pax_global_header 0000666 0000000 0000000 00000000064 15011667241 0014515 g ustar 00root root 0000000 0000000 52 comment=60fee63d830cf213a3591cd0218abf46f72ff9f3
humanloop-python-0.8.40b1/ 0000775 0000000 0000000 00000000000 15011667241 0015352 5 ustar 00root root 0000000 0000000 humanloop-python-0.8.40b1/.fernignore 0000664 0000000 0000000 00000000706 15011667241 0017514 0 ustar 00root root 0000000 0000000 # Specify files that shouldn't be modified by Fern
## Custom code
src/humanloop/evals
src/humanloop/prompt_utils.py
src/humanloop/path_utils.py
src/humanloop/client.py
src/humanloop/overload.py
src/humanloop/context.py
src/humanloop/error.py
mypy.ini
README.md
src/humanloop/decorators
src/humanloop/otel
src/humanloop/sync
src/humanloop/cli
pytest.ini
## Tests
tests/custom
tests/__init__.py
## CI
.github/workflows/ci.yml
## Config
.gitignore
humanloop-python-0.8.40b1/.github/ 0000775 0000000 0000000 00000000000 15011667241 0016712 5 ustar 00root root 0000000 0000000 humanloop-python-0.8.40b1/.github/workflows/ 0000775 0000000 0000000 00000000000 15011667241 0020747 5 ustar 00root root 0000000 0000000 humanloop-python-0.8.40b1/.github/workflows/ci.yml 0000664 0000000 0000000 00000004206 15011667241 0022067 0 ustar 00root root 0000000 0000000 name: ci
on: [push]
jobs:
compile:
runs-on: ubuntu-latest
strategy:
matrix:
python-version: ["3.9", "3.10", "3.11"]
steps:
- name: Checkout repo
uses: actions/checkout@v3
- name: Set up python
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Bootstrap poetry
run: |
curl -sSL https://install.python-poetry.org | python - -y --version 1.5.1
- name: Install dependencies
run: poetry install
- name: Compile
run: poetry run mypy .
test:
runs-on: ubuntu-latest
strategy:
matrix:
python-version: ["3.9", "3.10", "3.11"]
steps:
- name: Checkout repo
uses: actions/checkout@v3
- name: Set up python
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Bootstrap poetry
run: |
curl -sSL https://install.python-poetry.org | python - -y --version 1.5.1
- name: Install dependencies
run: poetry install
- name: Test
run: poetry run pytest -rP .
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
HUMANLOOP_API_KEY: ${{ secrets.HUMANLOOP_API_KEY }}
publish:
needs: [compile, test]
if: github.event_name == 'push' && contains(github.ref, 'refs/tags/')
runs-on: ubuntu-latest
steps:
- name: Checkout repo
uses: actions/checkout@v3
- name: Set up python
uses: actions/setup-python@v4
with:
python-version: 3.9
- name: Bootstrap poetry
run: |
curl -sSL https://install.python-poetry.org | python - -y --version 1.5.1
- name: Install dependencies
run: poetry install
- name: Publish to pypi
run: |
poetry config repositories.remote https://upload.pypi.org/legacy/
poetry --no-interaction -v publish --build --repository remote --username "$PYPI_USERNAME" --password "$PYPI_PASSWORD"
env:
PYPI_USERNAME: ${{ secrets.PYPI_USERNAME }}
PYPI_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
humanloop-python-0.8.40b1/.gitignore 0000664 0000000 0000000 00000000162 15011667241 0017341 0 ustar 00root root 0000000 0000000 dist/
.mypy_cache/
__pycache__/
poetry.toml
.ruff_cache/
.vscode
.env
tests/assets/*.jsonl
tests/assets/*.parquet
humanloop-python-0.8.40b1/README.md 0000664 0000000 0000000 00000022477 15011667241 0016645 0 ustar 00root root 0000000 0000000 # Humanloop Python Library
[](https://buildwithfern.com?utm_source=github&utm_medium=github&utm_campaign=readme&utm_source=https%3A%2F%2Fgithub.com%2Fhumanloop%2Fhumanloop-python)
[](https://pypi.python.org/pypi/humanloop)
The Humanloop Python library provides convenient access to the Humanloop API from Python.
## Installation
```sh
pip install humanloop
```
## Reference
A full reference for this library is available [here](https://github.com/humanloop/humanloop-python/blob/master/reference.md).
## Usage
Instantiate and use the client with the following:
```python
import datetime
from humanloop import Humanloop
client = Humanloop(
api_key="YOUR_API_KEY",
)
client.prompts.log(
path="persona",
prompt={
"model": "gpt-4",
"template": [
{
"role": "system",
"content": "You are {{person}}. Answer questions as this person. Do not break character.",
}
],
},
messages=[{"role": "user", "content": "What really happened at Roswell?"}],
inputs={"person": "Trump"},
created_at=datetime.datetime.fromisoformat(
"2024-07-19 00:29:35.178000+00:00",
),
provider_latency=6.5931549072265625,
output_message={
"content": "Well, you know, there is so much secrecy involved in government, folks, it's unbelievable. They don't want to tell you everything. They don't tell me everything! But about Roswell, it’s a very popular question. I know, I just know, that something very, very peculiar happened there. Was it a weather balloon? Maybe. Was it something extraterrestrial? Could be. I'd love to go down and open up all the classified documents, believe me, I would. But they don't let that happen. The Deep State, folks, the Deep State. They’re unbelievable. They want to keep everything a secret. But whatever the truth is, I can tell you this: it’s something big, very very big. Tremendous, in fact.",
"role": "assistant",
},
prompt_tokens=100,
output_tokens=220,
prompt_cost=1e-05,
output_cost=0.0002,
finish_reason="stop",
)
```
## Async Client
The SDK also exports an `async` client so that you can make non-blocking calls to our API.
```python
import asyncio
import datetime
from humanloop import AsyncHumanloop
client = AsyncHumanloop(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.prompts.log(
path="persona",
prompt={
"model": "gpt-4",
"template": [
{
"role": "system",
"content": "You are {{person}}. Answer questions as this person. Do not break character.",
}
],
},
messages=[
{"role": "user", "content": "What really happened at Roswell?"}
],
inputs={"person": "Trump"},
created_at=datetime.datetime.fromisoformat(
"2024-07-19 00:29:35.178000+00:00",
),
provider_latency=6.5931549072265625,
output_message={
"content": "Well, you know, there is so much secrecy involved in government, folks, it's unbelievable. They don't want to tell you everything. They don't tell me everything! But about Roswell, it’s a very popular question. I know, I just know, that something very, very peculiar happened there. Was it a weather balloon? Maybe. Was it something extraterrestrial? Could be. I'd love to go down and open up all the classified documents, believe me, I would. But they don't let that happen. The Deep State, folks, the Deep State. They’re unbelievable. They want to keep everything a secret. But whatever the truth is, I can tell you this: it’s something big, very very big. Tremendous, in fact.",
"role": "assistant",
},
prompt_tokens=100,
output_tokens=220,
prompt_cost=1e-05,
output_cost=0.0002,
finish_reason="stop",
)
asyncio.run(main())
```
## Exception Handling
When the API returns a non-success status code (4xx or 5xx response), a subclass of the following error
will be thrown.
```python
from humanloop.core.api_error import ApiError
try:
client.prompts.log(...)
except ApiError as e:
print(e.status_code)
print(e.body)
```
## Streaming
The SDK supports streaming responses, as well, the response will be a generator that you can loop over.
```python
import datetime
from humanloop import Humanloop
client = Humanloop(
api_key="YOUR_API_KEY",
)
response = client.prompts.call_stream(
version_id="string",
environment="string",
path="string",
id="string",
messages=[
{
"content": "string",
"name": "string",
"tool_call_id": "string",
"role": "user",
"tool_calls": [
{
"id": "string",
"type": "function",
"function": {"name": "string"},
}
],
}
],
prompt={"model": "string"},
inputs={"string": {"key": "value"}},
source="string",
metadata={"string": {"key": "value"}},
start_time=datetime.datetime.fromisoformat(
"2024-01-15 09:30:00+00:00",
),
end_time=datetime.datetime.fromisoformat(
"2024-01-15 09:30:00+00:00",
),
source_datapoint_id="string",
trace_parent_id="string",
user="string",
prompts_call_stream_request_environment="string",
save=True,
provider_api_keys={
"openai": "string",
"ai_21": "string",
"mock": "string",
"anthropic": "string",
"bedrock": "string",
"cohere": "string",
"openai_azure": "string",
"openai_azure_endpoint": "string",
},
num_samples=1,
return_inputs=True,
logprobs=1,
suffix="string",
)
for chunk in response:
yield chunk
```
## Local File Development
Humanloop allows you to clone files from your Humanloop workspace to your local filesystem and use them in your code.
### Syncing Files with the CLI
```bash
# Pull all files
humanloop pull
# Pull from a directory
humanloop pull --path="examples/chat"
# Pull a specific file
humanloop pull --path="examples/chat/basic.prompt"
# Pull versions deployed to specific environment
humanloop pull --environment="production"
```
### Using Local Files in the SDK
To use local files in your code:
```python
# Enable local file support
client = Humanloop(
api_key="YOUR_API_KEY",
use_local_files=True
)
# Use a local prompt file
response = client.prompts.call(
path="examples/chat/basic",
inputs={"query": "Hello world"}
)
```
For detailed instructions on syncing, see our [Guide to Syncing and Using Local Files](https://humanloop.com/docs/v5/guides/prompts/syncing-files).
For information about the `.prompt` and `.agent` file formats, see our [File Format Reference](https://humanloop.com/docs/v5/reference/prompt-and-agent-files).
## Pagination
Paginated requests will return a `SyncPager` or `AsyncPager`, which can be used as generators for the underlying object.
```python
from humanloop import Humanloop
client = Humanloop(
api_key="YOUR_API_KEY",
)
response = client.prompts.list(
size=1,
)
for item in response:
yield item
# alternatively, you can paginate page-by-page
for page in response.iter_pages():
yield page
```
## Advanced
### Retries
The SDK is instrumented with automatic retries with exponential backoff. A request will be retried as long
as the request is deemed retriable and the number of retry attempts has not grown larger than the configured
retry limit (default: 2).
A request is deemed retriable when any of the following HTTP status codes is returned:
- [408](https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/408) (Timeout)
- [429](https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/429) (Too Many Requests)
- [5XX](https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/500) (Internal Server Errors)
Use the `max_retries` request option to configure this behavior.
```python
client.prompts.log(..., request_options={
"max_retries": 1
})
```
### Timeouts
The SDK defaults to a 60 second timeout. You can configure this with a timeout option at the client or request level.
```python
from humanloop import Humanloop
client = Humanloop(
...,
timeout=20.0,
)
# Override timeout for a specific method
client.prompts.log(..., request_options={
"timeout_in_seconds": 1
})
```
### Custom Client
You can override the `httpx` client to customize it for your use-case. Some common use-cases include support for proxies
and transports.
```python
import httpx
from humanloop import Humanloop
client = Humanloop(
...,
httpx_client=httpx.Client(
proxies="http://my.test.proxy.example.com",
transport=httpx.HTTPTransport(local_address="0.0.0.0"),
),
)
```
## Contributing
While we value open-source contributions to this SDK, this library is generated programmatically.
Additions made directly to this library would have to be moved over to our generation code,
otherwise they would be overwritten upon the next generated release. Feel free to open a PR as
a proof of concept, but know that we will not be able to merge it as-is. We suggest opening
an issue first to discuss with us!
On the other hand, contributions to the README are always very welcome!
humanloop-python-0.8.40b1/mypy.ini 0000664 0000000 0000000 00000000374 15011667241 0017055 0 ustar 00root root 0000000 0000000 [mypy]
exclude = (?x)(
^src/humanloop/eval_utils/.*\.py$ # all files in "eval_utils" folder
| ^src/humanloop/prompt_utils\.py$ # single "prompt_utils.py" file
)
# Add ignore for referencing package
[mypy-referencing.*]
ignore_errors = True humanloop-python-0.8.40b1/poetry.lock 0000664 0000000 0000000 00000626571 15011667241 0017567 0 ustar 00root root 0000000 0000000 # This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand.
[[package]]
name = "annotated-types"
version = "0.7.0"
description = "Reusable constraint types to use with typing.Annotated"
optional = false
python-versions = ">=3.8"
groups = ["main", "dev"]
files = [
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[[package]]
name = "anthropic"
version = "0.51.0"
description = "The official Python library for the anthropic API"
optional = false
python-versions = ">=3.8"
groups = ["main", "dev"]
files = [
{file = "anthropic-0.51.0-py3-none-any.whl", hash = "sha256:b8b47d482c9aa1f81b923555cebb687c2730309a20d01be554730c8302e0f62a"},
{file = "anthropic-0.51.0.tar.gz", hash = "sha256:6f824451277992af079554430d5b2c8ff5bc059cc2c968cdc3f06824437da201"},
]
[package.dependencies]
anyio = ">=3.5.0,<5"
distro = ">=1.7.0,<2"
httpx = ">=0.25.0,<1"
jiter = ">=0.4.0,<1"
pydantic = ">=1.9.0,<3"
sniffio = "*"
typing-extensions = ">=4.10,<5"
[package.extras]
bedrock = ["boto3 (>=1.28.57)", "botocore (>=1.31.57)"]
vertex = ["google-auth[requests] (>=2,<3)"]
[[package]]
name = "anyio"
version = "4.9.0"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.9"
groups = ["main", "dev"]
files = [
{file = "anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c"},
{file = "anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028"},
]
[package.dependencies]
exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
idna = ">=2.8"
sniffio = ">=1.1"
typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
[package.extras]
doc = ["Sphinx (>=8.2,<9.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1) ; python_version >= \"3.10\"", "uvloop (>=0.21) ; platform_python_implementation == \"CPython\" and platform_system != \"Windows\" and python_version < \"3.14\""]
trio = ["trio (>=0.26.1)"]
[[package]]
name = "attrs"
version = "25.3.0"
description = "Classes Without Boilerplate"
optional = false
python-versions = ">=3.8"
groups = ["dev"]
files = [
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cov = ["cloudpickle ; platform_python_implementation == \"CPython\"", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
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docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"]
tests = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
tests-mypy = ["mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\""]
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{file = "wrapt-1.17.2-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:c86563182421896d73858e08e1db93afdd2b947a70064b813d515d66549e15f9"},
{file = "wrapt-1.17.2-cp39-cp39-win32.whl", hash = "sha256:f393cda562f79828f38a819f4788641ac7c4085f30f1ce1a68672baa686482bb"},
{file = "wrapt-1.17.2-cp39-cp39-win_amd64.whl", hash = "sha256:36ccae62f64235cf8ddb682073a60519426fdd4725524ae38874adf72b5f2aeb"},
{file = "wrapt-1.17.2-py3-none-any.whl", hash = "sha256:b18f2d1533a71f069c7f82d524a52599053d4c7166e9dd374ae2136b7f40f7c8"},
{file = "wrapt-1.17.2.tar.gz", hash = "sha256:41388e9d4d1522446fe79d3213196bd9e3b301a336965b9e27ca2788ebd122f3"},
]
[[package]]
name = "zipp"
version = "3.21.0"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
{file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
{file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
]
[package.extras]
check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
cover = ["pytest-cov"]
doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
enabler = ["pytest-enabler (>=2.2)"]
test = ["big-O", "importlib-resources ; python_version < \"3.9\"", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
type = ["pytest-mypy"]
[metadata]
lock-version = "2.1"
python-versions = ">=3.9,<4"
content-hash = "b13d162d3fbca536d5eabbb7baec8155cc8eaeffc3c26227397a572bddde5385"
humanloop-python-0.8.40b1/pyproject.toml 0000664 0000000 0000000 00000006111 15011667241 0020265 0 ustar 00root root 0000000 0000000 [project]
name = "humanloop"
[tool.poetry]
name = "humanloop"
version = "0.8.39"
description = ""
readme = "README.md"
authors = []
keywords = []
classifiers = [
"Intended Audience :: Developers",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Operating System :: OS Independent",
"Operating System :: POSIX",
"Operating System :: MacOS",
"Operating System :: POSIX :: Linux",
"Operating System :: Microsoft :: Windows",
"Topic :: Software Development :: Libraries :: Python Modules",
"Typing :: Typed"
]
packages = [
{ include = "humanloop", from = "src"}
]
[project.urls]
Repository = 'https://github.com/humanloop/humanloop-python'
[tool.poetry.dependencies]
python = ">=3.9,<4"
click = "^8.0.0"
deepdiff = "^8.2.0"
httpx = ">=0.21.2"
httpx-sse = "0.4.0"
mmh3 = "^5.1.0"
opentelemetry-api = ">=1.27.0"
opentelemetry-instrumentation-anthropic = ">=0.20"
opentelemetry-instrumentation-bedrock = ">=0.15"
opentelemetry-instrumentation-cohere = ">=0.20"
opentelemetry-instrumentation-groq = ">=0.29"
opentelemetry-instrumentation-openai = ">=0.20"
opentelemetry-instrumentation-replicate = ">=0.20"
opentelemetry-proto = ">=1.30.0"
opentelemetry-sdk = ">=1.27.0"
parse = ">=1"
protobuf = ">=5.29.3"
pydantic = ">= 1.9.2"
pydantic-core = "^2.18.2"
python-dotenv = "^1.1.0"
typing_extensions = ">= 4.0.0"
[tool.poetry.group.dev.dependencies]
mypy = "==1.13.0"
pytest = "^7.4.0"
pytest-asyncio = "^0.23.5"
python-dateutil = "^2.9.0"
types-python-dateutil = "^2.9.0.20240316"
anthropic = ">=0.37.1"
cohere = "^5.11.2"
groq = ">=0.11.0"
jsonschema = "^4.23.0"
numpy = "<2.0.0"
openai = "^1.52.2"
pandas = "^2.2.0"
parse-type = ">=0.6.4"
pyarrow = "^19.0.0"
pytest-retry = "^1.6.3"
pytest-xdist = "^3.6.1"
replicate = "^1.0.3"
ruff = "==0.11.5"
types-jsonschema = "^4.23.0.20240813"
types-protobuf = "^5.29.1.20250208"
[tool.pytest.ini_options]
testpaths = [ "tests" ]
asyncio_mode = "auto"
[tool.mypy]
plugins = ["pydantic.mypy"]
[tool.ruff]
line-length = 120
[tool.ruff.lint]
select = [
"E", # pycodestyle errors
"F", # pyflakes
"I", # isort
]
ignore = [
"E402", # Module level import not at top of file
"E501", # Line too long
"E711", # Comparison to `None` should be `cond is not None`
"E712", # Avoid equality comparisons to `True`; use `if ...:` checks
"E721", # Use `is` and `is not` for type comparisons, or `isinstance()` for insinstance checks
"E722", # Do not use bare `except`
"E731", # Do not assign a `lambda` expression, use a `def`
"F821", # Undefined name
"F841" # Local variable ... is assigned to but never used
]
[tool.ruff.lint.isort]
section-order = ["future", "standard-library", "third-party", "first-party"]
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
[tool.poetry.scripts]
humanloop = "humanloop.cli.__main__:cli"
humanloop-python-0.8.40b1/pytest.ini 0000664 0000000 0000000 00000000033 15011667241 0017377 0 ustar 00root root 0000000 0000000 [pytest]
addopts = -n auto
humanloop-python-0.8.40b1/reference.md 0000664 0000000 0000000 00000624461 15011667241 0017647 0 ustar 00root root 0000000 0000000 # Reference
## Prompts
client.prompts.log(...)
