diff --git a/.cursor/skills/sdk-migration/SKILL.md b/.cursor/skills/sdk-migration/SKILL.md new file mode 100644 index 0000000..867bf08 --- /dev/null +++ b/.cursor/skills/sdk-migration/SKILL.md @@ -0,0 +1,191 @@ +--- +name: sdk-migration +description: >- + Migration guide for breaking changes between subconscious-sdk versions. + Use when upgrading the SDK, fixing import errors after upgrade, or when + the user encounters removed types like ModelUsage or PlatformToolUsage. +--- + +# SDK Migration Guide + +## Migrating from 0.x to 1.0 + +Version 1.0 aligns the Python SDK's response types with the API wire format. +Response models are now Pydantic `BaseModel` instances instead of dataclasses, +and their fields match the JSON returned by the Subconscious API 1:1. + +### Summary of breaking changes + +- `Usage` is now a flat Pydantic model (`input_tokens`, `output_tokens`, `duration_ms`). The old `models` and `platform_tools` fields are removed. +- `ModelUsage` and `PlatformToolUsage` are deleted entirely. +- `ReasoningNode` is removed. Use `ReasoningTask` instead. +- `ReasoningTask.subtask` is renamed to `subtasks`. +- `ReasoningTask.tooluse` is now `Optional[AgentToolUse]` instead of `List[Any]`. +- `RunResult.reasoning` is now `Optional[List[ReasoningTask]]` instead of `Optional[ReasoningNode]`. +- `Run` has a new `error: Optional[RunError]` field for failed runs. +- `Run`, `RunResult`, `Usage`, `RunError`, `ReasoningTask`, and `AgentToolUse` are Pydantic `BaseModel` instead of `@dataclass`. +- `Engine` literal expanded: added `"tim-edge"`, `"tim-oss-local"`, `"tim-1.5"`, `"tim-gpt-heavy-tc"`. + +### Usage — before and after + +```python +# 0.x — broken; models/platform_tools were raw dicts, not dataclass instances +run.usage.models[0].input_tokens +run.usage.platform_tools[0].tool_id + +# 1.0 — flat, matches API wire format +run.usage.input_tokens # int +run.usage.output_tokens # int +run.usage.duration_ms # Optional[int] +``` + +### ReasoningTask — before and after + +```python +# 0.x +from subconscious import ReasoningNode +node.subtask # List[ReasoningNode] +node.tooluse # List[Any] + +# 1.0 — ReasoningNode is removed +from subconscious import ReasoningTask +task.subtasks # Optional[List[ReasoningTask]] +task.tooluse # Optional[AgentToolUse] +task.tooluse.tool_name # str +task.tooluse.tool_call_id # Optional[str] +task.tooluse.parameters # Dict[str, Any] +task.tooluse.tool_result # Optional[Any] +``` + +### RunResult.reasoning — before and after + +```python +# 0.x — single node +run.result.reasoning # Optional[ReasoningNode] + +# 1.0 — list of tasks (matches API) +run.result.reasoning # Optional[List[ReasoningTask]] +for task in run.result.reasoning or []: + print(task.title, task.thought) +``` + +### Run.error — new field + +```python +# 1.0 — error details on failed runs +if run.status == "failed" and run.error: + print(f"Error {run.error.code}: {run.error.message}") +``` + +### Removed exports + +```python +# 0.x — these imports will fail in 1.0 +from subconscious import ModelUsage # REMOVED +from subconscious import PlatformToolUsage # REMOVED +from subconscious import ReasoningNode # REMOVED + +# 1.0 — new exports +from subconscious import AgentToolUse +from subconscious import RunError +from subconscious import ReasoningTask +``` + +### Dataclass to Pydantic + +Attribute access is unchanged (`run.run_id`, `run.usage.input_tokens`), but +`isinstance` checks and construction patterns differ: + +```python +# 0.x +from dataclasses import is_dataclass +assert is_dataclass(run) + +# 1.0 +from pydantic import BaseModel +assert isinstance(run, BaseModel) + +# Construction still works the same way: +run = Run(run_id="run_abc", status="queued") +``` + +### Engine literal + +```python +# 0.x +Engine = Literal["tim", "tim-claude", "tim-claude-heavy"] + +# 1.0 +Engine = Literal[ + "tim", "tim-edge", "tim-claude", "tim-claude-heavy", + "tim-oss-local", "tim-1.5", "tim-gpt-heavy-tc", +] +``` + +### Quick find-and-replace patterns + +| Old pattern | Replacement | +|---|---| +| `from subconscious import ModelUsage` | Remove — no longer exists | +| `from subconscious import PlatformToolUsage` | Remove — no longer exists | +| `from subconscious import ReasoningNode` | `from subconscious import ReasoningTask` | +| `run.usage.models` | `run.usage.input_tokens` / `run.usage.output_tokens` | +| `run.usage.platform_tools` | Remove — no longer exists | +| `.subtask` (on ReasoningTask) | `.subtasks` | +| `ReasoningNode` (in type hints/code) | `ReasoningTask` | +| `run.result.reasoning.title` | `run.result.reasoning[0].title` (now a list) | +| `RunInput(..., reasoning_format=...)` | Remove — field no longer exists | +| `{'reasoningFormat': ...}` | Remove — field no longer exists | + +## `reasoning_format` removed + +The `reasoning_format` input field (aliased as `reasoningFormat` on the wire) is gone. If you were shaping the reasoning trace with a JSON/Pydantic schema, fold that guidance into your `instructions` or into `answer_format` instead — the agent's final output is the contract, and the reasoning trace is best treated as a read-only byproduct. + +```python +# Before +client.run( + engine='tim', + input={ + 'instructions': '...', + 'answerFormat': AnswerSchema, + 'reasoningFormat': ReasoningSchema, + }, +) + +# After +client.run( + engine='tim', + input={ + 'instructions': '...', + 'answerFormat': AnswerSchema, + }, +) +``` + +Requests that still include `reasoningFormat` are rejected by the API. + +## `RunResult.parsed_answer` + +`result.answer` is always a `str` on the wire, even when `answerFormat` is supplied — the API JSON-encodes the structured value. The SDK now attaches a `parsed_answer` companion field on every response that runs through the client (`run`, `get`, `wait`, `cancel`), populated via a best-effort `json.loads` of `answer`. + +```python +from pydantic import BaseModel + +class Person(BaseModel): + name: str + age: int + +run = client.run( + engine='tim', + input={ + 'instructions': 'return JSON for a person', + 'answerFormat': Person, + }, + options={'await_completion': True}, +) + +run.result.answer # '{"name":"ada","age":36}' (raw string) +run.result.parsed_answer # {'name': 'ada', 'age': 36} (decoded dict) +``` + +`parsed_answer` is typed as `Any` — validate with your Pydantic model of choice (`Person.model_validate(run.result.parsed_answer)`) if you want a typed instance. It is `None` when `answer` is empty or not valid JSON. diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index bcaa05c..cc024be 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -1,17 +1,34 @@ -name: Tests +name: CI on: - push: - branches: [main] pull_request: - branches: [main] + push: + branches: + - main jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - uses: astral-sh/ruff-action@v3 + with: + args: check + + - uses: astral-sh/ruff-action@v3 + with: + args: format --check + test: runs-on: ubuntu-latest strategy: matrix: - python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] + python-version: ["3.10", "3.11", "3.12", "3.13"] steps: - uses: actions/checkout@v4 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..9090bb2 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,7 @@ +repos: + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.12.0 + hooks: + - id: ruff + args: [--fix, --exit-non-zero-on-fix] + - id: ruff-format diff --git a/MIGRATION.md b/MIGRATION.md new file mode 100644 index 0000000..081715e --- /dev/null +++ b/MIGRATION.md @@ -0,0 +1,7 @@ +# Migration Guide + +For the full migration guide with before/after code examples, see the +[SDK Migration Skill](.cursor/skills/sdk-migration/SKILL.md). + +This file is also available on GitHub: +https://github.com/subconscious-systems/subconscious-python/blob/main/.cursor/skills/sdk-migration/SKILL.md diff --git a/README.md b/README.md index 38c18ae..d50b26a 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ PyPI version PyPI downloads docs - python version + python version license

@@ -21,11 +21,9 @@ ## Installation ```bash -pip install subconscious-sdk -# or uv add subconscious-sdk # or -poetry add subconscious-sdk +pip install subconscious-sdk ``` > **Note**: The package name is `subconscious-sdk` but you import it as `subconscious`. @@ -38,7 +36,7 @@ from subconscious import Subconscious client = Subconscious(api_key="your-api-key") run = client.run( - engine="tim-gpt", + engine="tim-claude", input={ "instructions": "Search for the latest AI news and summarize the top 3 stories", "tools": [{"type": "platform", "id": "fast_search"}], @@ -61,7 +59,7 @@ The simplest way to use the SDK—create a run and wait for completion: ```python run = client.run( - engine="tim-gpt", + engine="tim-claude", input={ "instructions": "Analyze the latest trends in renewable energy", "tools": [{"type": "platform", "id": "fast_search"}], @@ -79,7 +77,7 @@ Start a run without waiting, then check status later: ```python run = client.run( - engine="tim-gpt", + engine="tim-claude", input={ "instructions": "Generate a comprehensive report", "tools": [], @@ -97,7 +95,7 @@ print(status.status) # 'queued' | 'running' | 'succeeded' | 'failed' | 'cancele ```python run = client.run( - engine="tim-gpt", + engine="tim-claude", input={ "instructions": "Complex task", "tools": [{"type": "platform", "id": "fast_search"}], @@ -120,7 +118,7 @@ Stream text as it's generated: ```python for event in client.stream( - engine="tim-gpt", + engine="tim-claude", input={ "instructions": "Write a short essay about space exploration", "tools": [{"type": "platform", "id": "fast_search"}], @@ -142,7 +140,7 @@ Attach reusable knowledge packages to your runs. Skills use progressive disclosu ```python run = client.run( - engine="tim-gpt", + engine="tim-claude", input={ "instructions": "Build a REST API following our team standards", "tools": [{"type": "platform", "id": "web_search"}], @@ -170,7 +168,7 @@ class AnalysisResult(BaseModel): client = Subconscious(api_key="your-api-key") run = client.run( - engine="tim-gpt", + engine="tim-claude", input={ "instructions": "Analyze the latest news about electric vehicles", "tools": [{"type": "platform", "id": "fast_search"}], @@ -185,25 +183,78 @@ print(run.result.answer) # JSON string matching AnalysisResult The SDK automatically converts your Pydantic model to JSON Schema. You can also pass a raw JSON Schema dict if preferred. -For advanced use cases, you can also specify a `reasoningFormat` to structure the agent's reasoning output. +### Multimodal Input (Images) + +Send images alongside text instructions using the `Image` helper: + +```python +from subconscious import Subconscious, Image + +client = Subconscious(api_key="your-api-key") + +# From a local file +run = client.run( + engine="tim-claude", + input={ + "instructions": "Describe what you see in this image", + "content": [Image.from_path("photo.png")], + }, + options={"await_completion": True}, +) + +print(run.result.answer) +``` + +`Image` supports several sources: + +```python +# From a URL (server-side fetch) +Image.from_url("https://example.com/photo.jpg") + +# From a URL (client-side fetch, embeds as base64) +Image.from_url("https://example.com/photo.jpg", fetch=True) + +# From raw bytes +Image.from_bytes(image_bytes) + +# From a previously uploaded blob +Image.from_blob_ref(blob_key="abc123", mime="image/png") +``` + +Supported formats: PNG, JPEG, GIF, and WebP. + +### Image Tool Responses + +If you have a function tool that generates or fetches an image, return it as a `ToolResponse` with mixed text and image content. The agent will see the image and can reason about it. This is useful for tools that generate images, such as a screenshot tool for computer use. + +```python +from subconscious import ToolResponse, Image + +# Your tool endpoint receives a tool_call_id from the agent. +# Return an image alongside text using ToolResponse.build(): +response = ToolResponse.build( + tool_call_id="call_abc123", + content=[ + "Here is the generated chart:", + Image.from_path("chart.png"), + ], +) + +# Or from a URL +response = ToolResponse.build( + tool_call_id="call_abc123", + content=[ + "Screenshot captured successfully.", + Image.from_url("https://example.com/screenshot.png"), + ], +) +``` + +`ToolResponse.build()` accepts a string, a single content block, or a mixed list of strings and `Image` blocks — it normalizes everything into the wire format automatically. You can use this when building custom tools to use with Subconscious. ### Tools -**Simple Search Tools** — Use these tools to get started quickly in our playground or with our API. For example: `{"type": "platform", "id": "fast_search"}`. - -| Tool Name | API Name | Description | -| --------------------- | ---------------------- | ---------------------------------------------------------- | -| Fast Search | `fast_search` | Extremely fast search for simple factual lookups | -| Web Search | `web_search` | Comprehensive web search for detailed research | -| Fresh Search | `fresh_search` | Search the web for content from the last 7 days | -| Page Reader | `page_reader` | Extract content from a specific webpage URL | -| Find Similar | `find_similar` | Find similar links to a given URL | -| People Search | `people_search` | Search for people, profiles, and bios | -| Company Search | `company_search` | Search for companies, funding info, and business details | -| News Search | `news_search` | Search for news articles and press coverage | -| Tweet Search | `tweet_search` | Search for tweets and Twitter/X discussions | -| Research Paper Search | `research_paper_search`| Search for academic research papers and studies | -| Google Search | `google_search` | Search the web using Google | +**Platform tools** — Hosted search and retrieval tools like `fast_search`, `web_search`, `news_search`, and more. See the [tools documentation](https://docs.subconscious.dev/core-concepts/tools) for the full list. ```python # Platform tools (hosted by Subconscious) @@ -241,8 +292,8 @@ mcp_tool = { Function tools support two powerful features for injecting data at call time: -- **`headers`**: HTTP headers sent with the request to your tool endpoint -- **`defaults`**: Parameter values hidden from the model and injected automatically +- `**headers**`: HTTP headers sent with the request to your tool endpoint +- `**defaults**`: Parameter values hidden from the model and injected automatically ```python tool_with_headers_and_defaults = { @@ -278,11 +329,13 @@ tool_with_headers_and_defaults = { **How it works:** + | Feature | Where it goes | When | | ---------- | ------------------------------------- | ----------------------- | | `headers` | HTTP request headers | Sent to your tool's URL | | `defaults` | Merged into request body `parameters` | At tool call time | + **Default arguments flow:** 1. Define all parameters in `properties` (required for validation) @@ -301,10 +354,12 @@ Connect to any [Model Context Protocol](https://modelcontextprotocol.io/) server MCP servers that require authentication accept an `auth` object. The auth translates to an HTTP header sent with every tool call: -| Method | When to use | Header sent | -| --- | --- | --- | -| **Bearer** | Most common — OAuth tokens, JWTs, etc. | `Authorization: Bearer ` | -| **API key** | Service-specific API keys | `
