Add real-time, live data to your RAG pipeline.
A RAG pipeline retrieves from documents you indexed in the past. Some questions need the present: today's CVEs, current software versions, live weather, latest LLM specs. Dynamic Feed is a second retriever for the facts no embedding can hold fresh.
The call
One keyless POST returns fresh facts plus a stale-or-wrong check, each with its own source, observed-at timestamp, and Ed25519 signature ready to drop into a retrieval step:
curl -s -X POST https://dynamicfeed.ai/v1/batch \
-H 'Content-Type: application/json' \
-d '{"calls":[
{"tool":"software_version","args":{"product":"python"}},
{"tool":"reality_check","args":{"claim":"the latest stable Python is 3.12"}}
]}'Sample response
{
"calls": [
{"tool": "reality_check", "args": {"claim": "the latest stable Python is 3.12"}},
{"tool": "check_vulnerability", "args": {"package": "lodash", "version": "4.17.10", "ecosystem": "npm"}},
{"tool": "ai_models", "args": {}}
]
}Why live data
RAG grounds answers in retrieved text, but your index is frozen at ingest time. For time-sensitive questions, retrieval over stale chunks produces confident, outdated answers. Wire Dynamic Feed in as a live retriever: at query time, call the tool that matches the question (a version, a CVE, a forecast) and feed the result into the context alongside your vector hits. Every datapoint carries its source, an observed-at timestamp, and an Ed25519 signature so you can store the verifiable record next to the chunk in your trace. The signature establishes integrity and attribution under the published key; it does not independently prove time, truth, accuracy, or safety. Note there is no live crypto, metal, or stock price conversion here; the finance tools are macro and rates (interest_rates, treasury_yields, global_macro, china_data), not market prices.
Use it for
- Add a live retrieval step so time-sensitive queries don't hit a stale index
- Ground a generated answer in a citeable fact with source and timestamp
- Run reality_check over a draft answer to catch stale or wrong claims before returning
- Store the signature and provenance next to each retrieved chunk for auditable traces
FAQ
How is this different from just re-indexing my documents more often?
Re-indexing keeps your corpus current, but some facts change faster than any ingest job and live in sources you don't own: today's CVEs, the current stable release of a package, the latest LLM specs, live weather. Dynamic Feed is a runtime retriever for exactly those. Call it per query and merge the result into the context next to your vector hits.
Do I need an API key, and does it fit my existing retriever?
No key. POST https://dynamicfeed.ai/v1/batch is keyless, or point any MCP client at https://dynamicfeed.ai/mcp (Streamable HTTP). It returns plain JSON, so you can wrap it as a custom retriever or tool in LangChain, LlamaIndex, or CrewAI and call it inside your chain.
What does the signature on each result actually prove?
It establishes integrity and attribution under the published key, so altered bytes fail verification. It does not independently prove time, truth, accuracy, or safety. Treat it as a verifiable record you can store with the chunk, not as a correctness guarantee.