skald-sdk 0.2.0

Official Python SDK for Skald

Skald Python SDK

Official Python SDK for Skald.

Installation

pip install skald-sdk

Quick Start

import asyncio
from skald_sdk import Skald

async def main():
    # Initialize the client
    async with Skald("your-api-key") as skald:
        # Create a memo
        await skald.create_memo({
            "title": "Meeting Notes",
            "content": "Discussion about Q1 goals and objectives...",
            "metadata": {"priority": "high"},
            "tags": ["meeting", "q1"],
            "source": "notion"
        })

        # Search your knowledge base
        results = await skald.search({
            "query": "quarterly goals",
            "limit": 10
        })

        # Chat with your knowledge
        response = await skald.chat({
            "query": "What were the main discussion points?"
        })
        print(response["response"])

asyncio.run(main())

Features

• Full CRUD Operations: Create, read, update, and delete memos
• Semantic Search: Vector-based search for finding relevant content
• AI Chat: Natural language Q&A over your knowledge base
• Document Generation: AI-powered document creation from your memos
• Streaming Support: Real-time streaming for chat and document generation
• Type Safety: Full type hints for better IDE support
• Async/Await: Built on modern async Python patterns

Usage

Creating Memos

# Basic memo
await skald.create_memo({
    "title": "Product Requirements",
    "content": "We need to build a mobile app with..."
})

# With metadata and tags
await skald.create_memo({
    "title": "Technical Spec",
    "content": "Architecture overview...",
    "metadata": {
        "author": "[e-mail]",
        "version": "1.0"
    },
    "tags": ["technical", "architecture"],
    "source": "confluence",
    "reference_id": "TECH-123"
})

Retrieving Memos

# Get by UUID
memo = await skald.get_memo("550e8400-e29b-41d4-a716-446655440000")

# Get by reference ID
memo = await skald.get_memo("TECH-123", id_type="reference_id")

# List with pagination
response = await skald.list_memos({
    "page": 1,
    "page_size": 50
})

for memo in response["results"]:
    print(f"{memo['title']}: {memo['summary']}")

Updating and Deleting Memos

# Update memo
await skald.update_memo(
    "550e8400-e29b-41d4-a716-446655440000",
    {
        "title": "Updated Title",
        "content": "Updated content...",
        "metadata": {"status": "reviewed"}
    }
)

# Delete memo
await skald.delete_memo("550e8400-e29b-41d4-a716-446655440000")

Searching

# Semantic vector search
results = await skald.search({
    "query": "machine learning models",
    "limit": 10
})

# Text-based search
results = await skald.search({
    "query": "python",
    "limit": 20
})

# Search with filters
results = await skald.search({
    "query": "api documentation",
    "limit": 10,
    "filters": [
        {
            "field": "source",
            "operator": "eq",
            "value": "confluence",
            "filter_type": "native_field"
        },
        {
            "field": "category",
            "operator": "eq",
            "value": "technical",
            "filter_type": "custom_metadata"
        }
    ]
})

for result in results["results"]:
    print(f"{result['title']}: {result['content_snippet']}")
    print(f"Relevance: {result['distance']}")

Chat

# Non-streaming chat
response = await skald.chat({
    "query": "What are our main product features?"
})
print(response["response"])  # Includes [[N]] citations

# Streaming chat for real-time responses
async for event in skald.streamed_chat({
    "query": "Summarize our technical architecture"
}):
    if event["type"] == "token":
        print(event["content"], end="", flush=True)
    elif event["type"] == "done":
        print("\nDone!")

# Chat with filters
response = await skald.chat({
    "query": "What did we discuss in recent meetings?",
    "filters": [
        {
            "field": "tags",
            "operator": "in",
            "value": ["meeting"],
            "filter_type": "native_field"
        }
    ]
})

Document Generation

# Generate a complete document
response = await skald.generate_doc({
    "prompt": "Create a comprehensive PRD for a mobile app",
    "rules": "Use formal language. Include: Overview, Requirements, Timeline, Success Metrics"
})
print(response["response"])

# Streaming document generation
async for event in skald.streamed_generate_doc({
    "prompt": "Write a technical specification",
    "rules": "Include Architecture, Security, and Testing sections",
    "filters": [
        {
            "field": "tags",
            "operator": "in",
            "value": ["technical"],
            "filter_type": "native_field"
        }
    ]
}):
    if event["type"] == "token":
        print(event["content"], end="", flush=True)

Search Methods

• chunk_vector_search: Semantic search using AI embeddings (best for finding conceptually similar content)
• title_contains: Case-insensitive substring match in titles
• title_startswith: Case-insensitive prefix match in titles

Filter Types

Native Fields

Filter on built-in memo fields:

• title: Memo title
• source: Source system (e.g., "notion", "confluence")
• client_reference_id: Your external reference ID
• tags: Memo tags (use with in or not_in operators)

Custom Metadata

Filter on your custom metadata fields using filter_type: "custom_metadata".

Filter Operators

• eq: Equals
• neq: Not equals
• contains: Contains substring (case-insensitive)
• startswith: Starts with (case-insensitive)
• endswith: Ends with (case-insensitive)
• in: Value in array
• not_in: Value not in array

API Reference

Skald(api_key, base_url="https://api.useskald.com")

Main client class for interacting with Skald.

Methods:

CRUD Operations

• async create_memo(memo_data: MemoData) -> CreateMemoResponse
• async get_memo(memo_id: str, id_type: IdType = "memo_uuid") -> Memo
• async list_memos(params: Optional[ListMemosParams] = None) -> ListMemosResponse
• async update_memo(memo_id: str, update_data: UpdateMemoData, id_type: IdType = "memo_uuid") -> UpdateMemoResponse
• async delete_memo(memo_id: str, id_type: IdType = "memo_uuid") -> None

Search and Query

• async search(search_params: SearchRequest) -> SearchResponse
• async chat(chat_params: ChatRequest) -> ChatResponse
• async streamed_chat(chat_params: ChatRequest) -> AsyncIterator[ChatStreamEvent]
• async generate_doc(generate_params: GenerateDocRequest) -> GenerateDocResponse
• async streamed_generate_doc(generate_params: GenerateDocRequest) -> AsyncIterator[GenerateDocStreamEvent]

Type Definitions

The SDK includes comprehensive type definitions for all API operations. Import them from skald_sdk.types:

from skald_sdk.types import (
    MemoData,
    UpdateMemoData,
    SearchRequest,
    ChatRequest,
    Filter,
    SearchMethod,
    FilterOperator,
    FilterType,
)

Error Handling

The SDK raises exceptions for API errors:

try:
    memo = await skald.get_memo("invalid-id")
except Exception as e:
    print(f"Error: {e}")
    # Output: "Skald API error (404): Not Found"

Requirements

• Python 3.8+
• httpx >= 0.24.0
• typing-extensions >= 4.5.0 (for Python < 3.10)

Development

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=skald_sdk --cov-report=term-missing

# Type checking
mypy skald_sdk

# Format code
black skald_sdk tests

# Lint
ruff check skald_sdk tests

License

MIT

Support

• Documentation: https://docs.useskald.com
• Issues: https://github.com/skald-labs/skald-python/issues
• Email: [e-mail]

Related

• Skald Node.js SDK
• Skald API Documentation