Added enhanced linguistic analyzer and persona quality improver
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ajaysi
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@@ -46,14 +46,17 @@ class GeminiGroundedProvider:
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# Initialize the Gemini client with timeout configuration
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self.client = genai.Client(api_key=self.api_key)
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self.timeout = 60 # 60 second timeout for API calls (increased for research)
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self._cache: Dict[str, Any] = {}
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logger.info("✅ Gemini Grounded Provider initialized with native Google Search grounding")
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async def generate_grounded_content(
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self,
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prompt: str,
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self,
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prompt: str,
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content_type: str = "linkedin_post",
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temperature: float = 0.7,
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max_tokens: int = 2048
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max_tokens: int = 2048,
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urls: Optional[List[str]] = None,
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mode: str = "polished"
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) -> Dict[str, Any]:
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"""
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Generate grounded content using native Google Search grounding.
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@@ -73,14 +76,29 @@ class GeminiGroundedProvider:
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# Build the grounded prompt
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grounded_prompt = self._build_grounded_prompt(prompt, content_type)
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# Configure the grounding tool
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grounding_tool = types.Tool(
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google_search=types.GoogleSearch()
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)
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# Configure tools: Google Search and optional URL Context
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tools: List[Any] = [
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types.Tool(google_search=types.GoogleSearch())
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]
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if urls:
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try:
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# URL Context tool (ai.google.dev URL Context)
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tools.append(types.Tool(url_context=types.UrlContext()))
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logger.info(f"Enabled URL Context tool for {len(urls)} URLs")
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except Exception as tool_err:
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logger.warning(f"URL Context tool not available in SDK version: {tool_err}")
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# Apply mode presets (Draft vs Polished)
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model_id = "gemini-2.5-flash"
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if mode == "draft":
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model_id = "gemini-2.5-flash-lite"
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temperature = min(1.0, max(0.0, temperature))
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else:
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model_id = "gemini-2.5-flash"
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# Configure generation settings
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config = types.GenerateContentConfig(
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tools=[grounding_tool],
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tools=tools,
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max_output_tokens=max_tokens,
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temperature=temperature
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)
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@@ -90,20 +108,27 @@ class GeminiGroundedProvider:
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import concurrent.futures
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try:
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# Run the synchronous generate_content in a thread pool to make it awaitable
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loop = asyncio.get_event_loop()
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with concurrent.futures.ThreadPoolExecutor() as executor:
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response = await asyncio.wait_for(
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loop.run_in_executor(
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executor,
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lambda: self.client.models.generate_content(
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model="gemini-2.5-flash",
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contents=grounded_prompt,
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config=config,
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)
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),
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timeout=self.timeout
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)
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# Cache first
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cache_key = self._make_cache_key(model_id, grounded_prompt, urls)
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if cache_key in self._cache:
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logger.info("Cache hit for grounded content request")
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response = self._cache[cache_key]
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else:
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# Run the synchronous generate_content in a thread pool to make it awaitable
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loop = asyncio.get_event_loop()
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with concurrent.futures.ThreadPoolExecutor() as executor:
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response = await asyncio.wait_for(
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loop.run_in_executor(
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executor,
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lambda: self.client.models.generate_content(
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model=model_id,
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contents=self._inject_urls_into_prompt(grounded_prompt, urls) if urls else grounded_prompt,
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config=config,
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)
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),
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timeout=self.timeout
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)
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self._cache[cache_key] = response
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except asyncio.TimeoutError:
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raise Exception(f"Gemini API request timed out after {self.timeout} seconds")
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except Exception as api_error:
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@@ -112,14 +137,14 @@ class GeminiGroundedProvider:
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if "503" in error_str and "overloaded" in error_str:
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# Conservative retry for overloaded service (expensive API calls)
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response = await self._retry_with_backoff(
