379 lines
16 KiB
Python
379 lines
16 KiB
Python
"""
|
|
Research Intent Inference Service
|
|
|
|
Analyzes user input to understand their research intent.
|
|
Uses AI to infer:
|
|
- What the user wants to accomplish
|
|
- What questions need answering
|
|
- What deliverables they expect
|
|
|
|
Author: ALwrity Team
|
|
Version: 1.0
|
|
"""
|
|
|
|
import json
|
|
from typing import Dict, Any, List, Optional
|
|
from loguru import logger
|
|
|
|
from models.research_intent_models import (
|
|
ResearchIntent,
|
|
ResearchPurpose,
|
|
ContentOutput,
|
|
ExpectedDeliverable,
|
|
ResearchDepthLevel,
|
|
InputType,
|
|
IntentInferenceRequest,
|
|
IntentInferenceResponse,
|
|
ResearchQuery,
|
|
)
|
|
from models.research_persona_models import ResearchPersona
|
|
from .intent_prompt_builder import IntentPromptBuilder
|
|
|
|
|
|
class ResearchIntentInference:
|
|
"""
|
|
Infers user research intent from minimal input.
|
|
|
|
Instead of asking a formal questionnaire, this service
|
|
uses AI to understand what the user really wants.
|
|
"""
|
|
|
|
def __init__(self):
|
|
"""Initialize the intent inference service."""
|
|
self.prompt_builder = IntentPromptBuilder()
|
|
logger.info("ResearchIntentInference initialized")
|
|
|
|
async def infer_intent(
|
|
self,
|
|
user_input: str,
|
|
keywords: Optional[List[str]] = None,
|
|
research_persona: Optional[ResearchPersona] = None,
|
|
competitor_data: Optional[List[Dict]] = None,
|
|
industry: Optional[str] = None,
|
|
target_audience: Optional[str] = None,
|
|
) -> IntentInferenceResponse:
|
|
"""
|
|
Analyze user input and infer their research intent.
|
|
|
|
Args:
|
|
user_input: User's keywords, question, or goal
|
|
keywords: Extracted keywords (optional)
|
|
research_persona: User's research persona (optional)
|
|
competitor_data: Competitor analysis data (optional)
|
|
industry: Industry context (optional)
|
|
target_audience: Target audience context (optional)
|
|
|
|
Returns:
|
|
IntentInferenceResponse with inferred intent and suggested queries
|
|
"""
|
|
try:
|
|
logger.info(f"Inferring intent for: {user_input[:100]}...")
|
|
|
|
keywords = keywords or []
|
|
|
|
# Build the inference prompt
|
|
prompt = self.prompt_builder.build_intent_inference_prompt(
|
|
user_input=user_input,
|
|
keywords=keywords,
|
|
research_persona=research_persona,
|
|
competitor_data=competitor_data,
|
|
industry=industry,
|
|
target_audience=target_audience,
|
|
)
|
|
|
|
# Define the expected JSON schema
|
|
intent_schema = {
|
|
"type": "object",
|
|
"properties": {
|
|
"input_type": {"type": "string", "enum": ["keywords", "question", "goal", "mixed"]},
|
|
"primary_question": {"type": "string"},
|
|
"secondary_questions": {"type": "array", "items": {"type": "string"}},
|
|
"purpose": {"type": "string"},
|
|
"content_output": {"type": "string"},
|
|
"expected_deliverables": {"type": "array", "items": {"type": "string"}},
|
|
"depth": {"type": "string", "enum": ["overview", "detailed", "expert"]},
|
|
"focus_areas": {"type": "array", "items": {"type": "string"}},
|
|
"perspective": {"type": "string"},
|
|
"time_sensitivity": {"type": "string"},
|
|
"confidence": {"type": "number"},
|
|
"needs_clarification": {"type": "boolean"},
|
|
"clarifying_questions": {"type": "array", "items": {"type": "string"}},
|
|
"analysis_summary": {"type": "string"}
|
|
},
|
|
"required": [
|
|
"input_type", "primary_question", "purpose", "content_output",
|
|
"expected_deliverables", "depth", "confidence", "analysis_summary"
