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feat(providers): add pluggable provider registry and OpenAICompatibleProvider base class Refactor the provider system from hardcoded if/elif chains to a self-registering plugin architecture. Each provider module registers itself with ProviderRegistry on import, declaring its env var, model prefixes, and default models. ProviderManager and discovery now use the registry for instantiation and model routing instead of hardcoded logic. Adds OpenAICompatibleProvider base class for future providers that use the OpenAI-compatible API format (Together, Fireworks, Cerebras, etc). Closes #77

lmata 2026-03-07 21:51 trunk
Commit ae85592da49c4ccc5f62070f82f3000bf0c49bd1ef3434ecb2d2100189e14fda
Parent 613bc9ca307adee…
8 files changed +13 -2 +14 -1 +171 -2 +56 -53 +14 -1 +27 -50 +10 -1 +10 -1
~ video_processor/providers/__init__.py ~ video_processor/providers/anthropic_provider.py ~ video_processor/providers/base.py ~ video_processor/providers/discovery.py ~ video_processor/providers/gemini_provider.py ~ video_processor/providers/manager.py ~ video_processor/providers/ollama_provider.py ~ video_processor/providers/openai_provider.py
M video_processor/providers/__init__.py
+13 -2
--- video_processor/providers/__init__.py
+++ video_processor/providers/__init__.py
@@ -1,6 +1,17 @@
11
 """Provider abstraction layer for LLM, vision, and transcription APIs."""
22
3
-from video_processor.providers.base import BaseProvider, ModelInfo
3
+from video_processor.providers.base import (
4
+ BaseProvider,
5
+ ModelInfo,
6
+ OpenAICompatibleProvider,
7
+ ProviderRegistry,
8
+)
49
 from video_processor.providers.manager import ProviderManager
510
6
-__all__ = ["BaseProvider", "ModelInfo", "ProviderManager"]
11
+__all__ = [
12
+ "BaseProvider",
13
+ "ModelInfo",
14
+ "OpenAICompatibleProvider",
15
+ "ProviderManager",
16
+ "ProviderRegistry",
17
+]
718
--- video_processor/providers/__init__.py
+++ video_processor/providers/__init__.py
@@ -1,6 +1,17 @@
1 """Provider abstraction layer for LLM, vision, and transcription APIs."""
2
3 from video_processor.providers.base import BaseProvider, ModelInfo
 
 
 
 
 
4 from video_processor.providers.manager import ProviderManager
5
6 __all__ = ["BaseProvider", "ModelInfo", "ProviderManager"]
 
 
 
 
 
 
7
--- video_processor/providers/__init__.py
+++ video_processor/providers/__init__.py
@@ -1,6 +1,17 @@
1 """Provider abstraction layer for LLM, vision, and transcription APIs."""
2
3 from video_processor.providers.base import (
4 BaseProvider,
5 ModelInfo,
6 OpenAICompatibleProvider,
7 ProviderRegistry,
8 )
9 from video_processor.providers.manager import ProviderManager
10
11 __all__ = [
12 "BaseProvider",
13 "ModelInfo",
14 "OpenAICompatibleProvider",
15 "ProviderManager",
16 "ProviderRegistry",
17 ]
18
M video_processor/providers/anthropic_provider.py
+14 -1
--- video_processor/providers/anthropic_provider.py
+++ video_processor/providers/anthropic_provider.py
@@ -7,11 +7,11 @@
77
 from typing import Optional
88
99
 import anthropic
1010
 from dotenv import load_dotenv
1111
12
-from video_processor.providers.base import BaseProvider, ModelInfo
12
+from video_processor.providers.base import BaseProvider, ModelInfo, ProviderRegistry
1313
1414
 load_dotenv()
1515
 logger = logging.getLogger(__name__)
1616
1717
@@ -108,5 +108,18 @@
108108
 )
109109
 )
110110
 except Exception as e:
111111
 logger.warning(f"Failed to list Anthropic models: {e}")
112112
 return sorted(models, key=lambda m: m.id)
113
+
114
+
115
+ProviderRegistry.register(
116
+ name="anthropic",
117
+ provider_class=AnthropicProvider,
118
+ env_var="ANTHROPIC_API_KEY",
119
+ model_prefixes=["claude-"],
120
+ default_models={
121
+ "chat": "claude-sonnet-4-5-20250929",
122
+ "vision": "claude-sonnet-4-5-20250929",
123
+ "audio": "",
124
+ },
125
+)
113126
--- video_processor/providers/anthropic_provider.py
+++ video_processor/providers/anthropic_provider.py
@@ -7,11 +7,11 @@
7 from typing import Optional
8
9 import anthropic
10 from dotenv import load_dotenv
11
12 from video_processor.providers.base import BaseProvider, ModelInfo
13
14 load_dotenv()
15 logger = logging.getLogger(__name__)
16
17
@@ -108,5 +108,18 @@
108 )
109 )
110 except Exception as e:
111 logger.warning(f"Failed to list Anthropic models: {e}")
112 return sorted(models, key=lambda m: m.id)
 
 
 
 
 
 
 
 
 
 
 
 
 
