All In Rag

Features

RAG Pipelines - Provides a comprehensive framework for building retrieval-augmented generation pipelines that integrate external data sources.
Graph Knowledge Indexing - Structures knowledge as interconnected nodes and relationships to improve reasoning and semantic retrieval.
Graph Retrieval Augmented Generation - Combines knowledge graphs with retrieval-augmented generation to enhance reasoning and relationship mapping in responses.
Hybrid Search Systems - Provides a search system that fuses multiple retrieval methods, including dense and sparse search.
Knowledge Graph Retrieval Systems - Leverages graph topology and entity relationships to provide context-aware retrieval for generated responses.
Knowledge Graphs - Integrates structured knowledge graphs to provide reasoning and context for generated model responses.
RAG Frameworks - Acts as a comprehensive framework for building RAG pipelines that integrate unstructured data and knowledge graphs.
Hybrid Sparse-Dense Embeddings - Integrates dense semantic vectors with sparse keyword-based representations to increase retrieval precision.
Document and Unstructured Extraction - Implements automated parsing and splitting of unstructured documents and web content into machine-readable formats.
Vector Store Orchestrators - Orchestrates the indexing of text and multimodal content into vector databases for high-performance retrieval.
Hybrid Retrieval - Implements a hybrid retrieval engine combining dense vector similarity and sparse keyword matching.
Vector Indexing - Implements high-dimensional vector indexing for semantic retrieval of unstructured and multimodal content.
Model Output Formatting - Includes utilities for enforcing structured data schemas in language model responses.
Output Formatting Constraints - Enforces specific output schemas and formats on language model responses to ensure consistency.
RAG Evaluation Frameworks - Provides a toolkit for evaluating the performance of retrieval and generation processes in RAG workflows.
Constrained Decoding - Uses constrained decoding and validation to force model outputs into predefined structured formats.
Structured Output Enforcements - Enforces specific patterns and predefined schemas on language model responses to ensure output consistency.
Text-to-SQL Translators - Translates natural language queries into structured SQL commands for precise data retrieval from relational databases.
LLM Schema Outputs - Enforces predefined schemas and formats on language model responses for consistent downstream data processing.
Multi-Stage Pipeline Processing - Orchestrates multi-stage pipelines that chain data loading, text chunking, and indexing into a sequential workflow.
System Quality Evaluators - Provides a framework for applying custom metrics to quantify the performance of RAG workflows.
Retrieval Metrics - Provides quantitative measurement of retrieval quality using standardized metrics against ground-truth datasets.

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Features

Open-source alternatives to All In Rag

timescale/pgai

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Star history

Open-source alternatives to All In Rag

timescale/pgai

bragai/bRAG-langchain

nndl/llm-beginner

microsoft/poml