diff --git a/README.md b/README.md
index f8593fee3..a61a40633 100644
--- a/README.md
+++ b/README.md
@@ -2,11 +2,11 @@
-PrivateGPT is a production-ready AI project that allows you to ask questions to your documents using the power
-of Large Language Models (LLMs), even in scenarios without Internet connection. 100% private, no data leaves your
+PrivateGPT is a production-ready AI project that allows you to ask questions to your documents using the power
+of Large Language Models (LLMs), even in scenarios without Internet connection. 100% private, no data leaves your
execution environment at any point.
-The project provides an API offering all the primitives required to build private, context-aware AI applications.
+The project provides an API offering all the primitives required to build private, context-aware AI applications.
It follows and extends [OpenAI API standard](https://openai.com/blog/openai-api),
and supports both normal and streaming responses.
@@ -14,56 +14,56 @@ The API is divided into two logical blocks:
**High-level API**, which abstracts all the complexity of a RAG (Retrieval Augmented Generation)
pipeline implementation:
-- Ingestion of documents: internally managing document parsing,
+- Ingestion of documents: internally managing document parsing,
splitting, metadata extraction, embedding generation and storage.
-- Chat & Completions using context from ingested documents:
+- Chat & Completions using context from ingested documents:
abstracting the retrieval of context, the prompt engineering and the response generation.
**Low-level API**, which allows advanced users to implement their own complex pipelines:
- Embeddings generation: based on a piece of text.
- Contextual chunks retrieval: given a query, returns the most relevant chunks of text from the ingested documents.
-In addition to this, a working [Gradio UI](https://www.gradio.app/)
+In addition to this, a working [Gradio UI](https://www.gradio.app/)
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.
-> 👂 **Need help applying PrivateGPT to your specific use case?**
-> [Let us know more about it](https://forms.gle/4cSDmH13RZBHV9at7)
+> 👂 **Need help applying PrivateGPT to your specific use case?**
+> [Let us know more about it](https://forms.gle/4cSDmH13RZBHV9at7)
> and we'll try to help! We are refining PrivateGPT through your feedback.
## 🎞️ Overview
-DISCLAIMER: This README is not updated as frequently as the [documentation](https://docs.privategpt.dev/).
+DISCLAIMER: This README is not updated as frequently as the [documentation](https://docs.privategpt.dev/).
Please check it out for the latest updates!
### Motivation behind PrivateGPT
Generative AI is a game changer for our society, but adoption in companies of all size and data-sensitive
-domains like healthcare or legal is limited by a clear concern: **privacy**.
-Not being able to ensure that your data is fully under your control when using third-party AI tools
+domains like healthcare or legal is limited by a clear concern: **privacy**.
+Not being able to ensure that your data is fully under your control when using third-party AI tools
is a risk those industries cannot take.
### Primordial version
-The first version of PrivateGPT was launched in May 2023 as a novel approach to address the privacy
-concern by using LLMs in a complete offline way.
+The first version of PrivateGPT was launched in May 2023 as a novel approach to address the privacy
+concern by using LLMs in a complete offline way.
This was done by leveraging existing technologies developed by the thriving Open Source AI community:
-[LangChain](https://github.com/hwchase17/langchain), [LlamaIndex](https://www.llamaindex.ai/),
-[GPT4All](https://github.com/nomic-ai/gpt4all),
-[LlamaCpp](https://github.com/ggerganov/llama.cpp),
-[Chroma](https://www.trychroma.com/)
+[LangChain](https://github.com/hwchase17/langchain), [LlamaIndex](https://www.llamaindex.ai/),
+[GPT4All](https://github.com/nomic-ai/gpt4all),
+[LlamaCpp](https://github.com/ggerganov/llama.cpp),
+[Chroma](https://www.trychroma.com/)
and [SentenceTransformers](https://www.sbert.net/).
That version, which rapidly became a go-to project for privacy-sensitive setups and served as the seed
-for thousands of local-focused generative AI projects, was the foundation of what PrivateGPT is becoming nowadays;
-thus a simpler and more educational implementation to understand the basic concepts required
+for thousands of local-focused generative AI projects, was the foundation of what PrivateGPT is becoming nowadays;
+thus a simpler and more educational implementation to understand the basic concepts required
to build a fully local -and therefore, private- chatGPT-like tool.
-If you want to keep experimenting with it, we have saved it in the
+If you want to keep experimenting with it, we have saved it in the
[primordial branch](https://github.com/imartinez/privateGPT/branches) of the project.