-
#### 📝 Description
-
-
Log to a Prompt.
You can use query parameters `version_id`, or `environment`, to target
an existing version of the Prompt. Otherwise, the default deployed version will be chosen.
Instead of targeting an existing version explicitly, you can instead pass in
Prompt details in the request body. In this case, we will check if the details correspond
to an existing version of the Prompt. If they do not, we will create a new version. This is helpful
in the case where you are storing or deriving your Prompt details in code.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
import datetime
client = Humanloop(api_key="YOUR_API_KEY", )
client.prompts.log(path='persona', prompt={'model': 'gpt-4', 'template': [{'role': "system", 'content': 'You are {{person}}. Answer questions as this person. Do not break character.'}]}, messages=[{'role': "user", 'content': 'What really happened at Roswell?'}], inputs={'person': 'Trump'
}, created_at=datetime.datetime.fromisoformat("2024-07-18 23:29:35.178000+00:00", ), provider_latency=6.5931549072265625, output_message={'content': "Well, you know, there is so much secrecy involved in government, folks, it's unbelievable. They don't want to tell you everything. They don't tell me everything! But about Roswell, it's a very popular question. I know, I just know, that something very, very peculiar happened there. Was it a weather balloon? Maybe. Was it something extraterrestrial? Could be. I'd love to go down and open up all the classified documents, believe me, I would. But they don't let that happen. The Deep State, folks, the Deep State. They're unbelievable. They want to keep everything a secret. But whatever the truth is, I can tell you this: it's something big, very very big. Tremendous, in fact.", 'role': "assistant"}, prompt_tokens=100, output_tokens=220, prompt_cost=1e-05, output_cost=0.0002, finish_reason='stop', )
```
#### ⚙️ Parameters
-
-
**version_id:** `typing.Optional[str]` — A specific Version ID of the Prompt to log to.
-
**environment:** `typing.Optional[str]` — Name of the Environment identifying a deployed version to log to.
-
**run_id:** `typing.Optional[str]` — Unique identifier for the Run to associate the Log to.
-
**path:** `typing.Optional[str]` — Path of the Prompt, including the name. This locates the Prompt in the Humanloop filesystem and is used as as a unique identifier. For example: `folder/name` or just `name`.
-
**id:** `typing.Optional[str]` — ID for an existing Prompt.
-
**output_message:** `typing.Optional[ChatMessageParams]` — The message returned by the provider.
-
**prompt_tokens:** `typing.Optional[int]` — Number of tokens in the prompt used to generate the output.
-
**reasoning_tokens:** `typing.Optional[int]` — Number of reasoning tokens used to generate the output.
-
**output_tokens:** `typing.Optional[int]` — Number of tokens in the output generated by the model.
-
**prompt_cost:** `typing.Optional[float]` — Cost in dollars associated to the tokens in the prompt.
-
**output_cost:** `typing.Optional[float]` — Cost in dollars associated to the tokens in the output.
-
**finish_reason:** `typing.Optional[str]` — Reason the generation finished.
-
**messages:** `typing.Optional[typing.Sequence[ChatMessageParams]]` — The messages passed to the to provider chat endpoint.
-
**tool_choice:** `typing.Optional[PromptLogRequestToolChoiceParams]`
Controls how the model uses tools. The following options are supported:
- `'none'` means the model will not call any tool and instead generates a message; this is the default when no tools are provided as part of the Prompt.
- `'auto'` means the model can decide to call one or more of the provided tools; this is the default when tools are provided as part of the Prompt.
- `'required'` means the model must call one or more of the provided tools.
- `{'type': 'function', 'function': {name': }}` forces the model to use the named function.
-
**prompt:** `typing.Optional[PromptLogRequestPromptParams]`
The Prompt configuration to use. Two formats are supported:
- An object representing the details of the Prompt configuration
- A string representing the raw contents of a .prompt file
A new Prompt version will be created if the provided details do not match any existing version.
-
**start_time:** `typing.Optional[dt.datetime]` — When the logged event started.
-
**end_time:** `typing.Optional[dt.datetime]` — When the logged event ended.
-
**output:** `typing.Optional[str]` — Generated output from your model for the provided inputs. Can be `None` if logging an error, or if creating a parent Log with the intention to populate it later.
-
**created_at:** `typing.Optional[dt.datetime]` — User defined timestamp for when the log was created.
-
**error:** `typing.Optional[str]` — Error message if the log is an error.
-
**provider_latency:** `typing.Optional[float]` — Duration of the logged event in seconds.
-
**stdout:** `typing.Optional[str]` — Captured log and debug statements.
-
**provider_request:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Raw request sent to provider.
-
**provider_response:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Raw response received the provider.
-
**inputs:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — The inputs passed to the prompt template.
-
**source:** `typing.Optional[str]` — Identifies where the model was called from.
-
**metadata:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Any additional metadata to record.
-
**log_status:** `typing.Optional[LogStatus]` — Status of a Log. Set to `incomplete` if you intend to update and eventually complete the Log and want the File's monitoring Evaluators to wait until you mark it as `complete`. If log_status is not provided, observability will pick up the Log as soon as possible. Updating this from specified to unspecified is undefined behavior.
-
**source_datapoint_id:** `typing.Optional[str]` — Unique identifier for the Datapoint that this Log is derived from. This can be used by Humanloop to associate Logs to Evaluations. If provided, Humanloop will automatically associate this Log to Evaluations that require a Log for this Datapoint-Version pair.
-
**trace_parent_id:** `typing.Optional[str]` — The ID of the parent Log to nest this Log under in a Trace.
-
**user:** `typing.Optional[str]` — End-user ID related to the Log.
-
**prompt_log_request_environment:** `typing.Optional[str]` — The name of the Environment the Log is associated to.
-
**save:** `typing.Optional[bool]` — Whether the request/response payloads will be stored on Humanloop.
-
**log_id:** `typing.Optional[str]` — This will identify a Log. If you don't provide a Log ID, Humanloop will generate one for you.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.prompts.update_log(...)
-
#### 📝 Description
-
-
Update a Log.
Update the details of a Log with the given ID.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.prompts.update_log(id='id', log_id='log_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Prompt.
-
**log_id:** `str` — Unique identifier for the Log.
-
**output_message:** `typing.Optional[ChatMessageParams]` — The message returned by the provider.
-
**prompt_tokens:** `typing.Optional[int]` — Number of tokens in the prompt used to generate the output.
-
**reasoning_tokens:** `typing.Optional[int]` — Number of reasoning tokens used to generate the output.
-
**output_tokens:** `typing.Optional[int]` — Number of tokens in the output generated by the model.
-
**prompt_cost:** `typing.Optional[float]` — Cost in dollars associated to the tokens in the prompt.
-
**output_cost:** `typing.Optional[float]` — Cost in dollars associated to the tokens in the output.
-
**finish_reason:** `typing.Optional[str]` — Reason the generation finished.
-
**messages:** `typing.Optional[typing.Sequence[ChatMessageParams]]` — The messages passed to the to provider chat endpoint.
-
**tool_choice:** `typing.Optional[PromptLogUpdateRequestToolChoiceParams]`
Controls how the model uses tools. The following options are supported:
- `'none'` means the model will not call any tool and instead generates a message; this is the default when no tools are provided as part of the Prompt.
- `'auto'` means the model can decide to call one or more of the provided tools; this is the default when tools are provided as part of the Prompt.
- `'required'` means the model must call one or more of the provided tools.
- `{'type': 'function', 'function': {name': }}` forces the model to use the named function.
-
**output:** `typing.Optional[str]` — Generated output from your model for the provided inputs. Can be `None` if logging an error, or if creating a parent Log with the intention to populate it later.
-
**created_at:** `typing.Optional[dt.datetime]` — User defined timestamp for when the log was created.
-
**error:** `typing.Optional[str]` — Error message if the log is an error.
-
**provider_latency:** `typing.Optional[float]` — Duration of the logged event in seconds.
-
**stdout:** `typing.Optional[str]` — Captured log and debug statements.
-
**provider_request:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Raw request sent to provider.
-
**provider_response:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Raw response received the provider.
-
**inputs:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — The inputs passed to the prompt template.
-
**source:** `typing.Optional[str]` — Identifies where the model was called from.
-
**metadata:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Any additional metadata to record.
-
**start_time:** `typing.Optional[dt.datetime]` — When the logged event started.
-
**end_time:** `typing.Optional[dt.datetime]` — When the logged event ended.
-
**log_status:** `typing.Optional[LogStatus]` — Status of a Log. Set to `incomplete` if you intend to update and eventually complete the Log and want the File's monitoring Evaluators to wait until you mark it as `complete`. If log_status is not provided, observability will pick up the Log as soon as possible. Updating this from specified to unspecified is undefined behavior.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.prompts.call_stream(...)
-
#### 📝 Description
-
-
Call a Prompt.
Calling a Prompt calls the model provider before logging
the request, responses and metadata to Humanloop.
You can use query parameters `version_id`, or `environment`, to target
an existing version of the Prompt. Otherwise the default deployed version will be chosen.
Instead of targeting an existing version explicitly, you can instead pass in
Prompt details in the request body. In this case, we will check if the details correspond
to an existing version of the Prompt. If they do not, we will create a new version. This is helpful
in the case where you are storing or deriving your Prompt details in code.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
response = client.prompts.call_stream()
for chunk in response.data:
yield chunk
```
#### ⚙️ Parameters
-
-
**version_id:** `typing.Optional[str]` — A specific Version ID of the Prompt to log to.
-
**environment:** `typing.Optional[str]` — Name of the Environment identifying a deployed version to log to.
-
**path:** `typing.Optional[str]` — Path of the Prompt, including the name. This locates the Prompt in the Humanloop filesystem and is used as as a unique identifier. For example: `folder/name` or just `name`.
-
**id:** `typing.Optional[str]` — ID for an existing Prompt.
-
**messages:** `typing.Optional[typing.Sequence[ChatMessageParams]]` — The messages passed to the to provider chat endpoint.
-
**tool_choice:** `typing.Optional[PromptsCallStreamRequestToolChoiceParams]`
Controls how the model uses tools. The following options are supported:
- `'none'` means the model will not call any tool and instead generates a message; this is the default when no tools are provided as part of the Prompt.
- `'auto'` means the model can decide to call one or more of the provided tools; this is the default when tools are provided as part of the Prompt.
- `'required'` means the model must call one or more of the provided tools.
- `{'type': 'function', 'function': {name': }}` forces the model to use the named function.
-
**prompt:** `typing.Optional[PromptsCallStreamRequestPromptParams]`
The Prompt configuration to use. Two formats are supported:
- An object representing the details of the Prompt configuration
- A string representing the raw contents of a .prompt file
A new Prompt version will be created if the provided details do not match any existing version.
-
**inputs:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — The inputs passed to the prompt template.
-
**source:** `typing.Optional[str]` — Identifies where the model was called from.
-
**metadata:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Any additional metadata to record.
-
**start_time:** `typing.Optional[dt.datetime]` — When the logged event started.
-
**end_time:** `typing.Optional[dt.datetime]` — When the logged event ended.
-
**log_status:** `typing.Optional[LogStatus]` — Status of a Log. Set to `incomplete` if you intend to update and eventually complete the Log and want the File's monitoring Evaluators to wait until you mark it as `complete`. If log_status is not provided, observability will pick up the Log as soon as possible. Updating this from specified to unspecified is undefined behavior.
-
**source_datapoint_id:** `typing.Optional[str]` — Unique identifier for the Datapoint that this Log is derived from. This can be used by Humanloop to associate Logs to Evaluations. If provided, Humanloop will automatically associate this Log to Evaluations that require a Log for this Datapoint-Version pair.
-
**trace_parent_id:** `typing.Optional[str]` — The ID of the parent Log to nest this Log under in a Trace.
-
**user:** `typing.Optional[str]` — End-user ID related to the Log.
-
**prompts_call_stream_request_environment:** `typing.Optional[str]` — The name of the Environment the Log is associated to.
-
**save:** `typing.Optional[bool]` — Whether the request/response payloads will be stored on Humanloop.
-
**log_id:** `typing.Optional[str]` — This will identify a Log. If you don't provide a Log ID, Humanloop will generate one for you.
-
**provider_api_keys:** `typing.Optional[ProviderApiKeysParams]` — API keys required by each provider to make API calls. The API keys provided here are not stored by Humanloop. If not specified here, Humanloop will fall back to the key saved to your organization.
-
**num_samples:** `typing.Optional[int]` — The number of generations.
-
**return_inputs:** `typing.Optional[bool]` — Whether to return the inputs in the response. If false, the response will contain an empty dictionary under inputs. This is useful for reducing the size of the response. Defaults to true.
-
**logprobs:** `typing.Optional[int]` — Include the log probabilities of the top n tokens in the provider_response
-
**suffix:** `typing.Optional[str]` — The suffix that comes after a completion of inserted text. Useful for completions that act like inserts.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.prompts.call(...)
-
#### 📝 Description
-
-
Call a Prompt.
Calling a Prompt calls the model provider before logging
the request, responses and metadata to Humanloop.
You can use query parameters `version_id`, or `environment`, to target
an existing version of the Prompt. Otherwise the default deployed version will be chosen.
Instead of targeting an existing version explicitly, you can instead pass in
Prompt details in the request body. In this case, we will check if the details correspond
to an existing version of the Prompt. If they do not, we will create a new version. This is helpful
in the case where you are storing or deriving your Prompt details in code.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.prompts.call(version_id='prv_Wu6zx1lAWJRqOyL8nWuZk', path='persona', messages=[{'role': "user", 'content': 'What really happened at Roswell?'}], inputs={'person': 'Trump'
}, )
```
#### ⚙️ Parameters
-
-
**version_id:** `typing.Optional[str]` — A specific Version ID of the Prompt to log to.
-
**environment:** `typing.Optional[str]` — Name of the Environment identifying a deployed version to log to.
-
**path:** `typing.Optional[str]` — Path of the Prompt, including the name. This locates the Prompt in the Humanloop filesystem and is used as as a unique identifier. For example: `folder/name` or just `name`.
