Backend

Architecture & Infrastructure

Powered by LinkolnOS

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High-Level Overview

          ┌───────────────┐
          │   Telegram    │
          │   Frontend    │
          └───────────────┘
                 │
           HTTPS / Webhook
                 │
        ┌──────────────────┐
        │  API Gateway /   │
        │   Load Balancer  │
        └──────────────────┘
                 │
       ┌────────────────────┐
       │  Bot Orchestrator  │
       │   (Microservice)   │
       └────────────────────┘
         /       |        \
        /        |         \
┌────────────────┐    ┌───────────────┐
│ AI Inference   │    │ User Services │
│  Microservice  │    │  (Profiles,   │
│  (OpenAI, etc.)│    │  Knowledge,   │
└────────────────┘    │  Sessions)    │
        |             └───────────────┘
        |                     |
        |                     |
        |             ┌────────────────┐
        |             │   SQL/NoSQL    │
        |             │   Database(s)  │
        |             └────────────────┘
        |                     |
        |             ┌────────────────┐
        |             │  Object Store  │
        |             │ (S3 / IPFS)    │
        |             └────────────────┘
        |
        |
  ┌──────────────┐
  │ Analytics &   │
  │ Observability │
  │  (Prometheus, │
  │   Grafana)    │
  └──────────────┘

Telegram Frontend Layer

• Telegram Bot: The user interacts with Linkoln via Telegram messages (commands, queries, etc.). Telegram passes all messages to Linkoln through an HTTPS webhook that points to our infrastructure.

• Security & TLS: The webhook endpoint uses TLS certificates for secure transmission of messages from Telegram to our servers.

API Gateway / Load Balancer

• Reverse Proxy / Load Balancer: A service like Nginx, HAProxy, or Kong routes inbound requests from Telegram to the correct microservices—especially important when Linkoln scales horizontally across multiple instances.

• Rate Limiting & Throttling: Implemented at the gateway level to protect the system from spam or DDoS attacks, ensuring fair use of AI resources.

Bot Orchestrator Microservice

• Core Node.js/TypeScript Service: Receives the inbound Telegram updates from the API Gateway. It handles essential logic such as parsing user commands, session identification, and routing requests to the correct microservices.

• State Machine & Session Management: Maintains ephemeral in-memory state for ongoing user interactions, while also calling on the User Services microservice for persistent data (like knowledge base references or conversation states).

• Scalability: Deployed in a containerized environment (e.g., Docker + Kubernetes). Multiple Bot Orchestrator instances can be spun up to handle spikes in traffic.

AI Inference Microservice

• OpenAI Integration: For LLM-based functionality (chat completions, text generation), Linkoln calls an external or self-hosted LLM. This microservice encapsulates the logic for:

  • Model selection (e.g., GPT-4, GPT-3.5, or a fine-tuned model).

  • Prompt engineering (applying system messages, user context, etc.).

  • Handling retries and rate limits with the AI provider.

• Caching Layer: Frequent or repeated queries can be cached in Redis or Memcached to reduce API calls and latency.

• Model Observability: Logs usage metrics to tools like Prometheus (for custom metrics on tokens used, response times, etc.).

User Services Microservice

• Profiles & Knowledge Management: Stores and retrieves user data (e.g., user settings, custom links, documents) in a SQL or NoSQL DB, allowing each user (or group of users) to maintain a unique knowledge base.

• Session Persistence: If a user’s session or certain ephemeral data needs partial persistence (e.g., short-term logs or pinned references), it’s managed here. The system can mark what data is ephemeral vs. long-term.

• Access Control: (If needed) Potential for role-based access. For example, if Linkoln is used by organizations, different users might have different knowledge base privileges.

Database Layer

• Primary SQL/NoSQL DB: A robust database solution (e.g., PostgreSQL, MySQL, or MongoDB) houses user profiles, knowledge references, and conversation logs.

  • SQL is typically chosen if relational structure is needed.

  • NoSQL (like MongoDB) can be beneficial if the data is highly unstructured (e.g., arbitrary documents or dynamic knowledge).

• Object Store (S3 / IPFS): Large attachments, documents, or curated references can be stored in an object storage system such as Amazon S3, MinIO, or decentralized solutions like IPFS. Metadata is stored in the database, while bulky files remain in object storage to optimize database performance.

Observability & Analytics

• Monitoring: Prometheus collects real-time metrics (CPU, memory usage, response times, tokens consumed by AI calls). Grafana visualizes these metrics for performance insights.

• Logging: Each microservice sends logs to an ELK Stack (Elasticsearch, Logstash, Kibana) or similar. This central logging pipeline helps debug issues and track user interactions in an anonymized manner.

• Alerts & Incident Management: Systems like PagerDuty or Opsgenie can trigger alerts when thresholds are breached (high latency, AI timeouts, gateway errors, etc.).

• Analytics: A dedicated pipeline for usage analytics can aggregate data on how often certain features or knowledge-base items are queried. This can guide future improvements or expansions to Linkoln’s knowledge.

Security & Compliance

• Token Management: The Telegram bot token is stored in secure environment variables. Service-to-service communication tokens (for the AI microservice or third-party APIs) are also managed via an encrypted vault like HashiCorp Vault or AWS Secrets Manager.

• Data Encryption: At-rest encryption for databases and object storage ensures user data remains secure. SSL/TLS for in-transit encryption of all traffic.

• Role-Based Access Control (RBAC): If Linkoln is integrated within an enterprise environment, RBAC can ensure certain knowledge sets or admin functions are only accessible by authorized accounts.

• Compliance: Depending on the scope, Linkoln can be aligned with relevant data-protection standards (e.g., GDPR) by implementing user consent flows and robust data-handling policies.

Deployment & CI/CD

• Containerization: Each microservice (Bot Orchestrator, AI Inference, User Services) is containerized (e.g., Docker) and orchestrated via Kubernetes (EKS, GKE, or on-prem).

• Continuous Integration / Continuous Deployment: Using tools like GitHub Actions, GitLab CI, or Jenkins to automate builds, tests, and deployments. Configuration-as-code keeps infrastructure consistent across dev, staging, and production.

• Blue-Green or Canary Releases: For seamless updates, new versions of microservices can be rolled out gradually, reducing downtime and risk.

Keynote

This modular microservices architecture ensures that Linkoln remains scalable, fault-tolerant, and feature-rich. The combination of API Gateways, AI Inference Services, User-centric microservices, and robust DevOps practices allows Linkoln to deliver high-performance, personalized AI experiences in a secure, professional, and future-proof way.

Whether hosting a few dozen conversations per hour or thousands of concurrent queries, Linkoln’s multi-tiered setup can be expanded or streamlined to match the evolving demands of your community or enterprise.