"""
KnowledgeService protocol for external service operations in the Capture,
Extract, Assemble, Publish workflow.
This module defines the KnowledgeService protocol that handles interactions
with external knowledge services, including document registration and query
execution. This protocol is separate from the repository layer which only
handles local metadata persistence.
Concrete implementations of this protocol are provided for different external
services (Anthropic, OpenAI, etc.) and are created via factory functions.
"""
from datetime import datetime, timezone
from typing import (
TYPE_CHECKING,
Any,
Protocol,
runtime_checkable,
)
from pydantic import BaseModel, Field
if TYPE_CHECKING:
from julee.domain.models.knowledge_service_config import (
KnowledgeServiceConfig,
)
from julee.domain.models.document import Document
[docs]
class QueryResult(BaseModel):
"""Result of a knowledge service query execution."""
[docs]
query_id: str = Field(description="Unique identifier for this query execution")
[docs]
query_text: str = Field(description="The original query text that was executed")
[docs]
result_data: dict[str, Any] = Field(
default_factory=dict,
description="The structured result data from the query",
)
[docs]
execution_time_ms: int | None = Field(
default=None,
description="Time taken to execute the query in milliseconds",
)
[docs]
created_at: datetime | None = Field(
default_factory=lambda: datetime.now(timezone.utc)
)
[docs]
class FileRegistrationResult(BaseModel):
"""Result of registering a file with a knowledge service."""
[docs]
document_id: str = Field(description="The original document ID from our system")
[docs]
knowledge_service_file_id: str = Field(
description="The file identifier assigned by the knowledge service"
)
[docs]
created_at: datetime | None = Field(
default_factory=lambda: datetime.now(timezone.utc)
)
@runtime_checkable
[docs]
class KnowledgeService(Protocol):
"""
Protocol for interacting with external knowledge services.
This protocol defines the interface for external operations that were
moved out of the repository layer. Implementations handle the specifics
of different knowledge service APIs (Anthropic, OpenAI, etc.).
"""
[docs]
async def register_file(
self, config: "KnowledgeServiceConfig", document: Document
) -> FileRegistrationResult:
"""Register a document file with the external knowledge service.
This method registers a document with the external knowledge service,
allowing that service to analyze and index the document content for
future queries.
Args:
config: KnowledgeServiceConfig for the service to use
document: Document domain object to register
Returns:
FileRegistrationResult containing registration details and the
service's internal file identifier
.. rubric:: Implementation Notes
- Must be idempotent: re-registering same document returns same result
- Should handle service unavailability gracefully
- Must return the service's internal file ID for future queries
- Document content is accessed directly from the Document object
- Should handle various document formats and sizes
.. rubric:: Workflow Context
In Temporal workflows, this method is implemented as an activity
to ensure registration results are durably stored and consistent
across workflow replays.
"""
...
[docs]
async def execute_query(
self,
config: "KnowledgeServiceConfig",
query_text: str,
output_schema: dict[str, Any] | None = None,
service_file_ids: list[str] | None = None,
query_metadata: dict[str, Any] | None = None,
assistant_prompt: str | None = None,
) -> QueryResult:
"""Execute a query against the external knowledge service.
This method executes a text query against the knowledge service,
optionally scoping the query to specific documents that have been
previously registered with the service.
Args:
config: KnowledgeServiceConfig for the service to use
query_text: The query to execute (natural language or structured)
output_schema: Optional JSON schema for structured response.
When provided, the service will attempt to return
results conforming to this schema using structured
outputs or schema-guided prompting.
service_file_ids: Optional list of service file IDs to provide as
context for the query. These are the IDs returned
by the knowledge service from register_file
operations, and are included in the query to give
the service access to specific documents.
query_metadata: Optional service-specific metadata and
configuration options such as model selection,
temperature, max_tokens, etc. The structure depends
on the specific knowledge service being used.
assistant_prompt: Optional assistant message content to constrain
or prime the model's response. This is added as
the final assistant message before the model
generates its response, allowing control over
response format and structure.
Returns:
QueryResult containing query results and execution metadata
.. rubric:: Implementation Notes
- Must be idempotent: same query returns consistent results
- Service file IDs are provided as context to enhance query responses
- Should handle service unavailability gracefully
- Query results should be structured as domain objects
- Should track execution time and metadata
- Must handle various query formats (natural language, structured,
etc.)
- Should validate that service_file_ids exist in the service before
including them in the query context
.. rubric:: Workflow Context
In Temporal workflows, this method is implemented as an activity
to ensure query results are durably stored and can be replayed
consistently.
"""
...