# agentset.ai

> AI-optimized mirror of agentset.ai containing 50 pages totalling 25,980 words of clean markdown content, structured data, and semantic HTML. Original source: https://agentset.ai/. Last updated: 2026-04-30T22:29:27.079Z.

Each page is available as HTML (with JSON-LD structured data) and Markdown (text-only, ideal for LLMs and RAG).

## Homepage

- [Building Blocks for  AI Chat and Search](/site-root.html): The open-source platform to build AI apps that deliver reliable answers. Production-grade RAG in minutes, no expertise needed. (527 words)

## Articles & Blog Posts

- [blog/parsing-pdf-documents-at-scale/index.html](/blog/parsing-pdf-documents-at-scale/index.html) (1 words)
- [Tuesday, February 25, 2025](/blog/the-art-of-document-chunking-for-llm-applications/index.html): Explore the nuances of effective document chunking strategies for retrieval-augmented generation systems and how they impact LLM performance. (1,753 words)
- [blog/automate-business-workflows-with-ai-agents/index.html](/blog/automate-business-workflows-with-ai-agents/index.html) (1 words)
- [Sunday, March 8, 2026](/blog/gpt-5-4-rag-regression.html): OpenAI released GPT-5.4 with a Pro variant for extended reasoning. We tested both on our LLM-for-RAG leaderboard across three workloads. (411 words)
- [Cohere Rerank 4 Pro](/rerankers/cohere-rerank-4-pro/index.html): Complete guide to Cohere Rerank 4 Pro by Cohere. View performance metrics, leaderboard position, and compare with other reranking models. (303 words)
- [Friday, December 5, 2025](/blog/best-vector-db-for-rag/index.html): We reviewed seven popular vector databases to understand how they differ in deployment, cost, and where they fit in real RAG systems. (797 words)
- [Jina Reranker v2 Base Multilingual](/rerankers/jina-reranker-v2-base-multilingual/index.html): Complete guide to Jina Reranker v2 Base Multilingual by Jina AI. View performance metrics, leaderboard position, and compare with other reranking models. (288 words)
- [Friday, February 6, 2026](/blog/opus-4-6-in-rag.html): We evaluated Claude Opus 4.6 in a RAG setup across factual retrieval, synthesis, and scientific tasks versus 11 frontier models. (424 words)
- [Best Rerankers for RAG](/rerankers/index.html): Compare the best rerankers for RAG. Cohere Rerank, Voyage, Jina, and BGE reranker benchmarked on accuracy, latency, and cost. (918 words)
- [Saturday, December 13, 2025](/blog/cohere-reranker-v4/index.html): We benchmarked Cohere Rerank 4 Pro and Fast against v3.5 and other rerankers under the same RAG pipeline. (941 words)
- [Voyage AI Rerank 2.5](/rerankers/voyage-ai-rerank-25/index.html): Complete guide to Voyage AI Rerank 2.5 by Voyage AI. View performance metrics, leaderboard position, and compare with other reranking models. (275 words)
- [Friday, December 12, 2025](/blog/gpt5-2-on-rag.html): We plugged GPT-5.2 into our LLM RAG leaderboard and compared it against nine other frontier models under the same RAG pipeline. (763 words)
- [Cohere Rerank 3.5](/rerankers/cohere-rerank-35/index.html): Complete guide to Cohere Rerank 3.5 by Cohere. View performance metrics, leaderboard position, and compare with other reranking models. (273 words)
- [LLM Leaderboard for RAG](/llms/index.html): LLM leaderboard for RAG applications. Compare answer quality, faithfulness, latency, and cost across GPT-5, Claude, Gemini, and more. Benchmark data updated December 2025. (1,010 words)
- [Monday, March 2, 2026](/blog/zembed-1/index.html): ZeroEntropy released zembed-1, a 4B embedding model distilled from zerank-2 reranker. We tested it to see how it performs on real retrieval tasks. (386 words)
- [Thursday, May 1, 2025](/blog/building-effective-rag-pipelines-practical-guide/index.html): Learn how to design and implement robust retrieval-augmented generation (RAG) pipelines, from document processing to retrieval optimization. (946 words)
