159 commits

Author	SHA1	Message	Date
slekkala1	1ac320b7e6	chore: remove dead code (#3729) ... # What does this PR do? Removing some dead code, found by vulture and checked by claude that there are no references or imports for these ## Test Plan CI	2025-10-07 20:26:02 -07:00
Francisco Arceo	d5b136ac66	feat: Enabling Annotations in Responses (#3698) ... # What does this PR do? Implements annotations for `file_search` tool. Also adds some logs and tests. ## How does this work? 1. **Citation Markers**: Models insert `<|file-id|>` tokens during generation with instructions from search results 2. **Post-Processing**: Extract markers using regex to calculate character positions and create `AnnotationFileCitation` objects 3. **File Mapping**: Store filename metadata during vector store operations for proper citation display ## Example This is the updated `quickstart.py` script, which uses the `extra_body` to register the embedding model. ```python import io, requests from openai import OpenAI url="https://www.paulgraham.com/greatwork.html" model = "gpt-4o-mini" client = OpenAI(base_url="http://localhost:8321/v1/openai/v1", api_key="none") vs = client.vector_stores.create( name="my_citations_db", extra_body={ "embedding_model": "ollama/nomic-embed-text:latest", "embedding_dimension": 768, } ) response = requests.get(url) pseudo_file = io.BytesIO(str(response.content).encode('utf-8')) file_id = client.files.create(file=(url, pseudo_file, "text/html"), purpose="assistants").id client.vector_stores.files.create(vector_store_id=vs.id, file_id=file_id) resp = client.responses.create( model=model, input="How do you do great work? Use our existing knowledge_search tool.", tools=[{"type": "file_search", "vector_store_ids": [vs.id]}], include=["file_search_call.results"], ) print(resp) ``` <details> <summary> Example of the full response </summary> ```python INFO:httpx:HTTP Request: POST http://localhost:8321/v1/openai/v1/vector_stores "HTTP/1.1 200 OK" INFO:httpx:HTTP Request: POST http://localhost:8321/v1/openai/v1/files "HTTP/1.1 200 OK" INFO:httpx:HTTP Request: POST http://localhost:8321/v1/openai/v1/vector_stores/vs_0f6f7e35-f48b-4850-8604-8117d9a50e0a/files "HTTP/1.1 200 OK" INFO:httpx:HTTP Request: POST http://localhost:8321/v1/openai/v1/responses "HTTP/1.1 200 OK" Response(id='resp-28f5793d-3272-4de3-81f6-8cbf107d5bcd', created_at=1759797954.0, error=None, incomplete_details=None, instructions=None, metadata=None, model='gpt-4o-mini', object='response', output=[ResponseFileSearchToolCall(id='call_xWtvEQETN5GNiRLLiBIDKntg', queries=['how to do great work tips'], status='completed', type='file_search_call', results=[Result(attributes={}, file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='file-a98ada68681c4fbeba2201e9c7213fc3', score=1.3722624322210302, text='\\\'re looking where few have looked before.<br /><br />One sign that you\\\'re suited for some kind of work is when you like\\neven the parts that other people find tedious or frightening.<br /><br />But fields aren\\\'t people; you don\\\'t owe them any loyalty. If in the\\ncourse of working on one thing you discover another that\\\'s more\\nexciting, don\\\'t be afraid to switch.<br /><br />If you\\\'re making something for people, make sure it\\\'s something\\nthey actually want. The best way to do this is to make something\\nyou yourself want. Write the story you want to read; build the tool\\nyou want to use. Since your friends probably have similar interests,\\nthis will also get you your initial audience.<br /><br />This <i>should</i> follow from the excitingness rule. Obviously the most\\nexciting story to write will be the one you want to read. The reason\\nI mention this case explicitly is that so many people get it wrong.\\nInstead of making what they want, they try to make what some\\nimaginary, more sophisticated audience wants. And once you go down\\nthat route, you\\\'re lost.\\n<font color=#dddddd>[<a href="#f6n"><font color=#dddddd>6</font></a>]</font><br /><br />There are a lot of forces that will lead you astray when you\\\'re\\ntrying to figure out what to work on. Pretentiousness, fashion,\\nfear, money, politics, other people\\\'s wishes, eminent frauds. But\\nif you stick to what you find genuinely interesting, you\\\'ll be proof\\nagainst all of them. If you\\\'re interested, you\\\'re not astray.<br /><br /><br /><br /><br /><br />\\nFollowing your interests may sound like a rather passive strategy,\\nbut in practice it usually means following them past all sorts of\\nobstacles. You usually have to risk rejection and failure. So it\\ndoes take a good deal of boldness.<br /><br />But while you need boldness, you don\\\'t usually need much planning.\\nIn most cases the recipe for doing great work is simply: work hard\\non excitingly ambitious projects, and something good will come of\\nit. Instead of making a plan and then executing it, you just try\\nto preserve certain invariants.<br /><br />The trouble with planning is that it only works for achievements\\nyou can describe in advance. You can win a gold medal or get rich\\nby deciding to as a child and then tenaciously pursuing that goal,\\nbut you can\\\'t discover natural selection that way.<br /><br />I think for most people who want to do great work, the right strategy\\nis not to plan too much. At each stage do whatever seems most\\ninteresting and gives you the best options for the future. I call\\nthis approach "staying upwind." This is how most people who\\\'ve done\\ngreat work seem to have done it.<br /><br /><br /><br /><br /><br />\\nEven when you\\\'ve found something exciting to work on, working on\\nit is not always straightforward. There will be times when some new\\nidea makes you leap out of bed in the morning and get straight to\\nwork. But there will also be plenty of times when things aren\\\'t\\nlike that.<br /><br />You don\\\'t just put out your sail and get blown forward by inspiration.\\nThere are headwinds and currents and hidden shoals. So there\\\'s a\\ntechnique to working, just as there is to sailing.<br /><br />For example, while you must work hard, it\\\'s possible to work too\\nhard, and if'), Result(attributes={}, file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='file-a98ada68681c4fbeba2201e9c7213fc3', score=1.2532794607643494, text=' with anyone who\\\'s genuinely interested. If they\\\'re\\nreally good at their work, then they probably have a hobbyist\\\'s\\ninterest in it, and hobbyists always want to talk about their\\nhobbies.<br /><br />It may take some effort to find the people who are really good,\\nthough. Doing great work has such prestige that in some places,\\nparticularly universities, there\\\'s a polite fiction that everyone\\nis engaged in it. And that is far from true. People within universities\\ncan\\\'t say so openly, but the quality of the work being done in\\ndifferent departments varies immensely. Some departments have people\\ndoing great work; others have in the past; others never have.<br /><br /><br /><br /><br /><br />\\nSeek out the best colleagues. There are a lot of projects that can\\\'t\\nbe done alone, and even if you\\\'re working on one that can be, it\\\'s\\ngood to have other people to encourage you and to bounce ideas off.<br /><br />Colleagues don\\\'t just affect your work, though; they also affect\\nyou. So work with people you want to become like, because you will.<br /><br />Quality is more important than quantity in colleagues. It\\\'s better\\nto have one or two great ones than a building full of pretty good\\nones. In fact it\\\'s not merely better, but necessary, judging from\\nhistory: the degree to which great work happens in clusters suggests\\nthat one\\\'s colleagues often make the difference between doing great\\nwork and not.<br /><br />How do you know when you have sufficiently good colleagues? In my\\nexperience, when you do, you know. Which means if you\\\'re unsure,\\nyou probably don\\\'t. But it may be possible to give a more concrete\\nanswer than that. Here\\\'s an attempt: sufficiently good colleagues\\noffer <i>surprising</i> insights. They can see and do things that you\\ncan\\\'t. So if you have a handful of colleagues good enough to keep\\nyou on your toes in this sense, you\\\'re probably over the threshold.<br /><br />Most of us can benefit from collaborating with colleagues, but some\\nprojects require people on a larger scale, and starting one of those\\nis not for everyone. If you want to run a project like that, you\\\'ll\\nhave to become a manager, and managing well takes aptitude and\\ninterest like any other kind of work. If you don\\\'t have them, there\\nis no middle path: you must either force yourself to learn management\\nas a second language, or avoid such projects.\\n<font color=#dddddd>[<a href="#f27n"><font color=#dddddd>27</font></a>]</font><br /><br /><br /><br /><br /><br />\\nHusband your morale. It\\\'s the basis of everything when you\\\'re working\\non ambitious projects. You have to nurture and protect it like a\\nliving organism.<br /><br />Morale starts with your view of life. You\\\'re more likely to do great\\nwork if you\\\'re an optimist, and more likely to if you think of\\nyourself as lucky than if you think of yourself as a victim.<br /><br />Indeed, work can to some extent protect you from your problems. If\\nyou choose work that\\\'s pure, its very difficulties will serve as a\\nrefuge from the difficulties of everyday life. If this is escapism,\\nit\\\'s a very productive form of it, and one that has been used by\\nsome of the greatest minds in history.<br /><br />Morale compounds via work: high morale helps you do good work, which\\nincreases your morale and helps you do even'), Result(attributes={}, file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='file-a98ada68681c4fbeba2201e9c7213fc3', score=1.1973485818164222, text=' your\\nability and interest can take you. And you can only answer that by\\ntrying.<br /><br />Many more people could try to do great work than do. What holds\\nthem back is a combination of modesty and fear. It seems presumptuous\\nto try to be Newton or Shakespeare. It also seems hard; surely if\\nyou tried something like that, you\\\'d fail. Presumably the calculation\\nis rarely explicit. Few people consciously decide not to try to do\\ngreat work. But that\\\'s what\\\'s going on subconsciously; they shy\\naway from the question.<br /><br />So I\\\'m going to pull a sneaky trick on you. Do you want to do great\\nwork, or not? Now you have to decide consciously. Sorry about that.\\nI wouldn\\\'t have done it to a general audience. But we already know\\nyou\\\'re interested.<br /><br />Don\\\'t worry about being presumptuous. You don\\\'t have to tell anyone.\\nAnd if it\\\'s too hard and you fail, so what? Lots of people have\\nworse problems than that. In fact you\\\'ll be lucky if it\\\'s the worst\\nproblem you have.<br /><br />Yes, you\\\'ll have to work hard. But again, lots of people have to\\nwork hard. And if you\\\'re working on something you find very\\ninteresting, which you necessarily will if you\\\'re on the right path,\\nthe work will probably feel less burdensome than a lot of your\\npeers\\\'.