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chatting for a few rounds, the chat history can become quite long, especially when we have code and execution results in it. This can cause the problem of exceeding the context window of the LLMs. To solve the problem, one way is to summarize the chat history a few rounds ago, and only keep the latest rounds of the chat history. Another way is to use a vector database to store the chat history entries, and only retrieve the last few rounds of the relevant part given the current user request. However, in TaskWeaver, code is also part of the chat history. It is not an option to skip some intermediate code and execution results in order to correctly generate the code for the current user request. Therefore, we choose the first way to solve the problem. The following figure shows the idea of chat history summarization where the chat history is divided into two parts: Rounds to compress: this part is summarized and only the summary is kept in the chat history. If the context_summary already exists, a new summary is generated based on the previous summary adding the rounds to be summarized. Rounds to retain: this part is kept in the chat history without summarization. Imagine that, at the beginning, the ConversationSummary is empty. Once the chat history reaches the rounds_to_compress (default 2) rounds plus rounds_to_retain (default 3) rounds, the ConversationSummary is generated based on the rounds_to_compress rounds and the rounds_to_retain rounds are kept in the chat history. After that, there will be only rounds_to_retain rounds in the chat history. The next time the chat history reaches the rounds_to_compress rounds plus rounds_to_retain rounds, the ConversationSummary is generated based on the rounds_to_compress rounds and the previous ConversationSummary. We use these two parameters to control the frequency of the chat history summarization. An example of the chat history summarization in the Code Generator is shown below: { \"ConversationSummary\": \"The user requested the generation of 100 random numbers, which was successfully executed. Then, the user asked to show the top 5 largest numbers from the generated random numbers. The assistant provided a code snippet to sort the generated random numbers in descending order and select the top 5 largest numbers, which was also successfully executed. After that, the user requested to plot the distribution of the 100 numbers, which was successfully executed. The user then asked to count the frequency of numbers in each bin of the histogram and identify the bin with the most numbers for the 0.1 bin width, which was also successfully executed.\", \"Variables\": [ { \"name\": \"random_numbers_100\", \"type\": \"numpy array\", \"description\": \"An array containing 100 random numbers generated using np.random.rand()\" }, { \"name\": \"top_5_largest\", \"type\": \"numpy array\", \"description\": \"An array containing the top 5 largest numbers from the generated random numbers\" } ] } The JSON object has two fields: ConversationSummary: the summary of the chat history. Variables: the variables in the chat history that could be used in the current user request. The chat history summary of the Planner has only the ConversationSummary field. The actual code generated in the summarized rounds is ignored and only the variables are kept in the summary so that the LLM can still refer the these variables in future code generation. One thing to note is that chat history summarization requires call the LLM which incurs additional latency and cost. The prompts for chat history summarization could be found for planner and code generator.","s":"Prompt Compression","u":"/TaskWeaver/docs/compression","h":"","p":10},{"i":13,"t":"As explained above, there are two parameters in controlling the chat history summarization: round_compressor.rounds_to_compress (default 2) and round_compressor.rounds_to_retain (default 3). To enable the chat history summarization, you need to set planner.prompt_compression and code_generator.prompt_compression to true.","s":"Configurations","u":"/TaskWeaver/docs/compression","h":"#configurations","p":10},{"i":15,"t":"There are two types of examples: (1) planning examples and (2) code interpreter examples. Planning examples are used to demonstrate how to use TaskWeaver to plan for a specific task. Code generation examples are used to demonstrate how to generate code or orchestrate plugins to perform a specific task.","s":"Customizing Examples","u":"/TaskWeaver/docs/customization/example/","h":"","p":14},{"i":17,"t":"A planning example tells LLMs how to plan for a specific query from the user; talk to the code interpreter; receive the execution result from the code interpreter; and summarize the execution result. Before constructing the planning example, we strongly encourage you to go through the planner prompt. The following is an example of a planning example which contains 4 posts. Each post contains a message, a sender, a receiver, and a list of attachments. The first post is sent from the user to the planner. The message is \"count the rows of /home/data.csv\", which is the same as the user query. The second post is sent from the planner to the code interpreter. The message is \"Please load the data file /home/data.csv and count the rows of the loaded data\". The attachment list contains 3 attachments: The first attachment is the initial plan, which is a markdown string. The second attachment is the plan, which is a markdown string. The third attachment is the current plan step, which is a markdown string. The third post is sent from the code interpreter to the planner. The message is \"Load the data file /home/data.csv successfully and there are 100 rows in the data file\". The fourth post is sent from the planner to the user. The message is \"The data file /home/data.csv is loaded and there are 100 rows in the data file\". The attachment list contains 3 attachments: The first attachment is the initial plan, which is the same as the second attachment of the second post. The second attachment is the plan, which is the same as the third attachment of the second post. The third attachment is the current plan step, which is a markdown string. enabled: True rounds: - user_query: count the rows of /home/data.csv state: created post_list: - message: count the rows of /home/data.csv send_from: User send_to: Planner attachment_list: - message: Please load the data file /home/data.csv and count the rows of the loaded data send_from: Planner send_to: CodeInterpreter attachment_list: - type: init_plan content: |- 1. load the data file 2. count the rows of the loaded data <sequentially depends on 1> 3. report the result to the user <interactively depends on 2> - type: plan content: |- 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data 2. report the result to the user - type: current_plan_step content: 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data - message: Load the data file /home/data.csv successfully and there are 100 rows in the data file send_from: CodeInterpreter send_to: Planner attachment_list: - message: The data file /home/data.csv is loaded and there are 100 rows in the data file send_from: Planner send_to: User attachment_list: - type: init_plan content: |- 1. load the data file 2. count the rows of the loaded data <sequentially depends on 1> 3. report the result to the user <interactively depends on 2> - type: plan content: |- 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data 2. report the result to the user - type: current_plan_step content: 2. report the result to the user","s":"Planning Examples","u":"/TaskWeaver/docs/customization/example/","h":"#planning-examples","p":14},{"i":19,"t":"A code interpreter example tells LLMs how to generate code or orchestrate plugins to perform a specific task. The task is from the planner. Before constructing the code interpreter example, we strongly encourage you to read the code generator prompt. The following is an example of a code interpreter example which contains 2 posts. Each post contains a message, a sender, a receiver, and a list of attachments. The first post is sent from the planner to the code interpreter. The message is \"Please read file /abc/def.txt\". The second post is sent from the code interpreter to the planner. The message is \"read file /abc/def.txt\". The attachment list contains 6 attachments: The first attachment is the thought of the code interpreter, which is a markdown string. The second attachment is the generated code, which is in python. The third attachment is the verification status, which is CORRECT, INCORRECT, or NONE. The fourth attachment is the verification error message, which is a markdown string. The fifth attachment is the execution status, which is SUCCESS, FAILURE, or NONE. The sixth attachment is the execution result, which is a markdown string. enabled: True rounds: - user_query: read file /abc/def.txt state: finished post_list: - message: read file /abc/def.txt send_from: Planner send_to: CodeInterpreter attachment_list: [] - message: I'm sorry, I cannot find the file /abc/def.txt. An FileNotFoundException has been raised. send_from: CodeInterpreter send_to: Planner attachment_list: - type: thought content: \"{ROLE_NAME} will generate a code snippet to read the file /abc/def.txt and present the content to the user.\" - type: python content: |- file_path = \"/abc/def.txt\" with open(file_path, \"r\") as file: file_contents = file.read() print(file_contents) - type: verification content: CORRECT - type: code_error content: No code error. - type: execution_status content: FAILURE - type: execution_result content: FileNotFoundException, the file /abc/def.txt does not exist. In this example, verification is about whether the generated code is correct or not. We implemented a module to verify the generated code. If the code is syntactically incorrect, or the code violates the constraints, the verification status will be INCORRECT and some error messages will be returned. A verification of NONE means that the code has not been verified, which means verification has been turned off. In this example, execution_status is about whether the generated code can be executed successfully or not. If the execution is successful, the execution status will be SUCCESS and the execution result will be returned. Otherwise, the execution status will be FAILURE and some error messages will be returned. A execution_status of NONE means that the code has not been executed.","s":"Code Interpreter Examples","u":"/TaskWeaver/docs/customization/example/","h":"#code-interpreter-examples","p":14},{"i":21,"t":"The configuration file is located at project/taskweaver_config.json. You can edit this file to configure TaskWeaver. The configuration file is in JSON format. So for boolean values, use true or false instead of True or False. For null values, use null instead of None or \"null\". All other values should be strings in double quotes. The following table lists the parameters in the configuration file: Parameter Description Default Value llm.model The model name used by the language model. gpt-4 llm.backup_model The model name used for self-correction purposes. null llm.api_base The base URL of the OpenAI API. https://api.openai.com/v1 llm.api_key The API key of the OpenAI API. null llm.api_type The type of the OpenAI API, could be openai or azure. openai llm.api_version The version of the OpenAI API. 2023-07-01-preview llm.response_format The response format of the OpenAI API, could be json_object, text or null. json_object code_interpreter.code_verification_on Whether to enable code verification. false code_interpreter.plugin_only Whether to turn on the plugin only mode. false code_interpreter.allowed_modules The list of allowed modules to import in code generation. \"pandas\", \"matplotlib\", \"numpy\", \"sklearn\", \"scipy\", \"seaborn\", \"datetime\", \"typing\" logging.log_file The name of the log file. taskweaver.log logging.log_folder The folder to store the log file. logs plugin.base_path The folder to store plugins. ${AppBaseDir}/plugins planner.example_base_path The folder to store planner examples. ${AppBaseDir}/planner_examples planner.prompt_compression Whether to compress the chat history for planner. false planner.skip_planning Whether to skip LLM planning process and enable the default plan false code_generator.example_base_path The folder to store code interpreter examples. ${AppBaseDir}/codeinterpreter_examples code_generator.prompt_compression Whether to compress the chat history for code interpreter. false code_generator.enable_auto_plugin_selection Whether to enable auto plugin selection. false code_generator.auto_plugin_selection_topk The number of auto selected plugins in each round. 