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feat(shared): add model availability with fuzzy matching and fetch

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Implement fuzzyMatchModel() for case-insensitive substring matching with provider filtering. Add fetchAvailableModels() to get available models from OpenCode client with caching.

Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-opencode)

Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
This commit is contained in:
justsisyphus 2026-01-22 22:43:10 +09:00
parent f4a0d5ec40
commit bc62c23a85
2 changed files with 369 additions and 0 deletions
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251
src/shared/model-availability.test.ts Normal file
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import { describe, it, expect, beforeEach } from "bun:test"
import { fetchAvailableModels, fuzzyMatchModel, __resetModelCache } from "./model-availability"
describe("fetchAvailableModels", () => {
let mockClient: any
beforeEach(() => {
__resetModelCache()
})
it("#given API returns list of models #when fetchAvailableModels called #then returns Set of model IDs", async () => {
const mockModels = [
{ id: "openai/gpt-5.2", name: "GPT-5.2" },
{ id: "anthropic/claude-opus-4-5", name: "Claude Opus 4.5" },
{ id: "google/gemini-3-pro", name: "Gemini 3 Pro" },
]
mockClient = {
model: {
list: async () => mockModels,
},
}
const result = await fetchAvailableModels(mockClient)
expect(result).toBeInstanceOf(Set)
expect(result.size).toBe(3)
expect(result.has("openai/gpt-5.2")).toBe(true)
expect(result.has("anthropic/claude-opus-4-5")).toBe(true)
expect(result.has("google/gemini-3-pro")).toBe(true)
})
it("#given API fails #when fetchAvailableModels called #then returns empty Set without throwing", async () => {
mockClient = {
model: {
list: async () => {
throw new Error("API connection failed")
},
},
}
const result = await fetchAvailableModels(mockClient)
expect(result).toBeInstanceOf(Set)
expect(result.size).toBe(0)
})
it("#given API called twice #when second call made #then uses cached result without re-fetching", async () => {
let callCount = 0
const mockModels = [
{ id: "openai/gpt-5.2", name: "GPT-5.2" },
{ id: "anthropic/claude-opus-4-5", name: "Claude Opus 4.5" },
]
mockClient = {
model: {
list: async () => {
callCount++
return mockModels
},
},
}
const result1 = await fetchAvailableModels(mockClient)
const result2 = await fetchAvailableModels(mockClient)
expect(callCount).toBe(1)
expect(result1).toEqual(result2)
expect(result1.has("openai/gpt-5.2")).toBe(true)
})
it("#given empty model list from API #when fetchAvailableModels called #then returns empty Set", async () => {
mockClient = {
model: {
list: async () => [],
},
}
const result = await fetchAvailableModels(mockClient)
expect(result).toBeInstanceOf(Set)
expect(result.size).toBe(0)
})
it("#given API returns models with various formats #when fetchAvailableModels called #then extracts all IDs correctly", async () => {
const mockModels = [
{ id: "openai/gpt-5.2-codex", name: "GPT-5.2 Codex" },
{ id: "anthropic/claude-sonnet-4-5", name: "Claude Sonnet 4.5" },
{ id: "google/gemini-3-flash", name: "Gemini 3 Flash" },
{ id: "opencode/grok-code", name: "Grok Code" },
]
mockClient = {
model: {
list: async () => mockModels,
},
}
const result = await fetchAvailableModels(mockClient)
expect(result.size).toBe(4)
expect(result.has("openai/gpt-5.2-codex")).toBe(true)
expect(result.has("anthropic/claude-sonnet-4-5")).toBe(true)
expect(result.has("google/gemini-3-flash")).toBe(true)
expect(result.has("opencode/grok-code")).toBe(true)
})
})
describe("fuzzyMatchModel", () => {
// #given available models from multiple providers
// #when searching for a substring match
// #then return the matching model
it("should match substring in model name", () => {
const available = new Set([
"openai/gpt-5.2",
"openai/gpt-5.2-codex",
"anthropic/claude-opus-4-5",
])
const result = fuzzyMatchModel("gpt-5.2", available)
expect(result).toBe("openai/gpt-5.2")
})
// #given available models with partial matches
// #when searching for a substring
// #then return exact match if it exists
it("should prefer exact match over substring match", () => {
const available = new Set([
"openai/gpt-5.2",
"openai/gpt-5.2-codex",
"openai/gpt-5.2-ultra",
])
const result = fuzzyMatchModel("gpt-5.2", available)
expect(result).toBe("openai/gpt-5.2")
})
// #given available models with multiple substring matches
