[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-c62e1f5b7f154135-three-multi-llm-patterns-chain-parallel-route-summary":3,"summaries-facets-categories":308,"summary-related-c62e1f5b7f154135-three-multi-llm-patterns-chain-parallel-route-summary":3893},{"id":4,"title":5,"ai":6,"body":13,"categories":281,"created_at":283,"date_modified":283,"description":34,"extension":284,"faq":283,"featured":285,"kicker_label":283,"meta":286,"navigation":291,"path":292,"published_at":283,"question":283,"scraped_at":293,"seo":294,"sitemap":295,"source_id":296,"source_name":297,"source_type":298,"source_url":299,"stem":300,"tags":301,"thumbnail_url":283,"tldr":305,"tweet":283,"unknown_tags":306,"__hash__":307},"summaries\u002Fsummaries\u002Fc62e1f5b7f154135-three-multi-llm-patterns-chain-parallel-route-summary.md","Three Multi-LLM Patterns: Chain, Parallel, Route",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",4991,1548,11897,0.0017497,{"type":14,"value":15,"toc":276},"minimark",[16,21,25,28,70,73,151,154,158,161,164,189,192,196,199,202,250,253,256,269,272],[17,18,20],"h2",{"id":19},"sequential-chaining-builds-complex-outputs-step-by-step","Sequential Chaining Builds Complex Outputs Step-by-Step",[22,23,24],"p",{},"Chain multiple LLM calls where each step refines the previous output, ideal for tasks needing progressive transformation like data extraction and formatting. This trades latency for precision since calls run one after another.",[22,26,27],{},"Implement with a simple loop:",[29,30,35],"pre",{"className":31,"code":32,"language":33,"meta":34,"style":34},"language-python shiki shiki-themes github-light github-dark","def chain(input: str, prompts: list[str]) -> str:\n    result = input\n    for i, prompt in enumerate(prompts, 1):\n        result = llm_call(f\"{prompt}\\nInput: {result}\")\n    return result\n","python","",[36,37,38,46,52,58,64],"code",{"__ignoreMap":34},[39,40,43],"span",{"class":41,"line":42},"line",1,[39,44,45],{},"def chain(input: str, prompts: list[str]) -> str:\n",[39,47,49],{"class":41,"line":48},2,[39,50,51],{},"    result = input\n",[39,53,55],{"class":41,"line":54},3,[39,56,57],{},"    for i, prompt in enumerate(prompts, 1):\n",[39,59,61],{"class":41,"line":60},4,[39,62,63],{},"        result = llm_call(f\"{prompt}\\nInput: {result}\")\n",[39,65,67],{"class":41,"line":66},5,[39,68,69],{},"    return result\n",[22,71,72],{},"For a Q3 performance report, four chained prompts extract metrics (e.g., \"92 points customer satisfaction\"), convert to percentages (\"92%: customer satisfaction\"), sort descending, then format as a markdown table:",[74,75,76,91],"table",{},[77,78,79],"thead",{},[80,81,82,87],"tr",{},[83,84,86],"th",{"align":85},"left","Metric",[83,88,90],{"align":89},"center","Value",[92,93,94,103,111,119,127,135,143],"tbody",{},[80,95,96,100],{},[97,98,99],"td",{"align":85},"Customer Satisfaction",[97,101,102],{"align":89},"92%",[80,104,105,108],{},[97,106,107],{"align":85},"Employee Satisfaction",[97,109,110],{"align":89},"87%",[80,112,113,116],{},[97,114,115],{"align":85},"Product Adoption",[97,117,118],{"align":89},"78%",[80,120,121,124],{},[97,122,123],{"align":85},"Operating Margin",[97,125,126],{"align":89},"34%",[80,128,129,132],{},[97,130,131],{"align":85},"Revenue Growth",[97,133,134],{"align":89},"45%",[80,136,137,140],{},[97,138,139],{"align":85},"Market Share",[97,141,142],{"align":89},"23%",[80,144,145,148],{},[97,146,147],{"align":85},"Customer Churn",[97,149,150],{"align":89},"5%",[22,152,153],{},"This breaks down intricate formatting that a single prompt might hallucinate or mishandle.",[17,155,157],{"id":156},"parallel-execution-speeds-up-multi-stakeholder-analysis","Parallel Execution Speeds Up Multi-Stakeholder Analysis",[22,159,160],{},"Run identical prompts on multiple inputs concurrently using ThreadPoolExecutor (default 3 workers), cutting total latency for independent tasks like impact analysis across groups.",[22,162,163],{},"Code:",[29,165,167],{"className":31,"code":166,"language":33,"meta":34,"style":34},"def parallel(prompt: str, inputs: list[str], n_workers: int = 3) -> list[str]:\n    with ThreadPoolExecutor(max_workers=n_workers) as executor:\n        futures = [executor.submit(llm_call, f\"{prompt}\\nInput: {x}\") for x in inputs]\n        return [f.result() for f in futures]\n",[36,168,169,174,179,184],{"__ignoreMap":34},[39,170,171],{"class":41,"line":42},[39,172,173],{},"def parallel(prompt: str, inputs: list[str], n_workers: int = 3) -> list[str]:\n",[39,175,176],{"class":41,"line":48},[39,177,178],{},"    with ThreadPoolExecutor(max_workers=n_workers) as executor:\n",[39,180,181],{"class":41,"line":54},[39,182,183],{},"        futures = [executor.submit(llm_call, f\"{prompt}\\nInput: {x}\") for x in inputs]\n",[39,185,186],{"class":41,"line":60},[39,187,188],{},"        return [f.result() for f in futures]\n",[22,190,191],{},"Example analyzes market changes for customers (price-sensitive, tech-wanting), employees (job security), investors (growth-focused), and suppliers (capacity issues). Each gets tailored impacts and actions in parallel, e.g., for customers: highlight pricing strategies and