V4.14.4 features (#6036)

* feat: add query optimize and bill (#6021) * add query optimize and bill * perf: query extension * fix: embe model * remove log * remove log * fix: test --------- Co-authored-by: xxyyh <2289112474@qq> Co-authored-by: archer <545436317@qq.com> * feat: notice (#6013) * feat: record user's language * feat: notice points/dataset indexes; support count limit; update docker-compose.yml * fix: ts error * feat: send auth code i18n * chore: dataset notice limit * chore: adjust * fix: ts * fix: countLimit race condition; i18n en-prefix locale fallback to en --------- Co-authored-by: archer <545436317@qq.com> * perf: comment * perf: send inform code * fix: type error (#6029) * feat: add ip region for chat logs (#6010) * feat: add ip region for chat logs * refactor: use Geolite2.mmdb * fix: export chat logs * fix: return location directly * test: add unit test * perf: log show ip data * adjust commercial plans (#6008) * plan frontend * plan limit * coupon * discount coupon * fix * type * fix audit * type * plan name * legacy plan * track * feat: add discount coupon * fix * fix discount coupon * openapi * type * type * env * api type * fix * fix: simple agent plugin input & agent dashboard card (#6034) * refactor: remove gridfs (#6031) * fix: replace gridfs multer operations with s3 compatible ops * wip: s3 features * refactor: remove gridfs * fix * perf: mock test * doc * doc * doc * fix: test * fix: s3 * fix: mock s3 * remove invalid config * fix: init query extension * initv4144 (#6037) * chore: initv4144 * fix * version * fix: new plans (#6039) * fix: new plans * qr modal tip * fix: buffer raw text filename (#6040) * fix: initv4144 (#6041) * fix: pay refresh (#6042) * fix: migration shell * rename collection * clear timerlock * clear timerlock * perf: faq * perf: bill schema * fix: openapi * doc * fix: share var render * feat: delete dataset queue * plan usage display (#6043) * plan usage display * text * fix * fix: ts * perf: remove invalid code * perf: init shell * doc * perf: rename field * perf: avatar presign * init * custom plan text (#6045) * fix plans * fix * fixed * computed --------- Co-authored-by: archer <545436317@qq.com> * init shell * plan text & price page back button (#6046) * init * index * delete dataset * delete dataset * perf: delete dataset * init --------- Co-authored-by: YeYuheng <57035043+YYH211@users.noreply.github.com> Co-authored-by: xxyyh <2289112474@qq> Co-authored-by: Finley Ge <32237950+FinleyGe@users.noreply.github.com> Co-authored-by: Roy <whoeverimf5@gmail.com> Co-authored-by: heheer <heheer@sealos.io>
2026-01-16 07:00:25 +08:00 · 2025-12-08 01:44:15 +08:00
parent 9d72f238c0
commit 2ccb5b50c6
247 changed files with 7342 additions and 3819 deletions
--- a/test/mocks/core/ai/embedding.ts
+++ b/test/mocks/core/ai/embedding.ts
@@ -0,0 +1,131 @@
+import { vi } from 'vitest';
+
+/**
+ * Mock embedding generation utilities for testing
+ */
+
+/**
+ * Generate a deterministic normalized vector based on text content
+ * Uses a simple hash-based approach to ensure same text produces same vector
+ */
+export const generateMockEmbedding = (text: string, dimension: number = 1536): number[] => {
+  // Simple hash function to generate seed from text
+  let hash = 0;
+  for (let i = 0; i < text.length; i++) {
+    const char = text.charCodeAt(i);
+    hash = (hash << 5) - hash + char;
+    hash = hash & hash; // Convert to 32-bit integer
+  }
+
+  // Generate vector using seeded random
+  const vector: number[] = [];
+  let seed = Math.abs(hash);
+  for (let i = 0; i < dimension; i++) {
+    // Linear congruential generator
+    seed = (seed * 1103515245 + 12345) & 0x7fffffff;
