chore: lint with mypy and isort

FullStackWithLawrence · Nov 18, 2023 · b380d32 · b380d32
1 parent f5c72fc
commit b380d32
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diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -22,10 +22,7 @@ repos:
     rev: 5.12.0
     hooks:
       - id: isort
-  - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.7.0
-    hooks:
-      - id: mypy
+        args: ["--settings-path=pyproject.toml"]
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.5.0
     hooks:

diff --git a/api/terraform/python/lambda_langchain/lambda_handler.py b/api/terraform/python/lambda_langchain/lambda_handler.py
@@ -15,56 +15,58 @@
             https://python.langchain.com/docs/integrations/memory/aws_dynamodb
             https://bobbyhadz.com/blog/react-generate-unique-id
 """
-import os
 import json
-from dotenv import load_dotenv, find_dotenv
+import os
 
 # OpenAI imports
 import openai
+from dotenv import find_dotenv, load_dotenv
+from langchain.chains import LLMChain
 
 # Langchain imports
 from langchain.chat_models import ChatOpenAI
-from langchain.chains import LLMChain
 from langchain.memory import ConversationBufferMemory
 from langchain.prompts import (
     ChatPromptTemplate,
+    HumanMessagePromptTemplate,
     MessagesPlaceholder,
     SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
 )
 
-# from langchain.memory.chat_message_histories.in_memory import ChatMessageHistory
-# from langchain.schema.messages import HumanMessage, SystemMessage, AIMessage
-# from langchain.schema.messages import BaseMessage
-
 # local imports from 'layer_genai' virtual environment or AWS Lambda layer.
 from openai_utils.const import (
-    OpenAIEndPoint,
-    OpenAIMessageKeys,
-    HTTP_RESPONSE_OK,
     HTTP_RESPONSE_BAD_REQUEST,
     HTTP_RESPONSE_INTERNAL_SERVER_ERROR,
+    HTTP_RESPONSE_OK,
     VALID_CHAT_COMPLETION_MODELS,
     VALID_EMBEDDING_MODELS,
+    OpenAIEndPoint,
+    OpenAIMessageKeys,
 )
 from openai_utils.utils import (
-    http_response_factory,
-    exception_response_factory,
     dump_environment,
-    get_request_body,
-    parse_request,
+    exception_response_factory,
     get_content_for_role,
     get_message_history,
     get_messages_for_role,
+    get_request_body,
+    http_response_factory,
+    parse_request,
 )
 from openai_utils.validators import (
-    validate_item,
-    validate_request_body,
-    validate_messages,
     validate_completion_request,
     validate_embedding_request,
+    validate_item,
+    validate_messages,
+    validate_request_body,
 )
 
+
+# from langchain.memory.chat_message_histories.in_memory import ChatMessageHistory
+# from langchain.schema.messages import HumanMessage, SystemMessage, AIMessage
+# from langchain.schema.messages import BaseMessage
+
+
 ###############################################################################
 # ENVIRONMENT CREDENTIALS
 ###############################################################################
@@ -108,9 +110,7 @@ def handler(event, context, api_key=None, organization=None, pinecone_api_key=No
         # ----------------------------------------------------------------------
         request_body = get_request_body(event=event)
         validate_request_body(request_body=request_body)
-        end_point, model, messages, input_text, temperature, max_tokens = parse_request(
-            request_body
-        )
+        end_point, model, messages, input_text, temperature, max_tokens = parse_request(request_body)
         validate_messages(request_body=request_body)
         request_meta_data = {
             "request_meta_data": {
@@ -138,18 +138,12 @@ def handler(event, context, api_key=None, organization=None, pinecone_api_key=No
                     item_type="ChatCompletion models",
                 )
                 validate_completion_request(request_body)
-                system_message = get_content_for_role(
-                    messages, OpenAIMessageKeys.OPENAI_SYSTEM_MESSAGE_KEY
-                )
-                user_message = get_content_for_role(
-                    messages, OpenAIMessageKeys.OPENAI_USER_MESSAGE_KEY
-                )
+                system_message = get_content_for_role(messages, OpenAIMessageKeys.OPENAI_SYSTEM_MESSAGE_KEY)
+                user_message = get_content_for_role(messages, OpenAIMessageKeys.OPENAI_USER_MESSAGE_KEY)
 
