diff --git "a/src/notebooks/brainstorm.ipynb" "b/src/notebooks/brainstorm.ipynb" --- "a/src/notebooks/brainstorm.ipynb" +++ "b/src/notebooks/brainstorm.ipynb" @@ -2,11 +2,1218 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ - "import langchain" + "import os\n", + "import langchain\n", + "from langchain.prompts import ChatPromptTemplate\n", + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.memory import ConversationBufferMemory\n", + "from langchain.agents import initialize_agent\n", + "from langchain.tools import StructuredTool\n", + "from langchain.prompts import MessagesPlaceholder\n", + "from langchain.agents import create_pandas_dataframe_agent, AgentType\n", + "import pandas as pd\n", + "from IPython.display import display, HTML\n", + "from pygments import highlight\n", + "from pygments.lexers import PythonLexer\n", + "from pygments.formatters import HtmlFormatter\n", + "import json" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "langchain.debug = True\n", + "os.environ[\"OPENAI_API_KEY\"] = \"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\"\n", + "data_dir_path = os.path.join(os.getcwd(), \"data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "pandas_agent_model_kwargs = {\n", + " \"model_name\": \"gpt-3.5-turbo-0613\",\n", + " \"temperature\": 0,\n", + "}\n", + "pandas_agent_model = ChatOpenAI(**pandas_agent_model_kwargs)\n", + "\n", + "entries_a_df = pd.read_csv(os.path.join(data_dir_path, 'legal_entries_a.csv'))\n", + "entries_b_df = pd.read_csv(os.path.join(data_dir_path, 'legal_entries_b.csv'))\n", + "template_df = pd.read_csv(os.path.join(data_dir_path, 'legal_template.csv'))\n", + "\n", + "dataframes = [entries_a_df, entries_b_df, template_df]\n", + "\n", + "# Default naming convention defined in langchain.agents.agent_toolkits.pandas.base._get_multi_functions_input\n", + "langchain_df_name_to_df_map: dict[str, pd.DataFrame] = {\n", + " f'df{i + 1}': df\n", + " for i, df in enumerate(dataframes)\n", + "}\n", + "\n", + "pandas_agent = create_pandas_dataframe_agent(\n", + " pandas_agent_model,\n", + " dataframes,\n", + " agent_type=AgentType.OPENAI_FUNCTIONS,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# question = \"What are the differences between the table structure of all of the dataframes?\"\n", + "# res = pandas_agent.run(input=question)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "llm = ChatOpenAI(temperature=0.3, model=\"gpt-3.5-turbo-16k-0613\")\n", + "\n", + "def validate_function_input(function_input: str):\n", + " '''Always use this before calling any other function. It will raise an exception if the input is not valid JSON.'''\n", + " try:\n", + " json.loads(function_input)\n", + " return None\n", + " except Exception as e:\n", + " return 'Input is not valid JSON: ' + str(e)\n", + "\n", + "def read_from_database(question: str):\n", + " '''Use this to ask natural language questions about tables in the database. The input must be a string that can be parsed into JSON. So escape necessary characters.'''\n", + " if res := validate_function_input(question):\n", + " return res\n", + " return pandas_agent.run(question)\n", + "\n", + "def write_to_database(valid_python_code: str):\n", + " '''Use this to write to the database. The input must be a string must be valid python code that can be parsed into JSON. So escape necessary characters.'''\n", + " if res := validate_function_input(valid_python_code):\n", + " return res\n", + " return pandas_agent.run(\"Execute this code: \" + valid_python_code)\n", + "\n", + "tools = [\n", + " StructuredTool.from_function(read_from_database),\n", + " StructuredTool.from_function(write_to_database)\n", + "]\n", + "agent_kwargs = {\n", + " \"extra_prompt_messages\": [MessagesPlaceholder(variable_name=\"memory\")],\n", + "}\n", + "memory = ConversationBufferMemory(memory_key=\"memory\", return_messages=True)\n", + "agent = initialize_agent(\n", + " tools,\n", + " llm,\n", + " agent=AgentType.OPENAI_FUNCTIONS,\n", + " verbose=True,\n", + " agent_kwargs=agent_kwargs,\n", + " memory=memory,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor] Entering Chain run with input:\n", + "\u001b[0m{\n", + " \"input\": \"What are the differences between the table structure of all of the dataframes?\",\n", + " \"memory\": []\n", + "}\n", + "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:llm:ChatOpenAI] Entering LLM run with input:\n", + "\u001b[0m{\n", + " \"prompts\": [\n", + " \"System: You are a helpful AI assistant.\\nHuman: What are the differences between the table structure of all of the dataframes?\"\n", + " ]\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:llm:ChatOpenAI] [2.18s] Exiting LLM run with output:\n", + "\u001b[0m{\n", + " \"generations\": [\n", + " [\n", + " {\n", + " \"text\": \"\",\n", + " \"generation_info\": {\n", + " \"finish_reason\": \"function_call\"\n", + " },\n", + " \"message\": {\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"schema\",\n", + " \"messages\",\n", + " \"AIMessage\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"content\": \"\",\n", + " \"additional_kwargs\": {\n", + " \"function_call\": {\n", + " \"name\": \"read_from_database\",\n", + " \"arguments\": \"{\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\\\"\\n}\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " ]\n", + " ],\n", + " \"llm_output\": {\n", + " \"token_usage\": {\n", + " \"prompt_tokens\": 161,\n", + " \"completion_tokens\": 34,\n", + " \"total_tokens\": 195\n", + " },\n", + " \"model_name\": \"gpt-3.5-turbo-16k-0613\"\n", + " },\n", + " \"run\": null\n", + "}\n", + "\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 3:tool:read_from_database] Entering Tool run with input:\n", + "\u001b[0m\"{'question': \"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\"}\"\n", + "\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 3:tool:read_from_database] [0ms] Exiting Tool run with output:\n", + "\u001b[0m\"Input is not valid JSON: name 'json' is not defined\"\n", + "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 4:llm:ChatOpenAI] Entering LLM run with input:\n", + "\u001b[0m{\n", + " \"prompts\": [\n", + " \"System: You are a helpful AI assistant.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\"\n", + " ]\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 4:llm:ChatOpenAI] [2.15s] Exiting LLM run with output:\n", + "\u001b[0m{\n", + " \"generations\": [\n", + " [\n", + " {\n", + " \"text\": \"\",\n", + " \"generation_info\": {\n", + " \"finish_reason\": \"function_call\"\n", + " },\n", + " \"message\": {\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"schema\",\n", + " \"messages\",\n", + " \"AIMessage\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"content\": \"\",\n", + " \"additional_kwargs\": {\n", + " \"function_call\": {\n", + " \"name\": \"read_from_database\",\n", + " \"arguments\": \"{\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\\\"\\n}\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " ]\n", + " ],\n", + " \"llm_output\": {\n", + " \"token_usage\": {\n", + " \"prompt_tokens\": 217,\n", + " \"completion_tokens\": 34,\n", + " \"total_tokens\": 251\n", + " },\n", + " \"model_name\": \"gpt-3.5-turbo-16k-0613\"\n", + " },\n", + " \"run\": null\n", + "}\n", + "\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 5:tool:read_from_database] Entering Tool run with input:\n", + "\u001b[0m\"{'question': \"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\"}\"\n", + "\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 5:tool:read_from_database] [0ms] Exiting Tool run with output:\n", + "\u001b[0m\"Input is not valid JSON: name 'json' is not defined\"\n", + "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 6:llm:ChatOpenAI] Entering LLM run with input:\n", + "\u001b[0m{\n", + " \"prompts\": [\n", + " \"System: You are a helpful AI assistant.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\"\n", + " ]\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 6:llm:ChatOpenAI] [2.66s] Exiting LLM run with output:\n", + "\u001b[0m{\n", + " \"generations\": [\n", + " [\n", + " {\n", + " \"text\": \"\",\n", + " \"generation_info\": {\n", + " \"finish_reason\": \"function_call\"\n", + " },\n", + " \"message\": {\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"schema\",\n", + " \"messages\",\n", + " \"AIMessage\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"content\": \"\",\n", + " \"additional_kwargs\": {\n", + " \"function_call\": {\n", + " \"name\": \"read_from_database\",\n", + " \"arguments\": \"{\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\\\"\\n}\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " ]\n", + " ],\n", + " \"llm_output\": {\n", + " \"token_usage\": {\n", + " \"prompt_tokens\": 273,\n", + " \"completion_tokens\": 34,\n", + " \"total_tokens\": 307\n", + " },\n", + " \"model_name\": \"gpt-3.5-turbo-16k-0613\"\n", + " },\n", + " \"run\": null\n", + "}\n", + "\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 7:tool:read_from_database] Entering Tool run with input:\n", + "\u001b[0m\"{'question': \"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\"}\"\n", + "\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 7:tool:read_from_database] [0ms] Exiting Tool run with output:\n", + "\u001b[0m\"Input is not valid JSON: name 'json' is not defined\"\n", + "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 8:llm:ChatOpenAI] Entering LLM run with input:\n", + "\u001b[0m{\n", + " \"prompts\": [\n", + " \"System: You are a helpful AI assistant.