• docs/LLMs/llms.md (2 hunks)
• docs/connectors.md (6 hunks)
• docs/custom-head.md (1 hunks)
• docs/custom-instructions.md (1 hunks)
• docs/custom-prompts.md (3 hunks)
• docs/custom-whitelisted-dependencies.md (1 hunks)
• docs/examples.md (14 hunks)
• docs/getting-started.md (5 hunks)
• docs/skills.md (1 hunks)

docs/LLMs/llms.md (2)

• 58-64: The changes to the instantiation of SmartDataframe using the config parameter as a named argument are correctly applied and consistent with the PR objectives to fix outdated or incorrect code snippets in the documentation.

• 175-179: The update to the instantiation of SmartDataframe with the config parameter as a named argument is correctly applied here as well, ensuring that the llm configuration is passed correctly.

docs/connectors.md (7)

• 41-41: The import statement correctly reflects the renaming of DataBricksConnector to DatabricksConnector as per the PR objectives and the AI-generated overview.

• 41-44: The config parameter is correctly passed as a named argument to the PostgreSQLConnector, which aligns with the PR's objective to ensure that the config parameter is passed as a named argument.

• 71-74: The config parameter is correctly passed as a named argument to the MySQLConnector, which aligns with the PR's objective to ensure that the config parameter is passed as a named argument.

• 101-104: The config parameter is correctly passed as a named argument to the SqliteConnector, which aligns with the PR's objective to ensure that the config parameter is passed as a named argument.

• 152-155: The import statement correctly reflects the renaming of DataBricksConnector to DatabricksConnector as per the PR objectives and the AI-generated overview. However, the class name SnowFlakeConnector should be checked for consistency with the rest of the codebase, as it does not match the expected SnowflakeConnector naming convention.

Verification successful

---

The use of SnowFlakeConnector in the docs/connectors.md file is consistent with its usage across the codebase, as verified by the search results. There is no need to change the class name in the documentation based on the current naming convention in the code.

Scripts Executed

The following scripts were executed for the analysis:

---

Script:

#!/bin/bash
# Verify that the class name `SnowFlakeConnector` is consistent across the codebase.
rg 'SnowFlakeConnector' --files-with-matches

Length of output: 218

* 183-183: The import statement correctly reflects the renaming of `DataBricksConnector` to `DatabricksConnector` as per the PR objectives and the AI-generated overview.

• 209-212: The config parameter is not used with the YahooFinanceConnector, which is inconsistent with the PR's objective to ensure that the config parameter is passed as a named argument. However, this may be due to the nature of the YahooFinanceConnector which might not require a config dictionary. It's important to verify if this is the expected usage or if it's an oversight.

Verification successful

To verify if the YahooFinanceConnector typically requires a config parameter, we should look at the constructor signature in the source code. The file pandasai/connectors/yahoo_finance.py is likely to contain the class definition, so we'll check there first. We can use ast-grep to search for the class definition and its constructor to see if a config parameter is expected.

---

The previous ast-grep command failed due to a syntax error in the pattern. It seems that the pattern was not correctly formatted for ast-grep. Let's correct the syntax and try again to find the constructor of the YahooFinanceConnector class.

---

The ast-grep command failed again due to a syntax error. It appears that the pattern used is still not compatible with ast-grep. Since we are having difficulty with ast-grep, let's switch to a different approach to inspect the constructor of the YahooFinanceConnector class. We can use rg (ripgrep) to search for the __init__ method within the file that defines the YahooFinanceConnector class. We know from the previous rg output that the class is defined in pandasai/connectors/yahoo_finance.py.

---

The output from rg shows that the __init__ method of the YahooFinanceConnector class is calling super().__init__ with a parameter yahoo_finance_config. However, this output does not show the full signature of the __init__ method, which means we still don't have the complete information about whether a config parameter is expected or not.

To get the full signature, we should look at the lines around the __init__ method call. We can adjust the rg command to include some lines of context around the match.