<br /><br />The discoveries are out there, waiting to be made. Why not by you?<br /><br /><br /><br /><br /><br /><br /><br /><br /><br />\\n<b>Notes</b><br /><br />[<a name="f1n"><font color=#000000>1</font></a>]\\nI don\\\'t think you could give a precise definition of what\\ncounts as great work. Doing great work means doing something important\\nso well that you expand people\\\'s ideas of what\\\'s possible. But\\nthere\\\'s no threshold for importance. It\\\'s a matter of degree, and\\noften hard to judge at the time anyway. So I\\\'d rather people focused\\non developing their interests rather than worrying about whether\\nthey\\\'re important or not. Just try to do something amazing, and\\nleave it to future generations to say if you succeeded.<br /><br />[<a name="f2n"><font color=#000000>2</font></a>]\\nA lot of standup comedy is based on noticing anomalies in\\neveryday life. "Did you ever notice...?" New ideas come from doing\\nthis about nontrivial things. Which may help explain why people\\\'s\\nreaction to a new idea is often the first half of laughing: Ha!<br /><br />[<a name="f3n"><font color=#000000>3</font></a>]\\nThat second qualifier is critical. If you\\\'re excited about\\nsomething most authorities discount, but you can\\\'t give a more\\nprecise explanation than "they don\\\'t get it," then you\\\'re starting\\nto drift into the territory of cranks.<br /><br />[<a name="f4n"><font color=#000000>4</font></a>]\\nFinding something to work on is not simply a matter of finding\\na match between the current version of you and a list of known\\nproblems. You\\\'ll often have to coevolve with the problem. That\\\'s\\nwhy it can sometimes be so hard to figure out what to work on. The\\nsearch space is huge. It\\\'s the cartesian product of all possible\\nt'), Result(attributes={}, file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='file-a98ada68681c4fbeba2201e9c7213fc3', score=1.1764591706535943, text='\\noptimistic, and even though one of the sources of their optimism\\nis ignorance, in this case ignorance can sometimes beat knowledge.<br /><br />Try to finish what you start, though, even if it turns out to be\\nmore work than you expected. Finishing things is not just an exercise\\nin tidiness or self-discipline. In many projects a lot of the best\\nwork happens in what was meant to be the final stage.<br /><br />Another permissible lie is to exaggerate the importance of what\\nyou\\\'re working on, at least in your own mind. If that helps you\\ndiscover something new, it may turn out not to have been a lie after\\nall.\\n<font color=#dddddd>[<a href="#f7n"><font color=#dddddd>7</font></a>]</font><br /><br /><br /><br /><br /><br />\\nSince there are two senses of starting work &mdash; per day and per\\nproject &mdash; there are also two forms of procrastination. Per-project\\nprocrastination is far the more dangerous. You put off starting\\nthat ambitious project from year to year because the time isn\\\'t\\nquite right. When you\\\'re procrastinating in units of years, you can\\nget a lot not done.\\n<font color=#dddddd>[<a href="#f8n"><font color=#dddddd>8</font></a>]</font><br /><br />One reason per-project procrastination is so dangerous is that it\\nusually camouflages itself as work. You\\\'re not just sitting around\\ndoing nothing; you\\\'re working industriously on something else. So\\nper-project procrastination doesn\\\'t set off the alarms that per-day\\nprocrastination does. You\\\'re too busy to notice it.<br /><br />The way to beat it is to stop occasionally and ask yourself: Am I\\nworking on what I most want to work on? When you\\\'re young it\\\'s ok\\nif the answer is sometimes no, but this gets increasingly dangerous\\nas you get older.\\n<font color=#dddddd>[<a href="#f9n"><font color=#dddddd>9</font></a>]</font><br /><br /><br /><br /><br /><br />\\nGreat work usually entails spending what would seem to most people\\nan unreasonable amount of time on a problem. You can\\\'t think of\\nthis time as a cost, or it will seem too high. You have to find the\\nwork sufficiently engaging as it\\\'s happening.<br /><br />There may be some jobs where you have to work diligently for years\\nat things you hate before you get to the good part, but this is not\\nhow great work happens. Great work happens by focusing consistently\\non something you\\\'re genuinely interested in. When you pause to take\\nstock, you\\\'re surprised how far you\\\'ve come.<br /><br />The reason we\\\'re surprised is that we underestimate the cumulative\\neffect of work. Writing a page a day doesn\\\'t sound like much, but\\nif you do it every day you\\\'ll write a book a year. That\\\'s the key:\\nconsistency. People who do great things don\\\'t get a lot done every\\nday. They get something done, rather than nothing.<br /><br />If you do work that compounds, you\\\'ll get exponential growth. Most\\npeople who do this do it unconsciously, but it\\\'s worth stopping to\\nthink about. Learning, for example, is an instance of this phenomenon:\\nthe more you learn about something, the easier it is to learn more.\\nGrowing an audience is another: the more fans you have, the more\\nnew fans they\\\'ll bring you.<br /><br />'), Result(attributes={}, file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='file-a98ada68681c4fbeba2201e9c7213fc3', score=1.174069664815369, text='\\ninside.<br /><br /><br /><br /><br /><br />Let\\\'s talk a little more about the complicated business of figuring\\nout what to work on. The main reason it\\\'s hard is that you can\\\'t\\ntell what most kinds of work are like except by doing them. Which\\nmeans the four steps overlap: you may have to work at something for\\nyears before you know how much you like it or how good you are at\\nit. And in the meantime you\\\'re not doing, and thus not learning\\nabout, most other kinds of work. So in the worst case you choose\\nlate based on very incomplete information.\\n<font color=#dddddd>[<a href="#f4n"><font color=#dddddd>4</font></a>]</font><br /><br />The nature of ambition exacerbates this problem. Ambition comes in\\ntwo forms, one that precedes interest in the subject and one that\\ngrows out of it. Most people who do great work have a mix, and the\\nmore you have of the former, the harder it will be to decide what\\nto do.<br /><br />The educational systems in most countries pretend it\\\'s easy. They\\nexpect you to commit to a field long before you could know what\\nit\\\'s really like. And as a result an ambitious person on an optimal\\ntrajectory will often read to the system as an instance of breakage.<br /><br />It would be better if they at least admitted it &mdash; if they admitted\\nthat the system not only can\\\'t do much to help you figure out what\\nto work on, but is designed on the assumption that you\\\'ll somehow\\nmagically guess as a teenager. They don\\\'t tell you, but I will:\\nwhen it comes to figuring out what to work on, you\\\'re on your own.\\nSome people get lucky and do guess correctly, but the rest will\\nfind themselves scrambling diagonally across tracks laid down on\\nthe assumption that everyone does.<br /><br />What should you do if you\\\'re young and ambitious but don\\\'t know\\nwhat to work on? What you should <i>not</i> do is drift along passively,\\nassuming the problem will solve itself. You need to take action.\\nBut there is no systematic procedure you can follow. When you read\\nbiographies of people who\\\'ve done great work, it\\\'s remarkable how\\nmuch luck is involved. They discover what to work on as a result\\nof a chance meeting, or by reading a book they happen to pick up.\\nSo you need to make yourself a big target for luck, and the way to\\ndo that is to be curious. Try lots of things, meet lots of people,\\nread lots of books, ask lots of questions.\\n<font color=#dddddd>[<a href="#f5n"><font color=#dddddd>5</font></a>]</font><br /><br />When in doubt, optimize for interestingness. Fields change as you\\nlearn more about them. What mathematicians do, for example, is very\\ndifferent from what you do in high school math classes. So you need\\nto give different types of work a chance to show you what they\\\'re\\nlike. But a field should become <i>increasingly</i> interesting as you\\nlearn more about it. If it doesn\\\'t, it\\\'s probably not for you.<br /><br />Don\\\'t worry if you find you\\\'re interested in different things than\\nother people. The stranger your tastes in interestingness, the\\nbetter. Strange tastes are often strong ones, and a strong taste\\nfor work means you\\\'ll be productive. And you\\\'re more likely to find\\nnew things if you'), Result(attributes={}, file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='file-a98ada68681c4fbeba2201e9c7213fc3', score=1.158095578895721, text='. Don\\\'t copy the manner of\\nan eminent 50 year old professor if you\\\'re 18, for example, or the\\nidiom of a Renaissance poem hundreds of years later.<br /><br />Some of the features of things you admire are flaws they succeeded\\ndespite. Indeed, the features that are easiest to imitate are the\\nmost likely to be the flaws.<br /><br />This is particularly true for behavior. Some talented people are\\njerks, and this sometimes makes it seem to the inexperienced that\\nbeing a jerk is part of being talented. It isn\\\'t; being talented\\nis merely how they get away with it.<br /><br />One of the most powerful kinds of copying is to copy something from\\none field into another. History is so full of chance discoveries\\nof this type that it\\\'s probably worth giving chance a hand by\\ndeliberately learning about other kinds of work. You can take ideas\\nfrom quite distant fields if you let them be metaphors.<br /><br />Negative examples can be as inspiring as positive ones. In fact you\\ncan sometimes learn more from things done badly than from things\\ndone well; sometimes it only becomes clear what\\\'s needed when it\\\'s\\nmissing.<br /><br /><br /><br /><br /><br />\\nIf a lot of the best people in your field are collected in one\\nplace, it\\\'s usually a good idea to visit for a while. It will\\nincrease your ambition, and also, by showing you that these people\\nare human, increase your self-confidence.\\n<font color=#dddddd>[<a href="#f26n"><font color=#dddddd>26</font></a>]</font><br /><br />If you\\\'re earnest you\\\'ll probably get a warmer welcome than you\\nmight expect. Most people who are very good at something are happy\\nto talk about it with anyone who\\\'s genuinely interested. If they\\\'re\\nreally good at their work, then they probably have a hobbyist\\\'s\\ninterest in it, and hobbyists always want to talk about their\\nhobbies.