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Context Builder for OpenAI Agent
"""
from typing import List, Dict, Any
from datetime import datetime
class ContextBuilder:
def __init__(self, max_messages: int = 20, max_tokens: int = 8000):
"""
Initialize context builder with limits
Args:
max_messages: Maximum number of messages to include in context
max_tokens: Maximum token count for context
"""
self.max_messages = max_messages
self.max_tokens = max_tokens
def build_context(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""
Build conversation context for OpenAI agent
Args:
messages: List of message dictionaries from database
Returns:
List of messages formatted for OpenAI API
"""
# Get recent messages (newest first)
recent_messages = self._get_recent_messages(messages)
# Format messages for OpenAI
formatted_messages = self._format_messages(recent_messages)
return formatted_messages
def _get_recent_messages(
self, messages: List[Dict[str, Any]]
) -> List[Dict[str, Any]]:
"""
Get recent messages with token counting
Args:
messages: List of all messages
Returns:
List of recent messages within token limits
"""
# Sort messages by created_at (newest first)
sorted_messages = sorted(messages, key=lambda x: x["created_at"], reverse=True)
# Take up to max_messages
recent_messages = sorted_messages[: self.max_messages]
# Sort back to chronological order (oldest first)
recent_messages = sorted(recent_messages, key=lambda x: x["created_at"])
return recent_messages
def _format_messages(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""
Format messages for OpenAI API
Args:
messages: List of message dictionaries
Returns:
List of formatted messages
"""
formatted = []
for msg in messages:
formatted.append(
{
"role": "user" if msg["sender"] == "user" else "assistant",
"content": msg["content"],
}
)
return formatted
def count_tokens(self, messages: List[Dict[str, Any]]) -> int:
"""
Count tokens in message list
Args:
messages: List of message dictionaries
Returns:
Token count
"""
from tiktoken import encoding_for_model
import math
# Get GPT-4 tokenizer
tokenizer = encoding_for_model("gpt-4")
# Count tokens in all messages
token_count = 0
for msg in messages:
content = msg.get("content", "")
token_count += len(tokenizer.encode(content))
return token_count
def should_truncate(self, messages: List[Dict[str, Any]]) -> bool:
"""
Check if messages should be truncated based on token limits
Args:
messages: List of message dictionaries
Returns:
True if truncation is needed
"""
token_count = self.count_tokens(messages)
return token_count > self.max_tokens
def truncate_context(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""
Truncate context to fit within token limits
Args:
messages: List of message dictionaries
Returns:
Truncated list of messages
"""
# Start with all messages
current_messages = messages.copy()
# Keep removing oldest messages until within token limit
while (
current_messages and self.count_tokens(current_messages) > self.max_tokens
):
current_messages = current_messages[1:] # Remove oldest message
return current_messages
def get_context_summary(self, messages: List[Dict[str, Any]]) -> str:
"""
Create a summary of conversation context
Args:
messages: List of message dictionaries
Returns:
Summary string
"""
if not messages:
return "New conversation"
# Get last few messages for summary
recent = messages[-3:] if len(messages) > 3 else messages
summary_parts = []
for msg in recent:
role = "User" if msg["sender"] == "user" else "AI"
summary_parts.append(
f'{role}: {msg["content"][:50]}{"..." if len(msg["content"]) > 50 else ""}'
)
return " | ".join(summary_parts)
def validate_context(self, messages: List[Dict[str, Any]]) -> Dict[str, Any]:
"""
Validate context for processing
Args:
messages: List of message dictionaries
Returns:
Validation result with warnings if any
"""
validation = {"valid": True, "warnings": []}
# Check message count
if len(messages) > self.max_messages:
validation["valid"] = False
validation["warnings"].append(
f"Message count {len(messages)} exceeds maximum {self.max_messages}"
)
# Check token count
token_count = self.count_tokens(messages)
if token_count > self.max_tokens:
validation["valid"] = False
validation["warnings"].append(
f"Token count {token_count} exceeds maximum {self.max_tokens}"
)
return validation
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