| ### <img src="https://npkum.github.io/agenticai/images/docIQ_learning.gif" width="40" height="40"/> DocIQ: Knowledge Engine | |
| #### Secure, cloud/On-Premise Generative AI utilizing Open Source RAG Architecture | |
| **Architecture:** CPU-Optimized RAG | |
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| #### Overview | |
| **DocIQ** represents a paradigm shift in enterprise document intelligence. By leveraging a high-efficiency **Retrieval-Augmented Generation (RAG)** architecture, DocIQ delivers accurate, context-aware answers from internal knowledge bases without data ever leaving the application boundary. | |
| Unlike cloud-dependent solutions, DocIQ operates entirely on open-source technology, optimized for constrained environments (2 vCPU), demonstrating that enterprise-grade AI does not require massive GPU clusters—only smart architecture. | |
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| #### Technical Architecture | |
| The system utilizes a decoupled "retriever-generator" architecture designed for maximum throughput on minimal hardware. | |
| | Component | Technology / Model | Enterprise Function | | |
| | :--- | :--- | :--- | | |
| | **Generative LLM** | `MBZUAI/LaMini-Flan-T5-248M` | **Inference Engine:** A distilled 248M parameter model fine-tuned for instruction following, capable of running efficiently on CPUs while minimizing hallucinations. | | |
| | **Embedding Engine** | `sentence-transformers/all-MiniLM-L6-v2` | **Semantic Indexing:** Converts text to vector embeddings locally with high semantic density and millisecond latency. | | |
| | **Vector Database** | **ChromaDB** (Persistent) | **Knowledge Store:** Serverless, persistent vector storage allowing instant semantic search across thousands of document chunks. | | |
| | **Orchestration** | Python 3.10 + Streamlit | **Frontend/Backend:** Unified interface for ingestion, querying, and administrative management. | | |
| | **Caching Layer** | SQLite + Fuzzy Logic | **Performance:** Local caching database to store QA history and serve repeated questions instantly. | | |
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| #### Key Features | |
| #### 1. Role-Based Access Control (RBAC) | |
| Security is foundational. DocIQ implements session-based authentication to segregate duties, complying with standard data governance policies. | |
| * **Admin Role:** Full privileges for document ingestion, topic creation, sub-topic management, and database cleaning. | |
| * **Viewer Role:** Restricted "Read-Only" access. Viewers can query the knowledge base and provide feedback but cannot alter the vector index or delete audit logs. | |
| #### 2. Intelligent Adaptive Chunking | |
| To maximize the context window of efficient LLMs, DocIQ employs sophisticated segmentation strategies during ingestion: | |
| * **Adaptive NLTK Chunking:** Utilizes Natural Language Processing to respect sentence boundaries, ensuring semantic context is never severed mid-sentence. | |
| * **Recursive & Sliding Window:** Fallback mechanisms ensure robust handling of unstructured text and OCR data. | |
| * **Metadata Tagging:** Every chunk is tagged with `topic`, `sub_topic`, and `chunking_strategy` for precise retrieval filtering. | |
| #### 3. Integrated Hallucination Metrics & QA | |
| Trust is the barrier to AI adoption. DocIQ builds trust through transparent observability displayed with every answer: | |
| * **Cosine Similarity Score:** Mathematically verifies how closely the generated answer aligns with the source documents. | |
| * **ROUGE Metrics (2 & L):** Measures the n-gram overlap to ensure the model isn't inventing new facts. | |
| * **Source Citation:** Every answer includes a JSON log of the exact `source_docs` used to generate the response. | |
| #### 4. Smart Caching & Feedback Loops | |
| The system accelerates over time using a local Metadata Layer. | |
| * **Fuzzy Match Caching:** If a user asks a question similar to a previous one (e.g., >78% similarity), the system serves a pre-validated answer instantly, bypassing the inference engine. | |
| * **RLHF-Lite (Feedback):** Users can vote "Helpful" 👍 or "Not Helpful" 👎. This data is stored in SQLite to build a dataset for future model fine-tuning. | |
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| #### Operational Capabilities | |
| #### **Ingestion Module** | |
| > *"Turn static PDFs into dynamic knowledge."* | |
| * Auto-detection of topics and sub-topics from filenames. | |
| * Extraction of tabular data utilizing `Camelot`. | |
| * **OCR Fallback:** Automatically utilizes `Tesseract` and `pdf2image` if no text layer is detected in scanned PDFs. | |
| #### **Admin Console & Audit** | |
| > *"Full Observability."* | |
| * **Strategy Overview:** Visual analytics showing which chunking strategies are applied across the document estate. | |
| * **Topic Management:** Granular deletion capabilities (delete entire topics or specific sub-topics). | |
| * **Audit Logging:** Automatic generation of deletion reports confirming when and what data was purged. | |
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| #### Infrastructure & Cost Efficiency | |
| This application is engineered to run on : | |
| * **Compute:** 2 vCPU / 16GB RAM. | |
| * **GPU Requirement:** None. | |
| * **Data Sovereignty:** All processing is local. No data is transmitted to OpenAI, Anthropic, or Azure. | |
| * **API tokens Cost:** $0.00. | |
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| #### Conclusion | |
| DocIQ validates that secure, private, and intelligent Knowledge Management can be achieved using open-source tools. by intelligently combining `ChromaDB` for storage, `Sentence-Transformers` for understanding, and `LaMini` for generation, it provides an Enterprise-Grade RAG solution compliant with strict data privacy requirements. |