SystemSh0cker/private-gpt
1
0
Fork 0
mirror of https://github.com/zylon-ai/private-gpt.git synced 2025-12-22 10:45:42 +01:00
Code Issues Projects Releases Packages Activity

feat(docs): update documentation and fix preview-docs (#2000)
Some checks are pending
publish docs / publish-docs (push) Waiting to run
release-please / release-please (push) Waiting to run
tests / setup (push) Waiting to run
tests / ${{ matrix.quality-command }} (black) (push) Blocked by required conditions
tests / ${{ matrix.quality-command }} (mypy) (push) Blocked by required conditions
tests / ${{ matrix.quality-command }} (ruff) (push) Blocked by required conditions
tests / test (push) Blocked by required conditions
tests / all_checks_passed (push) Blocked by required conditions

Browse source 
* docs: add missing configurations

* docs: change HF embeddings by ollama

* docs: add disclaimer about Gradio UI

* docs: improve readability in concepts

* docs: reorder `Fully Local Setups`

* docs: improve setup instructions

* docs: prevent have duplicate documentation and use table to show different options

* docs: rename privateGpt to PrivateGPT

* docs: update ui image

* docs: remove useless header

* docs: convert to alerts ingestion disclaimers

* docs: add UI alternatives

* docs: reference UI alternatives in disclaimers

* docs: fix table

* chore: update doc preview version

* chore: add permissions

* chore: remove useless line

* docs: fixes

...
This commit is contained in:
Javier Martinez 2024-07-18 10:06:51 +02:00 • committed by GitHub
parent 01b7ccd064
commit 4523a30c8f
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
13 changed files with 161 additions and 100 deletions
Download patch file Download diff file Expand all files Collapse all files

40
fern/docs/pages/installation/concepts.mdx
View file

@ -8,18 +8,25 @@ It supports a variety of LLM providers, embeddings providers, and vector stores,
## Setup configurations available
You get to decide the setup for these 3 main components:
- LLM: the large language model provider used for inference. It can be local, or remote, or even OpenAI.
- Embeddings: the embeddings provider used to encode the input, the documents and the users' queries. Same as the LLM, it can be local, or remote, or even OpenAI.
- Vector store: the store used to index and retrieve the documents.
- **LLM**: the large language model provider used for inference. It can be local, or remote, or even OpenAI.
- **Embeddings**: the embeddings provider used to encode the input, the documents and the users' queries. Same as the LLM, it can be local, or remote, or even OpenAI.
- **Vector store**: the store used to index and retrieve the documents.
There is an extra component that can be enabled or disabled: the UI. It is a Gradio UI that allows to interact with the API in a more user-friendly way.
<Callout intent = "warning">
A working **Gradio UI client** is provided to test the API, together with a set of useful tools such as bulk
model download script, ingestion script, documents folder watch, etc. Please refer to the [UI alternatives](/manual/user-interface/alternatives) page for more UI alternatives.
</Callout>
### Setups and Dependencies
Your setup will be the combination of the different options available. You'll find recommended setups in the [installation](./installation) section.
PrivateGPT uses poetry to manage its dependencies. You can install the dependencies for the different setups by running `poetry install --extras "<extra1> <extra2>..."`.
Extras are the different options available for each component. For example, to install the dependencies for a a local setup with UI and qdrant as vector database, Ollama as LLM and HuggingFace as local embeddings, you would run
Extras are the different options available for each component. For example, to install the dependencies for a a local setup with UI and qdrant as vector database, Ollama as LLM and local embeddings, you would run:
`poetry install --extras "ui vector-stores-qdrant llms-ollama embeddings-huggingface"`.
```bash
poetry install --extras "ui vector-stores-qdrant llms-ollama embeddings-ollama"
```
Refer to the [installation](./installation) section for more details.
@ -37,18 +44,6 @@ will load the configuration from `settings.yaml` and `settings-ollama.yaml`.
## About Fully Local Setups
In order to run PrivateGPT in a fully local setup, you will need to run the LLM, Embeddings and Vector Store locally.
### Vector stores
The vector stores supported (Qdrant, ChromaDB and Postgres) run locally by default.
### Embeddings
For local Embeddings there are two options:
* (Recommended) You can use the 'ollama' option in PrivateGPT, which will connect to your local Ollama instance. Ollama simplifies a lot the installation of local LLMs.
* You can use the 'embeddings-huggingface' option in PrivateGPT, which will use HuggingFace.
In order for HuggingFace LLM to work (the second option), 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
```
### LLM
For local LLM there are two options:
* (Recommended) You can use the 'ollama' option in PrivateGPT, which will connect to your local Ollama instance. Ollama simplifies a lot the installation of local LLMs.
@ -58,3 +53,14 @@ In order for LlamaCPP powered LLM to work (the second option), you need to downl
```bash
poetry run python scripts/setup
```
### Embeddings
For local Embeddings there are two options:
* (Recommended) You can use the 'ollama' option in PrivateGPT, which will connect to your local Ollama instance. Ollama simplifies a lot the installation of local LLMs.
* You can use the 'embeddings-huggingface' option in PrivateGPT, which will use HuggingFace.
In order for HuggingFace LLM to work (the second option), 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
```
### Vector stores
The vector stores supported (Qdrant, ChromaDB and Postgres) run locally by default.