eco-features. Without parallelism, this serializes to 4x longer; concurrency delivers all results near-simultaneously at higher API cost.",[17,193,195],{"id":194},"routing-directs-inputs-to-specialized-experts","Routing Directs Inputs to Specialized Experts",[22,197,198],{},"Classify input content first, then route to a tailored prompt, improving relevance for varied tasks like support tickets. Adds upfront classification latency but leverages specialist personas for better outputs.",[22,200,201],{},"Router uses chain-of-thought in XML:",[29,203,205],{"className":31,"code":204,"language":33,"meta":34,"style":34},"def route(input: str, routes: dict[str, str]) -> str:\n    selector_prompt = f\"\"\"\n    Analyze... select from {routes.keys()}\n    \u003Creasoning>Explanation\u003C\u002Freasoning>\n    \u003Cselection>Team\u003C\u002Fselection>\n    Input: {input}\"\"\"\n    route_key = extract_xml(llm_call(selector_prompt), \"selection\").strip().lower()\n    return llm_call(f\"{routes[route_key]}\\nInput: {input}\")\n",[36,206,207,212,217,222,227,232,238,244],{"__ignoreMap":34},[39,208,209],{"class":41,"line":42},[39,210,211],{},"def route(input: str, routes: dict[str, str]) -> str:\n",[39,213,214],{"class":41,"line":48},[39,215,216],{},"    selector_prompt = f\"\"\"\n",[39,218,219],{"class":41,"line":54},[39,220,221],{},"    Analyze... select from {routes.keys()}\n",[39,223,224],{"class":41,"line":60},[39,225,226],{},"    \u003Creasoning>Explanation\u003C\u002Freasoning>\n",[39,228,229],{"class":41,"line":66},[39,230,231],{},"    \u003Cselection>Team\u003C\u002Fselection>\n",[39,233,235],{"class":41,"line":234},6,[39,236,237],{},"    Input: {input}\"\"\"\n",[39,239,241],{"class":41,"line":240},7,[39,242,243],{},"    route_key = extract_xml(llm_call(selector_prompt), \"selection\").strip().lower()\n",[39,245,247],{"class":41,"line":246},8,[39,248,249],{},"    return llm_call(f\"{routes[route_key]}\\nInput: {input}\")\n",[22,251,252],{},"Routes: billing (acknowledge charges, steps), technical (numbered fixes), account (security-first), product (feature education).",[22,254,255],{},"Ticket examples:",[257,258,259,263,266],"ul",{},[260,261,262],"li",{},"Login fail → account: Verifies security, recovery steps.",[260,264,265],{},"Unexpected charge → billing: Explains discrepancy, adjustment timeline.",[260,267,268],{},"Data export → product: Step-by-step guide, docs links.",[22,270,271],{},"Routing reasoning cites keywords (\"invalid password\" → security urgency) and intent, avoiding generic responses.",[273,274,275],"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":34,"searchDepth":48,"depth":48,"links":277},[278,279,280],{"id":19,"depth":48,"text":20},{"id":156,"depth":48,"text":157},{"id":194,"depth":48,"text":195},[282],"AI & LLMs",null,"md",false,{"content_references":287,"triage":288},[],{"relevance":66,"novelty":60,"quality":60,"actionability":66,"composite":289,"reasoning":290},4.55,"Category: AI & LLMs. The article provides practical patterns for using multiple LLMs, addressing specific pain points like latency and accuracy in AI feature development. It includes actionable code examples for chaining and parallel execution, making it immediately applicable for developers building AI-powered products.",true,"\u002Fsummaries\u002Fc62e1f5b7f154135-three-multi-llm-patterns-chain-parallel-route-summary","2026-04-15 15:32:57",{"title":5,"description":34},{"loc":292},"c62e1f5b7f154135","__oneoff__","article","https:\u002F\u002Fplatform.claude.com\u002Fcookbook\u002Fpatterns-agents-basic-workflows","summaries\u002Fc62e1f5b7f154135-three-multi-llm-patterns-chain-parallel-route-summary",[302,303,33,304],"llm","prompt-engineering","ai-automation","Chain LLMs sequentially for step-by-step refinement, run parallel calls for concurrent multi-input tasks, and route inputs to specialized prompts via classification—trading latency or cost for better 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GraphRAG for Complex Queries Across Articles",{"provider":7,"model":8,"input_tokens":3898,"output_tokens":3899,"processing_time_ms":3900,"cost_usd":3901},8492,2634,24122,0.00299275,{"type":14,"value":3903,"toc":4307},[3904,3908,3911,3931,3934,3945,3948,3956,3962,3968,3972,3975,3981,4001,4006,4021,4024,4044,4047,4052,4056,4063,4069,4083,4090,4146,4149,4154,4162,4165,4169,4174,4185,4191,4199,4202,4237,4243,4246,4257,4260,4265,4269,4295,4300,4305],[17,3905,3907],{"id":3906},"standard-rag-fails-on-interconnected-datagraphrag-fixes-it","Standard RAG Fails on Interconnected Data—GraphRAG Fixes It",[22,3909,3910],{},"Standard RAG chunks documents, embeds them, and retrieves similar vectors for LLM context. It works for simple fact lookups in small datasets but degrades with scale: accuracy drops 12% at 100k pages due to embedding overlap. Worse, chunks are isolated—no cross-document reasoning for questions like \"main themes across articles\" or \"company connections in disputes.