mirror of
https://github.com/simstudioai/sim.git
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fix(misc): remove duplicate docs page, update clopus 4.7 (#4200)
* fix(misc): remove duplicate docs page, update clopus 4.7 * fix(docs): consolidate duplicate docs and fix SDK API signatures - Remove duplicate custom-tools page (custom-tools/index.mdx → tools/custom-tools.mdx is canonical) - Remove comparison table from custom-tools per product preference - Fix permissions inconsistency: delete now requires Admin across all docs - Consolidate sdks/ into api-reference/ (sdks/ directory deleted) - Fix Python SDK docs: correct param is `input`, not `input_data` - Fix TypeScript SDK docs: correct signature is executeWorkflow(id, input, options) not options-object form - Add FAQ sections to both SDK reference pages * fix(docs): update SDKs card links from /sdks to /api-reference * fix(docs): update /sdks references to /api-reference in llms.txt files
This commit is contained in:
Waleed
@@ -62,7 +62,7 @@ ${Object.entries(sections)
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- Full documentation content: ${baseUrl}/llms-full.txt
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- Individual page content: ${baseUrl}/llms.mdx/[page-path]
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- API documentation: ${baseUrl}/sdks/
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- API documentation: ${baseUrl}/api-reference/
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- Tool integrations: ${baseUrl}/tools/
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## Statistics
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@@ -51,7 +51,7 @@ Willkommen bei Sim, einem visuellen Workflow-Builder für KI-Anwendungen. Erstel
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<Card title="MCP-Integration" href="/mcp">
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Externe Dienste mit dem Model Context Protocol verbinden
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</Card>
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<Card title="SDKs" href="/sdks">
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<Card title="SDKs" href="/api-reference">
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Sim in Ihre Anwendungen integrieren
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</Card>
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</Cards>
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@@ -65,14 +65,14 @@ Execute a workflow with optional input data.
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```python
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result = client.execute_workflow(
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"workflow-id",
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input_data={"message": "Hello, world!"},
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input={"message": "Hello, world!"},
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timeout=30.0 # 30 seconds
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)
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```
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**Parameters:**
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- `workflow_id` (str): The ID of the workflow to execute
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- `input_data` (dict, optional): Input data to pass to the workflow
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- `input` (dict, optional): Input data to pass to the workflow
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- `timeout` (float, optional): Timeout in seconds (default: 30.0)
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- `stream` (bool, optional): Enable streaming responses (default: False)
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- `selected_outputs` (list[str], optional): Block outputs to stream in `blockName.attribute` format (e.g., `["agent1.content"]`)
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@@ -144,7 +144,7 @@ Execute a workflow with automatic retry on rate limit errors using exponential b
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```python
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result = client.execute_with_retry(
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"workflow-id",
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input_data={"message": "Hello"},
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input={"message": "Hello"},
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timeout=30.0,
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max_retries=3, # Maximum number of retries
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initial_delay=1.0, # Initial delay in seconds
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@@ -155,7 +155,7 @@ result = client.execute_with_retry(
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**Parameters:**
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- `workflow_id` (str): The ID of the workflow to execute
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- `input_data` (dict, optional): Input data to pass to the workflow
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- `input` (dict, optional): Input data to pass to the workflow
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- `timeout` (float, optional): Timeout in seconds
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- `stream` (bool, optional): Enable streaming responses
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- `selected_outputs` (list, optional): Block outputs to stream
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@@ -359,7 +359,7 @@ def run_workflow():
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# Execute the workflow
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result = client.execute_workflow(
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"my-workflow-id",
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input_data={
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input={
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"message": "Process this data",
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"user_id": "12345"
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}
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@@ -488,7 +488,7 @@ def execute_async():
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# Start async execution
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result = client.execute_workflow(
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"workflow-id",
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input_data={"data": "large dataset"},
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input={"data": "large dataset"},
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async_execution=True # Execute asynchronously
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)
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@@ -533,7 +533,7 @@ def execute_with_retry_handling():
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# Automatically retries on rate limit
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result = client.execute_with_retry(
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"workflow-id",
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input_data={"message": "Process this"},
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input={"message": "Process this"},
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max_retries=5,
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initial_delay=1.0,
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max_delay=60.0,
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@@ -615,7 +615,7 @@ def execute_with_streaming():
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# Enable streaming for specific block outputs
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result = client.execute_workflow(
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"workflow-id",
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input_data={"message": "Count to five"},
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input={"message": "Count to five"},
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stream=True,
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selected_outputs=["agent1.content"] # Use blockName.attribute format
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)
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@@ -758,4 +758,15 @@ Configure the client using environment variables:
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## License
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Apache-2.0
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Apache-2.0
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import { FAQ } from '@/components/ui/faq'
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<FAQ items={[
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{ question: "Do I need to deploy a workflow before I can execute it via the SDK?", answer: "Yes. Workflows must be deployed before they can be executed through the SDK. You can use the validate_workflow() method to check whether a workflow is deployed and ready. If it returns False, deploy the workflow from the Sim UI first and create or select an API key during deployment." },
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{ question: "What is the difference between sync and async execution?", answer: "Sync execution (the default) blocks until the workflow completes and returns the full result. Async execution (async_execution=True) returns immediately with a task ID that you can poll using get_job_status(). Use async mode for long-running workflows to avoid request timeouts. Async job statuses include queued, processing, completed, failed, and cancelled." },
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{ question: "How does the SDK handle rate limiting?", answer: "The SDK provides built-in rate limiting support through the execute_with_retry() method. It uses exponential backoff (1s, 2s, 4s, 8s...) with 25% jitter to avoid thundering herd problems. If the API returns a retry-after header, that value is used instead. You can configure max_retries, initial_delay, max_delay, and backoff_multiplier. Use get_rate_limit_info() to check your current rate limit status." },
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{ question: "Can I use the Python SDK as a context manager?", answer: "Yes. The SimStudioClient supports Python's context manager protocol. Use it with the 'with' statement to automatically close the underlying HTTP session when you are done, which is especially useful for scripts that create and discard client instances." },
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{ question: "How do I handle different types of errors from the SDK?", answer: "The SDK raises SimStudioError with a code property for API-specific errors. Common error codes are UNAUTHORIZED (invalid API key), TIMEOUT (request timed out), RATE_LIMIT_EXCEEDED (too many requests), USAGE_LIMIT_EXCEEDED (billing limit reached), and EXECUTION_ERROR (workflow failed). Use the error code to implement targeted error handling and recovery logic." },
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{ question: "How do I monitor my API usage and remaining quota?", answer: "Use the get_usage_limits() method to check your current usage. It returns sync and async rate limit details (limit, remaining, reset time, whether you are currently limited), plus your current period cost, usage limit, and plan tier. This lets you monitor consumption and alert before hitting limits." },
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]} />
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@@ -78,16 +78,15 @@ new SimStudioClient(config: SimStudioConfig)
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Execute a workflow with optional input data.
