mirror of
https://gitee.com/mateos/mateclaw.git
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773 lines
34 KiB
Java
773 lines
34 KiB
Java
package vip.mate.llm.service;
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import com.fasterxml.jackson.databind.JsonNode;
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import com.fasterxml.jackson.databind.ObjectMapper;
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import lombok.RequiredArgsConstructor;
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import lombok.extern.slf4j.Slf4j;
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import org.springframework.http.HttpHeaders;
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import org.springframework.http.MediaType;
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import org.springframework.stereotype.Service;
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import org.springframework.util.StringUtils;
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import org.springframework.web.client.RestClient;
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import vip.mate.exception.MateClawException;
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import vip.mate.llm.model.*;
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import java.time.Duration;
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import java.util.*;
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import java.util.concurrent.CompletableFuture;
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import java.util.concurrent.ExecutorService;
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import java.util.concurrent.Executors;
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import java.util.concurrent.Semaphore;
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import java.util.concurrent.TimeUnit;
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import java.util.concurrent.TimeoutException;
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import java.util.stream.Collectors;
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@Slf4j
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@Service
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@RequiredArgsConstructor
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public class ModelDiscoveryService {
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private final ModelProviderService modelProviderService;
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private final ModelConfigService modelConfigService;
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private final ObjectMapper objectMapper;
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private static final Duration TIMEOUT = Duration.ofSeconds(10);
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// Virtual-thread executor for parallel model probing (lightweight, short-lived)
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private static final ExecutorService PROBE_EXECUTOR = Executors.newVirtualThreadPerTaskExecutor();
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// Probe concurrency cap — reduced from 5 to 3 because higher parallelism
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// triggered 429 Throttling.RateQuota on DashScope during bulk refresh
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private static final int MAX_PROBE_CONCURRENCY = 3;
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// Per-model probe timeout (short; we only need to know "yes/no usable")
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private static final long PROBE_TIMEOUT_SECONDS = 12;
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/**
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* Explicit deny list: model ids listed by DashScope compatible-mode that are known
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* to fail on the native protocol. Updated as we observe new failures.
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*/
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private static final Set<String> DASHSCOPE_NATIVE_DENY = Set.of(
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"qwen3.5-max",
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"qwen3.5-plus"
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);
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/**
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* Pattern matching DashScope model ids that use a dot-versioned family (e.g.
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* "qwen3.5-max", "qwen3.6-plus"). These are only offered on compatible-mode
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* and consistently fail on the native endpoint with
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* "[InvalidParameter] url error". Block them regardless of exact name.
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*/
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private static final java.util.regex.Pattern DASHSCOPE_NATIVE_UNSUPPORTED_PATTERN =
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java.util.regex.Pattern.compile("^qwen\\d+\\.\\d+.*", java.util.regex.Pattern.CASE_INSENSITIVE);
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/**
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* Parameter-size suffixes used by DashScope open-source base models (e.g.
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* {@code qwen3-0.6b}, {@code qwen3-8b}, {@code qwen3-32b}, {@code qwen3-30b-a3b}).
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* These are catalog entries, not DashScope-hosted chat endpoints, and the native
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* protocol returns {@code InvalidParameter: parameter.enable_thinking ...} for
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* any attempt to invoke them. Pre-filter them out of discovery.
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*/
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private static final java.util.regex.Pattern DASHSCOPE_OPEN_SOURCE_SIZE_PATTERN =
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java.util.regex.Pattern.compile("^qwen\\d+-\\d+(?:\\.\\d+)?b(?:-.*)?$", java.util.regex.Pattern.CASE_INSENSITIVE);
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/**
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* Prefixes that identify non-chat modalities (image generation, vision understanding,
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* TTS, ASR, omni/multimodal bases, realtime speech, live translation, OCR, speech-to-
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* speech, voice-clone). They are catalog-visible but have different endpoints than
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* the native chat-generation API, so probing them via chat always fails with
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* "url error". Blocking them up-front cuts discovery time and log noise dramatically.
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*/
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private static final Set<String> DASHSCOPE_NON_CHAT_PREFIXES = Set.of(
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// Vision understanding / OCR
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"qwen-vl-",
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"qwen3-vl-",
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// Image generation / edit
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"qwen-image-",
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"qwen3-image-",
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// TTS / ASR / speech-to-speech / voice
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"qwen-tts-",
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"qwen3-tts-",
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"qwen-asr-",
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"qwen3-asr-",
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"qwen-s2s-",
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"qwen3-s2s-",
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// Omni multimodal bases
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"qwen-omni-",
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"qwen3-omni-",
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// Audio understanding
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"qwen-audio-",
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// Live translation
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"qwen-livetranslate-",
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"qwen3-livetranslate-"
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);
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/**
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* Allow-list prefixes for DashScope models that are known to work on the native
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* chat protocol. An empty set means "no prefix filter" (we still apply DENY).