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lambda: self._make_api_request(grounded_prompt, config),
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lambda: self._make_api_request_with_model(grounded_prompt, config, model_id, urls),
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max_retries=1, # Only 1 retry to avoid excessive costs
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base_delay=5 # Longer delay
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)
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elif "429" in error_str:
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# Conservative retry for rate limits
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response = await self._retry_with_backoff(
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lambda: self._make_api_request(grounded_prompt, config),
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lambda: self._make_api_request_with_model(grounded_prompt, config, model_id, urls),
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max_retries=1, # Only 1 retry
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base_delay=10 # Much longer delay for rate limits
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)
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@@ -132,6 +157,15 @@ class GeminiGroundedProvider:
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# Process the grounded response
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result = self._process_grounded_response(response, content_type)
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# Attach URL Context metadata if present
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try:
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if hasattr(response, 'candidates') and response.candidates:
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candidate0 = response.candidates[0]
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if hasattr(candidate0, 'url_context_metadata') and candidate0.url_context_metadata:
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result['url_context_metadata'] = candidate0.url_context_metadata
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logger.info("Attached url_context_metadata to result")
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except Exception as meta_err:
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logger.warning(f"Unable to attach url_context_metadata: {meta_err}")
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logger.info(f"✅ Grounded content generated successfully with {len(result.get('sources', []))} sources")
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return result
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@@ -162,6 +196,41 @@ class GeminiGroundedProvider:
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),
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timeout=self.timeout
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)
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async def _make_api_request_with_model(self, grounded_prompt: str, config: Any, model_id: str, urls: Optional[List[str]] = None):
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"""Make the API request with explicit model id and optional URL injection."""
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import concurrent.futures
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loop = asyncio.get_event_loop()
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with concurrent.futures.ThreadPoolExecutor() as executor:
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resp = await asyncio.wait_for(
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loop.run_in_executor(
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executor,
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lambda: self.client.models.generate_content(
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model=model_id,
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contents=self._inject_urls_into_prompt(grounded_prompt, urls) if urls else grounded_prompt,
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config=config,
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)
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),
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timeout=self.timeout
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)
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self._cache[self._make_cache_key(model_id, grounded_prompt, urls)] = resp
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return resp
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def _inject_urls_into_prompt(self, prompt: str, urls: Optional[List[str]]) -> str:
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"""Append URLs to the prompt for URL Context tool to pick up (as per docs)."""
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if not urls:
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return prompt
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safe_urls = [u for u in urls if isinstance(u, str) and u.startswith("http")]
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if not safe_urls:
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return prompt
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urls_block = "\n".join(safe_urls[:20])
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return f"{prompt}\n\nSOURCE URLS (use url_context to retrieve content):\n{urls_block}"
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def _make_cache_key(self, model_id: str, prompt: str, urls: Optional[List[str]]) -> str:
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import hashlib
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u = "|".join((urls or [])[:20])
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base = f"{model_id}|{prompt}|{u}"
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return hashlib.sha256(base.encode("utf-8")).hexdigest()
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async def _retry_with_backoff(self, func, max_retries: int = 3, base_delay: float = 1.0):
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"""Retry a function with exponential backoff."""
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@@ -390,11 +390,19 @@ def gemini_structured_json_response(prompt, schema, temperature=0.7, top_p=0.9,
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)
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# Check for parsed content first (primary method for structured output)
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if hasattr(response, 'parsed') and response.parsed is not None:
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logger.info("Using response.parsed for structured output")
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return response.parsed
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if hasattr(response, 'parsed'):
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logger.info(f"Response has parsed attribute: {response.parsed is not None}")
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if response.parsed is not None:
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logger.info("Using response.parsed for structured output")
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return response.parsed
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else:
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logger.warning("Response.parsed is None, falling back to text parsing")
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# Debug: Check if there's any text content
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if hasattr(response, 'text') and response.text:
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logger.info(f"Text response length: {len(response.text)}")
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logger.debug(f"Text response preview: {response.text[:200]}...")
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# Check for text content as fallback
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# Check for text content as fallback (only if no parsed content)
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if hasattr(response, 'text') and response.text:
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logger.info("No parsed content, trying to parse text response")
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try:
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