3 session.max_internal_chat_round_num The maximum number of internal chat rounds between Planner and Code Interpreter. 10 session.code_interpreter_only Allow users to directly communicate with the Code Interpreter. false round_compressor.rounds_to_compress The number of rounds to compress. 2 round_compressor.rounds_to_retain The number of rounds to retain. 3 💡 ${AppBaseDir} is the project directory. 💡 Up to 11/30/2023, the json_object and text options of llm.response_format is only supported by the OpenAI models later than 1106. If you are using an older version of OpenAI model, you need to set the llm.response_format to null. 💡 Read this for more information for planner.prompt_compression and code_generator.prompt_compression.","s":"Configuration File","u":"/TaskWeaver/docs/configurations","h":"","p":20},{"i":23,"t":"In TaskWeaver, we support various embedding models to generate embeddings for auto plugin selection.","s":"Embedding","u":"/TaskWeaver/docs/customization/plugin/embedding","h":"","p":22},{"i":25,"t":"llm.embedding_api_type: The type of the embedding API. We support the following types: openai qwen ollama sentence_transformers llm.embedding_model: The embedding model name. The model name should be aligned with llm.embedding_api_type. We only list some embedding models we have tested below: openai text-embedding-ada-002 qwen text-embedding-v1 ollama llama2 sentence_transformers all-mpnet-base-v2 multi-qa-mpnet-base-dot-v1 all-distilroberta-v1 all-MiniLM-L12-v2 multi-qa-MiniLM-L6-cos-v1 You also can use other embedding models supported by the above embedding APIs.","s":"Embedding Configration","u":"/TaskWeaver/docs/customization/plugin/embedding","h":"#embedding-configration","p":22},{"i":27,"t":"In TaskWeaver, we provide an auto plugin selection mechanism to dynamically select the best plugin for each user request. It targets to solve the following two problems: An excessive number of plugins may cause confusion for LLM, leading to inaccuracies in generating the correct code. A large number of plugins could lead to increased token usage (potentially exceeding the token limit of LLM) and extended response time.","s":"Auto Plugin Selection","u":"/TaskWeaver/docs/customization/plugin/plugin_selection","h":"","p":26},{"i":29,"t":"Below is the overview workflow of the auto plugin selection mechanism. NOTE: the automatic plugin selection mechanism is only activated during the code generation process in the Code Interpreter and does not affect the planning process of the Planner. At the start of TaskWeaver initialization, the automatic plugin selector is activated to generate embedding vectors for all plugins, including their names and descriptions. The embedding vectors are created using the specified embedding model configured in the taskweaver_config.json file. For more information, please refer to the embedding documentation. When the Planner sends a request to the Code Interpreter, the auto plugin selection mechanism will be triggered. It will first generate an embedding vector for the request using the same embedding model. Then, it will calculate the cosine similarity between the request embedding vector and the embedding vectors of all plugins. It will select the top-k plugins with the highest cosine similarity scores and load them into the code_generator prompt. Upon completing the code generation, the code_generator employs one or more plugins to produce the desired code. We have established a plugin pool to store the plugins involved in the code generation process while filtering out any unused ones. During the subsequent automatic plugin selection phase, newly chosen plugins are appended to the existing ones.","s":"Auto Plugin Selection Overview","u":"/TaskWeaver/docs/customization/plugin/plugin_selection","h":"#auto-plugin-selection-overview","p":26},{"i":31,"t":"code_generator.enable_auto_plugin_selection: Whether to enable auto plugin selection. The default value is false. code_generator.auto_plugin_selection_topk: The number of auto selected plugins in each round. The default value is 3.","s":"Auto Plugin Selection Configuration","u":"/TaskWeaver/docs/customization/plugin/plugin_selection","h":"#auto-plugin-selection-configuration","p":26},{"i":33,"t":"We show the auto plugin selection mechanism in the following example. First, we start TaskWeaver with the auto plugin selection mechanism enabled. ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: Then we can check the log file task_weaver.log in the logs folder to see the auto plugin selector is initialized successfully because the Plugin embeddings generated message is printed. 2023-12-18 14:23:44,197 - INFO - Planner initialized successfully 2023-12-18 14:24:10,488 - INFO - Plugin embeddings generated 2023-12-18 14:24:10,490 - INFO - CodeInterpreter initialized successfully. 2023-12-18 14:24:10,490 - INFO - Session 20231218-062343-c18494b1 is initialized We ask TaskWeaver to \"search Xbox price for me\". The Planner instructs the Code Interpreter to search Xbox price. TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: search xbox price for me >>> [INIT_PLAN] 1. search xbox price 2. report the result to the user <interactively depends on 1> >>> [PLAN] 1. instruct CodeInterpreter to search xbox price 2. report the result to the user >>> [CURRENT_PLAN_STEP] 1. instruct CodeInterpreter to search xbox price >>> [SEND_TO] CodeInterpreter >>> [MESSAGE] Please search xbox price >>> [PLANNER->CODEINTERPRETER] Please search xbox price Back to the Code Interpreter, the auto plugin selection mechanism is triggered. We can check the log file task_weaver.log again to see the auto plugin selector selected the top-3 plugins with the highest cosine similarity scores. 023-12-18 14:24:34,513 - INFO - Planner talk to CodeInterpreter: Please search xbox price using klarna_search plugin 2023-12-18 14:24:34,669 - INFO - Selected plugins: ['klarna_search', 'sql_pull_data', 'paper_summary'] 2023-12-18 14:24:34,669 - INFO - Selected plugin pool: ['klarna_search', 'sql_pull_data', 'paper_summary'] Then the Code Interpreter will generate the code using the selected plugins. >>> [THOUGHT] ProgramApe will call the klarna_search plugin function to search for Xbox prices. >>> [PYTHON] search_results, description = klarna_search(query=\"xbox\") search_results, description >>> [VERIFICATION] NONE >>> [STATUS] SUCCESS >>> [RESULT] The execution of the generated python code above has succeeded The result of above Python code after execution is: ( name price url attributes 0 Microsoft Xbox Series X - Black Edition $399.00 https://www.klarna.com/us/shopping/pl/cl52/495... [Release Year:2020, Included Accessories:1 gam... 1 Microsoft Xbox Series S 1TB - Black $349.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Included Accessories:1 gamepad, Media Type:DV... .. ... ... ... ... 3 Xbox Series S – Starter Bundle $239.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Platform:Xbox One] 4 Microsoft Xbox Series X 1TB Console - Diablo I... $385.58 https://www.klarna.com/us/shopping/pl/cl52/320... [Release Year:2023, Included Accessories:1 gam... [5 rows x 4 columns], 'The response is a dataframe with the following columns: name, price, url, attributes. The attributes column is a list of tags. The price is in the format of $xx.xx.') >>> [CODEINTERPRETER->PLANNER] The following python code has been executed: ```python search_results, description = klarna_search(query=\"xbox\") search_results, description ``` The execution of the generated python code above has succeeded The result of above Python code after execution is: ( name price url attributes 0 Microsoft Xbox Series X - Black Edition $399.00 https://www.klarna.com/us/shopping/pl/cl52/495... [Release Year:2020, Included Accessories:1 gam... 1 Microsoft Xbox Series S 1TB - Black $349.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Included Accessories:1 gamepad, Media Type:DV... .. ... ... ... ... 3 Xbox Series S – Starter Bundle $239.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Platform:Xbox One] 4 Microsoft Xbox Series X 1TB Console - Diablo I... $385.58 https://www.klarna.com/us/shopping/pl/cl52/320... [Release Year:2023, Included Accessories:1 gam... [5 rows x 4 columns], 'The response is a dataframe with the following columns: name, price, url, attributes. The attributes column is a list of tags. The price is in the format of $xx.xx.') Finally, the Planner will report the result to the user. >>> [INIT_PLAN] 1. search xbox price using klarna_search plugin 2. report the result to the user <interactively depends on 1> >>> [PLAN] 1. instruct CodeInterpreter to search xbox price using klarna_search plugin 2. report the result to the user >>> [CURRENT_PLAN_STEP] 2. report the result to the user >>> [SEND_TO] User >>> [MESSAGE] Here are some Xbox prices I found: 1. Microsoft Xbox Series X - Black Edition: $399.00 2. Microsoft Xbox Series S 1TB - Black: $349.00 3. Xbox Series S – Starter Bundle: $239.00 4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58 Please note that these prices are subject to change and availability. >>> [PLANNER->USER] Here are some Xbox prices I found: 1. Microsoft Xbox Series X - Black Edition: $399.00 2. Microsoft Xbox Series S 1TB - Black: $349.00 3. Xbox Series S – Starter Bundle: $239.00 4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58 Please note that these prices are subject to change and availability. TaskWeaver: Here are some Xbox prices I found: 1. Microsoft Xbox Series X - Black Edition: $399.00 2. Microsoft Xbox Series S 1TB - Black: $349.00 3. Xbox Series S – Starter Bundle: $239.00 4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58 Please note that these prices are subject to change and availability.","s":"Auto Plugin Selection Example","u":"/TaskWeaver/docs/customization/plugin/plugin_selection","h":"#auto-plugin-selection-example","p":26},{"i":37,"t":"Using LLMs from OpenAI","s":"📄️ OpenAI","u":"/TaskWeaver/docs/llms/","h":"","p":35},{"i":39,"t":"Using LLMs from OpenAI/AOAI","s":"📄️ Azure OpenAI","u":"/TaskWeaver/docs/llms/","h":"","p":35},{"i":41,"t":"Using LLMs from LiteLLM","s":"📄️ LiteLLM","u":"/TaskWeaver/docs/llms/","h":"","p":35},{"i":43,"t":"1. Go to Ollama and follow the instructions to serve a LLM model on your local environment.","s":"📄️ Ollama","u":"/TaskWeaver/docs/llms/","h":"","p":35},{"i":45,"t":"1. Create an account on Google AI and get your API key.","s":"📄️ Gemini","u":"/TaskWeaver/docs/llms/","h":"","p":35},{"i":47,"t":"1. QWen (Tongyi Qianwen) is a LLM developed by Alibaba. Go to QWen and register an account and get the API key. More details can be found here (in Chinese).","s":"📄️ QWen","u":"/TaskWeaver/docs/llms/","h":"","p":35},{"i":49,"t":"Create an account on Azure OpenAI and get your API key. Add the following to your taskweaver_config.json file: { \"llm.api_base\":\"YOUR_AOAI_ENDPOINT\", \"llm.api_key\":\"YOUR_API_KEY\", \"llm.api_type\":\"azure\", \"llm.auth_mode\":\"api-key\", \"llm.model\":\"gpt-4-1106-preview\", \"llm.response_format\": \"json_object\" } tip llm.model is the model name you want to use. You can find the list of models here. info For gpt-4-1106-preview and gpt-3.5-turbo-1106, llm.response_format can be set to json_object. However, for the earlier models, which do not support JSON response explicitly, llm.response_format should be set to null. Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"Azure OpenAI","u":"/TaskWeaver/docs/llms/aoai","h":"","p":48},{"i":51,"t":"Create an account on Google AI and get your API key. Add the following content to your taskweaver_config.json file: { \"llm.api_type\": \"google_genai\", \"llm.google_genai.api_key\": \"YOUR_API_KEY\", \"llm.google_genai.model\": \"gemini-pro\" } Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"Gemini","u":"/TaskWeaver/docs/llms/gemini","h":"","p":50},{"i":53,"t":"info LiteLLM provides a unified interface to call 100+ LLMs using the same Input/Output format, including OpenAI, Huggingface, Anthropic, vLLM, Cohere, and even custom LLM API server. Taking LiteLLM as the bridge, many LLMs can be onboarded to TaskWeaver. Here we use the OpenAI Proxy Server provided by LiteLLM to make configuration. Install LiteLLM Proxy and configure the LLM server by following the instruction here. In general, there are a few steps: Install the package pip install litellm[proxy] Setup the API key and other necessary environment variables which vary by LLM. Taking Cohere as an example, it is required to setup export COHERE_API_KEY=my-api-key. Run LiteLLM proxy server by litellm --model MODEL_NAME --drop_params, for example, in Cohere, the model name can be command-nightly. The drop-params argument is used to ensure the API compatibility. Then, a server will be automatically started on http://0.0.0.0:8000. tip The full list of supported models by LiteLLM can be found in the page. Add the following content to your taskweaver_config.json file: { \"llm.api_base\": \"http://0.0.0.0:8000\", \"llm.api_key\": \"anything\", \"llm.model\": \"gpt-3.5-turbo\" } info llm.api_key and llm.model are mainly used as placeholder for API call, whose actual values are not used. If the configuration does not work, please refer to LiteLLM documents to locally test whether you can send requests to the LLM. Open a new terminal, start TaskWeaver and chat. You can refer to the Quick Start for more details.","s":"LiteLLM","u":"/TaskWeaver/docs/llms/liteLLM","h":"","p":52},{"i":55,"t":"Go to Ollama and follow the instructions to serve a LLM model on your local environment. We provide a short example to show how to configure the ollama in the following, which might change if ollama makes updates. install ollama and serve LLMs in local ## Install ollama on Linux & WSL2 curl https://ollama.ai/install.sh | sh ## Run the serving ollama serve ## Open another terminal and run the model ollama run llama2 tip We recommend deploying the LLM with a parameter scale exceeding 13B for enhanced performance (such as Llama 2 13B). info When serving LLMs via Ollama, it will by default start a server at http://localhost:11434, which will later be used as the API base in taskweaver_config.json. Add following configuration to taskweaver_config.json: { \"llm.api_base\": \"http://localhost:11434\", \"llm.api_key\": \"ARBITRARY_STRING\", \"llm.api_type\": \"ollama\", \"llm.model\": \"llama2:13b\" } NOTE: llm.api_base is the URL started in the Ollama LLM server and llm.model is the model name of Ollama LLM. Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"Ollama","u":"/TaskWeaver/docs/llms/ollama","h":"","p":54},{"i":57,"t":"Create an account on OpenAI and get your API key. Add the following to your taskweaver_config.json file: { \"llm.api_type\":\"openai\", \"llm.api_base\": \"https://api.openai.com/v1\", \"llm.api_key\": \"YOUR_API_KEY\", \"llm.model\": \"gpt-4-1106-preview\", \"llm.response_format\": \"json_object\" } tip llm.model is the model name you want to use. You can find the list of models here. info For gpt-4-1106-preview and gpt-3.5-turbo-1106, llm.response_format can be set to json_object. However, for the earlier models which do not support JSON response explicitly, llm.response_format should be set to null. Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"OpenAI","u":"/TaskWeaver/docs/llms/openai","h":"","p":56},{"i":59,"t":"QWen (Tongyi Qianwen) is a LLM developed by Alibaba. Go to QWen and register an account and get the API key. More details can be found here (in Chinese). Install the required packages dashscope. pip install dashscope Add the following configuration to taskweaver_config.json: { \"llm.api_type\": \"qwen\", \"llm.model\": \"qwen-max\", \"llm.api_key\": \"YOUR_API_KEY\" } NOTE: llm.model is the model name of QWen LLM API. You can find the model name in the QWen LLM model list. Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"QWen","u":"/TaskWeaver/docs/llms/qwen","h":"","p":58},{"i":61,"t":"TaskWeaver is a code-first agent framework for seamlessly planning and executing data analytics tasks. This innovative framework interprets user requests through coded snippets and efficiently coordinates a variety of plugins in the form of functions to execute data analytics tasks Highlighted Features Rich data structure - TaskWeaver allows you to work with rich data structures in Python, such as DataFrames, instead of having to work with text strings. Customized algorithms - TaskWeaver allows you to encapsulate your own algorithms into plugins (in the form of Python functions), and orchestrate them to achieve complex tasks. Incorporating domain-specific knowledge - TaskWeaver is designed to be easily incorporating domain-specific knowledge, such as the knowledge of execution flow, to improve the reliability of the AI copilot. Stateful conversation - TaskWeaver is designed to support stateful conversation. It can remember the context of the conversation and leverage it to improve the user experience. Code verification - TaskWeaver is designed to verify the generated code before execution. It can detect potential issues in the generated code and provide suggestions to fix them. Easy to use - TaskWeaver is designed to be easy to use. We provide a set of sample plugins and a tutorial to help you get started. Users can easily create their own plugins based on the sample plugins. TaskWeaver offers an open-box experience, allowing users to run a service immediately after installation. Easy to debug - TaskWeaver is designed to be easy to debug. We have detailed logs to help you understand what is going on during calling the LLM, the code generation, and execution process. Security consideration - TaskWeaver supports a basic session management to keep different users' data separate. The code execution is separated into different processes in order not to interfere with each other. Easy extension - TaskWeaver is designed to be easily extended to accomplish more complex tasks. You can create multiple AI copilots to act in different roles, and orchestrate them to achieve complex tasks.","s":"Overview","u":"/TaskWeaver/docs/overview","h":"","p":60},{"i":63,"t":"In TaskWeaver, the Planner is responsible for generating a plan to accomplish the user's task. The plan is a sequence of steps, where each step will be executed by the Code Interpreter. Taken the response from the Code Interpreter or new requests from the user as input, the Planner will update the plan and move on to the next step.","s":"Planner","u":"/TaskWeaver/docs/planner","h":"","p":62},{"i":65,"t":"planner.example_base_path: The folder to store planner examples. The default value is ${AppBaseDir}/planner_examples. If you want to create your own planner examples, you can add them to this folder. More details about example can referred to example. planner.prompt_compression: At times, lengthy conversations with the Planner may exceed the input limitations of the LLM model. To address this issue, we can compress the chat history and send it to the LLM model. The default value for this setting is false. More details about prompt_compression can be referred to prompt_compression. planner.skip_planning: In certain scenarios, there may be no need to use the Planner to generate complex plans for simple tasks. For instance, if a user wants to count the rows in a data file, the request can be sent directly to the Code Interpreter. When the majority of user requests involve simple tasks, enabling this option will create a dummy plan that is sent alongside the user request to the Code Interpreter directly, without LLM generation process. The fixed dummy plan is shown in dummy_plan.json. Here is an chat example: ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: generate 10 random numbers >>> [MESSAGE]eparing... <=�=> Please process this request: generate 10 random numbers >>> [SEND_TO] CodeInterpreter >>> [INIT_PLAN] 1. ask Code Interpreter to handle the request; 2. report the result to user <interactively depends on 1> >>> [PLAN] 1. ask Code Interpreter to handle user\\'s request; 2. report the result to user >>> [CURRENT_PLAN_STEP] 1. ask Code Interpreter to handle the request >>> [PLANNER->CODEINTERPRETER] Please process this request: generate 10 random numbers >>> [PYTHON]Starting... random_numbers = np.random.rand(10) random_numbers >>> [VERIFICATION] NONE >>> [STATUS]Starting... SUCCESS >>> [RESULT] The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.65294462, 0.26946084, 0.06244879, 0.78520418, 0.87067657, 0.24208003, 0.60249788, 0.30921069, 0.83811521, 0.05135891]) >>> [CODEINTERPRETER->PLANNER] The following python code has been executed: ```python random_numbers = np.random.rand(10) random_numbers ``` The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.65294462, 0.26946084, 0.06244879, 0.78520418, 0.87067657, 0.24208003, 0.60249788, 0.30921069, 0.83811521, 0.05135891]) >>> [INIT_PLAN]ting... <=�=> 1. ask Code Interpreter to handle the request; 2. report the result to user <interactively depends on 1> >>> [PLAN] 1. ask Code Interpreter to handle user's request; 2. report the result to user >>> [CURRENT_PLAN_STEP] 2. report the result to user >>> [SEND_TO] User >>> [MESSAGE] The random numbers are as follows: [0.65294462, 0.26946084, 0.06244879, 0.78520418, 0.87067657, 0.24208003, 0.60249788, 0.30921069, 0.83811521, 0.05135891] >>> [PLANNER->USER] The random numbers are as follows: [0.65294462, 0.26946084, 0.06244879, 0.78520418, 0.87067657, 0.24208003, 0.60249788, 0.30921069, 0.83811521, 0.05135891] TaskWeaver: The random numbers are as follows: [0.65294462, 0.26946084, 0.06244879, 0.78520418, 0.87067657, 0.24208003, 0.60249788, 0.30921069, 0.83811521, 0.05135891]","s":"Planner Configuration","u":"/TaskWeaver/docs/planner","h":"#planner-configuration","p":62},{"i":67,"t":"Plugins are the units that could be orchestrated by TaskWeaver. One could view the plugins as tools that the LLM can utilize to accomplish certain tasks. In TaskWeaver, each plugin is represented as a Python function that can be called within a code snippet. The orchestration is essentially the process of generating Python code snippets consisting of a certain number of plugins. One concrete example would be pulling data from database and apply anomaly detection. The generated code (simplified) looks like follows: df, data_description = sql_pull_data(query=\"pull data from time_series table\") anomaly_df, anomaly_description = anomaly_detection(df, time_col_name=\"ts\", value_col_name=\"val\")","s":"Plugin Introduction","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"","p":66},{"i":69,"t":"A plugin has two files: Plugin Implementation: a Python file that defines the plugin Plugin Schema: a file in yaml that defines the schema of the plugin","s":"Plugin Structure","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-structure","p":66},{"i":71,"t":"The plugin function needs to be implemented in Python. To be coordinated with the orchestration by TaskWeaver, a plugin python file consists of two parts: Plugin function implementation code TaskWeaver plugin decorator Here we exhibit an example of the anomaly detection plugin as the following code: import pandas as pd from pandas.api.types import is_numeric_dtype from taskWeaver.plugin import Plugin, register_plugin @register_plugin class AnomalyDetectionPlugin(Plugin): def __call__(self, df: pd.DataFrame, time_col_name: str, value_col_name: str): \"\"\" anomaly_detection function identifies anomalies from an input dataframe of time series. It will add a new column \"Is_Anomaly\", where each entry will be marked with \"True\" if the value is an anomaly or \"False\" otherwise. :param df: the input data, must be a dataframe :param time_col_name: name of the column that contains the datetime :param value_col_name: name of the column that contains the numeric values. :return df: a new df that adds an additional \"Is_Anomaly\" column based on the input df. :return description: the description about the anomaly detection results. \"\"\" try: df[time_col_name] = pd.to_datetime(df[time_col_name]) except Exception: print(\"Time column is not datetime\") return if not is_numeric_dtype(df[value_col_name]): try: df[value_col_name] = df[value_col_name].astype(float) except ValueError: print(\"Value column is not numeric\") return mean, std = df[value_col_name].mean(), df[value_col_name].std() cutoff = std * 3 lower, upper = mean - cutoff, mean + cutoff df[\"Is_Anomaly\"] = df[value_col_name].apply(lambda x: x < lower or x > upper) anomaly_count = df[\"Is_Anomaly\"].sum() description = \"There are {} anomalies in the time series data\".format(anomaly_count) self.ctx.add_artifact( name=\"anomaly_detection_results\", # a brief description of the artifact file_name=\"anomaly_detection_results.csv\", # artifact file name type=\"df\", # artifact data type, support chart/df/file/txt/svg val=df, # variable to be dumped ) return df, description You need to go through the following steps to implement your own plugin. import the TaskWeaver plugin decorator from taskWeaver.plugin import Plugin, register_plugin create your plugin class inherited from Plugin parent class (e.g., AnomalyDetectionPlugin(Plugin)), which is decorated by @register_plugin implement your plugin function in __call__ method of the plugin class. Most importantly, it is mandatory to include descriptions of your execution results in the return values of your plugin function. These descriptions can be utilized by the LLM to effectively summarize your execution results. 