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```typescript
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const result = await client.executeWorkflow('workflow-id', {
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input: { message: 'Hello, world!' },
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const result = await client.executeWorkflow('workflow-id', { message: 'Hello, world!' }, {
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timeout: 30000 // 30 seconds
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});
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```
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**Parameters:**
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- `workflowId` (string): The ID of the workflow to execute
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- `input` (any, optional): Input data to pass to the workflow
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- `options` (ExecutionOptions, optional):
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- `input` (any): Input data to pass to the workflow
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- `timeout` (number): Timeout in milliseconds (default: 30000)
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- `stream` (boolean): Enable streaming responses (default: false)
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- `selectedOutputs` (string[]): Block outputs to stream in `blockName.attribute` format (e.g., `["agent1.content"]`)
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@@ -158,8 +157,7 @@ if (status.status === 'completed') {
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Execute a workflow with automatic retry on rate limit errors using exponential backoff.
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```typescript
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const result = await client.executeWithRetry('workflow-id', {
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input: { message: 'Hello' },
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const result = await client.executeWithRetry('workflow-id', { message: 'Hello' }, {
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timeout: 30000
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}, {
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maxRetries: 3, // Maximum number of retries
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@@ -171,6 +169,7 @@ const result = await client.executeWithRetry('workflow-id', {
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**Parameters:**
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- `workflowId` (string): The ID of the workflow to execute
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- `input` (any, optional): Input data to pass to the workflow
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- `options` (ExecutionOptions, optional): Same as `executeWorkflow()`
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- `retryOptions` (RetryOptions, optional):
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- `maxRetries` (number): Maximum number of retries (default: 3)
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@@ -389,10 +388,8 @@ async function runWorkflow() {
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// Execute the workflow
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const result = await client.executeWorkflow('my-workflow-id', {
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input: {
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message: 'Process this data',
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userId: '12345'
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}
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});
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if (result.success) {
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@@ -508,8 +505,7 @@ app.post('/execute-workflow', async (req, res) => {
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try {
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const { workflowId, input } = req.body;
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const result = await client.executeWorkflow(workflowId, {
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input,
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const result = await client.executeWorkflow(workflowId, input, {
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timeout: 60000
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});
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@@ -555,8 +551,7 @@ export default async function handler(
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try {
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const { workflowId, input } = req.body;
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const result = await client.executeWorkflow(workflowId, {
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input,
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const result = await client.executeWorkflow(workflowId, input, {
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timeout: 30000
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});
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@@ -586,9 +581,7 @@ const client = new SimStudioClient({
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async function executeClientSideWorkflow() {
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try {
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const result = await client.executeWorkflow('workflow-id', {
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input: {
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userInput: 'Hello from browser'
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}
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});
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console.log('Workflow result:', result);
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@@ -642,10 +635,8 @@ Alternatively, you can manually provide files using the URL format:
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// Include files under the field name from your API trigger's input format
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const result = await client.executeWorkflow('workflow-id', {
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input: {
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documents: files, // Must match your workflow's "files" field name
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instructions: 'Analyze these documents'
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}
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});
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console.log('Result:', result);
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@@ -669,10 +660,8 @@ Alternatively, you can manually provide files using the URL format:
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// Include files under the field name from your API trigger's input format
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const result = await client.executeWorkflow('workflow-id', {
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input: {
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documents: [file], // Must match your workflow's "files" field name
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query: 'Summarize this document'
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}
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});
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```
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</Tab>
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@@ -712,8 +701,7 @@ export function useWorkflow(): UseWorkflowResult {
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setResult(null);
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try {
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const workflowResult = await client.executeWorkflow(workflowId, {
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input,
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const workflowResult = await client.executeWorkflow(workflowId, input, {
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timeout: 30000
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});
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setResult(workflowResult);
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@@ -774,8 +762,7 @@ const client = new SimStudioClient({
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async function executeAsync() {
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try {
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// Start async execution
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const result = await client.executeWorkflow('workflow-id', {
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input: { data: 'large dataset' },
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const result = await client.executeWorkflow('workflow-id', { data: 'large dataset' }, {
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async: true // Execute asynchronously
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});
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@@ -823,9 +810,7 @@ const client = new SimStudioClient({
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async function executeWithRetryHandling() {
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try {
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// Automatically retries on rate limit
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const result = await client.executeWithRetry('workflow-id', {
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input: { message: 'Process this' }
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}, {
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const result = await client.executeWithRetry('workflow-id', { message: 'Process this' }, {}, {
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maxRetries: 5,
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initialDelay: 1000,
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maxDelay: 60000,
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@@ -908,8 +893,7 @@ const client = new SimStudioClient({
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async function executeWithStreaming() {
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try {
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// Enable streaming for specific block outputs
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const result = await client.executeWorkflow('workflow-id', {
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input: { message: 'Count to five' },
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const result = await client.executeWorkflow('workflow-id', { message: 'Count to five' }, {
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stream: true,
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selectedOutputs: ['agent1.content'] // Use blockName.attribute format
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});
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@@ -1033,3 +1017,14 @@ function StreamingWorkflow() {
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## License
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||||
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Apache-2.0
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import { FAQ } from '@/components/ui/faq'
|
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|
||||
<FAQ items={[
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{ question: "Do I need to deploy a workflow before I can execute it via the SDK?", answer: "Yes. Workflows must be deployed before they can be executed through the SDK. You can use the validateWorkflow() method to check whether a workflow is deployed and ready. If it returns false, deploy the workflow from the Sim UI first and create or select an API key during deployment." },
|
||||
{ question: "What is the difference between sync and async execution?", answer: "Sync execution (the default) blocks until the workflow completes and returns the full result. Async execution returns immediately with a task ID that you can poll using getJobStatus(). Use async mode for long-running workflows to avoid request timeouts. Async job statuses include queued, processing, completed, failed, and cancelled." },
|
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{ question: "How does streaming work with the SDK?", answer: "Enable streaming by setting stream: true and specifying selectedOutputs with block names and attributes in blockName.attribute format (e.g., ['agent1.content']). The response uses Server-Sent Events (SSE) format, sending incremental chunks as the workflow executes. Each chunk includes the blockId and the text content. A final done event includes the execution metadata." },
|
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{ question: "How does the SDK handle rate limiting?", answer: "The SDK provides built-in rate limiting support through the executeWithRetry() method. It uses exponential backoff (1s, 2s, 4s, 8s...) with 25% jitter to avoid thundering herd problems. If the API returns a retry-after header, that value is used instead. You can configure maxRetries, initialDelay, maxDelay, and backoffMultiplier. Use getRateLimitInfo() to check your current rate limit status." },
|
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{ question: "Is it safe to use the SDK in browser-side code?", answer: "You can use the SDK in the browser, but you should not expose your API key in client-side code. In production, use a backend proxy server to handle SDK calls, or use a public API key with limited permissions. The SDK works with both Node.js and browser environments, but sensitive keys should stay server-side." },
|
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{ question: "How do I send files to a workflow through the SDK?", answer: "File objects are automatically detected and converted to base64 format. Include them in the input object under the field name that matches your workflow's API trigger input format. In the browser, pass File objects directly from file inputs. In Node.js, create File objects from buffers. You can also provide files as URL references with type, data, name, and mime fields." },
|
||||
]} />
|
||||
|
||||
@@ -1,100 +0,0 @@
|
||||
---
|
||||
title: Custom Tools
|
||||
description: Extend your agents with your own functions — defined by a schema and executed as JavaScript
|
||||
---
|
||||
|
||||
import { Image } from '@/components/ui/image'
|
||||
import { Callout } from 'fumadocs-ui/components/callout'
|
||||
|
||||
Custom Tools let you define your own functions that agents can call, without needing an external MCP server. You write a JSON schema describing the function and the JavaScript code that runs when the agent invokes it.