---

The new output from rg provides the full signature of the __init__ method for the YahooFinanceConnector class. It shows that the constructor accepts a stock_ticker parameter and a config parameter, which can be either an instance of YahooFinanceConnectorConfig or a dictionary. The config parameter is optional, as indicated by the default value None.

Given this information, the usage of YahooFinanceConnector in the docs/connectors.md file without a config parameter is correct, as the config parameter is not mandatory. This aligns with the PR's objective to ensure that the config parameter is passed as a named argument where required, and in this case, it is not required.

Therefore, the usage of YahooFinanceConnector without the config parameter in the documentation is consistent with the class definition and does not require any changes.

Scripts Executed

The following scripts were executed for the analysis:

---

Script:

#!/bin/bash
# Verify if the `YahooFinanceConnector` typically requires a `config` parameter or if this usage is correct.
rg 'YahooFinanceConnector' --files-with-matches

Length of output: 233

---

---

Script:

#!/bin/bash
# Search for the constructor of the YahooFinanceConnector class to see if it includes a `config` parameter.
ast-grep --lang python --pattern $'class YahooFinanceConnector($_) {
  def __init__($$$) {
    $$$
  }
}'

Length of output: 598

---

---

Script:

#!/bin/bash
# Correct the syntax and search for the constructor of the YahooFinanceConnector class to see if it includes a `config` parameter.
ast-grep --lang python --pattern $'class YahooFinanceConnector {
  def __init__($$$) {
    $$$
  }
}'

Length of output: 590

---

---

Script:

#!/bin/bash
# Use ripgrep to search for the __init__ method of the YahooFinanceConnector class.
rg "__init__" pandasai/connectors/yahoo_finance.py

Length of output: 115

---

---

Script:

#!/bin/bash
# Use ripgrep to search for the __init__ method of the YahooFinanceConnector class with some lines of context.
rg -C 3 "__init__" pandasai/connectors/yahoo_finance.py

Length of output: 471

docs/custom-head.md (1)

• 16-18: The update to instantiate SmartDataframe with the config parameter as a named argument is correct and aligns with the PR objective to standardize the instantiation across documentation.

docs/custom-instructions.md (1)

• 12-14: The change to the SmartDataframe instantiation correctly uses the config parameter as a named argument, which aligns with the PR's objective to standardize instantiation patterns in the documentation.

docs/custom-prompts.md (3)

• 34-37: The update to pass the config parameter as a named argument is correct and aligns with the PR's objective to standardize the instantiation of SmartDataframe.

• 61-64: The change to pass the config parameter as a named argument in the instantiation of SmartDataframe using MyCustomFileBasedPrompt is also correct and consistent with the PR's objective.

• 88-90: The instantiation of SmartDataframe without explicitly setting the my_custom_value attribute in the config parameter is consistent with the PR's objective to correct the documentation. However, it's important to ensure that MyCustomPrompt can handle cases where my_custom_value is not provided.

docs/custom-whitelisted-dependencies.md (1)

• 7-9: The updated code snippet correctly demonstrates how to instantiate SmartDataframe with the config parameter, including custom_whitelisted_dependencies. This is in line with the PR's objective to ensure that the config parameter is passed as a named argument.

docs/examples.md (14)

• 13-13: The import statement for OpenAI is correctly added according to the PR description and AI-generated summaries. This change is consistent across all examples.

• 22-22: The instantiation of OpenAI with an API token is correct and follows the pattern described in the PR description. However, it's important to ensure that YOUR_API_TOKEN is a placeholder and that actual API tokens are not exposed in the documentation.

• 25-25: The SmartDataframe instantiation now includes the config parameter with the llm key set to the OpenAI instance, which aligns with the PR's objective to pass the config parameter as a named argument.

• 27-27: The chat method is used correctly to demonstrate the conversational capabilities of the SmartDataframe. The output comment is informative and provides an example of the expected result.

• 322-322: The plot_salaries function is correctly renamed and updated to accept a DataFrame parameter. The comment within the function provides a clear description of its purpose and usage.

