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Documentation updates and default settings reviewed

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fern/docs/pages/installation/installation.mdx
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@ -1,8 +1,8 @@
## Installation and Settings
It is important that you review the Main Concepts before you start the installation process.
### Base requirements to run PrivateGPT
## Base requirements to run PrivateGPT
* Git clone PrivateGPT repository, and navigate to it:
* Clone PrivateGPT repository, and navigate to it:
```bash
git clone https://github.com/imartinez/privateGPT
@ -21,93 +21,128 @@ pyenv local 3.11
* Install [Poetry](https://python-poetry.org/docs/#installing-with-the-official-installer) for dependency management:
* Have a valid C++ compiler like gcc. See [Troubleshooting: C++ Compiler](#troubleshooting-c-compiler) for more details.
* Install `make` for scripts:
* Install `make` to be able to run the different scripts:
* osx: (Using homebrew): `brew install make`
* windows: (Using chocolatey) `choco install make`
### Install dependencies
## Install and run your desired setup
Install the dependencies:
PrivateGPT allows to customize the setup -from fully local to cloud based- by deciding the modules to use.
Here are the different options available:
- LLM: "local" (uses LlamaCPP), "ollama", "sagemaker", "openai", "openailike"
- Embeddings: "local" (uses HuggingFace embeddings), "openai", "sagemaker"
- Vector stores: "qdrant", "chroma", "postgres"
- UI: whether or not to enable UI (Gradio) or just go with the API
In order to only install the required dependencies, PrivateGPT offers different `extras` that can be combined during the installation process:
```bash
poetry install --with ui
poetry install --extras "<extra1> <extra2>..."
```
Verify everything is working by running `make run` (or `poetry run python -m private_gpt`) and navigate to
http://localhost:8001. You should see a [Gradio UI](https://gradio.app/) **configured with a mock LLM** that will
echo back the input. Below we'll see how to configure a real LLM.
Where `<extra>` can be any of the following:
### Settings
- ui: adds support for UI using Gradio
- local: adds support for local LLM and Embeddings using LlamaCPP - expect a messy installation process on some platforms
- openai: adds support for OpenAI LLM and Embeddings, requires OpenAI API key
- sagemaker: adds support for Amazon Sagemaker LLM and Embeddings, requires Sagemaker endpoints
- ollama: adds support for Ollama LLM, the easiest way to get a local LLM running
- openai-like: adds support for 3rd party LLM providers that are compatible with OpenAI's API
- qdrant: adds support for Qdrant vector store
- chroma: adds support for Chroma DB vector store
- postgres: adds support for Postgres vector store
<Callout intent="info">
The default settings of PrivateGPT should work out-of-the-box for a 100% local setup. **However**, as is, it runs exclusively on your CPU.
Skip this section if you just want to test PrivateGPT locally, and come back later to learn about more configuration options (and have better performances).
</Callout>
## Recommended Setups
<br />
There are just some examples of recommended setups. You can mix and match the different options to fit your needs.
You'll find more information in the Manual section of the documentation.
### Local LLM requirements
### Local, Ollama-powered setup
Install extra dependencies for local execution:
The easiest way to run PrivateGPT fully locally is to depend on Ollama for the LLM. Ollama provides a local LLM that is easy to install and use.
Go to [ollama.ai](https://ollama.ai/) and follow the instructions to install Ollama on your machine.
Once done, you can install PrivateGPT with the following command:
```bash
poetry install --with local
poetry install --extras "ui local ollama qdrant"
```
For PrivateGPT to run fully locally GPU acceleration is required
(CPU execution is possible, but very slow), however,
typical Macbook laptops or window desktops with mid-range GPUs lack VRAM to run
even the smallest LLMs. For that reason
**local execution is only supported for models compatible with [llama.cpp](https://github.com/ggerganov/llama.cpp)**
These two models are known to work well:
* https://huggingface.co/TheBloke/Llama-2-7B-chat-GGUF
* https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF (recommended)
To ease the installation process, use the `setup` script that will download both
the embedding and the LLM model and place them in the correct location (under `models` folder):
We are installing "local" dependency to support local embeddings, because Ollama doesn't support embeddings just yet. But they working on it!
In order for local embeddings to work, you need to download the embeddings model to the `models` folder. You can do so by running the `setup` script:
```bash
poetry run python scripts/setup
```
If you are ok with CPU execution, you can skip the rest of this section.
Once installed, you can run PrivateGPT. Make sure you have a working Ollama running locally before running the following command.
As stated before, llama.cpp is required and in
```bash
PGPT_PROFILES=ollama make run
```
PrivateGPT will use the already existing `settings-ollama.yaml` settings file, which is already configured to use Ollama LLM, local Embeddings, and Qdrant. Review it and adapt it to your needs (different LLM model, different Ollama port, etc.)
The UI will be available at http://localhost:8001
### Private, Sagemaker-powered setup
If you need more performance, you can run a version of PrivateGPT that relies on powerful AWS Sagemaker machines to serve the LLM and Embeddings.