-
**id:** `typing.Optional[str]` — ID for an existing Prompt.
-
**messages:** `typing.Optional[typing.Sequence[ChatMessageParams]]` — The messages passed to the to provider chat endpoint.
-
**tool_choice:** `typing.Optional[PromptsCallRequestToolChoiceParams]`
Controls how the model uses tools. The following options are supported:
- `'none'` means the model will not call any tool and instead generates a message; this is the default when no tools are provided as part of the Prompt.
- `'auto'` means the model can decide to call one or more of the provided tools; this is the default when tools are provided as part of the Prompt.
- `'required'` means the model must call one or more of the provided tools.
- `{'type': 'function', 'function': {name': }}` forces the model to use the named function.
-
**prompt:** `typing.Optional[PromptsCallRequestPromptParams]`
The Prompt configuration to use. Two formats are supported:
- An object representing the details of the Prompt configuration
- A string representing the raw contents of a .prompt file
A new Prompt version will be created if the provided details do not match any existing version.
-
**inputs:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — The inputs passed to the prompt template.
-
**source:** `typing.Optional[str]` — Identifies where the model was called from.
-
**metadata:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Any additional metadata to record.
-
**start_time:** `typing.Optional[dt.datetime]` — When the logged event started.
-
**end_time:** `typing.Optional[dt.datetime]` — When the logged event ended.
-
**log_status:** `typing.Optional[LogStatus]` — Status of a Log. Set to `incomplete` if you intend to update and eventually complete the Log and want the File's monitoring Evaluators to wait until you mark it as `complete`. If log_status is not provided, observability will pick up the Log as soon as possible. Updating this from specified to unspecified is undefined behavior.
-
**source_datapoint_id:** `typing.Optional[str]` — Unique identifier for the Datapoint that this Log is derived from. This can be used by Humanloop to associate Logs to Evaluations. If provided, Humanloop will automatically associate this Log to Evaluations that require a Log for this Datapoint-Version pair.
-
**trace_parent_id:** `typing.Optional[str]` — The ID of the parent Log to nest this Log under in a Trace.
-
**user:** `typing.Optional[str]` — End-user ID related to the Log.
-
**prompts_call_request_environment:** `typing.Optional[str]` — The name of the Environment the Log is associated to.
-
**save:** `typing.Optional[bool]` — Whether the request/response payloads will be stored on Humanloop.
-
**log_id:** `typing.Optional[str]` — This will identify a Log. If you don't provide a Log ID, Humanloop will generate one for you.
-
**provider_api_keys:** `typing.Optional[ProviderApiKeysParams]` — API keys required by each provider to make API calls. The API keys provided here are not stored by Humanloop. If not specified here, Humanloop will fall back to the key saved to your organization.
-
**num_samples:** `typing.Optional[int]` — The number of generations.
-
**return_inputs:** `typing.Optional[bool]` — Whether to return the inputs in the response. If false, the response will contain an empty dictionary under inputs. This is useful for reducing the size of the response. Defaults to true.
-
**logprobs:** `typing.Optional[int]` — Include the log probabilities of the top n tokens in the provider_response
-
**suffix:** `typing.Optional[str]` — The suffix that comes after a completion of inserted text. Useful for completions that act like inserts.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.prompts.list(...)
-
#### 📝 Description
-
-
Get a list of all Prompts.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
response = client.prompts.list(size=1, )
for item in response:
yield item
# alternatively, you can paginate page-by-page
for page in response.iter_pages():
yield page
```
#### ⚙️ Parameters
-
-
**page:** `typing.Optional[int]` — Page number for pagination.
-
**size:** `typing.Optional[int]` — Page size for pagination. Number of Prompts to fetch.
-
**name:** `typing.Optional[str]` — Case-insensitive filter for Prompt name.
-
**user_filter:** `typing.Optional[str]` — Case-insensitive filter for users in the Prompt. This filter matches against both email address and name of users.
-
**sort_by:** `typing.Optional[FileSortBy]` — Field to sort Prompts by
-
**order:** `typing.Optional[SortOrder]` — Direction to sort by.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.prompts.upsert(...)
-
#### 📝 Description
-
-
Create a Prompt or update it with a new version if it already exists.
Prompts are identified by the `ID` or their `path`. The parameters (i.e. the prompt template, temperature, model etc.) determine the versions of the Prompt.
You can provide `version_name` and `version_description` to identify and describe your versions.
Version names must be unique within a Prompt - attempting to create a version with a name
that already exists will result in a 409 Conflict error.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.prompts.upsert(path='Personal Projects/Coding Assistant', model='gpt-4o', endpoint="chat", template=[{'content': 'You are a helpful coding assistant specialising in {{language}}', 'role': "system"}], provider="openai", max_tokens=-1, temperature=0.7, version_name='coding-assistant-v1', version_description='Initial version', )
```
#### ⚙️ Parameters
-
-
**model:** `str` — The model instance used, e.g. `gpt-4`. See [supported models](https://humanloop.com/docs/reference/supported-models)
-
**path:** `typing.Optional[str]` — Path of the Prompt, including the name. This locates the Prompt in the Humanloop filesystem and is used as as a unique identifier. For example: `folder/name` or just `name`.
-
**id:** `typing.Optional[str]` — ID for an existing Prompt.
-
**endpoint:** `typing.Optional[ModelEndpoints]` — The provider model endpoint used.
-
**template:** `typing.Optional[PromptRequestTemplateParams]`
The template contains the main structure and instructions for the model, including input variables for dynamic values.
For chat models, provide the template as a ChatTemplate (a list of messages), e.g. a system message, followed by a user message with an input variable.
For completion models, provide a prompt template as a string.
Input variables should be specified with double curly bracket syntax: `{{input_name}}`.
-
**template_language:** `typing.Optional[TemplateLanguage]` — The template language to use for rendering the template.
-
**provider:** `typing.Optional[ModelProviders]` — The company providing the underlying model service.
-
**max_tokens:** `typing.Optional[int]` — The maximum number of tokens to generate. Provide max_tokens=-1 to dynamically calculate the maximum number of tokens to generate given the length of the prompt
-
**temperature:** `typing.Optional[float]` — What sampling temperature to use when making a generation. Higher values means the model will be more creative.
-
**top_p:** `typing.Optional[float]` — An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass.
-
**stop:** `typing.Optional[PromptRequestStopParams]` — The string (or list of strings) after which the model will stop generating. The returned text will not contain the stop sequence.
-
**presence_penalty:** `typing.Optional[float]` — Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the generation so far.
-
**frequency_penalty:** `typing.Optional[float]` — Number between -2.0 and 2.0. Positive values penalize new tokens based on how frequently they appear in the generation so far.
-
**other:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Other parameter values to be passed to the provider call.
-
**seed:** `typing.Optional[int]` — If specified, model will make a best effort to sample deterministically, but it is not guaranteed.
-
**response_format:** `typing.Optional[ResponseFormatParams]` — The format of the response. Only `{"type": "json_object"}` is currently supported for chat.
-
**reasoning_effort:** `typing.Optional[PromptRequestReasoningEffortParams]` — Guidance on how many reasoning tokens it should generate before creating a response to the prompt. OpenAI reasoning models (o1, o3-mini) expect a OpenAIReasoningEffort enum. Anthropic reasoning models expect an integer, which signifies the maximum token budget.
-
**tools:** `typing.Optional[typing.Sequence[ToolFunctionParams]]` — The tool specification that the model can choose to call if Tool calling is supported.
-
**linked_tools:** `typing.Optional[typing.Sequence[str]]` — The IDs of the Tools in your organization that the model can choose to call if Tool calling is supported. The default deployed version of that tool is called.
-
**attributes:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Additional fields to describe the Prompt. Helpful to separate Prompt versions from each other with details on how they were created or used.
-
**version_name:** `typing.Optional[str]` — Unique name for the Prompt version. Version names must be unique for a given Prompt.
-
**version_description:** `typing.Optional[str]` — Description of the version, e.g., the changes made in this version.
-
**description:** `typing.Optional[str]` — Description of the Prompt.
-
**tags:** `typing.Optional[typing.Sequence[str]]` — List of tags associated with this prompt.
-
**readme:** `typing.Optional[str]` — Long description of the Prompt.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.prompts.get(...)
-
#### 📝 Description
-
-
Retrieve the Prompt with the given ID.
By default, the deployed version of the Prompt is returned. Use the query parameters
`version_id` or `environment` to target a specific version of the Prompt.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.prompts.get(id='pr_30gco7dx6JDq4200GVOHa', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Prompt.
-
**version_id:** `typing.Optional[str]` — A specific Version ID of the Prompt to retrieve.
-
**environment:** `typing.Optional[str]` — Name of the Environment to retrieve a deployed Version from.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.prompts.delete(...)
-
#### 📝 Description
-
-
Delete the Prompt with the given ID.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.prompts.delete(id='pr_30gco7dx6JDq4200GVOHa', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Prompt.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.prompts.move(...)
-
#### 📝 Description
-
-
Move the Prompt to a different path or change the name.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.prompts.move(id='pr_30gco7dx6JDq4200GVOHa', path='new directory/new name', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Prompt.
-
**path:** `typing.Optional[str]` — Path of the Prompt including the Prompt name, which is used as a unique identifier.
-
**name:** `typing.Optional[str]` — Name of the Prompt.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.prompts.populate(...)
-
#### 📝 Description
-
-
Retrieve the Prompt with the given ID, including the populated template.
By default, the deployed version of the Prompt is returned. Use the query parameters
`version_id` or `environment` to target a specific version of the Prompt.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.prompts.populate(id='id', request={'key': 'value'
}, )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Prompt.
-
**request:** `typing.Dict[str, typing.Optional[typing.Any]]`
-
**version_id:** `typing.Optional[str]` — A specific Version ID of the Prompt to retrieve to populate the template.
-
**environment:** `typing.Optional[str]` — Name of the Environment to retrieve a deployed Version from to populate the template.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.prompts.list_versions(...)
-
#### 📝 Description
-
-
Get a list of all the versions of a Prompt.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.prompts.list_versions(id='pr_30gco7dx6JDq4200GVOHa', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Prompt.
-
**evaluator_aggregates:** `typing.Optional[bool]` — Whether to include Evaluator aggregate results for the versions in the response
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.prompts.delete_prompt_version(...)
-
#### 📝 Description
-
-
Delete a version of the Prompt.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.prompts.delete_prompt_version(id='id', version_id='version_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Prompt.
-
**version_id:** `str` — Unique identifier for the specific version of the Prompt.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.prompts.patch_prompt_version(...)
-
#### 📝 Description
-
-
Update the name or description of the Prompt version.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.prompts.patch_prompt_version(id='id', version_id='version_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Prompt.
-
**version_id:** `str` — Unique identifier for the specific version of the Prompt.
-
**name:** `typing.Optional[str]` — Name of the version.
-
**description:** `typing.Optional[str]` — Description of the version.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.prompts.set_deployment(...)
-
#### 📝 Description
-
-
Deploy Prompt to an Environment.
Set the deployed version for the specified Environment. This Prompt
will be used for calls made to the Prompt in this Environment.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.prompts.set_deployment(id='id', environment_id='environment_id', version_id='version_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Prompt.
-
**environment_id:** `str` — Unique identifier for the Environment to deploy the Version to.
-
**version_id:** `str` — Unique identifier for the specific version of the Prompt.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.prompts.remove_deployment(...)
-
#### 📝 Description
-
-
Remove deployed Prompt from the Environment.
Remove the deployed version for the specified Environment. This Prompt
will no longer be used for calls made to the Prompt in this Environment.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.prompts.remove_deployment(id='id', environment_id='environment_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Prompt.
-
**environment_id:** `str` — Unique identifier for the Environment to remove the deployment from.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.prompts.list_environments(...)
-
#### 📝 Description
-
-
List all Environments and their deployed versions for the Prompt.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.prompts.list_environments(id='pr_30gco7dx6JDq4200GVOHa', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Prompt.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.prompts.update_monitoring(...)
-
#### 📝 Description
-
-
Activate and deactivate Evaluators for monitoring the Prompt.
An activated Evaluator will automatically be run on all new Logs
within the Prompt for monitoring purposes.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.prompts.update_monitoring(id='pr_30gco7dx6JDq4200GVOHa', activate=[{'evaluator_version_id': 'evv_1abc4308abd'}], )
```
#### ⚙️ Parameters
-
-
**id:** `str`
-
**activate:** `typing.Optional[typing.Sequence[EvaluatorActivationDeactivationRequestActivateItemParams]]` — Evaluators to activate for Monitoring. These will be automatically run on new Logs.
-
**deactivate:** `typing.Optional[typing.Sequence[EvaluatorActivationDeactivationRequestDeactivateItemParams]]` — Evaluators to deactivate. These will not be run on new Logs.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.prompts.serialize(...)
-
#### 📝 Description
-
-
Serialize a Prompt to the .prompt file format.
Useful for storing the Prompt with your code in a version control system,
or for editing with an AI tool.
By default, the deployed version of the Prompt is returned. Use the query parameters
`version_id` or `environment` to target a specific version of the Prompt.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.prompts.serialize(id='id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Prompt.
-
**version_id:** `typing.Optional[str]` — A specific Version ID of the Prompt to retrieve.
-
**environment:** `typing.Optional[str]` — Name of the Environment to retrieve a deployed Version from.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.prompts.deserialize(...)
-
#### 📝 Description
-
-
Deserialize a Prompt from the .prompt file format.
This returns a subset of the attributes required by a Prompt.
This subset is the bit that defines the Prompt version (e.g. with `model` and `temperature` etc)
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.prompts.deserialize(prompt='prompt', )
```
#### ⚙️ Parameters
-
-
**prompt:** `str`
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.tools.call(...)
-
#### 📝 Description
-
-
Call a Tool.
Calling a Tool with inputs runs the tool's source code and logs the result and metadata to Humanloop.
You can use query parameters `version_id`, or `environment`, to target
an existing version of the Tool. Otherwise, the default deployed version will be chosen.
Instead of targeting an existing version explicitly, you can instead pass in
Tool details in the request body. In this case, we will check if the details correspond
to an existing version of the Tool. If they do not, we will create a new version. This is helpful
in the case where you are storing or deriving your Tool details in code.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.tools.call()
```
#### ⚙️ Parameters
-
-
**version_id:** `typing.Optional[str]` — A specific Version ID of the Tool to call.
-
**environment:** `typing.Optional[str]` — Name of the Environment identifying a deployed version to call.
-
**path:** `typing.Optional[str]` — Path of the Tool, including the name. This locates the Tool in the Humanloop filesystem and is used as as a unique identifier. For example: `folder/name` or just `name`.
-
**id:** `typing.Optional[str]` — ID for an existing Tool.
-
**tool:** `typing.Optional[ToolKernelRequestParams]` — Details of your Tool. A new Tool version will be created if the provided details are new.
-
**inputs:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — The inputs passed to the prompt template.
-
**source:** `typing.Optional[str]` — Identifies where the model was called from.
-
**metadata:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Any additional metadata to record.
-
**start_time:** `typing.Optional[dt.datetime]` — When the logged event started.
-
**end_time:** `typing.Optional[dt.datetime]` — When the logged event ended.
-
**log_status:** `typing.Optional[LogStatus]` — Status of a Log. Set to `incomplete` if you intend to update and eventually complete the Log and want the File's monitoring Evaluators to wait until you mark it as `complete`. If log_status is not provided, observability will pick up the Log as soon as possible. Updating this from specified to unspecified is undefined behavior.
-
**source_datapoint_id:** `typing.Optional[str]` — Unique identifier for the Datapoint that this Log is derived from. This can be used by Humanloop to associate Logs to Evaluations. If provided, Humanloop will automatically associate this Log to Evaluations that require a Log for this Datapoint-Version pair.
-
**trace_parent_id:** `typing.Optional[str]` — The ID of the parent Log to nest this Log under in a Trace.
-
**user:** `typing.Optional[str]` — End-user ID related to the Log.
-
**tool_call_request_environment:** `typing.Optional[str]` — The name of the Environment the Log is associated to.
-
**save:** `typing.Optional[bool]` — Whether the request/response payloads will be stored on Humanloop.
-
**log_id:** `typing.Optional[str]` — This will identify a Log. If you don't provide a Log ID, Humanloop will generate one for you.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.tools.log(...)
-
#### 📝 Description
-
-
Log to a Tool.
You can use query parameters `version_id`, or `environment`, to target
an existing version of the Tool. Otherwise the default deployed version will be chosen.
