The Big Data Project

We are looking for a specialist in LLM, RAG, and prompt engineering for auditing and improving an already created AI assistant for contact center operators of a network of medical centers. This is not a development from scratch. Currently, the assistant operates in the ChatGPT environment and uses: its own skill with instructions SKILL.md; a knowledge base in Project Sources; structured Markdown files automatically generated from CSV exports of the medical information system; separate indexes of prices, performers, departments, packages, equipment, and recommended service combinations. The database contains approximately: 2,700+ medical services; 90+ packages and complexes; 300+ recommended combinations; 600+ surgical interventions; prices by various departments; performers, addresses, preparation, equipment, and other reference information.What the assistant should do Upon the operator's request, the assistant should quickly provide a verified response: whether the required service is provided; the exact code, name, and price; in which departments it is available; which doctors or other specialists perform it; how to prepare; which package or complex is more advantageous to offer; which accompanying services are advisable to suggest; the sequence of comprehensive patient registration; what cheaper or alternative options are available; for operations — separately the base price and the estimated total cost of the surgical treatment case. The assistant should not invent prices, performers, preparation, medical indications, or transfer information between similar services.Existing problems The system is already operational but requires increased search stability and response quality. In particular: the model sometimes finds the main service but misses recommended combinations; does not always extract individual fields from large Markdown files; can find the base price of an operation but miss the total cost of the surgical case; results depend on the structure and size of the files in Project Sources; indexes, source routing, and search rules need optimization; it is necessary to ensure equally high-quality responses to short, inaccurate, and conversational operator queries. For example, a simple query "cholecystectomy" should immediately return available options for the operation, codes, base prices, total treatment costs, departments, performers, and related services.Specialist tasks Conduct an audit of the current SKILL.md, the structure of the knowledge base, and the search logic. Analyze the reasons for data omission during retrieval. Propose an optimal knowledge base architecture for ChatGPT. Improve or rewrite SKILL.md. Optimize the structure of Markdown files and compact indexes. Set up mandatory searches: packages and complexes; recommended combinations; prices by departments; performers; the cost of the surgical treatment case. Check the knowledge base generator from CSV exports and improve Python scripts if necessary. Create a set of control queries and criteria for evaluating responses. Conduct testing on real contact center scenarios. Provide final documentation for further updates and system support.Expected result We expect to have a stable assistant that: responds in Ukrainian; does not miss critically important data; returns only information confirmed by the knowledge base; correctly distinguishes between services, packages, and recommended combinations; shows code, name, price, department, and performer; for operations separates the base price and total treatment cost; offers the operator a specific scenario for further patient registration; works stably after subsequent updates of CSV exports; operates relatively quickly.Requirements for the performer A specialist with practical experience is needed: ChatGPT Projects, Custom GPT, or ChatGPT Skills; LLM, RAG, retrieval, and semantic search; prompt engineering; designing knowledge bases for language models; Markdown, CSV, JSON/JSONL; Python for data processing and transformation; testing the quality of LLM responses. Experience with medical information systems, contact centers, or large service catalogs will be an advantage. We are looking for not just a prompt author, but a specialist who understands the limitations of searching in large sources, context fragmentation, and ways to build reliable indexes.What to provide in the proposal Please briefly indicate: Your experience with ChatGPT, RAG, or corporate knowledge bases. Examples of similar implemented projects. How you would approach diagnosing the omission of individual fields in large files. Estimated timelines and costs for the audit and refinement. Whether you are willing to sign a confidentiality agreement. Personal data of patients will not be transferred within this project. The final cost of the work will be agreed upon after clarifying the Technical Task between the Customer and the Performer.

