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Steven Jannette
2026-08-12 12:19:55 +00:00
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@@ -30,6 +30,19 @@ Built with a React/Vote frontend and a Typescript/Node/Express backend, using An
Source Ingestion -> Parsing -> Chunking -> Embedding (using Voyage AI voyage-3 model) -> Storage (vector store) -> { user query submission } -> Evaluation of User Query -> Retrieval -> Ranking -> Response Generation (Using Anthopic's claude-opus-4-6 model) -> Response Groundedness Scoring (using Voyage AI rerank-r model) Source Ingestion -> Parsing -> Chunking -> Embedding (using Voyage AI voyage-3 model) -> Storage (vector store) -> { user query submission } -> Evaluation of User Query -> Retrieval -> Ranking -> Response Generation (Using Anthopic's claude-opus-4-6 model) -> Response Groundedness Scoring (using Voyage AI rerank-r model)
## Why Database Math Can't Replace Groundedness Scoring
Many recent additions to the Vector database/store product space use distance metrics (Cosine, L2, Inner Product) for Bi-Encoder Similarity. They compare the vector of the user query against the vectors of chunks independently.
The rerank-r model at the end of the Source Sentinel pipeline performs Cross-Encoder Evaluation, taking two entirely separate text inputs:
1) The generated response from claude-opus-4-6 and
2) The raw source chunks
It processes them simultaneously through deep attention layers to check for hallucinations, missing context, and factual alignment.
A vector database cosine metric only calculates how close two embeddings are in coordinate space. It **does not read the generated text of a response to verify if it accurately reflects the source chunks**.
## Prerequisites ## Prerequisites