118
fern/docs/pages/installation/installation.mdx
View file

@ -1,63 +1,101 @@
It is important that you review the Main Concepts before you start the installation process.
It is important that you review the [Main Concepts](../concepts) section to understand the different components of PrivateGPT and how they interact with each other.
## Base requirements to run PrivateGPT
* Clone PrivateGPT repository, and navigate to it:
### 1. Clone the PrivateGPT Repository
Clone the repository and navigate to it:
```bash
git clone https://github.com/zylon-ai/private-gpt
cd private-gpt
git clone https://github.com/zylon-ai/private-gpt
cd private-gpt
```
* Install Python `3.11` (*if you do not have it already*). Ideally through a python version manager like `pyenv`.
Earlier python versions are not supported.
* osx/linux: [pyenv](https://github.com/pyenv/pyenv)
* windows: [pyenv-win](https://github.com/pyenv-win/pyenv-win)
### 2. Install Python 3.11
If you do not have Python 3.11 installed, install it using a Python version manager like `pyenv`. Earlier Python versions are not supported.
#### macOS/Linux
Install and set Python 3.11 using [pyenv](https://github.com/pyenv/pyenv):
```bash
pyenv install 3.11
pyenv local 3.11
```
#### Windows
Install and set Python 3.11 using [pyenv-win](https://github.com/pyenv-win/pyenv-win):
```bash
pyenv install 3.11
pyenv local 3.11
```
* Install [Poetry](https://python-poetry.org/docs/#installing-with-the-official-installer) for dependency management:
### 3. Install `Poetry`
Install [Poetry](https://python-poetry.org/docs/#installing-with-the-official-installer) for dependency management:
Follow the instructions on the official Poetry website to install it.
* Install `make` to be able to run the different scripts:
* osx: (Using homebrew): `brew install make`
* windows: (Using chocolatey) `choco install make`
### 4. Optional: Install `make`
To run various scripts, you need to install `make`. Follow the instructions for your operating system:
#### macOS
(Using Homebrew):
```bash
brew install make
```
#### Windows
(Using Chocolatey):
```bash
choco install make
```
## Install and run your desired setup
## Install and Run Your Desired Setup
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: "llama-cpp", "ollama", "sagemaker", "openai", "openailike", "azopenai"
- Embeddings: "huggingface", "openai", "sagemaker", "azopenai"
- 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:
PrivateGPT allows customization of the setup, from fully local to cloud-based, by deciding the modules to use. To install only the required dependencies, PrivateGPT offers different `extras` that can be combined during the installation process:
```bash
poetry install --extras "<extra1> <extra2>..."
```
Where `<extra>` can be any of the following options described below.
Where `<extra>` can be any of the following:
### Available Modules
- ui: adds support for UI using Gradio
- llms-ollama: adds support for Ollama LLM, the easiest way to get a local LLM running, requires Ollama running locally
- llms-llama-cpp: adds support for local LLM using LlamaCPP - expect a messy installation process on some platforms
- llms-sagemaker: adds support for Amazon Sagemaker LLM, requires Sagemaker inference endpoints
- llms-openai: adds support for OpenAI LLM, requires OpenAI API key
- llms-openai-like: adds support for 3rd party LLM providers that are compatible with OpenAI's API
- llms-azopenai: adds support for Azure OpenAI LLM, requires Azure OpenAI inference endpoints
- embeddings-ollama: adds support for Ollama Embeddings, requires Ollama running locally
- embeddings-huggingface: adds support for local Embeddings using HuggingFace
- embeddings-sagemaker: adds support for Amazon Sagemaker Embeddings, requires Sagemaker inference endpoints
- embeddings-openai = adds support for OpenAI Embeddings, requires OpenAI API key
- embeddings-azopenai = adds support for Azure OpenAI Embeddings, requires Azure OpenAI inference endpoints
- vector-stores-qdrant: adds support for Qdrant vector store
- vector-stores-chroma: adds support for Chroma DB vector store
- vector-stores-postgres: adds support for Postgres vector store
You need to choose one option per category (LLM, Embeddings, Vector Stores, UI). Below are the tables listing the available options for each category.
#### LLM
| **Option** | **Description** | **Extra** |
|--------------|------------------------------------------------------------------------|---------------------|
| **ollama** | Adds support for Ollama LLM, requires Ollama running locally | llms-ollama |
| llama-cpp | Adds support for local LLM using LlamaCPP | llms-llama-cpp |
| sagemaker | Adds support for Amazon Sagemaker LLM, requires Sagemaker endpoints | llms-sagemaker |
| openai | Adds support for OpenAI LLM, requires OpenAI API key | llms-openai |
| openailike | Adds support for 3rd party LLM providers compatible with OpenAI's API | llms-openai-like |
| azopenai | Adds support for Azure OpenAI LLM, requires Azure endpoints | llms-azopenai |
| gemini | Adds support for Gemini LLM, requires Gemini API key | llms-gemini |
#### Embeddings
| **Option** | **Description** | **Extra** |
|------------------|--------------------------------------------------------------------------------|-------------------------|
| **ollama** | Adds support for Ollama Embeddings, requires Ollama running locally | embeddings-ollama |
| huggingface | Adds support for local Embeddings using HuggingFace | embeddings-huggingface |
| openai | Adds support for OpenAI Embeddings, requires OpenAI API key | embeddings-openai |
| sagemaker | Adds support for Amazon Sagemaker Embeddings, requires Sagemaker endpoints | embeddings-sagemaker |
| azopenai | Adds support for Azure OpenAI Embeddings, requires Azure endpoints | embeddings-azopenai |
| gemini | Adds support for Gemini Embeddings, requires Gemini API key | embeddings-gemini |
#### Vector Stores
| **Option** | **Description** | **Extra** |
|------------------|-----------------------------------------|-------------------------|
| **qdrant** | Adds support for Qdrant vector store | vector-stores-qdrant |
| chroma | Adds support for Chroma DB vector store | vector-stores-chroma |
| postgres | Adds support for Postgres vector store | vector-stores-postgres |
| clickhouse | Adds support for Clickhouse vector store| vector-stores-clickhouse|
#### UI
| **Option** | **Description** | **Extra** |
|--------------|------------------------------------------|-----------|
| Gradio | Adds support for UI using Gradio | ui |
<Callout intent = "warning">
A working **Gradio UI client** is provided to test the API, together with a set of useful tools such as bulk
model download script, ingestion script, documents folder watch, etc. Please refer to the [UI alternatives](/manual/user-interface/alternatives) page for more UI alternatives.
</Callout>
## Recommended Setups