                 # 2. initialize the LangChain ChatOpenAI model
                 # -------------------------------------------------------------
-                llm = ChatOpenAI(
-                    model=model, temperature=temperature, max_tokens=max_tokens
-                )
+                llm = ChatOpenAI(model=model, temperature=temperature, max_tokens=max_tokens)
                 prompt = ChatPromptTemplate(
                     messages=[
                         SystemMessagePromptTemplate.from_template(system_message),
@@ -165,9 +159,7 @@ def handler(event, context, api_key=None, organization=None, pinecone_api_key=No
                     return_messages=True,
                 )
                 message_history = get_message_history(messages)
-                user_messages = get_messages_for_role(
-                    message_history, OpenAIMessageKeys.OPENAI_USER_MESSAGE_KEY
-                )
+                user_messages = get_messages_for_role(message_history, OpenAIMessageKeys.OPENAI_USER_MESSAGE_KEY)
                 assistant_messages = get_messages_for_role(
                     message_history, OpenAIMessageKeys.OPENAI_ASSISTANT_MESSAGE_KEY
                 )
@@ -211,19 +203,15 @@ def handler(event, context, api_key=None, organization=None, pinecone_api_key=No
                 openai_results = openai.Moderation.create(input=input_text)
 
             case OpenAIEndPoint.Models:
-                openai_results = (
-                    openai.Model.retrieve(model) if model else openai.Model.list()
-                )
+                openai_results = openai.Model.retrieve(model) if model else openai.Model.list()
 
             case OpenAIEndPoint.Audio:
                 raise NotImplementedError("Audio support is coming soon")
 
     # handle anything that went wrong
     except (openai.APIError, ValueError, TypeError, NotImplementedError) as e:
         # 400 Bad Request
-        return http_response_factory(
-            status_code=HTTP_RESPONSE_BAD_REQUEST, body=exception_response_factory(e)
-        )
+        return http_response_factory(status_code=HTTP_RESPONSE_BAD_REQUEST, body=exception_response_factory(e))
     except (openai.OpenAIError, Exception) as e:
         # 500 Internal Server Error
         return http_response_factory(
@@ -232,6 +220,4 @@ def handler(event, context, api_key=None, organization=None, pinecone_api_key=No
         )
 
     # success!! return the results
-    return http_response_factory(
-        status_code=HTTP_RESPONSE_OK, body={**openai_results, **request_meta_data}
-    )
+    return http_response_factory(status_code=HTTP_RESPONSE_OK, body={**openai_results, **request_meta_data})
diff --git a/api/terraform/python/lambda_langchain/tests/test_01.py b/api/terraform/python/lambda_langchain/tests/test_01.py
@@ -2,11 +2,13 @@
 """
 Test requests to the OpenAI API via Langchain using the Lambda Layer, 'genai'.
 """
-import pytest
 import os
-from dotenv import load_dotenv, find_dotenv
-from lambda_langchain.tests.init import get_event
+
+import pytest
+from dotenv import find_dotenv, load_dotenv
 from lambda_langchain.lambda_handler import handler
+from lambda_langchain.tests.init import get_event
+
 
 # Load environment variables from .env file in all folders
 dotenv_path = find_dotenv()

diff --git a/api/terraform/python/lambda_openai/lambda_handler.py b/api/terraform/python/lambda_openai/lambda_handler.py
@@ -44,12 +44,14 @@
 
 import base64
 import json  # library for interacting with JSON data https://www.json.org/json-en.html
-import openai
 import os  # library for interacting with the operating system
 import platform  # library to view informatoin about the server host this Lambda runs on
 import sys  # libraries for error management
 import traceback  # libraries for error management
 
