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ALwrity AI Blog Writer - Added Google Grounding UI Implementation

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This commit is contained in:
ajaysi
2025-09-18 18:45:53 +05:30
parent 9f13daf443
commit 4d153b292d
72 changed files with 11944 additions and 1526 deletions
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78
backend/services/llm_providers/gemini_grounded_provider.py
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@@ -89,12 +89,13 @@ class GeminiGroundedProvider:
logger.warning(f"URL Context tool not available in SDK version: {tool_err}")
# Apply mode presets (Draft vs Polished)
model_id = "gemini-2.5-flash"
# Use Gemini 2.0 Flash for better content generation with grounding
model_id = "gemini-2.0-flash"
if mode == "draft":
model_id = "gemini-2.5-flash-lite"
model_id = "gemini-2.0-flash"
temperature = min(1.0, max(0.0, temperature))
else:
model_id = "gemini-2.5-flash"
model_id = "gemini-2.0-flash"
# Configure generation settings
config = types.GenerateContentConfig(
@@ -189,7 +190,7 @@ class GeminiGroundedProvider:
loop.run_in_executor(
executor,
lambda: self.client.models.generate_content(
model="gemini-2.5-flash",
model="gemini-2.0-flash",
contents=grounded_prompt,
config=config,
)
@@ -199,6 +200,10 @@ class GeminiGroundedProvider:
async def _make_api_request_with_model(self, grounded_prompt: str, config: Any, model_id: str, urls: Optional[List[str]] = None):
"""Make the API request with explicit model id and optional URL injection."""
logger.info(f"🔍 DEBUG: Making API request with model: {model_id}")
logger.info(f"🔍 DEBUG: Prompt length: {len(grounded_prompt)} characters")
logger.info(f"🔍 DEBUG: Prompt preview (first 300 chars): {grounded_prompt[:300]}...")
import concurrent.futures
loop = asyncio.get_event_loop()
with concurrent.futures.ThreadPoolExecutor() as executor:
@@ -310,23 +315,70 @@ class GeminiGroundedProvider:
Processed content with sources and citations
"""
try:
# Extract the main content
# Debug: Log response structure
logger.info(f"🔍 DEBUG: Response type: {type(response)}")
logger.info(f"🔍 DEBUG: Response has 'text': {hasattr(response, 'text')}")
logger.info(f"🔍 DEBUG: Response has 'candidates': {hasattr(response, 'candidates')}")
logger.info(f"🔍 DEBUG: Response has 'grounding_metadata': {hasattr(response, 'grounding_metadata')}")
if hasattr(response, 'grounding_metadata'):
logger.info(f"🔍 DEBUG: Grounding metadata: {response.grounding_metadata}")
if hasattr(response, 'candidates') and response.candidates:
logger.info(f"🔍 DEBUG: Number of candidates: {len(response.candidates)}")
candidate = response.candidates[0]
logger.info(f"🔍 DEBUG: Candidate type: {type(candidate)}")
logger.info(f"🔍 DEBUG: Candidate has 'content': {hasattr(candidate, 'content')}")
if hasattr(candidate, 'content') and candidate.content:
logger.info(f"🔍 DEBUG: Content type: {type(candidate.content)}")
# Check if content is a list or single object
if hasattr(candidate.content, '__iter__') and not isinstance(candidate.content, str):
try:
content_length = len(candidate.content) if candidate.content else 0
logger.info(f"🔍 DEBUG: Content is iterable, length: {content_length}")
except TypeError:
logger.info(f"🔍 DEBUG: Content is iterable but has no len() - treating as single object")
for i, part in enumerate(candidate.content):
logger.info(f"🔍 DEBUG: Part {i} type: {type(part)}")
logger.info(f"🔍 DEBUG: Part {i} has 'text': {hasattr(part, 'text')}")
if hasattr(part, 'text'):
logger.info(f"🔍 DEBUG: Part {i} text length: {len(part.text) if part.text else 0}")
else:
logger.info(f"🔍 DEBUG: Content is single object, has 'text': {hasattr(candidate.content, 'text')}")
if hasattr(candidate.content, 'text'):
logger.info(f"🔍 DEBUG: Content text length: {len(candidate.content.text) if candidate.content.text else 0}")
# Extract the main content - prioritize response.text as it's more reliable
content = ""
if hasattr(response, 'text'):
content = response.text
logger.info(f"🔍 DEBUG: response.text exists, value: '{response.text}', type: {type(response.text)}")
if response.text:
content = response.text
logger.info(f"🔍 DEBUG: Using response.text, length: {len(content)}")
else:
logger.info(f"🔍 DEBUG: response.text is empty or None")
elif hasattr(response, 'candidates') and response.candidates:
candidate = response.candidates[0]
if hasattr(candidate, 'content') and candidate.content:
# Extract text from content parts
text_parts = []
for part in candidate.content:
if hasattr(part, 'text'):
text_parts.append(part.text)
content = " ".join(text_parts)
# Handle both single Content object and list of parts
if hasattr(candidate.content, '__iter__') and not isinstance(candidate.content, str):
# Content is a list of parts
text_parts = []
for part in candidate.content:
if hasattr(part, 'text'):
text_parts.append(part.text)
content = " ".join(text_parts)
logger.info(f"🔍 DEBUG: Using candidate.content (list), extracted {len(text_parts)} parts, total length: {len(content)}")
else:
# Content is a single object
if hasattr(candidate.content, 'text'):
content = candidate.content.text
logger.info(f"🔍 DEBUG: Using candidate.content (single), text length: {len(content)}")
else:
logger.warning("🔍 DEBUG: candidate.content has no 'text' attribute")
logger.info(f"Extracted content length: {len(content) if content else 0}")
if not content:
logger.warning("No content extracted from response")
logger.warning("⚠️ No content extracted from Gemini response - using fallback content")
logger.warning("⚠️ This indicates Google Search grounding is not working properly")
content = "Generated content about the requested topic."
# Initialize result structure