|
||||
|
||||
## What Is a Custom Tool?
|
||||
|
||||
A custom tool has three parts:
|
||||
|
||||
| Part | Description |
|
||||
|------|-------------|
|
||||
| **Schema** | OpenAI function-calling format — name, description, and parameters. This is what the agent sees when deciding whether to call the tool. |
|
||||
| **Code** | JavaScript that runs when the tool is called. Parameters come in as variables matching the schema. |
|
||||
| **Scope** | Custom tools are workspace-scoped and available to every agent in that workspace. |
|
||||
|
||||
Use custom tools when you need tightly-scoped logic that doesn't warrant spinning up a full MCP server — one-off API calls, formatting helpers, internal utilities, etc.
|
||||
|
||||
## Creating a Custom Tool
|
||||
|
||||
1. Navigate to **Settings → Custom Tools**
|
||||
2. Click **Add**
|
||||
3. Fill out the **Schema** tab with your function definition
|
||||
|
||||
<div className="flex justify-center">
|
||||
<Image
|
||||
src="/static/blocks/custom-tool-schema.png"
|
||||
alt="Custom tool schema editor"
|
||||
width={550}
|
||||
height={500}
|
||||
className="my-6"
|
||||
/>
|
||||
</div>
|
||||
|
||||
<Callout type="info">
|
||||
Use the **Generate** button to have AI draft the schema from a plain-English description.
|
||||
</Callout>
|
||||
|
||||
4. Write your implementation in the **Code** tab
|
||||
5. Click **Save**
|
||||
|
||||
<Callout type="info">
|
||||
You can also create a custom tool directly from an Agent block — click **Add tool… → Create Tool** in the tool dropdown.
|
||||
</Callout>
|
||||
|
||||
## Schema Format
|
||||
|
||||
Custom tool schemas follow the OpenAI function-calling spec:
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"description": "Get the current weather for a city",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"city": {
|
||||
"type": "string",
|
||||
"description": "The city to get weather for"
|
||||
}
|
||||
},
|
||||
"required": ["city"]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The `name` must be lowercase, use underscores, and match what your code expects as input.
|
||||
|
||||
## Using Custom Tools in Agents
|
||||
|
||||
Once created, your tools become available in any Agent block:
|
||||
|
||||
1. Open an **Agent** block
|
||||
2. In the **Tools** section, click **Add tool…**
|
||||
3. Under **Custom Tools**, click the tool you want to add
|
||||
4. The agent can now call the tool the same way it calls MCP tools or built-in tools
|
||||
|
||||
## Custom Tools vs MCP Tools
|
||||
|
||||
| | **Custom Tools** | **MCP Tools** |
|
||||
|---|---|---|
|
||||
| **Defined** | Inline — schema + code in Sim | External MCP server |
|
||||
| **Hosting** | Runs inside Sim | Runs on your server |
|
||||
| **Best for** | Small, workspace-specific helpers | Shared tools, third-party services, complex integrations |
|
||||
| **Setup** | One modal | Deploy and register a server |
|
||||
|
||||
## Permission Requirements
|
||||
|
||||
| Action | Required Permission |
|
||||
|--------|-------------------|
|
||||
| Create or update custom tools | **Write** or **Admin** |
|
||||
| Delete custom tools | **Admin** |
|
||||
| Use custom tools in agents | **Read**, **Write**, or **Admin** |
|
||||
@@ -51,7 +51,7 @@ Welcome to Sim, the open-source AI workspace where teams build, deploy, and mana
|
||||
<Card title="MCP Integration" href="/mcp">
|
||||
Connect external services with Model Context Protocol
|
||||
</Card>
|
||||
<Card title="SDKs" href="/sdks">
|
||||
<Card title="SDKs" href="/api-reference">
|
||||
Integrate Sim into your applications
|
||||
</Card>
|
||||
</Cards>
|
||||
@@ -21,7 +21,6 @@
|
||||
"variables",
|
||||
"integrations",
|
||||
"credentials",
|
||||
"custom-tools",
|
||||
"---Platform---",
|
||||
"execution",
|
||||
"permissions",
|
||||
|
||||
@@ -1,4 +0,0 @@
|
||||
{
|
||||
"title": "SDKs",
|
||||
"pages": ["python", "typescript"]
|
||||
}
|
||||
@@ -1,772 +0,0 @@
|
||||
---
|
||||
title: Python
|
||||
---
|
||||
|
||||
import { Callout } from 'fumadocs-ui/components/callout'
|
||||
import { Card, Cards } from 'fumadocs-ui/components/card'
|
||||
import { Step, Steps } from 'fumadocs-ui/components/steps'
|
||||
import { Tab, Tabs } from 'fumadocs-ui/components/tabs'
|
||||
|
||||
The official Python SDK for Sim allows you to execute workflows programmatically from your Python applications using the official Python SDK.
|
||||
|
||||
<Callout type="info">
|
||||
The Python SDK supports Python 3.8+ with async execution support, automatic rate limiting with exponential backoff, and usage tracking.
