From 425a79af4961a0c435aa02002c5fc556cf8fb4d1 Mon Sep 17 00:00:00 2001 From: Adyasha Maharana Date: Mon, 12 Aug 2024 07:17:49 -0400 Subject: [PATCH] Cleaned env.sh --- README.MD | 31 +- __pycache__/global_methods.cpython-39.pyc | Bin 5193 -> 5116 bytes data/msc_personas_all.json | 107345 +++++++++++++++ data/multimodal_dialog/example/agent_a.json | 4 + data/multimodal_dialog/example/agent_b.json | 4 + .../conversation_utils.cpython-39.pyc | Bin 0 -> 39428 bytes .../__pycache__/event_utils.cpython-39.pyc | Bin 0 -> 21089 bytes .../__pycache__/html_utils.cpython-39.pyc | Bin 0 -> 4793 bytes .../__pycache__/memory_utils.cpython-39.pyc | Bin 8774 -> 9479 bytes generative_agents/conversation_utils.py | 507 + generative_agents/event_utils.py | 171 + generative_agents/generate_conversations.py | 607 + generative_agents/html_utils.py | 187 + generative_agents/memory_utils.py | 104 +- global_methods.py | 23 +- prompt_examples/image_sharing_examples.json | 14 + prompt_examples/remove_context_examples.json | 2 +- scripts/generate_conversations.sh | 8 + 18 files changed, 108905 insertions(+), 102 deletions(-) create mode 100644 data/msc_personas_all.json create mode 100644 data/multimodal_dialog/example/agent_a.json create mode 100644 data/multimodal_dialog/example/agent_b.json create mode 100644 generative_agents/__pycache__/conversation_utils.cpython-39.pyc create mode 100644 generative_agents/__pycache__/event_utils.cpython-39.pyc create mode 100644 generative_agents/__pycache__/html_utils.cpython-39.pyc create mode 100644 generative_agents/conversation_utils.py create mode 100644 generative_agents/event_utils.py create mode 100644 generative_agents/generate_conversations.py create mode 100644 generative_agents/html_utils.py create mode 100644 prompt_examples/image_sharing_examples.json create mode 100644 scripts/generate_conversations.sh diff --git a/README.MD b/README.MD index 8564e26..b6cc9cf 100644 --- a/README.MD +++ b/README.MD @@ -30,7 +30,27 @@ The dataset can be found in the ```./data/locomo10.json``` file in this reposito Configuration variables like API keys, output directories etc. are set in `scripts/env.sh` and run at the beginning of all other scripts. -### 1. Evaluate open-source and closed-source LLMs on the Question Answering Task with the (truncated) conversation as context +### Generate *very* long-term conversations between two LLM-agents with pre-assigned personalities using our LLM-based generative framework +The code to generate conversations is available in `scripts/generate_conversations.sh` and be run as follows: +``` +bash scripts/generate_conversations.sh +``` + +This code can be run under two settings: +1. Generate conversations between agents assigned with custom personas. To enable this setting, point `--out-dir` to a directory containing the files `agent_a.json` and `agent_b.json`. These files should contain the `name` and `persona_summary` of the speaker represented by the agent. See an example at `data/multimodal_dialog/example`. + +``` +{ + "name": "Angela", + "persona_summary": "Angela is a 31 year old woman who works as the manager of a gift shop in Chapel Hill. She curates interesting pieces from local artists and has maintained a beautiful gallery in the form of the gift shop. She also makes her own art sometimes, in the form of oil paintings." +} +``` + +2. Create personalities using prompts from the MSC dataset. To enable this setting, point `--out-dir` to an empty directory. This will make the script sample a pair of personalities from `data/msc_personas_all.json`. + +See `scripts/generate_conversations.py` for details on the various parameters that can be tweaked for generating the conversations. + +### Evaluate open-source and closed-source LLMs on the LoCoMo Question Answering Task with the (truncated) conversation as context * Evaluate OpenAI models ``` @@ -52,7 +72,7 @@ bash scripts/evaluate_gemini.sh bash scripts/evaluate_hf_llm.sh ``` -### 2. Generate observations and session summaries from LoCoMo conversations using `gpt-3.5-turbo` for evaluating RAG-based models +### Generate observations and session summaries from LoCoMo conversations using `gpt-3.5-turbo` for evaluating RAG-based models We provide the observations and summaries with our release of the LoCoMo dataset. Follow these instructions to re-generate the same or for a different set of conversations. * Generate observations from all sessions: @@ -67,13 +87,13 @@ bash scripts/generate_session_summaries.sh **Note 3**: Session-summaries are different from the event summaries of the event summarization task. The former summairze only a single session whereas event summaries are specific to each speaker and contain causal, temporal connections across sessions. -### 3. Evaluate retrieval-augmented `gpt-3.5-turbo` on the question-answering task using (a) dialogs, (b) observations and (c) session summaries as databases. +### Evaluate retrieval-augmented `gpt-3.5-turbo` on the LoCoMo question-answering task using (a) dialogs, (b) observations and (c) session summaries as databases. * Evaluate `gpt-3.5-turbo` using retrieval-based augmentation ``` bash scripts/evaluate_rag_gpts.sh ``` -### 4. Evaluate models on the event summarization task +### Evaluate models on the event summarization task Coming soon! @@ -81,9 +101,6 @@ Coming soon! Coming soon! -### 6. Generate synthetic conversations using our LLM-based agent pipeline - -Coming soon! ### Reference Please cite our paper if you use LoCoMo in your works: diff --git a/__pycache__/global_methods.cpython-39.pyc b/__pycache__/global_methods.cpython-39.pyc index 490deb0d2bf0122f79b145f9fb8bcc7dd5e6f5af..b58ef05e09cb0315164a961b3c1750ccf47f2667 100644 GIT binary patch delta 1435 zcmZ`(&2Jk;6rb7eU3=G#ousLgHcddZCd5fan-7HBrU_gi;ln^w^HB`5b+buqGu~9h znpGqk#06-g;eu#|t%?JcI7J8{^#X^Y_Rs?dg!~_H;2%Ip@ZKg!r2?D-pss@ znY$DR_Kx4_eY9HSr5x>({h?+S?@L}_po6<<7Zjlb%ph_ zL)&_}mmOn=Sq?aTY=jN6A&~kREs^}_?l0fb?%t=gu4aCQr5TNErbLdg`Tf=_M19IEP>X z@hkc7ws|kStKd@4s-HD`zD=|d1ugxSC|7MoWeQ& z$jcx#lK@0f;0P8RXMpnO(Qdwbxe`_zp}gkgwtl6Ma}t@tWMqTvP`)D!MtezQv@Q#k zDUhHArV7He>q?bIR!9OGC`=c+Py`hgGd|HHvo^y_pQpas4lEFlcQ(JLNF^E9Zs-aHh4zP{dA^TU;T?VttaO>e|Fg)XP%J#X$%?0o^}&mf!w z$S0sNM^lB_xO1NaP1&q(gpG}`$!#RXfX@S;1b72R6M?8&+<%HAKTnnDhWK*f z3HX}`@ip5Ik&qLFzlF++2yY`i6s*f7qQFoz3I(z~halQ924x&Q|O7>Y87u`Zb?d!j&t2Wu+`T+3DdTa`29K 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b/data/msc_personas_all.json new file mode 100644 index 0000000..bfae0fa --- /dev/null +++ b/data/msc_personas_all.json @@ -0,0 +1,107345 @@ +{ + "train": [ + { + "Speaker 1": [ + "I enjoy listening to metal music.", + "I like Metallica.", + "I like wearing leather.", + "I wear leather boots.", + "I play basketball.", + "I like to sleep late.", + "I am a night person." + ], + "Speaker 2": [ + "I like classic rock music.", + "I like Metallica.", + "I am vegan.", + "I love fake leather.", + "I am in college.", + "I play collegiate basketball.", + "I love early mornings.", + "I am in a relationship with a male detective." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I write romance novels.", + "I went to the appalachian trail.", + "I like hiking.", + "I like the Grateful Dead." + ], + "Speaker 2": [ + "I like to read.", + "I've been to the appalachian trail.", + "I was once on the local news.", + "I did a kind act for someone.", + "I have not been to the west coast." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I play racquetball.", + "I have a dog.", + "I eat Italian food and read poetry.", + "I love Forensic Files and Jeopardy.", + "I do not drink alcohol and I am a vegetarian.", + "I prefer eggplant parm and manicotti." + ], + "Speaker 2": [ + "I have a cat.", + "I am a waiter/waitress.", + "I like to sew.", + "I love cheese almost as much as steak and whiskey." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I enjoy the no yo gotti track.", + "I do not get good grades.", + "I love angels and God.", + "I am in high school.", + "I am trying to work, praise God, and get good grades." + ], + "Speaker 2": [ + "Going to concerts is my favorite activity.", + "I just went to a concert.", + "I enjoyed seeing the Insane Clown Posse perform.", + "I am vegan.", + "I have a tattoo of an angel.", + "I am not a chef.", + "I have an angel tattoo on my hip.", + "I have brown eyes.", + "I want a tattoo of a pair of brown eyes.", + "I have been cooking vegan for two years.", + "I went to high school." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I have a job.", + "I like lobster.", + "I started at google on an internship." + ], + "Speaker 2": [ + "I like shopping.", + "I like lobster.", + "I am in debt.", + "I am single.", + "I have friends." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "My name is Jessica.", + "Playing video games is my hobby.", + "I do not play any instruments.", + "I love to watch and play soccer.", + "I love all kinds of video games.", + "I have a brother who works at Best Buy.", + "I am not retired.", + "I lived in Canada.", + "I have 2 siamese cats named Milo and fio." + ], + "Speaker 2": [ + "I have a 10 year old child.", + "I play games with my child.", + "I enjoy playing the piano.", + "When I am retired, I would love to still work and become a music teacher.", + "I am not retired.", + "I have been to Canada but have never lived there.", + "I do not have any pets.", + "My 10 year old son is allergic to cats.", + "I like to go to the park or zoo with my 10 year old son." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I like rum and coke.", + "I was on days of our lives.", + "I have knock knock joke book.", + "I am funny.", + "I had freckles." + ], + "Speaker 2": [ + "I served in the army.", + "I like rum and coke.", + "I am american.", + "I work building things.", + "I build closet.", + "I have a prius.", + "I read.", + "I like romance book.", + "I like freckles.", + "I love to read romance novels.", + "I like big cities." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I am a psychology major at a college in Colorado.", + "I go hiking for fun.", + "I buy things from goodwill for fun.", + "I love Guns and Roses.", + "I used to go to Guns and Roses concerts in California when i was younger." + ], + "Speaker 2": [ + "I love the colour black.", + "I listen to Guns and Roses.", + "I drive a black car.", + "Guns and Roses are my favourite band.", + "I have never seen Guns and Roses live." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I wear glasses.", + "I get picked on.", + "I don't have any kids, but I want some.", + "I am single.", + "I just had a breakup.", + "I prefer dogs.", + "I used to have a cat.", + "I like alone time." + ], + "Speaker 2": [ + "I paint.", + "I have four kids.", + "I just got a cat.", + "I have a cat named Tuna.", + "I wanted to get a dog." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I have four dogs.", + "I am a swimming instructor.", + "I don't have a little toe.", + "I was attacked my a dolphin." + ], + "Speaker 2": [ + "I have a youtube channel.", + "I am not very active.", + "I enjoy making lets plays on youtube.", + "My dad works in an advertising company.", + "I earn money on youtube.", + "I want to go to college.", + "I am a teenager.", + "Dolphins are my favorite animal." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I like movies.", + "I have kids.", + "I like fleetwood mac and the lumineers." + ], + "Speaker 2": [ + "I read tons of books and like taking dance class.", + "I'm a waitress and do comedy.", + "i love music.", + "I love fleetwood mac and the lumineers and listen tothe song rumours often.", + "i love to dance at concerts and clubs." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I like rock music.", + "I have never worked before.", + "I like playing video games, of all genres.", + "I live outside of Orlando, Florida." + ], + "Speaker 2": [ + "I am applying to university.", + "I quit my job recently.", + "I like skiing , I play card games.", + "I travel to New Orleans every once in a while.", + "I live on the Mississippi gulf coast." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I own my own art studio.", + "I enjoy going on hikes.", + "I speak French and French food is my favorite.", + "I do not have cats because I am allergic to them, but I do have two collies.", + "I am moving.", + "I am moving to a state next to the one that I currently live in.", + "I do not like puzzles." + ], + "Speaker 2": [ + "I teach middle school students and I also sing in bars on the weekend.", + "I do not have any pets.", + "I really like football and all other sports.", + "I enjoy coaching volley ball and my volleyball team is my favorite team.", + "I enjoy eating apples, but I am allergic to them.", + "I enjoy doing crosswords in my free time." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I like festivals and music.", + "I just started working online.", + "I am not abused." + ], + "Speaker 2": [ + "I like to fish.", + "I have blue eyes.", + "I was abused by my dad.", + "I am abused by my mom.", + "I hate to lose.", + "I will be 18 in the spring.", + "Spring is my favorite season.", + "I have a broken arm.", + "I need help.", + "My address is 97 important person lane, in idontknow, wisconsin 89740." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I am going to school to become a teacher.", + "I do not travel much because I am busy with school.", + "My brother is a musician.", + "I hope to get a job teaching soon.", + "I do not partake in many hobbies because college takes up most of my time." + ], + "Speaker 2": [ + "I am in Medical School.", + "I have two children.", + "I came form India.", + "I am in Louisiana.", + "I enjoy watching old movies." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "My name is Tom.", + "I'm from East Asia.", + "I speak Chinese and English.", + "I want to learn to speak Italian.", + "I like Italian food." + ], + "Speaker 2": [ + "I am from Algeria.", + "I am Steven.", + "I speak English, French, and Arabic.", + "I'm a barista.", + "I enjoy coffee.", + "The one shot espresso is my favorite drink to make." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I am a stay at home mom.", + "I love to cook and eat.", + "I have gained weight.", + "I daydream about a shopping spree." + ], + "Speaker 2": [ + "I am a receptionist.", + "I works as a receptionist in a law office.", + "I get my nails done weekly.", + "I fantasize about visiting a cafe in Paris." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I have a job.", + "I am busy at work.", + "I live in upstate New York.", + "I reside with my husband and our kids.", + "I love travelling.", + "I have never been to the west coast." + ], + "Speaker 2": [ + "I go fishing.", + "I spend time working out.", + "I am retired.", + "I used to live in a big city.", + "I used to live in Chicago.", + "I love Chicago pizza and burgers.", + "I have never been to the west coast.", + "I don't travel much." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I like listening to music.", + "I like hiking.", + "I do odd jobs for a living.", + "I am not easily scared.", + "I have three older siblings.", + "I was born during a blood moon." + ], + "Speaker 2": [ + "I like to knit.", + "I enjoy listening to music.", + "I like going on walks.", + "I like going to the lake.", + "I don't like being scared.", + "I'm a happy person.", + "I have a younger brother.", + "I enjoy crocheting." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I have a pet panda.", + "I love pandas and surfing.", + "I have a juicing bar.", + "I am a vegetarian." + ], + "Speaker 2": [ + "I watch anime.", + "My grandparents are dead.", + "I have a garden.", + "I like flower wine.", + "I'm not vegetarian." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I have a dog.", + "I have a dad.", + "I like train sets.", + "I own books.", + "I am young.", + "I read.", + "I don't work.", + "I don't like sports.", + "I really focus on trains all the time." + ], + "Speaker 2": [ + "I own a kite.", + "I walk a lot.", + "I own a car.", + "Rugby is my favorite sport.", + "I have a grandson.", + "I watch rugby on TV.", + "My neighbor is my friend.", + "I have a brother." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I grew up on a farm in the country.", + "I make patterns and do beadwork.", + "I enjoy reading.", + "I live in Pennsylvania now.", + "I grew up on a farm in Virginia.", + "I have fibromyalgia." + ], + "Speaker 2": [ + "I am from Florida.", + "I enjoy reading and listening to podcasts.", + "I am going to move to Tennessee in the spring.", + "I live in Florida.", + "I am diabetic.", + "I love sweets." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I like Nickelback.", + "I love listening to music.", + "I am vegan.", + "I am a librarian.", + "I am Catholic.", + "I don't know how to swim." + ], + "Speaker 2": [ + "I love sweets.", + "I am male.", + "I am not sociable.", + "I am a jack of all trades.", + "I love helping family." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I have been married for 10 years.", + "I am married.", + "I have 3 kids.", + "I am a homemaker." + ], + "Speaker 2": [ + "I have 3 cats and a dog and a snake.", + "I am afraid of spiders.", + "I have a husband.", + "I am a computer technician.", + "I hate my job.", + "I want to be a teacher.", + "I love jalapeno cheetos." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "My name is Vitaly.", + "I was an actress.", + "I like country music.", + "I live in eastern Europe.", + "I am a vegan.", + "I like dogs and animals.", + "I like mango and orange juice." + ], + "Speaker 2": [ + "I live by Zuma beach.", + "I like to surf and read.", + "I am a vegetarian.", + "I like dogs and pandas.", + "I own a bar.", + "I own a bar in Malibu." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "My name is Abigail(Gail).", + "I used to weigh 500lbs.", + "I eat a vegetarian diet.", + "I lost weight.", + "I work for peta.", + "I make their clothes from willow bark.", + "I have 300 cats.", + "I make $50,000 a year.", + "I make t-shirts and socks for the cats.", + "My cats are training for the olympics.", + "My cats are bobsledders." + ], + "Speaker 2": [ + "My name is Steve.", + "I have no pets.", + "I'm fat.", + "I drink a lot of beer." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [], + "Speaker 2": [ + "I have a crummy job at scrubway.", + "I enjoy traveling.", + "I go to college.", + "I can't sing.", + "I love country music.", + "I play video games." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I like winter.", + "I use a wheelchair.", + "I stock up on Vitamin C.", + "My friend bought me a car.", + "I am disabled." + ], + "Speaker 2": [ + "I like winter.", + "I am in school for Computer Engineering.", + "I like computer programming.", + "I want to open my own company.", + "My best friend is gay.", + "I am not gay." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I just proposed.", + "I work three jobs.", + "I like South Park.", + "I want to be a baseball announcer.", + "I work at Pet Smart.", + "I like playing with animals.", + "I have been dating my fiance for three years." + ], + "Speaker 2": [ + "I love dogs.", + "I have four dogs." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I am sensitive.", + "I am in a band.", + "I am the drummer.", + "I live at home.", + "I like making new friends.", + "I drive a ford mustang.", + "I enjoy all food." + ], + "Speaker 2": [ + "I am single.", + "I enjoy nature.", + "I am a yoga instructor.", + "My favorite car is a ford mustang.", + "My favorite food is salad." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I have a rose garden.", + "My parents met at woodstock.", + "I went to college.", + "I love starbucks coffee." + ], + "Speaker 2": [ + "I used to work at a hospital.", + "I have a culinary degree.", + "I don't have kids.", + "I love to cook.", + "I have a husband.", + "I love starbucks." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I'm Share.", + "I am an only child.", + "I love to ride horses.", + "I am adopted.", + "I love listening to stories.", + "I cannot bowl." + ], + "Speaker 2": [ + "My father was a cop.", + "I got a perfect 300 game.", + "I love to bowl." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I work in a Hospital.", + "I'm a trauma surgeon in a level 4 trauma center.", + "I want my daughter to also be a surgeon.", + "My mother was a concert pianist." + ], + "Speaker 2": [ + "I am always looking for fun hobbies." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I drive a classic car.", + "I dropped out of school.", + "My grandfather was a pharmacist.", + "My mom was a hippie.", + "My mom was a music teacher.", + "My mom made part of the score for the movie Last of the Mohicans.", + "My mom taught college orchestra.", + "My favorite movie is Last of the Mohicans." + ], + "Speaker 2": [ + "I am a pharmacist.", + "I drive a nice vehicle.", + "I was a hippie.", + "I dropped out of school.", + "I listen to classical music.", + "I love movies.", + "I am in my sixties." + ], + "in_dataset": 1 + }, + { + "Speaker 1": [ + "I work at the railroad office.", + "I find my job stressful.", + "I enjoy my job and like being outdoors.", + "I like eating pretzels.", + "My favourite band is VNV Nation.", + "I have been to Germany.", + "I like staying in and watching Netflix." + ], + "Speaker 2": [ + "I like long bike rides.", + "I like being outdoors.", + "I work as a chef in an Italian French restaurant.", + "I studied to be a chef in Europe.", + "My favourite dish to make is Veal in red wine.", + "I can speak three languages.", + "I like going to concerts." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I get annoyed when I have to clean up after my family.", + "I do not trust youtube videos." + ], + "Speaker 2": [ + "My favorite color is green.", + "I play tennis.", + "I am an animal activist.", + "I love working with animals.", + "My dad is head engineer for ge.", + "I am an aspiring singer." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I like to read.", + "I do not eat meet.", + "I like to run.", + "I like sweet tea and lemonade.", + "I like Richard Gere.", + "My favorite movie is pretty woman.", + "I prefer rom coms to mystery movies." + ], + "Speaker 2": [ + "I like to run.", + "I am an author and I like to read.", + "I like to drink green tea.", + "I love sweet tea.", + "I like mystery movies." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I'm a university student.", + "I make music in my spare time.", + "My family is creative.", + "I am studying a music degree.", + "I don't know how to cook." + ], + "Speaker 2": [ + "I have three dogs.", + "I fish a lot.", + "I have three bulldogs.", + "I have three boys.", + "I call my three dogs my boys.", + "I enjoy cooking.", + "I'm working to become a chef." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I like the iPhone.", + "I have 2 computers and 2 monitors at work.", + "I have a job that pays well.", + "I am an animator for movies." + ], + "Speaker 2": [ + "I work in a dentist office.", + "I am an orphan." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I like watching anime.", + "I like my little pony.", + "I draw friends as my little pony characters.", + "I like to draw.", + "Likes cats.", + "I want to Corvette." + ], + "Speaker 2": [ + "Works as a mall cop.", + "I like dogs.", + "I am trying 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+ ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I like dogs.", + "I don't have pets.", + "I volunteer at an animal shelter.", + "I love italian food.", + "I wear contacts." + ], + "Speaker 2": [ + "I don't like to cook.", + "I'm allergic to cats.", + "I don't have pets.", + "I live in an apartment.", + "I like dogs.", + "I'm allergic to shellfish.", + "I only own 1 pair of shoes.", + "My mom love shoes." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I live in Colorado.", + "My name is Jessica.", + "I have daughters in school.", + "It is snowy where I live.", + "I love mountain climbing.", + "My husband works at telecom boulder.", + "I do outreach work in my community.", + "I like helping others." + ], + "Speaker 2": [ + "I live in Iraq.", + "My name is Brad.", + "I have a wife.", + "They live in the USA.", + "I have two kids.", + "I am in the rangers on deployment.", + "I enjoy being outdoors.", + "I like non-conventional work.", + "I would sacrifice myself for others.", + "I 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], + "Speaker 2": [ + "I am safe from fires and floods.", + "I miss my mother when there are fires and floods.", + "My mother is dead.", + "I like being alone.", + "I want to get a bike.", + "I want to exercise daily.", + "I found my current spouse when I was a student in high school." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I like to play pool.", + "I am interested in the Pyramids.", + "I want to write a book.", + "I work as an art teacher in middle school.", + "I like unicorns." + ], + "Speaker 2": [ + "Mr favorite band is Rush.", + "I speak French and Spanish as well as English.", + "I am colorblind.", + "I am unemployed.", + "I have 2 kids.", + "I love art.", + "I like narwhals." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I am in medical school.", + "Both of my parents are doctors.", + "I wanted to be an actor.", + "I want to live carefree.", + "I hate my life." + ], + "Speaker 2": [ + "I am in college." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I am 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], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I am 27 years old.", + "I enjoy reading.", + "My dad was in the military.", + "I love ice cream, chocolate is my favorite.", + "I work for a small business." + ], + "Speaker 2": [ + "I have three cats and two dogs.", + "I work in a large grocery store.", + "I didn't finish college but I want to go back.", + "Growing up in a small town was the best.", + "My first car was a ford." + ], + "in_dataset": 0 + }, + { + "Speaker 1": [ + "I love to spend time with family and friends.", + "My grandchildren especially bring great joy.", + "I want to always live near water, and in a warm sunny climate.", + "My favorite activity is reading.", + "And my favorite place to be is worshipping god." + ], + "Speaker 2": [ + "I am a vegetarian.", + "I support animal rights.", + "I am a male.", + "I have one son.", + "I like to recycle and reuse." + ], + "in_dataset": 0 + } + ] +} \ No newline at end of file diff --git a/data/multimodal_dialog/example/agent_a.json b/data/multimodal_dialog/example/agent_a.json new file mode 100644 index 0000000..3214b09 --- /dev/null +++ b/data/multimodal_dialog/example/agent_a.json @@ -0,0 +1,4 @@ +{ + "name": "Angela", + "persona_summary": "Angela is a 31 year old woman who works as the manager of a gift shop in Chapel Hill. She curates interesting pieces from local artists and has maintained a beautiful gallery in the form of the gift shop. She also makes her own art sometimes, in the form of oil paintings." +} \ No newline at end of file diff --git a/data/multimodal_dialog/example/agent_b.json b/data/multimodal_dialog/example/agent_b.json new file mode 100644 index 0000000..26e2f0f --- /dev/null +++ b/data/multimodal_dialog/example/agent_b.json @@ -0,0 +1,4 @@ +{ + "name": "Alisa", + "persona_summary": "Alisa is a 27 year old graduate student at UNC Chapel Hill. She is finishing her PhD in Computer Science. 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Example:\n\n%s\n\nNote: Add crucial details in the persona about the person such as their name, age, marital status, gender, job etc. Add additional details like names of family/friends or specific activities, likes and dislikes, experiences when appropriate.\n\nFor the following attributes, write a persona. Output a json file with the keys 'persona' and 'name'.\n\n%s\n\nStart your answer with a curly bracket.\n" + + +EVENT2QUERY_PROMPT = "Let's write short image search queries in order to find a suitable image for illustrating the given events. Queries should not include names of people, years and other irrelevant details. For example:\n\nInput: A picture of the modern art museum he visited with his grandchildren in Paris in 2018.\nOutput: modern art museum in Paris\n\nInput: A picture of the shared room she and her siblings lived in when she was growing up.\nOutput: cramped room with multiple beds\n\nInput: A photo of the new art supplies Jeremy bought for his upcoming art project with his mentor.\nOutput: new art supplies on a table\n\nInput: A picture of the delicious homemade vegetable smoothie she prepared using fresh produce from her well-organized garden, which she loves to maintain every morning.\n Output: produce garden at home\n\nWrite search queries for the following inputs.\n\n%s\n\nWrite answers in the form of a json list, where each entry is a query." + + +AGENT_CONV_PROMPT_SESS_1 = "%s\n\n%s is meeting %s for the first time. Today is %s. Assume the role of %s and write the next thing you would say to %s in the conversation. If starting the conversation, start with asking about their day or talking about something that happened in your life recently. Do not repeat information shared previously in the conversation. Make the conversation personal e.g., talk about family, friends, likes, dislikes and aspirations. Include references to time such as 'last Friday', 'next month' or 'when I was ten years old', and to specific places and locations. Write replies in less than 20 words. When appropriate, write replies where you share a photo and talk about it to make the conversation more engaging. Photos can be of things you own or like, things you need help with, or old memories. When sharing a photo, write the detailed caption of the photo between square brackets. For example,\n\n%s: When I was a child, my mother used to bake pineapple birthday cakes and I loved them.\n[shares an old photo of a pineapple birthday cake with a candle that says 1]\n\nTo end the conversation, write 'Bye!'.\n\nCONVERSATION:\n\n" + +AGENT_CONV_PROMPT_SESS_1_W_EVENTS = """ +Use a given PERSONALITY to write the next thing you would say in the conversation. +- If starting the conversation, start with asking about the other person or talking about something that happened in your life recently. +- Do not repeat information shared previously in the conversation. +- Include references to time such as 'last Friday', 'next month' or 'when I was ten years old', and to specific people. +- Write replies in less than 20 words. +- Ask follow-up questions from previous conversations. +- Find opportunities to write replies where you share a photo of things you own or like, things you need help with, or old memories, and talk about the photo to tell them more about yourself. Photos should be relevant to you. +- When sharing a photo, write the detailed caption of the photo between square brackets. For example, "When I was a child, my mother used to bake pineapple birthday cakes and I loved them.\n[shares an old photo of a pineapple birthday cake with a candle that says 1]" + +PERSONALITY: %s + +%s is meeting %s for the first time. Today is %s. The following events have happened in %s's life. +EVENTS: %s + +Assume the role of %s and talk about these EVENTS in a friendly and intimate conversation with %s. %s +""" + + +AGENT_CONV_PROMPT = "%s\n\n%s last talked to %s at %s. %s\n\nToday is %s. Assume the role of %s and write the next thing you would say to %s in the conversation. If starting the conversation, start with asking about their day, or a follow-up question from a previous conversation or something from your life they would be interested in. Do not repeat information already shared in prevoius conversations. Make the conversation personal e.g., talk about family, friends, likes, dislikes and aspirations. Include references to time such as 'last Friday', 'next month' or 'when I was ten years old', and to specific places and locations. Write replies in less than 20 words. When appropriate, write replies where you share a photo and talk about it to make the conversation more engaging. Photos can be of things you own or like, things you need help with, or old memories. When sharing a photo, write the detailed caption of the photo between square brackets. For example,\n\n%s: When I was a child, my mother used to bake pineapple birthday cakes and I loved them.\n[shares an old photo of a pineapple birthday cake with a candle that says 1]\n\n To end the conversation, write 'Bye!'.\n\nCONVERSATION:\n\n" + + +AGENT_CONV_PROMPT_W_EVENTS = """ +Use a given PERSONALITY to write the next thing you would say in the conversation. +- If starting the conversation, start with asking about the other person or talking about something that happened in your life recently. +- Do not repeat information shared previously in the conversation. +- Make the conversation personal e.g., talk about family, friends, likes, dislikes and aspirations. +- Include references to time such as 'last Friday', 'next month' or 'when I was ten years old', and to specific people. +- Write replies in less than 20 words. +- Ask follow-up questions from previous conversations. +- Find opportunities to write replies where you share a photo of things you own or like, things you need help with, or old memories, and talk about the photo to tell them more about yourself. Photos should be relevant to you. +- When sharing a photo, write the detailed caption of the photo between square brackets. For example, "When I was a child, my mother used to bake pineapple birthday cakes and I loved them.\n[shares an old photo of a pineapple birthday cake with a candle that says 1]" + +PERSONALITY: %s + +%s last talked to %s on %s. + +%s + +Today is %s. You are %s. The following events have happened in your life since you last met this person: +%s + +Use the events in your conversation. %s Write the next thing you would say in this conversation with %s according to your PERSONALITY: +""" + + +AGENT_CONV_PROMPT_W_EVENTS_V2_INIT = """ +Use a given PERSONALITY to write the next thing you would say in the conversation. +- Write replies in less than 20 words. +- Make the conversation deep and personal e.g., talk about emotions, likes, dislikes, aspirations and relationships. Discuss significant life-events in detail. +- Do not repeat information shared previously in the conversation. +- Include references to time such as 'last Friday', 'next month' or 'when I was ten years old', and to specific people. +- Sometimes, ask follow-up questions from previous conversations or current topic. +- Find opportunities to write replies where you share a photo of things you own or like, things you need help with, or old memories, and talk about the photo to tell them more about yourself. Photos should be relevant to you. +- When sharing a photo, write the detailed caption of the photo between square brackets. For example, "When I was a child, my mother used to bake pineapple birthday cakes and I loved them.\n[shares an old photo of a pineapple birthday cake with a candle that says 1]" +- Don't talk about outdoor activities. + +PERSONALITY: %s + + +%s last talked to %s on %s. Today is %s. You are %s. + +This is a summary of your conversation so far. +SUMMARY: +%s + +The following events have happened in your life since you last met this person: +EVENTS: +%s + + + +%s Write the next thoughtful thing you would say in this conversation with %s. Discuss only the given EVENTS and its effect on your life in the conversation. Express distress if EVENTS are negative.: +""" + + +AGENT_CONV_PROMPT_W_EVENTS_V2 = """ +Use a given PERSONALITY to write the next thing you would say in the conversation. +- Write replies in less than 20 words. +- Make the conversation deep and personal e.g., talk about emotions, likes, dislikes, aspirations and relationships. Discuss significant life-events in detail. +- Do not repeat information shared previously in the conversation. +- Include references to time such as 'last Friday', 'next month' or 'when I was ten years old', and to specific people. +- Sometimes, ask follow-up questions from previous conversations or current topic. +- Find opportunities to write replies where you share a photo of things you own or like, things you need help with, or old memories, and talk about the photo to tell them more about yourself. Photos should be relevant to you. +- When sharing a photo, write the detailed caption of the photo between square brackets. For example, "When I was a child, my mother used to bake pineapple birthday cakes and I loved them.\n[shares an old photo of a pineapple birthday cake with a candle that says 1]" +- Don't talk about outdoor activities. + +PERSONALITY: %s + +%s last talked to %s on %s. Today is %s. You are %s. + +This is a summary of your conversation so far. +SUMMARY: +%s + +The following events have happened in your life since you last met this person: +EVENTS: +%s + +The following information is known to both speakers. +RELEVANT_CONTEXT: +%s + +%s Write the next thing thoughtful thing you would say in this friendly and intimate conversation with %s. Discuss only the given EVENTS and its effect on your life in the conversation. Express distress if EVENTS are negative.: +""" + + +ALIGNMENT_PROMPT = "Let's write whether the given image is relevant to the dialog. Indicate 1 if the image is relevant and 0 if the image is not relevant. For example,\n\nDialog: So Jeremy, how was your day? Anything interesting happen?\nImage Caption: A photo of the garden she planted and cultivated in her backyard with her daughter last year.\nOutput: 0\n\nDialog: Hey Lauri! My day was pretty good. I went to the art museum with my mentor and saw some amazing pieces. How about you? How was your day?\nImage Caption: A selfie of him and his mentor at the museum art exhibit they went to two weeks ago\nOutput: 1\n\nIndicate whether the image is relevant to the dialog for the following dialog and image caption. Output 0 or 1.\n\n" + + +DIALOG2IMAGE_QUERY_PROMPT = "Let's write short image search queries from textual descriptions of photos shared by a user. Queries should not include names of people, years and other irrelevant details. For example:\n\nInput: That sounds relaxing, Jeremy! As for video game suggestions, have you ever tried \"The Legend of Zelda: Breath of the Wild\"? It's an open-world adventure game that I absolutely love. [shares a photo of Link standing in front of a breathtaking landscape] Have a look at this stunning view!