\"",[22,3912,3913,3914,3918,3919,3922,3923,3926,3927,3930],{},"GraphRAG layers a knowledge graph on top. LLMs extract ",[3915,3916,3917],"strong",{},"entities"," (e.g., organizations like OpenAI, events like lawsuits) and ",[3915,3920,3921],{},"relationships"," (e.g., \"defendant in\", \"trained on\"). These form nodes and edges in a property graph, capturing structure. Microsoft's approach adds ",[3915,3924,3925],{},"community detection"," (hierarchical Leiden algorithm groups related entities) and ",[3915,3928,3929],{},"community summaries"," (LLM-generated briefs per cluster).",[22,3932,3933],{},"Use GraphRAG for:",[257,3935,3936,3939,3942],{},[260,3937,3938],{},"Hundreds\u002Fthousands of interconnected docs (law, policy, research).",[260,3940,3941],{},"Global queries: patterns, trends, summaries.",[260,3943,3944],{},"Traceable answers.",[22,3946,3947],{},"Stick to standard RAG for:",[257,3949,3950,3953],{},[260,3951,3952],{},"Single-doc facts.",[260,3954,3955],{},"Speed\u002Fcost priority on small, non-relational data.",[22,3957,3958,3961],{},[3915,3959,3960],{},"Before\u002Fafter",": Standard RAG might retrieve unrelated chunks on \"AI copyright connections\"; GraphRAG traces paths like OpenAI → defendant in → NYT lawsuit → filed against → artists.",[3963,3964,3965],"blockquote",{},[22,3966,3967],{},"\"Standard rack has two more fundamental blind spots. The number one is each chunk is treated as an isolated fragment... no ability to reason across documents.\"",[17,3969,3971],{"id":3970},"scrape-real-world-data-without-browser-hassles","Scrape Real-World Data Without Browser Hassles",[22,3973,3974],{},"Start with live data: Use SerpApi's Google Search API for structured JSON results—no Selenium needed. Free tier covers testing.",[22,3976,3977,3980],{},[3915,3978,3979],{},"Collection steps",":",[3982,3983,3984,3987,3998],"ol",{},[260,3985,3986],{},"Define queries (e.g., \"AI intellectual property\", \"copyright generative AI\").",[260,3988,3989,3990,3993,3994,3997],{},"Call ",[36,3991,3992],{},"GoogleSearchResults"," with params: ",[36,3995,3996],{},"engine=\"google\", gl=\"us\", hl=\"en\", num=10",".",[260,3999,4000],{},"Dedupe URLs across queries → Pandas DataFrame.",[22,4002,4003,3980],{},[3915,4004,4005],{},"Enrich with full text",[257,4007,4008,4011,4018],{},[260,4009,4010],{},"Articles: Trafilatura extracts clean body text (strips nav\u002Fads\u002Ffooters).",[260,4012,4013,4014,4017],{},"YouTube: Regex video ID from URL → ",[36,4015,4016],{},"youtube_transcript_api"," for captions.",[260,4019,4020],{},"Filter successes (paywalls\u002Fcaptionless fail) → Save as CSV.",[22,4022,4023],{},"Code snippet for search:",[29,4025,4027],{"className":31,"code":4026,"language":33,"meta":34,"style":34},"import serpapi\nresults = GoogleSearchResults({'q': query, 'api_key': SERPAPI_KEY})\nraw = results.get_dict()\n",[36,4028,4029,4034,4039],{"__ignoreMap":34},[39,4030,4031],{"class":41,"line":42},[39,4032,4033],{},"import serpapi\n",[39,4035,4036],{"class":41,"line":48},[39,4037,4038],{},"results = GoogleSearchResults({'q': query, 'api_key': SERPAPI_KEY})\n",[39,4040,4041],{"class":41,"line":54},[39,4042,4043],{},"raw = results.get_dict()\n",[22,4045,4046],{},"For 20 articles on AI copyright, this yields ~10-20 usable full-text docs. Scales to any topic—swap queries.",[3963,4048,4049],{},[22,4050,4051],{},"\"SER API is what we're using to script Google News results... real time structured clean search results... no browser automation needed.\"",[17,4053,4055],{"id":4054},"ontology-driven-extraction-ensures-reliable-graphs","Ontology-Driven Extraction Ensures Reliable Graphs",[22,4057,4058,4059,4062],{},"Define ",[3915,4060,4061],{},"ontology"," first: List entity types (e.g., ORGANIZATION, PERSON, LAWSUIT) and relations (e.g., FILED_AGAINST, REGULATES, TRAINED_ON). Domain-specific—tailor to AI copyright (e.g., defendant_in).",[22,4064,4065,4068],{},[3915,4066,4067],{},"Extraction prompt"," (via LlamaIndex):",[257,4070,4071,4074,4077,4080],{},[260,4072,4073],{},"Input: Article text.",[260,4075,4076],{},"Output: Up to 20 entity-relation triplets per article.",[260,4078,4079],{},"Per entity: name, type, description.",[260,4081,4082],{},"Per relation: source, target, type, description.",[22,4084,4085,4086,4089],{},"Use ",[3915,4087,4088],{},"Pydantic models"," for structured output:",[29,4091,4093],{"className":31,"code":4092,"language":33,"meta":34,"style":34},"from pydantic import BaseModel\nclass ExtractedEntity(BaseModel):\n    name: str\n    type: str\n    description: str\nclass ExtractedRelationship(BaseModel):\n    source: str\n    target: str\n    relation: str\n    description: str\n",[36,4094,4095,4100,4105,4110,4115,4120,4125,4130,4135,4141],{"__ignoreMap":34},[39,4096,4097],{"class":41,"line":42},[39,4098,4099],{},"from pydantic import BaseModel\n",[39,4101,4102],{"class":41,"line":48},[39,4103,4104],{},"class ExtractedEntity(BaseModel):\n",[39,4106,4107],{"class":41,"line":54},[39,4108,4109],{},"    name: str\n",[39,4111,4112],{"class":41,"line":60},[39,4113,4114],{},"    type: str\n",[39,4116,4117],{"class":41,"line":66},[39,4118,4119],{},"    description: str\n",[39,4121,4122],{"class":41,"line":234},[39,4123,4124],{},"class ExtractedRelationship(BaseModel):\n",[39,4126,4127],{"class":41,"line":240},[39,4128,4129],{},"    source: str\n",[39,4131,4132],{"class":41,"line":246},[39,4133,4134],{},"    target: str\n",[39,4136,4138],{"class":41,"line":4137},9,[39,4139,4140],{},"    relation: str\n",[39,4142,4144],{"class":41,"line":4143},10,[39,4145,4119],{},[22,4147,4148],{},"Pass as OpenAI function-calling schema—auto-validates\u002Frejects bad outputs. No regex parsing.",[22,4150,4151,3980],{},[3915,4152,4153],{},"GraphRAGExtractor class",[3982,4155,4156,4159],{},[260,4157,4158],{},"LLM extracts → Pydantic → LlamaIndex EntityNode\u002FRelation objects.",[260,4160,4161],{},"Process 50 articles in parallel (GPT-4o-mini for cost).",[22,4163,4164],{},"Common mistake: Skipping ontology → hallucinated\u002Finconsistent entities. Fix: Explicit lists in prompt.