Pinecone jobs
We are seeking an experienced AI/LLM engineer to develop an end-to-end Retrieval-Augmented Generation (RAG) system for automated customer support chat. Scope of Work • Data Ingestion & Preprocessing: Parse and chunk internal documentation (PDFs, Markdown, FAQs, and ticket logs) with automated re-indexing. • Vector Storage & Semantic Search: Set up and optimize a vector database (e.g., Chroma, Pinecone, Weaviate, or pgvector) for hybrid/semantic retrieval. • LLM Pipeline: Integrate LLMs (OpenAI GPT-4/3.5, Claude, or open-source models) with prompt chaining that provides accurate answers and explicit source citations. • Backend & API: Build secure, low-latency REST/WebSocket endpoints using Python (FastAPI). • Frontend Interface: Lightweight c...
...products are uploaded * Results should combine visual similarity with product attributes * Admin should be able to re-index products and review search quality * The system should be designed to improve as the catalogue grows Please mention which AI model, vector database and search architecture you recommend. We are open to solutions such as OpenAI/CLIP-compatible embeddings, Google Vision, AWS, Pinecone, Qdrant, Weaviate, Elasticsearch/OpenSearch or another suitable stack. 4. Admin panel/CMS The admin dashboard should allow our team to: * Add and edit products without coding * Bulk import products through CSV or Excel * Upload multiple product images * Manage categories, subcategories, brands and collections * Manage product attributes and filter options * Manage prices, siz...
...developing conversational systems for voice and/or chat interfaces • Strong programming skills in Python, JavaScript/TypeScript, or similar languages • Experience with: • API design and integration (REST, GraphQL) • Cloud platforms (Azure, AWS, or GCP) • Microservices and distributed system design Familiarity with: • Prompt engineering and orchestration patterns • Vector databases (e.g., Pinecone, Redis, Azure AI Search) • CI/CD and DevOps practices Preferred Qualifications • Experience transitioning traditional IVR or chatbot systems to AI-driven agentic architectures • Experience with speech technologies (ASR, TTS, voice streaming frameworks) • Familiarity with agent frameworks such as Semantic Kernel, LangChai...
...Generation (RAG) layer that can pull that data on-demand for search, chat, and merchandising features. Here’s what I need built: • A lightweight service (Python preferred; open to Node.js) that connects securely to the existing MongoDB collection and exposes a clean retrieval interface. • An embedding/indexing routine so product documents can be vectorised and queried efficiently (e.g., with FAISS, Pinecone, or a native MongoDB Atlas Vector Search). • RAG pipeline logic that combines the retrieved product fields with a language model prompt to produce rich, real-time answers. • Environment configuration and clear instructions so I can run, extend, and monitor the service in staging and production. Acceptance criteria • Given a product ID ...
...web chat UI plus a simple REST/GraphQL endpoint so other apps can tap into the same knowledge base. • Include deployment scripts (Docker or similar) so the stack can be spun up quickly on our cloud account. Key technical notes Python is preferred; popular RAG toolkits such as LangChain, LlamaIndex or Haystack are welcome as long as the code remains modular and well-commented. ElasticSearch, Pinecone or another vector store may be used for embeddings. The language model can be OpenAI, Anthropic or an open-source alternative—just document any API keys or weights required. Acceptance criteria 1. Ask-answer cycle under three seconds for a standard query on a 1k-document set. 2. At least two citations returned with every answer. 3. One-command rebuild script suc...
...integrate different open-source instruction-tuned models such as Llama, Mistral, or similar Hugging Face model families based on performance, cost, and deployment requirements. The RAG pipeline will leverage Azure AI Search with vector search capabilities as the preferred vector store, supporting semantic search and hybrid retrieval (BM25 + embeddings) for improved accuracy. Alternatives such as Pinecone or pgvector may be evaluated depending on scalability and operational needs. The agentic workflow layer will support tool/function calling, retrieval workflows, prompt orchestration, content validation, and human-in-the-loop review processes using frameworks such as LangGraph, LangChain, and LlamaIndex. The platform will expose secure REST APIs with OAuth2/OIDC and JWT-based a...
