diff --git a/_posts/2024-09-30-extended-arena.md b/_posts/2024-09-30-extended-arena.md index 610155c..73ee65f 100644 --- a/_posts/2024-09-30-extended-arena.md +++ b/_posts/2024-09-30-extended-arena.md @@ -64,7 +64,7 @@ arrive. The Extended Online Arena Score amounts to running online logistic regression on the same feature set. The algorithm is as follows: -$$ \theta^{(t+1)} = \theta^{(t)} - \eta \nabla \ell(\sigma(X_t^\top \theta^{(t)}), Y_t) - \lambda \nabla \|\theta^{(t)}\|_p,$$ +$$ \theta^{(t+1)} = \theta^{(t)} - \eta \nabla \ell(\sigma(X_t^\top \theta^{(t)}), Y_t) - \lambda \nabla \|\theta^{(t)}\|\_p,$$ where $$\eta > 0$$ is the learning rate, and $$\nabla \|\cdot\|_p$$ is any valid subgradient of the $$\ell_p$$ norm. The benefit, and drawback, of the online score is that it never converges. diff --git a/_posts/2024-10-03-agent-arena.md b/_posts/2024-10-03-agent-arena.md index 885f033..15de677 100644 --- a/_posts/2024-10-03-agent-arena.md +++ b/_posts/2024-10-03-agent-arena.md @@ -216,7 +216,7 @@ In education-focused scenarios, LLM agents have the potential to offer rich, ste "Agent_A": { "Agent name": "sql agent plotter langchain (gpt-4o-2024-05-13)", "Code executed": "import os\nimport sys\nimport subprocess\nfrom composio_langchain import App, ComposioToolSet\nfrom langchain import hub\nfrom langchain.agents import AgentExecutor, create_openai_functions_agent\nfrom langchain_openai import ChatOpenAI\n\n# Ensure Composio apps are updated\nsubprocess.run([\"composio\", \"apps\", \"update\"])\n\n# Initialize the LLM with the OpenAI GPT-4o model and API key\nllm = ChatOpenAI(model=\"gpt-4o-2024-05-13\", openai_api_key=os.environ[\"OPENAI_API_KEY\"])\n\n# Pull the prompt template for the agent\nprompt = hub.pull(\"hwchase17/openai-functions-agent\")\n\n# Initialize the Composio ToolSet with the API key\ntoolset = ComposioToolSet(api_key=os.environ[\"COMPOSIO_API_KEY\"])\n\n# Get tools for SQL, File, and Code Interpreter operations\ntry:\n tools = toolset.get_tools(apps=[App.SQLTOOL, App.FILETOOL, App.CODEINTERPRETER])\nexcept Exception as e:\n print(f\"Error loading tools: {e}\")\n sys.exit(1)\n\n# Database file path from the argument\nif len(sys.argv) < 2:\n print(\"Please provide the database file path as an argument.\")\n sys.exit(1)\n\ndb_file_path = sys.argv[1]\n\n# Define the task to execute\nquery_task = f\"\"\"\nWrite sqlite query to get top 10 rows from the only table MOCK_DATA in the database {db_file_path} using sqltool, \nwrite the output in a file called log.txt and return the output.\n\"\"\"\n\n# Create the agent for SQL and File operations and execute the task\nquery_agent = create_openai_functions_agent(llm, tools, prompt)\nagent_executor = AgentExecutor(agent=query_agent, tools=tools, verbose=True, handle_parsing_errors=True)\nres = agent_executor.invoke({\"input\": query_task})\n\n# Check if the query execution was successful\nif \"output\" not in res:\n print(\"Error: Failed to execute SQL query.\")\n sys.exit(1)\n\n# Directory for generated files\nGENERATED_FILES_DIR = \"/tmp/generated_files\"\nos.makedirs(GENERATED_FILES_DIR, exist_ok=True)\njob_id = os.path.basename(db_file_path).split('_')[0]\n\n# Define the task for plotting graphs\nplot_task = f\"\"\"\nUsing the following extracted information, plot the graph between first name and salary: \n{res['output']} Save the plot as '{GENERATED_FILES_DIR}/salary_plot_{job_id}.png'. \nI would like to view the plot.