+import openai
+
+
 DEBUG_MODE = os.getenv("DEBUG_MODE", "False").lower() in ("true", "1", "t")
 HTTP_RESPONSE_OK = 200
 HTTP_RESPONSE_BAD_REQUEST = 400
@@ -108,11 +110,7 @@ def http_response_factory(status_code: int, body) -> dict:
     see https://docs.aws.amazon.com/lambda/latest/dg/python-handler.html
     """
     if status_code < 100 or status_code > 599:
-        raise ValueError(
-            "Invalid HTTP response code received: {status_code}".format(
-                status_code=status_code
-            )
-        )
+        raise ValueError("Invalid HTTP response code received: {status_code}".format(status_code=status_code))
 
     retval = {
         "isBase64Encoded": False,
@@ -165,21 +163,11 @@ def validate_messages(request_body):
         raise ValueError("dict key 'messages' should be a JSON list")
     for message in messages:
         if type(message) is not dict:
-            raise ValueError(
-                "invalid ojbect type {t} found in messages list".format(t=type(message))
-            )
+            raise ValueError("invalid ojbect type {t} found in messages list".format(t=type(message)))
         if "role" not in message:
-            raise ValueError(
-                "dict key 'role' not found in message {m}".format(
-                    m=json.dumps(message, indent=4)
-                )
-            )
+            raise ValueError("dict key 'role' not found in message {m}".format(m=json.dumps(message, indent=4)))
         if "content" not in message:
-            raise ValueError(
-                "dict key 'content' not found in message {m}".format(
-                    m=json.dumps(message, indent=4)
-                )
-            )
+            raise ValueError("dict key 'content' not found in message {m}".format(m=json.dumps(message, indent=4)))
 
 
 def validate_completion_request(request_body) -> None:
@@ -294,9 +282,7 @@ def handler(event, context):
                     item_type="ChatCompletion models",
                 )
                 validate_completion_request(request_body)
-                openai_results = openai.ChatCompletion.create(
-                    model=model, messages=messages
-                )
+                openai_results = openai.ChatCompletion.create(model=model, messages=messages)
 
             case OpenAIEndPoint.Embedding:
                 # https://platform.openai.com/docs/guides/embeddings/embeddings
@@ -319,19 +305,15 @@ def handler(event, context):
                 openai_results = openai.Moderation.create(input=input_text)
 
             case OpenAIEndPoint.Models:
-                openai_results = (
-                    openai.Model.retrieve(model) if model else openai.Model.list()
-                )
+                openai_results = openai.Model.retrieve(model) if model else openai.Model.list()
 
             case OpenAIEndPoint.Audio:
                 raise NotImplementedError("Audio support is coming soon")
 
     # handle anything that went wrong
     except (openai.APIError, ValueError, TypeError, NotImplementedError) as e:
         # 400 Bad Request
-        return http_response_factory(
-            status_code=HTTP_RESPONSE_BAD_REQUEST, body=exception_response_factory(e)
-        )
+        return http_response_factory(status_code=HTTP_RESPONSE_BAD_REQUEST, body=exception_response_factory(e))
     except (openai.OpenAIError, Exception) as e:
         # 500 Internal Server Error
         return http_response_factory(

diff --git a/api/terraform/python/lambda_openai/tests/test_init.py b/api/terraform/python/lambda_openai/tests/test_init.py
@@ -1,10 +1,12 @@
 # -*- coding: utf-8 -*-
 """Shared code for testing the lambda function"""
-from dotenv import load_dotenv, find_dotenv
-import os
 import json
+import os
+
+from dotenv import find_dotenv, load_dotenv
 from lambda_openai.lambda_handler import handler
 
+
 # Load environment variables from .env file in all folders
 dotenv_path = find_dotenv()
 if os.path.exists(dotenv_path):

diff --git a/api/terraform/python/lambda_openai_v2/lambda_handler.py b/api/terraform/python/lambda_openai_v2/lambda_handler.py
@@ -31,26 +31,27 @@
 # -----------------------
 import openai
 from openai_utils.const import (
-    OpenAIEndPoint,
-    HTTP_RESPONSE_OK,
     HTTP_RESPONSE_BAD_REQUEST,
     HTTP_RESPONSE_INTERNAL_SERVER_ERROR,
+    HTTP_RESPONSE_OK,
     VALID_CHAT_COMPLETION_MODELS,
     VALID_EMBEDDING_MODELS,
+    OpenAIEndPoint,
 )
 from openai_utils.utils import (
-    http_response_factory,
-    exception_response_factory,
     dump_environment,
+    exception_response_factory,
     get_request_body,
+    http_response_factory,
     parse_request,
 )
 from openai_utils.validators import (
-    validate_item,
     validate_completion_request,
     validate_embedding_request,
+    validate_item,
 )
 
