Adi Vamsi Sai’s Post

Built an AI agent that monitors our production databases and automatically optimizes slow queries. Database response times dropped 73% across our main application. Query execution went from 2.3 seconds average to 0.6 seconds. The agent runs every 15 minutes, analyzes query patterns, identifies bottlenecks, and applies index suggestions. It even rewrites inefficient joins when possible. Best part: it caught a recursive query that was burning through 40% of our server resources. Would have taken our team weeks to find manually. Running on a simple Python script with SQLAlchemy and some custom ML models for pattern recognition. What database performance issues are eating up your team's time? --- Want to automate your workflows or build AI-powered systems for your business? DM me — I help teams ship automation that actually works. #CaseStudy #Results #Automation #DatabaseOptimization #AIAgents #Python #Performance #DevOps

𝗜 𝗰𝘂𝘁 𝗮𝗻 𝗘𝗧𝗟 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲'𝘀 𝗽𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗹𝗮𝘁𝗲𝗻𝗰𝘆 𝗯𝘆 𝟳𝟬%. Three things worked. Only one of them was the one I expected. The context: we were processing 𝟱𝟬𝟬𝗞+ (𝗶𝗻𝘃𝗼𝗶𝗰𝗲𝘀) 𝗿𝗲𝗰𝗼𝗿𝗱𝘀 𝗮 𝘄𝗲𝗲𝗸. The pipeline was quietly becoming the bottleneck for every downstream dashboard. Management wanted more data ingested, not less — so optimization wasn't optional. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗺𝗼𝘃𝗲𝗱 𝘁𝗵𝗲 𝗻𝗲𝗲𝗱𝗹𝗲: 𝟭. 𝗣𝗿𝗼𝗳𝗶𝗹𝗲 𝗯𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲. My instinct was to parallelize first. The profiler told me 60% of the time was spent in a single nested loop doing membership checks against a list. Swapping the list for a set closed most of the gap before I wrote a single worker. 𝟮. 𝗜/𝗢-𝗯𝗼𝘂𝗻𝗱 𝘄𝗼𝗿𝗸 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗰𝗮𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝗰𝗼𝗿𝗲𝘀. I assumed multiprocessing would win. The CPU-bound transforms benefited less than expected. The biggest jump came from batching database writes and running them concurrently against the sink — not from parallelizing the Python code. 𝟯. 𝗕𝗼𝗿𝗶𝗻𝗴 𝗯𝗲𝗮𝘁𝘀 𝗰𝗹𝗲𝘃𝗲𝗿, 𝗮𝗹𝗺𝗼𝘀𝘁 𝗮𝗹𝘄𝗮𝘆𝘀. A weekly-batch analysis showed ~50% of our records didn't actually change week-over-week. A simple content-hash cache skipped all of that redundant work. Five lines of code. Bigger impact than the parallelism refactor. The takeaway I keep coming back to: measure first, then pick the cheapest fix that matches the real bottleneck. Engineers (me included) love the interesting solutions. The boring ones usually win. So tell me :— What's the most embarrassingly simple fix that ever saved you a week? #softwareengineering #python #backend #dataengineering #learninginpublic

Here’s the easiest way to fix context window limits: Stop putting documents in the prompt… Put them in a REPL instead. Instead of building complex RAG pipelines and other hacks to work around context limits, load the document as a variable in a persistent Python environment. This is the core idea behind Recursive Language Models (RLMs) as an orchestration technique. The model never sees the full document. It only gets metadata: • Size • Structure • Available functions • How to access it Then the model writes code to explore it. Each step runs inside a persistent REPL. Variables survive across iterations. So the model builds results progressively: • Filtered subsets • Intermediate buffers • Partial summaries • Structured outputs When deeper reasoning is needed, it spawns a sub-call: llm_query(prompt, chunk) Only that chunk goes to a worker model. The result returns to the REPL. The main context stays clean. Only small execution results get appended to history. This keeps the context window a constant size. Here's the takeaway: Traditional RAG → you engineer the context REPL loop → the model engineers its own context This is context engineering on autopilot. Load the large state into memory. Let the model inspect it. Keep the prompt minimal. Cleaner context. Lower cost. Better reasoning over large data. I broke down the full RLM mechanism in a recent newsletter. Check it out here: https://lnkd.in/dj5PWtSW