113
--- video_processor/providers/anthropic_provider.py
+++ video_processor/providers/anthropic_provider.py
@@ -7,11 +7,11 @@
7 from typing import Optional
8
9 import anthropic
10 from dotenv import load_dotenv
11
12 from video_processor.providers.base import BaseProvider, ModelInfo, ProviderRegistry
13
14 load_dotenv()
15 logger = logging.getLogger(__name__)
16
17
@@ -108,5 +108,18 @@
108 )
109 )
110 except Exception as e:
111 logger.warning(f"Failed to list Anthropic models: {e}")
112 return sorted(models, key=lambda m: m.id)
113
114
115 ProviderRegistry.register(
116 name="anthropic",
117 provider_class=AnthropicProvider,
118 env_var="ANTHROPIC_API_KEY",
119 model_prefixes=["claude-"],
120 default_models={
121 "chat": "claude-sonnet-4-5-20250929",
122 "vision": "claude-sonnet-4-5-20250929",
123 "audio": "",
124 },
125 )
126
M video_processor/providers/base.py
+171 -2
--- video_processor/providers/base.py
+++ video_processor/providers/base.py
@@ -1,12 +1,17 @@
1
-"""Abstract base class and shared types for provider implementations."""
1
+"""Abstract base class, registry, and shared types for provider implementations."""
22
3
+import base64
4
+import logging
5
+import os
36
 from abc import ABC, abstractmethod
47
 from pathlib import Path
5
-from typing import List, Optional
8
+from typing import Dict, List, Optional
69
710
 from pydantic import BaseModel, Field
11
+
12
+logger = logging.getLogger(__name__)
813
914
1015
 class ModelInfo(BaseModel):
1116
 """Information about an available model."""
1217
@@ -53,5 +58,169 @@
5358
 """Transcribe an audio file. Returns dict with 'text', 'segments', etc."""
5459
5560
 @abstractmethod
5661
 def list_models(self) -> list[ModelInfo]:
5762
 """Discover available models from this provider's API."""
63
+
64
+
65
+class ProviderRegistry:
66
+ """Registry for provider classes. Providers register themselves with metadata."""
67
+
68
+ _providers: Dict[str, Dict] = {}
69
+
70
+ @classmethod
71
+ def register(
72
+ cls,
73
+ name: str,
74
+ provider_class: type,
75
+ env_var: str = "",
76
+ model_prefixes: Optional[List[str]] = None,
77
+ default_models: Optional[Dict[str, str]] = None,
78
+ ) -> None:
79
+ """Register a provider class with its metadata."""
80
+ cls._providers[name] = {
81
+ "class": provider_class,
82
+ "env_var": env_var,
83
+ "model_prefixes": model_prefixes or [],
84
+ "default_models": default_models or {},
85
+ }
86
+
87
+ @classmethod
88
+ def get(cls, name: str) -> type:
89
+ """Return the provider class for a given name."""
90
+ if name not in cls._providers:
91
+ raise ValueError(f"Unknown provider: {name}")
92
+ return cls._providers[name]["class"]
93
+
94
+ @classmethod
95
+ def get_by_model(cls, model_id: str) -> Optional[str]:
96
+ """Return provider name for a model ID based on prefix matching."""
97
+ for name, info in cls._providers.items():
98
+ for prefix in info["model_prefixes"]:
99
+ if model_id.startswith(prefix):
100
+ return name
101
+ return None
102
+
103
+ @classmethod
104
+ def get_default_models(cls, name: str) -> Dict[str, str]:
105
+ """Return the default models dict for a provider."""
106
+ if name not in cls._providers:
107
+ return {}
108
+ return cls._providers[name].get("default_models", {})
109
+
110
+ @classmethod
111
+ def available(cls) -> List[str]:
112
+ """Return names of providers whose env var is set (or have no env var requirement)."""
113
+ result = []
114
+ for name, info in cls._providers.items():
115
+ env_var = info.get("env_var", "")
116
+ if not env_var:
117
+ # Providers without an env var (e.g. ollama) need special availability checks
118
+ result.append(name)
119
+ elif os.getenv(env_var, ""):
120
+ result.append(name)
121
+ return result
122
+
123
+ @classmethod
124
+ def all_registered(cls) -> Dict[str, Dict]:
125
+ """Return all registered providers and their metadata."""
126
+ return dict(cls._providers)
127
+
128
+
129
+class OpenAICompatibleProvider(BaseProvider):
130
+ """Base for providers using OpenAI-compatible APIs.
131
+
132
+ Suitable for Together, Fireworks, Cerebras, xAI, Azure, and similar services.
133
+ """
134
+
135
+ provider_name: str = ""
136
+ base_url: str = ""
137
+ env_var: str = ""
138
+
139
+ def __init__(self, api_key: Optional[str] = None, base_url: Optional[str] = None):
140
+ from openai import OpenAI
141
+
142
+ self._api_key = api_key or os.getenv(self.env_var, "")
143
+ self._base_url = base_url or self.base_url
144
+ self._client = OpenAI(api_key=self._api_key, base_url=self._base_url)
145
+ self._last_usage = None
146
+
147
+ def chat(
148
+ self,
149
+ messages: list[dict],
150
+ max_tokens: int = 4096,
151
+ temperature: float = 0.7,
152
+ model: Optional[str] = None,
153
+ ) -> str:
154
+ model = model or "gpt-4o"
155
+ response = self._client.chat.completions.create(
156
+ model=model,
157
+ messages=messages,
158
+ max_tokens=max_tokens,
159
+ temperature=temperature,
160
+ )
161
+ self._last_usage = {
162
+ "input_tokens": getattr(response.usage, "prompt_tokens", 0) if response.usage else 0,
163
+ "output_tokens": getattr(response.usage, "completion_tokens", 0)
164
+ if response.usage
165
+ else 0,
166
+ }
167
+ return response.choices[0].message.content or ""
168
+
169
+ def analyze_image(
170
+ self,
171
+ image_bytes: bytes,
172
+ prompt: str,
173
+ max_tokens: int = 4096,
174
+ model: Optional[str] = None,
175
+ ) -> str:
176
+ model = model or "gpt-4o"
177
+ b64 = base64.b64encode(image_bytes).decode()
178
+ response = self._client.chat.completions.create(
179
+ model=model,
180
+ messages=[
181
+ {
182
+ "role": "user",
183
+ "content": [
184
+ {"type": "text", "text": prompt},
185
+ {
186
+ "type": "image_url",
187
+ "image_url": {"url": f"data:image/jpeg;base64,{b64}"},
188
+ },
189
+ ],
190
+ }
191
+ ],
192
+ max_tokens=max_tokens,
193
+ )
194
+ self._last_usage = {
195
+ "input_tokens": getattr(response.usage, "prompt_tokens", 0) if response.usage else 0,
196
+ "output_tokens": getattr(response.usage, "completion_tokens", 0)
197
+ if response.usage
198
+ else 0,
199
+ }
200
+ return response.choices[0].message.content or ""
201
+
202
+ def transcribe_audio(
203
+ self,
204
+ audio_path: str | Path,
205
+ language: Optional[str] = None,
206
+ model: Optional[str] = None,
207
+ ) -> dict:
208
+ raise NotImplementedError(f"{self.provider_name} does not support audio transcription")
209
+
210
+ def list_models(self) -> list[ModelInfo]:
211
+ models = []
212
+ try:
213
+ for m in self._client.models.list():
214
+ mid = m.id
215
+ caps = ["chat"]
216
+ models.append(
217
+ ModelInfo(
218
+ id=mid,
219
+ provider=self.provider_name,
220
+ display_name=mid,
221
+ capabilities=caps,
222
+ )
223
+ )
224
+ except Exception as e:
225
+ logger.warning(f"Failed to list {self.provider_name} models: {e}")
226
+ return sorted(models, key=lambda m: m.id)
58227
--- video_processor/providers/base.py
+++ video_processor/providers/base.py
@@ -1,12 +1,17 @@
1 """Abstract base class and shared types for provider implementations."""
2
 
