base on Generate and auto-execute Python scripts in the cli [](https://discord.gg/zbvd9qx9Pb) # Rawdog An CLI assistant that responds by generating and auto-executing a Python script. https://github.com/AbanteAI/rawdog/assets/50287275/1417a927-58c1-424f-90a8-e8e63875dcda You'll be surprised how useful this can be: - "How many folders in my home directory are git repos?" ... "Plot them by disk size." - "Give me the pd.describe() for all the csv's in this directory" - "What ports are currently active?" ... "What are the Google ones?" ... "Cancel those please." Rawdog (Recursive Augmentation With Deterministic Output Generations) is a novel alternative to RAG (Retrieval Augmented Generation). Rawdog can self-select context by running scripts to print things, adding the output to the conversation, and then calling itself again. This works for tasks like: - "Setup the repo per the instructions in the README" - "Look at all these csv's and tell me if they can be merged or not, and why." - "Try that again." Please proceed with caution. This obviously has the potential to cause harm if so instructed. ### Quickstart 1. Install rawdog with pip: ``` pip install rawdog-ai ``` 2. Export your api key. See [Model selection](#model-selection) for how to use other providers ``` export OPENAI_API_KEY=your-api-key ``` 3. Choose a mode of interaction. Direct: Execute a single prompt and close ``` rawdog Plot the size of all the files and directories in cwd ``` Conversation: Initiate back-and-forth until you close. Rawdog can see its scripts and output. ``` rawdog >>> What can I do for you? (Ctrl-C to exit) >>> > | ``` ## Optional Arguments * `--leash`: (default False) Print and manually approve each script before executing. * `--retries`: (default 2) If rawdog's script throws an error, review the error and try again. ## Model selection Rawdog uses `litellm` for completions with 'gpt-4-turbo-preview' as the default. You can adjust the model or point it to other providers by modifying `~/.rawdog/config.yaml`. Some examples: To use gpt-3.5 turbo a minimal config is: ```yaml llm_model: gpt-3.5-turbo ``` To run mixtral locally with ollama a minimal config is (assuming you have [ollama](https://ollama.ai/) installed and a sufficient gpu): ```yaml llm_custom_provider: ollama llm_model: mixtral ``` To run claude-2.1 set your API key: ```bash export ANTHROPIC_API_KEY=your-api-key ``` and then set your config: ```yaml llm_model: claude-2.1 ``` If you have a model running at a local endpoint (or want to change the baseurl for some other reason) you can set the `llm_base_url`. For instance if you have an openai compatible endpoint running at http://localhost:8000 you can set your config to: ``` llm_base_url: http://localhost:8000 llm_model: openai/model # So litellm knows it's an openai compatible endpoint ``` Litellm supports a huge number of providers including Azure, VertexAi and Huggingface. See [their docs](https://docs.litellm.ai/docs/) for details on what environment variables, model names and llm_custom_providers you need to use for other providers. ", Assign "at most 3 tags" to the expected json: {"id":"7514","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"