\n\"\"\"\n\n# Create the agent for Code Interpreter operations\ncode_tool = toolset.get_tools(apps=[App.CODEINTERPRETER])\ncode_agent = create_openai_functions_agent(llm, code_tool, prompt)\nagent_executor = AgentExecutor(agent=code_agent, tools=code_tool, verbose=True, handle_parsing_errors=True)\n\n# Ensure the directory exists before saving the plot\ncreate_dir_command = f\"mkdir -p {GENERATED_FILES_DIR}\"\nagent_executor.invoke({\"input\": create_dir_command})\n\n# Execute the plotting task\nfinal_res = agent_executor.invoke({\"input\": plot_task})\n\n# Check if the plot generation was successful\nif final_res.get(\"error\"):\n print(f\"Error: {final_res['error']}\")\n sys.exit(1)\n\n# Display the plot URL\nplot_url = f\"file://{GENERATED_FILES_DIR}/salary_plot_{job_id}.png\"\nprint(f\"Here is the plot of the graph between first names and their respective salaries:\\n\\n![Salary Plot]({plot_url})\")", - "Agent output": "\u26a0\ufe0f Apps does not require update\u26a0\ufe0f Tags does not require update\u26a0\ufe0f Actions does not require update\u26a0\ufe0f Triggers does not require update* A new version of composio is available, run `pip install composio-core==0.5.28` to update.[2024-09-29 00:04:06,732][INFO] Logging is set to INFO, use `logging_level` argument or `COMPOSIO_LOGGING_LEVEL` change this/opt/render/project/src/.venv/lib/python3.11/site-packages/composio/client/collections.py:924: UserWarning: Using all the actions of an app is not recommended. Please use tags to filter actions or provide specific actions. We just pass the important actions to the agent, but this is not meant to be used in production. Check out https://docs.composio.dev/sdk/python/actions for more information. warnings.warn([2024-09-29 00:04:08,448][INFO] Executing `SQLTOOL_SQL_QUERY` with params={'query': 'SELECT * FROM MOCK_DATA LIMIT 10;', 'connection_string': '/tmp/sales.csv.txt'} and metadata={} connected_account_id=None[2024-09-29 00:04:08,454][ERROR] Error executing `SQLTOOL_SQL_QUERY`: SQLite error: file is not a database[2024-09-29 00:04:08,455][ERROR] Traceback (most recent call last): File \"/opt/render/project/src/.venv/lib/python3.11/site-packages/composio/tools/local/sqltool/actions/sql_query.py\", line 50, in execute cursor.execute(request.query)sqlite3.DatabaseError: file is not a databaseThe above exception was the direct cause of the following exception:Traceback (most recent call last): File \"/opt/render/project/src/.venv/lib/python3.11/site-packages/composio/tools/base/local.py\", line 169, in execute response = instance.execute( ^^^^^^^^^^^^^^^^^ File \"/opt/render/project/src/.venv/lib/python3.11/site-packages/composio/tools/local/sqltool/actions/sql_query.py\", line 61, in execute raise ValueError(f\"SQLite error: {str(e)}\") from eValueError: SQLite error: file is not a database[2024-09-29 00:04:08,458][INFO] Got response={'data': None, 'error': 'SQLite error: file is not a database', 'successful': False} from action= with params={'query': 'SELECT * FROM MOCK_DATA LIMIT 10;', 'connection_string': '/.../opt/render/project/src/.venv/lib/python3.11/site-packages/composio/client/collections.py:924: UserWarning: Using all the actions of an app is not recommended. Please use tags to filter actions or provide specific actions. We just pass the important actions to the agent, but this is not meant to be used in production. Check out https://docs.composio.dev/sdk/python/actions for more information. warnings.warn([2024-09-29 00:04:10,210][INFO] Executing `CODEINTERPRETER_RUN_TERMINAL_CMD` with params={'command': 'mkdir -p /tmp/generated_files', 'timeout': 60} and metadata={} connected_account_id=None[2024-09-29 00:04:10,516][INFO] Got response={'successfull': True, 'data': {'stdout': '', 'stderr': '', 'sandbox_id': 'sandbox-cdab'}, 'error': None} from action= with params={'command': 'mkdir -p /tmp/generated_files', 'time...