\nQuality check: Descriptions enrich context for later summaries.",[17,4166,4168],{"id":4167},"graph-construction-communities-and-local-global-querying","Graph Construction, Communities, and Local-Global Querying",[22,4170,4171,3980],{},[3915,4172,4173],{},"GraphRAGStore",[257,4175,4176,4179,4182],{},[260,4177,4178],{},"Insert extracted nodes\u002Fedges.",[260,4180,4181],{},"Run Leiden community detection → Clusters (e.g., \"OpenAI lawsuits\").",[260,4183,4184],{},"LLM summarizes each: Collect entities\u002Frelations → GPT-4o-mini brief.",[22,4186,4187,4190],{},[3915,4188,4189],{},"Query engine"," (two-step):",[3982,4192,4193,4196],{},[260,4194,4195],{},"GPT-4o-mini filters relevant communities (skip irrelevant to save tokens).",[260,4197,4198],{},"GPT-4o synthesizes: Per-community answers → Global response.",[22,4200,4201],{},"Code flow:",[29,4203,4205],{"className":31,"code":4204,"language":33,"meta":34,"style":34},"from llama_index.core.graph_stores import SimpleGraphStore\n# ...extraction...\ngraph_store = SimpleGraphStore()\n# Insert, detect_communities(), summarize_communities()\nquery_engine = GraphRAGQueryEngine(...)\nresponse = query_engine.query(\"Central companies in AI copyright disputes?\")\n",[36,4206,4207,4212,4217,4222,4227,4232],{"__ignoreMap":34},[39,4208,4209],{"class":41,"line":42},[39,4210,4211],{},"from llama_index.core.graph_stores import SimpleGraphStore\n",[39,4213,4214],{"class":41,"line":48},[39,4215,4216],{},"# ...extraction...\n",[39,4218,4219],{"class":41,"line":54},[39,4220,4221],{},"graph_store = SimpleGraphStore()\n",[39,4223,4224],{"class":41,"line":60},[39,4225,4226],{},"# Insert, detect_communities(), summarize_communities()\n",[39,4228,4229],{"class":41,"line":66},[39,4230,4231],{},"query_engine = GraphRAGQueryEngine(...)\n",[39,4233,4234],{"class":41,"line":234},[39,4235,4236],{},"response = query_engine.query(\"Central companies in AI copyright disputes?\")\n",[22,4238,4239,4242],{},[3915,4240,4241],{},"Visualization",": D3.js for interactive graph (nodes=entities, edges=relations, clusters colored).",[22,4244,4245],{},"Production tips:",[257,4247,4248,4251,4254],{},[260,4249,4250],{},"GPT-4o-mini for extraction\u002Fsummaries (volume), GPT-4o for queries (reasoning).",[260,4252,4253],{},"No chunking if articles short.",[260,4255,4256],{},"Parallel workers speed indexing.",[22,4258,4259],{},"Example query: \"Connections in AI copyright?\" → Traces OpenAI, NYT, artists via graph traversal.",[3963,4261,4262],{},[22,4263,4264],{},"\"Using a knowledge graph has been shown to improve LM response accuracy... sensemaking: understand connections, patterns and themes.\"",[17,4266,4268],{"id":4267},"key-takeaways","Key Takeaways",[257,4270,4271,4274,4277,4280,4283,4286,4289,4292],{},[260,4272,4273],{},"Switch to GraphRAG for cross-document reasoning; standard RAG for isolated facts.",[260,4275,4276],{},"Always define domain ontology first—prevents extraction drift.",[260,4278,4279],{},"SerpApi + Trafilatura = reliable scraping pipeline; dedupe and filter aggressively.",[260,4281,4282],{},"Pydantic + function calling = bulletproof structured extraction.",[260,4284,4285],{},"Community summaries enable efficient local-global querying—filter first, synthesize second.",[260,4287,4288],{},"Use cheaper models for indexing, premium for queries to optimize costs.",[260,4290,4291],{},"Visualize with D3.js to debug\u002Ftrace graph quality.",[260,4293,4294],{},"Test on real data like AI copyright: Start with GitHub repo, adapt ontology.",[3963,4296,4297],{},[22,4298,4299],{},"\"The ontology... tells the LLM exactly what types of entities and relationships it's allowed to extract.\"",[3963,4301,4302],{},[22,4303,4304],{},"\"At query time, these summaries are queried instead of the raw graph, which makes it particularly effective and fast for big picture questions.