@cogitator-ai/ 配置

Cogitator 的配置加载 。 支持YAML文件,环境变量,以及全Zod验证的程序覆盖.

安装

pnpm add @cogitator-ai/config

另见 配置 在网站上.

快速启动

YAML 配置

创建 cogitator.yml 在您的项目根中。 没有明确的 configPath, (中文(简体) ), loadConfig() 在当前目录中查看 cogitator.yaml, (中文(简体) ), cogitator.yml, (中文(简体) ), .cogitator.yaml 财务报告和审定财务报表 .cogitator.yml,顺序为:

llm:
  defaultProvider: openai
  defaultModel: gpt-6.1-sol
  providers:
    openai:
      apiKey: sk-xxx
    ollama:
      baseUrl: http://localhost:11434
      headers: # optional, sent with every chat request
        X-Gateway-Key: xxx

memory:
  adapter: redis
  redis:
    url: redis://localhost:6379

logging:
  level: info
  format: pretty

装入配置

import { loadConfig, defineConfig } from '@cogitator-ai/config';

// Load from file with env vars and overrides
const config = loadConfig({
  configPath: './cogitator.yml',
  overrides: {
    logging: { level: 'debug' },
  },
});

// The same config as another environment sees it, such as a deployed app's
const deployed = loadConfig({
  configPath: './cogitator.yml',
  env: { COGITATOR_LLM_DEFAULT_MODEL: 'ollama/llama3.2', OLLAMA_HOST: 'ollama' },
});

// Or define config programmatically with type safety
const programmatic = defineConfig({
  llm: {
    defaultProvider: 'openai',
    providers: {
      openai: { apiKey: process.env.OPENAI_API_KEY! },
    },
  },
});

既验证结果又抛出 Invalid configuration: … 与每个 与计划不符的Zod问题 结果是 CogitatorConfig准备运行时间 具有函数或对象的选项( 存储、 回调例如 security.pii.onDetect 或者说 memory.compaction.beforeCompact, (中文(简体) ), llm.backends, (中文(简体) ), prompts.versions 编号 : prompts.abTests 编号 : prompts.score, (中文(简体) ), runCheckpoints)不能来自YAML,在代码中添加:

import { Cogitator } from '@cogitator-ai/core';
import { loadConfig } from '@cogitator-ai/config';

const config = loadConfig();
const cog = new Cogitator({
  ...config,
  prompts: { ...config.prompts, score: (result) => (result.output.length > 0 ? 1 : 0) },
});

prompts.autoDeployWinner 可以来自YAML(prompts: { autoDeployWinner: true }) (中文(简体) ).