|
|
]
|
|
}
|
|
|
|
# Call LLM for intent inference
|
|
from services.llm_providers.main_text_generation import llm_text_gen
|
|
|
|
result = llm_text_gen(
|
|
prompt=prompt,
|
|
json_struct=intent_schema,
|
|
user_id=None
|
|
)
|
|
|
|
if isinstance(result, dict) and "error" in result:
|
|
logger.error(f"Intent inference failed: {result.get('error')}")
|
|
return self._create_fallback_response(user_input, keywords)
|
|
|
|
# Parse and validate the result
|
|
intent = self._parse_intent_result(result, user_input)
|
|
|
|
# Generate quick options for UI
|
|
quick_options = self._generate_quick_options(intent, result)
|
|
|
|
# Create response
|
|
response = IntentInferenceResponse(
|
|
success=True,
|
|
intent=intent,
|
|
analysis_summary=result.get("analysis_summary", "Research intent analyzed"),
|
|
suggested_queries=[], # Will be populated by query generator
|
|
suggested_keywords=self._extract_keywords_from_input(user_input, keywords),
|
|
suggested_angles=result.get("focus_areas", []),
|
|
quick_options=quick_options,
|
|
)
|
|
|
|
logger.info(f"Intent inferred: purpose={intent.purpose}, confidence={intent.confidence}")
|
|
return response
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error inferring intent: {e}")
|
|
return self._create_fallback_response(user_input, keywords or [])
|
|
|
|
def _parse_intent_result(self, result: Dict[str, Any], user_input: str) -> ResearchIntent:
|
|
"""Parse LLM result into ResearchIntent model."""
|
|
|
|
# Map string values to enums safely
|
|
input_type = self._safe_enum(InputType, result.get("input_type", "keywords"), InputType.KEYWORDS)
|
|
purpose = self._safe_enum(ResearchPurpose, result.get("purpose", "learn"), ResearchPurpose.LEARN)
|
|
content_output = self._safe_enum(ContentOutput, result.get("content_output", "general"), ContentOutput.GENERAL)
|
|
depth = self._safe_enum(ResearchDepthLevel, result.get("depth", "detailed"), ResearchDepthLevel.DETAILED)
|
|
|
|
# Parse expected deliverables
|
|
raw_deliverables = result.get("expected_deliverables", [])
|
|
expected_deliverables = []
|
|
for d in raw_deliverables:
|
|
try:
|
|
expected_deliverables.append(ExpectedDeliverable(d).value)
|
|
except ValueError:
|
|
# Skip invalid deliverables
|
|
pass
|
|
|
|
# Ensure we have at least some deliverables
|
|
if not expected_deliverables:
|
|
expected_deliverables = self._infer_deliverables_from_purpose(purpose)
|
|
|
|
return ResearchIntent(
|
|
primary_question=result.get("primary_question", user_input),
|
|
secondary_questions=result.get("secondary_questions", []),
|
|
purpose=purpose.value,
|
|
content_output=content_output.value,
|
|
expected_deliverables=expected_deliverables,
|
|
depth=depth.value,
|
|
focus_areas=result.get("focus_areas", []),
|
|
perspective=result.get("perspective"),
|
|
time_sensitivity=result.get("time_sensitivity"),
|
|
input_type=input_type.value,
|
|
original_input=user_input,
|
|
confidence=float(result.get("confidence", 0.7)),
|
|
needs_clarification=result.get("needs_clarification", False),
|
|
clarifying_questions=result.get("clarifying_questions", []),
|
|
)
|
|
|
|
def _safe_enum(self, enum_class, value: str, default):
|
|
"""Safely convert string to enum, returning default if invalid."""
|
|
try:
|
|
return enum_class(value)
|
|
except ValueError:
|
|
return default
|
|
|
|
def _infer_deliverables_from_purpose(self, purpose: ResearchPurpose) -> List[str]:
|
|
"""Infer expected deliverables based on research purpose."""
