Update twitter link.

README.md

@@ -2,68 +2,68 @@
 
 <img width="900"  alt="demo" src="https://lh3.googleusercontent.com/drive-viewer/AK7aPaBasLxbp49Hrwnmi_Ctii1oIM18nFJrBO0ERSE3wpkS-syjiQBE32_tUSdqnjn6etUDjUSkdJeFa8acqRb0lZbkZ6CyAw=s1600">
 
-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.
 
 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:<api>`. Each package contains an
   `<api>_router.py` (FastAPI layer) and an `<api>_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:<component>`. 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:<component>`. 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