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* Extend conservatively as new families are verified.
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*/
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private static final Set<String> DASHSCOPE_NATIVE_ALLOW_PREFIXES = Set.of(
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"qwen-", // qwen-max / qwen-plus / qwen-turbo / qwen-coder-* / qwen-long
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"qwen2-", // qwen2 series
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"qwen3-", // qwen3-max / qwen3-plus / qwen3-coder / qwen3-235b-*
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"deepseek-", // deepseek-v3.x / deepseek-r1*
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"baichuan",
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"yi-",
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"llama"
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);
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// ==================== 模型发现 ====================
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public DiscoverResult discoverModels(String providerId) {
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ModelProviderEntity provider = modelProviderService.getProviderConfig(providerId);
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if (!Boolean.TRUE.equals(provider.getSupportModelDiscovery())) {
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throw new MateClawException("err.llm.discovery_not_supported", "该供应商不支持模型发现: " + providerId);
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}
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ModelProtocol protocol = ModelProtocol.fromChatModel(provider.getChatModel());
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List<ModelInfoDTO> discovered = fetchRemoteModels(provider, protocol);
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// Layer 2: Protocol-aware allow/deny filtering. The listing endpoint
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// (compatible-mode /v1/models for DashScope) often returns models that
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// the native SDK does not accept — filter them out before the user sees
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// them.
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discovered = applyProtocolFilter(discovered, protocol, providerId);
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// Layer 3: Probe each remaining model with a real runtime-protocol call.
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// This catches any model the allow-list let through but the provider
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// actually rejects at request time. Failed probes are kept in the list
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// but marked probeOk=false so the UI can show a warning badge.
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discovered = probeInParallel(discovered, provider, protocol);
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// De-dupe against already-configured models for the "new" bucket
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Set<String> existingIds = modelConfigService.listModelsByProvider(providerId).stream()
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.map(ModelConfigEntity::getModelName)
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.collect(Collectors.toSet());
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// Only propose models that passed the probe (or were not probed) as "new"
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List<ModelInfoDTO> newModels = discovered.stream()
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.filter(m -> !existingIds.contains(m.getId()))
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.filter(m -> !Boolean.FALSE.equals(m.getProbeOk()))
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.toList();
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return new DiscoverResult(discovered, newModels, discovered.size(), newModels.size());
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}
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/**
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* Apply protocol-aware allow/deny filtering to the raw discovery list.
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* <p>
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* Currently only DashScope is filtered: the compatible-mode listing includes
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* many models the native SDK rejects. Other providers pass through unchanged.
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*/
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private List<ModelInfoDTO> applyProtocolFilter(List<ModelInfoDTO> discovered,
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ModelProtocol protocol,
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String providerId) {
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if (protocol != ModelProtocol.DASHSCOPE_NATIVE) {
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return discovered;
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}
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int before = discovered.size();
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List<ModelInfoDTO> filtered = discovered.stream()
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.filter(m -> isDashScopeModelIdAcceptable(m.getId()))
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.toList();
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if (filtered.size() < before) {
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log.info("[ModelDiscovery] Filtered {} -> {} DashScope models for provider={} (allow/deny rules)",
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before, filtered.size(), providerId);
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}
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return filtered;
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}
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/**
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* Return true if a DashScope model id is allowed on the native chat protocol.
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* Rejection rules (in order):
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* 1. Explicit DENY set (e.g. qwen3.5-max)
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* 2. Dot-version family pattern (qwen3.5-*, qwen3.6-*, ...)
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* 3. Non-chat modality prefix (vl, image, tts, asr, omni, audio, s2s, ocr, livetranslate)
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* 4. Open-source parameter-size suffix (qwen3-8b, qwen3-32b, qwen3-30b-a3b, qwen3-0.6b ...)
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* 5. Must start with a known ALLOW prefix (qwen-, qwen2-, qwen3-, deepseek-, ...)
