Siddharth Kothari
6672d2be57
Agentic schematic validation: datasheet extraction via Claude Console Skills, netlist/BOM design graph, per-IC direct datasheet review with page citations, capacitor derating, Next.js report UI. Extracted from the Pinscope cloud codebase. Auth and billing live in the private gateway repo behind stable seams (billing_hook.py, adapter files listed in CLAUDE.md).
101 lines
3.1 KiB
Python
101 lines
3.1 KiB
Python
"""Abstract LLMProvider + LLMSession interfaces."""
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from typing import Protocol
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from backend.services.llm.types import (
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Completion,
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Message,
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ToolChoice,
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ToolSchema,
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)
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class LLMSession(ABC):
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"""A multi-turn conversation with provider-specific cache lifecycle.
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Lifecycle::
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session = await provider.create_session(model=..., system=...)
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try:
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messages = [Message("user", [
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PdfBlock(path, cacheable=True),
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TextBlock(context, cacheable=True),
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])]
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for turn in range(N):
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completion = await session.complete(
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messages=messages, tools=..., tool_choice=...,
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)
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# process tool_calls, append to messages, repeat
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finally:
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await session.close()
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Caching: blocks with ``cacheable=True`` participate in provider caching.
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Anthropic stamps ``cache_control: ephemeral`` on each cacheable block on
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every call. Gemini collects all cacheable blocks (plus the system prompt)
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on the first ``complete()`` call into a ``CachedContent`` object and
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references it on subsequent calls. The system prompt is always cached.
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"""
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provider_name: str
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"""Provider identifier ("anthropic", "gemini") — used for api_logs."""
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model: str
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@abstractmethod
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async def complete(
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self,
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*,
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messages: list[Message],
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tools: list[ToolSchema] | None = None,
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tool_choice: ToolChoice = "auto",
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) -> Completion:
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"""Run one inference turn."""
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@abstractmethod
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async def close(self) -> None:
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"""Release any provider-side resources (e.g. delete a cache object).
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Safe to call multiple times."""
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class LLMProvider(Protocol):
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"""Top-level provider interface."""
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name: str
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async def create_session(
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self,
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*,
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model: str,
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system: str,
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max_tokens: int = 4096,
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temperature: float | None = None,
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) -> LLMSession:
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"""Construct a session. ``system`` is always cached by the session.
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``temperature`` — if not None, applied to every ``complete()`` call on
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this session. ``None`` means use the provider's default. Set to 0.0
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for deterministic-as-possible behavior in agentic loops where the same
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inputs should produce the same outputs."""
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...
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async def run_skill(
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self,
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*,
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skill_name: str,
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model: str,
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system: str,
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user_text: str,
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pdf_path: str | None,
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output_tool: ToolSchema,
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) -> tuple[dict, "Completion"]:
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"""Execute a managed Skill and return (forced-tool input, Completion).
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Anthropic uses Console Skills (skill_id + container + code_execution
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beta). Gemini raises ``NotImplementedError`` — there is no
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Gemini-managed-Skill equivalent today; if you want a Gemini path for
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skill-style extraction, inline the SKILL.md content as ``system`` and
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run validation locally."""
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...
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