[{"data":1,"prerenderedAt":113},["ShallowReactive",2],{"seo-verification":3,"marketplace-app-en-weaviate":6},{"google":4,"bing":5},"EycwPY2XMyTkVzas3n1ygeNJFGAH513qrMjfDljzsMQ","",{"key":7,"data":8},"marketplace-app-en-weaviate",{"slug":9,"slugs":10,"categorySlugs":11,"name":16,"description":17,"phase":18,"unavailableReason":19,"docsUrl":20,"logo":21,"github":21,"tagline":22,"longDescription":23,"features":24,"useCases":31,"steps":41,"faq":60,"specs":76,"compatibleOs":84,"relatedApps":85,"relatedPosts":106,"category":110},"weaviate",{"fr":9,"en":9,"ar":9,"es":9},{"fr":12,"en":13,"ar":14,"es":15},"ia","artificial-intelligence","الذكاء-الاصطناعي","inteligencia-artificial","Weaviate","Open-source vector database for semantic search and multimodal RAG. Combine text, images and structured metadata with built-in vectorizer modules or your own embeddings on your VPS.",2,"awaiting_qa","https:\u002F\u002Fservorbit.com\u002Fblog\u002Fhosting-weaviate-on-a-vps",null,"Self-hosted multimodal vector database — GraphQL, RAG and built-in vectorizers on your VPS.","Weaviate is an open-source vector database written in Go, designed for semantic search and retrieval-augmented generation at scale. Unlike pure vector stores, Weaviate ships built-in vectorizer modules — text2vec-openai, text2vec-cohere, img2vec — that embed your data at ingest time without any client-side preprocessing. Data, vectors and structured metadata live together in the same record, so a single GraphQL or REST query returns semantically relevant objects with all their properties.\n\nDeployed on a ServOrbit VPS, Weaviate becomes the memory and retrieval layer of your AI stack: feed it documents, images or knowledge-base entries and let your LLM (via LangChain, LlamaIndex or a direct API call) pull the most relevant context before generating a response. The loopback binding keeps all data on your machine — no embeddings or private content ever leave the server.",[25,26,27,28,29,30],"Built-in vectorizer modules — text2vec-openai, text2vec-cohere, img2vec — embed data at ingest, no client-side preprocessing","GraphQL and REST APIs — query by vector similarity, scalar filters or a combination in one request","Multi-modal collections — store text, images and structured metadata together in the same object","Native LangChain, LlamaIndex and Haystack integrations — drop-in replacement for managed vector stores","BSD-3-Clause licence — fully auditable, no telemetry, air-gap capable","Go runtime — low memory footprint, fast startup, deterministic GC pauses under load",[32,35,38],{"title":33,"body":34},"RAG pipeline memory","Index your documentation or knowledge base in Weaviate using the text2vec module of your choice. At query time, retrieve the top-k most relevant chunks and pass them as context to your LLM. The entire pipeline runs on your VPS — no data leaves the machine.",{"title":36,"body":37},"Semantic search across documents","Replace keyword-based search with meaning-based retrieval. A query for 'invoice dispute resolution' surfaces results about 'billing conflict handling' even when those words never appear, because Weaviate compares intent encoded as vectors.",{"title":39,"body":40},"Multi-modal retrieval","Store product images alongside their descriptions and metadata in the same Weaviate collection. Search by image similarity, text description or a combination — useful for e-commerce catalogues, design asset libraries or document archives that mix text and visuals.",[42,45,48,51,54,57],{"title":43,"body":44},"Order a ServOrbit VPS","Plan for at least 2 GB of RAM: Weaviate itself idles around 400–600 MB, with additional memory for your index and any vectorizer modules you enable. A 4 GB VPS is a comfortable starting point. The machine ships with Ubuntu 24.04 and Docker pre-installed.",{"title":46,"body":47},"Deploy from the Marketplace","In your ServOrbit client area: Marketplace → Artificial Intelligence → Weaviate → Deploy. The `semitechnologies\u002Fweaviate:1.27.2` image starts in seconds. The REST and GraphQL APIs answer on port 8080; the gRPC API listens on port 50051 — both restricted to `127.0.0.1`. The `weaviate_data` volume keeps your schema and objects across container restarts.",{"title":49,"body":50},"Connect for the first time","This Weaviate instance runs with anonymous access enabled — no API key is required to connect. The ports are bound to `127.0.0.1` only, so the database is not reachable from the public internet. To call the REST or GraphQL API from your workstation, open an SSH tunnel: `ssh -L 8080:127.0.0.1:\u003Cport> root@\u003Cvps-ip>` (replace `\u003Cport>` with the port shown in your client area), then query `http:\u002F\u002Flocalhost:8080\u002Fv1\u002Fschema`.",{"title":52,"body":53},"Create a class and ingest your data","Define your schema with a single REST call: `curl -X POST http:\u002F\u002Flocalhost:8080\u002Fv1\u002Fschema -H 'Content-Type: application\u002Fjson' -d '{\"class\":\"Document\",\"vectorizer\":\"none\",\"properties\":[{\"name\":\"content\",\"dataType\":[\"text\"]}]}'`. With `vectorizer: none` you supply your own embeddings on each import. Switch to `text2vec-openai` (and add `OPENAI_APIKEY` to the container env) to let Weaviate call the embedding model automatically at ingest time.",{"title":55,"body":56},"Query with GraphQL or REST","Run a nearest-neighbour search in GraphQL: `curl http:\u002F\u002Flocalhost:8080\u002Fv1\u002Fgraphql -d '{\"query\":\"{Get{Document(nearVector:{vector:[0.1,0.2,...]