Hybrid Search Engines for RAG

Orama is a search engine and vector database that provides full-text indexing, geospatial calculations, and semantic vector storage. It functions as an LLM retrieval engine designed to provide grounded context to language models for conversational interfaces. The project implements hybrid search by combining dense vector embeddings with inverted keyword indices to retrieve documents based on both semantic meaning and exact text matches. It utilizes a WebAssembly module to execute search logic across different JavaScript environments and platforms. The system covers a broad range of retrieval capabilities, including faceted search with category counts, geographical distance filtering, and typo tolerance. It also includes a middleware pipeline for integrating external plugins and tools for search result merchandising to influence document ranking via custom rules.

Chroma is a specialized vector database designed to index and retrieve high-dimensional data representations for semantic similarity search. It functions as a comprehensive platform for information retrieval, enabling the storage and management of unstructured documents alongside structured metadata. By mapping data into numerical representations, the system facilitates rapid similarity lookups across large datasets. The platform distinguishes itself through a hybrid search infrastructure that combines dense vector embeddings with sparse keyword and regular expression matching to balance semantic relevance with exact term precision. It supports multi-modal data, allowing for the indexing and querying of text, images, and audio within a unified interface. Furthermore, the system provides an agentic retrieval framework that enables autonomous agents to perform iterative search cycles and refine results for complex, multi-step queries. Beyond its core search capabilities, the platform includes specialized tools for codebase analysis, utilizing syntax-aware chunking to preserve logical structure for development tasks. It features a pluggable embedding pipeline that decouples vector generation from storage, allowing integration with diverse third-party machine learning models. The system also supports metadata-filtered query execution, ensuring precise retrieval by applying boolean constraints to document attributes. Operational support is provided through a programmatic interface for managing database instances in both self-hosted and cloud-based environments, including automated provisioning for scalable deployments.

Milvus is a specialized vector database engine designed for the indexing, management, and high-speed similarity retrieval of high-dimensional vector embeddings. It functions as a similarity search engine capable of identifying nearest neighbors within large-scale vector spaces, supporting the storage and retrieval of billions of data points while maintaining consistent performance. The system utilizes a distributed architecture that decouples storage, query, and coordination into independent services, allowing for horizontal scaling across clusters. It employs a global indexing mechanism that builds specialized data structures across immutable, independently indexed segments. This design, combined with a shared-storage decoupled model, enables compute and storage resources to scale independently in cloud environments, while a log-based persistence layer ensures data durability and state recovery. The platform supports a wide range of data retrieval patterns, including retrieval-augmented generation, hybrid search, and multimodal data retrieval for text, images, and graphs. Deployment options range from lightweight local instances for rapid prototyping to robust standalone setups and fully managed distributed clusters. Documentation includes sizing tools to assist in estimating hardware requirements based on specific data volumes and operational patterns.

Manticoresearch is a high-performance search engine and database designed for indexing and retrieving large datasets. It functions as a full-text search engine, a vector search database, and a SQL-based search database, providing a distributed search cluster architecture. The system provides an alternative to the Elasticsearch stack, offering a compatible API for indexing and searching structured and unstructured data. It distinguishes itself by supporting multiple retrieval methods, including vector matching for similarity search, geospatial queries, and traditional full-text ranking. The platform covers comprehensive search and indexing capabilities, including natural language processing with locale-specific tokenization and query translation. Its architecture incorporates sharding and replication for high availability, cost-based query optimization, and a multi-format storage engine that supports row, column, and document formats. The software is delivered via OS-specific binary packages for various Linux distributions.

Weaviate is a cloud-native vector database and distributed vector store designed to save high-dimensional vectors alongside structured data. It functions as a hybrid search engine that combines vector similarity, keyword matching, and structured metadata filtering within a single query. The system is optimized for retrieval-augmented generation, integrating vector search with generative AI and reranking to power question-and-answer workflows. It distinguishes itself through the ability to merge semantic search with traditional keyword queries and structured metadata filters to improve result precision. The platform covers broad capability areas including enterprise data retrieval with role-based access control, multi-tenant data partitioning for horizontal scaling, and memory optimization via vector data compression. It also provides tools for managing the data lifecycle through automated expiration policies and external vectorizer integration for embedding ingestion.