Instead of targeting an existing version explicitly, you can instead pass in
Tool details in the request body. In this case, we will check if the details correspond
to an existing version of the Tool, if not we will create a new version. This is helpful
in the case where you are storing or deriving your Tool details in code.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.tools.log(path='math-tool', tool={'function': {'name': 'multiply', 'description': 'Multiply two numbers', 'parameters': {'type': 'object'
, 'properties': {'a': {'type': 'number'}, 'b': {'type': 'number'}}
, 'required': ['a', 'b']
}}}, inputs={'a': 5
, 'b': 7
}, output='35', )
```
#### ⚙️ Parameters
-
-
**version_id:** `typing.Optional[str]` — A specific Version ID of the Tool to log to.
-
**environment:** `typing.Optional[str]` — Name of the Environment identifying a deployed version to log to.
-
**path:** `typing.Optional[str]` — Path of the Tool, including the name. This locates the Tool in the Humanloop filesystem and is used as as a unique identifier. For example: `folder/name` or just `name`.
-
**id:** `typing.Optional[str]` — ID for an existing Tool.
-
**tool:** `typing.Optional[ToolKernelRequestParams]` — Details of your Tool. A new Tool version will be created if the provided details are new.
-
**start_time:** `typing.Optional[dt.datetime]` — When the logged event started.
-
**end_time:** `typing.Optional[dt.datetime]` — When the logged event ended.
-
**output:** `typing.Optional[str]` — Generated output from your model for the provided inputs. Can be `None` if logging an error, or if creating a parent Log with the intention to populate it later.
-
**created_at:** `typing.Optional[dt.datetime]` — User defined timestamp for when the log was created.
-
**error:** `typing.Optional[str]` — Error message if the log is an error.
-
**provider_latency:** `typing.Optional[float]` — Duration of the logged event in seconds.
-
**stdout:** `typing.Optional[str]` — Captured log and debug statements.
-
**provider_request:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Raw request sent to provider.
-
**provider_response:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Raw response received the provider.
-
**inputs:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — The inputs passed to the prompt template.
-
**source:** `typing.Optional[str]` — Identifies where the model was called from.
-
**metadata:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Any additional metadata to record.
-
**log_status:** `typing.Optional[LogStatus]` — Status of a Log. Set to `incomplete` if you intend to update and eventually complete the Log and want the File's monitoring Evaluators to wait until you mark it as `complete`. If log_status is not provided, observability will pick up the Log as soon as possible. Updating this from specified to unspecified is undefined behavior.
-
**source_datapoint_id:** `typing.Optional[str]` — Unique identifier for the Datapoint that this Log is derived from. This can be used by Humanloop to associate Logs to Evaluations. If provided, Humanloop will automatically associate this Log to Evaluations that require a Log for this Datapoint-Version pair.
-
**trace_parent_id:** `typing.Optional[str]` — The ID of the parent Log to nest this Log under in a Trace.
-
**user:** `typing.Optional[str]` — End-user ID related to the Log.
-
**tool_log_request_environment:** `typing.Optional[str]` — The name of the Environment the Log is associated to.
-
**save:** `typing.Optional[bool]` — Whether the request/response payloads will be stored on Humanloop.
-
**log_id:** `typing.Optional[str]` — This will identify a Log. If you don't provide a Log ID, Humanloop will generate one for you.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.tools.update(...)
-
#### 📝 Description
-
-
Update a Log.
Update the details of a Log with the given ID.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.tools.update(id='id', log_id='log_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Prompt.
-
**log_id:** `str` — Unique identifier for the Log.
-
**output:** `typing.Optional[str]` — Generated output from your model for the provided inputs. Can be `None` if logging an error, or if creating a parent Log with the intention to populate it later.
-
**created_at:** `typing.Optional[dt.datetime]` — User defined timestamp for when the log was created.
-
**error:** `typing.Optional[str]` — Error message if the log is an error.
-
**provider_latency:** `typing.Optional[float]` — Duration of the logged event in seconds.
-
**stdout:** `typing.Optional[str]` — Captured log and debug statements.
-
**provider_request:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Raw request sent to provider.
-
**provider_response:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Raw response received the provider.
-
**inputs:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — The inputs passed to the prompt template.
-
**source:** `typing.Optional[str]` — Identifies where the model was called from.
-
**metadata:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Any additional metadata to record.
-
**start_time:** `typing.Optional[dt.datetime]` — When the logged event started.
-
**end_time:** `typing.Optional[dt.datetime]` — When the logged event ended.
-
**log_status:** `typing.Optional[LogStatus]` — Status of a Log. Set to `incomplete` if you intend to update and eventually complete the Log and want the File's monitoring Evaluators to wait until you mark it as `complete`. If log_status is not provided, observability will pick up the Log as soon as possible. Updating this from specified to unspecified is undefined behavior.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.tools.list(...)
-
#### 📝 Description
-
-
Get a list of all Tools.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
response = client.tools.list(size=1, )
for item in response:
yield item
# alternatively, you can paginate page-by-page
for page in response.iter_pages():
yield page
```
#### ⚙️ Parameters
-
-
**page:** `typing.Optional[int]` — Page offset for pagination.
-
**size:** `typing.Optional[int]` — Page size for pagination. Number of Tools to fetch.
-
**name:** `typing.Optional[str]` — Case-insensitive filter for Tool name.
-
**user_filter:** `typing.Optional[str]` — Case-insensitive filter for users in the Tool. This filter matches against both email address and name of users.
-
**sort_by:** `typing.Optional[FileSortBy]` — Field to sort Tools by
-
**order:** `typing.Optional[SortOrder]` — Direction to sort by.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.tools.upsert(...)
-
#### 📝 Description
-
-
Create a Tool or update it with a new version if it already exists.
Tools are identified by the `ID` or their `path`. The name, description and parameters determine the versions of the Tool.
You can provide `version_name` and `version_description` to identify and describe your versions.
Version names must be unique within a Tool - attempting to create a version with a name
that already exists will result in a 409 Conflict error.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.tools.upsert(path='math-tool', function={'name': 'multiply', 'description': 'Multiply two numbers', 'parameters': {'type': 'object'
, 'properties': {'a': {'type': 'number'}, 'b': {'type': 'number'}}
, 'required': ['a', 'b']
}}, version_name='math-tool-v1', version_description='Simple math tool that multiplies two numbers', )
```
#### ⚙️ Parameters
-
-
**path:** `typing.Optional[str]` — Path of the Tool, including the name. This locates the Tool in the Humanloop filesystem and is used as as a unique identifier. For example: `folder/name` or just `name`.
-
**id:** `typing.Optional[str]` — ID for an existing Tool.
-
**function:** `typing.Optional[ToolFunctionParams]` — Callable function specification of the Tool shown to the model for tool calling.
-
**source_code:** `typing.Optional[str]` — Code source of the Tool.
-
**setup_values:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Values needed to setup the Tool, defined in JSON Schema format: https://json-schema.org/
-
**attributes:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Additional fields to describe the Tool. Helpful to separate Tool versions from each other with details on how they were created or used.
-
**tool_type:** `typing.Optional[FilesToolType]` — Type of Tool.
-
**version_name:** `typing.Optional[str]` — Unique identifier for this Tool version. Each Tool can only have one version with a given name.
-
**version_description:** `typing.Optional[str]` — Description of the Version.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.tools.get(...)
-
#### 📝 Description
-
-
Retrieve the Tool with the given ID.
By default, the deployed version of the Tool is returned. Use the query parameters
`version_id` or `environment` to target a specific version of the Tool.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.tools.get(id='tl_789ghi', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Tool.
-
**version_id:** `typing.Optional[str]` — A specific Version ID of the Tool to retrieve.
-
**environment:** `typing.Optional[str]` — Name of the Environment to retrieve a deployed Version from.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.tools.delete(...)
-
#### 📝 Description
-
-
Delete the Tool with the given ID.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.tools.delete(id='tl_789ghi', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Tool.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.tools.move(...)
-
#### 📝 Description
-
-
Move the Tool to a different path or change the name.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.tools.move(id='tl_789ghi', path='new directory/new name', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Tool.
-
**path:** `typing.Optional[str]` — Path of the Tool including the Tool name, which is used as a unique identifier.
-
**name:** `typing.Optional[str]` — Name of the Tool, which is used as a unique identifier.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.tools.list_versions(...)
-
#### 📝 Description
-
-
Get a list of all the versions of a Tool.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.tools.list_versions(id='tl_789ghi', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for the Tool.
-
**evaluator_aggregates:** `typing.Optional[bool]` — Whether to include Evaluator aggregate results for the versions in the response
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.tools.delete_tool_version(...)
-
#### 📝 Description
-
-
Delete a version of the Tool.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.tools.delete_tool_version(id='id', version_id='version_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Tool.
-
**version_id:** `str` — Unique identifier for the specific version of the Tool.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.tools.update_tool_version(...)
-
#### 📝 Description
-
-
Update the name or description of the Tool version.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.tools.update_tool_version(id='id', version_id='version_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Tool.
-
**version_id:** `str` — Unique identifier for the specific version of the Tool.
-
**name:** `typing.Optional[str]` — Name of the version.
-
**description:** `typing.Optional[str]` — Description of the version.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.tools.set_deployment(...)
-
#### 📝 Description
-
-
Deploy Tool to an Environment.
Set the deployed version for the specified Environment. This Prompt
will be used for calls made to the Tool in this Environment.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.tools.set_deployment(id='tl_789ghi', environment_id='staging', version_id='tv_012jkl', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Tool.
-
**environment_id:** `str` — Unique identifier for the Environment to deploy the Version to.
-
**version_id:** `str` — Unique identifier for the specific version of the Tool.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.tools.remove_deployment(...)
-
#### 📝 Description
-
-
Remove deployed Tool from the Environment.
Remove the deployed version for the specified Environment. This Tool
will no longer be used for calls made to the Tool in this Environment.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.tools.remove_deployment(id='tl_789ghi', environment_id='staging', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Tool.
-
**environment_id:** `str` — Unique identifier for the Environment to remove the deployment from.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.tools.list_environments(...)
-
#### 📝 Description
-
-
List all Environments and their deployed versions for the Tool.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.tools.list_environments(id='tl_789ghi', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Tool.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.tools.update_monitoring(...)
-
#### 📝 Description
-
-
Activate and deactivate Evaluators for monitoring the Tool.
An activated Evaluator will automatically be run on all new Logs
within the Tool for monitoring purposes.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.tools.update_monitoring(id='tl_789ghi', activate=[{'evaluator_version_id': 'evv_1abc4308abd'}], )
```
#### ⚙️ Parameters
-
-
**id:** `str`
-
**activate:** `typing.Optional[typing.Sequence[EvaluatorActivationDeactivationRequestActivateItemParams]]` — Evaluators to activate for Monitoring. These will be automatically run on new Logs.
-
**deactivate:** `typing.Optional[typing.Sequence[EvaluatorActivationDeactivationRequestDeactivateItemParams]]` — Evaluators to deactivate. These will not be run on new Logs.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.tools.get_environment_variables(...)
-
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.tools.get_environment_variables(id='id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for File.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.tools.add_environment_variable(...)
-
#### 📝 Description
-
-
Add an environment variable to a Tool.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.tools.add_environment_variable(id='id', request=[{'name': 'name', 'value': 'value'}], )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Tool.
-
**request:** `typing.Sequence[FileEnvironmentVariableRequestParams]`
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.tools.delete_environment_variable(...)
-
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.tools.delete_environment_variable(id='id', name='name', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for File.
-
**name:** `str` — Name of the Environment Variable to delete.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.datasets.list(...)
-
#### 📝 Description
-
-
List all Datasets.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
response = client.datasets.list(size=1, )
for item in response:
yield item
# alternatively, you can paginate page-by-page
for page in response.iter_pages():
yield page
```
#### ⚙️ Parameters
-
-
**page:** `typing.Optional[int]` — Page offset for pagination.
-
**size:** `typing.Optional[int]` — Page size for pagination. Number of Datasets to fetch.
-
**name:** `typing.Optional[str]` — Case-insensitive filter for Dataset name.
-
**user_filter:** `typing.Optional[str]` — Case-insensitive filter for users in the Dataset. This filter matches against both email address and name of users.
-
**sort_by:** `typing.Optional[FileSortBy]` — Field to sort Datasets by
-
**order:** `typing.Optional[SortOrder]` — Direction to sort by.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.datasets.upsert(...)
-
#### 📝 Description
-
-
Create a Dataset or update it with a new version if it already exists.
Datasets are identified by the `ID` or their `path`. The datapoints determine the versions of the Dataset.
By default, the new Dataset version will be set to the list of Datapoints provided in
the request. You can also create a new version by adding or removing Datapoints from an existing version
by specifying `action` as `add` or `remove` respectively. In this case, you may specify
the `version_id` or `environment` query parameters to identify the existing version to base
the new version on. If neither is provided, the latest created version will be used.
You can provide `version_name` and `version_description` to identify and describe your versions.
Version names must be unique within a Dataset - attempting to create a version with a name
that already exists will result in a 409 Conflict error.
Humanloop also deduplicates Datapoints. If you try to add a Datapoint that already
exists, it will be ignored. If you intentionally want to add a duplicate Datapoint,
you can add a unique identifier to the Datapoint's inputs such as `{_dedupe_id: }`.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.datasets.upsert(path='datasets/support-queries', datapoints=[{'messages': [{'role': "user", 'content': 'How do i manage my organizations API keys?\n'}], 'target': {'response': 'Hey, thanks for your questions. Here are steps for how to achieve: 1. Log in to the Humanloop Dashboard \n\n2. Click on "Organization Settings."\n If you do not see this option, you might need to contact your organization admin to gain the necessary permissions.\n\n3. Within the settings or organization settings, select the option labeled "API Keys" on the left. Here you will be able to view and manage your API keys.\n\n4. You will see a list of existing API keys. You can perform various actions, such as:\n - **Generate New API Key:** Click on the "Generate New Key" button if you need a new API key.\n - **Revoke an API Key:** If you need to disable an existing key, find the key in the list and click the "Revoke" or "Delete" button.\n - **Copy an API Key:** If you need to use an existing key, you can copy it to your clipboard by clicking the "Copy" button next to the key.\n\n5. **Save and Secure API Keys:** Make sure to securely store any new or existing API keys you are using. Treat them like passwords and do not share them publicly.\n\nIf you encounter any issues or need further assistance, it might be helpful to engage with an engineer or your IT department to ensure you have the necessary permissions and support.\n\nWould you need help with anything else?'}}, {'messages': [{'role': "user", 'content': 'Hey, can do I use my code evaluator for monitoring my legal-copilot prompt?'}], 'target': {'response': 'Hey, thanks for your questions. Here are steps for how to achieve: 1. Navigate to your Prompt dashboard. \n 2. Select the `Monitoring` button on the top right of the Prompt dashboard \n 3. Within the model select the Version of the Evaluator you want to turn on for monitoring. \n\nWould you need help with anything else?'}}], version_name='Initial version', version_description='Add two new questions and answers', )
```
#### ⚙️ Parameters
-
-
**datapoints:** `typing.Sequence[CreateDatapointRequestParams]` — The Datapoints to create this Dataset version with. Modify the `action` field to determine how these Datapoints are used.
-
**version_id:** `typing.Optional[str]` — ID of the specific Dataset version to base the created Version on. Only used when `action` is `"add"` or `"remove"`.
-
**environment:** `typing.Optional[str]` — Name of the Environment identifying a deployed Version to base the created Version on. Only used when `action` is `"add"` or `"remove"`.
-
**include_datapoints:** `typing.Optional[bool]` — If set to `true`, include all Datapoints in the response. Defaults to `false`. Consider using the paginated List Datapoints endpoint instead.
-
**path:** `typing.Optional[str]` — Path of the Dataset, including the name. This locates the Dataset in the Humanloop filesystem and is used as as a unique identifier. For example: `folder/name` or just `name`.
-
**id:** `typing.Optional[str]` — ID for an existing Dataset.
-
**action:** `typing.Optional[UpdateDatesetAction]`
The action to take with the provided Datapoints.
- If `"set"`, the created version will only contain the Datapoints provided in this request.
- If `"add"`, the created version will contain the Datapoints provided in this request in addition to the Datapoints in the target version.
- If `"remove"`, the created version will contain the Datapoints in the target version except for the Datapoints provided in this request.
If `"add"` or `"remove"`, one of the `version_id` or `environment` query parameters may be provided.
-
**attributes:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Additional fields to describe the Dataset. Helpful to separate Dataset versions from each other with details on how they were created or used.
-
**version_name:** `typing.Optional[str]` — Unique name for the Dataset version. Version names must be unique for a given Dataset.
-
**version_description:** `typing.Optional[str]` — Description of the version, e.g., the changes made in this version.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.datasets.get(...)
-
#### 📝 Description
-
-
Retrieve the Dataset with the given ID.
