{"observation":{"id":"ebad59f9-0dac-4c0c-93b3-6c855d10c508","tool":"framia","tool_name":"Framia","criterion":"text-to-animation-accuracy","criterion_name":"Text-to-animation accuracy","criterion_definition":"Does the output match all stages in the input description?","criterion_evidence_type":"transformation","criterion_rank_role":"decisive","criterion_rank_role_reason":"This is the core requirement: the animation must match the described stages and sequence. (3 of 3 judges)","scenario":"rag-ingestion-pipeline","scenario_name":"RAG Ingestion Pipeline","group_tag":null,"scenario_description":"A text prompt asking a tool to create an animated flowchart of a RAG ingestion pipeline: document upload, text extraction, chunking, embeddings, and parallel storage into a vector database and a metadata store. It stresses sequential pipeline animation plus a branch into two output paths.","modality":"text","input_text":"Create an animated flowchart titled \"RAG Ingestion Pipeline\". A Document is uploaded and its text is extracted. The extracted text is split into smaller chunks. Each chunk is converted into embeddings. The generated embeddings are stored in a Vector Database, and the metadata is stored in a Metadata Store.","input_artifact_refs":[],"stresses":["Sequential process visualization","Parallel branching into two outputs","Label accuracy for pipeline stages","Animated flowchart generation from plain text"],"verdict":"failed","score":null,"score_total":null,"note":"The recap screen can add hallucinated extra boxes and misstate the final structure by drawing Metadata Store and Vector Database as a sequential arrow instead of parallel destinations.","evidence_state":"verified","source":null,"artifacts":[{"url":"https://d3epheqghktydj.cloudfront.net/research-media-image-c53e41d86530.png","role":"output","alt":null},{"url":"https://d3epheqghktydj.cloudfront.net/research-media-image-7d2ae60922b5.png","role":"output","alt":null}],"run_id":"e7fbb451-ba1e-4de0-ad94-41cde2700aef","study_title":"Generate Diagram Animations from Text Descriptions","study_kind":"generation","research_task":"86b8vp172","tested_at":null,"completeness":"input-and-output","input":{"state":"text","text":"Create an animated flowchart titled \"RAG Ingestion Pipeline\". A Document is uploaded and its text is extracted. The extracted text is split into smaller chunks. Each chunk is converted into embeddings. The generated embeddings are stored in a Vector Database, and the metadata is stored in a Metadata Store.","files":[],"modality":"text","stresses":["Sequential process visualization","Parallel branching into two outputs","Label accuracy for pipeline stages","Animated flowchart generation from plain text"]},"tool_page_slug":"framia-converge-ai","tool_url":"https://aidemos.com/tools/framia-converge-ai","permalink":"https://aidemos.com/evidence/ebad59f9-0dac-4c0c-93b3-6c855d10c508","api_url":"https://ai.aidemos.com/v1/observations/ebad59f9-0dac-4c0c-93b3-6c855d10c508"},"peers":[{"id":"85fe4637-d28d-4edd-a5d6-a223ab6d5538","tool":"academa-ai","tool_name":"Academa AI","verdict":"worked","score":null,"score_total":null,"note":"The tool preserved the full six-step RAG ingestion sequence and the split into two parallel outputs, with document upload, text extraction, chunking, embeddings, vector database storage, and metadata storage all represented in order.","artifact_count":2,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-academa-20ai-4b84b2191423.mp4","evidence_url":"https://aidemos.com/evidence/85fe4637-d28d-4edd-a5d6-a223ab6d5538"},{"id":"cf48ca25-620f-4f5b-8a19-6cbc68e76557","tool":"animg","tool_name":"AnimG","verdict":"failed","score":null,"score_total":null,"note":"The tool can fail to keep parallel storage targets visually distinct; in the tested RAG flowchart, Vector DB and Metadata Store end up stacked on top of each other instead of separating into two nodes.","artifact_count":1,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-image-0fa72d161a40.png","evidence_url":"https://aidemos.com/evidence/cf48ca25-620f-4f5b-8a19-6cbc68e76557"},{"id":"f2a181cc-508a-47ab-ab43-cd2ec5262720","tool":"claude-ai","tool_name":"Claude