: ` (header is typically `X-Api-Key` — check your MCP server's docs) | + +| Method | When to use | Header sent | +| ----------- | -------------------------------------- | ------------------------------------------------------------------------------------ | +| **Bearer** | Most common — OAuth tokens, JWTs, etc. | `Authorization: Bearer ` | +| **API key** | Service-specific API keys | `
: ` (header is typically `X-Api-Key` — check your MCP server's docs) | + ```python from subconscious import Subconscious, MCPTool, McpAuth @@ -313,7 +368,7 @@ client = Subconscious() # Basic — use all tools from an MCP server run = client.run( - engine="tim-gpt", + engine="tim-claude", input={ "instructions": "Find my recent meeting notes", "tools": [ @@ -325,7 +380,7 @@ run = client.run( # Filter to specific tools run = client.run( - engine="tim-gpt", + engine="tim-claude", input={ "instructions": "Search my documents", "tools": [ @@ -341,7 +396,7 @@ run = client.run( # With bearer auth (most common — e.g. OAuth tokens) # → sends header: { "Authorization": "Bearer " } run = client.run( - engine="tim-gpt", + engine="tim-claude", input={ "instructions": "Check my calendar", "tools": [ @@ -359,7 +414,7 @@ run = client.run( # The header name is typically "X-Api-Key" but may vary — # check the docs of the MCP server you are connecting to. run = client.run( - engine="tim-gpt", + engine="tim-claude", input={ "instructions": "Query the database", "tools": [ @@ -373,14 +428,16 @@ run = client.run( ) ``` -**`allowedTools` filtering:** +`**allowedTools` filtering:** + + +| Value | Behavior | +| --------------------- | ------------------------------------- | +| Omitted / `None` | All tools from the server are enabled | +| `["*"]` | All tools enabled (explicit wildcard) | +| `["search", "fetch"]` | Only these tools (case-insensitive) | +| `[]` | No tools (blocks all) | -| Value | Behavior | -| --- | --- | -| Omitted / `None` | All tools from the server are enabled | -| `["*"]` | All tools enabled (explicit wildcard) | -| `["search", "fetch"]` | Only these tools (case-insensitive) | -| `[]` | No tools (blocks all) | You can also pass MCP tools as plain dicts: @@ -416,7 +473,7 @@ Get a POST when runs complete instead of polling. ```python run = client.run( - engine="tim-gpt", + engine="tim-claude", input={"instructions": "Generate a report"}, output={"callbackUrl": "https://your-server.com/webhook"}, ) @@ -446,64 +503,31 @@ client.cancel(run.run_id) ## API Reference -### `Subconscious` - -The main client class. - -#### Constructor Options +See the full [API documentation](https://docs.subconscious.dev) for detailed reference. The `Subconscious` client exposes five core methods: `run()`, `stream()`, `get()`, `wait()`, and `cancel()`. -| Option | Type | Required | Default | -| ---------- | ----- | -------- | --------------------------------- | -| `api_key` | `str` | Yes | - | -| `base_url` | `str` | No | `https://api.subconscious.dev/v1` | +For available engines and pricing, see the [pricing page](https://docs.subconscious.dev/pricing). -#### Methods +A run's `status` field is one of: `queued`, `running`, `succeeded`, `failed`, `canceled`, or `timed_out`. -| Method | Description | -| ----------------------------- | --------------------- | -| `run(engine, input, options)` | Create a new run | -| `stream(engine, input)` | Stream text deltas | -| `get(run_id)` | Get run status | -| `wait(run_id, options)` | Poll until completion | -| `cancel(run_id)` | Cancel a running run | -### Engines +## Development -| Engine | Type | Description | Input | Output | -| --------------- | -------- | ------------------------------------------------------------------------------- | -------- | --------- | -| `tim` | Unified | Our flagship unified agent engine for a wide range of tasks | $2.00/1M | $8.00/1M | -| `tim-edge` | Unified | Highly efficient engine tuned for performance with search tools | $0.50/1M | $2.00/1M | -| `timini` | Compound | Complex reasoning engine for long-context and tool use backed by Gemini-3 Flash | $2.00/1M | $12.00/1M | -| `tim-gpt` | Compound | Complex reasoning engine for long-context and tool use backed by OpenAI GPT-4.1 | $2.00/1M | $8.00/1M | -| `tim-gpt-heavy` | Compound | Complex reasoning engine for long-context and tool use backed by OpenAI GPT-5.2 | $2.00/1M | $15.00/1M | +We recommend [uv](https://docs.astral.sh/uv/) as your package manager. See `pyproject.toml` for Python version requirements and dependencies. -### Run Status +## Upgrading -| Status | Description | -| ----------- | ---------------------- | -| `queued` | Waiting to start | -| `running` | Currently executing | -| `succeeded` | Completed successfully | -| `failed` | Encountered an error | -| `canceled` | Manually canceled | -| `timed_out` | Exceeded time limit | - -## Requirements - -- Python ≥ 3.8 -- requests +If you're upgrading from 0.x, see the +[Migration Guide](https://github.com/subconscious-systems/subconscious-python/blob/main/.cursor/skills/sdk-migration/SKILL.md) +for breaking changes and code examples. ## Contributing Contributions are welcome! Please feel free to submit a pull request. -## License - -Apache-2.0 - ## Support For support and questions: -- Documentation: https://docs.subconscious.dev -- Email: {hongyin,jack}@subconscious.dev +- Documentation: [https://docs.subconscious.dev](https://docs.subconscious.dev) +- Email: {hongyin,jack,dana,wei}@subconscious.dev + diff --git a/pyproject.toml b/pyproject.toml index c42b17c..cd25581 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "subconscious-sdk" -version = "0.3.0" +version = "1.0.0" description = "The official Python SDK for the Subconscious API" readme = "README.md" authors = [ @@ -12,16 +12,15 @@ authors = [ {name = "Jack O'Brien", email = "jack@subconscious.dev"} ] license = { text = "Apache-2.0" } -requires-python = ">=3.8" +requires-python = ">=3.10" classifiers = [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", "Topic :: Software Development :: Libraries :: Python Modules", "Topic :: Scientific/Engineering :: Artificial Intelligence", ] @@ -29,14 +28,15 @@ keywords = ["ai", "agent", "api", "sdk", "subconscious"] dependencies = [ "requests>=2.25.0", + "pydantic>=2.0.0", ] [project.optional-dependencies] dev = [ "pytest>=7.0.0", "pytest-cov>=4.0.0", - "black>=22.0.0", - "isort>=5.0.0", + "ruff>=0.12.0", + "pre-commit>=3.0.0", "mypy>=1.0.0", "types-requests>=2.28.0", ] @@ -50,15 +50,7 @@ Issues = "https://github.com/subconscious-systems/subconscious-python/issues" [tool.setuptools.packages.find] include = ["subconscious*"] -[tool.black] -line-length = 88 -target-version = ['py38'] - -[tool.isort] -profile = "black" -line_length = 88 - [tool.mypy] -python_version = "3.8" +python_version = "3.10" warn_return_any = true warn_unused_configs = true diff --git a/ruff.toml b/ruff.toml new file mode 100644 index 0000000..271b147 --- /dev/null +++ b/ruff.toml @@ -0,0 +1,48 @@ +target-version = "py310" +line-length = 100 +indent-width = 4 +required-version = ">=0.12.0" +show-fixes = true + +include = [ + "**.py" +] +exclude = [ + "**/__pycache__", + "**/.coverage", + "**/.ipynb_checkpoints", + "**/.mypy_cache", + "**/.nox", + "**/.pytest_cache", + "**/.ruff_cache", + "**/.tox", + "**/.venv", + "**/build", + "**/dist", + "**/env", + "**/node_modules", + "**/site-packages", + "**/htmlcov", + ".git", +] +force-exclude = true + +[lint] +select = ["ASYNC", "B", "E", "F", "I", "SIM", "UP", "W"] +ignore = ["E501"] + +[lint.per-file-ignores] +"__init__.py" = ["E402"] +"**/tests/**" = ["E402", "S101"] +"**/test_*.py" = ["S101"] + +[lint.isort] +known-first-party = ["subconscious"] +no-sections = false + +[format] +quote-style = "single" +indent-style = "space" +line-ending = "auto" +skip-magic-trailing-comma = false +docstring-code-format = true diff --git a/subconscious/__init__.py b/subconscious/__init__.py index 74e105b..5ed0450 100644 --- a/subconscious/__init__.py +++ b/subconscious/__init__.py @@ -5,81 +5,122 @@ """ from .client import Subconscious +from .content import Image +from .errors import ( + AuthenticationError, + NotFoundError, + RateLimitError, + RequestTooLargeError, + SubconsciousError, + ValidationError, +) from .types import ( + AgentToolUse, + # Multimodal content + AudioContent, + ContentBlock, + # Wire-format request models + CreateRunBody, + DeltaEvent, + DoneEvent, + Engine, + ErrorEvent, + FileContent, + FunctionTool, + ImageContent, + McpAuth, + MCPTool, + # Structured output + OutputSchema, + PlatformTool, + PollOptions, + ReasoningTask, # Run types Run, - RunStatus, - RunResult, + RunError, RunInput, + RunInputWire, RunOptions, + RunOptionsWire, + RunOutput, + RunOutputWire, RunParams, - ReasoningNode, - Engine, - Usage, - ModelUsage, - PlatformToolUsage, - PollOptions, - # Tool types - Tool, - PlatformTool, - FunctionTool, - MCPTool, - McpAuth, + RunResult, + RunStatus, + # Source types (shared across all content blocks) + Source, + SourceBase64, + SourceBlobRef, + SourceUrl, # Stream events StreamEvent, - DeltaEvent, - DoneEvent, - ErrorEvent, - # Structured output - OutputSchema, + TextContent, + # Tool types + Tool, + # Tool response + ToolResponse, + Usage, pydantic_to_schema, ) -from .errors import ( - SubconsciousError, - AuthenticationError, - RateLimitError, - NotFoundError, - ValidationError, -) -__version__ = "0.3.0" -__author__ = "Subconscious Systems" -__email__ = "contact@subconscious.dev" +__version__ = '1.0.0' +__author__ = 'Subconscious Systems' +__email__ = 'contact@subconscious.dev' __all__ = [ # Client - "Subconscious", + 'Subconscious', + 'RequestTooLargeError', + # Wire-format request models + 'RunInputWire', + 'RunOptionsWire', + 'RunOutputWire', + 'CreateRunBody', + # Multimodal content + 'Image', + 'ContentBlock', + 'TextContent', + 'ImageContent', + 'AudioContent', + 'FileContent', + 'Source', + 'SourceBase64', + 'SourceBlobRef', + 'SourceUrl', + # Tool response + 'ToolResponse', # Run types - "Run", - "RunStatus", - "RunResult", - "RunInput", - "RunOptions", - "RunParams", - "ReasoningNode", - "Engine", - "Usage", - "ModelUsage", - "PlatformToolUsage", - "PollOptions", + 'Run', + 'RunStatus', + 'RunResult', + 'RunInput', + 'RunOptions', + 'RunOutput', + 'RunParams', + 'RunError', + 'ReasoningTask', + 'AgentToolUse', + 'Engine', + 'Usage', + 'PollOptions', # Tool types - "Tool", - "PlatformTool", - "FunctionTool", - "MCPTool", - "McpAuth", + 'Tool', + 'PlatformTool', + 'FunctionTool', + 'MCPTool', + 'McpAuth', # Stream events - "StreamEvent", - "DeltaEvent", - "DoneEvent", - "ErrorEvent", + 'StreamEvent', + 'DeltaEvent', + 'DoneEvent', + 'ErrorEvent', # Structured output - "OutputSchema", - "pydantic_to_schema", + 'OutputSchema', + 'pydantic_to_schema', # Errors - "SubconsciousError", - "AuthenticationError", - "RateLimitError", - "NotFoundError", - "ValidationError", + 'SubconsciousError', + 'AuthenticationError', + 'RateLimitError', + 'NotFoundError', + 'ValidationError', ] diff --git a/subconscious/client.py b/subconscious/client.py index 01f547b..f17e39c 100644 --- a/subconscious/client.py +++ b/subconscious/client.py @@ -1,15 +1,18 @@ """Subconscious API client.""" +import contextlib import json import os import time +from collections.abc import Generator from pathlib import Path -from typing import Any, Dict, Generator, List, Optional, Union +from typing import Any import requests from .errors import raise_for_status from .types import ( + CreateRunBody, DeltaEvent, DoneEvent, Engine, @@ -18,16 +21,12 @@ Run, RunInput, RunOptions, - RunParams, - RunResult, RunStatus, StreamEvent, - Tool, - Usage, ) -def _resolve_api_key(explicit: Optional[str]) -> str: +def _resolve_api_key(explicit: str | None) -> str: """ Resolve the API key using a standard precedence chain: 1. Explicitly passed ``api_key`` argument @@ -37,80 +36,27 @@ def _resolve_api_key(explicit: Optional[str]) -> str: if explicit: return explicit - env_key = os.environ.get("SUBCONSCIOUS_API_KEY") + env_key = os.environ.get('SUBCONSCIOUS_API_KEY') if env_key: return env_key try: - config_path = Path.home() / ".subcon" / "config.json" + config_path = Path.home() / '.subcon' / 'config.json' config = json.loads(config_path.read_text()) - if config.get("subconscious_api_key"): - return config["subconscious_api_key"] + if config.get('subconscious_api_key'): + return config['subconscious_api_key'] except (OSError, json.JSONDecodeError, KeyError): pass raise ValueError( - "No API key found. Either:\n" - " • Pass api_key to the Subconscious constructor\n" - " • Set SUBCONSCIOUS_API_KEY environment variable\n" - " • Run `npx subconscious login` to authenticate" + 'No API key found. Either:\n' + ' • Pass api_key to the Subconscious constructor\n' + ' • Set SUBCONSCIOUS_API_KEY environment variable\n' + ' • Run `npx subconscious login` to authenticate' ) -def _resolve_schema(schema: Any) -> Optional[Dict[str, Any]]: - """ - Resolve a schema to a JSON Schema dict. - - Accepts: - - A Pydantic BaseModel class (calls model_json_schema() automatically) - - A dict (passed through as-is) - - None (returns None) - """ - if schema is None: - return None - - # Check if it's a Pydantic model class - if isinstance(schema, type) and hasattr(schema, "model_json_schema"): - return schema.model_json_schema() - - # Already a dict - if isinstance(schema, dict): - return schema - - # Unknown type - try to use it as-is - return schema - -# Python snake_case → API camelCase key mapping for tool serialization -_TOOL_KEY_MAP = { - "allowed_tools": "allowedTools", -} - - -def _normalize_tool(tool: Any) -> Dict[str, Any]: - """Convert a tool dataclass to an API-compatible dict. - - Strips None values and maps snake_case keys to camelCase. - """ - if not hasattr(tool, "__dict__"): - return tool - - result = {} - for k, v in tool.__dict__.items(): - if v is None: - continue - # Recursively normalize nested dataclasses (e.g. McpAuth) - if hasattr(v, "__dict__"): - v = { - _TOOL_KEY_MAP.get(nk, nk): nv - for nk, nv in v.