SynapseKit v1.5.0 Released with 12+ New Features

One of the biggest challenges in vector search is not retrieval itself. It is the query interface. qql-go was built to solve this particular problem in mind: agents first, humans too. The starting point was QQL (qdrant query language), originally shared by Kameshwara Pavan Kumar Mantha. The original idea, repo, and write-up came from that work. The idea brings the possibility of giving vector retrieval a cleaner interface for repeated use inside agent workflows. That is what led to qql-go: an independent Go port and extension of the idea. Repo: https://lnkd.in/gXjQdjaw The focus was simple: clean CLI, structured output, and a path that works well inside Skills. 👉 Install the Skill, and the agent can do the rest. That makes the whole thing much easier to start with, especially for Qdrant Cloud. Qdrant gives a very good entry point here: 1. free dense vectors (sentence-transformers/all-minilm-l6-v2) inference. 2. free BM25 (qdrant/bm25) inference. 3. free ColBERT multivector model. (answerdotai/answerai-colbert-small-v1). 4. 4 GB always-free cloud tier. So you can start with a real hybrid+reranking retrieval setup without spending money upfront. That is the part that matters. A retrieval interface becomes much more useful when it is: easy for agents to call, easy for humans to inspect, and cheap enough for people to actually adopt. Credit to Kameshwara Pavan Kumar Mantha for putting the original QQL idea out there and giving others something worth building on. 📖 Read the full article from the qql creator : https://lnkd.in/g_nh9T7s Original qql repo:- https://lnkd.in/gwppzjgw #Qdrant #Retrieval #AIEngineering #OpenSource #GoLang #DeveloperTools #Agents #VectorSearch #Skills

I built a recommendation engine that had to respond in under 200ms. Here's what I learned about the gap between "it works" and "it works at scale." The first version was straightforward. Python service, takes user behavioral data, scores items, returns a ranked list. In development it worked great. In production with real traffic, it was way too slow. The problem wasn't the algorithm. It was when we were doing the work. We were computing recommendations at request time. Every API call triggered a fresh scoring pass over the dataset. At low traffic, fine. At real traffic, timeouts. The fix was separating the work into two parts: → Precompute: a background pipeline that scored and ranked recommendations ahead of time based on behavioral signals, then wrote the results to Redis → Serve: the API just read from Redis. No computation at request time. Sub-200ms, consistently. But the harder part wasn't the caching. It was knowing which strategy to trust. We had multiple ranking approaches. Instead of picking one based on gut feeling, we ran them side by side and compared on three signals: 1. Engagement: did users actually click/act on what we recommended? 2. Latency: did the serving path stay fast? 3. Coverage: were we recommending the same 20 items to everyone, or actually personalizing? That comparison was more valuable than any single optimization. It turned "we think this ranking is better" into "here's the data, pick the tradeoff you want." The takeaway: personalization is easy to demo and hard to ship. The difference is knowing what to precompute, what to serve live, and having the discipline to measure which approach actually works instead of guessing. #softwareengineering #python #recommendationsystems

𝟕 𝐟𝐫𝐞𝐞 𝐀𝐈 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐭𝐨𝐨𝐥𝐬 𝐰𝐨𝐫𝐭𝐡 𝐤𝐧𝐨𝐰𝐢𝐧𝐠 𝐢𝐧 𝐀𝐩𝐫𝐢𝐥 𝟐𝟎𝟐𝟔. All verified as of this week. All open source or genuinely useful free tiers. 🛠️ 𝟏. 𝐋𝐀𝐍𝐆𝐅𝐔𝐒𝐄 Open source 𝐋𝐋𝐌 𝐨𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲. Trace every call, latency, token count, and retrieval quality. Self-host free with no per-trace fees. Works with LangChain, LlamaIndex, and raw API calls. 𝟐. 𝐐𝐃𝐑𝐀𝐍𝐓 Open source 𝐯𝐞𝐜𝐭𝐨𝐫 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞. Production-grade performance with a clean Python client. Free cloud tier up to 1GB. Best balance of performance and free access in 2026. 𝟑. 𝐎𝐋𝐋𝐀𝐌𝐀 Run 𝐋𝐋𝐌𝐬 𝐥𝐨𝐜𝐚𝐥𝐥𝐲 at zero API cost. Llama 3, Mistral, Phi-3, and Gemma, all on your hardware. Essential for offline, private, or cost-sensitive inference. 𝟒. 𝐑𝐀𝐆𝐀𝐒 Open source 𝐑𝐀𝐆 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧. Measures context precision, context recall, and answer faithfulness. The fastest way to know if your retrieval pipeline is actually working. 𝟓. 𝐎𝐏𝐄𝐍𝐑𝐎𝐔𝐓𝐄𝐑 Access 𝟐𝟎𝟎+ 𝐋𝐋𝐌𝐬 through one API key, GPT-4o, Claude, Gemini, Mistral, Llama 3. Free tier available. Prevents vendor lock-in and makes model switching trivial. 𝟔. 𝐏𝐇𝐎𝐄𝐍𝐈𝐗 (𝐀𝐑𝐈𝐙𝐄) Open source 𝐋𝐋𝐌 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐨𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲. Stronger for systematic testing across prompt versions than Langfuse. 𝟕. 𝐏𝐆𝐕𝐄𝐂𝐓𝐎𝐑 Vector search inside 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐬. If you are already on Postgres, add vector similarity search without running a separate database. Zero additional cost. 𝘚𝘢𝘷𝘦 𝘵𝘩𝘪𝘴 𝘧𝘰𝘳 𝘵𝘩𝘦 𝘯𝘦𝘹𝘵 𝘵𝘪𝘮𝘦 𝘺𝘰𝘶 𝘢𝘳𝘦 𝘦𝘷𝘢𝘭𝘶𝘢𝘵𝘪𝘯𝘨 𝘺𝘰𝘶𝘳 𝘴𝘵𝘢𝘤𝘬. 👇 𝐖𝐡𝐢𝐜𝐡 𝐨𝐧𝐞𝐬 𝐚𝐫𝐞 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐭𝐨𝐨𝐥𝐤𝐢𝐭? #OpenSource #AITools #LLMOps #RAG #DevTools #AIEngineering #VectorDatabase #MachineLearning

🚀 SynapseKit v1.6.0 is live on PyPI                                                                                                                                 Our biggest release yet and it's packed.                                                      When we started SynapseKit, the goal was simple: build the LLM framework we wished existed. Async-native, streaming-first, no bloat. Two hard dependencies. Today, v1.6.0 takes that foundation and adds serious production breadth. What's new: 🗄️22 vector store backends Vespa[Vespa.ai] · Redis[Redis] · Elasticsearch[Elastic] · OpenSearch[OpenSearch Project] · Supabase[Supabase] · Typesense · Marqo[Marqo] · Zilliz[Zilliz] · DuckDB[DuckDB] · ClickHouse[ClickHouse] · Cassandra: all with the same 3-line interface. Drop in whichever your infra already runs. 📄 64 document loaders  Firestore · Zendesk · Intercom · Freshdesk · Hacker News · Reddit · Twitter · Google Calendar · Trello : plus the full suite from prior releases. If your data lives somewhere, there's now a loader for it. 🔍 4 new retrieval strategies  RAPTOR (recursive abstractive tree) · Agentic RAG (tool-using retriever) · Document Augmentation (HyDE-style query + doc expansion) · Late Chunking(full-doc embeddings before splitting) 🤖 SwarmAgent —> spawn specialist sub-agents dynamically based on task complexity. Real multi-agent coordination, not just chaining. 🎤 VoiceAgent —> full STT → agent → TTS pipeline. OpenAI Whisper or local faster-whisper. pyttsx3 or OpenAI TTS. Mic/speaker streaming built in. 🧩 Plugin system —> PluginRegistry + BasePlugin. Package your integrations, publish them, load them with one line. ⏱️ 12 performance fixes —> semantic cache BLAS lookups, O(1) vector inserts, parallel ensemble retrieval, persistent HTTP sessions, rate limiter  deadlock fix, and more. And: CronTrigger · EventTrigger · StreamTrigger · AgentMemory · BrowserTool · TimedResumeGraph · ReplicateLLM · Agent Benchmarking Suite · Visual  Graph Builder 34 LLM providers. 64 loaders. 22 vector stores. 2 hard dependencies. The design constraint stays the same. The scope keeps growing. pip install synapsekit==1.6.0 Docs → https://lnkd.in/dcptxYin GitHub → https://lnkd.in/d2fGSPkX Huge thanks to every contributor who shipped PRs for this release, this wouldn't exist without you. 🙏  #OpenSource #Python #LLM #RAG #AI #MachineLearning #SynapseKit

In the last couple of days, while working on a RAG implementation, we realized Accuracy isn’t just about better embeddings or larger models. The real challenge we faced was context loss—situations where related information existed in the knowledge base but wasn’t retrieved together. This led to fragmented, inconsistent answers, even though the data was technically present. That’s when we explored an alternative framework: LightRAG. By combining graph-based knowledge structures with vector search, LightRAG enables: Deeper contextual understanding Relationship-aware retrieval Significantly more accurate and coherent responses Why LightRAG stood out 👇 ✅ Graph-aware indexing ✅ Dual-level retrieval (low-level details + high-level knowledge) ✅ Easy implementation using PostgreSQL with graph support ✅ Incremental updates for fast-changing data For anyone struggling with context fragmentation in traditional RAG pipelines, LightRAG offers a compelling and practical approach. Explore implementation details here: https://lnkd.in/dpbWmR8X #RAG #LightRAG #GenAI #LLM #GraphDatabase #PostgreSQL #AIEngineering #KnowledgeGraphs

 Research should compound. Every session should make the next one better.  I built Recall — an open-source research index that tracks reports, tags, and threads across topics. It remembers what you've already  researched so you never start from zero.  Flask API + SQLite + D3 tag graph dashboard. Reports stay on your machine. Metadata indexes on a server. Private network, TLS encrypted.     https://lnkd.in/eKyjGbsM  #OpenSource #AI #Python #ResearchTools

Why does Google never rank spam pages that just repeat the same keyword 100 times? The answer is an algorithm called BM25 — and it's been quietly powering Elasticsearch, Lucene, and most production search stacks for decades. Most devs learn about embeddings and vector search first. But skip BM25? You're missing the foundation that makes RAG pipelines actually work. Here's what BM25 solves that basic keyword search can't: → Keyword stuffing? Penalised. → Long documents dominating short ones? Normalised. → Common words like "the" outweighing rare ones? Weighted down. I wrote a beginner-friendly breakdown with: ✅ The formula explained in plain English ✅ Python code from scratch + rank-bm25 library ✅ An interactive demo you can run in your browser ✅ How to plug BM25 into a RAG pipeline If you're building search or RAG systems and haven't thought about BM25 yet — this one's for you. 👇 Full article (with live demo): https://lnkd.in/gbmFZaUD

I built a Model Context Protocol-powered doc assistant in Streamlit (with the help of Claude) and it taught me more than I expected about general application of Agents, LLMs and MCPs. 🧠 The idea is simple: query official library documentation using natural language, with Claude as the conductor. Select from a catalogue of Python and R libraries (pandas, PySpark, dbplyr, scikit-learn, and more), point it at GitHub-hosted docs via gitmcp.io, and ask anything. But the real insight came from connecting it to custom MCP servers. Here's what I learned: 🔗 You can mix official docs with any custom MCP server. Open-source tooling like a database (Supabase)? Hook it in. The architecture doesn't care where the knowledge lives, although system prompts can really help point the agent in the right direction, what is important is that there's an MCP endpoint to call. 🤖 The LLM is the conductor, not the worker. Claude doesn't know your codebase. But give it a set of MCP tools, and it figures out what to call, in what order with the help of an llms.txt file. Building this really help me turn the the concept of an "agent loop" to a real life use case. 🔑 Making AI tools accessible matters. The app accepts your own Anthropic API key directly in the browser, no server-side secrets needed for personal use. Lowering that barrier changes who can actually use the thing. 📚 Docs are just another data source. Once you think of documentation as something a model can query — not just read — the design space opens up. Structured retrieval, versioned docs, multi-repo search: it's all the same pattern. Other things I picked up along the way: → Token cost is real and visible. Tracking per-message cost ($1/$5 per 1M input/output tokens) immediately changed how I thought about Agent architecture. → Rate limits force you to think about server selection. Capping active MCP servers to 2 taught me to be intentional. The stack: Streamlit · Anthropic SDK · MCP Python client · gitmcp.io · claude-haiku-4-5 If you're exploring agentic patterns, happy to share and learn more about your use cases. #LLMs #DataScience #AgenticAI #DataEngineering

🚀 Built a scalable  RAG (Retrieval-Augmented Generation) system using FastAPI + LLMs Most people focus on the LLM. I learned the hard way: retrieval quality matters more. 🔧 Tech Stack: FastAPI | Qdrant | MongoDB | Redis | Gemini/Ollama ⚙️ What I built: • End-to-end pipeline: ingestion → chunking → embeddings → retrieval → response • Hybrid search (semantic + keyword) for better recall • Vector retrieval using Qdrant • Structured LLM outputs with schema validation 📈 Results: • 🎯 Improved answer accuracy with hybrid search • ⚡ ~40% lower API latency (caching + optimized queries) • 🧩 ~30% faster development (modular pipeline) 💡 Key takeaway: A strong RAG system isn’t about plugging in an LLM — it’s about retrieving the right context efficiently. what’s been your biggest challenge when building with LLMs? #AI #LLM #RAG #FastAPI #Python #GenerativeAI #Backend

Following up on my recent article about the Apache Cassandra Sidecar, I wanted to share a sneak peek of a project I’ve been working on behind the scenes! 🛠️ The open-source Cassandra Sidecar exposes an incredibly powerful REST API for node operations, but interacting with 40+ endpoints strictly through curl or Postman can get tedious. To bridge that gap, I’ve started building a custom, web-based UI wrapper around the Sidecar to serve as a visual control plane. It’s still very much a work in progress, but the core topology and monitoring components are finally coming to life. In the quick demo video below, I walk through: 1️⃣ 𝗧𝗵𝗲 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 & 𝗛𝗼𝘀𝘁𝘀: Dynamically adding a local Cassandra node into the cluster topology in real-time. 2️⃣ 𝗟𝗶𝘃𝗲 𝗛𝗲𝗮𝗹𝘁𝗵 𝗦𝘁𝗮𝘁𝘂𝘀: Mapping the Sidecar, Native, JMX, and Gossip health checks to a clean, visual status monitor. 3️⃣ 𝗦𝗰𝗵𝗲𝗺𝗮 𝗘𝘅𝗽𝗹𝗼𝗿𝗲𝗿: My favorite part! The Sidecar API returns the entire cluster schema as one massive, raw DDL string. I built a custom parser to transform that text block into a clean, nested Keyspace-to-Table tree. 🤖 𝗔 𝗾𝘂𝗶𝗰𝗸 𝗻𝗼𝘁𝗲 𝗼𝗻 𝗔𝗜 & 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆: While my day-to-day expertise lies in backend distributed systems and database administration, leveraging AI tools massively accelerated this build. Using AI to help scaffold the React/Tailwind frontend and write the complex Regex needed to parse the Sidecar’s raw schema outputs was a total game-changer. It’s amazing how much faster we can build full-stack solutions now! I am really excited about how this is shaping up. I'll be exploring more features in upcoming posts—🐘⚡ Let me know what you think of the interface so far! #ApacheCassandra #DistributedSystems #DatabaseAdministration #OpenSource #DataEngineering #GenerativeAI #SoftwareEngineering #Ksolves