Spaces:
Running
Running
| # Sources and implementation references - v2.0 final | |
| ## Final-release note | |
| v2.0 introduces no new runtime dependency or external architectural component. The final changes are benchmark alignment, UI/documentation polish, and local release-verification tooling. Existing official/source references below continue to document the underlying Gradio, FastAPI, Gemini, LangGraph, Qdrant and retrieval behavior. | |
| No tutorial source code is copied into RAGForge. The requested projects were used as architectural inspiration/checklists. | |
| ## Requested reference projects | |
| 1. Krish Naik — Enterprise Advanced RAG with Hybrid Search, Reranking, HyDE, CRAG, Self-RAG, Text2SQL, Caching and Guardrails in LangGraph | |
| https://www.krishnaik.in/project/enterprise-advanced-rag-with-hybrid-search-reranking-hyde-crag-self-rag-text2sql-caching-and-guardrails-in-langgraph | |
| 2. Krish Naik — Production Grade Cyclic RAG with LangGraph, GCP and Groq | |
| https://www.krishnaik.in/project/production-grade-cyclic-rag-with-langgraph-gcp-and-groq | |
| 3. Krish Naik — Building a RAG-Based Document Search Application | |
| https://www.krishnaik.in/project/building-a-rag-based-document-search-application | |
| 4. Krish Naik — Air India RAG Chatbot Development | |
| https://www.krishnaik.in/project/air-india-rag-chatbot-development | |
| 5. Educative — Building a Retrieval-Augmented Generation System Using FastAPI | |
| https://www.educative.io/projects/building-a-retrieval-augmented-generation-system-using-fastapi | |
| 6. ByteByteAI — AI Engineering curriculum / Ask-the-Web modules | |
| https://bytebyteai.com/c/ai-engineering | |
| ## Official implementation references | |
| - Gemini Interactions API migration / structured output | |
| https://ai.google.dev/gemini-api/docs/migrate-to-interactions | |
| - Gemini Interactions structured-output schema update | |
| https://ai.google.dev/gemini-api/docs/interactions-breaking-changes-may-2026 | |
| - LangGraph custom agentic RAG: document grading, conditional routing and query rewrite loop | |
| https://docs.langchain.com/oss/python/langgraph/agentic-rag | |
| - LangGraph Graph API / conditional edges | |
| https://docs.langchain.com/oss/python/langgraph/graph-api | |
| - Qdrant filtering/search relevance/hybrid retrieval | |
| https://qdrant.tech/documentation/search/filtering/ | |
| https://qdrant.tech/documentation/search/search-relevance/ | |
| https://qdrant.tech/documentation/search/hybrid-queries/ | |
| - FastEmbed | |
| https://github.com/qdrant/fastembed | |
| - Hugging Face Spaces documentation | |
| https://huggingface.co/docs/hub/spaces-overview | |
| - Gradio BrowserState / state lifecycle | |
| https://www.gradio.app/guides/state-in-blocks | |
| - Gradio Progress | |
| https://www.gradio.app/docs/gradio/progress | |
| ## Evaluation references | |
| - RAGAS paper - component-wise/reference-free evaluation of RAG pipelines | |
| https://arxiv.org/abs/2309.15217 | |
| - RAGAS context precision / context recall / faithfulness / response relevancy documentation | |
| https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/ | |
| - LangSmith RAG evaluation tutorial - datasets plus answer/retrieval evaluators | |
| https://docs.langchain.com/langsmith/evaluate-rag-tutorial | |
| - Gradio event progress controls - `show_progress=hidden` suppresses the automatic overlay when explicit progress is used | |
| https://www.gradio.app/docs/gradio/on | |
| - Gemini API rate limits - active limits are project/model dependent and visible in Google AI Studio | |
| https://ai.google.dev/gemini-api/docs/rate-limits | |
| - Gemini troubleshooting - bounded exponential backoff for 429/5xx and retry guidance | |
| https://ai.google.dev/gemini-api/docs/troubleshooting | |
| ## v1.5 evidence-driven runtime policy | |
| The adaptive reranker policy and incremental evaluation design are internal engineering decisions informed by RAGForge's own transparent bundled benchmark. They are not presented as universal claims that reranking is ineffective or that one evaluation design is optimal for every corpus. The explicit reranker ablation remains available so the decision can be revisited when corpus size/difficulty changes. | |
| ## v1.6 evaluation-driven policy | |
| Insight synthesis, hard-mode benchmark cases, table citation semantics, chunk-level reranker labels, profile comparison and evaluation-history deltas are RAGForge-specific engineering additions derived from the project's own observed evaluation gaps. They are not claims that one routing taxonomy or benchmark design is universally optimal. | |
| ## v1.7 evaluation-policy note | |
| v1.7 does not add a new external dependency or benchmark dataset. The new policies are derived from RAGForge's own auditable v1.6 run data: perfect source/chunk ablation quality with large reranker latency, a false-negative missing-answer case, and Markdown citation-coverage artifacts. | |
| ## v1.8 evaluation-policy note | |
| v1.8 adds no new external benchmark or hosted dependency. The context-budget policy is derived from RAGForge's own v1.7 auditable run: focused QA retained 100% source Recall@5/Hit@1/MRR while source Precision@5 remained roughly 47%, and generation dominated node latency. The new ablation therefore tests whether removing the focused-query distractor tail preserves recall before treating the optimization as beneficial. | |
| ## v1.9 implementation note | |
| v1.9 adds no external runtime dependency and no new third-party architecture source. Adaptive budgets, sentence compression, scale stress, workspace diagnostics and readiness scoring are implemented locally on top of the existing retrieval/evaluation stack. The scale harness reuses existing embedding vectors rather than calling an embedding or generation API. |