__dict__.items() - if nv is not None - } - key = _TOOL_KEY_MAP.get(k, k) - result[key] = v - return result - - -TERMINAL_STATUSES: List[RunStatus] = ["succeeded", "failed", "canceled", "timed_out"] +TERMINAL_STATUSES: list[RunStatus] = ['succeeded', 'failed', 'canceled', 'timed_out'] class Subconscious: @@ -128,12 +74,12 @@ class Subconscious: client = Subconscious() run = client.run( - engine="tim-gpt", + engine='tim-gpt', input={ - "instructions": "Search for the latest news about AI", - "tools": [{"type": "platform", "id": "fast_search"}], + 'instructions': 'Search for the latest news about AI', + 'tools': [{'type': 'platform', 'id': 'fast_search'}], }, - options={"await_completion": True}, + options={'await_completion': True}, ) print(run.result.answer) @@ -142,8 +88,8 @@ class Subconscious: def __init__( self, - api_key: Optional[str] = None, - base_url: str = "https://api.subconscious.dev/v1", + api_key: str | None = None, + base_url: str = 'https://api.subconscious.dev/v1', ): """ Initialize the Subconscious client. @@ -154,23 +100,23 @@ def __init__( base_url: API base URL (default: https://api.subconscious.dev/v1) """ self._api_key = _resolve_api_key(api_key) - self._base_url = base_url.rstrip("/") + self._base_url = base_url.rstrip('/') - def _headers(self) -> Dict[str, str]: + def _headers(self) -> dict[str, str]: """Get default request headers.""" return { - "Authorization": f"Bearer {self._api_key}", - "Content-Type": "application/json", + 'Authorization': f'Bearer {self._api_key}', + 'Content-Type': 'application/json', } def _request( self, method: str, path: str, - json_data: Optional[Dict[str, Any]] = None, - ) -> Dict[str, Any]: + json_data: dict[str, Any] | None = None, + ) -> dict[str, Any]: """Make an HTTP request to the API.""" - url = f"{self._base_url}{path}" + url = f'{self._base_url}{path}' response = requests.request( method=method, url=url, @@ -183,8 +129,8 @@ def _request( def run( self, engine: Engine, - input: Union[RunInput, Dict[str, Any]], - options: Optional[Union[RunOptions, Dict[str, Any]]] = None, + input: RunInput | dict[str, Any], + options: RunOptions | dict[str, Any] | None = None, ) -> Run: """ Create a new run. @@ -200,51 +146,28 @@ def run( Example: ```python from pydantic import BaseModel - + + class Result(BaseModel): answer: str confidence: float - + + run = client.run( - engine="tim-gpt", + engine='tim-gpt', input={ - "instructions": "Search for AI news", - "tools": [{"type": "platform", "id": "fast_search"}], - "answerFormat": Result, # Pass the Pydantic class directly + 'instructions': 'Search for AI news', + 'tools': [{'type': 'platform', 'id': 'fast_search'}], + 'answerFormat': Result, # Pass the Pydantic class directly }, - options={"await_completion": True}, + options={'await_completion': True}, ) ``` """ - # Normalize input - if isinstance(input, RunInput): - input_dict: Dict[str, Any] = { - "instructions": input.instructions, - "tools": input.tools, - } - if input.answer_format is not None: - input_dict["answerFormat"] = _resolve_schema(input.answer_format) - if input.reasoning_format is not None: - input_dict["reasoningFormat"] = _resolve_schema(input.reasoning_format) - else: - input_dict = dict(input) # Make a copy to avoid mutating - # Resolve Pydantic models to JSON Schema - if "answerFormat" in input_dict: - input_dict["answerFormat"] = _resolve_schema(input_dict["answerFormat"]) - if "reasoningFormat" in input_dict: - input_dict["reasoningFormat"] = _resolve_schema(input_dict["reasoningFormat"]) - - # Normalize tools to dicts - input_dict["tools"] = [_normalize_tool(t) for t in input_dict.get("tools", [])] - - # Make request - data = self._request( - "POST", - "/runs", - {"engine": engine, "input": input_dict}, - ) + payload = CreateRunBody.build(engine, input, options).to_dict() + data = self._request('POST', '/runs', payload) - run_id = data["runId"] + run_id = data['runId'] # Check if we should wait for completion await_completion = False @@ -252,7 +175,7 @@ class Result(BaseModel): if isinstance(options, RunOptions): await_completion = options.await_completion elif isinstance(options, dict): - await_completion = options.get("await_completion", False) + await_completion = options.get('await_completion', False) if not await_completion: return Run(run_id=run_id) @@ -269,13 +192,13 @@ def get(self, run_id: str) -> Run: Returns: The run with its current status and result (if completed) """ - data = self._request("GET", f"/runs/{run_id}") + data = self._request('GET', f'/runs/{run_id}') return self._parse_run(data) def wait( self, run_id: str, - options: Optional[Union[PollOptions, Dict[str, Any]]] = None, + options: PollOptions | dict[str, Any] | None = None, ) -> Run: """ Wait for a run to complete by polling. @@ -294,7 +217,7 @@ def wait( ```python run = client.wait( run_id, - options={"interval_ms": 2000, "max_attempts": 60}, + options={'interval_ms': 2000, 'max_attempts': 60}, ) ``` """ @@ -306,8 +229,8 @@ def wait( interval_ms = options.interval_ms max_attempts = options.max_attempts elif isinstance(options, dict): - interval_ms = options.get("interval_ms", 1000) - max_attempts = options.get("max_attempts") + interval_ms = options.get('interval_ms', 1000) + max_attempts = options.get('max_attempts') attempts = 0 while True: @@ -318,7 +241,7 @@ def wait( attempts += 1 if max_attempts is not None and attempts >= max_attempts: - raise TimeoutError(f"Polling exceeded max attempts ({max_attempts})") + raise TimeoutError(f'Polling exceeded max attempts ({max_attempts})') time.sleep(interval_ms / 1000) @@ -332,14 +255,14 @@ def cancel(self, run_id: str) -> Run: Returns: The canceled run """ - data = self._request("POST", f"/runs/{run_id}/cancel") + data = self._request('POST', f'/runs/{run_id}/cancel') return self._parse_run(data) def stream( self, engine: Engine, - input: Union[RunInput, Dict[str, Any]], - ) -> Generator[StreamEvent, None, Optional[Run]]: + input: RunInput | dict[str, Any], + ) -> Generator[StreamEvent, None, Run | None]: """ Create a streaming run that yields text deltas as they arrive. @@ -356,64 +279,46 @@ def stream( Example: ```python from pydantic import BaseModel - + + class Result(BaseModel): summary: str - + + for event in client.stream( - engine="tim-gpt", + engine='tim-gpt', input={ - "instructions": "Write an essay", - "tools": [], - "answerFormat": Result, # Pass the Pydantic class directly + 'instructions': 'Write an essay', + 'tools': [], + 'answerFormat': Result, # Pass the Pydantic class directly }, ): - if event.type == "delta": - print(event.content, end="", flush=True) - elif event.type == "done": - print("\\nDone!") + if event.type == 'delta': + print(event.content, end='', flush=True) + elif event.type == 'done': + print('\\nDone!') ``` Note: Rich streaming events (reasoning steps, tool calls) are coming soon. Currently provides text deltas only. """ - # Normalize input - if isinstance(input, RunInput): - input_dict: Dict[str, Any] = { - "instructions": input.instructions, - "tools": input.tools, - } - if input.answer_format is not None: - input_dict["answerFormat"] = _resolve_schema(input.answer_format) - if input.reasoning_format is not None: - input_dict["reasoningFormat"] = _resolve_schema(input.reasoning_format) - else: - input_dict = dict(input) # Make a copy to avoid mutating - # Resolve Pydantic models to JSON Schema - if "answerFormat" in input_dict: - input_dict["answerFormat"] = _resolve_schema(input_dict["answerFormat"]) - if "reasoningFormat" in input_dict: - input_dict["reasoningFormat"] = _resolve_schema(input_dict["reasoningFormat"]) - - # Normalize tools to dicts - input_dict["tools"] = [_normalize_tool(t) for t in input_dict.get("tools", [])] - - url = f"{self._base_url}/runs/stream" + payload = CreateRunBody.build(engine, input).to_dict() + url = f'{self._base_url}/runs/stream' headers = { **self._headers(), - "Accept": "text/event-stream", + 'Accept': 'text/event-stream', } response = requests.post( url, headers=headers, - json={"engine": engine, "input": input_dict}, + json=payload, stream=True, ) raise_for_status(response) - run_id = response.headers.get("x-run-id", "") + run_id = response.headers.get('x-run-id', '') is_error = False for line in response.iter_lines(decode_unicode=True): @@ -423,74 +328,58 @@ class Result(BaseModel): line = line.strip() # Skip heartbeat comments - if line.startswith(":"): + if line.startswith(':'): continue # Handle event type markers - if line.startswith("event:"): + if line.startswith('event:'): event_type = line[6:].strip() - is_error = event_type == "error" + is_error = event_type == 'error' continue # Handle data lines - if line.startswith("data:"): + if line.startswith('data:'): data_content = line[5:].strip() # Stream end - if data_content == "[DONE]": - yield DoneEvent(type="done", run_id=run_id) + if data_content == '[DONE]': + yield DoneEvent(type='done', run_id=run_id) continue try: payload = json.loads(data_content) # Meta event with run_id - if "run_id" in payload: - run_id = payload["run_id"] + if 'run_id' in payload: + run_id = payload['run_id'] continue # Error event - if is_error or "error" in payload: + if is_error or 'error' in payload: yield ErrorEvent( - type="error", + type='error', run_id=run_id, - message=payload.get("details") or payload.get("error", "Unknown error"), - code=payload.get("code"), + message=payload.get('details') or payload.get('error', 'Unknown error'), + code=payload.get('code'), ) is_error = False continue # OpenAI-compatible chunk with text delta - choices = payload.get("choices", []) + choices = payload.get('choices', []) if choices: - content = choices[0].get("delta", {}).get("content") + content = choices[0].get('delta', {}).get('content') if content: - yield DeltaEvent(type="delta", run_id=run_id, content=content) + yield DeltaEvent(type='delta', run_id=run_id, content=content) except json.JSONDecodeError: pass - return Run(run_id=run_id, status="succeeded") if run_id else None + return Run(run_id=run_id, status='succeeded') if run_id else None - def _parse_run(self, data: Dict[str, Any]) -> Run: - """Parse a run response from the API.""" - result = None - if "result" in data and data["result"]: - result = RunResult( - answer=data["result"].get("answer", ""), - reasoning=data["result"].get("reasoning"), - ) - - usage = None - if "usage" in data and data["usage"]: - usage = Usage( - models=data["usage"].get("models", []), - platform_tools=data["usage"].get("platformTools", []), - ) - - return Run( - run_id=data.get("runId", data.get("run_id", "")), - status=data.get("status"), - result=result, - usage=usage, - ) + def _parse_run(self, data: dict[str, Any]) -> Run: + run = Run.model_validate(data) + if run.result is not None and run.result.answer: + with contextlib.suppress(json.JSONDecodeError, ValueError): + run.result.parsed_answer = json.loads(run.result.answer) + return run diff --git a/subconscious/content.py b/subconscious/content.py new file mode 100644 index 0000000..f3f3078 --- /dev/null +++ b/subconscious/content.py @@ -0,0 +1,107 @@ +"""Multimodal content helpers — `Image` constructor + content type re-exports. + +Lets users build canonical ImageContent blocks from a path, raw bytes, a +remote URL, or a server-side blob_ref:: + + from subconscious import Client, Image, TextContent + + client.runs.create( + engine='tim-claude', + input={ + 'instructions': 'What is in this image?', + 'content': [Image.from_path('shot.png')], + }, + ) +""" + +from __future__ import annotations + +import base64 +import urllib.request +from pathlib import Path + +from .types import ( + ImageContent, + SourceBase64, + SourceBlobRef, + SourceUrl, +) + +# Mirror packages/common/schemas/content-block.ts MIME_ALLOWED. +_MIME_ALLOWED = frozenset({'image/png', 'image/jpeg', 'image/gif', 'image/webp'}) + + +def _detect_mime(data: bytes) -> str: + """Magic-byte mime detection — mirrors apps/api/src/core/blob-store/image-utils.ts.""" + if data[:8] == b'\x89PNG\r\n\x1a\n': + return 'image/png' + if data[:3] == b'\xff\xd8\xff': + return 'image/jpeg' + if data[:6] in (b'GIF87a', b'GIF89a'): + return 'image/gif' + if len(data) >= 12 and data[:4] == b'RIFF' and data[8:12] == b'WEBP': + return 'image/webp' + raise ValueError('unsupported image type — only PNG, JPEG, GIF, and WebP are accepted') + + +class Image: + """Static factory for ImageContent blocks. Use the constructor that matches + the bytes you have in hand.""" + + @staticmethod + def from_path(path: str | Path) -> ImageContent: + """Read an image file from disk and emit an ImageContent(base64) block.""" + data = Path(path).read_bytes() + mime = _detect_mime(data) + return ImageContent( + type='image', + source=SourceBase64( + kind='base64', + data=base64.b64encode(data).decode('ascii'), + mime=mime, + ), + ) + + @staticmethod + def from_bytes(data: bytes, mime: str | None = None) -> ImageContent: + """Wrap raw bytes as an ImageContent(base64) block. Mime is detected if not provided.""" + resolved_mime = mime or _detect_mime(data) + if resolved_mime not in _MIME_ALLOWED: + raise ValueError(f'mime {resolved_mime} not allowed') + return ImageContent( + type='image', + source=SourceBase64( + kind='base64', + data=base64.b64encode(data).decode('ascii'), + mime=resolved_mime, + ), + ) + + @staticmethod + def from_url(url: str, *, fetch: bool = False) -> ImageContent: + """Reference a remote image by URL. + + ``fetch=False`` (default) sends the URL through to the server, which + forwards it to the vendor when supported. ``fetch=True`` downloads the + bytes client-side and embeds them as base64 — useful when the vendor + doesn't fetch URLs (e.g. some Claude paths). + """ + if fetch: + with urllib.request.urlopen(url) as resp: # noqa: S310 — caller-controlled URL + return Image.from_bytes(resp.read()) + return ImageContent(type='image', source=SourceUrl(kind='url', url=url)) + + @staticmethod + def from_blob_ref(blob_key: str, mime: str) -> ImageContent: + """Reference an asset already stored server-side. Skip an upload roundtrip.""" + return ImageContent( + type='image', + source=SourceBlobRef( + kind='blob_ref', + blob_key=blob_key, + mime=mime, + ), + ) + + +__all__ = ['Image'] diff --git a/subconscious/errors.py b/subconscious/errors.py index 9decac9..0ec1350 100644 --- a/subconscious/errors.py +++ b/subconscious/errors.py @@ -1,19 +1,23 @@ """Custom exceptions for the Subconscious SDK.""" -from typing import Any, Dict, Literal, Optional +from typing import Any, Literal ErrorCode = Literal[ - "invalid_request", - "authentication_failed", - "permission_denied", - "not_found", - "rate_limited", - "internal_error", - "service_unavailable", - "timeout", + 'invalid_request', + 'authentication_failed', + 'permission_denied', + 'not_found', + 'rate_limited', + 'internal_error', + 'service_unavailable', + 'timeout', ] +class RequestTooLargeError(ValueError): + """Raised when the serialized run request exceeds the API size limit.""" + + class SubconsciousError(Exception): """Base exception for Subconscious API errors.""" @@ -22,7 +26,7 @@ def __init__( code: ErrorCode, message: str, status: int, - details: Optional[Dict[str, Any]] = None, + details: dict[str, Any] | None = None, ): super().__init__(message) self.code = code @@ -30,73 +34,108 @@ def __init__( self.details = details def __str__(self) -> str: - return f"{self.code}: {self.args[0]}" + return f'{self.code}: {self.args[0]}' class AuthenticationError(SubconsciousError): """Raised when API key is invalid or missing.""" - def __init__(self, message: str = "Invalid API key"): - super().__init__("authentication_failed", message, 401) + def __init__(self, message: str = 'Invalid API key'): + super().__init__('authentication_failed', message, 401) class RateLimitError(SubconsciousError): """Raised when rate limit is exceeded.""" - def __init__(self, message: str = "Rate limit exceeded"): - super().__init__("rate_limited", message, 429) + def __init__(self, message: str = 'Rate limit exceeded'): + super().