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\"\n", + " ]\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 8:llm:ChatOpenAI] [817ms] Exiting LLM run with output:\n", + "\u001b[0m{\n", + " \"generations\": [\n", + " [\n", + " {\n", + " \"text\": \"\",\n", + " \"generation_info\": {\n", + " \"finish_reason\": \"function_call\"\n", + " },\n", + " \"message\": {\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"schema\",\n", + " \"messages\",\n", + " \"AIMessage\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"content\": \"\",\n", + " \"additional_kwargs\": {\n", + " \"function_call\": {\n", + " \"name\": \"read_from_database\",\n", + " \"arguments\": \"{\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\\\"\\n}\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " ]\n", + " ],\n", + " \"llm_output\": {\n", + " \"token_usage\": {\n", + " \"prompt_tokens\": 329,\n", + " \"completion_tokens\": 34,\n", + " \"total_tokens\": 363\n", + " },\n", + " \"model_name\": \"gpt-3.5-turbo-16k-0613\"\n", + " },\n", + " \"run\": null\n", + "}\n", + "\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 9:tool:read_from_database] Entering Tool run with input:\n", + "\u001b[0m\"{'question': \"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\"}\"\n", + "\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 9:tool:read_from_database] [0ms] Exiting Tool run with output:\n", + "\u001b[0m\"Input is not valid JSON: name 'json' is not defined\"\n", + "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 10:llm:ChatOpenAI] Entering LLM run with input:\n", + "\u001b[0m{\n", + " \"prompts\": [\n", + " \"System: You are a helpful AI assistant.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\"\n", + " ]\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 10:llm:ChatOpenAI] [1.95s] Exiting LLM run with output:\n", + "\u001b[0m{\n", + " \"generations\": [\n", + " [\n", + " {\n", + " \"text\": \"\",\n", + " \"generation_info\": {\n", + " \"finish_reason\": \"function_call\"\n", + " },\n", + " \"message\": {\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"schema\",\n", + " \"messages\",\n", + " \"AIMessage\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"content\": \"\",\n", + " \"additional_kwargs\": {\n", + " \"function_call\": {\n", + " \"name\": \"read_from_database\",\n", + " \"arguments\": \"{\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\\\"\\n}\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " ]\n", + " ],\n", + " \"llm_output\": {\n", + " \"token_usage\": {\n", + " \"prompt_tokens\": 385,\n", + " \"completion_tokens\": 34,\n", + " \"total_tokens\": 419\n", + " },\n", + " \"model_name\": \"gpt-3.5-turbo-16k-0613\"\n", + " },\n", + " \"run\": null\n", + "}\n", + "\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 11:tool:read_from_database] Entering Tool run with input:\n", + "\u001b[0m\"{'question': \"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\"}\"\n", + "\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 11:tool:read_from_database] [0ms] Exiting Tool run with output:\n", + "\u001b[0m\"Input is not valid JSON: name 'json' is not defined\"\n", + "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 12:llm:ChatOpenAI] Entering LLM run with input:\n", + "\u001b[0m{\n", + " \"prompts\": [\n", + " \"System: You are a helpful AI assistant.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\"\n", + " ]\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 12:llm:ChatOpenAI] [613ms] Exiting LLM run with output:\n", + "\u001b[0m{\n", + " \"generations\": [\n", + " [\n", + " {\n", + " \"text\": \"\",\n", + " \"generation_info\": {\n", + " \"finish_reason\": \"function_call\"\n", + " },\n", + " \"message\": {\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"schema\",\n", + " \"messages\",\n", + " \"AIMessage\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"content\": \"\",\n", + " \"additional_kwargs\": {\n", + " \"function_call\": {\n", + " \"name\": \"read_from_database\",\n", + " \"arguments\": \"{\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\\\"\\n}\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " ]\n", + " ],\n", + " \"llm_output\": {\n", + " \"token_usage\": {\n", + " \"prompt_tokens\": 441,\n", + " \"completion_tokens\": 34,\n", + " \"total_tokens\": 475\n", + " },\n", + " \"model_name\": \"gpt-3.5-turbo-16k-0613\"\n", + " },\n", + " \"run\": null\n", + "}\n", + "\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 13:tool:read_from_database] Entering Tool run with input:\n", + "\u001b[0m\"{'question': \"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\"}\"\n", + "\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 13:tool:read_from_database] [0ms] Exiting Tool run with output:\n", + "\u001b[0m\"Input is not valid JSON: name 'json' is not defined\"\n", + "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 14:llm:ChatOpenAI] Entering LLM run with input:\n", + "\u001b[0m{\n", + " \"prompts\": [\n", + " \"System: You are a helpful AI assistant.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\"\n", + " ]\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 14:llm:ChatOpenAI] [613ms] Exiting LLM run with output:\n", + "\u001b[0m{\n", + " \"generations\": [\n", + " [\n", + " {\n", + " \"text\": \"\",\n", + " \"generation_info\": {\n", + " \"finish_reason\": \"function_call\"\n", + " },\n", + " \"message\": {\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"schema\",\n", + " \"messages\",\n", + " \"AIMessage\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"content\": \"\",\n", + " \"additional_kwargs\": {\n", + " \"function_call\": {\n", + " \"name\": \"read_from_database\",\n", + " \"arguments\": \"{\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\\\"\\n}\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " ]\n", + " ],\n", + " \"llm_output\": {\n", + " \"token_usage\": {\n", + " \"prompt_tokens\": 497,\n", + " \"completion_tokens\": 34,\n", + " \"total_tokens\": 531\n", + " },\n", + " \"model_name\": \"gpt-3.5-turbo-16k-0613\"\n", + " },\n", + " \"run\": null\n", + "}\n", + "\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 15:tool:read_from_database] Entering Tool run with input:\n", + "\u001b[0m\"{'question': \"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\"}\"\n", + "\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 15:tool:read_from_database] [0ms] Exiting Tool run with output:\n", + "\u001b[0m\"Input is not valid JSON: name 'json' is not defined\"\n", + "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 16:llm:ChatOpenAI] Entering LLM run with input:\n", + "\u001b[0m{\n", + " \"prompts\": [\n", + " \"System: You are a helpful AI assistant.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\"\n", + " ]\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 16:llm:ChatOpenAI] [2.25s] Exiting LLM run with output:\n", + "\u001b[0m{\n", + " \"generations\": [\n", + " [\n", + " {\n", + " \"text\": \"\",\n", + " \"generation_info\": {\n", + " \"finish_reason\": \"function_call\"\n", + " },\n", + " \"message\": {\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"schema\",\n", + " \"messages\",\n", + " \"AIMessage\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"content\": \"\",\n", + " \"additional_kwargs\": {\n", + " \"function_call\": {\n", + " \"name\": \"read_from_database\",\n", + " \"arguments\": \"{\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\\\"\\n}\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " ]\n", + " ],\n", + " \"llm_output\": {\n", + " \"token_usage\": {\n", + " \"prompt_tokens\": 553,\n", + " \"completion_tokens\": 34,\n", + " \"total_tokens\": 587\n", + " },\n", + " \"model_name\": \"gpt-3.5-turbo-16k-0613\"\n", + " },\n", + " \"run\": null\n", + "}\n", + "\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 17:tool:read_from_database] Entering Tool run with input:\n", + "\u001b[0m\"{'question': \"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\"}\"\n", + "\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 17:tool:read_from_database] [0ms] Exiting Tool run with output:\n", + "\u001b[0m\"Input is not valid JSON: name 'json' is not defined\"\n", + "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 18:llm:ChatOpenAI] Entering LLM run with input:\n", + "\u001b[0m{\n", + " \"prompts\": [\n", + " \"System: You are a helpful AI assistant.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\"\n", + " ]\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 18:llm:ChatOpenAI] [2.35s] Exiting LLM run with output:\n", + "\u001b[0m{\n", + " \"generations\": [\n", + " [\n", + " {\n", + " \"text\": \"\",\n", + " \"generation_info\": {\n", + " \"finish_reason\": \"function_call\"\n", + " },\n", + " \"message\": {\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"schema\",\n", + " \"messages\",\n", + " \"AIMessage\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"content\": \"\",\n", + " \"additional_kwargs\": {\n", + " \"function_call\": {\n", + " \"name\": \"read_from_database\",\n", + " \"arguments\": \"{\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\\\"\\n}\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " ]\n", + " ],\n", + " \"llm_output\": {\n", + " \"token_usage\": {\n", + " \"prompt_tokens\": 609,\n", + " \"completion_tokens\": 34,\n", + " \"total_tokens\": 643\n", + " },\n", + " \"model_name\": \"gpt-3.5-turbo-16k-0613\"\n", + " },\n", + " \"run\": null\n", + "}\n", + "\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 19:tool:read_from_database] Entering Tool run with input:\n", + "\u001b[0m\"{'question': \"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\"}\"\n", + "\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 19:tool:read_from_database] [0ms] Exiting Tool run with output:\n", + "\u001b[0m\"Input is not valid JSON: name 'json' is not defined\"\n", + "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 20:llm:ChatOpenAI] Entering LLM run with input:\n", + "\u001b[0m{\n", + " \"prompts\": [\n", + " \"System: You are a helpful AI assistant.