AI","verdict":"mixed","score":null,"score_total":null,"note":"Reproduces the requested ingestion pipeline structure and parallel branching correctly, including the split to both Vector Database and Metadata Store, but does not render the requested in-canvas title 'RAG Ingestion Pipeline'.","artifact_count":1,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-image-146ea34e9471.png","evidence_url":"https://aidemos.com/evidence/f2a181cc-508a-47ab-ab43-cd2ec5262720"},{"id":"0685f3f0-bb29-4281-95cb-ad54c5b2382c","tool":"easymotion","tool_name":"EasyMotion","verdict":"worked","score":null,"score_total":null,"note":"The rendered flowchart matches the five-step RAG ingestion sequence and splits the final stage into two parallel destinations: document/input, extraction, chunks, embeddings, then vector DB and metadata storage.","artifact_count":1,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-rag-20ingestion-20pipeline-20thumbnail-a6ddf26c6bab.png","evidence_url":"https://aidemos.com/evidence/0685f3f0-bb29-4281-95cb-ad54c5b2382c"},{"id":"9a01d3b2-b773-4fa6-9778-529d4d3c2b56","tool":"kodisc","tool_name":"Kodisc","verdict":"worked","score":null,"score_total":null,"note":"Renders the full RAG ingestion sequence correctly, including document upload, text extraction, chunking, embedding generation, and fan-out to both a vector database and a metadata store.","artifact_count":2,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-image-5e8d19e30c06.png","evidence_url":"https://aidemos.com/evidence/9a01d3b2-b773-4fa6-9778-529d4d3c2b56"},{"id":"9b6b45ac-4e17-4a9e-9e40-c0d01ef8c364","tool":"remotion-ai","tool_name":"Remotion AI","verdict":"worked","score":null,"score_total":null,"note":"The rendered flow matches the prompt’s full stage sequence and end branch: Document → Text Extraction → Chunking → Embeddings → split into Vector DB and Metadata Store.","artifact_count":1,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-remotionai-20output-201-b9f0ebe5619c.mp4","evidence_url":"https://aidemos.com/evidence/9b6b45ac-4e17-4a9e-9e40-c0d01ef8c364"},{"id":"826f5eff-fbc3-42ae-b1d0-327d30656d6b","tool":"replit","tool_name":"Replit","verdict":"worked","score":null,"score_total":null,"note":"On the tested prompt, the generated animation matched the requested pipeline structure exactly, including the split into both Vector Database and Metadata Store, and the report records zero spelling errors across all node labels.","artifact_count":2,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-replit-20output-201-8ed41f8146df.mp4","evidence_url":"https://aidemos.com/evidence/826f5eff-fbc3-42ae-b1d0-327d30656d6b"},{"id":"c037db16-70a0-4b54-be89-35c75f100c58","tool":"vismo-studio","tool_name":"Vismo Studio","verdict":"worked","score":null,"score_total":null,"note":"The tool rendered the full 5-step ingestion sequence in order: document upload, text extraction, chunking, embedding generation, and parallel storage into both a vector database and a metadata store.","artifact_count":2,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-vismo-animation-5be3d17b8f2c.mp4","evidence_url":"https://aidemos.com/evidence/c037db16-70a0-4b54-be89-35c75f100c58"},{"id":"6b169579-1465-4127-b778-684dc3055257","tool":"x-pilot","tool_name":"X-Pilot","verdict":"worked","score":null,"score_total":null,"note":"Renders the full RAG ingestion sequence correctly, including the sequential steps and the branch into both the Vector Database and Metadata Store.","artifact_count":1,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-x-pilot-20output-201-c2f122b1e124.mp4","evidence_url":"https://aidemos.com/evidence/6b169579-1465-4127-b778-684dc3055257"}],"other_criteria":[{"id":"0f559835-5a1d-41a2-a4f2-f9356906310d","criterion":"animation-smoothness","criterion_name":"Animation smoothness","rank_role":"decisive","verdict":"worked","score":null,"score_total":null,"note":"The motion is described as smooth and visually