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*/
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private static boolean isDashScopeModelIdAcceptable(String modelId) {
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if (modelId == null || modelId.isBlank()) return false;
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String lower = modelId.toLowerCase();
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if (DASHSCOPE_NATIVE_DENY.contains(lower)) return false;
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if (DASHSCOPE_NATIVE_UNSUPPORTED_PATTERN.matcher(lower).matches()) return false;
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if (DASHSCOPE_NON_CHAT_PREFIXES.stream().anyMatch(lower::startsWith)) return false;
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if (DASHSCOPE_OPEN_SOURCE_SIZE_PATTERN.matcher(lower).matches()) return false;
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if (DASHSCOPE_NATIVE_ALLOW_PREFIXES.isEmpty()) return true;
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return DASHSCOPE_NATIVE_ALLOW_PREFIXES.stream().anyMatch(lower::startsWith);
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}
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/**
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* Defensive guard for code paths that persist a model id without going through
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* discovery (e.g. the manual "Add model" form). Throws a MateClawException with
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* a user-friendly message if the id is known to be unusable under the provider's
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* runtime protocol.
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*/
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public static void assertModelIdAcceptable(String providerId, ModelProviderEntity provider, String modelId) {
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if (provider == null) return;
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ModelProtocol protocol = ModelProtocol.fromChatModel(provider.getChatModel());
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if (protocol == ModelProtocol.DASHSCOPE_NATIVE && !isDashScopeModelIdAcceptable(modelId)) {
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throw new MateClawException(
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"err.llm.model_not_supported",
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"Model id '" + modelId + "' is not supported on DashScope native protocol. " +
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"Dot-versioned families (e.g. qwen3.5-*, qwen3.6-*) are only available via compatible-mode. " +
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"Use an allowed id such as qwen-max / qwen-plus / qwen3-max."
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);
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}
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}
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/**
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* Probe each discovered model in parallel (bounded concurrency) using the same
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* protocol the runtime will use. Populates {@code probeOk}/{@code probeError}
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* on each DTO; does not remove failed entries so the UI can surface the reason.
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*/
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private List<ModelInfoDTO> probeInParallel(List<ModelInfoDTO> discovered,
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ModelProviderEntity provider,
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ModelProtocol protocol) {
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if (discovered.isEmpty()) return discovered;
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// OpenAI ChatGPT has no model-level test, skip probe for it
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if (protocol == ModelProtocol.OPENAI_CHATGPT) return discovered;
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Semaphore sem = new Semaphore(MAX_PROBE_CONCURRENCY);
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List<CompletableFuture<Void>> futures = new ArrayList<>(discovered.size());
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for (ModelInfoDTO dto : discovered) {
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futures.add(CompletableFuture.runAsync(() -> {
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try { sem.acquire(); }
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catch (InterruptedException ie) { Thread.currentThread().interrupt(); return; }
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try {
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sendTestPrompt(provider, protocol, dto.getId());
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dto.setProbeOk(true);
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} catch (Exception e) {
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dto.setProbeOk(false);
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dto.setProbeError(shortError(e));
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// DEBUG level — per-model probe failures are an expected part of bulk
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// discovery (DashScope lists many deprecated/restricted models). The
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// aggregate "Probe results: X passed, Y failed" summary below is
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// sufficient for normal operations. Enable DEBUG for ModelDiscovery
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// if you need to inspect individual reasons.
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log.debug("[ModelDiscovery] Probe failed for model={}: {}", dto.getId(), dto.getProbeError());
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} finally {
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sem.release();
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}
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}, PROBE_EXECUTOR));
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}