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\"\n", + " ]\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 20:llm:ChatOpenAI] [1.84s] Exiting LLM run with output:\n", + "\u001b[0m{\n", + " \"generations\": [\n", + " [\n", + " {\n", + " \"text\": \"\",\n", + " \"generation_info\": {\n", + " \"finish_reason\": \"function_call\"\n", + " },\n", + " \"message\": {\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"schema\",\n", + " \"messages\",\n", + " \"AIMessage\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"content\": \"\",\n", + " \"additional_kwargs\": {\n", + " \"function_call\": {\n", + " \"name\": \"read_from_database\",\n", + " \"arguments\": \"{\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\\\"\\n}\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " ]\n", + " ],\n", + " \"llm_output\": {\n", + " \"token_usage\": {\n", + " \"prompt_tokens\": 665,\n", + " \"completion_tokens\": 34,\n", + " \"total_tokens\": 699\n", + " },\n", + " \"model_name\": \"gpt-3.5-turbo-16k-0613\"\n", + " },\n", + " \"run\": null\n", + "}\n", + "\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 21:tool:read_from_database] Entering Tool run with input:\n", + "\u001b[0m\"{'question': \"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\"}\"\n", + "\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 21:tool:read_from_database] [0ms] Exiting Tool run with output:\n", + "\u001b[0m\"Input is not valid JSON: name 'json' is not defined\"\n", + "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 22:llm:ChatOpenAI] Entering LLM run with input:\n", + "\u001b[0m{\n", + " \"prompts\": [\n", + " \"System: You are a helpful AI assistant.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\"\n", + " ]\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 22:llm:ChatOpenAI] [1.84s] Exiting LLM run with output:\n", + "\u001b[0m{\n", + " \"generations\": [\n", + " [\n", + " {\n", + " \"text\": \"\",\n", + " \"generation_info\": {\n", + " \"finish_reason\": \"function_call\"\n", + " },\n", + " \"message\": {\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"schema\",\n", + " \"messages\",\n", + " \"AIMessage\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"content\": \"\",\n", + " \"additional_kwargs\": {\n", + " \"function_call\": {\n", + " \"name\": \"read_from_database\",\n", + " \"arguments\": \"{\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\\\"\\n}\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " ]\n", + " ],\n", + " \"llm_output\": {\n", + " \"token_usage\": {\n", + " \"prompt_tokens\": 721,\n", + " \"completion_tokens\": 34,\n", + " \"total_tokens\": 755\n", + " },\n", + " \"model_name\": \"gpt-3.5-turbo-16k-0613\"\n", + " },\n", + " \"run\": null\n", + "}\n", + "\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 23:tool:read_from_database] Entering Tool run with input:\n", + "\u001b[0m\"{'question': \"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\"}\"\n", + "\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 23:tool:read_from_database] [0ms] Exiting Tool run with output:\n", + "\u001b[0m\"Input is not valid JSON: name 'json' is not defined\"\n", + "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 24:llm:ChatOpenAI] Entering LLM run with input:\n", + "\u001b[0m{\n", + " \"prompts\": [\n", + " \"System: You are a helpful AI assistant.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\"\n", + " ]\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 24:llm:ChatOpenAI] [2.35s] Exiting LLM run with output:\n", + "\u001b[0m{\n", + " \"generations\": [\n", + " [\n", + " {\n", + " \"text\": \"\",\n", + " \"generation_info\": {\n", + " \"finish_reason\": \"function_call\"\n", + " },\n", + " \"message\": {\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"schema\",\n", + " \"messages\",\n", + " \"AIMessage\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"content\": \"\",\n", + " \"additional_kwargs\": {\n", + " \"function_call\": {\n", + " \"name\": \"read_from_database\",\n", + " \"arguments\": \"{\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\\\"\\n}\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " ]\n", + " ],\n", + " \"llm_output\": {\n", + " \"token_usage\": {\n", + " \"prompt_tokens\": 777,\n", + " \"completion_tokens\": 34,\n", + " \"total_tokens\": 811\n", + " },\n", + " \"model_name\": \"gpt-3.5-turbo-16k-0613\"\n", + " },\n", + " \"run\": null\n", + "}\n", + "\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 25:tool:read_from_database] Entering Tool run with input:\n", + "\u001b[0m\"{'question': \"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\"}\"\n", + "\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 25:tool:read_from_database] [0ms] Exiting Tool run with output:\n", + "\u001b[0m\"Input is not valid JSON: name 'json' is not defined\"\n", + "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 26:llm:ChatOpenAI] Entering LLM run with input:\n", + "\u001b[0m{\n", + " \"prompts\": [\n", + " \"System: You are a helpful AI assistant.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\"\n", + " ]\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 26:llm:ChatOpenAI] [758ms] Exiting LLM run with output:\n", + "\u001b[0m{\n", + " \"generations\": [\n", + " [\n", + " {\n", + " \"text\": \"\",\n", + " \"generation_info\": {\n", + " \"finish_reason\": \"function_call\"\n", + " },\n", + " \"message\": {\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"schema\",\n", + " \"messages\",\n", + " \"AIMessage\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"content\": \"\",\n", + " \"additional_kwargs\": {\n", + " \"function_call\": {\n", + " \"name\": \"read_from_database\",\n", + " \"arguments\": \"{\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\\\"\\n}\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " ]\n", + " ],\n", + " \"llm_output\": {\n", + " \"token_usage\": {\n", + " \"prompt_tokens\": 833,\n", + " \"completion_tokens\": 34,\n", + " \"total_tokens\": 867\n", + " },\n", + " \"model_name\": \"gpt-3.5-turbo-16k-0613\"\n", + " },\n", + " \"run\": null\n", + "}\n", + "\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 27:tool:read_from_database] Entering Tool run with input:\n", + "\u001b[0m\"{'question': \"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\"}\"\n", + "\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 27:tool:read_from_database] [0ms] Exiting Tool run with output:\n", + "\u001b[0m\"Input is not valid JSON: name 'json' is not defined\"\n", + "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 28:llm:ChatOpenAI] Entering LLM run with input:\n", + "\u001b[0m{\n", + " \"prompts\": [\n", + " \"System: You are a helpful AI assistant.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\"\n", + " ]\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 28:llm:ChatOpenAI] [1.91s] Exiting LLM run with output:\n", + "\u001b[0m{\n", + " \"generations\": [\n", + " [\n", + " {\n", + " \"text\": \"\",\n", + " \"generation_info\": {\n", + " \"finish_reason\": \"function_call\"\n", + " },\n", + " \"message\": {\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"schema\",\n", + " \"messages\",\n", + " \"AIMessage\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"content\": \"\",\n", + " \"additional_kwargs\": {\n", + " \"function_call\": {\n", + " \"name\": \"read_from_database\",\n", + " \"arguments\": \"{\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\\\"\\n}\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " ]\n", + " ],\n", + " \"llm_output\": {\n", + " \"token_usage\": {\n", + " \"prompt_tokens\": 889,\n", + " \"completion_tokens\": 34,\n", + " \"total_tokens\": 923\n", + " },\n", + " \"model_name\": \"gpt-3.5-turbo-16k-0613\"\n", + " },\n", + " \"run\": null\n", + "}\n", + "\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 29:tool:read_from_database] Entering Tool run with input:\n", + "\u001b[0m\"{'question': \"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\"}\"\n", + "\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 29:tool:read_from_database] [0ms] Exiting Tool run with output:\n", + "\u001b[0m\"Input is not valid JSON: name 'json' is not defined\"\n", + "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 30:llm:ChatOpenAI] Entering LLM run with input:\n", + "\u001b[0m{\n", + " \"prompts\": [\n", + " \"System: You are a helpful AI assistant.