...CAPA/NCR Agent * Audit Agent * Executive Intelligence Agent Agents should securely call application APIs/tools to retrieve real-time data. ## Recommended Architecture The AI layer should be implemented as an **independent AI service/microservice** connected to the existing application through secure APIs. Preferred technologies: * Python * FastAPI * OpenAI API * LangGraph / LangChain * Qdrant / Pinecone * Redis * PostgreSQL or existing database integration * Docker The architecture must allow future replacement of AI models/providers without rebuilding the entire system. ## Security Required: * Strict multi-tenant isolation * Role-based access control * Secure API authentication * Audit logging * Data protection * No cross-organization data exposure * Human approval befo...
...chatbots, AI agents, RAG, or AI workflows. * Mobile development experience with Flutter or native iOS (Swift) and Android (Kotlin) is a plus. * Must have published at least one production app on both the Apple App Store and Google Play Store. ## Additional Skills (Good to Have) * Experience with LangChain, LangGraph, CrewAI, AutoGen, MCP, or Vercel AI SDK. * Experience with Vector Databases such as Pinecone, Qdrant, Weaviate, or Supabase Vector. * Experience with Docker, AWS, Google Cloud, or Azure. * Experience with Firebase or Supabase. * Experience integrating Stripe, Razorpay, or other payment gateways. * Experience with AI automation tools such as n8n, , or Zapier. * AI-assisted development experience is preferred. * Please mention which AI tools you use to speed up develo...
...manuals, etc.) Ask questions in natural language and get instant, accurate answers Semantic search using vector embeddings — finds relevant info even if exact keywords don't match Reduces hallucinations by grounding responses in the actual document Scalable architecture — can handle single files or large document libraries Tech stack: LangChain / LlamaIndex • OpenAI / Claude API • Vector Database (Pinecone / ChromaDB / FAISS) • Python • [Streamlit / React frontend] Use cases: Perfect for legal document review, customer support knowledge bases, research assistants, internal company wikis, or any business drowning in PDFs that needs fast, reliable answers. I can customize this solution for your specific documents, industry, and use case &mdas...
I’m building a self-serve, AI-driven help desk and need a specialist who can turn ChatGPT into a friendly, always-on support agen...set of 100 unseen queries. • Integration code or no-code setup that plugs the workflow into Zendesk Chat and triggers within 1-2 seconds. • A quick reference guide so my non-technical team can tweak the system without breaking it. Tools that will probably come into play: OpenAI API, Python (or a no-code alternative like Zapier/Make), vector search for the knowledge base (Weights & Biases, Pinecone, or whatever you prefer), and basic analytics to track response quality over time. If you’ve already built something similar—or have a clever prompt structure that nails tone and accuracy—I’d love to see a shor...
...Integration * Digital Signature * Cloud Storage * Google Maps API (Optional) Technology Stack (Preferred) Frontend - React.js / Backend - .NET Core / Node.js / Python Mobile (Optional) - Flutter / React Native Database - PostgreSQL / MySQL Cloud - Microsoft Azure / AWS / Google Cloud AI Technologies * OpenAI GPT * LangChain * RAG (Retrieval-Augmented Generation) * Vector Database (Pinecone, Weaviate, Chroma, or similar) * OCR (Azure AI Document Intelligence, AWS Textract, or Google Vision AI) * Machine Learning Models for Lead Scoring & Fraud Detection Security Requirements * Multi-Factor Authentication (MFA) * Role-Based Access Control (RBAC) * Data Encryption (At Rest & In Transit) * Secure API Authentication * Audit Logs * GDPR-ready architecture * OWASP s...
...This entire project is to help my dad who was recently diagnosed with this disease and I need more power in my searches. I am told by my own ChatGPT research that "this would involve a Python stack and leverage current NLP/LLM tooling (e.g., transformers, LangChain, GPT-4, spaCy), PDF parsing utilities, and APIs such as PubMed, CrossRef, , and YouTube Data API. A vector store (Pinecone, FAISS, or similar) for semantic search plus a lightweight web dashboard or Streamlit front end will keep things user-friendly." Deliverables • Crawling & ingestion pipeline covering all stated sources • Information-extraction module with clearly defined JSON/CSV schema • Automatic literature-review generator with citation linking • Real-time monitoring/a...
