[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-b84eeafa7de4edcb-building-a-code-dataset-pipeline-with-nvidia-nemot-summary":3,"summaries-facets-categories":103,"summary-related-b84eeafa7de4edcb-building-a-code-dataset-pipeline-with-nvidia-nemot-summary":5677},{"id":4,"title":5,"ai":6,"body":13,"categories":63,"created_at":65,"date_modified":65,"description":57,"extension":66,"faq":65,"featured":67,"kicker_label":65,"meta":68,"navigation":85,"path":86,"published_at":87,"question":65,"scraped_at":87,"seo":88,"sitemap":89,"source_id":90,"source_name":91,"source_type":92,"source_url":93,"stem":94,"tags":95,"thumbnail_url":65,"tldr":100,"tweet":65,"unknown_tags":101,"__hash__":102},"summaries\u002Fsummaries\u002Fb84eeafa7de4edcb-building-a-code-dataset-pipeline-with-nvidia-nemot-summary.md","Building a Code Dataset Pipeline with NVIDIA Nemotron Metadata",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",10469,529,2579,0.00341075,{"type":14,"value":15,"toc":56},"minimark",[16,21,34,38,45,49],[17,18,20],"h2",{"id":19},"efficient-dataset-exploration-via-streaming","Efficient Dataset Exploration via Streaming",[22,23,24,25,29,30,33],"p",{},"Instead of downloading massive datasets, developers can use the Hugging Face ",[26,27,28],"code",{},"datasets"," library in streaming mode to inspect schema and metadata. By loading the ",[26,31,32],{},"nvidia\u002FNemotron-Pretraining-Code-v3"," dataset as a stream, you can perform exploratory data analysis (EDA) on a manageable sample (e.g., 30,000 rows) without local storage constraints. This approach allows for rapid iteration when identifying dataset structures, such as repository frequency, file extensions, and directory nesting depth.",[17,35,37],{"id":36},"metadata-to-source-reconstruction","Metadata-to-Source Reconstruction",[22,39,40,41,44],{},"Metadata indices often contain the necessary components—repository name, commit ID, and relative file path—to reconstruct raw GitHub URLs. By using ",[26,42,43],{},"urllib.parse.quote"," to handle path encoding, you can programmatically fetch source files from GitHub. The pipeline includes robust error handling to account for missing, deleted, or private repositories, ensuring the script remains resilient during execution.",[17,46,48],{"id":47},"token-estimation-and-scaling","Token Estimation and Scaling",[22,50,51,52,55],{},"To understand the computational requirements for training or fine-tuning, the pipeline incorporates token estimation. By using ",[26,53,54],{},"tiktoken"," (or a character-based fallback), you can calculate the token density of your sample. This provides a baseline to extrapolate the scale of the full dataset, which in this case contains approximately 146 million files and 173 billion tokens, helping developers estimate the compute resources required for pretraining tasks.",{"title":57,"searchDepth":58,"depth":58,"links":59},"",2,[60,61,62],{"id":19,"depth":58,"text":20},{"id":36,"depth":58,"text":37},{"id":47,"depth":58,"text":48},[64],"AI Automation",null,"md",false,{"content_references":69,"triage":80},[70,75,78],{"type":71,"title":72,"url":73,"context":74},"tool","NVIDIA Nemotron-Pretraining-Code-v3","https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fnvidia\u002FNemotron-Pretraining-Code-v3","mentioned",{"type":71,"title":76,"url":77,"context":74},"Hugging Face 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12:57:05",{"title":5,"description":57},{"loc":86},"b84eeafa7de4edcb","MarkTechPost","article","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F06\u002F09\u002Fbuilding-a-code-dataset-pipeline-from-nvidia-nemotron-pretraining-code-v3-metadata-with-streaming-pandas-and-tiktoken\u002F","summaries\u002Fb84eeafa7de4edcb-building-a-code-dataset-pipeline-with-nvidia-nemot-summary",[96,97,98,99],"python","ai-tools","data-science","llm","A