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\\nAI: {'name': 'read_from_database', 'arguments': '{\\\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = \\\\'public\\\\'\\\"\\\\n}'}\\nFunction: Input is not valid JSON: name 'json' is not defined\"\n", + " ]\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 30:llm:ChatOpenAI] [1.94s] Exiting LLM run with output:\n", + "\u001b[0m{\n", + " \"generations\": [\n", + " [\n", + " {\n", + " \"text\": \"\",\n", + " \"generation_info\": {\n", + " \"finish_reason\": \"function_call\"\n", + " },\n", + " \"message\": {\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"schema\",\n", + " \"messages\",\n", + " \"AIMessage\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"content\": \"\",\n", + " \"additional_kwargs\": {\n", + " \"function_call\": {\n", + " \"name\": \"read_from_database\",\n", + " \"arguments\": \"{\\n \\\"question\\\": \\\"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\\\"\\n}\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " ]\n", + " ],\n", + " \"llm_output\": {\n", + " \"token_usage\": {\n", + " \"prompt_tokens\": 945,\n", + " \"completion_tokens\": 34,\n", + " \"total_tokens\": 979\n", + " },\n", + " \"model_name\": \"gpt-3.5-turbo-16k-0613\"\n", + " },\n", + " \"run\": null\n", + "}\n", + "\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 31:tool:read_from_database] Entering Tool run with input:\n", + "\u001b[0m\"{'question': \"SELECT table_name, column_name, data_type FROM information_schema.columns WHERE table_schema = 'public'\"}\"\n", + "\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 31:tool:read_from_database] [0ms] Exiting Tool run with output:\n", + "\u001b[0m\"Input is not valid JSON: name 'json' is not defined\"\n", + "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor] [26.24s] Exiting Chain run with output:\n", + "\u001b[0m{\n", + " \"output\": \"Agent stopped due to iteration limit or time limit.\"\n", + "}\n" + ] + }, + { + "data": { + "text/html": [ + "
Agent stopped due to iteration limit or time limit.\n",
+       "
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor] Entering Chain run with input:\n", + "\u001b[0m[inputs]\n", + "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:llm:ChatOpenAI] Entering LLM run with input:\n", + "\u001b[0m{\n", + " \"prompts\": [\n", + " \"System: You are a helpful AI assistant.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: Agent stopped due to iteration limit or time limit.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: Agent stopped due to iteration limit or time limit.\\nHuman: Given these differences, please write an outline for a python script that would transform df1 to df3. Do not write code, just return an outline of the step you would take. Be precise and validate your work.\"\n", + " ]\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:llm:ChatOpenAI] [10.54s] Exiting LLM run with output:\n", + "\u001b[0m{\n", + " \"generations\": [\n", + " [\n", + " {\n", + " \"text\": \"To transform df1 to df3, you can follow the following outline:\\n\\n1. Create a new dataframe, df3, with the same columns as df1.\\n2. Iterate over each row in df1.\\n3. For each row, check if the value in the \\\"ID\\\" column exists in df2.\\n4. If the value exists in df2, retrieve the corresponding value from the \\\"New Column\\\" column in df2.\\n5. Assign the retrieved value to the \\\"New Column\\\" column in df3 for the corresponding row.\\n6. If the value does not exist in df2, assign a default value to the \\\"New Column\\\" column in df3 for the corresponding row.\\n7. Repeat steps 3-6 for the \\\"Name\\\" column.\\n8. Repeat steps 3-6 for the \\\"Age\\\" column.\\n9. Validate the transformed dataframe, df3, to ensure that it matches the structure of df3.\\n\\nTo validate the transformation, you can compare the structure and values of df3 with the expected structure and values of df3.\",\n", + " \"generation_info\": {\n", + " \"finish_reason\": \"stop\"\n", + " },\n", + " \"message\": {\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"schema\",\n", + " \"messages\",\n", + " \"AIMessage\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"content\": \"To transform df1 to df3, you can follow the following outline:\\n\\n1. Create a new dataframe, df3, with the same columns as df1.\\n2. Iterate over each row in df1.\\n3. For each row, check if the value in the \\\"ID\\\" column exists in df2.\\n4. If the value exists in df2, retrieve the corresponding value from the \\\"New Column\\\" column in df2.\\n5. Assign the retrieved value to the \\\"New Column\\\" column in df3 for the corresponding row.\\n6. If the value does not exist in df2, assign a default value to the \\\"New Column\\\" column in df3 for the corresponding row.\\n7. Repeat steps 3-6 for the \\\"Name\\\" column.\\n8. Repeat steps 3-6 for the \\\"Age\\\" column.\\n9. Validate the transformed dataframe, df3, to ensure that it matches the structure of df3.\\n\\nTo validate the transformation, you can compare the structure and values of df3 with the expected structure and values of df3.\",\n", + " \"additional_kwargs\": {}\n", + " }\n", + " }\n", + " }\n", + " ]\n", + " ],\n", + " \"llm_output\": {\n", + " \"token_usage\": {\n", + " \"prompt_tokens\": 256,\n", + " \"completion_tokens\": 218,\n", + " \"total_tokens\": 474\n", + " },\n", + " \"model_name\": \"gpt-3.5-turbo-16k-0613\"\n", + " },\n", + " \"run\": null\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor] [10.55s] Exiting Chain run with output:\n", + "\u001b[0m{\n", + " \"output\": \"To transform df1 to df3, you can follow the following outline:\\n\\n1. Create a new dataframe, df3, with the same columns as df1.\\n2. Iterate over each row in df1.\\n3. For each row, check if the value in the \\\"ID\\\" column exists in df2.\\n4. If the value exists in df2, retrieve the corresponding value from the \\\"New Column\\\" column in df2.\\n5. Assign the retrieved value to the \\\"New Column\\\" column in df3 for the corresponding row.\\n6. If the value does not exist in df2, assign a default value to the \\\"New Column\\\" column in df3 for the corresponding row.\\n7. Repeat steps 3-6 for the \\\"Name\\\" column.\\n8. Repeat steps 3-6 for the \\\"Age\\\" column.\\n9. Validate the transformed dataframe, df3, to ensure that it matches the structure of df3.\\n\\nTo validate the transformation, you can compare the structure and values of df3 with the expected structure and values of df3.\"\n", + "}\n" + ] + }, + { + "data": { + "text/html": [ + "
To transform df1 to df3, you can follow the following outline:\n",
+       "\n",
+       "1. Create a new dataframe, df3, with the same columns as df1.\n",
+       "2. Iterate over each row in df1.\n",
+       "3. For each row, check if the value in the "ID" column exists in df2.\n",
+       "4. If the value exists in df2, retrieve the corresponding value from the "New Column" column in df2.\n",
+       "5. Assign the retrieved value to the "New Column" column in df3 for the corresponding row.\n",
+       "6. If the value does not exist in df2, assign a default value to the "New Column" column in df3 for the corresponding row.\n",
+       "7. Repeat steps 3-6 for the "Name" column.\n",
+       "8. Repeat steps 3-6 for the "Age" column.\n",
+       "9. Validate the transformed dataframe, df3, to ensure that it matches the structure of df3.\n",
+       "\n",
+       "To validate the transformation, you can compare the structure and values of df3 with the expected structure and values of df3.\n",
+       "
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor] Entering Chain run with input:\n", + "\u001b[0m[inputs]\n", + "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:llm:ChatOpenAI] Entering LLM run with input:\n", + "\u001b[0m{\n", + " \"prompts\": [\n", + " \"System: You are a helpful AI assistant.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: Agent stopped due to iteration limit or time limit.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: Agent stopped due to iteration limit or time limit.\\nHuman: Given these differences, please write an outline for a python script that would transform df1 to df3. Do not write code, just return an outline of the step you would take. Be precise and validate your work.\\nAI: To transform df1 to df3, you can follow the following outline:\\n\\n1. Create a new dataframe, df3, with the same columns as df1.\\n2. Iterate over each row in df1.\\n3. For each row, check if the value in the \\\"ID\\\" column exists in df2.\\n4. If the value exists in df2, retrieve the corresponding value from the \\\"New Column\\\" column in df2.\\n5. Assign the retrieved value to the \\\"New Column\\\" column in df3 for the corresponding row.\\n6. If the value does not exist in df2, assign a default value to the \\\"New Column\\\" column in df3 for the corresponding row.\\n7. Repeat steps 3-6 for the \\\"Name\\\" column.\\n8. Repeat steps 3-6 for the \\\"Age\\\" column.\\n9. Validate the transformed dataframe, df3, to ensure that it matches the structure of df3.\\n\\nTo validate the transformation, you can compare the structure and values of df3 with the expected structure and values of df3.\\nHuman: Given these differences, please write an outline for a python script that would transform df1 to df3. Do not write code, just return an outline of the step you would take. Be precise and validate your work.\\nAI: To transform df1 to df3, you can follow the following outline:\\n\\n1. Create a new dataframe, df3, with the same columns as df1.\\n2. Iterate over each row in df1.\\n3. For each row, check if the value in the \\\"ID\\\" column exists in df2.\\n4. If the value exists in df2, retrieve the corresponding value from the \\\"New Column\\\" column in df2.\\n5. Assign the retrieved value to the \\\"New Column\\\" column in df3 for the corresponding row.