..., React, and modern UI frameworks (Tailwind CSS, etc.). Backend: Python, FastAPI or Django/DRF, PostgreSQL. Async & Streaming: Experience with Celery/Redis for background tasks and Server-Sent Events (SSE) or WebSockets for streaming LLM responses. AI/LLM Frameworks: Langchain, LlamaIndex, and commercial/open-source LLM integration. RAG & Data Architecture: Vector Databases (pgvector, Pinecone, ChromaDB, Qdrant), custom embedding generation, and advanced document chunking strategies. DevOps: Docker, AWS/GCP/Azure, and Vercel deployment. Deliverables & Milestones Milestone 1 (Scoping & Technical Architecture): Deliver a comprehensive technical specification detailing the database schema, API design, RAG architecture, and deployment strategy to get started. M...
...data, SSO (OIDC/SAML) and audit logging baked in from day one. Tech snapshot Prefer modern, cloud-native tooling (TypeScript/Node or Python, containerised micro-services, PostgreSQL, Redis, message queue, Terraform/Kubernetes). If you have a different stack that delivers the same scalability and speed, I’m open to it. AI components can leverage OpenAI or similar LLM APIs; vector search through Pinecone or an equivalent is welcome. Acceptance criteria 1. Functional, tested workflow-automation and knowledge modules deployed to a staging environment. 2. API documentation (OpenAPI) plus quick-start Postman collection. 3. Working connectors for one CRM, one email service and one project-management tool. 4. Secure, multi-tenant architecture review signed off after pen...
...procedures. Preferred Technical Skills: >Strong expertise in Python and AI/ML development. >Hands-on experience with Agentic AI frameworks (LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, or similar). >Experience building Generative AI and Retrieval-Augmented Generation (RAG) applications. >Fine-tuning and optimization of Large Language Models (LLMs). >Experience with vector databases such as Pinecone, Milvus, Weaviate, FAISS, or ChromaDB. >Knowledge of LangChain, LlamaIndex, and AI orchestration frameworks. >Strong understanding of PyTorch, Hugging Face Transformers, and model optimization techniques. >Experience deploying AI workloads on AWS (EKS, SageMaker, EC2, Bedrock, or equivalent cloud services). >Experience designing RESTful APIs, gRPC service...
... We are looking for someone with real production experience, not just prompt engineering. Required experience: * Dify * RAG (Retrieval-Augmented Generation) * Docker & Self-hosting * n8n * OpenAI / Claude / Gemini APIs * Knowledge base design * Prompt engineering * AI workflow design * Document generation (DOCX/PDF) Bonus: * LangChain / LlamaIndex * Azure / AWS * Vector databases (Qdrant, Pinecone, Weaviate, etc.) * Arabic language support Scope of Work You will help us: * Build production-ready Dify workflows * Design and optimize RAG pipelines * Organize and optimize knowledge bases * Improve prompt engineering and guardrails * Build contract review and drafting workflows * Generate Word/PDF documents from templates * Integrate Dify with n8n * Configure secure self-h...
...jobs and webhooks, while implementing robust error handling, rate limiting, and failover mechanisms to ensure production-grade reliability. The AI Specialist will design a sophisticated Retrieval-Augmented Generation (RAG) pipeline using LangChain and LangGraph, where they will select and fine-tune embedding models, architect optimal chunking strategies, and deploy a vector database (such as Pinecone or Chroma) to store both historical reviews and internal business knowledge—including product catalogs, FAQs, brand guidelines, and past customer interactions. This specialist will build a multi-agent orchestration framework where autonomous agents dynamically decide which tools to invoke: querying the vector store for similar past cases, escalating critical negative feedback ...
