| File | Agent Tag | Solved | Correct Groups | Mistakes | Embed Correct | Comments |
|---|---|---|---|---|---|---|
| data/word_list1.txt | v0.6.1 | No | 2 | 4 | 1 | difficulty with blue & purple groups |
| data/word_list2.txt | v0.6.1 | Yes | 4 | 1 | 2 | |
| data/word_list3.txt | v0.6.1 | Yes | 4 | 1 | 1 | |
| data/word_list4.txt | v0.6.1 | Yes | 4 | 1 | 2 | |
| data/word_list5.txt | v0.6.1 | Yes | 4 | 1 | 1 | |
| data/connection_puzzle_2024_10_23.png | v0.6.1 | No | 0 | 4 | 0 | |
| data/connection_puzzle_2024_10_26.png | v0.6.1 | Yes | 4 | 0 | 4 | |
| data/connection_puzzle_2024_10_27.png | v0.6.2 | Yes | 4 | 1 | 0 | |
| data/connection_puzzle_2024_10_28.png | v0.6.2 | No | 1 | 4 | 0 | |
| data/connection_puzzle_2024_11_02.png | v0.6.2 | No | 1 | 4 | 1 | one-away failed mulitple single-topic groups,random selection wrong one. |
python src/agent/app_embedvec.py
Enter 'file' to read words from a file or 'image' to read words from an image: image
Please enter the image file location: data/connection_puzzle_2024_11_02.png
Puzzle Words: ['board', 'drop', 'plank', 'sink', 'range', 'panel', 'stud', 'boat', 'chandelier', 'counter', 'cabinet', 'crunch', 'council', 'mountain climber', 'fridge', 'hoop']
Generating vocabulary for the words...this may take about a minute
Generating embeddings for the definitions
ENTERED EMBEDVEC RECOMMENDATION
(111, 111)
(111, 111)
candidate_lists size: 76
EMBEDVEC_RECOMMENDER: RECOMMENDED WORDS ['board', 'cabinet', 'council', 'panel'] with connection These words are connected by the theme of 'decision-making or advisory groups'.
Is the recommendation accepted? (y/g/b/p/o/n): g
Recommendation ['board', 'cabinet', 'council', 'panel'] is correct
ENTERED EMBEDVEC RECOMMENDATION
(84, 84)
(84, 84)
candidate_lists size: 53
EMBEDVEC_RECOMMENDER: RECOMMENDED WORDS ['crunch', 'drop', 'plank', 'sink'] with connection These words relate to actions involving downward movement or positioning.
Is the recommendation accepted? (y/g/b/p/o/n): n
Recommendation ['crunch', 'drop', 'plank', 'sink'] is incorrect
Changing the recommender from 'embedvec_recommender' to 'llm_recommender'
attempt_count: 1
words_remaining: ['hoop', 'fridge', 'mountain climber', 'crunch', 'counter', 'chandelier', 'boat', 'stud', 'range', 'sink', 'plank', 'drop']
LLM_RECOMMENDER: RECOMMENDED WORDS ['counter', 'hoop', 'range', 'sink'] with connection kitchen items
Is the recommendation accepted? (y/g/b/p/o/n): o
Recommendation ['hoop', 'counter', 'sink', 'range'] is incorrect, one away from correct
>>>Number of single topic groups: 1
>>>One-away group recommendations:
Recommended Group: ['counter', 'sink', 'range', 'fridge']
Connection Description: The word 'fridge' is most connected to the anchor words 'counter', 'sink', and 'range' through the common connection of a kitchen setting. A 'fridge' is an essential kitchen appliance used for storing food, similar to how a 'counter', 'sink', and 'range' are features or equipment commonly found in a kitchen.
using one_away_group_recommendation
LLM_RECOMMENDER: RECOMMENDED WORDS ['counter', 'fridge', 'range', 'sink'] with connection The word 'fridge' is most connected to the anchor words 'counter', 'sink', and 'range' through the common connection of a kitchen setting. A 'fridge' is an essential kitchen appliance used for storing food, similar to how a 'counter', 'sink', and 'range' are features or equipment commonly found in a kitchen.
Is the recommendation accepted? (y/g/b/p/o/n): y
Recommendation ['counter', 'sink', 'range', 'fridge'] is correct
attempt_count: 1
words_remaining: ['crunch', 'boat', 'mountain climber', 'drop', 'chandelier', 'hoop', 'plank', 'stud']
LLM_RECOMMENDER: RECOMMENDED WORDS ['crunch', 'drop', 'hoop', 'plank'] with connection exercise moves
Is the recommendation accepted? (y/g/b/p/o/n): n
Recommendation ['crunch', 'drop', 'plank', 'hoop'] is incorrect
attempt_count: 1
words_remaining: ['stud', 'plank', 'hoop', 'chandelier', 'drop', 'mountain climber', 'boat', 'crunch']
LLM_RECOMMENDER: RECOMMENDED WORDS ['chandelier', 'hoop', 'plank', 'stud'] with connection Types of Jewelry or Ornaments
Is the recommendation accepted? (y/g/b/p/o/n): o
FAILED TO SOLVE THE CONNECTION PUZZLE TOO MANY MISTAKES!!!
FINAL PUZZLE STATE:
{ 'found_count': 2,
'found_yellow': True,
'invalid_connections': [ ( '7bb7b7c46da39a4482168b08c206b83f',
['crunch', 'drop', 'plank', 'sink']),
( '9e13c461f52514df8d561595a077dae6',
['hoop', 'counter', 'sink', 'range']),
( '0287ce63790ae4dae2e62ebfc6055070',
['crunch', 'drop', 'plank', 'hoop']),
( 'ff1022807d633f7bc4e0756356409c88',
['stud', 'plank', 'hoop', 'chandelier'])],
'llm_retry_count': 0,
'llm_temperature': 0.7,
'mistake_count': 4,
'puzzle_recommender': 'llm_recommender',
'puzzle_status': 'initialized',
'puzzle_step': 'puzzle_completed',
'recommendation_count': 6,
'recommended_connection': '',
'recommended_correct': False,
'recommended_words': [],
'tool_to_use': 'END',
'vocabulary_df': word ... embedding
8 drop ... [0.00634157657623291, 0.000708345090970397, -0...
9 drop ... [0.028417417779564857, 0.01165621168911457, -0...
10 drop ... [0.022679604589939117, 0.005510931834578514, 0...
11 drop ... [0.033691562712192535, -0.0004736323026008904,...
12 drop ... [-0.024791566655039787, -0.02297668159008026, ...
.. ... ... ...
106 hoop ... [0.025540487840771675, 0.040016502141952515, -...
107 hoop ... [0.025241505354642868, 0.010754874907433987, 0...
108 hoop ... [0.052456941455602646, 0.015394971705973148, -...
109 hoop ... [0.012448335066437721, -0.019924946129322052, ...
110 hoop ... [0.0051789600402116776, -0.018043240532279015,...