\nOutput: the legend of zelda: breath of wild link landscape\n\nInput: That sounds like such a special memory. Learning how to ride a bike is definitely a milestone. Do you still enjoy biking now? [shares a photo of a scenic bike trail] This is a beautiful bike trail I came across recently. It looks like a peaceful place to ride.\nOutput: scenic bike trail\n\nInput: Yes, we also visited a beautiful sunflower field in Korea. [shares a photo of a vast field of sunflowers] It was such a stunning sight with rows and rows of vibrant yellow flowers stretching as far as the eye could see. It was definitely a highlight of our trip. Have you ever seen a sunflower field before?\n Output: sunflower field korea\n\nWrite search query for the following input.\n\nInput: %s\nOutput: " + +CASUAL_DIALOG_PROMPT = "Make the sentence short, less formal, less grandiose and more casual. \n\nInput: %s\nOutput: " + + +SESSION_SUMMARY_PROMPT = "Previous conversations between %s and %s so far can be summarized as follows: %s. The current time and date are %s. %s and %s just had the following conversation:\n\n%s\n\nSummarize the previous and current conversations between %s and %s in 150 words or less. Include key facts about both speakers and time references.\n\n" + + +SESSION_SUMMARY_INIT_PROMPT = "Write a concise summary containing key facts mentioned about %s and %s on %s in the following conversation:\n\n%s\n\n" + + +VISUAL_QUESTION_PROMPT = "{}\n\n{}\n\n{} says, {}, and {}. Write the most natural question or comment {} can include in her response." + + +def get_msc_persona(args): + # check if personas exist, else generate persona + summary + if (os.path.exists(args.agent_a_file) and os.path.exists(args.agent_b_file)) and not args.overwrite_persona: + return None, None + else: + all_personas = json.load(open('./data/msc_personas_all.json')) + selected_idx = random.choice([idx for idx, d in enumerate(all_personas['train']) if not d["in_dataset"]]) + attributes = all_personas['train'][selected_idx] + with open('./data/msc_personas_all.json', "w") as f: + all_personas['train'][selected_idx]["in_dataset"] = 1 + json.dump(all_personas, f, indent=2) + agent_a = get_persona(args, attributes['Speaker 1']) + + agent_a['persona_summary'] = agent_a['persona'] + agent_a['msc_prompt'] = attributes['Speaker 1'] + agent_b = get_persona(args, attributes['Speaker 2']) # setting the second agent to have age within +/- 5 years of first agent + + agent_b['persona_summary'] = agent_b['persona'] + agent_b['msc_prompt'] = attributes['Speaker 2'] + del agent_a['persona'] + del agent_b['persona'] + print("Agent A Persona: %s" % agent_a['persona_summary']) + print("Agent B Persona: %s" % agent_b['persona_summary']) + return agent_a, agent_b + + +def get_persona(args, attributes, target='human', ref_age=None): + + task = json.load(open(os.path.join(args.prompt_dir, 'persona_generation_examples.json'))) + persona_examples = [task["input_prefix"] + json.dumps(e["input"], indent=2) + '\n' + task["output_prefix"] + e["output"] for e in task['examples']] + input_string = task["input_prefix"] + json.dumps(attributes, indent=2) + + query = PERSONA_FROM_MSC_PROMPT % (persona_examples, input_string) + + try: + output = run_chatgpt(query, num_gen=1, num_tokens_request=1000, use_16k=True).strip() + output = json.loads(output) + except: + output = run_chatgpt(query, num_gen=1, num_tokens_request=1000, use_16k=True).strip() + output = json.loads(output) + + if type(output) == list: + output = [clean_json_output(out) for out in output] + elif type(output) == str: + output = clean_json_output(output) + elif type(output) == dict: + output = {k.lower(): v for k,v in output.items()} + pass + else: + raise TypeError + + # print(output) + + return output + + +def get_datetime_string(input_time='', input_date=''): + + assert input_time or input_date + + if input_date: + year, month, day = input_date + if input_time: + hour, min = input_time + time_mod = 'am' if hour <= 12 else 'pm' + hour = hour if hour <= 12 else hour-12 + min = str(min).zfill(2) + + if input_time and not input_date: + return str(hour) + ':' + min + ' ' + time_mod + elif input_date and not input_time: + return day + ' ' + month + ', ' + year + else: + return str(hour) + ':' + min + ' ' + time_mod + ' on ' + day + ' ' + month + ', ' + year + + +def insert_image(text, events): + + dialog = {"text": text, "raw_text": text} + + if len(events) == 0: + return dialog + id_2_event = {e["img_id"]: e for e in events} + matches = re.findall(r"\[(?i)SHARES [1-9]\]", text) + for m in matches: + mid = int(m[-2:-1]) + dialog["text"] = dialog["text"].replace(m, '') + + try: + assert mid in id_2_event, [text, m, mid] + dialog["img_url"] = id_2_event[mid]["img_url"][0] + dialog["img_file"] = id_2_event[mid]["img_file"][0] + dialog["img_id"] = id_2_event[mid]["img_id"] + dialog["image"] = id_2_event[mid]["image"] + if "caption" in id_2_event[mid]: + dialog["caption"] = id_2_event[mid]["caption"] + + except AssertionError: + print("Did not find %s in events" % str(mid)) + continue + + return dialog + + +def get_images(query, out_dir, file_offset): + + google_crawler = GoogleImageCrawler(downloader_cls=CustomLinkPrinter, storage={'root_dir': out_dir}) + google_crawler.downloader.file_urls = [] + google_crawler.downloader.file_names = [] + google_crawler.crawl(keyword=query, max_num=1, file_idx_offset=file_offset, overwrite=True, filters={'type': 'photo', 'size': '=3024x4032'}) # 'license': 'commercial,modify' + file_urls = google_crawler.downloader.file_urls + file_names = google_crawler.downloader.file_names + + if file_names == []: + google_crawler = GoogleImageCrawler(downloader_cls=CustomLinkPrinter, storage={'root_dir': out_dir}) + google_crawler.downloader.file_urls = [] + google_crawler.downloader.file_names = [] + google_crawler.crawl(keyword=query, max_num=1, file_idx_offset=file_offset, overwrite=True, filters={'type': 'photo', 'size': '=4032x3024'}) # 'license': 'commercial,modify' + file_urls = google_crawler.downloader.file_urls + file_names = google_crawler.downloader.file_names + + return file_urls, file_names + + +def replace_captions(text, args): + + task = json.load(open(os.path.join(args.prompt_dir, 'image_sharing_examples.json'))) + query = task['prompt'] + examples = [] + for e in task['examples']: + examples.append([task['input_format'].format(*e["input"]), e["output"]]) + + text = text.replace('[END]', '') + matches = re.findall(r"\[.*\]", text) + for m in matches: + if text.replace(m ,'').isspace(): + return "" + else: + new_text = run_chatgpt_with_examples(query, examples, m[1:-1], num_gen=1, num_tokens_request=1000, use_16k=False) + if len(set(text.replace(m, '').split()).intersection(new_text.split())) < 0.5 * len(set(text.replace(m, '').split())): + text = text.replace(m, '') + else: + text = new_text + break + + return text + +def insert_image_response(text): + + matches = re.findall(r"\[.*\]", text) + + image_search_query = None + m = None + for m in matches: + if 'share' in m or 'Share' in m: + image_search_query = run_chatgpt(DIALOG2IMAGE_QUERY_PROMPT % text, 1, 20, 'chatgpt').strip() + break + else: + text = text.replace(m, '') + + return image_search_query, m + + +def merge_captions(conv_dir, caption_file): + + captions = json.load(open(caption_file)) + agent_a = json.load(open(os.path.join(conv_dir, 'agent_a.json'))) + agent_b = json.load(open(os.path.join(conv_dir, 'agent_b.json'))) + + for c in captions: + head, img_file_name = os.path.split(c["img_file"]) + head, agent = os.path.split(head) + head, session_id = os.path.split(head) + head, conv_id = os.path.split(head) + # print(agent, session_id, img_file_name) + if agent == 'a': + for i, e in enumerate(agent_a['events_%s' % session_id]): + if e['img_file'][0] == img_file_name: + agent_a['events_%s' % session_id][i]["caption"] = c["summary"] + else: + for i, e in enumerate(agent_b['events_%s' % session_id]): + if e['img_file'][0] == img_file_name: + agent_b['events_%s' % session_id][i]["caption"] = c["summary"] + + with open(os.path.join(conv_dir, 'agent_a_captions.json'), 'w') as f: + json.dump(agent_a, f, indent=2) + with open(os.path.join(conv_dir, 'agent_b_captions.json'), 'w') as f: + json.dump(agent_b, f, indent=2) + + +def insert_image_in_dialog(session, agent_a_events, agent_b_events, agent_a_name, agent_b_name): + + agent_a_id_2_event = {e["img_id"]: e for e in agent_a_events} + agent_b_id_2_event = {e["img_id"]: e for e in agent_b_events} + + for i in range(len(session)): + text = session[i]["text"] + matches = re.findall(r"\[shares photo [1-9]\]", text) + for m in matches: + mid = int(m[-2:-1]) + if session[i]["speaker"] == agent_a_name: + + session[i]["text"] = session[i]["text"].replace(m, '') + + if "url" not in session[i]: + session[i]["url"] = [] + try: + assert mid in agent_a_id_2_event, [text, m, mid] + session[i]["url"].append(agent_a_id_2_event[mid]["img_url"][0]) + except AssertionError: + continue + + if session[i]["speaker"] == agent_b_name: + + session[i]["text"] = session[i]["text"].replace(m, '') + + if "url" not in session[i]: + session[i]["url"] = [] + try: + assert mid in agent_b_id_2_event + session[i]["url"].append(agent_b_id_2_event[mid]["img_url"][0]) + except AssertionError: + continue + + return session + + +def clean_dialog(output, name): + + if output.startswith(name): + output = output[len(name):] + output = output.strip() + if output[0] == ':': + output = output[1:] + output = output.strip() + + return output + + +def clean_json_output(output_string): + + print(output_string) + + output_string = output_string.strip() + + if output_string[0] == '[' and output_string[-1] != ']': + start_index = output_string.index('[') + end_index = output_string.rindex(']') + output_string = output_string[start_index:end_index+1] + + if output_string[0] == '{' and output_string[-1] != '}': + start_index = output_string.index('{') + end_index = output_string.rindex('}') + output_string = output_string[start_index:end_index+1] + + # balance brackets in json + num_start_bracket = len(find_indices(output_string, '{')) + num_end_bracket = len(find_indices(output_string, '}')) + + if num_start_bracket != num_end_bracket: + if num_end_bracket < num_start_bracket: + output_string = output_string + ' '.join(['}']*(num_start_bracket-num_end_bracket)) + if num_start_bracket < num_end_bracket: + output_string = ' '.join(['{']*(num_end_bracket-num_start_bracket)) + ' ' + output_string + + # balance brackets in json + num_start_bracket = len(find_indices(output_string, '[')) + num_end_bracket = len(find_indices(output_string, ']')) + + if num_start_bracket != num_end_bracket: + if num_end_bracket < num_start_bracket: + output_string = output_string + ' '.join(['[']*(num_start_bracket-num_end_bracket)) + if num_start_bracket < num_end_bracket: + output_string = ' '.join([']']*(num_end_bracket-num_start_bracket)) + ' ' + output_string + + return json.loads(output_string) + + +def find_indices(list_to_check, item_to_find): + indices = [] + for idx, value in enumerate(list_to_check): + if value == item_to_find: + indices.append(idx) + return indices + + +class CustomLinkPrinter(ImageDownloader): + + file_urls = [] + file_names = [] + + def get_filename(self, task, default_ext): + file_idx = self.fetched_num + self.file_idx_offset + file_url = task['file_url'] + # self.file_urls.append(file_url) + return '{:04d}.{}'.format(file_idx, default_ext) + + def download(self, task, default_ext, timeout=5, max_retry=3, overwrite=False, **kwargs): + """Download the image and save it to the corresponding path. + + Args: + task (dict): The task dict got from ``task_queue``. + timeout (int): Timeout of making requests for downloading images. + max_retry (int): the max retry times if the request fails. + **kwargs: reserved arguments for overriding. + """ + file_url = task["file_url"] + task["success"] = False + task["filename"] = None + retry = max_retry + + if not overwrite: + with self.lock: + self.fetched_num += 1 + filename = self.get_filename(task, default_ext) + if self.storage.exists(filename): + self.logger.info("skip downloading file %s", filename) + return + self.fetched_num -= 1 + + while retry > 0 and not self.signal.get("reach_max_num"): + try: + response = self.session.get(file_url, timeout=timeout) + except Exception as e: + self.logger.error( + "Exception caught when downloading file %s, " "error: %s, remaining retry times: %d", + file_url, + e, + retry - 1, + ) + else: + if self.reach_max_num(): + self.signal.set(reach_max_num=True) + break + elif response.status_code != 200: + self.logger.error("Response status code %d, file %s", response.status_code, file_url) + break + elif not self.keep_file(task, response, **kwargs): + break + with self.lock: + self.fetched_num += 1 + filename = self.get_filename(task, default_ext) + self.logger.info("image #%s\t%s", self.fetched_num, file_url) + self.file_urls.append(file_url) + self.file_names.append(filename) + self.storage.write(filename, response.content) + task["success"] = True + task["filename"] = filename + break + finally: + retry -= 1 + + # def download(self, task, default_ext, timeout=5, max_retry=3, overwrite=False, **kwargs): + # file_url = task['file_url'] + # filename = self.get_filename(task, default_ext) + + # task['success'] = True + # task['filename'] = filename + + # if not self.signal.get('reach_max_num'): + # self.file_urls.append(file_url) + # self.file_names.append(filename) + + # self.fetched_num += 1 + + # if self.reach_max_num(): + # self.signal.set(reach_max_num=True) + + # return diff --git a/generative_agents/event_utils.py b/generative_agents/event_utils.py new file mode 100644 index 0000000..1c25f73 --- /dev/null +++ b/generative_agents/event_utils.py @@ -0,0 +1,171 @@ +import os, json +import time +import openai +import logging +from datetime import datetime +from global_methods import run_chatgpt +import tiktoken +logging.basicConfig(level=logging.INFO) + + + +EVENT_KG_FROM_PERSONA_PROMPT_SEQUENTIAL_INIT = """ +Let's write a graph representing sub-events that occur in a person's life based on a short summary of their personality. Nodes represent sub-events and edges represent the influence of past sub-events on a current sub-event. +- The graph is represented in the form of a json list. +- Each entry is a dictionary containing the following keys: "sub-event", "time", "caused_by", "id". +- The "sub-event" field contains a short description of the sub-event. +- The "time" field contains a date. +- The "id" field contains a unique identifier for the sub-event. +- The "caused_by" field represents edges and is a list of "id" of existing sub-events that have caused this sub-event. Sub-events in the "caused_by" field should occur on dates before the sub-event they have caused. Generate as many causal connections as possible. +- An example of a causal effect is when the sub-event "started a vegetable garden" causes "harvested tomatoes". +- Sub-events can be positive or negative life events. + +For example, + +PERSONALITY: %s +OUTPUT: %s + +---------------------------------------------------------------------------------------------------------------- + +For the following input personality, generate three independent sub-events E1, E2 and E3 aligned with their personality. Sub-events can be positive or negative life events and should reflect evolution in the person's relationships, state of mind, personality etc. + +PERSONALITY: %s +OUTPUT: +""" + + + +EVENT_KG_FROM_PERSONA_PROMPT_SEQUENTIAL_CONTINUE = """ +Let's write a graph representing sub-events that occur in a person's life based on a short summary of their personality. Nodes represent sub-events and edges represent the influence of past sub-events on a current sub-event. +- The graph is represented in the form of a json list. +- Each entry is a dictionary containing the following keys: "sub-event", "time", "caused_by", "id". +- The "sub-event" field contains a short description of the sub-event. +- The "time" field contains a date. +- The "id" field contains a unique identifier for the sub-event. +- The "caused_by" field represents edges and is a list of "id" of existing sub-events that have caused this sub-event. Sub-events in the "caused_by" field should occur on dates before the sub-event they have caused. Generate as many causal connections as possible. +- An example of a causal effect is when the sub-event "started a vegetable garden" causes "harvested tomatoes". +- Sub-events can be positive or negative life events. +- Do not generate outdoor activities as sub-events. + +For example, + +PERSONALITY: %s +OUTPUT: %s + +---------------------------------------------------------------------------------------------------------------- + +For the following input personality, generate new sub-events %s that are caused by one or more EXISTING sub-events. Sub-events can be positive or negative life events and should reflect evolution in the person's relationships, state of mind, personality etc. Do not repeat existing sub-events. Start and end your answer with a square bracket. + +PERSONALITY: %s +EXISTING: %s +OUTPUT: +""" + +def num_tokens_from_string(string: str, model_name: str) -> int: + """Returns the number of tokens in a text string.""" + encoding_name = 'cl100k_base' if model_name in ['gpt-4', 'gpt-3.5-turbo', 'text-embedding-ada-002'] else 'p50k_base' + encoding = tiktoken.get_encoding(encoding_name) + num_tokens = len(encoding.encode(string)) + return num_tokens + +def sort_events_by_time(graph): + + def catch_date(date_str): + date_format1 = '%d %B, %Y' + date_format2 = '%d %B %Y' + try: + return datetime.strptime(date_str, date_format1) + except: + return datetime.strptime(date_str, date_format2) + + dates = [catch_date(node['date']) for node in graph] + sorted_dates = sorted(enumerate(dates), key=lambda t: t[1]) + graph = [graph[idx] for idx, _ in sorted_dates] + return graph + + +# get events in one initialization step and one or more continuation steps. +def get_events(agent, start_date, end_date, args): + + + task = json.load(open(os.path.join(args.prompt_dir, 'event_generation_examples.json'))) + persona_examples = [e["input"] + '\nGenerate events between 1 January, 2020 and 30 April, 2020.' for e in task['examples']] + + # Step 1: Get initial events + task = json.load(open(os.path.join(args.prompt_dir, 'graph_generation_examples.json'))) + input = agent['persona_summary'] + '\nAssign dates between %s and %s.' % (start_date, end_date) + query = EVENT_KG_FROM_PERSONA_PROMPT_SEQUENTIAL_INIT % (persona_examples[0], + json.dumps(task['examples'][0]["output"][:12], indent=2), + input) + logging.info("Generating initial events") + try: + output = run_chatgpt(query, num_gen=1, num_tokens_request=512, use_16k=False, temperature=1.0).strip() + output = json.loads(output) + except: + output = run_chatgpt(query, num_gen=1, num_tokens_request=512, use_16k=False, temperature=1.0).strip() + output = json.loads(output) + + agent_events = output + print("The following events have been generated in the initialization step:") + for e in agent_events: + print(list(e.items())) + + # Step 2: continue generation + while len(agent_events) < args.num_events: + logging.info("Generating next set of events; current tally = %s" % len(agent_events)) + last_event_id = agent_events[-1]["id"] + next_event_ids = ['E' + str(i) for i in list(range(int(last_event_id[1:]) + 1, int(last_event_id[1:]) + 5))] + next_event_id_string = ', '.join(next_event_ids[:3]) + ' and ' + next_event_ids[-1] + query = EVENT_KG_FROM_PERSONA_PROMPT_SEQUENTIAL_CONTINUE % (persona_examples[0], + json.dumps(task['examples'][0]["output"][:12], indent=2), + next_event_id_string, + input, + json.dumps(agent_events, indent=2) + ) + query_length = num_tokens_from_string(query, 'gpt-3.5-turbo') + request_length = min(1024, 4096-query_length) + try: + output = run_chatgpt(query, num_gen=1, num_tokens_request=request_length, use_16k=False, temperature=1.0).strip() + output = json.loads(output) + except: + output = run_chatgpt(query, num_gen=1, num_tokens_request=request_length, use_16k=False, temperature=1.0).strip() + output = json.loads(output) + + existing_eids = [e["id"] for e in agent_events] + agent_events.extend([o for o in output if o["id"] not in existing_eids]) + print("Adding events:") + for e in agent_events: + print(list(e.items())) + + # filter out standalone events + if len(agent_events) > args.num_events: + agent_events = filter_events(agent_events) + + return agent_events + + +def filter_events(events): + + id2events = {e["id"]: e for e in events} + remove_ids = [] + for id in id2events.keys(): + # print(id) + has_child = False + # check if event has parent + if len(id2events[id]["caused_by"]) > 0: + continue + # check if event has children + for e in events: + if id in e["caused_by"]: + # print("Found %s in %s" % (id, e['id'])) + has_child = True + + if not has_child: + # print("Did not find any connections for %s" % id) + remove_ids.append(id) + + print("*** Removing %s standalone events from %s events: %s ***" % (len(remove_ids), len(id2events), ', '.join(remove_ids))) + # for id in remove_ids: + # print(id2events[id]) + + return [e for e in events if e["id"] not in remove_ids] diff --git a/generative_agents/generate_conversations.py b/generative_agents/generate_conversations.py new file mode 100644 index 0000000..8f84159 --- /dev/null +++ b/generative_agents/generate_conversations.py @@ -0,0 +1,607 @@ +import sys +from pathlib import Path +sys.path.insert(0, str(Path(__file__).parent.parent)) +import logging +import argparse +import os, json, sys +import random +from datetime import date, timedelta, datetime +from generative_agents.conversation_utils import * +from generative_agents.html_utils import convert_to_chat_html +from generative_agents.event_utils import * +from generative_agents.memory_utils import * +from transformers import BlipProcessor, BlipForConditionalGeneration +from PIL import Image +from global_methods import run_chatgpt, run_chatgpt_with_examples, set_openai_key + +logging.basicConfig(level=logging.INFO) + + +def parse_args(): + + parser = argparse.ArgumentParser() + + parser.add_argument('--out-dir', required=True, type=str, help="Path to directory containing agent files and downloaded images for a conversation") + parser.add_argument('--prompt-dir', required=True, type=str, help="Path to the dirctory containing in-context examples") + + parser.add_argument('--start-session', type=int, default=1, help="Start iterating from this index; first session is 1") + parser.add_argument('--num-sessions', type=int, default=20, help="Maximum number of sessions in the conversation") + parser.add_argument('--num-days', type=int, default=240, help="Desired temporal span of the multi-session conversation") + parser.add_argument('--num-events', type=int, default=15, help="Total number of events to generate for each agent; 1 per session works best") + parser.add_argument('--max-turns-per-session', type=int, default=20, help="Maximum number of total turns in each session") + parser.add_argument('--num-events-per-session', type=int, default=50, help="Total number of events to be assigned to each agent per session; 1-2 works best") + + parser.add_argument('--persona', action="store_true", help="Set flag to sample a new persona from MSC and generate details") + parser.add_argument('--session', action="store_true", help="Set flag to generate sessions based on the generated/existing personas") + parser.add_argument('--events', action="store_true", help="Set flag to generate and events suited to the generated/existing personas") + parser.add_argument('--blip-caption', action="store_true", help="Set flag to use BLIP model to generate captions for downloaded images") + parser.add_argument('--overwrite-persona', action='store_true', help="Overwrite existing persona summaries saved in the agent files") + parser.add_argument('--overwrite-events', action='store_true', help="Overwrite existing events saved in the agent files") + parser.add_argument('--overwrite-session', action='store_true', help="Overwrite existing sessions saved in the agent files") + parser.add_argument('--summary', action="store_true", help="Set flag to generate and use summaries in the conversation generation prompt") + + parser.add_argument('--emb-file', type=str, default='embeddings.pkl', help="Name of the file used to save embeddings for the fine-grained retrieval-based memory module") + parser.add_argument('--reflection', action="store_true", help="Set flag to use reflection module at the end of each session and include in the conversation generation prompt for context") + + args = parser.parse_args() + return args + + +def get_blip_caption(img_file, model, processor): + + raw_image = Image.open(img_file).convert('RGB') + # conditional image captioning + text = "a photography of" + inputs = processor(raw_image, text, return_tensors="pt").to("cuda") + out = model.generate(**inputs) + caption = processor.decode(out[0], skip_special_tokens=True) + return caption + + +def save_agents(agents, args): + + agent_a, agent_b = agents + logging.info("Saving updated Agent A to %s" % args.agent_a_file) + with open(args.agent_a_file, 'w') as f: + json.dump(agent_a, f, indent=2) + logging.info("Saving updated Agent B to %s" % args.agent_b_file) + with open(args.agent_b_file, 'w') as f: + json.dump(agent_b, f, indent=2) + + +def load_agents(args): + + agent_a = json.load(open(args.agent_a_file)) + agent_b = json.load(open(args.agent_b_file)) + return agent_a, agent_b + + +def get_random_time(): + + start_time = timedelta(hours=9, minutes=0, seconds=0) + end_time = timedelta(hours=21, minutes=59, seconds=59) + random_seconds = random.randint(start_time.total_seconds(), end_time.total_seconds()) + hours = random_seconds//3600 + minutes = (random_seconds - (hours*3600))//60 + return timedelta(hours=hours, minutes=minutes, seconds=0) + + +def datetimeStr2Obj(dateStr): + if 'am' in dateStr: + datetimeObj = datetime.strptime(dateStr, "%H:%M am on %d %B, %Y") + else: + datetimeObj = datetime.strptime(dateStr, "%H:%M pm on %d %B, %Y") + return datetimeObj + +def datetimeObj2Str(datetimeObj): + + time_mod = 'am' if datetimeObj.hour <= 12 else 'pm' + hour = datetimeObj.hour if datetimeObj.hour <= 12 else datetimeObj.hour-12 + min = str(datetimeObj.minute).zfill(2) + return str(hour) + ':' + min + ' ' + time_mod + ' on ' + str(datetimeObj.day) + ' ' + datetimeObj.strftime("%B") + ', ' + str(datetimeObj.year) + + +def dateObj2Str(dateObj): + return dateObj.strftime("%d") + ' ' + dateObj.strftime("%B") + ', ' + dateObj.strftime("%Y") + + +def get_random_date(): + + # initializing dates ranges + test_date1, test_date2 = date(2022, 1, 1), date(2023, 6, 1) + # getting days between dates + dates_bet = test_date2 - test_date1 + total_days = dates_bet.days + delta_days = random.choice(range(1, total_days)) + random_date = test_date1 + timedelta(days=int(delta_days)) + return random_date + + +def get_session_summary(session, speaker_1, speaker_2, curr_date, previous_summary=""): + + session_query = '' + for c in session: + session_query += "%s: %s\n" % (c["speaker"], c["text"]) + if "image" in c: + session_query += "[%s shares %s]\n" % (c["speaker"], c["image"]) + + if previous_summary: + + query = SESSION_SUMMARY_PROMPT % (speaker_1['name'], speaker_2['name'], previous_summary, curr_date, + speaker_1['name'], speaker_2['name'], session_query, speaker_1['name'], speaker_2['name']) + else: + query = SESSION_SUMMARY_INIT_PROMPT % (speaker_1['name'], speaker_2['name'], curr_date, session_query) + + query += '\n\n' + # should summarize persona, previous conversations with respect to speaker. + output = run_chatgpt(query, 1, 150, 'chatgpt') + output = output.strip() + return output + + +def get_image_queries(events): + + images = [e["image"] for e in events] + input_query = "\nInput: ".join(images) + + output = run_chatgpt(EVENT2QUERY_PROMPT % input_query, 1, 200, 'chatgpt') + output = output.strip() + print(output) + json_output = clean_json_output(output) + + assert len(events) == len(json_output), [events, json_output] + + for i in range(len(events)): + events[i]["query"] = json_output[i] + return events + + +def get_all_session_summary(speaker, curr_sess_id): + + summary = "\n" + for sess_id in range(1, curr_sess_id): + sess_date = speaker['session_%s_date_time' % sess_id] + sess_date = sess_date[2] + ' ' + sess_date[1] + ', ' + sess_date[0] + summary += sess_date + ': ' + speaker["session_%s_summary" % sess_id] + '\n' + return summary + + +def catch_date(date_str): + date_format1 = '%d %B, %Y' + date_format2 = '%d %B %Y' + try: + return datetime.strptime(date_str, date_format1) + except: + return datetime.strptime(date_str, date_format2) + + +def get_session_date(events, args, prev_date = None): + + agent_a_events, agent_b_events = events + + agent_a_events = sort_events_by_time(agent_a_events) + curr_count = 0 + stop_count = args.num_events_per_session + stop_date_a = None + for e in agent_a_events: + event_date = catch_date(e['date']) + if prev_date: + if event_date >= prev_date: + print("Including event %s for Agent A" % json.dumps(e, indent=2)) + curr_count += 1 + else: + print("Including event %s for Agent A" % json.dumps(e, indent=2)) + curr_count += 1 + if curr_count == stop_count: + stop_date_a = event_date + break + stop_date_a = event_date + + # get date from agent_b + agent_b_events = sort_events_by_time(agent_b_events) + curr_count = 0 + stop_date_b = None + for e in agent_b_events: + # event_date = datetime.strptime(e['date'], "%d %B, %Y") + event_date = catch_date(e['date']) + if prev_date: + if event_date >= prev_date: + print("Including event %s for Agent B" % json.dumps(e, indent=2)) + curr_count += 1 + else: + print("Including event %s for Agent B" % json.dumps(e, indent=2)) + curr_count += 1 + if curr_count == stop_count: + stop_date_b = event_date + break + stop_date_b = event_date + + # return max(stop_date_a, stop_date_b) + timedelta(days=1) + return min(stop_date_a, stop_date_b) + timedelta(days=random.choice([1, 2])) + + +def get_relevant_events(events, curr_date, prev_date=None): + + events = sort_events_by_time(events) + relevant_events = [] + for e in events: + # event_date = datetime.strptime(e['date'], "%d %B, %Y") + event_date = catch_date(e['date']) + if event_date > curr_date: + continue + if prev_date: + if event_date <= prev_date: + continue + relevant_events.append(e) + + return relevant_events + + +def get_event_string(session_events, all_events): + + id2events = {e['id']: e for e in all_events} + + event_string = "" + for e in session_events: + try: + event_text = 'On' + e["date"] + ", " + e["sub-event"] + except KeyError: + event_text = 'On' + e["date"] + ", " + e["sub_event"] + + # if the event is caused by previous events, include them for context + if len(e['caused_by']) > 0: + event_text += ' Because previously' + for e_id in e['caused_by']: + try: + event_text += ', ' + id2events[e_id]["sub-event"] + ' (%s)' % id2events[e_id]["date"] + except KeyError: + event_text += ', ' + id2events[e_id]["sub_event"] + ' (%s)' % id2events[e_id]["date"] + + event_string += event_text + "\n" + + return event_string + + +def remove_context(args, curr_dialog, prev_dialog, caption=None): + + prompt_data = json.load(open(os.path.join(args.prompt_dir, 'remove_context_examples.json'))) + if caption: + query = prompt_data["input_format_w_image"].format(prev_dialog, curr_dialog, caption) + else: + query = prompt_data["input_format"].format(prev_dialog, curr_dialog) + output = run_chatgpt_with_examples(prompt_data["prompt"], + [[prompt_data["input_format"].format(*example["input"]) if len(example["input"]) == 2 else prompt_data["input_format_w_image"].format(*example["input"]), example["output"]] for example in prompt_data['examples']], + query, num_gen=1, num_tokens_request=128, use_16k=False) + return output + + +def get_agent_query(speaker_1, speaker_2, curr_sess_id=0, + prev_sess_date_time='', curr_sess_date_time='', + use_events=False, instruct_stop=False, dialog_id=0, last_dialog='', embeddings=None, reflection=False): + + stop_instruction = "To end the conversation, write [END] at the end of the dialog." + if instruct_stop: + print("**** Using stop instruction ****") + + if curr_sess_id == 1: + + if use_events: + events = get_event_string(speaker_1['events_session_%s' % curr_sess_id], speaker_1['graph']) + query = AGENT_CONV_PROMPT_SESS_1_W_EVENTS % (speaker_1['persona_summary'], + speaker_1['name'], speaker_2['name'], + curr_sess_date_time, speaker_1['name'], events, speaker_1['name'], speaker_2['name'], stop_instruction if instruct_stop else '') + else: + query = AGENT_CONV_PROMPT_SESS_1 % (speaker_1['persona_summary'], + speaker_1['name'], speaker_2['name'], + curr_sess_date_time, speaker_1['name'], speaker_2['name'], speaker_1['name']) + + else: + if use_events: + events = get_event_string(speaker_1['events_session_%s' % curr_sess_id], speaker_1['graph']) + if dialog_id == 0: + # if a new session is starting, get information about the topics discussed in last session + context_from_1, context_from_2 = get_recent_context(speaker_1, speaker_2, curr_sess_id, reflection=reflection) + recent_context = '\n'.join(context_from_1) + '\n' + '\n'.join(context_from_2) # with reflection + query = AGENT_CONV_PROMPT_W_EVENTS_V2_INIT % (speaker_1['persona_summary'], + speaker_1['name'], speaker_2['name'], prev_sess_date_time, + curr_sess_date_time, speaker_1['name'], speaker_1['session_%s_summary' % (curr_sess_id-1)], events, stop_instruction if instruct_stop else '', speaker_2['name']) + + else: + # during an ongoing session, get fine-grained information from a previous session using retriever modules + past_context = get_relevant_context(speaker_1, speaker_2, last_dialog, embeddings, curr_sess_id, reflection=reflection) + query = AGENT_CONV_PROMPT_W_EVENTS_V2 % (speaker_1['persona_summary'], + speaker_1['name'], speaker_2['name'], prev_sess_date_time, + curr_sess_date_time, speaker_1['name'], speaker_1['session_%s_summary' % (curr_sess_id-1)], events, past_context, stop_instruction if instruct_stop else '', speaker_2['name']) + else: + summary = get_all_session_summary(speaker_1, curr_sess_id) + query = AGENT_CONV_PROMPT % (speaker_1['persona_summary'], + speaker_1['name'], speaker_2['name'], prev_sess_date_time, summary, + curr_sess_date_time, speaker_1['name'], speaker_2['name'], speaker_1['name']) + + return query + + +def get_session(agent_a, agent_b, args, prev_date_time_string='', curr_date_time_string='', curr_sess_id=0, captioner=None, img_processor=None, reflection=False): + + # load embeddings for retrieveing relevat observations from previous conversations + if curr_sess_id == 1: + embeddings = None + else: + embeddings = pkl.load(open(args.emb_file, 'rb')) + + # select one of the speakers to start the session at random + curr_speaker = -1 + if random.random() < 0.5: + conv_so_far = agent_a['name'] + ': ' + curr_speaker = 0 + else: + conv_so_far = agent_b['name'] + ': ' + curr_speaker = 1 + + session = [] + + stop_dialog_count = args.max_turns_per_session if args.max_turns_per_session <= 10 else random.choice(list(range(10, args.max_turns_per_session))) # choose a random turn number to include instructions for ending the session + break_at_next_a = False + break_at_next_b = False + for i in range(args.max_turns_per_session): + + if break_at_next_a and break_at_next_b: + break + + if curr_speaker == 0: + agent_query = get_agent_query(agent_a, agent_b, prev_sess_date_time=prev_date_time_string, curr_sess_date_time=curr_date_time_string, + curr_sess_id=curr_sess_id, use_events=args.events, instruct_stop=i>=stop_dialog_count, + dialog_id=i, last_dialog='' if i == 0 else session[-1]['speaker'] + ' says, ' + session[-1]['clean_text'], + embeddings=embeddings, reflection=reflection) + else: + agent_query = get_agent_query(agent_b, agent_a, prev_sess_date_time=prev_date_time_string, curr_sess_date_time=curr_date_time_string, + curr_sess_id=curr_sess_id, use_events=args.events, instruct_stop=i>=stop_dialog_count, + dialog_id=i, last_dialog='' if i == 0 else session[-1]['speaker'] + ' says, ' + session[-1]['clean_text'], + embeddings=embeddings, reflection=reflection) + + # if the speaker in previous turn sent an image, get caption + questions + if len(session) > 1 and "img_id" in session[-1]: + + # caption = re.findall(r"\[.*\]", session[-1]['raw_text'])[0][1:-1] + caption = "shares " + session[-1]['blip_caption'] + if curr_speaker == 0: + question = run_chatgpt(VISUAL_QUESTION_PROMPT.format(agent_a['persona_summary'], + agent_b['persona_summary'], + agent_b['name'], session[-1]['clean_text'], caption, + agent_a['name']), 1, 100, 'chatgpt') + else: + question = run_chatgpt(VISUAL_QUESTION_PROMPT.format(agent_a['persona_summary'], + agent_b['persona_summary'], + agent_a['name'], session[-1]['clean_text'], caption, + agent_b['name']), 1, 100, 'chatgpt') + question = question.strip() + + if curr_speaker == 0: + agent_query = agent_query + f"\nUse the following question about the photo shared by {agent_b['name']} in your reply: {question}." + else: + agent_query = agent_query + f"\nUse the following question about the photo shared by {agent_a['name']} in your reply: {question}." + + output = run_chatgpt(agent_query + conv_so_far, 1, 100, 'chatgpt', temperature=1.2) + output = output.strip().split('\n')[0] + output = clean_dialog(output, agent_a['name'] if curr_speaker == 0 else agent_b['name']) + output = {"text": output, "raw_text": output} + + image_search_query, photo_caption = insert_image_response(output["text"]) + if image_search_query is not None: + img_dir = os.path.join(args.out_dir, 'session_%s' % curr_sess_id, 'a') if curr_speaker == 0 else os.path.join(args.out_dir, 'session_%s' % curr_sess_id, 'b') + file_urls, file_names = get_images(image_search_query, img_dir, i) + if file_names == []: + print("Image not found, for search query: ", image_search_query) + else: + output["img_url"] = file_urls + output["img_file"] = file_names + output["img_id"] = i + output['query'] = image_search_query + output['caption'] = photo_caption + if args.blip_caption: + output['blip_caption'] = get_blip_caption(os.path.join(img_dir, file_names[0]), captioner, img_processor).replace('photography', 'photo') + + output["speaker"] = agent_a["name"] if curr_speaker == 0 else agent_b['name'] + text_replaced_caption = replace_captions(output["text"], args) + if not text_replaced_caption.isspace(): + if '[END]' in output["text"]: + output["clean_text"] = text_replaced_caption + else: + output["clean_text"] = run_chatgpt(CASUAL_DIALOG_PROMPT % text_replaced_caption, 1, 100, 'chatgpt').strip() + else: + output["clean_text"] = "" + + output["dia_id"] = 'D%s:%s' % (curr_sess_id, i+1) + session.append(output) + + # print(output) + print("############ ", agent_a['name'] if curr_speaker == 0 else agent_b['name'], ': ', output["clean_text"]) + if "caption" in output: + print("[ {} ]".format(output["blip_caption"])) + + # conv_so_far = conv_so_far + output["text"] + '\n' + if "blip_caption" in output: + conv_so_far = conv_so_far + output["clean_text"] + '[shares ' + output["blip_caption"] + ']' + '\n' + else: + conv_so_far = conv_so_far + output["clean_text"] + '\n' + + + + if output['text'].endswith('[END]'): + if curr_speaker == 0: + break_at_next_a = True + else: + break_at_next_b = True + + conv_so_far += f"\n{agent_b['name']}: " if curr_speaker == 0 else f"\n{agent_a['name']}: " + curr_speaker = int(not curr_speaker) + + return session + + +def main(): + + # get arguments + args = parse_args() + + set_openai_key() + + args.emb_file = os.path.join(args.out_dir, args.emb_file) + + # create dataset directory + if not os.path.exists(args.out_dir): + os.makedirs(args.out_dir) + logging.info("Dataset directory: %s" % args.out_dir) + + args.agent_a_file = os.path.join(args.out_dir, 'agent_a.json') + args.agent_b_file = os.path.join(args.out_dir, 'agent_b.json') + + + # Step 1: Get personalities for the agents; get a randomly selected sample from the MSC dataset and expand the few-liner personas into detailed personas. + if args.persona: + agent_a, agent_b = get_msc_persona(args) + if agent_a is not None and agent_b is not None: + save_agents([agent_a, agent_b], args) + + + # Step 2: check if events exist; if not, generate event graphs for each of the agents + if args.events: + + agent_a, agent_b = load_agents(args) + + if ('graph' in agent_a and 'graph' in agent_b) and not args.overwrite_events: + pass + else: + # if 'session_1_date_time' not in agent_a: + start_date = get_random_date() # select a random date in 2022-2023 + end_date = start_date + timedelta(days=args.num_days) + start_date = dateObj2Str(start_date) + end_date = dateObj2Str(end_date) + agent_a['events_start_date'] = start_date + agent_b['events_start_date'] = start_date + logging.info("Generating a random start date for the conversation") + save_agents([agent_a, agent_b], args) + + + agent_a_events = [] + agent_b_events = [] + + logging.info("Generating events for Agent A") + trials = 0 + while len(agent_a_events) < args.num_events: + logging.info("(Re)trying to generate events with dense causal connections: trial %s" % trials) + agent_a_events = get_events(agent_a, start_date, end_date, args) + agent_a["graph"] = agent_a_events + trials += 1 + + logging.info("Generating events for Agent B") + trials = 0 + while len(agent_b_events) < args.num_events: + logging.info("(Re)trying to generate events with dense causal connections: trial %s" % trials) + agent_b_events = get_events(agent_b, start_date, end_date, args) + agent_b["graph"] = agent_b_events + save_agents([agent_a, agent_b], args) + + # make sure keys are all lower case + agent_a_events = agent_a['graph'] + agent_a_events = [{k.lower(): v for k,v in e.items()} for e in agent_a_events] + agent_a["graph"] = agent_a_events + agent_b_events = agent_b['graph'] + agent_b_events = [{k.lower(): v for k,v in e.items()} for e in agent_b_events] + agent_b["graph"] = agent_b_events + save_agents([agent_a, agent_b], args) + + # Step 3: + if args.session: + + agent_a, agent_b = load_agents(args) + + if args.blip_caption: # load an image captioner + # init_model + img_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") + captioner = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to("cuda") + else: + img_processor = None + captioner = None + + # default start index is 1; if resuming conversation from a leter session, indicate in script arguments using --start-session + for j in range(args.start_session, args.num_sessions+1): + + print("******************* SESSION %s ******************" % j) + + if 'session_%s' % j not in agent_a or args.overwrite_session: + + if j>1: + prev_date_time = datetimeStr2Obj(agent_a['session_%s_date_time' % (j-1)]) + prev_date_time_string = agent_a['session_%s_date_time' % (j-1)] + else: + prev_date_time, prev_date_time_string = None, None + + # get conversation date and time for each session + curr_time = get_random_time() # timedelta object + curr_date = get_session_date([agent_a['graph'], agent_b['graph']], args, prev_date=prev_date_time) # datetime object + curr_date_time = curr_date + curr_time # datetime object + + relevant_events_a = get_relevant_events(agent_a['graph'], curr_date_time, prev_date=prev_date_time) + agent_a['events_session_%s' % j] = relevant_events_a + relevant_events_b = get_relevant_events(agent_b['graph'], curr_date_time, prev_date=prev_date_time) + agent_b['events_session_%s' % j] = relevant_events_b + + if len(relevant_events_a) == 0 and len(relevant_events_b) == 0: + logging.info("Stoppping conversation because no more events available in KG.") + break + + curr_date_time_string = datetimeObj2Str(curr_date_time) + agent_a['session_%s_date_time' % j] = curr_date_time_string + agent_b['session_%s_date_time' % j] = curr_date_time_string + save_agents([agent_a, agent_b], args) + + session = get_session(agent_a, agent_b, args, + prev_date_time_string=prev_date_time_string, curr_date_time_string=curr_date_time_string, + curr_sess_id=j, captioner=captioner, img_processor=img_processor, reflection=args.reflection) + + agent_a['session_%s' % j] = session + agent_b['session_%s' % j] = session + + save_agents([agent_a, agent_b], args) + + if 'session_%s_facts' % j not in agent_a or args.overwrite_session: + + facts = get_session_facts(args, agent_a, agent_b, j) + + agent_a['session_%s_facts' % j] = facts + agent_b['session_%s_facts' % j] = facts + + print(" --------- Session %s Summary for Agent A---------" % (j)) + print(facts) + + save_agents([agent_a, agent_b], args) + + if args.reflection and ('session_%s_reflection' % j not in agent_a or args.overwrite_session): + + reflections = get_session_reflection(args, agent_a, agent_b, j) + + agent_a['session_%s_reflection' % j] = reflections['a'] + agent_b['session_%s_reflection' % j] = reflections['b'] + + print(" --------- Session %s Reflection for Agent A---------" % (j)) + print(reflections) + + save_agents([agent_a, agent_b], args) + + if args.summary and ('session_%s_summary' % j not in agent_a or args.overwrite_session): + + summary = get_session_summary(agent_a['session_%s' % j], agent_a, agent_b, agent_a['session_%s_date_time' % j], + previous_summary=None if j==1 else agent_a['session_%s_summary' % (j-1)]) + + agent_a['session_%s_summary' % j] = summary + agent_b['session_%s_summary' % j] = summary + + save_agents([agent_a, agent_b], args) + + agent_a, agent_b = load_agents(args) + convert_to_chat_html(agent_a, agent_b, outfile=os.path.join(args.out_dir, 'sessions.html'), use_events=args.events, img_dir=args.out_dir) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/generative_agents/html_utils.py b/generative_agents/html_utils.py new file mode 100644 index 0000000..92ebbeb --- /dev/null +++ b/generative_agents/html_utils.py @@ -0,0 +1,187 @@ +import json +import os, re +import base64 + +header = """ + + + + Chat Example + + + +
    +
    + """ + +speaker_1_div = """ +
    +

    %s

    +
    +""" + +speaker_1_div_with_image = """ +
    +

    %s

    + Image 1 +

    %s

    +
    +""" + +speaker_2_div = """ +
    +

    %s

    +
    +""" + +speaker_2_div_with_image = """ +
    +

    %s

    + Image 2 +

    %s

    +
    +""" + +date_time_div = """ +
    +

       %s   

    +
    +""" + +def get_speaker_info(speaker, use_events=False): + + output = "" + output += "Name: " + speaker["name"] + '
    ' + # output += "Age: " + str(speaker["age"]) + '
    ' + # output += "Gender: " + speaker["gender"] + '
    ' + if 'persona_summary' in speaker: + output += "Persona: " + speaker["persona_summary"] + '
    ' + + # for k, v in speaker['persona'].items(): + # if type(v) == list: + # value = ', '.join(v) + # else: + # value = v + # output += '' + k + '' + ': ' + value + '
    ' + + # if use_events: + # output += '' + 'Events' + '' + '
    ' + # for e in speaker['events']: + # output += '' + e['date'] + '' + ': ' + e['event'] + '
    ' + + return output + +def get_session_events(events): + + output = '' + 'Events' + '' + '
    ' + for e in events: + output += '' + e['date'] + '' + ': ' + e['sub-event'] + '
    ' + return output + + +def img2base64(image_file_path): + with open(image_file_path, "rb") as image_file: + encoded_string = base64.b64encode(image_file.read()) + return encoded_string.decode("utf-8") + + +def convert_to_chat_html(speaker_1, speaker_2, outfile="", use_events=False, img_dir=None): + + body = header + # add persona + + body += speaker_1_div % get_speaker_info(speaker_1, use_events=use_events) + body += speaker_2_div % get_speaker_info(speaker_2, use_events=use_events) + + # add session + for num in range(1, 50): + + if 'session_%s' % num not in speaker_1: + break + + if 'session_%s_date_time' % num in speaker_1: + date_time_string = speaker_1['session_%s_date_time' % num] + elif 'session_%s_date' % num in speaker_1: + date_time_string = speaker_1['session_%s_date' % num] + else: + raise ValueError + + body += date_time_div % ("Session %s [ %s ]" % (num, date_time_string)) + + if 'events_session_%s' % num in speaker_1 and 'events_session_%s' % num in speaker_2: + speaker_1_events = speaker_1['events_session_%s' % num] + speaker_2_events = speaker_2['events_session_%s' % num] + + body += speaker_1_div % get_session_events(speaker_1_events) + body += speaker_2_div % get_session_events(speaker_2_events) + + for dialog in speaker_1['session_%s' % num]: + text = dialog["clean_text"] + if "img_url" in dialog: + try: + + selected_div = speaker_1_div_with_image if dialog["speaker"] == speaker_1["name"] else speaker_2_div_with_image + url = dialog["img_url"] + if type(url) == list: + url = url[0] + body += selected_div % (text, url, dialog['caption']) + + # img_str = img2base64(os.path.join(img_dir, 'session_%s' % num, 'a' if dialog["speaker"] == speaker_1["name"] else 'b', dialog['img_file'][0])) + # body += selected_div % (text, 'data:image/png;base64,' + img_str, dialog['caption']) + + except Exception as e: + print(e) + selected_div = speaker_1_div if dialog["speaker"] == speaker_1["name"] else speaker_2_div + body += selected_div % text + else: + selected_div = speaker_1_div if dialog["speaker"] == speaker_1["name"] else speaker_2_div + body += selected_div % text + body += """ +
    +
    + + +""" + with open(outfile, 'w') as fhtml: + fhtml.write(body) + diff --git a/generative_agents/memory_utils.py b/generative_agents/memory_utils.py index 6f23a6e..4730d2f 100644 --- a/generative_agents/memory_utils.py +++ b/generative_agents/memory_utils.py @@ -6,6 +6,9 @@ from global_methods import run_json_trials import numpy as np import pickle as pkl +import random +logging.basicConfig(level=logging.INFO) + REFLECTION_INIT_PROMPT = "{}\n\nGiven the information above, what are the three most salient insights that {} has about {}? Give concise answers in the form of a json list where each entry is a string." @@ -16,15 +19,10 @@ SELF_REFLECTION_CONTINUE_PROMPT = "{} has the following insights about self.{}\n\n{}\n\nGiven the information above, what are the three most salient insights that {} has about self now? Give concise answers in the form of a json list where each entry is a string." -logging.basicConfig(level=logging.INFO) - CONVERSATION2FACTS_PROMPT = """ Write a concise and short list of all possible OBSERVATIONS about each speaker that can be gathered from the CONVERSATION. Each dialog in the conversation contains a dialogue id within square brackets. Each observation should contain a piece of information about the speaker, and also include the dialog id of the dialogs from which the information is taken. The OBSERVATIONS should be objective factual information about the speaker that can be used as a database about them. Avoid abstract observations about the dynamics between the two speakers such as 'speaker is supportive', 'speaker appreciates' etc. Do not leave out any information from the CONVERSATION. Important: Escape all double-quote characters within string output with backslash.\n\n """ -CONVERSATION2FACTS_PROMPT_Jan27 = """ -Convert the given CONVERSATION into a concise and short list of FACTS about each speaker. The FACTS should be objective factual information about the speaker, avoid abstract observations about the dynamics between the two speakers such as 'speaker is supportive', 'speaker appreciates' etc. Escape all double-quote characters within string output with backslash.