Add dataset card

README.md

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+---
+language:
+- en
+license: apache-2.0
+size_categories:
+- 100K<n<1M
+task_categories:
+- text-generation
+- question-answering
+tags:
+- rag
+- retrieval-augmented-generation
+- vector-databases
+- embeddings
+- langchain
+- llamaindex
+- sft
+- synthetic
+- chunking
+- hybrid-search
+pretty_name: RAG Systems SFT 100K
+---
+
+# RAG Systems SFT 100K
+
+A synthetic supervised fine-tuning dataset of 100,000 high-quality conversations covering Retrieval-Augmented Generation (RAG) systems — from basic pipelines to advanced multi-hop retrieval, evaluation, and production optimization. Designed to train AI assistants that can help engineers build, debug, and scale RAG applications.
+
+## Dataset Description
+
+This dataset covers the full spectrum of RAG system development across 12 specialized categories. Each record follows the ShareGPT format with a practitioner-level question and a detailed, implementation-ready response with working Python code.
+
+## Categories
+
+| Category | Description |
+|---|---|
+| `rag_architecture` | Basic pipeline, advanced patterns (HyDE, RAG-Fusion, FLARE, GraphRAG) |
+| `chunking_strategies` | Fixed-size, semantic, recursive, parent-child chunking |
+| `vector_databases` | Pinecone vs Weaviate vs ChromaDB vs pgvector |
+| `embedding_models` | text-embedding-3-small, BGE, sentence-transformers comparison |
+| `retrieval_evaluation` | RAGAS metrics, debugging RAG failures |
+| `hybrid_search` | BM25 + dense embeddings, RRF reranking |
+| `reranking` | Cross-encoder reranking with Cohere, FlashRank |
+| `contextual_compression` | LLMChainExtractor, EmbeddingsFilter, context window optimization |
+| `citation_generation` | Generating cited responses linked to source documents |
+| `multi_hop_rag` | Multi-step chained retrieval for complex questions |
+| `rag_production` | Caching, latency optimization, cost control |
+| `document_ingestion` | PDF, HTML, code, table parsing and loading |
+
+## Format
+
+ShareGPT format:
+```json
+{
+  "conversations": [
+    {"from": "human", "value": "...RAG question..."},
+    {"from": "gpt", "value": "...implementation-ready response with code..."}
+  ],
+  "metadata": {"category": "...", "context": "..."},
+  "id": "uuid"
+}
+```
+
+## Use Cases
+
+- Fine-tuning AI assistants for RAG system design and debugging
+- Training models to reason about retrieval quality and evaluation
+- Building AI-assisted RAG development tooling
+- Educating teams on production RAG patterns
+
+## Quality Notes
+
+All responses include working Python code examples using LangChain, LlamaIndex, and direct API integrations. Covers RAGAS evaluation, hybrid search implementation, and production deployment patterns.