Product introduction

FlerkenS is a Web3-based AI application product designed to provide users with personalized content and services while protecting users' data and privacy. FlerkenS uses LLM and other AI technologies to generate and recommend various types of content, including text, pictures, audio, video, etc., based on user behavior data and preferences. Users can interact with other users or intelligent agents through FlerkenS, share and obtain information, participate in communities and activities, and obtain rewards and values.

The core value of FlerkenS lies in:

- Provide users with high-quality and diverse content to meet the different needs and interests of users.

- Provide users with intelligent and friendly interactions, enhancing user engagement and experience.

- Provide users with a safe and fair platform to protect user data and privacy.

- Provide users with an artificial intelligence collaborative production platform, and use smart contracts to ensure the distribution of benefits.

LangChain technology introduction

LangChain is an LLM-based AI application development framework, which solves the following problems:

- LLMs have limited memory and cannot handle long texts or collections of documents, nor can they verify the reliability of information or provide sources of information.

- LLM's dialogue ability is limited by pre-trained data and models, and cannot be customized according to user needs and data.

- The application scenarios and functions of LLM are single, and it cannot be combined with other computing methods, knowledge or systems to achieve more complex and intelligent tasks.

LangChain provides convenience and support for LLM's AI application development through the following aspects:

- LangChain provides a common interface to access a variety of different underlying models, including open source or proprietary LLMs.

- LangChain provides a framework that can help users manage and design better prompts (Prompt), thereby improving the performance and effect of LLM.

- LangChain provides a central interface that can access long-term memory (Memory), external data (Indexes), other LLMs (Chains), and other agents (Agents), thereby expanding the capabilities and knowledge of LLM.

- LangChain provides an open source project (GitHub repository), which allows users to participate in contributions and improvements, and can also learn from other users' experiences and cases.

Application of LangChain technology in FlerkenS

LangChain technology mainly has the following application scenarios in FlerkenS:

- Content generation: FlerkenS uses the LLM module in LangChain to generate various types of content, including text, pictures, audio, video, etc., according to user input or selected prompts (Prompt). For example, a user can input "a poem about love", and then FlerkenS will call the ChatGPT model to generate a poem and display it on the interface. Users can also choose "a picture about space travel", and then FlerkenS will call the DALL-E model to generate a picture and display it on the interface. These contents are generated by LLM in real time according to prompts, and are not retrieved from existing databases. In this way, the novelty and diversity of the content can be guaranteed, and problems such as copyright can be avoided.

- Content recommendation: FlerkenS uses the Memory and Indexes modules in LangChain to retrieve and filter relevant content from external data sources (such as web pages, social media, blockchain, etc.) based on user behavior data and preferences, and recommend them to users. For example, the user can input "I want to learn programming", and then FlerkenS will obtain the programming-related content that the user has browsed or favorited before from Memory, and the latest or most popular programming-related content from Indexes, and follow the related Recommended to users after sorting by degrees or ratings. These contents are provided by external data sources and are not generated by LLM. This can ensure the reliability and authority of the content, and avoid the problem of information overload or redundancy.

- Content interaction: FlerkenS uses the Chains and Agents modules in LangChain to interact with other LLMs or intelligent agents according to the content type or topic input or selected by the user, and return valuable or interesting information or feedback. For example, the user can input "I want to listen to a song", and then FlerkenS will call a music agent (Music Agent) to obtain and play a song from the music platform according to the type of song or artist that the user has heard or liked before , and display the lyrics and related information. The user can also choose "I want to play a game", and then FlerkenS will call a game agent (Game Agent) to obtain and start a game from the game platform according to the game type or style that the user has played or liked before, and display Game screen and operation guide. These interactions are performed by LLM or intelligent agents based on user input or selection, not preset by FlerkenS. In this way, the flexibility and personalization of the interaction can be guaranteed, and the monotony or boring problem of the interaction can be avoided.