|
||||
</Callout>
|
||||
|
||||
## Installation
|
||||
|
||||
Install the SDK using pip:
|
||||
|
||||
```bash
|
||||
pip install simstudio-sdk
|
||||
```
|
||||
|
||||
## Quick Start
|
||||
|
||||
Here's a simple example to get you started:
|
||||
|
||||
```python
|
||||
from simstudio import SimStudioClient
|
||||
|
||||
# Initialize the client
|
||||
client = SimStudioClient(
|
||||
api_key="your-api-key-here",
|
||||
base_url="https://sim.ai" # optional, defaults to https://sim.ai
|
||||
)
|
||||
|
||||
# Execute a workflow
|
||||
try:
|
||||
result = client.execute_workflow("workflow-id")
|
||||
print("Workflow executed successfully:", result)
|
||||
except Exception as error:
|
||||
print("Workflow execution failed:", error)
|
||||
```
|
||||
|
||||
## API Reference
|
||||
|
||||
### SimStudioClient
|
||||
|
||||
#### Constructor
|
||||
|
||||
```python
|
||||
SimStudioClient(api_key: str, base_url: str = "https://sim.ai")
|
||||
```
|
||||
|
||||
**Parameters:**
|
||||
- `api_key` (str): Your Sim API key
|
||||
- `base_url` (str, optional): Base URL for the Sim API
|
||||
|
||||
#### Methods
|
||||
|
||||
##### execute_workflow()
|
||||
|
||||
Execute a workflow with optional input data.
|
||||
|
||||
```python
|
||||
result = client.execute_workflow(
|
||||
"workflow-id",
|
||||
input={"message": "Hello, world!"},
|
||||
timeout=30.0 # 30 seconds
|
||||
)
|
||||
```
|
||||
|
||||
**Parameters:**
|
||||
- `workflow_id` (str): The ID of the workflow to execute
|
||||
- `input` (dict, optional): Input data to pass to the workflow
|
||||
- `timeout` (float, optional): Timeout in seconds (default: 30.0)
|
||||
- `stream` (bool, optional): Enable streaming responses (default: False)
|
||||
- `selected_outputs` (list[str], optional): Block outputs to stream in `blockName.attribute` format (e.g., `["agent1.content"]`)
|
||||
- `async_execution` (bool, optional): Execute asynchronously (default: False)
|
||||
|
||||
**Returns:** `WorkflowExecutionResult | AsyncExecutionResult`
|
||||
|
||||
When `async_execution=True`, returns immediately with a task ID for polling. Otherwise, waits for completion.
|
||||
|
||||
##### get_workflow_status()
|
||||
|
||||
Get the status of a workflow (deployment status, etc.).
|
||||
|
||||
```python
|
||||
status = client.get_workflow_status("workflow-id")
|
||||
print("Is deployed:", status.is_deployed)
|
||||
```
|
||||
|
||||
**Parameters:**
|
||||
- `workflow_id` (str): The ID of the workflow
|
||||
|
||||
**Returns:** `WorkflowStatus`
|
||||
|
||||
##### validate_workflow()
|
||||
|
||||
Validate that a workflow is ready for execution.
|
||||
|
||||
```python
|
||||
is_ready = client.validate_workflow("workflow-id")
|
||||
if is_ready:
|
||||
# Workflow is deployed and ready
|
||||
pass
|
||||
```
|
||||
|
||||
**Parameters:**
|
||||
- `workflow_id` (str): The ID of the workflow
|
||||
|
||||
**Returns:** `bool`
|
||||
|
||||
##### get_job_status()
|
||||
|
||||
Get the status of an async job execution.
|
||||
|
||||
```python
|
||||
status = client.get_job_status("task-id-from-async-execution")
|
||||
print("Status:", status["status"]) # 'queued', 'processing', 'completed', 'failed'
|
||||
if status["status"] == "completed":
|
||||
print("Output:", status["output"])
|
||||
```
|
||||
|
||||
**Parameters:**
|
||||
- `task_id` (str): The task ID returned from async execution
|
||||
|
||||
**Returns:** `Dict[str, Any]`
|
||||
|
||||
**Response fields:**
|
||||
- `success` (bool): Whether the request was successful
|
||||
- `taskId` (str): The task ID
|
||||
- `status` (str): One of `'queued'`, `'processing'`, `'completed'`, `'failed'`, `'cancelled'`
|
||||
- `metadata` (dict): Contains `startedAt`, `completedAt`, and `duration`
|
||||
- `output` (any, optional): The workflow output (when completed)
|
||||
- `error` (any, optional): Error details (when failed)
|
||||
- `estimatedDuration` (int, optional): Estimated duration in milliseconds (when processing/queued)
|
||||
|
||||
##### execute_with_retry()
|
||||
|
||||
Execute a workflow with automatic retry on rate limit errors using exponential backoff.
|
||||
|
||||
```python
|
||||
result = client.execute_with_retry(
|
||||
"workflow-id",
|
||||
input={"message": "Hello"},
|
||||
timeout=30.0,
|
||||
max_retries=3, # Maximum number of retries
|
||||
initial_delay=1.0, # Initial delay in seconds
|
||||
max_delay=30.0, # Maximum delay in seconds
|
||||
backoff_multiplier=2.0 # Exponential backoff multiplier
|
||||
)
|
||||
```
|
||||
|
||||
**Parameters:**
|
||||
- `workflow_id` (str): The ID of the workflow to execute
|
||||
- `input` (dict, optional): Input data to pass to the workflow
|
||||
- `timeout` (float, optional): Timeout in seconds
|
||||
- `stream` (bool, optional): Enable streaming responses
|
||||
- `selected_outputs` (list, optional): Block outputs to stream
|
||||
- `async_execution` (bool, optional): Execute asynchronously
|
||||
- `max_retries` (int, optional): Maximum number of retries (default: 3)
|
||||
- `initial_delay` (float, optional): Initial delay in seconds (default: 1.0)
|
||||
- `max_delay` (float, optional): Maximum delay in seconds (default: 30.0)
|
||||
- `backoff_multiplier` (float, optional): Backoff multiplier (default: 2.0)
|
||||
|
||||
**Returns:** `WorkflowExecutionResult | AsyncExecutionResult`
|
||||
|
||||
The retry logic uses exponential backoff (1s → 2s → 4s → 8s...) with ±25% jitter to prevent thundering herd. If the API provides a `retry-after` header, it will be used instead.
|
||||
|
||||
##### get_rate_limit_info()
|
||||
|
||||
Get the current rate limit information from the last API response.
|
||||
|
||||
```python
|
||||
rate_limit_info = client.get_rate_limit_info()
|
||||
if rate_limit_info:
|
||||
print("Limit:", rate_limit_info.limit)
|
||||
print("Remaining:", rate_limit_info.remaining)
|
||||
print("Reset:", datetime.fromtimestamp(rate_limit_info.reset))
|
||||
```
|
||||
|
||||
**Returns:** `RateLimitInfo | None`
|
||||
|
||||
##### get_usage_limits()
|
||||
|
||||
Get current usage limits and quota information for your account.