### Present and Future of PrivateGPT
-PrivateGPT is now evolving towards becoming a gateway to generative AI models and primitives, including
-completions, document ingestion, RAG pipelines and other low-level building blocks.
+PrivateGPT is now evolving towards becoming a gateway to generative AI models and primitives, including
+completions, document ingestion, RAG pipelines and other low-level building blocks.
We want to make easier for any developer to build AI applications and experiences, as well as providing
-a suitable extensive architecture for the community to keep contributing.
+a suitable extensive architecture for the community to keep contributing.
Stay tuned to our [releases](https://github.com/imartinez/privateGPT/releases) to check all the new features and changes included.
@@ -72,31 +72,31 @@ Full documentation on installation, dependencies, configuration, running the ser
ingesting local documents, API details and UI features can be found here: https://docs.privategpt.dev/
## 🧩 Architecture
-Conceptually, PrivateGPT is an API that wraps a RAG pipeline and exposes its
+Conceptually, PrivateGPT is an API that wraps a RAG pipeline and exposes its
primitives.
-* The API is built using [FastAPI](https://fastapi.tiangolo.com/) and follows
+* The API is built using [FastAPI](https://fastapi.tiangolo.com/) and follows
[OpenAI's API scheme](https://platform.openai.com/docs/api-reference).
* The RAG pipeline is based on [LlamaIndex](https://www.llamaindex.ai/).
-The design of PrivateGPT allows to easily extend and adapt both the API and the
+The design of PrivateGPT allows to easily extend and adapt both the API and the
RAG implementation. Some key architectural decisions are:
* Dependency Injection, decoupling the different componentes and layers.
-* Usage of LlamaIndex abstractions such as `LLM`, `BaseEmbedding` or `VectorStore`,
- making it immediate to change the actual implementations of those abstractions.
-* Simplicity, adding as few layers and new abstractions as possible.
-* Ready to use, providing a full implementation of the API and RAG
- pipeline.
+* Usage of LlamaIndex abstractions such as `LLM`, `BaseEmbedding` or `VectorStore`,
+ making it immediate to change the actual implementations of those abstractions.
+* Simplicity, adding as few layers and new abstractions as possible.
+* Ready to use, providing a full implementation of the API and RAG
+ pipeline.
Main building blocks:
* APIs are defined in `private_gpt:server:`. Each package contains an
`_router.py` (FastAPI layer) and an `_service.py` (the
- service implementation). Each *Service* uses LlamaIndex base abstractions instead
+ service implementation). Each *Service* uses LlamaIndex base abstractions instead
of specific implementations,
decoupling the actual implementation from its usage.
-* Components are placed in
- `private_gpt:components:`. Each *Component* is in charge of providing
- actual implementations to the base abstractions used in the Services - for example
- `LLMComponent` is in charge of providing an actual implementation of an `LLM`
+* Components are placed in
+ `private_gpt:components:`. Each *Component* is in charge of providing
+ actual implementations to the base abstractions used in the Services - for example
+ `LLMComponent` is in charge of providing an actual implementation of an `LLM`
(for example `LlamaCPP` or `OpenAI`).
## 💡 Contributing
@@ -105,8 +105,8 @@ typing checks, just run `make check` before committing to make sure your code is
Remember to test your code! You'll find a tests folder with helpers, and you can run
tests using `make test` command.
-Interested in contributing to PrivateGPT? We have the following challenges ahead of us in case
-you want to give a hand:
+Interested in contributing to PrivateGPT? We have the following challenges ahead of us in case
+you want to give a hand:
### Improvements
- Better RAG pipeline implementation (improvements to both indexing and querying stages)
@@ -120,7 +120,7 @@ you want to give a hand:
### New features
- Implement concurrency lock to avoid errors when there are several calls to the local LlamaCPP model
-- API key-based request control to the API
+- API key-based request control to the API
- CORS support
- Support for Sagemaker
- Support Function calling
@@ -134,11 +134,11 @@ you want to give a hand:
- Dockerize the application for platforms outside linux (Docker Desktop for Mac and Windows)
- Document how to deploy to AWS, GCP and Azure.
-##
+##
-## 💬 Community
+## 💬 Community
Join the conversation around PrivateGPT on our:
-- [Twitter (aka X)](https://twitter.com/ivanmartit)
+- [Twitter (aka X)](https://twitter.com/PrivateGPT_AI)
- [Discord](https://discord.gg/bK6mRVpErU)
## 📖 Citation