Merge pull request #226 from MagnivOrg/dmugtasimov/dmi-52-support-cha…

…nges-in-python-sdk

Linters, formatting and testing improvements

.devcontainer/devcontainer.json

@@ -21,11 +21,10 @@
         "editor.formatOnSave": true,
         "python.analysis.autoImportCompletions": true,
         "[python]": {
-          "editor.defaultFormatter": "ms-python.black-formatter"
+          "editor.defaultFormatter": "charliermarsh.ruff"
         },
+        // TODO(dmu) HIGH: Make linter configuration consistent with .pre-commit-config.yaml
         "ruff.args": [
-          "--exclude",
-          "migrations",
           "--ignore",
           "E501,E711,E712",
           "--fix",

.editorconfig

@@ -0,0 +1,6 @@
+# See https://editorconfig.org/ for more info about this file
+[*.py]
+max_line_length = 120
+charset = utf-8
+indent_style = space
+indent_size = 4

.github/workflows/integration-tests.yml

@@ -26,5 +26,4 @@ jobs:
           python -m pip install behave langchain openai
           if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
       - name: Run Integration Tests
-        run: |
-          behave
+        run: behave

.github/workflows/release.yml

@@ -16,12 +16,14 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: "3.8"
+
       - name: Install poetry
         run: pipx install poetry
+
       - name: Running poetry install
         run: poetry install
+
       - name: Build and publish
         env:
           POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_PASSWORD }}
-        run: |
-          poetry publish --build
+        run: poetry publish --build

.pre-commit-config.yaml

@@ -1,20 +1,15 @@
 repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v5.0.0
+    hooks:
+      - id: end-of-file-fixer
+        exclude: (app/utils/evaluate/(javascript_code_wrapper|python_code_wrapper)\.txt|langchain_prompts\.txt)
+      - id: trailing-whitespace
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.0.290
+    rev: v0.9.4
     hooks:
       - id: ruff
-        args:
-          [--exclude, features, --ignore, "E501", --fix, --exit-non-zero-on-fix]
-
-  - repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 23.9.1
-    hooks:
-      - id: black
-        args: [--force-exclude, features]
-
-  - repo: https://github.com/pycqa/isort
-    rev: 5.12.0
-    hooks:
-      - id: isort
-        name: isort (python)
-        args: [--profile, black, --skip, features, --filter-files]
+        name: 'ruff: fix imports'
+        args: ["--select", "I", "--fix"]
+      - id: ruff
+      - id: ruff-format

Makefile

@@ -1,7 +1,13 @@
+RUN_TEST := test -f .env && set -a; . ./.env; set +a; poetry run pytest
+
 .PHONY: lint
 lint:
 	poetry run pre-commit run --all-files
 
 .PHONY: test
 test:
-	poetry run pytest
+	${RUN_TEST}
+
+.PHONY: test-sw
+test-sw:
+	${RUN_TEST} -vv --sw --show-capture=no

README.md

@@ -8,11 +8,11 @@
 <a href="https://docs.promptlayer.com"><img alt="Docs" src="https://custom-icon-badges.herokuapp.com/badge/docs-PL-green.svg?logo=cake&style=for-the-badge"></a>
 <a href="https://www.loom.com/share/196c42e43acd4a369d75e9a7374a0850"><img alt="Demo with Loom" src="https://img.shields.io/badge/Demo-loom-552586.svg?logo=loom&style=for-the-badge&labelColor=gray"></a>
 
----  
+---
 
 <div align="left">
 
-[PromptLayer](https://promptlayer.com/) is the first platform that allows you to track, manage, and share your GPT prompt engineering. PromptLayer acts a middleware between your code and OpenAI’s python library. 
+[PromptLayer](https://promptlayer.com/) is the first platform that allows you to track, manage, and share your GPT prompt engineering. PromptLayer acts a middleware between your code and OpenAI’s python library.
 
 PromptLayer records all your OpenAI API requests, allowing you to search and explore request history in the PromptLayer dashboard.
 
@@ -53,14 +53,14 @@ openai = promptlayer.openai
 
 ### Adding PromptLayer tags: `pl_tags`
 
-PromptLayer allows you to add tags through the `pl_tags` argument. This allows you to track and group requests in the dashboard. 
+PromptLayer allows you to add tags through the `pl_tags` argument. This allows you to track and group requests in the dashboard.
 