\"",[273,4306,275],{},{"title":34,"searchDepth":48,"depth":48,"links":4308},[4309,4310,4311,4312,4313],{"id":3906,"depth":48,"text":3907},{"id":3970,"depth":48,"text":3971},{"id":4054,"depth":48,"text":4055},{"id":4167,"depth":48,"text":4168},{"id":4267,"depth":48,"text":4268},[282],{"content_references":4316,"triage":4332},[4317,4323,4328],{"type":4318,"title":4319,"author":4320,"url":4321,"context":4322},"paper","GraphRAG paper","Microsoft Research","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2404.16130","cited",{"type":4324,"title":4325,"url":4326,"context":4327},"tool","SerpApi","https:\u002F\u002Fserpapi.link\u002Fthu-vu","recommended",{"type":4329,"title":4330,"url":4331,"context":4327},"other","graphRAG Git repo","https:\u002F\u002Fgithub.com\u002Fthu-vu92\u002FgraphRAG",{"relevance":66,"novelty":60,"quality":60,"actionability":60,"composite":4333,"reasoning":4334},4.35,"Category: AI & LLMs. The article provides a detailed explanation of how GraphRAG improves upon standard RAG for complex queries, addressing a specific pain point of interconnected data analysis. It includes actionable steps for implementing the solution, such as using SerpApi for data collection.","\u002Fsummaries\u002Fad972853080121bc-build-graphrag-for-complex-queries-across-articles-summary","2026-04-14 08:04:31","2026-04-19 01:20:43",{"title":3896,"description":34},{"loc":4335},"ad972853080121bc","AI Summaries (evaluation playlist)","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=JTVx6i6MzVw","summaries\u002Fad972853080121bc-build-graphrag-for-complex-queries-across-articles-summary",[302,303,33,304],"GraphRAG builds knowledge graphs from scraped articles to enable reasoning over interconnected data, outperforming standard RAG on global questions like themes and relationships in AI copyright disputes.",[304],"341Gx8g9rvMLH63Xt_hzigIvq8ab6Il3BJfkLjVzfZ0",{"id":4349,"title":4350,"ai":4351,"body":4356,"categories":4439,"created_at":283,"date_modified":283,"description":34,"extension":284,"faq":283,"featured":285,"kicker_label":283,"meta":4440,"navigation":291,"path":4451,"published_at":4452,"question":283,"scraped_at":4453,"seo":4454,"sitemap":4455,"source_id":4456,"source_name":4457,"source_type":298,"source_url":4458,"stem":4459,"tags":4460,"thumbnail_url":283,"tldr":4461,"tweet":283,"unknown_tags":4462,"__hash__":4463},"summaries\u002Fsummaries\u002F8808df43f033abad-python-rules-turn-financial-signals-into-thesis-ve-summary.md","Python Rules Turn Financial Signals into Thesis Verdicts",{"provider":7,"model":8,"input_tokens":4352,"output_tokens":4353,"processing_time_ms":4354,"cost_usd":4355},8673,1835,20575,0.00262945,{"type":14,"value":4357,"toc":4433},[4358,4362,4365,4369,4372,4392,4395,4399,4402,4419,4422,4426],[17,4359,4361],{"id":4360},"classify-theses-to-focus-evidence-on-relevant-signals","Classify Theses to Focus Evidence on Relevant Signals",[22,4363,4364],{},"First classify natural-language theses into one of 10 allowed claim types—controlled_downside, momentum_strength, low_risk, high_risk, valuation_attractive, valuation_expensive, business_quality, weak_business_quality, premium_justified, premium_not_justified—using a structured LLM prompt that returns JSON with claim_types array and short summary. Python validates against the allowed set to prevent hallucinations. This narrows evaluation: controlled_downside prioritizes drawdown and volatility; business_quality checks margins (>=25% operating, >=20% profit), ROA (>=10%), ROE (>=20%), growth (>0% YoY revenue\u002Fearnings), and revisions (>0 net EPS last 30d); valuation_attractive uses P\u002FE\u003C20 or forward P\u002FE below trailing.",[17,4366,4368],{"id":4367},"rule-based-evidence-sorting-builds-balanced-arguments","Rule-Based Evidence Sorting Builds Balanced Arguments",[22,4370,4371],{},"Feed classified thesis and signals (from Part 1: price metrics like ret_total, vol_annualized\u003C30% for low_risk, max_drawdown>-15% for controlled_downside, trend>0+positive return for momentum_strength; fundamentals like beta\u003C0.9\u002F >1.2, PE>30 for expensive) into build_evidence_blocks(). Hard-coded if-then rules sort into three buckets:",[257,4373,4374,4380,4386],{},[260,4375,4376,4379],{},[3915,4377,4378],{},"evidence_for",": e.g., drawdown -10% supports controlled_downside; vol 25% supports low_risk; operating margin 30% + ROE 25% + revenue growth 15% YoY hit business_quality (tracks hits, flags if zero).",[260,4381,4382,4385],{},[3915,4383,4384],{},"evidence_against",": e.g., drawdown -20%; P\u002FE 35 for attractive valuation; negative earnings growth.",[260,4387,4388,4391],{},[3915,4389,4390],{},"missing_evidence",": e.g., no drawdown data; insufficient quality metrics.",[22,4393,4394],{},"Beta always checked (>1.2 against, \u003C0.9 for); flags if no ret_to_vol. Ensures explicit gaps prevent overconfidence, turning raw signals into thesis-specific pros\u002Fcons.",[17,4396,4398],{"id":4397},"verdict-engine-balances-counts-with-claim-dependencies","Verdict Engine Balances Counts with Claim Dependencies",[22,4400,4401],{},"decide_verdict() counts evidence_for (n_for), evidence_against (n_against), missing (n_missing). Caps verdicts by claim:",[257,4403,4404,4407,4410,4413,4416],{},[260,4405,4406],{},"Quality\u002Fvaluation claims (business_quality etc.) unresolved or partially_supported if missing>=1 and against>0.",[260,4408,4409],{},"Pure support (n_for>0, n_against=0, missing\u003C2): \"supported\".",[260,4411,4412],{},"n_for > n_against: \"partially_supported\".",[260,4414,4415],{},"n_against >= n_for: \"weakly_supported\".",[260,4417,4418],{},"No evidence: \"unresolved_due_to_missing_evidence\".",[22,4420,4421],{},"Returns verdict + reason, e.g., \"partially_supported: The available evidence supports the thesis, but important evidence is still missing.