Day 11/30 is live: Structured Outputs (JSON Schema). json.loads() on a model response is not a parsing strategy — it is a guess. structured Output helps if multiple agents' communication needed. Day 10 tool calls return JSON. But "valid JSON" and "correct shape for your pipeline" are two different things. Structured outputs close that gap at the token level. What I cover today: → Constrained decoding: what it is and why it makes retry loops almost unnecessary → The four JSON Schema keywords that determine whether your schema is production-safe → The Pydantic BaseModel bridge: define once, validate everywhere, no manual parsing → 3-attempt retry strategy with exact error feedback injection → Four parse failure types and the correct response to each One rule that saves production incidents: Set additionalProperties: false on every object — top-level and nested. Without it, the model can invent fields that pass validation and silently corrupt downstream data. Schema drift is the silent killer: your code changes, your schema doesn't, old responses pass validation with a missing field. Version schemas alongside code. Next Day 12/30: Workflows vs Agents — when to chain steps in a fixed sequence and when to let the model decide what to do next. #GenAI #StructuredOutputs #JSONSchema #Pydantic #LLM #AIEngineering #Python #AIInProduction #ITWorld #AgenticAI #Pune #MAANG

This is how my agents actually "talk" to each other. 🤝💻 Everyone talks about "Multi-Agent Systems," but few people show the actual logic behind the handoff. To scale to 10,000 users (as I discussed yesterday), you need a structured way for agents to pass the baton without losing context. Here is the State Schema I’m using to manage the flow between my Researcher and Analyst: Python # The "Shared Memory" Object class ResearchState(TypedDict): query: str raw_sources: List[dict] # Researcher writes here analyzed_insights: str # Analyst writes here is_verified: bool # Critic toggles this iteration_count: int # Prevents infinite loops Why this works: TypedDict for Safety: The Analyst knows exactly what the Researcher is providing. No "guessing" what's in a raw string. Loop Control: The iteration_count acts as a kill-switch. If the Critic rejects the work 3 times, the system escalates to a human instead of burning tokens. Audit Trail: Because the state is a single object, we can save the entire "Thought Process" to our database for the Observability Dashboard I built on Tuesday. Building AI isn't just about the prompts; it's about the Data Structures that connect them. I’m pushing this State Machine logic to my GitHub today. Want to see the full implementation? Let me know in the comments! 👇 #AIEngineering #Python #CleanCode #MultiAgentSystems #BuildInPublic #SystemDesign #OpenSource #TechTips

"Stop breaking your AI's logic with 'Blind' Chunking" Most RAG (Retrieval-Augmented Generation) systems fail because they treat code like plain text. When you slice a Python script every 500 characters, you risk cutting a function in half. This leads to LLM "hallucinations" because the model only sees partial logic. How it works: AST Parsing: Instead of raw text slicing, I use Python's Abstract Syntax Tree (AST) to identify logical boundaries (Classes and Functions). Token-Aware Packing: Using the tiktoken tokenizer, I calculate exact costs and pack these logical blocks into chunks that never exceed the model’s context window. Semantic Retrieval: I integrated ChromaDB to store these chunks as high-dimensional vectors, enabling semantic search instead of just keyword matching. The Result: A context-aware ingestion pipeline that ensures the LLM always receives complete, logical code blocks, drastically increasing accuracy and reducing errors. Tech Stack: FastAPI | ChromaDB | Sentence-Transformers | Tiktoken Check out the repo here: https://lnkd.in/gkVYmnwW #AI #GenerativeAI #Python #FastAPI #MachineLearning #RAG #SoftwareEngineering

📣 SynapseKit v1.4.7 + v1.4.8 just dropped. Back to back. Huge thanks to Dhruv Garg and Abhay Krishna who drove most of this sprint. 🙌 Two themes in these releases: getting data in, and making workflows resilient. Getting data in: 5 new loaders The gap between "I have a RAG pipeline" and "I can actually feed it my company's data" is a loader problem. These close it: 📨 SlackLoader — pull channel messages directly into your pipeline 📝 NotionLoader — ingest pages and databases from Notion 📖 WikipediaLoader — single article or multiple, pipe-separated 📄 ArXivLoader — search arXiv, download PDFs, extract text automatically 📧 EmailLoader — any IMAP mailbox, stdlib only, zero extra dependencies SynapseKit now has 24 loaders. Your data is probably already covered. Better retrieval — ColBERT ColBERTRetriever brings late-interaction ColBERT via RAGatouille. Instead of comparing a single query vector against a single document vector, ColBERT scores every query token against every document token (MaxSim). On long documents the recall improvement is significant- single-vector approaches lose detail in the compression. Token-level scoring doesn't. Resilient graph workflows Subgraph error handling now ships with three strategies — retry with backoff, fallback to an alternative graph, skip and continue. Production workflows break. The question is whether they break gracefully. Where SynapseKit stands today: 27 providers · 9 vector backends · 42 tools · 24 loaders · 2 hard dependencies ⚡ pip install synapsekit==1.4.8 📖 https://lnkd.in/dvr6Nyhx 🔗 https://lnkd.in/d2fGSPkX #Python #LLM #RAG #AI #OpenSource #MachineLearning #Agents #SynapseKit

