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.
2.3 KiB
2.3 KiB
Real-World Examples
Practical examples of using Instructor for structured data extraction.
Data Extraction
class CompanyInfo(BaseModel):
name: str
founded: int
industry: str
employees: int
text = "Apple was founded in 1976 in the technology industry with 164,000 employees."
company = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[{"role": "user", "content": f"Extract: {text}"}],
response_model=CompanyInfo
)
Classification
class Sentiment(str, Enum):
POSITIVE = "positive"
NEGATIVE = "negative"
NEUTRAL = "neutral"
class Review(BaseModel):
sentiment: Sentiment
confidence: float = Field(ge=0.0, le=1.0)
review = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[{"role": "user", "content": "This product is amazing!"}],
response_model=Review
)
Multi-Entity Extraction
class Person(BaseModel):
name: str
role: str
class Entities(BaseModel):
people: list[Person]
organizations: list[str]
locations: list[str]
entities = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[{"role": "user", "content": "Tim Cook, CEO of Apple, spoke in Cupertino..."}],
response_model=Entities
)
Structured Analysis
class Analysis(BaseModel):
summary: str
key_points: list[str]
sentiment: Sentiment
actionable_items: list[str]
analysis = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[{"role": "user", "content": "Analyze: [long text]"}],
response_model=Analysis
)
Batch Processing
texts = ["text1", "text2", "text3"]
results = [
client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[{"role": "user", "content": text}],
response_model=YourModel
)
for text in texts
]
Streaming
for partial in client.messages.create_partial(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[{"role": "user", "content": "Generate report..."}],
response_model=Report
):
print(f"Progress: {partial.title}")
# Update UI in real-time