\" Forces humility on incomplete data.",[17,4423,4425],{"id":4424},"facts-builder-structures-inputs-for-memo-generation","Facts Builder Structures Inputs for Memo Generation",[22,4427,4428,4429,4432],{},"extract_company_context() pulls clean dict from fundamentals.General: name, code, exchange, sector, industry, country, market_cap, pe_ratio, beta, dividend_yield, description (skips None\u002Fempty). Combines with thesis, signals, evidence, verdict into single facts object as memo prompt context, avoiding scattered vars for reliable LLM outputs. Test in Jupyter: fetch AAPL prices\u002Ffundamentals 2026-01-01 to 04-01, compute signals, classify \"Apple looks attractive because downside has been controlled and business quality remains high.\", build evidence\u002Fverdict—outputs claim_types like ",[39,4430,4431],{},"\"controlled_downside\", \"business_quality\"",", balanced bullets, e.g., supported by low drawdown\u002Fhigh margins if data fits.",{"title":34,"searchDepth":48,"depth":48,"links":4434},[4435,4436,4437,4438],{"id":4360,"depth":48,"text":4361},{"id":4367,"depth":48,"text":4368},{"id":4397,"depth":48,"text":4398},{"id":4424,"depth":48,"text":4425},[319],{"content_references":4441,"triage":4449},[4442,4446],{"type":4324,"title":4443,"url":4444,"context":4445},"MCP","https:\u002F\u002Feodhd.com\u002Ffinancial-apis\u002Fmcp-server-for-financial-data-by-eodhd?utm_source=medium&utm_medium=post&utm_campaign=mcp_research_agent&utm_content=nikhil","mentioned",{"type":4329,"title":4447,"url":4448,"context":4445},"Building a Market Research Copilot using MCP and Python","https:\u002F\u002Fai.gopubby.com\u002Fbuilding-a-market-research-copilot-using-mcp-and-python-37dbdd74667f",{"relevance":66,"novelty":60,"quality":60,"actionability":60,"composite":4333,"reasoning":4450},"Category: AI & LLMs. The article provides a detailed framework for using LLMs and Python to classify financial theses and evaluate evidence, addressing practical applications for AI-powered product builders in finance. It offers specific methodologies and coding strategies that can be directly implemented, making it highly actionable.","\u002Fsummaries\u002F8808df43f033abad-python-rules-turn-financial-signals-into-thesis-ve-summary","2026-05-07 08:25:27","2026-05-07 11:23:43",{"title":4350,"description":34},{"loc":4451},"8808df43f033abad","Generative AI","https:\u002F\u002Fgenerativeai.pub\u002Ffrom-signals-to-verdicts-building-a-financial-research-copilot-with-mcp-and-python-63d5d7b662a8?source=rss----440100e76000---4","summaries\u002F8808df43f033abad-python-rules-turn-financial-signals-into-thesis-ve-summary",[302,303,33,304],"Classify stock theses into 10 claim types, map price\u002Ffundamentals signals to support\u002Fagainst\u002Fmissing evidence using thresholds like drawdown >-15% or P\u002FE\u003C20, then assign verdicts like 'supported' based on evidence counts and gaps for a research copilot.",[304],"eXupXvwsVi_nA_Wu4j6AQbHrKihZdBI0G5l1XXOpjqA",{"id":4465,"title":4466,"ai":4467,"body":4472,"categories":4547,"created_at":283,"date_modified":283,"description":34,"extension":284,"faq":283,"featured":285,"kicker_label":283,"meta":4548,"navigation":291,"path":4559,"published_at":4560,"question":283,"scraped_at":4561,"seo":4562,"sitemap":4563,"source_id":4564,"source_name":4565,"source_type":298,"source_url":4566,"stem":4567,"tags":4568,"thumbnail_url":283,"tldr":4569,"tweet":283,"unknown_tags":4570,"__hash__":4571},"summaries\u002Fsummaries\u002F462073626d1551b9-run-gpt-oss-20b-with-advanced-inference-in-colab-summary.md","Run GPT-OSS-20B with Advanced Inference in Colab",{"provider":7,"model":8,"input_tokens":4468,"output_tokens":4469,"processing_time_ms":4470,"cost_usd":4471},8775,1765,16218,0.00261485,{"type":14,"value":4473,"toc":4542},[4474,4478,4481,4492,4496,4499,4519,4526,4530,4533,4536,4539],[17,4475,4477],{"id":4476},"gpu-and-dependency-setup-for-reliable-loading","GPU and Dependency Setup for Reliable Loading",[22,4479,4480],{},"GPT-OSS-20B requires ~16GB VRAM (T4\u002FA100 recommended) and downloads ~40GB on first run. Install transformers>=4.51.0, accelerate, sentencepiece, protobuf, huggingface_hub, gradio, ipywidgets, and openai-harmony. Verify CUDA with torch.cuda.is_available() and check memory via torch.cuda.get_device_properties(0).total_memory. Load via AutoModelForCausalLM.from_pretrained(\"openai\u002Fgpt-oss-20b\", torch_dtype=torch.bfloat16, device_map=\"auto\", trust_remote_code=True) and AutoTokenizer. Use pipeline(\"text-generation\") for inference. Post-load, expect ~allocated\u002Freserved GPU memory printouts to confirm ~16GB usage. OpenAI recommends temperature=1.0, top_p=1.0; adjust to 0.8 for consistency.",[22,4482,4483,4484,4487,4488,4491],{},"Basic generation: Pass messages list like ",[39,4485,4486],{},"{'role': 'user', 'content': 'query'}"," to pipeline with max_new_tokens=256, do_sample=True, pad_token_id=tokenizer.eos_token_id. Extracts response from output[0][\"generated_text\"][-1]",[39,4489,4490],{},"\"content\"",". Handles QA, code gen, creative tasks effectively.",[17,4493,4495],{"id":4494},"configurable-reasoning-and-structured-outputs","Configurable Reasoning and Structured Outputs",[22,4497,4498],{},"Define ReasoningEffortController with three levels:",[257,4500,4501,4507,4513],{},[260,4502,4503,4506],{},[3915,4504,4505],{},"Low",": \"Be concise\", max_tokens=200, temp=0.7 → quick answers.",[260,4508,4509,4512],{},[3915,4510,4511],{},"Medium",": \"Think step-by-step\", max_tokens=400, temp=0.8 → balanced.",[260,4514,4515,4518],{},[3915,4516,4517],{},"High",": Multi-step CoT prompt, max_tokens=800, temp=1.0 → deep analysis.