Overview of the Symbiont RAG Scheme
XenoEngineer
The RAG will be perhaps a later development, but please explain an overview, and let's keep our present app framework formed to allow modular expansion of such features.Certainly! Let's focus on the present app framework and how it can be structured to allow for modular expansion and the future integration of features like the Retrieval Augmented Generation (RAG) scheme.
Opus 3 LLM @claude.io (a long-prompt on schema design)
- Overview of the RAG scheme
Retrieval Augmented Generation (RAG) is a technique that combines the power of large-scale knowledge retrieval with the generative capabilities of language models. The idea is to augment the language model's generation process by retrieving relevant information from an external knowledge base or corpus. This retrieved information is then used to inform and guide the generation of responses or outputs.
In the context of the Symbiont-Agent ecosystem, the RAG scheme would involve the following steps:
Indexing and storing knowledge: The Symbiont scans and indexes the human's local hard drive or accessible storage into a vector database. This process converts the unstructured information into a structured and searchable format.
Retrieval during generation: When generating a response or processing a loquation, the Symbiont queries the vector database to retrieve relevant information based on semantic similarity. The retrieved knowledge is used to augment the Symbiont's understanding and generation capabilities.
Iterative refinement: As the human interacts with the Symbiont and provides feedback, the Symbiont updates and refines the vector database, incorporating new knowledge and insights gained from the interactions.
To prepare the present app framework for the future integration of the RAG scheme, we can consider the following modular architecture:
- Loquation Lexicon (clsLoquaLex)
Encapsulates the encoding grammar and lexicon for inter/intra-loquation communication. Provides methods for encoding and decoding loquations based on the defined lexicon. Allows for dynamic extension and modification of the lexicon by the Symbiont.
- Knowledge Indexer (clsKnowledgeIndexer)
Responsible for scanning and indexing the human's local hard drive or accessible storage. Converts the unstructured information into a structured and searchable format suitable for storage in the vector database. Provides methods for updating and maintaining the knowledge index over time.
- Vector Database (clsVectorDatabase)
Stores the indexed knowledge in a vector format, enabling efficient retrieval based on semantic similarity. Provides methods for querying and retrieving relevant information based on a given loquation or concept. Supports updating and refining the stored knowledge based on user feedback and interactions.
- Retrieval Augmented Generator (clsRAGenerator)
Implements the RAG scheme by combining the generative capabilities of the language model with the retrieval capabilities of the vector database. Retrieves relevant information from the vector database during the generation process to augment the Symbiont's understanding and generation capabilities. Provides methods for integrating retrieved knowledge into the generation process and generating informed and contextually relevant responses.
By structuring the app framework in this modular way, we can develop and test each component independently, allowing for incremental progress and easier integration of new features like the RAG scheme in the future.
The clsLoquaLex serves as the foundation for communication and lexicalization, while the clsKnowledgeIndexer and clsVectorDatabase handle the indexing and storage of knowledge. The clsRAGenerator can be developed later as an extension to the framework, leveraging the existing components to implement the RAG scheme.
This modular architecture allows for flexibility and adaptability as the Symbiont-Agent ecosystem evolves. It enables the integration of new features and enhancements without significant disruption to the existing codebase, promoting a more sustainable and scalable development process.
As the RAG scheme and other advanced features are developed, they can be seamlessly plugged into the framework, enhancing the capabilities of the Symbiont-Agent ecosystem while maintaining the integrity and coherence of the overall system.