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"""Utility functions for Supermemory OpenAI middleware."""
import json
from typing import Optional, Any, Protocol
from openai.types.chat import ChatCompletionMessageParam
class Logger(Protocol):
"""Logger protocol for type safety."""
def debug(self, message: str, data: Optional[dict[str, Any]] = None) -> None:
"""Log debug message."""
...
def info(self, message: str, data: Optional[dict[str, Any]] = None) -> None:
"""Log info message."""
...
def warn(self, message: str, data: Optional[dict[str, Any]] = None) -> None:
"""Log warning message."""
...
def error(self, message: str, data: Optional[dict[str, Any]] = None) -> None:
"""Log error message."""
...
class SimpleLogger:
"""Simple logger implementation."""
def __init__(self, verbose: bool = False):
self.verbose: bool = verbose
def _log(self, level: str, message: str, data: Optional[dict[str, Any]] = None) -> None:
"""Internal logging method."""
if not self.verbose:
return
log_message = f"[supermemory] {message}"
if data:
log_message += f" {json.dumps(data, indent=2)}"
if level == "error":
print(f"ERROR: {log_message}", flush=True)
elif level == "warn":
print(f"WARN: {log_message}", flush=True)
else:
print(log_message, flush=True)
def debug(self, message: str, data: Optional[dict[str, Any]] = None) -> None:
"""Log debug message."""
self._log("debug", message, data)
def info(self, message: str, data: Optional[dict[str, Any]] = None) -> None:
"""Log info message."""
self._log("info", message, data)
def warn(self, message: str, data: Optional[dict[str, Any]] = None) -> None:
"""Log warning message."""
self._log("warn", message, data)
def error(self, message: str, data: Optional[dict[str, Any]] = None) -> None:
"""Log error message."""
self._log("error", message, data)
def create_logger(verbose: bool) -> Logger:
"""Create a logger instance.
Args:
verbose: Whether to enable verbose logging
Returns:
Logger instance
"""
return SimpleLogger(verbose)
def get_last_user_message(
messages: list[ChatCompletionMessageParam],
) -> str:
"""
Extract the last user message from an array of chat completion messages.
Searches through the messages array in reverse order to find the most recent
message with role "user" and returns its content as a string.
Args:
messages: Array of chat completion message parameters
Returns:
The content of the last user message, or empty string if none found
Example:
```python
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello there!"},
{"role": "assistant", "content": "Hi! How can I help you?"},
{"role": "user", "content": "What's the weather like?"}
]
last_message = get_last_user_message(messages)
# Returns: "What's the weather like?"
```
"""
for message in reversed(messages):
if message.get("role") == "user":
content = message.get("content", "")
if isinstance(content, str):
return content
elif isinstance(content, list):
# Handle content that is an array of content parts
text_parts = []
for part in content:
if isinstance(part, dict) and part.get("type") == "text":
text_parts.append(part.get("text", ""))
elif isinstance(part, str):
text_parts.append(part)
return " ".join(text_parts)
return ""
def get_conversation_content(
messages: list[ChatCompletionMessageParam],
) -> str:
"""
Convert an array of chat completion messages into a formatted conversation string.
Transforms the messages array into a readable conversation format where each
message is prefixed with its role (User/Assistant) and messages are separated
by double newlines.
Args:
messages: Array of chat completion message parameters
Returns:
Formatted conversation string with role prefixes
Example:
```python
messages = [
{"role": "user", "content": "Hello!"},
{"role": "assistant", "content": "Hi there!"},
{"role": "user", "content": "How are you?"}
]
conversation = get_conversation_content(messages)
# Returns: "User: Hello!\n\nAssistant: Hi there!\n\nUser: How are you?"
```
"""
conversation_parts = []
for message in messages:
role = message.get("role", "")
content = message.get("content", "")
# Format role
if role == "user":
role_display = "User"
elif role == "assistant":
role_display = "Assistant"
elif role == "system":
role_display = "System"
else:
role_display = role.capitalize()
# Extract content text
if isinstance(content, str):
content_text = content
elif isinstance(content, list):
# Handle content that is an array of content parts
text_parts = []
for part in content:
if isinstance(part, dict) and part.get("type") == "text":
text_parts.append(part.get("text", ""))
elif isinstance(part, str):
text_parts.append(part)
content_text = " ".join(text_parts)
else:
content_text = str(content)
if content_text:
conversation_parts.append(f"{role_display}: {content_text}")
return "\n\n".join(conversation_parts)
class DeduplicatedMemories:
"""Deduplicated memory strings organized by source."""
def __init__(self, static: list[str], dynamic: list[str], search_results: list[str]):
self.static = static
self.dynamic = dynamic
self.search_results = search_results
def deduplicate_memories(
static: Optional[list[Any]] = None,
dynamic: Optional[list[Any]] = None,
search_results: Optional[list[Any]] = None,
) -> DeduplicatedMemories:
"""
Deduplicates memory items across sources. Priority: Static > Dynamic > Search Results.
Same memory appearing in multiple sources is kept only in the highest-priority source.
"""
static_items = static or []
dynamic_items = dynamic or []
search_items = search_results or []
def extract_memory_text(item: Any) -> Optional[str]:
if item is None:
return None
if isinstance(item, dict):
memory = item.get("memory")
if isinstance(memory, str):
trimmed = memory.strip()
return trimmed if trimmed else None
return None
if isinstance(item, str):
trimmed = item.strip()
return trimmed if trimmed else None
return None
static_memories: list[str] = []
seen_memories: set[str] = set()
for item in static_items:
memory = extract_memory_text(item)
if memory is not None:
static_memories.append(memory)
seen_memories.add(memory)
dynamic_memories: list[str] = []
for item in dynamic_items:
memory = extract_memory_text(item)
if memory is not None and memory not in seen_memories:
dynamic_memories.append(memory)
seen_memories.add(memory)
search_memories: list[str] = []
for item in search_items:
memory = extract_memory_text(item)
if memory is not None and memory not in seen_memories:
search_memories.append(memory)
seen_memories.add(memory)
return DeduplicatedMemories(
static=static_memories,
dynamic=dynamic_memories,
search_results=search_memories,
)
def convert_profile_to_markdown(data: dict[str, Any]) -> str:
"""
Convert profile data to markdown based on profile.static and profile.dynamic properties.
Args:
data: Profile structure data
Returns:
Markdown string
Example:
```python
data = {
"profile": {
"static": ["User prefers Python", "Lives in San Francisco"],
"dynamic": ["Recently asked about AI"]
},
"searchResults": {
"results": [{"memory": "Likes coffee"}]
}
}
markdown = convert_profile_to_markdown(data)
# Returns formatted markdown with sections
```
"""
sections = []
profile = data.get("profile", {})
static_memories = profile.get("static", [])
dynamic_memories = profile.get("dynamic", [])
if static_memories:
sections.append("## Static Profile")
sections.append("\n".join(f"- {item}" for item in static_memories))
if dynamic_memories:
sections.append("## Dynamic Profile")
sections.append("\n".join(f"- {item}" for item in dynamic_memories))
return "\n\n".join(sections)
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