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Merged
ozanunal0 merged 2 commits into from
Jul 7, 2025
Merged

Dev #24

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5543c85
remove duplicate documentation deployment workflow
ozanunal0 Jul 7, 2025
54566bc
fix provider implementations to handle Pydantic ChatMessage objects
ozanunal0 Jul 7, 2025
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37 changes: 0 additions & 37 deletions .github/workflows/deploy-docs-simple.yml
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9 changes: 5 additions & 4 deletions app/providers/anthropic/provider.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,24 +25,25 @@ def __init__(self, api_key: str):
self.api_key = api_key
self.client = anthropic.AsyncAnthropic(api_key=self.api_key)

def _prepare_messages_for_anthropic(self, messages: List[Dict[str, str]]) -> List[Dict[str, str]]:
def _prepare_messages_for_anthropic(self, messages: List) -> List[Dict[str, str]]:
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Copilot AI Jul 7, 2025

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[nitpick] The parameter type hint is now a generic List; consider using List[ChatMessage] to make the expected input explicit.

Suggested change
def _prepare_messages_for_anthropic(self, messages: List) -> List[Dict[str, str]]:
def _prepare_messages_for_anthropic(self, messages: List[ChatMessage]) -> List[Dict[str, str]]:

Copilot uses AI. Check for mistakes.
"""
Prepare messages for Anthropic API format.

Anthropic's Messages API uses a similar format to OpenAI, but we need to
ensure proper structure and handle any system messages appropriately.

Args:
messages: List of messages in OpenAI format.
messages: List of ChatMessage objects.

Returns:
List of messages formatted for Anthropic API.
"""
anthropic_messages = []

for message in messages:
role = message["role"]
content = message["content"]
# Access Pydantic model attributes
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Copilot AI Jul 7, 2025

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[nitpick] This inline comment is redundant given the clear attribute access; consider removing it to keep the code concise.

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# Access Pydantic model attributes

Copilot uses AI. Check for mistakes.
role = message.role
content = message.content

# Anthropic supports user, assistant, and system roles
# System messages can be passed directly
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11 changes: 6 additions & 5 deletions app/providers/google/provider.py
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Expand Up @@ -25,24 +25,25 @@ def __init__(self, api_key: str):
self.api_key = api_key
genai.configure(api_key=self.api_key)

def _translate_messages_to_google(self, messages: List[Dict[str, str]]) -> List[Dict[str, str]]:
def _translate_messages_to_google(self, messages: List) -> List[Dict[str, str]]:
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Copilot AI Jul 7, 2025

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[nitpick] The parameter type hint uses an untyped List; consider specifying List[ChatMessage] for clarity and type safety.

Suggested change
def _translate_messages_to_google(self, messages: List) -> List[Dict[str, str]]:
def _translate_messages_to_google(self, messages: List[ChatMessage]) -> List[Dict[str, str]]:

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Copilot AI Jul 7, 2025

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The return type List[Dict[str, str]] doesn’t match the actual structure (parts is a List[str]); update to reflect Dict[str, List[str]] or a proper model.

Suggested change
def _translate_messages_to_google(self, messages: List) -> List[Dict[str, str]]:
def _translate_messages_to_google(self, messages: List) -> List[Dict[str, List[str]]]:

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"""
Translate OpenAI-format messages to Google Gemini format.

OpenAI uses: [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]
Google uses: [{"role": "user", "parts": ["..."]}, {"role": "model", "parts": ["..."]}]

Args:
messages: List of messages in OpenAI format.
messages: List of ChatMessage objects.

Returns:
List of messages in Google Gemini format.
"""
translated_messages = []

for message in messages:
role = message["role"]
content = message["content"]
# Access Pydantic model attributes
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[nitpick] This comment adds little value and could be removed to reduce noise; the code is self-explanatory.

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# Access Pydantic model attributes

Copilot uses AI. Check for mistakes.
role = message.role
content = message.content

# Map OpenAI roles to Google roles
if role == "user":
Expand Down Expand Up @@ -131,7 +132,7 @@ async def create_completion(self, request: ChatCompletionRequest) -> dict:
google_messages = self._translate_messages_to_google(request.messages)

# Estimate prompt tokens (rough approximation)
prompt_text = " ".join([msg["content"] for msg in request.messages])
prompt_text = " ".join([msg.content for msg in request.messages])
prompt_tokens = len(prompt_text.split())

# Configure generation parameters
Expand Down
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