配置参考

LLM 配置

llm:
  defaultProvider: openai # ollama | openai | anthropic | google | azure | bedrock | vllm | mistral | groq | together | deepseek | openrouter
  defaultModel: gpt-6.1-sol
  providers:
    ollama:
      baseUrl: http://localhost:11434
      headers: # optional, sent with every chat request
        X-Gateway-Key: xxx
    openai:
      apiKey: sk-xxx
      baseUrl: https://api.openai.com/v1 # optional, for proxies
      api: responses # optional: responses (default for api.openai.com) | chat-completions
      profile: lmstudio # optional: what the server at baseUrl takes, a preset or { server, maxTokensField, streamUsage, reasoningEffort }
      headers: # optional, sent with every request
        X-Gateway-Key: xxx
    anthropic:
      apiKey: sk-ant-xxx
      baseUrl: https://gateway.example.com/anthropic # optional, default https://api.anthropic.com
      headers: # optional
        X-Team: search
    google:
      apiKey: xxx
      baseUrl: https://gateway.example.com/gemini/v1beta # optional, with the API version
    azure:
      apiKey: xxx
      endpoint: https://xxx.openai.azure.com
      apiVersion: 2025-04-01-preview # optional, the default
      deployment: gpt-6.1-sol # optional
      model: gpt-5 # optional: the model the deployments serve, when their names do not say
    bedrock:
      region: us-east-1 # optional, can use AWS config chain
      accessKeyId: xxx # optional, uses AWS credentials chain
      secretAccessKey: xxx # optional
      endpoint: https://vpce-0abc.bedrock-runtime.us-east-1.vpce.amazonaws.com # optional: a VPC endpoint or a proxy
    vllm:
      baseUrl: http://localhost:8000
      profile: vllm # optional, the default
    mistral:
      apiKey: xxx
    groq:
      apiKey: xxx
    together:
      apiKey: xxx
    deepseek:
      apiKey: xxx
    openrouter: # chat models of every provider, and decision models through cog.decide()
      apiKey: xxx
      headers: # optional, e.g. HTTP-Referer and X-Title app attribution
        X-Title: My App
  pricing: # prices of your own in USD per million tokens, by model string or its tail after any '/'
    llama3.3:70b:
      input: 0.6
      output: 0.6
      inputCached: 0.06 # optional, also inputCacheWrite and inputCacheWrite1h
  retry: # or `false`; default: 2 retries with exponential backoff
    maxRetries: 3
    baseDelay: 1000
    maxDelay: 30000
    maxRetryAfter: 60000
    requestTimeout: 180000 # ms one call may take before it is aborted and retried, off by default
  promptCache: # or `false`; on by default
    ttl: 1h # 5m | 1h (Anthropic)
  modelCircuitBreaker: # when a model agents fall back from (fallbackModels) is skipped
    failureThreshold: 5 # failures in a row, default 5
    resetTimeout: 30000 # ms it is skipped, default 30000
    halfOpenRequests: 3 # successes needed afterwards, default 3
  plugins: # settings for backend plugins registered with registerLLMBackend, by provider name
    my-provider:
      endpoint: http://localhost:9000

内存配置

memory:
  adapter: postgres # thread store: memory | redis | postgres | sqlite | mongodb

  # In-memory (for development)
  inMemory:
    maxEntries: 1000

  # Redis
  redis:
    url: redis://localhost:6379
    # Or individual settings:
    host: localhost
    port: 6379
    password: secret
    keyPrefix: 'cogitator:'
    ttl: 3600 # seconds
    # Cluster mode:
    cluster:
      nodes:
        - host: redis-1
          port: 6379
        - host: redis-2
          port: 6379
      scaleReads: slave # master | slave | all

  # PostgreSQL with pgvector
  postgres:
    connectionString: postgresql://user:pass@localhost:5432/cogitator
    schema: public
    poolSize: 10

  # SQLite
  sqlite:
    path: ./data/cogitator.db
    walMode: true

  # MongoDB
  mongodb:
    uri: mongodb://localhost:27017
    database: cogitator
    collectionPrefix: cog_

  # Qdrant: embedding store searched by contextBuilder, next to the thread store
  qdrant:
    url: http://localhost:6333
    apiKey: xxx
    collection: cogitator
    dimensions: 1536

  # Embedding service for semantic search
  embedding:
    provider: openai # openai | ollama | google (apiKey required for openai and google)
    apiKey: sk-xxx
    model: text-embedding-3-small
    baseUrl: https://api.openai.com/v1 # optional, for every provider
    dimensions: 1536 # optional output size (e.g. text-embedding-3-*)

  # Context builder settings
  contextBuilder:
    maxTokens: 4000
    reserveTokens: 500
    strategy: recent # recent | relevant | hybrid
    includeSystemPrompt: true
    includeFacts: true
    includeSemanticContext: true
    includeGraphContext: false
    graphContextOptions:
      maxNodes: 20
      maxEdges: 50
      maxDepth: 3
      includeInferred: true
      entityTypes: [person, organization] # person | organization | location | concept | event | object | custom

  # Compaction of a run's thread after the run (hierarchical)
  compaction:
    at: 0.8 # share of the model's context window that starts it
    keepRecentTokens: 20000 # newest whole turns kept verbatim (default: the less of 20000 and a quarter of the window)
    chapterTokens: 4000
    chapterConcurrency: 1
    chaptersPerArc: 4
    arcsPerSaga: 4
    summaryModel: google/gemini-3.5-flash # default: the model the run ended on
    summaryPrompt: Keep every price that was quoted.