Brief I need a program in Python that runs on my PC (Windows) and creates faceless videos in the format of "voiceover + changing visuals from photos and clips" — like historical documentaries on YouTube (I will attach an example separately). I input a topic → the program writes a script, voices it, selects video/photos from free archives for each piece of text, edits them together → outputs a ready MP4. For me only. No website, no users, no sales. One user — me. How it works (step by step) 1. Input. A simple window opens. I enter the topic of the video and choose a voice from the list (**the list of voices is automatically pulled from ElevenLabs via API** — available on my account). I click "Create." (Second mode: insert a ready script instead of generating.) 2. Script. The program writes a script on the topic of the specified length via LLM API (OpenAI/Anthropic, key in settings). 3. Scene breakdown. LLM divides the script into scenes and returns for each: scene text; visual type: video or photo; search query (a detailed phrase of what should be in the frame); highlight mark + importance rating 1-10 (for intro, see below). 4. Voiceover. The text is sent to ElevenLabs API with the chosen voice → audio. The video sequence is cut to match the length of the audio for each scene. 5. Video/photo selection — from free sources via API (see the list below). A specific request is formed for each source (for accuracy). If one doesn't yield results — it tries the next. 6. Verification (maximum 3 steps per scene). Step 1: the program takes the first found option (video/photo) based on the request. Step 2: sends the frame to LLM — "does it fit the scene?". If it fits → stop. Step 3 (if it doesn't fit): the program switches to searching for photos (it's easier to find photos with an exact request than videos — this guarantees relevance) and takes it with enhanced motion (zoom + pan). No more than 3 steps per scene — this saves the LLM budget and ensures that the frame is relevant. 7. Assembly via ffmpeg/moviepy: clips and photos timed to the voiceover, photos are animated with zoom (Ken Burns effect), voice on top, simple transitions. Output: MP4 1920×1080. Video sequence rules (important — the program is responsible for this) First 60 seconds — intro teaser: a montage of the most impactful clips from the entire video (scenes with the highest importance rating that contain video) under a separate introductory text from LLM ("in this video you will learn..."), frames without explanations, creating intrigue. Then a transition to the main part. Alternation: video insert at least every ~6 seconds, no many photos in a row. Video share: at least ~40% of the time — live clips, the rest — photos with zoom. Frame length: 4-6 seconds (both photos and videos). No rapid cuts, no prolonged static shots. For purely historical topics where there is no video — photos with enhanced motion (zoom + pan). Sources (all free, with API) Modern video + photos: Pexels, Pixabay. Historical/archive (public domain): Wikimedia Commons, Archive.org, Library of Congress, Europeana, NASA, Smithsonian Open Access, Flickr Commons, openverse. Each source is a separate module, easy to add new ones. Use only public domain / free licenses with the right for commercial use. No parsing of other YouTube/sites, movie clips, images "from Google".Uniqueness of selection To ensure videos do not match others: take a random clip from the top results (not the first), maintain a database of already used clips (do not repeat), optionally — light processing of the clip (crop/mirror/speed). Options (enable/disable in settings) Photos only — if enabled, the video is assembled PURELY from photos, without video clips. Each photo MUST have motion (zoom and/or pan, Ken Burns effect) — even in this mode, there should be no static "dead" frames, minimal dynamics always. If disabled — standard mode (photos + video clips alternating, as per video sequence rules). No voiceover — if enabled, the video is assembled based on the text WITHOUT generating voice: the code DOES NOT call ElevenLabs and does not overlay voice (the video sequence is selected based on the text of the scenes, timing of frames — according to rules/parameters, without reference to audio). If disabled — it automatically generates voiceover based on the text through ElevenLabs, as usual. Atmospheric overlay — if enabled, a semi-transparent layer with floating particles/dust/glowing bokeh/light fog (particle/dust/bokeh/fog overlay, screen/add mode) is applied over the entire video sequence to make the frames look alive and cinematic. When installing the program, a set of 5-8 popular overlays (particles, dust,copybokeh, fog, light cinematic "grain") is placed in a local folder — I choose the needed one from the list. Adjustable transparency/brightness of the overlay (slider 0-100%), so the effect is neithercopytoo dull nor too pronounced — I adjust the strength myself. Ideally, the overlay should also be applicable to ALREADY finished videos separatelycopy(post-processing: take a ready MP4 → choose overlay → set transparency → save), not just during assembly. Where to get overlays for packaging (free license): Pexels, Pixabay (queriescopy"particle overlay", "bokeh overlay", "dust overlay", "light leaks", "film grain"), Mixkit, Videezy. The performer selects 5-8 pieces and places them in the program folder. Subtitles (embed or separate .srt). Clip processing for uniqueness. Resolution/format, video length, video share, search depth.Technical requirements Python. Modular structure (sources and LLM — through interchangeable modules, to easily replace or add). All API keys — in the settings file, not in the code. Simple window (GUI at the discretion of the performer — Tkinter/PyQt), launched by double-clicking. README with instructions, clear logs, comments in the code.What I provide API keys (ElevenLabs, LLM, where registration is needed — I will arrange). I will pay for any fees myself. Examples of video references (I will attach) and examples of topics for tests.Acceptance (ready if) I launch → window → I enter the topic, choose the voice → "Create" → I receive a ready MP4. The video sequence matches the meaning of the text, alternating video/photos, intro teaser 60 sec, voiceover on top. Works with at least 6 free sources, with fallback between them. Frame verification through LLM: max. 3 steps per scene (found → LLM checked → if not, photo with motion as a sure bet). Uniqueness: randomization + database of used. Only legal sources. There is a README, runs from scratch.Delivery of results All source code — in open view (all files), without obfuscation + compiled working version. I can run it myself from the sources according to the instructions (README: installation, keys, launch). The code must be clean, commented, and understandable, so **any other programmer can continue working** on it if needed (not tied to the author). **All everyday work — through the interface (buttons, fields, sliders, dropdown lists), WITHOUT the need to touch the code.** All settings (topic, voice, options, overlay, folders, length, formats) are changed in the program window, not by editing files. The code in hand — only as my property and insurance, not as a way to control the program. All rights to the code after payment — mine.Disk space management (important) The program should not fill up the disk. Implement: After assembling the video, all intermediate files (downloaded clips, temporary pieces, audio cuts) are automatically deleted — only the ready MP4 remains on the disk. Cache limit (parameter in settings, e.g., 5 GB): when exceeded, old downloaded files are automatically deleted (starting with the oldest). I set the folder for ready videos and for temporary files in the settings. Show how much space is occupied, and a button "clear cache" manually.Please specify in your response Examples of similar works (ffmpeg/moviepy, working with stock/archive APIs, ElevenLabs/LLM). Proposal for GUI. Does the solution use a database (which and why) — or are local files sufficient. Timeline and cost.

