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"""
src/states/vectorizationAgentState.py
Vectorization Agent State - handles text-to-vector conversion with multilingual BERT
"""
from typing import Optional, List, Dict, Any
from typing_extensions import TypedDict
class VectorizationAgentState(TypedDict, total=False):
"""
State for Vectorization Agent.
Converts text to vectors using language-specific BERT models.
Steps: Language Detection → Vectorization → Expert Summary
Note: This is a sequential graph, so no reducers needed.
Each node's output fully replaces the field value.
"""
# ===== INPUT =====
input_texts: List[Dict[str, Any]] # [{text, post_id, metadata}]
batch_id: str
# ===== LANGUAGE DETECTION =====
language_detection_results: List[Dict[str, Any]]
# [{post_id, text, language, confidence}]
# ===== VECTORIZATION =====
vector_embeddings: List[Dict[str, Any]]
# [{post_id, language, vector, model_used}]
# ===== CLUSTERING/ANOMALY =====
clustering_results: Optional[Dict[str, Any]]
anomaly_results: Optional[Dict[str, Any]]
# ===== TRENDING DETECTION =====
trending_results: Optional[Dict[str, Any]]
# {trending_topics, spike_alerts, entities_extracted}
# ===== EXPERT ANALYSIS =====
expert_summary: Optional[str] # LLM-generated summary combining all insights
opportunities: List[Dict[str, Any]] # Detected opportunities
threats: List[Dict[str, Any]] # Detected threats
# ===== PROCESSING STATUS =====
current_step: str
processing_stats: Dict[str, Any]
errors: List[str]
# ===== LLM OUTPUT =====
llm_response: Optional[str]
structured_output: Dict[str, Any]
# ===== INTEGRATION WITH PARENT GRAPH =====
domain_insights: List[Dict[str, Any]]
# ===== FINAL OUTPUT =====
final_output: Dict[str, Any]
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