Check whether an LLM response was drawn from its source. Grounding, not truth.

This is the main tool. It automatically selects the right method:

• If context is provided: uses SGI (Semantic Grounding Index) to check whether the response actually engaged the source material.

• If no context: uses DGI (Directional Grounding Index), a coarse signal with a known ceiling.

Both are deterministic — same inputs always produce the same score. No model in the scoring path. Scoring is embedding geometry.

IMPORTANT — how to report the result. A passing check means the response ENGAGED ITS SOURCE. It does NOT mean the facts are correct. A plausible wrong fact stated in the right frame (right topic, right terminology, one wrong number or date) will pass. Do not tell the user a passing check means the answer is "verified", "accurate" or "not hallucinated". Always surface the handoff field, and when escalate is true, say so: the case needs a second stage (an entailment check, a lookup against the source, or a judge).

Args: params (CheckInput): The question, response, and optional context.

Returns: str: JSON with a plain-language CHECK (Supported / Partly supported / Not supported by the document, or Looks grounded / Partly grounded / Not grounded), score, level, method, message, escalate, handoff, and the raw components.

Examples: - "Did this ChatGPT answer actually come from our policy document?" → provide question + response + the policy document as context - "Did this answer engage its source at all?" → provide question + response (no context needed)

Check whether an LLM response engaged a source document (SGI). Provenance, not truth.

SGI (Semantic Grounding Index) measures whether the response engaged with the provided context or stayed anchored to the question. This is the method to use for RAG pipeline verification — did the model actually use the retrieved documents?

The score is a ratio: dist(response, question) / dist(response, context). A high ratio means the response moved toward the context. A low ratio means it stayed near the question (possibly ignored the context).

IMPORTANT: this measures PROVENANCE. An answer that borrows the source's vocabulary and structure but changes one figure will pass. Surface the handoff field and escalate fact-level verification to a second stage.

Args: params (SGIInput): The question, source context, and LLM response.

Returns: str: JSON with a plain-language CHECK, the SGI score, and the two distances.

Examples: - Verifying a RAG chatbot used the retrieved documents - Checking if a summary is faithful to the source text - Auditing whether context was ignored in a customer support bot

Coarse context-free grounding signal, for when no source is available (DGI).

DGI (Directional Grounding Index) compares the question-to-response displacement against the direction typical of answers written from a source. No context is needed, so it works for open-ended chat and general Q&A.

IMPORTANT: this is the weakest signal here and it has a measured ceiling. With authorship held constant it reaches AUROC 0.606, and the ceiling of the entire embedding-similarity class is about 0.68. It is a ranking signal for triage, not a detector, and it is not a risk verdict. Prefer groundlens_sgi whenever a source is available. Never report a DGI score as evidence that an answer is true or false.

Args: params (DGIInput): The question and LLM response.

Returns: str: JSON with a plain-language CHECK, the DGI score, the magnitude, escalate and handoff.

Examples: - Ranking a batch of chat answers so a reviewer starts with the worst - Screening outputs when no source document exists