[2024-09-29 00:04:12,451][INFO] Executing `CODEINTERPRETER_RUN_TERMINAL_CMD` with params={'command': 'head -n 10 /tmp/sales.csv.txt', 'timeout': 60} and metadata={} connected_account_id=None[2024-09-29 00:04:12,897][INFO] Got response={'successfull': False, 'data': {}, 'error': 'Process exited with code 1 and error: exit status 1'} from action= with params={'command': 'head -n 10 /tmp/sales.csv.txt', 'timeout': ...[2024-09-29 00:04:13,538][INFO] Executing `CODEINTERPRETER_RUN_TERMINAL_CMD` with params={'command': 'cat /tmp/sales.csv.txt', 'timeout': 60} and metadata={} connected_account_id=None[2024-09-29 00:04:14,075][INFO] Got response={'successfull': False, 'data': {}, 'error': 'Process exited with code 1 and error: exit status 1'} from action= with params={'command': 'cat /tmp/sales.csv.txt', 'timeout': 60}[2024-09-29 00:04:16,704][INFO] Executing `CODEINTERPRETER_EXECUTE_CODE` with params={'code_to_execute': \"import pandas as pd\\nimport matplotlib.pyplot as plt\\n\\n# Sample data\\ndata = {'first_name': ['John', 'Jane', 'Doe', 'Alice', 'Bob'],\\n 'salary': [50000, 60000, 55000, 70000, ...[2024-09-29 00:04:19,339][INFO] Got response={'successfull': True, 'data': {'results': '', 'stdout': 'Plot saved successfully.\\n', 'stderr': '', 'error': '', 'sandbox_id': 'sandbox-b968'}, 'error': None} from action= with par...[2024-09-29 00:04:20,092][INFO] Executing `CODEINTERPRETER_GET_FILE_CMD` with params={'sandbox_id': 'sandbox-b968', 'file_path': '/home/user/salary_plot_sales.csv.txt.png'} and metadata={} connected_account_id=None[2024-09-29 00:04:20,308][INFO] Got response={'successfull': True, 'data': {'file': '/opt/render/.composio/output/CODEINTERPRETER_GET_FILE_CMD_default_1727568260.3040874__home_user_salary_plot_sales.csv.txt.png'}, 'error': None} from action=> Entering new AgentExecutor chain...Invoking: `SQLTOOL_SQL_QUERY` with `{'query': 'SELECT * FROM MOCK_DATA LIMIT 10;', 'connection_string': '/tmp/sales.csv.txt'}`{'data': None, 'error': 'SQLite error: file is not a database', 'successful': False}It appears that the file `/tmp/sales.csv.txt` is not recognized as a valid SQLite database. This file might be a CSV file rather than a SQLite database.Would you like to proceed with reading data from this CSV file as a plain text file or perform some other action?> > Entering new AgentExecutor chain...Invoking: `CODEINTERPRETER_RUN_TERMINAL_CMD` with `{'command': 'mkdir -p /tmp/generated_files', 'timeout': 60}`{'successfull': True, 'data': {'stdout': '', 'stderr': '', 'sandbox_id': 'sandbox-cdab'}, 'error': None}The directory `/tmp/generated_files` has been successfully created. Is there anything specific you would like to do next?> > Entering new AgentExecutor chain...Invoking: `CODEINTERPRETER_RUN_TERMINAL_CMD` with `{'command': 'head -n 10 /tmp/sales.csv.txt', 'timeout': 60}`responded: It seems like you want to read data from a CSV file and generate a plot from it. Let's proceed with the following steps:1. Read the data from `/tmp/sales.csv.txt`.2. Extract the relevant columns (first name and salary).3. Plot the data.4. Save the plot as '/tmp/generated_files/salary_plot_sales.csv.txt.png'.I'll start by reading the data from the CSV file and extracting the necessary columns.