 
 
3 from abc import ABC, abstractmethod
4 from pathlib import Path
5 from typing import List, Optional
6
7 from pydantic import BaseModel, Field
 
 
8
9
10 class ModelInfo(BaseModel):
11 """Information about an available model."""
12
@@ -53,5 +58,169 @@
53 """Transcribe an audio file. Returns dict with 'text', 'segments', etc."""
54
55 @abstractmethod
56 def list_models(self) -> list[ModelInfo]:
57 """Discover available models from this provider's API."""
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
58
--- video_processor/providers/base.py
+++ video_processor/providers/base.py
@@ -1,12 +1,17 @@
1 """Abstract base class, registry, and shared types for provider implementations."""
2
3 import base64
4 import logging
5 import os
6 from abc import ABC, abstractmethod
7 from pathlib import Path
8 from typing import Dict, List, Optional
9
10 from pydantic import BaseModel, Field
11
12 logger = logging.getLogger(__name__)
13
14
15 class ModelInfo(BaseModel):
16 """Information about an available model."""
17
@@ -53,5 +58,169 @@
58 """Transcribe an audio file. Returns dict with 'text', 'segments', etc."""
59
60 @abstractmethod
61 def list_models(self) -> list[ModelInfo]:
62 """Discover available models from this provider's API."""
63
64
65 class ProviderRegistry:
66 """Registry for provider classes. Providers register themselves with metadata."""
67
68 _providers: Dict[str, Dict] = {}
69
70 @classmethod
71 def register(
72 cls,
73 name: str,
74 provider_class: type,
75 env_var: str = "",
76 model_prefixes: Optional[List[str]] = None,
77 default_models: Optional[Dict[str, str]] = None,
78 ) -> None:
79 """Register a provider class with its metadata."""
80 cls._providers[name] = {
81 "class": provider_class,
82 "env_var": env_var,
83 "model_prefixes": model_prefixes or [],
84 "default_models": default_models or {},
85 }
86
87 @classmethod
88 def get(cls, name: str) -> type:
89 """Return the provider class for a given name."""
90 if name not in cls._providers:
91 raise ValueError(f"Unknown provider: {name}")
92 return cls._providers[name]["class"]
93
94 @classmethod
95 def get_by_model(cls, model_id: str) -> Optional[str]:
96 """Return provider name for a model ID based on prefix matching."""
97 for name, info in cls._providers.items():
98 for prefix in info["model_prefixes"]:
99 if model_id.startswith(prefix):
100 return name
101 return None
102
103 @classmethod
104 def get_default_models(cls, name: str) -> Dict[str, str]:
105 """Return the default models dict for a provider."""
106 if name not in cls._providers:
107 return {}
108 return cls._providers[name].get("default_models", {})
109
110 @classmethod
111 def available(cls) -> List[str]:
112 """Return names of providers whose env var is set (or have no env var requirement)."""
113 result = []
114 for name, info in cls._providers.items():
115 env_var = info.get("env_var", "")
116 if not env_var:
117 # Providers without an env var (e.g. ollama) need special availability checks
118 result.append(name)
119 elif os.getenv(env_var, ""):
120 result.append(name)
121 return result
122
123 @classmethod
124 def all_registered(cls) -> Dict[str, Dict]:
125 """Return all registered providers and their metadata."""
126 return dict(cls._providers)
127
128
129 class OpenAICompatibleProvider(BaseProvider):
130 """Base for providers using OpenAI-compatible APIs.
131
132 Suitable for Together, Fireworks, Cerebras, xAI, Azure, and similar services.
133 """
134
135 provider_name: str = ""
136 base_url: str = ""
137 env_var: str = ""
138
139 def __init__(self, api_key: Optional[str] = None, base_url: Optional[str] = None):
140 from openai import OpenAI
141
142 self._api_key = api_key or os.getenv(self.env_var, "")
143 self._base_url = base_url or self.base_url
144 self._client = OpenAI(api_key=self._api_key, base_url=self._base_url)
145 self._last_usage = None
146
147 def chat(
148 self,
149 messages: list[dict],
150 max_tokens: int = 4096,
151 temperature: float = 0.7,
152 model: Optional[str] = None,
153 ) -> str:
154 model = model or "gpt-4o"
155 response = self._client.chat.completions.create(
156 model=model,
157 messages=messages,
158 max_tokens=max_tokens,
159 temperature=temperature,
160 )
161 self._last_usage = {
162 "input_tokens": getattr(response.usage, "prompt_tokens", 0) if response.usage else 0,
163 "output_tokens": getattr(response.usage, "completion_tokens", 0)
164 if response.usage
165 else 0,
166 }
167 return response.choices[0].message.content or ""
168
169 def analyze_image(
170 self,
171 image_bytes: bytes,
172 prompt: str,
173 max_tokens: int = 4096,
174 model: Optional[str] = None,
175 ) -> str:
176 model = model or "gpt-4o"
177 b64 = base64.b64encode(image_bytes).decode()
178 response = self._client.chat.completions.create(
179 model=model,
180 messages=[
181 {
182 "role": "user",
183 "content": [
184 {"type": "text", "text": prompt},
185 {
186 "type": "image_url",
187 "image_url": {"url": f"data:image/jpeg;base64,{b64}"},
188 },
189 ],
190 }
191 ],
192 max_tokens=max_tokens,
193 )
194 self._last_usage = {
195 "input_tokens": getattr(response.usage, "prompt_tokens", 0) if response.usage else 0,
196 "output_tokens": getattr(response.usage, "completion_tokens", 0)
197 if response.usage
198 else 0,
199 }
200 return response.choices[0].message.content or ""
201
202 def transcribe_audio(
203 self,
204 audio_path: str | Path,
205 language: Optional[str] = None,
206 model: Optional[str] = None,
207 ) -> dict:
208 raise NotImplementedError(f"{self.provider_name} does not support audio transcription")
209
210 def list_models(self) -> list[ModelInfo]:
211 models = []
212 try:
213 for m in self._client.models.list():
214 mid = m.id