💡A key difference in a plugin implementation and a normal python function is that it always return a description of the result in natural language. As LLMs only understand natural language, it is important to let the model understand what the execution result is. In the example implementation above, the description says how many anomalies are detected. Behind the scene, only the description will be passed to the LLM model. In contrast, the execution result (e.g., df in the above example) is not handled by the LLM.","s":"Plugin Implementation","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-implementation","p":66},{"i":73,"t":"If the functionality of your plugin depends on additional libraries or packages, it is essential to ensure that they are installed before proceeding. If you wish to persist intermediate results, such as data, figures, or prompts, in your plugin implementation, TaskWeaver provides an add_artifact API that allows you to store these results in the workspace. In the example we provide, if you have performed anomaly detection and obtained results in the form of a CSV file, you can utilize the add_artifact API to save this file as an artifact. The artifacts are stored in the project/workspace/session_id/cwd folder in the project directory. self.ctx.add_artifact( name=\"anomaly_detection_results\", # a brief description of the artifact file_name=\"anomaly_detection_results.csv\", # artifact file name type=\"df\", # artifact data type, support chart/df/file/txt/svg val=df, # variable to be dumped )","s":"Important Notes","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#important-notes","p":66},{"i":75,"t":"The plugin schema is composed of several parts: name: The main function name of the Python code. enabled: determine whether the plugin is enabled for selection during conversations. The default value is true. descriptions: A brief description that introduces the plugin function. parameters: This section lists all the input parameter information. It includes the parameter's name, type, whether it is required or optional, and a description providing more details about the parameter. returns: This section lists all the return value information. It includes the return value's name, type, and description that provides information about the value that is returned by the function. Note: The addition of any extra fields would result in a validation failure within the plugin schema. The plugin schema is required to be written in YAML format. Here is the plugin schema example of the above anomaly detection plugin: name: anomaly_detection enabled: true required: false description: >- anomaly_detection function identifies anomalies from an input DataFrame of time series. It will add a new column \"Is_Anomaly\", where each entry will be marked with \"True\" if the value is an anomaly or \"False\" otherwise. parameters: - name: df type: DataFrame required: true description: >- the input data from which we can identify the anomalies with the 3-sigma algorithm. - name: time_col_name type: str required: true description: name of the column that contains the datetime - name: value_col_name type: str required: true description: name of the column that contains the numeric values. returns: - name: df type: DataFrame description: >- This DataFrame extends the input DataFrame with a newly-added column \"Is_Anomaly\" containing the anomaly detection result. - name: description type: str description: This is a string describing the anomaly detection results. Besides, we also set two optional fields as below: code: In cases where multiple plugins map to the same Python code (i.e., the plugin name is different from the code name), it is essential to specify the code name (code file) in the plugin schema to ensure clarity and accuracy. configurations: When using common code that requires some configuration parameter modifications for different plugins, it is important to specify these configuration parameters in the plugin schema.","s":"Plugin Schema","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-schema","p":66},{"i":78,"t":"You can install TaskWeaver by running the following command: # [optional] create a conda environment to isolate the dependencies # conda create -n taskweaver python=3.10 # conda activate taskweaver # clone the repository git clone https://github.com/microsoft/TaskWeaver.git cd TaskWeaver # install the requirements pip install -r requirements.txt","s":"Installation","u":"/TaskWeaver/docs/quickstart","h":"#installation","p":76},{"i":80,"t":"TaskWeaver runs as a process, you need to create a project directory to store plugins and configuration files. We provided a sample project directory in the project folder. You can copy the project folder to your workspace. A project directory typically contains the following files and folders: 📦project ┣ 📜taskweaver_config.json # the configuration file for TaskWeaver ┣ 📂plugins # the folder to store plugins ┣ 📂planner_examples # the folder to store planner examples ┣ 📂codeinterpreter_examples # the folder to store code interpreter examples ┣ 📂sample_data # the folder to store sample data used for evaluations ┣ 📂logs # the folder to store logs, will be generated after program starts ┗ 📂workspace # the directory stores session data， will be generated after program starts ┗ 📂 session_id ┣ 📂ces # the folder used by the code execution service ┗ 📂cwd # the current working directory to run the generated code","s":"Project Directory","u":"/TaskWeaver/docs/quickstart","h":"#project-directory","p":76},{"i":82,"t":"Before running TaskWeaver, you need to provide your OpenAI API key and other necessary information. You can do this by editing the taskweaver_config.json file. If you are using Azure OpenAI, you need to set the following parameters in the taskweaver_config.json file:","s":"OpenAI Configuration","u":"/TaskWeaver/docs/quickstart","h":"#openai-configuration","p":76},{"i":84,"t":"{ \"llm.api_base\": \"https://xxx.openai.azure.com/\", \"llm.api_key\": \"your_api_key\", \"llm.api_type\": \"azure\", \"llm.api_version\": \"the api version\", \"llm.model\": \"the model name, e.g., gpt-4\" }","s":"Azure OpenAI","u":"/TaskWeaver/docs/quickstart","h":"#azure-openai","p":76},{"i":86,"t":"{ \"llm.api_key\": \"the api key\", \"llm.model\": \"the model name, e.g., gpt-4\" } 💡 Only the latest OpenAI API supports the json_object response format. If you are using an older version of OpenAI API, you need to set the llm.response_format to null. More configuration options can be found in the configuration documentation.","s":"OpenAI","u":"/TaskWeaver/docs/quickstart","h":"#openai","p":76},{"i":88,"t":"# assume you are in the taskweaver folder # -p is the path to the project directory python -m taskweaver -p ./project/ This will start the TaskWeaver process and you can interact with it through the command line interface. If everything goes well, you will see the following prompt: ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: ___","s":"Start TaskWeaver","u":"/TaskWeaver/docs/quickstart","h":"#start-taskweaver","p":76},{"i":92,"t":"make sure you completed the taskweaver quickstart","s":"setup","u":"/TaskWeaver/docs/run_pytest","h":"#setup","p":89},{"i":94,"t":"execute ./scripts/run_pytest.sh from the project root","s":"run tests","u":"/TaskWeaver/docs/run_pytest","h":"#run-tests","p":89},{"i":96,"t":"the script temporarily sets PYTHONPATH and runs pytest no permanent environment changes pass additional pytest arguments as needed done! 🚀","s":"notes","u":"/TaskWeaver/docs/run_pytest","h":"#notes","p":89},{"i":98,"t":"session is the entrance of TaskWeaver. It is responsible for the communication between the user and TaskWeaver. You can refer to taskweaver_as_a_lib to see how to setup a TaskWeaver session and start chatting with TaskWeaver.","s":"Session","u":"/TaskWeaver/docs/session","h":"","p":97},{"i":100,"t":"max_internal_chat_round_num: the maximum number of internal chat rounds between Planner and Code Interpreter. If the number of internal chat rounds exceeds this number, the session will be terminated. The default value is 10. code_interpreter_only: allow users to directly communicate with the Code Interpreter. In this mode, users can only send messages to the Code Interpreter and receive messages from the Code Interpreter. Here is an example: ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: generate 10 random numbers >>> [PYTHON]Starting... import numpy as np random_numbers = np.random.rand(10) random_numbers >>> [VERIFICATION] NONE >>> [STATUS]Starting... SUCCESS >>> [RESULT] The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.09918602, 0.68732778, 0.44413814, 0.4756623 , 0.48302334, 0.8286594 , 0.80994359, 0.35677263, 0.45719317, 0.68240194]) >>> [CODEINTERPRETER->PLANNER] The following python code has been executed: ```python import numpy as np random_numbers = np.random.rand(10) random_numbers ``` The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.09918602, 0.68732778, 0.44413814, 0.4756623 , 0.48302334, 0.8286594 , 0.80994359, 0.35677263, 0.45719317, 0.68240194]) TaskWeaver: The following python code has been executed: ```python import numpy as np random_numbers = np.random.rand(10) random_numbers ``` The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.09918602, 0.68732778, 0.44413814, 0.4756623 , 0.48302334, 0.8286594 , 0.80994359, 0.35677263, 0.45719317, 0.68240194]) `````","s":"Session Configration","u":"/TaskWeaver/docs/session","h":"#session-configration","p":97},{"i":102,"t":"Follow the instruction in Quick Start to clone the repo and make configurations git clone https://github.com/microsoft/TaskWeaver.git cd TaskWeaver # install the requirements pip install -r requirements.txt { \"llm.api_key\": \"the api key\", \"llm.model\": \"the model name, e.g., gpt-4\" } Run the following command in terminal. # assume you are in the taskweaver folder # -p is the path to the project directory python -m taskweaver -p ./project/ This will start the TaskWeaver process and you can interact with it through the command line interface. If everything goes well, you will see the following prompt: ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: ___","s":"Terminal","u":"/TaskWeaver/docs/usage/cmd","h":"","p":101},{"i":104,"t":"If you want to use TaskWeaver as a library, you can refer to the following code example: from taskweaver.app.app import TaskWeaverApp # This is the folder that contains the taskweaver_config.json file and not the repo root. Defaults to \"./project/\" app_dir = \"./project/\" app = TaskWeaverApp(app_dir=app_dir) session = app.get_session() user_query = \"hello, what can you do?\" response_round = session.send_message(user_query, event_handler=lambda _type, _msg: print(f\"{_type}:\\n{_msg}\")) print(response_round.to_dict()) Note: event_handler: a callback function that is utilized to display the internal planning and execution steps of TaskWeaver. It takes two arguments: the message type (e.g., plan) and the message body. response_round: the response from TaskWeaver. which is an object of the Round class. An example of the Round object is shown below: { \"id\": \"round-20231201-043134-218a2681\", \"user_query\": \"hello, what can you do?\", \"state\": \"finished\", \"post_list\": [ { \"id\": \"post-20231201-043134-10eedcca\", \"message\": \"hello, what can you do?\", \"send_from\": \"User\", \"send_to\": \"Planner\", \"attachment_list\": [] }, { \"id\": \"post-20231201-043141-86a2aaff\", \"message\": \"I can help you with various tasks, such as counting rows in a data file, detecting anomalies in a dataset, searching for products on Klarna, summarizing research papers, and pulling data from a SQL database. Please provide more information about the task you want to accomplish, and I'll guide you through the process.