Feature/upgrade LlamaIndex to 0.10

.github/workflows/actions/install_dependencies/action.yml

@@ -25,6 +25,6 @@ runs:
         python-version: ${{ inputs.python_version }}
         cache: "poetry"
     - name: Install Dependencies
-      run: poetry install --with ui --no-root
+      run: poetry install --extras "ui vector-stores-qdrant" --no-root
       shell: bash
 

Dockerfile.external

@@ -14,7 +14,7 @@ FROM base as dependencies
 WORKDIR /home/worker/app
 COPY pyproject.toml poetry.lock ./
 
-RUN poetry install --with ui
+RUN poetry install --extras "ui vector-stores-qdrant"
 
 FROM base as app
 

Dockerfile.local

@@ -24,8 +24,7 @@ FROM base as dependencies
 WORKDIR /home/worker/app
 COPY pyproject.toml poetry.lock ./
 
-RUN poetry install --with local
-RUN poetry install --with ui
+RUN poetry install --extras "ui embeddings-huggingface llms-llama-cpp vector-stores-qdrant"
 
 FROM base as app
 

fern/docs.yml

@@ -30,15 +30,15 @@ navigation:
     layout:
       - section: Welcome
         contents:
-          - page: Welcome
+          - page: Introduction
             path: ./docs/pages/overview/welcome.mdx
-          - page: Quickstart
-            path: ./docs/pages/overview/quickstart.mdx
   # How to install privateGPT, with FAQ and troubleshooting
   - tab: installation
     layout:
       - section: Getting started
         contents:
+          - page: Main Concepts
+            path: ./docs/pages/installation/concepts.mdx
           - page: Installation
             path: ./docs/pages/installation/installation.mdx
   # Manual of privateGPT: how to use it and configure it

fern/docs/pages/installation/concepts.mdx

@@ -0,0 +1,59 @@
+PrivateGPT is a service that wraps a set of AI RAG primitives in a comprehensive set of APIs providing a private, secure, customizable and easy to use GenAI development framework.
+
+It uses FastAPI and LLamaIndex as its core frameworks. Those can be customized by changing the codebase itself.
+
+It supports a variety of LLM providers, embeddings providers, and vector stores, both local and remote. Those can be easily changed without changing the codebase.
+
+# Different Setups support
+
+## 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.
+
+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.
+
+### 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
+
+`poetry install --extras "ui vector-stores-qdrant llms-ollama embeddings-huggingface"`.
+
+Refer to the [installation](/installation) section for more details.
+
+### Setups and Configuration
+PrivateGPT uses yaml to define its configuration in files named `settings-<profile>.yaml`.
+Different configuration files can be created in the root directory of the project.
+PrivateGPT will load the configuration at startup from the profile specified in the `PGPT_PROFILES` environment variable.
+For example, running:
+```bash
+PGPT_PROFILES=ollama make run
+```
+will load the configuration from `settings.yaml` and `settings-ollama.yaml`.
+- `settings.yaml` is always loaded and contains the default configuration.
+- `settings-ollama.yaml` is loaded if the `ollama` profile is specified in the `PGPT_PROFILES` environment variable. It can override configuration from the default `settings.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 you need to install the 'embeddings-huggingface' extra dependencies. It will use Huggingface Embeddings.
+
+Note: Ollama will support Embeddings in the short term for easier installation, but it doesn't as of today.
+
+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
+```
+### 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.
+* You can use the 'llms-llama-cpp' option in PrivateGPT, which will use LlamaCPP. It works great on Mac with Metal most of the times (leverages Metal GPU), but it can be tricky in certain Linux and Windows distributions, depending on the GPU. In the installation document you'll find guides and troubleshooting.
+
+In order for local 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
+```