Unless `include_datapoints` is set to `true`, the response will not include
the Datapoints.
Use the List Datapoints endpoint (`GET /{id}/datapoints`) to efficiently
retrieve Datapoints for a large Dataset.
By default, the deployed version of the Dataset is returned. Use the query parameters
`version_id` or `environment` to target a specific version of the Dataset.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.datasets.get(id='ds_b0baF1ca7652', version_id='dsv_6L78pqrdFi2xa', include_datapoints=True, )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Dataset.
-
**version_id:** `typing.Optional[str]` — A specific Version ID of the Dataset to retrieve.
-
**environment:** `typing.Optional[str]` — Name of the Environment to retrieve a deployed Version from.
-
**include_datapoints:** `typing.Optional[bool]` — If set to `true`, include all Datapoints in the response. Defaults to `false`. Consider using the paginated List Datapoints endpoint instead.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.datasets.delete(...)
-
#### 📝 Description
-
-
Delete the Dataset with the given ID.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.datasets.delete(id='id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Dataset.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.datasets.move(...)
-
#### 📝 Description
-
-
Move the Dataset to a different path or change the name.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.datasets.move(id='id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Dataset.
-
**path:** `typing.Optional[str]` — Path of the Dataset including the Dataset name, which is used as a unique identifier.
-
**name:** `typing.Optional[str]` — Name of the Dataset, which is used as a unique identifier.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.datasets.list_datapoints(...)
-
#### 📝 Description
-
-
List all Datapoints for the Dataset with the given ID.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
response = client.datasets.list_datapoints(id='ds_b0baF1ca7652', size=1, )
for item in response:
yield item
# alternatively, you can paginate page-by-page
for page in response.iter_pages():
yield page
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Dataset.
-
**version_id:** `typing.Optional[str]` — A specific Version ID of the Dataset to retrieve.
-
**environment:** `typing.Optional[str]` — Name of the Environment to retrieve a deployed Version from.
-
**page:** `typing.Optional[int]` — Page number for pagination.
-
**size:** `typing.Optional[int]` — Page size for pagination. Number of Datapoints to fetch.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.datasets.list_versions(...)
-
#### 📝 Description
-
-
Get a list of the versions for a Dataset.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.datasets.list_versions(id='ds_b0baF1ca7652', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Dataset.
-
**include_datapoints:** `typing.Optional[ListVersionsDatasetsIdVersionsGetRequestIncludeDatapoints]` — If set to 'latest_saved', include datapoints for the latest saved version. Alternatively, 'latest_committed' (deprecated) includes datapoints for the latest committed version only.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.datasets.delete_dataset_version(...)
-
#### 📝 Description
-
-
Delete a version of the Dataset.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.datasets.delete_dataset_version(id='id', version_id='version_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Dataset.
-
**version_id:** `str` — Unique identifier for the specific version of the Dataset.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.datasets.update_dataset_version(...)
-
#### 📝 Description
-
-
Update the name or description of the Dataset version.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.datasets.update_dataset_version(id='id', version_id='version_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Dataset.
-
**version_id:** `str` — Unique identifier for the specific version of the Dataset.
-
**name:** `typing.Optional[str]` — Name of the version.
-
**description:** `typing.Optional[str]` — Description of the version.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.datasets.upload_csv(...)
-
#### 📝 Description
-
-
Add Datapoints from a CSV file to a Dataset.
This will create a new version of the Dataset with the Datapoints from the CSV file.
If either `version_id` or `environment` is provided, the new version will be based on the specified version,
with the Datapoints from the CSV file added to the existing Datapoints in the version.
If neither `version_id` nor `environment` is provided, the new version will be based on the version
of the Dataset that is deployed to the default Environment.
You can optionally provide a name and description for the new version using `version_name`
and `version_description` parameters.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.datasets.upload_csv(id='id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for the Dataset
-
**file:** `from __future__ import annotations
core.File` — See core.File for more documentation
-
**version_id:** `typing.Optional[str]` — ID of the specific Dataset version to base the created Version on.
-
**environment:** `typing.Optional[str]` — Name of the Environment identifying a deployed Version to base the created Version on.
-
**version_name:** `typing.Optional[str]` — Name for the new Dataset version.
-
**version_description:** `typing.Optional[str]` — Description for the new Dataset version.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.datasets.set_deployment(...)
-
#### 📝 Description
-
-
Deploy Dataset to Environment.
Set the deployed version for the specified Environment.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.datasets.set_deployment(id='ds_b0baF1ca7652', environment_id='staging', version_id='dsv_6L78pqrdFi2xa', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Dataset.
-
**environment_id:** `str` — Unique identifier for the Environment to deploy the Version to.
-
**version_id:** `str` — Unique identifier for the specific version of the Dataset.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.datasets.remove_deployment(...)
-
#### 📝 Description
-
-
Remove deployed Dataset from Environment.
Remove the deployed version for the specified Environment.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.datasets.remove_deployment(id='ds_b0baF1ca7652', environment_id='staging', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Dataset.
-
**environment_id:** `str` — Unique identifier for the Environment to remove the deployment from.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.datasets.list_environments(...)
-
#### 📝 Description
-
-
List all Environments and their deployed versions for the Dataset.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.datasets.list_environments(id='id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Dataset.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluators.log(...)
-
#### 📝 Description
-
-
Submit Evaluator judgment for an existing Log.
Creates a new Log. The evaluated Log will be set as the parent of the created Log.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluators.log(parent_id='parent_id', )
```
#### ⚙️ Parameters
-
-
**parent_id:** `str` — Identifier of the evaluated Log. The newly created Log will have this one set as parent.
-
**version_id:** `typing.Optional[str]` — ID of the Evaluator version to log against.
-
**environment:** `typing.Optional[str]` — Name of the Environment identifying a deployed version to log to.
-
**path:** `typing.Optional[str]` — Path of the Evaluator, including the name. This locates the Evaluator in the Humanloop filesystem and is used as as a unique identifier. For example: `folder/name` or just `name`.
-
**id:** `typing.Optional[str]` — ID for an existing Evaluator.
-
**start_time:** `typing.Optional[dt.datetime]` — When the logged event started.
-
**end_time:** `typing.Optional[dt.datetime]` — When the logged event ended.
-
**output:** `typing.Optional[str]` — Generated output from the LLM. Only populated for LLM Evaluator Logs.
-
**created_at:** `typing.Optional[dt.datetime]` — User defined timestamp for when the log was created.
-
**error:** `typing.Optional[str]` — Error message if the log is an error.
-
**provider_latency:** `typing.Optional[float]` — Duration of the logged event in seconds.
-
**stdout:** `typing.Optional[str]` — Captured log and debug statements.
-
**provider_request:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Raw request sent to provider. Only populated for LLM Evaluator Logs.
-
**provider_response:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Raw response received the provider. Only populated for LLM Evaluator Logs.
-
**inputs:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — The inputs passed to the prompt template.
-
**source:** `typing.Optional[str]` — Identifies where the model was called from.
-
**metadata:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Any additional metadata to record.
-
**log_status:** `typing.Optional[LogStatus]` — Status of a Log. Set to `incomplete` if you intend to update and eventually complete the Log and want the File's monitoring Evaluators to wait until you mark it as `complete`. If log_status is not provided, observability will pick up the Log as soon as possible. Updating this from specified to unspecified is undefined behavior.
-
**source_datapoint_id:** `typing.Optional[str]` — Unique identifier for the Datapoint that this Log is derived from. This can be used by Humanloop to associate Logs to Evaluations. If provided, Humanloop will automatically associate this Log to Evaluations that require a Log for this Datapoint-Version pair.
-
**trace_parent_id:** `typing.Optional[str]` — The ID of the parent Log to nest this Log under in a Trace.
-
**user:** `typing.Optional[str]` — End-user ID related to the Log.
-
**create_evaluator_log_request_environment:** `typing.Optional[str]` — The name of the Environment the Log is associated to.
-
**save:** `typing.Optional[bool]` — Whether the request/response payloads will be stored on Humanloop.
-
**log_id:** `typing.Optional[str]` — This will identify a Log. If you don't provide a Log ID, Humanloop will generate one for you.
-
**output_message:** `typing.Optional[ChatMessageParams]` — The message returned by the LLM. Only populated for LLM Evaluator Logs.
-
**judgment:** `typing.Optional[CreateEvaluatorLogRequestJudgmentParams]` — Evaluator assessment of the Log.
-
**marked_completed:** `typing.Optional[bool]` — Whether the Log has been manually marked as completed by a user.
-
**spec:** `typing.Optional[CreateEvaluatorLogRequestSpecParams]`
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluators.list(...)
-
#### 📝 Description
-
-
Get a list of all Evaluators.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
response = client.evaluators.list(size=1, )
for item in response:
yield item
# alternatively, you can paginate page-by-page
for page in response.iter_pages():
yield page
```
#### ⚙️ Parameters
-
-
**page:** `typing.Optional[int]` — Page offset for pagination.
-
**size:** `typing.Optional[int]` — Page size for pagination. Number of Evaluators to fetch.
-
**name:** `typing.Optional[str]` — Case-insensitive filter for Evaluator name.
-
**user_filter:** `typing.Optional[str]` — Case-insensitive filter for users in the Evaluator. This filter matches against both email address and name of users.
-
**sort_by:** `typing.Optional[FileSortBy]` — Field to sort Evaluators by
-
**order:** `typing.Optional[SortOrder]` — Direction to sort by.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluators.upsert(...)
-
#### 📝 Description
-
-
Create an Evaluator or update it with a new version if it already exists.
Evaluators are identified by the `ID` or their `path`. The spec provided determines the version of the Evaluator.
You can provide `version_name` and `version_description` to identify and describe your versions.
Version names must be unique within an Evaluator - attempting to create a version with a name
that already exists will result in a 409 Conflict error.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluators.upsert(path='Shared Evaluators/Accuracy Evaluator', spec={'arguments_type': "target_required", 'return_type': "number", 'evaluator_type': 'python', 'code': 'def evaluate(answer, target):\n return 0.5'}, version_name='simple-evaluator', version_description='Simple evaluator that returns 0.5', )
```
#### ⚙️ Parameters
-
-
**spec:** `EvaluatorRequestSpecParams`
-
**path:** `typing.Optional[str]` — Path of the Evaluator, including the name. This locates the Evaluator in the Humanloop filesystem and is used as as a unique identifier. For example: `folder/name` or just `name`.
-
**id:** `typing.Optional[str]` — ID for an existing Evaluator.
-
**version_name:** `typing.Optional[str]` — Unique name for the Evaluator version. Version names must be unique for a given Evaluator.
-
**version_description:** `typing.Optional[str]` — Description of the version, e.g., the changes made in this version.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluators.get(...)
-
#### 📝 Description
-
-
Retrieve the Evaluator with the given ID.
By default, the deployed version of the Evaluator is returned. Use the query parameters
`version_id` or `environment` to target a specific version of the Evaluator.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluators.get(id='ev_890bcd', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Evaluator.
-
**version_id:** `typing.Optional[str]` — A specific Version ID of the Evaluator to retrieve.
-
**environment:** `typing.Optional[str]` — Name of the Environment to retrieve a deployed Version from.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluators.delete(...)
-
#### 📝 Description
-
-
Delete the Evaluator with the given ID.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluators.delete(id='ev_890bcd', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Evaluator.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluators.move(...)
-
#### 📝 Description
-
-
Move the Evaluator to a different path or change the name.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluators.move(id='ev_890bcd', path='new directory/new name', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Evaluator.
-
**path:** `typing.Optional[str]` — Path of the Evaluator including the Evaluator name, which is used as a unique identifier.
-
**name:** `typing.Optional[str]` — Name of the Evaluator, which is used as a unique identifier.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluators.list_versions(...)
-
#### 📝 Description
-
-
Get a list of all the versions of an Evaluator.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluators.list_versions(id='ev_890bcd', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for the Evaluator.
-
**evaluator_aggregates:** `typing.Optional[bool]` — Whether to include Evaluator aggregate results for the versions in the response
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluators.delete_evaluator_version(...)
-
#### 📝 Description
-
-
Delete a version of the Evaluator.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluators.delete_evaluator_version(id='id', version_id='version_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Evaluator.
-
**version_id:** `str` — Unique identifier for the specific version of the Evaluator.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluators.update_evaluator_version(...)
-
#### 📝 Description
-
-
Update the name or description of the Evaluator version.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluators.update_evaluator_version(id='id', version_id='version_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Evaluator.
-
**version_id:** `str` — Unique identifier for the specific version of the Evaluator.
-
**name:** `typing.Optional[str]` — Name of the version.
-
**description:** `typing.Optional[str]` — Description of the version.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluators.set_deployment(...)
-
#### 📝 Description
-
-
Deploy Evaluator to an Environment.
Set the deployed version for the specified Environment. This Evaluator
will be used for calls made to the Evaluator in this Environment.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluators.set_deployment(id='ev_890bcd', environment_id='staging', version_id='evv_012def', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Evaluator.
-
**environment_id:** `str` — Unique identifier for the Environment to deploy the Version to.
-
**version_id:** `str` — Unique identifier for the specific version of the Evaluator.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluators.remove_deployment(...)
-
#### 📝 Description
-
-
Remove deployed Evaluator from the Environment.
Remove the deployed version for the specified Environment. This Evaluator
will no longer be used for calls made to the Evaluator in this Environment.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluators.remove_deployment(id='ev_890bcd', environment_id='staging', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Evaluator.
-
**environment_id:** `str` — Unique identifier for the Environment to remove the deployment from.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluators.list_environments(...)
-
#### 📝 Description
-
-
List all Environments and their deployed versions for the Evaluator.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluators.list_environments(id='ev_890bcd', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Evaluator.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluators.update_monitoring(...)
-
#### 📝 Description
-
-
Activate and deactivate Evaluators for monitoring the Evaluator.
An activated Evaluator will automatically be run on all new Logs
within the Evaluator for monitoring purposes.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluators.update_monitoring(id='id', )
```
#### ⚙️ Parameters
-
-
**id:** `str`
-
**activate:** `typing.Optional[typing.Sequence[EvaluatorActivationDeactivationRequestActivateItemParams]]` — Evaluators to activate for Monitoring. These will be automatically run on new Logs.
-
**deactivate:** `typing.Optional[typing.Sequence[EvaluatorActivationDeactivationRequestDeactivateItemParams]]` — Evaluators to deactivate. These will not be run on new Logs.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.flows.log(...)
-
#### 📝 Description
-
-
Log to a Flow.
You can use query parameters `version_id`, or `environment`, to target
an existing version of the Flow. Otherwise, the default deployed version will be chosen.
If you create the Flow Log with a `log_status` of `incomplete`, you should later update it to `complete`
in order to trigger Evaluators.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
import datetime
client = Humanloop(api_key="YOUR_API_KEY", )
client.flows.log(id='fl_6o701g4jmcanPVHxdqD0O', flow={'attributes': {'prompt': {'template': 'You are a helpful assistant helping with medical anamnesis', 'model': 'gpt-4o', 'temperature': 0.8}
, 'tool': {'name': 'retrieval_tool_v3', 'description': 'Retrieval tool for MedQA.', 'source_code': 'def retrieval_tool(question: str) -> str:\n pass\n'}
}}, inputs={'question': 'Patient with a history of diabetes and hypertension presents with chest pain and shortness of breath.'
}, output='The patient is likely experiencing a myocardial infarction. Immediate medical attention is required.', log_status="incomplete", start_time=datetime.datetime.fromisoformat("2024-07-08 21:40:35+00:00", ), end_time=datetime.datetime.fromisoformat("2024-07-08 21:40:39+00:00", ), )
```
#### ⚙️ Parameters
-
-
**version_id:** `typing.Optional[str]` — A specific Version ID of the Flow to log to.
-
**environment:** `typing.Optional[str]` — Name of the Environment identifying a deployed version to log to.
-
**messages:** `typing.Optional[typing.Sequence[ChatMessageParams]]` — List of chat messages that were used as an input to the Flow.
-
**output_message:** `typing.Optional[ChatMessageParams]` — The output message returned by this Flow.
-
**run_id:** `typing.Optional[str]` — Unique identifier for the Run to associate the Log to.
-
**path:** `typing.Optional[str]` — Path of the Flow, including the name. This locates the Flow in the Humanloop filesystem and is used as as a unique identifier. For example: `folder/name` or just `name`.
-
**id:** `typing.Optional[str]` — ID for an existing Flow.
-
**start_time:** `typing.Optional[dt.datetime]` — The start time of the Trace. Will be updated if a child Log with an earlier start time is added.
-
**end_time:** `typing.Optional[dt.datetime]` — The end time of the Trace. Will be updated if a child Log with a later end time is added.