  # Memory items extracted from a run's conversation after the run
  extraction:
    model: google/gemini-3.5-flash-lite # default: the model the run ended on
    prompt: Mind diets and allergies.
    windowTokens: 4000 # conversation one extraction call reads
    activationConfidence: 0.7 # confidence a quoted user item needs to become active
    scope: user # user | agent | thread
    timeZone: Europe/Lisbon # IANA, when the thread names none (default UTC)

  # The memory tool: notes under /memories every agent keeps across conversations
  files:
    scope: user # user | agent | thread
    readOnly: false
    indexChars: 2000 # characters the list of files takes in the context

  # Cards of what memory holds for the turn, before its first model call
  brief:
    model: google/gemini-3.5-flash-lite # a cheap model, the run's model by default
    maxCards: 4 # 1 to 4
    timeoutMs: 4000

  # What memory makes of the facts about each entity, rewritten when they change
  observations:
    maxEntities: 3 # observations one refresh writes at most
    minItems: 3 # facts an entity needs for an observation

  # A background pass every few runs: repeats go stale, lessons reach the playbook
  consolidation:
    everyRuns: 20
    afterCompaction: true
    maxChanges: 10 # items one pass marks as stale at most, 0 to leave items alone
    maxSubjects: 10 # subjects one pass asks about at most, the next pass goes on
    playbook: apply # apply | shadow | false
    showPlaybook: true

memory.compaction 由 MemoryCompactionConfigSchema (单位:千美元)at 0以上,最多为1个。 它的钩子 beforeCompact 财务报告和审定财务报表 afterCompact 是函数,所以它们设置在代码中。 见 运行后压缩。 。 。 .

memory.extraction 由 MemoryExtractionConfigSchema数字 : model 不为空, windowTokens 0以上的整个数字, activationConfidence 从0到1, scope 一个 user, (中文(简体) ), agent 财务报告和审定财务报表 thread,以及 timeZone 一个IANA时区。 内容 afterExtract 钩被设置在代码中. 见 运行后提取。 。 。 .

memory.files 由 MemoryFilesConfigSchema数字 : scope 一个 user, (中文(简体) ), agent 财务报告和审定财务报表 thread, (中文(简体) ), readOnly 布尔和 indexChars 0以上为整数。 有了它,运行时间 让每一个经纪人 memory 工具,并列出运行在上下文中看到的文件。 见 内存文件。 。 。 .

memory.brief 由 MemoryBriefConfigSchema数字 : model 不为空, maxCards 1至4个整数, timeoutMs 0以上和 maxTokens 整数在 0 以上,运行时间在从代码中构建简要时会投出相同的值。 见 会前简报。 。 。 .

memory.observations 由 MemoryObservationsConfigSchema数字 : model 不为空, maxEntities 财务报告和审定财务报表 minItems 0以上的全数。 见 意 见。 。 。 .

memory.consolidation 由 MemoryConsolidationConfigSchema数字 : model 不为空, everyRuns 0以上的整个数字, afterCompaction 财务报告和审定财务报表 showPlaybook 布尔斯人, maxChanges 整个数字从0, maxSubjects 0以上的整数和 playbook 一个 apply, (中文(简体) ), shadow 财务报告和审定财务报表 false。 。 afterConsolidate 钩子是一个函数,所以它被设置在代码中. 见 合并。 。 。 .

这个 Cogitator 运行时间构建商店 adapter 名称来自其部分: redis 需求 url, (中文(简体) ), host编号 :port 或者说 cluster, (中文(简体) ), postgres 需求 connectionString, (中文(简体) ), sqlite 需求 path 财务报告和审定财务报表 mongodb 需求 uri。一个 Postgres 存储其矢量列大小为 embedding。 。 。 . qdrant 不是一个线程商店: 保留其中之一为 adapter,并与 contextBuilder (单位:千美元)includeSemanticContext: true运行时间搜索 memory.qdrant 为语义上下文.

