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Models

Research

Science

About

Explore our next gen­er­a­tion AI sys­tems

Explore mod­els

Gemini

Specialized mod­els

World mod­els & em­bod­ied AI

Open mod­els

Our lat­est AI break­throughs and up­dates from the lab

Explore re­search

Breakthroughs

Learn more

Unlocking a new era of dis­cov­ery with AI

Explore sci­ence

Life sci­ences

Climate and sus­tain­abil­ity

Our mis­sion is to build AI re­spon­si­bly to ben­e­fit hu­man­ity

About Google DeepMind

Responsibility

Ensuring AI safety through proac­tive se­cu­rity, even against evolv­ing threats

Learn more

News

Discover our lat­est AI break­throughs, pro­jects, and up­dates

Learn more

Careers

We’re look­ing for peo­ple who want to make a real, pos­i­tive im­pact on the world

Learn more

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Gemini 3.1 Pro is the next it­er­a­tion in the Gemini 3 se­ries of mod­els, a suite of highly ca­pa­ble, na­tively mul­ti­modal rea­son­ing mod­els. As of this model card’s date of pub­li­ca­tion, Gemini 3.1 Pro is Google’s most ad­vanced model for com­plex tasks. Gemini 3.1 Pro can com­pre­hend vast datasets and chal­leng­ing prob­lems from mas­sively mul­ti­modal in­for­ma­tion sources, in­clud­ing text, au­dio, im­ages, video, and en­tire code repos­i­to­ries.

Text strings (e.g., a ques­tion, a prompt, doc­u­ment(s) to be sum­ma­rized), im­ages, au­dio, and video files, with a to­ken con­text win­dow of up to 1M.

Gemini 3.1 Pro is based on Gemini 3 Pro. For more in­for­ma­tion about the model ar­chi­tec­ture for Gemini 3.1 Pro, see the Gemini 3 Pro model card.

Gemini 3.1 Pro is based on Gemini 3 Pro. For more in­for­ma­tion about the train­ing dataset for Gemini 3.1 Pro, see the Gemini 3 Pro model card.

For more in­for­ma­tion about the train­ing data pro­cess­ing for Gemini 3.1 Pro, see the Gemini 3 Pro model card.

Gemini 3.1 Pro is based on Gemini 3 Pro. For more in­for­ma­tion about the hard­ware for Gemini 3.1 Pro and our con­tin­ued com­mit­ment to op­er­ate sus­tain­ably, see the Gemini 3 Pro model card.

Gemini 3.1 Pro is based on Gemini 3 Pro. For more in­for­ma­tion about the soft­ware for Gemini 3.1 Pro, see the Gemini 3 Pro model card.

Gemini 3.1 Pro was eval­u­ated across a range of bench­marks, in­clud­ing rea­son­ing, mul­ti­modal ca­pa­bil­i­ties, agen­tic tool use, multi-lin­gual per­for­mance, and long-con­text. Additional bench­marks and de­tails on ap­proach, re­sults and their method­olo­gies can be found at: deep­mind.google/​mod­els/​evals-method­ol­ogy/​gem­ini-3-1-pro.

Gemini 3.1 Pro sig­nif­i­cantly out­per­forms Gemini 3 Pro across a range of bench­marks re­quir­ing en­hanced rea­son­ing and mul­ti­modal ca­pa­bil­i­ties. Results as of February 2026 are listed be­low:

Gemini 3.1 Pro is the next it­er­a­tion in the Gemini 3 se­ries of mod­els, a suite of highly in­tel­li­gent and adap­tive mod­els, ca­pa­ble of help­ing with real-world com­plex­ity, solv­ing prob­lems that re­quire en­hanced rea­son­ing and in­tel­li­gence, cre­ativ­ity, strate­gic plan­ning and mak­ing im­prove­ments step-by-step. It is par­tic­u­larly well-suited for ap­pli­ca­tions that re­quire:

For more in­for­ma­tion about the known lim­i­ta­tions for Gemini 3.1 Pro, see the Gemini 3 Pro model card.

For more in­for­ma­tion about the ac­cept­able us­age for Gemini 3.1 Pro, see the Gemini 3 Pro model card.

For more in­for­ma­tion about the eval­u­a­tion ap­proach for Gemini 3.1 Pro, see the Gemini 3 Pro model card.

For more in­for­ma­tion about the safety poli­cies for Gemini 3.1 Pro, see the Gemini 3 Pro model card.

Results for some of the in­ter­nal safety eval­u­a­tions con­ducted dur­ing the de­vel­op­ment phase are listed be­low. The eval­u­a­tion re­sults are for au­to­mated eval­u­a­tions and not hu­man eval­u­a­tion or red team­ing. Scores are pro­vided as an ab­solute per­cent­age in­crease or de­crease in per­for­mance com­pared to the in­di­cated model, as de­scribed be­low. Overall, Gemini 3.1 Pro out­per­forms Gemini 3 Pro across both safety and tone, while keep­ing un­jus­ti­fied re­fusals low. We mark im­prove­ments in green and re­gres­sions in red. Safety eval­u­a­tions of Gemini 3.1 Pro pro­duced re­sults con­sis­tent with the orig­i­nal Gemini 3 Pro safety as­sess­ment.

Automated eval­u­a­tion mea­sur­ing mod­el’s abil­ity to re­spond to bor­der­line prompts while re­main­ing safe

We con­tinue to im­prove our in­ter­nal eval­u­a­tions, in­clud­ing re­fin­ing au­to­mated eval­u­a­tions to re­duce false pos­i­tives and neg­a­tives, as well as up­date query sets to en­sure bal­ance and main­tain a high stan­dard of re­sults. The per­for­mance re­sults re­ported be­low are com­puted with im­proved eval­u­a­tions and thus are not di­rectly com­pa­ra­ble with per­for­mance re­sults found in pre­vi­ous Gemini model cards. We ex­pect vari­a­tion in our au­to­mated safety eval­u­a­tions re­sults, which is why we re­view flagged con­tent to check for egre­gious or dan­ger­ous ma­te­r­ial. Our man­ual re­view con­firmed losses were over­whelm­ingly ei­ther a) false pos­i­tives or b) not egre­gious.

We con­duct man­ual red team­ing by spe­cial­ist teams who sit out­side of the model de­vel­op­ment team. High-level find­ings are fed back to the model team. For child safety eval­u­a­tions, Gemini 3.1 Pro sat­is­fied re­quired launch thresh­olds, which were de­vel­oped by ex­pert teams to pro­tect chil­dren on­line and meet Google’s com­mit­ments to child safety across our mod­els and Google prod­ucts. For con­tent safety poli­cies gen­er­ally, in­clud­ing child safety, we saw sim­i­lar safety per­for­mance com­pared to Gemini 3 Pro.

For more in­for­ma­tion about the risks and mit­i­ga­tions for Gemini 3.1 Pro, see the Gemini 3 Pro model card.

Our Frontier Safety Framework in­cludes rig­or­ous eval­u­a­tions that ad­dress risks of se­vere harm from fron­tier mod­els, cov­er­ing five risk do­mains: CBRN (chemical, bi­o­log­i­cal, ra­di­o­log­i­cal and nu­clear in­for­ma­tion risks), cy­ber, harm­ful ma­nip­u­la­tion, ma­chine learn­ing R&D and mis­align­ment. Our fron­tier safety strat­egy is based on a “safety buffer” to pre­vent mod­els from reach­ing crit­i­cal ca­pa­bil­ity lev­els (CCLs), i.e. if a fron­tier model does not reach the alert thresh­old for a CCL, we can as­sume mod­els de­vel­oped be­fore the next reg­u­lar test­ing in­ter­val will not reach that CCL. We con­duct con­tin­u­ous test­ing, eval­u­at­ing mod­els at a fixed ca­dence and when a sig­nif­i­cant ca­pa­bil­ity jump is de­tected. (Read more about this in our ap­proach to tech­ni­cal AGI safety.)Fol­low­ing FSF pro­to­cols, we con­ducted a full eval­u­a­tion of Gemini 3.1 Pro (focusing on Deep Think mode). We found that the model re­mains be­low alert thresh­olds for the CBRN, harm­ful ma­nip­u­la­tion, ma­chine learn­ing R&D, and mis­align­ment CCLs. As pre­vi­ous mod­els passed the alert thresh­old for cy­ber, we per­formed more ad­di­tional test­ing in this do­main on Gemini 3.1 Pro with and with­out Deep Think mode, and found that the model re­mains be­low the cy­ber CCL.More de­tails on our eval­u­a­tions and the mit­i­ga­tions we de­ploy can be found in the Gemini 3 Pro Frontier Safety Framework Report.

(Deep Think mode) The model can pro­vide highly ac­cu­rate and ac­tion­able in­for­ma­tion but still fails to of­fer novel or suf­fi­ciently com­plete and de­tailed in­struc­tions for crit­i­cal stages, to sig­nif­i­cantly en­hance the ca­pa­bil­i­ties of low to medium re­sourced threat ac­tors re­quired for the CCL. We con­tinue to de­ploy mit­i­ga­tions in this do­main.

(3.1 Pro) We con­ducted ad­di­tional test­ing on the model in this do­main as Gemini 3 Pro had pre­vi­ously reached the alert thresh­old. The model shows an in­crease in cy­ber ca­pa­bil­i­ties com­pared to Gemini 3 Pro. As with Gemini 3 Pro, the model has reached the alert thresh­old, but still does not reach the lev­els of up­lift re­quired for the CCL.

(Deep Think mode) Accounting for in­fer­ence costs, the model with Deep Think mode per­forms con­sid­er­ably worse than with­out Deep Think mode. Even at high lev­els of in­fer­ence, re­sults for the model with Deep Think mode do not sug­gest higher ca­pa­bil­ity than with­out Deep Think mode.