... * XGBoost / LightGBM / CatBoost * Strong SQL skills and experience working with large datasets. ### Generative AI Hands-on experience with: * Large Language Models (OpenAI, Claude, Gemini, Llama, etc.) * Multi-agent architectures * RAG systems * Prompt engineering * Tool calling / Function calling * Structured outputs * Context management * Memory architectures * Vector databases such as Pinecone, Qdrant, Weaviate, Milvus, Chroma, or FAISS * Embeddings and semantic search * Hybrid retrieval and reranking Experience with one or more frameworks: * LangGraph * LangChain * CrewAI * Google ADK * Semantic Kernel * AutoGen * LlamaIndex --- ## MLOps & Production Engineering Experience with: * Docker * Kubernetes * CI/CD pipelines * MLflow * Model versioning * Model deploym...
...skills and experience: Strong React and JavaScript/TypeScript. Hands-on experience integrating AI/LLM APIs into real products. Comfortable with backend work (Node.js, REST APIs) and a database (PostgreSQL, MongoDB, or similar). Good understanding of async flows, API keys/security, and rate limiting. Attention to detail and clean, maintainable code. Nice to have: Experience with vector databases (Pinecone, pgvector) or RAG pipelines. Familiarity with Next.js. Prior work with real-time features (WebSockets, streaming). To apply, please include: A short note on your relevant experience (React + AI integration specifically). Links to live projects or a portfolio showing similar work. Your availability and estimated hourly rate. Any questions you have about the project. I'm...
...processing pipeline should expose: • high-accuracy OCR for scanned pages, • field-level data extraction that can be fine-tuned per document type, and • a clean JSON export endpoint so downstream systems in law firms or finance teams can consume the results automatically. Expected flow 1. User uploads PDF(s). 2. Service performs OCR where needed. 3. Embeddings are generated and stored (FAISS, Pinecone, or similar—your choice as long as latency stays under two seconds for typical 100-page bundles). 4. A web chat box accepts text input and streams answers back, citing sources. 5. An API call returns the full extracted data set in JSON. Acceptance criteria • Chat responses ≤2 s on 100-page test set. • OCR accuracy on supplied samples ...
...low-quality scanned/PDF inputs, and building pipelines that degrade gracefully rather than silently producing wrong answers. **LLM/RAG** - Production experience building tool-calling LLM architectures with hard output constraints — not prototype chatbots. You should already understand why prompt-level suppression is not a security boundary. - RAG pipeline experience: embeddings, vector DB (ChromaDB, Pinecone, Weaviate, or equivalent), retrieval tuning. - Comfort building schema-enforced, denylist-constrained generation for a regulated domain. **Otto-specific** - Context isolation between subscription tiers within a single LLM session — the candidate must be able to explain, unprompted, why passing full paid-tier data into a shared context window and relying on promp...
...Predictive analysis that flags potential bottlenecks or capacity issues at least two weeks in advance, using past trends. 3. A simple chat interface (web or Slack) so team members can ask follow-up questions and request custom slices of the data. Tech preferences Claude (latest model via Anthropic API) as the core LLM, Python for the orchestration layer, and a lightweight vector store (e.g., Pinecone or Chroma) for context retrieval. I’m open to alternatives if you can justify gains in speed, cost, or accuracy. Deliverables • Clean, well-commented code repository • Deployment scripts (Docker or serverless) • A short read-me explaining how to add new data sources and adjust reporting schedules • One live demo session to walk through setu...
...Sonnet 3.5 deployed in my AWS account (EC2 or an equivalent managed service is fine) and wired up to those embeddings so that users can chat in plain text and receive interactive language-learning guidance based on the content. Here is the workflow I have in mind: • Generate 384-dimensional embeddings for the entire document, verify their quality, and load them into a persistent vector store (Pinecone, Amazon Kendra, or Faiss—whichever you prefer and can justify). • Spin up Claude Sonnet 3.5 in AWS and expose it through a simple web front end or an API endpoint; no voice features are required, text chat only. • Connect the model to the vector store so that retrieval-augmented generation powers the responses. The chatbot’s role is strictly Interact...