沙盒配置

sandbox:
  defaults:
    type: docker # docker | native | wasm | os
    image: python:3.11-slim
    timeout: 30000
    workdir: /workspace
    user: sandbox
    env:
      PYTHONUNBUFFERED: '1'
    mounts:
      - source: /srv/data
        target: /workspace/data
        readOnly: true
    # wasmModule / wasmFunction / wasi: defaults for wasm tools
    resources:
      memory: 512m
      cpus: 0.5
      cpuShares: 512
      pidsLimit: 100
    network:
      mode: none # none | bridge | host
      # allowedHosts: [api.example.com]  # wasm only; the docker executor rejects it
      dns:
        - 8.8.8.8

  pool:
    maxSize: 10
    idleTimeoutMs: 60000
    reuseContainers: false # true: reuse containers between executions with the same settings

  # false: fail Docker-sandboxed tools when Docker is unavailable instead of running them on the host
  allowNativeFallback: true

  docker:
    socketPath: /var/run/docker.sock
    # Or TCP:
    host: localhost
    port: 2375

  wasm:
    wasmModule: ./tools.wasm
    memoryPages: 256
    functionName: run
    wasi: true
    cacheSize: 100

sandbox.allowNativeFallback 默认为 true: Docker-sandboxed工具在无法使用Docker时在主机上被解开并运行,同时发出警告. sandbox.pool.reuseContainers 默认为 false:每次执行都得到一个新鲜的容器; true 更快, 但让一个执行留下的文件和进程到达下一个执行 .

反射配置

reflection:
  enabled: true
  reflectAfterToolCall: true
  reflectAfterError: true
  reflectAtEnd: true
  storeInsights: true
  maxInsightsPerAgent: 50
  minConfidenceToStore: 0.7
  useSmallModelForReflection: true
  reflectionModel: gpt-6-luna

护栏配置

guardrails:
  enabled: true
  model: gpt-6-luna
  filterInput: true
  filterOutput: true
  filterToolCalls: true
  filterToolResults: false
  enableCritiqueRevision: true
  maxRevisionIterations: 3
  revisionConfidenceThreshold: 0.8
  strictMode: false
  logViolations: true
  constitution: # optional: your own principles instead of the default constitution
    id: support
    name: Support desk
    version: 1.0.0
    customizable: true
    strictMode: false
    principles:
      - id: no-refund-promises
        name: No refund promises
        description: Never promise refunds the policy does not allow
        category: custom # ethics | safety | privacy | legal | custom
        critiquePrompt: Does the reply promise a refund?
        revisionPrompt: Rewrite the reply without promising a refund.
        severity: medium
        appliesTo: [output] # input | output | tool
  thresholds:
    violence: high
    hate: high
    sexual: medium
    self-harm: high
    illegal: high
    privacy: medium
    misinformation: medium
    manipulation: medium

成本运行配置

costRouting:
  enabled: true
  autoSelectModel: true
  preferLocal: true
  minCapabilityMatch: 0.3
  trackCosts: true
  ollamaUrl: http://localhost:11434
  budget:
    maxCostPerRun: 0.10
    maxCostPerHour: 5.00
    maxCostPerDay: 50.00
    warningThreshold: 0.8

安全配置

security:
  promptInjection:
    detectInjection: true
    detectJailbreak: true
    detectRoleplay: true
    detectEncoding: true
    detectContextManipulation: true
    patterns: # your own regular expression sources, compiled to RegExp
      - 'reveal (the|your) system prompt'
    classifier: local # local | llm
    llmModel: openai/gpt-6-luna # for classifier: llm
    action: block # block | warn | log
    threshold: 0.7
    failMode: secure # when the classifier throws: secure (default) fails the check, open lets the input through
    allowlist:
      - ignore the previous search
  pii:
    mode: mask # mask | redact | block
    detect: [email, phone, credit_card, iban, ssn, ip_address, api_key]
    custom:
      - type: customer_id
        pattern: 'CUS-\d{6}' # a regular expression source, compiled to a RegExp

