Slides for FaVe suite

[tfius]

Below is a slide deck outline for short descriptions and placeholders for links. Each slide focuses on one product, providing a succinct description and highlighting its unique features.

---

Slide 1: FaVe

Decentralized Vector Store

FairOS Vector Store
FaVe is a cutting-edge decentralized vector store built on FairOS and Swarm decentralized storage, designed to provide fast, secure, and scalable vector-based search capabilities.

• Built on FairOS and Swarm
• Decentralized and Secure
• Free your embedings from data siloses

Learn More About FaVe

---

Slide 2: FaVe Python Client

Integration into LangChain

The FaVe Python Client is a powerful tool for seamless integration into LangChain, offering an easy-to-use API for interacting with the FaVe decentralized vector store.

• Easy-to-Use API
• Seamless Integration for Python projects
• use it with LangChain

Explore FaVe Python Client

---

Slide 3: Vectorizer

Generate Embeddings

Vectorizer is a specialized tool designed to generate high-quality embeddings, making it easier to index and search through complex data sets.

• High-Quality Embeddings
• Easy to Use
• Versatile Applications

Discover Vectorizer

---

Slide 4: w3kipedia

Wikipedia Content Search

w3kipedia is a practical example of FaVe's capabilities, allowing users to index and search through Wikipedia content with unparalleled speed and accuracy.

• Fast and Accurate Search on Wikipedia Content
• Showcases FaVe Capabilities
• ?

Explore w3kipedia-fave

---

Slide 5: FaVeDemo

LangChain Integration with localGPT and Q&A on documents

FaVeDemo is an innovative solution that integrates LangChain with local GPT and provides Q&A functionalities for documents stored in FaVe, offering a complete and secure data management solution.

• Local GPT with Q&A on your FaVe documents
• Ingest Local Data, stored on FaVe/FairOs/Swarm
• Complete Demo Solution, easily extendable

Learn More About FaVeDemo

---

[tfius]

Potential Questions

FaVe Decentralized Vector Store

1. What are the advantages of using FairOS and Swarm for decentralized storage?
2. How does FaVe ensure the security of the data?
3. What types of data can be stored and searched using FaVe?
4. How does FaVe compare to traditional vector stores?
5. Are there any costs associated with using FaVe?

FaVe Python Client - Integration into LangChain

1. How does the Python client simplify the integration process with LangChain?
2. Are there any prerequisites or dependencies for using the FaVe Python Client?
3. How does the client handle errors or failed requests?
4. Is the client compatible with other programming languages or just Python?
5. Can the client be used in cloud environments or serverless architectures?

Vectorizer - Generate Embeddings

1. What types of embeddings can the Vectorizer generate?
2. How does Vectorizer ensure the quality of the embeddings?
3. Are there any size or format limitations for the data being processed?
4. Can Vectorizer be integrated with other machine learning or data processing tools?
5. How does Vectorizer handle different languages or character sets?

w3kipedia - Wikipedia Content Search

1. How is w3kipedia different from the regular Wikipedia search?
2. Are there any limitations on the amount or type of content that can be searched?
3. How does w3kipedia handle updates or changes to Wikipedia content?
4. Can w3kipedia be used to search other large datasets or just Wikipedia?
5. Is there a user interface or API for w3kipedia?

FaVeDemo LangChain Integration with localGPT and Q&A on documents

1. How does FaVeDemo integrate with local GPT for Q&A functionalities?
2. What types of documents can be stored and queried using FaVeDemo?
3. How does FaVeDemo ensure the privacy and security of the documents?
4. Can FaVeDemo be customized or extended for specific use cases?
5. Are there any sample datasets or demos available to test FaVeDemo's capabilities?

[tfius]

FaVe Decentralized Vector Store

1. FairOS and Swarm Advantages: FairOS and Swarm offer decentralized storage solutions that ensure data redundancy, fault tolerance, and enhanced security. Their peer-to-peer nature ensures data availability even if some nodes fail.
2. FaVe Security: FaVe employs end-to-end encryption and leverages the inherent security features of FairOS and Swarm to ensure data integrity and confidentiality.
3. Types of Data: FaVe can store and search any data that can be represented as vectors, including text, images, and more complex data structures.
4. Comparison: Unlike traditional vector stores, FaVe offers decentralized storage, ensuring better data availability, redundancy, and security.
5. Costs: FaVe operates on a Swarm decentralized platform, which does have associated costs.