We con­tinue to de­ploy mit­i­ga­tions in this do­main.

(Deep Think mode) Evaluations in­di­cated that the model showed higher ma­nip­u­la­tive ef­fi­cacy for be­lief change met­rics com­pared to a non-AI base­line, with the max­i­mum odds ra­tio of 3.6x, which is the same as Gemini 3 Pro, and did not reach the alert thresh­old.

(Deep Think mode) The model shows gains on RE-Bench com­pared to Gemini 3 Pro, with a hu­man-nor­malised av­er­age score of 1.27 com­pared to Gemini 3 Pro’s score of 1.04. On one par­tic­u­lar chal­lenge, Optimise LLM Foundry, it scores dou­ble the hu­man-nor­malised base­line score (reducing the run­time of a fine-tun­ing script from 300 sec­onds to 47 sec­onds, com­pared to the hu­man ref­er­ence so­lu­tion of 94 sec­onds). However, the mod­el’s av­er­age per­for­mance across all chal­lenges re­mains be­neath the alert thresh­old for the CCLs.

(Deep Think mode) On stealth eval­u­a­tions, the model per­forms sim­i­larly to Gemini 3 Pro. On sit­u­a­tional aware­ness, the model is stronger than Gemini 3 Pro: on three chal­lenges which no other model has been able to con­sis­tently solve, max to­kens, con­text size mod, and over­sight fre­quency, the model achieves a suc­cess rate of al­most 100%. However, its per­for­mance on other chal­lenges is in­con­sis­tent, and thus the model does not reach the alert thresh­old.

This post is an elab­o­ra­tion on a com­ment I made on Hacker News re­cently, on a blog post that showed an in­crease in vol­ume and de­cline in qual­ity among the “Show HN” sub­mis­sons. I don’t ac­tu­ally mind AI-aided de­vel­op­ment, a tool is a tool and should be used if you find it use­ful, but I think the vibe coded Show HN pro­jects are over­all pretty bor­ing. They gen­er­ally don’t have a lot of work put into them, and as a re­sult, the au­thor (pilot?) has­n’t gen­er­ally thought too much about the prob­lem space, and so there is­n’t re­ally much of a dis­cus­sion to be had.The cool part about pre-AI show HN is you got to talk to some­one who had thought about a prob­lem for way longer than you had. It was a real op­por­tu­nity to learn some­thing new, to get an en­tirely dif­fer­ent per­spec­tive.I feel like this is what AI has done to the pro­gram­ming dis­cus­sion. It draws in bor­ing peo­ple with bor­ing pro­jects who don’t have any­thing in­ter­est­ing to say about pro­gram­ming.

This is­n’t some­thing that is lim­ited to Show HN or even Hacker News, it’s some­thing you see every­where.

While part of this phe­nom­e­non is likely just an up­swing of peo­ple who don’t usu­ally do pro­gram­ming that get swept up in the fun of build­ing a prod­uct, I want to build an ar­gu­ment that it’s much worse than that.

AI mod­els are ex­tremely bad at orig­i­nal think­ing, so any think­ing that is of­floaded to a LLM is as a re­sult usu­ally not very orig­i­nal, even if they’re very good at treat­ing your in­puts to the dis­cus­sion as amaz­ing ge­nius level in­sights.

This may be a fea­ture if you are ex­plor­ing a topic you are un­fa­mil­iar with, but it’s a fa­tal flaw if you are writ­ing a blog post or de­sign­ing a prod­uct or try­ing to do some other form of orig­i­nal work.

Some will ar­gue that this is why you need a hu­man in the loop to steer the work and do the high level think­ing. That premise is fun­da­men­tally flawed. Original ideas are the re­sult of the very work you’re of­fload­ing on LLMs. Having hu­mans in the loop does­n’t make the AI think more like peo­ple, it makes the hu­man thought more like AI out­put.

The way hu­man be­ings tend to have orig­i­nal ideas is to im­merse in a prob­lem for a long pe­riod of time, which is some­thing that flat out does­n’t hap­pen when LLMs do the think­ing. You get shal­low, sur­face-level ideas in­stead.

Ideas are then fur­ther re­fined when you try to ar­tic­u­late them. This is why we make stu­dents write es­says. It’s also why we make pro­fes­sors teach un­der­grad­u­ates.

Prompting an AI model is not ar­tic­u­lat­ing an idea. You get the out­put, but in terms of ideation the out­put is dis­card­able. It’s the work that mat­ters.

You don’t get build mus­cle us­ing an ex­ca­va­tor to lift weights. You don’t pro­duce in­ter­est­ing thoughts us­ing a GPU to think.

Your browser does not sup­port the au­dio el­e­ment.

This con­tent is gen­er­ated by Google AI. Generative AI is ex­per­i­men­tal

Last week, we re­leased a ma­jor up­date to Gemini 3 Deep Think to solve mod­ern chal­lenges across sci­ence, re­search and en­gi­neer­ing. Today, we’re re­leas­ing the up­graded core in­tel­li­gence that makes those break­throughs pos­si­ble: Gemini 3.1 Pro. We are ship­ping 3.1 Pro across our con­sumer and de­vel­oper prod­ucts to bring this progress in in­tel­li­gence to your every­day ap­pli­ca­tions. For de­vel­op­ers in pre­view via the Gemini API in Google AI Studio, Gemini CLI, our agen­tic de­vel­op­ment plat­form Google Antigravity and Android StudioFor en­ter­prises in Vertex AI and Gemini EnterpriseFor con­sumers via the Gemini app and NotebookLMBuilding on the Gemini 3 se­ries, 3.1 Pro rep­re­sents a step for­ward in core rea­son­ing. 3.1 Pro is a smarter, more ca­pa­ble base­line for com­plex prob­lem-solv­ing. This is re­flected in our progress on rig­or­ous bench­marks. On ARC-AGI-2, a bench­mark that eval­u­ates a mod­el’s abil­ity to solve en­tirely new logic pat­terns, 3.1 Pro achieved a ver­i­fied score of 77.1%. This is more than dou­ble the rea­son­ing per­for­mance of 3 Pro.

3.1 Pro is de­signed for tasks where a sim­ple an­swer is­n’t enough, tak­ing ad­vanced rea­son­ing and mak­ing it use­ful for your hard­est chal­lenges. This im­proved in­tel­li­gence can help in prac­ti­cal ap­pli­ca­tions — whether you’re look­ing for a clear, vi­sual ex­pla­na­tion of a com­plex topic, a way to syn­the­size data into a sin­gle view, or bring­ing a cre­ative pro­ject to life.

Code-based an­i­ma­tion: 3.1 Pro can gen­er­ate web­site-ready, an­i­mated SVGs di­rectly from a text prompt. Because these are built in pure code rather than pix­els, they re­main crisp at any scale and main­tain in­cred­i­bly small file sizes com­pared to tra­di­tional video.

Complex sys­tem syn­the­sis: 3.1 Pro uti­lizes ad­vanced rea­son­ing to bridge the gap be­tween com­plex APIs and user-friendly de­sign. In this ex­am­ple, the model built a live aero­space dash­board, suc­cess­fully con­fig­ur­ing a pub­lic teleme­try stream to vi­su­al­ize the International Space Station’s or­bit.

Interactive de­sign: 3.1 Pro codes a com­plex 3D star­ling mur­mu­ra­tion. It does­n’t just gen­er­ate the vi­sual code; it builds an im­mer­sive ex­pe­ri­ence where users can ma­nip­u­late the flock with hand-track­ing and lis­ten to a gen­er­a­tive score that shifts based on the birds’ move­ment. For re­searchers and de­sign­ers, this pro­vides a pow­er­ful way to pro­to­type sen­sory-rich in­ter­faces.

Creative cod­ing: 3.1 Pro can trans­late lit­er­ary themes into func­tional code. When prompted to build a mod­ern per­sonal port­fo­lio for Emily Brontë’s “Wuthering Heights,” the model did­n’t just sum­ma­rize the text. It rea­soned through the nov­el’s at­mos­pheric tone to de­sign a sleek, con­tem­po­rary in­ter­face, cre­at­ing a web­site that cap­tures the essence of the pro­tag­o­nist.

Since re­leas­ing Gemini 3 Pro in November, your feed­back and the pace of progress have dri­ven these rapid im­prove­ments. We are re­leas­ing 3.1 Pro in pre­view to­day to val­i­date these up­dates and con­tinue to make fur­ther ad­vance­ments in ar­eas such as am­bi­tious agen­tic work­flows be­fore we make it gen­er­ally avail­able soon.Start­ing to­day, Gemini 3.1 Pro in the Gemini app is rolling out with higher lim­its for users with the Google AI Pro and Ultra plans. 3.1 Pro is also now avail­able on NotebookLM ex­clu­sively for Pro and Ultra users. And de­vel­op­ers and en­ter­prises can ac­cess 3.1 Pro now in pre­view in the Gemini API via AI Studio, Antigravity, Vertex AI, Gemini Enterprise, Gemini CLI and Android Studio.We can’t wait to see what you build and dis­cover with it.

Your page may be load­ing slowly be­cause you’re build­ing op­ti­mized sources. If you in­tended on us­ing un­com­piled sources, please click this link.

Google Cloud Console has failed to load JavaScript sources from www.gsta­tic.com.

Possible rea­sons are:www.gsta­tic.com or its IP ad­dresses are blocked by your net­work ad­min­is­tra­tor­Google has tem­porar­ily blocked your ac­count or net­work due to ex­ces­sive au­to­mated re­quest­sPlease con­tact your net­work ad­min­is­tra­tor for fur­ther as­sis­tance.

Your house is qui­etly plot­ting to break while you sleep—and you’re dream­ing about re­do­ing the kitchen.

mi­casa tracks main­te­nance, pro­jects, in­ci­dents, ap­pli­ances, ven­dors, quotes, and doc­u­ments—all from your ter­mi­nal.