🚀 #Day11 of #100DaysOfGenAIDataEngineering Topic: Async Processing in Python (Speeding Up Data Pipelines) If your pipeline waits for every task to finish one by one… you’re wasting time and compute. Today, I focused on asynchronous processing in Python — a key technique to make pipelines faster and more efficient. 🔹 What I did today: - Learned difference between: - Synchronous vs Asynchronous execution - Explored asyncio basics - Used: - "async" and "await" - Built a script to: - Fetch data from multiple APIs concurrently - Compared: - Sequential API calls vs async calls - Observed performance improvements 🔹 Why this is important: In real-world pipelines: - Multiple API calls - I/O-heavy operations (network, file reads) Using synchronous approach: ❌ Slow execution ❌ Idle waiting time Using async: ✅ Faster execution ✅ Better resource utilization ✅ Scalable ingestion pipelines In GenAI systems: - Multiple LLM/API calls - Parallel data retrieval (RAG pipelines) Async = speed advantage. 🔹 Who should do this: - Data Engineers working with API-heavy pipelines - Engineers building real-time or near real-time systems - Anyone optimizing for performance and cost If your pipeline is slow, you’re losing efficiency. 🔹 Key Learnings: - Use async for I/O-bound tasks (not CPU-bound) - Don’t overcomplicate — use it where it adds value - Parallelism = performance boost - Measure before and after optimization 🔥 “Speed is not a luxury in data engineering. It’s a requirement.” Day 11 complete. Faster pipelines, better engineering. Follow along if you're building towards GenAI Data Engineering mastery in 2026. #GenAI #Python #AsyncIO #DataEngineering #Performance #AI #LearningInPublic

The hardest bug I ever fixed: clean Ctrl+C in an async AI pipeline. When a user presses Ctrl+C during a streaming response with active tool calls, you need to — in order, without race conditions: 1. Cancel the HTTP stream gracefully 2. Abort any in-flight tool executions 3. Clean up temporary state (partial files, temp directories) 4. Preserve conversation history up to the interruption point 5. Return to a clean prompt — ready for the next input Each step can fail. And each failure mode is different. What if Ctrl+C fires between two tool calls? What if the stream buffer hasn't flushed? What if cleanup itself gets interrupted by a second Ctrl+C? What if an async tool call returns after cancellation and tries to write to a closed context? Python's signal handling + asyncio cancellation made it possible. But every edge case took hours to find — because you can only reproduce them by hitting Ctrl+C at exactly the right millisecond. The lesson I keep coming back to: the undo path is always harder than the happy path. And in developer tools, the undo path is what determines whether people trust your software. Stack: Python + Claude API GitHub: https://lnkd.in/ghn_8iKA Full case study: https://lnkd.in/gtg49D-S #Python #Claude #CLI #AsyncPython #Architecture #BuildInPublic #SoftwareEngineering

Spending some time deep in architecture decisions for a side project, and honestly, this is the part I love most. The stack is taking shape: 🔹 FastAPI + Python on the backend 🔹 React + Vite on the frontend 🔹 PostgreSQL with pgvector for semantic search 🔹 LlamaIndex for the RAG pipeline 🔹 Anthropic + OpenAI APIs for generation The domain is healthcare-adjacent. I'm keeping the specifics close to the chest for now, but the core challenge is interesting: how do you build a system that retrieves clinically relevant evidence, reranks it, and generates structured recommendations that a practitioner can actually trust and trace back to sources? Some of the design decisions I've been enjoying: • Separating QueryBuilder, VectorRetriever, Reranker, and PlanGenerator into composable pipeline stages • A human-in-the-loop approval flow before any recommendation goes live • Audit trails baked in from day one, not bolted on later Still a lot of road ahead, but it's the kind of project that makes you a better engineer regardless of where it lands. Building in public (ish). Happy to geek out with anyone working on RAG systems or clinical AI. 🙌 #RAG #FastAPI #pgvector #LLM #Python #AIEngineering #BuildingInPublic