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try {
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CompletableFuture.allOf(futures.toArray(new CompletableFuture[0]))
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.get(PROBE_TIMEOUT_SECONDS * Math.max(1, discovered.size() / MAX_PROBE_CONCURRENCY + 1),
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TimeUnit.SECONDS);
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} catch (TimeoutException te) {
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log.warn("[ModelDiscovery] Probe batch timeout; {} models may be marked unknown",
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futures.stream().filter(f -> !f.isDone()).count());
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} catch (Exception e) {
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log.warn("[ModelDiscovery] Probe batch wait failed: {}", e.getMessage());
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}
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long passed = discovered.stream().filter(m -> Boolean.TRUE.equals(m.getProbeOk())).count();
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long failed = discovered.stream().filter(m -> Boolean.FALSE.equals(m.getProbeOk())).count();
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log.info("[ModelDiscovery] Probe results: {} passed, {} failed, {} unknown (of {})",
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passed, failed, discovered.size() - passed - failed, discovered.size());
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return discovered;
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}
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private String shortError(Exception e) {
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String msg = extractErrorMessage(e);
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if (msg == null) return "unknown error";
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// Clip to ~120 chars so the UI tooltip stays usable
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return msg.length() > 120 ? msg.substring(0, 120) + "..." : msg;
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}
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// ==================== 连接测试 ====================
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public TestResult testConnection(String providerId) {
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ModelProviderEntity provider = modelProviderService.getProviderConfig(providerId);
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ModelProtocol protocol = ModelProtocol.fromChatModel(provider.getChatModel());
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long start = System.currentTimeMillis();
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try {
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if (Boolean.TRUE.equals(provider.getSupportModelDiscovery())) {
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// 支持模型发现的 provider：调用模型列表 API 验证连接
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fetchRemoteModels(provider, protocol);
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long latency = System.currentTimeMillis() - start;
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return TestResult.ok(latency, "连接成功");
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} else {
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// 不支持模型发现（如智谱）：用第一个已配置模型发送测试请求
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List<ModelConfigEntity> models = modelConfigService.listModelsByProvider(providerId);
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if (models.isEmpty()) {
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throw new MateClawException("err.llm.no_model_for_test", "该供应商没有已配置的模型，无法测试连接");
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}
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String testModelId = models.get(0).getModelName();
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String response = sendTestPrompt(provider, protocol, testModelId);
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long latency = System.currentTimeMillis() - start;
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return TestResult.ok(latency, response);
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}
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} catch (Exception e) {
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long latency = System.currentTimeMillis() - start;
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return TestResult.fail(latency, extractErrorMessage(e));
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}
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}
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// ==================== 单模型测试 ====================
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public TestResult testModel(String providerId, String modelId) {
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ModelProviderEntity provider = modelProviderService.getProviderConfig(providerId);
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ModelProtocol protocol = ModelProtocol.fromChatModel(provider.getChatModel());
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long start = System.currentTimeMillis();
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try {
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String response = sendTestPrompt(provider, protocol, modelId);
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long latency = System.currentTimeMillis() - start;
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return TestResult.ok(latency, response);
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} catch (Exception e) {
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long latency = System.currentTimeMillis() - start;
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return TestResult.fail(latency, extractErrorMessage(e));
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}
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}
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// ==================== 批量添加发现的模型 ====================
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public int batchAddModels(String providerId, List<String> modelIds) {