When did I last change the fur­nace fil­ter?What if we fi­nally did the back­yard?How much would it ac­tu­ally cost to…Quotes side by side, ven­dor his­tory, and the math you need to ac­tu­ally de­cide. Is the dish­washer still un­der war­ranty?Ap­pli­ance track­ing with pur­chase dates, war­ranty sta­tus, and main­te­nance his­tory tied to each one.The base­ment is leak­ing again.Log in­ci­dents with sever­ity and lo­ca­tion, link them to ap­pli­ances and ven­dors, and re­solve them when fixed.Who did we use last time?A ven­dor di­rec­tory with con­tact info, quote his­tory, and every job they’ve done for you.At­tach files—man­u­als, in­voices, pho­tos—di­rectly to pro­jects and ap­pli­ances. Stored in the same SQLite file.

or grab a bi­nary from the lat­est re­leaseLinux, ma­cOS, and Windows bi­na­ries are avail­able for amd64 and ar­m64.mi­casa –demo # poke around with sam­ple data

mi­casa # start fresh with your own house

mi­casa –print-path # show where the data­base lives­Linux, ma­cOS, Windows. One SQLite file, your ma­chine. Back it up with cp.

Vim-style modal keys. nav to browse, edit to change things. Sort by any col­umn, jump to columns with fuzzy search, hide what you don’t need, drill into re­lated records. The full list is in the key­bind­ing ref­er­ence.

Microsoft SQL now sup­ports na­tive vec­tor search ca­pa­bil­i­ties in Azure SQL and SQL data­base in Microsoft Fabric. We also re­leased the langchain-sqlserver pack­age, en­abling the man­age­ment of SQL Server as a Vectorstore in LangChain. In this step-by-step tu­to­r­ial, we will show you how to add gen­er­a­tive AI fea­tures to your own ap­pli­ca­tions with just a few lines of code us­ing Azure SQL DB, LangChain, and LLMs.

The Harry Potter se­ries, writ­ten by J. K. Rowling, is a glob­ally beloved col­lec­tion of seven books that fol­low the jour­ney of a young wiz­ard, Harry Potter, and his friends as they bat­tle the dark forces led by the evil Voldemort. Its cap­ti­vat­ing plot, rich char­ac­ters, and imag­i­na­tive world have made it one of the most fa­mous and cher­ished se­ries in lit­er­ary his­tory

By us­ing a well-known dataset, we can cre­ate en­gag­ing and re­lat­able ex­am­ples that res­onate with a wide au­di­ence

This Sample dataset from Kag­gle con­tains 7 .txt files of 7 books of Harry Potter. For this demo we will only be us­ing the first book — Harry Potter and the Sorcerer’s Stone.

Whether you’re a tech en­thu­si­ast or a Potterhead, we have two ex­cit­ing use cases to ex­plore:

A Q&A sys­tem that lever­ages the power of SQL Vector Store & LangChain to pro­vide ac­cu­rate and con­text-rich an­swers from the Harry Potter Book.

Next, we will push the cre­ative lim­its of the ap­pli­ca­tion by teach­ing it to gen­er­ate new AI-driven Harry Potter fan fic­tion based on our ex­ist­ing dataset of Harry Potter books. This fea­ture is sure to de­light Potterheads, al­low­ing them to ex­plore new ad­ven­tures and cre­ate their own mag­i­cal sto­ries.

The code lives in an in­te­gra­tion pack­age langchain-sqlserver

!pip in­stall langchain-sqlserver==0.1.1

In this ex­am­ple, we will use a dataset con­sist­ing of text files from the Harry Potter books, which are stored in Azure Blob Storage

LangChain has a seam­less in­te­gra­tion with Azure­Blob­Stor­age, mak­ing it easy to load doc­u­ments di­rectly from Azure Blob Storage.

Additionally, LangChain pro­vides a method to split long text into smaller chunks, us­ing langchain-text-split­ter which is es­sen­tial since Azure OpenAI em­bed­dings have an in­put to­ken limit.

In this ex­am­ple we use Azure OpenAI to gen­er­ate em­bed­dings of the split doc­u­ments, how­ever you can use any of the dif­fer­ent em­bed­dings pro­vided in LangChain.

After split­ting the long text files of Harry Potter books into smaller chunks, you can gen­er­ate vec­tor em­bed­dings for each chunk us­ing the Text Embedding Model avail­able through Azure­Ope­nAI. Notice how we can ac­com­plish this in just a few lines of code!

* First, ini­tial­ize the vec­tor store and set up the em­bed­dings us­ing AzureOpenAI

* Once we have our Vector Store we can add items to our vec­tor store by us­ing the ad­d_­doc­u­ments func­tion

Once your vec­tor store has been cre­ated and the rel­e­vant doc­u­ments have been added you can now per­form sim­i­lar­ity search.

