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f172f7d4aa
- Introduced new skills tools: `skills_categories`, `skills_list`, and `skill_view` in `model_tools.py`, allowing for better organization and access to skill-related functionalities. - Updated `toolsets.py` to include a new `skills` toolset, providing a dedicated space for skill tools. - Enhanced `batch_runner.py` to recognize and validate skills tools during batch processing. - Added comprehensive tool definitions for skills tools, ensuring compatibility with OpenAI's expected format. - Created new shell script `test_skills_kimi.sh` for testing skills tool functionality with Kimi K2.5. - Added example skill files demonstrating the structure and usage of skills within the Hermes-Agent framework, including `SKILL.md` for example and audiocraft skills. - Improved documentation for skills tools and their integration into the existing tool framework, ensuring clarity for future development and usage.
107 lines
2.3 KiB
Markdown
107 lines
2.3 KiB
Markdown
# Real-World Examples
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Practical examples of using Instructor for structured data extraction.
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## Data Extraction
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```python
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class CompanyInfo(BaseModel):
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name: str
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founded: int
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industry: str
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employees: int
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text = "Apple was founded in 1976 in the technology industry with 164,000 employees."
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company = client.messages.create(
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model="claude-sonnet-4-5-20250929",
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max_tokens=1024,
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messages=[{"role": "user", "content": f"Extract: {text}"}],
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response_model=CompanyInfo
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)
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```
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## Classification
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```python
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class Sentiment(str, Enum):
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POSITIVE = "positive"
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NEGATIVE = "negative"
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NEUTRAL = "neutral"
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class Review(BaseModel):
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sentiment: Sentiment
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confidence: float = Field(ge=0.0, le=1.0)
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review = client.messages.create(
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model="claude-sonnet-4-5-20250929",
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max_tokens=1024,
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messages=[{"role": "user", "content": "This product is amazing!"}],
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response_model=Review
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)
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```
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## Multi-Entity Extraction
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```python
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class Person(BaseModel):
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name: str
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role: str
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class Entities(BaseModel):
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people: list[Person]
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organizations: list[str]
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locations: list[str]
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entities = client.messages.create(
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model="claude-sonnet-4-5-20250929",
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max_tokens=1024,
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messages=[{"role": "user", "content": "Tim Cook, CEO of Apple, spoke in Cupertino..."}],
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response_model=Entities
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)
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```
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## Structured Analysis
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```python
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class Analysis(BaseModel):
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summary: str
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key_points: list[str]
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sentiment: Sentiment
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actionable_items: list[str]
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analysis = client.messages.create(
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model="claude-sonnet-4-5-20250929",
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max_tokens=1024,
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messages=[{"role": "user", "content": "Analyze: [long text]"}],
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response_model=Analysis
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)
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```
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## Batch Processing
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```python
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texts = ["text1", "text2", "text3"]
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results = [
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client.messages.create(
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model="claude-sonnet-4-5-20250929",
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max_tokens=1024,
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messages=[{"role": "user", "content": text}],
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response_model=YourModel
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)
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for text in texts
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]
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```
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## Streaming
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```python
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for partial in client.messages.create_partial(
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model="claude-sonnet-4-5-20250929",
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max_tokens=1024,
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messages=[{"role": "user", "content": "Generate report..."}],
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response_model=Report
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):
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print(f"Progress: {partial.title}")
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# Update UI in real-time
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```
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