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@MissuulLangchain

미슐랭체인

Langchain과 같은 라이브러리 및 framework을 적극활용하여 RAG에 대해 깊게 파봅시다!! 그후엔 미슐랭처럼 맛집까지 정복해보자구요!!
README.md

0. Data Preprocessing

  • Chunk Optimization
  • Embedding Model Optimization (DAPT, TAPT, etc .. )
  • Make MetaData
  • RAFT

1. Query Analysis (= Pre-Retriever)

  • Multi Query
  • Decomposition
  • HyDE
  • 부정어가 섞여있는 query의 경우 부정적인 쿼리를 filtering해서 가져올 수 있는지? (ex, 산은 좋고, 바다는 싫어 -> 바다에 대한 relevant docs는 걸러내야 함)
  • 자연어 query를 넣었을 때와 한국어 토크나이징을 수행한 query의 relevant docs 추출 성능비교

2. Query Routing (= Pre-Retriever)

  • Logical Routing
  • Semantic Routing

3. Retriever & Indexing

  • Lexical : BM25
  • Lexcial : BM42 (advanced)
  • Semantic : DPR
  • Semantic : PDR
  • Ensemble : Hybrid Search
  • Indexing : FAISS
  • Indexing : HNSW
  • RDB + Vector (= Docker + ParadeDB)

4. Filtering (= Post-Retriever)

  • Meta-Data Filtering
  • Similarity threshold cut-off
  • Passage Filtering : Refine
  • Passage Filtering : Compressor

5. Re-Ranking (= Post-Retriever)

  • API Base
  • LM Base
  • LLM Base
  • Language Specific Model Base

6. Prompting

  • Long-Context Reorder (-> Lost in the middle)
  • Zero-shot & Few-shot
  • Prompt-Chaining
  • CoT
  • ToT
  • TDB
  • ReAct
  • Reflextion

7. Generation & Inference

  • LLM Base Generation
  • Data type(fp16, fp32, bp16, nf16, etc..)
  • vllm
  • Ollama
  • DSPy

8. Hallucination Control

  • CRAG
  • Self-RAG
  • etc..

9. Evaluation

  • Evaluation (each RAG step)
  • Relevance (hitrate)
  • Groundtruth

10. Methology

  • Memory moudle (from Modular RAG)
  • Tabular Data Reasoning
  • RE-Questioning
  • RAG Module Implementation

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  1. RAG-is-all-you-need RAG-is-all-you-need Public

    "RAG is all you need"

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  • RAG-is-all-you-need Public

    "RAG is all you need"

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