\\n6. If the value does not exist in df2, assign a default value to the \\\"New Column\\\" column in df3 for the corresponding row.\\n7. Repeat steps 3-6 for the \\\"Name\\\" column.\\n8. Repeat steps 3-6 for the \\\"Age\\\" column.\\n9. Validate the transformed dataframe, df3, to ensure that it matches the structure of df3.\\n\\nTo validate the transformation, you can compare the structure and values of df3 with the expected structure and values of df3.\\nHuman: Excellent! Now, please write the code to transform df1 to df3. Your output should be 100% valid python code. The only explanations should be in the comments. Go!\"\n", + " ]\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:llm:ChatOpenAI] [21.71s] Exiting LLM run with output:\n", + "\u001b[0m{\n", + " \"generations\": [\n", + " [\n", + " {\n", + " \"text\": \"Sure! Here's the code to transform df1 to df3:\\n\\n```python\\nimport pandas as pd\\n\\n# Create a new dataframe, df3, with the same columns as df1\\ndf3 = pd.DataFrame(columns=df1.columns)\\n\\n# Iterate over each row in df1\\nfor index, row in df1.iterrows():\\n # Check if the value in the \\\"ID\\\" column exists in df2\\n if row['ID'] in df2['ID'].values:\\n # Retrieve the corresponding value from the \\\"New Column\\\" column in df2\\n new_value = df2.loc[df2['ID'] == row['ID'], 'New Column'].values[0]\\n else:\\n # Assign a default value if the value does not exist in df2\\n new_value = 'Default Value'\\n \\n # Assign the retrieved value to the \\\"New Column\\\" column in df3 for the corresponding row\\n df3.at[index, 'New Column'] = new_value\\n \\n # Repeat the same steps for the \\\"Name\\\" column\\n if row['Name'] in df2['Name'].values:\\n new_value = df2.loc[df2['Name'] == row['Name'], 'New Column'].values[0]\\n else:\\n new_value = 'Default Value'\\n df3.at[index, 'Name'] = new_value\\n \\n # Repeat the same steps for the \\\"Age\\\" column\\n if row['Age'] in df2['Age'].values:\\n new_value = df2.loc[df2['Age'] == row['Age'], 'New Column'].values[0]\\n else:\\n new_value = 'Default Value'\\n df3.at[index, 'Age'] = new_value\\n\\n# Validate the transformed dataframe, df3, to ensure that it matches the structure of df3\\n# You can compare the structure and values of df3 with the expected structure and values of df3\\n```\\n\\nPlease note that you need to replace `df1` and `df2` with the actual names of your dataframes. Also, make sure you have the pandas library installed before running this code.\",\n", + " \"generation_info\": {\n", + " \"finish_reason\": \"stop\"\n", + " },\n", + " \"message\": {\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"schema\",\n", + " \"messages\",\n", + " \"AIMessage\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"content\": \"Sure! Here's the code to transform df1 to df3:\\n\\n```python\\nimport pandas as pd\\n\\n# Create a new dataframe, df3, with the same columns as df1\\ndf3 = pd.DataFrame(columns=df1.columns)\\n\\n# Iterate over each row in df1\\nfor index, row in df1.iterrows():\\n # Check if the value in the \\\"ID\\\" column exists in df2\\n if row['ID'] in df2['ID'].values:\\n # Retrieve the corresponding value from the \\\"New Column\\\" column in df2\\n new_value = df2.loc[df2['ID'] == row['ID'], 'New Column'].values[0]\\n else:\\n # Assign a default value if the value does not exist in df2\\n new_value = 'Default Value'\\n \\n # Assign the retrieved value to the \\\"New Column\\\" column in df3 for the corresponding row\\n df3.at[index, 'New Column'] = new_value\\n \\n # Repeat the same steps for the \\\"Name\\\" column\\n if row['Name'] in df2['Name'].values:\\n new_value = df2.loc[df2['Name'] == row['Name'], 'New Column'].values[0]\\n else:\\n new_value = 'Default Value'\\n df3.at[index, 'Name'] = new_value\\n \\n # Repeat the same steps for the \\\"Age\\\" column\\n if row['Age'] in df2['Age'].values:\\n new_value = df2.loc[df2['Age'] == row['Age'], 'New Column'].values[0]\\n else:\\n new_value = 'Default Value'\\n df3.at[index, 'Age'] = new_value\\n\\n# Validate the transformed dataframe, df3, to ensure that it matches the structure of df3\\n# You can compare the structure and values of df3 with the expected structure and values of df3\\n```\\n\\nPlease note that you need to replace `df1` and `df2` with the actual names of your dataframes. Also, make sure you have the pandas library installed before running this code.\",\n", + " \"additional_kwargs\": {}\n", + " }\n", + " }\n", + " }\n", + " ]\n", + " ],\n", + " \"llm_output\": {\n", + " \"token_usage\": {\n", + " \"prompt_tokens\": 788,\n", + " \"completion_tokens\": 440,\n", + " \"total_tokens\": 1228\n", + " },\n", + " \"model_name\": \"gpt-3.5-turbo-16k-0613\"\n", + " },\n", + " \"run\": null\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor] [21.71s] Exiting Chain run with output:\n", + "\u001b[0m{\n", + " \"output\": \"Sure! Here's the code to transform df1 to df3:\\n\\n```python\\nimport pandas as pd\\n\\n# Create a new dataframe, df3, with the same columns as df1\\ndf3 = pd.DataFrame(columns=df1.columns)\\n\\n# Iterate over each row in df1\\nfor index, row in df1.iterrows():\\n # Check if the value in the \\\"ID\\\" column exists in df2\\n if row['ID'] in df2['ID'].values:\\n # Retrieve the corresponding value from the \\\"New Column\\\" column in df2\\n new_value = df2.loc[df2['ID'] == row['ID'], 'New Column'].values[0]\\n else:\\n # Assign a default value if the value does not exist in df2\\n new_value = 'Default Value'\\n \\n # Assign the retrieved value to the \\\"New Column\\\" column in df3 for the corresponding row\\n df3.at[index, 'New Column'] = new_value\\n \\n # Repeat the same steps for the \\\"Name\\\" column\\n if row['Name'] in df2['Name'].values:\\n new_value = df2.loc[df2['Name'] == row['Name'], 'New Column'].values[0]\\n else:\\n new_value = 'Default Value'\\n df3.at[index, 'Name'] = new_value\\n \\n # Repeat the same steps for the \\\"Age\\\" column\\n if row['Age'] in df2['Age'].values:\\n new_value = df2.loc[df2['Age'] == row['Age'], 'New Column'].values[0]\\n else:\\n new_value = 'Default Value'\\n df3.at[index, 'Age'] = new_value\\n\\n# Validate the transformed dataframe, df3, to ensure that it matches the structure of df3\\n# You can compare the structure and values of df3 with the expected structure and values of df3\\n```\\n\\nPlease note that you need to replace `df1` and `df2` with the actual names of your dataframes. Also, make sure you have the pandas library installed before running this code.\"\n", + "}\n" + ] + }, + { + "data": { + "text/html": [ + "
Sure! Here's the code to transform df1 to df3:\n",
+       "\n",
+       "```python\n",
+       "import pandas as pd\n",
+       "\n",
+       "# Create a new dataframe, df3, with the same columns as df1\n",
+       "df3 = pd.DataFrame(columns=df1.columns)\n",
+       "\n",
+       "# Iterate over each row in df1\n",
+       "for index, row in df1.iterrows():\n",
+       "    # Check if the value in the "ID" column exists in df2\n",
+       "    if row['ID'] in df2['ID'].values:\n",
+       "        # Retrieve the corresponding value from the "New Column" column in df2\n",
+       "        new_value = df2.loc[df2['ID'] == row['ID'], 'New Column'].values[0]\n",
+       "    else:\n",
+       "        # Assign a default value if the value does not exist in df2\n",
+       "        new_value = 'Default Value'\n",
+       "    \n",
+       "    # Assign the retrieved value to the "New Column" column in df3 for the corresponding row\n",
+       "    df3.at[index, 'New Column'] = new_value\n",
+       "    \n",
+       "    # Repeat the same steps for the "Name" column\n",
+       "    if row['Name'] in df2['Name'].values:\n",
+       "        new_value = df2.loc[df2['Name'] == row['Name'], 'New Column'].values[0]\n",
+       "    else:\n",
+       "        new_value = 'Default Value'\n",
+       "    df3.at[index, 'Name'] = new_value\n",
+       "    \n",
+       "    # Repeat the same steps for the "Age" column\n",
+       "    if row['Age'] in df2['Age'].values:\n",
+       "        new_value = df2.loc[df2['Age'] == row['Age'], 'New Column'].values[0]\n",
+       "    else:\n",
+       "        new_value = 'Default Value'\n",
+       "    df3.at[index, 'Age'] = new_value\n",
+       "\n",
+       "# Validate the transformed dataframe, df3, to ensure that it matches the structure of df3\n",
+       "# You can compare the structure and values of df3 with the expected structure and values of df3\n",
+       "```\n",
+       "\n",
+       "Please note that you need to replace `df1` and `df2` with the actual names of your dataframes. Also, make sure you have the pandas library installed before running this code.\n",
+       "
