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Solvi is a Python library for building decision systems from developer-defined functions, checks, and typed questions. It can plan which computations to run, attach reasons and source evidence to answers, and record a replayable execution trace. It does not supply sound decision rules or guarantee that an answer is correct simply because the process can be audited.
What is Solvi for?
Solvi’s project documentation describes a workflow in which developers write ordinary Python functions and checks, then ask typed questions—for example, a yes-or-no question or a choice among specified options. A strategist selects the functions and checks needed to answer those questions, using their dependencies and checkpoints. Catalog entries can compute facts, test conditions, extract text spans, or provide rules for answering.
This makes Solvi a framework for running developer-defined decision logic, not a ready-made authority on what a decision should be. The developer still chooses the inputs, rules, constraints, and checks that shape the outcome.
How can you check a Solvi decision?
Inspect the answer’s reason and evidence
The package says an answer can include confidence and an inspectable reason. Depending on how the system is configured, that reason may show rule inputs, a formula based on computed facts, or a quotation with character offsets in source text. For document extraction, those quotes and offsets can help a reviewer locate the supporting passage. They establish where a result came from; they do not, by themselves, prove that the passage is accurate or that the rule interpreted it correctly.
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Replay the recorded execution
Solvi describes each execution step as hashed and linked to the next in a chain. Replay is intended to recompute steps and flag mismatches, broken links, or invalid quote spans. For model-backed steps, the trace records model identity and fingerprint, so a replay can expose a model change. These are mechanisms described by the project, not independent validation that every implementation or decision is correct.
What happens if a rule or computation fails?
Solvi’s package description says: “When something cannot be computed, a function fails, or a rule returns an answer outside the allowed options, solvi abstains instead of guessing.” It also states: “A failed hard check always overrides any model confidence.” In practical terms, the system can decline to produce an answer when a required computation fails or a configured hard check rejects the result.
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Abstention is a safeguard around the logic developers define. It cannot catch every bad input, missing rule, or flawed assumption unless the system has been designed to identify it. A decision can be reproducible and still be wrong if its evidence or rules are inadequate.
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What limits apply to the associated base model?
The project’s 2026 base-model card describes a 150-million-parameter ModernBERT-base cross-encoder. Its stated performance description is 50 milliseconds per question on a CPU; this is a project-reported figure, not an independently measured benchmark. The card’s reported evaluation figures also come with important boundaries:
- It reports 56.3% on the Fast Decisions development split for zero-shot choice questions, and says zero-shot choice performance is not improved over previous base models.
- For contract evidence supporting an answer, it reports 46% for the base model versus 62% for the large model.
- It describes English-only support and says inputs are truncated beyond 512 tokens, or 1,024 in block layout.
- It warns that the shipped act/escalate thresholds are indicative, were fitted on development data, and may be over-confident on new real text. It recommends calibration on labeled examples representative of the deployment stream and recalibration when the input domain shifts.
These limitations describe the associated base model and its card; they should not be assumed to apply identically to every possible Solvi configuration.
Can Solvi make legal, medical, or credit decisions?
The associated base-model card says the model should not be used alone for medical, legal, or credit decisions. That warning matters even when a system produces source quotations, confidence values, or a replayable trace: auditability does not replace appropriate expertise, validated rules, representative data, or human review for high-stakes decisions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Is Solvi 1.0 released?
The cited PyPI listing does not substantiate a 1.0 release. It labels the project Alpha, describes version 0.4.0, and shows release history through 0.6.1 dated September 28, 2026. The listing therefore does not verify the “1.0” in the title; its exact meaning remains unresolved.
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