\", \"send_from\": \"Planner\", \"send_to\": \"User\", \"attachment_list\": [ { \"id\": \"atta-20231201-043141-6bc4da86\", \"type\": \"init_plan\", \"content\": \"1. list the available functions\" }, { \"id\": \"atta-20231201-043141-6f29f6c9\", \"type\": \"plan\", \"content\": \"1. list the available functions\" }, { \"id\": \"atta-20231201-043141-76186c7a\", \"type\": \"current_plan_step\", \"content\": \"1. list the available functions\" } ] } ] }","s":"Library","u":"/TaskWeaver/docs/usage/library","h":"","p":103},{"i":106,"t":"Follow the instruction in Quick Start to clone the repo and make configurations git clone https://github.com/microsoft/TaskWeaver.git cd TaskWeaver # install the requirements pip install -r requirements.txt { \"llm.api_key\": \"the api key\", \"llm.model\": \"the model name, e.g., gpt-4\" } Install the chainlit package by pip install chainlit if you don't have it in your env. Start the service by running the following command. cd playground/UI/ chainlit run app.py Open the browser with http://localhost:8000 and you can start the trial. Below are some screenshots:","s":"Web 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chatting for a few rounds, the chat history can become quite long, especially when we have code and execution results in it. This can cause the problem of exceeding the context window of the LLMs. To solve the problem, one way is to summarize the chat history a few rounds ago, and only keep the latest rounds of the chat history. Another way is to use a vector database to store the chat history entries, and only retrieve the last few rounds of the relevant part given the current user request. However, in TaskWeaver, code is also part of the chat history. It is not an option to skip some intermediate code and execution results in order to correctly generate the code for the current user request. Therefore, we choose the first way to solve the problem. The following figure shows the idea of chat history summarization where the chat history is divided into two parts: Rounds to compress: this part is summarized and only the summary is kept in the chat history. If the context_summary already exists, a new summary is generated based on the previous summary adding the rounds to be summarized. Rounds to retain: this part is kept in the chat history without summarization. Imagine that, at the beginning, the ConversationSummary is empty. Once the chat history reaches the rounds_to_compress (default 2) rounds plus rounds_to_retain (default 3) rounds, the ConversationSummary is generated based on the rounds_to_compress rounds and the rounds_to_retain rounds are kept in the chat history. After that, there will be only rounds_to_retain rounds in the chat history. The next time the chat history reaches the rounds_to_compress rounds plus rounds_to_retain rounds, the ConversationSummary is generated based on the rounds_to_compress rounds and the previous ConversationSummary. We use these two parameters to control the frequency of the chat history summarization. An example of the chat history summarization in the Code Generator is shown below: { \"ConversationSummary\": \"The user requested the generation of 100 random numbers, which was successfully executed. Then, the user asked to show the top 5 largest numbers from the generated random numbers. The assistant provided a code snippet to sort the generated random numbers in descending order and select the top 5 largest numbers, which was also successfully executed. After that, the user requested to plot the distribution of the 100 numbers, which was successfully executed. The user then asked to count the frequency of numbers in each bin of the histogram and identify the bin with the most numbers for the 0.1 bin width, which was also successfully executed.\", \"Variables\": [ { \"name\": \"random_numbers_100\", \"type\": \"numpy array\", \"description\": \"An array containing 100 random numbers generated using np.random.rand()\" }, { \"name\": \"top_5_largest\", \"type\": \"numpy array\", \"description\": \"An array containing the top 5 largest numbers from the generated random numbers\" } ] } The JSON object has two fields: ConversationSummary: the summary of the chat history. Variables: the variables in the chat history that could be used in the current user request. The chat history summary of the Planner has only the ConversationSummary field. The actual code generated in the summarized rounds is ignored and only the variables are kept in the summary so that the LLM can still refer the these variables in future code generation. One thing to note is that chat history summarization requires call the LLM which incurs additional latency and cost. The prompts for chat history summarization could be found for planner and code generator.","s":"Prompt Compression","u":"/TaskWeaver/docs/compression","h":"","p":10},{"i":13,"t":"As explained above, there are two parameters in controlling the chat history summarization: round_compressor.rounds_to_compress (default 2) and round_compressor.rounds_to_retain (default 3). To enable the chat history summarization, you need to set planner.prompt_compression and code_generator.prompt_compression to true.","s":"Configurations","u":"/TaskWeaver/docs/compression","h":"#configurations","p":10},{"i":15,"t":"The configuration file is located at project/taskweaver_config.json. You can edit this file to configure TaskWeaver. The configuration file is in JSON format. So for boolean values, use true or false instead of True or False. For null values, use null instead of None or \"null\". All other values should be strings in double quotes. The following table lists the parameters in the configuration file: Parameter Description Default Value llm.model The model name used by the language model. gpt-4 llm.backup_model The model name used for self-correction purposes. null llm.api_base The base URL of the OpenAI API. https://api.openai.com/v1 llm.api_key The API key of the OpenAI API. null llm.api_type The type of the OpenAI API, could be openai or azure. openai llm.api_version The version of the OpenAI API. 2023-07-01-preview llm.response_format The response format of the OpenAI API, could be json_object, text or null. json_object code_interpreter.code_verification_on Whether to enable code verification. false code_interpreter.plugin_only Whether to turn on the plugin only mode. false code_interpreter.allowed_modules The list of allowed modules to import in code generation. \"pandas\", \"matplotlib\", \"numpy\", \"sklearn\", \"scipy\", \"seaborn\", \"datetime\", \"typing\" logging.log_file The name of the log file. taskweaver.log logging.log_folder The folder to store the log file. logs plugin.base_path The folder to store plugins. ${AppBaseDir}/plugins planner.example_base_path The folder to store planner examples. ${AppBaseDir}/planner_examples planner.prompt_compression Whether to compress the chat history for planner. false planner.skip_planning Whether to skip LLM planning process and enable the default plan false code_generator.example_base_path The folder to store code interpreter examples. ${AppBaseDir}/codeinterpreter_examples code_generator.prompt_compression Whether to compress the chat history for code interpreter. false code_generator.enable_auto_plugin_selection Whether to enable auto plugin selection. false code_generator.auto_plugin_selection_topk The number of auto selected plugins in each round. 3 session.max_internal_chat_round_num The maximum number of internal chat rounds between Planner and Code Interpreter. 10 session.code_interpreter_only Allow users to directly communicate with the Code Interpreter. false round_compressor.rounds_to_compress The number of rounds to compress. 2 round_compressor.rounds_to_retain The number of rounds to retain. 3 💡 ${AppBaseDir} is the project directory. 💡 Up to 11/30/2023, the json_object and text options of llm.response_format is only supported by the OpenAI models later than 1106. If you are using an older version of OpenAI model, you need to set the llm.response_format to null. 💡 Read this for more information for planner.prompt_compression and code_generator.prompt_compression.","s":"Configuration File","u":"/TaskWeaver/docs/configurations","h":"","p":14},{"i":17,"t":"There are two types of examples: (1) planning examples and (2) code interpreter examples. Planning examples are used to demonstrate how to use TaskWeaver to plan for a specific task. Code generation examples are used to demonstrate how to generate code or orchestrate plugins to perform a specific task.","s":"Customizing Examples","u":"/TaskWeaver/docs/customization/example/","h":"","p":16},{"i":19,"t":"A planning example tells LLMs how to plan for a specific query from the user; talk to the code interpreter; receive the execution result from the code interpreter; and summarize the execution result. Before constructing the planning example, we strongly encourage you to go through the planner prompt. The following is an example of a planning example which contains 4 posts. Each post contains a message, a sender, a receiver, and a list of attachments. The first post is sent from the user to the planner. The message is \"count the rows of /home/data.csv\", which is the same as the user query. The second post is sent from the planner to the code interpreter. The message is \"Please load the data file /home/data.csv and count the rows of the loaded data\". The attachment list contains 3 attachments: The first attachment is the initial plan, which is a markdown string. The second attachment is the plan, which is a markdown string. The third attachment is the current plan step, which is a markdown string. The third post is sent from the code interpreter to the planner. The message is \"Load the data file /home/data.csv successfully and there are 100 rows in the data file\". The fourth post is sent from the planner to the user. The message is \"The data file /home/data.csv is loaded and there are 100 rows in the data file\". The attachment list contains 3 attachments: The first attachment is the initial plan, which is the same as the second attachment of the second post. The second attachment is the plan, which is the same as the third attachment of the second post. The third attachment is the current plan step, which is a markdown string. enabled: True rounds: - user_query: count the rows of /home/data.csv state: created post_list: - message: count the rows of /home/data.csv send_from: User send_to: Planner attachment_list: - message: Please load the data file /home/data.csv and count the rows of the loaded data send_from: Planner send_to: CodeInterpreter attachment_list: - type: init_plan content: |- 1. load the data file 2. count the rows of the loaded data <sequentially depends on 1> 3. report the result to the user <interactively depends on 2> - type: plan content: |- 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data 2. report the result to the user - type: current_plan_step content: 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data - message: Load the data file /home/data.csv successfully and there are 100 rows in the data file send_from: CodeInterpreter send_to: Planner attachment_list: - message: The data file /home/data.csv is loaded and there are 100 rows in the data file send_from: Planner send_to: User attachment_list: - type: init_plan content: |- 1. load the data file 2. count the rows of the loaded data <sequentially depends on 1> 3. report the result to the user <interactively depends on 2> - type: plan content: |- 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data 2. report the result to the user - type: current_plan_step content: 2. report the result to the user","s":"Planning Examples","u":"/TaskWeaver/docs/customization/example/","h":"#planning-examples","p":16},{"i":21,"t":"A code interpreter example tells LLMs how to generate code or orchestrate plugins to perform a specific task. The task is from the planner. Before constructing the code interpreter example, we strongly encourage you to read the code generator prompt. The following is an example of a code interpreter example which contains 2 posts. Each post contains a message, a sender, a receiver, and a list of attachments. The first post is sent from the planner to the code interpreter. The message is \"Please read file /abc/def.txt\". The second post is sent from the code interpreter to the planner. The message is \"read file /abc/def.txt\". The attachment list contains 6 attachments: The first attachment is the thought of the code interpreter, which is a markdown string. The second attachment is the generated code, which is in python. The third attachment is the verification status, which is CORRECT, INCORRECT, or NONE. The fourth attachment is the verification error message, which is a markdown string. The fifth attachment is the execution status, which is SUCCESS, FAILURE, or NONE. The sixth attachment is the execution result, which is a markdown string. enabled: True rounds: - user_query: read file /abc/def.txt state: finished post_list: - message: read file /abc/def.txt send_from: Planner send_to: CodeInterpreter attachment_list: [] - message: I'm sorry, I cannot find the file /abc/def.txt. An FileNotFoundException has been raised. send_from: CodeInterpreter send_to: Planner attachment_list: - type: thought content: \"{ROLE_NAME} will generate a code snippet to read the file /abc/def.txt and present the content to the user.