},limit:5){content _additional{certainty}}}}\"}' -H 'Content-Type: application\u002Fjson'`. For structured access, the REST `\u002Fv1\u002Fobjects` and `\u002Fv1\u002Fbatch` endpoints are available via the official Python, Go, TypeScript and Java clients.",{"title":58,"body":59},"Secure the instance","Enable API-key authentication by adding `AUTHENTICATION_APIKEY_ENABLED=true` and `AUTHENTICATION_APIKEY_ALLOWED_KEYS=\u003Cyour-key>` to the container environment variables and restarting it. Once a key is set, every request must include the `Authorization: Bearer \u003Ckey>` header. The instance can then be safely published behind a domain, with the nginx vhost and TLS certificate ServOrbit manages.",[61,64,67,70,73],{"q":62,"a":63},"What is the difference between Weaviate and Qdrant?","Both are open-source vector databases, but their design philosophies differ. Qdrant (Rust) focuses on pure vector similarity search with payload filters — it is schema-free and brings no vectorization. Weaviate (Go) adds a schema layer and built-in vectorizer modules that call embedding models at ingest time, plus a GraphQL API and native multi-modal support. Choose Qdrant for raw performance on pre-embedded vectors; choose Weaviate when you want the database to handle embedding and you need GraphQL query syntax or multi-modal collections.",{"q":65,"a":66},"How much RAM does Weaviate need?","Weaviate itself uses 400–600 MB at idle. Total memory scales with your index: HNSW parameters, the number of objects and their vector dimension are the main factors. A 4 GB VPS comfortably handles collections of a few hundred thousand objects with 1536-dimensional vectors. Enable binary quantisation for large-scale deployments to cut memory by up to 32×.",{"q":68,"a":69},"Do I need an OpenAI key to use Weaviate?","No. Setting `DEFAULT_VECTORIZER_MODULE=none` (the default in this deployment) means Weaviate never calls an external embedding API. You supply your own float vectors on every import. The text2vec-openai, text2vec-cohere and other cloud modules are optional — enable them only if you want Weaviate to call the embedding service automatically.",{"q":71,"a":72},"Can I use Weaviate alongside Qdrant on the same VPS?","Yes. Weaviate listens on port 8080 (REST\u002FGraphQL) and 50051 (gRPC), Qdrant on 6333 (REST) and 6334 (gRPC) — no port conflict. Both are single-container deployments. On a 4 GB VPS, running both at once is possible but tight; a 8 GB VPS gives comfortable headroom for both databases plus an LLM inference tool like Ollama.",{"q":74,"a":75},"Which frameworks integrate natively with Weaviate?","LangChain, LlamaIndex, Haystack and DSPy all ship a Weaviate vector store connector. The official client libraries cover Python, Go, TypeScript and Java. Any HTTP-capable tool can also use the REST or GraphQL API directly.",{"ram":77,"cpu":78,"disk":79,"stack":80,"port":83},"2 GB","1 vCPU","20 GB",[81,82],"Docker","Go","8080",[],[86,94,100],{"name":87,"slug":88,"categorySlug":13,"categoryName":89,"categoryColor":90,"logo":91,"tagline":92,"description":93},"Qdrant","qdrant","Artificial Intelligence","text-purple-400 bg-purple-500\u002F10","https:\u002F\u002Fcdn.jsdelivr.net\u002Fgh\u002Fselfhst\u002Ficons\u002Fsvg\u002Fqdrant.svg","Self-hosted vector database for semantic search and RAG — the missing infrastructure layer for your LLM stack.","Self-hosted vector database for semantic search and RAG. Store, index and query high-dimensional embeddings with HNSW indexing — the missing infrastructure layer for your LLM stack.",{"name":95,"slug":96,"categorySlug":13,"categoryName":89,"categoryColor":90,"logo":97,"tagline":98,"description":99},"Ollama","ollama","https:\u002F\u002Fcdn.simpleicons.org\u002Follama","Run open-source LLMs on your VPS — OpenAI-compatible API, zero per-token cost.","Run open-source LLMs locally via a dead-simple API. Pull Llama 3, Mistral, Qwen or DeepSeek in one command — OpenAI-compatible, zero per-token cost.",{"name":101,"slug":102,"categorySlug":13,"categoryName":89,"categoryColor":90,"logo":103,"tagline":104,"description":105},"Open WebUI","open-webui","https:\u002F\u002Fcdn.jsdelivr.net\u002Fgh\u002Fselfhst\u002Ficons\u002Fsvg\u002Fopen-webui.svg","Web interface for your LLMs — Ollama, OpenAI, Mistral — hosted on your own server.","Web interface to interact with your local or remote LLMs. Your data stays on your infrastructure — no third party involved.",[107,108,109],"heberger-weaviate-vps","heberger-qdrant-vps","heberger-ollama-vps",{"key":12,"slug":13,"name":89,"objective":111,"icon":112,"color":90},"Build, host and run AI solutions.","ai",1790092237878]