We are developing a real-time integration with the external service Trainer. We send structured snapshots of the state, receive recommendations, and display them in a pop-up bubble. The task is stable and complete data transmission for the correct operation of the Trainer. We are looking for a developer for the chain: data processing → HTTP communication → overlay. We need people with the following skills Python is good, Java basics, API HTTP/JSON-APIs The project is 90% ready but there are some inconsistencies

A local Python script needs to be developed to automatically fill a Google Sheet with data from the company's internal service. Main logic: 1. Connect to the Google Sheet. 2. Find rows where the ID is filled but two target values are missing. 3. Form a link based on the template: https://internal-service.example/item/{ID} 4. Retrieve the two values (via API, if it exists, otherwise via Playwright). 5. Write the values back to the Google Sheet. 6. Mark the row as processed. 7. Continue processing the next rows. Requirements: • Python • Google Sheets API • Priority to use the official API • If no API — Playwright • No OCR, screen recognition, or mouse coordinates • Confidential data must not be logged • Configuration via .env • Test mode (without writing to the sheet) • Do not process already filled rows • Batch write changes to Google Sheets • Proper error handling and retries It is necessary to provide: - source code; - requirements.txt; - example .env.example; - installation instructions; - running instructions; - brief architecture description. Before starting implementation, please: 1. Suggest an architecture. 2. List the necessary accesses. 3. Ask clarifying questions. 4. Indicate the cost, deadlines, and estimated number of hours.

As part of enhancing the cybersecurity level of our infrastructure, we need to abandon the practice of storing "eternal" and static API keys, passwords, and integration tokens in the configuration files (.env, appsettings.json, config.yaml) of our microservices. Business Goal: Create a single secure storage point for confidential data (secrets) with a mechanism for their automatic updating (rotation) in external systems on a schedule. Our other services will request current tokens "on the fly" via API, which will minimize damage in case of compromise of any system component.Security Model and Encryption (Crypto Core) No secret should be stored in plaintext in the database. Upon application startup, a Master Key is passed to the environment variables. If the key is missing or has an invalid length, the service should fail at the initialization stage with a clear error in the logs. Each secret is encrypted before being written to the database using this Master Key. Upon request, it is decrypted in memory and returned in the response body.Audit Logging (Audit Trail) Any action with secrets (creation, reading by the service, successful or unsuccessful rotation) must be recorded in a separate log file audit.log (or a separate table in the database). Strict Taboo: It is strictly prohibited to record the actual values of secrets in the audit log (neither in plaintext nor in encrypted form).