\" - type: python content: |- file_path = \"/abc/def.txt\" with open(file_path, \"r\") as file: file_contents = file.read() print(file_contents) - type: verification content: CORRECT - type: code_error content: No code error. - type: execution_status content: FAILURE - type: execution_result content: FileNotFoundException, the file /abc/def.txt does not exist. In this example, verification is about whether the generated code is correct or not. We implemented a module to verify the generated code. If the code is syntactically incorrect, or the code violates the constraints, the verification status will be INCORRECT and some error messages will be returned. A verification of NONE means that the code has not been verified, which means verification has been turned off. In this example, execution_status is about whether the generated code can be executed successfully or not. If the execution is successful, the execution status will be SUCCESS and the execution result will be returned. Otherwise, the execution status will be FAILURE and some error messages will be returned. A execution_status of NONE means that the code has not been executed.","s":"Code Interpreter Examples","u":"/TaskWeaver/docs/customization/example/","h":"#code-interpreter-examples","p":16},{"i":23,"t":"In TaskWeaver, we provide an auto plugin selection mechanism to dynamically select the best plugin for each user request. It targets to solve the following two problems: An excessive number of plugins may cause confusion for LLM, leading to inaccuracies in generating the correct code. A large number of plugins could lead to increased token usage (potentially exceeding the token limit of LLM) and extended response time.","s":"Auto Plugin Selection","u":"/TaskWeaver/docs/customization/plugin/plugin_selection","h":"","p":22},{"i":25,"t":"Below is the overview workflow of the auto plugin selection mechanism. NOTE: the automatic plugin selection mechanism is only activated during the code generation process in the Code Interpreter and does not affect the planning process of the Planner. At the start of TaskWeaver initialization, the automatic plugin selector is activated to generate embedding vectors for all plugins, including their names and descriptions. The embedding vectors are created using the specified embedding model configured in the taskweaver_config.json file. For more information, please refer to the embedding documentation. When the Planner sends a request to the Code Interpreter, the auto plugin selection mechanism will be triggered. It will first generate an embedding vector for the request using the same embedding model. Then, it will calculate the cosine similarity between the request embedding vector and the embedding vectors of all plugins. It will select the top-k plugins with the highest cosine similarity scores and load them into the code_generator prompt. Upon completing the code generation, the code_generator employs one or more plugins to produce the desired code. We have established a plugin pool to store the plugins involved in the code generation process while filtering out any unused ones. During the subsequent automatic plugin selection phase, newly chosen plugins are appended to the existing ones.","s":"Auto Plugin Selection Overview","u":"/TaskWeaver/docs/customization/plugin/plugin_selection","h":"#auto-plugin-selection-overview","p":22},{"i":27,"t":"code_generator.enable_auto_plugin_selection: Whether to enable auto plugin selection. The default value is false. code_generator.auto_plugin_selection_topk: The number of auto selected plugins in each round. The default value is 3.","s":"Auto Plugin Selection Configuration","u":"/TaskWeaver/docs/customization/plugin/plugin_selection","h":"#auto-plugin-selection-configuration","p":22},{"i":29,"t":"We show the auto plugin selection mechanism in the following example. First, we start TaskWeaver with the auto plugin selection mechanism enabled. ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: Then we can check the log file task_weaver.log in the logs folder to see the auto plugin selector is initialized successfully because the Plugin embeddings generated message is printed. 2023-12-18 14:23:44,197 - INFO - Planner initialized successfully 2023-12-18 14:24:10,488 - INFO - Plugin embeddings generated 2023-12-18 14:24:10,490 - INFO - CodeInterpreter initialized successfully. 2023-12-18 14:24:10,490 - INFO - Session 20231218-062343-c18494b1 is initialized We ask TaskWeaver to \"search Xbox price for me\". The Planner instructs the Code Interpreter to search Xbox price. TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: search xbox price for me >>> [INIT_PLAN] 1. search xbox price 2. report the result to the user <interactively depends on 1> >>> [PLAN] 1. instruct CodeInterpreter to search xbox price 2. report the result to the user >>> [CURRENT_PLAN_STEP] 1. instruct CodeInterpreter to search xbox price >>> [SEND_TO] CodeInterpreter >>> [MESSAGE] Please search xbox price >>> [PLANNER->CODEINTERPRETER] Please search xbox price Back to the Code Interpreter, the auto plugin selection mechanism is triggered. We can check the log file task_weaver.log again to see the auto plugin selector selected the top-3 plugins with the highest cosine similarity scores. 023-12-18 14:24:34,513 - INFO - Planner talk to CodeInterpreter: Please search xbox price using klarna_search plugin 2023-12-18 14:24:34,669 - INFO - Selected plugins: ['klarna_search', 'sql_pull_data', 'paper_summary'] 2023-12-18 14:24:34,669 - INFO - Selected plugin pool: ['klarna_search', 'sql_pull_data', 'paper_summary'] Then the Code Interpreter will generate the code using the selected plugins. >>> [THOUGHT] ProgramApe will call the klarna_search plugin function to search for Xbox prices. >>> [PYTHON] search_results, description = klarna_search(query=\"xbox\") search_results, description >>> [VERIFICATION] NONE >>> [STATUS] SUCCESS >>> [RESULT] The execution of the generated python code above has succeeded The result of above Python code after execution is: ( name price url attributes 0 Microsoft Xbox Series X - Black Edition $399.00 https://www.klarna.com/us/shopping/pl/cl52/495... [Release Year:2020, Included Accessories:1 gam... 1 Microsoft Xbox Series S 1TB - Black $349.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Included Accessories:1 gamepad, Media Type:DV... .. ... ... ... ... 3 Xbox Series S – Starter Bundle $239.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Platform:Xbox One] 4 Microsoft Xbox Series X 1TB Console - Diablo I... $385.58 https://www.klarna.com/us/shopping/pl/cl52/320... [Release Year:2023, Included Accessories:1 gam... [5 rows x 4 columns], 'The response is a dataframe with the following columns: name, price, url, attributes. The attributes column is a list of tags. The price is in the format of $xx.xx.') >>> [CODEINTERPRETER->PLANNER] The following python code has been executed: ```python search_results, description = klarna_search(query=\"xbox\") search_results, description ``` The execution of the generated python code above has succeeded The result of above Python code after execution is: ( name price url attributes 0 Microsoft Xbox Series X - Black Edition $399.00 https://www.klarna.com/us/shopping/pl/cl52/495... [Release Year:2020, Included Accessories:1 gam... 1 Microsoft Xbox Series S 1TB - Black $349.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Included Accessories:1 gamepad, Media Type:DV... .. ... ... ... ... 3 Xbox Series S – Starter Bundle $239.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Platform:Xbox One] 4 Microsoft Xbox Series X 1TB Console - Diablo I... $385.58 https://www.klarna.com/us/shopping/pl/cl52/320... [Release Year:2023, Included Accessories:1 gam... [5 rows x 4 columns], 'The response is a dataframe with the following columns: name, price, url, attributes. The attributes column is a list of tags. The price is in the format of $xx.xx.') Finally, the Planner will report the result to the user. >>> [INIT_PLAN] 1. search xbox price using klarna_search plugin 2. report the result to the user <interactively depends on 1> >>> [PLAN] 1. instruct CodeInterpreter to search xbox price using klarna_search plugin 2. report the result to the user >>> [CURRENT_PLAN_STEP] 2. report the result to the user >>> [SEND_TO] User >>> [MESSAGE] Here are some Xbox prices I found: 1. Microsoft Xbox Series X - Black Edition: $399.00 2. Microsoft Xbox Series S 1TB - Black: $349.00 3. Xbox Series S – Starter Bundle: $239.00 4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58 Please note that these prices are subject to change and availability. >>> [PLANNER->USER] Here are some Xbox prices I found: 1. Microsoft Xbox Series X - Black Edition: $399.00 2. Microsoft Xbox Series S 1TB - Black: $349.00 3. Xbox Series S – Starter Bundle: $239.00 4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58 Please note that these prices are subject to change and availability. TaskWeaver: Here are some Xbox prices I found: 1. Microsoft Xbox Series X - Black Edition: $399.00 2. Microsoft Xbox Series S 1TB - Black: $349.00 3. Xbox Series S – Starter Bundle: $239.00 4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58 Please note that these prices are subject to change and availability.","s":"Auto Plugin Selection Example","u":"/TaskWeaver/docs/customization/plugin/plugin_selection","h":"#auto-plugin-selection-example","p":22},{"i":32,"t":"In TaskWeaver, we support various embedding models to generate embeddings for auto plugin selection.","s":"Embedding","u":"/TaskWeaver/docs/customization/plugin/embedding","h":"","p":31},{"i":34,"t":"llm.embedding_api_type: The type of the embedding API. We support the following types: openai qwen ollama sentence_transformers llm.embedding_model: The embedding model name. The model name should be aligned with llm.embedding_api_type. We only list some embedding models we have tested below: openai text-embedding-ada-002 qwen text-embedding-v1 ollama llama2 sentence_transformers all-mpnet-base-v2 multi-qa-mpnet-base-dot-v1 all-distilroberta-v1 all-MiniLM-L12-v2 multi-qa-MiniLM-L6-cos-v1 You also can use other embedding models supported by the above embedding APIs.","s":"Embedding Configration","u":"/TaskWeaver/docs/customization/plugin/embedding","h":"#embedding-configration","p":31},{"i":37,"t":"Using LLMs from OpenAI","s":"📄️ OpenAI","u":"/TaskWeaver/docs/llms/","h":"","p":35},{"i":39,"t":"Using LLMs from OpenAI/AOAI","s":"📄️ Azure OpenAI","u":"/TaskWeaver/docs/llms/","h":"","p":35},{"i":41,"t":"Using LLMs from LiteLLM","s":"📄️ LiteLLM","u":"/TaskWeaver/docs/llms/","h":"","p":35},{"i":43,"t":"1. Go to Ollama and follow the instructions to serve a LLM model on your local environment.","s":"📄️ Ollama","u":"/TaskWeaver/docs/llms/","h":"","p":35},{"i":45,"t":"1. Create an account on Google AI and get your API key.","s":"📄️ Gemini","u":"/TaskWeaver/docs/llms/","h":"","p":35},{"i":47,"t":"1. QWen (Tongyi Qianwen) is a LLM developed by Alibaba. Go to QWen and register an account and get the API key. More details can be found here (in Chinese).","s":"📄️ QWen","u":"/TaskWeaver/docs/llms/","h":"","p":35},{"i":49,"t":"Create an account on Azure OpenAI and get your API key. Add the following to your taskweaver_config.json file: { \"llm.api_base\":\"YOUR_AOAI_ENDPOINT\", \"llm.api_key\":\"YOUR_API_KEY\", \"llm.api_type\":\"azure\", \"llm.auth_mode\":\"api-key\", \"llm.model\":\"gpt-4-1106-preview\", \"llm.response_format\": \"json_object\" } tip llm.model is the model name you want to use. You can find the list of models here. info For gpt-4-1106-preview and gpt-3.5-turbo-1106, llm.response_format can be set to json_object. However, for the earlier models, which do not support JSON response explicitly, llm.response_format should be set to null. Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"Azure OpenAI","u":"/TaskWeaver/docs/llms/aoai","h":"","p":48},{"i":51,"t":"info LiteLLM provides a unified interface to call 100+ LLMs using the same Input/Output format, including OpenAI, Huggingface, Anthropic, vLLM, Cohere, and even custom LLM API server. Taking LiteLLM as the bridge, many LLMs can be onboarded to TaskWeaver. Here we use the OpenAI Proxy Server provided by LiteLLM to make configuration. Install LiteLLM Proxy and configure the LLM server by following the instruction here. In general, there are a few steps: Install the package pip install litellm[proxy] Setup the API key and other necessary environment variables which vary by LLM. Taking Cohere as an example, it is required to setup export COHERE_API_KEY=my-api-key. Run LiteLLM proxy server by litellm --model MODEL_NAME --drop_params, for example, in Cohere, the model name can be command-nightly. The drop-params argument is used to ensure the API compatibility. Then, a server will be automatically started on http://0.0.0.0:8000. tip The full list of supported models by LiteLLM can be found in the page. Add the following content to your taskweaver_config.json file: { \"llm.api_base\": \"http://0.0.0.0:8000\", \"llm.api_key\": \"anything\", \"llm.model\": \"gpt-3.5-turbo\" } info llm.api_key and llm.model are mainly used as placeholder for API call, whose actual values are not used. If the configuration does not work, please refer to LiteLLM documents to locally test whether you can send requests to the LLM. Open a new terminal, start TaskWeaver and chat. You can refer to the Quick Start for more details.","s":"LiteLLM","u":"/TaskWeaver/docs/llms/liteLLM","h":"","p":50},{"i":53,"t":"Go to Ollama and follow the instructions to serve a LLM model on your local environment. We provide a short example to show how to configure the ollama in the following, which might change if ollama makes updates. install ollama and serve LLMs in local ## Install ollama on Linux & WSL2 curl https://ollama.ai/install.sh | sh ## Run the serving ollama serve ## Open another terminal and run the model ollama run llama2 tip We recommend deploying the LLM with a parameter scale exceeding 13B for enhanced performance (such as Llama 2 13B). info When serving LLMs via Ollama, it will by default start a server at http://localhost:11434, which will later be used as the API base in taskweaver_config.json. Add following configuration to taskweaver_config.json: { \"llm.api_base\": \"http://localhost:11434\", \"llm.api_key\": \"ARBITRARY_STRING\", \"llm.api_type\": \"ollama\", \"llm.model\": \"llama2:13b\" } NOTE: llm.api_base is the URL started in the Ollama LLM server and llm.model is the model name of Ollama LLM. Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"Ollama","u":"/TaskWeaver/docs/llms/ollama","h":"","p":52},{"i":55,"t":"Create an account on Google AI and get your API key. Add the following content to your taskweaver_config.json file: { \"llm.api_type\": \"google_genai\", \"llm.google_genai.api_key\": \"YOUR_API_KEY\", \"llm.google_genai.model\": \"gemini-pro\" } Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"Gemini","u":"/TaskWeaver/docs/llms/gemini","h":"","p":54},{"i":57,"t":"Create an account on OpenAI and get your API key. Add the following to your taskweaver_config.json file: { \"llm.api_type\":\"openai\", \"llm.api_base\": \"https://api.openai.com/v1\", \"llm.api_key\": \"YOUR_API_KEY\", \"llm.model\": \"gpt-4-1106-preview\", \"llm.response_format\": \"json_object\" } tip llm.model is the model name you want to use. You can find the list of models here. info For gpt-4-1106-preview and gpt-3.5-turbo-1106, llm.response_format can be set to json_object. However, for the earlier models which do not support JSON response explicitly, llm.response_format should be set to null. Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"OpenAI","u":"/TaskWeaver/docs/llms/openai","h":"","p":56},{"i":59,"t":"QWen (Tongyi Qianwen) is a LLM developed by Alibaba. Go to QWen and register an account and get the API key. More details can be found here (in Chinese). Install the required packages dashscope. pip install dashscope Add the following configuration to taskweaver_config.json: { \"llm.api_type\": \"qwen\", \"llm.model\": \"qwen-max\", \"llm.api_key\": \"YOUR_API_KEY\" } NOTE: llm.model is the model name of QWen LLM API. You can find the model name in the QWen LLM model list. Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"QWen","u":"/TaskWeaver/docs/llms/qwen","h":"","p":58},{"i":61,"t":"TaskWeaver is a code-first agent framework for seamlessly planning and executing data analytics tasks. This innovative framework interprets user requests through coded snippets and efficiently coordinates a variety of plugins in the form of functions to execute data analytics tasks Highlighted Features Rich data structure - TaskWeaver allows you to work with rich data structures in Python, such as DataFrames, instead of having to work with text strings. Customized algorithms - TaskWeaver allows you to encapsulate your own algorithms into plugins (in the form of Python functions), and orchestrate them to achieve complex tasks. Incorporating domain-specific knowledge - TaskWeaver is designed to be easily incorporating domain-specific knowledge, such as the knowledge of execution flow, to improve the reliability of the AI copilot. Stateful conversation - TaskWeaver is designed to support stateful conversation. It can remember the context of the conversation and leverage it to improve the user experience. Code verification - TaskWeaver is designed to verify the generated code before execution. It can detect potential issues in the generated code and provide suggestions to fix them. Easy to use - TaskWeaver is designed to be easy to use. We provide a set of sample plugins and a tutorial to help you get started. Users can easily create their own plugins based on the sample plugins. TaskWeaver offers an open-box experience, allowing users to run a service immediately after installation. Easy to debug - TaskWeaver is designed to be easy to debug. We have detailed logs to help you understand what is going on during calling the LLM, the code generation, and execution process. Security consideration - TaskWeaver supports a basic session management to keep different users' data separate. The code execution is separated into different processes in order not to interfere with each other. Easy extension - TaskWeaver is designed to be easily extended to accomplish more complex tasks. You can create multiple AI copilots to act in different roles, and orchestrate them to achieve complex tasks.","s":"Overview","u":"/TaskWeaver/docs/overview","h":"","p":60},{"i":63,"t":"In TaskWeaver, the Planner is responsible for generating a plan to accomplish the user's task. The plan is a sequence of steps, where each step will be executed by the Code Interpreter. Taken the response from the Code Interpreter or new requests from the user as input, the Planner will update the plan and move on to the next step.","s":"Planner","u":"/TaskWeaver/docs/planner","h":"","p":62},{"i":65,"t":"planner.example_base_path: The folder to store planner examples. The default value is ${AppBaseDir}/planner_examples. If you want to create your own planner examples, you can add them to this folder. More details about example can referred to example. planner.prompt_compression: At times, lengthy conversations with the Planner may exceed the input limitations of the LLM model. To address this issue, we can compress the chat history and send it to the LLM model. The default value for this setting is false. More details about prompt_compression can be referred to prompt_compression. planner.skip_planning: In certain scenarios, there may be no need to use the Planner to generate complex plans for simple tasks. For instance, if a user wants to count the rows in a data file, the request can be sent directly to the Code Interpreter. When the majority of user requests involve simple tasks, enabling this option will create a dummy plan that is sent alongside the user request to the Code Interpreter directly, without LLM generation process. The fixed dummy plan is shown in dummy_plan.json. Here is an chat example: ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: generate 10 random numbers >>> [MESSAGE]eparing... <=�=> Please process this request: generate 10 random numbers >>> [SEND_TO] CodeInterpreter >>> [INIT_PLAN] 1. ask Code Interpreter to handle the request; 2. report the result to user <interactively depends on 1> >>> [PLAN] 1. ask Code Interpreter to handle user\\'s request; 2. report the result to user >>> [CURRENT_PLAN_STEP] 1. ask Code Interpreter to handle the request >>> [PLANNER->CODEINTERPRETER] Please process this request: generate 10 random numbers >>> [PYTHON]Starting... random_numbers = np.random.rand(10) random_numbers >>> [VERIFICATION] NONE >>> [STATUS]Starting... SUCCESS >>> [RESULT] The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.65294462, 0.26946084, 0.06244879, 0.78520418, 0.87067657, 0.24208003, 0.60249788, 0.30921069, 0.83811521, 0.05135891]) >>> [CODEINTERPRETER->PLANNER] The following python code has been executed: ```python random_numbers = np.random.rand(10) random_numbers ``` The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.65294462, 0.26946084, 0.06244879, 0.78520418, 0.87067657, 0.24208003, 0.60249788, 0.30921069, 0.83811521, 0.05135891]) >>> [INIT_PLAN]ting... <=�=> 1. ask Code Interpreter to handle the request; 2. report the result to user <interactively depends on 1> >>> [PLAN] 1. ask Code Interpreter to handle user's request; 2. report the result to user >>> [CURRENT_PLAN_STEP] 2. report the result to user >>> [SEND_TO] User >>> [MESSAGE] The random numbers are as follows: [0.65294462, 0.26946084, 0.06244879, 0.78520418, 0.87067657, 0.24208003, 0.60249788, 0.30921069, 0.83811521, 0.05135891] >>> [PLANNER->USER] The random numbers are as follows: [0.65294462, 0.26946084, 0.06244879, 0.78520418, 0.87067657, 0.24208003, 0.60249788, 0.30921069, 0.83811521, 0.05135891] TaskWeaver: The random numbers are as follows: [0.65294462, 0.26946084, 0.06244879, 0.78520418, 0.87067657, 0.24208003, 0.60249788, 0.30921069, 0.83811521, 0.05135891]","s":"Planner Configuration","u":"/TaskWeaver/docs/planner","h":"#planner-configuration","p":62},{"i":67,"t":"Plugins are the units that could be orchestrated by TaskWeaver. One could view the plugins as tools that the LLM can utilize to accomplish certain tasks. In TaskWeaver, each plugin is represented as a Python function that can be called within a code snippet. The orchestration is essentially the process of generating Python code snippets consisting of a certain number of plugins. One concrete example would be pulling data from database and apply anomaly detection. The generated code (simplified) looks like follows: df, data_description = sql_pull_data(query=\"pull data from time_series table\") anomaly_df, anomaly_description = anomaly_detection(df, time_col_name=\"ts\", value_col_name=\"val\")","s":"Plugin Introduction","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"","p":66},{"i":69,"t":"A plugin has two files: Plugin Implementation: a Python file that defines the plugin Plugin Schema: a file in yaml that defines the schema of the plugin","s":"Plugin Structure","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-structure","p":66},{"i":71,"t":"The plugin function needs to be implemented in Python. To be coordinated with the orchestration by TaskWeaver, a plugin python file consists of two parts: Plugin function implementation code TaskWeaver plugin decorator Here we exhibit an example of the anomaly detection plugin as the following code: import pandas as pd from pandas.api.types import is_numeric_dtype from taskWeaver.plugin import Plugin, register_plugin @register_plugin class AnomalyDetectionPlugin(Plugin): def __call__(self, df: pd.DataFrame, time_col_name: str, value_col_name: str): \"\"\" anomaly_detection function identifies anomalies from an input dataframe of time series. It will add a new column \"Is_Anomaly\", where each entry will be marked with \"True\" if the value is an anomaly or \"False\" otherwise. :param df: the input data, must be a dataframe :param time_col_name: name of the column that contains the datetime :param value_col_name: name of the column that contains the numeric values. :return df: a new df that adds an additional \"Is_Anomaly\" column based on the input df. :return description: the description about the anomaly detection results. \"\"\" try: df[time_col_name] = pd.to_datetime(df[time_col_name]) except Exception: print(\"Time column is not datetime\") return if not is_numeric_dtype(df[value_col_name]): try: df[value_col_name] = df[value_col_name].astype(float) except ValueError: print(\"Value column is not numeric\") return mean, std = df[value_col_name].mean(), df[value_col_name].std() cutoff = std * 3 lower, upper = mean - cutoff, mean + cutoff df[\"Is_Anomaly\"] = df[value_col_name].apply(lambda x: x < lower or x > upper) anomaly_count = df[\"Is_Anomaly\"].sum() description = \"There are {} anomalies in the time series data\".format(anomaly_count) self.ctx.add_artifact( name=\"anomaly_detection_results\", # a brief description of the artifact file_name=\"anomaly_detection_results.csv\", # artifact file name type=\"df\", # artifact data type, support chart/df/file/txt/svg val=df, # variable to be dumped ) return df, description You need to go through the following steps to implement your own plugin. import the TaskWeaver plugin decorator from taskWeaver.plugin import Plugin, register_plugin create your plugin class inherited from Plugin parent class (e.g., AnomalyDetectionPlugin(Plugin)), which is decorated by @register_plugin implement your plugin function in __call__ method of the plugin class. Most importantly, it is mandatory to include descriptions of your execution results in the return values of your plugin function. These descriptions can be utilized by the LLM to effectively summarize your execution results. 