practical guide to streaming, analyzing, and sampling large-scale code metadata from NVIDIA's Nemotron-Pretraining-Code-v3 dataset without downloading the entire multi-gigabyte 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This is technically difficult because filters often conflict with the pre-indexed 'aisles,' requiring complex engineering to maintain performance.",[5706,5747,5748,5751],{},[5709,5749,5750],{},"Hybrid Search",": Combining vector similarity with traditional keyword or structured data filtering.",{"title":57,"searchDepth":58,"depth":58,"links":5753},[5754,5755,5756],{"id":5690,"depth":58,"text":5691},{"id":5697,"depth":58,"text":5698},{"id":5727,"depth":58,"text":5728},[112],{"content_references":5759,"triage":5763},[5760],{"type":71,"title":5761,"url":5762,"context":74},"FAISS","https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Ffaiss",{"relevance":81,"novelty":5764,"quality":82,"actionability":82,"composite":5765,"reasoning":5766},3,4.15,"Category: AI & LLMs. The article provides a detailed explanation of vector search techniques, specifically focusing on Approximate Nearest Neighbor (ANN) methods, which are crucial for AI-powered product builders dealing with large datasets. It offers actionable insights into the trade-offs between speed and accuracy in search systems, making it relevant for developers looking to implement efficient search functionalities.","\u002Fsummaries\u002F58aa82efe57a452b-vector-search-explained-from-brute-force-to-ann-summary","2026-06-23 04:50:43","2026-06-23 12:56:47",{"title":5680,"description":57},{"loc":5767},"58aa82efe57a452b","Level Up Coding","https:\u002F\u002Flevelup.gitconnected.com\u002Fvector-search-explained-visually-how-databases-find-a-needle-in-5-million-vectors-bb67825b1a75?source=rss----5517fd7b58a6---4","summaries\u002F58aa82efe57a452b-vector-search-explained-from-brute-force-to-ann-summary",[99,97,98],"Vector search scales by replacing linear scans with 'aisles'—grouping similar vectors into clusters defined by centroids—allowing systems to ignore irrelevant data and return results in milliseconds.",[],"HK3k-5y-hEVvC2ZT8cQOkj4KbzVl2TV_np9iv9o01iE",{"id":5781,"title":5782,"ai":5783,"body":5789,"categories":5843,"created_at":65,"date_modified":65,"description":57,"extension":66,"faq":65,"featured":67,"kicker_label":65,"meta":5844,"navigation":85,"path":5874,"published_at":5875,"question":65,"scraped_at":5876,"seo":5877,"sitemap":5878,"source_id":5879,"source_name":5880,"source_type":92,"source_url":5881,"stem":5882,"tags":5883,"thumbnail_url":65,"tldr":5884,"tweet":65,"unknown_tags":5885,"__hash__":5886},"summaries\u002Fsummaries\u002F352a655761b08b28-35b-models-on-rtx-4090-turboquant-kv-compression-u-summary.md","35B Models on RTX 4090: TurboQuant KV Compression Unlocks 32K Context",{"provider":7,"model":5784,"input_tokens":5785,"output_tokens":5786,"processing_time_ms":5787,"cost_usd":5788},"x-ai\u002Fgrok-4.1-fast",6914,2052,13171,0.00190195,{"type":14,"value":5790,"toc":5837},[5791,5795,5798,5802,5805,5809,5815,5821,5827,5830,5834],[17,5792,5794],{"id":5793},"q4_k_m-quantization-delivers-90-95-quality-at-60-original-size","Q4_K_M Quantization Delivers 90-95% Quality at 60% Original Size",[22,5796,5797],{},"Q4_K_M GGUF compresses model weights to ~0.6 GB per billion parameters (7B → 4GB, 32B → 19GB, 70B → 40GB) by storing weights in 4 bits with K-quant block grouping and M-medium mixed precision for sensitive layers. This preserves 90-95% of FP16 accuracy, making it the default for local runs on HuggingFace\u002FOllama. Dense 35B models need 21-22GB VRAM for weights alone on RTX 4090 (24GB total), leaving ~2GB for KV cache—insufficient beyond short contexts. MoE 35B (e.g., Qwen2.5-35B-A3B) activates only 3B params\u002Ftoken, fitting in ~20GB with 1.2GB KV at 64K context due to fewer active heads, reducing TurboQuant's necessity.",[17,5799,5801],{"id":5800},"turboquant-stacks-on-weights-for-long-context-memory-wins","TurboQuant Stacks on Weights for Long-Context Memory Wins",[22,5803,5804],{},"TurboQuant compresses KV cache to 2-4 bits at inference (PolarQuant + QJL, e.g., bits=3) without touching weights, enabling dense models like Mistral Small 3.1 24B or Qwen2.5-32B (64 layers, 8 GQA heads, head_dim=128) to handle 32K context on 24GB VRAM. Formula: 2 × layers × heads × head_dim × seq_len × bytes\u002Felement. Without it, 16K context KV hits ~4GB (total ~24GB borderline); with turbo3, drops to ~1.2GB, freeing space for 32K (~2.4GB). Fused Triton kernels compute attention on compressed KV, speeding up >8K contexts (major at 32K+). Asymmetric K@3bits\u002FV@2bits saves more with zero quality loss empirically.",[17,5806,5808],{"id":5807},"three-paths-to-turboquant-on-24gb-gpus-today","Three Paths to TurboQuant on 24GB GPUs Today",[22,5810,5811,5814],{},[5709,5812,5813],{},"PyPI turboquant-kv",": Wrap HF Transformers (load_in_4bit) with TurboQuantModel(bits=3).enable_decoder_fused_attention() for Python scripts; handles 512+ new tokens on long inputs.",[22,5816,5817,5820],{},[5709,5818,5819],{},"vLLM fork (0xSero\u002Fturboquant)",": install_turboquant_vllm(bits=3, head_dim=128) before LLM(model, gpu_memory_utilization=0.92); prebuilt codebooks for d=128\u002F256 at 2\u002F3\u002F4 bits; server-friendly.",[22,5822,5823,5826],{},[5709,5824,5825],{},"llama.cpp fork (turboquant_plus)",": Build with CUDA, run llama-server -m model-Q4_K_M.gguf --cache-type-k turbo3 --cache-type-v turbo2 -c 32768 -ngl 99. Turbo4 ≈ q8_0 quality, turbo3 best tradeoff, turbo2 extreme. Fits 32K on Qwen2.5-32B (19GB weights + \u003C4GB KV).",[22,5828,5829],{},"Quality holds ≥8B models; speedups context-dependent (\u003C2K: memory only). Experimental—await Google impl (Q2-Q3 2026), llama.cpp #20969, vLLM #38171 merges.",[17,5831,5833],{"id":5832},"optimal-stack-q4_k_m-gguf-turboquant_plus-turbo32","Optimal Stack: Q4_K_M GGUF + turboquant_plus turbo3\u002F2",[22,5835,5836],{},"Download Q4_K_M GGUF, use llama.cpp fork at 16-32K context. Achieves reliable 35B dense inference where defaults crash; 128K impossible (KV still GBs post-compression).",{"title":57,"searchDepth":58,"depth":58,"links":5838},[5839,5840,5841,5842],{"id":5793,"depth":58,"text":5794},{"id":5800,"depth":58,"text":5801},{"id":5807,"depth":58,"text":5808},{"id":5832,"depth":58,"text":5833},[112],{"content_references":5845,"triage":5872},[5846,5850,5853,5857,5860,5863,5866,5869],{"type":5847,"title":5848,"url":5849,"context":74},"other","GGUF","https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Fgguf",{"type":5847,"title":5851,"url":5852,"context":74},"AWQ","https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Ftransformers\u002Fquantization\u002Fawq",{"type":5847,"title":5854,"url":5855,"context":5856},"VRAM Requirements for AI Models","https:\u002F\u002Fwillitrunai.com\u002Fblog\u002Fvram-requirements-for-ai-models","cited",{"type":71,"title":5858,"context":5859},"turboquant-kv","recommended",{"type":71,"title":5861,"url":5862,"context":5859},"0xSero\u002Fturboquant","https:\u002F\u002Fgithub.com\u002F0xSero\u002Fturboquant.git",{"type":71,"title":5864,"url":5865,"context":5859},"turboquant_plus","https:\u002F\u002Fgithub.com\u002FTheTom\u002Fturboquant_plus.git",{"type":5847,"title":5867,"url":5868,"context":74},"llama.cpp discussion #20969","https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fllama.cpp\u002Fdiscussions\u002F20969",{"type":5847,"title":5870,"url":5871,"context":74},"vLLM issue #38171","https:\u002F\u002Fgithub.com\u002Fvllm-project\u002Fvllm\u002Fissues\u002F38171",{"relevance":81,"novelty":82,"quality":82,"actionability":82,"composite":83,"reasoning":5873},"Category: AI & LLMs. The article provides in-depth technical insights on running large language models efficiently, addressing the pain point of integrating AI features into products. It offers specific implementation paths for using TurboQuant, which is actionable for developers looking to optimize AI model performance.","\u002Fsummaries\u002F352a655761b08b28-35b-models-on-rtx-4090-turboquant-kv-compression-u-summary","2026-04-15 12:31:01","2026-04-15 15:39:14",{"title":5782,"description":57},{"loc":5874},"352a655761b08b28","Towards AI","https:\u002F\u002Fpub.towardsai.net\u002Frunning-a-35b-model-locally-with-turboquant-whats-actually-possible-right-now-1ac5327430b0?source=rss----98111c9905da---4","summaries\u002F352a655761b08b28-35b-models-on-rtx-4090-turboquant-kv-compression-u-summary",[99,97,96],"Stack Q4_K_M weight quantization with TurboQuant's 3-bit KV cache compression to run dense 35B models at 32K context on 24GB VRAM, fitting weights (20GB) + KV cache (under 4GB) with room to spare—use llama.cpp forks today.",[],"sGRm9blteDamXpWHWdqmPUk1Seq5Nct_nBmywP1bFT0",{"id":5888,"title":5889,"ai":5890,"body":5895,"categories":6248,"created_at":65,"date_modified":65,"description":57,"extension":66,"faq":65,"featured":67,"kicker_label":65,"meta":6249,"navigation":85,"path":6250,"published_at":6251,"question":65,"scraped_at":65,"seo":6252,"sitemap":6253,"source_id":6254,"source_name":5773,"source_type":92,"source_url":6255,"stem":6256,"tags":6257,"thumbnail_url":65,"tldr":6258,"tweet":65,"unknown_tags":6259,"__hash__":6260},"summaries\u002Fsummaries\u002Fllm-as-judge-evaluates-rag-keyword-beats-vector-summary.md","LLM-as-Judge Evaluates RAG: Keyword Beats Vector",{"provider":7,"model":5784,"input_tokens":5891,"output_tokens":5892,"processing_time_ms":5893,"cost_usd":5894},5849,1975,17506,0.0021348,{"type":14,"value":5896,"toc":6243},[5897,5901,5908,5914,5934,5939,5957,5972,5976,5987,6008,6011,6082,6085,6112,6115,6135,6138,6173,6177,6180,6232,6239],[17,5898,5900],{"id":5899},"rag-needs-automated-internal-evaluation-for-optimization","RAG Needs Automated Internal Evaluation for Optimization",[22,5902,5903,5904,5907],{},"RAG systems require quantitative evaluation to compare optimizations like retrieval strategies, avoiding manual checks that are slow and subjective—integrate into CI\u002FCD pipelines like unit tests. Focus on ",[5709,5905,5906],{},"internal evaluation"," of retrieval and generation modules:",[22,5909,5910,5913],{},[5709,5911,5912],{},"Retrieval metrics",":",[5703,5915,5916,5922,5928],{},[5706,5917,5918,5921],{},[5709,5919,5920],{},"Relevance",": Retrieved chunks match query?",[5706,5923,5924,5927],{},[5709,5925,5926],{},"Coverage",": All relevant database chunks fetched?",[5706,5929,5930,5933],{},[5709,5931,5932],{},"Correctness",": High signal-to-noise ratio, relevant chunks ranked top?",[22,5935,5936,5913],{},[5709,5937,5938],{},"Generation metrics",[5703,5940,5941,5946,5952],{},[5706,5942,5943,5945],{},[5709,5944,5920],{},": Answer aligns with query, no off-topic drift?",[5706,5947,5948,5951],{},[5709,5949,5950],{},"Factuality",": Answer grounded in retrieved sources, no hallucinations?",[5706,5953,5954,5956],{},[5709,5955,5932],{},": Answer factually accurate?",[22,5958,5959,5960,5963,5964,5967,5968,5971],{},"Prefer ",[5709,5961,5962],{},"LLM-as-a-judge"," over traditional NLP metrics (ROUGE, BLEU) for nuanced semantic judgment. Ground evaluators in production setups like Azure AI Search indexes (e.g., ",[26,5965,5966],{},"rag-evalution-chris"," with 50 chunks from employee handbook PDFs, vectorized in ",[26,5969,5970],{},"text_vector"," field).",[17,5973,5975],{"id":5974},"azure-sdk-evaluators-automate-llm-as-judge-scoring","Azure SDK Evaluators Automate LLM-as-Judge Scoring",[22,5977,5978,5979,5982,5983,5986],{},"Leverage ",[26,5980,5981],{},"azure.ai.evaluation"," package with GPT-4 (",[26,5984,5985],{},"gpt-4.1"," deployment) for zero-shot scoring (1.0-5.0 scale). Key evaluators:",[5703,5988,5989,5999],{},[5706,5990,5991,5994,5995,5998],{},[5709,5992,5993],{},"GroundednessEvaluator",": Measures answer's fidelity to sources—scores drop if facts can't be verified in context, even if externally true. Input: ",[26,5996,5997],{},"response=answer, context=sources",".",[5706,6000,6001,6004,6005,5998],{},[5709,6002,6003],{},"RelevanceEvaluator",": Checks query-response alignment and contextual fit. Input: ",[26,6006,6007],{},"query=user_question, response=answer, context=sources",[22,6009,6010],{},"Setup clients for Azure AI Search and OpenAI:",[6012,6013,6016],"pre",{"className":6014,"code":6015,"language":96,"meta":57,"style":57},"language-python shiki shiki-themes github-light github-dark","import os\nfrom azure.search.documents import SearchClient\nfrom azure.search.documents.models import VectorizedQuery\nfrom openai import AzureOpenAI\n# Load env vars: AZURE_SEARCH_*, AZURE_OPENAI_*\nopenai_client = AzureOpenAI(api_key=AZURE_OPENAI_API_KEY, azure_endpoint=AZURE_OPENAI_ENDPOINT, api_version=\"2024-10-21\")\nsearch_client = SearchClient(endpoint=AZURE_SEARCH_ENDPOINT, index_name=AZURE_SEARCH_INDEX_NAME, credential=AzureKeyCredential(AZURE_SEARCH_ADMIN_KEY))\n\ndef get_embedding_vector(query: str) -> list[float]:\n    response = openai_client.embeddings.create(model=AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME, input=[query])\n    return response.data[0].embedding\n",[26,6017,6018,6026,6031,6036,6041,6046,6052,6058,6064,6070,6076],{"__ignoreMap":57},[6019,6020,6023],"span",{"class":6021,"line":6022},"line",1,[6019,6024,6025],{},"import os\n",[6019,6027,6028],{"class":6021,"line":58},[6019,6029,6030],{},"from azure.search.documents import SearchClient\n",[6019,6032,6033],{"class":6021,"line":5764},[6019,6034,6035],{},"from azure.search.documents.models import VectorizedQuery\n",[6019,6037,6038],{"class":6021,"line":82},[6019,6039,6040],{},"from openai import AzureOpenAI\n",[6019,6042,6043],{"class":6021,"line":81},[6019,6044,6045],{},"# Load env vars: AZURE_SEARCH_*, AZURE_OPENAI_*\n",[6019,6047,6049],{"class":6021,"line":6048},6,[6019,6050,6051],{},"openai_client = AzureOpenAI(api_key=AZURE_OPENAI_API_KEY, azure_endpoint=AZURE_OPENAI_ENDPOINT, api_version=\"2024-10-21\")\n",[6019,6053,6055],{"class":6021,"line":6054},7,[6019,6056,6057],{},"search_client = SearchClient(endpoint=AZURE_SEARCH_ENDPOINT, index_name=AZURE_SEARCH_INDEX_NAME, credential=AzureKeyCredential(AZURE_SEARCH_ADMIN_KEY))\n",[6019,6059,6061],{"class":6021,"line":6060},8,[6019,6062,6063],{"emptyLinePlaceholder":85},"\n",[6019,6065,6067],{"class":6021,"line":6066},9,[6019,6068,6069],{},"def get_embedding_vector(query: str) -> list[float]:\n",[6019,6071,6073],{"class":6021,"line":6072},10,[6019,6074,6075],{},"    response = openai_client.embeddings.create(model=AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME, input=[query])\n",[6019,6077,6079],{"class":6021,"line":6078},11,[6019,6080,6081],{},"    return response.data[0].embedding\n",[22,6083,6084],{},"Retrieval (top=5):",[5703,6086,6087,6096,6104],{},[5706,6088,6089,6092,6093],{},[5709,6090,6091],{},"Keyword",": ",[26,6094,6095],{},"search_client.search(search_text=user_question)",[5706,6097,6098,6092,6101],{},[5709,6099,6100],{},"Vector",[26,6102,6103],{},"search_client.search(None, vector_queries=[VectorizedQuery(vector=get_embedding_vector(user_question), k_nearest_neighbors=50, fields=\"text_vector\")])",[5706,6105,6106,6092,6109],{},[5709,6107,6108],{},"Hybrid (semantic)",[26,6110,6111],{},"search_client.search(user_question, vector_queries=[...], query_type=\"semantic\", semantic_configuration_name=\"rag-evaluation-chris-semantic-configuration\")",[22,6113,6114],{},"Generation prompt enforces grounding:",[6012,6116,6118],{"className":6014,"code":6117,"language":96,"meta":57,"style":57},"SYSTEM_MESSAGE = \"\"\"Answer ONLY with facts from sources. Use [source] citations.\"\"\"\nresponse = openai_client.chat.completions.create(model=AZURE_OPENAI_LLM_DEPLOYMENT_NAME, messages=[{\"role\": \"system\", \"content\": SYSTEM_MESSAGE}, {\"role\": \"user\", \"content\": user_question + \"\\nSources: \" + sources}])\nanswer = response.choices[0].message.content\n",[26,6119,6120,6125,6130],{"__ignoreMap":57},[6019,6121,6122],{"class":6021,"line":6022},[6019,6123,6124],{},"SYSTEM_MESSAGE = \"\"\"Answer ONLY with facts from sources. Use [source] citations.\"\"\"\n",[6019,6126,6127],{"class":6021,"line":58},[6019,6128,6129],{},"response = openai_client.chat.completions.create(model=AZURE_OPENAI_LLM_DEPLOYMENT_NAME, messages=[{\"role\": \"system\", \"content\": SYSTEM_MESSAGE}, {\"role\": \"user\", \"content\": user_question + \"\\nSources: \" + sources}])\n",[6019,6131,6132],{"class":6021,"line":5764},[6019,6133,6134],{},"answer = response.choices[0].message.content\n",[22,6136,6137],{},"Evaluate:",[6012,6139,6141],{"className":6014,"code":6140,"language":96,"meta":57,"style":57},"from azure.ai.evaluation import AzureOpenAIModelConfiguration, GroundednessEvaluator, RelevanceEvaluator\nmodel_config = {\"azure_endpoint\": AZURE_OPENAI_ENDPOINT, \"azure_deployment\": AZURE_OPENAI_LLM_DEPLOYMENT_NAME, \"api_key\": AZURE_OPENAI_API_KEY}\nrelevance_eval = RelevanceEvaluator(model_config)\ngroundedness_eval = GroundednessEvaluator(model_config)\nrelevance_score = relevance_eval(query=user_question, response=answer, context=sources)\ngroundedness_score = groundedness_eval(response=answer, context=sources)\n",[26,6142,6143,6148,6153,6158,6163,6168],{"__ignoreMap":57},[6019,6144,6145],{"class":6021,"line":6022},[6019,6146,6147],{},"from azure.ai.evaluation import AzureOpenAIModelConfiguration, GroundednessEvaluator, RelevanceEvaluator\n",[6019,6149,6150],{"class":6021,"line":58},[6019,6151,6152],{},"model_config = {\"azure_endpoint\": AZURE_OPENAI_ENDPOINT, \"azure_deployment\": AZURE_OPENAI_LLM_DEPLOYMENT_NAME, \"api_key\": AZURE_OPENAI_API_KEY}\n",[6019,6154,6155],{"class":6021,"line":5764},[6019,6156,6157],{},"relevance_eval = RelevanceEvaluator(model_config)\n",[6019,6159,6160],{"class":6021,"line":82},[6019,6161,6162],{},"groundedness_eval = GroundednessEvaluator(model_config)\n",[6019,6164,6165],{"class":6021,"line":81},[6019,6166,6167],{},"relevance_score = relevance_eval(query=user_question, response=answer, context=sources)\n",[6019,6169,6170],{"class":6021,"line":6048},[6019,6171,6172],{},"groundedness_score = groundedness_eval(response=answer, context=sources)\n",[17,6174,6176],{"id":6175},"keyword-search-wins-for-simple-queries-enables-agentic-rag","Keyword Search Wins for Simple Queries, Enables Agentic RAG",[22,6178,6179],{},"On query \"What does a product manager do?