mongodb-developer/GenAI-Showcase
GenAI Showcase
This project is a collection of generative AI implementations focused on the development of AI agents, retrieval-augmented generation pipelines, and vector search integration. It provides a framework for connecting managed cloud databases to language models to create context-aware applications.
The project covers the orchestration of autonomous agents that use multi-step reasoning and external tools to complete tasks. It includes implementations for semantic retrieval using high-dimensional embeddings and the use of model-agnostic prompting to ensure consistent outputs across different large language models.
Additional capabilities include the use of ground-truth evaluation frameworks to measure the accuracy and reliability of AI performance. The project also demonstrates the setup of cloud database accounts for storing and managing application data and vector embeddings.
Features
- Retrieval-Augmented Generation - Connects MongoDB vector search with language models to provide grounded and context-aware responses.
- Agentic Tool Orchestration - Orchestrates autonomous agents that call external functions and tools to complete multi-step tasks.
- AI Agent Development - Provides tools and environments for developing autonomous AI agents capable of multi-step reasoning.
- RAG Pipelines - Combines external data stores with language models to improve response accuracy via RAG pipelines.
- Semantic Vector Search - Implements semantic retrieval using high-dimensional embeddings to find documents based on conceptual meaning.
- Autonomous AI Agents - Provides frameworks for building autonomous agents that use reasoning and tool-use to complete complex tasks.
- Vector Database Integrations - Integrates vector databases to store and manage document embeddings as a source of truth for AI context.
- Ground-Truth Scoring - Implements methods to compare model outputs against ground-truth datasets using scoring metrics.
- AI Evaluation Frameworks - Uses AI evaluation frameworks to measure the accuracy and reliability of model outputs.
- Prompt Templates - Uses standardized templates to ensure consistent output across different large language models.
- RAG Context Retrieval - Retrieves relevant document segments from a vector database to ground language model responses.
- AI Observability and Evaluation - Measures the accuracy and reliability of generative AI outputs using standardized evaluation frameworks.