[84 rows x 3 columns],
'words_remaining': [ 'stud',
'plank',
'hoop',
'chandelier',
'drop',
'mountain climber',
'boat',
'crunch'],
'workflow_instructions': '**Instructions**\n'
'\n'
'use "setup_puzzle" tool to initialize the puzzle '
'if the "puzzle_status" is not initialized.\n'
'\n'
'if "puzzle_step" is "puzzle_completed" then use '
'"END" tool.\n'
'\n'
'Use the table to select the appropriate tool.\n'
'\n'
'|puzzle_recommender| puzzle_step | tool |\n'
'| --- | --- | --- |\n'
'|embedvec_recommender| next_recommendation | '
'get_embedvec_recommendation |\n'
'|embedvec_recommender| have_recommendation | '
'apply_recommendation |\n'
'|llm_recommender| next_recommendation | '
'get_recommendation |\n'
'|llm_recommender| have_recommendation | '
'apply_recommendation |\n'
'\n'
'If no tool is selected, use "ABORT" tool.\n'}
| File | Agent Tag | Solved | Correct Groups | Mistakes | Comments |
|---|---|---|---|---|---|
| data/word_list1.txt | v0.5.0 | No | 1 | 4 | |
| data/word_list2.txt | v0.5.0 | Yes | 4 | 2 | |
| data/word_list3.txt | v0.5.0 | No | 2 | 4 | |
| data/word_list4.txt | v0.5.0 | Yes | 4 | 1 | |
| data/word_list5.txt | v0.5.0 | Yes | 4 | 0 | |
| data/connection_puzzle_2024_10_23.png | v0.5.0 | No | 0 | 4 | |
| data/connection_puzzle_2024_10_26.png | v0.5.0 | Yes | 4 | 0 | |
| data/connection_puzzle_2024_10_27.png | v0.5.0 | Yes | 4 | 2 | |
| data/connection_puzzle_2024_10_28.png | v0.5.0 | No | 2 | 4 | |
| data/connection_puzzle_2024_11_02.png | v0.5.0 | No | 1 | 4 |
Expected Solution
🟡 GRASSY AREA: GREEN ,LAWN ,PARK ,YARD
🟢 DEAL WITH: ADDRESS ,ANSWER ,FIELD ,HANDLE
🔵 MOVIES WITH “S” REMOVED: CAR ,GOODFELLA ,JAW ,SWINGER
🟣 ___ LAW: CRIMINAL ,HARVARD ,LEMON ,NATURAL
Example Run
$ python src/agent/app_embedvec.py
Enter 'file' to read words from a file or 'image' to read words from an image: file
Please enter the word file location: data/word_list1.txt
Puzzle Words: ['goodfella', 'jaw', 'answer', 'handle', 'park', 'lemon', 'yard', 'field', 'natural', 'car', 'harvard', 'swinger', 'green', 'criminal', 'address', 'lawn']
Generating vocabulary for the words...this may take about a minute
Generating embeddings for the definitions
ENTERED EMBEDVEC RECOMMENDATION
(101, 101)
(101, 101)
candidate_lists size: 61
RECOMMENDED WORDS ['green', 'lawn', 'park', 'yard'] with connection This group is uniquely connected by the theme of grassy or green areas, typically used for recreation or surrounding a building.
Is the recommendation accepted? (y/g/b/p/n): y
Recommendation ['green', 'lawn', 'park', 'yard'] is correct
ENTERED EMBEDVEC RECOMMENDATION
(75, 75)
(75, 75)
candidate_lists size: 41
RECOMMENDED WORDS ['criminal', 'goodfella', 'handle', 'swinger'] with connection This group uniquely combines illegal activities and lifestyle elements, unlike the others.
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['criminal', 'goodfella', 'handle', 'swinger'] is incorrect
Changing the recommender from 'embedvec_recommender' to 'llm_recommender'
RECOMMENDED WORDS ['address', 'answer', 'field', 'jaw'] with connection Common words that can follow 'bone'
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['address', 'field', 'jaw', 'answer'] is incorrect
RECOMMENDED WORDS ['car', 'criminal', 'lemon', 'natural'] with connection Types of things that can be described as 'bad'
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['criminal', 'lemon', 'car', 'natural'] is incorrect
RECOMMENDED WORDS ['car', 'handle', 'jaw', 'lemon'] with connection Parts of a Vehicle
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['handle', 'jaw', 'lemon', 'car'] is incorrect
FAILED TO SOLVE THE CONNECTION PUZZLE TOO MANY MISTAKES!!!
FINAL PUZZLE STATE:
{ 'found_count': 1,
'found_yellow': True,
'invalid_connections': [ ['criminal', 'goodfella', 'handle', 'swinger'],
['address', 'field', 'jaw', 'answer'],
['criminal', 'lemon', 'car', 'natural'],
['handle', 'jaw', 'lemon', 'car']],
'llm_temperature': 0.7,
'mistake_count': 4,
'puzzle_recommender': 'llm_recommender',
'puzzle_status': 'initialized',
'puzzle_step': 'puzzle_completed',
'recommendation_count': 5,
'recommended_connection': '',
'recommended_correct': False,
'recommended_words': [],
'tool_to_use': 'END',
'vocabulary_df': word ... embedding
0 goodfella ... [-0.0018825688166543841, -0.020176813006401062...
1 goodfella ... [0.012982374057173729, -0.024880854412913322, ...
2 goodfella ... [0.002593545475974679, -0.03611481562256813, -...
3 jaw ... [0.009509989060461521, -0.012200391851365566, ...
4 jaw ... [0.015597725287079811, 0.040123581886291504, 0...
.. ... ... ...
93 address ... [0.02411642298102379, -0.009190985932946205, -...
94 address ... [0.02462298423051834, -0.03992646187543869, 0....
95 address ... [0.020654963329434395, 0.002632375340908766, 0...
96 address ... [0.06892802566289902, -0.01271691732108593, 0....
97 address ... [0.03520861640572548, -0.062401093542575836, -...
[75 rows x 3 columns],
'words_remaining': [ 'address',
'handle',
'harvard',
'goodfella',
'field',
'jaw',
'answer',
'lemon',
'natural',
'swinger',
'car',
'criminal'],
'workflow_instructions': '**Instructions**\n'
'\n'
'use "setup_puzzle" tool to initialize the puzzle '
'if the "puzzle_status" is not initialized.\n'
'\n'
'if "puzzle_step" is "puzzle_completed" then use '
'"END" tool.\n'
'\n'
'Use the table to select the appropriate tool.\n'
'\n'
'|puzzle_recommender| puzzle_step | tool |\n'
'| --- | --- | --- |\n'
'|embedvec_recommender| next_recommendation | '
'get_embedvec_recommendation |\n'
'|embedvec_recommender| have_recommendation | '
'apply_recommendation |\n'
'|llm_recommender| next_recommendation | '
'get_recommendation |\n'
'|llm_recommender| have_recommendation | '
'apply_recommendation |\n'
'\n'
'If no tool is selected, use "ABORT" tool.\n'}
Expected Solution
🟡 BRING ABOUT: GENERATE ,INSPIRE ,PROMPT ,PROVOKE
🟢 THINGS THAT ARE OFTEN SCENTED: CANDLE ,INCENSE ,LOTION ,SOAP
🔵 THINGS THAT MIGHT STING: INSULT ,JELLYFISH ,NETTLE ,WASP
🟣 VIDEO GAME FRANCHISES: CIVILIZATION ,HALO ,MADDEN ,METROID
Example Run
$ python src/agent/app_embedvec.py
Enter 'file' to read words from a file or 'image' to read words from an image: file
Please enter the word file location: data/word_list2.txt
Puzzle Words: ['inspire', 'madden', 'jellyfish', 'metroid', 'insult', 'candle', 'halo', 'provoke', 'soap', 'generate', 'incense', 'civilization', 'lotion', 'wasp', 'prompt', 'nettle']
Generating vocabulary for the words...this may take about a minute
Generating embeddings for the definitions
ENTERED EMBEDVEC RECOMMENDATION
(86, 86)
(86, 86)
candidate_lists size: 58
RECOMMENDED WORDS ['incense', 'madden', 'nettle', 'provoke'] with connection All words are connected by the theme of making someone angry or annoyed.
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['incense', 'madden', 'nettle', 'provoke'] is incorrect
Changing the recommender from 'embedvec_recommender' to 'llm_recommender'
RECOMMENDED WORDS ['insult', 'jellyfish', 'nettle', 'wasp'] with connection Things that sting or irritate
Is the recommendation accepted? (y/g/b/p/n): b
Recommendation ['nettle', 'wasp', 'jellyfish', 'insult'] is correct
RECOMMENDED WORDS ['civilization', 'halo', 'incense', 'metroid'] with connection Video Game Titles
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['metroid', 'halo', 'civilization', 'incense'] is incorrect
RECOMMENDED WORDS ['candle', 'incense', 'lotion', 'soap'] with connection Items used for fragrance or hygiene
Is the recommendation accepted? (y/g/b/p/n): g
Recommendation ['soap', 'candle', 'incense', 'lotion'] is correct
RECOMMENDED WORDS ['generate', 'inspire', 'prompt', 'provoke'] with connection Words related to eliciting a response or action
Is the recommendation accepted? (y/g/b/p/n): y
Recommendation ['inspire', 'provoke', 'generate', 'prompt'] is correct
RECOMMENDED WORDS ['civilization', 'halo', 'madden', 'metroid'] with connection Video Game Titles
Is the recommendation accepted? (y/g/b/p/n): p
Recommendation ['halo', 'metroid', 'civilization', 'madden'] is correct
SOLVED THE CONNECTION PUZZLE!!!
FINAL PUZZLE STATE:
{ 'found_blue': True,
'found_count': 4,
'found_purple': True,
'found_yellow': True,
'invalid_connections': [ ['incense', 'madden', 'nettle', 'provoke'],
['metroid', 'halo', 'civilization', 'incense']],
'llm_temperature': 0.7,
'mistake_count': 2,
'puzzle_recommender': 'llm_recommender',
'puzzle_status': 'initialized',
'puzzle_step': 'puzzle_completed',
'recommendation_count': 6,
'recommended_connection': '',
'recommended_correct': False,
'recommended_words': [],
'tool_to_use': 'END',
'vocabulary_df': word ... embedding
0 inspire ... [0.05667409673333168, -0.02952421084046364, -0...
1 inspire ... [0.011845439672470093, 0.008845405653119087, -...
2 inspire ... [0.0391249917447567, -0.0660882368683815, -0.1...
3 inspire ... [0.0076184929348528385, -0.0359916090965271, -...
4 inspire ... [0.04250592365860939, -0.03686268627643585, -0...
.. ... ... ...
81 prompt ... [-0.014272868633270264, 0.023768790066242218, ...
82 nettle ... [-0.02215682715177536, -0.00010512269363971427...
83 nettle ... [-0.005328505765646696, -0.00879666954278946, ...
84 nettle ... [0.01180578675121069, -0.04750848188996315, -0...
85 nettle ... [0.02716865763068199, -0.03534795343875885, -0...
[86 rows x 3 columns],
'words_remaining': [],
'workflow_instructions': '**Instructions**\n'
'\n'
'use "setup_puzzle" tool to initialize the puzzle '
'if the "puzzle_status" is not initialized.\n'
'\n'
'if "puzzle_step" is "puzzle_completed" then use '
'"END" tool.\n'
'\n'
'Use the table to select the appropriate tool.\n'
'\n'
'|puzzle_recommender| puzzle_step | tool |\n'
'| --- | --- | --- |\n'
'|embedvec_recommender| next_recommendation | '
'get_embedvec_recommendation |\n'
'|embedvec_recommender| have_recommendation | '
'apply_recommendation |\n'
'|llm_recommender| next_recommendation | '
'get_recommendation |\n'
'|llm_recommender| have_recommendation | '
'apply_recommendation |\n'
'\n'
'If no tool is selected, use "ABORT" tool.\n'}
Expected Solution
🟡 FOOTBALL POSITIONS: CENTER ,GUARD ,QUARTERBACK ,SAFETY
🟢 CABLE CHANNELS: DISCOVERY ,HISTORY ,NICKELODEON ,OXYGEN
🔵 FICTIONAL CLOWNS: HOMEY ,JOKER ,PENNYWISE ,RONALD
🟣 WHAT “D” MIGHT STAND FOR: DEFENSE ,DEMOCRAT ,DIMENSIONAL ,DRIVE
Example Run
$ python src/agent/app_embedvec.py
Enter 'file' to read words from a file or 'image' to read words from an image: file
Please enter the word file location: data/word_list3.txt
Puzzle Words: ['center', 'pennywise', 'democrat', 'safety', 'oxygen', 'history', 'guard', 'homey', 'joker', 'quarterback', 'ronald', 'defense', 'discovery', 'drive', 'nickelodeon', 'dimensional']
Generating vocabulary for the words...this may take about a minute
Generating embeddings for the definitions
ENTERED EMBEDVEC RECOMMENDATION
(97, 97)
(97, 97)
candidate_lists size: 64
RECOMMENDED WORDS ['defense', 'drive', 'guard', 'safety'] with connection This group is unique as it relates to general safety and protection concepts, rather than being strictly tied to sports roles.
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['defense', 'drive', 'guard', 'safety'] is incorrect
Changing the recommender from 'embedvec_recommender' to 'llm_recommender'
RECOMMENDED WORDS ['discovery', 'drive', 'history', 'nickelodeon'] with connection TV Channels
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['nickelodeon', 'drive', 'discovery', 'history'] is incorrect
RECOMMENDED WORDS ['joker', 'nickelodeon', 'pennywise', 'ronald'] with connection Clowns or clown-related characters
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['joker', 'pennywise', 'ronald', 'nickelodeon'] is incorrect
RECOMMENDED WORDS ['center', 'guard', 'quarterback', 'safety'] with connection positions in American football
Is the recommendation accepted? (y/g/b/p/n): y
Recommendation ['quarterback', 'center', 'guard', 'safety'] is correct
RECOMMENDED WORDS ['homey', 'joker', 'pennywise', 'ronald'] with connection Clowns
Is the recommendation accepted? (y/g/b/p/n): b
Recommendation ['joker', 'pennywise', 'ronald', 'homey'] is correct
RECOMMENDED WORDS ['democrat', 'dimensional', 'discovery', 'oxygen'] with connection Words beginning with 'D'
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['oxygen', 'democrat', 'dimensional', 'discovery'] is incorrect
FAILED TO SOLVE THE CONNECTION PUZZLE TOO MANY MISTAKES!!!
FINAL PUZZLE STATE:
{ 'found_blue': True,
'found_count': 2,
'found_yellow': True,
'invalid_connections': [ ['defense', 'drive', 'guard', 'safety'],
['nickelodeon', 'drive', 'discovery', 'history'],
['joker', 'pennywise', 'ronald', 'nickelodeon'],
[ 'oxygen',
'democrat',
'dimensional',
'discovery']],
'llm_temperature': 0.7,
'mistake_count': 4,
'puzzle_recommender': 'llm_recommender',
'puzzle_status': 'initialized',
'puzzle_step': 'puzzle_completed',
'recommendation_count': 6,
'recommended_connection': '',
'recommended_correct': False,
'recommended_words': [],
'tool_to_use': 'END',
'vocabulary_df': word ... embedding
0 center ... [0.02066771127283573, -0.031914301216602325, 0...
1 center ... [0.005799443461000919, -0.020684584975242615, ...
2 center ... [0.005490818060934544, -0.012460795231163502, ...
3 center ... [-0.00505771953612566, 1.8607530364533886e-05,...
4 center ... [-0.0031464514322578907, 0.01062652189284563, ...
.. ... ... ...
92 dimensional ... [0.011221064254641533, -0.022305285558104515, ...
93 dimensional ... [0.022211303934454918, 0.007923364639282227, -...
94 dimensional ... [-0.02473333291709423, -0.017410408705472946, ...
95 dimensional ... [0.02035861276090145, 0.01731749251484871, -0....
96 dimensional ... [-0.005879412870854139, -0.019015125930309296,...
[97 rows x 3 columns],
'words_remaining': [ 'oxygen',
'democrat',
'dimensional',
'drive',
'defense',
'history',
'discovery',
'nickelodeon'],
'workflow_instructions': '**Instructions**\n'
'\n'
'use "setup_puzzle" tool to initialize the puzzle '
'if the "puzzle_status" is not initialized.\n'
'\n'
'if "puzzle_step" is "puzzle_completed" then use '
'"END" tool.\n'
'\n'
'Use the table to select the appropriate tool.\n'
'\n'
'|puzzle_recommender| puzzle_step | tool |\n'
'| --- | --- | --- |\n'
'|embedvec_recommender| next_recommendation | '
'get_embedvec_recommendation |\n'
'|embedvec_recommender| have_recommendation | '
'apply_recommendation |\n'
'|llm_recommender| next_recommendation | '
'get_recommendation |\n'
'|llm_recommender| have_recommendation | '
'apply_recommendation |\n'
'\n'
'If no tool is selected, use "ABORT" tool.\n'}
Expected Solution
🟡 RUMMAGE: COMB ,DIG ,ROOT ,SIFT
🟢 SOUNDS OF THUNDER: CLAP ,PEAL ,ROLL ,RUMBLE
🔵 WAYS TO WEAR YOUR HAIR UP: BUN ,BRAID ,PONY ,TWIST
🟣 THINGS THAT CAN HAVE LEAVES: BOOK ,SALAD ,TABLE ,TREE
Example Run
$ python src/agent/app_embedvec.py
Enter 'file' to read words from a file or 'image' to read words from an image: file
Please enter the word file location: data/word_list4.txt
Puzzle Words: ['rumble', 'table', 'pony', 'sift', 'roll', 'bun', 'tree', 'twist', 'salad', 'clap', 'comb', 'peal', 'dig', 'braid', 'root', 'book']
Generating vocabulary for the words...this may take about a minute
Generating embeddings for the definitions
ENTERED EMBEDVEC RECOMMENDATION
(120, 120)
(120, 120)
candidate_lists size: 74
RECOMMENDED WORDS ['comb', 'dig', 'root', 'sift'] with connection All words are connected by the theme of searching or examining thoroughly.
Is the recommendation accepted? (y/g/b/p/n): y
Recommendation ['comb', 'dig', 'root', 'sift'] is correct
ENTERED EMBEDVEC RECOMMENDATION
(93, 93)
(93, 93)
candidate_lists size: 38
RECOMMENDED WORDS ['braid', 'bun', 'roll', 'twist'] with connection The group is connected by the theme of hairstyles, as all words can describe different hair arrangements or styles.
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['braid', 'bun', 'roll', 'twist'] is incorrect
Changing the recommender from 'embedvec_recommender' to 'llm_recommender'
RECOMMENDED WORDS ['clap', 'peal', 'roll', 'rumble'] with connection types of sounds
Is the recommendation accepted? (y/g/b/p/n): y
Recommendation ['peal', 'clap', 'rumble', 'roll'] is correct
RECOMMENDED WORDS ['book', 'salad', 'table', 'tree'] with connection Types of leaves (tree leaf, salad leaf, table leaf, and book leaf)
Is the recommendation accepted? (y/g/b/p/n): p
Recommendation ['tree', 'salad', 'table', 'book'] is correct
RECOMMENDED WORDS ['braid', 'bun', 'pony', 'twist'] with connection types of hairstyles
Is the recommendation accepted? (y/g/b/p/n): b
Recommendation ['pony', 'braid', 'bun', 'twist'] is correct
SOLVED THE CONNECTION PUZZLE!!!
FINAL PUZZLE STATE:
{ 'found_blue': True,
'found_count': 4,
'found_purple': True,
'found_yellow': True,
'invalid_connections': [['braid', 'bun', 'roll', 'twist']],
'llm_temperature': 0.7,
'mistake_count': 1,
'puzzle_recommender': 'llm_recommender',
'puzzle_status': 'initialized',
'puzzle_step': 'puzzle_completed',
'recommendation_count': 5,
'recommended_connection': '',
'recommended_correct': False,
'recommended_words': [],
'tool_to_use': 'END',
'vocabulary_df': word ... embedding
0 rumble ... [0.007194223813712597, -0.03427945449948311, -...
1 rumble ... [-0.012329446151852608, -0.012000261805951595,...
2 rumble ... [0.0026637613773345947, -0.020794421434402466,...
3 rumble ... [0.006952341180294752, -0.049337465316057205, ...
4 rumble ... [0.047436319291591644, 0.011532682925462723, -...
.. ... ... ...
115 book ... [-0.023059912025928497, -0.04290644824504852, ...
116 book ... [-0.017393656075000763, -0.04099489748477936, ...
117 book ... [-0.009654626250267029, -0.027533341199159622,...
118 book ... [0.013451640494167805, -0.033327944576740265, ...
119 book ... [0.011979937553405762, -0.0443277433514595, -0...
[93 rows x 3 columns],
'words_remaining': [],
'workflow_instructions': '**Instructions**\n'
'\n'
'use "setup_puzzle" tool to initialize the puzzle '
'if the "puzzle_status" is not initialized.\n'
'\n'
'if "puzzle_step" is "puzzle_completed" then use '
'"END" tool.\n'
'\n'
'Use the table to select the appropriate tool.\n'
'\n'
'|puzzle_recommender| puzzle_step | tool |\n'
'| --- | --- | --- |\n'
'|embedvec_recommender| next_recommendation | '
'get_embedvec_recommendation |\n'
'|embedvec_recommender| have_recommendation | '
'apply_recommendation |\n'
'|llm_recommender| next_recommendation | '
'get_recommendation |\n'
'|llm_recommender| have_recommendation | '
'apply_recommendation |\n'
'\n'
'If no tool is selected, use "ABORT" tool.\n'}
Expected Solution
🟡 MAKE GOOD ON, AS A PROMISE: FULFILL ,HONOR ,KEEP ,UPHOLD
🟢 BEDDING: BLANKET ,SHAM ,SHEET ,THROW
🔵 ACTIONS IN CARD GAMES: DISCARD ,DRAW ,PASS ,PLAY
🟣 CABINET DEPARTMENTS: ENERGY ,JUSTICE ,LABOR ,STATE
Example Run
python src/agent/app_embedvec.py
Enter 'file' to read words from a file or 'image' to read words from an image: file
Please enter the word file location: data/word_list5.txt
Puzzle Words: ['uphold', 'discard', 'honor', 'energy', 'state', 'play', 'justice', 'labor', 'pass', 'fulfill', 'draw', 'keep', 'blanket', 'sham', 'sheet', 'throw']
Generating vocabulary for the words...this may take about a minute
Generating embeddings for the definitions
ENTERED EMBEDVEC RECOMMENDATION
(133, 133)
(133, 133)
candidate_lists size: 86
RECOMMENDED WORDS ['fulfill', 'honor', 'keep', 'uphold'] with connection These words are all connected by the theme of fulfilling obligations or commitments.
Is the recommendation accepted? (y/g/b/p/n): y
Recommendation ['fulfill', 'honor', 'keep', 'uphold'] is correct
ENTERED EMBEDVEC RECOMMENDATION
(107, 107)
(107, 107)
candidate_lists size: 54
RECOMMENDED WORDS ['blanket', 'sham', 'sheet', 'throw'] with connection All words are related to household fabric items used for covering or decoration.
Is the recommendation accepted? (y/g/b/p/n): g
Recommendation ['blanket', 'sham', 'sheet', 'throw'] is correct
ENTERED EMBEDVEC RECOMMENDATION
(78, 78)
(78, 78)
candidate_lists size: 25
RECOMMENDED WORDS ['discard', 'draw', 'pass', 'play'] with connection This group uniquely centers around actions commonly associated with games or sports.
Is the recommendation accepted? (y/g/b/p/n): b
Recommendation ['discard', 'draw', 'pass', 'play'] is correct
ENTERED EMBEDVEC RECOMMENDATION
(29, 29)
(29, 29)
candidate_lists size: 1
RECOMMENDED WORDS ['energy', 'justice', 'labor', 'state'] with connection The words relate to different societal and functional aspects but do not share a single theme.
Is the recommendation accepted? (y/g/b/p/n): p
Recommendation ['energy', 'justice', 'labor', 'state'] is correct
SOLVED THE CONNECTION PUZZLE!!!
FINAL PUZZLE STATE:
{ 'found_blue': True,
'found_count': 4,
'found_purple': True,
'found_yellow': True,
'invalid_connections': [],
'llm_temperature': 0.7,
'mistake_count': 0,
'puzzle_recommender': 'embedvec_recommender',
'puzzle_status': 'initialized',
'puzzle_step': 'puzzle_completed',
'recommendation_count': 4,
'recommended_connection': '',
'recommended_correct': False,
'recommended_words': [],
'tool_to_use': 'END',
'vocabulary_df': Empty DataFrame
Columns: [word, definition, embedding]
Index: [],
'words_remaining': [],
'workflow_instructions': '**Instructions**\n'
'\n'
'use "setup_puzzle" tool to initialize the puzzle '
'if the "puzzle_status" is not initialized.\n'
'\n'
'if "puzzle_step" is "puzzle_completed" then use '
'"END" tool.\n'
'\n'
'Use the table to select the appropriate tool.\n'
'\n'
'|puzzle_recommender| puzzle_step | tool |\n'
'| --- | --- | --- |\n'
'|embedvec_recommender| next_recommendation | '
'get_embedvec_recommendation |\n'
'|embedvec_recommender| have_recommendation | '
'apply_recommendation |\n'
'|llm_recommender| next_recommendation | '
'get_recommendation |\n'
'|llm_recommender| have_recommendation | '
'apply_recommendation |\n'
'\n'
'If no tool is selected, use "ABORT" tool.\n'}
Puzzle Grid Screenshot
Expected Solution
Example Run
$ python src/agent/app_embedvec.py
Enter 'file' to read words from a file or 'image' to read words from an image: image
Please enter the image file location: data/connection_puzzle_2024_10_23.png
Puzzle Words: ['jeans', 'jacket', 'rain', 'haze', 'goblin', 'jay', 'beret', 'cab', 'moon', 'pages', 'heart', 'thumb', 'prose', 'salad', 'whale', 'journalism']
Generating vocabulary for the words...this may take about a minute
Generating embeddings for the definitions
ENTERED EMBEDVEC RECOMMENDATION
(93, 93)
(93, 93)
candidate_lists size: 75
RECOMMENDED WORDS ['beret', 'jacket', 'jeans', 'pages'] with connection The words 'beret', 'jacket', and 'jeans' all relate to clothing or fashion, whereas 'pages' refers to a section of a website, making this group less cohesive compared to others.
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['beret', 'jacket', 'jeans', 'pages'] is incorrect
Changing the recommender from 'embedvec_recommender' to 'llm_recommender'
RECOMMENDED WORDS ['heart', 'moon', 'rain', 'whale'] with connection parts of the song 'Hallelujah'
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['heart', 'rain', 'moon', 'whale'] is incorrect
RECOMMENDED WORDS ['beret', 'cab', 'jacket', 'jeans'] with connection Types of Clothing
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['cab', 'jacket', 'jeans', 'beret'] is incorrect
RECOMMENDED WORDS ['goblin', 'jay', 'salad', 'thumb'] with connection green
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['jay', 'goblin', 'thumb', 'salad'] is incorrect
FAILED TO SOLVE THE CONNECTION PUZZLE TOO MANY MISTAKES!!!
FINAL PUZZLE STATE:
{ 'found_count': 0,
'invalid_connections': [ ['beret', 'jacket', 'jeans', 'pages'],
['heart', 'rain', 'moon', 'whale'],
['cab', 'jacket', 'jeans', 'beret'],
['jay', 'goblin', 'thumb', 'salad']],
'llm_temperature': 0.7,
'mistake_count': 4,
'puzzle_recommender': 'llm_recommender',
'puzzle_status': 'initialized',
'puzzle_step': 'puzzle_completed',
'recommendation_count': 4,
'recommended_connection': '',
'recommended_correct': False,
'recommended_words': [],
'tool_to_use': 'END',
'vocabulary_df': word ... embedding
0 jeans ... [-0.010398581624031067, 0.034614380449056625, ...
1 jeans ... [0.07001741975545883, 0.027637239545583725, -0...
2 jeans ... [0.002482770476490259, 0.019244685769081116, -...
3 jeans ... [0.04950800910592079, 0.028868574649095535, -0...
4 jeans ... [0.0724964514374733, 0.01916358433663845, -0.1...
.. ... ... ...
88 whale ... [0.01513548195362091, 0.04323774576187134, -0....
89 journalism ... [-0.007067571394145489, -0.012600105255842209,...
90 journalism ... [0.0007792948163114488, -0.015096179209649563,...
91 journalism ... [0.009174425154924393, -0.01982419565320015, -...
92 journalism ... [-0.014559044502675533, -0.003283234778791666,...
[93 rows x 3 columns],
'words_remaining': [ 'pages',
'heart',
'beret',
'jay',
'rain',
'goblin',
'prose',
'jeans',
'thumb',
'jacket',
'moon',
'whale',
'journalism',
'haze',
'salad',
'cab'],
'workflow_instructions': '**Instructions**\n'
'\n'
'use "setup_puzzle" tool to initialize the puzzle '
'if the "puzzle_status" is not initialized.\n'
'\n'
'if "puzzle_step" is "puzzle_completed" then use '
'"END" tool.\n'
'\n'
'Use the table to select the appropriate tool.\n'
'\n'
'|puzzle_recommender| puzzle_step | tool |\n'
'| --- | --- | --- |\n'
'|embedvec_recommender| next_recommendation | '
'get_embedvec_recommendation |\n'
'|embedvec_recommender| have_recommendation | '
'apply_recommendation |\n'
'|llm_recommender| next_recommendation | '
'get_recommendation |\n'
'|llm_recommender| have_recommendation | '
'apply_recommendation |\n'
'\n'
'If no tool is selected, use "ABORT" tool.\n'}
Puzzle Grid Screenshot
Expected Solution
Example Run
$ python src/agent/app_embedvec.py
Enter 'file' to read words from a file or 'image' to read words from an image: image
Please enter the image file location: data/connection_puzzle_2024_10_26.png
Puzzle Words: ['swing', 'throw', 'rocker', 'roller', 'rattle', 'thread', 'cradle', 'spool', 'can', 'ruffle', 'chat', 'hammock', 'conversation', 'silo', 'chain', 'faze']
Generating vocabulary for the words...this may take about a minute
Generating embeddings for the definitions
ENTERED EMBEDVEC RECOMMENDATION
(118, 118)
(118, 118)
candidate_lists size: 75
RECOMMENDED WORDS ['cradle', 'hammock', 'rocker', 'swing'] with connection The words are connected by the theme of objects or actions related to gentle rocking or swaying motion.
Is the recommendation accepted? (y/g/b/p/n): b
Recommendation ['cradle', 'hammock', 'rocker', 'swing'] is correct
ENTERED EMBEDVEC RECOMMENDATION
(88, 88)
(88, 88)
candidate_lists size: 46
RECOMMENDED WORDS ['faze', 'rattle', 'ruffle', 'throw'] with connection All four words relate to causing disturbance or being unsettled.
Is the recommendation accepted? (y/g/b/p/n): y
Recommendation ['faze', 'rattle', 'ruffle', 'throw'] is correct
ENTERED EMBEDVEC RECOMMENDATION
(61, 61)
(61, 61)
candidate_lists size: 22
RECOMMENDED WORDS ['chain', 'chat', 'conversation', 'thread'] with connection These words are connected by the theme of communication and connectivity, representing linked exchanges or sequences.
Is the recommendation accepted? (y/g/b/p/n): g
Recommendation ['chain', 'chat', 'conversation', 'thread'] is correct
ENTERED EMBEDVEC RECOMMENDATION
(28, 28)
(28, 28)
candidate_lists size: 1
RECOMMENDED WORDS ['can', 'roller', 'silo', 'spool'] with connection All four words refer to cylindrical objects or structures.
Is the recommendation accepted? (y/g/b/p/n): p
Recommendation ['can', 'roller', 'silo', 'spool'] is correct
SOLVED THE CONNECTION PUZZLE!!!
FINAL PUZZLE STATE:
{ 'found_blue': True,
'found_count': 4,
'found_purple': True,
'found_yellow': True,
'invalid_connections': [],
'llm_temperature': 0.7,
'mistake_count': 0,
'puzzle_recommender': 'embedvec_recommender',
'puzzle_status': 'initialized',
'puzzle_step': 'puzzle_completed',
'recommendation_count': 4,
'recommended_connection': '',
'recommended_correct': False,
'recommended_words': [],
'tool_to_use': 'END',
'vocabulary_df': Empty DataFrame
Columns: [word, definition, embedding]
Index: [],
'words_remaining': [],
'workflow_instructions': '**Instructions**\n'
'\n'
'use "setup_puzzle" tool to initialize the puzzle '
'if the "puzzle_status" is not initialized.\n'
'\n'
'if "puzzle_step" is "puzzle_completed" then use '
'"END" tool.\n'
'\n'
'Use the table to select the appropriate tool.\n'
'\n'
'|puzzle_recommender| puzzle_step | tool |\n'
'| --- | --- | --- |\n'
'|embedvec_recommender| next_recommendation | '
'get_embedvec_recommendation |\n'
'|embedvec_recommender| have_recommendation | '
'apply_recommendation |\n'
'|llm_recommender| next_recommendation | '
'get_recommendation |\n'
'|llm_recommender| have_recommendation | '
'apply_recommendation |\n'
'\n'
'If no tool is selected, use "ABORT" tool.\n'}
Puzzle Grid Screenshot
Expected Solution
Example Run
$ python src/agent/app_embedvec.py
Enter 'file' to read words from a file or 'image' to read words from an image: image
Please enter the image file location: data/connection_puzzle_2024_10_27.png
Puzzle Words: ['fresh', 'prince', 'bel', 'air', 'quality', 'bar', 'cute', 'mermaid', 'lux', 'wise', 'tramp', 'mood', 'feeling', 'smart', 'mole', 'rascals']
Generating vocabulary for the words...this may take about a minute
Generating embeddings for the definitions
ENTERED EMBEDVEC RECOMMENDATION
(107, 107)
(107, 107)
candidate_lists size: 77
RECOMMENDED WORDS ['air', 'feeling', 'mood', 'quality'] with connection This group is connected by the theme of impressions and attributes, focusing on the qualities and impressions given by someone or something.
