55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
"""
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Embedding processing for retain pipeline.
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Handles augmenting fact texts with temporal information and generating embeddings.
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"""
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import logging
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from . import embedding_utils
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from .types import ExtractedFact
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logger = logging.getLogger(__name__)
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def augment_texts_with_dates(facts: list[ExtractedFact], format_date_fn) -> list[str]:
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"""
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Augment fact texts with readable dates for better temporal matching.
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This allows queries like "camping in June" to match facts that happened in June.
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Args:
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facts: List of ExtractedFact objects
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format_date_fn: Function to format datetime to readable string
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Returns:
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List of augmented text strings (same length as facts)
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"""
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augmented_texts = []
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for fact in facts:
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# Use occurred_start as the representative date
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fact_date = fact.occurred_start or fact.mentioned_at
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readable_date = format_date_fn(fact_date)
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# Augment text with date for embedding (but store original text in DB)
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augmented_text = f"{fact.fact_text} (happened in {readable_date})"
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augmented_texts.append(augmented_text)
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return augmented_texts
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async def generate_embeddings_batch(embeddings_model, texts: list[str]) -> list[list[float]]:
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"""
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Generate embeddings for a batch of texts.
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Args:
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embeddings_model: Embeddings model instance
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texts: List of text strings to embed
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Returns:
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List of embedding vectors (same length as texts)
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"""
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if not texts:
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return []
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embeddings = await embedding_utils.generate_embeddings_batch(embeddings_model, texts)
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return embeddings
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