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ModelProviderEntity provider = modelProviderService.getProviderConfig(providerId);
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ModelProtocol protocol = ModelProtocol.fromChatModel(provider.getChatModel());
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Set<String> existingIds = modelConfigService.listModelsByProvider(providerId).stream()
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.map(ModelConfigEntity::getModelName)
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.collect(Collectors.toSet());
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int added = 0;
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int skipped = 0;
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for (String modelId : modelIds) {
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if (modelId == null || modelId.isBlank()) continue;
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if (existingIds.contains(modelId)) continue;
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// Defense-in-depth: never add a DashScope model that fails the protocol-aware check
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if (protocol == ModelProtocol.DASHSCOPE_NATIVE && !isDashScopeModelIdAcceptable(modelId)) {
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log.warn("[ModelDiscovery] Refusing to add {} — blocked by DashScope native protocol filter", modelId);
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skipped++;
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continue;
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}
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modelConfigService.addModelToProvider(providerId, modelId, modelId, false);
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added++;
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}
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if (skipped > 0) {
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log.info("[ModelDiscovery] batchAddModels: added={}, skipped(deny)={}", added, skipped);
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}
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return added;
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}
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// ==================== 协议分派：模型列表 ====================
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private List<ModelInfoDTO> fetchRemoteModels(ModelProviderEntity provider, ModelProtocol protocol) {
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return switch (protocol) {
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case OPENAI_COMPATIBLE -> fetchOpenAiCompatibleModels(provider);
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case DASHSCOPE_NATIVE -> fetchDashScopeModels(provider);
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case GEMINI_NATIVE -> fetchGeminiModels(provider);
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case ANTHROPIC_MESSAGES -> fetchAnthropicModels(provider);
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case OPENAI_CHATGPT -> throw new MateClawException("err.llm.chatgpt_no_discovery", "ChatGPT OAuth provider 不支持模型发现");
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};
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}
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private List<ModelInfoDTO> fetchOpenAiCompatibleModels(ModelProviderEntity provider) {
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String baseUrl = normalizeBaseUrl(provider.getBaseUrl());
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if (!StringUtils.hasText(baseUrl)) {
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throw new MateClawException("err.llm.base_url_missing", "Base URL 未配置");
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}
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String apiKey = provider.getApiKey();
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RestClient client = RestClient.builder()
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.baseUrl(baseUrl)
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.defaultHeader(HttpHeaders.ACCEPT, MediaType.APPLICATION_JSON_VALUE)
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.build();
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RestClient.RequestHeadersSpec<?> spec = client.get().uri("/v1/models");
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if (modelProviderService.hasUsableApiKey(apiKey)) {
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spec = spec.header(HttpHeaders.AUTHORIZATION, "Bearer " + apiKey.trim());
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}
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// 添加自定义 headers（从 generateKwargs 中读取）
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Map<String, Object> kwargs = modelProviderService.readProviderGenerateKwargs(provider);
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applyCustomHeaders(spec, kwargs);
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String body = spec.retrieve().body(String.class);
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return parseOpenAiModelsResponse(body);
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}
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private List<ModelInfoDTO> fetchDashScopeModels(ModelProviderEntity provider) {
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String apiKey = provider.getApiKey();
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if (!modelProviderService.hasUsableApiKey(apiKey)) {
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throw new MateClawException("err.llm.dashscope_key_missing", "DashScope API Key 未配置");
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}
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// DashScope 兼容模式暴露了 OpenAI 兼容的 /v1/models 端点
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RestClient client = RestClient.builder()
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.baseUrl("https://dashscope.aliyuncs.com/compatible-mode")
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.defaultHeader(HttpHeaders.ACCEPT, MediaType.APPLICATION_JSON_VALUE)
