Context and Database (RAG)

Our framework extends the capabilities of a standard RAG system, enabling us to generate and analyze images alongside text-based Retrieval Augmented Generation.

Breakdown

• Raw Data Sources: Utilizes a variety of raw data formats, including sitemaps, PDFs, and CSV files, for input.

• Information Extraction: Employs OCR, PDF data extraction, and web crawling techniques to gather and format raw data.

• Data Preparation and Embeddings: Processes raw data into structured embeddings for AI analysis.

• Vector Database: Stores and indexes processed data for efficient retrieval using vector embeddings and cosine similarity.

• Retrieval Tool and LLM: Harnesses a retrieval tool to fetch pertinent data, which is then interpreted by our LLM for generating responses.

• Custom Dynamic Tool and Image Generation: Engages a dynamic tool to refine user prompts for the image generation module, interfacing with OpenAI services to produce visual content.

• Mint NFT: Integrates with blockchain technology to mint generated images as NFTs.

• Image Generation: Displays the final generated image as a result of the user's prompt.

These components utilize a variety of technologies and libraries, including OpenAI's APIs for image generation, FastAPI for backend operations, Supabase for database interactions, and custom-built tools for data handling and NFT minting.

FAQ Knowledge Base Retrieval

Our system includes a sophisticated FAQ knowledge base retrieval mechanism that ensures quick and accurate responses to user inquiries. This is achieved through:

• Data Embeddings: Transforming FAQ content into embeddings using vectorization techniques.

• Cosine Similarity: Utilizing cosine similarity to match user queries with the most relevant FAQ entries.

• Efficient Retrieval: Quickly fetching pertinent information from the knowledge base, enhancing the responsiveness and accuracy of the system.

Chat History Retrieval for Context Handling

To maintain context and coherence in ongoing conversations, we have implemented an advanced chat history retrieval system:

• Vector Embeddings: Storing chat history as vector embeddings to facilitate efficient context retrieval.

• Cosine Similarity: Leveraging cosine similarity to match current user inputs with relevant past interactions.

• Context Summarization: Summarizing previous exchanges to maintain continuity and relevance, while optimizing token usage and reducing processing costs.

• Token Limitation Management: Addressing AI token limitations through efficient context management, ensuring cost-effective and coherent responses.

Enhanced Context Management and Token Optimization

One of the significant advancements in our system is the ability to retrieve and maintain chat history using vector embeddings and cosine similarity. This retrieval mechanism ensures that the context is preserved across interactions, enabling more coherent and relevant responses from the language model. By summarizing and condensing the context, we effectively tackle the challenge of AI token limitations, ensuring efficient and cost-effective processing.

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By incorporating these enhancements, our framework offers a robust solution that bridges text and image generation with advanced retrieval and context summarization capabilities, setting a new standard in interactive AI applications.