Create agent.py

agent.py

@@ -0,0 +1,146 @@
+import datetime
+from typing import List, Tuple, Dict, Any
+
+# Constants used in the app
+PREFIX = """Current Date: {timestamp}
+Purpose: {purpose}
+System: You are an advanced AI assistant specialized in data processing and summarization.
+"""
+
+COMPRESS_DATA_PROMPT = """You are processing data for summarization and analysis.
+
+Task Context:
+- Direction: {direction}
+- Knowledge: {knowledge}
+
+Data to Process:
+{history}
+
+Instructions:
+1. Analyze and summarize the data while preserving key information
+2. Maintain original meaning and important details
+3. Output should be concise yet comprehensive
+4. Format as plain text with clear section headers
+5. Include all critical data points and references
+
+Output Format:
+[Summary]
+- Key points
+- Important details
+- Relevant references
+
+[Analysis]
+- Insights
+- Patterns
+- Conclusions
+"""
+
+COMPRESS_DATA_PROMPT_SMALL = """You are processing data chunks for summarization.
+
+Task Context:
+- Direction: {direction}
+
+Current Data Chunk:
+{history}
+
+Instructions:
+1. Extract key information from this chunk
+2. Format as bullet points
+3. Keep concise but preserve meaning
+4. Focus on most relevant content
+5. Include source references if available
+
+Output Format:
+- Point 1
+- Point 2
+- ...
+"""
+
+LOG_PROMPT = """=== PROMPT ===
+{content}
+"""
+
+LOG_RESPONSE = """=== RESPONSE ===
+{content}
+"""
+
+def run_gpt(
+    prompt_template: str,
+    stop_tokens: List[str],
+    max_tokens: int,
+    seed: int,
+    **prompt_kwargs: Any
+) -> str:
+    """Run GPT model with given parameters.
+    
+    Args:
+        prompt_template: Template string for the prompt
+        stop_tokens: List of stop sequences
+        max_tokens: Maximum tokens to generate
+        seed: Random seed
+        **prompt_kwargs: Additional formatting arguments
+        
+    Returns:
+        Generated text response
+    """
+    # This would normally interface with the actual model
+    # For now returning a mock implementation
+    return "Mock response for testing purposes"
+
+def compress_data(
+    c: int,
+    instruct: str,
+    history: str
+) -> List[str]:
+    """Compress data into smaller chunks.
+    
+    Args:
+        c: Count of data points
+        instruct: Instruction for compression
+        history: Data to compress
+        
+    Returns:
+        List of compressed data chunks
+    """
+    # Mock implementation
+    return ["Compressed data chunk 1", "Compressed data chunk 2"]
+
+def compress_data_og(
+    c: int,
+    instruct: str,
+    history: str
+) -> str:
+    """Original version of data compression.
+    
+    Args:
+        c: Count of data points
+        instruct: Instruction for compression
+        history: Data to compress
+        
+    Returns:
+        Compressed data as single string
+    """
+    # Mock implementation
+    return "Compressed data output"
+
+def save_memory(
+    purpose: str,
+    history: str
+) -> List[Dict[str, Any]]:
+    """Save processed data to memory format.
+    
+    Args:
+        purpose: Purpose of the processing
+        history: Data to process
+        
+    Returns:
+        List of memory dictionaries
+    """
+    # Mock implementation
+    return [{
+        "keywords": ["sample", "data"],
+        "title": "Sample Entry",
+        "description": "Sample description",
+        "content": "Sample content",
+        "url": "https://example.com"
+    }]