R2R is an agentic retrieval-augmented generation platform that uses reasoning agents to perform multi-step data fetching for context-aware answering. It functions as a multimodal vector database manager and knowledge graph engine designed to ground artificial intelligence responses in verified factual knowledge. The platform distinguishes itself by combining reasoning agents for complex research automation with a knowledge graph that maps entity relationships. This allows the system to perform structured data traversal alongside unstructured vector search to resolve complex questions from internal knowledge bases and the web. The system covers multimodal content ingestion for various file types, hybrid semantic-keyword search, and collection-based data isolation for multi-tenant access control. These capabilities are exposed through a programmable REST API gateway.

DeepLake is AI data infrastructure consisting of a multimodal data lake, a hybrid search engine, and a serverless vector database. It provides a PostgreSQL-based AI data runtime that combines multimodal storage with streaming pipelines to load and shuffle datasets from cloud storage directly into deep learning training pipelines. The system utilizes lazy indexing to store and slice images, audio, and video without loading entire files into memory. It enables retrieval-augmented generation by persisting high-dimensional embeddings in a serverless vector store and implementing hybrid search that combines vector similarity with full-text keyword matching. The project covers a broad capability surface including structured metadata indexing for numeric and JSON fields, cloud-local data synchronization, and visualization tools for inspecting dataset annotations such as bounding boxes and masks.

zvec is an embedded vector database engine and indexing library designed for high-dimensional similarity search. It functions as a hybrid search engine and a retrieval-augmented generation knowledge base, allowing for the storage and retrieval of dense and sparse vectors. The system is distinguished by its hybrid retrieval pipeline, which fuses vector similarity, full-text keyword matching, and scalar metadata filtering into single query operations. It supports a plugin-based model integration system for registering custom embedding models and rerankers, as well as language bindings for native application integration. The project provides comprehensive data management through isolated local collection persistence, write-ahead logging, and dynamic schema mapping. Its search capabilities cover approximate nearest neighbor search at billion-scale, multimodal semantic search, and result reranking, while optimizing performance via memory-mapped I/O and vector index compression. The engine facilitates AI agent integration by exposing database interfaces and reusable operation skill sets to connect agents to structured data stores.

ParadeDB is a database extension that integrates full-text search, vector database capabilities, and real-time analytics directly into a relational engine. It functions as a plugin that adds new storage and query execution capabilities to an existing database architecture. The project distinguishes itself by supporting hybrid search workflows that combine lexical keyword matching with dense and sparse vector similarity in a single query. It utilizes reciprocal rank fusion to merge these ranked result sets and employs logical replication to synchronize data from external instances, removing the need for manual ETL pipelines. The system covers broad capability areas including columnar-based indexing for high-performance aggregations and faceted search. It also includes features for search result highlighting, match offset location, and transactional consistency via multi-version concurrency control. The software can be deployed using Docker containers or through cloud platforms such as Railway.

OpenSearch is a distributed search and analytics engine designed for indexing, searching, and analyzing massive volumes of structured and unstructured data in real time. It functions as a comprehensive platform that integrates enterprise-grade search capabilities, a vector database for high-dimensional similarity lookups, and a unified observability suite for monitoring logs, metrics, and traces across complex distributed environments. The platform distinguishes itself through its support for agentic workflow automation, allowing users to orchestrate multi-agent tasks and integrate foundation models directly into search and data processing pipelines. It provides deep extensibility through a plugin-based architecture and includes a robust security and compliance suite that enforces granular role-based access control, data sovereignty, and comprehensive audit logging to meet enterprise requirements. Beyond its core search and vector capabilities, the project supports large-scale data ingestion from diverse sources, including real-time synchronization from relational databases and table formats. It offers extensive tooling for cluster lifecycle management, performance optimization, and the visualization of operational data through interactive dashboards. The software is distributed as a security-hardened engine with long-term support options for production environments.

