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6 | 6 | "description": "This recipe provides a blueprint for developers to create their own AI-powered chat applications using Streamlit.",
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7 | 7 | "name": "ChatBot",
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8 | 8 | "repository": "https://github.com/containers/ai-lab-recipes",
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9 |
| - "ref": "v1.7.0.1", |
| 9 | + "ref": "v1.7.0.2", |
10 | 10 | "icon": "natural-language-processing",
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11 | 11 | "categories": ["natural-language-processing"],
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12 | 12 | "basedir": "recipes/natural_language_processing/chatbot",
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28 | 28 | "description": "This recipe provides a blueprint for developers to create their own AI-powered chat applications with the pydantic framework using Streamlit",
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29 | 29 | "name": "Chatbot PydanticAI",
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30 | 30 | "repository": "https://github.com/containers/ai-lab-recipes",
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31 |
| - "ref": "v1.7.0.1", |
| 31 | + "ref": "v1.7.0.2", |
32 | 32 | "icon": "natural-language-processing",
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33 | 33 | "categories": ["natural-language-processing"],
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34 | 34 | "basedir": "recipes/natural_language_processing/chatbot-pydantic-ai",
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|
43 | 43 | "description": "This recipe shows how ReAct can be used to create an intelligent music discovery assistant with Spotify API.",
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44 | 44 | "name": "ReAct Agent Application",
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45 | 45 | "repository": "https://github.com/containers/ai-lab-recipes",
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46 |
| - "ref": "v1.7.0.1", |
| 46 | + "ref": "v1.7.0.2", |
47 | 47 | "icon": "natural-language-processing",
|
48 | 48 | "categories": ["natural-language-processing"],
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49 | 49 | "basedir": "recipes/natural_language_processing/agents",
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|
65 | 65 | "description": "This recipe guides into creating custom LLM-powered summarization applications using Streamlit.",
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66 | 66 | "name": "Summarizer",
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67 | 67 | "repository": "https://github.com/containers/ai-lab-recipes",
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68 |
| - "ref": "v1.7.0.1", |
| 68 | + "ref": "v1.7.0.2", |
69 | 69 | "icon": "natural-language-processing",
|
70 | 70 | "categories": ["natural-language-processing"],
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71 | 71 | "basedir": "recipes/natural_language_processing/summarizer",
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|
88 | 88 | "description": "This recipes showcases how to leverage LLM to build your own custom code generation application.",
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89 | 89 | "name": "Code Generation",
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90 | 90 | "repository": "https://github.com/containers/ai-lab-recipes",
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91 |
| - "ref": "v1.7.0.1", |
| 91 | + "ref": "v1.7.0.2", |
92 | 92 | "icon": "generator",
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93 | 93 | "categories": ["natural-language-processing"],
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94 | 94 | "basedir": "recipes/natural_language_processing/codegen",
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109 | 109 | "description": "This application illustrates how to integrate RAG (Retrieval Augmented Generation) into LLM applications enabling to interact with your own documents.",
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110 | 110 | "name": "RAG Chatbot",
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111 | 111 | "repository": "https://github.com/containers/ai-lab-recipes",
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112 |
| - "ref": "v1.7.0.1", |
| 112 | + "ref": "v1.7.0.2", |
113 | 113 | "icon": "natural-language-processing",
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114 | 114 | "categories": ["natural-language-processing"],
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115 | 115 | "basedir": "recipes/natural_language_processing/rag",
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|
131 | 131 | "description": "This application illustrates how to integrate RAG (Retrieval Augmented Generation) into LLM applications written in Node.js enabling to interact with your own documents.",
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132 | 132 | "name": "Node.js RAG Chatbot",
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133 | 133 | "repository": "https://github.com/containers/ai-lab-recipes",
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134 |
| - "ref": "v1.7.0.1", |
| 134 | + "ref": "v1.7.0.2", |
135 | 135 | "icon": "natural-language-processing",
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136 | 136 | "categories": ["natural-language-processing"],
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137 | 137 | "basedir": "recipes/natural_language_processing/rag-nodejs",
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153 | 153 | "description": "This is a Java Quarkus-based recipe demonstrating how to create an AI-powered chat applications.",
