Groq chat vanna integration

[Major-wagh]

🔧 Changes

• Integration with Groq Chat:
  • Added a new Groq_Chat class to enable interactions with the Groq API.
  • The class includes methods for message handling (system_message, user_message, and assistant_message) and submitting prompts.
• Environment Configuration:
  • Updated GitHub Actions workflow (.github/workflows/tests.yml) to include GROQ_API_KEY as a secret for running tests.
• Unit Tests:
  • Extended tests/test_vanna.py with a new test class, VannaGroq, combining VannaDB_VectorStore and Groq_Chat functionality.
  • Added a test (test_vn_groq) that validates SQL generation and execution for retrieving the top 10 customers by sales.

🚀 How to Test

1. Set up the environment:

   • Ensure the GROQ_API_KEY environment variable is available.
   • Update .env or GitHub Secrets for testing if required.

2. Run the tests:

   • Use the following command to execute the updated test suite:

     pytest tests/test_vanna.py

   • Confirm that all tests, including test_vn_groq, pass successfully.

3. Validate Groq Integration:

   • Instantiate the VannaGroq class in an environment with a valid Groq API key.
   • Test its ability to execute a prompt and retrieve data via SQL.

📝 Notes

• The Groq_Chat class intelligently selects the model based on token count and supports configurations for temperature, engine, and API key.
• The integration is designed to ensure compatibility with the existing Vanna architecture while being extensible for future enhancements.

Let me know if there’s anything else you'd like to include!

[llamapreview]

Auto Pull Request Review from LlamaPReview

1. Overview

1.1 PR Summary

• Business value and requirements alignment: This PR introduces integration with the Groq Chat API, expanding Vanna's LLM capabilities. The integration aims to enhance SQL generation and execution by combining VannaDB_VectorStore and Groq_Chat functionalities.
• Key components modified:
  • Added a new Groq_Chat class for interacting with the Groq API.
  • Updated GitHub Actions workflow to include GROQ_API_KEY for running tests.
  • Extended tests/test_vanna.py with a new test class VannaGroq to validate SQL generation and execution.
• Impact assessment: The integration introduces a new dependency on the Groq API, which impacts system stability and security. Proper handling of the GROQ_API_KEY and robust error handling are critical.
• System dependencies and integration impacts: The new Groq_Chat class interacts with the existing VannaDB_VectorStore, affecting how Vanna leverages LLMs for SQL generation and execution.

1.2 Architecture Changes

• System design modifications: Introduction of the Groq_Chat class to handle interactions with the Groq API. This class includes methods for message handling and submitting prompts, dynamically selecting models based on token count.
• Component interactions: The VannaGroq class combines VannaDB_VectorStore and Groq_Chat functionalities, enabling SQL generation and execution using the Groq API.
• Integration points: The Groq_Chat class is integrated into the existing Vanna architecture, with configuration options for temperature, engine, and API key. The VannaGroq class demonstrates how this integration can be used in practice.

2. Detailed Technical Analysis

2.1 Code Logic Deep-Dive

Core Logic Changes

• src/vanna/groq/groq_chat.py - init
  • Submitted PR Code:

    import os

    from groq import Groq

    from ..base import VannaBase

    class Groq_Chat(VannaBase):
        def __init__(self, client=None, config=None):
            VannaBase.__init__(self, config=config)

            # default parameters - can be overridden using config
            self.temperature = 0.7

            if "temperature" in config:
                self.temperature = config["temperature"]

            if "model" in config:
                model = config["model"]

            if client is not None:
                self.client = client
                return

            if config is None and client is None:
                self.client = Groq(api_key=os.getenv("GROQ_API_KEY"))
                return

            if "api_key" in config:
                self.client = Groq(api_key=config["api_key"])