215 caps = ["chat"]
216 models.append(
217 ModelInfo(
218 id=mid,
219 provider=self.provider_name,
220 display_name=mid,
221 capabilities=caps,
222 )
223 )
224 except Exception as e:
225 logger.warning(f"Failed to list {self.provider_name} models: {e}")
226 return sorted(models, key=lambda m: m.id)
227
M video_processor/providers/discovery.py
+56 -53
--- video_processor/providers/discovery.py
+++ video_processor/providers/discovery.py
@@ -4,17 +4,27 @@
44
 import os
55
 from typing import Optional
66
77
 from dotenv import load_dotenv
88
9
-from video_processor.providers.base import ModelInfo
9
+from video_processor.providers.base import ModelInfo, ProviderRegistry
1010
1111
 load_dotenv()
1212
 logger = logging.getLogger(__name__)
1313
1414
 _cached_models: Optional[list[ModelInfo]] = None
1515
16
+
17
+def _ensure_providers_registered() -> None:
18
+ """Import all built-in provider modules so they register themselves."""
19
+ if ProviderRegistry.all_registered():
20
+ return
21
+ import video_processor.providers.anthropic_provider # noqa: F401
22
+ import video_processor.providers.gemini_provider # noqa: F401
23
+ import video_processor.providers.ollama_provider # noqa: F401
24
+ import video_processor.providers.openai_provider # noqa: F401
25
+
1626
1727
 def discover_available_models(
1828
 api_keys: Optional[dict[str, str]] = None,
1929
 force_refresh: bool = False,
2030
 ) -> list[ModelInfo]:
@@ -26,70 +36,63 @@
2636
 """
2737
 global _cached_models
2838
 if _cached_models is not None and not force_refresh:
2939
 return _cached_models
3040
41
+ _ensure_providers_registered()
42
+
3143
 keys = api_keys or {
3244
 "openai": os.getenv("OPENAI_API_KEY", ""),
3345
 "anthropic": os.getenv("ANTHROPIC_API_KEY", ""),
3446
 "gemini": os.getenv("GEMINI_API_KEY", ""),
3547
 }
3648
3749
 all_models: list[ModelInfo] = []
3850
39
- # OpenAI
40
- if keys.get("openai"):
41
- try:
42
- from video_processor.providers.openai_provider import OpenAIProvider
43
-
44
- provider = OpenAIProvider(api_key=keys["openai"])
45
- models = provider.list_models()
46
- logger.info(f"Discovered {len(models)} OpenAI models")
47
- all_models.extend(models)
48
- except Exception as e:
49
- logger.info(f"OpenAI discovery skipped: {e}")
50
-
51
- # Anthropic
52
- if keys.get("anthropic"):
53
- try:
54
- from video_processor.providers.anthropic_provider import AnthropicProvider
55
-
56
- provider = AnthropicProvider(api_key=keys["anthropic"])
57
- models = provider.list_models()
58
- logger.info(f"Discovered {len(models)} Anthropic models")
59
- all_models.extend(models)
60
- except Exception as e:
61
- logger.info(f"Anthropic discovery skipped: {e}")
62
-
63
- # Gemini (API key or service account)
64
- gemini_key = keys.get("gemini")
65
- gemini_creds = os.getenv("GOOGLE_APPLICATION_CREDENTIALS", "")
66
- if gemini_key or gemini_creds:
67
- try:
68
- from video_processor.providers.gemini_provider import GeminiProvider
69
-
70
- provider = GeminiProvider(
71
- api_key=gemini_key or None,
72
- credentials_path=gemini_creds or None,
73
- )
74
- models = provider.list_models()
75
- logger.info(f"Discovered {len(models)} Gemini models")
76
- all_models.extend(models)
77
- except Exception as e:
78
- logger.warning(f"Gemini discovery failed: {e}")
79
-
80
- # Ollama (local, no API key needed)
81
- try:
82
- from video_processor.providers.ollama_provider import OllamaProvider
83
-
84
- if OllamaProvider.is_available():
85
- provider = OllamaProvider()
86
- models = provider.list_models()
87
- logger.info(f"Discovered {len(models)} Ollama models")
88
- all_models.extend(models)
89
- except Exception as e:
90
- logger.info(f"Ollama discovery skipped: {e}")
51
+ for name, info in ProviderRegistry.all_registered().items():
52
+ env_var = info.get("env_var", "")
53
+ provider_class = info["class"]
54
+
55
+ if name == "ollama":
56
+ # Ollama: no API key, check server availability
57
+ try:
58
+ if provider_class.is_available():
59
+ provider = provider_class()
60
+ models = provider.list_models()
61
+ logger.info(f"Discovered {len(models)} Ollama models")
62
+ all_models.extend(models)
63
+ except Exception as e:
64
+ logger.info(f"Ollama discovery skipped: {e}")
65
+ continue
66
+
67
+ # For key-based providers, check the api_keys dict first, then env var
68
+ key = keys.get(name, "")
69
+ if not key and env_var:
70
+ key = os.getenv(env_var, "")
71
+
72
+ # Special case: Gemini also supports service account credentials
73
+ gemini_creds = ""
74
+ if name == "gemini":
75
+ gemini_creds = os.getenv("GOOGLE_APPLICATION_CREDENTIALS", "")
76
+
77
+ if not key and not gemini_creds:
78
+ continue
79
+
80
+ try:
81
+ # Handle provider-specific constructor args
82
+ if name == "gemini":
83
+ provider = provider_class(
84
+ api_key=key or None,
85
+ credentials_path=gemini_creds or None,
86
+ )
87
+ else:
88
+ provider = provider_class(api_key=key)
89
+ models = provider.list_models()
90
+ logger.info(f"Discovered {len(models)} {name.capitalize()} models")
91
+ all_models.extend(models)
92
+ except Exception as e:
93
+ logger.info(f"{name.capitalize()} discovery skipped: {e}")
9194
9295
 # Sort by provider then id
9396
 all_models.sort(key=lambda m: (m.provider, m.id))
9497
 _cached_models = all_models
9598
 logger.info(f"Total discovered models: {len(all_models)}")
9699
--- video_processor/providers/discovery.py
+++ video_processor/providers/discovery.py
@@ -4,17 +4,27 @@
4 import os
5 from typing import Optional
6
7 from dotenv import load_dotenv
8
9 from video_processor.providers.base import ModelInfo
10
11 load_dotenv()
12 logger = logging.getLogger(__name__)
13
14 _cached_models: Optional[list[ModelInfo]] = None
15
 
