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processes.",{"redirection":40,"thumbnail":2294,"tags":2296,"date":2297,"hidden":40},{"src":2295,"provider":74},"/assets/blog/thumbnails/european-financial-review-th.png",[228],"2023-09-24","/news/european-financial-review",{"title":2276,"description":2292},{"loc":2298},"9.news/869.european-financial-review","fCHz0-hkdrUrwwiMr7H6BRNOh-qbOaVufmBzqVArkQ4",{"id":2304,"title":2305,"author":2306,"body":2310,"description":2324,"extension":37,"meta":2325,"navigation":40,"path":2330,"seo":2331,"sitemap":2332,"stem":2333,"__hash__":2334},"content/9.news/871.wearewomen-article.md","Enabling AI to unlearn and self-correct like a human",{"name":2307,"description":33,"img":2308,"website":2309},"We Are Tech 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News","/assets/content/blog/techeu-avatar.png","https://tech.eu/",{"type":11,"value":2439,"toc":2450},[2440,2442,2448],[14,2441,17],{"id":16},[19,2443,21,2444,28],{},[23,2445,2446],{"href":2446,"rel":2447},"https://tech.eu/2023/08/22/pathway-machine-unlearning/",[27],[30,2449],{"url":2446},{"title":33,"searchDepth":34,"depth":34,"links":2451},[],"Pathway innovates real-time data processing, combating outdated or inaccurate data in AI models",{"redirection":40,"layout":39,"thumbnail":2454,"tags":2456,"date":2457,"hidden":40},{"src":2455,"provider":74},"/assets/blog/thumbnails/techeu-th.png",[228],"2023-08-22","/news/techeu-realtime-value-to-logistics",{"title":2432,"description":2452},{"loc":2458},"9.news/875.techeu-realtime-value-to-logistics","WsBuBxZA6mUkj0GqJfOWebk1ycLQMERD0Gb8JB5g-mo",{"id":2464,"title":2465,"author":2466,"body":2467,"description":2481,"extension":37,"meta":2482,"navigation":40,"path":2487,"seo":2488,"sitemap":2489,"stem":2490,"__hash__":2491},"content/9.news/876.la-revue-ia-article.md","LLM and real-time learning article for 'La revue IA'",{"id":1153,"url":1154,"name":1155,"description":1156,"img":1157,"provider":74,"linkedin":1158},{"type":11,"value":2468,"toc":2479},[2469,2471,2477],[14,2470,17],{"id":16},[19,2472,21,2473,28],{},[23,2474,2475],{"href":2475,"rel":2476},"https://larevueia.fr/llm-et-apprentissage-en-temps-reel/",[27],[30,2478],{"url":2475},{"title":33,"searchDepth":34,"depth":34,"links":2480},[],"Claire Nouet, COO at Pathway wrote an article about LLM and real-time learning describing limits of static models, vector and index database utility for LLM models, and Pathway LLM App",{"redirection":40,"layout":39,"lang":697,"thumbnail":2483,"tags":2485,"date":2486,"hidden":40},{"src":2484,"provider":74},"/assets/blog/thumbnails/la-revue-th.png",[228],"2023-08-27","/news/la-revue-ia-article",{"title":2465,"description":2481},{"loc":2487},"9.news/876.la-revue-ia-article","y3Gnbde2Zk3NSchYpKJp4TSKGWjyT6SL2Z8lnU-6UPg",{"id":2493,"title":2494,"author":2495,"body":2497,"description":2554,"extension":37,"meta":2555,"navigation":40,"path":2560,"seo":2561,"sitemap":2562,"stem":2563,"__hash__":2564},"content/9.news/877.maddyness-gen-ai-mapping.md","Pathway named as a promising Generative AI leader (in French)",{"name":1393,"img":2496,"website":1395},"/assets/content/blog/maddyness-avatar.png",{"type":11,"value":2498,"toc":2552},[2499,2502,2515,2523,2530,2533,2539,2545],[14,2500,2494],{"id":2501},"pathway-named-as-a-promising-generative-ai-leader-in-french",[19,2503,2504,2505,2510,2511,1804],{},"The generative AI market was worth almost $40 billion in 2022 and should approach $70 billion by the end of this year, according to ",[23,2506,2509],{"href":2507,"rel":2508},"https://www.bloomberg.com/company/press/generative-ai-to-become-a-1-3-trillion-market-by-2032-research-finds/",[27],"Bloomberg Intelligence",". And this is just the beginning, as the market is expected to reach $1,300 billion by 2032 (Source: ",[23,2512,2514],{"href":2507,"rel":2513},[27],"Bloomberg: Generative AI to Become a $1.3 Trillion Market by 2032, Research Finds.",[19,2516,2517,2522],{},[23,2518,2521],{"href":2519,"rel":2520},"https://www.resonance.vc/",[27],"Resonance Venture",", a French Venture Capital, released a mapping of the main GenAI players in France, including established French companies such as Hugging Face.",[19,2524,2525,2529],{},[23,2526,1803],{"href":2527,"rel":2528},"https://pathway.com/",[27]," is the single, integrated processing layer for real-time intelligence. It allows easy mix-and-match of batch, streaming, and LLM architectures - all within one engine.",[19,2531,2532],{},"Real-time learning is made possible by an effective and scalable engine, which powers LLMs and machine learning models. These models are automatically updated thanks to a framework that combines streaming and batch data, and which is user-friendly and flexible for developers, data engineers, and data scientists. Leading experts in the field of artificial intelligence make up the team, which is headed by Zuzanna Stamirowska. They include CTO Jan Chorowski, co-authors of Geoff Hinton and Yoshua Bengio, as well as Business Angel Lukasz Kaiser, who co-authored Tensor Flow and is also known as the \"T\" in ChatGPT.",[19,2534,2535,2538],{},[1217,2536,2537],{},"Zuzanna Stamirowska, CEO & Co-Founder of Pathway",", comments: “Our mission has been to enable real-time data processing, while giving developers a simple experience regardless of whether they work with batch, streaming, or LLM systems. Pathway is truly facilitating the convergence of historical and real-time data for the first time.”",[2540,2541],"article-img",{":zoomable":2542,"alt":2543,"src":2544},"true","A list of companies in the ecosystem where Pathway has a place in data preparation","assets/content/blog/ecosysteme-gen-ai-francais.png",[19,2546,2547,2548],{},"Read the full article on Maddyness:\n",[23,2549,2550],{"href":2550,"rel":2551},"https://www.maddyness.com/2023/07/21/france-europe-ia-generative/",[27],{"title":33,"searchDepth":34,"depth":34,"links":2553},[],"Maddyness reposted a mapping of future European GenAI leaders",{"layout":39,"thumbnail":2556,"tags":2558,"date":2559,"hidden":40},{"src":2557},"/assets/content/blog/maddyness-gen-ai-mapping-th.png",[228],"2023-07-26","/news/maddyness-gen-ai-mapping",{"title":2494,"description":2554},{"loc":2560},"9.news/877.maddyness-gen-ai-mapping","Eihm6ie265YMlNhHk1f07Qd20bUSgCAPSi1bGAZB4Gw",{"id":2566,"title":2567,"author":2568,"body":2569,"description":2583,"extension":37,"meta":2584,"navigation":40,"path":2588,"seo":2589,"sitemap":2590,"stem":2591,"__hash__":2592},"content/9.news/878.maddyness-article-about-pathway.md","French deep tech start-up announces the general launch of its data processing engine",{"name":1393,"img":1394,"provider":74,"website":1395},{"type":11,"value":2570,"toc":2581},[2571,2573,2579],[14,2572,17],{"id":16},[19,2574,21,2575,28],{},[23,2576,2577],{"href":2577,"rel":2578},"https://www.maddyness.com/2023/07/26/pathway-ia/",[27],[30,2580],{"url":2577},{"title":33,"searchDepth":34,"depth":34,"links":2582},[],"Comment Pathway veut permettre aux IA d’apprendre et «d’oublier» en temps réel",{"redirection":40,"lang":697,"thumbnail":2585,"tags":2587,"date":2559,"hidden":40},{"src":2586},"/assets/content/blog/maddyness-th.png",[228],"/news/maddyness-article-about-pathway",{"title":2567,"description":2583},{"loc":2588},"9.news/878.maddyness-article-about-pathway","6bw1TzK3-BpHJ_7S92d75rmNoDj1q17ICFs0s1cz7Yc",{"id":2594,"title":2595,"author":2596,"body":2600,"description":2614,"extension":37,"meta":2615,"navigation":40,"path":2619,"seo":2620,"sitemap":2621,"stem":2622,"__hash__":2623},"content/9.news/879.nextweb-article.md","AI startup launches ‘fastest data processing engine’ on the market",{"name":2597,"img":2598,"linkedin":2599},"The Next Web","/assets/content/blog/thenextweb-avatar.png","https://thenextweb.com/",{"type":11,"value":2601,"toc":2612},[2602,2604,2610],[14,2603,17],{"id":16},[19,2605,21,2606,28],{},[23,2607,2608],{"href":2608,"rel":2609},"https://thenextweb.com/news/ai-startup-launches-fastest-data-processing-engine-market",[27],[30,2611],{"url":2608},{"title":33,"searchDepth":34,"depth":34,"links":2613},[],"Female-led Pathway says its system can 'forget' in real-time, like a human",{"redirection":40,"thumbnail":2616,"tags":2618,"date":2559,"hidden":40},{"src":2617},"/assets/content/blog/thenextweb-th.png",[228],"/news/nextweb-article",{"title":2595,"description":2614},{"loc":2619},"9.news/879.nextweb-article","C8KvjMP7iX7Mb6iHM6TrsS7HoN3kLvL_NofgN_RZmfY",{"id":2625,"title":2626,"author":2627,"body":2628,"description":2657,"extension":37,"meta":2658,"navigation":40,"path":2663,"seo":2664,"sitemap":2665,"stem":2666,"__hash__":2667},"content/9.news/880.200-startups-disrupting-supply-chains.md","Pathway Named as a Top Startup Disrupting Supply Chains",{"id":1153,"url":1154,"name":1155,"description":1156,"img":1157,"provider":74,"linkedin":1158},{"type":11,"value":2629,"toc":2655},[2630,2634,2637,2640],[14,2631,2633],{"id":2632},"pathway-is-listed-as-a-top-start-up-disrupting-supply-chains","Pathway is listed as a top start-up disrupting supply chains.",[19,2635,2636],{},"Pathway has been named in the Management and optimization category, notably thanks to its public success stories with leading logistics companies and supply chain departments.",[19,2638,2639],{},"Pathway Logistics App is the lighthouse data platform built in the Pathway framework. The one-stop-shop cloud-based application is providing immediately actionable insights on top of data for logistics assets, including IoT data and status data.",[19,2641,2642,2643,2647,2648,2650,2651,1785],{},"Discover our success stories with ",[23,2644,2646],{"href":2645},"/success-stories/db-schenker","DB Schenker",", ",[23,2649,201],{"href":1182}," and ",[23,2652,2654],{"href":2653},"/success-stories/cma-cgm","CMA CGM",{"title":33,"searchDepth":34,"depth":34,"links":2656},[],"Stratégies Logistique listed the 200 top startups disrupting supply chains, and rewarded Pathway in the Management and Optimization category",{"layout":39,"thumbnail":2659,"tags":2661,"date":2662,"hidden":40},{"src":2660},"/assets/content/blog/strategies-logistique-th.png",[228],"2023-05-11","/news/200-startups-disrupting-supply-chains",{"title":2626,"description":2657},{"loc":2663},"9.news/880.200-startups-disrupting-supply-chains","8QHr8UZiBnsMUC8YH-TnPkRlCqvgnaS0iUcDJmS3Il8",{"id":2669,"title":2670,"author":2671,"body":2674,"description":2688,"extension":37,"meta":2689,"navigation":40,"path":2694,"seo":2695,"sitemap":2696,"stem":2697,"__hash__":2698},"content/9.news/881.le-point.md","Pathway CEO featured in the ranking of the next generation of geniuses by the French national weekly Le Point",{"name":2672,"img":2673},"Le Point","/assets/content/blog/le-point-avatar.png",{"type":11,"value":2675,"toc":2686},[2676,2678,2684],[14,2677,17],{"id":16},[19,2679,21,2680,28],{},[23,2681,2682],{"href":2682,"rel":2683},"https://www.lepoint.fr/sciences-nature/palmares-des-inventeurs-du-point-la-releve-du-genie-francais-22-06-2023-2525696_1924.php",[27],[30,2685],{"url":2682},{"title":33,"searchDepth":34,"depth":34,"links":2687},[],"An exceptional jury (including Alain Aspect, the 2022 Nobel Prize in Physics) has selected Pathway among the teams whose breakthroughs will change our lives.",{"layout":39,"redirection":40,"thumbnail":2690,"tags":2692,"date":2693,"lang":697,"hidden":40},{"src":2691},"/assets/content/blog/le-point-th.png",[228],"2023-06-22","/news/le-point",{"title":2670,"description":2688},{"loc":2694},"9.news/881.le-point","OUZIcSjAo36ly-2a3b9vVD5VujQtcdg1FYAt1ZO5Cuo",{"id":2700,"title":2701,"author":2702,"body":2704,"description":2748,"extension":37,"meta":2749,"navigation":40,"path":2132,"seo":2753,"sitemap":2754,"stem":2755,"__hash__":2756},"content/9.news/882.gartner-market-guide-supply-chain.md","Pathway is a Representative Vendor in Gartner 2023 Market Guide for Analytics and Decision Intelligence Platforms in Supply Chain",{"name":2174,"img":2175,"website":2703},"https://www.gartner.com/account/signin?method=initialize&TARGET=https%3A%2F%2Fwww.gartner.com%2Fmyhomepage",{"type":11,"value":2705,"toc":2746},[2706,2710,2713,2716,2729,2737],[14,2707,2709],{"id":2708},"pathway-was-selected-as-a-representative-vendor-in-the-2023-gartner-market-guide-for-analytics-and-decision-intelligence-platforms-in-supply-chain","Pathway was selected as a Representative Vendor in the 2023 Gartner Market Guide for Analytics and Decision Intelligence Platforms in Supply Chain.",[19,2711,2712],{},"According to Gartner analysts Christian Titze and Noha Tohamy, ”By 2026, 50% of organizations will have to evaluate analytics and business intelligence (ABI) and data science and machine learning (DSML) platforms as a single platform due to market convergence.”",[19,2714,2715],{},"At Pathway we are proud to enable industry leaders to “achieve contextualized, connected, and continuous insights” through:",[1193,2717,2718,2723],{},[1196,2719,2720,2722],{},[1217,2721,1803],{},": the most powerful data processing framework, currently used for real-time anomaly detection, predictive analytics, IoT and logs data observability, recommender systems, and alerting, and which works particularly well with data in motion: data tables, live events data, etc.",[1196,2724,2725,2728],{},[1217,2726,2727],{},"Pathway Logistics App",": our lighthouse data platform built in the Pathway framework. It is a one-stop-shop cloud-based application to provide immediately actionable insights on top of data for logistics assets, including IoT data and status data.",[19,2730,2731,2732,2736],{},"With “functional teams ",[2733,2734,2735],"span",{},"..."," looking to speed up cross-functional decision making on the basis of more near-real-time and broader datasets”, Pathway is best positioned to deliver value to Enterprise clients",[19,2738,2739,2740,2745],{},"For Gartner clients, feel free to read the full ",[23,2741,2744],{"href":2742,"rel":2743},"https://www.gartner.com/document/4478399?ref=solrAll&refval=374406409&",[27],"Gartner Market Guide"," for Analytics and Decision Intelligence Platforms in Supply Chain, and do reach out!",{"title":33,"searchDepth":34,"depth":34,"links":2747},[],"Gartner published its latest edition of its Market Guide for Analytics and Decision Intelligence Platforms in Supply Chain, and named Pathway a Representative Vendor",{"layout":39,"thumbnail":2750,"tags":2751,"date":2752,"enterprise":40,"hidden":40},{"src":859,"provider":74},[228],"2023-06-26",{"title":2701,"description":2748},{"loc":2132},"9.news/882.gartner-market-guide-supply-chain","iY5aaP1BB-LlLWHL9cIHys1WV38iLrGhHf_DmxJ_GmI",{"id":2758,"title":2759,"author":2760,"body":2761,"description":2794,"extension":37,"meta":2795,"navigation":40,"path":2799,"seo":2800,"sitemap":2801,"stem":2802,"__hash__":2803},"content/9.news/883.vivatech-by-the-french-prime.md","Pathway awarded at VivaTech by the French Prime Minister Elisabeth Borne",{"id":1359,"url":1360,"name":1361,"description":1362,"img":1363,"provider":74,"linkedin":1364},{"type":11,"value":2762,"toc":2792},[2763,2766,2769,2772,2776,2783,2786,2789],[14,2764,2759],{"id":2765},"pathway-awarded-at-vivatech-by-the-french-prime-minister-elisabeth-borne",[19,2767,2768],{},"Pathway is proud to announce that Zuzanna Stamirowska, CEO at Pathway was awarded at Viva Technology, Europe’s biggest tech event, held in Paris, France.",[19,2770,2771],{},"Elisabeth Borne, the French Prime Minister awarded Zuzanna Stamirowska for her performance on stage and the achievements of Pathway as the most powerful data processing framework to power real-time data products and pipelines. This happened a few weeks after the CIO of Goldman Sachs declared that “going from batch to real-time (processing) was like going from printed newspapers to the Internet.