...cost-effectiveness, scalability, and clean system architecture. Preferred Technology Stack We welcome equivalent recommendations; however, our preferred technology stack includes: • Frontend: React or • Backend: Python (FastAPI) • AI: OpenAI API (preferred), with support for Anthropic Claude or equivalent • AI Workflow: LangChain or LlamaIndex • Database: PostgreSQL with a Vector Database (e.g., Pinecone, Weaviate, or pgvector) • Cloud: Microsoft Azure or AWS • Authentication: Auth0, Clerk, or equivalent MVP Scope A detailed functional specification, technical requirements, and product documentation will be provided to the selected developer following the execution of a Non-Disclosure Agreement (NDA). What We're Looking For We are seeking ...
...reason through processes, generate outputs, execute actions, communicate with humans, and escalate when human review is required. **Technical architecture we need** We are looking for developers with experience in building advanced AI agent systems using tools such as: * n8n * OpenAI, Claude, Gemini, or other LLMs * RAG systems * Vector databases * Structured databases * Airtable * Supabase * Pinecone * AWS * Azure * APIs * Webhooks * CRM integrations * Gmail and email systems * WhatsApp * Slack * Notion * HubSpot * Holded * Airtable * Lovable * Internal dashboards and client-facing interfaces Our digital workers normally need several layers of reasoning, validation, and execution. Depending on the project, a worker may include 5 to 7 layers, such as: * Input interpretation ...
...logging (Loki), metrics (Prometheus), tracing (Tempo) · Write API docs (Swagger) and deployment runbook · Train the Full Stack team on using the AI APIs --- REQUIREMENTS (Must Have): · Python (5+ years professional) · FastAPI or Flask (expert) · vLLM or TensorRT-LLM (production deployment) · RunPod, AWS SageMaker, or similar GPU cloud · Docker & Docker Compose (advanced) · Vector DBs (Qdrant, Pinecone, or Weaviate) · PostgreSQL with pgvector · Redis (caching + queue) · Git & CI/CD · Linux admin (Ubuntu, shell scripting) Nice to have: Llama/Qwen/DeepSeek experience, AWQ/GPTQ quantisation, NATS, Kong, Vault, Prometheus/Grafana/Loki/Tempo, MinIO/S3, Whisper, FLUX/Stable Diffusion. --- H...
...underlying code relevant from an interview standpoint (not toy examples). Goal: cover realistic scenarios and architectures from interview standpoint around: 1. Agent Anatomy — (Prompt + Tools + Memory) × LLM * Prompting, CoT/ReAct, structured output, model selection * Tool calling, RAG-as-a-tool, short-/long-term memory 2. Knowledge + Topology * Chunking, embeddings, vector DBs (pgvector/Pinecone) * Hybrid retrieval, RRF, reranking, retrieval evaluation metrics * Single-agent ReAct, planning/reflection, orchestrator-worker, handoffs, MCP/A2A 3. Reliability Envelope * Guardrails, prompt-injection defense, LLM-as-judge evaluation * Caching/cost optimization, observability, Human-in-the-Loop (HITL) Note: The above topics are broad; coverin...
migration from pinecone to json format for the db and other changes discussed via chat.
...LinkedIn-ready format (PDF or images). • Publish the generated carousel directly to my LinkedIn account via API/integration. • Maintain a log of generated topics to avoid duplicate content. • Provide a simple dashboard or management interface to upload new documents and manage the knowledge base. Preferred Skills: • RAG systems (LangChain, LlamaIndex, Haystack, etc.) • Vector databases (Pinecone, Qdrant, Weaviate, ChromaDB) • OpenAI, Claude, or Gemini APIs • LinkedIn API integration • Canva API or automated carousel generation • Workflow automation (n8n, Make, Zapier, Airflow, Python) • Cloud deployment and scheduling Nice to Have: • Experience building AI content automation systems • Experience with LinkedIn g...