 
 
 
 
 
 
 
 
 
16
17 def discover_available_models(
18 api_keys: Optional[dict[str, str]] = None,
19 force_refresh: bool = False,
20 ) -> list[ModelInfo]:
@@ -26,70 +36,63 @@
26 """
27 global _cached_models
28 if _cached_models is not None and not force_refresh:
29 return _cached_models
30
 
 
31 keys = api_keys or {
32 "openai": os.getenv("OPENAI_API_KEY", ""),
33 "anthropic": os.getenv("ANTHROPIC_API_KEY", ""),
34 "gemini": os.getenv("GEMINI_API_KEY", ""),
35 }
36
37 all_models: list[ModelInfo] = []
38
39 # OpenAI
40 if keys.get("openai"):
41 try:
42 from video_processor.providers.openai_provider import OpenAIProvider
43
44 provider = OpenAIProvider(api_key=keys["openai"])
45 models = provider.list_models()
46 logger.info(f"Discovered {len(models)} OpenAI models")
47 all_models.extend(models)
48 except Exception as e:
49 logger.info(f"OpenAI discovery skipped: {e}")
50
51 # Anthropic
52 if keys.get("anthropic"):
53 try:
54 from video_processor.providers.anthropic_provider import AnthropicProvider
55
56 provider = AnthropicProvider(api_key=keys["anthropic"])
57 models = provider.list_models()
58 logger.info(f"Discovered {len(models)} Anthropic models")
59 all_models.extend(models)
60 except Exception as e:
61 logger.info(f"Anthropic discovery skipped: {e}")
62
63 # Gemini (API key or service account)
64 gemini_key = keys.get("gemini")
65 gemini_creds = os.getenv("GOOGLE_APPLICATION_CREDENTIALS", "")
66 if gemini_key or gemini_creds:
67 try:
68 from video_processor.providers.gemini_provider import GeminiProvider
69
70 provider = GeminiProvider(
71 api_key=gemini_key or None,
72 credentials_path=gemini_creds or None,
73 )
74 models = provider.list_models()
75 logger.info(f"Discovered {len(models)} Gemini models")
76 all_models.extend(models)
77 except Exception as e:
78 logger.warning(f"Gemini discovery failed: {e}")
79
80 # Ollama (local, no API key needed)
81 try:
82 from video_processor.providers.ollama_provider import OllamaProvider
83
84 if OllamaProvider.is_available():
85 provider = OllamaProvider()
86 models = provider.list_models()
87 logger.info(f"Discovered {len(models)} Ollama models")
88 all_models.extend(models)
89 except Exception as e:
90 logger.info(f"Ollama discovery skipped: {e}")
91
92 # Sort by provider then id
93 all_models.sort(key=lambda m: (m.provider, m.id))
94 _cached_models = all_models
95 logger.info(f"Total discovered models: {len(all_models)}")
96
--- video_processor/providers/discovery.py
+++ video_processor/providers/discovery.py
@@ -4,17 +4,27 @@
4 import os
5 from typing import Optional
6
7 from dotenv import load_dotenv
8
9 from video_processor.providers.base import ModelInfo, ProviderRegistry
10
11 load_dotenv()
12 logger = logging.getLogger(__name__)
13
14 _cached_models: Optional[list[ModelInfo]] = None
15
16
17 def _ensure_providers_registered() -> None:
18 """Import all built-in provider modules so they register themselves."""
19 if ProviderRegistry.all_registered():
20 return
21 import video_processor.providers.anthropic_provider # noqa: F401
22 import video_processor.providers.gemini_provider # noqa: F401
23 import video_processor.providers.ollama_provider # noqa: F401
24 import video_processor.providers.openai_provider # noqa: F401
25
26
27 def discover_available_models(
28 api_keys: Optional[dict[str, str]] = None,
29 force_refresh: bool = False,
30 ) -> list[ModelInfo]:
@@ -26,70 +36,63 @@
36 """
37 global _cached_models
38 if _cached_models is not None and not force_refresh:
39 return _cached_models
40
41 _ensure_providers_registered()
42
43 keys = api_keys or {
44 "openai": os.getenv("OPENAI_API_KEY", ""),
45 "anthropic": os.getenv("ANTHROPIC_API_KEY", ""),
46 "gemini": os.getenv("GEMINI_API_KEY", ""),
47 }
48
49 all_models: list[ModelInfo] = []
50
51 for name, info in ProviderRegistry.all_registered().items():
52 env_var = info.get("env_var", "")
53 provider_class = info["class"]
54
55 if name == "ollama":
56 # Ollama: no API key, check server availability
57 try:
58 if provider_class.is_available():
59 provider = provider_class()
60 models = provider.list_models()
61 logger.info(f"Discovered {len(models)} Ollama models")
62 all_models.extend(models)
63 except Exception as e:
64 logger.info(f"Ollama discovery skipped: {e}")
65 continue
66
67 # For key-based providers, check the api_keys dict first, then env var
68 key = keys.get(name, "")
69 if not key and env_var:
70 key = os.getenv(env_var, "")
71
72 # Special case: Gemini also supports service account credentials
73 gemini_creds = ""
74 if name == "gemini":
75 gemini_creds = os.getenv("GOOGLE_APPLICATION_CREDENTIALS", "")
76
77 if not key and not gemini_creds:
78 continue
79
80 try:
81 # Handle provider-specific constructor args
82 if name == "gemini":
83 provider = provider_class(
84 api_key=key or None,
85 credentials_path=gemini_creds or None,
86 )
87 else:
88 provider = provider_class(api_key=key)
89 models = provider.list_models()
90 logger.info(f"Discovered {len(models)} {name.capitalize()} models")
91 all_models.extend(models)
92 except Exception as e:
93 logger.info(f"{name.capitalize()} discovery skipped: {e}")
 
 
 
 
 
 
 
 
 
94
95 # Sort by provider then id
96 all_models.sort(key=lambda m: (m.provider, m.id))
97 _cached_models = all_models
98 logger.info(f"Total discovered models: {len(all_models)}")
99
M video_processor/providers/gemini_provider.py
+14 -1
--- video_processor/providers/gemini_provider.py
+++ video_processor/providers/gemini_provider.py
@@ -5,11 +5,11 @@
55
 from pathlib import Path
66
 from typing import Optional
77
88
 from dotenv import load_dotenv
99
10
-from video_processor.providers.base import BaseProvider, ModelInfo
10
+from video_processor.providers.base import BaseProvider, ModelInfo, ProviderRegistry
1111
1212
 load_dotenv()
1313
 logger = logging.getLogger(__name__)
1414
1515
 # Capabilities inferred from model id patterns
@@ -218,5 +218,18 @@
218218
 )
219219
 )
220220
 except Exception as e:
221221
 logger.warning(f"Failed to list Gemini models: {e}")
222222
 return sorted(models, key=lambda m: m.id)
223
+
224
+
225
+ProviderRegistry.register(
226
+ name="gemini",
227
+ provider_class=GeminiProvider,
228
+ env_var="GEMINI_API_KEY",
229
+ model_prefixes=["gemini-"],
230
+ default_models={
231
+ "chat": "gemini-2.5-flash",
232
+ "vision": "gemini-2.5-flash",
233
+ "audio": "gemini-2.5-flash",
234
+ },
235
+)
223236
--- video_processor/providers/gemini_provider.py
+++ video_processor/providers/gemini_provider.py
@@ -5,11 +5,11 @@
5 from pathlib import Path
6 from typing import Optional
7
8 from dotenv import load_dotenv
9
10 from video_processor.providers.base import BaseProvider, ModelInfo
11
12 load_dotenv()
13 logger = logging.getLogger(__name__)
14
15 # Capabilities inferred from model id patterns
@@ -218,5 +218,18 @@
218 )
219 )
220 except Exception as e:
221 logger.warning(f"Failed to list Gemini models: {e}")
222 return sorted(models, key=lambda m: m.id)
 
