model_name=\"openai/gpt-oss-120b\")\n",
"\n",
"# --- Create the Pandas DataFrame Agent ---\n",
"# Note: allow_dangerous_code=True is required for the agent to execute python code.\n",
"# This should only be used in a secure, sandboxed environment.\n",
"agent = create_pandas_dataframe_agent(\n",
" llm,\n",
" df,\n",
" verbose=True,\n",
" allow_dangerous_code=True\n",
")\n",
"\n",
"# --- Define Queries for the Agent to Perform ---\n",
"queries = [\n",
" \"Give me a summary of the dataframe, including the data types and null values.\",\n",
" \"What is the total revenue from all sales?\",\n",
" \"Which product had the highest quantity sold?\",\n",
" \"What was the average unit price for products in the 'Electronics' category?\",\n",
"]\n",
"\n",
"# --- Run the Queries ---\n",
"for query in queries:\n",
" print(f\"--- Running Query ---\")\n",
" print(f\"User Query: {query}\")\n",
" try:\n",
" response = agent.invoke(query)\n",
" print(f\"\\nAgent Response: {response['output']}\\n\")\n",
" except Exception as e:\n",
" print(f\"An error occurred: {e}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b208a483",
"metadata": {},
"outputs": [],
"source": [
"You are absolutely right. My apologies. In a true multi-agent supervisor pattern, the supervisor LLM must be the one making the routing decisions. A deterministic Python function breaks that pattern.\n",
"\n",
"The problem you're facing is a fascinating one: the LLM is correctly reasoning but failing at the last moment by hallucinating the tool name (json instead of Router). This is a known issue with some models when using the with_structured_output method.\n",
"\n",
"Let's force the LLM to do the right thing. There are two primary strategies to make this work reliably.\n",
"\n",
"Strategy 1: (Recommended) Bind the Tool Explicitly to the LLM\n",
"\n",
"This is the most modern and robust way to handle tool-calling with LangChain. Instead of just asking for structured output, you explicitly bind the tool (your Router Pydantic model) to the LLM. This gives the model a much stronger signal about the exact tool it is expected to use.\n",
"\n",
"This method is less prone to the \"hallucinated name\" error because the model is primed to use the specific tool you've given it.\n",
"\n",
"Here is how you modify your supervisor_node:\n",
"\n",
"code\n",
"Python\n",
"download\n",
"content_copy\n",
"expand_less\n",
"\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"from typing import Literal\n",
"\n",
"# Make sure your Router model is defined correctly\n",
"class Router(BaseModel):\n",
" \"\"\"Represents the routing decision and the reasoning behind it.\"\"\"\n",
" next: Literal[\"Analyzer_node\", \"__end__\"] = Field(\n",
" ...,\n",
" description=\"The next node to route to. Must be one of 'Analyzer_node' or '__end__'.\"\n",
" )\n",
" reasoning: str = Field(\n",
" ...,\n",
" description=\"A concise explanation of why this routing decision was made.\"\n",
" )\n",
"\n",
"# ... inside your supervisor_node ...\n",
"def supervisor_node(self, state: AgentStateModel):\n",
" print(\"...\")\n",
"\n",
" # --- NEW: Bind the Router tool to the LLM ---\n",
" # This creates a new runnable that is primed to use the Router tool.\n",
" llm_with_router_tool = self.tool_disabled_llm.bind_tools([Router])\n",
"\n",
" # Your prompt can be simplified, as the model now knows its job is to call the tool.\n",
" prompt = ChatPromptTemplate.from_messages([\n",
" (\"system\", \"You are the supervisor. Your job is to review the current state and decide the next step. \"\n",
" \"You must call the `Router` tool to indicate your decision.\"),\n",
" (\"user\", \"Here is the current state of the 'Analysis' field: {analysis_state}. \"\n",
" \"If the field is an empty list (`[]`), route to 'Analyzer_node'. \"\n",
" \"If it contains any data, route to '__end__'. Provide your reasoning.\"),\n",
" ])\n",
"\n",
" # Create the chain: prompt -> LLM with tool -> (optional) parser\n",
" # The output of the LLM will now be a message containing a `tool_calls` attribute.\n",
" chain = prompt | llm_with_router_tool\n",
"\n",
" print(\"***********************Invoking LLM for routing decision************************\")\n",
" \n",
" # Invoke the chain with the current state analysis\n",
" response_message = chain.invoke({\n",
" \"analysis_state\": str(state.Analysis) # Pass the state as a string for clarity\n",
" })\n",
"\n",