...Development Team Senior AI Architect with expertise in designing and deploying scalable AI systems. Required Expertise OpenAI APIs and LLMs Agentic AI & Multi-Agent Systems RAG (Retrieval-Augmented Generation) Vector Databases Conversational Memory Systems Context Management Prompt Engineering Function/Tool Calling AI Workflow Orchestration Experience with LangGraph, LangChain, LlamaIndex, Pinecone, Weaviate, Qdrant, or similar technologies is preferred. Travel-Tech Experience (Major Advantage) The AI must understand: Flight booking workflows Fare rules and restrictions Baggage policies Transit and travel requirements Airport intelligence Alternative airport suggestions Layover validation Date optimization OTA-style booking logic API & Backend Requirements Expected...
...Semantic Kernel, AutoGen, CrewAI, Haystack, or similar. * Good understanding of machine learning, NLP, model evaluation, and data preprocessing. * Experience working with APIs, databases, cloud services, and production software systems. * Ability to analyze model outputs, debug failures, and improve system quality. ## Preferred Qualifications * Experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, Qdrant, Chroma, or Azure AI Search. * Experience with cloud platforms such as Azure, AWS, or Google Cloud. * Experience with model serving or inference frameworks such as vLLM, TGI, Triton, Ray Serve, or FastAPI. * Experience building production-grade AI agents, chatbots, copilots, document intelligence systems, or workflow automation tools. * Knowledg...
...Semantic Kernel, AutoGen, CrewAI, Haystack, or similar. * Good understanding of machine learning, NLP, model evaluation, and data preprocessing. * Experience working with APIs, databases, cloud services, and production software systems. * Ability to analyze model outputs, debug failures, and improve system quality. ## Preferred Qualifications * Experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, Qdrant, Chroma, or Azure AI Search. * Experience with cloud platforms such as Azure, AWS, or Google Cloud. * Experience with model serving or inference frameworks such as vLLM, TGI, Triton, Ray Serve, or FastAPI. * Experience building production-grade AI agents, chatbots, copilots, document intelligence systems, or workflow automation tools. * Knowledg...
...Sonnet 3.5 deployed in my AWS account (EC2 or an equivalent managed service is fine) and wired up to those embeddings so that users can chat in plain text and receive interactive language-learning guidance based on the content. Here is the workflow I have in mind: • Generate 384-dimensional embeddings for the entire document, verify their quality, and load them into a persistent vector store (Pinecone, Amazon Kendra, or Faiss—whichever you prefer and can justify). • Spin up Claude Sonnet 3.5 in AWS and expose it through a simple web front end or an API endpoint; no voice features are required, text chat only. • Connect the model to the vector store so that retrieval-augmented generation powers the responses. The chatbot’s role is strictly Interact...
...Sonnet 3.5 deployed in my AWS account (EC2 or an equivalent managed service is fine) and wired up to those embeddings so that users can chat in plain text and receive interactive language-learning guidance based on the content. Here is the workflow I have in mind: • Generate 384-dimensional embeddings for the entire document, verify their quality, and load them into a persistent vector store (Pinecone, Amazon Kendra, or Faiss—whichever you prefer and can justify). • Spin up Claude Sonnet 3.5 in AWS and expose it through a simple web front end or an API endpoint; no voice features are required, text chat only. • Connect the model to the vector store so that retrieval-augmented generation powers the responses. The chatbot’s role is strictly Interact...
...Infrastructure & Security – Deploy on AWS/GCP/Azure with Docker and Kubernetes, implement secure authentication (JWT/OAuth), and ensure data privacy and system resilience. You Have: • Proven expertise building LLM-powered systems and agentic AI applications • Hands-on experience with LangChain, AutoGen, or CrewAI • Strong Python backend development skills • Proficiency with vector databases (FAISS, Pinecone, Weaviate) • Cloud deployment and scalable architecture experience • Familiarity with OpenAI, Anthropic, Hugging Face APIs Nice to Have: Multi-agent collaboration systems, AI evaluation/monitoring, production AI system experience This role combines full-stack development with frontier AI—ideal for engineers passionate about bui...