__init__('rate_limited', message, 429) class NotFoundError(SubconsciousError): """Raised when a resource is not found.""" - def __init__(self, message: str = "Resource not found"): - super().__init__("not_found", message, 404) + def __init__(self, message: str = 'Resource not found'): + super().__init__('not_found', message, 404) class ValidationError(SubconsciousError): """Raised when request validation fails.""" - def __init__(self, message: str, details: Optional[Dict[str, Any]] = None): - super().__init__("invalid_request", message, 400, details) + def __init__(self, message: str, details: dict[str, Any] | None = None): + super().__init__('invalid_request', message, 400, details) def _status_to_code(status: int) -> ErrorCode: """Map HTTP status codes to error codes.""" - mapping: Dict[int, ErrorCode] = { - 400: "invalid_request", - 401: "authentication_failed", - 403: "permission_denied", - 404: "not_found", - 429: "rate_limited", - 503: "service_unavailable", - 504: "timeout", + mapping: dict[int, ErrorCode] = { + 400: 'invalid_request', + 401: 'authentication_failed', + 403: 'permission_denied', + 404: 'not_found', + 429: 'rate_limited', + 503: 'service_unavailable', + 504: 'timeout', } - return mapping.get(status, "internal_error") + return mapping.get(status, 'internal_error') def raise_for_status(response) -> None: - """Raise appropriate exception based on response status.""" - if response.status_code >= 400: - try: - body = response.json() - error = body.get("error", {}) - code = error.get("code", _status_to_code(response.status_code)) - message = error.get("message", response.text or f"HTTP {response.status_code}") - details = error.get("details") - except Exception: - code = _status_to_code(response.status_code) - message = response.text or f"HTTP {response.status_code}" - details = None - - if code == "authentication_failed": - raise AuthenticationError(message) - elif code == "rate_limited": - raise RateLimitError(message) - elif code == "not_found": - raise NotFoundError(message) - elif code == "invalid_request": - raise ValidationError(message, details) - else: - raise SubconsciousError(code, message, response.status_code, details) - + """Raise appropriate exception based on response status. + + Extracts error info from three wire shapes the server may return: + + - Canonical SDK shape: ``{"error": {"code", "message", "details"}}`` + - Express default: ``{"error": "Internal server error"}`` + - Plain text/unknown: falls back to ``response.text`` + + Enriches the message with method + URL so users get enough context + to correlate with server logs (especially for 5xx responses where + the body is generic). + """ + if response.status_code < 400: + return + + try: + body = response.json() + except Exception: + body = None + + error_field = body.get('error') if isinstance(body, dict) else None + if isinstance(error_field, dict): + code = error_field.get('code', _status_to_code(response.status_code)) + message = error_field.get('message') or str(error_field) + details = error_field.get('details') + elif isinstance(error_field, str): + code = _status_to_code(response.status_code) + message = error_field + details = None + else: + code = _status_to_code(response.status_code) + message = response.text or f'HTTP {response.status_code}' + details = None + + # Context suffix: "[GET /v1/runs/]" and a request id when the + # server surfaces one. Skipped quietly if the request object isn't + # reachable (e.g., requests.Response without a bound PreparedRequest). + request = getattr(response, 'request', None) + method = getattr(request, 'method', None) + url = getattr(request, 'url', None) + request_id = response.headers.get('x-request-id') if response.headers else None + suffix_parts = [] + if method and url: + suffix_parts.append(f'{method} {url}') + if request_id: + suffix_parts.append(f'request_id={request_id}') + if suffix_parts: + message = f'{message} [{" ".join(suffix_parts)}]' + + if code == 'authentication_failed': + raise AuthenticationError(message) + elif code == 'rate_limited': + raise RateLimitError(message) + elif code == 'not_found': + raise NotFoundError(message) + elif code == 'invalid_request': + raise ValidationError(message, details) + else: + raise SubconsciousError(code, message, response.status_code, details) diff --git a/subconscious/types.py b/subconscious/types.py index 99ee906..1be12d8 100644 --- a/subconscious/types.py +++ b/subconscious/types.py @@ -1,117 +1,155 @@ """Type definitions for the Subconscious SDK.""" -from typing import Any, Dict, List, Literal, Optional, Union +import json from dataclasses import dataclass, field +from typing import Annotated, Any, ClassVar, Literal -# Engine types -Engine = Literal["tim-edge", "tim-gpt", "tim-gpt-heavy"] +from pydantic import BaseModel, ConfigDict, Discriminator, Field + +from .errors import RequestTooLargeError + +Engine = str # JSON Schema types for structured output -class OutputSchema(Dict[str, Any]): +class OutputSchema(dict[str, Any]): """ JSON Schema for structured output format. - + Expected structure: - $defs: Optional definitions for complex types - properties: Dict of property names to their JSON Schema definitions - required: List of required property names - title: Title for the schema - type: "object" - + Use pydantic_to_schema() to convert a Pydantic model to this format. """ + pass -def pydantic_to_schema(model: type, title: Optional[str] = None) -> OutputSchema: +def pydantic_to_schema(model: type, title: str | None = None) -> OutputSchema: """ Convert a Pydantic model to the JSON Schema format expected by Subconscious. - + Note: You typically don't need to call this directly. The SDK automatically - converts Pydantic models passed to answerFormat/reasoningFormat. - + converts Pydantic models passed to answerFormat. + Args: model: A Pydantic BaseModel class title: Optional title override (defaults to model class name) - + Returns: - An OutputSchema compatible with answerFormat/reasoningFormat + An OutputSchema compatible with answerFormat """ - # Get the JSON Schema from Pydantic schema = model.model_json_schema() - - # Create the output schema - result = OutputSchema({ - "type": "object", - "title": title or schema.get("title", model.__name__), - "properties": schema.get("properties", {}), - "required": schema.get("required", list(schema.get("properties", {}).keys())), - }) - - # Include $defs if present (for complex nested types) - if "$defs" in schema: - result["$defs"] = schema["$defs"] - + + result = OutputSchema( + { + 'type': 'object', + 'title': title or schema.get('title', model.__name__), + 'properties': schema.get('properties', {}), + 'required': schema.get('required', list(schema.get('properties', {}).keys())), + } + ) + + if '$defs' in schema: + result['$defs'] = schema['$defs'] + return result + # Run status types -RunStatus = Literal["queued", "running", "succeeded", "failed", "canceled", "timed_out"] +RunStatus = Literal['queued', 'running', 'succeeded', 'failed', 'canceled', 'timed_out'] -@dataclass -class ReasoningNode: - """A node in the reasoning tree.""" +# --------------------------------------------------------------------------- +# Response types — Pydantic models mirroring the API wire format 1:1. +# Field aliases match the camelCase keys returned by GET /v1/runs/:runId. +# populate_by_name=True allows both snake_case and camelCase construction. +# --------------------------------------------------------------------------- - title: str - thought: str - tooluse: List[Any] = field(default_factory=list) - subtask: List["ReasoningNode"] = field(default_factory=list) - conclusion: str = "" +class AgentToolUse(BaseModel): + """A tool call within a reasoning step. -@dataclass -class RunResult: - """The result of a completed run.""" + Maps to ``AgentToolUse`` in the monorepo (schemas/index.ts). + """ - answer: str - reasoning: Optional[ReasoningNode] = None + model_config = ConfigDict(populate_by_name=True) + tool_name: str + tool_call_id: str | None = None + parameters: dict[str, Any] = Field(default_factory=dict) + tool_result: Any | None = None -@dataclass -class ModelUsage: - """Token usage for a specific model.""" - engine: str - input_tokens: int - output_tokens: int - total_tokens: int +class ReasoningTask(BaseModel): + """A node in the reasoning tree. Recursive via ``subtasks``. + Maps to ``ReasoningTask`` in the monorepo (schemas/index.ts). + All fields are optional because the tree is built incrementally + during streaming. + """ -@dataclass -class PlatformToolUsage: - """Usage statistics for a platform tool.""" + model_config = ConfigDict(populate_by_name=True) - tool_id: str - calls: int + title: str | None = None + thought: str | None = None + tooluse: AgentToolUse | None = None + subtasks: list['ReasoningTask'] | None = None + conclusion: str | None = None -@dataclass -class Usage: - """Usage statistics for a run.""" +class RunResult(BaseModel): + """The result of a completed run. - models: List[ModelUsage] = field(default_factory=list) - platform_tools: List[PlatformToolUsage] = field(default_factory=list) + Maps to ``ReasoningOutput`` in the monorepo (schemas/index.ts). + """ + answer: str = '' + reasoning: list[ReasoningTask] | None = None + parsed_answer: Any = None + """Best-effort ``json.loads`` of ``answer``, populated by the SDK after + receiving the response. Never sent on the wire. ``None`` when ``answer`` + is empty or not valid JSON.""" -@dataclass -class Run: - """Represents an agent run.""" - run_id: str - status: Optional[RunStatus] = None - result: Optional[RunResult] = None - usage: Optional[Usage] = None +class Usage(BaseModel): + """Token usage for a run. + + Maps to ``RunUsage`` in the monorepo (schemas/index.ts). + Flat structure matching the API wire format exactly. + """ + + model_config = ConfigDict(populate_by_name=True) + + input_tokens: int = Field(default=0, alias='inputTokens') + output_tokens: int = Field(default=0, alias='outputTokens') + duration_ms: int | None = Field(default=None, alias='durationMs') + + +class RunError(BaseModel): + """Error details for a failed run. + + Maps to ``RunError`` in the monorepo (schemas/index.ts). + """ + + code: str = '' + message: str = '' + + +class Run(BaseModel): + """Represents an agent run — mirrors GET /v1/runs/:runId response.""" + + model_config = ConfigDict(populate_by_name=True) + + run_id: str = Field(default='', alias='runId') + status: RunStatus | None = None + result: RunResult | None = None + usage: Usage | None = None + error: RunError | None = None # Tool types @@ -120,14 +158,14 @@ class PlatformTool: """A platform-hosted tool.""" id: str - type: Literal["platform"] = "platform" - options: Dict[str, Any] = field(default_factory=dict) + type: Literal['platform'] = 'platform' + options: dict[str, Any] = field(default_factory=dict) @dataclass class FunctionTool: """A custom function tool. - + Attributes: name: The tool name the model will use to call it description: Description of what the tool does @@ -140,15 +178,15 @@ class FunctionTool: """ name: str - type: Literal["function"] = "function" - description: Optional[str] = None - parameters: Dict[str, Any] = field(default_factory=dict) - url: Optional[str] = None - method: Optional[str] = None - timeout: Optional[int] = None - headers: Optional[Dict[str, str]] = None + type: Literal['function'] = 'function' + description: str | None = None + parameters: dict[str, Any] = field(default_factory=dict) + url: str | None = None + method: str | None = None + timeout: int | None = None + headers: dict[str, str] | None = None """HTTP headers sent when calling this tool's endpoint.""" - defaults: Optional[Dict[str, Any]] = None + defaults: dict[str, Any] | None = None """Parameter values hidden from model and injected at call time.""" @@ -173,9 +211,9 @@ class McpAuth: in (e.g. ``"X-Api-Key"``). """ - type: Literal["bearer", "api_key"] + type: Literal['bearer', 'api_key'] token: str - header: Optional[str] = None + header: str | None = None @dataclass @@ -190,13 +228,108 @@ class MCPTool: """ url: str - type: Literal["mcp"] = "mcp" - allowed_tools: Optional[List[str]] = None - auth: Optional[McpAuth] = None + type: Literal['mcp'] = 'mcp' + allowed_tools: list[str] | None = None + auth: McpAuth | None = None # Tool union type -Tool = Union[PlatformTool, FunctionTool, MCPTool, Dict[str, Any]] +Tool = PlatformTool | FunctionTool | MCPTool | dict[str, Any] + + +# Multimodal content — mirrors timlarge/src/timlarge/types.py canonical types. +# A single Source union is shared across all content blocks (text, image, audio, file). + + +class TextContent(BaseModel): + type: Literal['text'] + text: str + + +class SourceBase64(BaseModel): + kind: Literal['base64'] + data: str + mime: str + + +class SourceBlobRef(BaseModel): + kind: Literal['blob_ref'] + blob_key: str + mime: str + size_bytes: int | None = None + attachment_id: str | None = None + presigned_url: str | None = None + presigned_expires_at: str | None = None + + +class SourceUrl(BaseModel): + kind: Literal['url'] + url: str + mime: str | None = None + + +Source = Annotated[SourceBase64 | SourceBlobRef | SourceUrl, Discriminator('kind')] + + +class ImageContent(BaseModel): + type: Literal['image'] + source: Source + + +class AudioContent(BaseModel): + type: Literal['audio'] + source: Source + + +class FileContent(BaseModel): + type: Literal['file'] + source: Source + filename: str | None = None + mime: str | None = None + + +ContentBlock = Annotated[ + TextContent | ImageContent | AudioContent | FileContent, + Discriminator('type'), +] + + +# Flexible input accepted by ToolResponse.build(). +_ToolResponseItem = str | TextContent | ImageContent | AudioContent | FileContent +ToolResponseContent = _ToolResponseItem | list[_ToolResponseItem] + + +class ToolResponse(BaseModel): + """Canonical envelope returned by a FunctionTool HTTP endpoint. + + Reuses the ContentBlock union (text, image, audio, file) from run input. + Use ``ToolResponse.build(tool_call_id, content)`` to wrap plain strings, + a single content block, or a mixed list; the strict constructor + ``ToolResponse(tool_call_id=..., content=[...])