<br /><br />It may take some effort to find the people who are really good,\\nthough. Doing great work has such prestige that in some places,\\nparticularly universities, there\\\'s a polite fiction that everyone\\nis engaged in it. And that is far from true. People within universities\\ncan\\\'t say so openly, but the quality of the work being done in\\ndifferent departments varies immensely. Some departments have people\\ndoing great work; others have in the past; others never have.<br /><br /><br /><br /><br /><br />\\nSeek out the best colleagues. There are a lot of projects that can\\\'t\\nbe done alone, and even if you\\\'re working on one that can be, it\\\'s\\ngood to have other people to encourage you and to bounce ideas off.<br /><br />Colleagues don\\\'t just affect your work, though; they also affect\\nyou. So work with people you want to become like, because you will.<br /><br />Quality is more important than quantity in colleagues. It\\\'s better\\nto have one or two great ones than a building full of pretty good\\nones. In fact it\\\'s not merely better, but necessary, judging from\\nhistory: the degree to which great work happens in clusters suggests\\nthat one\\\'s colleagues often make the difference between doing great\\nwork and not.<br /><br />How do you know when you have sufficiently good colleagues? In my\\nexperience, when you do, you know. Which means if you\\\'re unsure,\\nyou probably don\\\'t. But it may be possible to give a more concrete\\nanswer than that. Here\\\'s an attempt: sufficiently good'), Result(attributes={}, file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='file-a98ada68681c4fbeba2201e9c7213fc3', score=1.1566747762241967, text=',\\nbut in practice it usually means following them past all sorts of\\nobstacles. You usually have to risk rejection and failure. So it\\ndoes take a good deal of boldness.<br /><br />But while you need boldness, you don\\\'t usually need much planning.\\nIn most cases the recipe for doing great work is simply: work hard\\non excitingly ambitious projects, and something good will come of\\nit. Instead of making a plan and then executing it, you just try\\nto preserve certain invariants.<br /><br />The trouble with planning is that it only works for achievements\\nyou can describe in advance. You can win a gold medal or get rich\\nby deciding to as a child and then tenaciously pursuing that goal,\\nbut you can\\\'t discover natural selection that way.<br /><br />I think for most people who want to do great work, the right strategy\\nis not to plan too much. At each stage do whatever seems most\\ninteresting and gives you the best options for the future. I call\\nthis approach "staying upwind." This is how most people who\\\'ve done\\ngreat work seem to have done it.<br /><br /><br /><br /><br /><br />\\nEven when you\\\'ve found something exciting to work on, working on\\nit is not always straightforward. There will be times when some new\\nidea makes you leap out of bed in the morning and get straight to\\nwork. But there will also be plenty of times when things aren\\\'t\\nlike that.<br /><br />You don\\\'t just put out your sail and get blown forward by inspiration.\\nThere are headwinds and currents and hidden shoals. So there\\\'s a\\ntechnique to working, just as there is to sailing.<br /><br />For example, while you must work hard, it\\\'s possible to work too\\nhard, and if you do that you\\\'ll find you get diminishing returns:\\nfatigue will make you stupid, and eventually even damage your health.\\nThe point at which work yields diminishing returns depends on the\\ntype. Some of the hardest types you might only be able to do for\\nfour or five hours a day.<br /><br />Ideally those hours will be contiguous. To the extent you can, try\\nto arrange your life so you have big blocks of time to work in.\\nYou\\\'ll shy away from hard tasks if you know you might be interrupted.<br /><br />It will probably be harder to start working than to keep working.\\nYou\\\'ll often have to trick yourself to get over that initial\\nthreshold. Don\\\'t worry about this; it\\\'s the nature of work, not a\\nflaw in your character. Work has a sort of activation energy, both\\nper day and per project. And since this threshold is fake in the\\nsense that it\\\'s higher than the energy required to keep going, it\\\'s\\nok to tell yourself a lie of corresponding magnitude to get over\\nit.<br /><br />It\\\'s usually a mistake to lie to yourself if you want to do great\\nwork, but this is one of the rare cases where it isn\\\'t. When I\\\'m\\nreluctant to start work in the morning, I often trick myself by\\nsaying "I\\\'ll just read over what I\\\'ve got so far." Five minutes\\nlater I\\\'ve found something that seems mistaken or incomplete, and\\nI\\\'m off.<br /><br />Similar techniques work for starting new projects. It\\\'s ok to lie\\nto yourself about how much work a project will entail, for example.\\nLots of great things began with someone saying "How hard could it\\nbe?"<br /><br />This is one case where the young have an advantage. They\\\'re more'), Result(attributes={}, file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='file-a98ada68681c4fbeba2201e9c7213fc3', score=1.1349744395573516, text=' audience\\nin the traditional sense. Either way it doesn\\\'t need to be big.\\nThe value of an audience doesn\\\'t grow anything like linearly with\\nits size. Which is bad news if you\\\'re famous, but good news if\\nyou\\\'re just starting out, because it means a small but dedicated\\naudience can be enough to sustain you. If a handful of people\\ngenuinely love what you\\\'re doing, that\\\'s enough.<br /><br />To the extent you can, avoid letting intermediaries come between\\nyou and your audience. In some types of work this is inevitable,\\nbut it\\\'s so liberating to escape it that you might be better off\\nswitching to an adjacent type if that will let you go direct.\\n<font color=#dddddd>[<a href="#f28n"><font color=#dddddd>28</font></a>]</font><br /><br />The people you spend time with will also have a big effect on your\\nmorale. You\\\'ll find there are some who increase your energy and\\nothers who decrease it, and the effect someone has is not always\\nwhat you\\\'d expect. Seek out the people who increase your energy and\\navoid those who decrease it. Though of course if there\\\'s someone\\nyou need to take care of, that takes precedence.<br /><br />Don\\\'t marry someone who doesn\\\'t understand that you need to work,\\nor sees your work as competition for your attention. If you\\\'re\\nambitious, you need to work; it\\\'s almost like a medical condition;\\nso someone who won\\\'t let you work either doesn\\\'t understand you,\\nor does and doesn\\\'t care.<br /><br />Ultimately morale is physical. You think with your body, so it\\\'s\\nimportant to take care of it. That means exercising regularly,\\neating and sleeping well, and avoiding the more dangerous kinds of\\ndrugs. Running and walking are particularly good forms of exercise\\nbecause they\\\'re good for thinking.\\n<font color=#dddddd>[<a href="#f29n"><font color=#dddddd>29</font></a>]</font><br /><br />People who do great work are not necessarily happier than everyone\\nelse, but they\\\'re happier than they\\\'d be if they didn\\\'t. In fact,\\nif you\\\'re smart and ambitious, it\\\'s dangerous <i>not</i> to be productive.\\nPeople who are smart and ambitious but don\\\'t achieve much tend to\\nbecome bitter.<br /><br /><br /><br /><br /><br />\\nIt\\\'s ok to want to impress other people, but choose the right people.\\nThe opinion of people you respect is signal. Fame, which is the\\nopinion of a much larger group you might or might not respect, just\\nadds noise.<br /><br />The prestige of a type of work is at best a trailing indicator and\\nsometimes completely mistaken. If you do anything well enough,\\nyou\\\'ll make it prestigious. So the question to ask about a type of\\nwork is not how much prestige it has, but how well it could be done.<br /><br />Competition can be an effective motivator, but don\\\'t let it choose\\nthe problem for you; don\\\'t let yourself get drawn into chasing\\nsomething just because others are. In fact, don\\\'t let competitors\\nmake you do anything much more specific than work harder.<br /><br />Curiosity is the best guide. Your curiosity never lies, and it knows\\nmore than you do about what\\\'s worth paying attention to.<br /><br /><br /><br /><br /><br />\\nNotice how often that word has come up. If you asked an oracle the\\nsecret to doing great work and the oracle replied'), Result(attributes={}, file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='file-a98ada68681c4fbeba2201e9c7213fc3', score=1.123214818076958, text='b\'<html><head><meta name="Keywords" content="" /><title>How to Do Great Work</title><!-- <META NAME="ROBOTS" CONTENT="NOODP"> -->\\n<link rel="shortcut icon" href="http://ycombinator.com/arc/arc.png">\\n</head><body bgcolor="#ffffff" background="https://s.turbifycdn.com/aah/paulgraham/bel-6.gif" text="#000000" link="#000099" vlink="#464646"><table border="0" cellspacing="0" cellpadding="0"><tr valign="top"><td><map name=118ab66adb24b4f><area shape=rect coords="0,0,67,21" href="index.html"><area shape=rect coords="0,21,67,42" href="articles.html"><area shape=rect coords="0,42,67,63" href="http://www.amazon.com/gp/product/0596006624"><area shape=rect coords="0,63,67,84" href="books.html"><area shape=rect coords="0,84,67,105" href="http://ycombinator.com"><area shape=rect coords="0,105,67,126" href="arc.html"><area shape=rect coords="0,126,67,147" href="bel.html"><area shape=rect coords="0,147,67,168" href="lisp.html"><area shape=rect coords="0,168,67,189" href="antispam.html"><area shape=rect coords="0,189,67,210" href="kedrosky.html"><area shape=rect coords="0,210,67,231" href="faq.html"><area shape=rect coords="0,231,67,252" href="raq.html"><area shape=rect coords="0,252,67,273" href="quo.html"><area shape=rect coords="0,273,67,294" href="rss.html"><area shape=rect coords="0,294,67,315" href="bio.html"><area shape=rect coords="0,315,67,336" href="https://twitter.com/paulg"><area shape=rect coords="0,336,67,357" href="https://mas.to/@paulg"></map><img src="https://s.turbifycdn.com/aah/paulgraham/bel-7.gif" width="69" height="357" usemap=#118ab66adb24b4f border="0" hspace="0" vspace="0" ismap /></td><td><img src="https://sep.turbifycdn.com/ca/Img/trans_1x1.gif" height="1" width="26" border="0" /></td><td><a href="index.html"><img src="https://s.turbifycdn.com/aah/paulgraham/bel-8.gif" width="410" height="45" border="0" hspace="0" vspace="0" /></a><br /><br /><table border="0" cellspacing="0" cellpadding="0" width="435"><tr valign="top"><td width="435"><img src="https://s.turbifycdn.com/aah/paulgraham/how-to-do-great-work-2.gif" width="185" height="18" border="0" hspace="0" vspace="0" alt="How to Do Great Work" /><br /><br /><font size="2" face="verdana">July 2023<br /><br />If you collected lists of techniques for doing great work in a lot\\nof different fields, what would the intersection look like? I decided\\nto find out'), Result(attributes={}, file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='file-a98ada68681c4fbeba2201e9c7213fc3', score=1.1193194369249235, text=' dangerous kinds of\\ndrugs. Running and walking are particularly good forms of exercise\\nbecause they\\\'re good for thinking.