dir-assistant 1.0.3

Chat with your current directory's files using a local or API LLM.

dir-assistant

Chat with your current directory's files using a local or API LLM.

Dir-assistant has local platform support for CPU (OpenBLAS), Cuda, ROCm, Metal, Vulkan, and SYCL.

Dir-assistant has API support for all major LLM APIs. More info in the LiteLLM Docs.

Dir-assistant uses a unique method for finding the most important files to include when submitting your prompt to an LLM called CGRAG (Contextually Guided Retrieval-Augmented Generation). You can read this blog post for more information about how it works.

This project runs local LLMs via the fantastic llama-cpp-python package and runs API LLMS using the also fantastic LiteLLM package.

New Features

• Now installable via pip
• Thorough CLI functionality including platform installation, model downloading, and config editing.
• User files have been moved to appropriate home hidden directories.
• Config now has llama.cpp completion options exposed (top_k, frequency_penalty, etc.)

Quickstart

In this section are recipes to run dir-assistant in basic capacity to get you started quickly.

Quickstart with Local Default Model (Phi 3 128k)

To get started locally, you can download a default llm model. Default configuration with this model requires 11GB of memory on most hardware or 8GB on nvidia GPUs due to flash attention availability (enabled by default). You will be able to adjust the configuration to fit higher or lower memory requirements. To run via CPU:

pip install dir-assistant
dir-assistant models download-embed
dir-assistant models download-llm
cd directory/to/chat/with
dir-assistant

To run with hardware acceleration, use the platform subcommand:

...
dir-assistant platform cuda
cd directory/to/chat/with
dir-assistant

See which platforms are supported using -h:

dir-assistant platform -h

Quickstart with API Model

To get started using an API model, you can use Google Gemini 1.5 Flash, which is currently free. To begin, you need to sign up for Google AI Studio and create an API key. After you create your API key, enter the following commands:

pip install dir-assistant
dir-assistant models download-embed
dir-assistant setkey GEMINI_API_KEY xxxxxYOURAPIKEYHERExxxxx
cd directory/to/chat/with
dir-assistant

You can optionally hardware-accelerate your local embedding model so indexing is quicker:

...
dir-assistant platform cuda
cd directory/to/chat/with
dir-assistant

See which platforms are supported using -h:

dir-assistant platform -h

Install

Install with pip:

pip install dir-assistant

The default configuration for dir-assistant is API-mode. If you download an LLM model with download-llm, local-mode will automatically be set. To change from API-mode to local-mode, set the ACTIVE_MODEL_IS_LOCAL setting.

Embedding Model Download

You must download an embedding model regardless of whether you are running in local or API mode. You can download a good default embedding model with:

dir-assistant models download-embed

If you would like to use another embedding model, open the models directory with:

dir-assistant models

Note: The embedding model will be hardware accelerated after using the platform subcommand. To disable hardware acceleration, change n_gpu_layers = -1 to n_gpu_layers = 0 in the config.

Optional: Select A Hardware Platform

By default dir-assistant is installed with CPU-only compute support. It will work properly without this step, but if you would like to hardware accelerate dir-assistant, use the command below to compile llama-cpp-python with your hardware's support.

dir-assistant platform cuda

Available options: cpu, cuda, rocm, metal, vulkan, sycl

Note: The embedding model and the local llm model will be run with acceleration after selecting a platform. To disable hardware acceleration change n_gpu_layers = -1 to n_gpu_layers = 0 in the config.

For Platform Install Issues

System dependencies may be required for the platform command and are outside the scope of these instructions.

If you have any issues building llama-cpp-python, the project's install instructions may offer more info: https://github.com/abetlen/llama-cpp-python

API Configuration

If you wish to use an API LLM, you will need to configure it. To configure which LLM API dir-assistant uses, you must edit LITELLM_MODEL and the appropriate API key in your configuration. To open your configuration file, enter:

dir-assistant config open

Once editing the file, change:

[DIR_ASSISTANT]
LITELLM_MODEL = "gemini/gemini-1.5-flash-latest"
LITELLM_CONTEXT_SIZE = 500000
...
[DIR_ASSISTANT.LITELLM_API_KEYS]
GEMINI_API_KEY = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

LiteLLM supports all major LLM APIs, including APIs hosted locally. View the available options in the LiteLLM providers list.