 *Tags are not required but we recommend them!*
 
 ```python
 openai.Completion.create(
-  engine="text-ada-001", 
-  prompt="My name is", 
+  engine="text-ada-001",
+  prompt="My name is",
   pl_tags=["name-guessing", "pipeline-2"]
 )
 ```
@@ -69,11 +69,11 @@ After making your first few requests, you should be able to see them in the Prom
 
 ## Using the REST API
 
-This Python library is a wrapper over PromptLayer's REST API. If you use another language, like Javascript, just interact directly with the API. 
+This Python library is a wrapper over PromptLayer's REST API. If you use another language, like Javascript, just interact directly with the API.
 
 Here is an example request below:
 
-```jsx
+```python
 import requests
 request_response = requests.post(
   "https://api.promptlayer.com/track-request",

promptlayer/__init__.py

@@ -1,4 +1,4 @@
 from .promptlayer import AsyncPromptLayer, PromptLayer
 
-__version__ = "1.0.37"
+__version__ = "1.0.38"
 __all__ = ["PromptLayer", "AsyncPromptLayer", "__version__"]

promptlayer/promptlayer.py

@@ -61,9 +61,7 @@ def __init__(
         self.api_key = api_key
         self.templates = TemplateManager(api_key)
         self.group = GroupManager(api_key)
-        self.tracer_provider, self.tracer = self._initialize_tracer(
-            api_key, enable_tracing
-        )
+        self.tracer_provider, self.tracer = self._initialize_tracer(api_key, enable_tracing)
         self.track = TrackManager(api_key)
 
     def __getattr__(
@@ -233,9 +231,7 @@ def run(
                 span.set_attribute("prompt_name", prompt_name)
                 span.set_attribute("function_input", str(_run_internal_kwargs))
                 pl_run_span_id = hex(span.context.span_id)[2:].zfill(16)
-                result = self._run_internal(
-                    **_run_internal_kwargs, pl_run_span_id=pl_run_span_id
-                )
+                result = self._run_internal(**_run_internal_kwargs, pl_run_span_id=pl_run_span_id)
                 span.set_attribute("function_output", str(result))
                 return result
         else:
@@ -285,18 +281,12 @@ def run_workflow(
 
             if not return_all_outputs:
                 if is_workflow_results_dict(results):
-                    output_nodes = [
-                        node_data
-                        for node_data in results.values()
-                        if node_data.get("is_output_node")
-                    ]
+                    output_nodes = [node_data for node_data in results.values() if node_data.get("is_output_node")]
 
                     if not output_nodes:
                         raise Exception(json.dumps(results, indent=4))
 
-                    if not any(
-                        node.get("status") == "SUCCESS" for node in output_nodes
-                    ):
+                    if not any(node.get("status") == "SUCCESS" for node in output_nodes):
                         raise Exception(json.dumps(results, indent=4))
 
             return results
@@ -364,14 +354,10 @@ def __init__(
         self.api_key = api_key
         self.templates = AsyncTemplateManager(api_key)
         self.group = AsyncGroupManager(api_key)
-        self.tracer_provider, self.tracer = self._initialize_tracer(
-            api_key, enable_tracing
-        )
+        self.tracer_provider, self.tracer = self._initialize_tracer(api_key, enable_tracing)
         self.track = AsyncTrackManager(api_key)
 
-    def __getattr__(
-        self, name: Union[Literal["openai"], Literal["anthropic"], Literal["prompts"]]
-    ):
+    def __getattr__(self, name: Union[Literal["openai"], Literal["anthropic"], Literal["prompts"]]):
         if name == "openai":
             import openai as openai_module
 
@@ -400,9 +386,7 @@ async def run_workflow(
         input_variables: Optional[Dict[str, Any]] = None,
         metadata: Optional[Dict[str, str]] = None,
         workflow_label_name: Optional[str] = None,
-        workflow_version: Optional[
-            int
-        ] = None,  # This is the version number, not the version ID
+        workflow_version: Optional[int] = None,  # This is the version number, not the version ID
         return_all_outputs: Optional[bool] = False,
     ) -> Dict[str, Any]:
         try:
@@ -448,9 +432,7 @@ async def run(
                 span.set_attribute("prompt_name", prompt_name)
                 span.set_attribute("function_input", str(_run_internal_kwargs))
                 pl_run_span_id = hex(span.context.span_id)[2:].zfill(16)
-                result = await self._run_internal(
-                    **_run_internal_kwargs, pl_run_span_id=pl_run_span_id
-                )
+                result = await self._run_internal(**_run_internal_kwargs, pl_run_span_id=pl_run_span_id)
                 span.set_attribute("function_output", str(result))
                 return result
         else:
@@ -563,9 +545,7 @@ async def _run_internal(
             input_variables=input_variables,
             metadata=metadata,
         )
-        prompt_blueprint = await self.templates.get(
-            prompt_name, get_prompt_template_params
-        )
+        prompt_blueprint = await self.templates.get(prompt_name, get_prompt_template_params)
         prompt_blueprint_model = self._validate_and_extract_model_from_prompt_blueprint(
             prompt_blueprint=prompt_blueprint, prompt_name=prompt_name
         )

promptlayer/promptlayer_base.py

@@ -15,9 +15,7 @@ class PromptLayerBase(object):
         "_tracer",
     ]
 