13
fern/docs/pages/installation/troubleshooting.mdx
View file

@ -1,44 +1,31 @@
# Downloading Gated and Private Models
Many models are gated or private, requiring special access to use them. Follow these steps to gain access and set up your environment for using these models.
## Accessing Gated Models
1. **Request Access:**
Follow the instructions provided [here](https://huggingface.co/docs/hub/en/models-gated) to request access to the gated model.
2. **Generate a Token:**
Once you have access, generate a token by following the instructions [here](https://huggingface.co/docs/hub/en/security-tokens).
3. **Set the Token:**
Add the generated token to your `settings.yaml` file:
```yaml
huggingface:
access_token: <your-token>
```
Alternatively, set the `HF_TOKEN` environment variable:
```bash
export HF_TOKEN=<your-token>
```
# Tokenizer Setup
PrivateGPT uses the `AutoTokenizer` library to tokenize input text accurately. It connects to HuggingFace's API to download the appropriate tokenizer for the specified model.
## Configuring the Tokenizer
1. **Specify the Model:**
In your `settings.yaml` file, specify the model you want to use:
```yaml
llm:
tokenizer: mistralai/Mistral-7B-Instruct-v0.2
```
2. **Set Access Token for Gated Models:**
If you are using a gated model, ensure the `access_token` is set as mentioned in the previous section.
This configuration ensures that PrivateGPT can download and use the correct tokenizer for the model you are working with.