\"",[2773,2774],"tweet",{"tweet-url":2775},"https://twitter.com/Elisabeth_Borne/status/1669798321550925837",[19,2777,2778,2779,2782],{},"Pathway was brought to life by a stellar team: the CTO Jan Chorowski worked with the Godfathers of AI, Geoff Hinton, and Yoshua Bengio, the CSO Adrian Kosowski had his Ph.D. at 20 and is a world-class expert in high-scale distributed computing, and ",[23,2780,1361],{"href":1364,"rel":2781},[27]," is the author of the state of the art model for forecasting of maritime trade. Pathway is supported by business angels such as Lukasz Kaiser, known to be behind the “T” in GPT.",[19,2784,2785],{},"“Very soon real-time will become the norm for data processing and it’s a game changer for everybody starting from financial services, Formula 1,  supply chains, online marketing, retail, energy…  the list goes on.” declared Zuzanna Stamirowska during her pitch in front of the Viva Tech assembly.",[19,2787,2788],{},"Watch Pathway Winning Pitch",[1174,2790],{"src":2791},"https://www.youtube.com/watch?v=iSRUMsM15uw",{"title":33,"searchDepth":34,"depth":34,"links":2793},[],"The real-time revolution starts now. 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devmio",{"name":2808,"description":33,"img":2809},"Devmio","/assets/content/blog/devmio-avatar.png",{"type":11,"value":2811,"toc":2823},[2812,2814,2820],[14,2813,17],{"id":16},[19,2815,21,2816,28],{},[23,2817,2818],{"href":2818,"rel":2819},"https://devm.io/careers/women-in-tech-stamirowska",[27],[30,2821],{"url":2822},"https://devm.io/careers/women-in-tech-stamirowska/",{"title":33,"searchDepth":34,"depth":34,"links":2824},[],"The key to overcoming challenges is to see them as temporary 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Paris-Saclay",{"name":2840,"description":33,"img":2841},"Paris-Saclay","/assets/content/blog/paris-saclay-avatar.png",{"type":11,"value":2843,"toc":2854},[2844,2846,2852],[14,2845,17],{"id":16},[19,2847,21,2848,28],{},[23,2849,2850],{"href":2850,"rel":2851},"https://epa-paris-saclay.fr/actualites-et-decryptages/toutes-nos-publications/traitement-de-donnees-en-temps-reel-la-voie-pathway/",[27],[30,2853],{"url":2850},{"title":33,"searchDepth":34,"depth":34,"links":2855},[],"Traitement de données en temps réel, la voie Pathway",{"layout":39,"redirection":40,"thumbnail":2858,"tags":2860,"date":681,"lang":697,"hidden":40},{"src":2859},"/assets/content/blog/paris-saclay-th.png",[228],"/news/paris-saclay",{"title":2838,"description":2856},{"loc":2861},"9.news/885.paris-saclay","O251ObVwyoWAaDWKZxIy2oUkJsFD6lvwL8etWTRTpM4",{"id":2867,"title":2868,"author":2869,"body":2873,"description":2887,"extension":37,"meta":2888,"navigation":40,"path":2893,"seo":2894,"sitemap":2895,"stem":2896,"__hash__":2897},"content/9.news/886.la-jaune-et-la-rouge.md","La Poste shared their IoT roadmap, and how Pathway helps them with their strategic objectives",{"name":2870,"description":2871,"img":2872},"La Jaune et la Rouge","Polytechnique Alumni","/assets/content/blog/la-june-avatar.png",{"type":11,"value":2874,"toc":2885},[2875,2877,2883],[14,2876,17],{"id":16},[19,2878,21,2879,28],{},[23,2880,2881],{"href":2881,"rel":2882},"https://www.lajauneetlarouge.com/nous-travaillons-avec-liot-depuis-longtemps/",[27],[30,2884],{"url":2881},{"title":33,"searchDepth":34,"depth":34,"links":2886},[],"Interview with Jean-Paul Fabre, Technology Manager at the Innovation and Information Systems department of the Mail & Parcel Services division of La 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Other data regarding the operation or functioning of vehicles can also be analyzed through AI.",[19,4340,4341],{},"The benefits of applying LiveAI™ to transport operations include:",[1193,4343,4344,4347],{},[1196,4345,4346],{},"Increased operational efficiency: More accurate and reliable predictions of arrival times, enhanced dynamic management of planned and unplanned diversions and disruptions, and new high-performance tools for operators.",[1196,4348,4349],{},"Improved customer experience: Providing reliable, real-time passenger information, both in normal and disrupted situations to optimize user experience. The automated management of new arrival times when routes are diverted also reduces passenger waiting times and inconvenience.",[4351,4352,4355],"quote",{"name":4353,"title":4354},"Edouard Hénaut","CEO France at Transdev",[19,4356,4357],{},"Pathway shows that real-time data processing and AI integrate seamlessly into our chain of business tools. The solution complements our operating and passenger information systems, without adding complexity or implementation delays, while guaranteeing reliable results. Effectively ensuring daily mobility requires expert use of the real-time data we produce, transform, and deliver to both passengers and local authorities clients. Our partnership with Pathway strengthens our expertise in this area and enables significant gains for the benefit of service quality.",[4351,4359,4362],{"name":4360,"title":4361},"Laurent Mahieu","Director of the Hauts-de-France and Grand-Est Regions at Transdev, President of DK’BUS",[19,4363,4364],{},"The experimentation on the DK’BUS network, which serves the Urban Community of Dunkirk, has shown significant gains in terms of information quality. The accuracy and reliability of the information delivered has improved, enabling the disappearance of theoretical arrival times, better prediction of waiting time estimations and efficient dynamic management of unscheduled deviations. We look forward to deploying and offering this quality of information to our passengers daily. The DK’Bus team, led by General Manager Nicolas Gaillard, already has great ideas to introduce it to our travelers! Pathway is undeniably the high-performance solution for monitoring and visualizing our activity to become even more reactive and continuously improve the data we produce.",[4351,4366,4368],{"name":1361,"title":4367},"CEO and co-founder of Pathway",[19,4369,4370],{},"Transportation is a dynamic industry, and an understanding of the real-time situation is critical for navigating mobility challenges. Applying AI and the most modern data processing to these challenges makes a very tangible difference to communities. We are proud to provide AI that reduces wait times and unpredictability, in turn attracting more people to public transport and boosting quality of life for residents of cities around the world.",[19,4372,4373],{},"Pathway's technology is proven with over 46,000 installations and users and is supported through the ZEBOX ecosystem that gathers companies like CMA CGM, Transdev or VINCI.  Pathway now also delivers for major clients such as NATO and La Poste. The company recently raised $10 million in seed funding and has a growing community of developers based in over 100 countries.",[1188,4375,4377],{"id":4376},"about-transdev","About Transdev",[19,4379,4380,4381],{},"Operator and leading independent private mobility group, Transdev empowers freedom to move every day thanks to safe, reliable and innovative solutions that serve the common good. Present in 19 countries, Transdev transports an average of 12.8 million passengers daily, operating all transportation modes and resolutely committed to the ecological transition. The Group employs more than 105,000 women and men serving its passengers, consolidating its position as the world leader in public transportation. Transdev advises and supports local authorities and companies in a long-term partnership. Transdev is jointly owned by Caisse des Dépôts (66%) and the Rethmann Group (34%). In 2024, Transdev reported sales of €10.05 billion. For more information: ",[23,4382,4385],{"href":4383,"rel":4384},"http://www.transdev.com",[27],"www.transdev.com",[1188,4387,4389],{"id":4388},"about-pathway","About Pathway",[4391,4392],"pathway-about",{},{"title":33,"searchDepth":34,"depth":34,"links":4394},[4395,4396,4397],{"id":4334,"depth":34,"text":4335},{"id":4376,"depth":34,"text":4377},{"id":4388,"depth":34,"text":4389},"Transdev and Pathway announce a strategic partnership to revolutionize public transport using LiveAI™, enhancing real-time passenger information and operational efficiency across mobility 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funding",{"name":4412,"img":4413,"provider":74},"ITBrief","/assets/blog/avatars/itbrief-av.png",{"type":11,"value":4415,"toc":4426},[4416,4418,4424],[14,4417,17],{"id":16},[19,4419,21,4420,28],{},[23,4421,4422],{"href":4422,"rel":4423},"https://itbrief.news/story/victor-szczerba-assumes-cco-role-at-pathway-post-funding",[27],[30,4425],{"url":4422},{"title":33,"searchDepth":34,"depth":34,"links":4427},[],"Victor Szczerba has been appointed Chief Commercial Officer at Pathway, following its USD $10 million funding, to enhance its LiveAI™ technology and 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Insights","etedge-insights.com",{"type":11,"value":4446,"toc":4457},[4447,4449,4455],[14,4448,17],{"id":16},[19,4450,21,4451,28],{},[23,4452,4453],{"href":4453,"rel":4454},"https://etedge-insights.com/technology/artificial-intelligence/why-todays-ai-struggles-with-the-real-world-and-what-comes-next/",[27],[30,4456],{"url":4453},{"title":33,"searchDepth":34,"depth":34,"links":4458},[],"Why AI's biggest challenge is memory—and how post-transformer architectures could enable continuous 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AI","unite.ai",{"type":11,"value":4477,"toc":4488},[4478,4480,4486],[14,4479,17],{"id":16},[19,4481,21,4482,28],{},[23,4483,4484],{"href":4484,"rel":4485},"https://www.unite.ai/zuzanna-stamirowska-co-founder-and-ceo-of-pathway-interview-series/",[27],[30,4487],{"url":4484},{"title":33,"searchDepth":34,"depth":34,"links":4489},[],{"layout":39,"redirection":40,"tags":4491,"date":3798,"thumbnail":4492},[228],{"src":4493,"provider":74},"/assets/blog/thumbnails/zuzanna-stamirowska-co-founder-and-ceo-of-pathway-interview-series-th.png","/news/zuzanna-stamirowska-co-founder-and-ceo-of-pathway-interview-series",{"title":4472,"description":33},{"loc":4494},"9.news/zuzanna-stamirowska-co-founder-and-ceo-of-pathway-interview-series","__oquUzUBg3yWehTG8IOEd0HmtaYY207jjOBr4uJnvA",{"id":4500,"title":59,"author":1110,"body":4501,"description":4537,"extension":37,"meta":4538,"navigation":40,"path":60,"seo":4539,"sitemap":4540,"stem":61,"__hash__":4541},"content/framework/blog/1.index.md",{"type":11,"value":4502},[4503,4507,4522,4534],[14,4504,4506],{"id":4505},"in-the-news","In the news",[19,4508,4509,4510,4515,4516,4521],{},"Read about ",[2733,4511,4514],{"className":4512},[4513],"text-primary-500","Pathway’s"," latest ",[2733,4517,4520],{"className":4518},[4519],"text-secondary-500","media mentions, press releases"," and more!",[4523,4524,4531],"modal",{"className":4525,"name":4530},[2045,2046,1181,4526,4527,4528,4529],"my-4","mb-10","block","text-base","Newsletter",[19,4532,4533],{},"Subscribe to our newsletter!",[4535,4536],"articles",{},"Read about Pathway’s latest media mentions, press releases and more!",{"layout":65,"aside":66,"toc":66,"single":40},{"title":59,"description":4537},{"loc":60},"shdvdCYJ6w8bdpejuMQbMXk7dIJ7X57V5A2-R5_Ixek",{"id":4543,"title":68,"author":4544,"body":4545,"description":4691,"extension":37,"meta":4692,"navigation":40,"path":69,"seo":4695,"sitemap":4696,"stem":70,"__hash__":4697},"content/framework/blog/1.pathway-open-beta-announced.md",{"id":1359,"url":1360,"name":1361,"description":1362,"img":1363,"provider":74,"linkedin":1364},{"type":11,"value":4546,"toc":4687},[4547,4551,4569,4573,4576,4579,4593,4598,4601,4604,4615,4618,4625,4629,4632,4646,4651,4658,4665,4677],[14,4548,4550],{"id":4549},"pathway-is-now-available-to-all-developers","Pathway is now available to all developers!",[19,4552,4553,4554,4560,4561,4563],{},"Pathway - the stream processing framework which takes care of data updates for you - announces a ",[23,4555,4556],{"href":3022},[4557,4558,4559],"b",{},"$4.5M funding round",", and opens to all developers. You can try it out in a cloud notebook directly from your browser, or run it on a local Linux machine.",[2959,4562],{},[23,4564,4568],{"className":4565,"bold":33,"size":4567,"href":1783},[4566,2045,2046],"mb-0!","large","Run it now",[1188,4570,4572],{"id":4571},"why-should-you-use-pathway","Why should you use Pathway?",[19,4574,4575],{},"Pathway is a programming framework which allows you to work with streaming data as if you were working with static data, in batch mode.",[19,4577,4578],{},"Have you ever tried to make sense of streaming data? If so, there is a high chance that you encountered at least one of these issues:",[1193,4580,4581,4584,4587,4590],{},[1196,4582,4583],{},"There are these annoying data updates that need to be taken care of",[1196,4585,4586],{},"One needs to use the same logic to handle real-time and historical data",[1196,4588,4589],{},"Debugging is a nightmare, because how can you debug something against unknown data?",[1196,4591,4592],{},"Not to mention applying proper Machine Learning on top of streaming data to draw business insights from it. Business insights, which are necessary for key decision-making.",[19,4594,4595],{},[1645,4596],{"alt":33,"src":4597},"/assets/content/blog/difficulties-streaming-data.svg",[19,4599,4600],{},"If these are problems you have been up against, you are in the right place.