feat(config): expose router generation + narration knobs in config file
The router/narration model behaviour was hardcoded (Main built RouterConfig() with defaults). Surface it under the [router] section so it's tunable without a rebuild: [router] conversation_keep_last, retrieval_k, token_budget [router.generation] temperature, top_p, max_tokens (chat/steering) [router.narration] temperature, top_p, max_tokens, max_per_run ConfigLoader parses the new sections (adds asDouble); Main maps the config-layer RouterConfig onto the domain RouterConfig and threads narration.max_per_run into ServerModule. All values default to the previous constants, so behaviour is unchanged when the sections are absent. Documents the block in sample-config.toml and adds parser tests for present/absent cases.
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@@ -14,10 +14,12 @@ import com.correx.core.config.CorrexConfig
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import com.correx.core.config.ProviderConfig
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import com.correx.core.context.builder.DefaultContextPackBuilder
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import com.correx.core.context.compression.DefaultContextCompressor
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import com.correx.core.context.model.TokenBudget
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import com.correx.core.events.EventDispatcher
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import com.correx.core.events.stores.EventStore
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import com.correx.core.inference.CapabilityScore
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import com.correx.core.inference.DefaultInferenceRouter
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import com.correx.core.inference.GenerationConfig
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import com.correx.core.inference.InferenceProjector
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import com.correx.core.inference.InferenceRepository
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import com.correx.core.inference.InferenceRouter
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@@ -246,10 +248,26 @@ fun main() {
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// persisted vectors survive restart but the metadata map does not (no-op for
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// non-rehydratable backends like in_memory).
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runBlocking { L3MetadataRehydrator(eventStore).rehydrate(l3MemoryStore) }
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// Map the config-file [router] block onto the domain RouterConfig the facade uses.
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val domainRouterConfig = RouterConfig(
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conversationKeepLast = routerConfig.conversationKeepLast,
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retrievalK = routerConfig.retrievalK,
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tokenBudget = TokenBudget(limit = routerConfig.tokenBudget),
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generationConfig = GenerationConfig(
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temperature = routerConfig.generation.temperature,
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topP = routerConfig.generation.topP,
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maxTokens = routerConfig.generation.maxTokens,
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),
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narrationGenerationConfig = GenerationConfig(
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temperature = routerConfig.narration.temperature,
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topP = routerConfig.narration.topP,
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maxTokens = routerConfig.narration.maxTokens,
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),
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)
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val routerFacade = InfrastructureModule.createRouterFacade(
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eventStore = eventStore,
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inferenceRouter = inferenceRouter,
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config = RouterConfig(),
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config = domainRouterConfig,
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tokenizer = firstProvider.tokenizer,
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embedder = embedder,
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l3MemoryStore = l3MemoryStore,
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@@ -289,6 +307,7 @@ fun main() {
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modelSwapper = modelSwapper,
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resourceProbe = resourceProbe,
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workspaceResolver = workspaceResolver,
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narrationMaxPerRun = routerConfig.narration.maxPerRun,
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)
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module.start()
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log.info("==============================")
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@@ -233,8 +233,11 @@ object ConfigLoader {
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val toolsFileReadSection = sections["tools.file_read"] ?: emptyMap()
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val toolsFileWriteSection = sections["tools.file_write"] ?: emptyMap()
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val toolsFileEditSection = sections["tools.file_edit"] ?: emptyMap()
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val routerSection = sections["router"] ?: emptyMap()
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val routerEmbedderSection = sections["router.embedder"] ?: emptyMap()
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val routerL3Section = sections["router.l3"] ?: emptyMap()
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val routerGenerationSection = sections["router.generation"] ?: emptyMap()
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val routerNarrationSection = sections["router.narration"] ?: emptyMap()
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val modelsSection = sections["models"] ?: emptyMap()
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val server = ServerConfig(
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@@ -368,9 +371,27 @@ object ConfigLoader {
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bitWidth = asInt(routerL3Section["bit_width"], 4),
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)
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val generation = GenerationSettings(
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temperature = asDouble(routerGenerationSection["temperature"], 0.7),
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topP = asDouble(routerGenerationSection["top_p"], 0.9),
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maxTokens = asInt(routerGenerationSection["max_tokens"], 512),
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)
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val narration = NarrationSettings(
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temperature = asDouble(routerNarrationSection["temperature"], 0.7),
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topP = asDouble(routerNarrationSection["top_p"], 0.9),
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maxTokens = asInt(routerNarrationSection["max_tokens"], 1024),
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maxPerRun = asInt(routerNarrationSection["max_per_run"], 100),
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)
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val router = RouterConfig(
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embedder = embedder,
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l3 = l3,
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conversationKeepLast = asInt(routerSection["conversation_keep_last"], 6),
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retrievalK = asInt(routerSection["retrieval_k"], 5),
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tokenBudget = asInt(routerSection["token_budget"], 4096),
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generation = generation,
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narration = narration,
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)
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val models = modelsList.mapNotNull { modelMap ->
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@@ -457,6 +478,16 @@ object ConfigLoader {
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}
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}
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private fun asDouble(value: Any?, default: Double = 0.0): Double {
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return when (value) {