You need to have access to sagemaker inference endpoints for the LLM and / or the embeddings, and have AWS credentials properly configured.
Edit the `settings-sagemaker.yaml` file to include the correct Sagemaker endpoints.
Then, install PrivateGPT with the following command:
```bash
poetry install --extras "ui sagemaker qdrant"
```
Once installed, you can run PrivateGPT. Make sure you have a working Ollama running locally before running the following command.
```bash
PGPT_PROFILES=sagemaker make run
```
PrivateGPT will use the already existing `settings-sagemaker.yaml` settings file, which is already configured to use Sagemaker LLM and Embeddings endpoints, and Qdrant.
The UI will be available at http://localhost:8001
### Local, Llama-CPP powered setup
If you want to run PrivateGPT fully locally without relying on Ollama, you can run the following command:
```bash
poetry install --extras "ui local qdrant"
```
In order for local LLM and embeddings to work, you need to download the models to the `models` folder. You can do so by running the `setup` script:
```bash
poetry run python scripts/setup
```
Once installed, you can run PrivateGPT with the following command:
```bash
PGPT_PROFILES=local make run
```
PrivateGPT will load the already existing `settings-local.yaml` file, which is already configured to use LlamaCPP and Qdrant.
The UI will be available at http://localhost:8001
#### Llama-CPP support
For PrivateGPT to run fully locally without Ollama, Llama.cpp is required and in
particular [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
is used.
You'll need to have a valid C++ compiler like gcc installed. See [Troubleshooting: C++ Compiler](#troubleshooting-c-compiler) for more details.
> It's highly encouraged that you fully read llama-cpp and llama-cpp-python documentation relevant to your platform.
> Running into installation issues is very likely, and you'll need to troubleshoot them yourself.
#### Customizing low level parameters
Currently, not all the parameters of `llama.cpp` and `llama-cpp-python` are available at PrivateGPT's `settings.yaml` file.
In case you need to customize parameters such as the number of layers loaded into the GPU, you might change
these at the `llm_component.py` file under the `private_gpt/components/llm/llm_component.py`.
##### Available LLM config options
The `llm` section of the settings allows for the following configurations:
- `mode`: how to run your llm
- `max_new_tokens`: this lets you configure the number of new tokens the LLM will generate and add to the context window (by default Llama.cpp uses `256`)
Example:
```yaml
llm:
mode: local
max_new_tokens: 256
```
If you are getting an out of memory error, you might also try a smaller model or stick to the proposed
recommended models, instead of custom tuning the parameters.
#### OSX GPU support
##### Llama-CPP OSX GPU support
You will need to build [llama.cpp](https://github.com/ggerganov/llama.cpp) with metal support.
@ -127,7 +162,7 @@ More information is available in the documentation of the libraries themselves:
* [llama-cpp-python's documentation](https://llama-cpp-python.readthedocs.io/en/latest/#installation-with-hardware-acceleration)
* [llama.cpp](https://github.com/ggerganov/llama.cpp#build)
#### Windows NVIDIA GPU support
##### Llama-CPP Windows NVIDIA GPU support
Windows GPU support is done through CUDA.
Follow the instructions on the original [llama.cpp](https://github.com/ggerganov/llama.cpp) repo to install the required
@ -160,7 +195,7 @@ Note that llama.cpp offloads matrix calculations to the GPU but the performance
still hit heavily due to latency between CPU and GPU communication. You might need to tweak
batch sizes and other parameters to get the best performance for your particular system.
#### Linux NVIDIA GPU support and Windows-WSL
##### Llama-CPP Linux NVIDIA GPU support and Windows-WSL
Linux GPU support is done through CUDA.
Follow the instructions on the original [llama.cpp](https://github.com/ggerganov/llama.cpp) repo to install the required
@ -188,7 +223,7 @@ llama_new_context_with_model: total VRAM used: 4857.93 MB (model: 4095.05 MB, co
AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 0 | VSX = 0 |
```
### Known issues and Troubleshooting
##### Llama-CPP Known issues and Troubleshooting
Execution of LLMs locally still has a lot of sharp edges, specially when running on non Linux platforms.
You might encounter several issues:
@ -205,7 +240,7 @@ If, during your installation, something does not go as planned, retry in *verbos
For example, when installing packages with `pip install`, you can add the option `-vvv` to show the details of the installation.
#### Troubleshooting: C++ Compiler
##### Llama-CPP Troubleshooting: C++ Compiler
If you encounter an error while building a wheel during the `pip install` process, you may need to install a C++
compiler on your computer.
@ -227,7 +262,7 @@ To install a C++ compiler on Windows 10/11, follow these steps:
Store and search for Xcode and install it. **Or** you can install the command line tools by running `xcode-select --install`.
2. If not, you can install clang or gcc with homebrew `brew install gcc`
#### Troubleshooting: Mac Running Intel
##### Llama-CPP Troubleshooting: Mac Running Intel
When running a Mac with Intel hardware (not M1), you may run into _clang: error: the clang compiler does not support '
-march=native'_ during pip install.