...mode: detects emergency keywords across all 6 languages and instantly switches to direct, no-emoji, no-slang response mode • WhatsApp channel working end-to-end (Meta Cloud API or Twilio sandbox) • Simple web chat fallback UI if WhatsApp setup takes too long --- TECH STACK EXPECTED • LLM: Claude API (Anthropic) or OpenAI GPT-4o or Equivalent • RAG: pgvector or lightweight vector store (Chroma, Pinecone free tier) • WhatsApp: Meta Cloud API or Twilio sandbox • Backend: Node.js / NestJS or Python FastAPI • Demo KB: we provide PDFs, Word files, and images for at least 10 destinations/operators --- TIMELINE Strict 48-hour delivery from project start. This is a demo, not production — clean code matters but speed is the priority. --- DEL...
...without touching code. – User accounts with basic matter tracking, document uploads and encrypted storage. – Deployment to my existing domains with staging and production environments. Stack guidance I’m comfortable with React or on the front end and Node, Django or similar on the back, so long as you can justify your choice. For AI, feel free to pull in OpenAI / GPT-4, LangChain, Pinecone (or an equivalent vector store). You just need to justify your choice and it's fine if the team is okay with it. Acceptance criteria (final hand-off) 1. Two responsive websites live on our servers, SSL enabled. 2. AI chatbot answers our seeded questions with <3 sec average latency and 95 % accuracy on a test set we’ll supply. 3. Admin dashboard dem...
...should use RAG to answer questions based on the knowledge base. The developer should be familiar with: * Embeddings * Vector databases * Semantic search * Document chunking * Retrieval pipelines * Prompting with retrieved context * Reducing hallucinations * Handling “I don’t know” cases when the answer is not in the knowledge base Possible vector database options include: * Qdrant * Weaviate * Pinecone * Chroma * PostgreSQL with pgvector * Another recommended option The final solution should ensure that the chatbot only answers based on the available knowledge base whenever possible. ## Expected Deliverables The selected freelancer will be expected to deliver: * A working Hexabot-based WhatsApp chatbot. * A configured WhatsApp integration. * A Wiki-based...
...Google Sheets, Airtable or Supabase * Data normalization Deliverables 1. Fix Help Scout attachment extraction 2. Download and process quotation PDFs automatically 3. Extract structured quote information using AI 4. Store extracted data in a database (Google Sheets initially) 5. Document the workflow 6. Provide recommendations for scaling to a vector database (Pinecone or Supabase) Bonus Experience with: * RAG systems * Pinecone * Supabase Vector Search * AI-powered quote recommendation systems End Goal I want an AI assistant that can receive inquiries such as: "Ég þarf 50 boli með logo" (in Icelandic meaning: I need 50 t-shirts) and instantly retrieve similar historical quotations, showing: * closest quantity below * closest quantity ...
Title: AI Multilingual Search Platform Developer Needed (Mobile App + Website) Description: I am l...PAN/tax * company registration * municipality procedures * police procedures * required documents * official processes Technical Requirements: * OpenAI API integration * RAG AI architecture * Vector database integration * OCR for scanned documents * Nepali Unicode support * Scalable backend architecture Preferred Stack: * Flutter * React * Python FastAPI or Node.js * PostgreSQL * pgvector/Pinecone Requirements: * Previous AI/RAG experience * Full source code delivery * Clean scalable architecture * Deployment support * Good UI/UX Please send: * Similar projects completed * Estimated budget * Estimated timeline * Team size * Recommended architecture * Monthly API/server cost...
...chatbots, pre-built integrations (Intercom, HubSpot, Xero) powered by their own knowledge base. We are model-agnostic by design. REQUIRED: BOTH TIERS This role requires genuine production experience across both tiers. Please only apply if you can honestly demonstrate both. TIER 1 — SMB Delivery: · n8n (workflow orchestration) · Notion (knowledge base and 2nd Brain) · OpenRouter (model routing) · Pinecone / Supabase pgvector (vector DB) · RAG pipelines — chunking, embedding, retrieval · Slack bot deployment · DigitalOcean (hosting and snapshot deployments) · Pre-built AI agent integrations (Intercom, HubSpot, etc.) TIER 2 — Enterprise Delivery: · LangChain / LlamaIndex (Python) · Azure Open...
