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feat: implement Pydantic AI dependency injection with RunContext and deps_type #190
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,22 @@ | ||
| from dataclasses import dataclass | ||
| from pathlib import Path | ||
| from typing import Any | ||
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| from rich.console import Console | ||
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| from .services import QueryProtocol | ||
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| @dataclass | ||
| class RAGDeps: | ||
| project_root: Path | ||
| ingestor: QueryProtocol | ||
| cypher_generator: Any | ||
| code_retriever: Any | ||
| file_reader: Any | ||
| file_writer: Any | ||
| file_editor: Any | ||
| shell_commander: Any | ||
| directory_lister: Any | ||
| document_analyzer: Any | ||
| console: Console | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,2 @@ | ||
| class LLMGenerationError(Exception): | ||
| pass |
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The
RAGDepsdataclass usesAnyfor most of its fields. This undermines the goal of "Explicit, type-safe dependency injection" mentioned in the PR description. To improve type safety and developer experience, you should use specific class types for these dependencies.To avoid circular imports that this might cause, you can use a
typing.TYPE_CHECKINGblock and string forward references.First, add these imports at the top of the file:
Then, update the dataclass fields: