e89f2f4b7b
Created 10 detailed GitHub issues covering: - Project activation and management UI (#1-2) - Worker node coordination and visualization (#3-4) - Automated GitHub repository scanning (#5) - Intelligent model-to-issue matching (#6) - Multi-model task execution system (#7) - N8N workflow integration (#8) - Hive-Bzzz P2P bridge (#9) - Peer assistance protocol (#10) Each issue includes detailed specifications, acceptance criteria, technical implementation notes, and dependency mapping. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
48 lines
1.0 KiB
Plaintext
48 lines
1.0 KiB
Plaintext
1 - "Mr. Green killed Colonel Mustard in the study with the candlestick. Mr. Green is not a very nice fellow."
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2 - "Professor Plumb has a green plant in his study."
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3 - "Miss Scarlett watered Professor Plumb's green plant while he was away from his office last week."
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l1 = 19
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l2 = 9
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l3 = 16
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q1 - "green"
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q1 = [0.0, 0.71]
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1 = [0.0, 0.0747]
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2 = [0.0, 0.1555]
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3 = [0.0, 0.0875]
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green : total count = 4, idf = 0.71
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mr : total count = 2, idf = 1.40
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the : total count = 2, idf = 1.40
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plant : total count = 2, idf = 1.40
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q2 = "Mr. Green"
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q2 = [1.4, 0.71]
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1 = [0.147, 0.0747]
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2 = [0, 0.1555]
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3 = [0, 0.0875]
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q3 = "the green plant"
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q3 = [0.5, 0.25, 0.5]
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1 = [1, 0.5, 0]
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2 = [0, 0.25, 0.5]
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3 = [0, 0.25, 0.5]
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Inverse Index as a trie
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values are {docId: score} where score is the sum of tf across fields, with multipliers applied
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when querying calculate the idf and multiply it by the tf
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for a multi term query generate a vector using the idf
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find all the documents that match both queries, and generate a tf*idf
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word: {
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totalCount: 123,
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docs:
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}
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