\" (50-chunk index):",[6181,6182,6183,6198],"table",{},[6184,6185,6186],"thead",{},[6187,6188,6189,6193,6196],"tr",{},[6190,6191,6192],"th",{},"Method",[6190,6194,6195],{},"Groundedness",[6190,6197,5920],{},[6199,6200,6201,6212,6222],"tbody",{},[6187,6202,6203,6206,6209],{},[6204,6205,6091],"td",{},[6204,6207,6208],{},"4.5",[6204,6210,6211],{},"5.0",[6187,6213,6214,6217,6220],{},[6204,6215,6216],{},"Hybrid",[6204,6218,6219],{},"4.0",[6204,6221,6208],{},[6187,6223,6224,6226,6229],{},[6204,6225,6100],{},[6204,6227,6228],{},"3.0",[6204,6230,6231],{},"3.5",[22,6233,6234,6235,6238],{},"Keyword search topped scores unexpectedly for this task, proving automated eval reveals trade-offs (e.g., vector struggles with exact phrasing). This closes the loop for ",[5709,6236,6237],{},"Agentic RAG",": reliable metrics select best retrieval for self-improving agents.",[6240,6241,6242],"style",{},"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 .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":57,"searchDepth":58,"depth":58,"links":6244},[6245,6246,6247],{"id":5899,"depth":58,"text":5900},{"id":5974,"depth":58,"text":5975},{"id":6175,"depth":58,"text":6176},[],{},"\u002Fsummaries\u002Fllm-as-judge-evaluates-rag-keyword-beats-vector-summary","2026-04-08 21:21:17",{"title":5889,"description":57},{"loc":6250},"20b8d035b68db639","https:\u002F\u002Funknown","summaries\u002Fllm-as-judge-evaluates-rag-keyword-beats-vector-summary",[99,96,97],"Use Azure SDK's GroundednessEvaluator (1-5 scale: answer fidelity to sources) and RelevanceEvaluator (query-response alignment) to automate RAG scoring; keyword search outperformed vector\u002Fhybrid on 'product manager duties' query.",[],"WbpHbWCJqCpSbai5hGyHaCpcDDTRGieYViPox5fj2mc",{"id":6262,"title":6263,"ai":6264,"body":6269,"categories":6350,"created_at":65,"date_modified":65,"description":57,"extension":66,"faq":65,"featured":67,"kicker_label":65,"meta":6351,"navigation":85,"path":6374,"published_at":65,"question":65,"scraped_at":6375,"seo":6376,"sitemap":6377,"source_id":6378,"source_name":6379,"source_type":92,"source_url":6380,"stem":6381,"tags":6382,"thumbnail_url":65,"tldr":6383,"tweet":65,"unknown_tags":6384,"__hash__":6385},"summaries\u002Fsummaries\u002F8fee41411642a9b7-harmony-render-gpt-oss-response-format-in-rust-pyt-summary.md","Harmony: Render gpt-oss Response Format in Rust\u002FPython",{"provider":7,"model":5784,"input_tokens":6265,"output_tokens":6266,"processing_time_ms":6267,"cost_usd":6268},5558,1823,8018,0.00151625,{"type":14,"value":6270,"toc":6345},[6271,6275,6278,6281,6289,6292,6296,6307,6310,6314],[17,6272,6274],{"id":6273},"harmony-format-enables-structured-gpt-oss-outputs","Harmony Format Enables Structured gpt-oss Outputs",[22,6276,6277],{},"gpt-oss models demand the harmony response format for correct operation, as they were trained specifically on it. This format structures conversations, reasoning traces, and function calls using special tokens like \u003C|start|>role\u003C|message|> and \u003C|end|>. It supports multiple channels (e.g., analysis, commentary, final) for separating chain-of-thought, tool preambles, and responses, plus tool namespaces and structured outputs with instruction hierarchies. Without harmony, gpt-oss fails; providers like HuggingFace, Ollama, or vLLM handle it automatically, but custom inference requires manual prompting.",[22,6279,6280],{},"Example system prompt specifies channels and tools:",[6012,6282,6287],{"className":6283,"code":6285,"language":6286},[6284],"language-text","\u003C|start|>system\u003C|message|>You are ChatGPT... Reasoning: high # Valid channels: analysis, commentary, final... Calls to 'functions' must go to commentary.\u003C|end|>\n\u003C|start|>developer\u003C|message|># Instructions Always respond in riddles # Tools ## functions namespace functions { type get_location = () => any; type get_current_weather = (_: {location: string...}) => any; }\u003C|end|>\n\u003C|start|>user\u003C|message|>What is the weather like in SF?