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85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
"""OSS provider definitions for LLM, embedder, and vector store."""
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from __future__ import annotations
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import os
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from typing import Any
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LLM_PROVIDERS: dict[str, dict[str, Any]] = {
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"openai": {
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"label": "OpenAI",
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"needs_key": True,
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"env_var": "OPENAI_API_KEY",
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"default_model": "gpt-5-mini",
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},
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"ollama": {
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"label": "Ollama (local)",
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"needs_key": False,
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"default_model": "llama3.1:8b",
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"default_url": "http://localhost:11434",
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"pip_dep": "ollama",
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},
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}
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EMBEDDER_PROVIDERS: dict[str, dict[str, Any]] = {
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"openai": {
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"label": "OpenAI",
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"needs_key": True,
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"env_var": "OPENAI_API_KEY",
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"default_model": "text-embedding-3-small",
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"dims": 1536,
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},
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"ollama": {
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"label": "Ollama (local)",
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"needs_key": False,
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"default_model": "nomic-embed-text",
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"default_url": "http://localhost:11434",
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"dims": 768,
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"pip_dep": "ollama",
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},
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}
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VECTOR_PROVIDERS: dict[str, dict[str, Any]] = {
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"qdrant": {
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"label": "Qdrant",
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"default_config": {"path": os.path.expanduser("~/.hermes/mem0_qdrant")},
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"pip_dep": "qdrant-client",
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},
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"pgvector": {
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"label": "PGVector",
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"default_config": {"host": "localhost", "port": 5432, "user": os.getenv("USER", "postgres"), "dbname": "postgres"},
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"pip_dep": "psycopg2-binary",
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},
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}
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KNOWN_DIMS: dict[str, int] = {
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"text-embedding-3-small": 1536,
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"text-embedding-3-large": 3072,
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"text-embedding-ada-002": 1536,
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"nomic-embed-text": 768,
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}
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def validate_oss_config(oss_config: dict) -> list[str]:
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"""Validate an OSS config dict. Returns list of error strings (empty = valid)."""
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errors: list[str] = []
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for section, registry in [("llm", LLM_PROVIDERS), ("embedder", EMBEDDER_PROVIDERS),
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("vector_store", VECTOR_PROVIDERS)]:
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block = oss_config.get(section)
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if not block or not isinstance(block, dict):
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errors.append(f"Missing required section: {section}")
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continue
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provider_id = block.get("provider", "")
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if provider_id not in registry:
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valid = ", ".join(registry.keys())
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errors.append(f"Unknown {section} provider '{provider_id}'. Valid: {valid}")
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vs = oss_config.get("vector_store", {})
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if vs.get("provider") == "pgvector":
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cfg = vs.get("config", {})
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if not cfg.get("user"):
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errors.append("PGVector requires 'user' in vector_store.config")
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return errors
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