\\n<font color=#dddddd>[<a href="#f29n"><font color=#dddddd>29</font></a>]</font><br /><br />People who do great work are not necessarily happier than everyone\\nelse, but they\\\'re happier than they\\\'d be if they didn\\\'t. In fact,\\nif you\\\'re smart and ambitious, it\\\'s dangerous <i>not</i> to be productive.\\nPeople who are smart and ambitious but don\\\'t achieve much tend to\\nbecome bitter.<br /><br /><br /><br /><br /><br />\\nIt\\\'s ok to want to impress other people, but choose the right people.\\nThe opinion of people you respect is signal. Fame, which is the\\nopinion of a much larger group you might or might not respect, just\\nadds noise.<br /><br />The prestige of a type of work is at best a trailing indicator and\\nsometimes completely mistaken. If you do anything well enough,\\nyou\\\'ll make it prestigious. So the question to ask about a type of\\nwork is not how much prestige it has, but how well it could be done.<br /><br />Competition can be an effective motivator, but don\\\'t let it choose\\nthe problem for you; don\\\'t let yourself get drawn into chasing\\nsomething just because others are. In fact, don\\\'t let competitors\\nmake you do anything much more specific than work harder.<br /><br />Curiosity is the best guide. Your curiosity never lies, and it knows\\nmore than you do about what\\\'s worth paying attention to.<br /><br /><br /><br /><br /><br />\\nNotice how often that word has come up. If you asked an oracle the\\nsecret to doing great work and the oracle replied with a single\\nword, my bet would be on "curiosity."<br /><br />That doesn\\\'t translate directly to advice. It\\\'s not enough just to\\nbe curious, and you can\\\'t command curiosity anyway. But you can\\nnurture it and let it drive you.<br /><br />Curiosity is the key to all four steps in doing great work: it will\\nchoose the field for you, get you to the frontier, cause you to\\nnotice the gaps in it, and drive you to explore them. The whole\\nprocess is a kind of dance with curiosity.<br /><br /><br /><br /><br /><br />\\nBelieve it or not, I tried to make this essay as short as I could.\\nBut its length at least means it acts as a filter. If you made it\\nthis far, you must be interested in doing great work. And if so\\nyou\\\'re already further along than you might realize, because the\\nset of people willing to want to is small.<br /><br />The factors in doing great work are factors in the literal,\\nmathematical sense, and they are: ability, interest, effort, and\\nluck. Luck by definition you can\\\'t do anything about, so we can\\nignore that. And we can assume effort, if you do in fact want to\\ndo great work. So the problem boils down to ability and interest.\\nCan you find a kind of work where your ability and interest will\\ncombine to yield an explosion of new ideas?<br /><br />Here there are grounds for optimism. There are so many different\\nways to do great work, and even more that are still undiscovered.\\nOut of all those different types of work, the one you\\\'re most suited\\nfor is probably a pretty close match. Probably a comically close\\nmatch. It\\\'s just a question of finding it, and how far into it')]), ResponseOutputMessage(id='msg_3591ea71-8b35-4efd-a5ad-c1c250801971', content=[ResponseOutputText(annotations=[AnnotationFileCitation(file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='https://www.paulgraham.com/greatwork.html', index=361, type='file_citation'), AnnotationFileCitation(file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='https://www.paulgraham.com/greatwork.html', index=676, type='file_citation'), AnnotationFileCitation(file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='https://www.paulgraham.com/greatwork.html', index=948, type='file_citation'), AnnotationFileCitation(file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='https://www.paulgraham.com/greatwork.html', index=1259, type='file_citation'), AnnotationFileCitation(file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='https://www.paulgraham.com/greatwork.html', index=1520, type='file_citation'), AnnotationFileCitation(file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='https://www.paulgraham.com/greatwork.html', index=1747, type='file_citation')], text='To do great work, consider the following principles:\n\n1. **Follow Your Interests**: Engage in work that genuinely excites you. If you find an area intriguing, pursue it without being overly concerned about external pressures or norms. You should create things that you would want for yourself, as this often aligns with what others in your circle might want too.\n\n2. **Work Hard on Ambitious Projects**: Ambition is vital, but it should be tempered by genuine interest. Instead of detailed planning for the future, focus on exciting projects that keep your options open. This approach, known as "staying upwind," allows for adaptability and can lead to unforeseen achievements.\n\n3. **Choose Quality Colleagues**: Collaborating with talented colleagues can significantly affect your own work. Seek out individuals who offer surprising insights and whom you admire. The presence of good colleagues can elevate the quality of your work and inspire you.\n\n4. **Maintain High Morale**: Your attitude towards work and life affects your performance. Cultivating optimism and viewing yourself as lucky rather than victimized can boost your productivity. It’s essential to care for your physical health as well since it directly impacts your mental faculties and morale.\n\n5. **Be Consistent**: Great work often comes from cumulative effort. Daily progress, even in small amounts, can result in substantial achievements over time. Emphasize consistency and make the work engaging, as this reduces the perceived burden of hard labor.\n\n6. **Embrace Curiosity**: Curiosity is a driving force that can guide you in selecting fields of interest, pushing you to explore uncharted territories. Allow it to shape your work and continually seek knowledge and insights.\n\nBy focusing on these aspects, you can create an environment conducive to great work and personal fulfillment.', type='output_text', logprobs=None)], role='assistant', status='completed', type='message')], parallel_tool_calls=False, temperature=None, tool_choice=None, tools=None, top_p=None, background=None, conversation=None, max_output_tokens=None, max_tool_calls=None, previous_response_id=None, prompt=None, prompt_cache_key=None, reasoning=None, safety_identifier=None, service_tier=None, status='completed', text=ResponseTextConfig(format=ResponseFormatText(type='text'), verbosity=None), top_logprobs=None, truncation=None, usage=None, user=None) In [34]: resp.output[1].content[0].text Out[34]: 'To do great work, consider the following principles:\n\n1. **Follow Your Interests**: Engage in work that genuinely excites you. If you find an area intriguing, pursue it without being overly concerned about external pressures or norms. You should create things that you would want for yourself, as this often aligns with what others in your circle might want too.\n\n2. **Work Hard on Ambitious Projects**: Ambition is vital, but it should be tempered by genuine interest. Instead of detailed planning for the future, focus on exciting projects that keep your options open. This approach, known as "staying upwind," allows for adaptability and can lead to unforeseen achievements.\n\n3. **Choose Quality Colleagues**: Collaborating with talented colleagues can significantly affect your own work. Seek out individuals who offer surprising insights and whom you admire. The presence of good colleagues can elevate the quality of your work and inspire you.\n\n4. **Maintain High Morale**: Your attitude towards work and life affects your performance. Cultivating optimism and viewing yourself as lucky rather than victimized can boost your productivity. It’s essential to care for your physical health as well since it directly impacts your mental faculties and morale.\n\n5. **Be Consistent**: Great work often comes from cumulative effort. Daily progress, even in small amounts, can result in substantial achievements over time. Emphasize consistency and make the work engaging, as this reduces the perceived burden of hard labor.\n\n6. **Embrace Curiosity**: Curiosity is a driving force that can guide you in selecting fields of interest, pushing you to explore uncharted territories. Allow it to shape your work and continually seek knowledge and insights.\n\nBy focusing on these aspects, you can create an environment conducive to great work and personal fulfillment.' ``` </details> The relevant output looks like this: ```python >resp.output[1].content[0].annotations [AnnotationFileCitation(file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='https://www.paulgraham.com/greatwork.html', index=361, type='file_citation'), AnnotationFileCitation(file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='https://www.paulgraham.com/greatwork.html', index=676, type='file_citation'), AnnotationFileCitation(file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='https://www.paulgraham.com/greatwork.html', index=948, type='file_citation'), AnnotationFileCitation(file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='https://www.paulgraham.com/greatwork.html', index=1259, type='file_citation'), AnnotationFileCitation(file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='https://www.paulgraham.com/greatwork.html', index=1520, type='file_citation'), AnnotationFileCitation(file_id='file-a98ada68681c4fbeba2201e9c7213fc3', filename='https://www.paulgraham.com/greatwork.html', index=1747, type='file_citation')]``` And ```python In [144]: print(resp.output[1].content[0].text) To do great work, consider the following principles: 1. **Follow Your Interests**: Engage in work that genuinely excites you. If you find an area intriguing, pursue it without being overly concerned about external pressures or norms. You should create things that you would want for yourself, as this often aligns with what others in your circle might want too. 2. **Work Hard on Ambitious Projects**: Ambition is vital, but it should be tempered by genuine interest. Instead of detailed planning for the future, focus on exciting projects that keep your options open. This approach, known as "staying upwind," allows for adaptability and can lead to unforeseen achievements. 3. **Choose Quality Colleagues**: Collaborating with talented colleagues can significantly affect your own work. Seek out individuals who offer surprising insights and whom you admire. The presence of good colleagues can elevate the quality of your work and inspire you. 4. **Maintain High Morale**: Your attitude towards work and life affects your performance. Cultivating optimism and viewing yourself as lucky rather than victimized can boost your productivity. It’s essential to care for your physical health as well since it directly impacts your mental faculties and morale. 5. **Be Consistent**: Great work often comes from cumulative effort. Daily progress, even in small amounts, can result in substantial achievements over time. Emphasize consistency and make the work engaging, as this reduces the perceived burden of hard labor. 6. **Embrace Curiosity**: Curiosity is a driving force that can guide you in selecting fields of interest, pushing you to explore uncharted territories. Allow it to shape your work and continually seek knowledge and insights. By focusing on these aspects, you can create an environment conducive to great work and personal fulfillment. ``` And the code below outputs only periods highlighting that the position/index behaves as expected—i.e., the annotation happens at the end of the sentence. ```python print([resp.output[1].content[0].text[j.index] for j in resp.output[1].content[0].annotations]) Out[41]: ['.', '.', '.', '.', '.', '.'] ``` ## Test Plan Unit tests added. --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-10-07 14:00:56 -04:00