\nAt Pathway, we design the programming framework which quietly takes care of data updates for you.",[19,4602,4603],{},"It gives you:",[1193,4605,4606,4609,4612],{},[1196,4607,4608],{},"A native real-time approach. Every task is either real-time streaming or streaming with historical data (backfilling), no need for batch, no hacks required.",[1196,4610,4611],{},"Reactivity.",[1196,4613,4614],{},"Full power of Python (to make all your ML dreams come true) with an extra SQL syntax layer coming soon (to make sure all data engineers are happy with their Pathway pipelines, too).",[19,4616,4617],{},"In the design of Pathway's streaming engine, we opted for ease-of-use and scalability.",[4351,4619,4622],{"name":4620,"title":4621},"Lukasz Kaiser","Co-author of Tensor Flow and co-inventor of Transformers, now at OpenAI - and an angel investor in Pathway.",[19,4623,4624],{},"In Machine Learning, the key to success of a programming framework is how to combine usability with scalability. This was the axis of competition between Google's TensorFlow and Facebook's PyTorch during the deep learning revolution. Today, Pathway has taken into account the lessons learned during this battle of giants, and embedded them in the compiler of its real-time data processing framework.",[1188,4626,4628],{"id":4627},"what-does-all-this-mean-in-practice-for-a-developer","What does all this mean in practice, for a developer?",[19,4630,4631],{},"That you can write as if you were writing a batch data processing pipeline (well, it's actually a little more than that, as we support loops and iteration!) and have it run on streaming data.",[19,4633,4634,4635,4639,4640,4645],{},"For a start, check out this simple ",[23,4636,4638],{"href":4637},"/developers/templates/etl/lsh_chapter1","example of classification of handwritten digits",". All of it is captured by the code below. We approach this task with a classifier from Pathway's standard library, in this case, k-Nearest-Neighbors (",[23,4641,4644],{"href":4642,"rel":4643},"https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm",[27],"read more on Wikipedia","). Pathway builds up the corresponding control flow graph, and updates it in streaming mode.",[19,4647,4648],{},[1645,4649],{"alt":33,"src":4650},"/assets/content/blog/classification-control-flow-pathway.svg",[19,4652,4653,4654,1785],{},"Using Pathway means that all Machine Learning outcomes are updated as the models learn with new samples and improve over time. Classification decisions for tested elements will also be revisited whenever they change. Such an approach is called reactive processing of streaming data. If you would like to learn more about this topic, we explain it in detail in ",[23,4655,4657],{"href":4656},"/blog/pydata","this fresh video talk",[19,4659,4660,4661,1785],{},"You will also find many more examples in our Documentation - and we are also sharing with you the whole examples pack at ",[23,4662,4663],{"href":4663,"rel":4664},"https://github.com/pathwaycom/pathway-examples",[27],[19,4666,4667,4668,4671,4672,4676],{},"All of this is now open for you to play with it, test, and have fun. You can even ",[23,4669,4670],{"href":1783},"run it"," in a cloud notebook from your browser, unless you prefer to ",[4673,4674,4675],"code",{},"pip install"," directly on to your own Linux machine.",[19,4678,4679,4680],{},"If you have some feedback on Pathway, or just some streaming use cases that are leaving you with sleepless nights, we would love to know.\n",[1217,4681,4682,4686],{},[23,4683,4685],{"href":4684},"https://discord.com/invite/pathway","Join us on Discord"," or drop us a line!",{"title":33,"searchDepth":34,"depth":34,"links":4688},[4689,4690],{"id":4571,"depth":34,"text":4572},{"id":4627,"depth":34,"text":4628},"Pathway - the streaming programming framework which takes care of data updates for you - is now available to all developers.",{"layout":39,"thumbnail":4693,"tags":4694,"date":77,"hidden":40},{"src":73,"provider":74},[39,76],{"title":68,"description":4691},{"loc":69},"BaAkCMeb3W1SpF7tGK48g-lFSBWY8sX_iAPfNB20NoA",{"id":4699,"title":79,"author":4700,"body":4701,"description":79,"extension":37,"meta":4720,"navigation":40,"path":80,"seo":4723,"sitemap":4724,"stem":81,"__hash__":4725},"content/framework/blog/1000.Ilab2021.md",{"id":1359,"url":1360,"name":1361,"description":1362,"img":1363,"provider":74,"linkedin":1364},{"type":11,"value":4702,"toc":4718},[4703,4707,4715],[14,4704,4706],{"id":4705},"pathway-ex-navalgo-named-2021-i-lab-laureate","Pathway (Ex-NavAlgo) named 2021 i-Lab Laureate",[19,4708,4709,4710,1785],{},"Feeling honored to share today that we are a 2021 laureate of the prestigious i-Lab contest organized by ",[23,4711,4714],{"href":4712,"rel":4713},"https://www.bpifrance.fr/",[27],"BPI France",[19,4716,4717],{},"Launched in 1999, this contest awards French most promising Deeptech startups, and has historically paved the way for success.",{"title":33,"searchDepth":34,"depth":34,"links":4719},[],{"layout":39,"aside":66,"thumbnail":4721,"tags":4722,"date":86,"hidden":40},{"src":84,"provider":74,"contain":40},[39],{"title":79,"description":79},{"loc":80},"acIeSyLHYBDc0NjAlOkyKOWpFm29wSRTSjLkT3bFHEU",{"id":4727,"title":88,"author":4728,"body":4729,"description":6671,"extension":37,"meta":6672,"navigation":40,"path":89,"seo":6676,"sitemap":6677,"stem":90,"__hash__":6678},"content/framework/blog/1001.gemini-rag.md",{"id":557,"url":1635,"name":1636,"img":257,"provider":74},{"type":11,"value":4730,"toc":6652},[4731,4735,4739,4750,4763,4780,4786,4790,4816,4820,4825,4831,4837,4842,4856,4860,4863,4877,4881,4892,4895,4900,4904,4918,4921,4935,4939,4945,4948,4951,4955,4962,4968,4971,4974,4978,4982,4985,4988,4992,5024,5027,5031,5037,5041,5048,5090,5094,5097,5118,5141,5344,5348,5351,5549,5553,5560,5567,5583,5677,5681,5684,5784,5788,5792,5795,5889,5893,5908,5981,5985,5988,6174,6178,6181,6258,6262,6265,6340,6344,6347,6412,6502,6572,6624,6632,6635,6639,6642,6648],[4732,4733],"true-img",{"alt":4734,"src":93},"blog banner",[14,4736,4738],{"id":4737},"multimodal-rag-with-pathway-and-gemini","Multimodal RAG with Pathway and Gemini",[19,4740,4741,4742,4745,4746,4749],{},"The recent release of ",[1217,4743,4744],{},"Google Gemini 1.5",", with its impressive ",[1217,4747,4748],{},"1 million token context length window",", has sparked discussions about the future of RAG. However, it hasn't rendered it obsolete. This system still offers unique advantages, especially in curating and optimizing the context provided to the model, ensuring relevance and accuracy. What is particularly interesting is how these advancements can be harnessed to enhance our projects and streamline our workflows.",[19,4751,4752,4753,4756,4757,2650,4759,4762],{},"In this article, you'll learn how to set up a ",[1217,4754,4755],{},"Multimodal Retrieval-Augmented Generation (MM-RAG)"," system using ",[1217,4758,1803],{},[1217,4760,4761],{},"Google Gemini",". You will walk through each step comprehensively, ensuring a solid understanding of both the theoretical and practical aspects of implementing Multimodal LLM and RAG applications.",[19,4764,4765,4766,2650,4769,4771,4772,4775,4776,1785],{},"You'll explore how to leverage the capabilities of ",[1217,4767,4768],{},"Gemini 1.5 Flash",[1217,4770,1803],{}," together. If you're interested in building RAG pipelines with OpenAI, we also have an article on ",[1217,4773,4774],{},"Multimodal RAG using GPT-4o",", which you can check out ",[23,4777,4779],{"href":4778},"/developers/templates/rag/multimodal-rag","here",[19,4781,4782,4783,1785],{},"If you want to skip the explanations, you can directly find the code ",[23,4784,4779],{"href":4785},"#hands-on-multimodal-rag-with-google-gemini",[1188,4787,4789],{"id":4788},"what-this-article-will-cover","What this article will cover:",[1193,4791,4792,4795,4798,4801,4804,4807,4810,4813],{},[1196,4793,4794],{},"What is Retrieval-Augmented Generation (RAG)?",[1196,4796,4797],{},"Multimodality in LLMs",[1196,4799,4800],{},"Why is Multimodal RAG (MM-RAG) Needed?",[1196,4802,4803],{},"What is Multimodal RAG and Use Cases?",[1196,4805,4806],{},"Gemini Models",[1196,4808,4809],{},"Release of Gemini 1.5 and its impact on RAG architectures",[1196,4811,4812],{},"Comparing LlamaIndex and Pathway",[1196,4814,4815],{},"Hands-on Multimodal RAG with Google Gemini",[1188,4817,4819],{"id":4818},"foundational-concepts","Foundational Concepts",[4821,4822,4824],"h3",{"id":4823},"why-is-multimodal-rag-needed","Why is Multimodal Rag needed?",[19,4826,4827,4830],{},[1217,4828,4829],{},"Retrieval-Augmented Generation (RAG)"," enhances large language models by incorporating external knowledge sources before generating responses. This approach ensures relevant and accurate output. In today's data-rich world, documents often combine text and images to convey information comprehensively. However, most Retrieval Augmented Generation (RAG) systems overlook the valuable insights locked within images. As Multimodal Large Language Models (LLMs) gain prominence, it's crucial to explore how we can leverage visual content alongside text in RAG, unlocking a deeper understanding of the information landscape.",[19,4832,4833,4836],{},[1217,4834,4835],{},"Multimodal RAG"," is an advanced form of Retrieval-Augmented Generation (RAG) that goes beyond text to incorporate various data types like images, charts, and tables. This expanded capability allows for a deeper understanding of complex information, leading to more accurate and informative outputs.",[4838,4839,4841],"h4",{"id":4840},"two-options-for-multimodal-rag","Two options for Multimodal RAG",[1757,4843,4844,4850],{},[1196,4845,4846,4849],{},[1217,4847,4848],{},"Multimodal Embeddings"," -\nThe multimodal embeddings model generates vectors based on the input you provide, which can include a combination of image, text, and video data. The image embedding vector and text embedding vector are in the same semantic space with the same dimensionality. Consequently, these vectors can be used interchangeably for use cases like searching image by text, or searching video by image.\nUtilize multimodal embeddings to integrate text and images, retrieve relevant content through similarity search, and then provide both the raw image and text chunks to a multimodal LLM for answer synthesis.",[1196,4851,4852,4855],{},[1217,4853,4854],{},"Text Embeddings"," -\nGenerate text summaries of images using a multimodal LLM, embed and retrieve the text, and then pass the text chunks to the LLM for answer synthesis.",[4838,4857,4859],{"id":4858},"comparing-text-based-and-multimodal-rag","Comparing text-based and multimodal RAG",[19,4861,4862],{},"Multimodal RAG offers several advantages over text-based RAG:",[1193,4864,4865,4871],{},[1196,4866,4867,4870],{},[1217,4868,4869],{},"Enhanced knowledge access",": Multimodal RAG can access and process both textual and visual information, providing a richer and more comprehensive knowledge base for the LLM.",[1196,4872,4873,4876],{},[1217,4874,4875],{},"Improved reasoning capabilities",": By incorporating visual cues, multimodal RAG can make better informed inferences across different types of data modalities.",[4838,4878,4880],{"id":4879},"key-advantages-of-mm-rag","Key Advantages of MM-RAG:",[1193,4882,4883,4886,4889],{},[1196,4884,4885],{},"Comprehensive Understanding: Processes multiple data formats for a better picture.",[1196,4887,4888],{},"Improved Performance: Visual data enhances efficiency in complex tasks.",[1196,4890,4891],{},"Versatile Applications: Useful in finance, healthcare, scientific research, and more.",[4821,4893,4806],{"id":4894},"gemini-models",[19,4896,4897,4899],{},[1217,4898,104],{}," is Google's most capable and general AI model to date. Google has released several Gemini model variants, each tailored for different use cases and performance requirements.",[4838,4901,4903],{"id":4902},"main-gemini-models","Main Gemini Models:",[1193,4905,4906,4909,4912,4915],{},[1196,4907,4908],{},"Gemini Ultra: The most powerful and advanced model, capable of handling complex tasks and offering state-of-the-art performance.",[1196,4910,4911],{},"Gemini Pro: A versatile model that balances performance and efficiency, suitable for a wide range of applications.",[1196,4913,4914],{},"Gemini Advanced: Designed for a broader set of tasks, offering a good balance of capabilities.",[1196,4916,4917],{},"Gemini Lite: A smaller, more efficient model focused on speed and responsiveness, ideal for resource-constrained environments.",[19,4919,4920],{},"Additional Variants:",[1193,4922,4923,4926,4929,4932],{},[1196,4924,4925],{},"Gemini 1.5 Flash: Optimized for high-volume, cost-effective applications.",[1196,4927,4928],{},"Gemini 1.5 Pro: Offers a balance of performance and capabilities.",[1196,4930,4931],{},"Gemini 1.0 Pro Vision: Includes vision capabilities for processing images and videos.",[1196,4933,4934],{},"Gemini 1.0 Pro: Text-based model for general language tasks.",[4838,4936,4938],{"id":4937},"benefits-of-building-with-gemini","Benefits of Building with Gemini:",[19,4940,4941,4944],{},[1217,4942,4943],{},"Free Credits",": Google Cloud offers new users up to $300 in free credits. This can be used to experiment with Gemini models and other Google Cloud services.