|
||||
|
||||
```python
|
||||
limits = client.get_usage_limits()
|
||||
print("Sync requests remaining:", limits.rate_limit["sync"]["remaining"])
|
||||
print("Async requests remaining:", limits.rate_limit["async"]["remaining"])
|
||||
print("Current period cost:", limits.usage["currentPeriodCost"])
|
||||
print("Plan:", limits.usage["plan"])
|
||||
```
|
||||
|
||||
**Returns:** `UsageLimits`
|
||||
|
||||
**Response structure:**
|
||||
```python
|
||||
{
|
||||
"success": bool,
|
||||
"rateLimit": {
|
||||
"sync": {
|
||||
"isLimited": bool,
|
||||
"limit": int,
|
||||
"remaining": int,
|
||||
"resetAt": str
|
||||
},
|
||||
"async": {
|
||||
"isLimited": bool,
|
||||
"limit": int,
|
||||
"remaining": int,
|
||||
"resetAt": str
|
||||
},
|
||||
"authType": str # 'api' or 'manual'
|
||||
},
|
||||
"usage": {
|
||||
"currentPeriodCost": float,
|
||||
"limit": float,
|
||||
"plan": str # e.g., 'free', 'pro'
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
##### set_api_key()
|
||||
|
||||
Update the API key.
|
||||
|
||||
```python
|
||||
client.set_api_key("new-api-key")
|
||||
```
|
||||
|
||||
##### set_base_url()
|
||||
|
||||
Update the base URL.
|
||||
|
||||
```python
|
||||
client.set_base_url("https://my-custom-domain.com")
|
||||
```
|
||||
|
||||
##### close()
|
||||
|
||||
Close the underlying HTTP session.
|
||||
|
||||
```python
|
||||
client.close()
|
||||
```
|
||||
|
||||
## Data Classes
|
||||
|
||||
### WorkflowExecutionResult
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class WorkflowExecutionResult:
|
||||
success: bool
|
||||
output: Optional[Any] = None
|
||||
error: Optional[str] = None
|
||||
logs: Optional[List[Any]] = None
|
||||
metadata: Optional[Dict[str, Any]] = None
|
||||
trace_spans: Optional[List[Any]] = None
|
||||
total_duration: Optional[float] = None
|
||||
```
|
||||
|
||||
### AsyncExecutionResult
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class AsyncExecutionResult:
|
||||
success: bool
|
||||
task_id: str
|
||||
status: str # 'queued'
|
||||
created_at: str
|
||||
links: Dict[str, str] # e.g., {"status": "/api/jobs/{taskId}"}
|
||||
```
|
||||
|
||||
### WorkflowStatus
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class WorkflowStatus:
|
||||
is_deployed: bool
|
||||
deployed_at: Optional[str] = None
|
||||
needs_redeployment: bool = False
|
||||
```
|
||||
|
||||
### RateLimitInfo
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class RateLimitInfo:
|
||||
limit: int
|
||||
remaining: int
|
||||
reset: int
|
||||
retry_after: Optional[int] = None
|
||||
```
|
||||
|
||||
### UsageLimits
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class UsageLimits:
|
||||
success: bool
|
||||
rate_limit: Dict[str, Any]
|
||||
usage: Dict[str, Any]
|
||||
```
|
||||
|
||||
### SimStudioError
|
||||
|
||||
```python
|
||||
class SimStudioError(Exception):
|
||||
def __init__(self, message: str, code: Optional[str] = None, status: Optional[int] = None):
|
||||
super().__init__(message)
|
||||
self.code = code
|
||||
self.status = status
|
||||
```
|
||||
|
||||
**Common error codes:**
|
||||
- `UNAUTHORIZED`: Invalid API key
|
||||
- `TIMEOUT`: Request timed out
|
||||
- `RATE_LIMIT_EXCEEDED`: Rate limit exceeded
|
||||
- `USAGE_LIMIT_EXCEEDED`: Usage limit exceeded
|
||||
- `EXECUTION_ERROR`: Workflow execution failed
|
||||
|
||||
## Examples
|
||||
|
||||
### Basic Workflow Execution
|
||||
|
||||
<Steps>
|
||||
<Step title="Initialize the client">
|
||||
Set up the SimStudioClient with your API key.
|
||||
</Step>
|
||||
<Step title="Validate the workflow">
|
||||
Check if the workflow is deployed and ready for execution.
|
||||
</Step>
|
||||
<Step title="Execute the workflow">
|
||||
Run the workflow with your input data.
|
||||
</Step>
|
||||
<Step title="Handle the result">
|
||||
Process the execution result and handle any errors.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
```python
|
||||
import os
|
||||
from simstudio import SimStudioClient
|
||||
|
||||
client = SimStudioClient(api_key=os.getenv("SIM_API_KEY"))
|
||||
|
||||
def run_workflow():
|
||||
try:
|
||||
# Check if workflow is ready
|
||||
is_ready = client.validate_workflow("my-workflow-id")
|
||||
if not is_ready:
|
||||
raise Exception("Workflow is not deployed or ready")
|
||||
|
||||
# Execute the workflow
|
||||
result = client.execute_workflow(
|
||||
"my-workflow-id",
|
||||
input={
|
||||
"message": "Process this data",
|
||||
"user_id": "12345"
|
||||
}
|
||||
)
|
||||
|
||||
if result.success:
|
||||
print("Output:", result.output)
|
||||
print("Duration:", result.metadata.get("duration") if result.metadata else None)
|
||||
else:
|
||||
print("Workflow failed:", result.error)
|
||||
|
||||
except Exception as error:
|
||||
print("Error:", error)
|
||||
|
||||
run_workflow()
|
||||
```
|
||||
|
||||
### Error Handling
|
||||
|
||||
Handle different types of errors that may occur during workflow execution:
|
||||
|
||||
```python
|
||||
from simstudio import SimStudioClient, SimStudioError
|
||||
import os
|
||||
|
||||
client = SimStudioClient(api_key=os.getenv("SIM_API_KEY"))
|
||||
|
||||
def execute_with_error_handling():
|
||||
try:
|
||||
result = client.execute_workflow("workflow-id")
|
||||
return result
|
||||
except SimStudioError as error:
|
||||
if error.code == "UNAUTHORIZED":
|
||||
print("Invalid API key")
|
||||
elif error.code == "TIMEOUT":
|
||||
print("Workflow execution timed out")
|
||||
elif error.code == "USAGE_LIMIT_EXCEEDED":
|
||||
print("Usage limit exceeded")
|
||||
elif error.code == "INVALID_JSON":
|
||||
print("Invalid JSON in request body")
|
||||
else:
|
||||
print(f"Workflow error: {error}")
|
||||
raise
|
||||
except Exception as error:
|
||||
print(f"Unexpected error: {error}")
|
||||
raise
|
||||
```
|
||||
|
||||
### Context Manager Usage
|
||||
|
||||
Use the client as a context manager to automatically handle resource cleanup:
|
||||
|
||||
```python
|
||||
from simstudio import SimStudioClient
|
||||
import os
|
||||
|
||||
# Using context manager to automatically close the session
|
||||
with SimStudioClient(api_key=os.getenv("SIM_API_KEY")) as client:
|
||||
result = client.execute_workflow("workflow-id")
|
||||
print("Result:", result)
|
||||
# Session is automatically closed here
|
||||
```
|
||||
|
||||
### Batch Workflow Execution
|
||||
|
||||
Execute multiple workflows efficiently:
|
||||
|
||||
```python
|
||||
from simstudio import SimStudioClient
|
||||
import os
|
||||
|
||||
client = SimStudioClient(api_key=os.getenv("SIM_API_KEY"))
|
||||
|
||||
def execute_workflows_batch(workflow_data_pairs):
|
||||
"""Execute multiple workflows with different input data."""
