Research in भारतीयज्ञानप्रणाली (bhāratīyajñānapraṇālī - Indian Knowledge Systems) (IKS) presents a unique challenge: knowledge is vast, multi-lingual, and highly fragmented across diverse sources. Scholars and students spend a disproportionate amount of time locating, accessing, and cross-referencing information from ancient texts (often stored in protected digital archives), modern academic papers (in shared drives), and the general body of knowledge from contemporary AI models. This manual process is slow, prone to oversight, and creates a significant barrier to performing deep, comparative analysis and discovering novel connections between disparate sources.
The opportunity exists to dramatically accelerate IKS research by providing a unified, intelligent platform that can interface with these varied knowledge sources simultaneously. By leveraging cutting-edge AI, we can empower researchers to move beyond simple information retrieval and engage in high-level analytical and comparative work.
भारतीयज्ञानप्रणालीनां कृते संज्ञानात्मकसंशोधनमञ्चः (bhāratīyajñānapraṇālīnāṁ kr̥tē saṁjñānātmakasaṁśōdhanamañcaḥ - Cognitive Research Platform for Indian Knowledge Systemswill be a secure, web-based conversational assistant designed specifically for the needs of IKS researchers. It will provide a single, intuitive chat interface to query multiple knowledge sources, including:
Curated Local Archives: Digitized texts and manuscripts stored in a secure file system.
Collaborative Research Drives: Modern papers, articles, and notes stored in a shared Google Drive.
Foundational AI Models: Direct access to the general knowledge of multiple world-class LLMs like Google's Gemini, OpenAI's ChatGPT, and others.
The core innovation of भारतीयज्ञानप्रणालीनां कृते संज्ञानात्मकसंशोधनमञ्चः (bhāratīyajñānapraṇālīnāṁ kr̥tē saṁjñānātmakasaṁśōdhanamañcaḥ - Cognitive Research Platform for Indian Knowledge Systems) is its Concordance Engine, which will not only fetch information from these sources but also perform a comparative analysis, highlighting similarities, differences, and contradictions. The platform will support natural, conversational follow-ups and maintain a persistent history of research sessions, creating a powerful, long-term workspace for every scholar. Access will be secured via their existing institutional Google accounts, ensuring ease of use and robust security.
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संज्ञानात्मकसंशोधनमञ्चस्य कार्यात्मकानि आवश्यकतानि (saṁjñānātmakasaṁśōdhanamañcasya kāryātmakāni āvaśyakatāni - Functional requirements of a cognitive research platform)
- Problem and Opportunity: Addresses the challenges of fragmented IKS knowledge and proposes a solution to centralize and intelligently analyze diverse sources using AI.
- Proposed Solution: Describes the platform as a secure, web-based conversational assistant that queries curated local archives, collaborative research drives, and foundational AI models. Its core innovation is a "Concordance Engine" for comparative analysis.
- Key Business Goals: Lists goals such as accelerating research cycles, deepening analytical insight, enhancing accessibility, and ensuring secure and persistent research.
- Actors in the system: Identifies "IKS Researcher" as the end user and "System Admin" as an administrative role.
- Knowledge Sources: Categorizes the various sources the platform will access, including personal repositories (Local File System, Google Drive) and Public LLM based GenAI platforms (Google's Gemini AI, OpenAI's ChatGPT, DeepSeek, Anthropic's Claude).
- Functional Use Cases: Details specific functionalities and user stories, including:
- Single Source Query (UC-01): Allows users to query a single selected knowledge source for targeted answers.
- Multi-Source Concordance Query (UC-02): Enables querying multiple sources simultaneously for a synthesized, comparative report.
- Conversational Follow-up (UC-03): Supports natural, context-aware follow-up questions within a conversation.
- View Chat History (UC-04): Allows users to access and resume previous research sessions.
- Secure Access (UC-05): Ensures authentication via existing institutional Google accounts.