The vec­tor­store also sup­ports a set of fil­ters that can be ap­plied against the meta­data fields of the doc­u­ments. By ap­ply­ing fil­ters based on spe­cific meta­data at­trib­utes, users can limit the scope of their searches, con­cen­trat­ing only on the most rel­e­vant data sub­sets.

Performing a sim­ple sim­i­lar­ity search can be done as fol­lows with the sim­i­lar­i­ty_search_with­_s­core

The Q&A func­tion al­lows users to ask spe­cific ques­tions about the story, char­ac­ters, and events, and get con­cise, con­text-rich an­swers. This not only en­hances their un­der­stand­ing of the books but also makes them feel like they’re part of the mag­i­cal uni­verse.

The LangChain Vector store sim­pli­fies build­ing so­phis­ti­cated Q&A sys­tems by en­abling ef­fi­cient sim­i­lar­ity searches to find the top 10 rel­e­vant doc­u­ments based on the user’s query.

The re­triever is cre­ated from the vec­tor_­s­tore, and the ques­tion-an­swer chain is built us­ing the cre­ate_stuff_­doc­u­ments_chain func­tion.

A prompt tem­plate is crafted us­ing the Chat­Prompt­Tem­plate class, en­sur­ing struc­tured and con­text-rich re­sponses.

Often in Q&A ap­pli­ca­tions it’s im­por­tant to show users the sources that were used to gen­er­ate the an­swer. LangChain’s built-in cre­ate_re­trieval_chain will prop­a­gate re­trieved source doc­u­ments to the out­put un­der the “context” key:

Read more about Langchain RAG tu­to­ri­als & the ter­mi­nolo­gies men­tioned above here

We can now ask the user ques­tion and re­ceive re­sponses from the Q&A System:

Potterheads are known for their cre­ativ­ity and pas­sion for the se­ries. With this they can craft their own sto­ries based on user prompt given , ex­plore new ad­ven­tures, and even cre­ate al­ter­nate end­ings. Whether it’s imag­in­ing a new duel be­tween Harry and Voldemort or craft­ing a per­son­al­ized Hogwarts bed­time story for you kiddo, the pos­si­bil­i­ties are end­less.

The fan fic­tion func­tion uses the em­bed­dings in the vec­tor store to gen­er­ate new sto­ries

* Retrieving Relevant Passages: When a user pro­vides a prompt for a fan fic­tion story, the func­tion first re­trieves rel­e­vant pas­sages from the SQL vec­tor store. The vec­tor store con­tains em­bed­dings of the text from the Harry Potter books, which al­lows it to find pas­sages that are con­tex­tu­ally sim­i­lar to the user’s prompt.

* Formatting the Retrieved Passages: The re­trieved pas­sages are then for­mat­ted into a co­her­ent con­text. This in­volves com­bin­ing the text from the re­trieved pas­sages into a sin­gle string that can be used as in­put for the lan­guage model.

* Generating the Story: The for­mat­ted con­text, along with the user’s prompt, is fed into a lan­guage model GPT4o to gen­er­ate the fan fic­tion story. The lan­guage model uses the con­text to en­sure that the gen­er­ated story is rel­e­vant and co­her­ent, in­cor­po­rat­ing el­e­ments from the re­trieved pas­sages.

Let’s imag­ine we prompt with this

Don’t miss the dis­cus­sion around the “Vector Support in SQL — Public Preview” by young Davide on the Hogwarts Express! Even the Wizards are ex­cited!

As you can see along with gen­er­at­ing the story it also men­tions the sources of in­spi­ra­tion from the Vector Store above

Hence, by com­bin­ing the Q&A sys­tem with the fan fic­tion gen­er­a­tor of­fers a unique and im­mer­sive read­ing ex­pe­ri­ence. If users come across a puz­zling mo­ment in the books, they can ask the Q&A sys­tem for clar­i­fi­ca­tion. If they’re in­spired by a par­tic­u­lar scene, they can use the fan fic­tion gen­er­a­tor to ex­pand on it and cre­ate their own ver­sion of events. This in­ter­ac­tive ap­proach makes read­ing more en­gag­ing and en­joy­able.

You can find this note­book in the GitHub Repo along with other sam­ples: https://​github.com/​Azure-Sam­ples/​azure-sql-db-vec­tor-search.

We’d love to hear your thoughts on this fea­ture! Please share how you’re us­ing it in the com­ments be­low and let us know any feed­back or sug­ges­tions for fu­ture im­prove­ments. If you have spe­cific re­quests, don’t for­get to sub­mit them through the Azure SQL and SQL Server feed­back por­tal, where other users can also con­tribute and help us pri­or­i­tize fu­ture de­vel­op­ments. We look for­ward to hear­ing your ideas!

Work con­tin­ues for the Vibrant Visuals up­date to come to Minecraft Java, and as part of that they’re switch­ing the ren­der­ing from OpenGL to Vulkan.