LangChain technology plays a role in personal behavior data collection and AI personalized service under the Web3 underlying architecture

LangChain technology plays a role in personal behavior data collection and AI personalized service under the Web3 underlying architecture mainly in the following aspects:

- Data ownership: Under the Web3 underlying architecture, users own the ownership of the data they have generated or used, and can protect the security and privacy of their own data through encryption and distributed storage. LangChain technology supports users to store their own data in the Memory module and control access to the data through keys. In this way, users can not only avoid their data being misused or leaked, but also gain benefits or value by selling or sharing their data.

- Data utilization: Under the Web3 underlying architecture, users can freely use data provided by themselves or others through smart contracts and other methods, and realize diversified and innovative functions through decentralized applications (DApps). LangChain technology supports users to use data provided by themselves or others as input or output, and interact with LLM or intelligent agents to generate or obtain valuable or interesting content or services. For example, a user can use an article written by himself as an input to call a writing agent (Writing Agent) to obtain improvement or optimization suggestions for the article. Users can also take a picture they like as an output and call an Art Agent (Art Agent) to generate an NFT (non-fungible token) related to the picture.

- Data incentives: Under the Web3 underlying architecture, users can obtain data incentives and rewards through tokens and other methods, and realize data circulation and value conversion through decentralized exchanges (DEX) and other methods. LangChain technology supports users to motivate and reward data providers or users through tokens, thereby promoting data sharing and collaboration. For example, users can use tokens to buy or sell content generated or recommended by themselves or others, and adjust the price of tokens according to the quality or popularity of the content. Users can also participate in or initiate communities or activities through tokens, and distribute token revenues according to the influence or effect of communities or activities.

LangChain technology plays a role in personal behavior data collection and AI personalized service under the Web3 underlying architecture

LangChain technology plays a role in personal behavior data collection and AI personalized service under the Web3 underlying architecture mainly in the following aspects:

- Data ownership: Under the Web3 underlying architecture, users own the ownership of the data they have generated or used, and can protect the security and privacy of their own data through encryption and distributed storage. LangChain technology supports users to store their own data in the Memory module and control access to the data through keys. In this way, users can not only avoid their data being misused or leaked, but also gain benefits or value by selling or sharing their data.

- Data utilization: Under the Web3 underlying architecture, users can freely use data provided by themselves or others through smart contracts and other methods, and realize diversified and innovative functions through decentralized applications (DApps). LangChain technology supports users to use data provided by themselves or others as input or output, and interact with LLM or intelligent agents to generate or obtain valuable or interesting content or services. For example, a user can use an article written by himself as an input to call a writing agent (Writing Agent) to obtain improvement or optimization suggestions for the article. Users can also take a picture they like as an output and call an Art Agent (Art Agent) to generate an NFT (non-fungible token) related to the picture.

- Data incentives: Under the Web3 underlying architecture, users can obtain data incentives and rewards through tokens and other methods, and realize data circulation and value conversion through decentralized exchanges (DEX) and other methods. LangChain technology supports users to motivate and reward data providers or users through tokens, thereby promoting data sharing and collaboration. For example, users can use tokens to buy or sell content generated or recommended by themselves or others, and adjust the price of tokens according to the quality or popularity of the content. Users can also participate in or initiate communities or activities through tokens, and distribute token revenues according to the influence or effect of communities or activities.

Summarize

LangChain technology is an AI application development framework based on LLM, which can solve some problems of LLM itself, and also provide a flexible and powerful framework for the integration of LLM with other resources or systems. FlerkenS is a Web3-based AI application product that uses LangChain technology to provide users with personalized content and services while protecting users' data and privacy. LangChain technology has three main application scenarios in FlerkenS: content generation, content recommendation, and content interaction. LangChain technology plays a role in personal behavior data collection and AI personalized service under the Web3 underlying architecture. There are three main aspects: data ownership, data utilization, and data incentives.