 
 
 
 
 
 
 
 
 
 
 
 
223
--- video_processor/providers/gemini_provider.py
+++ video_processor/providers/gemini_provider.py
@@ -5,11 +5,11 @@
5 from pathlib import Path
6 from typing import Optional
7
8 from dotenv import load_dotenv
9
10 from video_processor.providers.base import BaseProvider, ModelInfo, ProviderRegistry
11
12 load_dotenv()
13 logger = logging.getLogger(__name__)
14
15 # Capabilities inferred from model id patterns
@@ -218,5 +218,18 @@
218 )
219 )
220 except Exception as e:
221 logger.warning(f"Failed to list Gemini models: {e}")
222 return sorted(models, key=lambda m: m.id)
223
224
225 ProviderRegistry.register(
226 name="gemini",
227 provider_class=GeminiProvider,
228 env_var="GEMINI_API_KEY",
229 model_prefixes=["gemini-"],
230 default_models={
231 "chat": "gemini-2.5-flash",
232 "vision": "gemini-2.5-flash",
233 "audio": "gemini-2.5-flash",
234 },
235 )
236
M video_processor/providers/manager.py
+27 -50
--- video_processor/providers/manager.py
+++ video_processor/providers/manager.py
@@ -4,16 +4,28 @@
44
 from pathlib import Path
55
 from typing import Optional
66
77
 from dotenv import load_dotenv
88
9
-from video_processor.providers.base import BaseProvider, ModelInfo
9
+from video_processor.providers.base import BaseProvider, ModelInfo, ProviderRegistry
1010
 from video_processor.providers.discovery import discover_available_models
1111
 from video_processor.utils.usage_tracker import UsageTracker
1212
1313
 load_dotenv()
1414
 logger = logging.getLogger(__name__)
15
+
16
+
17
+def _ensure_providers_registered() -> None:
18
+ """Import all built-in provider modules so they register themselves."""
19
+ if ProviderRegistry.all_registered():
20
+ return
21
+ # Each module registers itself on import via ProviderRegistry.register()
22
+ import video_processor.providers.anthropic_provider # noqa: F401
23
+ import video_processor.providers.gemini_provider # noqa: F401
24
+ import video_processor.providers.ollama_provider # noqa: F401
25
+ import video_processor.providers.openai_provider # noqa: F401
26
+
1527
1628
 # Default model preference rankings (tried in order)
1729
 _VISION_PREFERENCES = [
1830
 ("gemini", "gemini-2.5-flash"),
1931
 ("openai", "gpt-4o"),
@@ -57,10 +69,11 @@
5769
 chat_model : override model for chat/LLM tasks
5870
 transcription_model : override model for transcription
5971
 provider : force all tasks to a single provider ('openai', 'anthropic', 'gemini')
6072
 auto : if True and no model specified, pick the best available
6173
 """
74
+ _ensure_providers_registered()
6275
 self.auto = auto
6376
 self._providers: dict[str, BaseProvider] = {}
6477
 self._available_models: Optional[list[ModelInfo]] = None
6578
 self.usage = UsageTracker()
6679
@@ -79,67 +92,31 @@
7992
 self._forced_provider = provider
8093
8194
 @staticmethod
8295
 def _default_for_provider(provider: str, capability: str) -> str:
8396
 """Return the default model for a provider/capability combo."""
84
- defaults = {
85
- "openai": {"chat": "gpt-4o", "vision": "gpt-4o", "audio": "whisper-1"},
86
- "anthropic": {
87
- "chat": "claude-sonnet-4-5-20250929",
88
- "vision": "claude-sonnet-4-5-20250929",
89
- "audio": "",
90
- },
91
- "gemini": {
92
- "chat": "gemini-2.5-flash",
93
- "vision": "gemini-2.5-flash",
94
- "audio": "gemini-2.5-flash",
95
- },
96
- "ollama": {
97
- "chat": "",
98
- "vision": "",
99
- "audio": "",
100
- },
101
- }
102
- return defaults.get(provider, {}).get(capability, "")
97
+ defaults = ProviderRegistry.get_default_models(provider)
98
+ if defaults:
99
+ return defaults.get(capability, "")
100
+ # Fallback for unregistered providers
101
+ return ""
103102
104103
 def _get_provider(self, provider_name: str) -> BaseProvider:
105104
 """Lazily initialize and cache a provider instance."""
106105
 if provider_name not in self._providers:
107
- if provider_name == "openai":
108
- from video_processor.providers.openai_provider import OpenAIProvider
109
-
110
- self._providers[provider_name] = OpenAIProvider()
111
- elif provider_name == "anthropic":
112
- from video_processor.providers.anthropic_provider import AnthropicProvider
113
-
114
- self._providers[provider_name] = AnthropicProvider()
115
- elif provider_name == "gemini":
116
- from video_processor.providers.gemini_provider import GeminiProvider
117
-
118
- self._providers[provider_name] = GeminiProvider()
119
- elif provider_name == "ollama":
120
- from video_processor.providers.ollama_provider import OllamaProvider
121
-
122
- self._providers[provider_name] = OllamaProvider()
123
- else:
124
- raise ValueError(f"Unknown provider: {provider_name}")
106
+ _ensure_providers_registered()
107
+ provider_class = ProviderRegistry.get(provider_name)
108
+ self._providers[provider_name] = provider_class()
125109
 return self._providers[provider_name]
126110
127111
 def _provider_for_model(self, model_id: str) -> str:
128112
 """Infer the provider from a model id."""
129
- if (
130
- model_id.startswith("gpt-")
131
- or model_id.startswith("o1")
132
- or model_id.startswith("o3")
133
- or model_id.startswith("o4")
134
- or model_id.startswith("whisper")
135
- ):
136
- return "openai"
137
- if model_id.startswith("claude-"):
138
- return "anthropic"
139
- if model_id.startswith("gemini-"):
140
- return "gemini"
113
+ _ensure_providers_registered()
114
+ # Check registry prefix matching first
115
+ provider_name = ProviderRegistry.get_by_model(model_id)
116
+ if provider_name:
117
+ return provider_name
141118
 # Try discovery (exact match, then prefix match for ollama name:tag format)
142119
 models = self._get_available_models()
143120
 for m in models:
144121
 if m.id == model_id:
145122
 return m.provider
146123
--- video_processor/providers/manager.py
+++ video_processor/providers/manager.py
@@ -4,16 +4,28 @@
4 from pathlib import Path
5 from typing import Optional
6
7 from dotenv import load_dotenv
8
9 from video_processor.providers.base import BaseProvider, ModelInfo
10 from video_processor.providers.discovery import discover_available_models
11 from video_processor.utils.usage_tracker import UsageTracker
12
13 load_dotenv()
14 logger = logging.getLogger(__name__)
 