💡A key difference in a plugin implementation and a normal python function is that it always return a description of the result in natural language. As LLMs only understand natural language, it is important to let the model understand what the execution result is. In the example implementation above, the description says how many anomalies are detected. Behind the scene, only the description will be passed to the LLM model. In contrast, the execution result (e.g., df in the above example) is not handled by the LLM.","s":"Plugin Implementation","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-implementation","p":66},{"i":73,"t":"If the functionality of your plugin depends on additional libraries or packages, it is essential to ensure that they are installed before proceeding. If you wish to persist intermediate results, such as data, figures, or prompts, in your plugin implementation, TaskWeaver provides an add_artifact API that allows you to store these results in the workspace. In the example we provide, if you have performed anomaly detection and obtained results in the form of a CSV file, you can utilize the add_artifact API to save this file as an artifact. The artifacts are stored in the project/workspace/session_id/cwd folder in the project directory. self.ctx.add_artifact( name=\"anomaly_detection_results\", # a brief description of the artifact file_name=\"anomaly_detection_results.csv\", # artifact file name type=\"df\", # artifact data type, support chart/df/file/txt/svg val=df, # variable to be dumped )","s":"Important Notes","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#important-notes","p":66},{"i":75,"t":"The plugin schema is composed of several parts: name: The main function name of the Python code. enabled: determine whether the plugin is enabled for selection during conversations. The default value is true. descriptions: A brief description that introduces the plugin function. parameters: This section lists all the input parameter information. It includes the parameter's name, type, whether it is required or optional, and a description providing more details about the parameter. returns: This section lists all the return value information. It includes the return value's name, type, and description that provides information about the value that is returned by the function. Note: The addition of any extra fields would result in a validation failure within the plugin schema. The plugin schema is required to be written in YAML format. Here is the plugin schema example of the above anomaly detection plugin: name: anomaly_detection enabled: true required: false description: >- anomaly_detection function identifies anomalies from an input DataFrame of time series. It will add a new column \"Is_Anomaly\", where each entry will be marked with \"True\" if the value is an anomaly or \"False\" otherwise. parameters: - name: df type: DataFrame required: true description: >- the input data from which we can identify the anomalies with the 3-sigma algorithm. - name: time_col_name type: str required: true description: name of the column that contains the datetime - name: value_col_name type: str required: true description: name of the column that contains the numeric values. returns: - name: df type: DataFrame description: >- This DataFrame extends the input DataFrame with a newly-added column \"Is_Anomaly\" containing the anomaly detection result. - name: description type: str description: This is a string describing the anomaly detection results. Besides, we also set two optional fields as below: code: In cases where multiple plugins map to the same Python code (i.e., the plugin name is different from the code name), it is essential to specify the code name (code file) in the plugin schema to ensure clarity and accuracy. configurations: When using common code that requires some configuration parameter modifications for different plugins, it is important to specify these configuration parameters in the plugin schema.","s":"Plugin Schema","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-schema","p":66},{"i":78,"t":"You can install TaskWeaver by running the following command: # [optional] create a conda environment to isolate the dependencies # conda create -n taskweaver python=3.10 # conda activate taskweaver # clone the repository git clone https://github.com/microsoft/TaskWeaver.git cd TaskWeaver # install the requirements pip install -r requirements.txt","s":"Installation","u":"/TaskWeaver/docs/quickstart","h":"#installation","p":76},{"i":80,"t":"TaskWeaver runs as a process, you need to create a project directory to store plugins and configuration files. We provided a sample project directory in the project folder. You can copy the project folder to your workspace. A project directory typically contains the following files and folders: 📦project ┣ 📜taskweaver_config.json # the configuration file for TaskWeaver ┣ 📂plugins # the folder to store plugins ┣ 📂planner_examples # the folder to store planner examples ┣ 📂codeinterpreter_examples # the folder to store code interpreter examples ┣ 📂sample_data # the folder to store sample data used for evaluations ┣ 📂logs # the folder to store logs, will be generated after program starts ┗ 📂workspace # the directory stores session data， will be generated after program starts ┗ 📂 session_id ┣ 📂ces # the folder used by the code execution service ┗ 📂cwd # the current working directory to run the generated code","s":"Project Directory","u":"/TaskWeaver/docs/quickstart","h":"#project-directory","p":76},{"i":82,"t":"Before running TaskWeaver, you need to provide your OpenAI API key and other necessary information. You can do this by editing the taskweaver_config.json file. If you are using Azure OpenAI, you need to set the following parameters in the taskweaver_config.json file:","s":"OpenAI Configuration","u":"/TaskWeaver/docs/quickstart","h":"#openai-configuration","p":76},{"i":84,"t":"{ \"llm.api_base\": \"https://xxx.openai.azure.com/\", \"llm.api_key\": \"your_api_key\", \"llm.api_type\": \"azure\", \"llm.api_version\": \"the api version\", \"llm.model\": \"the model name, e.g., gpt-4\" }","s":"Azure OpenAI","u":"/TaskWeaver/docs/quickstart","h":"#azure-openai","p":76},{"i":86,"t":"{ \"llm.api_key\": \"the api key\", \"llm.model\": \"the model name, e.g., gpt-4\" } 💡 Only the latest OpenAI API supports the json_object response format. If you are using an older version of OpenAI API, you need to set the llm.response_format to null. More configuration options can be found in the configuration documentation.","s":"OpenAI","u":"/TaskWeaver/docs/quickstart","h":"#openai","p":76},{"i":88,"t":"# assume you are in the taskweaver folder # -p is the path to the project directory python -m taskweaver -p ./project/ This will start the TaskWeaver process and you can interact with it through the command line interface. If everything goes well, you will see the following prompt: ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: ___","s":"Start TaskWeaver","u":"/TaskWeaver/docs/quickstart","h":"#start-taskweaver","p":76},{"i":92,"t":"make sure you completed the taskweaver quickstart","s":"setup","u":"/TaskWeaver/docs/run_pytest","h":"#setup","p":89},{"i":94,"t":"execute ./scripts/run_pytest.sh from the project root","s":"run tests","u":"/TaskWeaver/docs/run_pytest","h":"#run-tests","p":89},{"i":96,"t":"the script temporarily sets PYTHONPATH and runs pytest no permanent environment changes pass additional pytest arguments as needed done! 🚀","s":"notes","u":"/TaskWeaver/docs/run_pytest","h":"#notes","p":89},{"i":98,"t":"session is the entrance of TaskWeaver. It is responsible for the communication between the user and TaskWeaver. You can refer to taskweaver_as_a_lib to see how to setup a TaskWeaver session and start chatting with TaskWeaver.","s":"Session","u":"/TaskWeaver/docs/session","h":"","p":97},{"i":100,"t":"max_internal_chat_round_num: the maximum number of internal chat rounds between Planner and Code Interpreter. If the number of internal chat rounds exceeds this number, the session will be terminated. The default value is 10. code_interpreter_only: allow users to directly communicate with the Code Interpreter. In this mode, users can only send messages to the Code Interpreter and receive messages from the Code Interpreter. Here is an example: ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: generate 10 random numbers >>> [PYTHON]Starting... import numpy as np random_numbers = np.random.rand(10) random_numbers >>> [VERIFICATION] NONE >>> [STATUS]Starting... SUCCESS >>> [RESULT] The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.09918602, 0.68732778, 0.44413814, 0.4756623 , 0.48302334, 0.8286594 , 0.80994359, 0.35677263, 0.45719317, 0.68240194]) >>> [CODEINTERPRETER->PLANNER] The following python code has been executed: ```python import numpy as np random_numbers = np.random.rand(10) random_numbers ``` The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.09918602, 0.68732778, 0.44413814, 0.4756623 , 0.48302334, 0.8286594 , 0.80994359, 0.35677263, 0.45719317, 0.68240194]) TaskWeaver: The following python code has been executed: ```python import numpy as np random_numbers = np.random.rand(10) random_numbers ``` The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.09918602, 0.68732778, 0.44413814, 0.4756623 , 0.48302334, 0.8286594 , 0.80994359, 0.35677263, 0.45719317, 0.68240194]) `````","s":"Session Configration","u":"/TaskWeaver/docs/session","h":"#session-configration","p":97},{"i":102,"t":"Follow the instruction in Quick Start to clone the repo and make configurations git clone https://github.com/microsoft/TaskWeaver.git cd TaskWeaver # install the requirements pip install -r requirements.txt { \"llm.api_key\": \"the api key\", \"llm.model\": \"the model name, e.g., gpt-4\" } Run the following command in terminal. # assume you are in the taskweaver folder # -p is the path to the project directory python -m taskweaver -p ./project/ This will start the TaskWeaver process and you can interact with it through the command line interface. If everything goes well, you will see the following prompt: ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: ___","s":"Terminal","u":"/TaskWeaver/docs/usage/cmd","h":"","p":101},{"i":104,"t":"If you want to use TaskWeaver as a library, you can refer to the following code example: from taskweaver.app.app import TaskWeaverApp # This is the folder that contains the taskweaver_config.json file and not the repo root. Defaults to \"./project/\" app_dir = \"./project/\" app = TaskWeaverApp(app_dir=app_dir) session = app.get_session() user_query = \"hello, what can you do?\" response_round = session.send_message(user_query, event_handler=lambda _type, _msg: print(f\"{_type}:\\n{_msg}\")) print(response_round.to_dict()) Note: event_handler: a callback function that is utilized to display the internal planning and execution steps of TaskWeaver. It takes two arguments: the message type (e.g., plan) and the message body. response_round: the response from TaskWeaver. which is an object of the Round class. An example of the Round object is shown below: { \"id\": \"round-20231201-043134-218a2681\", \"user_query\": \"hello, what can you do?\", \"state\": \"finished\", \"post_list\": [ { \"id\": \"post-20231201-043134-10eedcca\", \"message\": \"hello, what can you do?\", \"send_from\": \"User\", \"send_to\": \"Planner\", \"attachment_list\": [] }, { \"id\": \"post-20231201-043141-86a2aaff\", \"message\": \"I can help you with various tasks, such as counting rows in a data file, detecting anomalies in a dataset, searching for products on Klarna, summarizing research papers, and pulling data from a SQL database. Please provide more information about the task you want to accomplish, and I'll guide you through the process.\", \"send_from\": \"Planner\", \"send_to\": \"User\", \"attachment_list\": [ { \"id\": \"atta-20231201-043141-6bc4da86\", \"type\": \"init_plan\", \"content\": \"1. list the available functions\" }, { \"id\": \"atta-20231201-043141-6f29f6c9\", \"type\": \"plan\", \"content\": \"1. list the available functions\" }, { \"id\": \"atta-20231201-043141-76186c7a\", \"type\": \"current_plan_step\", \"content\": \"1. list the available functions\" } ] } ] }","s":"Library","u":"/TaskWeaver/docs/usage/library","h":"","p":103},{"i":106,"t":"Follow the instruction in Quick Start to clone the repo and make configurations git clone https://github.com/microsoft/TaskWeaver.git cd TaskWeaver # install the requirements pip install -r requirements.txt { \"llm.api_key\": \"the api key\", \"llm.model\": \"the model name, e.g., gpt-4\" } Install the chainlit package by pip install chainlit if you don't have it in your env. Start the service by running the following command. cd playground/UI/ chainlit run app.py Open the browser with http://localhost:8000 and you can start the trial. Below are some screenshots:","s":"Web 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