+    vector.push((seed / 0x7fffffff) * 2 - 1); // Range [-1, 1]
+  }
+
+  // Normalize the vector (L2 norm = 1)
+  const norm = Math.sqrt(vector.reduce((sum, val) => sum + val * val, 0));
+  return vector.map((val) => val / norm);
+};
+
+/**
+ * Generate multiple mock embeddings for a list of texts
+ */
+export const generateMockEmbeddings = (texts: string[], dimension: number = 1536): number[][] => {
+  return texts.map((text) => generateMockEmbedding(text, dimension));
+};
+
+/**
+ * Create a mock response for getVectorsByText
+ */
+export const createMockVectorsResponse = (
+  texts: string | string[],
+  dimension: number = 1536
+): { tokens: number; vectors: number[][] } => {
+  const textArray = Array.isArray(texts) ? texts : [texts];
+  const vectors = generateMockEmbeddings(textArray, dimension);
+
+  // Estimate tokens (roughly 1 token per 4 characters)
+  const tokens = textArray.reduce((sum, text) => sum + Math.ceil(text.length / 4), 0);
+
+  return { tokens, vectors };
+};
+
+/**
+ * Generate a vector similar to another vector with controlled similarity
+ * @param baseVector - The base vector to create similarity from
+ * @param similarity - Target cosine similarity (0-1), higher means more similar
+ */
+export const generateSimilarVector = (baseVector: number[], similarity: number = 0.9): number[] => {
+  const dimension = baseVector.length;
+  const noise = generateMockEmbedding(`noise_${Date.now()}_${Math.random()}`, dimension);
+
+  // Interpolate between base vector and noise
+  const vector = baseVector.map((val, i) => val * similarity + noise[i] * (1 - similarity));
+
+  // Normalize
+  const norm = Math.sqrt(vector.reduce((sum, val) => sum + val * val, 0));
+  return vector.map((val) => val / norm);
+};
+
+/**
+ * Generate a vector orthogonal (dissimilar) to the given vector
+ */
+export const generateOrthogonalVector = (baseVector: number[]): number[] => {
+  const dimension = baseVector.length;
+  const randomVector = generateMockEmbedding(`orthogonal_${Date.now()}`, dimension);
+
+  // Gram-Schmidt orthogonalization
+  const dotProduct = baseVector.reduce((sum, val, i) => sum + val * randomVector[i], 0);
+  const vector = randomVector.map((val, i) => val - dotProduct * baseVector[i]);
+
+  // Normalize
+  const norm = Math.sqrt(vector.reduce((sum, val) => sum + val * val, 0));
+  return vector.map((val) => val / norm);
+};
+
+/**
+ * Mock implementation for getVectorsByText
+ * Automatically generates embeddings based on input text
+ */
+export const mockGetVectorsByText = vi.fn(
+  async ({
+    input,
+    type
+  }: {
+    model: any;
+    input: string[] | string;
+    type?: string;
+  }): Promise<{ tokens: number; vectors: number[][] }> => {
+    const texts = Array.isArray(input) ? input : [input];
+    return createMockVectorsResponse(texts);
+  }
+);
+
+/**
+ * Setup global mock for embedding module
+ */
+vi.mock('@fastgpt/service/core/ai/embedding', async (importOriginal) => {
+  const actual = (await importOriginal()) as any;
+  return {
+    ...actual,
+    getVectorsByText: mockGetVectorsByText
+  };
+});
+
+/**
+ * Setup global mock for AI model module
+ */
+vi.mock('@fastgpt/service/core/ai/model', async (importOriginal) => {
+  const actual = (await importOriginal()) as any;
+  return {
+    ...actual,
+    getEmbeddingModel: vi.fn().mockReturnValue({
+      model: 'text-embedding-ada-002',
+      name: 'text-embedding-ada-002'
+    })
+  };
+});
--- a/test/mocks/core/ai/llm.ts
+++ b/test/mocks/core/ai/llm.ts
@@ -0,0 +1,139 @@
+import { vi } from 'vitest';
+import type { ChatCompletion } from '@fastgpt/global/core/ai/type';
+
+/**
+ * Mock LLM response utilities for testing
+ */
+
+/**
+ * Create a mock non-streaming response with reason and text
