xmemory-ai 0.1.0

Python client library for the Xmemory API

xmemory

Python client library for the Xmemory API.

Quick start

from xmemory import xmemory_instance

mem = xmemory_instance(
    url="https://api.xmemory.ai",   # or set XMEM_API_URL env var
    instance_id="<your-instance-id>",
    token="<your-token>",           # or set XMEM_AUTH_TOKEN env var
)

mem.write("Alice is a software engineer who loves Python.")
result = mem.read("What does Alice do?")
print(result.reader_result)

Configuration

Parameter	Env var	Default	Description
url	XMEM_API_URL	https://api.xmemory.ai	Base URL of the Xmemory API
instance_id	—	None	Instance to read/write against
token	XMEM_AUTH_TOKEN	None	Bearer token for authentication
timeout	—	60	Default request timeout in seconds

Methods

check_health() → None

Verify that the Xmemory API is reachable. Raises XmemoryHealthCheckError on failure. Unlike the constructor, this method performs the actual connectivity check, so you can call it whenever you need to confirm the service is alive.

try:
    mem.check_health()
    print("API is up")
except XmemoryHealthCheckError as e:
    print(f"API is unreachable: {e}")

generate_schema(schema_description, *, old_schema_yml=None, timeout=None) → GenerateSchemaResponse

Ask the API to generate a YML schema from a plain-text description.

resp = mem.generate_schema("People with name, role, and location.")
print(resp.generated_schema)

Optionally pass old_schema_yml to refine an existing schema.

create_instance(schema_text, schema_type, *, timeout=None) → bool

Create a new instance with the given schema. On success, the new instance_id is saved automatically and used for subsequent calls.

write(text, *, timeout=None, extract_write_id=None) → WriteResponse

Extract structured objects from text and store them in the instance.

resp = mem.write("Bob joined the team on Monday as a designer.")
print(resp.status)          # "ok" or "error"
print(resp.cleaned_objects) # written objects

read(query, *, timeout=None, read_id=None) → ReadResponse

Query the instance and get a natural-language answer.

resp = mem.read("Who is on the team?")
print(resp.reader_result)

extract(text, *, timeout=None, extract_write_id=None) → ExtractionResponse

Extract structured objects from text without writing them to the instance.

resp = mem.extract("Carol is a manager based in Berlin.")
print(resp.objects_extracted)

from xmemory import SchemaType

ok = mem.create_instance(schema_yml, SchemaType.YML)
ok = mem.create_instance(schema_json, SchemaType.JSON)

update_schema(schema_text, schema_type, *, timeout=None) → bool

Update the schema of the current instance.

ok = mem.update_schema(new_schema_yml, SchemaType.YML)

Error handling

All errors raise XmemoryAPIError (or its subclass XmemoryHealthCheckError for connectivity failures).

from xmemory import XmemoryAPIError, XmemoryHealthCheckError, xmemory_instance

mem = xmemory_instance(url="http://localhost:8000", instance_id="abc")

try:
    mem.check_health()
except XmemoryHealthCheckError as e:
    print(f"Could not reach the API: {e}")

try:
    resp = mem.read("something")
except XmemoryAPIError as e:
    print(f"API error (HTTP {e.status}): {e}")

Package publishing to pip

python -m pip install --upgrade build twine
python -m build

#test with test.pypi.org (separate account and API key required)
python -m twine upload --repository testpypi dist/*

#publish the real version when ready
python -m twine upload dist/*

#test the package
pip install xmemory