\nYou can also seamlessly integrate MM-RAG applications with Google's Vertex AI platform for streamlined machine learning workflows.",[4821,4946,4809],{"id":4947},"release-of-gemini-15-and-its-impact-on-rag-architectures",[19,4949,4950],{},"The Gemini 1.5 Flash model, released on May 24, 2024, revolutionized AI with its enhanced speed, efficiency, cost-effectiveness, long context window, and multimodal reasoning capabilities.",[4838,4952,4954],{"id":4953},"did-google-gemini-15-kill-the-need-of-rag","Did Google Gemini 1.5 Kill the need of RAG?",[19,4956,4957,4958,4961],{},"In one word ",[1217,4959,4960],{},"“No”",". Gemini 1.5, with a 1M context length window, has sparked a new debate about whether RAG (Retrieval Augmented Generation) is still relevant or not. LLMs commonly struggle with hallucination. To address this challenge, two solutions were introduced, one involving an increased context window and the other utilizing RAG. Gemini 1.5 outperforms Claude 2.1 and GPT-4 Turbo as it can assimilate entire code bases, process over 100 papers, and various documents, but it surely hasn’t killed RAG.",[19,4963,4964,4965,1785],{},"RAG leverages your private knowledge database for effective Q&A while ensuring the security of sensitive information like trade secrets, confidential IP, GDPR-protected data, and internal documents. For more detailed insights explore our article on Private RAG with Connected Data Sources using Mistral, Ollama, and Pathway ",[23,4966,4779],{"href":4967},"/developers/templates/rag/private-rag-ollama-mistral",[19,4969,4970],{},"Additionally in traditional RAG pipelines, you can enhance performance by tweaking the retrieval process, changing the embedding model, adjusting chunking strategies, or improving source data. However, with a \"stuff-the-context-window-1M-tokens\" strategy, your only option is to improve the source data since all data is given to the model within the token limit. Additionally the context window may be filled with many relevant facts, but 40% or more of them are “lost” to the model. If you want to make sure the model is actually using the context you are sending it, you are best off curating it first and only sending the most relevant context. In other words, doing traditional RAG.",[19,4972,4973],{},"Here in this template you will use the Gemini 1.5 Flash but you can also use other multimodal models by gemini accordingly.",[4732,4975],{"alt":4976,"src":4977},"Gemini 1.5 flash overview","/assets/content/showcases/gemini_rag/gemini1.5flashtable.png",[4821,4979,4981],{"id":4980},"multimodality-with-gemini-15-flash","Multimodality with Gemini-1.5-Flash",[19,4983,4984],{},"Gemini 1.5 Flash is the newest addition to the Gemini family of large language models, and it’s specifically designed to be fast, efficient, and cost-effective for high-volume tasks. This is achieved by being a lighter model than the Gemini 1.5 Pro.",[19,4986,4987],{},"According to the paper from Google DeepMind, Gemini 1.5 Flash is “a more lightweight variant designed for efficiency with minimal regression in quality” and uses the transformer decoder model architecture “and multimodal capabilities as Gemini 1.5 Pro, designed for efficient utilization of tensor processing units (TPUs) with lower latency for model serving.”",[4821,4989,4991],{"id":4990},"gemini-15-flash-key-features","Gemini 1.5 Flash: Key Features",[1193,4993,4994,5000,5006,5012,5018],{},[1196,4995,4996,4999],{},[1217,4997,4998],{},"Speed and Efficiency",": Fastest Gemini model at 60 tokens/second, ideal for real-time tasks, reducing costs by delaying autoscaling.",[1196,5001,5002,5005],{},[1217,5003,5004],{},"Cost-Effective",": 1/10 the price of Gemini 1.5 Pro and cheaper than GPT-3.5.",[1196,5007,5008,5011],{},[1217,5009,5010],{},"Long Context Window",": Processes up to one million tokens, handling one hour of video, 11 hours of audio, or 700,000 words without losing accuracy.",[1196,5013,5014,5017],{},[1217,5015,5016],{},"Multimodal Reasoning",": Understands text, images, audio, video, PDFs, and tables. Supports function calling and real-time data access.",[1196,5019,5020,5023],{},[1217,5021,5022],{},"Great Performance",": High performance with large context windows, excelling in long-document QA, long-video QA, and long-context ASR.",[4732,5025],{"alt":4976,"src":5026},"/assets/content/showcases/gemini_rag/gemini1.5flashdetails.png",[1188,5028,5030],{"id":5029},"hands-on-multimodal-rag-with-google-gemini","Hands on Multimodal RAG with Google Gemini",[19,5032,5033],{},[1645,5034],{"alt":5035,"src":5036},"Gemini RAG overview","/assets/content/showcases/gemini_rag/RAG_diagram.png",[4821,5038,5040],{"id":5039},"step-1-installation","Step 1: Installation",[19,5042,5043,5044,5047],{},"First, we need to install the required packages: pathway",[2733,5045,5046],{},"all",", litellm==1.40.0 and google-generativeai.",[5049,5050,5054],"pre",{"className":5051,"code":5052,"language":5053,"meta":33,"style":33},"language-python shiki shiki-themes material-theme-palenight","!pip install 'pathway[all]>=0.14.0' litellm==1.40.0\n","python",[4673,5055,5056],{"__ignoreMap":33},[2733,5057,5060,5064,5068,5072,5074,5077,5080,5084,5086],{"class":5058,"line":5059},"line",1,[2733,5061,5063],{"class":5062},"s0W1g","!pip install ",[2733,5065,5067],{"class":5066},"sAklC","'",[2733,5069,5071],{"class":5070},"sfyAc","pathway[all]>=0.14.0",[2733,5073,5067],{"class":5066},[2733,5075,5076],{"class":5062}," litellm",[2733,5078,5079],{"class":5066},"==",[2733,5081,5083],{"class":5082},"sx098","1.40",[2733,5085,1785],{"class":5066},[2733,5087,5089],{"class":5088},"s-wAU","0\n",[4821,5091,5093],{"id":5092},"step-2-imports-and-environment-setup","Step 2: Imports and Environment Setup",[19,5095,5096],{},"Next, we import the necessary libraries and set up the environment variables.",[5049,5098,5100],{"className":5051,"code":5099,"language":5053,"meta":33,"style":33},"import logging\nimport os\n",[4673,5101,5102,5111],{"__ignoreMap":33},[2733,5103,5104,5108],{"class":5058,"line":5059},[2733,5105,5107],{"class":5106},"s6cf3","import",[2733,5109,5110],{"class":5062}," logging\n",[2733,5112,5113,5115],{"class":5058,"line":34},[2733,5114,5107],{"class":5106},[2733,5116,5117],{"class":5062}," os\n",[5049,5119,5121],{"className":5051,"code":5120,"language":5053,"meta":33,"style":33},"import google.generativeai as genai\n",[4673,5122,5123],{"__ignoreMap":33},[2733,5124,5125,5127,5130,5132,5135,5138],{"class":5058,"line":5059},[2733,5126,5107],{"class":5106},[2733,5128,5129],{"class":5062}," google",[2733,5131,1785],{"class":5066},[2733,5133,5134],{"class":5088},"generativeai",[2733,5136,5137],{"class":5106}," as",[2733,5139,5140],{"class":5062}," genai\n",[5049,5142,5144],{"className":5051,"code":5143,"language":5053,"meta":33,"style":33},"import litellm\n\nimport pathway as pw\n\nfrom pathway.udfs import DiskCache, ExponentialBackoffRetryStrategy\nfrom pathway.xpacks.llm import embedders, llms, parsers, prompts, splitters\nfrom pathway.xpacks.llm.question_answering import BaseRAGQuestionAnswerer\nfrom pathway.xpacks.llm.vector_store import VectorStoreServer\n\n# Set the logging level for LiteLLM to DEBUG\nos.environ[\"LITELLM_LOG\"] = \"DEBUG\"  # to help in debugging\n",[4673,5145,5146,5153,5158,5171,5176,5201,5243,5268,5293,5298,5305],{"__ignoreMap":33},[2733,5147,5148,5150],{"class":5058,"line":5059},[2733,5149,5107],{"class":5106},[2733,5151,5152],{"class":5062}," litellm\n",[2733,5154,5155],{"class":5058,"line":34},[2733,5156,5157],{"emptyLinePlaceholder":40},"\n",[2733,5159,5160,5162,5165,5168],{"class":5058,"line":1767},[2733,5161,5107],{"class":5106},[2733,5163,5164],{"class":5062}," pathway ",[2733,5166,5167],{"class":5106},"as",[2733,5169,5170],{"class":5062}," pw\n",[2733,5172,5174],{"class":5058,"line":5173},4,[2733,5175,5157],{"emptyLinePlaceholder":40},[2733,5177,5179,5182,5185,5187,5190,5192,5195,5198],{"class":5058,"line":5178},5,[2733,5180,5181],{"class":5106},"from",[2733,5183,5184],{"class":5062}," pathway",[2733,5186,1785],{"class":5066},[2733,5188,5189],{"class":5062},"udfs ",[2733,5191,5107],{"class":5106},[2733,5193,5194],{"class":5062}," DiskCache",[2733,5196,5197],{"class":5066},",",[2733,5199,5200],{"class":5062}," ExponentialBackoffRetryStrategy\n",[2733,5202,5204,5206,5208,5210,5213,5215,5218,5220,5223,5225,5228,5230,5233,5235,5238,5240],{"class":5058,"line":5203},6,[2733,5205,5181],{"class":5106},[2733,5207,5184],{"class":5062},[2733,5209,1785],{"class":5066},[2733,5211,5212],{"class":5062},"xpacks",[2733,5214,1785],{"class":5066},[2733,5216,5217],{"class":5062},"llm ",[2733,5219,5107],{"class":5106},[2733,5221,5222],{"class":5062}," embedders",[2733,5224,5197],{"class":5066},[2733,5226,5227],{"class":5062}," llms",[2733,5229,5197],{"class":5066},[2733,5231,5232],{"class":5062}," parsers",[2733,5234,5197],{"class":5066},[2733,5236,5237],{"class":5062}," prompts",[2733,5239,5197],{"class":5066},[2733,5241,5242],{"class":5062}," splitters\n",[2733,5244,5246,5248,5250,5252,5254,5256,5258,5260,5263,5265],{"class":5058,"line":5245},7,[2733,5247,5181],{"class":5106},[2733,5249,5184],{"class":5062},[2733,5251,1785],{"class":5066},[2733,5253,5212],{"class":5062},[2733,5255,1785],{"class":5066},[2733,5257,98],{"class":5062},[2733,5259,1785],{"class":5066},[2733,5261,5262],{"class":5062},"question_answering ",[2733,5264,5107],{"class":5106},[2733,5266,5267],{"class":5062}," BaseRAGQuestionAnswerer\n",[2733,5269,5271,5273,5275,5277,5279,5281,5283,5285,5288,5290],{"class":5058,"line":5270},8,[2733,5272,5181],{"class":5106},[2733,5274,5184],{"class":5062},[2733,5276,1785],{"class":5066},[2733,5278,5212],{"class":5062},[2733,5280,1785],{"class":5066},[2733,5282,98],{"class":5062},[2733,5284,1785],{"class":5066},[2733,5286,5287],{"class":5062},"vector_store ",[2733,5289,5107],{"class":5106},[2733,5291,5292],{"class":5062}," VectorStoreServer\n",[2733,5294,5296],{"class":5058,"line":5295},9,[2733,5297,5157],{"emptyLinePlaceholder":40},[2733,5299,5301],{"class":5058,"line":5300},10,[2733,5302,5304],{"class":5303},"saEQR","# Set the logging level for LiteLLM to DEBUG\n",[2733,5306,5308,5311,5313,5316,5319,5322,5325,5327,5330,5333,5336,5339,5341],{"class":5058,"line":5307},11,[2733,5309,5310],{"class":5062},"os",[2733,5312,1785],{"class":5066},[2733,5314,5315],{"class":5088},"environ",[2733,5317,5318],{"class":5066},"[",[2733,5320,5321],{"class":5066},"\"",[2733,5323,5324],{"class":5070},"LITELLM_LOG",[2733,5326,5321],{"class":5066},[2733,5328,5329],{"class":5066},"]",[2733,5331,5332],{"class":5066}," =",[2733,5334,5335],{"class":5066}," \"",[2733,5337,5338],{"class":5070},"DEBUG",[2733,5340,5321],{"class":5066},[2733,5342,5343],{"class":5303},"  # to help in debugging\n",[4821,5345,5347],{"id":5346},"step-3-api-key-setup-and-license-key-setup","Step 3: API Key Setup and License Key Setup",[19,5349,5350],{},"Set up the API key and the Pathway license key:",[5049,5352,5354],{"className":5051,"code":5353,"language":5053,"meta":33,"style":33},"# Api key setup\nGEMINI_API_KEY = \"Paste your Gemini API Key here\"\n\nos.environ[\"GEMINI_API_KEY\"] = GEMINI_API_KEY\nos.environ[\"TESSDATA_PREFIX\"] = \"/usr/share/tesseract/tessdata/\"\ngenai.configure(api_key=GEMINI_API_KEY)\n\n# License key setup\npw.set_license_key(\"demo-license-key-with-telemetry\")\n\nlogging.basicConfig(\n    level=logging.INFO, format=\"%(asctime)s - %(levelname)s - %(message)s\"\n)\n",[4673,5355,5356,5361,5377,5381,5405,5433,5458,5462,5467,5488,5492,5505,5544],{"__ignoreMap":33},[2733,5357,5358],{"class":5058,"line":5059},[2733,5359,5360],{"class":5303},"# Api key setup\n",[2733,5362,5363,5366,5369,5371,5374],{"class":5058,"line":34},[2733,5364,5365],{"class":5062},"GEMINI_API_KEY ",[2733,5367,5368],{"class":5066},"=",[2733,5370,5335],{"class":5066},[2733,5372,5373],{"class":5070},"Paste your Gemini API Key here",[2733,5375,5376],{"class":5066},"\"\n",[2733,5378,5379],{"class":5058,"line":1767},[2733,5380,5157],{"emptyLinePlaceholder":40},[2733,5382,5383,5385,5387,5389,5391,5393,5396,5398,5400,5402],{"class":5058,"line":5173},[2733,5384,5310],{"class":5062},[2733,5386,1785],{"class":5066},[2733,5388,5315],{"class":5088},[2733,5390,5318],{"class":5066},[2733,5392,5321],{"class":5066},[2733,5394,5395],{"class":5070},"GEMINI_API_KEY",[2733,5397,5321],{"class":5066},[2733,5399,5329],{"class":5066},[2733,5401,5332],{"class":5066},[2733,5403,5404],{"class":5062}," GEMINI_API_KEY\n",[2733,5406,5407,5409,5411,5413,5415,5417,5420,5422,5424,5426,5428,5431],{"class":5058,"line":5178},[2733,5408,5310],{"class":5062},[2733,5410,1785],{"class":5066},[2733,5412,5315],{"class":5088},[2733,5414,5318],{"class":5066},[2733,5416,5321],{"class":5066},[2733,5418,5419],{"class":5070},"TESSDATA_PREFIX",[2733,5421,5321],{"class":5066},[2733,5423,5329],{"class":5066},[2733,5425,5332],{"class":5066},[2733,5427,5335],{"class":5066},[2733,5429,5430],{"class":5070},"/usr/share/tesseract/tessdata/",[2733,5432,5376],{"class":5066},[2733,5434,5435,5438,5440,5444,5447,5451,5453,5455],{"class":5058,"line":5203},[2733,5436,5437],{"class":5062},"genai",[2733,5439,1785],{"class":5066},[2733,5441,5443],{"class":5442},"sdLwU","configure",[2733,5445,5446],{"class":5066},"(",[2733,5448,5450],{"class":5449},"s7ZW3","api_key",[2733,5452,5368],{"class":5066},[2733,5454,5395],{"class":5442},[2733,5456,5457],{"class":5066},")\n",[2733,5459,5460],{"class":5058,"line":5245},[2733,5461,5157],{"emptyLinePlaceholder":40},[2733,5463,5464],{"class":5058,"line":5270},[2733,5465,5466],{"class":5303},"# License key