• Analysis:
  • The __init__ method handles the initialization of the Groq client. It prioritizes a provided client instance, then checks for an api_key in the config, and finally falls back to the GROQ_API_KEY environment variable.
  • A potential issue arises if an invalid api_key is provided either through the config or the environment variable. The current implementation doesn't include explicit error handling for invalid API keys during initialization. This could lead to exceptions later when the client is used, making debugging harder.
  • The initial review correctly identifies the importance of secure handling of GROQ_API_KEY, but doesn't delve into the immediate consequences of an invalid key during object instantiation.
• LlamaPReview Suggested Improvements:

    import os

    from groq import Groq
    from groq._exceptions import AuthenticationError  # Import specific exception

    from ..base import VannaBase

    class Groq_Chat(VannaBase):
        def __init__(self, client=None, config=None):
            VannaBase.__init__(self, config=config)

            # default parameters - can be overridden using config
            self.temperature = 0.7

            if "temperature" in config:
                self.temperature = config["temperature"]

            if "model" in config:
                model = config["model"]

            if client is not None:
                self.client = client
                return

            try:
                if config is None and client is None:
                    api_key = os.getenv("GROQ_API_KEY")
                    if not api_key:
                        raise ValueError("GROQ_API_KEY environment variable not set.")
                    self.client = Groq(api_key=api_key)
                    return

                if "api_key" in config:
                    self.client = Groq(api_key=config["api_key"])
            except AuthenticationError as e:
                raise ValueError(f"Invalid Groq API key provided: {e}") from e
            except ValueError as e:
                raise ValueError(f"Error initializing Groq client: {e}") from e

• Improvement rationale:
  • Technical benefits: Adds explicit error handling for invalid API keys during initialization. This improves the robustness of the Groq_Chat class by catching potential authentication errors early. By importing AuthenticationError from the groq library, we can specifically handle API key issues. A ValueError is raised with a more informative message, including the original exception for better debugging. It also handles the case where the environment variable is not set.
  • Business value: Prevents unexpected failures later in the application lifecycle due to invalid API keys, leading to a better user experience.
  • Risk assessment: Reduces the risk of runtime errors related to authentication and improves the clarity of error messages.

Core Logic Changes

• src/vanna/groq/groq_chat.py - submit_prompt
  • Submitted PR Code:

    def submit_prompt(self, prompt, **kwargs) -> str:
        if prompt is None:
            raise Exception("Prompt is None")

        if len(prompt) == 0:
            raise Exception("Prompt is empty")

        # Count the number of tokens in the message log
        # Use 4 as an approximation for the number of characters per token
        num_tokens = 0
        for message in prompt:
            num_tokens += len(message["content"]) / 4

        if kwargs.get("model", None) is not None:
            model = kwargs.get("model", None)
            print(
                f"Using model {model} for {num_tokens} tokens (approx)"
            )
            response = self.client.chat.completions.create(
                model=model,
                messages=prompt,
                stop=None,
                temperature=self.temperature,
            )
        elif kwargs.get("engine", None) is not None:
            engine = kwargs.get("engine", None)
            print(
                f"Using model {engine} for {num_tokens} tokens (approx)"
            )
            response = self.client.chat.completions.create(
                engine=engine,
                messages=prompt,
                stop=None,
                temperature=self.temperature,
            )
        elif self.config is not None and "engine" in self.config:
            print(
                f"Using engine {self.config['engine']} for {num_tokens} tokens (approx)"
            )
            response = self.client.chat.completions.create(
                engine=self.config["engine"],
                messages=prompt,
                stop=None,
                temperature=self.temperature,
            )
        elif self.config is not None and "model" in self.config:
            print(
                f"Using model {self.config['model']} for {num_tokens} tokens (approx)"
            )
            response = self.client.chat.completions.create(
                model=self.config["model"],
                messages=prompt,
                stop=None,
                temperature=self.temperature,
            )
        else:
            if num_tokens > 3500:
                model = "llama-3.1-8b-instant"
            else:
                model = "llama3-8b-8192"

            print(f"Using model {model} for {num_tokens} tokens (approx)")
            response = self.client.chat.completions.create(
                model=model,
                messages=prompt,
                stop=None,
                temperature=self.temperature,
            )

        # Find the first response from the chatbot that has text in it (some responses may not have text)
        for choice in response.choices:
            if "text" in choice:
                return choice.text

        # If no response with text is found, return the first response's content (which may be empty)
        return response.choices[0].message.content