-
**output:** `typing.Optional[str]` — Generated output from your model for the provided inputs. Can be `None` if logging an error, or if creating a parent Log with the intention to populate it later.
-
**created_at:** `typing.Optional[dt.datetime]` — User defined timestamp for when the log was created.
-
**error:** `typing.Optional[str]` — Error message if the log is an error.
-
**provider_latency:** `typing.Optional[float]` — Duration of the logged event in seconds.
-
**stdout:** `typing.Optional[str]` — Captured log and debug statements.
-
**provider_request:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Raw request sent to provider.
-
**provider_response:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Raw response received the provider.
-
**inputs:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — The inputs passed to the prompt template.
-
**source:** `typing.Optional[str]` — Identifies where the model was called from.
-
**metadata:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Any additional metadata to record.
-
**log_status:** `typing.Optional[LogStatus]` — Status of the Flow Log. When a Log is updated from `incomplete` to `complete`, it becomes available to Monitoring Evaluators. Flow Logs cannot have an unspecified status: they must start as `incomplete` to allow children to be added. Provide `complete` if you don't intend to add children to the trace.
-
**source_datapoint_id:** `typing.Optional[str]` — Unique identifier for the Datapoint that this Log is derived from. This can be used by Humanloop to associate Logs to Evaluations. If provided, Humanloop will automatically associate this Log to Evaluations that require a Log for this Datapoint-Version pair.
-
**trace_parent_id:** `typing.Optional[str]` — The ID of the parent Log to nest this Log under in a Trace.
-
**user:** `typing.Optional[str]` — End-user ID related to the Log.
-
**flow_log_request_environment:** `typing.Optional[str]` — The name of the Environment the Log is associated to.
-
**save:** `typing.Optional[bool]` — Whether the request/response payloads will be stored on Humanloop.
-
**log_id:** `typing.Optional[str]` — This will identify a Log. If you don't provide a Log ID, Humanloop will generate one for you.
-
**flow:** `typing.Optional[FlowKernelRequestParams]` — Flow used to generate the Trace.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.flows.update_log(...)
-
#### 📝 Description
-
-
Update the status, inputs, output of a Flow Log.
Marking a Flow Log as complete will trigger any monitoring Evaluators to run.
Inputs and output (or error) must be provided in order to mark it as complete.
The end_time log attribute will be set to match the time the log is marked as complete.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.flows.update_log(log_id='medqa_experiment_0001', inputs={'question': 'Patient with a history of diabetes and normal tension presents with chest pain and shortness of breath.'
}, output='The patient is likely experiencing a myocardial infarction. Immediate medical attention is required.', log_status="complete", )
```
#### ⚙️ Parameters
-
-
**log_id:** `str` — Unique identifier of the Flow Log.
-
**messages:** `typing.Optional[typing.Sequence[ChatMessageParams]]` — List of chat messages that were used as an input to the Flow.
-
**output_message:** `typing.Optional[ChatMessageParams]` — The output message returned by this Flow.
-
**inputs:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — The inputs passed to the Flow Log.
-
**output:** `typing.Optional[str]` — The output of the Flow Log. Provide None to unset existing `output` value. Provide either this, `output_message` or `error`.
-
**error:** `typing.Optional[str]` — The error message of the Flow Log. Provide None to unset existing `error` value. Provide either this, `output_message` or `output`.
-
**log_status:** `typing.Optional[LogStatus]` — Status of the Flow Log. When a Flow Log is updated to `complete`, no more Logs can be added to it. Monitoring Evaluators will only run on `complete` Flow Logs.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.flows.get(...)
-
#### 📝 Description
-
-
Retrieve the Flow with the given ID.
By default, the deployed version of the Flow is returned. Use the query parameters
`version_id` or `environment` to target a specific version of the Flow.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.flows.get(id='fl_6o701g4jmcanPVHxdqD0O', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Flow.
-
**version_id:** `typing.Optional[str]` — A specific Version ID of the Flow to retrieve.
-
**environment:** `typing.Optional[str]` — Name of the Environment to retrieve a deployed Version from.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.flows.delete(...)
-
#### 📝 Description
-
-
Delete the Flow with the given ID.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.flows.delete(id='fl_6o701g4jmcanPVHxdqD0O', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Flow.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.flows.move(...)
-
#### 📝 Description
-
-
Move the Flow to a different path or change the name.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.flows.move(id='fl_6o701g4jmcanPVHxdqD0O', path='new directory/new name', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Flow.
-
**path:** `typing.Optional[str]` — Path of the Flow including the Flow name, which is used as a unique identifier.
-
**name:** `typing.Optional[str]` — Name of the Flow.
-
**directory_id:** `typing.Optional[str]` — Unique identifier for the Directory to move Flow to. Starts with `dir_`.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.flows.list(...)
-
#### 📝 Description
-
-
Get a list of Flows.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
response = client.flows.list(size=1, )
for item in response:
yield item
# alternatively, you can paginate page-by-page
for page in response.iter_pages():
yield page
```
#### ⚙️ Parameters
-
-
**page:** `typing.Optional[int]` — Page number for pagination.
-
**size:** `typing.Optional[int]` — Page size for pagination. Number of Flows to fetch.
-
**name:** `typing.Optional[str]` — Case-insensitive filter for Flow name.
-
**user_filter:** `typing.Optional[str]` — Case-insensitive filter for users in the Flow. This filter matches against both email address and name of users.
-
**sort_by:** `typing.Optional[FileSortBy]` — Field to sort Flows by
-
**order:** `typing.Optional[SortOrder]` — Direction to sort by.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.flows.upsert(...)
-
#### 📝 Description
-
-
Create or update a Flow.
Flows can also be identified by the `ID` or their `path`.
You can provide `version_name` and `version_description` to identify and describe your versions.
Version names must be unique within a Flow - attempting to create a version with a name
that already exists will result in a 409 Conflict error.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.flows.upsert(path='Personal Projects/MedQA Flow', attributes={'prompt': {'template': 'You are a helpful medical assistant helping with medical anamnesis. Answer {{question}}', 'model': 'gpt-4o', 'temperature': 0.8}
, 'tool': {'name': 'retrieval_tool_v3', 'description': 'Retrieval tool for MedQA.', 'source_code': 'def retrieval_tool(question: str) -> str:\n pass\n'}
, 'version_name': 'medqa-flow-v1'
, 'version_description': 'Initial version'
}, )
```
#### ⚙️ Parameters
-
-
**attributes:** `typing.Dict[str, typing.Optional[typing.Any]]` — A key-value object identifying the Flow Version.
-
**path:** `typing.Optional[str]` — Path of the Flow, including the name. This locates the Flow in the Humanloop filesystem and is used as as a unique identifier. For example: `folder/name` or just `name`.
-
**id:** `typing.Optional[str]` — ID for an existing Flow.
-
**version_name:** `typing.Optional[str]` — Unique name for the Flow version. Version names must be unique for a given Flow.
-
**version_description:** `typing.Optional[str]` — Description of the version, e.g., the changes made in this version.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.flows.list_versions(...)
-
#### 📝 Description
-
-
Get a list of all the versions of a Flow.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.flows.list_versions(id='fl_6o701g4jmcanPVHxdqD0O', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Flow.
-
**evaluator_aggregates:** `typing.Optional[bool]` — Whether to include Evaluator aggregate results for the versions in the response
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.flows.delete_flow_version(...)
-
#### 📝 Description
-
-
Delete a version of the Flow.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.flows.delete_flow_version(id='id', version_id='version_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Flow.
-
**version_id:** `str` — Unique identifier for the specific version of the Flow.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.flows.update_flow_version(...)
-
#### 📝 Description
-
-
Update the name or description of the Flow version.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.flows.update_flow_version(id='id', version_id='version_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Flow.
-
**version_id:** `str` — Unique identifier for the specific version of the Flow.
-
**name:** `typing.Optional[str]` — Name of the version.
-
**description:** `typing.Optional[str]` — Description of the version.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.flows.set_deployment(...)
-
#### 📝 Description
-
-
Deploy Flow to an Environment.
Set the deployed version for the specified Environment. This Flow
will be used for calls made to the Flow in this Environment.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.flows.set_deployment(id='fl_6o701g4jmcanPVHxdqD0O', environment_id='staging', version_id='flv_6o701g4jmcanPVHxdqD0O', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Flow.
-
**environment_id:** `str` — Unique identifier for the Environment to deploy the Version to.
-
**version_id:** `str` — Unique identifier for the specific version of the Flow.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.flows.remove_deployment(...)
-
#### 📝 Description
-
-
Remove deployed Flow from the Environment.
Remove the deployed version for the specified Environment. This Flow
will no longer be used for calls made to the Flow in this Environment.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.flows.remove_deployment(id='fl_6o701g4jmcanPVHxdqD0O', environment_id='staging', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Flow.
-
**environment_id:** `str` — Unique identifier for the Environment to remove the deployment from.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.flows.list_environments(...)
-
#### 📝 Description
-
-
List all Environments and their deployed versions for the Flow.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.flows.list_environments(id='fl_6o701g4jmcanPVHxdqD0O', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Flow.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.flows.update_monitoring(...)
-
#### 📝 Description
-
-
Activate and deactivate Evaluators for monitoring the Flow.
An activated Evaluator will automatically be run on all new "completed" Logs
within the Flow for monitoring purposes.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.flows.update_monitoring(id='fl_6o701g4jmcanPVHxdqD0O', activate=[{'evaluator_version_id': 'evv_1abc4308abd'}], )
```
#### ⚙️ Parameters
-
-
**id:** `str`
-
**activate:** `typing.Optional[typing.Sequence[EvaluatorActivationDeactivationRequestActivateItemParams]]` — Evaluators to activate for Monitoring. These will be automatically run on new Logs.
-
**deactivate:** `typing.Optional[typing.Sequence[EvaluatorActivationDeactivationRequestDeactivateItemParams]]` — Evaluators to deactivate. These will not be run on new Logs.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.log(...)
-
#### 📝 Description
-
-
Create an Agent Log.
You can use query parameters `version_id`, or `environment`, to target
an existing version of the Agent. Otherwise, the default deployed version will be chosen.
If you create the Agent Log with a `log_status` of `incomplete`, you should later update it to `complete`
in order to trigger Evaluators.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.agents.log(path='Banking/Teller Agent', agent={'provider': "anthropic", 'endpoint': "chat", 'model': 'claude-3-7-sonnet-latest', 'reasoning_effort': 1024, 'template': [{'role': "system", 'content': 'You are a helpful digital assistant, helping users navigate our digital banking platform.'}], 'max_iterations': 3, 'tools': [{'type': 'file', 'link': {'file_id': 'pr_1234567890', 'version_id': 'prv_1234567890'}, 'on_agent_call': "continue"}, {'type': 'inline', 'json_schema': {'name': 'stop', 'description': 'Call this tool when you have finished your task.', 'parameters': {'type': 'object'
, 'properties': {'output': {'type': 'string', 'description': 'The final output to return to the user.'}}
, 'additionalProperties': False
, 'required': ['output']
}, 'strict': True}, 'on_agent_call': "stop"}]}, )
```
#### ⚙️ Parameters
-
-
**version_id:** `typing.Optional[str]` — A specific Version ID of the Agent to log to.
-
**environment:** `typing.Optional[str]` — Name of the Environment identifying a deployed version to log to.
-
**run_id:** `typing.Optional[str]` — Unique identifier for the Run to associate the Log to.
-
**path:** `typing.Optional[str]` — Path of the Agent, including the name. This locates the Agent in the Humanloop filesystem and is used as as a unique identifier. For example: `folder/name` or just `name`.
-
**id:** `typing.Optional[str]` — ID for an existing Agent.
-
**output_message:** `typing.Optional[ChatMessageParams]` — The message returned by the provider.
-
**prompt_tokens:** `typing.Optional[int]` — Number of tokens in the prompt used to generate the output.
-
**reasoning_tokens:** `typing.Optional[int]` — Number of reasoning tokens used to generate the output.
-
**output_tokens:** `typing.Optional[int]` — Number of tokens in the output generated by the model.
-
**prompt_cost:** `typing.Optional[float]` — Cost in dollars associated to the tokens in the prompt.
-
**output_cost:** `typing.Optional[float]` — Cost in dollars associated to the tokens in the output.
-
**finish_reason:** `typing.Optional[str]` — Reason the generation finished.
-
**messages:** `typing.Optional[typing.Sequence[ChatMessageParams]]` — The messages passed to the to provider chat endpoint.
-
**tool_choice:** `typing.Optional[AgentLogRequestToolChoiceParams]`
Controls how the model uses tools. The following options are supported:
- `'none'` means the model will not call any tool and instead generates a message; this is the default when no tools are provided as part of the Prompt.
- `'auto'` means the model can decide to call one or more of the provided tools; this is the default when tools are provided as part of the Prompt.
- `'required'` means the model must call one or more of the provided tools.
- `{'type': 'function', 'function': {name': }}` forces the model to use the named function.
-
**agent:** `typing.Optional[AgentLogRequestAgentParams]`
The Agent configuration to use. Two formats are supported:
- An object representing the details of the Agent configuration
- A string representing the raw contents of a .agent file
A new Agent version will be created if the provided details do not match any existing version.
-
**start_time:** `typing.Optional[dt.datetime]` — When the logged event started.
-
**end_time:** `typing.Optional[dt.datetime]` — When the logged event ended.
-
**output:** `typing.Optional[str]` — Generated output from your model for the provided inputs. Can be `None` if logging an error, or if creating a parent Log with the intention to populate it later.
-
**created_at:** `typing.Optional[dt.datetime]` — User defined timestamp for when the log was created.
-
**error:** `typing.Optional[str]` — Error message if the log is an error.
-
**provider_latency:** `typing.Optional[float]` — Duration of the logged event in seconds.
-
**stdout:** `typing.Optional[str]` — Captured log and debug statements.
-
**provider_request:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Raw request sent to provider.
-
**provider_response:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Raw response received the provider.
-
**inputs:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — The inputs passed to the prompt template.
-
**source:** `typing.Optional[str]` — Identifies where the model was called from.
-
**metadata:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Any additional metadata to record.
-
**log_status:** `typing.Optional[LogStatus]` — Status of a Log. Set to `incomplete` if you intend to update and eventually complete the Log and want the File's monitoring Evaluators to wait until you mark it as `complete`. If log_status is not provided, observability will pick up the Log as soon as possible. Updating this from specified to unspecified is undefined behavior.
-
**source_datapoint_id:** `typing.Optional[str]` — Unique identifier for the Datapoint that this Log is derived from. This can be used by Humanloop to associate Logs to Evaluations. If provided, Humanloop will automatically associate this Log to Evaluations that require a Log for this Datapoint-Version pair.
-
**trace_parent_id:** `typing.Optional[str]` — The ID of the parent Log to nest this Log under in a Trace.
-
**user:** `typing.Optional[str]` — End-user ID related to the Log.
-
**agent_log_request_environment:** `typing.Optional[str]` — The name of the Environment the Log is associated to.
-
**save:** `typing.Optional[bool]` — Whether the request/response payloads will be stored on Humanloop.
-
**log_id:** `typing.Optional[str]` — This will identify a Log. If you don't provide a Log ID, Humanloop will generate one for you.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.update_log(...)
-
#### 📝 Description
-
-
Update a Log.
Update the details of a Log with the given ID.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.agents.update_log(id='ag_1234567890', log_id='log_1234567890', messages=[{'role': "user", 'content': 'I need to withdraw $1000'}, {'role': "assistant", 'content': 'Of course! Would you like to use your savings or checking account?'}], output_message={'role': "assistant", 'content': "I'm sorry, I can't help with that."}, log_status="complete", )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Agent.
-
**log_id:** `str` — Unique identifier for the Log.
-
**messages:** `typing.Optional[typing.Sequence[ChatMessageParams]]` — List of chat messages that were used as an input to the Flow.
-
**output_message:** `typing.Optional[ChatMessageParams]` — The output message returned by this Flow.
-
**inputs:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — The inputs passed to the Flow Log.
-
**output:** `typing.Optional[str]` — The output of the Flow Log. Provide None to unset existing `output` value. Provide either this, `output_message` or `error`.
-
**error:** `typing.Optional[str]` — The error message of the Flow Log. Provide None to unset existing `error` value. Provide either this, `output_message` or `output`.
-
**log_status:** `typing.Optional[LogStatus]` — Status of the Flow Log. When a Flow Log is updated to `complete`, no more Logs can be added to it. Monitoring Evaluators will only run on `complete` Flow Logs.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.call_stream(...)
-
#### 📝 Description
-
-
Call an Agent. The Agent will run on the Humanloop runtime and return a completed Agent Log.
If the Agent requires a tool call that cannot be ran by Humanloop, execution will halt. To continue,
pass the ID of the incomplete Log and the required tool call to the /agents/continue endpoint.