Is the recommendation accepted? (y/g/b/p/n): g
Recommendation ['air', 'feeling', 'mood', 'quality'] is correct
ENTERED EMBEDVEC RECOMMENDATION
(82, 82)
(82, 82)
candidate_lists size: 49
RECOMMENDED WORDS ['bar', 'bel', 'lux', 'mole'] with connection This group is unique as all words are units of measurement in different scientific fields.
Is the recommendation accepted? (y/g/b/p/n): b
Recommendation ['bar', 'bel', 'lux', 'mole'] is correct
ENTERED EMBEDVEC RECOMMENDATION
(56, 56)
(56, 56)
candidate_lists size: 23
RECOMMENDED WORDS ['fresh', 'mermaid', 'rascals', 'tramp'] with connection The words in this group are associated with playful or cheeky behavior and characteristics.
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['fresh', 'mermaid', 'rascals', 'tramp'] is incorrect
Changing the recommender from 'embedvec_recommender' to 'llm_recommender'
RECOMMENDED WORDS ['cute', 'prince', 'smart', 'wise'] with connection Words associated with positive attributes
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['smart', 'cute', 'prince', 'wise'] is incorrect
RECOMMENDED WORDS ['cute', 'fresh', 'smart', 'wise'] with connection qualities or attributes
Is the recommendation accepted? (y/g/b/p/n): y
Recommendation ['fresh', 'smart', 'wise', 'cute'] is correct
RECOMMENDED WORDS ['mermaid', 'prince', 'rascals', 'tramp'] with connection Characters in Disney Movies
Is the recommendation accepted? (y/g/b/p/n): p
Recommendation ['mermaid', 'prince', 'rascals', 'tramp'] is correct
SOLVED THE CONNECTION PUZZLE!!!
FINAL PUZZLE STATE:
{ 'found_blue': True,
'found_count': 4,
'found_purple': True,
'found_yellow': True,
'invalid_connections': [ ['fresh', 'mermaid', 'rascals', 'tramp'],
['smart', 'cute', 'prince', 'wise']],
'llm_temperature': 0.7,
'mistake_count': 2,
'puzzle_recommender': 'llm_recommender',
'puzzle_status': 'initialized',
'puzzle_step': 'puzzle_completed',
'recommendation_count': 6,
'recommended_connection': '',
'recommended_correct': False,
'recommended_words': [],
'tool_to_use': 'END',
'vocabulary_df': word ... embedding
0 fresh ... [0.0009076713467948139, -0.006530973128974438,...
1 fresh ... [-0.0027019931003451347, -0.01262784842401743,...
2 fresh ... [0.005221983417868614, 0.015111403539776802, -...
3 fresh ... [-0.0008461514953523874, -0.016223935410380363...
4 fresh ... [-4.65069169877097e-05, -0.024564167484641075,...
...
92 smart ... [0.01592598855495453, -0.014212200418114662, -...
93 smart ... [-0.050580061972141266, 0.034565459936857224, ...
102 rascals ... [0.04702338948845863, -0.022611195221543312, -...
103 rascals ... [0.035631995648145676, -0.023638218641281128, ...
104 rascals ... [0.03260764479637146, -0.028950713574886322, -...
105 rascals ... [0.040093712508678436, -0.018147410824894905, ...
106 rascals ... [0.0366840697824955, 0.03016420267522335, -0.0...
[56 rows x 3 columns],
'words_remaining': [],
'workflow_instructions': '**Instructions**\n'
'\n'
'use "setup_puzzle" tool to initialize the puzzle '
'if the "puzzle_status" is not initialized.\n'
'\n'
'if "puzzle_step" is "puzzle_completed" then use '
'"END" tool.\n'
'\n'
'Use the table to select the appropriate tool.\n'
'\n'
'|puzzle_recommender| puzzle_step | tool |\n'
'| --- | --- | --- |\n'
'|embedvec_recommender| next_recommendation | '
'get_embedvec_recommendation |\n'
'|embedvec_recommender| have_recommendation | '
'apply_recommendation |\n'
'|llm_recommender| next_recommendation | '
'get_recommendation |\n'
'|llm_recommender| have_recommendation | '
'apply_recommendation |\n'
'\n'
'If no tool is selected, use "ABORT" tool.\n'}
Puzzle Grid Screenshot
Expected Solution
Example Run
$ python src/agent/app_embedvec.py
Enter 'file' to read words from a file or 'image' to read words from an image: image
Please enter the image file location: data/connection_puzzle_2024_10_28.png
Puzzle Words: ['spell', 'love', 'please', 'hold', 'presto', 'mean', 'thank you', 'shazam', 'audible', 'abracadabra', 'signify', 'tidal', 'suggest', 'have', 'pandora', 'cherish']
Generating vocabulary for the words...this may take about a minute
Generating embeddings for the definitions
ENTERED EMBEDVEC RECOMMENDATION
(88, 88)
(88, 88)
candidate_lists size: 55
RECOMMENDED WORDS ['abracadabra', 'presto', 'shazam', 'spell'] with connection All words are related to magic or magical transformations.
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['abracadabra', 'presto', 'shazam', 'spell'] is incorrect
Changing the recommender from 'embedvec_recommender' to 'llm_recommender'
RECOMMENDED WORDS ['cherish', 'love', 'please', 'thank you'] with connection Expressions of Affection or Politeness
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['cherish', 'thank you', 'please', 'love'] is incorrect
RECOMMENDED WORDS ['audible', 'pandora', 'suggest', 'tidal'] with connection Music Streaming Services
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['audible', 'tidal', 'suggest', 'pandora'] is incorrect
RECOMMENDED WORDS ['cherish', 'have', 'hold', 'love'] with connection Words related to affection or possession
Is the recommendation accepted? (y/g/b/p/n): b
Recommendation ['have', 'hold', 'cherish', 'love'] is correct
RECOMMENDED WORDS ['mean', 'signify', 'spell', 'suggest'] with connection Synonyms for 'indicate'
Is the recommendation accepted? (y/g/b/p/n): g
Recommendation ['mean', 'signify', 'suggest', 'spell'] is correct
RECOMMENDED WORDS ['audible', 'pandora', 'presto', 'tidal'] with connection Music streaming services
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['audible', 'tidal', 'pandora', 'presto'] is incorrect
FAILED TO SOLVE THE CONNECTION PUZZLE TOO MANY MISTAKES!!!