-    def __init__(
-        self, obj, function_name="", provider_type="openai", api_key=None, tracer=None
-    ):
+    def __init__(self, obj, function_name="", provider_type="openai", api_key=None, tracer=None):
         object.__setattr__(self, "_obj", obj)
         object.__setattr__(self, "_function_name", function_name)
         object.__setattr__(self, "_provider_type", provider_type)
@@ -29,29 +27,22 @@ def __getattr__(self, name):
 
         if (
             name != "count_tokens"  # fix for anthropic count_tokens
-            and not re.match(
-                r"<class 'anthropic\..*Error'>", str(attr)
-            )  # fix for anthropic errors
-            and not re.match(
-                r"<class 'openai\..*Error'>", str(attr)
-            )  # fix for openai errors
+            and not re.match(r"<class 'anthropic\..*Error'>", str(attr))  # fix for anthropic errors
+            and not re.match(r"<class 'openai\..*Error'>", str(attr))  # fix for openai errors
             and (
                 inspect.isclass(attr)
                 or inspect.isfunction(attr)
                 or inspect.ismethod(attr)
-                or str(type(attr))
-                == "<class 'anthropic.resources.completions.Completions'>"
-                or str(type(attr))
-                == "<class 'anthropic.resources.completions.AsyncCompletions'>"
+                or str(type(attr)) == "<class 'anthropic.resources.completions.Completions'>"
+                or str(type(attr)) == "<class 'anthropic.resources.completions.AsyncCompletions'>"
                 or str(type(attr)) == "<class 'anthropic.resources.messages.Messages'>"
-                or str(type(attr))
-                == "<class 'anthropic.resources.messages.AsyncMessages'>"
+                or str(type(attr)) == "<class 'anthropic.resources.messages.AsyncMessages'>"
                 or re.match(r"<class 'openai\.resources.*'>", str(type(attr)))
             )
         ):
             return PromptLayerBase(
                 attr,
-                function_name=f'{object.__getattribute__(self, "_function_name")}.{name}',
+                function_name=f"{object.__getattribute__(self, '_function_name')}.{name}",
                 provider_type=object.__getattribute__(self, "_provider_type"),
                 api_key=object.__getattribute__(self, "_api_key"),
                 tracer=object.__getattribute__(self, "_tracer"),
@@ -77,16 +68,10 @@ def __call__(self, *args, **kwargs):
 
         if tracer:
             with tracer.start_as_current_span(function_name) as llm_request_span:
-                llm_request_span_id = hex(llm_request_span.context.span_id)[2:].zfill(
-                    16
-                )
-                llm_request_span.set_attribute(
-                    "provider", object.__getattribute__(self, "_provider_type")
-                )
+                llm_request_span_id = hex(llm_request_span.context.span_id)[2:].zfill(16)
+                llm_request_span.set_attribute("provider", object.__getattribute__(self, "_provider_type"))
                 llm_request_span.set_attribute("function_name", function_name)
-                llm_request_span.set_attribute(
-                    "function_input", str({"args": args, "kwargs": kwargs})
-                )
+                llm_request_span.set_attribute("function_input", str({"args": args, "kwargs": kwargs}))
 
                 if inspect.isclass(function_object):
                     result = PromptLayerBase(
@@ -101,9 +86,7 @@ def __call__(self, *args, **kwargs):
 
                 function_response = function_object(*args, **kwargs)
 
-                if inspect.iscoroutinefunction(function_object) or inspect.iscoroutine(
-                    function_response
-                ):
+                if inspect.iscoroutinefunction(function_object) or inspect.iscoroutine(function_response):
                     return async_wrapper(
                         function_response,
                         return_pl_id,
@@ -146,9 +129,7 @@ def __call__(self, *args, **kwargs):
 
             function_response = function_object(*args, **kwargs)
 
-            if inspect.iscoroutinefunction(function_object) or inspect.iscoroutine(
-                function_response
-            ):
+            if inspect.iscoroutinefunction(function_object) or inspect.iscoroutine(function_response):
                 return async_wrapper(
                     function_response,
                     return_pl_id,