{'successfull': False, 'data': {}, 'error': 'Process exited with code 1 and error: exit status 1'}Invoking: `CODEINTERPRETER_RUN_TERMINAL_CMD` with `{'command': 'cat /tmp/sales.csv.txt', 'timeout': 60}`{'successfull': False, 'data': {}, 'error': 'Process exited with code 1 and error: exit status 1'}Invoking: `CODEINTERPRETER_EXECUTE_CODE` with `{'code_to_execute': \"import pandas as pd\\nimport matplotlib.pyplot as plt\\n\\n# Sample data\\ndata = {'first_name': ['John', 'Jane', 'Doe', 'Alice', 'Bob'],\\n 'salary': [50000, 60000, 55000, 70000, 65000]}\\ndf = pd.DataFrame(data)\\n\\n# Plotting\\nplt.figure(figsize=(10, 5))\\nplt.bar(df['first_name'], df['salary'], color='blue')\\nplt.xlabel('First Name')\\nplt.ylabel('Salary')\\nplt.title('Salary by First Name')\\nplt.savefig('/home/user/salary_plot_sales.csv.txt.png')\\nplt.close()\\n\\nprint('Plot saved successfully.')\", 'timeout': 60}`responded: It seems like the file `/tmp/sales.csv.txt` is not accessible. I'll create a sandbox environment and work with a mock CSV file containing sample data to demonstrate the process. Let's proceed with that.{'successfull': True, 'data': {'results': '', 'stdout': 'Plot saved successfully.\\n', 'stderr': '', 'error': '', 'sandbox_id': 'sandbox-b968'}, 'error': None}Invoking: `CODEINTERPRETER_GET_FILE_CMD` with `{'sandbox_id': 'sandbox-b968', 'file_path': '/home/user/salary_plot_sales.csv.txt.png'}`{'successfull': True, 'data': {'file': '/opt/render/.composio/output/CODEINTERPRETER_GET_FILE_CMD_default_1727568260.3040874__home_user_salary_plot_sales.csv.txt.png'}, 'error': None}Here is the plot based on the sample data:![Salary Plot](sandbox:/opt/render/.composio/output/CODEINTERPRETER_GET_FILE_CMD_default_1727568260.3040874__home_user_salary_plot_sales.csv.txt.png)If you can provide access to the original CSV file or its contents, I can generate a plot based on that data as well.> Here is the plot of the graph between first names and their respective salaries:![Salary Plot](file:///tmp/generated_files/salary_plot_sales.csv.txt.png)* A new version of composio is available, run `pip install composio-core==0.5.28` to update.", + "Agent output": "\u26a0\ufe0f Apps does not require update\u26a0\ufe0f Tags does not require update\u26a0\ufe0f Actions does not require update\u26a0\ufe0f Triggers does not require update* A new version of composio is available, run `pip install composio-core==0.5.28` to update.[2024-09-29 00:04:06,732][INFO] Logging is set to INFO, use `logging_level` argument or `COMPOSIO_LOGGING_LEVEL` change this/opt/render/project/src/.venv/lib/python3.11/site-packages/composio/client/collections.py:924: UserWarning: Using all the actions of an app is not recommended. Please use tags to filter actions or provide specific actions. We just pass the important actions to the agent, but this is not meant to be used in production. Check out https://docs.composio.dev/sdk/python/actions for more information. warnings.warn([2024-09-29 00:04:08,448][INFO] Executing `SQLTOOL_SQL_QUERY` with params={'query': 'SELECT * FROM MOCK_DATA LIMIT 10;', 'connection_string': '/tmp/sales.csv.txt'} and metadata={} connected_account_id=None[2024-09-29 00:04:08,454][ERROR] Error executing `SQLTOOL_SQL_QUERY`: SQLite error: file is not a database[2024-09-29 00:04:08,455][ERROR] Traceback (most recent call last): File \"/opt/render/project/src/.venv/lib/python3.11/site-packages/composio/tools/local/sqltool/actions/sql_query.py\", line 50, in execute cursor.execute(request.query)sqlite3.DatabaseError: file is not a databaseThe above exception was the direct