- [Tuesday, November 25, 2025](/blog/opus-4-5-eval.html): An evaluation of Opus 4.5 inside a real retrieval setup, compared against Gemini 3 Pro and GPT 5.1 across five behaviors that matter for RAG. (785 words)
- [Monday, March 10, 2025](/blog/building-a-proof-of-concept-rag-system-in-an-afternoon.html): A practical guide to quickly building a functional retrieval-augmented generation system to demonstrate the value of AI-powered document search. (1,429 words)
- [Thursday, December 25, 2025](/blog/multimodal-vs-text-embeddings/index.html): We compared a text-based and a multimodal embedding pipeline across text, tables, and charts to see where multimodal actually helps. (712 words)
- [Vector Database Leaderboard](/vector-databases/index.html): Compare vector databases for RAG: Pinecone, Qdrant, Milvus, Weaviate, Chroma, and more—benchmarked on cost, features, and use cases. (1,008 words)
- [Monday, January 26, 2026](/blog/how-to-detect-hallucinations-in-rag/index.html): RAG helps, but hallucinations still happen. We tested four detection methods to find the best for production—comparing accuracy, latency, and cost. (1,040 words)
- [Cohere Rerank 4 Fast](/rerankers/cohere-rerank-4-fast/index.html): Complete guide to Cohere Rerank 4 Fast by Cohere. View performance metrics, leaderboard position, and compare with other reranking models. (288 words)
- [Monday, February 9, 2026](/blog/voyage-4/index.html): We tested Gemini 3 inside an actual retrieval setup and compared it directly with GPT-5.1 across five areas that matter for RAG. (457 words)
- [Voyage AI Rerank 2.5 Lite](/rerankers/voyage-ai-rerank-25-lite/index.html): Complete guide to Voyage AI Rerank 2.5 Lite by Voyage AI. View performance metrics, leaderboard position, and compare with other reranking models. (279 words)
- [Sunday, November 16, 2025](/blog/embedding-models-converged/index.html): We compared 13 embedding models across 8 datasets using an LLM judge and ELO scoring. The result: almost all of them perform in the same narrow band. (391 words)
- [Wednesday, February 18, 2026](/blog/sonnet-4-6-in-rag.html): We evaluated Claude Sonnet 4.6 in a RAG setup across factual retrieval, synthesis, and scientific tasks compared to frontier models. (328 words)
- [Wednesday, March 11, 2026](/blog/gemini-2-embedding/index.html): Google released Gemini Embedding 2, their first natively multimodal embedding model. We ran it against 17 models across 7 datasets. It takes #1 with 1605 Elo, but the top three are within 18 points of each other. (446 words)
- [Friday, November 7, 2025](/blog/best-reranker/index.html): We benchmarked eight leading rerankers to find which performs best for real-world RAG pipelines—comparing speed, accuracy, and relevance. (478 words)
- [Wednesday, November 19, 2025](/blog/gemini-3-vs-gpt5-1.html): We tested Gemini 3 inside an actual retrieval setup and compared it directly with GPT-5.1 across five areas that matter for RAG. (481 words)
- [Friday, March 27, 2026](/blog/rag-for-the-ai-sdk/index.html): A practical guide to implementing retrieval-augmented generation with the AI SDK — from ingesting documents into vectors to retrieving context at query time. (429 words)
- [Graphlit vs Agentset](/compare/graphlit-vs-agentset/index.html): Compare Graphlit and Agentset RAG platforms. See how Agentset offers superior features, better performance, and more flexible pricing for your AI needs. (450 words)
- [Vectara vs Agentset](/compare/vectara-vs-agentset/index.html): Compare Vectara and Agentset RAG platforms. See how Agentset offers superior features, better performance, and more flexible pricing for your AI needs. (464 words)
- [Thursday, December 18, 2025](/blog/gemini-3-flash/index.html): We evaluated Gemini 3 Flash in a RAG setup to understand where it excels and where it falls short—focusing on factual retrieval and grounding. (410 words)