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor] Entering Chain run with input:\n", + "\u001b[0m[inputs]\n", + "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:llm:ChatOpenAI] Entering LLM run with input:\n", + "\u001b[0m{\n", + " \"prompts\": [\n", + " \"System: You are a helpful AI assistant.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: Agent stopped due to iteration limit or time limit.\\nHuman: What are the differences between the table structure of all of the dataframes?\\nAI: Agent stopped due to iteration limit or time limit.\\nHuman: Given these differences, please write an outline for a python script that would transform df1 to df3. Do not write code, just return an outline of the step you would take. Be precise and validate your work.\\nAI: To transform df1 to df3, you can follow the following outline:\\n\\n1. Create a new dataframe, df3, with the same columns as df1.\\n2. Iterate over each row in df1.\\n3. For each row, check if the value in the \\\"ID\\\" column exists in df2.\\n4. If the value exists in df2, retrieve the corresponding value from the \\\"New Column\\\" column in df2.\\n5. Assign the retrieved value to the \\\"New Column\\\" column in df3 for the corresponding row.\\n6. If the value does not exist in df2, assign a default value to the \\\"New Column\\\" column in df3 for the corresponding row.\\n7. Repeat steps 3-6 for the \\\"Name\\\" column.\\n8. Repeat steps 3-6 for the \\\"Age\\\" column.\\n9. Validate the transformed dataframe, df3, to ensure that it matches the structure of df3.\\n\\nTo validate the transformation, you can compare the structure and values of df3 with the expected structure and values of df3.\\nHuman: Given these differences, please write an outline for a python script that would transform df1 to df3. Do not write code, just return an outline of the step you would take. Be precise and validate your work.\\nAI: To transform df1 to df3, you can follow the following outline:\\n\\n1. Create a new dataframe, df3, with the same columns as df1.\\n2. Iterate over each row in df1.\\n3. For each row, check if the value in the \\\"ID\\\" column exists in df2.\\n4. If the value exists in df2, retrieve the corresponding value from the \\\"New Column\\\" column in df2.\\n5. Assign the retrieved value to the \\\"New Column\\\" column in df3 for the corresponding row.\\n6. If the value does not exist in df2, assign a default value to the \\\"New Column\\\" column in df3 for the corresponding row.\\n7. Repeat steps 3-6 for the \\\"Name\\\" column.\\n8. Repeat steps 3-6 for the \\\"Age\\\" column.\\n9. Validate the transformed dataframe, df3, to ensure that it matches the structure of df3.\\n\\nTo validate the transformation, you can compare the structure and values of df3 with the expected structure and values of df3.\\nHuman: Excellent! Now, please write the code to transform df1 to df3. Your output should be 100% valid python code. The only explanations should be in the comments. Go!\\nAI: Sure! Here's the code to transform df1 to df3:\\n\\n```python\\nimport pandas as pd\\n\\n# Create a new dataframe, df3, with the same columns as df1\\ndf3 = pd.DataFrame(columns=df1.columns)\\n\\n# Iterate over each row in df1\\nfor index, row in df1.iterrows():\\n # Check if the value in the \\\"ID\\\" column exists in df2\\n if row['ID'] in df2['ID'].values:\\n # Retrieve the corresponding value from the \\\"New Column\\\" column in df2\\n new_value = df2.loc[df2['ID'] == row['ID'], 'New Column'].values[0]\\n else:\\n # Assign a default value if the value does not exist in df2\\n new_value = 'Default Value'\\n \\n # Assign the retrieved value to the \\\"New Column\\\" column in df3 for the corresponding row\\n df3.at[index, 'New Column'] = new_value\\n \\n # Repeat the same steps for the \\\"Name\\\" column\\n if row['Name'] in df2['Name'].values:\\n new_value = df2.loc[df2['Name'] == row['Name'], 'New Column'].values[0]\\n else:\\n new_value = 'Default Value'\\n df3.at[index, 'Name'] = new_value\\n \\n # Repeat the same steps for the \\\"Age\\\" column\\n if row['Age'] in df2['Age'].values:\\n new_value = df2.loc[df2['Age'] == row['Age'], 'New Column'].values[0]\\n else:\\n new_value = 'Default Value'\\n df3.at[index, 'Age'] = new_value\\n\\n# Validate the transformed dataframe, df3, to ensure that it matches the structure of df3\\n# You can compare the structure and values of df3 with the expected structure and values of df3\\n```\\n\\nPlease note that you need to replace `df1` and `df2` with the actual names of your dataframes. Also, make sure you have the pandas library installed before running this code.\\nHuman: Excellent! Now, please write the code to transform df1 to df3. Your output should be 100% valid python code. The only explanations should be in the comments. Go!\\nAI: Sure! Here's the code to transform df1 to df3:\\n\\n```python\\nimport pandas as pd\\n\\n# Create a new dataframe, df3, with the same columns as df1\\ndf3 = pd.DataFrame(columns=df1.columns)\\n\\n# Iterate over each row in df1\\nfor index, row in df1.iterrows():\\n # Check if the value in the \\\"ID\\\" column exists in df2\\n if row['ID'] in df2['ID'].values:\\n # Retrieve the corresponding value from the \\\"New Column\\\" column in df2\\n new_value = df2.loc[df2['ID'] == row['ID'], 'New Column'].values[0]\\n else:\\n # Assign a default value if the value does not exist in df2\\n new_value = 'Default Value'\\n \\n # Assign the retrieved value to the \\\"New Column\\\" column in df3 for the corresponding row\\n df3.at[index, 'New Column'] = new_value\\n \\n # Repeat the same steps for the \\\"Name\\\" column\\n if row['Name'] in df2['Name'].values:\\n new_value = df2.loc[df2['Name'] == row['Name'], 'New Column'].values[0]\\n else:\\n new_value = 'Default Value'\\n df3.at[index, 'Name'] = new_value\\n \\n # Repeat the same steps for the \\\"Age\\\" column\\n if row['Age'] in df2['Age'].values:\\n new_value = df2.loc[df2['Age'] == row['Age'], 'New Column'].values[0]\\n else:\\n new_value = 'Default Value'\\n df3.at[index, 'Age'] = new_value\\n\\n# Validate the transformed dataframe, df3, to ensure that it matches the structure of df3\\n# You can compare the structure and values of df3 with the expected structure and values of df3\\n```\\n\\nPlease note that you need to replace `df1` and `df2` with the actual names of your dataframes. Also, make sure you have the pandas library installed before running this code.\\nHuman: This is a good start, but you included text that wasn't in python code. Do not do this. Only return valid python code. Please try again.\"\n", + " ]\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:llm:ChatOpenAI] [4.19s] Exiting LLM run with output:\n", + "\u001b[0m{\n", + " \"generations\": [\n", + " [\n", + " {\n", + " \"text\": \"\",\n", + " \"generation_info\": {\n", + " \"finish_reason\": \"function_call\"\n", + " },\n", + " \"message\": {\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"schema\",\n", + " \"messages\",\n", + " \"AIMessage\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"content\": \"\",\n", + " \"additional_kwargs\": {\n", + " \"function_call\": {\n", + " \"name\": \"python\",\n", + " \"arguments\": \"import pandas as pd\\n\\n# Create a new dataframe, df3, with the same columns as df1\\ndf3 = pd.DataFrame(columns=df1.columns)\\n\\n# Iterate over each row in df1\\nfor index, row in df1.iterrows():\\n # Check if the value in the \\\"ID\\\" column exists in df2\\n if row['ID'] in df2['ID'].values:\\n # Retrieve the corresponding value from the \\\"New Column\\\" column in df2\\n new_value = df2.loc[df2['ID'] == row['ID'], 'New Column'].values[0]\\n else:\\n # Assign a default value if the value does not exist in df2\\n new_value = 'Default Value'\\n \\n # Assign the retrieved value to the \\\"New Column\\\" column in df3 for the corresponding row\\n df3.at[index, 'New Column'] = new_value\\n \\n # Repeat the same steps for the \\\"Name\\\" column\\n if row['Name'] in df2['Name'].values:\\n new_value = df2.loc[df2['Name'] == row['Name'], 'New Column'].values[0]\\n else:\\n new_value = 'Default Value'\\n df3.at[index, 'Name'] = new_value\\n \\n # Repeat the same steps for the \\\"Age\\\" column\\n if row['Age'] in df2['Age'].values:\\n new_value = df2.loc[df2['Age'] == row['Age'], 'New Column'].values[0]\\n else:\\n new_value = 'Default Value'\\n df3.at[index, 'Age'] = new_value\\n\\n# Validate the transformed dataframe, df3, to ensure that it matches the structure of df3\\n# You can compare the structure and values of df3 with the expected structure and values of df3\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " ]\n", + " ],\n", + " \"llm_output\": {\n", + " \"token_usage\": {\n", + " \"prompt_tokens\": 1752,\n", + " \"completion_tokens\": 384,\n", + " \"total_tokens\": 2136\n", + " },\n", + " \"model_name\": \"gpt-3.5-turbo-16k-0613\"\n", + " },\n", + " \"run\": null\n", + "}\n", + "\u001b[31;1m\u001b[1;3m[chain/error]\u001b[0m \u001b[1m[1:chain:AgentExecutor] [4.19s] Chain run errored with error:\n", + "\u001b[0m\"OutputParserException('Could not parse tool input: {\\\\'name\\\\': \\\\'python\\\\', \\\\'arguments\\\\': \\\\'import pandas as pd\\\\\\\\n\\\\\\\\n# Create a new dataframe, df3, with the same columns as df1\\\\\\\\ndf3 = pd.DataFrame(columns=df1.columns)\\\\\\\\n\\\\\\\\n# Iterate over each row in df1\\\\\\\\nfor index, row in df1.iterrows():\\\\\\\\n # Check if the value in the \\\"ID\\\" column exists in df2\\\\\\\\n if row[\\\\\\\\\\\\'ID\\\\\\\\\\\\'] in df2[\\\\\\\\\\\\'ID\\\\\\\\\\\\'].values:\\\\\\\\n # Retrieve the corresponding value from the \\\"New Column\\\" column in df2\\\\\\\\n new_value = df2.loc[df2[\\\\\\\\\\\\'ID\\\\\\\\\\\\'] == row[\\\\\\\\\\\\'ID\\\\\\\\\\\\'], \\\\\\\\\\\\'New Column\\\\\\\\\\\\'].values[0]\\\\\\\\n else:\\\\\\\\n # Assign a default value if the value does not exist in df2\\\\\\\\n new_value = \\\\\\\\\\\\'Default Value\\\\\\\\\\\\'\\\\\\\\n \\\\\\\\n # Assign the retrieved value to the \\\"New Column\\\" column in df3 for the corresponding row\\\\\\\\n df3.at[index, \\\\\\\\\\\\'New Column\\\\\\\\\\\\'] = new_value\\\\\\\\n \\\\\\\\n # Repeat the same steps for the \\\"Name\\\" column\\\\\\\\n if row[\\\\\\\\\\\\'Name\\\\\\\\\\\\'] in df2[\\\\\\\\\\\\'Name\\\\\\\\\\\\'].values:\\\\\\\\n new_value = df2.loc[df2[\\\\\\\\\\\\'Name\\\\\\\\\\\\'] == row[\\\\\\\\\\\\'Name\\\\\\\\\\\\'], \\\\\\\\\\\\'New Column\\\\\\\\\\\\'].values[0]\\\\\\\\n else:\\\\\\\\n new_value = \\\\\\\\\\\\'Default Value\\\\\\\\\\\\'\\\\\\\\n df3.at[index, \\\\\\\\\\\\'Name\\\\\\\\\\\\'] = new_value\\\\\\\\n \\\\\\\\n # Repeat the same steps for the \\\"Age\\\" column\\\\\\\\n if row[\\\\\\\\\\\\'Age\\\\\\\\\\\\'] in df2[\\\\\\\\\\\\'Age\\\\\\\\\\\\'].values:\\\\\\\\n new_value = df2.loc[df2[\\\\\\\\\\\\'Age\\\\\\\\\\\\'] == row[\\\\\\\\\\\\'Age\\\\\\\\\\\\'], \\\\\\\\\\\\'New Column\\\\\\\\\\\\'].values[0]\\\\\\\\n else:\\\\\\\\n new_value = \\\\\\\\\\\\'Default Value\\\\\\\\\\\\'\\\\\\\\n df3.at[index, \\\\\\\\\\\\'Age\\\\\\\\\\\\'] = new_value\\\\\\\\n\\\\\\\\n# Validate the transformed dataframe, df3, to ensure that it matches the structure of df3\\\\\\\\n# You can compare the structure and values of df3 with the expected structure and values of df3\\\\'} because the `arguments` is not valid JSON.')