setup\n",[2733,5468,5469,5472,5474,5477,5479,5481,5484,5486],{"class":5058,"line":5295},[2733,5470,5471],{"class":5062},"pw",[2733,5473,1785],{"class":5066},[2733,5475,5476],{"class":5442},"set_license_key",[2733,5478,5446],{"class":5066},[2733,5480,5321],{"class":5066},[2733,5482,5483],{"class":5070},"demo-license-key-with-telemetry",[2733,5485,5321],{"class":5066},[2733,5487,5457],{"class":5066},[2733,5489,5490],{"class":5058,"line":5300},[2733,5491,5157],{"emptyLinePlaceholder":40},[2733,5493,5494,5497,5499,5502],{"class":5058,"line":5307},[2733,5495,5496],{"class":5062},"logging",[2733,5498,1785],{"class":5066},[2733,5500,5501],{"class":5442},"basicConfig",[2733,5503,5504],{"class":5066},"(\n",[2733,5506,5508,5511,5513,5515,5517,5520,5522,5525,5527,5529,5532,5534,5537,5539,5542],{"class":5058,"line":5507},12,[2733,5509,5510],{"class":5449},"    level",[2733,5512,5368],{"class":5066},[2733,5514,5496],{"class":5442},[2733,5516,1785],{"class":5066},[2733,5518,5519],{"class":5088},"INFO",[2733,5521,5197],{"class":5066},[2733,5523,5524],{"class":5449}," format",[2733,5526,5368],{"class":5066},[2733,5528,5321],{"class":5066},[2733,5530,5531],{"class":5082},"%(asctime)s",[2733,5533,4316],{"class":5070},[2733,5535,5536],{"class":5082},"%(levelname)s",[2733,5538,4316],{"class":5070},[2733,5540,5541],{"class":5082},"%(message)s",[2733,5543,5376],{"class":5066},[2733,5545,5547],{"class":5058,"line":5546},13,[2733,5548,5457],{"class":5066},[4821,5550,5552],{"id":5551},"step-4-upload-your-file","Step 4: Upload your file",[19,5554,5555,5556,5559],{},"Create a ",[4673,5557,5558],{},"./data"," directory if it doesn't already exist. This is where the uploaded files will be stored. Then upload your pdf documents.",[19,5561,5562,5563,5566],{},"You can also omit this cell if you are running locally on your system - in that case create a ",[4673,5564,5565],{},"data"," folder in the current directory and copy the files and comment out this cell.",[5049,5568,5570],{"className":5051,"code":5569,"language":5053,"meta":33,"style":33},"!mkdir -p data\n",[4673,5571,5572],{"__ignoreMap":33},[2733,5573,5574,5577,5580],{"class":5058,"line":5059},[2733,5575,5576],{"class":5062},"!mkdir ",[2733,5578,5579],{"class":5066},"-",[2733,5581,5582],{"class":5062},"p data\n",[5049,5584,5586],{"className":5051,"code":5585,"language":5053,"meta":33,"style":33},"# Demo pdf for testing\n!wget -q -P ./data/ https://github.com/pathwaycom/llm-app/raw/main/templates/multimodal_rag/data/20230203_alphabet_10K.pdf\n",[4673,5587,5588,5593],{"__ignoreMap":33},[2733,5589,5590],{"class":5058,"line":5059},[2733,5591,5592],{"class":5303},"# Demo pdf for testing\n",[2733,5594,5595,5598,5600,5603,5605,5608,5611,5613,5615,5618,5621,5624,5626,5629,5631,5634,5636,5638,5640,5643,5645,5648,5650,5653,5655,5658,5660,5663,5665,5667,5669,5672,5674],{"class":5058,"line":34},[2733,5596,5597],{"class":5062},"!wget ",[2733,5599,5579],{"class":5066},[2733,5601,5602],{"class":5062},"q ",[2733,5604,5579],{"class":5066},[2733,5606,5607],{"class":5062},"P ",[2733,5609,5610],{"class":5066},"./",[2733,5612,5565],{"class":5062},[2733,5614,1802],{"class":5066},[2733,5616,5617],{"class":5062}," https",[2733,5619,5620],{"class":5066},"://",[2733,5622,5623],{"class":5062},"github",[2733,5625,1785],{"class":5066},[2733,5627,5628],{"class":5088},"com",[2733,5630,1802],{"class":5066},[2733,5632,5633],{"class":5062},"pathwaycom",[2733,5635,1802],{"class":5066},[2733,5637,98],{"class":5062},[2733,5639,5579],{"class":5066},[2733,5641,5642],{"class":5062},"app",[2733,5644,1802],{"class":5066},[2733,5646,5647],{"class":5062},"raw",[2733,5649,1802],{"class":5066},[2733,5651,5652],{"class":5062},"main",[2733,5654,1802],{"class":5066},[2733,5656,5657],{"class":5062},"templates",[2733,5659,1802],{"class":5066},[2733,5661,5662],{"class":5062},"multimodal_rag",[2733,5664,1802],{"class":5066},[2733,5666,5565],{"class":5062},[2733,5668,1802],{"class":5066},[2733,5670,5671],{"class":5062},"20230203_alphabet_10K",[2733,5673,1785],{"class":5066},[2733,5675,5676],{"class":5088},"pdf\n",[4838,5678,5680],{"id":5679},"reading-pdf-data","Reading PDF Data",[19,5682,5683],{},"Next, we read the PDF data from a folder.",[5049,5685,5687],{"className":5051,"code":5686,"language":5053,"meta":33,"style":33},"# Read the PDF data\nfolder = pw.io.fs.read(\n    path=\"./data/\",\n    format=\"binary\",\n    with_metadata=True,\n)\nsources = [folder]  # you can add any other Pathway connector here!\n",[4673,5688,5689,5694,5721,5738,5754,5762,5766],{"__ignoreMap":33},[2733,5690,5691],{"class":5058,"line":5059},[2733,5692,5693],{"class":5303},"# Read the PDF data\n",[2733,5695,5696,5699,5701,5704,5706,5709,5711,5714,5716,5719],{"class":5058,"line":34},[2733,5697,5698],{"class":5062},"folder ",[2733,5700,5368],{"class":5066},[2733,5702,5703],{"class":5062}," pw",[2733,5705,1785],{"class":5066},[2733,5707,5708],{"class":5088},"io",[2733,5710,1785],{"class":5066},[2733,5712,5713],{"class":5088},"fs",[2733,5715,1785],{"class":5066},[2733,5717,5718],{"class":5442},"read",[2733,5720,5504],{"class":5066},[2733,5722,5723,5726,5728,5730,5733,5735],{"class":5058,"line":1767},[2733,5724,5725],{"class":5449},"    path",[2733,5727,5368],{"class":5066},[2733,5729,5321],{"class":5066},[2733,5731,5732],{"class":5070},"./data/",[2733,5734,5321],{"class":5066},[2733,5736,5737],{"class":5066},",\n",[2733,5739,5740,5743,5745,5747,5750,5752],{"class":5058,"line":5173},[2733,5741,5742],{"class":5449},"    format",[2733,5744,5368],{"class":5066},[2733,5746,5321],{"class":5066},[2733,5748,5749],{"class":5070},"binary",[2733,5751,5321],{"class":5066},[2733,5753,5737],{"class":5066},[2733,5755,5756,5759],{"class":5058,"line":5178},[2733,5757,5758],{"class":5449},"    with_metadata",[2733,5760,5761],{"class":5066},"=True,\n",[2733,5763,5764],{"class":5058,"line":5203},[2733,5765,5457],{"class":5066},[2733,5767,5768,5771,5773,5776,5779,5781],{"class":5058,"line":5245},[2733,5769,5770],{"class":5062},"sources ",[2733,5772,5368],{"class":5066},[2733,5774,5775],{"class":5066}," [",[2733,5777,5778],{"class":5062},"folder",[2733,5780,5329],{"class":5066},[2733,5782,5783],{"class":5303},"  # you can add any other Pathway connector here!\n",[4821,5785,5787],{"id":5786},"step-5-document-processing-and-question-answering-setup","Step 5: Document Processing and Question Answering Setup",[4838,5789,5791],{"id":5790},"setting-up-litellm-chat","Setting Up LiteLLM Chat",[19,5793,5794],{},"Set up a LiteLLM chat instance with retry and cache strategies:",[5049,5796,5798],{"className":5051,"code":5797,"language":5053,"meta":33,"style":33},"# Setup LiteLLM chat\nchat = llms.LiteLLMChat(\n    model=\"gemini/gemini-1.5-flash\",  # Model specified for LiteLLM\n    retry_strategy=ExponentialBackoffRetryStrategy(max_retries=6, backoff_factor=2.5),\n    temperature=0.0,\n)\n",[4673,5799,5800,5805,5821,5840,5873,5885],{"__ignoreMap":33},[2733,5801,5802],{"class":5058,"line":5059},[2733,5803,5804],{"class":5303},"# Setup LiteLLM chat\n",[2733,5806,5807,5810,5812,5814,5816,5819],{"class":5058,"line":34},[2733,5808,5809],{"class":5062},"chat ",[2733,5811,5368],{"class":5066},[2733,5813,5227],{"class":5062},[2733,5815,1785],{"class":5066},[2733,5817,5818],{"class":5442},"LiteLLMChat",[2733,5820,5504],{"class":5066},[2733,5822,5823,5826,5828,5830,5833,5835,5837],{"class":5058,"line":1767},[2733,5824,5825],{"class":5449},"    model",[2733,5827,5368],{"class":5066},[2733,5829,5321],{"class":5066},[2733,5831,5832],{"class":5070},"gemini/gemini-1.5-flash",[2733,5834,5321],{"class":5066},[2733,5836,5197],{"class":5066},[2733,5838,5839],{"class":5303},"  # Model specified for LiteLLM\n",[2733,5841,5842,5845,5847,5850,5852,5855,5857,5860,5862,5865,5867,5870],{"class":5058,"line":5173},[2733,5843,5844],{"class":5449},"    retry_strategy",[2733,5846,5368],{"class":5066},[2733,5848,5849],{"class":5442},"ExponentialBackoffRetryStrategy",[2733,5851,5446],{"class":5066},[2733,5853,5854],{"class":5449},"max_retries",[2733,5856,5368],{"class":5066},[2733,5858,5859],{"class":5082},"6",[2733,5861,5197],{"class":5066},[2733,5863,5864],{"class":5449}," backoff_factor",[2733,5866,5368],{"class":5066},[2733,5868,5869],{"class":5082},"2.5",[2733,5871,5872],{"class":5066},"),\n",[2733,5874,5875,5878,5880,5883],{"class":5058,"line":5178},[2733,5876,5877],{"class":5449},"    temperature",[2733,5879,5368],{"class":5066},[2733,5881,5882],{"class":5082},"0.0",[2733,5884,5737],{"class":5066},[2733,5886,5887],{"class":5058,"line":5203},[2733,5888,5457],{"class":5066},[4838,5890,5892],{"id":5891},"setting-up-embedder","Setting Up Embedder",[19,5894,5895,5896,5899,5900,5903,5904,5907],{},"Let's utilize Gemini embedders. The ",[4673,5897,5898],{},"GeminiEmbedder"," class in Pathway provides an interface for interacting with Gemini embedders. It generates semantic embeddings with a specified model, providing methods for single items (",[4673,5901,5902],{},"embed","), batches (",[4673,5905,5906],{},"embed_batch","), and direct calls.",[5049,5909,5911],{"className":5051,"code":5910,"language":5053,"meta":33,"style":33},"# Setup embedder\nembedder = embedders.GeminiEmbedder(\n    model=\"models/embedding-001\",\n    retry_strategy=ExponentialBackoffRetryStrategy(max_retries=6, backoff_factor=2.5),\n)  # Specify embedder here\n",[4673,5912,5913,5918,5933,5948,5974],{"__ignoreMap":33},[2733,5914,5915],{"class":5058,"line":5059},[2733,5916,5917],{"class":5303},"# Setup embedder\n",[2733,5919,5920,5923,5925,5927,5929,5931],{"class":5058,"line":34},[2733,5921,5922],{"class":5062},"embedder ",[2733,5924,5368],{"class":5066},[2733,5926,5222],{"class":5062},[2733,5928,1785],{"class":5066},[2733,5930,5898],{"class":5442},[2733,5932,5504],{"class":5066},[2733,5934,5935,5937,5939,5941,5944,5946],{"class":5058,"line":1767},[2733,5936,5825],{"class":5449},[2733,5938,5368],{"class":5066},[2733,5940,5321],{"class":5066},[2733,5942,5943],{"class":5070},"models/embedding-001",[2733,5945,5321],{"class":5066},[2733,5947,5737],{"class":5066},[2733,5949,5950,5952,5954,5956,5958,5960,5962,5964,5966,5968,5970,5972],{"class":5058,"line":5173},[2733,5951,5844],{"class":5449},[2733,5953,5368],{"class":5066},[2733,5955,5849],{"class":5442},[2733,5957,5446],{"class":5066},[2733,5959,5854],{"class":5449},[2733,5961,5368],{"class":5066},[2733,5963,5859],{"class":5082},[2733,5965,5197],{"class":5066},[2733,5967,5864],{"class":5449},[2733,5969,5368],{"class":5066},[2733,5971,5869],{"class":5082},[2733,5973,5872],{"class":5066},[2733,5975,5976,5978],{"class":5058,"line":5178},[2733,5977,1804],{"class":5066},[2733,5979,5980],{"class":5303},"  # Specify embedder here\n",[4838,5982,5984],{"id":5983},"setting-up-parser","Setting Up Parser",[19,5986,5987],{},"Next, we set up a parser for the document store.",[5049,5989,5991],{"className":5051,"code":5990,"language":5053,"meta":33,"style":33},"# Setup parser\ntable_args = {\n    \"parsing_algorithm\": \"llm\",  # for tables\n    \"llm\": chat,\n    \"prompt\": prompts.DEFAULT_MD_TABLE_PARSE_PROMPT,\n}\n\nimage_args = {\n    \"parsing_algorithm\": \"llm\",  # for images\n    \"llm\": chat,\n    \"prompt\": prompts.DEFAULT_IMAGE_PARSE_PROMPT,\n}\n\nparser = parsers.DoclingParser(multimodal_llm=chat)\n",[4673,5992,5993,5998,6008,6032,6047,6067,6072,6076,6085,6106,6120,6139,6143,6147],{"__ignoreMap":33},[2733,5994,5995],{"class":5058,"line":5059},[2733,5996,5997],{"class":5303},"# Setup parser\n",[2733,5999,6000,6003,6005],{"class":5058,"line":34},[2733,6001,6002],{"class":5062},"table_args ",[2733,6004,5368],{"class":5066},[2733,6006,6007],{"class":5066}," {\n",[2733,6009,6010,6013,6016,6018,6021,6023,6025,6027,6029],{"class":5058,"line":1767},[2733,6011,6012],{"class":5066},"    \"",[2733,6014,6015],{"class":5070},"parsing_algorithm",[2733,6017,5321],{"class":5066},[2733,6019,6020],{"class":5066},":",[2733,6022,5335],{"class":5066},[2733,6024,98],{"class":5070},[2733,6026,5321],{"class":5066},[2733,6028,5197],{"class":5066},[2733,6030,6031],{"class":5303},"  # for tables\n",[2733,6033,6034,6036,6038,6040,6042,6045],{"class":5058,"line":5173},[2733,6035,6012],{"class":5066},[2733,6037,98],{"class":5070},[2733,6039,5321],{"class":5066},[2733,6041,6020],{"class":5066},[2733,6043,6044],{"class":5062}," chat",[2733,6046,5737],{"class":5066},[2733,6048,6049,6051,6054,6056,6058,6060,6062,6065],{"class":5058,"line":5178},[2733,6050,6012],{"class":5066},[2733,6052,6053],{"class":5070},"prompt",[2733,6055,5321],{"class":5066},[2733,6057,6020],{"class":5066},[2733,6059,5237],{"class":5062},[2733,6061,1785],{"class":5066},[2733,6063,6064],{"class":5088},"DEFAULT_MD_TABLE_PARSE_PROMPT",[2733,6066,5737],{"class":5066},[2733,6068,6069],{"class":5058,"line":5203},[2733,6070,6071],{"class":5066},"}\n",[2733,6073,6074],{"class":5058,"line":5245},[2733,6075,5157],{"emptyLinePlaceholder":40},[2733,6077,6078,6081,6083],{"class":5058,"line":5270},[2733,6079,6080],{"class":5062},"image_args ",[2733,6082,5368],{"class":5066},[2733,6084,6007],{"class":5066},[2733,6086,6087,6089,6091,6093,6095,6097,6099,6101,6103],{"class":5058,"line":5295},[2733,6088,6012],{"class":5066},[2733,6090,6015],{"class":5070},[2733,6092,5321],{"class":5066},[2733,6094,6020],{"class":5066},[2733,6096,5335],{"class":5066},[2733,6098,98],{"class":5070},[2733,6100,5321],{"class":5066},[2733,6102,5197],{"class":5066},[2733,6104,6105],{"class":5303},"  # for images\n",[2733,6107,6108,6110,6112,6114,6116,6118],{"class":5058,"line":5300},[2733,6109,6012],{"class":5066},[2733,6111,98],{"class":5070},[2733,6113,5321],{"class":5066},[2733,6115,6020],{"class":5066},[2733,6117,6044],{"class":5062},[2733,6119,5737],{"class":5066},[2733,6121,6122,6124,6126,6128,6130,6132,6134,6137],{"class":5058,"line":5307},[2733,6123,6012],{"class":5066},[2733,6125,6053],{"class":5070},[2733,6127,5321],{"class":5066},[2733,6129,6020],{"class":5066},[2733,6131,5237],{"class":5062},[2733,6133,1785],{"class":5066},[2733,6135,6136],{"class":5088},"DEFAULT_IMAGE_PARSE_PROMPT",[2733,6138,5737],{"class":5066},[2733,6140,6141],{"class":5058,"line":5507},[2733,6142,6071],{"class":5066},[2733,6144,6145],{"class":5058,"line":5546},[2733,6146,5157],{"emptyLinePlaceholder":40},[2733,6148,6150,6153,6155,6157,6159,6162,6164,6167,6169,6172],{"class":5058,"line":6149},14,[2733,6151,6152],{"class":5062},"parser ",[2733,6154,5368],{"class":5066},[2733,6156,5232],{"class":5062},[2733,6158,1785],{"class":5066},[2733,6160,6161],{"class":5442},"DoclingParser",[2733,6163,5446],{"class":5066},[2733,6165,6166],{"class":5449},"multimodal_llm",[2733,6168,5368],{"class":5066},[2733,6170,6171],{"class":5442},"chat",[2733,6173,5457],{"class":5066},[4838,6175,6177],{"id":6176},"setting-up-document-store","Setting Up Document Store",[19,6179,6180],{},"We will set up the document store with the sources, embedder, and parser.",[5049,6182,6184],{"className":5051,"code":6183,"language":5053,"meta":33,"style":33},"# Setup document store\n# splitter = splitters.TokenCountSplitter()\ndoc_store = VectorStoreServer(\n    *sources,\n    embedder=embedder,\n    splitter=splitter,\n    parser=parser,\n)\n",[4673,6185,6186,6191,6196,6208,6218,6230,6242,6254],{"__ignoreMap":33},[2733,6187,6188],{"class":5058,"line":5059},[2733,6189,6190],{"class":5303},"# Setup document store\n",[2733,6192,6193],{"class":5058,"line":34},[2733,6194,6195],{"class":5303},"# splitter = splitters.TokenCountSplitter()\n",[2733,6197,6198,6201,6203,6206],{"class":5058,"line":1767},[2733,6199,6200],{"class":5062},"doc_store ",[2733,6202,5368],{"class":5066},[2733,6204,6205],{"class":5442}," VectorStoreServer",[2733,6207,5504],{"class":5066},[2733,6209,6210,6213,6216],{"class":5058,"line":5173},[2733,6211,6212],{"class":5066},"    *",[2733,6214,6215],{"class":5442},"sources",[2733,6217,5737],{"class":5066},[2733,6219,6220,6223,6225,6228],{"class":5058,"line":5178},[2733,6221,6222],{"class":5449},"    embedder",[2733,6224,5368],{"class":5066},[2733,6226,6227],{"class":5442},"embedder",[2733,6229,5737],{"class":5066},[2733,6231,6232,6235,6237,6240],{"class":5058,"line":5203},[2733,6233,6234],{"class":5449},"    splitter",[2733,6236,5368],{"class":5066},[2733,6238,6239],{"class":5442},"splitter",[2733,6241,5737],{"class":5066},[2733,6243,6244,6247,6249,6252],{"class":5058,"line":5245},[2733,6245,6246],{"class":5449},"    parser",[2733,6248,5368],{"class":5066},[2733,6250,6251],{"class":5442},"parser",[2733,6253,5737],{"class":5066},[2733,6255,6256],{"class":5058,"line":5270},[2733,6257,5457],{"class":5066},[4821,6259,6261],{"id":6260},"step-6-setting-up-question-answerer-application","Step 6: Setting Up Question Answerer Application",[19,6263,6264],{},"We will set up the question answerer application using the LiteLLM-based chat object.",[5049,6266,6268],{"className":5051,"code":6267,"language":5053,"meta":33,"style":33},"# Setup question answerer application\napp = BaseRAGQuestionAnswerer(\n        llm=chat,  # Using the LiteLLM-based chat object\n        indexer=doc_store, search_topk=2,\n        short_prompt_template=prompts.prompt_qa)\n",[4673,6269,6270,6275,6287,6301,6323],{"__ignoreMap":33},[2733,6271,6272],{"class":5058,"line":5059},[2733,6273,6274],{"class":5303},"# Setup question answerer application\n",[2733,6276,6277,6280,6282,6285],{"class":5058,"line":34},[2733,6278,6279],{"class":5062},"app ",[2733,6281,5368],{"class":5066},[2733,6283,6284],{"class":5442}," BaseRAGQuestionAnswerer",[2733,6286,5504],{"class":5066},[2733,6288,6289,6292,6294,6296,6298],{"class":5058,"line":1767},[2733,6290,6291],{"class":5449},"        llm",[2733,6293,5368],{"class":5066},[2733,6295,6171],{"class":5442},[2733,6297,5197],{"class":5066},[2733,6299,6300],{"class":5303},"  # Using the LiteLLM-based chat object\n",[2733,6302,6303,6306,6308,6311,6313,6316,6318,6321],{"class":5058,"line":5173},[2733,6304,6305],{"class":5449},"        indexer",[2733,6307,5368],{"class":5066},[2733,6309,6310],{"class":5442},"doc_store",[2733,6312,5197],{"class":5066},[2733,6314,6315],{"class":5449}," search_topk",[2733,6317,5368],{"class":5066},[2733,6319,6320],{"class":5082},"2",[2733,6322,5737],{"class":5066},[2733,6324,6325,6328,6330,6333,6335,6338],{"class":5058,"line":5178},[2733,6326,6327],{"class":5449},"        short_prompt_template",[2733,6329,5368],{"class":5066},[2733,6331,6332],{"class":5442},"prompts",[2733,6334,1785],{"class":5066},[2733,6336,6337],{"class":5088},"prompt_qa",[2733,6339,5457],{"class":5066},[4838,6341,6343],{"id":6342},"building-and-running-the-server","Building and Running the Server",[19,6345,6346],{},"Finally, we build and run the server.",[5049,6348,6350],{"className":5051,"code":6349,"language":5053,"meta":33,"style":33},"# Build and run the server\napp_host = \"0.0.0.0\"\napp_port = 8000\napp.build_server(host=app_host, port=app_port)\n",[4673,6351,6352,6357,6371,6381],{"__ignoreMap":33},[2733,6353,6354],{"class":5058,"line":5059},[2733,6355,6356],{"class":5303},"# Build and run the server\n",[2733,6358,6359,6362,6364,6366,6369],{"class":5058,"line":34},[2733,6360,6361],{"class":5062},"app_host ",[2733,6363,5368],{"class":5066},[2733,6365,5335],{"class":5066},[2733,6367,6368],{"class":5070},"0.0.0.0",[2733,6370,5376],{"class":5066},[2733,6372,6373,6376,6378],{"class":5058,"line":1767},[2733,6374,6375],{"class":5062},"app_port ",[2733,6377,5368],{"class":5066},[2733,6379,6380],{"class":5082}," 8000\n",[2733,6382,6383,6385,6387,6390,6392,6395,6397,6400,6402,6405,6407,6410],{"class":5058,"line":5173},[2733,6384,5642],{"class":5062},[2733,6386,1785],{"class":5066},[2733,6388,6389],{"class":5442},"build_server",[2733,6391,5446],{"class":5066},[2733,6393,6394],{"class":5449},"host",[2733,6396,5368],{"class":5066},[2733,6398,6399],{"class":5442},"app_host",[2733,6401,5197],{"class":5066},[2733,6403,6404],{"class":5449}," port",[2733,6406,5368],{"class":5066},[2733,6408,6409],{"class":5442},"app_port",[2733,6411,5457],{"class":5066},[5049,6413,6415],{"className":5051,"code":6414,"language":5053,"meta":33,"style":33},"import threading\nt = threading.Thread(target=app.run_server, name=\"BaseRAGQuestionAnswerer\")\nt.daemon = True\nthr = t.start()\n",[4673,6416,6417,6424,6469,6484],{"__ignoreMap":33},[2733,6418,6419,6421],{"class":5058,"line":5059},[2733,6420,5107],{"class":5106},[2733,6422,6423],{"class":5062}," threading\n",[2733,6425,6426,6429,6431,6434,6436,6439,6441,6444,6446,6448,6450,6453,6455,6458,6460,6462,6465,6467],{"class":5058,"line":34},[2733,6427,6428],{"class":5062},"t ",[2733,6430,5368],{"class":5066},[2733,6432,6433],{"class":5062}," threading",[2733,6435,1785],{"class":5066},[2733,6437,6438],{"class":5442},"Thread",[2733,6440,5446],{"class":5066},[2733,6442,6443],{"class":5449},"target",[2733,6445,5368],{"class":5066},[2733,6447,5642],{"class":5442},[2733,6449,1785],{"class":5066},[2733,6451,6452],{"class":5088},"run_server",[2733,6454,5197],{"class":5066},[2733,6456,6457],{"class":5449}," name",[2733,6459,5368],{"class":5066},[2733,6461,5321],{"class":5066},[2733,6463,6464],{"class":5070},"BaseRAGQuestionAnswerer",[2733,6466,5321],{"class":5066},[2733,6468,5457],{"class":5066},[2733,6470,6471,6474,6476,6479,6481],{"class":5058,"line":1767},[2733,6472,6473],{"class":5062},"t",[2733,6475,1785],{"class":5066},[2733,6477,6478],{"class":5088},"daemon",[2733,6480,5332],{"class":5066},[2733,6482,6483],{"class":5066}," True\n",[2733,6485,6486,6489,6491,6494,6496,6499],{"class":5058,"line":5173},[2733,6487,6488],{"class":5062},"thr ",[2733,6490,5368],{"class":5066},[2733,6492,6493],{"class":5062}," t",[2733,6495,1785],{"class":5066},[2733,6497,6498],{"class":5442},"start",[2733,6500,6501],{"class":5066},"()\n",[5049,6503,6505],{"className":5051,"code":6504,"language":5053,"meta":33,"style":33},"from pathway.xpacks.llm.question_answering import RAGClient\n\n# Initialize the RAG client\nclient = RAGClient(host=\"0.0.0.0\", port=8000)\n",[4673,6506,6507,6530,6534,6539],{"__ignoreMap":33},[2733,6508,6509,6511,6513,6515,6517,6519,6521,6523,6525,6527],{"class":5058,"line":5059},[2733,6510,5181],{"class":5106},[2733,6512,5184],{"class":5062},[2733,6514,1785],{"class":5066},[2733,6516,5212],{"class":5062},[2733,6518,1785],{"class":5066},[2733,6520,98],{"class":5062},[2733,6522,1785],{"class":5066},[2733,6524,5262],{"class":5062},[2733,6526,5107],{"class":5106},[2733,6528,6529],{"class":5062}," RAGClient\n",[2733,6531,6532],{"class":5058,"line":34},[2733,6533,5157],{"emptyLinePlaceholder":40},[2733,6535,6536],{"class":5058,"line":1767},[2733,6537,6538],{"class":5303},"# Initialize the RAG client\n",[2733,6540,6541,6544,6546,6549,6551,6553,6555,6557,6559,6561,6563,6565,6567,6570],{"class":5058,"line":5173},[2733,6542,6543],{"class":5062},"client ",[2733,6545,5368],{"class":5066},[2733,6547,6548],{"class":5442}," RAGClient",[2733,6550,5446],{"class":5066},[2733,6552,6394],{"class":5449},[2733,6554,5368],{"class":5066},[2733,6556,5321],{"class":5066},[2733,6558,6368],{"class":5070},[2733,6560,5321],{"class":5066},[2733,6562,5197],{"class":5066},[2733,6564,6404],{"class":5449},[2733,6566,5368],{"class":5066},[2733,6568,6569],{"class":5082},"8000",[2733,6571,5457],{"class":5066},[5049,6573,6575],{"className":5051,"code":6574,"language":5053,"meta":33,"style":33},"# Example usage\n\nresponse = client.answer(\"What is the Total Stockholders' equity as of December 31, 2022?\")\nprint(response)\n\n",[4673,6576,6577,6582,6586,6612],{"__ignoreMap":33},[2733,6578,6579],{"class":5058,"line":5059},[2733,6580,6581],{"class":5303},"# Example usage\n",[2733,6583,6584],{"class":5058,"line":34},[2733,6585,5157],{"emptyLinePlaceholder":40},[2733,6587,6588,6591,6593,6596,6598,6601,6603,6605,6608,6610],{"class":5058,"line":1767},[2733,6589,6590],{"class":5062},"response ",[2733,6592,5368],{"class":5066},[2733,6594,6595],{"class":5062}," client",[2733,6597,1785],{"class":5066},[2733,6599,6600],{"class":5442},"answer",[2733,6602,5446],{"class":5066},[2733,6604,5321],{"class":5066},[2733,6606,6607],{"class":5070},"What is the Total Stockholders' equity as of December 31, 2022?",[2733,6609,5321],{"class":5066},[2733,6611,5457],{"class":5066},[2733,6613,6614,6617,6619,6622],{"class":5058,"line":5173},[2733,6615,6616],{"class":5442},"print",[2733,6618,5446],{"class":5066},[2733,6620,6621],{"class":5442},"response",[2733,6623,5457],{"class":5066},[5049,6625,6630],{"className":6626,"code":6628,"language":6629},[6627],"language-text","$256,144 million\n","text",[4673,6631,6628],{"__ignoreMap":33},[19,6633,6634],{},"Now your chatbot is now running live! You can ask any questions and get information from your documents instantly.",[1188,6636,6638],{"id":6637},"conclusion","Conclusion",[19,6640,6641],{},"This article demonstrated how to implement a Multimodal RAG service using Pathway and Gemini. The setup leverages the capabilities of LiteLLM to process and query multimodal data effectively. If you're looking for a cost-effective alternative, consider using the Gemini Mini, which provides great performance at a lower cost.",[19,6643,6644,6645,6647],{},"For more detailed insights and an alternative approach, check out our article on multimodal RAG using GPT-4o ",[23,6646,4779],{"href":4778},". This will give you another perspective on how to handle multimodal RAG applications using different models and techniques.\nBy following the steps outlined above, you can efficiently integrate and utilize various data types to enhance your AI applications, ensuring more accurate and contextually rich outputs.",[6649,6650,6651],"style",{},"html pre.shiki code .s0W1g, html code.shiki .s0W1g{--shiki-default:#BABED8}html pre.shiki code .sAklC, html code.shiki .sAklC{--shiki-default:#89DDFF}html pre.shiki code .sfyAc, html code.shiki .sfyAc{--shiki-default:#C3E88D}html pre.shiki code .sx098, html code.shiki .sx098{--shiki-default:#F78C6C}html pre.shiki code .s-wAU, html code.shiki .s-wAU{--shiki-default:#F07178}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html pre.shiki code .s6cf3, html code.shiki .s6cf3{--shiki-default:#89DDFF;--shiki-default-font-style:italic}html pre.shiki code .saEQR, html code.shiki .saEQR{--shiki-default:#676E95;--shiki-default-font-style:italic}html pre.shiki code .sdLwU, html code.shiki .sdLwU{--shiki-default:#82AAFF}html pre.shiki code .s7ZW3, html code.shiki .s7ZW3{--shiki-default:#BABED8;--shiki-default-font-style:italic}",{"title":33,"searchDepth":34,"depth":34,"links":6653},[6654,6655,6662,6670],{"id":4788,"depth":34,"text":4789},{"id":4818,"depth":34,"text":4819,"children":6656},[6657,6658,6659,6660,6661],{"id":4823,"depth":1767,"text":4824},{"id":4894,"depth":1767,"text":4806},{"id":4947,"depth":1767,"text":4809},{"id":4980,"depth":1767,"text":4981},{"id":4990,"depth":1767,"text":4991},{"id":5029,"depth":34,"text":5030,"children":6663},[6664,6665,6666,6667,6668,6669],{"id":5039,"depth":1767,"text":5040},{"id":5092,"depth":1767,"text":5093},{"id":5346,"depth":1767,"text":5347},{"id":5551,"depth":1767,"text":5552},{"id":5786,"depth":1767,"text":5787},{"id":6260,"depth":1767,"text":6261},{"id":6637,"depth":34,"text":6638},"End-to-end template showing how you can launch a document processing RAG pipeline that utilizes Gemini and Pathway",{"aside":40,"layout":39,"thumbnail":6673,"date":95,"tags":6674,"keywords":6675,"notebook_export_path":111,"run_template":112,"hidden":40},{"src":93,"fit":94},[97,98],[100,101,102,103,104,105,106,107,108,109,110],{"title":88,"description":6671},{"loc":89},"pgWjERJ2NvHTeunGcu3MKJlhcra6lra69ndyvdPs00M",{"id":6680,"title":114,"author":6681,"body":6687,"description":33,"extension":37,"meta":7873,"navigation":40,"path":115,"seo":7877,"sitemap":7878,"stem":116,"__hash__":7879},"content/framework/blog/1002.langchain-integration.md",{"id":6682,"url":6683,"name":6684,"description":6685,"img":257,"provider":74,"linkedin":6686},"szymon","szymon-dudycz","Szymon Dudycz","Algorithm and Data Processing Magician","https://www.linkedin.com/in/szymon-dudycz-19ab2962/",{"type":11,"value":6688,"toc":7865},[6689,6692,6695,6709,6712,6716,6731,6751,6755,6772,6779,6801,6804,6912,6918,7065,7075,7258,7274,7278,7287,7324,7377,7428,7431,7469,7472,7505,7509,7512,7764,7767,7792,7796,7808,7827,7862],[14,6690,114],{"id":6691},"langchain-and-pathway-rag-apps-with-always-up-to-date-knowledge",[19,6693,6694],{},"You can now use Pathway in your RAG applications which enables always up-to-date knowledge from your documents to LLMs with Langchaing integration.",[19,6696,6697,6698,6703,6704,1785],{},"Pathway is now available on ",[23,6699,6702],{"href":6700,"rel":6701},"https://python.langchain.com/docs/integrations/vectorstores/pathway/",[27],"Langchain",", a framework for developing applications powered by large language models (LLMs).