Searchkit is a TypeScript monorepo that provides a library and server-side API to bridge Algolia's InstantSearch UI components with Elasticsearch. It transforms InstantSearch search parameters into Elasticsearch queries, executes them via a configurable transporter (supporting API keys, basic auth, cloud_id, custom headers), and transforms responses back into InstantSearch-compatible format. The core package (`searchkit`) handles request/response transformation, query rules (conditions and actions like pin, boost, rewrite), facets (string, numeric, date, nested, geo), filters, sorting (with nested fields and array modes), highlighting/snippets (with wildcard and nested path support), and KNN/hybrid search. The `@searchkit/api` package provides a Node.js client factory for building search endpoints, and `@searchkit/instantsearch-client` offers a browser-side adapter that can connect either to a Searchkit API endpoint or directly to Elasticsearch. The monorepo also includes an analytics plugin for Elasticsearch Behavioral Analytics, extensive functional tests using nock snapshots, sample datasets, and integration examples for Next.js, Express, Cloudflare Workers, Vue, and plain JavaScript. The architecture is config-driven, with a `SearchkitConfig` object defining connection, search settings, facets, filters, sorting, and query rules, plus hooks for customizing queries and responses.
Typesense is a high-performance, open-source search engine written in C++17, designed as a drop-in alternative to Algolia and Elasticsearch. It provides a RESTful HTTP API for full-text search, faceted navigation, geospatial filtering, vector search (with HNSW), and hybrid (semantic + keyword) search. The architecture is a distributed, Raft-based clustered system with leader election, log replication, and snapshotting, ensuring high availability and consistency. The server integrates with external AI providers (OpenAI, Google, Azure, Cloudflare) for embedding generation and natural language query processing, and supports on-device ONNX models for text, image, and voice search. The codebase is modular, with singleton managers for collections, embeddings, analytics, rate limiting, and authentication, all built on top of a RocksDB-backed key-value store. Extensive test coverage includes C++ unit tests (gtest), TypeScript integration tests (Bun), and benchmark infrastructure (k6, InfluxDB). While primarily an application, it incorporates data-intensive features like statistical sketches (TDigest, CountMinSketch), custom indexing data structures (ART, numeric trie, hat-trie), and analytics pipelines, giving it a hybrid character.
This repository is the official Algolia API client library for Go (v4). It is a generated SDK that provides typed Go structs, serialization, and HTTP transport for interacting with all Algolia services including Search, Analytics, Recommendations, Ingestion, A/B Testing, Personalization, Monitoring, Insights, Agent Studio, and Query Suggestions. The code is overwhelmingly auto-generated by OpenAPI Generator from Algolia's shared API specs, with a hand-written core infrastructure layer for HTTP transport (rate-limiting, retry with host failover, stateful host management) and utilities (nullable types, deep merge, SSE streaming). The library is well-structured and follows consistent patterns across all service packages, but the heavy reliance on code generation leads to significant duplication of identical models across packages and a large number of boilerplate files. The hand-written transport layer is robust and well-tested, demonstrating good engineering practices. This SDK is suitable for any Go application needing to integrate with Algolia's search and data platforms.
Hermes is an open-source document management system developed by HashiCorp for creating, reviewing, approving, and publishing internal technical documents (RFCs, PRDs, FRDs). It provides a full-stack application with a Go REST API backend, an Ember.js web frontend, and a Microsoft Word add-in (React/Fluent UI) for editing document metadata directly in documents. The system supports dual content backends: Google Workspace (Drive, Docs, People API) and Microsoft SharePoint/Microsoft Graph, with file storage, permission management, and email notifications. Search is powered by Algolia with faceted filtering, and persistent storage uses PostgreSQL via GORM, though the architecture is in transition from Algolia as the primary data store to PostgreSQL. Authentication supports Google OAuth, Microsoft OAuth, and AWS ALB OIDC/Okta. The API exposes REST endpoints under `/api/v1/` and `/api/v2/` for documents, drafts, approvals, reviews, projects, people, products, and subscriptions. Key background processes include a document indexer that synchronizes file metadata to Algolia and a migration command to move data from Algolia to PostgreSQL. The Ember frontend provides a dashboard, document browsing with advanced search, project management, and settings. The Office add-in enables users to manage related resources, update document status, and edit custom fields without leaving Word. Externally, Hermes depends on Google Workspace APIs, Microsoft Graph API, Algolia, PostgreSQL, and optionally Datadog for tracing and Google Analytics for frontend metrics. It is configured via HCL files and environment variables, with Docker and Docker Compose support for local development. The project is in alpha stage and not an official HashiCorp product, but it is actively developed with CI/CD pipelines, extensive unit/integration tests (Go, Ember QUnit), and a comprehensive test harness using Mirage for the frontend.