11cc14d707
Sovereign backup of all Hermes Agent configuration and data. Excludes: secrets, auth tokens, sessions, caches, code (separate repo). Tracked: - config.yaml (model, fallback chain, toolsets, display prefs) - SOUL.md (Timmy personality charter) - memories/ (persistent MEMORY.md + USER.md) - skills/ (371 files — full skill library) - cron/jobs.json (scheduled tasks) - channel_directory.json (platform channels) - hooks/ (custom hooks)
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