Query Conduit: A Coordination and Configuration Management System for LLM Inference

Query Conduit is a coordination system specifically designed for LLM inference workflows. Positioned as a middleware layer between the application logic layer and underlying model services, it provides unified configuration management, inference task scheduling, and professional data analysis processing capabilities. It helps developers build scalable AI application architectures and decouples business logic from complex system-level operations.

With the widespread application of LLMs, developers' challenges have evolved from calling model APIs to efficiently managing complex inference workflows: they need to handle multiple model calls, system configuration management, asynchronous task queues, and professional data analysis simultaneously. These complexities have spurred the demand for specialized infrastructure that unifies the coordination of LLM inference, configuration management, and data processing.

As a middleware layer, Query Conduit sits between application logic and underlying model services, handling system-level issues such as request routing, load balancing, configuration hot updates, and heterogeneous data source processing. The advantage of the layered design lies in decoupling: developers can focus on business logic without worrying about model API differences, rate-limiting strategies, or multi-environment configuration management.

Query Conduit includes three core modules:

1. Query Coordination: Manages the lifecycle of inference requests, implements intelligent routing (dynamically selects endpoints based on load/latency/cost), and supports multi-model chain calls and parallel execution;
2. Configuration Management: Provides centralized storage and distribution of configurations, supports hot loading and version control rollback, covering dimensions such as model parameters, prompt templates, and security policies;
3. Data Processing Pipeline: Handles mixed structured/unstructured inputs, provides cleaning, format conversion, and batch processing functions to ensure data quality consistency.

The design follows three principles:

• Scalability: Plug-in architecture allows new models/data sources/logic to be accessed via standard interfaces;
• Observability: Built-in metric collection and logging for key paths, monitoring indicators such as inference latency, error rate, and cost;
• Fault Tolerance: Implements multi-level retries, circuit breaking, graceful degradation, etc., to handle anomalies like network fluctuations and rate limits.

Applicable to multiple scenarios:

• Enterprise Knowledge Q&A: Coordinates collaboration among multiple dedicated models and manages knowledge base configurations for different departments;
• Intelligent Customer Service: Handles high-concurrency queries, routes to optimal models/combinations, and adjusts parameters in real time to optimize responses;
• AI-Assisted Programming: Manages model configurations for code generation/testing/documentation tasks, collects quality metrics for continuous optimization.

Compatible with existing tech stacks:

• Adapters support mainstream LLM services (OpenAI/Anthropic/Azure OpenAI/local models) and integrate with LangChain/LlamaIndex frameworks;
• Exposes RESTful API and gRPC interfaces;
• Supports containerized deployment, provides Kubernetes Operator for cluster management, and is compatible with cloud-native observability tools like Prometheus/Grafana/Jaeger.

As an open-source project, the community is actively building it, with the roadmap including enhanced multi-modal support, intelligent routing algorithms, and out-of-the-box data components. Recommendations: For scenarios with high inference complexity, strong configuration requirements, or the need to handle heterogeneous data streams, introducing Query Conduit can reduce maintenance costs and improve system stability.