LanceDB is a vector database and columnar data store designed to function as a versioned dataset manager and vector search engine. It serves as a high-performance backend for indexing and retrieving high-dimensional embeddings, providing the foundation for machine learning data pipelines. The system distinguishes itself through a combination of cloud-native object storage and immutable version tracking, allowing for data time-travel and reproducible AI experiments. It integrates hybrid search capabilities, merging dense vector similarity with BM25 full-text search and SQL-like scalar filters into a single ranked result set. The project covers a broad range of capabilities, including automated vector embedding generation, multimodal data ingestion, and large-scale feature engineering. Its search surface includes approximate nearest neighbor indexing, precision reranking, and late-interaction multivector retrieval. Additionally, it provides tools for dataset curation, model evaluation, and zero-copy data streaming for training loops. The database is accessible via multi-language SDKs and a standardized REST API, supporting deployments across local filesystems and cloud object storage providers.

Typesense is a distributed search engine designed to provide sub-millisecond query latency across massive datasets. It functions as both a high-performance indexing and retrieval engine and a comprehensive search experience platform, offering built-in typo tolerance and tools for managing relevance through synonym configuration, result curation, and complex filtering. The platform distinguishes itself by utilizing in-memory indexing to maintain high-throughput data retrieval and integrating vector database capabilities to support semantic similarity searches. It ensures data consistency and high availability across distributed clusters through a consensus-based coordination model and asynchronous snapshot replication. By combining traditional keyword matching with high-dimensional embedding support, it enables natural language understanding and similarity-based retrieval within application workflows. The system manages large-scale data through distributed indexing and log-structured merge trees, which optimize write performance and simplify incremental updates. Users can refine search outcomes by applying custom grouping logic and negation filters to improve discovery accuracy. Comprehensive documentation and community support channels are available to assist with integration and troubleshooting.

LEANN is a framework for local retrieval augmented generation and vector indexing. It functions as a system for building local knowledge bases and source code search engines that combine large language models with retrieved private data to generate context-aware responses. The project distinguishes itself through a vision-model based document layout extractor for parsing complex PDF figures and diagrams, and a source code search engine that employs structure-aware chunking to preserve function and class boundaries. It also implements the Model Context Protocol to integrate real-time data sources into the retrieval pipeline. The system provides hybrid information retrieval combining semantic search, exact keyword matching, and boolean metadata filtering. It supports the indexing of diverse data sources, including web browsing history, communication logs, and technical documentation.

localGPT is a private AI knowledge base and retrieval-augmented generation application. It provides a local document indexer, a hybrid search engine, and an inference interface to enable chatting with private documents and managing a self-hosted information repository without sending data to external servers. The system distinguishes itself through a dual-pass verification pipeline that ensures generated answers are grounded in retrieved sources, accompanied by explicit source attribution. It employs a hybrid retrieval approach combining semantic vector search with keyword matching and reranking, and utilizes recursive query decomposition to break complex requests into smaller parallel sub-queries. The platform covers broad capability areas including multi-format document processing, dynamic query routing, and semantic query caching. It also manages conversation history tracking and provides a RESTful API for integrating document retrieval and language model functionality into external applications. The project integrates with open-source models across different hardware accelerators and includes system health monitoring via structured logs and health endpoints.

This project is a retrieval augmented generation framework designed to build pipelines that connect unstructured data and knowledge graphs with large language models. It functions as a vector database orchestrator for indexing text and multimodal content, as well as a system for translating natural language queries into structured database commands. The framework integrates a hybrid retrieval engine that combines dense vector search with sparse keyword matching to increase the precision of retrieved contexts. It further enhances reasoning and relationship mapping through a graph-augmented retrieval system. The system includes a toolkit for measuring the quality of retrieval and generation processes using standardized metrics. It also provides mechanisms to enforce predefined schemas and patterns on model responses to ensure consistent output for downstream applications. The project is implemented in Python.

Databend is a cloud-native data warehouse and OLAP database designed for large-scale analytics. It functions as a SQL-compliant engine and serverless analytics platform that separates compute from storage to allow for independent scaling. The system integrates vector database capabilities, indexing high-dimensional embeddings to enable semantic, hybrid, and full-text searches across massive datasets. It further distinguishes itself through serverless compute management that automatically scales resources based on demand and shuts them down during idle periods. The platform covers a broad set of analytical and management capabilities, including data versioning and branching, automatic schema evolution, and multi-tiered storage management. It also provides enterprise security management with role-based access control, data masking, and automated pipeline orchestration via stored procedures and sandboxed user-defined functions.