" # --- NEW: Extract the arguments from the tool call ---\n",
" # The response is now a message object, not the parsed output directly.\n",
" tool_call = response_message.tool_calls[0]\n",
" if tool_call['name'] == \"Router\":\n",
" routing_decision = Router(**tool_call['args'])\n",
" goto = routing_decision.next\n",
" reasoning = routing_decision.reasoning\n",
" else:\n",
" # Fallback in case of another hallucination, though less likely now\n",
" print(f\"Warning: LLM called an unexpected tool: {tool_call['name']}\")\n",
" goto = \"__end__\" \n",
" reasoning = \"LLM failed to call the correct Router tool.\"\n",
"\n",
" print(f\"********************************this is my goto*************************\\n{goto}\")\n",
" print(f\"********************************\\n{reasoning}\")\n",
"\n",
" if goto == \"__end__\":\n",
" goto = END\n",
"\n",
" return Command(\n",
" goto=goto,\n",
" update={'next': goto, 'current_reasoning': reasoning}\n",
" )\n",
"\n",
"Why this works better:\n",
"\n",
".bind_tools([Router]) explicitly tells the Groq API that the Router tool is available for this call. The LLM is heavily incentivized to use it.\n",
"\n",
"The output is a standard AIMessage with a tool_calls attribute, which is a more standard and less \"magical\" way of handling tool use than with_structured_output.\n",
"\n",
"We manually parse the arguments from the tool call, giving us full control and visibility.\n",
"\n",
"Strategy 2: Force JSON Mode with a Specific Prompt\n",
"\n",
"If you want to stick closer to your original code, you can try to force the model into a JSON-only output mode with a very strong prompt. This is essentially \"prompt engineering\" a solution.\n",
"\n",
"code\n",
"Python\n",
"download\n",
"content_copy\n",
"expand_less\n",
"IGNORE_WHEN_COPYING_START\n",
"IGNORE_WHEN_COPYING_END\n",
"# ... inside your supervisor_node ...\n",
"def supervisor_node(self, state: AgentStateModel):\n",
" print(\"...\")\n",
"\n",
" # Your Router Pydantic model is the same.\n",
" # We will use it with with_structured_output.\n",
"\n",
" # --- NEW: A much more forceful prompt ---\n",
" # We explicitly tell the model its output format.\n",
" system_prompt = (\n",
" \"You are a supervisor agent. Your sole purpose is to act as a JSON router. \"\n",
" \"You will be given the current state of a task. \"\n",
" \"You must respond with ONLY a valid JSON object that conforms to the following Pydantic schema: \"\n",
" \"```json\\n\"\n",
" \"{\\\"title\\\": \\\"Router\\\", \\\"description\\\": \\\"...\\\", \\\"properties\\\": {\\\"next\\\": ..., \\\"reasoning\\\": ...}}\\n\"\n",
" \"```\\n\"\n",
" \"Do not add any other text, explanation, or markdown formatting around the JSON object.\"\n",
" )\n",
" \n",
" human_prompt = (\n",
" f\"Current 'Analysis' state: {state.Analysis}. \"\n",
" \"If 'Analysis' is empty, set 'next' to 'Analyzer_node'. \"\n",
" \"If 'Analysis' contains data, set 'next' to '__end__'. \"\n",
" \"Provide your reasoning in the 'reasoning' field.\"\n",
" )\n",
"\n",
" messages_for_llm = [\n",
" SystemMessage(content=system_prompt),\n",
" HumanMessage(content=human_prompt),\n",
" ]\n",
"\n",
" print(\"***********************Invoking LLM for routing decision************************\")\n",
" \n",
" # The invoke call remains the same\n",
" response = self.tool_disabled_llm.with_structured_output(Router).invoke(messages_for_llm)\n",
" \n",
" goto = response.next\n",
" reasoning = response.reasoning\n",
"\n",
" print(f\"********************************this is my goto*************************\\n{goto}\")\n",
" print(f\"********************************\\n{reasoning}\")\n",
"\n",
" if goto == \"__end__\":\n",
" goto = END\n",
"\n",
" return Command(\n",
" goto=goto,\n",
" update={'next': goto, 'current_reasoning': reasoning}\n",
" )\n",
"\n",
"Why this might work:\n",
"\n",
"By explicitly telling the model to only output JSON and even showing it a schema snippet, you reduce its creative freedom and nudge it away from thinking it needs to wrap its output in a tool call.\n",
"\n",
"This approach relies more on the model's ability to follow instructions perfectly, which can still be brittle.\n",
"\n",
"Recommendation: Use Strategy 1 (bind_tools). It is the more modern, reliable, and idiomatic LangChain approach for this problem. It aligns with how models are being fine-tuned for tool use and is less susceptible to prompt-following errors."
]
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