ALwrity version 0.5.4

This commit is contained in:
ajaysi
2025-08-09 23:14:16 +05:30
parent 01fe1e0a9c
commit 5c08b6e007
42 changed files with 3514 additions and 2148 deletions

View File

@@ -24,6 +24,8 @@ import asyncio
import json
import re
from typing import Optional, Dict, Any
# Configure standard logging
import logging
logging.basicConfig(level=logging.INFO, format='[%(asctime)s-%(levelname)s-%(module)s-%(lineno)d]- %(message)s')
@@ -170,63 +172,107 @@ def gemini_pro_text_gen(prompt, temperature=0.7, top_p=0.9, top_k=40, max_tokens
logger.error(f"Error in Gemini Pro text generation: {e}")
return str(e)
def _dict_to_types_schema(schema: Dict[str, Any]) -> types.Schema:
"""Convert a lightweight dict schema to google.genai.types.Schema."""
if not isinstance(schema, dict):
raise ValueError("response_schema must be a dict compatible with types.Schema")
def _convert(node: Dict[str, Any]) -> types.Schema:
node_type = (node.get("type") or "OBJECT").upper()
if node_type == "OBJECT":
props = node.get("properties") or {}
props_types: Dict[str, types.Schema] = {}
for key, prop in props.items():
if isinstance(prop, dict):
props_types[key] = _convert(prop)
else:
props_types[key] = types.Schema(type=types.Type.STRING)
return types.Schema(type=types.Type.OBJECT, properties=props_types if props_types else None)
elif node_type == "ARRAY":
items_node = node.get("items")
if isinstance(items_node, dict):
item_schema = _convert(items_node)
else:
item_schema = types.Schema(type=types.Type.STRING)
return types.Schema(type=types.Type.ARRAY, items=item_schema)
elif node_type == "NUMBER":
return types.Schema(type=types.Type.NUMBER)
elif node_type == "BOOLEAN":
return types.Schema(type=types.Type.BOOLEAN)
else:
return types.Schema(type=types.Type.STRING)
return _convert(schema)
@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
def gemini_structured_json_response(prompt, schema, temperature=0.7, top_p=0.9, top_k=40, max_tokens=2048, system_prompt=None):
"""
Generate structured JSON response using Google's Gemini Pro model.
Args:
prompt (str): The input text to generate completion for
schema (dict): The JSON schema to follow for the response
temperature (float, optional): Controls randomness. Defaults to 0.7
top_p (float, optional): Controls diversity. Defaults to 0.9
top_k (int, optional): Controls vocabulary size. Defaults to 40
max_tokens (int, optional): Maximum number of tokens to generate. Defaults to 2048
system_prompt (str, optional): System instructions for the model
Returns:
dict: The generated structured JSON response
"""
try:
# Configure the model
client = genai.Client(api_key=os.getenv('GEMINI_API_KEY'))
# Set up generation config
generation_config = {
"temperature": temperature,
"top_p": top_p,
"top_k": top_k,
"max_output_tokens": max_tokens,
}
# Generate content with structured response
response = client.models.generate_content(
model='gemini-2.5-pro',
contents=prompt,
config=types.GenerateContentConfig(
system_instruction=system_prompt,
max_output_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
top_k=top_k,
response_mime_type='application/json',
response_schema=schema
),
)
# Parse the response
# Build config using official SDK schema type
try:
# First try to get the parsed response
if hasattr(response, 'parsed'):
return response.parsed
# If parsed is not available, try to parse the text
response_text = response.text
return json.loads(response_text)
except json.JSONDecodeError as e:
logger.error(f"Error parsing JSON response: {e}")
return {"error": f"Failed to parse JSON response: {e}", "raw_response": response_text}
types_schema = _dict_to_types_schema(schema) if isinstance(schema, dict) else schema
except Exception as conv_err:
logger.warning(f"Schema conversion warning, defaulting to OBJECT: {conv_err}")
types_schema = types.Schema(type=types.Type.OBJECT)
generation_config = types.GenerateContentConfig(
system_instruction=system_prompt,
max_output_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
top_k=top_k,
response_mime_type='application/json',
response_schema=types_schema
)
response = client.models.generate_content(
model='gemini-2.5-flash',
contents=prompt,
config=generation_config,
)
# Prefer parsed if present and non-empty; otherwise parse text with fallbacks
try:
parsed = getattr(response, 'parsed', None)
if parsed:
return parsed if isinstance(parsed, dict) else json.loads(json.dumps(parsed))
text = (response.text or '').strip()
# Strip markdown code fences if present
if text.startswith('```'):
# remove leading ```json or ``` and trailing ```
if text.lower().startswith('```json'):
text = text[7:]
else:
text = text[3:]
if text.endswith('```'):
text = text[:-3]
text = text.strip()
try:
return json.loads(text)
except json.JSONDecodeError:
# Fallback: extract likely JSON object substring
first = text.find('{')
last = text.rfind('}')
if first != -1 and last != -1 and last > first:
candidate = text[first:last+1]
try:
return json.loads(candidate)
except json.JSONDecodeError:
pass
# Final fallback: regex any object
import re
match = re.search(r'\{[\s\S]*\}', text)
if match:
return json.loads(match.group(0))
raise
except Exception as e:
logger.error(f"Error parsing structured response: {e}")
return {"error": f"Failed to parse JSON response: {e}", "raw_response": (response.text or '')}
except Exception as e:
logger.error(f"Error in Gemini Pro structured JSON generation: {e}")
return {"error": str(e)}