Product catalogs, help centers, and document archives all fail the same way: visitors type a few words, get the wrong results, and leave. Apache Solr is the search platform behind fast, relevant answers, returning ranked matches with faceted filters, highlighted snippets, spell suggestions, and geospatial distance in a single query. Index documents through HTTP updates or data import handlers, shape fields in the schema, and watch changes appear through near-real-time indexing without rebuilding the corpus. SolrCloud turns one index into a distributed collection: shards split large corpora across nodes, replicas copy each shard for failover and query load, and ZooKeeper coordinates configuration so every node serves the same schema. Dense vector fields store embeddings beside your text, and the KNN parser retrieves nearest neighbors through HNSW graphs, so semantic search runs in the same engine as keyword search instead of a second system. A combined query handler merges keyword and vector rankings with reciprocal rank fusion, and an enrichment processor can call language models at index time to tag or summarize documents as they arrive. Streaming expressions add analytics over results, from rollups and percentiles to joins across collections, while JWT authentication and authorization rules protect the admin console and APIs. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

Perplexity's search experience without Perplexity: Vane deploys Perplexica, an open-source AI answer engine built as the self-hosted alternative. Instead of returning a page of links, it reads your question, searches the live web through the SearxNG metasearch engine, and composes a direct answer with cited sources. Retrieval quality comes from embeddings and similarity search: fetched pages are re-ranked against the query so the model answers from the most relevant passages rather than whatever ranked first. Two query modes cover different needs - Normal mode runs a straightforward web search, while Copilot mode generates multiple reformulated queries and actively pulls content from top matches for harder questions. Focus modes specialize retrieval for academic papers, YouTube, Reddit discussions, Wolfram Alpha calculations, or the general web. The answering model is your choice: OpenAI-compatible APIs or fully local LLMs such as Llama 3 and Mixtral through Ollama, which keeps queries entirely on your infrastructure. Because SearxNG pulls live results, answers reflect current information, and no search history is tracked.

Live web search plus an LLM of your choice: Farfalle is an open-source, self-hosted answer engine in the Perplexity mold. Queries route through one of several search providers - self-hosted SearXNG for a fully independent stack, or Tavily, Serper, and Bing APIs - and the model composes a cited answer from the retrieved results. Model flexibility is the core design: run llama3, mistral, gemma, or phi3 locally through Ollama for zero per-query cost and full privacy, use cloud models like GPT-4o or Groq-hosted Llama 3 for speed, or route to any provider via LiteLLM. An Expert Search mode uses an agent that plans a multi-step search strategy and executes it for harder questions, and chat history keeps prior research sessions available. The stack is a Next.js and shadcn/ui frontend over a FastAPI backend with Redis rate limiting, shipped as a pre-built Docker image. A browser search-engine entry pointing at your instance makes it the default search from the address bar. Paired with SearXNG and Ollama, the whole pipeline runs with no external API at all.