GanymedeNil/document.ai
Document.ai
document.ai is a local document question-answering system and vector database search engine. It functions as a retrieval-augmented generation knowledge base that combines a vector database with language models to generate answers grounded in private local datasets.
The project features a domain-specific embedding pipeline and a fine-tuned language model framework. These tools allow for the use of specialized embedding models and mathematical theme extraction to improve retrieval accuracy for professional terminology and technical subject areas.
The system covers vector-based semantic retrieval, supervised model fine-tuning, and query-to-question mapping to match user inputs against historical verified answers. It integrates a pipeline for processing documents into mathematical representations and refining retrieved context into structured responses.
Features
- RAG-Based - Combines retrieved local document context with a language model to generate grounded and natural responses.
- Retrieval-Augmented Generation - Grounds language model responses by retrieving relevant documents from a local vector store.
- Semantic Vector Search - Implements semantic retrieval based on the mathematical distance between query and document embeddings.
- Vector Store Retrievals - Queries vector databases to retrieve semantically relevant document context based on user input.
- Domain-Specific Embedding Pipelines - Implements a specialized embedding pipeline using mathematical theme extraction to improve technical retrieval accuracy.
- Domain-Specific Fine-Tuning - Fine-tunes language models on specialized datasets to increase expertise in technical subject areas.
- Model Fine-Tuning - Provides a pipeline for fine-tuning language models on specialized datasets to enhance domain expertise.
- Supervised Fine-Tuning - Adapts base models using supervised datasets to increase expertise in technical subject areas.
- Local Question Answerers - Provides a locally-run question-answering system for interacting with private document datasets.
- Semantic Document Retrieval - Fetches relevant document chunks from a vector database by converting user queries into embeddings.
- Knowledge Base Construction - Implements an end-to-end pipeline for parsing documents and generating embeddings for a local knowledge base.
- Local Knowledge Bases - Combines a vector database with a language model to build a private knowledge base from local datasets.
- Document Embedding Pipelines - Provides a pipeline for processing raw documents into embeddings for use in a vector store.
- Vector Search Engines - Implements a retrieval system that uses vector embeddings to find relevant context for prompts.
- LLM Fine-Tuning - Includes a framework for fine-tuning language models on specialized datasets to increase domain expertise.
- Conversational Answer Generation - Refines retrieved document context through a language model to generate structured conversational answers.
- Query-to-Answer Mapping - Reduces retrieval ambiguity by matching user queries against a database of historical verified answers.
- Theme Extraction - Employs mathematical algorithms to isolate core semantic themes from documents for improved vector representation.
- Historical Answer Mapping - Matches user inputs against a database of historical questions to retrieve previously verified and accurate answers.
- Embedding Service Integrations - Integrates specialized embedding services to better represent professional terminology within the vector space.
- Vector Databases - Optimizes a vector database using mathematical algorithms and custom embeddings for specialized terminology.
- Vector Indexing - Optimizes vector indexing by extracting core mathematical themes to create more accurate representations.
- Custom Embedding Logic - Implements custom embedding logic to increase retrieval accuracy for professional and specialized terminology.
- Document Q&A - Local knowledge base solution using vector databases and GPT.
- Natural Language Processing - Listed in the “Natural Language Processing” section of the FunNLP awesome list.
- Knowledge Management - Universal local knowledge base powered by vector databases.
