[FEAT] Groq provider #342
[FEAT] Groq provider #342
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WalkthroughThe pull request introduces a new Groq API provider into the system. It adds several new JSON configuration and output files, a new API class ( Changes
Sequence Diagram(s)sequenceDiagram
participant C as Client
participant G as GroqApi
participant L as LLMEngine
C->>G: Initiate text__chat (or llm__chat)
G->>L: Call corresponding LLMEngine method with parameters
L-->>G: Return response or error details
G-->>C: Send back formatted chat response
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Actionable comments posted: 3
🧹 Nitpick comments (3)
edenai_apis/apis/groq/errors.py (1)
5-5: Add Documentation for the ERRORS Dictionary.
TheERRORSvariable is correctly typed asProviderErrorListsand initialized as an empty dictionary. To improve maintainability, consider adding an inline comment or docstring explaining its purpose, such as which error codes or messages might be expected in the future and how they will be utilized by the Groq API.edenai_apis/apis/groq/__init__.py (1)
1-1: Add__all__to explicitly define exportsWhile the import is correct for exposing
GroqApiat the package level, consider adding an__all__list to explicitly define what's exported from this module and fix the static analysis warning.from .groq_api import GroqApi + +__all__ = ["GroqApi"]🧰 Tools
🪛 Ruff (0.8.2)
1-1:
.groq_api.GroqApiimported but unused; consider removing, adding to__all__, or using a redundant alias(F401)
edenai_apis/apis/__init__.py (1)
69-69: Add__all__to properly handle importsThe import statement correctly adds Groq to the available APIs, but to address the static analysis warning about unused imports, consider adding an
__all__list that includes all the API classes.# At the beginning of the file after imports +__all__ = [ + "AffindaApi", "Ai21labsApi", "AlephAlphaApi", "AmazonApi", "AnthropicApi", + # ... other API classes ... + "DeepseekApi", "TogetheraiApi", "GroqApi" +]This would also help with the "THIS NEEDS TO BE DONE AUTOMATICALLY" comment by making it easier to automate updates to the exports list in the future.
🧰 Tools
🪛 Ruff (0.8.2)
69-69:
.groq.GroqApiimported but unused; consider removing, adding to__all__, or using a redundant alias(F401)
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📒 Files selected for processing (9)
edenai_apis/api_keys/groq_settings_template.json(1 hunks)edenai_apis/apis/__init__.py(1 hunks)edenai_apis/apis/groq/__init__.py(1 hunks)edenai_apis/apis/groq/errors.py(1 hunks)edenai_apis/apis/groq/groq_api.py(1 hunks)edenai_apis/apis/groq/info.json(1 hunks)edenai_apis/apis/groq/outputs/llm/chat_output.json(1 hunks)edenai_apis/apis/groq/outputs/text/chat_output.json(1 hunks)edenai_apis/features/text/chat/chat_args.py(1 hunks)
🧰 Additional context used
🧬 Code Definitions (2)
edenai_apis/apis/groq/__init__.py (1)
edenai_apis/apis/groq/groq_api.py (1)
GroqApi(25-172)
edenai_apis/apis/__init__.py (1)
edenai_apis/apis/groq/groq_api.py (1)
GroqApi(25-172)
🪛 Ruff (0.8.2)
edenai_apis/apis/groq/__init__.py
1-1: .groq_api.GroqApi imported but unused; consider removing, adding to __all__, or using a redundant alias
(F401)
edenai_apis/apis/__init__.py
69-69: .groq.GroqApi imported but unused; consider removing, adding to __all__, or using a redundant alias
(F401)
edenai_apis/apis/groq/groq_api.py
22-22: Redefinition of unused ChatDataClass from line 16
(F811)
28-28: Do not use mutable data structures for argument defaults
Replace with None; initialize within function
(B006)
100-100: Do not use mutable data structures for argument defaults
Replace with None; initialize within function
(B006)
🔇 Additional comments (6)
edenai_apis/apis/groq/errors.py (1)
1-3: Review Import Statement and Type Import.