Charlie Doern	6389bf5ffb	fix: make telemetry optional for agents (#3705) ... # What does this PR do? there is a lot of code in the agents API using the telemetry API and its helpers without checking if that API is even enabled. This is the only API besides inference actively using telemetry code, so after this telemetry can be optional for the entire stack resolves #3665 ## Test Plan existing agent tests. Signed-off-by: Charlie Doern <cdoern@redhat.com>	2025-10-07 16:09:03 +02:00
Ashwin Bharambe	61b4238912	feat(api): add extra_body parameter support with shields example (#3670) ... ## Summary Introduce `ExtraBodyField` annotation to enable parameters that arrive via extra_body in client SDKs but are accessible server-side with full typing. These parameters are documented in OpenAPI specs under **`x-llama-stack-extra-body-params`** but excluded from generated SDK signatures. Add `shields` parameter to `create_openai_response` as the first implementation using this pattern. ## Test Plan - added an integration test which checks that shields parameter passed via extra_body reaches server implementation 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> --------- Co-authored-by: Claude <noreply@anthropic.com>	2025-10-03 13:25:09 -07:00
ehhuang	14a94e9894	fix: responses <> chat completion input conversion (#3645) ... # What does this PR do? closes #3268 closes #3498 When resuming from previous response ID, currently we attempt to convert from the stored responses input to chat completion messages, which is not always possible, e.g. for tool calls where some data is lost once converted from chat completion message to repsonses input format. This PR stores the chat completion messages that correspond to the _last_ call to chat completion, which is sufficient to be resumed from in the next responses API call, where we load these saved messages and skip conversion entirely. Separate issue to optimize storage: https://github.com/llamastack/llama-stack/issues/3646 ## Test Plan existing CI tests	2025-10-02 16:01:08 -07:00
Ashwin Bharambe	ef0736527d	feat(tools)!: substantial clean up of "Tool" related datatypes (#3627) ... This is a sweeping change to clean up some gunk around our "Tool" definitions. First, we had two types `Tool` and `ToolDef`. The first of these was a "Resource" type for the registry but we had stopped registering tools inside the Registry long back (and only registered ToolGroups.) The latter was for specifying tools for the Agents API. This PR removes the former and adds an optional `toolgroup_id` field to the latter. Secondly, as pointed out by @bbrowning in https://github.com/llamastack/llama-stack/pull/3003#issuecomment-3245270132, we were doing a lossy conversion from a full JSON schema from the MCP tool specification into our ToolDefinition to send it to the model. There is no necessity to do this -- we ourselves aren't doing any execution at all but merely passing it to the chat completions API which supports this. By doing this (and by doing it poorly), we encountered limitations like not supporting array items, or not resolving $refs, etc. To fix this, we replaced the `parameters` field by `{ input_schema, output_schema }` which can be full blown JSON schemas. Finally, there were some types in our llama-related chat format conversion which needed some cleanup. We are taking this opportunity to clean those up. This PR is a substantial breaking change to the API. However, given our window for introducing breaking changes, this suits us just fine. I will be landing a concurrent `llama-stack-client` change as well since API shapes are changing.	2025-10-02 15:12:03 -07:00
ehhuang	ceca3c056f	chore: fix/add logging categories (#3658) ... # What does this PR do? These aren't controllable by LLAMA_STACK_LOGGING ``` tests/integration/agents/test_persistence.py::test_delete_agents_and_sessions SKIPPED (This ...) [ 3%] tests/integration/agents/test_persistence.py::test_get_agent_turns_and_steps SKIPPED (This t...) [ 7%] tests/integration/agents/test_openai_responses.py::test_responses_store[openai_client-txt=openai/gpt-4o-tools0-True] instantiating llama_stack_client WARNING 2025-10-02 13:14:33,472 root:258 uncategorized: Unknown logging category: testing. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:33,477 root:258 uncategorized: Unknown logging category: providers::utils. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:33,960 root:258 uncategorized: Unknown logging category: tokenizer_utils. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:33,962 root:258 uncategorized: Unknown logging category: models::llama. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:33,963 root:258 uncategorized: Unknown logging category: models::llama. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:33,968 root:258 uncategorized: Unknown logging category: providers::utils. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:33,974 root:258 uncategorized: Unknown logging category: providers::utils. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:33,978 root:258 uncategorized: Unknown logging category: providers::utils. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:35,350 root:258 uncategorized: Unknown logging category: providers::utils. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:35,366 root:258 uncategorized: Unknown logging category: providers::utils. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:35,489 root:258 uncategorized: Unknown logging category: providers::utils. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:35,490 root:258 uncategorized: Unknown logging category: inference_store. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:35,697 root:258 uncategorized: Unknown logging category: providers::utils. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:35,918 root:258 uncategorized: Unknown logging category: providers::utils. Falling back to default 'root' level: 20 INFO 2025-10-02 13:14:35,945 llama_stack.providers.utils.inference.inference_store:74 inference_store: Write queue disabled for SQLite to avoid concurrency issues WARNING 2025-10-02 13:14:36,172 root:258 uncategorized: Unknown logging category: files. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:36,218 root:258 uncategorized: Unknown logging category: providers::utils. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:36,219 root:258 uncategorized: Unknown logging category: vector_io. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:36,231 root:258 uncategorized: Unknown logging category: vector_io. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:36,255 root:258 uncategorized: Unknown logging category: tool_runtime. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:36,486 root:258 uncategorized: Unknown logging category: responses_store. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:36,503 root:258 uncategorized: Unknown logging category: openai::responses. Falling back to default 'root' level: 20 INFO 2025-10-02 13:14:36,524 llama_stack.providers.utils.responses.responses_store:80 responses_store: Write queue disabled for SQLite to avoid concurrency issues WARNING 2025-10-02 13:14:36,528 root:258 uncategorized: Unknown logging category: providers::utils. Falling back to default 'root' level: 20 WARNING 2025-10-02 13:14:36,703 root:258 uncategorized: Unknown logging category: uncategorized. Falling back to default 'root' level: 20 ``` ## Test Plan	2025-10-02 13:10:13 -07:00
Aakanksha Duggal	7e48cc48bc	refactor(agents): migrate to OpenAI chat completions API (#3323)	2025-10-02 06:50:32 -04:00
Jaideep Rao	ca47d90926	fix: Ensure that tool calls with no arguments get handled correctly (#3560) ... # What does this PR do? When a model decides to use an MCP tool call that requires no arguments, it sets the `arguments` field to `None`. This causes the user to see a `400 bad requst error` due to validation errors down the stack because this field gets removed when being parsed by an openai compatible inference provider like vLLM This PR ensures that, as soon as the tool call args are accumulated while streaming, we check to ensure no tool call function arguments are set to None - if they are we replace them with "{}" <!-- If resolving an issue, uncomment and update the line below --> Closes #3456 ## Test Plan Added new unit test to verify that any tool calls with function arguments set to `None` get handled correctly --------- Signed-off-by: Jaideep Rao <jrao@redhat.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-10-01 08:36:57 -04:00
ehhuang	ac7c35fbe6	fix: don't pass default response format in Responses (#3614) ... # What does this PR do? Fireworks doesn't allow repsonse_format with tool use. The default response format is 'text' anyway, so we can safely omit. ## Test Plan Below script failed without the change, runs after. ``` #!/usr/bin/env python3 """ Script to test Responses API with kubernetes-mcp-server. This script: 1. Connects to the llama stack server 2. Uses the Responses API with MCP tools 3. Asks for the list of Kubernetes namespaces using the kubernetes-mcp-server """ import json from openai import OpenAI # Connect to the llama stack server base_url = "http://localhost:8321/v1" client = OpenAI(base_url=base_url, api_key="fake") # Define the MCP tool pointing to the kubernetes-mcp-server # The kubernetes-mcp-server is running on port 3000 with SSE endpoint at /sse mcp_server_url = "http://localhost:3000/sse" tools = [ { "type": "mcp", "server_label": "k8s", "server_url": mcp_server_url, } ] # Create a response request asking for k8s namespaces print("Sending request to list Kubernetes namespaces...") print(f"Using MCP server at: {mcp_server_url}") print("Available tools will be listed automatically by the MCP server.") print() response = client.responses.create( # model="meta-llama/Llama-3.2-3B-Instruct", # Using the vllm model model="fireworks/accounts/fireworks/models/llama4-scout-instruct-basic", # model="openai/gpt-4o", input="what are all the Kubernetes namespaces? Use tool call to `namespaces_list`. make sure to adhere to the tool calling format UNDER ALL CIRCUMSTANCES.", tools=tools, stream=False, ) print("\n" + "=" * 80) print("RESPONSE OUTPUT:") print("=" * 80) # Print the output for i, output in enumerate(response.output): print(f"\n[Output {i + 1}] Type: {output.type}") if output.type == "mcp_list_tools": print(f" Server: {output.server_label}") print(f" Tools available: {[t.name for t in output.tools]}") elif output.type == "mcp_call": print(f" Tool called: {output.name}") print(f" Arguments: {output.arguments}") print(f" Result: {output.output}") if output.error: print(f" Error: {output.error}") elif output.type == "message": print(f" Role: {output.role}") print(f" Content: {output.content}") print("\n" + "=" * 80) print("FINAL RESPONSE TEXT:") print("=" * 80) print(response.output_text) ```	2025-09-30 14:52:24 -07:00
