Phoenix - Your AI AssistantIntegrate OpenAI/Claude/local LLMs into your app (API-ready)
Delivery in
5 days
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What you get with this Offer
I will plug AI into your existing product by integrating OpenAI/Claude or a local LLM and wiring it to your data and workflows.
Deliverables (fixed scope):
- LLM provider integration (OpenAI, Claude, or local) with secure key handling
- 1 data source connection (DB/API/files) to ground responses (RAG/basic retrieval)
- Function calling/tools: up to 3 functions (e.g., search, CRUD action, ticket creation)
- Caching and rate limiting to control cost and stability
- Observability: structured logs + basic tracing/metrics hooks for prompts, latency, errors
- Deployment to 1 target environment (your cloud/VPS/container platform)
- Quick start notes: how to run, configure env vars, and test
- 7 days bugfix support after delivery
Notes:
- Works with your existing backend (Node/Python/etc.).
- Any extra data sources, advanced agent workflows, UI work, or fine-tuning can be added as a separate offer.
Deliverables (fixed scope):
- LLM provider integration (OpenAI, Claude, or local) with secure key handling
- 1 data source connection (DB/API/files) to ground responses (RAG/basic retrieval)
- Function calling/tools: up to 3 functions (e.g., search, CRUD action, ticket creation)
- Caching and rate limiting to control cost and stability
- Observability: structured logs + basic tracing/metrics hooks for prompts, latency, errors
- Deployment to 1 target environment (your cloud/VPS/container platform)
- Quick start notes: how to run, configure env vars, and test
- 7 days bugfix support after delivery
Notes:
- Works with your existing backend (Node/Python/etc.).
- Any extra data sources, advanced agent workflows, UI work, or fine-tuning can be added as a separate offer.
What the Freelancer needs to start the work
Please provide:
1) Your codebase access method (repo/archive) and tech stack (Node/Python/etc.)
2) Which model/provider(s) you want (OpenAI/Claude/local) and API credentials (or you will add them)
3) Target use case/user flow + success criteria
4) Data source details (choose 1): DB connection string, API docs/keys, or sample files
5) List of up to 3 functions/tools you want the LLM to call + expected inputs/outputs
6) Preferred cache/rate limit approach (or I can propose defaults)
7) Deployment target (AWS/GCP/Azure/VPS/Docker) + access method
8) Any compliance constraints (PII redaction, logging limits, regions)
9) Test accounts/sample data to validate end-to-end