 
 
 
 
 
 
 
 
 
 
 
15
16 # Default model preference rankings (tried in order)
17 _VISION_PREFERENCES = [
18 ("gemini", "gemini-2.5-flash"),
19 ("openai", "gpt-4o"),
@@ -57,10 +69,11 @@
57 chat_model : override model for chat/LLM tasks
58 transcription_model : override model for transcription
59 provider : force all tasks to a single provider ('openai', 'anthropic', 'gemini')
60 auto : if True and no model specified, pick the best available
61 """
 
62 self.auto = auto
63 self._providers: dict[str, BaseProvider] = {}
64 self._available_models: Optional[list[ModelInfo]] = None
65 self.usage = UsageTracker()
66
@@ -79,67 +92,31 @@
79 self._forced_provider = provider
80
81 @staticmethod
82 def _default_for_provider(provider: str, capability: str) -> str:
83 """Return the default model for a provider/capability combo."""
84 defaults = {
85 "openai": {"chat": "gpt-4o", "vision": "gpt-4o", "audio": "whisper-1"},
86 "anthropic": {
87 "chat": "claude-sonnet-4-5-20250929",
88 "vision": "claude-sonnet-4-5-20250929",
89 "audio": "",
90 },
91 "gemini": {
92 "chat": "gemini-2.5-flash",
93 "vision": "gemini-2.5-flash",
94 "audio": "gemini-2.5-flash",
95 },
96 "ollama": {
97 "chat": "",
98 "vision": "",
99 "audio": "",
100 },
101 }
102 return defaults.get(provider, {}).get(capability, "")
103
104 def _get_provider(self, provider_name: str) -> BaseProvider:
105 """Lazily initialize and cache a provider instance."""
106 if provider_name not in self._providers:
107 if provider_name == "openai":
108 from video_processor.providers.openai_provider import OpenAIProvider
109
110 self._providers[provider_name] = OpenAIProvider()
111 elif provider_name == "anthropic":
112 from video_processor.providers.anthropic_provider import AnthropicProvider
113
114 self._providers[provider_name] = AnthropicProvider()
115 elif provider_name == "gemini":
116 from video_processor.providers.gemini_provider import GeminiProvider
117
118 self._providers[provider_name] = GeminiProvider()
119 elif provider_name == "ollama":
120 from video_processor.providers.ollama_provider import OllamaProvider
121
122 self._providers[provider_name] = OllamaProvider()
123 else:
124 raise ValueError(f"Unknown provider: {provider_name}")
125 return self._providers[provider_name]
126
127 def _provider_for_model(self, model_id: str) -> str:
128 """Infer the provider from a model id."""
129 if (
130 model_id.startswith("gpt-")
131 or model_id.startswith("o1")
132 or model_id.startswith("o3")
133 or model_id.startswith("o4")
134 or model_id.startswith("whisper")
135 ):
136 return "openai"
137 if model_id.startswith("claude-"):
138 return "anthropic"
139 if model_id.startswith("gemini-"):
140 return "gemini"
141 # Try discovery (exact match, then prefix match for ollama name:tag format)
142 models = self._get_available_models()
143 for m in models:
144 if m.id == model_id:
145 return m.provider
146
--- video_processor/providers/manager.py
+++ video_processor/providers/manager.py
@@ -4,16 +4,28 @@
4 from pathlib import Path
5 from typing import Optional
6
7 from dotenv import load_dotenv
8
9 from video_processor.providers.base import BaseProvider, ModelInfo, ProviderRegistry
10 from video_processor.providers.discovery import discover_available_models
11 from video_processor.utils.usage_tracker import UsageTracker
12
13 load_dotenv()
14 logger = logging.getLogger(__name__)
15
16
17 def _ensure_providers_registered() -> None:
18 """Import all built-in provider modules so they register themselves."""
19 if ProviderRegistry.all_registered():
20 return
21 # Each module registers itself on import via ProviderRegistry.register()
22 import video_processor.providers.anthropic_provider # noqa: F401
23 import video_processor.providers.gemini_provider # noqa: F401
24 import video_processor.providers.ollama_provider # noqa: F401
25 import video_processor.providers.openai_provider # noqa: F401
26
27
28 # Default model preference rankings (tried in order)
29 _VISION_PREFERENCES = [
30 ("gemini", "gemini-2.5-flash"),
31 ("openai", "gpt-4o"),
@@ -57,10 +69,11 @@
69 chat_model : override model for chat/LLM tasks
70 transcription_model : override model for transcription
71 provider : force all tasks to a single provider ('openai', 'anthropic', 'gemini')
72 auto : if True and no model specified, pick the best available
73 """
74 _ensure_providers_registered()
75 self.auto = auto
76 self._providers: dict[str, BaseProvider] = {}
77 self._available_models: Optional[list[ModelInfo]] = None
78 self.usage = UsageTracker()
79
@@ -79,67 +92,31 @@
92 self._forced_provider = provider
93
94 @staticmethod
95 def _default_for_provider(provider: str, capability: str) -> str:
96 """Return the default model for a provider/capability combo."""
97 defaults = ProviderRegistry.get_default_models(provider)
98 if defaults:
99 return defaults.get(capability, "")
100 # Fallback for unregistered providers
101 return ""
 
 
 
 
 
 
 