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is Double -> value
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is Int -> value.toDouble()
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is Long -> value.toDouble()
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is String -> value.toDoubleOrNull() ?: default
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else -> default
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}
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}
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private fun asBoolean(value: Any?, default: Boolean = false): Boolean {
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return when (value) {
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is Boolean -> value
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@@ -89,6 +89,29 @@ data class ProviderConfig(
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data class RouterConfig(
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val embedder: EmbedderConfig = EmbedderConfig(),
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val l3: L3Config = L3Config(),
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val conversationKeepLast: Int = 6,
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val retrievalK: Int = 5,
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val tokenBudget: Int = 4096,
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val generation: GenerationSettings = GenerationSettings(),
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val narration: NarrationSettings = NarrationSettings(),
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)
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/** Sampling/length knobs for router chat + steering replies. */
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@Serializable
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data class GenerationSettings(
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val temperature: Double = 0.7,
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val topP: Double = 0.9,
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val maxTokens: Int = 512,
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)
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/** Router narration knobs. Larger maxTokens than chat so reasoning models can
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* finish "thinking" and still emit the line; maxPerRun caps narrations per run. */
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@Serializable
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data class NarrationSettings(
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val temperature: Double = 0.7,
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val topP: Double = 0.9,
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val maxTokens: Int = 1024,
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val maxPerRun: Int = 100,
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)
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@Serializable
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@@ -415,4 +415,51 @@ class ConfigLoaderTest {
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assertEquals("/home/user/projects", result.tools.allowedWorkspaceRoots[0])
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assertEquals("/tmp/work", result.tools.allowedWorkspaceRoots[1])
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}
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@Test
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fun `parseToml parses router generation and narration knobs`() {
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val toml = """
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[router]
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conversation_keep_last = 8
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retrieval_k = 3
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token_budget = 8192
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[router.generation]
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temperature = 0.4
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top_p = 0.8
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max_tokens = 600
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[router.narration]
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temperature = 0.55
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top_p = 0.95
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max_tokens = 2048
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max_per_run = 25
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""".trimIndent()
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val parseTomlMethod = ConfigLoader::class.java.getDeclaredMethod("parseToml", String::class.java)
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parseTomlMethod.isAccessible = true
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val result = parseTomlMethod.invoke(ConfigLoader, toml) as CorrexConfig
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assertEquals(8, result.router.conversationKeepLast)
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assertEquals(3, result.router.retrievalK)
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assertEquals(8192, result.router.tokenBudget)
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assertEquals(0.4, result.router.generation.temperature)
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assertEquals(0.8, result.router.generation.topP)
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assertEquals(600, result.router.generation.maxTokens)
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assertEquals(0.55, result.router.narration.temperature)
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assertEquals(2048, result.router.narration.maxTokens)
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assertEquals(25, result.router.narration.maxPerRun)
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}
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@Test
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fun `parseToml uses router defaults when sections absent`() {
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val parseTomlMethod = ConfigLoader::class.java.getDeclaredMethod("parseToml", String::class.java)
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parseTomlMethod.isAccessible = true
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val result = parseTomlMethod.invoke(ConfigLoader, "[server]\nhost = \"x\"") as CorrexConfig
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assertEquals(6, result.router.conversationKeepLast)
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assertEquals(512, result.router.generation.maxTokens)
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assertEquals(1024, result.router.narration.maxTokens)
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assertEquals(100, result.router.narration.maxPerRun)
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}
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}
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@@ -74,6 +74,26 @@ capabilities = { General = 1.0, Coding = 0.7, Reasoning = 0.6, Summarization = 0
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# capabilities = { General = 1.0, Coding = 0.7, Reasoning = 0.6, Summarization = 0.8, ToolCalling = 0.5 }
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# Router configuration (optional, defaults shown below)
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[router]
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conversation_keep_last = 6 # how many recent chat turns to keep in context
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retrieval_k = 5 # L3 memory hits to retrieve per query
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token_budget = 4096 # context budget for router prompts
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# Sampling/length for router chat + steering replies.
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[router.generation]
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temperature = 0.7
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top_p = 0.9
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max_tokens = 512
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# Router narration (the live workflow commentary). max_tokens is higher than chat so
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# reasoning models can finish "thinking" and still emit the line; max_per_run caps how
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# many narrations fire per workflow run.
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[router.narration]
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temperature = 0.7
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top_p = 0.9
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max_tokens = 1024
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max_per_run = 100
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[router.embedder]
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backend = "noop" # or "llamacpp"
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dimension = 1536
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