|
||||
results = []
|
||||
|
||||
for workflow_id, input_data in workflow_data_pairs:
|
||||
try:
|
||||
# Validate workflow before execution
|
||||
if not client.validate_workflow(workflow_id):
|
||||
print(f"Skipping {workflow_id}: not deployed")
|
||||
continue
|
||||
|
||||
result = client.execute_workflow(workflow_id, input_data)
|
||||
results.append({
|
||||
"workflow_id": workflow_id,
|
||||
"success": result.success,
|
||||
"output": result.output,
|
||||
"error": result.error
|
||||
})
|
||||
|
||||
except Exception as error:
|
||||
results.append({
|
||||
"workflow_id": workflow_id,
|
||||
"success": False,
|
||||
"error": str(error)
|
||||
})
|
||||
|
||||
return results
|
||||
|
||||
# Example usage
|
||||
workflows = [
|
||||
("workflow-1", {"type": "analysis", "data": "sample1"}),
|
||||
("workflow-2", {"type": "processing", "data": "sample2"}),
|
||||
]
|
||||
|
||||
results = execute_workflows_batch(workflows)
|
||||
for result in results:
|
||||
print(f"Workflow {result['workflow_id']}: {'Success' if result['success'] else 'Failed'}")
|
||||
```
|
||||
|
||||
### Async Workflow Execution
|
||||
|
||||
Execute workflows asynchronously for long-running tasks:
|
||||
|
||||
```python
|
||||
import os
|
||||
import time
|
||||
from simstudio import SimStudioClient
|
||||
|
||||
client = SimStudioClient(api_key=os.getenv("SIM_API_KEY"))
|
||||
|
||||
def execute_async():
|
||||
try:
|
||||
# Start async execution
|
||||
result = client.execute_workflow(
|
||||
"workflow-id",
|
||||
input={"data": "large dataset"},
|
||||
async_execution=True # Execute asynchronously
|
||||
)
|
||||
|
||||
# Check if result is an async execution
|
||||
if hasattr(result, 'task_id'):
|
||||
print(f"Task ID: {result.task_id}")
|
||||
print(f"Status endpoint: {result.links['status']}")
|
||||
|
||||
# Poll for completion
|
||||
status = client.get_job_status(result.task_id)
|
||||
|
||||
while status["status"] in ["queued", "processing"]:
|
||||
print(f"Current status: {status['status']}")
|
||||
time.sleep(2) # Wait 2 seconds
|
||||
status = client.get_job_status(result.task_id)
|
||||
|
||||
if status["status"] == "completed":
|
||||
print("Workflow completed!")
|
||||
print(f"Output: {status['output']}")
|
||||
print(f"Duration: {status['metadata']['duration']}")
|
||||
else:
|
||||
print(f"Workflow failed: {status['error']}")
|
||||
|
||||
except Exception as error:
|
||||
print(f"Error: {error}")
|
||||
|
||||
execute_async()
|
||||
```
|
||||
|
||||
### Rate Limiting and Retry
|
||||
|
||||
Handle rate limits automatically with exponential backoff:
|
||||
|
||||
```python
|
||||
import os
|
||||
from simstudio import SimStudioClient, SimStudioError
|
||||
|
||||
client = SimStudioClient(api_key=os.getenv("SIM_API_KEY"))
|
||||
|
||||
def execute_with_retry_handling():
|
||||
try:
|
||||
# Automatically retries on rate limit
|
||||
result = client.execute_with_retry(
|
||||
"workflow-id",
|
||||
input={"message": "Process this"},
|
||||
max_retries=5,
|
||||
initial_delay=1.0,
|
||||
max_delay=60.0,
|
||||
backoff_multiplier=2.0
|
||||
)
|
||||
|
||||
print(f"Success: {result}")
|
||||
except SimStudioError as error:
|
||||
if error.code == "RATE_LIMIT_EXCEEDED":
|
||||
print("Rate limit exceeded after all retries")
|
||||
|
||||
# Check rate limit info
|
||||
rate_limit_info = client.get_rate_limit_info()
|
||||
if rate_limit_info:
|
||||
from datetime import datetime
|
||||
reset_time = datetime.fromtimestamp(rate_limit_info.reset)
|
||||
print(f"Rate limit resets at: {reset_time}")
|
||||
|
||||
execute_with_retry_handling()
|
||||
```
|
||||
|
||||
### Usage Monitoring
|
||||
|
||||
Monitor your account usage and limits:
|
||||
|
||||
```python
|
||||
import os
|
||||
from simstudio import SimStudioClient
|
||||
|
||||
client = SimStudioClient(api_key=os.getenv("SIM_API_KEY"))
|
||||
|
||||
def check_usage():
|
||||
try:
|
||||
limits = client.get_usage_limits()
|
||||
|
||||
print("=== Rate Limits ===")
|
||||
print("Sync requests:")
|
||||
print(f" Limit: {limits.rate_limit['sync']['limit']}")
|
||||
print(f" Remaining: {limits.rate_limit['sync']['remaining']}")
|
||||
print(f" Resets at: {limits.rate_limit['sync']['resetAt']}")
|
||||
print(f" Is limited: {limits.rate_limit['sync']['isLimited']}")
|
||||
|
||||
print("\nAsync requests:")
|
||||
print(f" Limit: {limits.rate_limit['async']['limit']}")
|
||||
print(f" Remaining: {limits.rate_limit['async']['remaining']}")
|
||||
print(f" Resets at: {limits.rate_limit['async']['resetAt']}")
|
||||
print(f" Is limited: {limits.rate_limit['async']['isLimited']}")
|
||||
|
||||
print("\n=== Usage ===")
|
||||
print(f"Current period cost: ${limits.usage['currentPeriodCost']:.2f}")
|
||||
print(f"Limit: ${limits.usage['limit']:.2f}")
|
||||
print(f"Plan: {limits.usage['plan']}")
|
||||
|
||||
percent_used = (limits.usage['currentPeriodCost'] / limits.usage['limit']) * 100
|
||||
print(f"Usage: {percent_used:.1f}%")
|
||||
|
||||
if percent_used > 80:
|
||||
print("⚠️ Warning: You are approaching your usage limit!")