• Analysis:
  • The submit_prompt method handles sending prompts to the Groq API. It includes logic for selecting the model based on an approximate token count.
  • The approximation of tokens using "number of characters / 4" is a potential source of inaccuracy. Different tokens can have varying lengths, and this approximation might lead to incorrect model selection, especially around the threshold of 3500 tokens. The Groq API likely has a more accurate way to determine token count, which should be used for more reliable model selection.
  • The initial review highlights the dynamic model selection but doesn't question the accuracy of the token estimation.
  • The code iterates through response.choices looking for a choice with "text". If none is found, it returns the content of the first choice, which could be empty. This behavior might not be ideal, and it might be better to return None or raise an exception if no meaningful text is found.
• LlamaPReview Suggested Improvements:

    from groq._exceptions import GroqError  # Import specific exception for API errors
    import tiktoken # Import tiktoken for more accurate token counting

    def submit_prompt(self, prompt, **kwargs) -> str:
        if prompt is None:
            raise ValueError("Prompt cannot be None") # Changed to ValueError

        if not prompt: # More Pythonic way to check for empty list
            raise ValueError("Prompt cannot be empty") # Changed to ValueError

        # Use tiktoken for more accurate token counting
        encoding = tiktoken.encoding_for_model(kwargs.get("model") or self.config.get("model") or "llama3-8b-8192") # Default model if not specified
        num_tokens = sum(len(encoding.encode(message["content"])) for message in prompt)

        selected_model = None
        if kwargs.get("model"):
            selected_model = kwargs.get("model")
            print(f"Using model {selected_model} for {num_tokens} tokens")
        elif kwargs.get("engine"):
            selected_model = kwargs.get("engine")
            print(f"Using engine {selected_model} for {num_tokens} tokens")
        elif self.config is not None and "engine" in self.config:
            selected_model = self.config["engine"]
            print(f"Using engine {selected_model} for {num_tokens} tokens")
        elif self.config is not None and "model" in self.config:
            selected_model = self.config["model"]
            print(f"Using model {selected_model} for {num_tokens} tokens")
        else:
            if num_tokens > 3500:
                selected_model = "llama-3.1-8b-instant"
            else:
                selected_model = "llama3-8b-8192"
            print(f"Using model {selected_model} for {num_tokens} tokens")

        try:
            response = self.client.chat.completions.create(
                model=selected_model,
                messages=prompt,
                stop=None,
                temperature=self.temperature,
            )
        except GroqError as e:
            raise Exception(f"Error communicating with Groq API: {e}") from e

        # Find the first response from the chatbot that has text in it
        for choice in response.choices:
            if hasattr(choice, 'text') and choice.text: # More robust check
                return choice.text

        # If no response with text is found, return None or raise an exception
        if response.choices:
            return response.choices[0].message.content # Keep the original behavior for now, but consider changing
        else:
            return None # Or raise an exception: raise Exception("No response received from Groq API")

• Improvement rationale:
  • Technical benefits: Uses the tiktoken library for more accurate token counting, improving the reliability of model selection. This addresses a potential inaccuracy in the original implementation. Adds specific exception handling for GroqError to provide more informative error messages when the API call fails. Improves the check for text in the response choices using hasattr and checking for truthiness.
  • Business value: More accurate model selection can lead to better performance and cost efficiency. Improved error handling makes the integration more robust.
  • Risk assessment: Reduces the risk of incorrect model selection due to inaccurate token estimation. Improves the handling of API errors, making the system more resilient. The behavior when no text is found is made more explicit, although the original behavior is maintained for now to minimize the scope of the change. A follow-up could consider raising an exception in this scenario.

