123db4219f
Phase 1 prerequisite for local LLM support. Adapters now declare capability flags (supports_tool_calling, max_context_tokens, etc.) so the engine can reason about what backend is available. OpenAI adapter accepts fusion_accounting.openai_base_url config -- point it at LM Studio (http://host.docker.internal:1234/v1) or Ollama (http://host.docker.internal:11434/v1) and the existing OpenAI adapter works unchanged. Implementation note: existing Odoo AbstractModel adapters (fusion.accounting.adapter.openai/claude) are preserved untouched to avoid breaking the chat panel; the new plain-Python OpenAIAdapter and ClaudeAdapter classes (LLMProvider subclasses) are added alongside them. Made-with: Cursor
45 lines
1.7 KiB
Python
45 lines
1.7 KiB
Python
"""LLMProvider contract - every adapter must conform.
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Phase 1 generalisation: makes local LLM (Ollama, LM Studio, vLLM, llamafile,
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llama.cpp HTTP server) a one-config-line drop-in via the OpenAI-compatible
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HTTP API surface that all of them expose.
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"""
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class LLMProvider:
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"""Contract every LLM backend must satisfy. Adapters declare capabilities
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as class attributes; the engine inspects them before calling optional methods."""
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supports_tool_calling: bool = False
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supports_streaming: bool = False
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max_context_tokens: int = 4096
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supports_embeddings: bool = False
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def __init__(self, env):
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self.env = env
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def complete(self, *, system, messages, max_tokens=2048, temperature=0.0) -> dict:
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"""Plain text completion. Required for ALL providers.
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Returns: {'content': str, 'tokens_used': int, 'model': str}
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"""
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raise NotImplementedError
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def complete_with_tools(self, *, system, messages, tools, max_tokens=2048) -> dict:
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"""Tool-calling completion. Optional - caller checks supports_tool_calling first.
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Returns: {'content': str, 'tool_calls': [{'name': str, 'arguments': dict}], ...}
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"""
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raise NotImplementedError(
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f"{type(self).__name__} does not support tool-calling. "
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f"Check supports_tool_calling before calling.")
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def embed(self, texts: list[str]) -> list[list[float]]:
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"""Embeddings. Optional - caller checks supports_embeddings first.
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Returns: list of float vectors, one per input text.
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"""
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raise NotImplementedError(
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f"{type(self).__name__} does not support embeddings. "
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f"Check supports_embeddings before calling.")
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