|
||||
|
||||
except Exception as error:
|
||||
print(f"Error checking usage: {error}")
|
||||
|
||||
check_usage()
|
||||
```
|
||||
|
||||
### Streaming Workflow Execution
|
||||
|
||||
Execute workflows with real-time streaming responses:
|
||||
|
||||
```python
|
||||
from simstudio import SimStudioClient
|
||||
import os
|
||||
|
||||
client = SimStudioClient(api_key=os.getenv("SIM_API_KEY"))
|
||||
|
||||
def execute_with_streaming():
|
||||
"""Execute workflow with streaming enabled."""
|
||||
try:
|
||||
# Enable streaming for specific block outputs
|
||||
result = client.execute_workflow(
|
||||
"workflow-id",
|
||||
input={"message": "Count to five"},
|
||||
stream=True,
|
||||
selected_outputs=["agent1.content"] # Use blockName.attribute format
|
||||
)
|
||||
|
||||
print("Workflow result:", result)
|
||||
except Exception as error:
|
||||
print("Error:", error)
|
||||
|
||||
execute_with_streaming()
|
||||
```
|
||||
|
||||
The streaming response follows the Server-Sent Events (SSE) format:
|
||||
|
||||
```
|
||||
data: {"blockId":"7b7735b9-19e5-4bd6-818b-46aae2596e9f","chunk":"One"}
|
||||
|
||||
data: {"blockId":"7b7735b9-19e5-4bd6-818b-46aae2596e9f","chunk":", two"}
|
||||
|
||||
data: {"event":"done","success":true,"output":{},"metadata":{"duration":610}}
|
||||
|
||||
data: [DONE]
|
||||
```
|
||||
|
||||
**Flask Streaming Example:**
|
||||
|
||||
```python
|
||||
from flask import Flask, Response, stream_with_context
|
||||
import requests
|
||||
import json
|
||||
import os
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
@app.route('/stream-workflow')
|
||||
def stream_workflow():
|
||||
"""Stream workflow execution to the client."""
|
||||
|
||||
def generate():
|
||||
response = requests.post(
|
||||
'https://sim.ai/api/workflows/WORKFLOW_ID/execute',
|
||||
headers={
|
||||
'Content-Type': 'application/json',
|
||||
'X-API-Key': os.getenv('SIM_API_KEY')
|
||||
},
|
||||
json={
|
||||
'message': 'Generate a story',
|
||||
'stream': True,
|
||||
'selectedOutputs': ['agent1.content']
|
||||
},
|
||||
stream=True
|
||||
)
|
||||
|
||||
for line in response.iter_lines():
|
||||
if line:
|
||||
decoded_line = line.decode('utf-8')
|
||||
if decoded_line.startswith('data: '):
|
||||
data = decoded_line[6:] # Remove 'data: ' prefix
|
||||
|
||||
if data == '[DONE]':
|
||||
break
|
||||
|
||||
try:
|
||||
parsed = json.loads(data)
|
||||
if 'chunk' in parsed:
|
||||
yield f"data: {json.dumps(parsed)}\n\n"
|
||||
elif parsed.get('event') == 'done':
|
||||
yield f"data: {json.dumps(parsed)}\n\n"
|
||||
print("Execution complete:", parsed.get('metadata'))
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
return Response(
|
||||
stream_with_context(generate()),
|
||||
mimetype='text/event-stream'
|
||||
)
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run(debug=True)
|
||||
```
|
||||
|
||||
### Environment Configuration
|
||||
|
||||
Configure the client using environment variables:
|
||||
|
||||
<Tabs items={['Development', 'Production']}>
|
||||
<Tab value="Development">
|
||||
```python
|
||||
import os
|
||||
from simstudio import SimStudioClient
|
||||
|
||||
# Development configuration
|
||||
client = SimStudioClient(
|
||||
api_key=os.getenv("SIM_API_KEY")
|
||||
base_url=os.getenv("SIM_BASE_URL", "https://sim.ai")
|
||||
)
|
||||
```
|
||||
</Tab>
|
||||
<Tab value="Production">
|
||||
```python
|
||||
import os
|
||||
from simstudio import SimStudioClient
|
||||
|
||||
# Production configuration with error handling
|
||||
api_key = os.getenv("SIM_API_KEY")
|
||||
if not api_key:
|
||||
raise ValueError("SIM_API_KEY environment variable is required")
|
||||
|
||||
client = SimStudioClient(
|
||||
api_key=api_key,
|
||||
base_url=os.getenv("SIM_BASE_URL", "https://sim.ai")
|
||||
)
|
||||
```
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
## Getting Your API Key
|
||||
|
||||
<Steps>
|
||||
<Step title="Log in to Sim">
|
||||
Navigate to [Sim](https://sim.ai) and log in to your account.
|
||||
</Step>
|
||||
<Step title="Open your workflow">
|
||||
Navigate to the workflow you want to execute programmatically.
|
||||
</Step>
|
||||
<Step title="Deploy your workflow">
|
||||
Click on "Deploy" to deploy your workflow if it hasn't been deployed yet.
|
||||
</Step>
|
||||
<Step title="Create or select an API key">
|
||||
During the deployment process, select or create an API key.