The agent will run for the maximum number of iterations, or until it encounters a stop condition,
according to its configuration.
You can use query parameters `version_id`, or `environment`, to target
an existing version of the Agent. Otherwise the default deployed version will be chosen.
Instead of targeting an existing version explicitly, you can instead pass in
Agent details in the request body. A new version is created if it does not match
any existing ones. This is helpful in the case where you are storing or deriving
your Agent details in code.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
response = client.agents.call_stream()
for chunk in response.data:
yield chunk
```
#### ⚙️ Parameters
-
-
**version_id:** `typing.Optional[str]` — A specific Version ID of the Agent to log to.
-
**environment:** `typing.Optional[str]` — Name of the Environment identifying a deployed version to log to.
-
**path:** `typing.Optional[str]` — Path of the Agent, including the name. This locates the Agent in the Humanloop filesystem and is used as as a unique identifier. For example: `folder/name` or just `name`.
-
**id:** `typing.Optional[str]` — ID for an existing Agent.
-
**messages:** `typing.Optional[typing.Sequence[ChatMessageParams]]` — The messages passed to the to provider chat endpoint.
-
**tool_choice:** `typing.Optional[AgentsCallStreamRequestToolChoiceParams]`
Controls how the model uses tools. The following options are supported:
- `'none'` means the model will not call any tool and instead generates a message; this is the default when no tools are provided as part of the Prompt.
- `'auto'` means the model can decide to call one or more of the provided tools; this is the default when tools are provided as part of the Prompt.
- `'required'` means the model must call one or more of the provided tools.
- `{'type': 'function', 'function': {name': }}` forces the model to use the named function.
-
**agent:** `typing.Optional[AgentsCallStreamRequestAgentParams]`
The Agent configuration to use. Two formats are supported:
- An object representing the details of the Agent configuration
- A string representing the raw contents of a .agent file
A new Agent version will be created if the provided details do not match any existing version.
-
**inputs:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — The inputs passed to the prompt template.
-
**source:** `typing.Optional[str]` — Identifies where the model was called from.
-
**metadata:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Any additional metadata to record.
-
**start_time:** `typing.Optional[dt.datetime]` — When the logged event started.
-
**end_time:** `typing.Optional[dt.datetime]` — When the logged event ended.
-
**log_status:** `typing.Optional[LogStatus]` — Status of a Log. Set to `incomplete` if you intend to update and eventually complete the Log and want the File's monitoring Evaluators to wait until you mark it as `complete`. If log_status is not provided, observability will pick up the Log as soon as possible. Updating this from specified to unspecified is undefined behavior.
-
**source_datapoint_id:** `typing.Optional[str]` — Unique identifier for the Datapoint that this Log is derived from. This can be used by Humanloop to associate Logs to Evaluations. If provided, Humanloop will automatically associate this Log to Evaluations that require a Log for this Datapoint-Version pair.
-
**trace_parent_id:** `typing.Optional[str]` — The ID of the parent Log to nest this Log under in a Trace.
-
**user:** `typing.Optional[str]` — End-user ID related to the Log.
-
**agents_call_stream_request_environment:** `typing.Optional[str]` — The name of the Environment the Log is associated to.
-
**save:** `typing.Optional[bool]` — Whether the request/response payloads will be stored on Humanloop.
-
**log_id:** `typing.Optional[str]` — This will identify a Log. If you don't provide a Log ID, Humanloop will generate one for you.
-
**provider_api_keys:** `typing.Optional[ProviderApiKeysParams]` — API keys required by each provider to make API calls. The API keys provided here are not stored by Humanloop. If not specified here, Humanloop will fall back to the key saved to your organization.
-
**return_inputs:** `typing.Optional[bool]` — Whether to return the inputs in the response. If false, the response will contain an empty dictionary under inputs. This is useful for reducing the size of the response. Defaults to true.
-
**include_trace_children:** `typing.Optional[bool]` — If true, populate `trace_children` for the returned Agent Log. Only applies when not streaming. Defaults to false.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.call(...)
-
#### 📝 Description
-
-
Call an Agent. The Agent will run on the Humanloop runtime and return a completed Agent Log.
If the Agent requires a tool call that cannot be ran by Humanloop, execution will halt. To continue,
pass the ID of the incomplete Log and the required tool call to the /agents/continue endpoint.
The agent will run for the maximum number of iterations, or until it encounters a stop condition,
according to its configuration.
You can use query parameters `version_id`, or `environment`, to target
an existing version of the Agent. Otherwise the default deployed version will be chosen.
Instead of targeting an existing version explicitly, you can instead pass in
Agent details in the request body. A new version is created if it does not match
any existing ones. This is helpful in the case where you are storing or deriving
your Agent details in code.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.agents.call(path='Banking/Teller Agent', messages=[{'role': "user", 'content': "I'd like to deposit $1000 to my savings account from my checking account."}], )
```
#### ⚙️ Parameters
-
-
**version_id:** `typing.Optional[str]` — A specific Version ID of the Agent to log to.
-
**environment:** `typing.Optional[str]` — Name of the Environment identifying a deployed version to log to.
-
**path:** `typing.Optional[str]` — Path of the Agent, including the name. This locates the Agent in the Humanloop filesystem and is used as as a unique identifier. For example: `folder/name` or just `name`.
-
**id:** `typing.Optional[str]` — ID for an existing Agent.
-
**messages:** `typing.Optional[typing.Sequence[ChatMessageParams]]` — The messages passed to the to provider chat endpoint.
-
**tool_choice:** `typing.Optional[AgentsCallRequestToolChoiceParams]`
Controls how the model uses tools. The following options are supported:
- `'none'` means the model will not call any tool and instead generates a message; this is the default when no tools are provided as part of the Prompt.
- `'auto'` means the model can decide to call one or more of the provided tools; this is the default when tools are provided as part of the Prompt.
- `'required'` means the model must call one or more of the provided tools.
- `{'type': 'function', 'function': {name': }}` forces the model to use the named function.
-
**agent:** `typing.Optional[AgentsCallRequestAgentParams]`
The Agent configuration to use. Two formats are supported:
- An object representing the details of the Agent configuration
- A string representing the raw contents of a .agent file
A new Agent version will be created if the provided details do not match any existing version.
-
**inputs:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — The inputs passed to the prompt template.
-
**source:** `typing.Optional[str]` — Identifies where the model was called from.
-
**metadata:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Any additional metadata to record.
-
**start_time:** `typing.Optional[dt.datetime]` — When the logged event started.
-
**end_time:** `typing.Optional[dt.datetime]` — When the logged event ended.
-
**log_status:** `typing.Optional[LogStatus]` — Status of a Log. Set to `incomplete` if you intend to update and eventually complete the Log and want the File's monitoring Evaluators to wait until you mark it as `complete`. If log_status is not provided, observability will pick up the Log as soon as possible. Updating this from specified to unspecified is undefined behavior.
-
**source_datapoint_id:** `typing.Optional[str]` — Unique identifier for the Datapoint that this Log is derived from. This can be used by Humanloop to associate Logs to Evaluations. If provided, Humanloop will automatically associate this Log to Evaluations that require a Log for this Datapoint-Version pair.
-
**trace_parent_id:** `typing.Optional[str]` — The ID of the parent Log to nest this Log under in a Trace.
-
**user:** `typing.Optional[str]` — End-user ID related to the Log.
-
**agents_call_request_environment:** `typing.Optional[str]` — The name of the Environment the Log is associated to.
-
**save:** `typing.Optional[bool]` — Whether the request/response payloads will be stored on Humanloop.
-
**log_id:** `typing.Optional[str]` — This will identify a Log. If you don't provide a Log ID, Humanloop will generate one for you.
-
**provider_api_keys:** `typing.Optional[ProviderApiKeysParams]` — API keys required by each provider to make API calls. The API keys provided here are not stored by Humanloop. If not specified here, Humanloop will fall back to the key saved to your organization.
-
**return_inputs:** `typing.Optional[bool]` — Whether to return the inputs in the response. If false, the response will contain an empty dictionary under inputs. This is useful for reducing the size of the response. Defaults to true.
-
**include_trace_children:** `typing.Optional[bool]` — If true, populate `trace_children` for the returned Agent Log. Only applies when not streaming. Defaults to false.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.continue_call_stream(...)
-
#### 📝 Description
-
-
Continue an incomplete Agent call.
This endpoint allows continuing an existing incomplete Agent call, by passing the tool call
requested by the Agent. The Agent will resume processing from where it left off.
The messages in the request will be appended to the original messages in the Log. You do not
have to provide the previous conversation history.
The original log must be in an incomplete state to be continued.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
response = client.agents.continue_call_stream(log_id='log_id', messages=[{'role': "user"}], )
for chunk in response.data:
yield chunk
```
#### ⚙️ Parameters
-
-
**log_id:** `str` — This identifies the Agent Log to continue.
-
**messages:** `typing.Sequence[ChatMessageParams]` — The additional messages with which to continue the Agent Log. Often, these should start with the Tool messages with results for the previous Assistant message's tool calls.
-
**provider_api_keys:** `typing.Optional[ProviderApiKeysParams]` — API keys required by each provider to make API calls. The API keys provided here are not stored by Humanloop. If not specified here, Humanloop will fall back to the key saved to your organization.
-
**include_trace_children:** `typing.Optional[bool]` — If true, populate `trace_children` for the returned Agent Log. Defaults to false.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.continue_call(...)
-
#### 📝 Description
-
-
Continue an incomplete Agent call.
This endpoint allows continuing an existing incomplete Agent call, by passing the tool call
requested by the Agent. The Agent will resume processing from where it left off.
The messages in the request will be appended to the original messages in the Log. You do not
have to provide the previous conversation history.
The original log must be in an incomplete state to be continued.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.agents.continue_call(log_id='log_1234567890', messages=[{'role': "tool", 'content': '{"type": "checking", "balance": 5200}', 'tool_call_id': 'tc_1234567890'}], )
```
#### ⚙️ Parameters
-
-
**log_id:** `str` — This identifies the Agent Log to continue.
-
**messages:** `typing.Sequence[ChatMessageParams]` — The additional messages with which to continue the Agent Log. Often, these should start with the Tool messages with results for the previous Assistant message's tool calls.
-
**provider_api_keys:** `typing.Optional[ProviderApiKeysParams]` — API keys required by each provider to make API calls. The API keys provided here are not stored by Humanloop. If not specified here, Humanloop will fall back to the key saved to your organization.
-
**include_trace_children:** `typing.Optional[bool]` — If true, populate `trace_children` for the returned Agent Log. Defaults to false.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.list(...)
-
#### 📝 Description
-
-
Get a list of all Agents.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
response = client.agents.list(size=1, )
for item in response:
yield item
# alternatively, you can paginate page-by-page
for page in response.iter_pages():
yield page
```
#### ⚙️ Parameters
-
-
**page:** `typing.Optional[int]` — Page number for pagination.
-
**size:** `typing.Optional[int]` — Page size for pagination. Number of Agents to fetch.
-
**name:** `typing.Optional[str]` — Case-insensitive filter for Agent name.
-
**user_filter:** `typing.Optional[str]` — Case-insensitive filter for users in the Agent. This filter matches against both email address and name of users.
-
**sort_by:** `typing.Optional[FileSortBy]` — Field to sort Agents by
-
**order:** `typing.Optional[SortOrder]` — Direction to sort by.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.upsert(...)
-
#### 📝 Description
-
-
Create an Agent or update it with a new version if it already exists.
Agents are identified by the `ID` or their `path`. The parameters (i.e. the template, temperature, model etc.) and
tools determine the versions of the Agent.
You can provide `version_name` and `version_description` to identify and describe your versions.
Version names must be unique within an Agent - attempting to create a version with a name
that already exists will result in a 409 Conflict error.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.agents.upsert(path='Banking/Teller Agent', provider="anthropic", endpoint="chat", model='claude-3-7-sonnet-latest', reasoning_effort=1024, template=[{'role': "system", 'content': 'You are a helpful digital assistant, helping users navigate our digital banking platform.'}], max_iterations=3, tools=[{'type': 'inline', 'json_schema': {'name': 'stop', 'description': 'Call this tool when you have finished your task.', 'parameters': {'type': 'object'
, 'properties': {'output': {'type': 'string', 'description': 'The final output to return to the user.'}}
, 'additionalProperties': False
, 'required': ['output']
}, 'strict': True}, 'on_agent_call': "stop"}], version_name='teller-agent-v1', version_description='Initial version', )
```
#### ⚙️ Parameters
-
-
**model:** `str` — The model instance used, e.g. `gpt-4`. See [supported models](https://humanloop.com/docs/reference/supported-models)
-
**path:** `typing.Optional[str]` — Path of the Agent, including the name. This locates the Agent in the Humanloop filesystem and is used as as a unique identifier. For example: `folder/name` or just `name`.
-
**id:** `typing.Optional[str]` — ID for an existing Agent.
-
**endpoint:** `typing.Optional[ModelEndpoints]` — The provider model endpoint used.
-
**template:** `typing.Optional[AgentRequestTemplateParams]`
The template contains the main structure and instructions for the model, including input variables for dynamic values.
For chat models, provide the template as a ChatTemplate (a list of messages), e.g. a system message, followed by a user message with an input variable.
For completion models, provide a prompt template as a string.
Input variables should be specified with double curly bracket syntax: `{{input_name}}`.
-
**template_language:** `typing.Optional[TemplateLanguage]` — The template language to use for rendering the template.
-
**provider:** `typing.Optional[ModelProviders]` — The company providing the underlying model service.
-
**max_tokens:** `typing.Optional[int]` — The maximum number of tokens to generate. Provide max_tokens=-1 to dynamically calculate the maximum number of tokens to generate given the length of the prompt
-
**temperature:** `typing.Optional[float]` — What sampling temperature to use when making a generation. Higher values means the model will be more creative.
-
**top_p:** `typing.Optional[float]` — An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass.
-
**stop:** `typing.Optional[AgentRequestStopParams]` — The string (or list of strings) after which the model will stop generating. The returned text will not contain the stop sequence.
-
**presence_penalty:** `typing.Optional[float]` — Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the generation so far.
-
**frequency_penalty:** `typing.Optional[float]` — Number between -2.0 and 2.0. Positive values penalize new tokens based on how frequently they appear in the generation so far.
-
**other:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Other parameter values to be passed to the provider call.
-
**seed:** `typing.Optional[int]` — If specified, model will make a best effort to sample deterministically, but it is not guaranteed.
-
**response_format:** `typing.Optional[ResponseFormatParams]` — The format of the response. Only `{"type": "json_object"}` is currently supported for chat.
-
**reasoning_effort:** `typing.Optional[AgentRequestReasoningEffortParams]` — Guidance on how many reasoning tokens it should generate before creating a response to the prompt. OpenAI reasoning models (o1, o3-mini) expect a OpenAIReasoningEffort enum. Anthropic reasoning models expect an integer, which signifies the maximum token budget.
-
**tools:** `typing.Optional[typing.Sequence[AgentRequestToolsItemParams]]`
-
**attributes:** `typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]]` — Additional fields to describe the Prompt. Helpful to separate Prompt versions from each other with details on how they were created or used.
-
**max_iterations:** `typing.Optional[int]` — The maximum number of iterations the Agent can run. This is used to limit the number of times the Agent model is called.
-
**version_name:** `typing.Optional[str]` — Unique name for the Prompt version. Each Prompt can only have one version with a given name.
-
**version_description:** `typing.Optional[str]` — Description of the Version.
-
**description:** `typing.Optional[str]` — Description of the Prompt.
-
**tags:** `typing.Optional[typing.Sequence[str]]` — List of tags associated with this prompt.
-
**readme:** `typing.Optional[str]` — Long description of the Prompt.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.delete_agent_version(...)
-
#### 📝 Description
-
-
Delete a version of the Agent.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.agents.delete_agent_version(id='ag_1234567890', version_id='agv_1234567890', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Agent.
-
**version_id:** `str` — Unique identifier for the specific version of the Agent.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.patch_agent_version(...)
-
#### 📝 Description
-
-
Update the name or description of the Agent version.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.agents.patch_agent_version(id='ag_1234567890', version_id='agv_1234567890', name='teller-agent-v2', description='Updated version', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Agent.
-
**version_id:** `str` — Unique identifier for the specific version of the Agent.
-
**name:** `typing.Optional[str]` — Name of the version.
-
**description:** `typing.Optional[str]` — Description of the version.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.get(...)
-
#### 📝 Description
-
-
Retrieve the Agent with the given ID.
By default, the deployed version of the Agent is returned. Use the query parameters
`version_id` or `environment` to target a specific version of the Agent.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.agents.get(id='ag_1234567890', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Agent.