Core Logic Changes

• tests/test_vanna.py - VannaGroq and test_vn_groq
  • Submitted PR Code:

    class VannaGroq(VannaDB_VectorStore, Groq_Chat):
        def __init__(self, config=None):
            VannaDB_VectorStore.__init__(self, vanna_model=MY_VANNA_MODEL, vanna_api_key=MY_VANNA_API_KEY, config=config)
            Groq_Chat.__init__(self, config=config)

    vn_groq = VannaGroq(config={'api_key': os.environ['GROQ_API_KEY']})
    vn_groq.connect_to_sqlite('https://vanna.ai/Chinook.sqlite')

    def test_vn_groq():
        sql = vn_groq.generate_sql("What are the top 10 customers by sales?")
        df = vn_groq.run_sql(sql)
        assert len(df) == 10

• Analysis:
  • The VannaGroq class uses multiple inheritance, combining VannaDB_VectorStore and Groq_Chat. While this might seem convenient for combining functionalities, it can lead to the "diamond problem" and make understanding the method resolution order more complex, especially as the classes evolve.
  • The test case test_vn_groq focuses on a happy path scenario. It doesn't include tests for error conditions, such as what happens when the Groq API returns an error, or when the generated SQL is invalid.
  • The initial review mentions the need for robust testing but doesn't specifically address the limitations of the current test case.
• LlamaPReview Suggested Improvements:

    class VannaGroq: # Favor composition over inheritance
        def __init__(self, db_vector_store_config=None, groq_chat_config=None):
            self.db_vector_store = VannaDB_VectorStore(vanna_model=MY_VANNA_MODEL, vanna_api_key=MY_VANNA_API_KEY, config=db_vector_store_config)
            self.groq_chat = Groq_Chat(config=groq_chat_config)

        def connect_to_sqlite(self, *args, **kwargs):
            self.db_vector_store.connect_to_sqlite(*args, **kwargs)

        def generate_sql(self, *args, **kwargs):
            return self.groq_chat.submit_prompt([self.groq_chat.user_message(self.db_vector_store.generate_sql(*args, **kwargs))]) # Example of how to chain calls

        def run_sql(self, *args, **kwargs):
            return self.db_vector_store.run_sql(*args, **kwargs)

    vn_groq = VannaGroq(groq_chat_config={'api_key': os.environ['GROQ_API_KEY']})
    vn_groq.connect_to_sqlite('https://vanna.ai/Chinook.sqlite')

    def test_vn_groq_success():
        sql = vn_groq.generate_sql("What are the top 10 customers by sales?")
        df = vn_groq.run_sql(sql)
        assert len(df) == 10

    def test_vn_groq_api_error(mocker): # Using pytest-mock
        mocker.patch.object(vn_groq.groq_chat.client.chat.completions, 'create', side_effect=GroqError("API Error"))
        with pytest.raises(Exception, match="Error communicating with Groq API"):
            vn_groq.generate_sql("What are the top 10 customers by sales?")

    def test_vn_groq_invalid_sql(mocker):
        mocker.patch.object(vn_groq, 'run_sql', side_effect=Exception("Invalid SQL"))
        sql = vn_groq.generate_sql("What are the top 10 customers by sales?")
        with pytest.raises(Exception, match="Invalid SQL"):
            vn_groq.run_sql(sql)

• Improvement rationale:
  • Technical benefits: Replaces multiple inheritance with composition in VannaGroq. This improves code clarity and reduces the risk of issues associated with the diamond problem. Adds new test cases to cover API error scenarios and invalid SQL generation, making the test suite more comprehensive. Uses pytest-mock to mock API calls and simulate error conditions.
  • Business value: More robust testing ensures the integration is reliable and handles errors gracefully.
  • Risk assessment: Reduces the risk of unexpected behavior due to complex inheritance structures and improves the confidence in the integration's stability through more comprehensive testing.

2.2 Implementation Quality

• Code organization and structure: The new Groq_Chat class is well-organized and follows a clear structure. The use of configuration parameters and environment variables for initialization is a good practice.
• Design patterns usage: The class uses the factory pattern for initializing the Groq client, which is appropriate for this use case.
• Error handling approach: The initial implementation lacks robust error handling, especially for invalid API keys and API errors. The suggested improvements address these issues by adding explicit error handling and using specific exceptions.
• Resource management: The class manages resources efficiently, with clear initialization and usage of the Groq client.

3. Critical Findings

3.1 Potential Issues

• 🔴 Critical Issues

  • Invalid API Key Handling: The current implementation does not handle invalid API keys during initialization, which can lead to runtime errors.
    • Impact: Potential application failures due to authentication errors.
    • Recommendation: Add explicit error handling for invalid API keys during initialization.

