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13784a6 5ea3240 13784a6 5ea3240 13784a6 5ea3240 13784a6 d6ee0a6 ef2c57c bb5d2bb 27716f7 b0af996 03bed0b f79a242 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | # Reference projects and implementation sources
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.
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