+ * This simulates a complete response from models that support reasoning (like o1)
+ */
+export const createMockCompleteResponseWithReason = (options?: {
+  content?: string;
+  reasoningContent?: string;
+  finishReason?: 'stop' | 'length' | 'content_filter';
+  promptTokens?: number;
+  completionTokens?: number;
+}): ChatCompletion => {
+  const {
+    content = 'This is the answer to your question.',
+    reasoningContent = 'First, I need to analyze the question...',
+    finishReason = 'stop',
+    promptTokens = 100,
+    completionTokens = 50
+  } = options || {};
+
+  return {
+    id: `chatcmpl-${Date.now()}`,
+    object: 'chat.completion',
+    created: Math.floor(Date.now() / 1000),
+    model: 'gpt-4o',
+    choices: [
+      {
+        index: 0,
+        message: {
+          role: 'assistant',
+          content,
+          reasoning_content: reasoningContent,
+          refusal: null
+        } as any,
+        logprobs: null,
+        finish_reason: finishReason
+      }
+    ],
+    usage: {
+      prompt_tokens: promptTokens,
+      completion_tokens: completionTokens,
+      total_tokens: promptTokens + completionTokens
+    },
+    system_fingerprint: 'fp_test'
+  } as ChatCompletion;
+};
+
+/**
+ * Create a mock non-streaming response with tool calls
+ * This simulates a response where the model decides to call tools/functions
+ */
+export const createMockCompleteResponseWithTool = (options?: {
+  toolCalls?: Array<{
+    id?: string;
+    name: string;
+    arguments: string | Record<string, any>;
+  }>;
+  finishReason?: 'tool_calls' | 'stop';
+  promptTokens?: number;
+  completionTokens?: number;
+}): ChatCompletion => {
+  const {
+    toolCalls = [
+      {
+        id: 'call_test_001',
+        name: 'get_weather',
+        arguments: { location: 'Beijing', unit: 'celsius' }
+      }
+    ],
+    finishReason = 'tool_calls',
+    promptTokens = 120,
+    completionTokens = 30
+  } = options || {};
+
+  return {
+    id: `chatcmpl-${Date.now()}`,
+    object: 'chat.completion',
+    created: Math.floor(Date.now() / 1000),
+    model: 'gpt-4o',
+    choices: [
+      {
+        index: 0,
+        message: {
+          role: 'assistant',
+          content: null,
+          refusal: null,
+          tool_calls: toolCalls.map((call, index) => ({
+            id: call.id || `call_${Date.now()}_${index}`,
+            type: 'function' as const,
+            function: {
+              name: call.name,
+              arguments:
+                typeof call.arguments === 'string' ? call.arguments : JSON.stringify(call.arguments)
+            }
+          }))
+        },
+        logprobs: null,
+        finish_reason: finishReason
+      }
+    ],
+    usage: {
+      prompt_tokens: promptTokens,
+      completion_tokens: completionTokens,
+      total_tokens: promptTokens + completionTokens
+    },
+    system_fingerprint: 'fp_test'
+  } as ChatCompletion;
+};
+
+/**
+ * Mock implementation for createChatCompletion
+ * Can be configured to return different types of responses based on test needs
+ */
+export const mockCreateChatCompletion = vi.fn(
+  async (body: any, options?: any): Promise<ChatCompletion> => {
+    // Default: return response with text
+    if (body.tools && body.tools.length > 0) {
+      return createMockCompleteResponseWithTool();
+    }
+    return createMockCompleteResponseWithReason();
+  }
+);
+
+/**
+ * Setup global mock for LLM request module
+ */
+vi.mock('@fastgpt/service/core/ai/llm/request', async (importOriginal) => {
+  const actual = (await importOriginal()) as any;
+  return {
+    ...actual,
+    createChatCompletion: mockCreateChatCompletion
+  };
+});