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154 | 154 | "name": "Java-based ChatBot (Quarkus)",
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155 | 155 | "repository": "https://github.com/containers/ai-lab-recipes",
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156 |
| - "ref": "v1.7.0.1", |
| 156 | + "ref": "v1.7.0.2", |
157 | 157 | "icon": "natural-language-processing",
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158 | 158 | "categories": ["natural-language-processing"],
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159 | 159 | "basedir": "recipes/natural_language_processing/chatbot-java-quarkus",
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175 | 175 | "description": "This is a NodeJS based recipe demonstrating how to create an AI-powered chat applications.",
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176 | 176 | "name": "Node.js based ChatBot",
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177 | 177 | "repository": "https://github.com/containers/ai-lab-recipes",
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178 |
| - "ref": "v1.7.0.1", |
| 178 | + "ref": "v1.7.0.2", |
179 | 179 | "icon": "natural-language-processing",
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180 | 180 | "categories": ["natural-language-processing"],
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181 | 181 | "basedir": "recipes/natural_language_processing/chatbot-nodejs",
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197 | 197 | "description": "This recipes guides into multiple function calling use cases, showing the ability to structure data and chain multiple tasks, using Streamlit.",
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198 | 198 | "name": "Function calling",
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199 | 199 | "repository": "https://github.com/containers/ai-lab-recipes",
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200 |
| - "ref": "v1.7.0.1", |
| 200 | + "ref": "v1.7.0.2", |
201 | 201 | "icon": "natural-language-processing",
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202 | 202 | "categories": ["natural-language-processing"],
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203 | 203 | "basedir": "recipes/natural_language_processing/function_calling",
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|
215 | 215 | "description": "This recipes guides into multiple function calling use cases, showing the ability to structure data and chain multiple tasks, using Streamlit.",
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216 | 216 | "name": "Node.js Function calling",
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217 | 217 | "repository": "https://github.com/containers/ai-lab-recipes",
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218 |
| - "ref": "v1.7.0.1", |
| 218 | + "ref": "v1.7.0.2", |
219 | 219 | "icon": "natural-language-processing",
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220 | 220 | "categories": ["natural-language-processing"],
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221 | 221 | "basedir": "recipes/natural_language_processing/function-calling-nodejs",
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233 | 233 | "description": "This demo provides a recipe to build out a custom Graph RAG (Graph Retrieval Augmented Generation) application using the repo LightRag which abstracts Microsoft's GraphRag implementation. It consists of two main components; the Model Service, and the AI Application with a built in Database.",
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234 | 234 | "name": "Graph RAG Chat Application",
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235 | 235 | "repository": "https://github.com/containers/ai-lab-recipes",
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236 |
| - "ref": "v1.7.0.1", |
| 236 | + "ref": "v1.7.0.2", |
237 | 237 | "icon": "natural-language-processing",
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238 | 238 | "categories": ["natural-language-processing"],
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239 | 239 | "basedir": "recipes/natural_language_processing/graph-rag",
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|
248 | 248 | "description": "This application demonstrate how to use LLM for transcripting an audio into text.",
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249 | 249 | "name": "Audio to Text",
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250 | 250 | "repository": "https://github.com/containers/ai-lab-recipes",
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251 |
| - "ref": "v1.7.0.1", |
| 251 | + "ref": "v1.7.0.2", |
252 | 252 | "icon": "generator",
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253 | 253 | "categories": ["audio"],
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254 | 254 | "basedir": "recipes/audio/audio_to_text",
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263 | 263 | "description": "This recipe illustrates how to use LLM to interact with images and build object detection applications.",
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264 | 264 | "name": "Object Detection",
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265 | 265 | "repository": "https://github.com/containers/ai-lab-recipes",
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266 |
| - "ref": "v1.7.0.1", |
| 266 | + "ref": "v1.7.0.2", |
267 | 267 | "icon": "generator",
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268 | 268 | "categories": ["computer-vision"],
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269 | 269 | "basedir": "recipes/computer_vision/object_detection",
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