+
 DEBUG_MODE = os.getenv("DEBUG_MODE", "False").lower() in ("true", "1", "t")
 
 # https://platform.openai.com/api_keys
@@ -71,9 +72,7 @@ def handler(event, context):
     try:
         openai_results = {}
         request_body = get_request_body(event=event)
-        end_point, model, messages, input_text, temperature, max_tokens = parse_request(
-            request_body
-        )
+        end_point, model, messages, input_text, temperature, max_tokens = parse_request(request_body)
         request_meta_data = {
             "request_meta_data": {
                 "lambda": "lambda_openai_v2",
@@ -121,19 +120,15 @@ def handler(event, context):
                 openai_results = openai.Moderation.create(input=input_text)
 
             case OpenAIEndPoint.Models:
-                openai_results = (
-                    openai.Model.retrieve(model) if model else openai.Model.list()
-                )
+                openai_results = openai.Model.retrieve(model) if model else openai.Model.list()
 
             case OpenAIEndPoint.Audio:
                 raise NotImplementedError("Audio support is coming soon")
 
     # handle anything that went wrong
     except (openai.APIError, ValueError, TypeError, NotImplementedError) as e:
         # 400 Bad Request
-        return http_response_factory(
-            status_code=HTTP_RESPONSE_BAD_REQUEST, body=exception_response_factory(e)
-        )
+        return http_response_factory(status_code=HTTP_RESPONSE_BAD_REQUEST, body=exception_response_factory(e))
     except (openai.OpenAIError, Exception) as e:
         # 500 Internal Server Error
         return http_response_factory(

diff --git a/api/terraform/python/lambda_openai_v2/tests/__init__.py b/api/terraform/python/lambda_openai_v2/tests/__init__.py
@@ -1,6 +1,8 @@
-from dotenv import load_dotenv, find_dotenv
 import os
 
+from dotenv import find_dotenv, load_dotenv
+
+
 # Load environment variables from .env file in all folders
 dotenv_path = find_dotenv()
 if os.path.exists(dotenv_path):

diff --git a/api/terraform/python/lambda_openai_v2/tests/test_01.py b/api/terraform/python/lambda_openai_v2/tests/test_01.py
@@ -2,11 +2,13 @@
 """
 Test requests to the OpenAI API using the Lambda Layer, 'genai'.
 """
-import pytest
 import os
-from dotenv import load_dotenv, find_dotenv
-from lambda_openai_v2.tests.init import get_event
+
+import pytest
+from dotenv import find_dotenv, load_dotenv
 from lambda_openai_v2.lambda_handler import handler
+from lambda_openai_v2.tests.init import get_event
+
 
 # Load environment variables from .env file in all folders
 dotenv_path = find_dotenv()

diff --git a/api/terraform/python/layer_genai/openai_utils/const.py b/api/terraform/python/layer_genai/openai_utils/const.py
@@ -1,8 +1,10 @@
 # -*- coding: utf-8 -*-
 """A module containing constants for the OpenAI API."""
 import os
+
 import openai
 
+
 HTTP_RESPONSE_OK = 200
 HTTP_RESPONSE_BAD_REQUEST = 400
 HTTP_RESPONSE_INTERNAL_SERVER_ERROR = 500

diff --git a/api/terraform/python/layer_genai/openai_utils/setup.py b/api/terraform/python/layer_genai/openai_utils/setup.py
@@ -1,8 +1,8 @@
 # -*- coding: utf-8 -*-
 """Setup for openai_utils package."""
-from setuptools import setup, find_packages
-
 from setup_utils import get_semantic_version
+from setuptools import find_packages, setup
+
 
 setup(
     name="openai_utils",