 
 
 
 
 
 
 
102
103 def _get_provider(self, provider_name: str) -> BaseProvider:
104 """Lazily initialize and cache a provider instance."""
105 if provider_name not in self._providers:
106 _ensure_providers_registered()
107 provider_class = ProviderRegistry.get(provider_name)
108 self._providers[provider_name] = provider_class()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
109 return self._providers[provider_name]
110
111 def _provider_for_model(self, model_id: str) -> str:
112 """Infer the provider from a model id."""
113 _ensure_providers_registered()
114 # Check registry prefix matching first
115 provider_name = ProviderRegistry.get_by_model(model_id)
116 if provider_name:
117 return provider_name
 
 
 
 
 
 
 
118 # Try discovery (exact match, then prefix match for ollama name:tag format)
119 models = self._get_available_models()
120 for m in models:
121 if m.id == model_id:
122 return m.provider
123
M video_processor/providers/ollama_provider.py
+10 -1
--- video_processor/providers/ollama_provider.py
+++ video_processor/providers/ollama_provider.py
@@ -7,11 +7,11 @@
77
 from typing import Optional
88
99
 import requests
1010
 from openai import OpenAI
1111
12
-from video_processor.providers.base import BaseProvider, ModelInfo
12
+from video_processor.providers.base import BaseProvider, ModelInfo, ProviderRegistry
1313
1414
 logger = logging.getLogger(__name__)
1515
1616
 # Known vision-capable model families (base name before the colon/tag)
1717
 _VISION_FAMILIES = {
@@ -168,5 +168,14 @@
168168
 )
169169
 )
170170
 except Exception as e:
171171
 logger.warning(f"Failed to list Ollama models: {e}")
172172
 return sorted(models, key=lambda m: m.id)
173
+
174
+
175
+ProviderRegistry.register(
176
+ name="ollama",
177
+ provider_class=OllamaProvider,
178
+ env_var="",
179
+ model_prefixes=[],
180
+ default_models={"chat": "", "vision": "", "audio": ""},
181
+)
173182
--- video_processor/providers/ollama_provider.py
+++ video_processor/providers/ollama_provider.py
@@ -7,11 +7,11 @@
7 from typing import Optional
8
9 import requests
10 from openai import OpenAI
11
12 from video_processor.providers.base import BaseProvider, ModelInfo
13
14 logger = logging.getLogger(__name__)
15
16 # Known vision-capable model families (base name before the colon/tag)
17 _VISION_FAMILIES = {
@@ -168,5 +168,14 @@
168 )
169 )
170 except Exception as e:
171 logger.warning(f"Failed to list Ollama models: {e}")
172 return sorted(models, key=lambda m: m.id)
 
 
 
 
 
 
 
 
 
173
--- video_processor/providers/ollama_provider.py
+++ video_processor/providers/ollama_provider.py
@@ -7,11 +7,11 @@
7 from typing import Optional
8
9 import requests
10 from openai import OpenAI
11
12 from video_processor.providers.base import BaseProvider, ModelInfo, ProviderRegistry
13
14 logger = logging.getLogger(__name__)
15
16 # Known vision-capable model families (base name before the colon/tag)
17 _VISION_FAMILIES = {
@@ -168,5 +168,14 @@
168 )
169 )
170 except Exception as e:
171 logger.warning(f"Failed to list Ollama models: {e}")
172 return sorted(models, key=lambda m: m.id)
173
174
175 ProviderRegistry.register(
176 name="ollama",
177 provider_class=OllamaProvider,
178 env_var="",
179 model_prefixes=[],
180 default_models={"chat": "", "vision": "", "audio": ""},
181 )
182
M video_processor/providers/openai_provider.py
+10 -1
--- video_processor/providers/openai_provider.py
+++ video_processor/providers/openai_provider.py
@@ -7,11 +7,11 @@
77
 from typing import Optional
88
99
 from dotenv import load_dotenv
1010
 from openai import OpenAI
1111
12
-from video_processor.providers.base import BaseProvider, ModelInfo
12
+from video_processor.providers.base import BaseProvider, ModelInfo, ProviderRegistry
1313
1414
 load_dotenv()
1515
 logger = logging.getLogger(__name__)
1616
1717
 # Models known to have vision capability
@@ -225,5 +225,14 @@
225225
 )
226226
 )
227227
 except Exception as e:
228228
 logger.warning(f"Failed to list OpenAI models: {e}")
229229
 return sorted(models, key=lambda m: m.id)
230
+
231
+
232
+ProviderRegistry.register(
233
+ name="openai",
234
+ provider_class=OpenAIProvider,
235
+ env_var="OPENAI_API_KEY",
236
+ model_prefixes=["gpt-", "o1", "o3", "o4", "whisper"],
237
+ default_models={"chat": "gpt-4o", "vision": "gpt-4o", "audio": "whisper-1"},
238
+)
230239
--- video_processor/providers/openai_provider.py
+++ video_processor/providers/openai_provider.py
@@ -7,11 +7,11 @@
7 from typing import Optional
8
9 from dotenv import load_dotenv
10 from openai import OpenAI
11
12 from video_processor.providers.base import BaseProvider, ModelInfo
13
14 load_dotenv()
15 logger = logging.getLogger(__name__)
16
17 # Models known to have vision capability
@@ -225,5 +225,14 @@
225 )
226 )
227 except Exception as e:
228 logger.warning(f"Failed to list OpenAI models: {e}")
229 return sorted(models, key=lambda m: m.id)
 
 
 
 
 
 
 
 
 
230
--- video_processor/providers/openai_provider.py
+++ video_processor/providers/openai_provider.py
@@ -7,11 +7,11 @@
7 from typing import Optional
8
9 from dotenv import load_dotenv
10 from openai import OpenAI
11
12 from video_processor.providers.base import BaseProvider, ModelInfo, ProviderRegistry
13
14 load_dotenv()
15 logger = logging.getLogger(__name__)
16
17 # Models known to have vision capability
@@ -225,5 +225,14 @@
225 )
226 )
227 except Exception as e:
228 logger.warning(f"Failed to list OpenAI models: {e}")
229 return sorted(models, key=lambda m: m.id)
230
231
232 ProviderRegistry.register(
233 name="openai",
234 provider_class=OpenAIProvider,
235 env_var="OPENAI_API_KEY",
236 model_prefixes=["gpt-", "o1", "o3", "o4", "whisper"],
237 default_models={"chat": "gpt-4o", "vision": "gpt-4o", "audio": "whisper-1"},
238 )
239

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