FaVe Python client Integration into LangChain

1. Simplifying Integration: The FaVe Python Client provides a set of easy-to-use APIs that abstract the complexities of interacting with the FaVe store, making integration with LangChain straightforward.
2. Prerequisites: The client requires Python and certain dependencies, which are listed in the requirements.txt file and running FaVe server (https://github.com/fairDataSociety/FaVe).
3. Error Handling: The client has built-in error handling mechanisms that provide informative feedback on failed requests or issues.
4. Compatibility: Currently, the client is designed for Python, but there's potential for developing clients for other languages in the future via OpenAPI specs and CodeGen.
5. Cloud and Serverless: The client is designed to be flexible and can be used in various environments, including cloud and serverless architectures.

Vectorizer - Generate Embeddings

Vectorizer is provided as simple example.

1. Types of Embeddings: Vectorizer can generate embeddings for text. Not been tested on images, or audio, or anything else.
2. Quality Assurance: Vectorizer uses sample underlying model.
3. Size and Format: While Vectorizer is versatile, there might be limitations based on the underlying model.
4. Integration: Vectorizer is meant as example.
5. Languages: Vectorizer is designed to handle English language, and its performance is based on underlying model.
   (https://github.com/fairDataSociety/huggingface-vectorizer)

In LangChain you can provide custom embedings.

FaVeClient("sotuTen", "text", "http://localhost:1234", llama)

w3kipedia> Wikipedia Content Search

1. Difference from Wikipedia: w3kipedia leverages FaVe's capabilities to provide search results
2. Limitations: w3kipedia is designed to text content, but like any system, there might be performance variations based on the complexity of queries and number of documents in FaVe.
3. Updates and Changes: w3kipedia is a demo. Updates and Changes might happen depending on community.
4. Other Datasets: While w3kipedia is tailored for Wikipedia, the underlying technology can be adapted to search other datasets.
5. Interface: w3kipedia is a demo that offers both a user interface for direct searches.

FaVeDemo - LangChain Integration with localGPT and Q&A on documents

1. Integration with local GPT: FaVeDemo integrates with local GPT models to provide real-time Q&A functionalities on documents stored in FaVe.
2. Types of Documents: FaVeDemo can handle text formats, including text, PDFs, and more can be added easily.
3. Privacy and Security: FaVeDemo ensures data privacy by storing documents in FaVe, which uses decentralized storage with encryption. The local GPT ensures that Q&A operations are secure and don't expose data.
4. Customization: FaVeDemo is simple demo, modular, allowing developers to extend or customize it for specific use cases.
5. Datasets: FaVeDemo put your docs in correct folder and run ingest.

[asabya]

Too good !

[asabya]

Slide 1: FaVe

Lets clearify FaVe as acronym. FairOS Vector store. So we don't have to say "FaVe decentralized vector store".

[tfius]

added to first slide

[asabya]

Can we add this in the slides?

I'm not an expert. I have run many manual and automated tests and it seems generally correct. This is not ready to be used in production yet.

[tfius]

I don't think so. The production part we can put somewhere.

[asabya]

Slide 4: w3kipedia

Wikipedia Content Search

w3kipedia is a practical example of FaVe's capabilities, allowing users to index and search through Wikipedia content with unparalleled speed and accuracy.

• Fast and Accurate Search on Wikipedia Content
• Showcases FaVe Capabilities
• ?

Semantic search?

[tfius]

% FaVe
% Sabyasachi Patra, Tadej Fius
% 27.9.2023

FaVe Decentralized Vector Store

• FairOS Vector Store on Swarm

FaVe

Designed to provide fast, secure, and scalable vector-based search capabilities.

• Secured by FairOS and Swarm
• Decentralized and Secure
• Free embedings from siloses
• Learn More About FaVe

::: notes
Speaker notes go here
:::

FaVe Python client

Easy-to-use, seamless integration for interacting with the FaVe.

• Easy-to-Use API
• Seamless Python Integration
• Use it with LangChain
• Explore FaVe Python Client

::: notes
Speaker notes go here
:::

Generate Embeddings with Vectorizer

High-quality embeddings for indexes and search.

• High-Quality Embeddings
• Easy to Use
• Versatile Applications
• Discover Vectorizer

::: notes
Speaker notes go here
:::

w3kipedia Content Search

An example of FaVe's capabilities how to index and search content.

• Semantic Search Content
• Showcases FaVe Capabilities
• Explore w3kipedia-fave

::: notes
Speaker notes go here
:::

FaVeDemo Integration with LangChain, localGPT and Q&A on documents

Chat with documents securely - data management solution.

• Ingest data into FaVe
• LocalGPT with FaVe as retriever
• Easily extendable solution
• Learn More About FaVeDemo

::: notes
Speaker notes go here
:::

[tfius]

build it with pandoc -t dzslides -s slides.md -o slides.htm

[tfius]

demo video
https://github.com/fairDataSociety/FaVe/assets/1674145/197fe30b-d666-412b-af61-7de2ff90dea7