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.defaultHeader(HttpHeaders.AUTHORIZATION, "Bearer " + apiKey.trim())
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.build();
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String body = client.get().uri("/v1/models").retrieve().body(String.class);
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return parseOpenAiModelsResponse(body);
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}
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private List<ModelInfoDTO> fetchGeminiModels(ModelProviderEntity provider) {
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String apiKey = provider.getApiKey();
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if (!modelProviderService.hasUsableApiKey(apiKey)) {
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throw new MateClawException("err.llm.gemini_key_missing", "Gemini API Key 未配置");
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}
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RestClient client = RestClient.builder()
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.baseUrl("https://generativelanguage.googleapis.com")
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.defaultHeader(HttpHeaders.ACCEPT, MediaType.APPLICATION_JSON_VALUE)
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.build();
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String body = client.get()
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.uri("/v1beta/models?key={key}", apiKey.trim())
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.retrieve()
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.body(String.class);
|
||
return parseGeminiModelsResponse(body);
|
||
}
|
||
|
||
private List<ModelInfoDTO> fetchAnthropicModels(ModelProviderEntity provider) {
|
||
String apiKey = provider.getApiKey();
|
||
if (!modelProviderService.hasUsableApiKey(apiKey)) {
|
||
throw new MateClawException("err.llm.anthropic_key_missing", "Anthropic API Key 未配置");
|
||
}
|
||
|
||
String baseUrl = StringUtils.hasText(provider.getBaseUrl())
|
||
? normalizeBaseUrl(provider.getBaseUrl())
|
||
: "https://api.anthropic.com";
|
||
|
||
RestClient client = RestClient.builder()
|
||
.baseUrl(baseUrl)
|
||
.defaultHeader(HttpHeaders.ACCEPT, MediaType.APPLICATION_JSON_VALUE)
|
||
.defaultHeader("x-api-key", apiKey.trim())
|
||
.defaultHeader("anthropic-version", "2023-06-01")
|
||
.build();
|
||
|
||
String body = client.get().uri("/v1/models").retrieve().body(String.class);
|
||
return parseAnthropicModelsResponse(body);
|
||
}
|
||
|
||
// ==================== 协议分派：单模型测试 ====================
|
||
|
||
private String sendTestPrompt(ModelProviderEntity provider, ModelProtocol protocol, String modelId) {
|
||
return switch (protocol) {
|
||
case OPENAI_COMPATIBLE -> sendOpenAiTestPrompt(provider, modelId);
|
||
case DASHSCOPE_NATIVE -> sendDashScopeTestPrompt(provider, modelId);
|
||
case GEMINI_NATIVE -> sendGeminiTestPrompt(provider, modelId);
|
||
case ANTHROPIC_MESSAGES -> sendAnthropicTestPrompt(provider, modelId);
|
||
case OPENAI_CHATGPT -> throw new MateClawException("err.llm.chatgpt_no_test", "ChatGPT OAuth provider 不支持模型测试");
|
||
};
|
||
}
|
||
|
||
private String sendOpenAiTestPrompt(ModelProviderEntity provider, String modelId) {
|
||
String baseUrl = normalizeBaseUrl(provider.getBaseUrl());
|
||
if (!StringUtils.hasText(baseUrl)) {
|
||
throw new MateClawException("err.llm.base_url_missing", "Base URL 未配置");
|
||
}
|
||
|
||
Map<String, Object> requestBody = Map.of(
|
||
"model", modelId,
|
||
"messages", List.of(Map.of("role", "user", "content", "请回复：连接正常")),
|
||
"max_tokens", 10,
|
||
"temperature", 0
|
||
);
|
||
|
||
// 从 generateKwargs 读取 completionsPath（智谱等用 /chat/completions 而非 /v1/chat/completions）
|
||
Map<String, Object> kwargs = modelProviderService.readProviderGenerateKwargs(provider);
|
||
String completionsPath = resolveCompletionsPath(baseUrl, kwargs);
|
||
|
||
RestClient.RequestHeadersSpec<?> spec = RestClient.builder()
|
||
.baseUrl(baseUrl)
|
||
.defaultHeader(HttpHeaders.CONTENT_TYPE, MediaType.APPLICATION_JSON_VALUE)
|
||
.build()
|
||
.post()
|
||
.uri(completionsPath)
|
||
.body(requestBody);
|
||
|
||
if (modelProviderService.hasUsableApiKey(provider.getApiKey())) {
|
||
spec = spec.header(HttpHeaders.AUTHORIZATION, "Bearer " + provider.getApiKey().trim());
|
||
}
|
||
applyCustomHeaders(spec, kwargs);
|
||
|
||
String body = spec.retrieve().body(String.class);
|
||
return extractOpenAiChatContent(body);
|
||
}
|
||
|
||
/**
|
||
* Test a DashScope model using the **native** endpoint
|
||
* ({@code /api/v1/services/aigc/text-generation/generation}).
|
||
* <p>
|
||
* This matches the protocol Spring AI Alibaba's {@code DashScopeChatModel} uses
|
||
* at runtime. Using compatible-mode for testing (as the previous implementation
|
||
* did) was the root cause of "test passed but chat fails" — compatible-mode
|
||
* accepts a broader set of model names than the native API does.
|
||
*/
|
||
private String sendDashScopeTestPrompt(ModelProviderEntity provider, String modelId) {
|
||
String apiKey = provider.getApiKey();
|
||
if (!modelProviderService.hasUsableApiKey(apiKey)) {
|
||
throw new MateClawException("err.llm.dashscope_key_missing", "DashScope API Key 未配置");
|
||
}
|
||
|
||
// DashScope native request shape: input.messages + parameters
|
||
Map<String, Object> requestBody = Map.of(
|
||
"model", modelId,
|
||
"input", Map.of(
|
||
"messages", List.of(Map.of("role", "user", "content", "ping"))
|
||
),
|
||
"parameters", Map.of(
|
||
"max_tokens", 1,
|
||
"temperature", 0,
|
||
"result_format", "message"
|
||
)
|
||
);
|
||
|
||
String body = RestClient.builder()
|
||
.baseUrl("https://dashscope.aliyuncs.com")
|
||
.defaultHeader(HttpHeaders.CONTENT_TYPE, MediaType.APPLICATION_JSON_VALUE)
|
||
.defaultHeader(HttpHeaders.AUTHORIZATION, "Bearer " + apiKey.trim())
|
||
.build()
|
||
.post()
|
||
.uri("/api/v1/services/aigc/text-generation/generation")
|
||
.body(requestBody)
|
||
.retrieve()
|
||
.body(String.class);
|
||
return extractDashScopeNativeContent(body);
|
||
}
|
||
|
||
/**
|
||
* Extract content from DashScope native response:
|
||
* {@code { "output": { "choices": [ { "message": { "content": "..." } } ] } } }
|
||
* Falls back to the raw body preview if the shape differs.