\"\n" + ] + }, + { + "ename": "OutputParserException", + "evalue": "Could not parse tool input: {'name': 'python', 'arguments': 'import pandas as pd\\n\\n# Create a new dataframe, df3, with the same columns as df1\\ndf3 = pd.DataFrame(columns=df1.columns)\\n\\n# Iterate over each row in df1\\nfor index, row in df1.iterrows():\\n # Check if the value in the \"ID\" column exists in df2\\n if row[\\'ID\\'] in df2[\\'ID\\'].values:\\n # Retrieve the corresponding value from the \"New Column\" column in df2\\n new_value = df2.loc[df2[\\'ID\\'] == row[\\'ID\\'], \\'New Column\\'].values[0]\\n else:\\n # Assign a default value if the value does not exist in df2\\n new_value = \\'Default Value\\'\\n \\n # Assign the retrieved value to the \"New Column\" column in df3 for the corresponding row\\n df3.at[index, \\'New Column\\'] = new_value\\n \\n # Repeat the same steps for the \"Name\" column\\n if row[\\'Name\\'] in df2[\\'Name\\'].values:\\n new_value = df2.loc[df2[\\'Name\\'] == row[\\'Name\\'], \\'New Column\\'].values[0]\\n else:\\n new_value = \\'Default Value\\'\\n df3.at[index, \\'Name\\'] = new_value\\n \\n # Repeat the same steps for the \"Age\" column\\n if row[\\'Age\\'] in df2[\\'Age\\'].values:\\n new_value = df2.loc[df2[\\'Age\\'] == row[\\'Age\\'], \\'New Column\\'].values[0]\\n else:\\n new_value = \\'Default Value\\'\\n df3.at[index, \\'Age\\'] = new_value\\n\\n# Validate the transformed dataframe, df3, to ensure that it matches the structure of df3\\n# You can compare the structure and values of df3 with the expected structure and values of df3'} because the `arguments` is not valid JSON.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mJSONDecodeError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Desktop/projects/zero-mapper/venv/lib/python3.9/site-packages/langchain/agents/openai_functions_agent/base.py:113\u001b[0m, in \u001b[0;36m_parse_ai_message\u001b[0;34m(message)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 113\u001b[0m _tool_input \u001b[39m=\u001b[39m json\u001b[39m.\u001b[39;49mloads(function_call[\u001b[39m\"\u001b[39;49m\u001b[39marguments\u001b[39;49m\u001b[39m\"\u001b[39;49m])\n\u001b[1;32m 114\u001b[0m \u001b[39mexcept\u001b[39;00m JSONDecodeError:\n", + "File \u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/json/__init__.py:346\u001b[0m, in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m 343\u001b[0m \u001b[39mif\u001b[39;00m (\u001b[39mcls\u001b[39m \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m object_hook \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m\n\u001b[1;32m 344\u001b[0m parse_int \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m parse_float \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m\n\u001b[1;32m 345\u001b[0m parse_constant \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m object_pairs_hook \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m kw):\n\u001b[0;32m--> 346\u001b[0m \u001b[39mreturn\u001b[39;00m _default_decoder\u001b[39m.\u001b[39;49mdecode(s)\n\u001b[1;32m 347\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mcls\u001b[39m \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/json/decoder.py:337\u001b[0m, in \u001b[0;36mJSONDecoder.decode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m 333\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Return the Python representation of ``s`` (a ``str`` instance\u001b[39;00m\n\u001b[1;32m 334\u001b[0m \u001b[39mcontaining a JSON document).\u001b[39;00m\n\u001b[1;32m 335\u001b[0m \n\u001b[1;32m 336\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 337\u001b[0m obj, end \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mraw_decode(s, idx\u001b[39m=\u001b[39;49m_w(s, \u001b[39m0\u001b[39;49m)\u001b[39m.\u001b[39;49mend())\n\u001b[1;32m 338\u001b[0m end \u001b[39m=\u001b[39m _w(s, end)\u001b[39m.\u001b[39mend()\n", + "File \u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/json/decoder.py:355\u001b[0m, in \u001b[0;36mJSONDecoder.raw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mStopIteration\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m--> 355\u001b[0m \u001b[39mraise\u001b[39;00m JSONDecodeError(\u001b[39m\"\u001b[39m\u001b[39mExpecting value\u001b[39m\u001b[39m\"\u001b[39m, s, err\u001b[39m.\u001b[39mvalue) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 356\u001b[0m \u001b[39mreturn\u001b[39;00m obj, end\n", + "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mOutputParserException\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[46], line 10\u001b[0m\n\u001b[1;32m 8\u001b[0m formatter \u001b[39m=\u001b[39m HtmlFormatter()\n\u001b[1;32m 9\u001b[0m \u001b[39mfor\u001b[39;00m question \u001b[39min\u001b[39;00m questions:\n\u001b[0;32m---> 10\u001b[0m res \u001b[39m=\u001b[39m agent\u001b[39m.\u001b[39;49mrun(question)\n\u001b[1;32m 11\u001b[0m memory\u001b[39m.\u001b[39mchat_memory\u001b[39m.\u001b[39madd_user_message(question)\n\u001b[1;32m 12\u001b[0m memory\u001b[39m.\u001b[39mchat_memory\u001b[39m.\u001b[39madd_ai_message(res)\n", + "File \u001b[0;32m~/Desktop/projects/zero-mapper/venv/lib/python3.9/site-packages/langchain/chains/base.py:475\u001b[0m, in \u001b[0;36mChain.run\u001b[0;34m(self, callbacks, tags, metadata, *args, **kwargs)\u001b[0m\n\u001b[1;32m 473\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m!=\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 474\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39m`run` supports only one positional argument.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 475\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m(args[\u001b[39m0\u001b[39;49m], callbacks\u001b[39m=\u001b[39;49mcallbacks, tags\u001b[39m=\u001b[39;49mtags, metadata\u001b[39m=\u001b[39;49mmetadata)[\n\u001b[1;32m 476\u001b[0m _output_key\n\u001b[1;32m 477\u001b[0m ]\n\u001b[1;32m 479\u001b[0m \u001b[39mif\u001b[39;00m kwargs \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m args:\n\u001b[1;32m 480\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m(kwargs, callbacks\u001b[39m=\u001b[39mcallbacks, tags\u001b[39m=\u001b[39mtags, metadata\u001b[39m=\u001b[39mmetadata)[\n\u001b[1;32m 481\u001b[0m _output_key\n\u001b[1;32m 482\u001b[0m ]\n", + "File \u001b[0;32m~/Desktop/projects/zero-mapper/venv/lib/python3.9/site-packages/langchain/chains/base.py:282\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info)\u001b[0m\n\u001b[1;32m 280\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mKeyboardInterrupt\u001b[39;00m, \u001b[39mException\u001b[39;00m) \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 281\u001b[0m run_manager\u001b[39m.\u001b[39mon_chain_error(e)\n\u001b[0;32m--> 282\u001b[0m \u001b[39mraise\u001b[39;00m e\n\u001b[1;32m 283\u001b[0m run_manager\u001b[39m.\u001b[39mon_chain_end(outputs)\n\u001b[1;32m 284\u001b[0m final_outputs: Dict[\u001b[39mstr\u001b[39m, Any] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprep_outputs(\n\u001b[1;32m 285\u001b[0m inputs, outputs, return_only_outputs\n\u001b[1;32m 286\u001b[0m )\n", + "File \u001b[0;32m~/Desktop/projects/zero-mapper/venv/lib/python3.9/site-packages/langchain/chains/base.py:276\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info)\u001b[0m\n\u001b[1;32m 270\u001b[0m run_manager \u001b[39m=\u001b[39m callback_manager\u001b[39m.\u001b[39mon_chain_start(\n\u001b[1;32m 271\u001b[0m dumpd(\u001b[39mself\u001b[39m),\n\u001b[1;32m 272\u001b[0m inputs,\n\u001b[1;32m 273\u001b[0m )\n\u001b[1;32m 274\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 275\u001b[0m outputs \u001b[39m=\u001b[39m (\n\u001b[0;32m--> 276\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call(inputs, run_manager\u001b[39m=\u001b[39;49mrun_manager)\n\u001b[1;32m 277\u001b[0m \u001b[39mif\u001b[39;00m new_arg_supported\n\u001b[1;32m 278\u001b[0m \u001b[39melse\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_call(inputs)\n\u001b[1;32m 279\u001b[0m )\n\u001b[1;32m 280\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mKeyboardInterrupt\u001b[39;00m, \u001b[39mException\u001b[39;00m) \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 281\u001b[0m run_manager\u001b[39m.\u001b[39mon_chain_error(e)\n", + "File \u001b[0;32m~/Desktop/projects/zero-mapper/venv/lib/python3.9/site-packages/langchain/agents/agent.py:1036\u001b[0m, in \u001b[0;36mAgentExecutor._call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m 1034\u001b[0m \u001b[39m# We now enter the agent loop (until it returns something).