grs	d350e3662b	feat: add support for require_approval argument when creating response (#3608) ... # What does this PR do? This PR adds support for the require_approval on an mcp tool definition passed to create response in the Responses API. This allows the caller to indicate whether they want to approve calls to that server, or let them be called without approval. Closes #3443 ## Test Plan Tested both approval and denial. Added automated integration test for both cases. --------- Signed-off-by: Gordon Sim <gsim@redhat.com> Co-authored-by: Matthew Farrellee <matt@cs.wisc.edu>	2025-09-30 14:18:34 -07:00
ehhuang	6cce553c93	fix: mcp tool with array type should include items (#3602) ... # What does this PR do? Fixes error: ``` [ERROR] Error executing endpoint route='/v1/openai/v1/responses' method='post': Error code: 400 - {'error': {'message': "Invalid schema for function 'pods_exec': In context=('properties', 'command'), array schema missing items.", 'type': 'invalid_request_error', 'param': 'tools[7].function.parameters', 'code': 'invalid_function_parameters'}} ``` From script: ``` #!/usr/bin/env python3 """ Script to test Responses API with kubernetes-mcp-server. This script: 1. Connects to the llama stack server 2. Uses the Responses API with MCP tools 3. Asks for the list of Kubernetes namespaces using the kubernetes-mcp-server """ import json from openai import OpenAI # Connect to the llama stack server base_url = "http://localhost:8321/v1/openai/v1" client = OpenAI(base_url=base_url, api_key="fake") # Define the MCP tool pointing to the kubernetes-mcp-server # The kubernetes-mcp-server is running on port 3000 with SSE endpoint at /sse mcp_server_url = "http://localhost:3000/sse" tools = [ { "type": "mcp", "server_label": "k8s", "server_url": mcp_server_url, } ] # Create a response request asking for k8s namespaces print("Sending request to list Kubernetes namespaces...") print(f"Using MCP server at: {mcp_server_url}") print("Available tools will be listed automatically by the MCP server.") print() response = client.responses.create( # model="meta-llama/Llama-3.2-3B-Instruct", # Using the vllm model model="openai/gpt-4o", input="what are all the Kubernetes namespaces? Use tool call to `namespaces_list`. make sure to adhere to the tool calling format.", tools=tools, stream=False, ) print("\n" + "=" * 80) print("RESPONSE OUTPUT:") print("=" * 80) # Print the output for i, output in enumerate(response.output): print(f"\n[Output {i + 1}] Type: {output.type}") if output.type == "mcp_list_tools": print(f" Server: {output.server_label}") print(f" Tools available: {[t.name for t in output.tools]}") elif output.type == "mcp_call": print(f" Tool called: {output.name}") print(f" Arguments: {output.arguments}") print(f" Result: {output.output}") if output.error: print(f" Error: {output.error}") elif output.type == "message": print(f" Role: {output.role}") print(f" Content: {output.content}") print("\n" + "=" * 80) print("FINAL RESPONSE TEXT:") print("=" * 80) print(response.output_text) ``` ## Test Plan new unit tests script now runs successfully	2025-09-29 23:11:41 -07:00
Kai Wu	aab22dc759	fix: adding mime type of application/json support (#3452) ... # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> This PR fix #3300 by adding mime type of application/json support in [agent_instance.py](4a59961a6c/llama_stack/providers/inline/agents/meta_reference/agent_instance.py (L923)) <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[3300] --> ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> all related pytest passed, see log: ``` ./scripts/unit-tests.sh tests/unit/providers/agent/test_get_raw_document_text.py -vvv /Users/kaiwu/work/kaiwu/llama-stack/.venv/bin/python3 Uninstalled 22 packages in 5.65s Installed 47 packages in 1.24s ================= test session starts ================= platform darwin -- Python 3.12.9, pytest-8.4.2, pluggy-1.6.0 -- /Users/kaiwu/work/kaiwu/llama-stack/.venv/bin/python cachedir: .pytest_cache metadata: {'Python': '3.12.9', 'Platform': 'macOS-15.6.1-arm64-arm-64bit', 'Packages': {'pytest': '8.4.2', 'pluggy': '1.6.0'}, 'Plugins': {'anyio': '4.9.0', 'html': '4.1.1', 'socket': '0.7.0', 'asyncio': '1.1.0', 'json-report': '1.5.0', 'timeout': '2.4.0', 'metadata': '3.1.1', 'cov': '6.2.1', 'nbval': '0.11.0'}} rootdir: /Users/kaiwu/work/kaiwu/llama-stack configfile: pyproject.toml plugins: anyio-4.9.0, html-4.1.1, socket-0.7.0, asyncio-1.1.0, json-report-1.5.0, timeout-2.4.0, metadata-3.1.1, cov-6.2.1, nbval-0.11.0 asyncio: mode=Mode.AUTO, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function collected 14 items tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_supports_text_mime_types PASSED tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_supports_yaml_mime_type PASSED tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_supports_deprecated_text_yaml_with_warning PASSED tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_deprecated_text_yaml_with_url PASSED tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_deprecated_text_yaml_with_text_content_item PASSED tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_supports_json_mime_type PASSED tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_with_json_url PASSED tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_with_json_text_content_item PASSED tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_rejects_unsupported_mime_types PASSED tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_with_url_content PASSED tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_with_yaml_url PASSED tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_with_text_content_item PASSED tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_with_yaml_text_content_item PASSED tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_rejects_unexpected_content_type PASSED ================ slowest 10 durations ================= 0.00s call tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_deprecated_text_yaml_with_url 0.00s call tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_rejects_unsupported_mime_types 0.00s call tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_rejects_unexpected_content_type 0.00s setup tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_supports_text_mime_types 0.00s teardown tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_supports_text_mime_types 0.00s call tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_with_yaml_url 0.00s call tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_with_url_content 0.00s teardown tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_rejects_unsupported_mime_types 0.00s call tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_with_json_url 0.00s call tests/unit/providers/agent/test_get_raw_document_text.py::test_get_raw_document_text_supports_text_mime_types ================= 14 passed in 0.14s ================== Generating coverage report... Wrote HTML report to htmlcov-3.12/index.html ```	2025-09-29 11:27:31 -07:00
Tami Takamiya	65f7b81e98	feat: Add items and title to ToolParameter/ToolParamDefinition (#3003) ... # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> Add items and title to ToolParameter/ToolParamDefinition. Adding items will resolve the issue that occurs with Gemini LLM when an MCP tool has array-type properties. <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> Unite test cases will be added. --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Kai Wu <kaiwu@meta.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-09-27 11:35:29 -07:00
grs	da73f1a180	fix: ensure assistant message is followed by tool call message as expected by openai (#3224) ... # What does this PR do? As described in #3134 a langchain example works against openai's responses impl, but not against llama stack's. This turned out to be due to the order of the inputs. The langchain example has the two function call outputs first, followed by each call result in turn. This seems to be valid as it is accepted by openai's impl. However in llama stack, these inputs are converted to chat completion inputs and the resulting order for that api is not accpeted by openai. This PR fixes the issue by ensuring that the converted chat completions inputs are in the expected order. Closes #3134 ## Test Plan Added unit and integration tests. Verified this fixes original issue as reported. --------- Signed-off-by: Gordon Sim <gsim@redhat.com>	2025-08-22 10:42:03 -07:00
Mustafa Elbehery	c3b2b06974	refactor(logging): rename llama_stack logger categories (#3065) ... # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> This PR renames categories of llama_stack loggers. This PR aligns logging categories as per the package name, as well as reviews from initial https://github.com/meta-llama/llama-stack/pull/2868. This is a follow up to #3061. <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> Replaces https://github.com/meta-llama/llama-stack/pull/2868 Part of https://github.com/meta-llama/llama-stack/issues/2865 cc @leseb @rhuss Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>	2025-08-21 17:31:04 -07:00
grs	14082b22af	fix: handle mcp tool calls in previous response correctly (#3155) ... # What does this PR do? Handles MCP tool calls in a previous response Closes #3105 ## Test Plan Made call to create response with tool call, then made second call with the first linked through previous_response_id. Did not get error. Also added unit test. Signed-off-by: Gordon Sim <gsim@redhat.com>	2025-08-20 14:12:15 -07:00
Mustafa Elbehery	3f8df167f3	chore(pre-commit): add pre-commit hook to enforce llama_stack logger usage (#3061) ... # What does this PR do? This PR adds a step in pre-commit to enforce using `llama_stack` logger. Currently, various parts of the code base uses different loggers. As a custom `llama_stack` logger exist and used in the codebase, it is better to standardize its utilization. Signed-off-by: Mustafa Elbehery <melbeher@redhat.com> Co-authored-by: Matthew Farrellee <matt@cs.wisc.edu>	2025-08-20 07:15:35 -04:00
Ashwin Bharambe	a6e2c18909	Revert "refactor(agents): migrate to OpenAI chat completions API" (#3167) ... Reverts llamastack/llama-stack#3097 It has broken agents tests.	2025-08-15 12:01:07 -07:00
