**Build production LLM applications with prompt management, RAG pipelines, guardrails, evaluation harnesses, and cost tracking — using a mock LLM client that runs without API keys.**
Browse the actual product documentation and code examples included in this toolkit.
Key features of LLM Application Framework
• **Mock LLM client** — deterministic LLM simulator for testing without API keys, with token counting, streaming simulation, and error injection for resilience testing
• **Prompt registry** — versioned prompt templates with `{{variable}}` substitution, validation, fingerprinting, bulk loading from YAML, and composition (system + few-shot + context + query)
• **RAG pipeline** — document chunking with configurable overlap, in-memory vector store with cosine similarity, token-budget-aware context assembly, and source citation
• **Guardrails** — input validation (prompt injection detection, content length, topic filtering) and output validation (PII detection/redaction, format compliance) composable into chains
• **Evaluation harness** — ROUGE-L, BLEU, word overlap, factual consistency scoring, answer relevance, format compliance, output length checks, and A/B comparison
• **Cost tracker** — per-model pricing, running cost accumulation, budget alerts with callbacks, cost breakdown by model and prompt, and monthly cost estimation**Mock LLM client** — deterministic LLM simulator for testing without API keys, with token counting, streaming simulation, and error injection for resilience testing
**Prompt registry** — versioned prompt templates with `{{variable}}` substitution, validation, fingerprinting, bulk loading from YAML, and composition (system + few-shot + context + query)
**RAG pipeline** — document chunking with configurable overlap, in-memory vector store with cosine similarity, token-budget-aware context assembly, and source citation
**Guardrails** — input validation (prompt injection detection, content length, topic filtering) and output validation (PII detection/redaction, format compliance) composable into chains
**Evaluation harness** — ROUGE-L, BLEU, word overlap, factual consistency scoring, answer relevance, format compliance, output length checks, and A/B comparison
**Cost tracker** — per-model pricing, running cost accumulation, budget alerts with callbacks, cost breakdown by model and prompt, and monthly cost estimation
Configure LLM Application Framework parameters to see how the product works.
pip install -r requirements.txt