IA-QA Eval & RAG — 38 tools

Quality 87 / 100good

39 tools, about 11,729 tokens to load them all · tool set e7c727a0b5c9 · tools last changed 2026-11-10 · checked 2026-11-14 07:03 UTC

Tool changes

• 2026-11-10rerank_evaluatechangeddescription
  before

  Evaluate RAG retrieval: rank passages against a query, compute Precision@k / Recall@k and a PASS/FAIL verdict from your relevance labels. Modes: (1) give every passage the `score` YOUR reranker produced — deterministic, offline, the mode to gate CI on; (2) no scores, no key — a local BM25 keyword baseline, never a judgement of a neural reranker; (3) api_key for an NVIDIA account that still has reranking (NVIDIA retired the hosted endpoints on 2026-05-18).

  after · 96% of words in common

  Evaluate RAG retrieval: rank passages against a query, compute Precision@k / Recall@k and a PASS/FAIL verdict from your relevance labels (INCONCLUSIVE without them). Modes: (1) give every passage the `score` YOUR reranker produced — deterministic, offline, the mode to gate CI on; (2) no scores, no key — a local BM25 keyword baseline, never a judgement of a neural reranker; (3) api_key for an NVIDIA account that still has reranking (NVIDIA retired the hosted endpoints on 2026-05-18).

• 2026-11-10run_vlm_test_suitechangeddescription, output schema
  before

  Test a vision model (OpenAI, Anthropic or Gemini) on one image: each test case asks a question and asserts on the answer (contains, not_contains, json_format, min_length, max_length, semantic_contains = TF-IDF cosine ≥ 0.4). Returns per-case PASS/FAIL, the pass rate, an overall PASS/WARNING/FAIL against threshold, and latency. BYOK: your key for the model's provider; the image is fetched only from a public address.

  after · 89% of words in common

  Test a vision model (OpenAI, Anthropic or Gemini) on one image: each test case asks a question and asserts on the answer (contains, not_contains, json_format, min_length, max_length, semantic_contains = TF-IDF cosine ≥ 0.4). Returns per-case PASS/FAIL, the pass rate, `overall` PASS/WARNING/FAIL against threshold (N/A when no assertion could run), and latency. BYOK: your key for the model's provider; the image is fetched only from a public address.

• 2026-11-10run_semantic_testschangedoutput schema
• 2026-11-09skill_interviewchangedinput schema
• 2026-11-08rate_toolchangedannotations
• 2026-11-08extract_json_from_textchangeddescription
  before

  Extract the first valid JSON object or array embedded in chaotic LLM output (surrounded by markdown fences, prose, or explanatory text). Handles ```json blocks and inline JSON. Call this whenever an LLM returns structured data mixed with explanation text instead of raw JSON.

  after · 65% of words in common

  Extract the first valid JSON object or array embedded in chaotic LLM output (surrounded by markdown fences, prose, or explanatory text). Handles ```json blocks and inline JSON.

• 2026-11-08count_tokenschangeddescription
  before

  Estimate the token count of a text string using the cl100k_base approximation (~4 chars/token). Call this BEFORE sending any text to an LLM API to check if it fits within the model context window and to estimate cost. Returns token estimate, character count, and word count.

  after · 57% of words in common

  Estimate the token count of a text string using the cl100k_base approximation (~4 chars/token). Shows whether a text fits the context window of a model and what it costs. Returns token estimate, character count, and word count.

• 2026-11-08embedding_similaritychangeddescription
  before

  Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for true semantic similarity with vector embeddings, use run_semantic_tests with mode="embeddings" and your OpenAI API key. Supports single pair or batch mode with pipe-separated pairs. Useful for RAG retrieval testing, semantic search evaluation, and text deduplication.

  after · 93% of words in common

  Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: true vector embeddings are run_semantic_tests with mode="embeddings" and an OpenAI key. Supports single pair or batch mode with pipe-separated pairs. Useful for RAG retrieval testing, semantic search evaluation, and text deduplication.

• 2026-11-08response_quality_scorechangeddescription
  before

  Score an LLM response against the criteria you pass: coverage of expected_keywords and compliance with max_length. Returns a 0-100 score over the criteria actually measured — or total_score null with the reason when none is given, because relevance and correctness cannot be read off word overlap (a correct one-word answer shares no words with its question). For meaning against a reference answer, use run_semantic_tests. Also returns unscored signals: question-term overlap, average sentence length, markdown.

  after · 97% of words in common

  Score an LLM response against the criteria you pass: coverage of expected_keywords and compliance with max_length. Returns a 0-100 score over the criteria actually measured — or total_score null with the reason when none is given, because relevance and correctness cannot be read off word overlap (a correct one-word answer shares no words with its question). Meaning against a reference answer: run_semantic_tests. Also returns unscored signals: question-term overlap, average sentence length, markdown.

• 2026-11-08consistency_checkchangeddescription
  before

  Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast, deterministic, no API key needed. Limitations: relies on surface-level word matching — "Paris is the capital of France" vs "Paris is the French capital" may score low despite semantic equivalence. For true semantic consistency, use run_semantic_tests with embedding mode. Essential for determinism testing.

  after · 95% of words in common

  Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast, deterministic, no API key needed. Limitations: relies on surface-level word matching — "Paris is the capital of France" vs "Paris is the French capital" may score low despite semantic equivalence. True semantic consistency: run_semantic_tests in embedding mode. Essential for determinism testing.

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