|
||||
</Step>
|
||||
<Step title="Copy the API key">
|
||||
Copy the API key to use in your Python application.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## Requirements
|
||||
|
||||
- Python 3.8+
|
||||
- requests >= 2.25.0
|
||||
|
||||
## License
|
||||
|
||||
Apache-2.0
|
||||
|
||||
import { FAQ } from '@/components/ui/faq'
|
||||
|
||||
<FAQ items={[
|
||||
{ question: "Do I need to deploy a workflow before I can execute it via the SDK?", answer: "Yes. Workflows must be deployed before they can be executed through the SDK. You can use the validate_workflow() method to check whether a workflow is deployed and ready. If it returns False, deploy the workflow from the Sim UI first and create or select an API key during deployment." },
|
||||
{ question: "What is the difference between sync and async execution?", answer: "Sync execution (the default) blocks until the workflow completes and returns the full result. Async execution (async_execution=True) returns immediately with a task ID that you can poll using get_job_status(). Use async mode for long-running workflows to avoid request timeouts. Async job statuses include queued, processing, completed, failed, and cancelled." },
|
||||
{ question: "How does the SDK handle rate limiting?", answer: "The SDK provides built-in rate limiting support through the execute_with_retry() method. It uses exponential backoff (1s, 2s, 4s, 8s...) with 25% jitter to avoid thundering herd problems. If the API returns a retry-after header, that value is used instead. You can configure max_retries, initial_delay, max_delay, and backoff_multiplier. Use get_rate_limit_info() to check your current rate limit status." },
|
||||
{ question: "Can I use the Python SDK as a context manager?", answer: "Yes. The SimStudioClient supports Python's context manager protocol. Use it with the 'with' statement to automatically close the underlying HTTP session when you are done, which is especially useful for scripts that create and discard client instances." },
|
||||
{ question: "How do I handle different types of errors from the SDK?", answer: "The SDK raises SimStudioError with a code property for API-specific errors. Common error codes are UNAUTHORIZED (invalid API key), TIMEOUT (request timed out), RATE_LIMIT_EXCEEDED (too many requests), USAGE_LIMIT_EXCEEDED (billing limit reached), and EXECUTION_ERROR (workflow failed). Use the error code to implement targeted error handling and recovery logic." },
|
||||
{ question: "How do I monitor my API usage and remaining quota?", answer: "Use the get_usage_limits() method to check your current usage. It returns sync and async rate limit details (limit, remaining, reset time, whether you are currently limited), plus your current period cost, usage limit, and plan tier. This lets you monitor consumption and alert before hitting limits." },
|
||||
]} />
|
||||
File diff suppressed because it is too large
Load Diff
@@ -151,7 +151,7 @@ From **Settings → Custom Tools** you can:
|
||||
|--------|-------------------|
|
||||
| View custom tools | **Read**, **Write**, or **Admin** |
|
||||
| Create or edit tools | **Write** or **Admin** |
|
||||
| Delete tools | **Write** or **Admin** |
|
||||
| Delete tools | **Admin** |
|
||||
|
||||
<FAQ items={[
|
||||
{ question: "Can I use custom tools in standalone blocks (not agents)?", answer: "No. Custom tools are designed for use within Agent blocks, where the AI model decides when to call them. For deterministic tool execution, use the Function block instead." },
|
||||
|
||||
@@ -51,7 +51,7 @@ Bienvenido a Sim, un constructor visual de flujos de trabajo para aplicaciones d
|
||||
<Card title="Integración MCP" href="/mcp">
|
||||
Conecta servicios externos con el Protocolo de Contexto de Modelo
|
||||
</Card>
|
||||
<Card title="SDKs" href="/sdks">
|
||||
<Card title="SDKs" href="/api-reference">
|
||||
Integra Sim en tus aplicaciones
|
||||
</Card>
|
||||
</Cards>
|
||||
@@ -51,7 +51,7 @@ Bienvenue sur Sim, un constructeur visuel de flux de travail pour les applicatio
|
||||
<Card title="Intégration MCP" href="/mcp">
|
||||
Connectez des services externes avec le protocole de contexte de modèle
|
||||
</Card>
|
||||
<Card title="SDKs" href="/sdks">
|
||||
<Card title="SDKs" href="/api-reference">
|
||||
Intégrez Sim dans vos applications
|
||||
</Card>
|
||||
</Cards>
|
||||
@@ -51,7 +51,7 @@ Simへようこそ。AIアプリケーション用のビジュアルワークフ
|
||||
<Card title="MCP統合" href="/mcp">
|
||||
モデルコンテキストプロトコルで外部サービスを接続する
|
||||
</Card>
|
||||
<Card title="SDK" href="/sdks">
|
||||
<Card title="SDK" href="/api-reference">
|
||||
アプリケーションにSimを統合する
|
||||
</Card>
|
||||
</Cards>
|
||||
@@ -51,7 +51,7 @@ import { Card, Cards } from 'fumadocs-ui/components/card'
|
||||
<Card title="MCP 集成" href="/mcp">
|
||||
使用模型上下文协议连接外部服务
|
||||
</Card>
|
||||
<Card title="SDKs" href="/sdks">
|
||||
<Card title="SDKs" href="/api-reference">
|
||||
将 Sim 集成到您的应用程序中
|
||||
</Card>
|
||||
</Cards>
|
||||
@@ -12,6 +12,7 @@ import {
|
||||
getMaxOutputTokensForModel,
|
||||
getThinkingCapability,
|
||||
supportsNativeStructuredOutputs,
|
||||
supportsTemperature,
|
||||
} from '@/providers/models'
|
||||
import type { ProviderRequest, ProviderResponse, TimeSegment } from '@/providers/types'
|
||||
import { ProviderError } from '@/providers/types'
|
||||
@@ -298,7 +299,9 @@ export async function executeAnthropicProviderRequest(
|
||||
system: systemPrompt,
|
||||
max_tokens:
|
||||
Number.parseInt(String(request.maxTokens)) || getMaxOutputTokensForModel(request.model),
|
||||
temperature: Number.parseFloat(String(request.temperature ?? 0.7)),
|
||||
...(supportsTemperature(request.model) && {
|
||||
temperature: Number.parseFloat(String(request.temperature ?? 0.7)),
|
||||
}),
|
||||
}
|
||||
|
||||
if (request.responseFormat) {
|
||||
|
||||
@@ -528,11 +528,10 @@ export const PROVIDER_DEFINITIONS: Record<string, ProviderDefinition> = {
|
||||
updatedAt: '2026-04-16',
|
||||
},
|
||||
capabilities: {
|
||||
temperature: { min: 0, max: 1 },
|
||||
nativeStructuredOutputs: true,
|
||||
maxOutputTokens: 128000,
|
||||
thinking: {
|
||||
levels: ['low', 'medium', 'high', 'max'],
|
||||
levels: ['low', 'medium', 'high', 'xhigh', 'max'],
|
||||
default: 'high',
|
||||
},
|
||||
},
|
||||
|
||||
@@ -56,4 +56,4 @@ The Sim blog covers announcements, technical deep-dives, and guides for building
|
||||
- MCP Protocol: https://docs.sim.ai/mcp
|
||||
- Self-Hosting: https://docs.sim.ai/self-hosting
|
||||
- API Reference: https://docs.sim.ai/api-reference/getting-started
|
||||
- SDKs: https://docs.sim.ai/sdks
|
||||
- SDKs: https://docs.sim.ai/api-reference
|
||||
|
||||
Reference in New Issue
Block a user