...Sonnet 3.5 deployed in my AWS account (EC2 or an equivalent managed service is fine) and wired up to those embeddings so that users can chat in plain text and receive interactive language-learning guidance based on the content. Here is the workflow I have in mind: • Generate 384-dimensional embeddings for the entire document, verify their quality, and load them into a persistent vector store (Pinecone, Amazon Kendra, or Faiss—whichever you prefer and can justify). • Spin up Claude Sonnet 3.5 in AWS and expose it through a simple web front end or an API endpoint; no voice features are required, text chat only. • Connect the model to the vector store so that retrieval-augmented generation powers the responses. The chatbot’s role is strictly Interact...
...Requirements We require a dashboard/interface for our team to interact with the AI and manage the generated assets. The required feature modules include: 1. Data Ingestion & RAG (Retrieval-Augmented Generation) Ability to upload raw PDFs and course overviews. System must accurately parse text, tables, and basic diagrams to use as context for content generation. Suggested tools: LlamaParse, Pinecone/Milvus, OpenAI models. 2. LLM Orchestration & Script Generation AI generation of highly structured course outlines, lecture scripts, and quiz questions based strictly on the uploaded source materials. Human-in-the-loop interface: The content creator must be able to review, edit, and approve all generated text scripts before they are sent to expensive media APIs (like vid...
...a plain-language instruction from the chat UI, break it into a basic sequence of sub-tasks, and then execute each step without additional prompts. Typical actions include: opening pages through Playwright or Selenium, reading or writing local files, running terminal commands, and calling external REST APIs. Throughout the run it should store and retrieve context via a vector database (Chroma, Pinecone, or FAISS) so conversations remain coherent and previously gathered knowledge is reusable. Architecture guidelines • Language: Python • Framework: FastAPI or Flask (whichever you’re faster with) • Orchestration: LangChain or LangGraph for tool routing and memory • Containerisation: Docker, with an easy “docker compose up” to launch ev...
...never notice a hand-off to a human. Here is what I need you to do: • Design and implement the core large-language-model pipeline (GPT-4, Claude, or another strong model of your choice). • Integrate retrieval-augmented generation so the bot can pull from my existing knowledge base and keep answers grounded. • Orchestrate the prompts, embeddings, and vector search (LangChain, LlamaIndex, Pinecone or similar) for speed and reliability. • Wrap the model in a clean, well-documented API that I can drop into a web or mobile front end. *Cost optimization by combining different models Acceptance criteria – The chat responds in under two seconds for 95 % of queries. – Hallucination rate is demonstrably below 3 % on a held-out test set we w...
...relevant article link on our site (or an authoritative external document source if nothing in our library matches). We are open to whichever stack you prefer—OpenAI / ChatGPT, DeepAI, Perplexity, Anthropic, or a comparable LLM—provided you can prove hands-on experience building RAG workflows. Cost-effectiveness matters, so factor in token usage, caching, or lightweight vector stores such as FAISS, Pinecone, or similar to keep the running costs low. Key points you’ll handle • Connect the LLM to a vector index of our existing blog archive (about 1 000 posts) • Build or adapt a WordPress plugin/widget that captures the user prompt, calls the model, retrieves context, and returns the summary + link output in a friendly chat UI • Train or fine-...
...workflows using LangGraph, CrewAI, or similar frameworks • Develop RAG pipelines for university data retrieval • Implement persistent memory for long-term student tracking • Integrate backend with FlutterFlow or React frontend • Create semester-by-semester planning logic Required Skills: • Strong Python experience • LangChain / LangGraph • OpenAI or Claude API integration • Vector databases (Pinecone, Weaviate, Supabase Vector) • RAG architecture • AI agent orchestration Bonus: • Experience with MCP (Model Context Protocol) • Prior AI planner / assistant projects Project Details: • Remote work • Budget: $1000/month • MVP timeline: 4–6 weeks To Apply: Please send: • Portfolio...