\u001b[39;00m\n\u001b[1;32m 1035\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_should_continue(iterations, time_elapsed):\n\u001b[0;32m-> 1036\u001b[0m next_step_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_take_next_step(\n\u001b[1;32m 1037\u001b[0m name_to_tool_map,\n\u001b[1;32m 1038\u001b[0m color_mapping,\n\u001b[1;32m 1039\u001b[0m inputs,\n\u001b[1;32m 1040\u001b[0m intermediate_steps,\n\u001b[1;32m 1041\u001b[0m run_manager\u001b[39m=\u001b[39;49mrun_manager,\n\u001b[1;32m 1042\u001b[0m )\n\u001b[1;32m 1043\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(next_step_output, AgentFinish):\n\u001b[1;32m 1044\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_return(\n\u001b[1;32m 1045\u001b[0m next_step_output, intermediate_steps, run_manager\u001b[39m=\u001b[39mrun_manager\n\u001b[1;32m 1046\u001b[0m )\n", + "File \u001b[0;32m~/Desktop/projects/zero-mapper/venv/lib/python3.9/site-packages/langchain/agents/agent.py:844\u001b[0m, in \u001b[0;36mAgentExecutor._take_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)\u001b[0m\n\u001b[1;32m 842\u001b[0m raise_error \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m 843\u001b[0m \u001b[39mif\u001b[39;00m raise_error:\n\u001b[0;32m--> 844\u001b[0m \u001b[39mraise\u001b[39;00m e\n\u001b[1;32m 845\u001b[0m text \u001b[39m=\u001b[39m \u001b[39mstr\u001b[39m(e)\n\u001b[1;32m 846\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandle_parsing_errors, \u001b[39mbool\u001b[39m):\n", + "File \u001b[0;32m~/Desktop/projects/zero-mapper/venv/lib/python3.9/site-packages/langchain/agents/agent.py:833\u001b[0m, in \u001b[0;36mAgentExecutor._take_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)\u001b[0m\n\u001b[1;32m 830\u001b[0m intermediate_steps \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_prepare_intermediate_steps(intermediate_steps)\n\u001b[1;32m 832\u001b[0m \u001b[39m# Call the LLM to see what to do.\u001b[39;00m\n\u001b[0;32m--> 833\u001b[0m output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49magent\u001b[39m.\u001b[39;49mplan(\n\u001b[1;32m 834\u001b[0m intermediate_steps,\n\u001b[1;32m 835\u001b[0m callbacks\u001b[39m=\u001b[39;49mrun_manager\u001b[39m.\u001b[39;49mget_child() \u001b[39mif\u001b[39;49;00m run_manager \u001b[39melse\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m 836\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49minputs,\n\u001b[1;32m 837\u001b[0m )\n\u001b[1;32m 838\u001b[0m \u001b[39mexcept\u001b[39;00m OutputParserException \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 839\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandle_parsing_errors, \u001b[39mbool\u001b[39m):\n", + "File \u001b[0;32m~/Desktop/projects/zero-mapper/venv/lib/python3.9/site-packages/langchain/agents/openai_functions_agent/base.py:222\u001b[0m, in \u001b[0;36mOpenAIFunctionsAgent.plan\u001b[0;34m(self, intermediate_steps, callbacks, with_functions, **kwargs)\u001b[0m\n\u001b[1;32m 217\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 218\u001b[0m predicted_message \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mllm\u001b[39m.\u001b[39mpredict_messages(\n\u001b[1;32m 219\u001b[0m messages,\n\u001b[1;32m 220\u001b[0m callbacks\u001b[39m=\u001b[39mcallbacks,\n\u001b[1;32m 221\u001b[0m )\n\u001b[0;32m--> 222\u001b[0m agent_decision \u001b[39m=\u001b[39m _parse_ai_message(predicted_message)\n\u001b[1;32m 223\u001b[0m \u001b[39mreturn\u001b[39;00m agent_decision\n", + "File \u001b[0;32m~/Desktop/projects/zero-mapper/venv/lib/python3.9/site-packages/langchain/agents/openai_functions_agent/base.py:115\u001b[0m, in \u001b[0;36m_parse_ai_message\u001b[0;34m(message)\u001b[0m\n\u001b[1;32m 113\u001b[0m _tool_input \u001b[39m=\u001b[39m json\u001b[39m.\u001b[39mloads(function_call[\u001b[39m\"\u001b[39m\u001b[39marguments\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[1;32m 114\u001b[0m \u001b[39mexcept\u001b[39;00m JSONDecodeError:\n\u001b[0;32m--> 115\u001b[0m \u001b[39mraise\u001b[39;00m OutputParserException(\n\u001b[1;32m 116\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCould not parse tool input: \u001b[39m\u001b[39m{\u001b[39;00mfunction_call\u001b[39m}\u001b[39;00m\u001b[39m because \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 117\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mthe `arguments` is not valid JSON.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 118\u001b[0m )\n\u001b[1;32m 120\u001b[0m \u001b[39m# HACK HACK HACK:\u001b[39;00m\n\u001b[1;32m 121\u001b[0m \u001b[39m# The code that encodes tool input into Open AI uses a special variable\u001b[39;00m\n\u001b[1;32m 122\u001b[0m \u001b[39m# name called `__arg1` to handle old style tools that do not expose a\u001b[39;00m\n\u001b[1;32m 123\u001b[0m \u001b[39m# schema and expect a single string argument as an input.\u001b[39;00m\n\u001b[1;32m 124\u001b[0m \u001b[39m# We unpack the argument here if it exists.\u001b[39;00m\n\u001b[1;32m 125\u001b[0m \u001b[39m# Open AI does not support passing in a JSON array as an argument.\u001b[39;00m\n\u001b[1;32m 126\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39m__arg1\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m _tool_input:\n", + "\u001b[0;31mOutputParserException\u001b[0m: Could not parse tool input: {'name': 'python', 'arguments': 'import pandas as pd\\n\\n# Create a new dataframe, df3, with the same columns as df1\\ndf3 = pd.DataFrame(columns=df1.columns)\\n\\n# Iterate over each row in df1\\nfor index, row in df1.iterrows():\\n # Check if the value in the \"ID\" column exists in df2\\n if row[\\'ID\\'] in df2[\\'ID\\'].values:\\n # Retrieve the corresponding value from the \"New Column\" column in df2\\n new_value = df2.loc[df2[\\'ID\\'] == row[\\'ID\\'], \\'New Column\\'].values[0]\\n else:\\n # Assign a default value if the value does not exist in df2\\n new_value = \\'Default Value\\'\\n \\n # Assign the retrieved value to the \"New Column\" column in df3 for the corresponding row\\n df3.at[index, \\'New Column\\'] = new_value\\n \\n # Repeat the same steps for the \"Name\" column\\n if row[\\'Name\\'] in df2[\\'Name\\'].values:\\n new_value = df2.loc[df2[\\'Name\\'] == row[\\'Name\\'], \\'New Column\\'].values[0]\\n else:\\n new_value = \\'Default Value\\'\\n df3.at[index, \\'Name\\'] = new_value\\n \\n # Repeat the same steps for the \"Age\" column\\n if row[\\'Age\\'] in df2[\\'Age\\'].values:\\n new_value = df2.loc[df2[\\'Age\\'] == row[\\'Age\\'], \\'New Column\\'].values[0]\\n else:\\n new_value = \\'Default Value\\'\\n df3.at[index, \\'Age\\'] = new_value\\n\\n# Validate the transformed dataframe, df3, to ensure that it matches the structure of df3\\n# You can compare the structure and values of df3 with the expected structure and values of df3'} because the `arguments` is not valid JSON." + ] + } + ], + "source": [ + "questions = [\"What are the differences between the table structure of all of the dataframes?\",\n", + " \"Given these differences, please write an outline for a python script that would transform df1 to df3. Do not write code, just return an outline of the step you would take. Be precise and validate your work.\",\n", + " \"Excellent! Now, please write the code to transform df1 to df3. Your output should be 100% valid python code. The only explanations should be in the comments. Go!\",\n", + " \"This is a good start, but you included text that wasn't in python code. Do not do this. Only return valid python code. Please try again.\",\n", + " \"Good. Now copy the the original df1 and execute this code on the copy. Do not modify df1. Then compare this new dataframe to df1. Is all of the data there? Is it mapping properly to the new columns? If not, please fix it.\",\n", + " \"No, I actually want you to run this code. Generate a new csv. This will be called df1_transformed.csv.\"\n", + "]\n", + "formatter = HtmlFormatter()\n", + "for question in questions:\n", + " res = agent.run(question)\n", + " memory.chat_memory.add_user_message(question)\n", + " memory.chat_memory.add_ai_message(res)\n", + " highlighted_code = highlight(res, PythonLexer(), formatter)\n", + " display(HTML(highlighted_code))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "formatter = HtmlFormatter()\n", + "highlighted_code = highlight(res, PythonLexer(), formatter)\n", + "display(HTML(highlighted_code))" ] }, { @@ -14,7 +1221,31 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "from langchain.agents import create_sql_agent\n", + "from langchain.agents.agent_toolkits import SQLDatabaseToolkit\n", + "from langchain.sql_database import SQLDatabase\n", + "from langchain.llms.openai import OpenAI\n", + "from langchain.agents import AgentExecutor\n", + "db = SQLDatabase.from_uri(\"sqlite:///../../../../../notebooks/data/test.db\")\n", + "toolkit = SQLDatabaseToolkit(db=db, llm=OpenAI(temperature=0))\n", + "agent_executor = create_sql_agent(\n", + " llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\"),\n", + " toolkit=toolkit,\n", + " verbose=True,\n", + " agent_type=AgentType.OPENAI_FUNCTIONS\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# have a tool for each individual function.\n", + "# When you do a full query of the table, bypass the standard return and add directly to the gradio chat. Then add a message to the chat with the head of the query. And return a short message to the user that it has been added to the chat. Maybe it should generate a file download, too." + ] } ], "metadata": {