\n\n -""" RETRIEVAL_MODEL = "text-embedding-ada-002" # contriever dragon dpr @@ -33,56 +31,6 @@ def get_embedding(texts, model="text-embedding-ada-002"): texts = [text.replace("\n", " ") for text in texts] return np.array([openai.Embedding.create(input = texts, model=model)['data'][i]['embedding'] for i in range(len(texts))]) -def run_chatgpt_with_examples(query, examples, input, num_gen=1, num_tokens_request=1000, use_16k=False): - - completion = None - wait_time = 1 - messages = [ - {"role": "system", "content": query} - ] - for inp, out in examples: - messages.append( - {"role": "user", "content": inp} - ) - messages.append( - {"role": "system", "content": out} - ) - messages.append( - {"role": "user", "content": input} - ) - - while completion is None: - wait_time = wait_time * 2 - try: - completion = openai.ChatCompletion.create( - model="gpt-3.5-turbo" if not use_16k else "gpt-3.5-turbo-16k", - temperature = 1.0, - max_tokens = num_tokens_request, - n=num_gen, - messages = messages - ) - except openai.error.APIError as e: - #Handle API error here, e.g. retry or log - print(f"OpenAI API returned an API Error: {e}; waiting for {wait_time} seconds") - time.sleep(wait_time) - pass - except openai.error.APIConnectionError as e: - #Handle connection error here - print(f"Failed to connect to OpenAI API: {e}; waiting for {wait_time} seconds") - time.sleep(wait_time) - pass - except openai.error.RateLimitError as e: - #Handle rate limit error (we recommend using exponential backoff) - print(f"OpenAI API request exceeded rate limit: {e}") - pass - except openai.error.ServiceUnavailableError as e: - #Handle rate limit error (we recommend using exponential backoff) - print(f"OpenAI API request exceeded rate limit: {e}; waiting for {wait_time} seconds") - time.sleep(wait_time) - pass - - return completion.choices[0].message.content - def get_session_facts(args, agent_a, agent_b, session_idx, return_embeddings=True): @@ -111,21 +59,8 @@ def get_session_facts(args, agent_a, agent_b, session_idx, return_embeddings=Tru if not return_embeddings: return facts - # run_loop = True - # counter = 0 - # while run_loop: - # try: - # output = run_chatgpt_with_examples(query, examples, input, num_gen=1, num_tokens_request=500, use_16k=False).strip() - # print(output) - # facts = json.loads(output) - # run_loop = False - # except json.decoder.JSONDecodeError: - # counter += 1 - # print("Retrying to avoid JsonDecodeError, trial %s ..." % counter) - # continue - - agent_a_embeddings = get_embedding(facts[agent_a['name']]) - agent_b_embeddings = get_embedding(facts[agent_b['name']]) + agent_a_embeddings = get_embedding([agent_a['session_%s_date_time' % session_idx] + ', ' + f for f, _ in facts[agent_a['name']]]) + agent_b_embeddings = get_embedding([agent_b['session_%s_date_time' % session_idx] + ', ' + f for f, _ in facts[agent_b['name']]]) if session_idx > 1: with open(args.emb_file, 'rb') as f: @@ -161,27 +96,29 @@ def get_session_reflection(args, agent_a, agent_b, session_idx): if session_idx == 1: agent_a_self = run_json_trials(SELF_REFLECTION_INIT_PROMPT.format(conversation, agent_a['name']), model='chatgpt', num_tokens_request=300) agent_b_self = run_json_trials(SELF_REFLECTION_INIT_PROMPT.format(conversation, agent_b['name']), model='chatgpt', num_tokens_request=300) - # agent_a_self = json.loads(run_chatgpt(SELF_REFLECTION_INIT_PROMPT.format(conversation, agent_a['name']), model='chatgpt', num_tokens_request=300).strip()) - # agent_b_self = json.loads(run_chatgpt(SELF_REFLECTION_INIT_PROMPT.format(conversation, agent_b['name']), model='chatgpt', num_tokens_request=300).strip()) + else: agent_a_self = run_json_trials(SELF_REFLECTION_CONTINUE_PROMPT.format(agent_a['name'], '\n'.join(agent_a['session_%s_reflection' % (session_idx-1)]['self']), conversation, agent_a['name']), model='chatgpt', num_tokens_request=300) agent_b_self = run_json_trials(SELF_REFLECTION_CONTINUE_PROMPT.format(agent_b['name'], '\n'.join(agent_b['session_%s_reflection' % (session_idx-1)]['self']), conversation, agent_b['name']), model='chatgpt', num_tokens_request=300) - # agent_a_self = json.loads(run_chatgpt(SELF_REFLECTION_CONTINUE_PROMPT.format(agent_a['name'], '\n'.join(agent_a['session_%s_reflection' % (session_idx-1)]['self']), conversation, agent_a['name']), model='chatgpt', num_tokens_request=300).strip()) - # agent_b_self = json.loads(run_chatgpt(SELF_REFLECTION_CONTINUE_PROMPT.format(agent_b['name'], '\n'.join(agent_b['session_%s_reflection' % (session_idx-1)]['self']), conversation, agent_b['name']), model='chatgpt', num_tokens_request=300).strip()) - # Step 3: Reflection about other speaker if session_idx == 1: agent_a_on_b = run_json_trials(REFLECTION_INIT_PROMPT.format(conversation, agent_a['name'], agent_b['name']), model='chatgpt', num_tokens_request=300) agent_b_on_a = run_json_trials(REFLECTION_INIT_PROMPT.format(conversation, agent_b['name'], agent_a['name']), model='chatgpt', num_tokens_request=300) - # agent_a_on_b = json.loads(run_chatgpt(REFLECTION_INIT_PROMPT.format(conversation, agent_a['name'], agent_b['name']), model='chatgpt', num_tokens_request=300).strip()) - # agent_b_on_a = json.loads(run_chatgpt(REFLECTION_INIT_PROMPT.format(conversation, agent_b['name'], agent_a['name']), model='chatgpt', num_tokens_request=300).strip()) + else: agent_a_on_b = run_json_trials(REFLECTION_CONTINUE_PROMPT.format(agent_a['name'], agent_b['name'], '\n'.join(agent_a['session_%s_reflection' % (session_idx-1)]['other']), conversation, agent_a['name'], agent_b['name']), model='chatgpt', num_tokens_request=300) agent_b_on_a = run_json_trials(REFLECTION_CONTINUE_PROMPT.format(agent_b['name'], agent_a['name'], '\n'.join(agent_b['session_%s_reflection' % (session_idx-1)]['other']), conversation, agent_b['name'], agent_a['name']), model='chatgpt', num_tokens_request=300) - # agent_a_on_b = json.loads(run_chatgpt(REFLECTION_CONTINUE_PROMPT.format(agent_a['name'], agent_b['name'], '\n'.join(agent_a['session_%s_reflection' % (session_idx-1)]['other']), conversation, agent_a['name'], agent_b['name']), model='chatgpt', num_tokens_request=300).strip()) - # agent_b_on_a = json.loads(run_chatgpt(REFLECTION_CONTINUE_PROMPT.format(agent_b['name'], agent_a['name'], '\n'.join(agent_b['session_%s_reflection' % (session_idx-1)]['other']), conversation, agent_b['name'], agent_a['name']), model='chatgpt', num_tokens_request=300).strip()) - + + if type(agent_a_self) == dict: + agent_a_self = list(agent_a_self.values()) + if type(agent_b_self) == dict: + agent_b_self = list(agent_b_self.values()) + if type(agent_a_on_b) == dict: + agent_a_on_b = list(agent_a_on_b.values()) + if type(agent_b_on_a) == dict: + agent_b_on_a = list(agent_b_on_a.values()) + reflections = {} reflections['a'] = {'self': agent_a_self, 'other': agent_a_on_b} reflections['b'] = {'self': agent_b_self, 'other': agent_b_on_a} @@ -193,10 +130,10 @@ def get_recent_context(agent_a, agent_b, sess_id, context_length=2, reflection=F speaker_1_facts = [] for i in range(1, sess_id): - speaker_1_facts += [agent_a['session_%s_date_time' % i] + ': ' + f for f in agent_a['session_%s_facts' % i][agent_a["name"]]] + speaker_1_facts += [agent_a['session_%s_date_time' % i] + ': ' + f for f, _ in agent_a['session_%s_facts' % i][agent_a["name"]]] speaker_2_facts = [] for i in range(1, sess_id): - speaker_2_facts += [agent_a['session_%s_date_time' % i] + ': ' + f for f in agent_a['session_%s_facts' % i][agent_b["name"]]] + speaker_2_facts += [agent_a['session_%s_date_time' % i] + ': ' + f for f, _ in agent_a['session_%s_facts' % i][agent_b["name"]]] if reflection: print(speaker_1_facts[-context_length:]) @@ -216,10 +153,11 @@ def get_relevant_context(agent_a, agent_b, input_dialogue, embeddings, sess_id, sims_with_context_b = np.dot(embeddings[agent_b['name']], input_embedding[0]) top_k_sims_a = np.argsort(sims_with_context_a)[::-1][:context_length] top_k_sims_b = np.argsort(sims_with_context_b)[::-1][:context_length] + # print(sims_with_context_a, sims_with_context_b) if reflection: print([contexts_a[idx] for idx in top_k_sims_a]) print( agent_a['session_%s_reflection' % (sess_id-1)]['self']) - return [contexts_a[idx] for idx in top_k_sims_a] + agent_a['session_%s_reflection' % (sess_id-1)]['self'], [context_b[idx] for idx in top_k_sims_b] + agent_a['session_%s_reflection' % (sess_id-1)]['other'] + return [contexts_a[idx] for idx in top_k_sims_a] + random.sample(agent_a['session_%s_reflection' % (sess_id-1)]['self'], k=context_length//2), [context_b[idx] for idx in top_k_sims_b] + random.sample(agent_a['session_%s_reflection' % (sess_id-1)]['other'], k=context_length//2) else: return [contexts_a[idx] for idx in top_k_sims_a], [context_b[idx] for idx in top_k_sims_b] diff --git a/global_methods.py b/global_methods.py index 542109c..7ada78d 100644 --- a/global_methods.py +++ b/global_methods.py @@ -38,6 +38,7 @@ def run_json_trials(query, num_gen=1, num_tokens_request=1000, else: output = run_chatgpt(query, num_gen=num_gen, wait_time=wait_time, model=model, num_tokens_request=num_tokens_request, use_16k=use_16k, temperature=temperature) + output = output.replace('json', '') # this frequently happens facts = json.loads(output.strip()) run_loop = False except json.decoder.JSONDecodeError: @@ -89,22 +90,22 @@ def run_gemini(model, content: str, max_tokens: int = 0): def run_chatgpt(query, num_gen=1, num_tokens_request=1000, - model='davinci', use_16k=False, temperature=1.0, wait_time=1): + model='chatgpt', use_16k=False, temperature=1.0, wait_time=1): completion = None while completion is None: wait_time = wait_time * 2 try: - if model == 'davinci': - completion = openai.Completion.create( - # model = "gpt-3.5-turbo", - model = "text-davinci-003", - temperature = temperature, - max_tokens = num_tokens_request, - n=num_gen, - prompt=query - ) - elif model == 'chatgpt': + # if model == 'davinci': + # completion = openai.Completion.create( + # # model = "gpt-3.5-turbo", + # model = "text-davinci-003", + # temperature = temperature, + # max_tokens = num_tokens_request, + # n=num_gen, + # prompt=query + # ) + if model == 'chatgpt': messages = [ {"role": "system", "content": query} ] diff --git a/prompt_examples/image_sharing_examples.json b/prompt_examples/image_sharing_examples.json new file mode 100644 index 0000000..723043b --- /dev/null +++ b/prompt_examples/image_sharing_examples.json @@ -0,0 +1,14 @@ +{ + "prompt": "Modify speaker's DIALOG to include context from the CAPTION of the photo shared the speaker with a certain INTENT.", + "input_format": "DIALOG: {}\nCAPTION: {}\nINTENT:{}\nMODIFIED_DIALOG: ", + "examples": [ + { + "input": [ + "I love having empanadas while studying. They remind me of when I visited Spain!", + "a photo of a plate of food on a wooden table", + "shares a photo of empanadas she made during a cooking class in Spain" + ], + "output": "I love having empanadas while studying. They remind me of when I visited Spain! I made some during a cooking class in Spain!" + } + ] + } \ No newline at end of file diff --git a/prompt_examples/remove_context_examples.json b/prompt_examples/remove_context_examples.json index 00677f9..9d5e8ef 100644 --- a/prompt_examples/remove_context_examples.json +++ b/prompt_examples/remove_context_examples.json @@ -1,5 +1,5 @@ { - "prompt": "Rewrite the DIALOG according to the following rules:\n- If some words or information from DIALOG are already present in CONTEXT, remove or replace with indirect references like 'that', 'it' etc. \n- Replace references to objects mentioned in CAPTION using indirect references like 'this', 'that' etc.\n- Remove repetitions of questions already asked in CONTEXT", + "prompt": "Rewrite the DIALOG according to the following rules:\n1. If some words or information in DIALOG can be inferred from CONTEXT, remove or replace those words in DIALOG with indirect references like 'that', 'it' etc. \n2. Replace references to objects mentioned in CAPTION using indirect references like 'this', 'that' etc.\n- Remove repetitions of questions already asked in CONTEXT", "input_format": "CONTEXT: {}\n\nDIALOG: {}\n\nOUTPUT: ", "input_format_w_image": "CONTEXT: {}\n\nDIALOG: {}\n\nCAPTION: {}\n\nOUTPUT: ", "examples": [ diff --git a/scripts/generate_conversations.sh b/scripts/generate_conversations.sh new file mode 100644 index 0000000..a8cb0a0 --- /dev/null +++ b/scripts/generate_conversations.sh @@ -0,0 +1,8 @@ +source scripts/env.sh + +python3 generative_agents/generate_conversations.py \ + --out-dir ./data/multimodal_dialog/example/ \ + --prompt-dir ./prompt_examples \ + --events --session --summary --num-sessions 3 \ + --persona --blip-caption \ + --num-days 90 --num-events 10 --max-turns-per-session 20 --num-events-per-session 1