The import forProviderErrorListsfromedenai_apis.utils.exceptionis clear and well-formatted. Ensure that the structure defined byProviderErrorListsaligns with how error mappings are intended to be managed in the Groq API implementation.edenai_apis/api_keys/groq_settings_template.json (1)
1-3: LGTM! Template file structure is correctThe template file for Groq API settings follows the proper structure with an empty API key placeholder.
edenai_apis/features/text/chat/chat_args.py (1)
31-31: LGTM! Default model properly configuredThe default model setting for Groq has been correctly added to the settings dictionary, following the same pattern as other providers.
edenai_apis/apis/groq/info.json (1)
1-17: Looks good!No issues found with this configuration file. The JSON syntax is valid and each version field is clearly defined for the corresponding chat categories.
edenai_apis/apis/groq/outputs/llm/chat_output.json (1)
1-41: No concerns here!This JSON response structure is well-formed and clear. Each field (choices, usage, cost, etc.) is suitably descriptive.
edenai_apis/apis/groq/outputs/text/chat_output.json (1)
1-67: Clean and well-structured!Everything in this JSON layout looks consistent; the presence of both original and standardized responses helps maintain clarity.
| class GroqApi(ProviderInterface, TextInterface, LlmInterface): | ||
| provider_name = "groq" | ||
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| def __init__(self, api_keys: Dict = {}) -> None: |
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🛠️ Refactor suggestion
Use an immutable default for function parameters.
Using a mutable default ({}) for api_keys can lead to unintended state sharing across instances. Replace it with None and initialize internally.
- def __init__(self, api_keys: Dict = {}):
+ def __init__(self, api_keys: Optional[Dict] = None):
+ if api_keys is None:
+ api_keys = {}
self.api_settings = load_provider(
...📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def __init__(self, api_keys: Dict = {}) -> None: | |
| def __init__(self, api_keys: Optional[Dict] = None) -> None: | |
| if api_keys is None: | |
| api_keys = {} | |
| self.api_settings = load_provider( | |
| ... |
🧰 Tools
🪛 Ruff (0.8.2)
28-28: Do not use mutable data structures for argument defaults
Replace with None; initialize within function
(B006)
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Actionable comments posted: 1
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📒 Files selected for processing (1)
edenai_apis/apis/groq/groq_api.py(1 hunks)
🔇 Additional comments (3)
edenai_apis/apis/groq/groq_api.py (3)
7-13: Avoid overshadowing imports.Re-importing
ChatDataClassfrom two different modules can cause confusion and overshadow one of them. Consider aliasing or removing the redundant import if only one is needed.
19-19: Use an immutable default forapi_keys.Defining
api_keys: Dict = {}can lead to unintended state sharing across instances. Switch toNoneand initialize internally.- def __init__(self, api_keys: Dict = {}): + def __init__(self, api_keys: Optional[Dict] = None): + if api_keys is None: + api_keys = {} self.api_settings = load_provider( ...
61-61: Use an immutable default formessages.Defining
messages: List = []can lead to unintended state sharing. Replace withNoneand initialize within the function.- def llm__chat( - self, - messages: List = [], + def llm__chat( + self, + messages: Optional[List] = None, ... + if messages is None: + messages = [] ...
| from typing import Dict, List, Literal, Optional, Type, Union | ||
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| import httpx | ||
| from openai import BaseModel |
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💡 Verification agent
🧩 Analysis chain
Verify if BaseModel is correctly imported from the openai module.
The openai Python library typically doesn't expose BaseModel. If you intended to use Pydantic's BaseModel, please adjust the import statement accordingly.
Please run the following web search to confirm:
🌐 Web query:
Does the openai Python library export a `BaseModel` class?