- [What’s happening at Agentset.](/blog/index.html): Stay informed with product updates, company news, and insights on how to sell smarter at your company. (870 words)
- [Credal vs Agentset](/compare/credal-vs-agentset/index.html): Compare Credal and Agentset RAG platforms. See how Agentset offers superior features, better performance, and more flexible pricing for your AI needs. (419 words)
- [Tuesday, April 15, 2025](/blog/is-rag-dead/index.html): OpenAI released the GPT 4.1 models supporting 1M token context window. Gemini supports up to 10M tokens in research. Is the RAG era over? (415 words)
- [Pricing that scales with you](/pricing/index.html): Start building RAG applications for free with Agentset. Flexible pricing that scales from hobbyists to enterprise teams processing millions of documents. (356 words)
- [embeddings/compare/qwen3-embedding-8b-vs-qwen3-embedding-4b/index.html](/embeddings/compare/qwen3-embedding-8b-vs-qwen3-embedding-4b/index.html) (1 words)
- [Privacy Policy](/privacy/index.html): Learn how Agentset collects, uses, and protects your personal data and documents. We are SOC 2 certified and HIPAA compliant for enterprise security. (543 words)
- [Ragie vs Agentset](/compare/ragie-vs-agentset/index.html): Compare Ragie and Agentset RAG platforms. See how Agentset offers superior features, better performance, and more flexible pricing for your AI needs. (442 words)
- [Monday, October 27, 2025](/blog/cohere-vs-zerank-comparison/index.html): We compared Cohere v3.5 and ZeRank-1 in a RAG pipeline using a BEIR subset and a custom dataset — analyzing accuracy, latency, and LLM preference. (289 words)
- [BAAI/bge-m3 vs Qwen3 Embedding 0.6B](/embeddings/compare/baaibge-m3-vs-qwen3-embedding-06b/index.html): Compare BAAI/bge-m3 vs Qwen3 Embedding 0.6B embedding models for RAG. Side-by-side analysis of accuracy, retrieval quality, latency, and cost across real-world datasets. (522 words)
- [Performance Rankings](/leaderboard/index.html): RAG leaderboards for embeddings, rerankers, LLMs, and vector DBs—benchmarked results to pick the best stack for quality, latency, and cost. (102 words)
- [Terms of Service](/terms/index.html): Read the terms and conditions for using Agentset RAG services. Covers account usage, data processing, intellectual property, and service agreements. (569 words)
- [Careers](/careers/index.html): Join the Agentset team. We're a fully remote company building the future of RAG infrastructure. High autonomy, fast shipping, and great people. See open positions. (27 words)
- [Engineers to RAG Specialists](/about/index.html): Meet the team behind Agentset. Former NASA and Twitter engineers who built RAG for 9 million pages and created the tool they wish they had from the start. (240 words)
- [Best Embedding Models for RAG](/embeddings/index.html): Compare the best embedding models for RAG. Benchmarks for OpenAI, Cohere, Voyage, Jina, and open-source models ranked by accuracy, speed, and cost. (918 words)
- [Schedule a demo](/schedule-demo/index.html): Book a personalized demo with the Agentset team. See how our RAG platform can improve your AI accuracy, reduce hallucinations, and scale to enterprise needs. (30 words)
- [Qdrant vs Elasticsearch](/vector-databases/compare/qdrant-vs-elasticsearch/index.html): Compare Qdrant vs Elasticsearch for RAG applications. Detailed analysis of deployment options, cost, licensing, indexing methods, and cloud provider support. (345 words)

## Resources

- [Full Page Index](/index.html): Browse all cached pages with rich metadata
- [About This Cache](/content/about.html): Methodology, technical details, and usage guidelines
- [XML Sitemap](/content/sitemap.xml): Machine-readable sitemap for crawler discovery
- [Robots.txt](/content/robots.txt): Crawler directives