Announced to­day (February 18th) by Mojang de­vel­op­ers, it’s a huge change for such a game and will take time - but it will be worth it in the end so they can take ad­van­tage of all the mod­ern fea­tures avail­able for both vi­sual im­prove­ments and bet­ter per­for­mance.

They note clearly that their aim is to “keep Minecraft: Java Edition playable for al­most any PC-operating sys­tem, in­clud­ing ma­cOS and Linux”. For the ma­cOS side of things, they’ll use a trans­la­tion layer since Apple don’t sup­port Vulkan di­rectly (they made their own API with Metal).

For mod­ders, they’re sug­gest­ing they start mak­ing prepa­ra­tions to move away from OpenGL

Switching from OpenGL to Vulkan will have an im­pact on the mods that cur­rently use OpenGL for ren­der­ing, and we an­tic­i­pate that up­dat­ing from OpenGL to Vulkan will take mod­ders more ef­fort than the up­dates you un­der­take for each of our re­leases.

To start with, we rec­om­mend our mod­ding com­mu­nity look at mov­ing away from OpenGL us­age. We en­cour­age au­thors to try to reuse as much of the in­ter­nal ren­der­ing APIs as pos­si­ble, to make this tran­si­tion as easy as pos­si­ble. If that is not suf­fi­cient for your needs, then come and talk to us!

It does mean that play­ers on re­ally old de­vices that don’t sup­port Vulkan will be left out, but Vulkan has been sup­ported go­ing back to some pretty old GPUs. You’ve got time though, as they’ll be rolling out Vulkan along­side OpenGL in snap­shots (development re­leases) “sometime over the sum­mer”. You’ll be able to tog­gle be­tween them dur­ing the test­ing pe­riod un­til Mojang be­lieve it’s ready. OpenGL will be en­tirely re­moved even­tu­ally once they’re happy with per­for­mance and sta­bil­ity.

Things are busy in Pebbleland! We’re get­ting close to ship­ping 3 new hard­ware prod­ucts and all the as­so­ci­ated soft­ware that comes along with them. Overall, things feel good. I’d say the amount of last minute shenani­gans is at the nor­mal amount. Getting new hard­ware into ‘production’ is a pretty wild and ex­cit­ing process. Building hard­ware is an ex­er­cise in bal­anc­ing com­pet­ing pri­or­i­ties of cost, qual­ity and speed. In the last mile push to get into pro­duc­tion, things can change quickly for the best (woohoo! the wa­ter­proof test fi­nally passes, we can move to the next stage), or less good (uh, the pro­duc­tion line needs 3 more test fix­tures to test Index 01 mic per­for­mance, and a ma­jor pro­duc­tion test soft­ware up­date…that’ll be a lot more money). Unlike with soft­ware, you can’t eas­ily fix hard­ware is­sues af­ter you ship! Making these last minute de­ci­sions is some­times pretty stress­ful but hey, that’s the world of mak­ing hard­ware.

We’re in the Production Verification Test (PVT) phase right now, the last stop be­fore Mass Production (MP). During this phase we man­u­fac­tured hun­dreds of PT2s in a se­ries of test builds, un­cov­ered a bunch of is­sues, and fixed a bunch of is­sues. Just be­fore the fac­to­ries shut down for the lu­nar New Year, we got the good news that all the tests passed on the last build!

We fo­cused most of January on im­prov­ing the wa­ter­proof­ing on the watch (flash back to last sum­mer when we worked on this for Pebble 2 Duo!). I trav­eled to visit the fac­tory (travelogue here) and worked through a lot of open is­sues. Above is a video of the speaker wa­ter­proof test­ing from the pro­duc­tion line. Good news is that we fixed all the is­sues, tests are pass­ing and it looks like we’ll be able to cer­tify PT2 with a wa­ter­proof rat­ing of 30m or 3ATM! This means you can get your watch wet, wear it while swim­ming (but not in hot tubs/​saunas) and gen­er­ally not worry about it. It’s not a dive watch, though. Also, don’t ex­pose it to hot wa­ter (this could weaken the wa­ter­proof seals), or high pres­sure wa­ter. It’s not in­vin­ci­ble.

Snapshot of our mass pro­duc­tion plan (output counts are cu­mu­la­tive)

The fac­tory is closed now for Lunar New Year and will re­open around the end of Feb. As of to­day, mass pro­duc­tion is sched­uled to start on March 9. It will take the pro­duc­tion line a lit­tle while to spin up to­wards our tar­get out­put of 500 watches per day. Finished watches ship from the fac­tory once a week to our dis­tri­b­u­tion cen­ter (which takes ~1 week), then get packed for ship­ping (a few days to a week), then get de­liv­ered to you (~7-10 days). These dates and es­ti­mates are ALL sub­ject to change - if we run into a prob­lem, pro­duc­tion shuts down un­til we fix it. Delays can and most likely will hap­pen.