|
|
|
|
purpose_deliverables = {
|
|
ResearchPurpose.LEARN: [
|
|
ExpectedDeliverable.DEFINITIONS.value,
|
|
ExpectedDeliverable.EXAMPLES.value,
|
|
ExpectedDeliverable.KEY_STATISTICS.value,
|
|
],
|
|
ResearchPurpose.CREATE_CONTENT: [
|
|
ExpectedDeliverable.KEY_STATISTICS.value,
|
|
ExpectedDeliverable.EXPERT_QUOTES.value,
|
|
ExpectedDeliverable.EXAMPLES.value,
|
|
ExpectedDeliverable.CASE_STUDIES.value,
|
|
],
|
|
ResearchPurpose.MAKE_DECISION: [
|
|
ExpectedDeliverable.PROS_CONS.value,
|
|
ExpectedDeliverable.COMPARISONS.value,
|
|
ExpectedDeliverable.BEST_PRACTICES.value,
|
|
],
|
|
ResearchPurpose.COMPARE: [
|
|
ExpectedDeliverable.COMPARISONS.value,
|
|
ExpectedDeliverable.PROS_CONS.value,
|
|
ExpectedDeliverable.KEY_STATISTICS.value,
|
|
],
|
|
ResearchPurpose.SOLVE_PROBLEM: [
|
|
ExpectedDeliverable.STEP_BY_STEP.value,
|
|
ExpectedDeliverable.BEST_PRACTICES.value,
|
|
ExpectedDeliverable.CASE_STUDIES.value,
|
|
],
|
|
ResearchPurpose.FIND_DATA: [
|
|
ExpectedDeliverable.KEY_STATISTICS.value,
|
|
ExpectedDeliverable.CITATIONS.value,
|
|
],
|
|
ResearchPurpose.EXPLORE_TRENDS: [
|
|
ExpectedDeliverable.TRENDS.value,
|
|
ExpectedDeliverable.PREDICTIONS.value,
|
|
ExpectedDeliverable.KEY_STATISTICS.value,
|
|
],
|
|
ResearchPurpose.VALIDATE: [
|
|
ExpectedDeliverable.CITATIONS.value,
|
|
ExpectedDeliverable.KEY_STATISTICS.value,
|
|
ExpectedDeliverable.EXPERT_QUOTES.value,
|
|
],
|
|
ResearchPurpose.GENERATE_IDEAS: [
|
|
ExpectedDeliverable.EXAMPLES.value,
|
|
ExpectedDeliverable.TRENDS.value,
|
|
ExpectedDeliverable.CASE_STUDIES.value,
|
|
],
|
|
}
|
|
|
|
return purpose_deliverables.get(purpose, [ExpectedDeliverable.KEY_STATISTICS.value])
|
|
|
|
def _generate_quick_options(self, intent: ResearchIntent, result: Dict[str, Any]) -> List[Dict[str, Any]]:
|
|
"""Generate quick options for UI confirmation."""
|
|
|
|
options = []
|
|
|
|
# Purpose option
|
|
options.append({
|
|
"id": "purpose",
|
|
"label": "Research Purpose",
|
|
"value": intent.purpose,
|
|
"display": self._purpose_display(intent.purpose),
|
|
"alternatives": [p.value for p in ResearchPurpose],
|
|
"confidence": result.get("confidence", 0.7),
|
|
})
|
|
|
|
# Content output option
|
|
if intent.content_output != ContentOutput.GENERAL.value:
|
|
options.append({
|
|
"id": "content_output",
|
|
"label": "Content Type",
|
|
"value": intent.content_output,
|
|
"display": intent.content_output.replace("_", " ").title(),
|
|
"alternatives": [c.value for c in ContentOutput],
|
|
"confidence": result.get("confidence", 0.7),
|
|
})
|
|
|
|
# Deliverables option
|
|
options.append({
|
|
"id": "deliverables",
|
|
"label": "What I'll Find",
|
|
"value": intent.expected_deliverables,
|
|
"display": [d.replace("_", " ").title() for d in intent.expected_deliverables[:4]],
|
|
"alternatives": [d.value for d in ExpectedDeliverable],
|
|
"confidence": result.get("confidence", 0.7),
|
|
"multi_select": True,
|
|
})
|
|
|
|
# Depth option
|
|
options.append({
|
|
"id": "depth",
|
|
"label": "Research Depth",
|
|
"value": intent.depth,
|
|
"display": intent.depth.title(),
|
|
"alternatives": [d.value for d in ResearchDepthLevel],
|
|
"confidence": result.get("confidence", 0.7),
|
|
})
|
|
|
|
return options
|
|
|
|
def _purpose_display(self, purpose: str) -> str:
|
|
"""Get display-friendly purpose text."""