There is a convenience subcommand for modifying and adding API keys:

dir-assistant setkey GEMINI_API_KEY xxxxxYOURAPIKEYHERExxxxx

However, in most cases you will need to modify other options when changing APIs.

Local LLM Model Download

If you want to use a local LLM, you can download a low requirements default model (Phi 3 128k) with:

dir-assistant models download-llm

Note: The local LLM model will be hardware accelerated after using the platform subcommand. To disable hardware acceleration, change n_gpu_layers = -1 to n_gpu_layers = 0 in the config.

Configuring A Custom Local Model

If you would like to use a custom local LLM model, download a GGUF model and place it in your models directory. Huggingface has numerous GGUF models to choose from. The models directory can be opened in a file browser using this command:

dir-assistant models

After putting your gguf in the models directory, you must configure dir-assistant to use it:

dir-assistant config open

Edit the following setting:

[DIR_ASSISTANT]
LLM_MODEL = "Mistral-Nemo-Instruct-2407.Q6_K.gguf"

Llama.cpp Options

Llama.cpp provides a large number of options to customize how your local model is run. Most of these options are exposed via llama-cpp-python. You can configure them with the [DIR_ASSISTANT.LLAMA_CPP_OPTIONS], [DIR_ASSISTANT.LLAMA_CPP_EMBED_OPTIONS], and [DIR_ASSISTANT.LLAMA_CPP_COMPLETION_OPTIONS] sections in the config file.

The options available for llama-cpp-python are documented in the Llama constructor documentation.

What the options do is also documented in the llama.cpp CLI documentation.

The most important llama-cpp-python options are related to tuning the LLM to your system's VRAM:

• Setting n_ctx lower will reduce the amount of VRAM required to run, but will decrease the amount of file text that can be included when running a prompt.
• CONTEXT_FILE_RATIO sets the proportion of prompt history to file text to be included when sent to the LLM. Higher ratios mean more file text and less prompt history. More file text generally improves comprehension.
• If your llm n_ctx times CONTEXT_FILE_RATIO is smaller than your embed n_ctx, your file text chunks have the potential to be larger than your llm context, and thus will not be included. To ensure all files can be included, make sure your embed context is smaller than n_ctx times CONTEXT_FILE_RATIO.
• Larger embed n_ctx will chunk your files into larger sizes, which allows LLMs to understand them more easily.
• n_batch must be smaller than the n_ctx of a model, but setting it higher will probably improve performance.

For other tips about tuning Llama.cpp, explore their documentation and do some google searches.

Running

dir-assistant

Running dir-assistant will scan all files recursively in your current directory. The most relevant files will automatically be sent to the LLM when you enter a prompt.

Upgrading

Some version upgrades may have incompatibility issues in the embedding index cache. Use this command to delete the index cache so it may be regenerated:

dir-assistant clear

Additional Help

Use the -h argument with any command or subcommand to view more information. If your problem is beyond the scope of the helptext, please report a github issue.

Contributors

We appreciate contributions from the community! For a list of contributors and how you can contribute, please see CONTRIBUTORS.md.

Limitations

• Only tested on Ubuntu 22.04. Please let us know if you run it successfully on other platforms by submitting an issue.
• Dir-assistant only detects and reads text files at this time.

Todos

• API LLMs
• RAG
• File caching (improve startup time)
• CGRAG (Contextually-Guided Retrieval-Augmented Generation)
• Multi-line input
• File watching (automatically reindex changed files)
• Single-step pip install
• Model download
• Web search
• API Embedding models
• Simple mode for better compatibility with external script automations