|
||
*/
|
||
private String extractDashScopeNativeContent(String body) {
|
||
try {
|
||
JsonNode root = objectMapper.readTree(body);
|
||
JsonNode choices = root.path("output").path("choices");
|
||
if (choices.isArray() && choices.size() > 0) {
|
||
String content = choices.get(0).path("message").path("content").asText("");
|
||
if (!content.isBlank()) return content;
|
||
}
|
||
// Older shape: output.text
|
||
String legacyText = root.path("output").path("text").asText("");
|
||
if (!legacyText.isBlank()) return legacyText;
|
||
} catch (Exception ignored) {}
|
||
return body == null ? "" : (body.length() > 200 ? body.substring(0, 200) : body);
|
||
}
|
||
|
||
private String sendGeminiTestPrompt(ModelProviderEntity provider, String modelId) {
|
||
String apiKey = provider.getApiKey();
|
||
if (!modelProviderService.hasUsableApiKey(apiKey)) {
|
||
throw new MateClawException("err.llm.gemini_key_missing", "Gemini API Key 未配置");
|
||
}
|
||
|
||
Map<String, Object> requestBody = Map.of(
|
||
"contents", List.of(Map.of(
|
||
"parts", List.of(Map.of("text", "请回复：连接正常"))
|
||
)),
|
||
"generationConfig", Map.of("maxOutputTokens", 10, "temperature", 0)
|
||
);
|
||
|
||
String body = RestClient.builder()
|
||
.baseUrl("https://generativelanguage.googleapis.com")
|
||
.defaultHeader(HttpHeaders.CONTENT_TYPE, MediaType.APPLICATION_JSON_VALUE)
|
||
.build()
|
||
.post()
|
||
.uri("/v1beta/models/{model}:generateContent?key={key}", modelId, apiKey.trim())
|
||
.body(requestBody)
|
||
.retrieve()
|
||
.body(String.class);
|
||
return extractGeminiContent(body);
|
||
}
|
||
|
||
private String sendAnthropicTestPrompt(ModelProviderEntity provider, String modelId) {
|
||
String apiKey = provider.getApiKey();
|
||
if (!modelProviderService.hasUsableApiKey(apiKey)) {
|
||
throw new MateClawException("err.llm.anthropic_key_missing", "Anthropic API Key 未配置");
|
||
}
|
||
|
||
String baseUrl = StringUtils.hasText(provider.getBaseUrl())
|
||
? normalizeBaseUrl(provider.getBaseUrl())
|
||
: "https://api.anthropic.com";
|
||
|
||
Map<String, Object> requestBody = Map.of(
|
||
"model", modelId,
|
||
"messages", List.of(Map.of("role", "user", "content", "请回复：连接正常")),
|
||
"max_tokens", 10
|
||
);
|
||
|
||
String body = RestClient.builder()
|
||
.baseUrl(baseUrl)
|
||
.defaultHeader(HttpHeaders.CONTENT_TYPE, MediaType.APPLICATION_JSON_VALUE)
|
||
.defaultHeader("x-api-key", apiKey.trim())
|
||
.defaultHeader("anthropic-version", "2023-06-01")
|
||
.build()
|
||
.post()
|
||
.uri("/v1/messages")
|
||
.body(requestBody)
|
||
.retrieve()
|
||
.body(String.class);
|
||
return extractAnthropicContent(body);
|
||
}
|
||
|
||
// ==================== JSON 解析 ====================
|
||
|
||
private List<ModelInfoDTO> parseOpenAiModelsResponse(String body) {
|
||
try {
|
||
JsonNode root = objectMapper.readTree(body);
|
||
JsonNode data = root.path("data");
|
||
if (!data.isArray()) {
|
||
return Collections.emptyList();
|
||
}
|
||
List<ModelInfoDTO> models = new ArrayList<>();
|
||
for (JsonNode node : data) {
|
||
String id = node.path("id").asText("");
|
||
if (StringUtils.hasText(id)) {
|
||
models.add(new ModelInfoDTO(id, id));
|
||
}
|
||
}
|
||
return models;
|
||
} catch (Exception e) {
|
||
log.warn("解析 OpenAI 模型列表失败: {}", e.getMessage());
|
||
return Collections.emptyList();
|
||
}
|
||
}
|
||
|
||
private List<ModelInfoDTO> parseGeminiModelsResponse(String body) {
|
||
try {
|
||
JsonNode root = objectMapper.readTree(body);
|
||
JsonNode models = root.path("models");
|
||
if (!models.isArray()) {
|
||
return Collections.emptyList();
|
||
}
|
||
List<ModelInfoDTO> result = new ArrayList<>();
|
||
for (JsonNode node : models) {
|
||
String name = node.path("name").asText("");
|
||
String displayName = node.path("displayName").asText(name);
|
||
// Gemini 返回 "models/gemini-1.5-pro" 格式，去掉 "models/" 前缀
|
||
if (name.startsWith("models/")) {
|
||
name = name.substring(7);
|
||
}
|