💡 Result:
The OpenAI Python library does not export a BaseModel class directly. However, the library is often used in conjunction with Pydantic's BaseModel for structured output validation when integrating with OpenAI's API. Here’s a breakdown:
-
Official OpenAI Library Functionality
The standardopenaiPython package (PyPI source [1][5][11]) focuses on API client interactions and does not include a nativeBaseModelclass. For example:from openai import OpenAI client = OpenAI()
-
Pydantic Integration
Structured output validation (e.g., enforcing JSON schemas for responses) typically requires thepydantic.BaseModelclass from the separate Pydantic library. This is commonly used with OpenAI via third-party tools like Instructor (documentation [6][10]):from pydantic import BaseModel import instructor from openai import OpenAI client = instructor.patch(OpenAI()) class User(BaseModel): # Uses Pydantic's BaseModel name: str age: int
-
Community Examples
Multiple search results show developers combining OpenAI’s library with Pydantic for tasks like parsing API responses, batch processing, and schema validation [3][12][15]. For instance:- Defining response models with Pydantic to enforce structure [6][10][15].
- Using OpenAI’s API with
response_model=User(a Pydantic model) via Instructor [6].
Key Takeaway: While the OpenAI library itself doesn’t provide BaseModel, Pydantic’s BaseModel is widely used alongside it for structured data workflows. Install Pydantic separately (pip install pydantic) and import it directly.
Citations:
- 1: https://pypi.org/project/openai/0.26.5/
- 2: https://pypi.org/project/openai-python-api/
- 3: https://pydantic.dev/articles/llm-intro
- 4: https://ai.pydantic.dev/api/models/openai/
- 5: https://platform.openai.com/docs/libraries/python-library
- 6: https://python.useinstructor.com/integrations/openai/
- 7: https://platform.openai.com/docs/api-reference/introduction
- 8: https://platform.openai.com/docs/api-reference?lang=python
- 9: https://github.com/ollama/ollama/blob/main/docs/openai.md
- 10: https://python.useinstructor.com
- 11: https://platform.openai.com/docs/quickstart
- 12: https://community.openai.com/t/using-pydantic-structured-outputs-in-batch-mode/955756
- 13: https://community.openai.com/t/how-to-define-pydantic-json-schema/988192
- 14: https://platform.openai.com/docs/api-reference/
- 15: https://www.datacamp.com/tutorial/open-ai-structured-outputs
- 16: https://platform.openai.com/docs/libraries
- 17: https://community.openai.com/t/where-is-the-documentation-for-the-python-openai-sdk/583643
- 18: https://github.com/openai/openai-python
- 19: https://github.com/openai/openai-python/blob/main/src/openai/types/model.py
- 20: https://platform.openai.com/docs/guides/structured-outputs
Fix incorrect BaseModel import in edenai_apis/apis/groq/groq_api.py.
It appears that the OpenAI Python library does not export a BaseModel class. If your intention is to use Pydantic's BaseModel for output validation, please update the import to:
from pydantic import BaseModel- Location:
edenai_apis/apis/groq/groq_api.py, line 4 - Issue: Importing
BaseModelfromopenaiinstead of Pydantic. - Suggested Fix: Replace the import with the one shown above to correctly reference Pydantic’s
BaseModel.
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There's one comment from the rabbit that I think it's pertinent: https://github.com/edenai/edenai-apis/pull/342/files#r2011774829
The ChatDataClass is being shadowed
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Actionable comments posted: 0
🧹 Nitpick comments (1)
edenai_apis/apis/groq/info.json (1)
1-20: Overall Structure & Metadata Validity Check
The JSON file correctly defines the metadata and version information for the new Groq API provider in a clear, well-structured format. The_metadatasection and the individual provider configurations fortext,multimodal, andllmall conform to a consistent pattern.Points to Consider:
- Verify the Privacy URL: Ensure that
"https://groq.com/privacy-policy"is accurate and reachable.- Optional Metadata Enhancements: Consider including additional provider details such as a
provider_nameor a briefdescriptionto enhance clarity and future extensibility (e.g., if similar info files for other providers are expected).- Versioning Strategy: All
chatconfigurations are marked as"v1". As the provider evolves, it might be beneficial to include more granular versioning or additional configuration keys if needed.
Summary by CodeRabbit