-
**version_id:** `typing.Optional[str]` — A specific Version ID of the Agent to retrieve.
-
**environment:** `typing.Optional[str]` — Name of the Environment to retrieve a deployed Version from.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.delete(...)
-
#### 📝 Description
-
-
Delete the Agent with the given ID.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.agents.delete(id='ag_1234567890', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Agent.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.move(...)
-
#### 📝 Description
-
-
Move the Agent to a different path or change the name.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.agents.move(id='ag_1234567890', path='new directory/new name', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Agent.
-
**path:** `typing.Optional[str]` — Path of the Flow including the Flow name, which is used as a unique identifier.
-
**name:** `typing.Optional[str]` — Name of the Flow.
-
**directory_id:** `typing.Optional[str]` — Unique identifier for the Directory to move Flow to. Starts with `dir_`.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.list_versions(...)
-
#### 📝 Description
-
-
Get a list of all the versions of a Agent.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.agents.list_versions(id='ag_1234567890', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Agent.
-
**evaluator_aggregates:** `typing.Optional[bool]` — Whether to include Evaluator aggregate results for the versions in the response
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.set_deployment(...)
-
#### 📝 Description
-
-
Deploy Agent to an Environment.
Set the deployed version for the specified Environment. This Agent
will be used for calls made to the Agent in this Environment.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.agents.set_deployment(id='id', environment_id='environment_id', version_id='version_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Agent.
-
**environment_id:** `str` — Unique identifier for the Environment to deploy the Version to.
-
**version_id:** `str` — Unique identifier for the specific version of the Agent.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.remove_deployment(...)
-
#### 📝 Description
-
-
Remove deployed Agent from the Environment.
Remove the deployed version for the specified Environment. This Agent
will no longer be used for calls made to the Agent in this Environment.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.agents.remove_deployment(id='id', environment_id='environment_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Agent.
-
**environment_id:** `str` — Unique identifier for the Environment to remove the deployment from.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.list_environments(...)
-
#### 📝 Description
-
-
List all Environments and their deployed versions for the Agent.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.agents.list_environments(id='ag_1234567890', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Agent.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.update_monitoring(...)
-
#### 📝 Description
-
-
Activate and deactivate Evaluators for monitoring the Agent.
An activated Evaluator will automatically be run on all new Logs
within the Agent for monitoring purposes.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.agents.update_monitoring(id='ag_1234567890', activate=[{'evaluator_version_id': 'ev_1234567890'}, {'evaluator_id': 'ev_2345678901', 'environment_id': 'env_1234567890'}], deactivate=[{'evaluator_version_id': 'ev_0987654321'}], )
```
#### ⚙️ Parameters
-
-
**id:** `str`
-
**activate:** `typing.Optional[typing.Sequence[EvaluatorActivationDeactivationRequestActivateItemParams]]` — Evaluators to activate for Monitoring. These will be automatically run on new Logs.
-
**deactivate:** `typing.Optional[typing.Sequence[EvaluatorActivationDeactivationRequestDeactivateItemParams]]` — Evaluators to deactivate. These will not be run on new Logs.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.serialize(...)
-
#### 📝 Description
-
-
Serialize an Agent to the .agent file format.
Useful for storing the Agent with your code in a version control system,
or for editing with an AI tool.
By default, the deployed version of the Agent is returned. Use the query parameters
`version_id` or `environment` to target a specific version of the Agent.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.agents.serialize(id='id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Agent.
-
**version_id:** `typing.Optional[str]` — A specific Version ID of the Agent to retrieve.
-
**environment:** `typing.Optional[str]` — Name of the Environment to retrieve a deployed Version from.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.agents.deserialize(...)
-
#### 📝 Description
-
-
Deserialize an Agent from the .agent file format.
This returns a subset of the attributes required by an Agent.
This subset is the bit that defines the Agent version (e.g. with `model` and `temperature` etc)
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.agents.deserialize(agent='agent', )
```
#### ⚙️ Parameters
-
-
**agent:** `str`
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.directories.list()
-
#### 📝 Description
-
-
Retrieve a list of all Directories.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.directories.list()
```
#### ⚙️ Parameters
-
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.directories.create(...)
-
#### 📝 Description
-
-
Creates a Directory.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.directories.create()
```
#### ⚙️ Parameters
-
-
**name:** `typing.Optional[str]` — Name of the directory to create.
-
**parent_id:** `typing.Optional[str]` — ID of the parent directory. Starts with `dir_`.
-
**path:** `typing.Optional[str]` — Path to create the directory in, relative to the root directory. If the path does not exist, it will be created. Includes name, e.g. `path/to/directory`.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.directories.get(...)
-
#### 📝 Description
-
-
Fetches a directory by ID.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.directories.get(id='id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — String ID of directory. Starts with `dir_`.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.directories.delete(...)
-
#### 📝 Description
-
-
Delete the Directory with the given ID.
The Directory must be empty (i.e. contain no Directories or Files).
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.directories.delete(id='id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Directory. Starts with `dir_`.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.directories.update(...)
-
#### 📝 Description
-
-
Update the Directory with the given ID.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.directories.update(id='id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Directory. Starts with `dir_`.
-
**name:** `typing.Optional[str]` — Name to set for the directory.
-
**parent_id:** `typing.Optional[str]` — ID of the parent directory. Specify this to move directories. Starts with `dir_`.
-
**path:** `typing.Optional[str]` — Path to move the directory to, relative to the root directory. Specify this to move directories. Includes name, e.g. `path/to/directory`.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.files.list_files(...)
-
#### 📝 Description
-
-
Get a paginated list of files.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.files.list_files()
```
#### ⚙️ Parameters
-
-
**page:** `typing.Optional[int]` — Page offset for pagination.
-
**size:** `typing.Optional[int]` — Page size for pagination. Number of files to fetch.
-
**name:** `typing.Optional[str]` — Case-insensitive filter for file name.
-
**path:** `typing.Optional[str]` — Path of the directory to filter for. Returns files in this directory and all its subdirectories.
-
**template:** `typing.Optional[bool]` — Filter to include only template files.
-
**type:** `typing.Optional[typing.Union[FileType, typing.Sequence[FileType]]]` — List of file types to filter for.
-
**environment:** `typing.Optional[str]` — Case-sensitive filter for files with a deployment in the specified environment. Requires the environment name.
-
**sort_by:** `typing.Optional[FileSortBy]` — Field to sort files by
-
**order:** `typing.Optional[SortOrder]` — Direction to sort by.
-
**include_raw_file_content:** `typing.Optional[bool]` — Whether to include the raw file content in the response. Currently only supported for Agents and Prompts.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.files.retrieve_by_path(...)
-
#### 📝 Description
-
-
Retrieve a File by path.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.files.retrieve_by_path(path='path', )
```
#### ⚙️ Parameters
-
-
**path:** `str` — Path of the File to retrieve.
-
**environment:** `typing.Optional[str]` — Name of the Environment to retrieve a deployed Version from.
-
**include_raw_file_content:** `typing.Optional[bool]` — Whether to include the raw file content in the response. Currently only supported for Agents and Prompts.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluations.list(...)
-
#### 📝 Description
-
-
Retrieve a list of Evaluations for the specified File.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
response = client.evaluations.list(file_id='pr_30gco7dx6JDq4200GVOHa', size=1, )
for item in response:
yield item
# alternatively, you can paginate page-by-page
for page in response.iter_pages():
yield page
```
#### ⚙️ Parameters
-
-
**file_id:** `str` — Filter by File ID. Only Evaluations for the specified File will be returned.
-
**page:** `typing.Optional[int]` — Page number for pagination.
-
**size:** `typing.Optional[int]` — Page size for pagination. Number of Evaluations to fetch.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluations.create(...)
-
#### 📝 Description
-
-
Create an Evaluation.
Create a new Evaluation by specifying the File to evaluate, and a name
for the Evaluation.
You can then add Runs to this Evaluation using the `POST /evaluations/{id}/runs` endpoint.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluations.create(evaluators=[{'version_id': 'version_id'}], )
```
#### ⚙️ Parameters
-
-
**evaluators:** `typing.Sequence[CreateEvaluationRequestEvaluatorsItemParams]` — The Evaluators used to evaluate.
-
**file:** `typing.Optional[FileRequestParams]` — The File to associate with the Evaluation. This File contains the Logs you're evaluating.
-
**name:** `typing.Optional[str]` — Name of the Evaluation to help identify it. Must be unique within the associated File.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluations.add_evaluators(...)
-
#### 📝 Description
-
-
Add Evaluators to an Evaluation.
The Evaluators will be run on the Logs generated for the Evaluation.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluations.add_evaluators(id='id', evaluators=[{'version_id': 'version_id'}], )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Evaluation.
-
**evaluators:** `typing.Sequence[AddEvaluatorsRequestEvaluatorsItemParams]` — The Evaluators to add to this Evaluation.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluations.remove_evaluator(...)
-
#### 📝 Description
-
-
Remove an Evaluator from an Evaluation.
The Evaluator will no longer be run on the Logs in the Evaluation.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluations.remove_evaluator(id='id', evaluator_version_id='evaluator_version_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Evaluation.
-
**evaluator_version_id:** `str` — Unique identifier for Evaluator Version.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluations.get(...)
-
#### 📝 Description
-
-
Get an Evaluation.
This includes the Evaluators associated with the Evaluation and metadata about the Evaluation,
such as its name.
To get the Runs associated with the Evaluation, use the `GET /evaluations/{id}/runs` endpoint.
To retrieve stats for the Evaluation, use the `GET /evaluations/{id}/stats` endpoint.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluations.get(id='ev_567yza', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Evaluation.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluations.delete(...)
-
#### 📝 Description
-
-
Delete an Evaluation.
The Runs and Evaluators in the Evaluation will not be deleted.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluations.delete(id='ev_567yza', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Evaluation.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluations.list_runs_for_evaluation(...)
-
#### 📝 Description
-
-
List all Runs for an Evaluation.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluations.list_runs_for_evaluation(id='id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Evaluation.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluations.create_run(...)
-
#### 📝 Description
-
-
Create an Evaluation Run.
Optionally specify the Dataset and version to be evaluated.
Humanloop will automatically start generating Logs and running Evaluators where
`orchestrated=true`. If you are generating Logs yourself, you can set `orchestrated=false`
and then generate and submit the required Logs via the API.
If `dataset` and `version` are provided, you can set `use_existing_logs=True` to reuse existing Logs,
avoiding generating new Logs unnecessarily. Logs that are associated with the specified Version and have `source_datapoint_id`
referencing a datapoint in the specified Dataset will be associated with the Run.
To keep updated on the progress of the Run, you can poll the Run using
the `GET /evaluations/{id}/runs` endpoint and check its status.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluations.create_run(id='id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Evaluation.
-
**dataset:** `typing.Optional[CreateRunRequestDatasetParams]` — Dataset to use in this Run.
-
**version:** `typing.Optional[CreateRunRequestVersionParams]` — Version to use in this Run.
-
**orchestrated:** `typing.Optional[bool]` — Whether the Run is orchestrated by Humanloop. If `True`, Humanloop will generate Logs for the Run; `dataset` and `version` must be provided. If `False`, a log for the Prompt/Tool should be submitted by the user via the API.
-
**use_existing_logs:** `typing.Optional[bool]` — If `True`, the Run will be initialized with existing Logs associated with the Dataset and Version. If `False`, the Run will be initialized with no Logs. Can only be set to `True` when both `dataset` and `version` are provided.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluations.add_existing_run(...)
-
#### 📝 Description
-
-
Add an existing Run to the specified Evaluation.
This is useful if you want to compare the Runs in this Evaluation with an existing Run
that exists within another Evaluation.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluations.add_existing_run(id='id', run_id='run_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Evaluation.
-
**run_id:** `str` — Unique identifier for Run.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluations.remove_run(...)
-
#### 📝 Description
-
-
Remove a Run from an Evaluation.
The Logs and Versions used in the Run will not be deleted.
If this Run is used in any other Evaluations, it will still be available in those Evaluations.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluations.remove_run(id='id', run_id='run_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Evaluation.
-
**run_id:** `str` — Unique identifier for Run.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluations.update_evaluation_run(...)
-
#### 📝 Description
-
-
Update an Evaluation Run.
Specify `control=true` to use this Run as the control Run for the Evaluation.
You can cancel a running/pending Run, or mark a Run that uses external or human Evaluators as completed.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluations.update_evaluation_run(id='id', run_id='run_id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Evaluation.
-
**run_id:** `str` — Unique identifier for Run.
-
**control:** `typing.Optional[bool]` — If `True`, this Run will be used as the control in the Evaluation. Stats for other Runs will be compared to this Run. This will replace any existing control Run.
-
**status:** `typing.Optional[EvaluationStatus]` — Used to set the Run to `cancelled` or `completed`. Can only be used if the Run is currently `pending` or `running`.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluations.add_logs_to_run(...)
-
#### 📝 Description
-
-
Add the specified Logs to a Run.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluations.add_logs_to_run(id='id', run_id='run_id', log_ids=['log_ids'], )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Evaluation.
-
**run_id:** `str` — Unique identifier for Run.
-
**log_ids:** `typing.Sequence[str]` — The IDs of the Logs to add to the Run.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluations.get_stats(...)
-
#### 📝 Description
-
-
Get Evaluation Stats.
Retrieve aggregate stats for the specified Evaluation. This includes the number of generated Logs for each Run and the
corresponding Evaluator statistics (such as the mean and percentiles).
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluations.get_stats(id='id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Evaluation.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.evaluations.get_logs(...)
-
#### 📝 Description
-
-
Get the Logs associated to a specific Evaluation.
This returns the Logs associated to all Runs within with the Evaluation.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.evaluations.get_logs(id='id', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — String ID of evaluation. Starts with `ev_` or `evr_`.
-
**page:** `typing.Optional[int]` — Page number for pagination.
-
**size:** `typing.Optional[int]` — Page size for pagination. Number of Logs to fetch.
-
**run_id:** `typing.Optional[typing.Union[str, typing.Sequence[str]]]` — Filter by Run IDs. Only Logs for the specified Runs will be returned.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.logs.list(...)
-
#### 📝 Description
-
-
List all Logs for the given filter criteria.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
response = client.logs.list(file_id='file_123abc', size=1, )
for item in response:
yield item
# alternatively, you can paginate page-by-page
for page in response.iter_pages():
yield page
```
#### ⚙️ Parameters
-
-
**file_id:** `str` — Unique identifier for the File to list Logs for.
-
**page:** `typing.Optional[int]` — Page number for pagination.
-
**size:** `typing.Optional[int]` — Page size for pagination. Number of Logs to fetch.
-
**version_id:** `typing.Optional[str]` — If provided, only Logs belonging to the specified Version will be returned.
-
**id:** `typing.Optional[typing.Union[str, typing.Sequence[str]]]` — If provided, returns Logs whose IDs contain any of the specified values as substrings.
-
**search:** `typing.Optional[str]` — If provided, only Logs that contain the provided string in its inputs and output will be returned.
-
**metadata_search:** `typing.Optional[str]` — If provided, only Logs that contain the provided string in its metadata will be returned.
-
**start_date:** `typing.Optional[dt.datetime]` — If provided, only Logs created after the specified date will be returned.
-
**end_date:** `typing.Optional[dt.datetime]` — If provided, only Logs created before the specified date will be returned.
-
**include_parent:** `typing.Optional[bool]` — If true, include the full parent Log in the response. Only applicable when retrieving Evaluator Logs.
-
**in_trace_filter:** `typing.Optional[typing.Union[bool, typing.Sequence[bool]]]` — If true, return Logs that are associated to a Trace. False, return Logs that are not associated to a Trace.
-
**sample:** `typing.Optional[int]` — If provided, limit the response to a random subset of logs from the filtered results. (This will be an approximate sample, not a strict limit.)
-
**include_trace_children:** `typing.Optional[bool]` — If true, populate `trace_children` for the retrieved Logs. Only applicable when retrieving Flow or Agent Logs.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.logs.delete(...)
-
#### 📝 Description
-
-
Delete Logs with the given IDs.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.logs.delete(id='prv_Wu6zx1lAWJRqOyL8nWuZk', )
```
#### ⚙️ Parameters
-
-
**id:** `typing.Optional[typing.Union[str, typing.Sequence[str]]]` — Unique identifiers for the Logs to delete.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.
client.logs.get(...)
-
#### 📝 Description
-
-
Retrieve the Log with the given ID.
#### 🔌 Usage
-
-
```python
from humanloop import Humanloop
client = Humanloop(api_key="YOUR_API_KEY", )
client.logs.get(id='prv_Wu6zx1lAWJRqOyL8nWuZk', )
```
#### ⚙️ Parameters
-
-
**id:** `str` — Unique identifier for Log.
-
**request_options:** `typing.Optional[RequestOptions]` — Request-specific configuration.