polished, with consistent camera framing and no broken transitions reported for the RAG run.","artifact_count":1,"evidence_url":"https://aidemos.com/evidence/0f559835-5a1d-41a2-a4f2-f9356906310d"},{"id":"671ff77a-9a3e-40cc-addd-d06215703c43","criterion":"visual-clarity","criterion_name":"Visual clarity","rank_role":"decisive","verdict":"failed","score":null,"score_total":null,"note":"Label rendering is unreliable on this pipeline: the title and key node names are repeatedly garbled or misspelled, so the diagram is not readable without outside context.","artifact_count":4,"evidence_url":"https://aidemos.com/evidence/671ff77a-9a3e-40cc-addd-d06215703c43"}],"appears_in":[{"page_type":"ranking","slug":"diagram-animation-generators","title":"Best AI Tools to Generate Diagram Animations from Text Descriptions","url":"https://aidemos.com/best/diagram-animation-generators","binding":"run"}],"same_scenario":[{"id":"85fe4637-d28d-4edd-a5d6-a223ab6d5538","tool":"academa-ai","tool_name":"Academa AI","verdict":"worked","score":null,"score_total":null,"note":"The tool preserved the full six-step RAG ingestion sequence and the split into two parallel outputs, with document upload, text extraction, chunking, embeddings, vector database storage, and metadata storage all represented in order."},{"id":"cf48ca25-620f-4f5b-8a19-6cbc68e76557","tool":"animg","tool_name":"AnimG","verdict":"failed","score":null,"score_total":null,"note":"The tool can fail to keep parallel storage targets visually distinct; in the tested RAG flowchart, Vector DB and Metadata Store end up stacked on top of each other instead of separating into two nodes."},{"id":"f2a181cc-508a-47ab-ab43-cd2ec5262720","tool":"claude-ai","tool_name":"Claude AI","verdict":"mixed","score":null,"score_total":null,"note":"Reproduces the requested ingestion pipeline structure and parallel branching correctly, including the split to both Vector Database and Metadata Store, but does not render the requested in-canvas title 'RAG Ingestion Pipeline'."},{"id":"0685f3f0-bb29-4281-95cb-ad54c5b2382c","tool":"easymotion","tool_name":"EasyMotion","verdict":"worked","score":null,"score_total":null,"note":"The rendered flowchart matches the five-step RAG ingestion sequence and splits the final stage into two parallel destinations: document/input, extraction, chunks, embeddings, then vector DB and metadata storage."},{"id":"9a01d3b2-b773-4fa6-9778-529d4d3c2b56","tool":"kodisc","tool_name":"Kodisc","verdict":"worked","score":null,"score_total":null,"note":"Renders the full RAG ingestion sequence correctly, including document upload, text extraction, chunking, embedding generation, and fan-out to both a vector database and a metadata store."},{"id":"9b6b45ac-4e17-4a9e-9e40-c0d01ef8c364","tool":"remotion-ai","tool_name":"Remotion AI","verdict":"worked","score":null,"score_total":null,"note":"The rendered flow matches the prompt’s full stage sequence and end branch: Document → Text Extraction → Chunking → Embeddings → split into Vector DB and Metadata Store."},{"id":"826f5eff-fbc3-42ae-b1d0-327d30656d6b","tool":"replit","tool_name":"Replit","verdict":"worked","score":null,"score_total":null,"note":"On the tested prompt, the generated animation matched the requested pipeline structure exactly, including the split into both Vector Database and Metadata Store, and the report records zero spelling errors across all node labels."},{"id":"c037db16-70a0-4b54-be89-35c75f100c58","tool":"vismo-studio","tool_name":"Vismo Studio","verdict":"worked","score":null,"score_total":null,"note":"The tool rendered the full 5-step ingestion sequence in order: document upload, text extraction, chunking, embedding generation, and parallel storage into both a vector database and a metadata store."},{"id":"6b169579-1465-4127-b778-684dc3055257","tool":"x-pilot","tool_name":"X-Pilot","verdict":"worked","score":null,"score_total":null,"note":"Renders the full RAG ingestion sequence correctly, including the sequential steps and the branch into both the Vector Database and Metadata Store."}]}