背景配置

context:
  enabled: true
  strategy: hybrid # truncate | sliding-window | summarize | hybrid
  compressionThreshold: 0.8
  outputReserve: 0.15
  summaryModel: openai/gpt-6-luna
  windowSize: 10

部署配置

阅读者 @cogitator-ai/deploy 财务报告和审定财务报表 cogitator deploy数字 :

deploy:
  target: fly # docker | fly
  server: express # express | fastify | hono | koa
  port: 3000
  registry: registry.fly.io
  image: my-agents
  region: ams
  instances: 2
  services:
    redis: true
    postgres: false
  env:
    NODE_ENV: production
  secrets: [OPENAI_API_KEY]
  health:
    path: /cogitator/health
    interval: 30s
    timeout: 5s
  resources:
    memory: 512mb
    cpu: 1

限制配置

limits:
  maxConcurrentRuns: 10 # runs over the limit wait in order
  defaultTimeout: 120000 # ms, for runs and agents that set no timeout
  maxTokensPerRun: 100000 # checked before each model call
  maxToolResultLength: 100000 # characters of a tool result the model reads

触发配置( 实验)

内容 experimental_triggers 文件可以持有:常规,民意测验间隔和限制. 代理商和工作流程的常规名称,存储和汇来自代码,而秘密则来自其环境变量({ env: NAME }),而不是作为文件中的文本.

experimental_triggers:
  pollInterval: 1000
  routines:
    - id: digest
      prompt: Summarize the news of the day
      target: { agent: assistant }
      overlap: skip
      catchUp: latest
      triggers:
        - { kind: cron, cron: '0 18 * * 1-5', timezone: Europe/Moscow }
        - { kind: github, secret: { env: GITHUB_WEBHOOK_SECRET }, events: [pull_request.opened] }

日志配置

logging:
  level: info # debug | info | warn | error | silent
  format: pretty # json | pretty
  destination: console # console | file
  filePath: ./logs/cogitator.log

A 类 Cogitator 创建于 logging 将它安装为全过程的日志器(上一次这样的运行时间获胜). destination: file 每个条目附加一行 filePath作为JSON,除非 format 设置;没有 filePath,或者在没有文件系统的运行时,它会登录到控制台. createLoggerFromConfig() 从 @cogitator-ai/core 在代码中建立相同的日志 .

环境变量

支持的提供者、限制和部署设置可通过环境变量与 COGITATOR_ 前缀 :

标准供应商 env vars 也被支持:

OPENAI_API_KEY=sk-xxx
ANTHROPIC_API_KEY=sk-ant-xxx
ANTHROPIC_BASE_URL=https://gateway.example.com/anthropic   # optional proxy or gateway
GOOGLE_API_KEY=xxx            # or GEMINI_API_KEY
OLLAMA_URL=http://localhost:11434   # or OLLAMA_BASE_URL / OLLAMA_HOST, see below
OLLAMA_API_KEY=xxx            # Ollama Cloud / authenticated Ollama
AZURE_OPENAI_API_KEY=xxx
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com
AZURE_OPENAI_DEPLOYMENT=gpt-6.1-sol
AWS_REGION=us-east-1          # Bedrock region, credentials come from the AWS SDK chain
MISTRAL_API_KEY=xxx
GROQ_API_KEY=xxx
TOGETHER_API_KEY=xxx
DEEPSEEK_API_KEY=xxx
OPENROUTER_API_KEY=xxx        # or OPENROUTER_BASE_URL for a proxy

loadConfig 读取 COGITATOR_ANTHROPIC_BASE_URL, (中文(简体) ), ANTHROPIC_BASE_URL 财务报告和审定财务报表 COGITATOR_GOOGLE_BASE_URL 与来自环境的 API 密钥一起进入配置, 完毕 cogitator.yml页:1 COGITATOR_ 变量。 Anthropic SDK也读作 ANTHROPIC_BASE_URL 在没有的情况下,由自己负责 baseUrl 已配置.