\nPrepend system prompt to messages; scales token budget and detail for logic puzzles, improving accuracy on complex queries.",[22,4520,4521,4522,4525],{},"For JSON: StructuredOutputGenerator enforces schema via strict system prompt (\"ONLY output valid JSON matching schema, no markdown\"). Cleans response (strip ```json blocks), parses with json.loads(), retries up to 2x on JSONDecodeError by appending error feedback. Examples: Entity extraction schema {'name': 'str', 'type': 'str', 'description': 'str', 'key_facts': ",[39,4523,4524],{},"'str'","}; recipe schema with prep_time_minutes (int), ingredients list of dicts. Reduces hallucinations, ensures type safety for APIs.",[17,4527,4529],{"id":4528},"stateful-chats-streaming-tools-and-batch-efficiency","Stateful Chats, Streaming, Tools, and Batch Efficiency",[22,4531,4532],{},"ConversationManager maintains history list, prepends system + history to each chat() call (max_new_tokens=300, temp=0.8). Supports get_history_length(), clear_history(), context_summary(). Enables memory across turns, e.g., recalling user name\u002Ffield.",[22,4534,4535],{},"Streaming: Use TextIteratorStreamer(tokenizer, skip_prompt=True) with model.generate(inputs from tokenizer.apply_chat_template(), streamer=streamer, max_new_tokens=200) in thread. Prints tokens live, reveals decoding speed\u002Fbehavior.",[22,4537,4538],{},"Tools via ToolExecutor: Decorator @register(name, desc) for funcs like calculator (safe eval with math whitelist), get_time(), simulated weather\u002Fsearch. Prompt lists tools; model outputs \"TOOL: name\\nARGS: json\". Parse, execute, feed result back for final response. Loops once for math\u002Ftime\u002Fweather queries.",[22,4540,4541],{},"Batch: batch_generate(prompts, batch_size=2) processes in chunks via pipeline on list of message lists. Handles 5+ prompts efficiently, e.g., trivia QA, cutting per-call overhead for throughput testing.",{"title":34,"searchDepth":48,"depth":48,"links":4543},[4544,4545,4546],{"id":4476,"depth":48,"text":4477},{"id":4494,"depth":48,"text":4495},{"id":4528,"depth":48,"text":4529},[],{"content_references":4549,"triage":4557},[4550,4553,4555],{"type":4329,"title":4551,"url":4552,"context":4445},"GPT-OSS","https:\u002F\u002Fgithub.com\u002Fopenai\u002Fgpt-oss",{"type":4324,"title":4554,"context":4445},"openai\u002Fgpt-oss-20b",{"type":4324,"title":4556,"context":4445},"openai-harmony",{"relevance":66,"novelty":60,"quality":60,"actionability":66,"composite":289,"reasoning":4558},"Category: AI & LLMs. The article provides a comprehensive guide on running the GPT-OSS-20B model with advanced inference techniques, addressing practical applications for developers looking to implement AI features. It includes specific instructions for setup and configuration, making it immediately actionable for the target audience.","\u002Fsummaries\u002F462073626d1551b9-run-gpt-oss-20b-with-advanced-inference-in-colab-summary","2026-04-18 03:39:46","2026-04-19 01:22:40",{"title":4466,"description":34},{"loc":4559},"462073626d1551b9","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F17\u002Fa-end-to-end-coding-guide-to-running-openai-gpt-oss-open-weight-models-with-advanced-inference-workflows\u002F","summaries\u002F462073626d1551b9-run-gpt-oss-20b-with-advanced-inference-in-colab-summary",[302,33,303,304],"Load OpenAI's 40GB GPT-OSS-20B model in Colab on T4 GPU using MXFP4 quantization and torch.bfloat16; implement reasoning controls, JSON schemas, multi-turn memory, streaming, tools, and batch processing for production workflows.",[304],"5xsSHgnQFdKsGuJLfTqq1VV18UgOrSbHj50lJTt0CPU",{"id":4573,"title":4574,"ai":4575,"body":4580,"categories":4661,"created_at":283,"date_modified":283,"description":34,"extension":284,"faq":283,"featured":285,"kicker_label":283,"meta":4662,"navigation":291,"path":4668,"published_at":4669,"question":283,"scraped_at":4670,"seo":4671,"sitemap":4672,"source_id":4673,"source_name":4565,"source_type":298,"source_url":4674,"stem":4675,"tags":4676,"thumbnail_url":283,"tldr":4677,"tweet":283,"unknown_tags":4678,"__hash__":4679},"summaries\u002Fsummaries\u002F68a7b0ecb194f703-fix-tokenization-drift-by-matching-sft-token-patte-summary.md","Fix Tokenization Drift by Matching SFT Token Patterns",{"provider":7,"model":8,"input_tokens":4576,"output_tokens":4577,"processing_time_ms":4578,"cost_usd":4579},9688,1789,16407,0.0028096,{"type":14,"value":4581,"toc":4656},[4582,4586,4597,4600,4604,4607,4610,4614,4617,4643,4646],[17,4583,4585],{"id":4584},"leading-spaces-and-formatting-create-entirely-new-token-sequences","Leading Spaces and Formatting Create Entirely New Token