Search Foundation APIs for Reader, web search, embeddings, and reranking
Jina AI documents Search Foundation APIs at docs.jina.ai for RAG and agent pipelines. The Reader API at POST https://r.jina.ai/ converts a single URL into LLM-friendly markdown or JSON with optional headers such as X-Engine (browser/direct), X-Return-Format, and X-Respond-With readerlm-v2 per docs. The Search API at POST https://s.jina.ai/ returns SERP-style web results optimized for downstream LLM use with query parameters q, gl, hl, num, and page. Embeddings API at POST https://api.jina.ai/v1/embeddings supports models including jina-embeddings-v5-text-small, jina-embeddings-v5-text-nano, jina-embeddings-v4, jina-embeddings-v3, and jina-clip-v2 with task types such as retrieval.query and retrieval.passage. Reranker API at POST https://api.jina.ai/v1/rerank includes jina-reranker-v3 for refining retrieval hits. EU endpoints eu.r.jina.ai and eu.s.jina.ai keep processing in EU jurisdiction per docs.
Use cases
- RAG pipelines fetching clean page content without custom HTML parsers
- Agent web research via s.jina.ai before LLM synthesis
- Semantic search stacks pairing embeddings with vector DB entries on this site
- Reranking first-stage retrieval hits for higher-precision RAG chunks
- Pair with jina-mcp-server when exposing Reader/Search tools to MCP clients
Key features
- Reader API (r.jina.ai) for URL-to-markdown extraction with browser/direct engines
- Search API (s.jina.ai) for SERP-style web search with LLM-optimized output
- Embeddings API with v5-text-small/nano, v4 multimodal, v3, and clip-v2 models
- Reranker API with jina-reranker-v3 and multimodal jina-reranker-m0
- Batch embeddings endpoint for large-scale async indexing jobs
Who Is It For?
- Developers building RAG or agent stacks needing URL reading and web search APIs
- Search teams evaluating embedders alongside Voyage AI or Cohere on this site
- Teams requiring EU-resident Reader/Search endpoints per Jina docs
Frequently Asked Questions
- Does Jina AI host vector storage?
- Jina docs describe Reader, Search, Embeddings, and Reranker APIs; vector storage is typically handled by separate databases integrated into your stack.
- What is the difference between r.jina.ai and s.jina.ai?
- Docs say r.jina.ai reads a known URL into LLM-friendly content, while s.jina.ai searches the web and returns SERP-formatted results.
- Which embedding models does Jina document as latest?
- Jina docs list jina-embeddings-v5-text-nano and jina-embeddings-v5-text-small as latest text embeddings, with jina-reranker-v3 as the latest reranker.
Related
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3 Indexed items
Voyage AI
Voyage AI documents state-of-the-art embedding models and rerankers at docs.voyageai.com for retrieval-augmented generation and semantic search. The embeddings API at POST https://api.voyageai.com/v1/embeddings converts text into dense vectors; the Python `voyageai` package exposes `voyageai.Client.embed()` with models such as voyage-4-large, voyage-4, voyage-4-lite, voyage-code-3, and domain-specific variants per docs.voyageai.com/docs/embeddings. Docs describe `input_type` query vs document prompts for retrieval tasks, optional `output_dimension` (256–2048), and `output_dtype` quantization (float, int8, binary). Rerankers score query–document relevance to refine initial retrieval results. Voyage AI is part of MongoDB's AI stack (MongoDB docs reference Voyage embeddings) and integrates with vector stores and LLMs as modular RAG components.
Chroma
Chroma documents an open-source embedding database at docs.trychroma.com for storing and querying vectors, metadata, and full-text fields in Python and JavaScript clients. Official guides cover ephemeral in-memory collections, persistent local storage, self-hosted server deployments, and Chroma Cloud at trychroma.com with authentication tokens. The docs describe collection CRUD, `add`/`query`/`get`/`update`/`delete` APIs, embedding functions (default and third-party), hybrid search, and multitenancy patterns for RAG and agent memory workloads per the documentation index.
Typesense
Typesense documents an open-source search engine at typesense.org/docs for fast typo-tolerant keyword search, faceting, and vector retrieval. Vector search docs at typesense.org/docs/30.2/api/vector-search describe KNN search on imported embeddings or auto-generated embeddings via OpenAI, Google PaLM API, or built-in Hugging Face models in huggingface.co/typesense/models (use the `ts` namespace prefix). Features include semantic search, hybrid search with rank fusion and adjustable `alpha` weighting, similar-document queries by ID, HNSW approximate search with optional `flat_search_cutoff` brute-force mode, and cosine `vector_distance` scoring. Deploy via Typesense Cloud or self-hosted Docker/binaries with REST API and official client libraries.