Build Your Own Enterprise RAG - E2E Tech for Text, Tables, Images, Audio & Video

About the Speakers -
Ritu Jain, https://www.linkedin.com/in/ritu-jain-1316355a/
https://www.linkedin.com/in/sailee-mogale/
https://www.linkedin.com/in/ameyanadkarni/
What will be covered?

# 🔹 Enterprise RAG / RAC Pipeline – Techniques, Tools, Frameworks & Platforms

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## 1. Data Ingestion & Preparation

• Sources: text docs (PDF, Word), tables (CSV, SQL), images, audio, video, APIs, web.
• Tools/Frameworks: LangChain, LlamaIndex, Haystack, custom ETL.
• Techniques:
• Text extraction (PDFPlumber, Apache Tika).
• OCR (Tesseract, LayoutLM, DocTR).
• Speech-to-text (Whisper, Deepgram).
• Video transcription/scene detection.

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## 2. Chunking & Segmentation

• Text: fixed-size, sliding window, recursive (LangChain), semantic split.
• Tables: row-wise, column-wise, schema-aware chunking.
• Images: patch-based, caption-based (BLIP, SAM).
• Audio: transcript-based chunking.
• Video: frame sampling, scene segmentation, timeline-based splits.

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## 3. Embeddings (Vectorization)

Purpose: convert raw inputs into dense vectors that capture semantic meaning → used for similarity, retrieval, clustering.

• Inputs & Outputs by Modality:
• Text: string → vector (e.g., 768–1536 dims).
• Tables: row/column → vector capturing structured meaning.
• Images: pixels → vector encoding visual features.
• Audio: waveform/spectrogram → vector encoding phonetic/semantic features.
• Video: frames+audio → temporal multimodal vector.
• Popular Models:
• Text (general): OpenAI text-embedding-3, Cohere, HuggingFace E5, MiniLM, Instructor.
• Domain-specific: BioBERT, SciBERT, FinBERT, LegalBERT.
• Multilingual: LaBSE, mUSE, multilingual-E5.
• Images: CLIP, BLIP, Florence.
• Audio: Wav2Vec2, Whisper embeddings.
• Video: VideoCLIP, VIOLET.

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## 4. Vector Databases / Vector Stores

• Open-source: FAISS, Milvus, Weaviate, Qdrant, pgvector.
• Managed/Cloud: Pinecone, Chroma Cloud, Vertex AI Matching Engine, Azure Cognitive Search, AWS Kendra/OpenSearch.
• Hybrid Search: Vespa, Elastic, Weaviate (BM25 + dense).

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## 5. Indexing & Search Techniques

• Structures: Flat, IVF, HNSW, PQ.
• Hybrid Search: combine sparse (BM25) + dense (embeddings).
• Specialized Indexing:
• Text → inverted + semantic.
• Tables → schema/key indexing.
• Images → perceptual hashing + vectors.
• Audio/Video → fingerprinting, temporal indexing.

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## 6. Retrieval & Augmentation

• Frameworks: LangChain retrievers, LlamaIndex query engines, Haystack retrievers.
• Techniques:
• Top-K similarity, Maximal Marginal Relevance (MMR).
• Reranking: cross-encoders (Cohere Rerank, bge-reranker).
• Adaptive retrieval (context window control).

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## 7. Generation Layer (LLM Integration)

• LLMs: GPT-4/5, Claude, Llama 3, Gemini, Mistral, Falcon.
• Frameworks: LangChain, LlamaIndex, Semantic Kernel.
• Strategies:
• Direct RAG prompting.
• Multi-query retrieval.
• Chain-of-thought, citations, tool-augmented responses.

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## 8. Orchestration & Application Layer

• Frameworks: LangChain, LlamaIndex, Semantic Kernel, Haystack, DSPy.
• Agents & Pipelines: LangChain Agents, CrewAI, AutoGPT.
• Integrations: REST APIs, GraphQL, enterprise connectors (SharePoint, Salesforce, Slack).

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## 9. Evaluation & Monitoring

• Metrics: precision@k, recall, MRR, nDCG, hallucination rate.
• Tools: Ragas, DeepEval, TruLens, LangSmith, Arize AI, Weights & Biases.
• Continuous Improvement: human feedback loops, active learning.

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## 10. Deployment & Scaling

• Serving: FastAPI, BentoML, TorchServe, HuggingFace Inference Endpoints.
• Platforms: Kubernetes, Docker, Ray, Airflow.
• Enterprise Concerns: auth, security, compliance (GDPR, HIPAA), caching (Redis, Vespa), cost optimization.