\u003C|end|>\n\u003C|start|>assistant\n","text",[26,6288,6285],{"__ignoreMap":57},[22,6290,6291],{},"This mimics OpenAI's Responses API, easing transition for familiar users. See full guide at cookbook.openai.com\u002Farticles\u002Fopenai-harmony.",[17,6293,6295],{"id":6294},"python-and-rust-libraries-for-encodingparsing","Python and Rust Libraries for Encoding\u002FParsing",[22,6297,6298,6299,6302,6303,6306],{},"Install Python via ",[26,6300,6301],{},"pip install openai-harmony"," for high-level dataclasses mirroring chat structures (Role, Message). Rust core handles rendering\u002Fparsing via ",[26,6304,6305],{},"cargo add harmony",", with full docs at docs\u002Fpython.md and docs\u002Frust.md.",[22,6308,6309],{},"Architecture: Rust crate (src\u002F) with chat.rs for data structures, encoding.rs for logic, tiktoken tokenizer, and registry.rs for encodings. Python wrapper (python\u002Fopenai_harmony\u002F) uses pyo3 FFI bindings, producing openai_harmony.*.so. Repo includes tests\u002F, test-data\u002F, demo\u002Fharmony-demo, and AGENTS.md.",[17,6311,6313],{"id":6312},"local-development-and-testing","Local Development and Testing",[22,6315,6316,6317,6320,6321,6324,6325,6328,6329,6332,6333,6336,6337,6340,6341,6344],{},"Use ",[26,6318,6319],{},"maturin develop"," to build Rust extension into virtualenv, then ",[26,6322,6323],{},"pip install -e ."," for Python wrapper. Run Rust tests with ",[26,6326,6327],{},"cargo test",", Python with ",[26,6330,6331],{},"pytest tests",", or both via ",[26,6334,6335],{},".\u002Frun_checks.sh",". Optional: ",[26,6338,6339],{},"cargo fmt",", ",[26,6342,6343],{},"ruff check .",". Ensures Rust\u002FPython parity for performance-critical rendering.",{"title":57,"searchDepth":58,"depth":58,"links":6346},[6347,6348,6349],{"id":6273,"depth":58,"text":6274},{"id":6294,"depth":58,"text":6295},{"id":6312,"depth":58,"text":6313},[],{"content_references":6352,"triage":6372},[6353,6356,6358,6361,6364,6367,6370],{"type":71,"title":6354,"url":6355,"context":74},"gpt-oss","https:\u002F\u002Fopenai.com\u002Fopen-models",{"type":71,"title":6354,"url":6357,"context":5859},"https:\u002F\u002Fgpt-oss.com",{"type":5847,"title":6359,"url":6360,"context":74},"gpt-oss Model Card","https:\u002F\u002Fopenai.com\u002Findex\u002Fgpt-oss-model-card\u002F",{"type":5847,"title":6362,"url":6363,"context":5856},"OpenAI Harmony Guide","https:\u002F\u002Fcookbook.openai.com\u002Farticles\u002Fopenai-harmony",{"type":5847,"title":6365,"url":6366,"context":74},"gpt-oss Cookbook","https:\u002F\u002Fcookbook.openai.com\u002Ftopic\u002Fgpt-oss",{"type":71,"title":6368,"url":6369,"context":74},"pyo3","https:\u002F\u002Fpyo3.rs\u002F",{"type":71,"title":6371,"context":74},"maturin",{"relevance":81,"novelty":5764,"quality":82,"actionability":82,"composite":5765,"reasoning":6373},"Category: AI & LLMs. The article provides a detailed explanation of the harmony response format necessary for gpt-oss models, addressing a specific pain point for developers integrating AI models into their products. It includes practical installation instructions and examples, making it actionable for developers.","\u002Fsummaries\u002F8fee41411642a9b7-harmony-render-gpt-oss-response-format-in-rust-pyt-summary","2026-04-16 03:07:33",{"title":6263,"description":57},{"loc":6374},"8fee41411642a9b7","__oneoff__","https:\u002F\u002Fgithub.com\u002Fopenai\u002Fharmony","summaries\u002F8fee41411642a9b7-harmony-render-gpt-oss-response-format-in-rust-pyt-summary",[99,97,96],"OpenAI's harmony library encodes\u002Fdecodes the harmony response format required for gpt-oss open-weight models in custom inference setups, mimicking the OpenAI API with multi-channel support for reasoning and tools.",[],"C5U8PWt3Bm5l1z20k8w-gzxDIGVbaFty5WmLaRh64O0"]