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Improve multi-entity contextual grounding in memory extraction
Enhanced DISCRETE_EXTRACTION_PROMPT with explicit multi-entity handling instructions and improved test robustness to focus on core grounding functionality. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>
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tests/test_thread_aware_grounding.py

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@@ -90,13 +90,13 @@ async def test_thread_aware_pronoun_resolution(self):
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), "Memories should contain the grounded name 'John'"
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# Ideally, there should be minimal or no ungrounded pronouns
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ungrounded_pronouns = [
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"he ",
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"his ",
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"him ",
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] # Note: spaces to avoid false positives
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# Use word boundary matching to avoid false positives like "the" containing "he"
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import re
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ungrounded_pronouns = [r"\bhe\b", r"\bhis\b", r"\bhim\b"]
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ungrounded_count = sum(
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all_memory_text.lower().count(pronoun) for pronoun in ungrounded_pronouns
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len(re.findall(pattern, all_memory_text, re.IGNORECASE))
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for pattern in ungrounded_pronouns
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)
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print(f"Ungrounded pronouns found: {ungrounded_count}")
@@ -227,8 +227,14 @@ async def test_multi_entity_conversation(self):
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# Still consider it a pass if we have some entity grounding
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# Check for reduced pronoun usage - this is the key improvement
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pronouns = ["he ", "she ", "his ", "her ", "him "]
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pronoun_count = sum(all_memory_text.lower().count(p) for p in pronouns)
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# Use word boundary matching to avoid false positives like "the" containing "he"
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import re
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pronouns = [r"\bhe\b", r"\bshe\b", r"\bhis\b", r"\bher\b", r"\bhim\b"]
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pronoun_count = sum(
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len(re.findall(pattern, all_memory_text, re.IGNORECASE))
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for pattern in pronouns
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)
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print(f"Remaining pronouns: {pronoun_count}")
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# The main success criterion: significantly reduced pronoun usage

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