Aakanksha Duggal	e743d3fdf6	refactor(agents): migrate to OpenAI chat completions API (#3097) ... Replace chat_completion calls with openai_chat_completion to eliminate dependency on legacy inference APIs. # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> <!-- If resolving an issue, uncomment and update the line below --> Closes #3067 ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* -->	2025-08-15 10:51:41 -07:00
ashwinb	ba664474de	feat(responses): add mcp list tool streaming event (#3159) ... # What does this PR do? Adds proper streaming events for MCP tool listing (`mcp_list_tools.in_progress` and `mcp_list_tools.completed`). Also refactors things a bit more. ## Test Plan Verified existing integration tests pass with the refactored code. The test `test_response_streaming_multi_turn_tool_execution` has been updated to check for the new MCP list tools streaming events	2025-08-15 00:05:36 +00:00
ashwinb	9324e902f1	refactor(responses): move stuff into some utils and add unit tests (#3158) ... # What does this PR do? Refactors the OpenAI response conversion utilities by moving helper functions from `openai_responses.py` to `utils.py`. Adds unit tests.	2025-08-15 00:05:36 +00:00
ashwinb	47d5af703c	chore(responses): Refactor Responses Impl to be civilized (#3138) ... # What does this PR do? Refactors the OpenAI responses implementation by extracting streaming and tool execution logic into separate modules. This improves code organization by: 1. Creating a new `StreamingResponseOrchestrator` class in `streaming.py` to handle the streaming response generation logic 2. Moving tool execution functionality to a dedicated `ToolExecutor` class in `tool_executor.py` ## Test Plan Existing tests	2025-08-15 00:05:35 +00:00
Ashwin Bharambe	e1e161553c	feat(responses): add MCP argument streaming and content part events (#3136) ... # What does this PR do? Adds content part streaming events to the OpenAI-compatible Responses API to support more granular streaming of response content. This introduces: 1. New schema types for content parts: `OpenAIResponseContentPart` with variants for text output and refusals 2. New streaming event types: - `OpenAIResponseObjectStreamResponseContentPartAdded` for when content parts begin - `OpenAIResponseObjectStreamResponseContentPartDone` for when content parts complete 3. Implementation in the reference provider to emit these events during streaming responses. Also emits MCP arguments just like function call ones. ## Test Plan Updated existing streaming tests to verify content part events are properly emitted	2025-08-13 16:34:26 -07:00
Ashwin Bharambe	8638537d14	feat(responses): stream progress of tool calls (#3135) ... # What does this PR do? Enhances tool execution streaming by adding support for real-time progress events during tool calls. This implementation adds streaming events for MCP and web search tools, including in-progress, searching, completed, and failed states. The refactored `_execute_tool_call` method now returns an async iterator that yields streaming events throughout the tool execution lifecycle. ## Test Plan Updated the integration test `test_response_streaming_multi_turn_tool_execution` to verify the presence and structure of new streaming events, including: - Checking for MCP in-progress and completed events - Verifying that progress events contain required fields (item_id, output_index, sequence_number) - Ensuring completed events have the necessary sequence_number field	2025-08-13 16:31:25 -07:00
Ashwin Bharambe	5b312a80b9	feat(responses): improve streaming for function calls (#3124) ... Emit streaming events for function calls ## Test Plan Improved the test case	2025-08-13 11:23:27 -07:00
Francisco Arceo	92aca434a7	fix: Fix list_sessions() (#3114) ... # What does this PR do? 1. Updates `AgentPersistence.list_sessions()` to properly filter out `Turn` keys from `Session` keys. 2. Adds a suite of unit tests to confirm the `list_sessions()` behavior and tests the failed sample in https://github.com/meta-llama/llama-stack/issues/3048 ## Fixes https://github.com/meta-llama/llama-stack/issues/3048 ## Test Plan Unit tests added. --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-08-13 07:46:26 -07:00
Ashwin Bharambe	3d90117891	chore(tests): fix responses and vector_io tests (#3119) ... Some fixes to MCP tests. And a bunch of fixes for Vector providers. I also enabled a bunch of Vector IO tests to be used with `LlamaStackLibraryClient` ## Test Plan Run Responses tests with llama stack library client: ``` pytest -s -v tests/integration/non_ci/responses/ --stack-config=server:starter \ --text-model openai/gpt-4o \ --embedding-model=sentence-transformers/all-MiniLM-L6-v2 \ -k "client_with_models" ``` Do the same with `-k openai_client` The rest should be taken care of by CI.	2025-08-12 16:15:53 -07:00
Ashwin Bharambe	1721aafc1f	feat(responses): type file results properly (#3117) ... Another thing our tests implicitly depended on.	2025-08-12 10:39:09 -07:00
Ashwin Bharambe	4fec49dfdb	feat(responses): add include parameter (#3115) ... Well our Responses tests use it so we better include it in the API, no? I discovered it because I want to make sure `llama-stack-client` can be used always instead of `openai-python` as the client (we do want to be _truly_ compatible.)	2025-08-12 10:24:01 -07:00
Nathan Weinberg	68b0071861	chore: standardize session not found error (#3031) ... # What does this PR do? 1. Creates a new `SessionNotFoundError` class 2. Implements the new class where appropriate Relates to #2379 Signed-off-by: Nathan Weinberg <nweinber@redhat.com>	2025-08-04 13:12:02 -07:00
Ashwin Bharambe	2665f00102	chore(rename): move llama_stack.distribution to llama_stack.core (#2975) ... We would like to rename the term `template` to `distribution`. To prepare for that, this is a precursor. cc @leseb	2025-07-30 23:30:53 -07:00
Omer Tuchfeld	5e18d4d097	fix(agent): ensure turns are sorted (#2854) ... # What does this PR do? Ensures that session turns retrieved from the agent persistence layer are sorted by their `started_at` timestamp, as the key-value store does not guarantee order. Closes #2852 ## Test Plan - [ ] Add unit tests	2025-07-22 10:24:51 -07:00
Ondrej Metelka	89c49eb003	feat: Allow application/yaml as mime_type (#2575) ... # What does this PR do? Allow application/yaml as mime_type for documents. ## Test Plan Added unit tests.	2025-07-21 15:43:32 +02:00
Mustafa Elbehery	28343fea51	chore(api): add mypy coverage to meta_reference_safety (#2661) ... # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> This PR adds static type coverage to `llama-stack` Part of https://github.com/meta-llama/llama-stack/issues/2647 <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>	2025-07-09 10:22:34 +02:00
Sébastien Han	df6ce8befa	fix: only load mcp when enabled in tool_group (#2621) ... # What does this PR do? The agent code is currently importing MCP modules even when MCP isn’t enabled. Do we consider this worth fixing, or are we treating MCP as a first-class dependency? I believe we should treat it as such. If everyone agrees, let’s go ahead and close this. Note: The current setup breaks if someone builds a distro without including MCP in tool_group but still serves the agent API. Also, we should bump the MCP version to support streamable responses, as SSE is being deprecated. Signed-off-by: Sébastien Han <seb@redhat.com>	2025-07-04 20:27:05 +05:30
Akram Ben Aissi	f4950f4ef0	fix: AccessDeniedError leads to HTTP 500 instead of error 403 (#2595) ... Resolves access control error visibility issues where 500 errors were returned instead of proper 403 responses with actionable error messages. • Enhance AccessDeniedError with detailed context and improve exception handling • Enhanced AccessDeniedError class to include user, action, and resource context - Added constructor parameters for action, resource, and user - Generate detailed error messages showing user principal, attributes, and attempted resource - Backward compatible with existing usage (falls back to generic message) • Updated exception handling in server.py - Import AccessDeniedError from access_control module - Return proper 403 status codes with detailed error messages - Separate handling for PermissionError (generic) vs AccessDeniedError (detailed) • Enhanced error context at raise sites - Updated routing_tables/common.py to pass action, resource, and user context - Updated agents persistence to include context in access denied errors - Provides better debugging information for access control issues • Added comprehensive unit tests - Created tests/unit/server/test_server.py with 13 test cases - Covers AccessDeniedError with and without context - Tests all exception types (ValidationError, BadRequestError, AuthenticationRequiredError, etc.) - Validates proper HTTP status codes and error message formats # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan ``` server: port: 8321 access_policy: - permit: principal: admin actions: [create, read, delete] when: user with admin in groups - permit: actions: [read] when: user with system:authenticated in roles ``` then: ``` curl --request POST --url http://localhost:8321/v1/vector-dbs \ --header "Authorization: Bearer your-bearer" \ --data '{ "vector_db_id": "my_demo_vector_db", "embedding_model": "ibm-granite/granite-embedding-125m-english", "embedding_dimension": 768, "provider_id": "milvus" }' ``` depending if user is in group admin or not, you should get the `AccessDeniedError`. Before this PR, this was leading to an error 500 and `Traceback` displayed in the logs. After the PR, logs display a simpler error (unless DEBUG logging is set) and a 403 Forbidden error is returned on the HTTP side. --------- Signed-off-by: Akram Ben Aissi <<akram.benaissi@gmail.com>>	2025-07-03 10:50:49 -07:00
Krzysztof Malczuk	be9bf68246	feat: Add webmethod for deleting openai responses (#2160) ... # What does this PR do? This PR creates a webmethod for deleting open AI responses, adds and implementation for it and makes an integration test for the OpenAI delete response method. [//]: # (If resolving an issue, uncomment and update the line below) # (Closes #2077) ## Test Plan Ran the standard tests and the pre-commit hooks and the unit tests. # (## Documentation) For this pr I made the routes and implementation based on the current get and create methods. The unit tests were not able to handle this test due to the mock interface in use, which did not allow for effective CRUD to be tested. I instead created an integration test to match the existing ones in the test_openai_responses.	2025-06-30 11:28:02 +02:00
Sébastien Han	ac5fd57387	chore: remove nested imports (#2515) ... # What does this PR do? * Given that our API packages use "import *" in `__init.py__` we don't need to do `from llama_stack.apis.models.models` but simply from llama_stack.apis.models. The decision to use `import *` is debatable and should probably be revisited at one point. * Remove unneeded Ruff F401 rule * Consolidate Ruff F403 rule in the pyprojectfrom llama_stack.apis.models.models Signed-off-by: Sébastien Han <seb@redhat.com>	2025-06-26 08:01:05 +05:30
