MANISH AI

What an agent can do with the InferenceFlow corpus

manishklach/InferenceFlow: Prepaid asynchronous LLM inference platform: FastAPI, PostgreSQL-backed queues, deadline-aware scheduling, modular GPU workers, vLLM…

• Look up how InferenceFlow implements asynchronous, developer-tools, docker, with answers cited to file and line.
• Reuse a worked example: the commit “Add hosted mock pipeline audit and owner-encrypted evidence export” (6 files · Python).
• Answer from InferenceFlow's real Python source instead of memory (AI, prompts and agents).

• Where does InferenceFlow handle “Add hosted mock pipeline audit and owner-encrypted evidence export”?
• How does InferenceFlow implement asynchronous?
• What does scripts do in InferenceFlow?

Search the GitHub repository manishklach/InferenceFlow with XERJ (https://hub.xerj.org/corpus/manishklach--inferenceflow).
Run these in a shell, in order, and check each result before the next:
1. xerj --help  (success: it prints usage). If xerj is missing: curl -fsSL https://xerj.org/get | sh
2. git clone https://github.com/manishklach/InferenceFlow InferenceFlow && git -C InferenceFlow checkout be0b3c489b656c4e8ceea7cd6af9514e9abb8a07
3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data
4. xerj autoindex ./InferenceFlow --url http://127.0.0.1:9200 --prefix inferenceflow --progress plain
5. xerj autoindex map --url http://127.0.0.1:9200
Then answer my question from the indexed files and cite the ax_path of every hit. Query syntax: https://xerj.org/llms.txt
Licence: Apache-2.0. Keep the copyright notice and credit the authors.

corpus manishklach--inferenceflow · AI, prompts and agents
Related corpora: adalflow, ann-benchmarks, aawhan0/ModelDock, MrKyaw/Onnx_Rust_Web_Assembly_Triton_Promethus_Grafana
AI search and retrieval with XERJ · Case study: reference coding for agents