observicia 0.1.7

Cloud Native Observability SDK for LLM applications

Observicia SDK

Observicia is a Cloud Native observability and policy control SDK for LLM applications. It provides seamless integration with CNCF native observability stack while offering comprehensive token tracking, policy enforcement, and PII protection capabilities.

Features

• Token Tracking and Management

  • Real-time token usage monitoring across providers
  • Stream-aware token counting
  • Token usage retention and cleanup
  • Per-session token tracking

• Policy Enforcement

  • Integration with Open Policy Agent (OPA)
  • Support for multiple policy evaluation levels
  • Risk level assessment (low, medium, high, critical)
  • Custom policy definition support
  • Synchronous and asynchronous policy evaluation

• LLM Provider Integration

  • OpenAI (fully implemented)
    • Chat completions (sync/async)
    • Text completions (sync/async)
    • Embeddings
    • Image generation
    • File operations
    • Streaming support
  • Basic scaffolding for:
    • Anthropic
    • LiteLLM
    • WatsonX

• Observability Features

  • OpenTelemetry integration
  • Span-based tracing for all LLM operations
  • Configurable logging (console, file, OTLP)
  • Mermaid diagram generation from telemetry data
  • Detailed request/response tracing
  • Custom attribute tracking

Quick Start

1. Install the SDK:

pip install observicia

2. Create a configuration file (observicia_config.yaml):

service_name: patient-rag-app
otel_endpoint: null
opa_endpoint: http://opa-server:8181/
policies:
  - name: pii_check
    path: policies/pii
    description: Check for PII in responses
    required_trace_level: enhanced
    risk_level: high
  - name: prompt_compliance
    path: policies/prompt_compliance
    description: Check for prompt compliance
    required_trace_level: basic
    risk_level: medium
logging:
  file: "rag-app.json"
  telemetry:
    enabled: true
    format: "json"
  messages:
    enabled: true
    level: "INFO"
  chat:
    enabled: true
    level: "both"
    file: "rag-chat.json"

3. Initialize in your code:

from observicia import init

# Initialize Observicia
init()

# Then import openai to so that OpenAI code is instrumented
from openai import OpenAI
client = OpenAI()

Deployment

Prerequisites

• Kubernetes cluster with:
  • OpenTelemetry Collector
  • Open Policy Agent
  • Jaeger (optional)
  • Prometheus (optional)

Example Kubernetes Deployment

The SDK includes Kubernetes manifests for deploying:

• OpenTelemetry Collector
• Open Policy Agent
• Jaeger
• Prometheus
• PII detection service
• Prompt compliance service

See the deploy/k8s directory for complete deployment manifests.

Examples

The SDK includes two example applications:

1. Simple Chat Application (examples/simple-chat)

   • Basic chat interface using OpenAI
   • Demonstrates token tracking and tracing
   • Shows streaming response handling

2. RAG Application (examples/rag-app)

   • Retrieval-Augmented Generation example
   • Shows policy enforcement for PII protection
   • Demonstrates context tracking

Architecture

flowchart TB
    App[Application] --> SDK[Observicia SDK]
    SDK --> Providers[LLM Providers]
    SDK --> OPA[Open Policy Agent]
    SDK --> OTEL[OpenTelemetry Collector]
    OTEL --> Jaeger[Jaeger]
    OTEL --> Prom[Prometheus]
    OPA --> PII[PII Detection Service]
    OPA --> Compliance[Prompt Compliance Service]

Core Components

• Context Manager: Manages trace context and session tracking
• Policy Engine: Handles policy evaluation and enforcement
• Token Tracker: Monitors token usage across providers
• Patch Manager: Manages LLM provider SDK instrumentation
• Tracing Manager: Handles OpenTelemetry integration

Development Status

• ✅ Core Framework
• ✅ OpenAI Integration
• ✅ Basic Policy Engine
• ✅ Token Tracking
• ✅ OpenTelemetry Integration
• 🚧 Additional Provider Support
• 🚧 Advanced Policy Features
• 🚧 UI Components

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.