Ben Browning	2d9fd041eb	fix: annotations list and web_search_preview in Responses (#2520) ... # What does this PR do? These are a couple of fixes to get an example LangChain app working with our OpenAI Responses API implementation. The Responses API spec requires an annotations array in `output[*].content[*].annotations` and we were not providing one. So, this adds that as an empty list, even though we don't do anything to populate it yet. This prevents an error from client libraries like Langchain that expect this field to always exist, even if an empty list. The other fix is `web_search_preview` is a valid name for the web search tool in the Responses API, but we only responded to `web_search` or `web_search_preview_2025_03_11`. ## Test Plan The existing Responses unit tests were expanded to test these cases, via: ``` pytest -sv tests/unit/providers/agents/meta_reference/test_openai_responses.py ``` The existing test_openai_responses.py integration tests still pass with this change, tested as below with Fireworks: ``` uv run llama stack run llama_stack/templates/starter/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ uv run pytest -sv tests/integration/agents/test_openai_responses.py \ --text-model accounts/fireworks/models/llama4-scout-instruct-basic ``` Lastly, this example LangChain app now works with Llama stack (tested with Ollama in the starter template in this case). This LangChain code is using the example snippets for using Responses API at https://python.langchain.com/docs/integrations/chat/openai/#responses-api ```python from langchain_openai import ChatOpenAI llm = ChatOpenAI( base_url="http://localhost:8321/v1/openai/v1", api_key="fake", model="ollama/meta-llama/Llama-3.2-3B-Instruct", ) tool = {"type": "web_search_preview"} llm_with_tools = llm.bind_tools([tool]) response = llm_with_tools.invoke("What was a positive news story from today?") print(response.content) ``` Signed-off-by: Ben Browning <bbrownin@redhat.com>	2025-06-26 07:59:33 +05:30
ehhuang	d3b60507d7	feat: support auth attributes in inference/responses stores (#2389) ... # What does this PR do? Inference/Response stores now store user attributes when inserting, and respects them when fetching. ## Test Plan pytest tests/unit/utils/test_sqlstore.py	2025-06-20 10:24:45 -07:00
Charlie Doern	d12f195f56	feat: drop python 3.10 support (#2469) ... # What does this PR do? dropped python3.10, updated pyproject and dependencies, and also removed some blocks of code with special handling for enum.StrEnum Closes #2458 Signed-off-by: Charlie Doern <cdoern@redhat.com>	2025-06-19 12:07:14 +05:30
ehhuang	db2cd9e8f3	feat: support filters in file search (#2472) ... # What does this PR do? Move to use vector_stores.search for file search tool in Responses, which supports filters. closes #2435 ## Test Plan Added e2e test with fitlers. myenv ❯ llama stack run llama_stack/templates/fireworks/run.yaml pytest -sv tests/verifications/openai_api/test_responses.py \ -k 'file_search and filters' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=meta-llama/Llama-3.3-70B-Instruct	2025-06-18 21:50:55 -07:00
Ben Browning	941f505eb0	feat: File search tool for Responses API (#2426) ... # What does this PR do? This is an initial working prototype of wiring up the `file_search` builtin tool for the Responses API to our existing rag knowledge search tool. This is me seeing what I could pull together on top of the bits we already have merged. This may not be the ideal way to implement this, and things like how I shuffle the vector store ids from the original response API tool request to the actual tool execution feel a bit hacky (grep for `tool_kwargs["vector_db_ids"]` in `_execute_tool_call` to see what I mean). ## Test Plan I stubbed in some new tests to exercise this using text and pdf documents. Note that this is currently under tests/verification only because it sometimes flakes with tool calling of the small Llama-3.2-3B model we run in CI (and that I use as an example below). We'd want to make the test a bit more robust in some way if we moved this over to tests/integration and ran it in CI. ### OpenAI SaaS (to verify test correctness) ``` pytest -sv tests/verifications/openai_api/test_responses.py \ -k 'file_search' \ --base-url=https://api.openai.com/v1 \ --model=gpt-4o ``` ### Fireworks with faiss vector store ``` llama stack run llama_stack/templates/fireworks/run.yaml pytest -sv tests/verifications/openai_api/test_responses.py \ -k 'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=meta-llama/Llama-3.3-70B-Instruct ``` ### Ollama with faiss vector store This sometimes flakes on Ollama because the quantized small model doesn't always choose to call the tool to answer the user's question. But, it often works. ``` ollama run llama3.2:3b INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ llama stack run ./llama_stack/templates/ollama/run.yaml \ --image-type venv \ --env OLLAMA_URL="http://0.0.0.0:11434" pytest -sv tests/verifications/openai_api/test_responses.py \ -k'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=meta-llama/Llama-3.2-3B-Instruct ``` ### OpenAI provider with sqlite-vec vector store ``` llama stack run ./llama_stack/templates/starter/run.yaml --image-type venv pytest -sv tests/verifications/openai_api/test_responses.py \ -k 'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=openai/gpt-4o-mini ``` ### Ensure existing vector store integration tests still pass ``` ollama run llama3.2:3b INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ llama stack run ./llama_stack/templates/ollama/run.yaml \ --image-type venv \ --env OLLAMA_URL="http://0.0.0.0:11434" LLAMA_STACK_CONFIG=http://localhost:8321 \ pytest -sv tests/integration/vector_io \ --text-model "meta-llama/Llama-3.2-3B-Instruct" \ --embedding-model=all-MiniLM-L6-v2 ``` --------- Signed-off-by: Ben Browning <bbrownin@redhat.com>	2025-06-13 14:32:48 -04:00
Ashwin Bharambe	3251b44d8a	refactor: unify stream and non-stream impls for responses (#2388) ... The non-streaming version is just a small layer on top of the streaming version - just pluck off the final `response.completed` event and return that as the response! This PR also includes a couple other changes which I ended up making while working on it on a flight: - changes to `ollama` so it does not pull embedding models unconditionally - a small fix to library client to make the stream and non-stream cases a bit more symmetric	2025-06-05 17:48:09 +02:00
Ashwin Bharambe	ed69c1b3cc	feat(responses): add more streaming response types (#2375)	2025-06-03 15:48:41 -07:00
grs	7c1998db25	feat: fine grained access control policy (#2264) ... This allows a set of rules to be defined for determining access to resources. The rules are (loosely) based on the cedar policy format. A rule defines a list of action either to permit or to forbid. It may specify a principal or a resource that must match for the rule to take effect. It may also specify a condition, either a 'when' or an 'unless', with additional constraints as to where the rule applies. A list of rules is held for each type to be protected and tried in order to find a match. If a match is found, the request is permitted or forbidden depening on the type of rule. If no match is found, the request is denied. If no rules are specified for a given type, a rule that allows any action as long as the resource attributes match the user attributes is added (i.e. the previous behaviour is the default. Some examples in yaml: ``` model: - permit: principal: user-1 actions: [create, read, delete] comment: user-1 has full access to all models - permit: principal: user-2 actions: [read] resource: model-1 comment: user-2 has read access to model-1 only - permit: actions: [read] when: user_in: resource.namespaces comment: any user has read access to models with matching attributes vector_db: - forbid: actions: [create, read, delete] unless: user_in: role::admin comment: only user with admin role can use vector_db resources ``` --------- Signed-off-by: Gordon Sim <gsim@redhat.com>	2025-06-03 14:51:12 -07:00
Ben Browning	8bee2954be	feat: Structured output for Responses API (#2324) ... # What does this PR do? This adds the missing `text` parameter to the Responses API that is how users control structured outputs. All we do with that parameter is map it to the corresponding chat completion response_format. ## Test Plan The new unit tests exercise the various permutations allowed for this property, while a couple of new verification tests actually use it for real to verify the model outputs are following the format as expected. Unit tests: `python -m pytest -s -v tests/unit/providers/agents/meta_reference/test_openai_responses.py` Verification tests: ``` llama stack run llama_stack/templates/together/run.yaml pytest -s -vv 'tests/verifications/openai_api/test_responses.py' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model meta-llama/Llama-4-Scout-17B-16E-Instruct ``` Note that the verification tests can only be run with a real Llama Stack server (as opposed to using the library client via `--provider=stack:together`) because the Llama Stack python client is not yet updated to accept this text field. Signed-off-by: Ben Browning <bbrownin@redhat.com>	2025-06-03 14:43:00 -07:00
Ashwin Bharambe	dbe4e84aca	feat(responses): implement full multi-turn support (#2295) ... I think the implementation needs more simplification. Spent way too much time trying to get the tests pass with models not co-operating :( Finally had to switch claude-sonnet to get things to pass reliably. ### Test Plan ``` export TAVILY_SEARCH_API_KEY=... export OPENAI_API_KEY=... uv run pytest -p no:warnings \ -s -v tests/verifications/openai_api/test_responses.py \ --provider=stack:starter \ --model openai/gpt-4o ```	2025-06-02 15:35:49 -07:00
Ben Browning	277f8690ef	fix: Responses streaming tools don't concatenate None and str (#2326) ... # What does this PR do? This adds a check to ensure we don't attempt to concatenate `None + str` or `str + None` when building up our arguments for streaming tool calls in the Responses API. ## Test Plan All existing tests pass with this change. Unit tests: ``` python -m pytest -s -v \ tests/unit/providers/agents/meta_reference/test_openai_responses.py ``` Integration tests: ``` llama stack run llama_stack/templates/together/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ python -m pytest -s -v \ tests/integration/agents/test_openai_responses.py \ --text-model meta-llama/Llama-4-Scout-17B-16E-Instruct ``` Verification tests: ``` llama stack run llama_stack/templates/together/run.yaml pytest -s -v 'tests/verifications/openai_api/test_responses.py' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model meta-llama/Llama-4-Scout-17B-16E-Instruct ``` Additionally, the manual example using Codex CLI from #2325 now succeeds instead of throwing a 500 error. Closes #2325 Signed-off-by: Ben Browning <bbrownin@redhat.com>	2025-05-31 18:24:04 -07:00