\nYou can now query Pathway and access up-to-date documents for your RAG applications from LangChain using ",[23,6705,6708],{"href":6706,"rel":6707},"https://api.python.langchain.com/en/latest/vectorstores/langchain_community.vectorstores.pathway.PathwayVectorClient.html",[27],"PathwayVectorClient",[19,6710,6711],{},"With this new integration, you will be able to use Pathway Vector Store natively in LangChain. In this guide, you will have a quick dive into Pathway + LangChain to learn how to create a simple, yet powerful RAG solution.",[1188,6713,6715],{"id":6714},"prerequisites","Prerequisites",[19,6717,6718,6719,6722,6723,6726,6727,6730],{},"To work with LangChain you need to install ",[4673,6720,6721],{},"langchain"," package, as it is not a dependence of Pathway. In the example in this guide you will also use ",[4673,6724,6725],{},"OpenAIEmbeddings"," class for which you need ",[4673,6728,6729],{},"langchain_openai"," package.",[5049,6732,6734],{"className":5051,"code":6733,"language":5053,"meta":33,"style":33},"!pip install langchain\n!pip install langchain_community\n!pip install langchain_openai\n\n",[4673,6735,6736,6741,6746],{"__ignoreMap":33},[2733,6737,6738],{"class":5058,"line":5059},[2733,6739,6740],{"class":5062},"!pip install langchain\n",[2733,6742,6743],{"class":5058,"line":34},[2733,6744,6745],{"class":5062},"!pip install langchain_community\n",[2733,6747,6748],{"class":5058,"line":1767},[2733,6749,6750],{"class":5062},"!pip install langchain_openai\n",[1188,6752,6754],{"id":6753},"using-langchain-components-in-pathway-vector-store","Using LangChain components in Pathway Vector Store",[19,6756,6757,6758,6764,6765,6771],{},"When using Pathway ",[23,6759,6761],{"href":6760},"/developers/api-docs/pathway-xpacks-llm/vectorstore#pathway.xpacks.llm.vector_store.VectorStoreServer",[4673,6762,6763],{},"VectorStoreServer",", you can use LangChain embedder and splitter for processing documents. To do that, use ",[23,6766,6768],{"href":6767},"/developers/api-docs/pathway-xpacks-llm/vectorstore#pathway.xpacks.llm.vector_store.VectorStoreServer.from_langchain_components",[4673,6769,6770],{},"from_langchain_components"," class method.",[19,6773,6774,6775,1785],{},"To start, you need to create a folder Pathway will listen to. Feel free to skip this if you already have a folder on which you want to build your RAG application. You can also use Google Drive, Sharepoint, or any other source from ",[23,6776,6778],{"href":6777},"/developers/api-docs/pathway-io","pathway-io",[5049,6780,6782],{"className":5051,"code":6781,"language":5053,"meta":33,"style":33},"!mkdir -p 'data/'\n",[4673,6783,6784],{"__ignoreMap":33},[2733,6785,6786,6788,6790,6793,6795,6798],{"class":5058,"line":5059},[2733,6787,5576],{"class":5062},[2733,6789,5579],{"class":5066},[2733,6791,6792],{"class":5062},"p ",[2733,6794,5067],{"class":5066},[2733,6796,6797],{"class":5070},"data/",[2733,6799,6800],{"class":5066},"'\n",[19,6802,6803],{},"To run this example you also need to set OpenAI API key, or change the embedder.",[5049,6805,6807],{"className":5051,"code":6806,"language":5053,"meta":33,"style":33},"import os\nimport getpass\n\n# Set OpenAI API Key\nif \"OPENAI_API_KEY\" in os.environ:\n    api_key = os.environ[\"OPENAI_API_KEY\"]\nelse:\n    api_key = getpass.getpass(\"OpenAI API Key:\")\n",[4673,6808,6809,6815,6822,6826,6831,6856,6880,6887],{"__ignoreMap":33},[2733,6810,6811,6813],{"class":5058,"line":5059},[2733,6812,5107],{"class":5106},[2733,6814,5117],{"class":5062},[2733,6816,6817,6819],{"class":5058,"line":34},[2733,6818,5107],{"class":5106},[2733,6820,6821],{"class":5062}," getpass\n",[2733,6823,6824],{"class":5058,"line":1767},[2733,6825,5157],{"emptyLinePlaceholder":40},[2733,6827,6828],{"class":5058,"line":5173},[2733,6829,6830],{"class":5303},"# Set OpenAI API Key\n",[2733,6832,6833,6836,6838,6841,6843,6846,6849,6851,6853],{"class":5058,"line":5178},[2733,6834,6835],{"class":5106},"if",[2733,6837,5335],{"class":5066},[2733,6839,6840],{"class":5070},"OPENAI_API_KEY",[2733,6842,5321],{"class":5066},[2733,6844,6845],{"class":5066}," in",[2733,6847,6848],{"class":5062}," os",[2733,6850,1785],{"class":5066},[2733,6852,5315],{"class":5088},[2733,6854,6855],{"class":5066},":\n",[2733,6857,6858,6861,6863,6865,6867,6869,6871,6873,6875,6877],{"class":5058,"line":5203},[2733,6859,6860],{"class":5062},"    api_key ",[2733,6862,5368],{"class":5066},[2733,6864,6848],{"class":5062},[2733,6866,1785],{"class":5066},[2733,6868,5315],{"class":5088},[2733,6870,5318],{"class":5066},[2733,6872,5321],{"class":5066},[2733,6874,6840],{"class":5070},[2733,6876,5321],{"class":5066},[2733,6878,6879],{"class":5066},"]\n",[2733,6881,6882,6885],{"class":5058,"line":5245},[2733,6883,6884],{"class":5106},"else",[2733,6886,6855],{"class":5066},[2733,6888,6889,6891,6893,6896,6898,6901,6903,6905,6908,6910],{"class":5058,"line":5270},[2733,6890,6860],{"class":5062},[2733,6892,5368],{"class":5066},[2733,6894,6895],{"class":5062}," getpass",[2733,6897,1785],{"class":5066},[2733,6899,6900],{"class":5442},"getpass",[2733,6902,5446],{"class":5066},[2733,6904,5321],{"class":5066},[2733,6906,6907],{"class":5070},"OpenAI API Key:",[2733,6909,5321],{"class":5066},[2733,6911,5457],{"class":5066},[19,6913,6914,6915,6917],{},"To run the server use Pathway filesystem connector to read files from the ",[4673,6916,5565],{}," folder.",[5049,6919,6921],{"className":5051,"code":6920,"language":5053,"meta":33,"style":33},"import pathway as pw\n\nfrom pathway.xpacks.llm.vector_store import VectorStoreServer\nfrom langchain_openai import OpenAIEmbeddings\nfrom langchain.text_splitter import CharacterTextSplitter\n\ndata = pw.io.fs.read(\n    \"./data\",\n    format=\"binary\",\n    mode=\"streaming\",\n    with_metadata=True,\n)\n",[4673,6922,6923,6933,6937,6959,6971,6988,6992,7015,7025,7039,7055,7061],{"__ignoreMap":33},[2733,6924,6925,6927,6929,6931],{"class":5058,"line":5059},[2733,6926,5107],{"class":5106},[2733,6928,5164],{"class":5062},[2733,6930,5167],{"class":5106},[2733,6932,5170],{"class":5062},[2733,6934,6935],{"class":5058,"line":34},[2733,6936,5157],{"emptyLinePlaceholder":40},[2733,6938,6939,6941,6943,6945,6947,6949,6951,6953,6955,6957],{"class":5058,"line":1767},[2733,6940,5181],{"class":5106},[2733,6942,5184],{"class":5062},[2733,6944,1785],{"class":5066},[2733,6946,5212],{"class":5062},[2733,6948,1785],{"class":5066},[2733,6950,98],{"class":5062},[2733,6952,1785],{"class":5066},[2733,6954,5287],{"class":5062},[2733,6956,5107],{"class":5106},[2733,6958,5292],{"class":5062},[2733,6960,6961,6963,6966,6968],{"class":5058,"line":5173},[2733,6962,5181],{"class":5106},[2733,6964,6965],{"class":5062}," langchain_openai ",[2733,6967,5107],{"class":5106},[2733,6969,6970],{"class":5062}," OpenAIEmbeddings\n",[2733,6972,6973,6975,6978,6980,6983,6985],{"class":5058,"line":5178},[2733,6974,5181],{"class":5106},[2733,6976,6977],{"class":5062}," langchain",[2733,6979,1785],{"class":5066},[2733,6981,6982],{"class":5062},"text_splitter ",[2733,6984,5107],{"class":5106},[2733,6986,6987],{"class":5062}," CharacterTextSplitter\n",[2733,6989,6990],{"class":5058,"line":5203},[2733,6991,5157],{"emptyLinePlaceholder":40},[2733,6993,6994,6997,6999,7001,7003,7005,7007,7009,7011,7013],{"class":5058,"line":5245},[2733,6995,6996],{"class":5062},"data ",[2733,6998,5368],{"class":5066},[2733,7000,5703],{"class":5062},[2733,7002,1785],{"class":5066},[2733,7004,5708],{"class":5088},[2733,7006,1785],{"class":5066},[2733,7008,5713],{"class":5088},[2733,7010,1785],{"class":5066},[2733,7012,5718],{"class":5442},[2733,7014,5504],{"class":5066},[2733,7016,7017,7019,7021,7023],{"class":5058,"line":5270},[2733,7018,6012],{"class":5066},[2733,7020,5558],{"class":5070},[2733,7022,5321],{"class":5066},[2733,7024,5737],{"class":5066},[2733,7026,7027,7029,7031,7033,7035,7037],{"class":5058,"line":5295},[2733,7028,5742],{"class":5449},[2733,7030,5368],{"class":5066},[2733,7032,5321],{"class":5066},[2733,7034,5749],{"class":5070},[2733,7036,5321],{"class":5066},[2733,7038,5737],{"class":5066},[2733,7040,7041,7044,7046,7048,7051,7053],{"class":5058,"line":5300},[2733,7042,7043],{"class":5449},"    mode",[2733,7045,5368],{"class":5066},[2733,7047,5321],{"class":5066},[2733,7049,7050],{"class":5070},"streaming",[2733,7052,5321],{"class":5066},[2733,7054,5737],{"class":5066},[2733,7056,7057,7059],{"class":5058,"line":5307},[2733,7058,5758],{"class":5449},[2733,7060,5761],{"class":5066},[2733,7062,7063],{"class":5058,"line":5507},[2733,7064,5457],{"class":5066},[19,7066,7067,7068,7071,7072,7074],{},"And then pass them to the server, which will split them using ",[4673,7069,7070],{},"CharacterTextSplitter"," and embed them using ",[4673,7073,6725],{},", both from LangChain.",[5049,7076,7078],{"className":5051,"code":7077,"language":5053,"meta":33,"style":33},"embeddings = OpenAIEmbeddings(api_key=api_key)\nsplitter = CharacterTextSplitter()\n\nhost = \"127.0.0.1\"\nport = 8666\n\nserver = VectorStoreServer.from_langchain_components(\n    data, embedder=embeddings, splitter=splitter\n)\nserver.run_server(host, port=port, with_cache=True, cache_backend=pw.persistence.Backend.filesystem(\"./Cache\"), threaded=True)\n",[4673,7079,7080,7100,7112,7116,7130,7140,7144,7159,7184,7188],{"__ignoreMap":33},[2733,7081,7082,7085,7087,7090,7092,7094,7096,7098],{"class":5058,"line":5059},[2733,7083,7084],{"class":5062},"embeddings ",[2733,7086,5368],{"class":5066},[2733,7088,7089],{"class":5442}," OpenAIEmbeddings",[2733,7091,5446],{"class":5066},[2733,7093,5450],{"class":5449},[2733,7095,5368],{"class":5066},[2733,7097,5450],{"class":5442},[2733,7099,5457],{"class":5066},[2733,7101,7102,7105,7107,7110],{"class":5058,"line":34},[2733,7103,7104],{"class":5062},"splitter ",[2733,7106,5368],{"class":5066},[2733,7108,7109],{"class":5442}," CharacterTextSplitter",[2733,7111,6501],{"class":5066},[2733,7113,7114],{"class":5058,"line":1767},[2733,7115,5157],{"emptyLinePlaceholder":40},[2733,7117,7118,7121,7123,7125,7128],{"class":5058,"line":5173},[2733,7119,7120],{"class":5062},"host ",[2733,7122,5368],{"class":5066},[2733,7124,5335],{"class":5066},[2733,7126,7127],{"class":5070},"127.0.0.1",[2733,7129,5376],{"class":5066},[2733,7131,7132,7135,7137],{"class":5058,"line":5178},[2733,7133,7134],{"class":5062},"port ",[2733,7136,5368],{"class":5066},[2733,7138,7139],{"class":5082}," 8666\n",[2733,7141,7142],{"class":5058,"line":5203},[2733,7143,5157],{"emptyLinePlaceholder":40},[2733,7145,7146,7149,7151,7153,7155,7157],{"class":5058,"line":5245},[2733,7147,7148],{"class":5062},"server ",[2733,7150,5368],{"class":5066},[2733,7152,6205],{"class":5062},[2733,7154,1785],{"class":5066},[2733,7156,6770],{"class":5442},[2733,7158,5504],{"class":5066},[2733,7160,7161,7164,7166,7169,7171,7174,7176,7179,7181],{"class":5058,"line":5270},[2733,7162,7163],{"class":5442},"    data",[2733,7165,5197],{"class":5066},[2733,7167,7168],{"class":5449}," embedder",[2733,7170,5368],{"class":5066},[2733,7172,7173],{"class":5442},"embeddings",[2733,7175,5197],{"class":5066},[2733,7177,7178],{"class":5449}," splitter",[2733,7180,5368],{"class":5066},[2733,7182,7183],{"class":5442},"splitter\n",[2733,7185,7186],{"class":5058,"line":5295},[2733,7187,5457],{"class":5066},[2733,7189,7190,7193,7195,7197,7199,7201,7203,7205,7207,7210,7212,7215,7218,7221,7223,7225,7227,7230,7232,7235,7237,7240,7242,7244,7247,7249,7252,7255],{"class":5058,"line":5300},[2733,7191,7192],{"class":5062},"server",[2733,7194,1785],{"class":5066},[2733,7196,6452],{"class":5442},[2733,7198,5446],{"class":5066},[2733,7200,6394],{"class":5442},[2733,7202,5197],{"class":5066},[2733,7204,6404],{"class":5449},[2733,7206,5368],{"class":5066},[2733,7208,7209],{"class":5442},"port",[2733,7211,5197],{"class":5066},[2733,7213,7214],{"class":5449}," with_cache",[2733,7216,7217],{"class":5066},"=True,",[2733,7219,7220],{"class":5449}," cache_backend",[2733,7222,5368],{"class":5066},[2733,7224,5471],{"class":5442},[2733,7226,1785],{"class":5066},[2733,7228,7229],{"class":5088},"persistence",[2733,7231,1785],{"class":5066},[2733,7233,7234],{"class":5088},"Backend",[2733,7236,1785],{"class":5066},[2733,7238,7239],{"class":5442},"filesystem",[2733,7241,5446],{"class":5066},[2733,7243,5321],{"class":5066},[2733,7245,7246],{"class":5070},"./Cache",[2733,7248,5321],{"class":5066},[2733,7250,7251],{"class":5066},"),",[2733,7253,7254],{"class":5449}," threaded",[2733,7256,7257],{"class":5066},"=True)\n",[19,7259,7260,7261,7266,7267,7269,7270,7273],{},"The server is now running and ready for querying with a ",[23,7262,7264],{"href":7263},"/developers/api-docs/pathway-xpacks-llm/vectorstore#pathway.xpacks.llm.vector_store.VectorStoreClient",[4673,7265,6763],{}," or with a ",[4673,7268,6708],{}," from ",[4673,7271,7272],{},"langchain-community"," described in the next Section.",[1188,7275,7277],{"id":7276},"using-pathway-as-a-vector-store-in-langchain-pipelines","Using Pathway as a Vector Store in LangChain pipelines",[19,7279,7280,7281,7283,7284,1785],{},"Once you have a ",[4673,7282,6763],{}," running you can access it from LangChain pipeline by using ",[23,7285,6708],{"href":6706,"rel":7286},[27],[19,7288,7289,7290,2947,7293,2650,7295,7297,7298,7300,7301,7303,7304,7309,7310,7313,7314,2650,7319,1785],{},"To do that you need to provide either the ",[4673,7291,7292],{},"url",[4673,7294,6394],{},[4673,7296,7209],{}," of