`` remains available for + callers holding already-normalized blocks. + """ + + tool_call_id: str | None = None + content: list[ContentBlock] + is_error: bool = False + + @classmethod + def build( + cls, + tool_call_id: str | None, + content: ToolResponseContent, + *, + is_error: bool = False, + ) -> 'ToolResponse': + """Build a tool response from text, an image, audio, a file, or a mixed list.""" + + def _wrap(item: _ToolResponseItem) -> ContentBlock: + if isinstance(item, str): + return TextContent(type='text', text=item) + return item + + blocks = [_wrap(i) for i in content] if isinstance(content, list) else [_wrap(content)] + return cls(tool_call_id=tool_call_id, content=blocks, is_error=is_error) @dataclass @@ -204,18 +337,319 @@ class RunInput: """Input configuration for a run.""" instructions: str - tools: List[Tool] = field(default_factory=list) - answer_format: Optional[OutputSchema] = None + tools: list[Tool] = field(default_factory=list) + resources: list[str] | None = None + skills: list[str] | None = None + """Names of skills to inject into the system prompt for this run. Skills are + reusable prompt fragments resolved by name at request time (platform/public + skills are global; org skills are org-scoped). Unknown names fail the request.""" + answer_format: OutputSchema | None = None """JSON Schema for the answer output format. Use pydantic_to_schema() to generate from Pydantic.""" - reasoning_format: Optional[OutputSchema] = None - """JSON Schema for the reasoning output format. Use pydantic_to_schema() to generate from Pydantic.""" + content: list[ContentBlock] | None = None + """Canonical multimodal content blocks (TextContent or ImageContent). Use the + ``Image`` helper to build ImageContent blocks from a path/bytes/URL/blob_key.""" + + +# --------------------------------------------------------------------------- +# Request wire-format models — Pydantic models for the POST /v1/runs body. +# Mirrors the exact camelCase shape the API expects so the SDK validates +# payload structure at construction time. +# --------------------------------------------------------------------------- + + +class RunInputWire(BaseModel): + """Wire-format model for the ``input`` field of POST /v1/runs. + + Mirrors the exact camelCase shape the API expects, so the SDK can + validate the payload structure at construction time rather than + discovering mismatches at the network boundary. + + Construct via ``RunInputWire.from_run_input()`` which handles + schema resolution, tool normalization, and content serialization. + """ + + model_config = ConfigDict(populate_by_name=True) + + _TOOL_KEY_MAP: ClassVar[dict[str, str]] = { + 'allowed_tools': 'allowedTools', + } + + instructions: str + tools: list[dict[str, Any]] = Field(default_factory=list) + content: list[dict[str, Any]] | None = None + resources: list[str] | None = None + skills: list[str] | None = Field( + default=None, + description=( + 'Names of skills to inject into the system prompt for this run. ' + 'Skills are reusable prompt fragments resolved by name at request time ' + '(platform/public skills are global; org skills are org-scoped). ' + 'Unknown names fail the request.' + ), + ) + answer_format: dict[str, Any] | None = Field(default=None, alias='answerFormat') + + @classmethod + def from_run_input(cls, input: RunInput | dict[str, Any]) -> 'RunInputWire': + """Build from a user-facing ``RunInput`` or raw dict. + + Resolves Pydantic-class schemas, normalizes tool dataclasses to + dicts with camelCase keys, and serializes content blocks. + """ + if isinstance(input, RunInput): + return cls( + instructions=input.instructions, + tools=[cls._normalize_tool(t) for t in input.tools], + resources=input.resources or None, + skills=input.skills or None, + content=( + [cls._normalize_content_block(b) for b in input.content] + if input.content + else None + ), + answer_format=cls._resolve_schema(input.answer_format), + ) + + raw = dict(input) + return cls( + instructions=raw['instructions'], + tools=[cls._normalize_tool(t) for t in raw.get('tools', [])], + content=( + [cls._normalize_content_block(b) for b in raw['content']] + if raw.get('content') + else None + ), + resources=raw.get('resources') or None, + skills=raw.get('skills') or None, + answer_format=cls._resolve_schema(raw.get('answerFormat')), + ) + + @staticmethod + def _resolve_schema(schema: Any) -> dict[str, Any] | None: + """Resolve a schema to a JSON Schema dict. + + Accepts a Pydantic BaseModel class (calls ``model_json_schema()``), + a dict (passed through), or None. + """ + if schema is None: + return None + if isinstance(schema, type) and hasattr(schema, 'model_json_schema'): + return schema.model_json_schema() + if isinstance(schema, dict): + return schema + return schema + + @classmethod + def _normalize_tool(cls, tool: Any) -> dict[str, Any]: + """Convert a tool dataclass to an API-compatible dict. + + Strips None values and maps snake_case keys to camelCase. + """ + if not hasattr(tool, '__dict__'): + return tool + + result = {} + for k, v in tool.__dict__.items(): + if v is None: + continue + if hasattr(v, '__dict__'): + v = { + cls._TOOL_KEY_MAP.get(nk, nk): nv + for nk, nv in v.__dict__.items() + if nv is not None + } + key = cls._TOOL_KEY_MAP.get(k, k) + result[key] = v + return result + + @staticmethod + def _normalize_content_block(block: Any) -> Any: + """Convert a Pydantic ContentBlock (or dict) to a JSON-ready dict.""" + if hasattr(block, 'model_dump'): + return block.model_dump(mode='json', exclude_none=True) + return block + + +class RunOptionsWire(BaseModel): + """Wire-format model for the ``options`` field of POST /v1/runs. + + Mirrors the server Zod schema: ``timeout`` and ``max_step_tokens`` + (snake_case) are the only server-recognized fields. Client-side flags + like ``await_completion`` are excluded — they never reach the server. + """ + + model_config = ConfigDict(populate_by_name=True) + + timeout: int | None = Field( + default=None, + description='Maximum run duration in seconds (1–3600). Server cancels the run if exceeded.', + ) + max_step_tokens: int | None = Field( + default=None, + description='Per-step token ceiling (256–20000). Caps tokens generated in any single reasoning step.', + ) + + +class RunOutputWire(BaseModel): + """Wire-format model for the ``output`` field of POST /v1/runs. + + Server Zod schema uses camelCase keys (``callbackUrl``, ``responseContent``); + we mirror that exactly via Pydantic field aliases. + """ + + model_config = ConfigDict(populate_by_name=True) + + callback_url: str | None = Field( + default=None, + alias='callbackUrl', + description=( + 'Webhook URL the server POSTs to when the run reaches a terminal state. ' + 'Useful for async workflows where polling or streaming is impractical.' + ), + ) + response_content: Literal['full', 'answer_only'] | None = Field( + default=None, + alias='responseContent', + description=( + 'Shape of the run result. "full" returns the complete reasoning tree ' + 'and answer; "answer_only" returns just the final answer. Defaults to full.' + ), + ) + + +class CreateRunBody(BaseModel): + """Wire-format model for the full POST /v1/runs request body. + + Validates the top-level shape (``engine`` + ``input`` [+ optional + ``options`` and ``output``]) and serializes to the dict the API expects + via ``to_dict()``. Size validation is built in — ``to_dict()`` raises + ``RequestTooLargeError`` if the payload exceeds the API limit. + """ + + MAX_REQUEST_BYTES: ClassVar[int] = 5 * 1024 * 1024 + + engine: Engine + input: RunInputWire + options: RunOptionsWire | None = None + output: RunOutputWire | None = None + + @classmethod + def build( + cls, + engine: Engine, + input: RunInput | dict[str, Any], + options: 'RunOptions | dict[str, Any] | None' = None, + ) -> 'CreateRunBody': + """Construct from user-facing types, normalizing input + server options. + + ``await_completion`` is intentionally stripped from the wire body — + it's a client-side polling toggle that must not reach the server. + """ + return cls( + engine=engine, + input=RunInputWire.from_run_input(input), + options=cls._wire_options(options), + output=cls._wire_output(options), + ) + + @staticmethod + def _wire_options(options: 'RunOptions | dict[str, Any] | None') -> RunOptionsWire | None: + if options is None: + return None + if isinstance(options, RunOptions): + timeout = options.timeout + mst = options.max_step_tokens + else: + timeout = options.get('timeout') + mst = options.get('max_step_tokens') + if timeout is None and mst is None: + return None + return RunOptionsWire(timeout=timeout, max_step_tokens=mst) + + @staticmethod + def _wire_output(options: 'RunOptions | dict[str, Any] | None') -> RunOutputWire | None: + if options is None: + return None + if isinstance(options, RunOptions): + out = options.output + if out is None: + return None + if out.callback_url is None and out.response_content is None: + return None + return RunOutputWire( + callback_url=out.callback_url, + response_content=out.response_content, + ) + raw = options.get('output') + if not raw: + return None + # Accept both snake_case and camelCase from dict users. + callback = raw.get('callback_url') if isinstance(raw, dict) else None + if callback is None and isinstance(raw, dict): + callback = raw.get('callbackUrl') + response_content = raw.get('response_content') if isinstance(raw, dict) else None + if response_content is None and isinstance(raw, dict): + response_content = raw.get('responseContent') + if callback is None and response_content is None: + return None + return RunOutputWire(callback_url=callback, response_content=response_content) + + def to_dict(self) -> dict[str, Any]: + """Serialize to the JSON-ready dict sent over the wire. + + Raises ``RequestTooLargeError`` if the serialized body exceeds + the API size limit. + """ + d = self.model_dump(by_alias=True, exclude_none=True) + serialized = json.dumps(d) + if len(serialized.encode('utf-8')) > self.MAX_REQUEST_BYTES: + raise RequestTooLargeError( + f'request body exceeds {self.MAX_REQUEST_BYTES} bytes — split images ' + 'across multiple turns or upload via /v1/internal/attachments first' + ) + return d + + +@dataclass +class RunOutput: + """Server-side output delivery options (webhook, response payload shape). + + Carried in the request body as a top-level ``output`` object. + """ + + callback_url: str | None = None + """Webhook URL the server POSTs to when the run reaches a terminal state. + Useful for async workflows where polling or streaming is impractical.""" + + response_content: Literal['full', 'answer_only'] | None = None + """Shape of the run result. 'full' returns the complete reasoning tree + and answer; 'answer_only' returns just the final answer. Defaults to 'full'.""" @dataclass class RunOptions: - """Options for creating a run.""" + """Options for creating a run. + + ``await_completion`` is purely a client-side flag that toggles polling + inside :meth:`Subconscious.run`; it is never sent to the server. The + remaining fields (``timeout``, ``max_step_tokens``, ``output``) are + serialized into the POST /v1/runs body as server-side runtime limits. + """ await_completion: bool = False + """Client-side only — never sent to the server. When True, ``client.run()`` + polls ``GET /v1/runs/:id`` until the run reaches a terminal state before + returning. When False (default), ``run()`` returns as soon as the run is + accepted, with only ``run_id`` populated.""" + + timeout: int | None = None + """Maximum run duration in seconds (1–3600). Server cancels the run if exceeded.""" + + max_step_tokens: int | None = None + """Per-step token ceiling (256–20000). Caps tokens generated in any single reasoning step.""" + + output: RunOutput | None = None + """Server-side output delivery options (webhook, response payload shape).""" @dataclass @@ -224,7 +658,7 @@ class RunParams: engine: Engine input: RunInput - options: Optional[RunOptions] = None + options: RunOptions | None = None @dataclass @@ -232,7 +666,7 @@ class PollOptions: """Options for polling a run.""" interval_ms: int = 1000 - max_attempts: Optional[int] = None + max_attempts: int | None = None # Stream event types @@ -240,28 +674,27 @@ class PollOptions: class DeltaEvent: """Text delta event - emitted as text is generated.""" - type: Literal["delta"] = "delta" - run_id: str = "" - content: str = "" + type: Literal['delta'] = 'delta' + run_id: str = '' + content: str = '' @dataclass class DoneEvent: """Stream completed successfully.""" - type: Literal["done"] = "done" - run_id: str = "" + type: Literal['done'] = 'done' + run_id: str = '' @dataclass class ErrorEvent: """Stream encountered an error.""" - type: Literal["error"] = "error" - run_id: str = "" - message: str = "" - code: Optional[str] = None - + type: Literal['error'] = 'error' + run_id: str = '' + message: str = '' + code: str | None = None -StreamEvent = Union[DeltaEvent, DoneEvent, ErrorEvent] +StreamEvent = DeltaEvent | DoneEvent | ErrorEvent diff --git a/tests/test_content.py b/tests/test_content.py new file mode 100644 index 0000000..77b8cbc --- /dev/null +++ b/tests/test_content.py @@ -0,0 +1,173 @@ +"""Tests for the multimodal Image helper and client serialization.""" + +from __future__ import annotations + +import base64 +import json +from pathlib import Path + +import pytest +from pydantic import BaseModel + +from subconscious import ( + CreateRunBody, + Image, + ImageContent, + PlatformTool, + RequestTooLargeError, + RunInput, + RunInputWire, +) + +# 1x1 PNG (valid magic bytes). +PNG_HEAD = b'\x89PNG\r\n\x1a\n' +PNG_BYTES = PNG_HEAD + b'\x00' * 100 + + +def test_image_from_bytes_detects_mime(): + img = Image.from_bytes(PNG_BYTES) + assert isinstance(img, ImageContent) + assert img.source.kind == 'base64' + assert img.source.mime == 'image/png' + # The data is raw base64 (no data: prefix) + decoded = base64.b64decode(img.source.data) + assert decoded == PNG_BYTES + + +def test_image_from_path(tmp_path: Path): + p = tmp_path / 'shot.png' + p.write_bytes(PNG_BYTES) + img = Image.from_path(p) + assert img.source.mime == 'image/png' + + +def test_image_from_url_default_does_not_fetch(): + img = Image.from_url('https://example.com/x.png') + assert img.source.kind == 'url' + # Pydantic's AnyUrl is not equal to a raw str — compare the string form. + assert str(img.source.url) == 'https://example.com/x.png' + + +def test_image_from_blob_ref(): + img = Image.from_blob_ref( + 'org/00000000-0000-0000-0000-000000000000/run/r1/screenshot/x.png', + mime='image/png', + ) + assert img.source.kind == 'blob_ref' + assert img.source.mime == 'image/png' + + +def test_image_rejects_non_image_bytes(): + with pytest.raises(ValueError, match='unsupported image type'): + Image.from_bytes(b'not an image at all') + + +def test_image_rejects_disallowed_mime(): + with pytest.raises(ValueError, match='not allowed'): + Image.from_bytes(b'whatever', mime='image/bmp') + + +def test_oversize_payload_rejected(): + huge = 'x' * (6 * 1024 * 1024) + body = CreateRunBody.build(engine='tim-claude', input={'instructions': huge, 'tools': []}) + with pytest.raises(RequestTooLargeError): + body.to_dict() + + +# --------------------------------------------------------------------------- +# RunInputWire / CreateRunBody — wire-format Pydantic models +# --------------------------------------------------------------------------- + + +class TestRunInputWire: + def test_from_dict_basic(self): + wire = RunInputWire.from_run_input( + { + 'instructions': 'do stuff', + 'tools': [{'type': 'platform', 'id': 'fast_search'}], + } + ) + assert wire.instructions == 'do stuff' + assert wire.tools == [{'type': 'platform', 'id': 'fast_search'}] + assert wire.answer_format is None + + def test_from_dict_with_pydantic_schema(self): + class MyFormat(BaseModel): + answer: str + + wire = RunInputWire.from_run_input( + { + 'instructions': 'go', + 'answerFormat': MyFormat, + } + ) + assert wire.answer_format is not None + assert 'properties' in wire.answer_format + assert 'answer' in wire.answer_format['properties'] + + def test_from_run_input_dataclass(self): + inp = RunInput( + instructions='hello', + tools=[PlatformTool(id='fast_search')], + ) + wire = RunInputWire.from_run_input(inp) + assert wire.instructions == 'hello' + assert wire.tools[0]['type'] == 'platform' + assert wire.tools[0]['id'] == 'fast_search' + + def test_model_dump_uses_camel_case(self): + class Schema(BaseModel): + value: int + + wire = RunInputWire.from_run_input( + { + 'instructions': 'test', + 'answerFormat': Schema, + } + ) + dumped = wire.model_dump(by_alias=True, exclude_none=True) + assert 'answerFormat' in dumped + assert 'answer_format' not in dumped + + def test_none_fields_excluded(self): + wire = RunInputWire.from_run_input({'instructions': 'minimal'}) + dumped = wire.model_dump(by_alias=True, exclude_none=True) + assert 'answerFormat' not in dumped + assert 'content' not in dumped + assert 'instructions' in dumped + assert 'tools' in dumped + + def test_content_blocks_serialized(self): + img = Image.from_bytes(PNG_BYTES) + wire = RunInputWire.from_run_input( + { + 'instructions': 'look', + 'content': [img], + } + ) + assert wire.content is not None + assert wire.content[0]['type'] == 'image' + assert wire.content[0]['source']['kind'] == 'base64' + + +class TestCreateRunBody: + def test_build_and_to_dict(self): + body = CreateRunBody.build( + engine='tim-claude', + input={'instructions': 'search', 'tools': []}, + ) + d = body.to_dict() + assert d['engine'] == 'tim-claude' + assert d['input']['instructions'] == 'search' + assert d['input']['tools'] == [] + json.dumps(d) # must be JSON-serializable + + def test_round_trip_with_content(self): + img = Image.from_bytes(PNG_BYTES) + body = CreateRunBody.build( + engine='tim-edge', + input={'instructions': 'look', 'content': [img]}, + ) + d = body.to_dict() + assert d['input']['content'][0]['type'] == 'image' + json.dumps(d) diff --git a/tests/test_polling.py b/tests/test_polling.py new file mode 100644 index 0000000..5a3e987 --- /dev/null +++ b/tests/test_polling.py @@ -0,0 +1,239 @@ +"""Tests for client.wait() polling behavior and end-to-end +client.run(..., await_completion=True) flow. Mocks the HTTP layer +(``client._request``) so the actual request loop is exercised.""" + +import contextlib +from unittest.mock import patch + +import pytest + +from subconscious.client import Subconscious +from subconscious.types import RunOptions + + +def _request_sequence(responses): + """Build a side_effect callable that returns each response in order, + recording every call for assertion.""" + calls = [] + iterator = iter(responses) + last = [None] + + def fake(method, path, payload=None): + calls.append({'method': method, 'path': path, 'payload': payload}) + with contextlib.suppress(StopIteration): + last[0] = next(iterator) + return last[0] + + return fake, calls + + +class TestWaitPolling: + def test_polls_through_non_terminal_states_until_terminal(self): + client = Subconscious(api_key='test-key') + fake, calls = _request_sequence( + [ + {'runId': 'r-1', 'status': 'queued'}, + {'runId': 'r-1', 'status': 'running'}, + {'runId': 'r-1', 'status': 'running'}, + { + 'runId': 'r-1', + 'status': 'succeeded', + 'result': {'answer': 'done'}, + }, + ] + ) + + with patch.object(client, '_request', side_effect=fake): + run = client.wait('r-1', {'interval_ms': 0}) + + assert run.status == 'succeeded' + assert run.result.answer == 'done' + assert len(calls) == 4 + for c in calls: + assert c == {'method': 'GET', 'path': '/runs/r-1', 'payload': None} + + def test_returns_failed_run_without_throwing(self): + client = Subconscious(api_key='test-key') + fake, _calls = _request_sequence( + [ + { + 'runId': 'r-2', + 'status': 'failed', + 'error': {'code': 'engine_error', 'message': 'boom'}, + } + ] + ) + with patch.object(client, '_request', side_effect=fake): + run = client.wait('r-2', {'interval_ms': 0}) + + assert run.status == 'failed' + assert run.error.code == 'engine_error' + + @pytest.mark.parametrize('terminal_status', ['succeeded', 'failed', 'canceled', 'timed_out']) + def test_each_terminal_status_exits_loop(self, terminal_status): + client = Subconscious(api_key='test-key') + fake, calls = _request_sequence([{'runId': 'r-t', 'status': terminal_status}]) + with patch.object(client, '_request', side_effect=fake): + run = client.wait('r-t', {'interval_ms': 0}) + assert run.status == terminal_status + assert len(calls) == 1 # single GET, no extra iterations + + def test_raises_timeout_when_max_attempts_exhausted(self): + client = Subconscious(api_key='test-key') + fake, _calls = _request_sequence([{'runId': 'r-3', 'status': 'running'}] * 10) + with ( + patch.object(client, '_request', side_effect=fake), + pytest.raises(TimeoutError, match='exceeded max attempts'), + ): + client.wait('r-3', {'interval_ms': 0, 'max_attempts': 3}) + + def test_accepts_poll_options_dataclass(self): + from subconscious.types import PollOptions + + client = Subconscious(api_key='test-key') + fake, _calls = _request_sequence([{'runId': 'r-4', 'status': 'succeeded'}]) + with patch.object(client, '_request', side_effect=fake): + run = client.wait('r-4', PollOptions(interval_ms=0, max_attempts=5)) + assert run.status == 'succeeded' + + +class TestRunAwaitCompletionEndToEnd: + def test_run_posts_then_polls_to_terminal(self): + """client.run(..., await_completion=True) should POST /runs, + then poll GET /runs/:id until terminal, and return the final Run.""" + client = Subconscious(api_key='test-key') + call_log = [] + + def fake(method, path, payload=None): + call_log.append({'method': method, 'path': path}) + if method == 'POST' and path == '/runs': + return {'runId': 'r-end'} + if method == 'GET' and path == '/runs/r-end': + # Progress through states based on number of prior GETs. + get_count = sum( + 1 for c in call_log if c['method'] == 'GET' and c['path'] == '/runs/r-end' + ) + if get_count == 1: + return {'runId': 'r-end', 'status': 'queued'} + if get_count == 2: + return {'runId': 'r-end', 'status': 'running'} + return { + 'runId': 'r-end', + 'status': 'succeeded', + 'result': {'answer': '42'}, + } + raise AssertionError(f'unexpected call {method} {path}') + + with ( + patch.object(client, '_request', side_effect=fake), + patch('subconscious.client.time.sleep'), + ): + # Patch time.sleep so the default 1000ms interval inside wait() + # doesn't stall the test (run() calls wait() without options). + run = client.run( + 'tim', + {'instructions': 'hi'}, + RunOptions(await_completion=True), + ) + + assert run.status == 'succeeded' + assert run.result.answer == '42' + # 1 POST + 3 GET polls = 4 calls total + assert len(call_log) == 4 + assert call_log[0] == {'method': 'POST', 'path': '/runs'} + for c in call_log[1:]: + assert c == {'method': 'GET', 'path': '/runs/r-end'} + + def test_run_without_await_completion_does_not_poll(self): + """When await_completion is absent/False, run() should return + immediately with only run_id — no GET calls.""" + client = Subconscious(api_key='test-key') + call_log = [] + + def fake(method, path, payload=None): + call_log.append({'method': method, 'path': path}) + return {'runId': 'r-nowait'} + + with patch.object(client, '_request', side_effect=fake): + run = client.run('tim', {'instructions': 'hi'}) + + assert run.run_id == 'r-nowait' + assert len(call_log) == 1 + assert call_log[0] == {'method': 'POST', 'path': '/runs'} + + +class TestParsedAnswerIntegration: + """The SDK attaches a best-effort ``parsed_answer`` on every response + path that flows through ``_parse_run`` (``get``, ``wait``, ``cancel``, + and transitively ``run(..., await_completion=True)``).""" + + def test_get_populates_parsed_answer_when_answer_is_json(self): + client = Subconscious(api_key='test-key') + fake, _ = _request_sequence( + [ + { + 'runId': 'r-json', + 'status': 'succeeded', + 'result': {'answer': '{"name":"ada","age":36}'}, + } + ] + ) + with patch.object(client, '_request', side_effect=fake): + run = client.get('r-json') + + assert run.result.answer == '{"name":"ada","age":36}' + assert run.result.parsed_answer == {'name': 'ada', 'age': 36} + + def test_get_leaves_parsed_answer_none_when_answer_is_plain_text(self): + client = Subconscious(api_key='test-key') + fake, _ = _request_sequence( + [{'runId': 'r-txt', 'status': 'succeeded', 'result': {'answer': 'free text'}}] + ) + with patch.object(client, '_request', side_effect=fake): + run = client.get('r-txt') + + assert run.result.answer == 'free text' + assert run.result.parsed_answer is None + + def test_cancel_populates_parsed_answer(self): + client = Subconscious(api_key='test-key') + fake, _ = _request_sequence( + [ + { + 'runId': 'r-c', + 'status': 'canceled', + 'result': {'answer': '{"partial":true}'}, + } + ] + ) + with patch.object(client, '_request', side_effect=fake): + run = client.cancel('r-c') + + assert run.status == 'canceled' + assert run.result.parsed_answer == {'partial': True} + + def test_run_with_await_completion_populates_parsed_answer(self): + client = Subconscious(api_key='test-key') + call_log = [] + + def fake(method, path, payload=None): + call_log.append({'method': method, 'path': path}) + if method == 'POST': + return {'runId': 'r-ac'} + return { + 'runId': 'r-ac', + 'status': 'succeeded', + 'result': {'answer': '[1,2,3]'}, + } + + with ( + patch.object(client, '_request', side_effect=fake), + patch('subconscious.client.time.sleep'), + ): + run = client.run( + 'tim', + {'instructions': 'hi'}, + RunOptions(await_completion=True), + ) + + assert run.result.parsed_answer == [1, 2, 3] diff --git a/tests/test_run_options.py b/tests/test_run_options.py new file mode 100644 index 0000000..f8d8855 --- /dev/null +++ b/tests/test_run_options.py @@ -0,0 +1,207 @@ +"""Tests for skills on RunInput and the widened RunOptions (timeout, +max_step_tokens, output) — including wire-format serialization and the +guarantee that client-side ``await_completion`` is stripped from the body.""" + +from unittest.mock import MagicMock, patch + +from subconscious.client import Subconscious +from subconscious.types import ( + CreateRunBody, + RunInput, + RunInputWire, + RunOptions, + RunOutput, +) + + +class TestSkillsRoundTrip: + def test_skills_from_dataclass_input(self): + wire = RunInputWire.from_run_input( + RunInput(instructions='hi', skills=['web-search', 'python']) + ) + d = wire.model_dump(by_alias=True, exclude_none=True) + assert d['skills'] == ['web-search', 'python'] + + def test_skills_from_dict_input(self): + wire = RunInputWire.from_run_input({'instructions': 'hi', 'skills': ['s1']}) + d = wire.model_dump(by_alias=True, exclude_none=True) + assert d['skills'] == ['s1'] + + def test_no_skills_key_when_absent(self): + wire = RunInputWire.from_run_input({'instructions': 'hi'}) + d = wire.model_dump(by_alias=True, exclude_none=True) + assert 'skills' not in d + + def test_empty_skills_list_is_normalized_to_none(self): + # ``skills or None`` in from_run_input collapses [] → None so the + # wire omits the key entirely (no benefit to sending an empty array). + wire = RunInputWire.from_run_input({'instructions': 'hi', 'skills': []}) + d = wire.model_dump(by_alias=True, exclude_none=True) + assert 'skills' not in d + + +class TestServerOptionsSerialization: + def test_timeout_and_max_step_tokens_on_wire(self): + body = CreateRunBody.build( + 'tim', + {'instructions': 'hi'}, + RunOptions(timeout=120, max_step_tokens=1000), + ) + d = body.to_dict() + assert d['options'] == {'timeout': 120, 'max_step_tokens': 1000} + + def test_dict_options_accepted(self): + body = CreateRunBody.build( + 'tim', + {'instructions': 'hi'}, + {'timeout': 60, 'max_step_tokens': 300}, + ) + assert body.to_dict()['options'] == {'timeout': 60, 'max_step_tokens': 300} + + def test_partial_options_only_includes_set_fields(self): + body = CreateRunBody.build('tim', {'instructions': 'hi'}, RunOptions(timeout=30)) + assert body.to_dict()['options'] == {'timeout': 30} + + def test_no_options_key_when_only_await_completion_set(self): + body = CreateRunBody.build('tim', {'instructions': 'hi'}, RunOptions(await_completion=True)) + assert 'options' not in body.to_dict() + + def test_no_options_key_when_options_is_none(self): + body = CreateRunBody.build('tim', {'instructions': 'hi'}, None) + assert 'options' not in body.to_dict() + + +class TestOutputBlockSerialization: + def test_output_camelcase_on_wire(self): + body = CreateRunBody.build( + 'tim', + {'instructions': 'hi'}, + RunOptions( + output=RunOutput( + callback_url='https://example.com/hook', + response_content='answer_only', + ) + ), + ) + d = body.to_dict() + assert d['output'] == { + 'callbackUrl': 'https://example.com/hook', + 'responseContent': 'answer_only', + } + + def test_output_from_dict_accepts_snake_case(self): + body = CreateRunBody.build( + 'tim', + {'instructions': 'hi'}, + {'output': {'callback_url': 'https://x.com', 'response_content': 'full'}}, + ) + assert body.to_dict()['output'] == { + 'callbackUrl': 'https://x.com', + 'responseContent': 'full', + } + + def test_output_from_dict_accepts_camel_case(self): + body = CreateRunBody.build( + 'tim', + {'instructions': 'hi'}, + {'output': {'callbackUrl': 'https://x.com', 'responseContent': 'full'}}, + ) + assert body.to_dict()['output'] == { + 'callbackUrl': 'https://x.com', + 'responseContent': 'full', + } + + def test_partial_output_only_includes_set_fields(self): + body = CreateRunBody.build( + 'tim', + {'instructions': 'hi'}, + RunOptions(output=RunOutput(response_content='full')), + ) + assert body.to_dict()['output'] == {'responseContent': 'full'} + + def test_no_output_key_when_unset(self): + body = CreateRunBody.build('tim', {'instructions': 'hi'}, RunOptions(timeout=10)) + assert 'output' not in body.to_dict() + + +class TestAwaitCompletionStripped: + def test_await_completion_never_appears_on_wire(self): + body = CreateRunBody.build( + 'tim', + {'instructions': 'hi'}, + RunOptions(await_completion=True, timeout=30, max_step_tokens=500), + ) + import json + + serialized = json.dumps(body.to_dict()) + assert 'await_completion' not in serialized + assert 'awaitCompletion' not in serialized + # Server-side fields still make it through. + assert body.to_dict()['options'] == {'timeout': 30, 'max_step_tokens': 500} + + def test_await_completion_triggers_wait(self): + client = Subconscious(api_key='test-key') + with ( + patch.object(client, '_request', return_value={'runId': 'r-1'}), + patch.object(client, 'wait') as mock_wait, + ): + mock_wait.return_value = MagicMock(run_id='r-1', status='succeeded') + client.run( + 'tim', + {'instructions': 'hi'}, + RunOptions(await_completion=True), + ) + mock_wait.assert_called_once_with('r-1') + + def test_no_wait_when_await_completion_false(self): + client = Subconscious(api_key='test-key') + with ( + patch.object(client, '_request', return_value={'runId': 'r-1'}), + patch.object(client, 'wait') as mock_wait, + ): + result = client.run('tim', {'instructions': 'hi'}) + mock_wait.assert_not_called() + assert result.run_id == 'r-1' + + +class TestMinimalBody: + def test_empty_options_produces_engine_plus_input_only(self): + body = CreateRunBody.build('tim', {'instructions': 'hi'}, None) + d = body.to_dict() + assert