// #when searching for a substring
// #then return the shorter model name (more specific)
it("should prefer shorter model name when multiple matches exist", () => {
const available = new Set([
"openai/gpt-5.2-ultra",
"openai/gpt-5.2-ultra-mega",
])
const result = fuzzyMatchModel("gpt-5.2", available)
expect(result).toBe("openai/gpt-5.2-ultra")
})
// #given available models with claude variants
// #when searching for claude-opus
// #then return matching claude-opus model
it("should match claude-opus to claude-opus-4-5", () => {
const available = new Set([
"anthropic/claude-opus-4-5",
"anthropic/claude-sonnet-4-5",
])
const result = fuzzyMatchModel("claude-opus", available)
expect(result).toBe("anthropic/claude-opus-4-5")
})
// #given available models from multiple providers
// #when providers filter is specified
// #then only search models from specified providers
it("should filter by provider when providers array is given", () => {
const available = new Set([
"openai/gpt-5.2",
"anthropic/claude-opus-4-5",
"google/gemini-3",
])
const result = fuzzyMatchModel("gpt", available, ["openai"])
expect(result).toBe("openai/gpt-5.2")
})
// #given available models from multiple providers
// #when providers filter excludes matching models
// #then return null
it("should return null when provider filter excludes all matches", () => {
const available = new Set([
"openai/gpt-5.2",
"anthropic/claude-opus-4-5",
])
const result = fuzzyMatchModel("claude", available, ["openai"])
expect(result).toBeNull()
})
// #given available models
// #when no substring match exists
// #then return null
it("should return null when no match found", () => {
const available = new Set([
"openai/gpt-5.2",
"anthropic/claude-opus-4-5",
])
const result = fuzzyMatchModel("gemini", available)
expect(result).toBeNull()
})
// #given available models with different cases
// #when searching with different case
// #then match case-insensitively
it("should match case-insensitively", () => {
const available = new Set([
"openai/gpt-5.2",
"anthropic/claude-opus-4-5",
])
const result = fuzzyMatchModel("GPT-5.2", available)
expect(result).toBe("openai/gpt-5.2")
})
// #given available models with exact match and longer variants
// #when searching for exact match
// #then return exact match first
it("should prioritize exact match over longer variants", () => {
const available = new Set([
"anthropic/claude-opus-4-5",
"anthropic/claude-opus-4-5-extended",
])
const result = fuzzyMatchModel("claude-opus-4-5", available)
expect(result).toBe("anthropic/claude-opus-4-5")
})
// #given available models with multiple providers
// #when multiple providers are specified
// #then search all specified providers
it("should search all specified providers", () => {
const available = new Set([
"openai/gpt-5.2",
"anthropic/claude-opus-4-5",
"google/gemini-3",
])
const result = fuzzyMatchModel("gpt", available, ["openai", "google"])
expect(result).toBe("openai/gpt-5.2")
})
// #given available models with provider prefix
// #when searching with provider filter
// #then only match models with correct provider prefix
it("should only match models with correct provider prefix", () => {
const available = new Set([
"openai/gpt-5.2",
"anthropic/gpt-something",
])
const result = fuzzyMatchModel("gpt", available, ["openai"])
expect(result).toBe("openai/gpt-5.2")
})
// #given empty available set
// #when searching
// #then return null
it("should return null for empty available set", () => {
const available = new Set<string>()
const result = fuzzyMatchModel("gpt", available)
expect(result).toBeNull()
})
})

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src/shared/model-availability.ts Normal file
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/**
* Fuzzy matching utility for model names
* Supports substring matching with provider filtering and priority-based selection
*/
import { log } from "./logger"
/**
* Fuzzy match a target model name against available models
*
* @param target - The model name or substring to search for (e.g., "gpt-5.2", "claude-opus")
* @param available - Set of available model names in format "provider/model-name"
* @param providers - Optional array of provider names to filter by (e.g., ["openai", "anthropic"])
* @returns The matched model name or null if no match found
*
* Matching priority:
* 1. Exact match (if exists)
* 2. Shorter model name (more specific)
*
* Matching is case-insensitive substring match.