• 326-327: The plotting code within the plot_salaries function is correct, but it's important to ensure that the path temp_chart.png is intended to be a temporary file or if it should be saved to a user-defined path as demonstrated in other examples.

• 215-215: The instantiation of SmartDataframe with the config parameter is repeated in multiple examples. While this repetition is necessary to show different use cases, it's important to ensure that the documentation clearly explains the purpose of the config parameter to avoid confusion.

• 216-216: The example demonstrates chaining commands using the chat method. It's important to verify that the output comment accurately reflects the capabilities of the SmartDataframe and that the example is clear and understandable for users.

• 256-258: The Agent class is instantiated with a config parameter that includes the llm key set to an instance of OpenAI, which is consistent with the changes made to SmartDataframe and SmartDatalake instantiations.

• 160-165: The SmartDataframe instantiation includes multiple configuration options, demonstrating the flexibility of the config parameter. It's important to ensure that the save_charts_path is explained clearly in the documentation and that the example uses a placeholder or a generic path that users can easily adapt.

• 198-198: The instantiation of SmartDatalake with the config parameter is consistent with the changes made to SmartDataframe. This example helps to demonstrate the use of the library with multiple dataframes.

• 299-299: The addition of custom functions to the Agent class is an advanced feature that should be explained clearly in the documentation. The example should demonstrate how users can define their own skills and add them to the agent.

• 322-322: The plot_salaries skill function is correctly annotated with the @skill decorator, indicating that it's a custom skill that can be added to the Agent. The function's docstring provides a clear explanation of its purpose.

• 326-327: The plotting code within the plot_salaries function uses matplotlib to generate a bar chart. It's important to ensure that the code follows best practices for plotting and that the saved chart is handled securely and appropriately.

docs/getting-started.md (4)

• 47-47: The addition of the OpenAI import statement is consistent with the PR objectives to ensure proper instantiation and usage of the OpenAI module in the examples.

• 58-60: The instantiation of the SmartDataframe with the config parameter as a named argument is a good practice and aligns with the PR objectives to correct parameter passing in the documentation.

• 128-128: The import of the OpenAI module here is consistent with the PR objectives and ensures that the Agent class is instantiated with the correct configuration.

• 138-140: The instantiation of the Agent class with the config parameter including an llm key set to an instance of OpenAI is in line with the PR objectives and the AI-generated overview. This change ensures that the Agent is configured correctly.

docs/skills.md (1)

• 86-98: The parameter names in the plot_salaries function have been updated from name and salary to names and salaries. This change is consistent with the PR objectives and the AI-generated overview.

• docs/LLMs/llms.md (2 hunks)
• docs/connectors.md (6 hunks)
• docs/custom-head.md (1 hunks)
• docs/custom-instructions.md (1 hunks)
• docs/custom-prompts.md (3 hunks)
• docs/custom-whitelisted-dependencies.md (1 hunks)
• docs/examples.md (14 hunks)
• docs/getting-started.md (5 hunks)
• docs/skills.md (3 hunks)
• examples/skills_example.py (1 hunks)

• docs/LLMs/llms.md

• docs/connectors.md
• docs/custom-head.md
• docs/custom-instructions.md
• docs/custom-prompts.md
• docs/custom-whitelisted-dependencies.md
• docs/getting-started.md
• docs/skills.md

docs/examples.md (3)

• 13-13: The addition of the OpenAI import from the pandasai.llm package is consistent with the PR's objective to integrate the OpenAI module in examples.

• 22-25: The SmartDataframe instantiation now correctly includes the config parameter as a named argument, aligning with the PR's objective to fix instantiation patterns.

• 27-27: The chat method is correctly used with the SmartDataframe object, demonstrating the interactive capabilities as intended in the PR.

examples/skills_example.py (2)

• 24-30: The function signature and docstring for plot_salaries have been updated to use plural parameter names, which is consistent with the PR objective to correct parameter naming in the documentation.

• 24-36: Ensure that the plot_salaries function is tested with the new parameter names to confirm that the agent's skill addition and subsequent chat interaction work as expected with the updated function signature.