何时 OLLAMA_API_KEY 设置时没有 Ollama URL (从 env 或 YAML), Ollama 基础 URL 默认为 https://ollama.com; 否则默认为 http://localhost:11434。环境中的 API 键永远不会取代 baseUrl 在 YAML 中配置 .

OLLAMA_BASE_URL, (中文(简体) ), OLLAMA_URL 财务报告和审定财务报表 OLLAMA_HOST (第一集胜出)读取奥拉玛的读取方式 OLLAMA_HOST数字 : gpu-box 财务报告和审定财务报表 0.0.0.0 平均端口11434,一个通配符地址,例如 0.0.0.0 以 localhost,以及 http:// 或者说 https:// 保持计划自己的端口。 其他工具也设置了这些变量(OLLAMA_HOST=0.0.0.0 制造 ollama serve ,所以它们只有在 cogitator.yml 没有时才适用 llm.providers.ollama.baseUrl。 。 。 . COGITATOR_OLLAMA_BASE_URL 覆盖 YAML 值, 如每 个 COGITATOR_ 变量. loadEnvDefaults() 返回此图层 resolveOllamaHost(value) 分析一个单一的值.

贝德罗克证书留在AWS SDK: AWS_ACCESS_KEY_ID, (中文(简体) ), AWS_SECRET_ACCESS_KEY, (中文(简体) ), AWS_SESSION_TOKEN, (中文(简体) ), AWS_PROFILE,SSO和实例角色 所有工作随着SDK的解决, 所以临时的证书保留他们的会话符号。 静态证书只来自 COGITATOR_BEDROCK_* 或者说 llm.providers.bedrock 在YAML中( 编号:accessKeyId, (中文(简体) ), secretAccessKey, (中文(简体) ), sessionToken, (中文(简体) ), profile) (中文(简体) ).

PROVIDER_ENV 列出每个内置提供者的每个变量(什么 loadConfig 读取,提供商SDK读取的内容,以及提供商无法在没有的情况下运行的内容). cogitator deploy 使用同一个表格来决定部署需要哪些秘密.

无 configPath, (中文(简体) ), loadConfig 读取第一个 cogitator.yml, (中文(简体) ), cogitator.yaml, (中文(简体) ), .cogitator.yml 财务报告和审定财务报表 .cogitator.yaml 在工作目录中. findConfigFile(dir) 找到同样的方式.

${VAR} YAML 参考文献

YAML 文件的字符串值可以引用环境变量:

llm:
  providers:
    openai:
      apiKey: ${OPENAI_API_KEY}
      baseUrl: ${OPENAI_BASE_URL:-https://api.openai.com/v1}

被替换的值总是字符串,所以对字符串字段(键, URL, 模型名)使用引用 —— 数字字段如 deploy.port 必须保持文字.

优先权顺序

配置在此顺序下被深植( 后推翻更早 ) :

1. YAML 配置文件 (单位:千美元)cogitator.yml; 最低优先级)
2. 环境变量 (单位:千美元)COGITATOR_* 和标准提供者变量)
3. 程序覆盖 (最高优先级)

Fields nobody sets stay unset, 运行时间应用自己的默认值 。 默认的是奥拉玛 baseUrl (见上文)。 未知的顶级密钥被验证后丢出.

奥拉玛接受 llm.providers.ollama.headers 用于在非流式和流式聊天请求中需要额外 HTTP 标题的网关 /api/chat。运行时间保持 Content-Type: application/json,则 apiKey 优先于习惯 Authorization 不论弹壳都用头 没有 apiKey,保留自定义授权.