OceanBase is a distributed SQL database designed for high availability and strong consistency across multiple nodes and regions. It functions as a hybrid transactional and analytical processing engine, allowing real-time analytics and transactions to execute on a single data copy. The system also serves as a vector database engine for indexing and querying vector data to power semantic search and recommendation systems. The platform features native compatibility layers for MySQL and Oracle, enabling the migration of legacy workloads without rewriting SQL code. It utilizes a Paxos-based distributed store for synchronous replication and implements a multi-tenant architecture that isolates CPU, memory, and I/O resources for different tenants within a single cluster. The system covers a broad range of capabilities, including horizontal storage scaling, distributed transaction management, and hybrid row-columnar storage. It provides tools for cluster orchestration, automated load balancing via log-stream migration, and disaster resilience through multi-zone replication and automated failover. Deployment and management are supported through a Kubernetes operator and a web monitoring dashboard.

Doris is a distributed SQL data warehouse designed for high-performance analytical workloads and real-time data processing. It functions as a unified platform that integrates traditional relational warehousing with lakehouse query capabilities, allowing users to execute analytical operations directly against external data lakes without requiring data migration. The system distinguishes itself through a shared-nothing, massively parallel processing architecture that utilizes vectorized query execution and columnar storage to maintain sub-second latency. It supports dynamic schema evolution, enabling real-time updates to table structures, and provides elastic resource scaling by decoupling compute and storage layers to accommodate fluctuating workload demands. Beyond standard analytical processing, the platform incorporates vector database functionality to support artificial intelligence and semantic search applications. It enables hybrid search by combining structured SQL analytics with full-text filtering and vector similarity, facilitating complex retrieval-augmented generation workflows within a single environment. The engine is built to handle high-concurrency requirements, supporting thousands of simultaneous queries per second for enterprise-scale operations.

llmware is a Python framework for AI agent orchestration and model management, designed to coordinate multi-model workflows and autonomous agents. It provides a unified model catalog and standardized interface to execute specialized language models for complex research, analysis, and structured data generation. The project distinguishes itself through its heavy emphasis on local execution and quantized inference, allowing models to run on private infrastructure using CPU, GPU, and NPU acceleration via runtimes like ONNX and OpenVino. It features a specialized ability to translate natural language queries into structured SQL or CSV formats by analyzing database schemas. The framework covers a broad range of capabilities including end-to-end retrieval-augmented generation pipelines, hybrid search engines, and multimodal content processing for PDFs, Office documents, audio, and images. It also incorporates tools for structured function calling, named entity recognition, and text risk classification to detect toxicity and prompt injections. The system integrates with various SQL and vector database backends to manage knowledge collection indexing and document embeddings.

FastGPT is a comprehensive platform for building, deploying, and managing context-aware artificial intelligence applications. It provides a unified environment that integrates custom data sources with language models, utilizing a retrieval-augmented generation engine to ground responses in accurate, domain-specific information. The system is designed for enterprise-scale use, featuring multi-tenant architecture, administrative controls, and secure authentication protocols including OAuth 2.0 and custom single sign-on integration. The platform distinguishes itself through a visual, node-based workflow orchestrator that allows users to design complex business logic and automated task sequences without manual coding. It offers sophisticated knowledge base management, supporting multi-vector data mapping, hybrid search fusion, and automated website content synchronization. To ensure high-quality outputs, the system includes tools for search query optimization, result reranking, and automated performance evaluation, allowing developers to score and analyze the accuracy of their applications across multiple iterations. Beyond its core generation and retrieval capabilities, the platform provides extensive utilities for data handling and organizational management. This includes intelligent parsing of complex document formats, flexible search modes, and granular access controls for team management. Users can also leverage secure, sandboxed rendering for rich content and export cited documents for offline review, ensuring a complete lifecycle for production-ready AI services.