Educhain refactored #40
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WalkthroughThe changes across multiple files primarily involve the transition from using the Changes
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Actionable comments posted: 3
🧹 Outside diff range and nitpick comments (10)
docs/features/mcq_from_data.md (1)
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1-70: Ensure consistency throughout the documentationWhile the code snippet has been updated to use the new
Educhainclient, the rest of the documentation still refers to the old API. This inconsistency could lead to confusion for users. Please review and update the entire document to ensure it accurately reflects the current API and usage patterns.Key areas to focus on:
- Update the introductory paragraph to mention the
Educhainclient if necessary.- Revise the "Customization Options" table to accurately reflect the current parameters and their usage with the
Educhainclient.- Update the "Output Format" section if the return type or structure has changed with the new API.
- Review and modify the "Pro Tips" section to ensure the advice is still applicable and add any new tips related to using the
Educhainclient.- Check if the "Supported Data Sources" section needs any updates based on the new implementation.
To address these points, please perform a thorough review of the entire document and make the necessary updates to align all sections with the new
Educhainclient usage.docs/getting-started/quick-start.md (4)
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14-29: Approve changes with a minor suggestion.The updates to the code block reflect a more object-oriented approach, which is a good improvement. The
Educhainclient now encapsulates the functionality, providing better organization and potentially easier extensibility.Consider renaming the
clientvariable to something more specific, likeeduchain_client, to enhance code readability:-client = Educhain() +educhain_client = Educhain() -questions = client.qna_engine.generate_questions( +questions = educhain_client.qna_engine.generate_questions(
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37-44: Approve changes with a suggestion for documentation.The updates to this code block are consistent with the previous changes, maintaining the new object-oriented approach. The customization options remain the same, which is good for backward compatibility.
Consider adding a brief comment explaining each customization option to enhance the documentation:
client = Educhain() +# Customize question generation with various parameters: +# - topic: The subject area for the questions +# - level: Difficulty level (e.g., "Beginner", "Intermediate", "Advanced") +# - num: Number of questions to generate +# - question_type: Type of questions (e.g., "conceptual", "practical") +# - language: Language for the questions and answers questions = client.qna_engine.generate_questions( topic="Machine Learning", level="Intermediate", num=3, question_type="conceptual", language="English" )
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53-60: Approve changes with a correction for the import statement.The updates to this code block are consistent with the previous changes, maintaining the new object-oriented approach for generating lesson plans.
There's a minor issue in the import statement. Please correct it as follows:
-from educhain import Educhain() +from educhain import EduchainThe
Educhainis a class, not a function, so it shouldn't be called in the import statement.
101-110: Approve changes with suggestions for additional documentation.The updates to this code block represent a significant improvement in flexibility for language model selection and configuration. The integration with Google's AI services through ChatGoogleGenerativeAI is a notable addition.
Consider adding more documentation to explain the new configuration process:
- Add a comment explaining the purpose of ChatGoogleGenerativeAI and the model being used.
- Explain how to obtain and set the GOOGLE_API_KEY.
- Describe the purpose of LLMConfig and how it's used in the Educhain client.
Here's an example of how you might enhance the documentation:
# Import necessary components for custom language model configuration from langchain_google_genai import ChatGoogleGenerativeAI from educhain import Educhain, LLMConfig # Configure a custom language model using Google's Generative AI # Here we're using the 'gemini-1.5-flash-exp-0827' model # Note: Ensure you have set up your Google API key as an environment variable or replace "GOOGLE_API_KEY" with your actual key gemini_flash = ChatGoogleGenerativeAI( model="gemini-1.5-flash-exp-0827", google_api_key="GOOGLE_API_KEY") # Create an LLMConfig object with the custom model # This configuration will be used to initialize the Educhain client flash_config = LLMConfig(custom_model=gemini_flash) # Initialize the Educhain client with the custom language model configuration client = Educhain(flash_config)Additionally, consider adding a note about the security implications of handling API keys and best practices for key management.
docs/index.md (1)
34-41: Code example updated to reflect new API usage.The code example has been successfully updated to use the new
Educhainclass andgenerate_questionsmethod. This change aligns well with the PR objectives of refactoring and updating documentation. The new structure makes the API usage more intuitive by encapsulating functionality within theEduchainclass.Consider adding a brief comment above the code example to explain the purpose of the code, such as:
# Example: Generating 5 beginner-level questions on Indian HistoryThis would provide additional context for users new to the library.