Sequences",[22,4587,4588,4589,4592,4593,4596],{},"Tokenization drift occurs when subtle changes like adding a leading space alter token IDs and sequence lengths, pushing inputs outside the model's trained distribution. Using GPT-2 tokenizer (vocab size 50,257, same BPE as GPT-4\u002FLLaMA\u002FMistral), test pairs like \" classify\" vs \"classify\": space version gets single token ",[39,4590,4591],{},"36509",", no-space splits to ",[39,4594,4595],{},"4871, 1958",". All 7 tested words (classify, answer, positive, negative, sentiment, output, label) produce different IDs—deltas range from \u003C100 (low risk, e.g., label) to >500 (high risk, e.g., classify at 31,638 delta). This changes attention computation since sequence lengths differ, making \"apple\" and \" apple\" as distinct to the model as unrelated words.",[22,4598,4599],{},"SFT models learn specific structures (newlines, colons, prefixes). Deviations like removing newlines drop Jaccard overlap with canonical SFT template (\"Below is a customer review. Classify the sentiment.\\n\\nReview: {review}\\n\\nSentiment:\") to 80%; no leading space on \"Review\" to 85%; colon-to-dash to 70%; rewording instruction to 50%. Lower overlap signals higher OOD risk: >80% low risk, 60-80% medium, \u003C60% high, correlating to accuracy drops.",[17,4601,4603],{"id":4602},"jaccard-overlap-quantifies-ood-risk-from-prompt-variants","Jaccard Overlap Quantifies OOD Risk from Prompt Variants",[22,4605,4606],{},"Canonical SFT overlap is 100%. Variants show: no newlines 80% (medium risk), missing space 85% (low), dash instead of colon 70% (medium), reworded (\"Determine the sentiment... Answer:\") 50% (high). On sample \"The product exceeded all my expectations. Highly recommend!\", these shifts mean the model processes unfamiliar token space, leading to unpredictable outputs despite unchanged logic or data.",[22,4608,4609],{},"Visual deltas confirm: high-ID gaps (>500) for most words indicate severe drift. Thresholds guide safety—stay above 80% overlap to mimic training distribution, avoiding degradation without retraining.",[17,4611,4613],{"id":4612},"apo-loop-auto-selects-high-overlap-prompts-for-stable-performance","APO Loop Auto-Selects High-Overlap Prompts for Stable Performance",[22,4615,4616],{},"Implement Automated Prompt Optimization on 8-sample validation set (balanced positive\u002Fnegative\u002Fneutral reviews). Test 5 candidates:",[257,4618,4619,4622,4625,4628,4640],{},[260,4620,4621],{},"A (no formatting: \"Classify: {review} Answer:\");",[260,4623,4624],{},"B (minimal: \"Review: {review}\\nSentiment:\");",[260,4626,4627],{},"C (SFT-aligned: full template with newlines\u002Fcolons);",[260,4629,4630,4631,4635,4636],{},"D (XML: \"",[4632,4633,4634],"review",{},"{review}","\\n",[4637,4638,4639],"sentiment",{},"\");",[260,4641,4642],{},"E (full instruction: \"You are a sentiment classifier... Output...\").",[22,4644,4645],{},"Simulate accuracy: base 85%, scaled by overlap factor (0.5 + 0.5*Jaccard) minus OOD penalty (e.g., 0.18 for A, 0.02 for C), clipped 40-95%, plus noise. Results: A 38%, B 50%, C 88%, D 63%, E 75%. APO picks C (\"Variant C -- SFT-aligned\") at 88% accuracy—33% better than worst, proving closest SFT match wins.",[22,4647,4648,4649,4655],{},"In production, replace simulation with real model evals on validation data. Full code: ",[4650,4651,4652],"a",{"href":4652,"rel":4653},"https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Agents-Projects-Tutorials\u002Fblob\u002Fmain\u002FNLP\u002FTokenization_Drift.ipynb",[4654],"nofollow",". This keeps prompts in-distribution, stabilizing performance across pipeline changes.",{"title":34,"searchDepth":48,"depth":48,"links":4657},[4658,4659,4660],{"id":4584,"depth":48,"text":4585},{"id":4602,"depth":48,"text":4603},{"id":4612,"depth":48,"text":4613},[282],{"content_references":4663,"triage":4666},[4664],{"type":4329,"title":4665,"url":4652,"context":4445},"Tokenization_Drift.ipynb",{"relevance":66,"novelty":60,"quality":60,"actionability":60,"composite":4333,"reasoning":4667},"Category: AI & LLMs. The article provides a deep dive into tokenization drift, a critical issue for AI product builders, and offers actionable strategies like Jaccard token overlap to measure risk and Automated Prompt Optimization to enhance model performance. This directly addresses the audience's need for practical applications in AI integration.","\u002Fsummaries\u002F68a7b0ecb194f703-fix-tokenization-drift-by-matching-sft-token-patte-summary","2026-05-03 07:06:45","2026-05-03 17:01:43",{"title":4574,"description":34},{"loc":4668},"68a7b0ecb194f703","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F03\u002Fwhat-is-tokenization-drift-and-how-to-fix-it\u002F","summaries\u002F68a7b0ecb194f703-fix-tokenization-drift-by-matching-sft-token-patte-summary",[303,302,33],"Minor formatting like spaces or newlines causes tokenization drift, shifting prompts out-of-distribution and dropping accuracy. Use Jaccard token overlap (>80% safe) to measure risk; Automated Prompt Optimization (APO) selects best templates, boosting simulated accuracy from 40-50% to 83%.",[],"fLlnSu0xltWQumszVZ_1GwcHUHHo4fwQpqnVb1YmSMM"]