the running ",[4673,7299,6763],{},". In the code example below, you will connect to the ",[4673,7302,6763],{}," defined in the previous Section, so make sure it's running before making queries. Alternatively, you can also use a publicly available ",[23,7305,7308],{"href":7306,"rel":7307},"https://pathway.com/solutions",[27],"demo pipeline"," to test your client. Its REST API you can access at ",[4673,7311,7312],{},"https://demo-document-indexing.pathway.stream",". This demo ingests documents from ",[23,7315,7318],{"href":7316,"rel":7317},"https://drive.google.com/drive/u/0/folders/1cULDv2OaViJBmOfG5WB0oWcgayNrGtVs",[27],"Google Drive",[23,7320,7323],{"href":7321,"rel":7322},"https://navalgo.sharepoint.com/sites/ConnectorSandbox/Shared%20Documents/Forms/AllItems.aspx?id=%2Fsites%2FConnectorSandbox%2FShared%20Documents%2FIndexerSandbox&p=true&ga=1",[27],"Sharepoint",[5049,7325,7327],{"className":5051,"code":7326,"language":5053,"meta":33,"style":33},"from langchain_community.vectorstores import PathwayVectorClient\n\nclient = PathwayVectorClient(host=host, port=port)\n",[4673,7328,7329,7346,7350],{"__ignoreMap":33},[2733,7330,7331,7333,7336,7338,7341,7343],{"class":5058,"line":5059},[2733,7332,5181],{"class":5106},[2733,7334,7335],{"class":5062}," langchain_community",[2733,7337,1785],{"class":5066},[2733,7339,7340],{"class":5062},"vectorstores ",[2733,7342,5107],{"class":5106},[2733,7344,7345],{"class":5062}," PathwayVectorClient\n",[2733,7347,7348],{"class":5058,"line":34},[2733,7349,5157],{"emptyLinePlaceholder":40},[2733,7351,7352,7354,7356,7359,7361,7363,7365,7367,7369,7371,7373,7375],{"class":5058,"line":1767},[2733,7353,6543],{"class":5062},[2733,7355,5368],{"class":5066},[2733,7357,7358],{"class":5442}," PathwayVectorClient",[2733,7360,5446],{"class":5066},[2733,7362,6394],{"class":5449},[2733,7364,5368],{"class":5066},[2733,7366,6394],{"class":5442},[2733,7368,5197],{"class":5066},[2733,7370,6404],{"class":5449},[2733,7372,5368],{"class":5066},[2733,7374,7209],{"class":5442},[2733,7376,5457],{"class":5066},[5049,7378,7380],{"className":5051,"code":7379,"language":5053,"meta":33,"style":33},"query = \"What is Pathway?\"\ndocs = client.similarity_search(query)\nprint(docs)\n",[4673,7381,7382,7396,7417],{"__ignoreMap":33},[2733,7383,7384,7387,7389,7391,7394],{"class":5058,"line":5059},[2733,7385,7386],{"class":5062},"query ",[2733,7388,5368],{"class":5066},[2733,7390,5335],{"class":5066},[2733,7392,7393],{"class":5070},"What is Pathway?",[2733,7395,5376],{"class":5066},[2733,7397,7398,7401,7403,7405,7407,7410,7412,7415],{"class":5058,"line":34},[2733,7399,7400],{"class":5062},"docs ",[2733,7402,5368],{"class":5066},[2733,7404,6595],{"class":5062},[2733,7406,1785],{"class":5066},[2733,7408,7409],{"class":5442},"similarity_search",[2733,7411,5446],{"class":5066},[2733,7413,7414],{"class":5442},"query",[2733,7416,5457],{"class":5066},[2733,7418,7419,7421,7423,7426],{"class":5058,"line":1767},[2733,7420,6616],{"class":5442},[2733,7422,5446],{"class":5066},[2733,7424,7425],{"class":5442},"docs",[2733,7427,5457],{"class":5066},[19,7429,7430],{},"As you can see, the LLM cannot respond clearly as it lacks current knowledge, but this is where Pathway shines. Add new data to the folder Pathway is listening to, then ask our agent again to see how it responds.\nTo do that, you can download the repo readme of Pathway into our data folder:",[5049,7432,7434],{"className":5051,"code":7433,"language":5053,"meta":33,"style":33},"!wget 'https://raw.githubusercontent.com/pathwaycom/pathway/main/README.md' -O 'data/pathway_readme.md' -q -nc\n",[4673,7435,7436],{"__ignoreMap":33},[2733,7437,7438,7440,7442,7445,7447,7450,7453,7455,7458,7460,7462,7464,7466],{"class":5058,"line":5059},[2733,7439,5597],{"class":5062},[2733,7441,5067],{"class":5066},[2733,7443,7444],{"class":5070},"https://raw.githubusercontent.com/pathwaycom/pathway/main/README.md",[2733,7446,5067],{"class":5066},[2733,7448,7449],{"class":5066}," -",[2733,7451,7452],{"class":5062},"O ",[2733,7454,5067],{"class":5066},[2733,7456,7457],{"class":5070},"data/pathway_readme.md",[2733,7459,5067],{"class":5066},[2733,7461,7449],{"class":5066},[2733,7463,5602],{"class":5062},[2733,7465,5579],{"class":5066},[2733,7467,7468],{"class":5062},"nc\n",[19,7470,7471],{},"Try again to query with the new data:",[5049,7473,7475],{"className":5051,"code":7474,"language":5053,"meta":33,"style":33},"docs = client.similarity_search(query)\nprint(docs)\n",[4673,7476,7477,7495],{"__ignoreMap":33},[2733,7478,7479,7481,7483,7485,7487,7489,7491,7493],{"class":5058,"line":5059},[2733,7480,7400],{"class":5062},[2733,7482,5368],{"class":5066},[2733,7484,6595],{"class":5062},[2733,7486,1785],{"class":5066},[2733,7488,7409],{"class":5442},[2733,7490,5446],{"class":5066},[2733,7492,7414],{"class":5442},[2733,7494,5457],{"class":5066},[2733,7496,7497,7499,7501,7503],{"class":5058,"line":34},[2733,7498,6616],{"class":5442},[2733,7500,5446],{"class":5066},[2733,7502,7425],{"class":5442},[2733,7504,5457],{"class":5066},[4821,7506,7508],{"id":7507},"rag-pipeline-in-langchain","RAG pipeline in LangChain",[19,7510,7511],{},"The next step is to write a chain in LangChain. The next example implements a simple RAG, that given a question, retrieves documents from Pathway Vector Store. These are then used as a context for the given question in a prompt sent to the OpenAI chat.",[5049,7513,7515],{"className":5051,"code":7514,"language":5053,"meta":33,"style":33},"from langchain_core.output_parsers import StrOutputParser\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.runnables import RunnablePassthrough\nfrom langchain_openai import ChatOpenAI\n\nretriever = client.as_retriever()\n\ntemplate = \"\"\"\nYou are smart assistant that helps users with their documents on Google Drive and Sharepoint.\nGiven a context, respond to the user question.\nCONTEXT:\n{context}\nQUESTION: {question}\nYOUR ANSWER:\"\"\"\n\nprompt = ChatPromptTemplate.from_template(template)\nllm = ChatOpenAI()\nchain = (\n    {\"context\": retriever, \"question\": RunnablePassthrough()}\n    | prompt\n    | llm\n    | StrOutputParser()\n)\n",[4673,7516,7517,7534,7550,7566,7577,7581,7597,7601,7611,7616,7621,7626,7631,7639,7647,7652,7674,7686,7697,7732,7741,7749,7759],{"__ignoreMap":33},[2733,7518,7519,7521,7524,7526,7529,7531],{"class":5058,"line":5059},[2733,7520,5181],{"class":5106},[2733,7522,7523],{"class":5062}," langchain_core",[2733,7525,1785],{"class":5066},[2733,7527,7528],{"class":5062},"output_parsers ",[2733,7530,5107],{"class":5106},[2733,7532,7533],{"class":5062}," StrOutputParser\n",[2733,7535,7536,7538,7540,7542,7545,7547],{"class":5058,"line":34},[2733,7537,5181],{"class":5106},[2733,7539,7523],{"class":5062},[2733,7541,1785],{"class":5066},[2733,7543,7544],{"class":5062},"prompts ",[2733,7546,5107],{"class":5106},[2733,7548,7549],{"class":5062}," ChatPromptTemplate\n",[2733,7551,7552,7554,7556,7558,7561,7563],{"class":5058,"line":1767},[2733,7553,5181],{"class":5106},[2733,7555,7523],{"class":5062},[2733,7557,1785],{"class":5066},[2733,7559,7560],{"class":5062},"runnables ",[2733,7562,5107],{"class":5106},[2733,7564,7565],{"class":5062}," RunnablePassthrough\n",[2733,7567,7568,7570,7572,7574],{"class":5058,"line":5173},[2733,7569,5181],{"class":5106},[2733,7571,6965],{"class":5062},[2733,7573,5107],{"class":5106},[2733,7575,7576],{"class":5062}," ChatOpenAI\n",[2733,7578,7579],{"class":5058,"line":5178},[2733,7580,5157],{"emptyLinePlaceholder":40},[2733,7582,7583,7586,7588,7590,7592,7595],{"class":5058,"line":5203},[2733,7584,7585],{"class":5062},"retriever ",[2733,7587,5368],{"class":5066},[2733,7589,6595],{"class":5062},[2733,7591,1785],{"class":5066},[2733,7593,7594],{"class":5442},"as_retriever",[2733,7596,6501],{"class":5066},[2733,7598,7599],{"class":5058,"line":5245},[2733,7600,5157],{"emptyLinePlaceholder":40},[2733,7602,7603,7606,7608],{"class":5058,"line":5270},[2733,7604,7605],{"class":5062},"template ",[2733,7607,5368],{"class":5066},[2733,7609,7610],{"class":5066}," \"\"\"\n",[2733,7612,7613],{"class":5058,"line":5295},[2733,7614,7615],{"class":5070},"You are smart assistant that helps users with their documents on Google Drive and Sharepoint.\n",[2733,7617,7618],{"class":5058,"line":5300},[2733,7619,7620],{"class":5070},"Given a context, respond to the user question.\n",[2733,7622,7623],{"class":5058,"line":5307},[2733,7624,7625],{"class":5070},"CONTEXT:\n",[2733,7627,7628],{"class":5058,"line":5507},[2733,7629,7630],{"class":5082},"{context}\n",[2733,7632,7633,7636],{"class":5058,"line":5546},[2733,7634,7635],{"class":5070},"QUESTION: ",[2733,7637,7638],{"class":5082},"{question}\n",[2733,7640,7641,7644],{"class":5058,"line":6149},[2733,7642,7643],{"class":5070},"YOUR ANSWER:",[2733,7645,7646],{"class":5066},"\"\"\"\n",[2733,7648,7650],{"class":5058,"line":7649},15,[2733,7651,5157],{"emptyLinePlaceholder":40},[2733,7653,7655,7658,7660,7663,7665,7668,7670,7672],{"class":5058,"line":7654},16,[2733,7656,7657],{"class":5062},"prompt 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retriever",[2733,7716,5197],{"class":5066},[2733,7718,5335],{"class":5066},[2733,7720,7721],{"class":5070},"question",[2733,7723,5321],{"class":5066},[2733,7725,6020],{"class":5066},[2733,7727,7728],{"class":5442}," RunnablePassthrough",[2733,7730,7731],{"class":5066},"()}\n",[2733,7733,7735,7738],{"class":5058,"line":7734},20,[2733,7736,7737],{"class":5066},"    |",[2733,7739,7740],{"class":5062}," prompt\n",[2733,7742,7744,7746],{"class":5058,"line":7743},21,[2733,7745,7737],{"class":5066},[2733,7747,7748],{"class":5062}," llm\n",[2733,7750,7752,7754,7757],{"class":5058,"line":7751},22,[2733,7753,7737],{"class":5066},[2733,7755,7756],{"class":5442}," StrOutputParser",[2733,7758,6501],{"class":5066},[2733,7760,7762],{"class":5058,"line":7761},23,[2733,7763,5457],{"class":5066},[19,7765,7766],{},"Now you have a RAG chain written in LangChain that uses Pathway as its Vector Store. Test it by asking some question.",[5049,7768,7770],{"className":5051,"code":7769,"language":5053,"meta":33,"style":33},"chain.invoke(\"What is Pathway?\")\n",[4673,7771,7772],{"__ignoreMap":33},[2733,7773,7774,7777,7779,7782,7784,7786,7788,7790],{"class":5058,"line":5059},[2733,7775,7776],{"class":5062},"chain",[2733,7778,1785],{"class":5066},[2733,7780,7781],{"class":5442},"invoke",[2733,7783,5446],{"class":5066},[2733,7785,5321],{"class":5066},[2733,7787,7393],{"class":5070},[2733,7789,5321],{"class":5066},[2733,7791,5457],{"class":5066},[4821,7793,7795],{"id":7794},"vector-store-statistics","Vector Store statistics",[19,7797,7798,7799,7804,7805,7807],{},"Just like ",[23,7800,7801],{"href":7263},[4673,7802,7803],{},"VectorStoreClient"," from the Pathway LLM xpack, ",[4673,7806,6708],{}," gives you two methods for getting information about indexed documents.",[19,7809,7810,7811,7818,7819,7826],{},"The first one is ",[23,7812,7815],{"href":7813,"rel":7814},"https://api.python.langchain.com/en/latest/vectorstores/langchain_community.vectorstores.pathway.PathwayVectorClient.html#langchain_community.vectorstores.pathway.PathwayVectorClient.get_vectorstore_statistics",[27],[4673,7816,7817],{},"get_vectorstore_statistics"," and gives essential statistics on the state of the vector store, like the number of indexed files and the timestamp of the last updated one. The second one is ",[23,7820,7823],{"href":7821,"rel":7822},"https://api.python.langchain.com/en/latest/vectorstores/langchain_community.vectorstores.pathway.PathwayVectorClient.html#langchain_community.vectorstores.pathway.PathwayVectorClient.get_input_files",[27],[4673,7824,7825],{},"get_input_files",", which gets the list of indexed files along with the associated metadata.",[5049,7828,7830],{"className":5051,"code":7829,"language":5053,"meta":33,"style":33},"print(client.get_vectorstore_statistics())\nprint(client.get_input_files())\n",[4673,7831,7832,7848],{"__ignoreMap":33},[2733,7833,7834,7836,7838,7841,7843,7845],{"class":5058,"line":5059},[2733,7835,6616],{"class":5442},[2733,7837,5446],{"class":5066},[2733,7839,7840],{"class":5442},"client",[2733,7842,1785],{"class":5066},[2733,7844,7817],{"class":5442},[2733,7846,7847],{"class":5066},"())\n",[2733,7849,7850,7852,7854,7856,7858,7860],{"class":5058,"line":34},[2733,7851,6616],{"class":5442},[2733,7853,5446],{"class":5066},[2733,7855,7840],{"class":5442},[2733,7857,1785],{"class":5066},[2733,7859,7825],{"class":5442},[2733,7861,7847],{"class":5066},[6649,7863,7864],{},"html pre.shiki code .s0W1g, html code.shiki .s0W1g{--shiki-default:#BABED8}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html pre.shiki code .sAklC, html code.shiki .sAklC{--shiki-default:#89DDFF}html pre.shiki code .sfyAc, html code.shiki .sfyAc{--shiki-default:#C3E88D}html pre.shiki code .s6cf3, html code.shiki .s6cf3{--shiki-default:#89DDFF;--shiki-default-font-style:italic}html pre.shiki code .saEQR, html code.shiki .saEQR{--shiki-default:#676E95;--shiki-default-font-style:italic}html pre.shiki code .s-wAU, html code.shiki .s-wAU{--shiki-default:#F07178}html pre.shiki code .sdLwU, html code.shiki .sdLwU{--shiki-default:#82AAFF}html pre.shiki code .s7ZW3, html code.shiki .s7ZW3{--shiki-default:#BABED8;--shiki-default-font-style:italic}html pre.shiki code .sx098, html code.shiki .sx098{--shiki-default:#F78C6C}",{"title":33,"searchDepth":34,"depth":34,"links":7866},[7867,7868,7869],{"id":6714,"depth":34,"text":6715},{"id":6753,"depth":34,"text":6754},{"id":7276,"depth":34,"text":7277,"children":7870},[7871,7872],{"id":7507,"depth":1767,"text":7508},{"id":7794,"depth":1767,"text":7795},{"layout":39,"date":118,"hidden":40,"thumbnail":7874,"tags":7875,"notebook_export_path":122,"keywords":7876},{"src":120},[97,98,46],[100,101,102,103,124,108],{"title":114,"description":33},{"loc":115},"bg_TqVKqcxb1uD2Bj0KQcx_-HRvp_C3sMuvhQGF-dGA",[7881,7882],{"title":1063,"path":1064,"stem":1065,"children":-1},{"title":1076,"path":1077,"stem":1078,"children":-1},1775364319109]