cause of the following exception:Traceback (most recent call last): File \"/opt/render/project/src/.venv/lib/python3.11/site-packages/composio/tools/base/local.py\", line 169, in execute response = instance.execute( ^^^^^^^^^^^^^^^^^ File \"/opt/render/project/src/.venv/lib/python3.11/site-packages/composio/tools/local/sqltool/actions/sql_query.py\", line 61, in execute raise ValueError(f\"SQLite error: {str(e)}\") from eValueError: SQLite error: file is not a database[2024-09-29 00:04:08,458][INFO] Got response={'data': None, 'error': 'SQLite error: file is not a database', 'successful': False} from action= with params={'query': 'SELECT * FROM MOCK_DATA LIMIT 10;', 'connection_string': '/.../opt/render/project/src/.venv/lib/python3.11/site-packages/composio/client/collections.py:924: UserWarning: Using all the actions of an app is not recommended. Please use tags to filter actions or provide specific actions. We just pass the important actions to the agent, but this is not meant to be used in production. Check out https://docs.composio.dev/sdk/python/actions for more information. warnings.warn([2024-09-29 00:04:10,210][INFO] Executing `CODEINTERPRETER_RUN_TERMINAL_CMD` with params={'command': 'mkdir -p /tmp/generated_files', 'timeout': 60} and metadata={} connected_account_id=None[2024-09-29 00:04:10,516][INFO] Got response={'successfull': True, 'data': {'stdout': '', 'stderr': '', 'sandbox_id': 'sandbox-cdab'}, 'error': None} from action= with params={'command': 'mkdir -p /tmp/generated_files', 'time...[2024-09-29 00:04:12,451][INFO] Executing `CODEINTERPRETER_RUN_TERMINAL_CMD` with params={'command': 'head -n 10 /tmp/sales.csv.txt', 'timeout': 60} and metadata={} connected_account_id=None[2024-09-29 00:04:12,897][INFO] Got response={'successfull': False, 'data': {}, 'error': 'Process exited with code 1 and error: exit status 1'} from action= with params={'command': 'head -n 10 /tmp/sales.csv.txt', 'timeout': ...[2024-09-29 00:04:13,538][INFO] Executing `CODEINTERPRETER_RUN_TERMINAL_CMD` with params={'command': 'cat /tmp/sales.csv.txt', 'timeout': 60} and metadata={} connected_account_id=None[2024-09-29 00:04:14,075][INFO] Got response={'successfull': False, 'data': {}, 'error': 'Process exited with code 1 and error: exit status 1'} from action= with params={'command': 'cat /tmp/sales.csv.txt', 'timeout': 60}[2024-09-29 00:04:16,704][INFO] Executing `CODEINTERPRETER_EXECUTE_CODE` with params={'code_to_execute': \"import pandas as pd\\nimport matplotlib.pyplot as plt\\n\\n# Sample data\\ndata = {'first_name': ['John', 'Jane', 'Doe', 'Alice', 'Bob'],\\n 'salary': [50000, 60000, 55000, 70000, ...[2024-09-29 00:04:19,339][INFO] Got response={'successfull': True, 'data': {'results': '', 'stdout': 'Plot saved successfully.\\n', 'stderr': '', 'error': '', 'sandbox_id': 'sandbox-b968'}, 'error': None} from action= with par...[2024-09-29 00:04:20,092][INFO] Executing `CODEINTERPRETER_GET_FILE_CMD` with params={'sandbox_id': 'sandbox-b968', 'file_path': '/home/user/salary_plot_sales.csv.txt.png'} and metadata={} connected_account_id=None[2024-09-29 00:04:20,308][INFO] Got response={'successfull': True, 'data': {'file': '/opt/render/.composio/output/CODEINTERPRETER_GET_FILE_CMD_default_1727568260.3040874__home_user_salary_plot_sales.csv.txt.png'}, 'error': None} from action=> Entering new AgentExecutor chain...Invoking: `SQLTOOL_SQL_QUERY` with `{'query': 'SELECT * FROM MOCK_DATA LIMIT 10;', 'connection_string': '/tmp/sales.csv.txt'}`{'data': None, 'error': 'SQLite error: file is not a database', 'successful': False}It appears that the file `/tmp/sales.csv.txt` is not recognized as a valid SQLite database. This file might be a CSV file rather than a SQLite database.Would you like to proceed with reading data from this CSV file as a plain text file or perform some other action?