set(d.keys()) == {'engine', 'input'} + assert d['engine'] == 'tim' + assert d['input'] == {'instructions': 'hi', 'tools': []} + + def test_run_options_with_defaults_only_produces_minimal_body(self): + body = CreateRunBody.build('tim', {'instructions': 'hi'}, RunOptions()) + assert set(body.to_dict().keys()) == {'engine', 'input'} + + +class TestPassthroughFromClient: + def test_client_run_sends_full_body(self): + client = Subconscious(api_key='test-key') + captured: dict = {} + + def fake_request(method, path, payload=None): + captured['payload'] = payload + return {'runId': 'r-1'} + + with patch.object(client, '_request', side_effect=fake_request): + client.run( + 'tim', + RunInput(instructions='hi', skills=['web-search']), + RunOptions( + await_completion=False, + timeout=90, + max_step_tokens=700, + output=RunOutput(callback_url='https://x.com'), + ), + ) + + payload = captured['payload'] + assert payload['input']['skills'] == ['web-search'] + assert payload['options'] == {'timeout': 90, 'max_step_tokens': 700} + assert payload['output'] == {'callbackUrl': 'https://x.com'} + import json + + assert 'await_completion' not in json.dumps(payload) diff --git a/tests/test_tool_response.py b/tests/test_tool_response.py new file mode 100644 index 0000000..c4121a0 --- /dev/null +++ b/tests/test_tool_response.py @@ -0,0 +1,341 @@ +"""Tests for the ToolResponse envelope + builder.""" + +from __future__ import annotations + +import pytest +from pydantic import ValidationError + +from subconscious import ( + AudioContent, + FileContent, + Image, + ImageContent, + SourceBase64, + SourceBlobRef, + SourceUrl, + TextContent, + ToolResponse, +) + +PNG_BYTES = b'\x89PNG\r\n\x1a\n' + b'\x00' * 32 + + +def test_build_from_string(): + resp = ToolResponse.build('tc_1', 'done') + dumped = resp.model_dump(mode='json', exclude_none=True) + assert dumped == { + 'tool_call_id': 'tc_1', + 'content': [{'type': 'text', 'text': 'done'}], + 'is_error': False, + } + + +def test_build_from_image(): + resp = ToolResponse.build('tc_1', Image.from_bytes(PNG_BYTES)) + assert len(resp.content) == 1 + block = resp.content[0] + assert isinstance(block, ImageContent) + assert block.source.kind == 'base64' + assert block.source.mime == 'image/png' + + +def test_build_from_mixed_list(): + resp = ToolResponse.build('tc_1', ['here:', Image.from_bytes(PNG_BYTES)]) + assert len(resp.content) == 2 + assert isinstance(resp.content[0], TextContent) + assert resp.content[0].text == 'here:' + assert isinstance(resp.content[1], ImageContent) + + +def test_build_preserves_is_error(): + resp = ToolResponse.build('tc_err', 'rate limited', is_error=True) + assert resp.is_error is True + + +def test_strict_constructor_round_trip(): + resp = ToolResponse( + tool_call_id='tc_1', + content=[ + TextContent(type='text', text='hi'), + Image.from_bytes(PNG_BYTES), + ], + ) + assert resp.model_dump(mode='json', exclude_none=True)['content'][0] == { + 'type': 'text', + 'text': 'hi', + } + + +# --------------------------------------------------------------------------- +# Canonical wire-shape contract +# --------------------------------------------------------------------------- +# +# These assertions pin the exact JSON structure the SDK sends to the API +# for a tool response. They mirror the canonical ``ToolResponse`` / +# ``ContentBlock`` / ``Source`` types in +# ``timlarge/src/timlarge/types.py``. If the canonical types change, +# these fail first — keeping the two sides in sync without needing a +# live API round-trip. + + +def test_wire_shape_text_only(): + dumped = ToolResponse.build('tc_1', 'ok').model_dump(mode='json', exclude_none=True) + assert set(dumped.keys()) == {'tool_call_id', 'content', 'is_error'} + assert dumped['content'] == [{'type': 'text', 'text': 'ok'}] + + +def test_wire_shape_image_base64_source(): + dumped = ToolResponse.build('tc_1', Image.from_bytes(PNG_BYTES)).model_dump( + mode='json', exclude_none=True + ) + source = dumped['content'][0]['source'] + # Exactly the three keys the canonical ``SourceBase64`` requires. + assert set(source.keys()) == {'kind', 'data', 'mime'} + assert source['kind'] == 'base64' + assert source['mime'] == 'image/png' + assert isinstance(source['data'], str) and source['data'] + + +def test_wire_shape_image_url_source(): + from subconscious import Image as _Image + + dumped = ToolResponse.build('tc_1', _Image.from_url('https://example.com/x.png')).model_dump( + mode='json', exclude_none=True + ) + source = dumped['content'][0]['source'] + assert source['kind'] == 'url' + assert source['url'] == 'https://example.com/x.png' + + +def test_wire_shape_image_blob_ref_source(): + from subconscious import Image as _Image + + dumped = ToolResponse.build( + 'tc_1', + _Image.from_blob_ref('org/00000000-0000-0000-0000-000000000000/run/r/x.png', 'image/png'), + ).model_dump(mode='json', exclude_none=True) + source = dumped['content'][0]['source'] + assert source['kind'] == 'blob_ref' + assert source['blob_key'].startswith('org/') + assert source['mime'] == 'image/png' + # Optional fields excluded when unset — keeps the wire payload compact. + assert 'attachment_id' not in source + + +# --------------------------------------------------------------------------- +# Optional tool_call_id +# --------------------------------------------------------------------------- + + +def test_tool_call_id_is_optional(): + resp = ToolResponse(content=[TextContent(type='text', text='hi')]) + assert resp.tool_call_id is None + + +def test_build_with_no_tool_call_id(): + resp = ToolResponse.build(None, 'done') + dumped = resp.model_dump(mode='json', exclude_none=True) + assert 'tool_call_id' not in dumped + assert dumped['content'] == [{'type': 'text', 'text': 'done'}] + + +def test_tool_call_id_present_when_set(): + resp = ToolResponse.build('call_abc', 'done') + assert resp.tool_call_id == 'call_abc' + + +# --------------------------------------------------------------------------- +# AudioContent paths +# --------------------------------------------------------------------------- + + +def test_build_from_audio_content(): + audio = AudioContent( + type='audio', + source=SourceBase64(kind='base64', data='AAAA', mime='audio/wav'), + ) + resp = ToolResponse.build('tc_1', audio) + assert len(resp.content) == 1 + assert isinstance(resp.content[0], AudioContent) + + +def test_audio_base64_wire_shape(): + audio = AudioContent( + type='audio', + source=SourceBase64(kind='base64', data='AAAA', mime='audio/mp3'), + ) + dumped = ToolResponse.build('tc_1', audio).model_dump(mode='json', exclude_none=True) + block = dumped['content'][0] + assert block['type'] == 'audio' + assert block['source'] == {'kind': 'base64', 'data': 'AAAA', 'mime': 'audio/mp3'} + + +def test_audio_url_wire_shape(): + audio = AudioContent( + type='audio', + source=SourceUrl(kind='url', url='https://example.com/clip.wav', mime='audio/wav'), + ) + dumped = ToolResponse.build('tc_1', audio).model_dump(mode='json', exclude_none=True) + source = dumped['content'][0]['source'] + assert source['kind'] == 'url' + assert source['url'] == 'https://example.com/clip.wav' + assert source['mime'] == 'audio/wav' + + +def test_audio_blob_ref_wire_shape(): + audio = AudioContent( + type='audio', + source=SourceBlobRef(kind='blob_ref', blob_key='org/run/r1/clip.mp3', mime='audio/mp3'), + ) + dumped = ToolResponse.build('tc_1', audio).model_dump(mode='json', exclude_none=True) + source = dumped['content'][0]['source'] + assert source['kind'] == 'blob_ref' + assert source['blob_key'] == 'org/run/r1/clip.mp3' + assert source['mime'] == 'audio/mp3' + assert 'size_bytes' not in source + assert 'attachment_id' not in source + + +def test_audio_blob_ref_with_metadata(): + audio = AudioContent( + type='audio', + source=SourceBlobRef( + kind='blob_ref', + blob_key='org/run/r1/clip.mp3', + mime='audio/mp3', + size_bytes=4096, + attachment_id='att_xyz', + ), + ) + dumped = ToolResponse.build('tc_1', audio).model_dump(mode='json', exclude_none=True) + source = dumped['content'][0]['source'] + assert source['size_bytes'] == 4096 + assert source['attachment_id'] == 'att_xyz' + + +# --------------------------------------------------------------------------- +# FileContent paths +# --------------------------------------------------------------------------- + + +def test_build_from_file_content(): + f = FileContent( + type='file', + source=SourceBase64(kind='base64', data='AAAA', mime='application/pdf'), + filename='report.pdf', + ) + resp = ToolResponse.build('tc_1', f) + assert len(resp.content) == 1 + assert isinstance(resp.content[0], FileContent) + + +def test_file_base64_wire_shape(): + f = FileContent( + type='file', + source=SourceBase64(kind='base64', data='AAAA', mime='application/pdf'), + filename='doc.pdf', + mime='application/pdf', + ) + dumped = ToolResponse.build('tc_1', f).model_dump(mode='json', exclude_none=True) + block = dumped['content'][0] + assert block['type'] == 'file' + assert block['source']['mime'] == 'application/pdf' + assert block['filename'] == 'doc.pdf' + assert block['mime'] == 'application/pdf' + + +def test_file_url_wire_shape(): + f = FileContent( + type='file', + source=SourceUrl(kind='url', url='https://example.com/data.csv'), + filename='data.csv', + ) + dumped = ToolResponse.build('tc_1', f).model_dump(mode='json', exclude_none=True) + block = dumped['content'][0] + assert block['type'] == 'file' + assert block['source']['kind'] == 'url' + assert block['source']['url'] == 'https://example.com/data.csv' + assert block['filename'] == 'data.csv' + + +def test_file_optional_fields_excluded(): + f = FileContent( + type='file', + source=SourceBase64(kind='base64', data='AAAA', mime='text/plain'), + ) + dumped = ToolResponse.build('tc_1', f).model_dump(mode='json', exclude_none=True) + block = dumped['content'][0] + assert 'filename' not in block + assert 'mime' not in block + + +# --------------------------------------------------------------------------- +# Mixed-modality lists +# --------------------------------------------------------------------------- + + +def test_mixed_list_text_audio_file(): + resp = ToolResponse.build( + 'tc_1', + [ + 'summary text', + AudioContent( + type='audio', + source=SourceBase64(kind='base64', data='AAAA', mime='audio/wav'), + ), + FileContent( + type='file', + source=SourceBase64(kind='base64', data='BBBB', mime='application/pdf'), + filename='report.pdf', + ), + ], + ) + assert len(resp.content) == 3 + assert isinstance(resp.content[0], TextContent) + assert isinstance(resp.content[1], AudioContent) + assert isinstance(resp.content[2], FileContent) + + +def test_mixed_list_image_and_audio(): + resp = ToolResponse.build( + 'tc_1', + [ + Image.from_bytes(PNG_BYTES), + AudioContent( + type='audio', + source=SourceUrl(kind='url', url='https://example.com/clip.wav'), + ), + ], + ) + assert isinstance(resp.content[0], ImageContent) + assert isinstance(resp.content[1], AudioContent) + + +# --------------------------------------------------------------------------- +# Source discriminator validation +# --------------------------------------------------------------------------- + + +def test_source_rejects_invalid_kind(): + with pytest.raises(ValidationError): + AudioContent( + type='audio', + source={'kind': 'unknown', 'data': 'AAA', 'mime': 'audio/wav'}, # type: ignore[arg-type] + ) + + +def test_audio_accepts_arbitrary_mime(): + # Source types accept any MIME string — no type-level restriction. + audio = AudioContent( + type='audio', + source=SourceBase64(kind='base64', data='AAAA', mime='audio/ogg; codecs=opus'), + ) + assert audio.source.mime == 'audio/ogg; codecs=opus' + + +def test_file_accepts_arbitrary_mime(): + f = FileContent( + type='file', + source=SourceBase64(kind='base64', data='AAAA', mime='application/vnd.ms-excel'), + ) + assert f.source.mime == 'application/vnd.ms-excel' diff --git a/tests/test_types.py b/tests/test_types.py index 85b9e00..ff16c1b 100644 --- a/tests/test_types.py +++ b/tests/test_types.py @@ -1,144 +1,555 @@ -"""Tests for type definitions and tool serialization.""" - -import pytest +"""Tests for type definitions, tool serialization, and run parsing.""" +from subconscious.client import Subconscious from subconscious.types import ( + AgentToolUse, FunctionTool, McpAuth, MCPTool, PlatformTool, + ReasoningTask, + Run, + RunError, + RunInputWire, + RunResult, Tool, + Usage, ) -from subconscious.client import _normalize_tool - # --------------------------------------------------------------------------- # MCPTool construction # --------------------------------------------------------------------------- + class TestMCPTool: def test_basic_construction(self): - tool = MCPTool(url="https://example.com/mcp") - assert tool.url == "https://example.com/mcp" - assert tool.type == "mcp" + tool = MCPTool(url='https://example.com/mcp') + assert tool.url == 'https://example.com/mcp' + assert tool.type == 'mcp' assert tool.allowed_tools is None assert tool.auth is None def test_with_allowed_tools(self): - tool = MCPTool(url="https://x.com/mcp", allowed_tools=["search", "fetch"]) - assert tool.allowed_tools == ["search", "fetch"] + tool = MCPTool(url='https://x.com/mcp', allowed_tools=['search', 'fetch']) + assert tool.allowed_tools == ['search', 'fetch'] def test_with_wildcard(self): - tool = MCPTool(url="https://x.com/mcp", allowed_tools=["*"]) - assert tool.allowed_tools == ["*"] + tool = MCPTool(url='https://x.com/mcp', allowed_tools=['*']) + assert tool.allowed_tools == ['*'] def test_with_empty_list_blocks_all(self): - tool = MCPTool(url="https://x.com/mcp", allowed_tools=[]) + tool = MCPTool(url='https://x.com/mcp', allowed_tools=[]) assert tool.allowed_tools == [] def test_with_bearer_auth(self): - auth = McpAuth(type="bearer", token="tok123") - tool = MCPTool(url="https://x.com/mcp", auth=auth) + auth = McpAuth(type='bearer', token='tok123') + tool = MCPTool(url='https://x.com/mcp', auth=auth) assert tool.auth is not None - assert tool.auth.type == "bearer" - assert tool.auth.token == "tok123" + assert tool.auth.type == 'bearer' + assert tool.auth.token == 'tok123' assert tool.auth.header is None def test_with_api_key_auth(self): - auth = McpAuth(type="api_key", token="key456", header="X-Api-Key") - tool = MCPTool(url="https://x.com/mcp", auth=auth) - assert tool.auth.type == "api_key" - assert tool.auth.token == "key456" - assert tool.auth.header == "X-Api-Key" + auth = McpAuth(type='api_key', token='key456', header='X-Api-Key') + tool = MCPTool(url='https://x.com/mcp', auth=auth) + assert tool.auth.type == 'api_key' + assert tool.auth.token == 'key456' + assert tool.auth.header == 'X-Api-Key' # --------------------------------------------------------------------------- # Tool union type assignability # --------------------------------------------------------------------------- + class TestToolUnion: def test_platform_tool_is_tool(self): - tool: Tool = PlatformTool(id="fast_search") - assert tool.type == "platform" + tool: Tool = PlatformTool(id='fast_search') + assert tool.type == 'platform' def test_function_tool_is_tool(self): - tool: Tool = FunctionTool(name="my_func") - assert tool.type == "function" + tool: Tool = FunctionTool(name='my_func') + assert tool.type == 'function' def test_mcp_tool_is_tool(self): - tool: Tool = MCPTool(url="https://x.com/mcp") - assert tool.type == "mcp" + tool: Tool = MCPTool(url='https://x.com/mcp') + assert tool.type == 'mcp' def test_dict_is_tool(self): - tool: Tool = {"type": "custom", "name": "raw"} - assert tool["type"] == "custom" + tool: Tool = {'type': 'custom', 'name': 'raw'} + assert tool['type'] == 'custom' # --------------------------------------------------------------------------- -# Serialization (_normalize_tool) +# Tool serialization via RunInputWire # --------------------------------------------------------------------------- -class TestNormalizeTool: + +def _wire_tools(tools): + """Helper: build a RunInputWire and return the serialized tools list.""" + wire = RunInputWire.from_run_input({'instructions': 'x', 'tools': tools}) + return wire.tools + + +class TestToolSerialization: def test_mcp_key_mapping(self): - tool = MCPTool( - url="https://x.com/mcp", - allowed_tools=["search", "fetch"], - ) - result = _normalize_tool(tool) - assert "allowedTools" in result - assert result["allowedTools"] == ["search", "fetch"] - assert "allowed_tools" not in result - assert result["url"] == "https://x.com/mcp" - assert result["type"] == "mcp" + tool = MCPTool(url='https://x.com/mcp', allowed_tools=['search', 'fetch']) + [result] = _wire_tools([tool]) + assert 'allowedTools' in result + assert result['allowedTools'] == ['search', 'fetch'] + assert 'allowed_tools' not in result + assert result['url'] == 'https://x.com/mcp' + assert result['type'] == 'mcp' def test_mcp_with_auth_nested_serialization(self): - auth = McpAuth(type="bearer", token="tok123") - tool = MCPTool(url="https://x.com/mcp", auth=auth) - result = _normalize_tool(tool) - assert isinstance(result["auth"], dict) - assert result["auth"]["type"] == "bearer" - assert result["auth"]["token"] == "tok123" - # header is None so should be stripped - assert "header" not in result["auth"] + auth = McpAuth(type='bearer', token='tok123') + tool = MCPTool(url='https://x.com/mcp', auth=auth) + [result] = _wire_tools([tool]) + assert isinstance(result['auth'], dict) + assert result['auth']['type'] == 'bearer' + assert result['auth']['token'] == 'tok123' + assert 'header' not in result['auth'] def test_strips_none_values(self): - tool = MCPTool(url="https://x.com/mcp") - result = _normalize_tool(tool) - assert "allowedTools" not in result - assert "auth" not in result - assert set(result.keys()) == {"url", "type"} + tool = MCPTool(url='https://x.com/mcp') + [result] = _wire_tools([tool]) + assert 'allowedTools' not in result + assert 'auth' not in result + assert set(result.keys()) == {'url', 'type'} def test_dict_passthrough(self): - raw = {"type": "custom", "name": "raw"} - result = _normalize_tool(raw) + raw = {'type': 'custom', 'name': 'raw'} + [result] = _wire_tools([raw]) assert result == raw + def test_function_tool(self): + tool = FunctionTool( + name='my_tool', + description='Does stuff', + url='https://x.com/tool', + method='POST', + parameters={'type': 'object', 'properties': {}}, + headers={'X-Key': 'val'}, + defaults={'org': 'acme'}, + ) + [result] = _wire_tools([tool]) + assert result['name'] == 'my_tool' + assert result['type'] == 'function' + assert result['headers'] == {'X-Key': 'val'} + assert result['defaults'] == {'org': 'acme'} + + def test_platform_tool(self): + tool = PlatformTool(id='fast_search', options={'limit': 10}) + [result] = _wire_tools([tool]) + assert result['id'] == 'fast_search' + assert result['type'] == 'platform' + assert result['options'] == {'limit': 10} + + # --------------------------------------------------------------------------- -# Backward compatibility +# _parse_run — camelCase API response deserialization # --------------------------------------------------------------------------- -class TestBackwardCompat: - def test_function_tool_unchanged(self): - tool = FunctionTool( - name="my_tool", - description="Does stuff", - url="https://x.com/tool", - method="POST", - parameters={"type": "object", "properties": {}}, - headers={"X-Key": "val"}, - defaults={"org": "acme"}, + +class TestParseRun: + """Validates that _parse_run (via Run.model_validate) correctly + deserializes the camelCase JSON returned by GET /v1/runs/:runId.""" + + def _parse(self, data): + return Subconscious._parse_run(None, data) + + def test_full_succeeded_response(self): + data = { + 'runId': 'run_abc', + 'status': 'succeeded', + 'result': { + 'answer': 'hello world', + 'reasoning': [ + { + 'title': 'Search', + 'thought': 'I need to search', + 'tooluse': { + 'tool_name': 'web_search', + 'tool_call_id': 'call_1', + 'parameters': {'query': 'AI news'}, + 'tool_result': { + 'content': [{'type': 'text', 'text': 'results'}], + 'is_error': False, + }, + }, + 'subtasks': [{'title': 'Sub-step', 'thought': 'Analyzing...'}], + 'conclusion': 'Found results', + } + ], + }, + 'usage': { + 'inputTokens': 150, + 'outputTokens': 42, + 'durationMs': 1234, + }, + } + run = self._parse(data) + + assert run.run_id == 'run_abc' + assert run.status == 'succeeded' + assert run.error is None + + # Result + assert isinstance(run.result, RunResult) + assert run.result.answer == 'hello world' + assert isinstance(run.result.reasoning, list) + assert len(run.result.reasoning) == 1 + + task = run.result.reasoning[0] + assert isinstance(task, ReasoningTask) + assert task.title == 'Search' + assert task.thought == 'I need to search' + assert task.conclusion == 'Found results' + + # Tool use + assert isinstance(task.tooluse, AgentToolUse) + assert task.tooluse.tool_name == 'web_search' + assert task.tooluse.tool_call_id == 'call_1' + assert task.tooluse.parameters == {'query': 'AI news'} + assert task.tooluse.tool_result is not None + + # Subtasks + assert isinstance(task.subtasks, list) + assert len(task.subtasks) == 1 + assert task.subtasks[0].title == 'Sub-step' + + # Usage — flat structure + assert isinstance(run.usage, Usage) + assert run.usage.input_tokens == 150 + assert run.usage.output_tokens == 42 + assert run.usage.duration_ms == 1234 + + def test_failed_response_with_error(self): + data = { + 'runId': 'run_fail', + 'status': 'failed', + 'error': {'code': '500', 'message': 'Engine crashed'}, + 'usage': {'inputTokens': 10, 'outputTokens': 0}, + } + run = self._parse(data) + + assert run.run_id == 'run_fail' + assert run.status == 'failed' + assert isinstance(run.error, RunError) + assert run.error.code == '500' + assert run.error.message == 'Engine crashed' + assert run.result is None + assert run.usage.input_tokens == 10 + + def test_queued_response_minimal(self): + data = {'runId': 'run_q', 'status': 'queued'} + run = self._parse(data) + + assert run.run_id == 'run_q' + assert run.status == 'queued' + assert run.result is None + assert run.usage is None + assert run.error is None + + def test_usage_without_duration(self): + data = { + 'runId': 'run_nodur', + 'status': 'succeeded', + 'result': {'answer': 'ok'}, + 'usage': {'inputTokens': 1, 'outputTokens': 1}, + } + run = self._parse(data) + assert run.usage.duration_ms is None + assert run.usage.input_tokens == 1 + assert run.usage.output_tokens == 1 + + def test_multiple_reasoning_tasks(self): + data = { + 'runId': 'run_multi', + 'status': 'succeeded', + 'result': { + 'answer': 'final', + 'reasoning': [ + {'title': 'Step 1', 'thought': 'first'}, + {'title': 'Step 2', 'thought': 'second', 'conclusion': 'done'}, + {'title': 'Step 3'}, + ], + }, + } + run = self._parse(data) + assert len(run.result.reasoning) == 3 + assert run.result.reasoning[0].title == 'Step 1' + assert run.result.reasoning[1].conclusion == 'done' + assert run.result.reasoning[2].thought is None + + def test_deeply_nested_subtasks(self): + data = { + 'runId': 'run_deep', + 'status': 'succeeded', + 'result': { + 'answer': 'nested', + 'reasoning': [ + { + 'title': 'L0', + 'subtasks': [ + { + 'title': 'L1', + 'subtasks': [ + { + 'title': 'L2', + 'thought': 'leaf node', + } + ], + } + ], + } + ], + }, + } + run = self._parse(data) + l0 = run.result.reasoning[0] + l1 = l0.subtasks[0] + l2 = l1.subtasks[0] + assert l0.title == 'L0' + assert l1.title == 'L1' + assert l2.title == 'L2' + assert l2.thought == 'leaf node' + assert l2.subtasks is None + + def test_empty_reasoning_list(self): + data = { + 'runId': 'run_empty_r', + 'status': 'succeeded', + 'result': {'answer': 'answer only', 'reasoning': []}, + } + run = self._parse(data) + assert run.result.reasoning == [] + assert run.result.answer == 'answer only' + + def test_result_without_reasoning(self): + data = { + 'runId': 'run_no_r', + 'status': 'succeeded', + 'result': {'answer': 'simple answer'}, + } + run = self._parse(data) + assert run.result.answer == 'simple answer' + assert run.result.reasoning is None + + def test_canceled_status(self): + data = {'runId': 'run_cancel', 'status': 'canceled'} + run = self._parse(data) + assert run.status == 'canceled' + + def test_timed_out_status(self): + data = {'runId': 'run_to', 'status': 'timed_out'} + run = self._parse(data) + assert run.status == 'timed_out' + + def test_running_status(self): + data = {'runId': 'run_ing', 'status': 'running'} + run = self._parse(data) + assert run.status == 'running' + assert run.result is None + assert run.usage is None + + def test_tooluse_without_result(self): + data = { + 'runId': 'run_tu', + 'status': 'succeeded', + 'result': { + 'answer': 'ok', + 'reasoning': [ + { + 'title': 'Search', + 'tooluse': { + 'tool_name': 'web_search', + 'parameters': {'q': 'test'}, + }, + } + ], + }, + } + run = self._parse(data) + tu = run.result.reasoning[0].tooluse + assert tu.tool_name == 'web_search' + assert tu.tool_call_id is None + assert tu.tool_result is None + assert tu.parameters == {'q': 'test'} + + def test_tooluse_with_string_result(self): + data = { + 'runId': 'run_str_tr', + 'status': 'succeeded', + 'result': { + 'answer': 'ok', + 'reasoning': [ + { + 'tooluse': { + 'tool_name': 'calc', + 'parameters': {}, + 'tool_result': '42', + }, + } + ], + }, + } + run = self._parse(data) + assert run.result.reasoning[0].tooluse.tool_result == '42' + + def test_tooluse_with_complex_result(self): + nested_result = { + 'content': [{'type': 'text', 'text': 'hi'}], + 'is_error': False, + '_attachments': [{'type': 'image', 'source': {'kind': 'blob_ref', 'blob_key': 'k1'}}], + } + data = { + 'runId': 'run_cplx', + 'status': 'succeeded', + 'result': { + 'answer': 'ok', + 'reasoning': [ + { + 'tooluse': { + 'tool_name': 'browse', + 'parameters': {}, + 'tool_result': nested_result, + }, + } + ], + }, + } + run = self._parse(data) + tr = run.result.reasoning[0].tooluse.tool_result + assert isinstance(tr, dict) + assert tr['content'][0]['text'] == 'hi' + assert tr['_attachments'][0]['source']['blob_key'] == 'k1' + + def test_extra_unknown_fields_tolerated(self): + """The API may add new fields in the future; they should not + cause validation errors.""" + data = { + 'runId': 'run_extra', + 'status': 'succeeded', + 'result': {'answer': 'ok', 'some_new_field': True}, + 'usage': {'inputTokens': 1, 'outputTokens': 1, 'newMetric': 99}, + 'newTopLevel': 'surprise', + } + run = self._parse(data) + assert run.run_id == 'run_extra' + assert run.usage.input_tokens == 1 + + def test_error_without_usage(self): + data = { + 'runId': 'run_err_no_u', + 'status': 'failed', + 'error': {'code': 'timeout', 'message': 'timed out'}, + } + run = self._parse(data) + assert run.error.code == 'timeout' + assert run.usage is None + + +# --------------------------------------------------------------------------- +# Pydantic model construction — snake_case and alias paths +# --------------------------------------------------------------------------- + + +class TestModelConstruction: + """Test that response Pydantic models can be constructed both via + snake_case kwargs (Pythonic) and camelCase aliases (wire format).""" + + def test_run_snake_case_construction(self): + run = Run(run_id='run_1', status='queued') + assert run.run_id == 'run_1' + assert run.status == 'queued' + + def test_run_alias_construction(self): + run = Run.model_validate({'runId': 'run_2', 'status': 'running'}) + assert run.run_id == 'run_2' + + def test_usage_snake_case_construction(self): + u = Usage(input_tokens=10, output_tokens=20, duration_ms=500) + assert u.input_tokens == 10 + assert u.output_tokens == 20 + assert u.duration_ms == 500 + + def test_usage_alias_construction(self): + u = Usage.model_validate({'inputTokens': 5, 'outputTokens': 3}) + assert u.input_tokens == 5 + assert u.output_tokens == 3 + assert u.duration_ms is None + + def test_usage_defaults(self): + u = Usage() + assert u.input_tokens == 0 + assert u.output_tokens == 0 + assert u.duration_ms is None + + def test_run_error_construction(self): + e = RunError(code='429', message='rate limited') + assert e.code == '429' + assert e.message == 'rate limited' + + def test_run_error_defaults(self): + e = RunError() + assert e.code == '' + assert e.message == '' + + def test_agent_tool_use_construction(self): + tu = AgentToolUse(tool_name='search', parameters={'q': 'test'}) + assert tu.tool_name == 'search' + assert tu.tool_call_id is None + assert tu.tool_result is None + + def test_reasoning_task_all_optional(self): + task = ReasoningTask() + assert task.title is None + assert task.thought is None + assert task.tooluse is None + assert task.subtasks is None + assert task.conclusion is None + + def test_run_result_defaults(self): + r = RunResult() + assert r.answer == '' + assert r.reasoning is None + + +# --------------------------------------------------------------------------- +# Serialization round-trip — model_dump produces alias keys +# --------------------------------------------------------------------------- + + +class TestSerialization: + """Verify model_dump(by_alias=True) produces the camelCase wire format + so the SDK could also serialize these objects for logging/debugging.""" + + def test_usage_round_trip(self): + u = Usage.model_validate({'inputTokens': 100, 'outputTokens': 50, 'durationMs': 3000}) + dumped = u.model_dump(by_alias=True) + assert dumped == {'inputTokens': 100, 'outputTokens': 50, 'durationMs': 3000} + + def test_run_round_trip(self): + data = { + 'runId': 'run_rt', + 'status': 'succeeded', + 'result': {'answer': 'ok', 'reasoning': None}, + 'usage': {'inputTokens': 1, 'outputTokens': 2, 'durationMs': None}, + 'error': None, + } + run = Run.model_validate(data) + dumped = run.model_dump(by_alias=True) + assert dumped['runId'] == 'run_rt' + assert dumped['usage']['inputTokens'] == 1 + + def test_reasoning_task_round_trip(self): + task = ReasoningTask( + title='T', + tooluse=AgentToolUse(tool_name='calc', parameters={'x': 1}), + subtasks=[ReasoningTask(thought='leaf')], ) - assert tool.name == "my_tool" - assert tool.type == "function" - result = _normalize_tool(tool) - assert result["name"] == "my_tool" - assert result["headers"] == {"X-Key": "val"} - assert result["defaults"] == 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