Fix reference formatting for Gemini models and improve message handling
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@ -127,7 +127,7 @@ class LLMInterface:
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return params
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def generate_completion(self, messages: List[Dict[str, str]], stream: bool = False) -> Union[str, Any]:
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async def generate_completion(self, messages: List[Dict[str, str]], stream: bool = False) -> Union[str, Any]:
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"""
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Generate a completion using the configured LLM.
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@ -139,41 +139,64 @@ class LLMInterface:
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If stream is False, returns the completion text as a string
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If stream is True, returns the completion response object for streaming
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"""
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try:
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params = self._get_completion_params()
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# Get provider from model config
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provider = self.model_config.get('provider', 'openai').lower()
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# Special handling for Gemini models
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if "gemini" in params.get('model', '').lower():
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# Format messages for Gemini models
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# Special handling for Gemini models - they use 'user' and 'model' roles
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if provider == 'gemini':
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formatted_messages = []
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for msg in messages:
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role = msg['role']
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# Gemini uses 'user' and 'model' roles (not 'assistant')
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if role == 'assistant':
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role = 'model'
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# For system messages, convert to user messages with a prefix
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if role == 'system':
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# Map 'system' to 'user' for the first message
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if role == 'system' and not formatted_messages:
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formatted_messages.append({
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"role": "user",
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"content": f"System instruction: {msg['content']}"
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'role': 'user',
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'content': msg['content']
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})
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# Map 'assistant' to 'model'
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elif role == 'assistant':
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formatted_messages.append({
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'role': 'model',
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'content': msg['content']
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})
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# Keep 'user' as is
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else:
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formatted_messages.append({"role": role, "content": msg['content']})
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params['messages'] = formatted_messages
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formatted_messages.append(msg)
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else:
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params['messages'] = messages
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formatted_messages = messages
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params['stream'] = stream
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response = completion(**params)
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# Get completion parameters
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params = self._get_completion_params()
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try:
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# Generate completion
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if stream:
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response = litellm.completion(
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messages=formatted_messages,
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stream=True,
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**params
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)
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return response
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else:
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return response.choices[0].message.content
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response = litellm.completion(
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messages=formatted_messages,
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**params
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)
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# Extract content from response
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content = response.choices[0].message.content
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# Process thinking tags if enabled
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if hasattr(self, 'process_thinking_tags') and self.process_thinking_tags:
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content = self._process_thinking_tags(content)
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return content
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except Exception as e:
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print(f"Error generating completion: {e}")
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return f"Error: {str(e)}"
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error_msg = f"Error generating completion: {str(e)}"
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print(error_msg)
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# Return error message in a user-friendly format
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return f"I encountered an error while processing your request: {str(e)}"
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def enhance_query(self, query: str) -> str:
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"""
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@ -135,57 +135,64 @@ class ReportSynthesizer:
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If stream is False, returns the completion text as a string
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If stream is True, returns the completion response object for streaming
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"""
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try:
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params = self._get_completion_params()
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# Get provider from model config
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provider = self.model_config.get('provider', 'groq').lower()
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# Special handling for Gemini models
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if "gemini" in params.get('model', '').lower():
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# Format messages for Gemini models
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# Special handling for Gemini models - they use 'user' and 'model' roles
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if provider == 'gemini':
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formatted_messages = []
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for msg in messages:
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role = msg['role']
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# Gemini uses 'user' and 'model' roles (not 'assistant')
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if role == 'assistant':
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role = 'model'
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# For system messages, convert to user messages with a prefix
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if role == 'system':
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# Map 'system' to 'user' for the first message
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if role == 'system' and not formatted_messages:
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formatted_messages.append({
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"role": "user",
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"content": f"System instruction: {msg['content']}"
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'role': 'user',
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'content': msg['content']
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})
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# Map 'assistant' to 'model'
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elif role == 'assistant':
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formatted_messages.append({
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'role': 'model',
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'content': msg['content']
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})
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# Keep 'user' as is
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else:
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formatted_messages.append({"role": role, "content": msg['content']})
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params['messages'] = formatted_messages
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formatted_messages.append(msg)
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else:
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params['messages'] = messages
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formatted_messages = messages
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params['stream'] = stream
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logger.info(f"Generating completion with model: {params.get('model')}")
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logger.info(f"Provider: {self.model_config.get('provider')}")
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response = completion(**params)
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# Get completion parameters
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params = self._get_completion_params()
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try:
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# Generate completion
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if stream:
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response = litellm.completion(
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messages=formatted_messages,
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stream=True,
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**params
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)
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return response
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else:
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response = litellm.completion(
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messages=formatted_messages,
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**params
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)
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# Extract content from response
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content = response.choices[0].message.content
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# Process <thinking> tags if enabled
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# Process thinking tags if enabled
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if self.process_thinking_tags:
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content = self._process_thinking_tags(content)
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return content
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except Exception as e:
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logger.error(f"Error generating completion: {e}")
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logger.error(f"Model params: {params}")
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error_msg = f"Error generating completion: {str(e)}"
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logger.error(error_msg)
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# More detailed error for debugging
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if hasattr(e, '__dict__'):
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for key, value in e.__dict__.items():
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logger.error(f"Error detail - {key}: {value}")
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return f"Error: {str(e)}"
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# Return error message in a user-friendly format
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return f"I encountered an error while processing your request: {str(e)}"
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def _process_thinking_tags(self, content: str) -> str:
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"""
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@ -360,13 +367,36 @@ class ReportSynthesizer:
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detail_level_manager = get_report_detail_level_manager()
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template = detail_level_manager.get_template_modifier(detail_level, query_type)
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# Add specific instructions for references formatting
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reference_instructions = """
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When including references, use a consistent format:
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[1] Author(s). Title. Publication. Year. URL (if available)
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If author information is not available, use the website or organization name.
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Always ensure the References section is complete and properly formatted at the end of the report.
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Do not use placeholders like "Document X" for references - provide actual titles.
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Ensure all references are properly closed with brackets and there are no incomplete references.
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"""
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# Special handling for Gemini models
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if "gemini" in self.model_name.lower():
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reference_instructions += """
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IMPORTANT: Due to token limitations, ensure the References section is completed properly.
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If you feel you might run out of tokens, start the References section earlier and make it more concise.
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Never leave the References section incomplete or cut off mid-reference.
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"""
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# Create the prompt for synthesizing the report
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messages = [
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{"role": "system", "content": f"""You are an expert research assistant tasked with creating comprehensive, well-structured reports.
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{template}
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Format the report in Markdown with clear headings, subheadings, and bullet points where appropriate.
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Make the report readable, engaging, and informative while maintaining academic rigor."""},
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Make the report readable, engaging, and informative while maintaining academic rigor.
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{reference_instructions}"""},
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{"role": "user", "content": f"""Query: {query}
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Information from sources:
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@ -378,6 +408,38 @@ class ReportSynthesizer:
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# Generate the report
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report = await self.generate_completion(messages)
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# Check if the report might be cut off at the end
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if report.strip().endswith('[') or report.strip().endswith(']') or report.strip().endswith('...'):
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logger.warning("Report appears to be cut off at the end. Attempting to fix references section.")
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# Try to fix the references section by generating it separately
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try:
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# Extract what we have so far without the incomplete references
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if "References" in report:
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report_without_refs = report.split("References")[0].strip()
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else:
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report_without_refs = report
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# Generate just the references section
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ref_messages = [
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{"role": "system", "content": "You are an expert at formatting reference lists. Create a properly formatted References section for the following documents:"},
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{"role": "user", "content": f"""Here are the documents used in the report:
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{context}
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Create a complete, properly formatted References section in Markdown format.
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Use the format: [1] Title. Source URL
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Make sure all references are complete and properly formatted."""}
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]
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references = await self.generate_completion(ref_messages)
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# Combine the report with the fixed references
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report = f"{report_without_refs}\n\n## References\n\n{references}"
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except Exception as e:
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logger.error(f"Error fixing references section: {str(e)}")
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return report
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async def synthesize_report(self, chunks: List[Dict[str, Any]], query: str, query_type: str = "exploratory", detail_level: str = "standard") -> str:
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