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modelx / src /states /vectorizationAgentState.py

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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]

	"""
	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]