• 🟡 Warnings

  • Inaccurate Token Counting: The approximation of tokens using "number of characters / 4" can lead to incorrect model selection.
    • Potential risks: Incorrect model selection can affect performance and cost efficiency.
    • Suggested improvements: Use a more accurate token counting method, such as the tiktoken library.

3.2 Code Quality Concerns

• Maintainability aspects: The use of multiple inheritance in VannaGroq can lead to maintainability issues as the classes evolve.
• Readability issues: The current implementation of submit_prompt has nested conditionals that can be simplified for better readability.
• Performance bottlenecks: The token counting approximation can lead to performance issues due to incorrect model selection.

4. Security Assessment

• Authentication/Authorization impacts: The GROQ_API_KEY is a sensitive piece of information that needs to be handled securely.
• Data handling concerns: Ensure that user prompts and API responses are handled securely and that no sensitive information is inadvertently exposed.
• Input validation: Validate the GROQ_API_KEY during initialization to prevent runtime errors.
• Security best practices: Follow best practices for handling API keys and sensitive data.
• Potential security risks: Invalid API keys can lead to authentication errors and potential security risks.
• Mitigation strategies: Add explicit error handling for invalid API keys and ensure secure handling of the GROQ_API_KEY.
• Security testing requirements: Include tests for handling invalid API keys and secure data handling.

5. Testing Strategy

5.1 Test Coverage

• Unit test analysis: The current test suite includes a happy path test for SQL generation and execution. Additional tests are needed to cover error conditions.
• Integration test requirements: Ensure that the integration with VannaDB_VectorStore functions as expected and that SQL generation and execution using Groq are accurate.
• Edge cases coverage: Include tests for edge cases, such as invalid API keys, API errors, and invalid SQL generation.

5.2 Test Recommendations

Suggested Test Cases

  def test_vn_groq_success():
      sql = vn_groq.generate_sql("What are the top 10 customers by sales?")
      df = vn_groq.run_sql(sql)
      assert len(df) == 10

  def test_vn_groq_api_error(mocker): # Using pytest-mock
      mocker.patch.object(vn_groq.groq_chat.client.chat.completions, 'create', side_effect=GroqError("API Error"))
      with pytest.raises(Exception, match="Error communicating with Groq API"):
          vn_groq.generate_sql("What are the top 10 customers by sales?")

  def test_vn_groq_invalid_sql(mocker):
      mocker.patch.object(vn_groq, 'run_sql', side_effect=Exception("Invalid SQL"))
      sql = vn_groq.generate_sql("What are the top 10 customers by sales?")
      with pytest.raises(Exception, match="Invalid SQL"):
          vn_groq.run_sql(sql)

• Coverage improvements: Add tests for error conditions and edge cases to improve test coverage.
• Performance testing needs: Ensure that the integration performs well under different scenarios, including high token counts and API errors.

6. Documentation & Maintenance

• Documentation updates needed (API, architecture, configuration): Update the documentation to include information about the new Groq_Chat class and its configuration options.
• Long-term maintenance considerations: Ensure that the integration is maintainable and that the code is easy to understand and modify.
• Technical debt and monitoring requirements: Monitor the integration for any issues and address technical debt as needed.

7. Deployment & Operations

• Deployment impact and strategy: The integration introduces a new dependency on the Groq API, which needs to be considered during deployment. Ensure that the GROQ_API_KEY is securely managed.
• Key operational considerations: Monitor the Groq API for any issues and ensure that the integration is reliable and performant.

8. Summary & Recommendations

8.1 Key Action Items

1. Add explicit error handling for invalid API keys during initialization.
2. Use a more accurate token counting method, such as the tiktoken library, for model selection.
3. Replace multiple inheritance with composition in VannaGroq to improve code clarity and maintainability.
4. Add tests for error conditions and edge cases to improve test coverage.

8.2 Future Considerations

• Technical evolution path: Continuously improve the integration with the Groq API and address any issues that arise.
• Business capability evolution: Expand the capabilities of the integration to support more use cases and improve performance.
• System integration impacts: Ensure that the integration is reliable and performant, and that it aligns with the overall system architecture.

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