docs/features/mcq_generation.md (3)
11-14: Approve changes and suggest documentation updateThe changes in the code snippet reflect an improved, more object-oriented approach to using the Educhain library. The transition from directly importing
qna_engineto using theEduchainclass provides better encapsulation and potentially more flexibility.To maintain consistency, please review and update the rest of the documentation, including other code snippets and explanations, to reflect this new usage pattern. This includes:
- Updating any mentions of
qna_enginetoEduchainwhere appropriate.- Explaining the need to instantiate an
Educhainclient before use.- Updating method names from
generate_mcqtogenerate_questionsthroughout the document.
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1-58: Summary of changes and recommendationsThe updates to the MCQ generation documentation reflect significant improvements in the Educhain library's structure and usage. The transition to an object-oriented approach with the
Educhainclass enhances flexibility and encapsulation.To ensure the documentation remains accurate and helpful:
- Update all references to
qna_engineandgenerate_mcqthroughout the document to reflect the newEduchainclass andgenerate_questionsmethod.- Verify that the customization options table includes all available options for the new
generate_questionsmethod.- Confirm that the output format example accurately represents the current output of the
generate_questionsmethod.- Consider adding a brief explanation of the
Educhainclass and why users need to instantiate a client.These changes will help maintain consistency and clarity in the documentation, ensuring users can effectively utilize the updated Educhain library.
Missing
generate_questionsmethod inEduchainclassThe
generate_questionsmethod was not found in theEduchainclass. Please verify if this method has been removed or renamed and ensure that the documentation reflects the current implementation accurately.🔗 Analysis chain
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19-25: Verify completeness of customization optionsThe customization options table appears to be consistent with the new
generate_questionsmethod. However, it's important to ensure that all available options are represented.Please review the
Educhainclass andgenerate_questionsmethod documentation to confirm that all available customization options are included in this table. If there are any new options introduced with theEduchainclass, they should be added to maintain comprehensive documentation.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for the Educhain class and generate_questions method definition ast-grep --lang python --pattern $'class Educhain: $$$ def generate_questions($$$): $$$'Length of output: 97
Script:
#!/bin/bash # Search for the Educhain class definition ast-grep --lang python --pattern $'class Educhain: $$$'Length of output: 3186
Script:
#!/bin/bash # Search for the generate_questions method in any class ast-grep --lang python --pattern $'def generate_questions($$$): $$$'Length of output: 71
cookbook/educhain_claude3_5_sonnet.ipynb (1)
91-98: LGTM: Updated Educhain usage with improved modularityThe changes in this cell reflect an update to the Educhain library's API, resulting in a more modular and flexible approach:
- The import statement now includes
EduchainandLLMConfig, allowing for better configuration options.- An
LLMConfigobject is created with the sonnet model, enabling easy swapping of different LLM configurations.- The Educhain client is instantiated with the
sonnet_config, providing a cleaner setup.- The method for generating questions has been updated from
generate_mcqtogenerate_questions, which may indicate expanded functionality.These changes improve the overall structure and maintainability of the code.
Consider adding a brief comment explaining the purpose of the
LLMConfigobject and why the sonnet model is being used. This would enhance the educational value of the notebook for users who may be new to Educhain or this specific setup.
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
📒 Files selected for processing (6)
- cookbook/Convert_any_webpage_to_quiz.ipynb (4 hunks)
- cookbook/educhain_claude3_5_sonnet.ipynb (7 hunks)
- docs/features/mcq_from_data.md (1 hunks)
- docs/features/mcq_generation.md (1 hunks)
- docs/getting-started/quick-start.md (4 hunks)
- docs/index.md (1 hunks)
🔇 Additional comments not posted (13)
docs/getting-started/quick-start.md (1)
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1-110: Overall improvements with minor suggestions for enhancement.The changes in this documentation file consistently reflect a shift towards a more object-oriented and flexible approach in the Educhain library. The introduction of the
Educhainclient and the new language model configuration process significantly improve the library's structure and usability.Key improvements:
- Consistent use of the
Educhainclient across all examples.- More flexible language model configuration with Google's AI services integration.