||
if (StringUtils.hasText(name)) {
|
||
result.add(new ModelInfoDTO(name, displayName));
|
||
}
|
||
}
|
||
return result;
|
||
} catch (Exception e) {
|
||
log.warn("解析 Gemini 模型列表失败: {}", e.getMessage());
|
||
return Collections.emptyList();
|
||
}
|
||
}
|
||
|
||
private List<ModelInfoDTO> parseAnthropicModelsResponse(String body) {
|
||
try {
|
||
JsonNode root = objectMapper.readTree(body);
|
||
JsonNode data = root.path("data");
|
||
if (!data.isArray()) {
|
||
return Collections.emptyList();
|
||
}
|
||
List<ModelInfoDTO> models = new ArrayList<>();
|
||
for (JsonNode node : data) {
|
||
String id = node.path("id").asText("");
|
||
String displayName = node.path("display_name").asText(id);
|
||
if (StringUtils.hasText(id)) {
|
||
models.add(new ModelInfoDTO(id, displayName));
|
||
}
|
||
}
|
||
return models;
|
||
} catch (Exception e) {
|
||
log.warn("解析 Anthropic 模型列表失败: {}", e.getMessage());
|
||
return Collections.emptyList();
|
||
}
|
||
}
|
||
|
||
private String extractOpenAiChatContent(String body) {
|
||
try {
|
||
JsonNode root = objectMapper.readTree(body);
|
||
return root.path("choices").path(0).path("message").path("content").asText("连接正常");
|
||
} catch (Exception e) {
|
||
return "连接正常（响应解析异常）";
|
||
}
|
||
}
|
||
|
||
private String extractGeminiContent(String body) {
|
||
try {
|
||
JsonNode root = objectMapper.readTree(body);
|
||
return root.path("candidates").path(0).path("content").path("parts").path(0).path("text").asText("连接正常");
|
||
} catch (Exception e) {
|
||
return "连接正常（响应解析异常）";
|
||
}
|
||
}
|
||
|
||
private String extractAnthropicContent(String body) {
|
||
try {
|
||
JsonNode root = objectMapper.readTree(body);
|
||
return root.path("content").path(0).path("text").asText("连接正常");
|
||
} catch (Exception e) {
|
||
return "连接正常（响应解析异常）";
|
||
}
|
||
}
|
||
|
||
// ==================== 工具方法 ====================
|
||
|
||
/**
|
||
* 从 generateKwargs 中解析 completionsPath，处理 baseUrl 与路径前缀的重叠。
|
||
* 例如：baseUrl 以 /v4 结尾，completionsPath 为 /chat/completions → 最终 /chat/completions
|
||
* baseUrl 以 /v1 结尾，completionsPath 为 /v1/chat/completions → 最终 /chat/completions
|
||
*/
|
||
private String resolveCompletionsPath(String baseUrl, Map<String, Object> kwargs) {
|
||
String path = "/v1/chat/completions";
|
||
if (kwargs != null) {
|
||
Object raw = kwargs.get("completionsPath");
|
||
if (raw instanceof String value && StringUtils.hasText(value)) {
|
||
path = value.trim();
|
||
if (!path.startsWith("/")) {
|
||
path = "/" + path;
|
||
}
|
||
}
|
||
}
|
||
// 避免路径重叠：如果 baseUrl 以 /v1 结尾且 path 以 /v1/ 开头，去掉重复
|
||
if (baseUrl != null && baseUrl.endsWith("/v1") && path.startsWith("/v1/")) {
|
||
path = path.substring(3);
|
||
}
|
||
return path;
|
||
}
|
||
|
||
private String normalizeBaseUrl(String baseUrl) {
|
||
if (!StringUtils.hasText(baseUrl)) {
|
||
return null;
|
||
}
|
||
String normalized = baseUrl.trim();
|
||
if (normalized.endsWith("/")) {
|
||
normalized = normalized.substring(0, normalized.length() - 1);
|
||
}
|
||
if (normalized.endsWith("/v1")) {
|
||
normalized = normalized.substring(0, normalized.length() - 3);
|
||
}
|
||
return normalized;
|
||
}
|
||
|
||
@SuppressWarnings("unchecked")
|
||
private void applyCustomHeaders(RestClient.RequestHeadersSpec<?> spec, Map<String, Object> kwargs) {
|
||
if (kwargs == null) {
|
||
return;
|
||
}
|
||
Object customHeaders = kwargs.get("customHeaders");
|
||
if (customHeaders instanceof Map) {
|
||
((Map<String, Object>) customHeaders).forEach((key, value) -> {
|
||
if (value != null) {
|
||
spec.header(key, value.toString());
|
||
}
|
||
});
|
||
}
|
||
}
|
||
|
||
private String extractErrorMessage(Exception e) {
|
||
String msg = e.getMessage();
|
||
if (msg == null || msg.isBlank()) {
|
||
return "未知错误: " + e.getClass().getSimpleName();
|
||
}
|
||
// 截取合理长度
|
||
if (msg.length() > 200) {
|
||
msg = msg.substring(0, 200) + "...";
|
||
}
|
||
return msg;
|
||
}
|
||
}
|