What every­one’s been wait­ing for…when will your PT2 ar­rive 🙂

Based on cur­rent sched­ule, the first mass pro­duc­tion PT2s will ar­rive on wrists dur­ing the be­gin­ning of April. We should wrap up de­liv­er­ing all pre-or­dered Pebble Time 2s two months later by the be­gin­ning of June. If your watch had an ini­tial date of December, it should ar­rive in April and if your ini­tial date was April, it should ar­rive in June. Unfortunately we can’t pre­dict when your spe­cific watch will ar­rive - please don’t email to ask, we’ll just send you a link to this blog post.

A few weeks be­fore your watch is sched­uled to ship, we’ll email link for you to con­firm your ad­dress (change if now if you’d like), pick op­tional ac­ces­sories (extra charg­ers and straps) and pay any tar­iffs/​VAT/​taxes owed. For US or­ders, the tar­iff amount is $10 per watch. For other coun­tries, VAT/taxes will be cal­cu­lated and charged dur­ing or­der con­fir­ma­tion. When the watch is de­liv­ered you won’t need to pay any­thing else or deal with cus­toms forms.

Index 01 is also in the Production Verification Test (PVT) phase. We’ve man­u­fac­tured sev­eral hun­dred so far. Waterproof test­ing went well (it’s rated 1m of sub­mer­sion, ipx8). You’ll be able to wash your hands, wash dishes, shower, get it wet etc but you can’t swim with it on. PTV is pro­ceed­ing well, but we’re not fin­ished yet. We’re still aim­ing to start mass pro­duc­tion dur­ing March, but we don’t have a firm start date yet.

In or­der news, we’re work­ing an Index 01 ring sizer kit that will be avail­able for $10 (hopefully in­clud­ing world­wide ship­ping, work­ing on that now). This will let you mea­sure your in­dex fin­ger and find your ex­act Pebble-specific ring size. We will ask every­one to mea­sure their ring size, ei­ther by or­der­ing an Index 01 sizer kit or 3D print­ing the kit, be­cause our sizes are dif­fer­ent than Oura or other rings.

We’re also con­sid­er­ing of­fer­ing size 14 and 15. It’s a big up­front ex­pense (~$50,000) to of­fer these sizes due to ad­di­tional tool­ing that will be needed, so we’re col­lect­ing in­ter­est - sign up here if you would like Index 01 in these sizes!

Things are rolling along. We fin­ished the Design Verification 1 (DVT1) phase just be­fore the Lunar New Year hol­i­day started. Work is pro­gress­ing well. One of the huge speed-ups to the pro­gram over­all is that the elec­tri­cal de­sign is al­most iden­ti­cal to Pebble Time 2. This means our (two per­son) firmware team can code new fea­tures or bug fixes for PT2 and they work im­me­di­ately on PR2! After the lu­nar new year, we’ll fo­cus on wa­ter­proof test­ing and last minute tweaks be­fore the cur­rent es­ti­mated pro­duc­tion start date in late May.

Our soft­ware out­put has been tremen­dous - we’re fix­ing bugs left, right and cen­ter and adding lots of new fea­tures to PebbleOS (changelog) and the Pebble mo­bile app (changelog).

Here are some high­lights:

* Weather now works (in sun­rise/​sun­set time­line pins and the Weather app)

* WhatsApp calls show up as calls (on Android)

* Fixed a ma­jor back­ground crash bug in Pebble iOS that caused weather and other apps to not fetch live data.

* Many old Pebble apps/​faces use weather APIs that no longer work (Yahoo, OpenWeather). The Pebble mo­bile app now catches these net­work re­quests and re­turns data from Open-Meteo - keep­ing old watch­faces work­ing!

* Pebble Appstore is now ‘native’ in­side the Pebble mo­bile app (in v1.0.11.1 on beta chan­nels to­day). We’ve also up­dated the Pebble Appstore on the web at apps.repeb­ble.com . If you’re a de­vel­oper and don’t see the lat­est ver­sion of your app or watch­face, please make sure to im­port them (takes ~2 min­utes).

* Now you can fil­ter out older apps with non-work­ing set­tings pages or com­pan­ion apps. Or fil­ter specif­i­cally for apps that are open source!

* Some PebbleKit 1.0 Android apps should work again (thanks Google for giv­ing us back com.get­peb­ble.an­droid.provider.basalt). But devs - please up­grade your apps to PebbleKit 2.0 Android for new com­pan­ion apps (more info and repo)

* Watch set­tings can now be ad­justed in the Pebble mo­bile app. Your set­tings are saved and synced to all your Pebble watches.

* Thanks to many com­mu­nity con­tri­bu­tions, there are now many new app icons for no­ti­fi­ca­tions for apps that did­n’t ex­ist 10 years ago!

* Most PebbleOS work has been go­ing into fac­tory ver­i­fi­ca­tion sw for Obelix

* Left handed mode - wear your Pebble on right hand with but­tons flipped (thanks Claudio!)

* Health data is now synced from watch to phone (thanks Michael!)

We’ve also made some great ad­vances on the SDK and de­vel­oper front…ex­pect an up­date very soon 😉

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