Laminar Project Description
What is the project about?
Laminar is an all-in-one open-source platform designed for engineering AI products, specifically focusing on tracing, evaluating, labeling, and analyzing data generated by Large Language Models (LLMs).
What problem does it solve?
Laminar addresses the challenges of monitoring, evaluating, and improving AI applications built with LLMs. It provides tools to understand LLM behavior, assess performance, manage data, and refine models. It helps in debugging, and quality assurance of LLM applications.
What are the features of the project?
- Tracing: Automatic tracing of AI frameworks and SDKs (LangChain, OpenAI, Anthropic, etc.) using OpenTelemetry, capturing inputs/outputs, latency, cost, token count, and function calls. Image tracing is supported, and audio tracing is planned.
- Evaluations: Local offline evaluations and online evaluations using hosted LLMs or Python scripts.
- Labels: A simple UI for fast data labeling.
- Datasets: Export of production trace data to datasets, running evaluations on golden datasets, and indexing datasets for semantic similarity search to improve prompts.
- Built for Scale: Designed for high performance and low overhead, using gRPC for trace transmission.
- Modern Open-Source Stack: Utilizes RabbitMQ, Postgres, Clickhouse, and Qdrant.
- Dashboards: User-friendly dashboards for visualizing traces, evaluations, and labels.
What are the technologies used in the project?
- Core Backend: Rust
- Frontend: Next.js
- Message Queue: RabbitMQ
- Databases: Postgres (application data), Clickhouse (analytics)
- Vector Database: Qdrant
- Tracing: OpenTelemetry, OpenLLMetry
- Client Libraries: Python, TypeScript (Node.js)
- Other: Docker, gRPC, Python sandbox
What are the benefits of the project?
- Improved LLM Application Quality: Provides tools for monitoring, evaluation, and data management, leading to better performing and more reliable AI products.
- Simplified Development: Easy integration with existing AI frameworks and SDKs.
- Scalability: Designed to handle large volumes of data and traces.
- Open Source: Free to use and contribute to.
- Comprehensive Solution: Offers a wide range of features for managing the entire LLM application lifecycle.
- Fast and Efficient: Built with performance in mind, using technologies like Rust and gRPC.
What are the use cases of the project?
- Monitoring LLM Performance: Tracking key metrics like latency, cost, and token usage.
- Debugging LLM Applications: Identifying and resolving issues in LLM interactions.
- Evaluating LLM Outputs: Assessing the quality and accuracy of LLM responses.
- Data Labeling: Creating labeled datasets for training and fine-tuning LLMs.
- Dataset Management: Organizing and managing data used in LLM applications.
- Improving Prompts: Using semantic search to find relevant examples for few-shot prompting.
- Building and maintaining any LLM-powered application.