* If providers array is given, only models starting with "provider/" are considered.
*
* @example
* const available = new Set(["openai/gpt-5.2", "openai/gpt-5.2-codex", "anthropic/claude-opus-4-5"])
* fuzzyMatchModel("gpt-5.2", available) // → "openai/gpt-5.2"
* fuzzyMatchModel("claude", available, ["openai"]) // → null (provider filter excludes anthropic)
*/
export function fuzzyMatchModel(
target: string,
available: Set<string>,
providers?: string[],
): string | null {
log("[fuzzyMatchModel] called", { target, availableCount: available.size, providers })
if (available.size === 0) {
log("[fuzzyMatchModel] empty available set")
return null
}
const targetLower = target.toLowerCase()
// Filter by providers if specified
let candidates = Array.from(available)
if (providers && providers.length > 0) {
const providerSet = new Set(providers)
candidates = candidates.filter((model) => {
const [provider] = model.split("/")
return providerSet.has(provider)
})
log("[fuzzyMatchModel] filtered by providers", { candidateCount: candidates.length, candidates: candidates.slice(0, 10) })
}
if (candidates.length === 0) {
log("[fuzzyMatchModel] no candidates after filter")
return null
}
// Find all matches (case-insensitive substring match)
const matches = candidates.filter((model) =>
model.toLowerCase().includes(targetLower),
)
log("[fuzzyMatchModel] substring matches", { targetLower, matchCount: matches.length, matches })
if (matches.length === 0) {
return null
}
// Priority 1: Exact match
const exactMatch = matches.find((model) => model.toLowerCase() === targetLower)
if (exactMatch) {
log("[fuzzyMatchModel] exact match found", { exactMatch })
return exactMatch
}
// Priority 2: Shorter model name (more specific)
const result = matches.reduce((shortest, current) =>
current.length < shortest.length ? current : shortest,
)
log("[fuzzyMatchModel] shortest match", { result })
return result
}
let cachedModels: Set<string> | null = null
export async function fetchAvailableModels(client: any): Promise<Set<string>> {
if (cachedModels !== null) {
log("[fetchAvailableModels] returning cached models", { count: cachedModels.size, models: Array.from(cachedModels).slice(0, 20) })
return cachedModels
}
try {
const models = await client.model.list()
const modelSet = new Set<string>()
log("[fetchAvailableModels] raw response", { isArray: Array.isArray(models), length: Array.isArray(models) ? models.length : 0, sample: Array.isArray(models) ? models.slice(0, 5) : models })
if (Array.isArray(models)) {
for (const model of models) {
if (model.id && typeof model.id === "string") {
modelSet.add(model.id)
}
}
}
log("[fetchAvailableModels] parsed models", { count: modelSet.size, models: Array.from(modelSet) })
cachedModels = modelSet
return modelSet
} catch (err) {
log("[fetchAvailableModels] error", { error: String(err) })
return new Set<string>()
}
}
export function __resetModelCache(): void {
cachedModels = null
}