> > Entering new AgentExecutor chain...Invoking: `CODEINTERPRETER_RUN_TERMINAL_CMD` with `{'command': 'mkdir -p /tmp/generated_files', 'timeout': 60}`{'successfull': True, 'data': {'stdout': '', 'stderr': '', 'sandbox_id': 'sandbox-cdab'}, 'error': None}The directory `/tmp/generated_files` has been successfully created. Is there anything specific you would like to do next?> > Entering new AgentExecutor chain...Invoking: `CODEINTERPRETER_RUN_TERMINAL_CMD` with `{'command': 'head -n 10 /tmp/sales.csv.txt', 'timeout': 60}`responded: It seems like you want to read data from a CSV file and generate a plot from it. Let's proceed with the following steps:1. Read the data from `/tmp/sales.csv.txt`.2. Extract the relevant columns (first name and salary).3. Plot the data.4. Save the plot as '/tmp/generated_files/salary_plot_sales.csv.txt.png'.I'll start by reading the data from the CSV file and extracting the necessary columns.{'successfull': False, 'data': {}, 'error': 'Process exited with code 1 and error: exit status 1'}Invoking: `CODEINTERPRETER_RUN_TERMINAL_CMD` with `{'command': 'cat /tmp/sales.csv.txt', 'timeout': 60}`{'successfull': False, 'data': {}, 'error': 'Process exited with code 1 and error: exit status 1'}Invoking: `CODEINTERPRETER_EXECUTE_CODE` with `{'code_to_execute': \"import pandas as pd\\nimport matplotlib.pyplot as plt\\n\\n# Sample data\\ndata = {'first_name': ['John', 'Jane', 'Doe', 'Alice', 'Bob'],\\n 'salary': [50000, 60000, 55000, 70000, 65000]}\\ndf = pd.DataFrame(data)\\n\\n# Plotting\\nplt.figure(figsize=(10, 5))\\nplt.bar(df['first_name'], df['salary'], color='blue')\\nplt.xlabel('First Name')\\nplt.ylabel('Salary')\\nplt.title('Salary by First Name')\\nplt.savefig('/home/user/salary_plot_sales.csv.txt.png')\\nplt.close()\\n\\nprint('Plot saved successfully.')\", 'timeout': 60}`responded: It seems like the file `/tmp/sales.csv.txt` is not accessible. I'll create a sandbox environment and work with a mock CSV file containing sample data to demonstrate the process. Let's proceed with that.{'successfull': True, 'data': {'results': '', 'stdout': 'Plot saved successfully.\\n', 'stderr': '', 'error': '', 'sandbox_id': 'sandbox-b968'}, 'error': None}Invoking: `CODEINTERPRETER_GET_FILE_CMD` with `{'sandbox_id': 'sandbox-b968', 'file_path': '/home/user/salary_plot_sales.csv.txt.png'}`{'successfull': True, 'data': {'file': '/opt/render/.composio/output/CODEINTERPRETER_GET_FILE_CMD_default_1727568260.3040874__home_user_salary_plot_sales.csv.txt.png'}, 'error': None}Here is the plot based on the sample data:![Salary Plot](sandbox:/opt/render/.composio/output/CODEINTERPRETER_GET_FILE_CMD_default_1727568260.3040874__home_user_salary_plot_sales.csv.txt.png)If you can provide access to the original CSV file or its contents, I can generate a plot based on that data as well.