tdfs4ds.agentops.package¶
Render, and optionally write, the AgentOps deployment package.
Follows the repo's generate/write idiom (scheduling/script.py,
migration/script.py): :func:generate_agentops_package returns the artifact
as text so it can be read before anything touches disk;
:func:write_agentops_package is the side-effecting variant.
Two things the AgentOps tutorial gets wrong or leaves implicit, handled here:
agent_runtime/ is the platform's, not ours. It arrives by scaffolding a
template from demo_agents/ and must be present in the destination before the
zip is built — a package without it registers and then fails at pod start. We
write alongside an existing scaffold rather than creating one.
The tutorial's own zip step says it excludes __pycache__ and .pyc, but
its os.walk applies no filter at all. Ours actually filters — stale bytecode
compiled against a different interpreter is a genuinely confusing failure inside
a container.
module-attribute
¶
module-attribute
¶
module-attribute
¶
DEFAULT_DOCKER_IMAGE = 'awsedge01.labsteradata.net:443/tdproduct-docker-stable/avmo/vmo-python-base:3.11.5'
Current value of attr: an override first, else module config or env.
Pass-through variables are never module attributes — they are read straight
from os.environ by the serve layer and reconnect — so they have to be
looked up there or they would silently export as unset.
Split the running configuration into the AgentOps halves.
Parameters¶
backend : str, optional
Vector store backend to classify against. Defaults to the running
tdfs4ds.VECTOR_STORE_BACKEND.
overrides : dict, optional
Values that win over the session, keyed by bare attribute name — see
:func:store_overrides. Used to state the deployment's feature store
explicitly instead of inheriting whatever the session happened to have.
Returns¶
dict
{"config": {ENV_NAME: value}, "runtime": {ENV_NAME: value},
"dropped": [ENV_NAME], "missing_runtime": [ENV_NAME]} where every key
is the full TDFS4DS_-prefixed environment variable name.
``missing_runtime`` names the **required** runtime variables that are
not set in this session — the deployment must supply them, and the
caller surfaces them rather than letting the pod discover it. An unset
*optional* runtime variable is simply absent, not a finding.
Build the :data:STORE_ATTRS override mapping from explicit arguments.
Only arguments actually given appear, so a caller that names just the database does not blank out the rest.
Render config.yaml, listing runtime variables as commented placeholders.
The commented entries matter: without them the file silently omits half the configuration, and whoever reads it later cannot tell what the deployment is expected to supply.
generate_agentops_package(*, mcp_enabled=False, backend=None, auth='service', database=None, database_view=None, databases_readonly=None, data_domain=None, td_host=None)
Render the deployment package.
Model, embedding and Vector Store settings are read from the running session — you have proved those work by asking the agent questions, and reproducing them is the point. The feature store is stated, not inherited: it is a deployment target, and a pod that silently picks up whatever the session had is impossible to review at the call site.
Nothing is written and nothing is sent anywhere.
Parameters¶
mcp_enabled : bool, default False
Baked into the generated agent as the mcp_enabled argument to
:func:tdfs4ds.consumer_agent. Off by default; True is
Enterprise-edition only and adds per-caller MCP authentication.
backend : str, optional
Vector store backend to classify against. Defaults to the running
tdfs4ds.VECTOR_STORE_BACKEND.
auth : {'service', 'jwt'}, default 'service'
Credential model for the generated agent.
database, database_view, data_domain : str, optional
The feature store the deployed agent serves. Each falls back to the
running session when omitted; :func:tdfs4ds.deploy_to_agentops
requires database so a deployment always names its target.
databases_readonly : list of str, optional
Additional stores attached read-only (cross-store federation).
td_host : str, optional
The database host the POD connects to, when it differs from the
session's TDFS4DS_TD_HOST (a pod that cannot resolve the name gets
its address). Only runtime_config changes; the session is untouched.
Returns¶
dict
{"files": {name: text}, "runtime_config": {...}, "dropped": [...],
"missing_runtime": [...], "version": str, "auth": str, "store": {...}}.
``runtime_config`` goes in the deployment payload, **not** on disk.
``store`` reports the resolved feature store, so "which database will
this read?" is answerable before anything is deployed.
The feature store the generated package will serve.
Reported by the preflight and by dry_run so the answer to "which
database will the pod read?" is visible before anything is deployed rather
than inferred from a failure afterwards.
tdfs4ds-agent-<database>-<8 hex>, lower case, [a-z0-9-] only.
The database is the part a reader of the registry needs; the suffix keeps
two deployments of the same store apart. A name with no usable database
falls back to the historical tdfs4ds-consumer-agent-<uuid>.
The registry description of an agent serving store (pure, ASCII).
States what does not change after the deploy: the feature store (and its view database and read-only stores), its data domains, what the agent answers, the MCP and logon choices and the tdfs4ds version. No counts: the store keeps growing after the deploy, and a count would go stale.
write_agentops_package(dest=None, *, mcp_enabled=False, backend=None, auth='service', database=None, database_view=None, databases_readonly=None, data_domain=None, agent_runtime='auto', make_zip=True, td_host=None)
Write the package into dest and (by default) build the deployable zip.
Parameters¶
dest : str, optional
The scaffolded agent directory — the one holding agent_runtime/.
Defaults to {tdfs4ds.AGENTOPS_PACKAGE_DIR}. Created when absent,
with a warning: a package with no agent_runtime/ registers fine and
then fails at pod start, so the gap is worth naming early.
make_zip : bool, default True
Also build <dest>.zip next to the directory — the artifact uploaded
to POST /agent_definitions.
Returns¶
dict
The :func:generate_agentops_package result plus "dest",
"zip_path" and "written" (the file names written).
Make sure dest/agent_runtime exists, and report where it came from.
A platform-supplied copy always wins. Its host may depend on details beyond the two public symbols, and only the platform's own file can be authoritative about that - so 'auto' never overwrites one.
Returns {"source": 'platform'|'generated'|'missing', "path": str}.
The first platform agent_runtime found on this machine, or None.
True when path holds the agent_runtime tdfs4ds writes itself.
Stable hash of a directory's .py contents, for provenance.
Zip agent_dir the way AgentOps expects: paths relative to the dir.
deployment_payload(agent_definition_id, runtime_config, *, deployment_name=None, environment='dev', docker_image=None)
Build the POST /agents payload with the constraints already applied.
The result is sent to the API verbatim, so it carries only fields the API defines — no generation timestamp or other bookkeeping that a strict endpoint would reject.
The tutorial's payload is sized for a toy agent. Three fields differ here, and each is load-bearing rather than a preference:
resources
512Mi does not hold teradataml + langchain + pandas + networkx.
replicas: 1
Conversation memory is an in-process MemorySaver; a second replica
answers with no history.
enable_memory: False
The platform's PostgreSQL checkpointer cannot be injected into the
tdfs4ds graph, so enabling it would provision a database nothing reads.