Schema 验证

所有配置均使用 Zod 计划验证 :

import {
  CogitatorConfigSchema,
  LLMConfigSchema,
  MemoryConfigSchema,
  ReflectionConfigSchema,
} from '@cogitator-ai/config';

// Validate raw config
const result = CogitatorConfigSchema.safeParse(rawConfig);
if (!result.success) {
  console.error('Invalid config:', result.error.issues);
}

// Type-safe config
import type { CogitatorConfigInput, CogitatorConfigOutput } from '@cogitator-ai/config';

可用的外观

KnowledgeGraphConfigSchema 财务报告和审定财务报表 PromptOptimizationConfigSchema 自动验证这些配置;它们不是 CogitatorConfigSchema。 。 。 .

实例

开发配置

# cogitator.dev.yml
llm:
  defaultProvider: ollama
  providers:
    ollama:
      baseUrl: http://localhost:11434

memory:
  adapter: memory
  inMemory:
    maxEntries: 100

logging:
  level: debug
  format: pretty

sandbox:
  defaults:
    type: native # no Docker needed

生产配置

# cogitator.prod.yml
llm:
  defaultProvider: openai
  defaultModel: gpt-6.1-sol
  providers:
    openai:
      apiKey: sk-prod-openai
    anthropic:
      apiKey: sk-ant-prod

memory:
  adapter: postgres
  postgres:
    connectionString: postgresql://user:[email protected]:5432/cogitator
    poolSize: 20
  embedding:
    provider: openai
    apiKey: sk-prod-openai
  contextBuilder:
    maxTokens: 8000
    strategy: hybrid

reflection:
  enabled: true
  storeInsights: true

guardrails:
  enabled: true
  strictMode: true

costRouting:
  enabled: true
  autoSelectModel: true
  budget:
    maxCostPerDay: 100.00

logging:
  level: info
  format: json
  destination: file
  filePath: /var/log/cogitator/app.log

sandbox:
  defaults:
    type: docker
    resources:
      memory: 256m
      cpus: 0.25
  pool:
    maxSize: 20

API 参考

加载Config( 选项)

从文件、 环境和 上载配置 .

interface LoadConfigOptions {
  configPath?: string; // Path to YAML file
  skipEnv?: boolean; // Skip loading from environment variables
  skipYaml?: boolean; // Skip loading from YAML file
  overrides?: CogitatorConfigInput; // Programmatic overrides
}

const config = loadConfig({
  configPath: './cogitator.yml',
  overrides: { logging: { level: 'debug' } },
});

定义 Config( 配置)

类型安全配置定义帮助器.

const config = defineConfig({
  llm: {
    defaultProvider: 'openai',
    providers: {
      openai: { apiKey: 'sk-xxx' },
    },
  },
});

装入 YamlConfig( 路径 )

装入并分析 YAML 配置文件并解析 ${VAR} 参考文献。 没有路径, 它会搜索默认文件名; 不存在的路径丢出 Config file not found. 回归 null 如果找不到配置文件或文件为空; 当文件无法解析(信件包括路径)或其顶层不是映射时丢出。 尚未验证结果(loadConfig - 做那个.

import { loadYamlConfig } from '@cogitator-ai/config';

const config = loadYamlConfig('./cogitator.yml');

装入 EnvConfig ()

从环境变量中装入配置 .

import { loadEnvConfig } from '@cogitator-ai/config';

const config = loadEnvConfig();

interpolateEnv( 值, env?) / interpolateEnvString( 值, env?)

应用 ${VAR} 替换到解析的配置对象或单个字符串(由 loadYamlConfig) (中文(简体) ).

import { interpolateEnvString } from '@cogitator-ai/config';

interpolateEnvString('${HOST:-localhost}:${PORT}', { PORT: '8080' }); // 'localhost:8080'

parseDotenv( 内容) / 加载DotenvFile( 路径)

解析 .env 文档(KEY=VALUE, (中文(简体) ), export 前缀,单/双引出有逃生,内含注释). loadDotenvFile 返回时 {} 当文件不存在时。 两个函数都变异 process.env。 。 。 .

import { loadDotenvFile } from '@cogitator-ai/config';

const env = { ...loadDotenvFile('.env'), ...process.env };

许可证

麻省理工学院