FINAL PUZZLE STATE:
{ 'found_blue': True,
'found_count': 2,
'invalid_connections': [ ['abracadabra', 'presto', 'shazam', 'spell'],
['cherish', 'thank you', 'please', 'love'],
['audible', 'tidal', 'suggest', 'pandora'],
['audible', 'tidal', 'pandora', 'presto']],
'llm_temperature': 0.7,
'mistake_count': 4,
'puzzle_recommender': 'llm_recommender',
'puzzle_status': 'initialized',
'puzzle_step': 'puzzle_completed',
'recommendation_count': 6,
'recommended_connection': '',
'recommended_correct': False,
'recommended_words': [],
'tool_to_use': 'END',
'vocabulary_df': word ... embedding
0 spell ... [0.0124284029006958, -0.009453979320824146, -0...
1 spell ... [-0.008444534614682198, -0.016062842682003975,...
2 spell ... [0.021861810237169266, -0.04352385550737381, -...
3 spell ... [-0.0009696248453110456, 0.0028450379613786936...
4 spell ... [0.08166498690843582, -0.01926223188638687, -0...
.. ... ... ...
83 pandora ... [0.017673371359705925, 0.04376263543963432, -0...
84 cherish ... [0.042832907289266586, -0.019683465361595154, ...
85 cherish ... [0.042156513780355453, -0.00019362549937795848...
86 cherish ... [0.030510470271110535, -0.06236092373728752, -...
87 cherish ... [0.02435368299484253, -0.030057914555072784, -...
[88 rows x 3 columns],
'words_remaining': [ 'audible',
'thank you',
'presto',
'abracadabra',
'please',
'pandora',
'shazam',
'tidal'],
'workflow_instructions': '**Instructions**\n'
'\n'
'use "setup_puzzle" tool to initialize the puzzle '
'if the "puzzle_status" is not initialized.\n'
'\n'
'if "puzzle_step" is "puzzle_completed" then use '
'"END" tool.\n'
'\n'
'Use the table to select the appropriate tool.\n'
'\n'
'|puzzle_recommender| puzzle_step | tool |\n'
'| --- | --- | --- |\n'
'|embedvec_recommender| next_recommendation | '
'get_embedvec_recommendation |\n'
'|embedvec_recommender| have_recommendation | '
'apply_recommendation |\n'
'|llm_recommender| next_recommendation | '
'get_recommendation |\n'
'|llm_recommender| have_recommendation | '
'apply_recommendation |\n'
'\n'
'If no tool is selected, use "ABORT" tool.\n'}
Puzzle Grid Screenshot
Expected Solution
Example Run
$ python src/agent/app_embedvec.py
Enter 'file' to read words from a file or 'image' to read words from an image: image
Please enter the image file location: data/connection_puzzle_2024_11_02.png
Puzzle Words: ['board', 'drop', 'plank', 'sink', 'range', 'panel', 'stud', 'boat', 'chandelier', 'counter', 'cabinet', 'crunch', 'council', 'mountain climber', 'fridge', 'hoop']
Generating vocabulary for the words...this may take about a minute
Generating embeddings for the definitions
ENTERED EMBEDVEC RECOMMENDATION
(118, 118)
(118, 118)
candidate_lists size: 76
RECOMMENDED WORDS ['board', 'cabinet', 'council', 'panel'] with connection Connected by the theme of groups of people in decision-making roles.
Is the recommendation accepted? (y/g/b/p/n): g
Recommendation ['board', 'cabinet', 'council', 'panel'] is correct
ENTERED EMBEDVEC RECOMMENDATION
(88, 88)
(88, 88)
candidate_lists size: 55
RECOMMENDED WORDS ['counter', 'hoop', 'plank', 'stud'] with connection This group of words all relate to construction or structural elements, with each word representing a component that can be part of a building or structure.
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['counter', 'hoop', 'plank', 'stud'] is incorrect
Changing the recommender from 'embedvec_recommender' to 'llm_recommender'
RECOMMENDED WORDS ['chandelier', 'counter', 'fridge', 'sink'] with connection Kitchen or home fixtures
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['fridge', 'sink', 'counter', 'chandelier'] is incorrect
RECOMMENDED WORDS ['chandelier', 'fridge', 'range', 'sink'] with connection Household Appliances/Fixtures
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['sink', 'fridge', 'chandelier', 'range'] is incorrect
RECOMMENDED WORDS ['boat', 'crunch', 'drop', 'mountain climber'] with connection Exercise or Fitness Terms
Is the recommendation accepted? (y/g/b/p/n): n
Recommendation ['boat', 'mountain climber', 'drop', 'crunch'] is incorrect
FAILED TO SOLVE THE CONNECTION PUZZLE TOO MANY MISTAKES!!!
FINAL PUZZLE STATE:
{ 'found_count': 1,
'invalid_connections': [ ['counter', 'hoop', 'plank', 'stud'],
['fridge', 'sink', 'counter', 'chandelier'],
['sink', 'fridge', 'chandelier', 'range'],
['boat', 'mountain climber', 'drop', 'crunch']],
'llm_temperature': 0.7,
'mistake_count': 4,
'puzzle_recommender': 'llm_recommender',
'puzzle_status': 'initialized',
'puzzle_step': 'puzzle_completed',
'recommendation_count': 5,
'recommended_connection': '',
'recommended_correct': False,
'recommended_words': [],
'tool_to_use': 'END',
'vocabulary_df': word ... embedding
11 drop ... [0.01210901141166687, 0.004545072093605995, -0...
12 drop ... [0.0022516821045428514, 0.0007056481554172933,...
13 drop ... [0.015226753428578377, 0.005197887774556875, 0...
14 drop ... [0.00290021114051342, -0.011369054205715656, -...
15 drop ... [-0.01719829812645912, 0.007968463934957981, -...
.. ... ... ...
113 hoop ... [0.007062331773340702, -0.04914180934429169, -...
114 hoop ... [0.004792096558958292, 6.375028169713914e-05, ...
115 hoop ... [0.01196839939802885, -0.0013090437278151512, ...
116 hoop ... [0.030083850026130676, -0.012810706160962582, ...
117 hoop ... [0.00035316916182637215, 0.004819720052182674,...
[88 rows x 3 columns],
'words_remaining': [ 'boat',
'mountain climber',
'fridge',
'sink',
'counter',
'drop',
'crunch',
'plank',
'range',
'stud',
'hoop',
'chandelier'],
'workflow_instructions': '**Instructions**\n'
'\n'
'use "setup_puzzle" tool to initialize the puzzle '
'if the "puzzle_status" is not initialized.\n'
'\n'
'if "puzzle_step" is "puzzle_completed" then use '
'"END" tool.\n'
'\n'
'Use the table to select the appropriate tool.\n'
'\n'
'|puzzle_recommender| puzzle_step | tool |\n'
'| --- | --- | --- |\n'
'|embedvec_recommender| next_recommendation | '
'get_embedvec_recommendation |\n'
'|embedvec_recommender| have_recommendation | '
'apply_recommendation |\n'
'|llm_recommender| next_recommendation | '
'get_recommendation |\n'
'|llm_recommender| have_recommendation | '
'apply_recommendation |\n'
'\n'
'If no tool is selected, use "ABORT" tool.\n'}