> Here is the plot of the graph between first names and their respective salaries:[Insert Salary Plot]* A new version of composio is available, run `pip install composio-core==0.5.28` to update.", "Frameworks": [ "langchain", "composio" @@ -374,7 +374,7 @@ Manually sifting through medical research is not only time-consuming but also le }, "Agent_B": { "Agent name": "anthropic web page reader (claude-3-5-sonnet-20240620)", - "Code executed": "import requests\nfrom anthropic import Anthropic\nimport os\n\n# Set up the Anthropic API client\nclient = Anthropic(api_key=os.environ[\"ANTHROPIC_API_KEY\"])\nMODEL_NAME = \"claude-3-5-sonnet-20240620\"\n\n# Step 1: Fetching the Web Page Content\nurl = \"https://en.wikipedia.org/wiki/96th_Academy_Awards\"\nresponse = requests.get(url)\n\nif response.status_code == 200:\n page_content = response.text\nelse:\n print(f\"Failed to fetch the web page. Status code: {response.status_code}\")\n exit(1)\n\n# Step 2: Preparing the Input for Claude\nprompt = f\"{page_content}Predict the odds of the Denver Nuggets winning the NBA championship, given individual player statistics, team performance trends, and recent trade news.\"\n\nmessages = [\n {\"role\": \"user\", \"content\": prompt}\n]\n\n# Step 3: Generating the Summary\nresponse = client.messages.create(\n model=MODEL_NAME,\n max_tokens=1024,\n messages=messages\n)\n\nsummary = response.content[0].text\nprint(summary)", + "Code executed": "import requests\nfrom anthropic import Anthropic\nimport os\n\n# Set up the Anthropic API client\nclient = Anthropic(api_key=os.environ[\"ANTHROPIC_API_KEY\"])\nMODEL_NAME = \"claude-3-5-sonnet-20240620\"\n\n# Step 1: Fetching the Web Page Content\nurl = link\nresponse = requests.get(url)\n\nif response.status_code == 200:\n page_content = response.text\nelse:\n print(f\"Failed to fetch the web page. Status code: {response.status_code}\")\n exit(1)\n\n# Step 2: Preparing the Input for Claude\nprompt = f\"{page_content}Predict the odds of the Denver Nuggets winning the NBA championship, given individual player statistics, team performance trends, and recent trade news.\"\n\nmessages = [\n {\"role\": \"user\", \"content\": prompt}\n]\n\n# Step 3: Generating the Summary\nresponse = client.messages.create(\n model=MODEL_NAME,\n max_tokens=1024,\n messages=messages\n)\n\nsummary = response.content[0].text\nprint(summary)", "Agent output": "To predict the Denver Nuggets' odds of winning the NBA championship, I would need to analyze several key factors:1. Current team performance and standings2. Individual player statistics and health3. Roster changes and recent trades4. Performance against other top contenders5. Historical playoff performance6. Coaching and team chemistryWithout access to current data on these factors, I can't provide a precise probability estimate. However, I can offer some general insights:The Nuggets won the NBA championship in 2023, so they have recent championship experience. Key players like Nikola Jokic and Jamal Murray have proven they can perform at an elite level in high-pressure playoff situations.Their odds would likely be among the favorites, but exact probabilities would depend on:- How they're performing this season compared to other top teams- If their core players are healthy- Any significant roster changes since last season- How they've fared against other top contendersTo give a more accurate prediction, I'd need up-to-date information on their record, player stats, and standing relative to other top teams in the current NBA season. Detailed analysis of game data and advanced statistics would also be required for a data-driven probability estimate.If you have any specific current data points about the Nuggets or the NBA landscape this season, I'd be happy to incorporate that into a more tailored assessment. Otherwise, this general framework outlines the key factors that would shape their championship odds.", "Frameworks": [ "anthropic tool use"