- Maintained backward compatibility in terms of function parameters.
Suggestions for further enhancement:
- Consider adding more inline comments to explain the purpose and usage of new classes and methods.
- Ensure consistency in variable naming (e.g.,
clientvseduchain_client).- Provide more context about API key management and security best practices.
These changes will greatly benefit users in understanding and effectively utilizing the updated Educhain library.
docs/index.md (3)
34-34: LGTM: Colab badge link updated.The Colab badge link has been successfully updated. This change enhances user accessibility by providing a direct link to the Colab notebook, which aligns well with the documentation's purpose of helping users get started quickly.
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1-1: Clarification needed: Removal of roadmap sectionThe AI summary indicates that the roadmap section has been removed entirely from the document. While this change is not directly visible in the provided code, it's a significant modification that affects the documentation's content.
Could you please clarify the reasoning behind removing the roadmap section? If the intention is to keep the documentation focused on current features, consider adding a link to an external roadmap or a brief mention of where users can find information about future developments.
For example, you could add a section like this:
## 🗺️ Project Roadmap To stay up-to-date with our future plans and upcoming features, please visit our [GitHub Issues](https://github.com/satvik314/educhain/issues) page or check our [official roadmap](https://educhain.in/roadmap).This would help maintain transparency about the project's direction while keeping the main documentation concise.
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1-1: Summary of changes in docs/index.mdThe updates to this documentation file successfully reflect the refactoring of the Educhain API, particularly the transition from using
qna_engineto theEduchainclass. The code example has been appropriately updated, and the Colab badge link has been refreshed. These changes align well with the PR objectives of refactoring and updating documentation.However, the removal of the roadmap section, as mentioned in the AI summary, is a significant change that may benefit from further explanation. Consider adding a brief mention or link to where users can find information about future developments to maintain transparency about the project's direction.
Overall, these changes enhance the documentation's accuracy and usability, effectively supporting the Educhain project's evolution.
docs/features/mcq_generation.md (1)
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28-43: Verify output format consistencyThe presented output format appears to be consistent with what we would expect from a multiple-choice question generator. However, it's crucial to ensure that this format accurately represents the output of the new
generate_questionsmethod.Please confirm that the
generate_questionsmethod of theEduchainclass returns the exact same JSON structure as shown in this documentation. If there are any differences, update the documentation accordingly.cookbook/Convert_any_webpage_to_quiz.ipynb (2)
20-20: LGTM: Colab badge link updated.The Colab badge link has been successfully updated. This change improves the accessibility of the notebook by providing the correct link to open it in Google Colab.
46-46: LGTM: Execution counts reset.The execution counts for the code cells have been reset to null. This is a good practice when committing Jupyter notebooks to version control as it helps prevent unnecessary conflicts and keeps the focus on actual code changes.
Also applies to: 92-92, 121-121
cookbook/educhain_claude3_5_sonnet.ipynb (6)
6-6: LGTM: Colab badge link updatedThe Colab badge link has been successfully updated to point to the latest version of the notebook. This change ensures that users can access the most up-to-date version of the notebook in Colab.
25-25: LGTM: Execution count resetThe execution count for this cell has been reset to null. This is a good practice when committing Jupyter notebooks to version control as it helps prevent unnecessary diffs and conflicts.
63-63: LGTM: Execution count resetThe execution count for this cell has also been reset to null, maintaining consistency with the previous cell and adhering to best practices for version control of Jupyter notebooks.
77-77: LGTM: Execution count resetThe execution count for this cell has been reset to null, consistent with the previous cells. This change maintains the overall consistency of the notebook and aligns with version control best practices.
136-136: LGTM: Execution count resetThe execution count for this final cell has been reset to null, completing the consistent reset of execution counts throughout the notebook. This change aligns with best practices for version control of Jupyter notebooks.
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1-191: Overall: Notebook successfully updated with improved Educhain integrationThis review has covered all changes in the
educhain_claude3_5_sonnet.ipynbnotebook. The updates primarily focus on:
- Refreshing the Colab badge link.
- Updating the Educhain library usage with a more modular approach.
- Resetting execution counts for all code cells.
These changes enhance the notebook's maintainability and align with best practices for Jupyter notebook version control. The updated Educhain integration provides a more flexible setup for working with different LLM configurations.