- Extensible Knowledge Base (UC-06): Describes the system's modular architecture for adding new knowledge sources.
- Non-Functional Requirements (NFRs): Covers aspects like performance (response time, concurrency), security (authentication, data encryption, least privilege), scalability (stateless services, scalable AI services), availability, usability, maintainability (CI/CD, monitoring), and compliance.
संज्ञानात्मकसंशोधनमञ्चस्य तकनीकीनिर्माणम् (saṁjñānātmakasaṁśōdhanamañcasya takanīkīnirmāṇam - Technical design of a cognitive research platform)
Key Components and Architecture:
- Cloud-Native Microservices: The system is built on a decoupled microservice architecture deployed on Google Kubernetes Engine (GKE).
- Conversational AI Agent (Concorde): Designed to answer questions by retrieving, comparing, and synthesizing information from multiple knowledge sources (local file system and Google Drive).
- Core Technologies: Leverages Angular (Frontend), Node.js/Express.js (API Tier), Python/Flask (Concordance Engine, Retriever Agents), MongoDB (chat history, agentic memory), Redis (caching), and Google Cloud services like Vertex AI, Pub/Sub, and GKE.
- Concordance Engine: The "brain" of the RAG (Retrieval-Augmented Generation) system, orchestrating retrieval agents and LLMs using LangChain via an event-driven flow.
- RAG Retriever Agents: Specialized microservices for data retrieval from specific sources (e.g., Local File Retriever, Google Drive Retriever), interacting with Vertex AI for embedding and vector search.
- Security Gateway: A secure entry point handling OIDC/OAuth 2.0 authentication and routing authenticated traffic.
- Core LLM & AI Tier: Utilizes managed Google AI services like Vertex AI Vector Search and Gemini Models for embedding, vector search, and text generation.
Data Flows and Integration:
- Authentication Flow (OIDC): Managed by the Security Gateway, redirecting users to an external Identity Provider (IdP), exchanging an authorization code for a JWT, and establishing a secure session.
- RAG Query & Concordance Flow:
- MCP Assembly (API Tier): User query initiates assembly of a Model Context Protocol (MCP) object including chat history and desired sources.
- Orchestration (Concordance Engine): Receives the MCP, dispatches parallel retrieval jobs to RAG Retriever Agents (for RAG sources) or directly to the LLM Aggregator (for direct LLM sources).
- Context Retrieval (Retriever Agents): Agents fetch context from their specific data sources, generate embeddings via Vertex AI, and query Vertex AI Vector Search.
- Concordance & Synthesis: The Concordance Engine aggregates results from all jobs and uses a reasoning LLM (e.g., Gemini 1.5 Pro) to perform concordance analysis and synthesize the final answer.
- Response & History: The answer is returned to the user, and the exchange is saved to MongoDB.
- Data Contracts: Defined by JSON schemas for MCP (Model Context Protocol) for user context and A2A (Agent-to-Agent) protocols for inter-agent communication.
Deployment, DevSecOps, and Scalability:
- Deployment: The system is defined by Kubernetes YAML manifests (Namespace, Deployments, Services, PersistentVolumeClaim, Ingress, Secrets) and containerized with Docker.
- DevSecOps Pipeline: A robust CI/CD pipeline ensures continuous integration, security scanning (SCA, SAST, DAST, Container, IaC), deployment, and monitoring, using tools like Google Cloud Build, GitHub Actions, Snyk, CodeQL, OWASP ZAP, and Google Cloud's operations suite.
- Scalability & Resilience: Microservices can be independently scaled, and GKE handles pod failures, ensuring system resilience.
- Security: Centralized authentication, Kubernetes Secrets for sensitive data, network policies for restricted communication, and regular security scans.
Future Considerations (v2.0):
- Support for adding new RAG agents.
- Implementation of a Redis cache for common queries.
- Enhanced MongoDB schema for advanced state management in multi-turn interactions.