|
|
display_map = {
|
|
"learn": "Understand this topic",
|
|
"create_content": "Create content about this",
|
|
"make_decision": "Make a decision",
|
|
"compare": "Compare options",
|
|
"solve_problem": "Solve a problem",
|
|
"find_data": "Find specific data",
|
|
"explore_trends": "Explore trends",
|
|
"validate": "Validate information",
|
|
"generate_ideas": "Generate ideas",
|
|
}
|
|
return display_map.get(purpose, purpose.replace("_", " ").title())
|
|
|
|
def _extract_keywords_from_input(self, user_input: str, keywords: List[str]) -> List[str]:
|
|
"""Extract and enhance keywords from user input."""
|
|
|
|
# Start with provided keywords
|
|
extracted = list(keywords) if keywords else []
|
|
|
|
# Simple extraction from input (split on common delimiters)
|
|
words = user_input.lower().replace(",", " ").replace(";", " ").split()
|
|
|
|
# Filter out common words
|
|
stop_words = {
|
|
"the", "a", "an", "is", "are", "was", "were", "be", "been", "being",
|
|
"have", "has", "had", "do", "does", "did", "will", "would", "could",
|
|
"should", "may", "might", "must", "shall", "can", "need", "dare",
|
|
"to", "of", "in", "for", "on", "with", "at", "by", "from", "up",
|
|
"about", "into", "through", "during", "before", "after", "above",
|
|
"below", "between", "under", "again", "further", "then", "once",
|
|
"here", "there", "when", "where", "why", "how", "all", "each",
|
|
"few", "more", "most", "other", "some", "such", "no", "nor", "not",
|
|
"only", "own", "same", "so", "than", "too", "very", "just", "and",
|
|
"but", "if", "or", "because", "as", "until", "while", "i", "we",
|
|
"you", "they", "what", "which", "who", "whom", "this", "that",
|
|
"these", "those", "am", "want", "write", "blog", "post", "article",
|
|
}
|
|
|
|
for word in words:
|
|
if word not in stop_words and len(word) > 2 and word not in extracted:
|
|
extracted.append(word)
|
|
|
|
return extracted[:15] # Limit to 15 keywords
|
|
|
|
def _create_fallback_response(self, user_input: str, keywords: List[str]) -> IntentInferenceResponse:
|
|
"""Create a fallback response when AI inference fails."""
|
|
|
|
# Create a basic intent from the input
|
|
fallback_intent = ResearchIntent(
|
|
primary_question=f"What are the key insights about: {user_input}?",
|
|
secondary_questions=[
|
|
f"What are the latest trends in {user_input}?",
|
|
f"What are best practices for {user_input}?",
|
|
],
|
|
purpose=ResearchPurpose.LEARN.value,
|
|
content_output=ContentOutput.GENERAL.value,
|
|
expected_deliverables=[
|
|
ExpectedDeliverable.KEY_STATISTICS.value,
|
|
ExpectedDeliverable.EXAMPLES.value,
|
|
ExpectedDeliverable.BEST_PRACTICES.value,
|
|
],
|
|
depth=ResearchDepthLevel.DETAILED.value,
|
|
focus_areas=[],
|
|
input_type=InputType.KEYWORDS.value,
|
|
original_input=user_input,
|
|
confidence=0.5,
|
|
needs_clarification=True,
|
|
clarifying_questions=[
|
|
"What type of content are you creating?",
|
|
"What specific aspects are you most interested in?",
|
|
],
|
|
)
|
|
|
|
return IntentInferenceResponse(
|
|
success=True, # Still return success, just with lower confidence
|
|
intent=fallback_intent,
|
|
analysis_summary=f"Basic research analysis for: {user_input}",
|
|
suggested_queries=[],
|
|
suggested_keywords=keywords,
|
|
suggested_angles=[],
|
|
quick_options=[],
|
|
)
|