To further improve the educational value of this notebook, consider adding brief comments explaining the purpose of new components like
LLMConfigand the choice of the sonnet model.
| from educhain import Educhain | ||
|
|
||
| questions = generate_mcqs_from_data( | ||
| client = Educhain() | ||
| questions = client.qna_engine.generate_questions_from_data( |
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Update code snippet and align documentation with new API
The code snippet has been updated to use the new Educhain client, which is a good improvement. However, there are a few inconsistencies and areas for improvement:
-
The method name
generate_questions_from_datadoesn't match the one mentioned in the AI summary (generate_mcqs_from_data). Please verify if this is the correct method name. -
The parameters
learning_objectiveanddifficulty_levelare included in the method call. These should be explained in the "Customization Options" table. -
The rest of the documentation needs to be updated to reflect the new API usage. This includes:
- Updating the "Customization Options" table to include the new parameters and remove any obsolete ones.
- Revising the "Output Format" section if the return type has changed.
- Checking if the "Pro Tips" and "Supported Data Sources" sections need any modifications.
To address these points, please:
- Confirm the correct method name and update it consistently throughout the document.
- Update the "Customization Options" table to include all current parameters:
| Option | Description | Example Values |
|--------|-------------|----------------|
| `source` | Data source for question generation | PDF file path, URL, or text content |
| `source_type` | Type of the data source | "pdf", "url", "text" |
| `num` | Number of questions to generate | 5, 10, 20 |
| `learning_objective` | Goal of the questions | "Understand AI basics", "Apply ML concepts" |
| `difficulty_level` | Difficulty of the questions | "Beginner", "Intermediate", "Advanced" |
| `llm` | Custom language model (optional) | ChatOpenAI(model="gpt-4") |
| `prompt_template` | Custom prompt template (optional) | "Generate questions about {topic}..." |-
Review and update the "Output Format" section if the return type has changed with the new API.
-
Scan through the rest of the document to ensure all information is up-to-date with the new
Educhainclient usage.
| "outputs": [] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "from educhain import qna_engine\n", | ||
| "from educhain import Educhain\n", | ||
| "from langchain_openai import ChatOpenAI\n", | ||
| "\n", |
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🛠️ Refactor suggestion
Consider making the model name configurable.
The code specifies a particular model ("gpt-4o-mini") for the ChatOpenAI instance. This might not be flexible for all users, and it's unclear if this is a publicly available model.
Consider the following improvements:
- Make the model name configurable, perhaps through an environment variable or a configuration file.
- Add a comment explaining the choice of this specific model and any requirements for using it.
Here's a suggested implementation:
import os
# Use an environment variable with a default value
model_name = os.getenv('OPENAI_MODEL_NAME', 'gpt-4o-mini')
# Add a comment explaining the model choice
# The gpt-4o-mini model is used for its balance of performance and efficiency.
# Ensure you have access to this model in your OpenAI account.
gpt_4o_mini = ChatOpenAI(model=model_name)| "from educhain import Educhain\n", | ||
| "from langchain_openai import ChatOpenAI\n", | ||
| "\n", | ||
| "gpt_4o_mini = ChatOpenAI(model = \"gpt-4o-mini\")\n", | ||
| "\n", | ||
| "client = Educhain()\n", | ||
| "\n", |
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Update qna_engine usage to client.
The import statement has been correctly updated to use Educhain instead of qna_engine, and a new Educhain client has been instantiated. However, the subsequent code still uses qna_engine, which will cause an error.
Please update the qna_engine usage to use the new client instance. Here's the suggested change:
client = Educhain()
-url_mcqs = qna_engine.generate_mcqs_from_data(
+url_mcqs = client.generate_mcqs_from_data(
source="https://en.wikipedia.org/wiki/Butterfly_effect",
source_type="url",
num=5,
llm = gpt_4o_mini
)Committable suggestion was skipped due to low confidence.
Added cookbooks and refactored documentations present in docs and other snippets with newer codes for educhain
Summary by CodeRabbit
Release Notes
New Features
Educhainclass, enhancing the structure and organization of the quiz generation process.Documentation
Educhainlibrary, including updated import statements and method calls for generating questions and lesson plans.Bug Fixes