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Chat Engine

ChatEngine

A class for interacting with a language model to generate documentation and ideas.

Attributes:

Name Type Description
llm

The language model instance used for generating text.
settings

Settings object containing configuration parameters.
project_manager

The project manager instance (currently unused).
Source code in repo_agent/chat_engine.py 
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class ChatEngine:
    """
    A class for interacting with a language model to generate documentation and ideas.

    Attributes:
        llm: The language model instance used for generating text.
        settings: Settings object containing configuration parameters.
        project_manager: The project manager instance (currently unused).
    """

    def __init__(self, project_manager):
        """
        Configures the chat engine with settings for the language model, including API key, base URL, timeout, model name, and temperature.

        Args:
            project_manager: The project manager instance. This is not directly used in initialization but kept for potential future use.

        Returns:
            None

        """

        setting = SettingsManager.get_setting()
        self.llm = OpenAILike(
            context_window=20000,
            api_key=setting.chat_completion.openai_api_key.get_secret_value(),
            api_base=setting.chat_completion.openai_base_url,
            timeout=setting.chat_completion.request_timeout,
            model=setting.chat_completion.model,
            temperature=setting.chat_completion.temperature,
            max_retries=1,
            is_chat_model=True,
        )

    def build_prompt(self, doc_item: DocItem, main_idea="", context_length=20000):
        """
        Creates a prompt to improve code documentation by gathering information about the code’s type, name, content, docstring, and file path. It also includes details of objects that call or are called by the current code, along with their documentation and code, to provide context. A user-defined main idea can be included to focus the enhancement process.

        Args:
            doc_item: A DocItem object containing code information and references.
            main_idea: An optional main idea to incorporate into the prompt.
            context_length: The maximum length of the context window (default is 20000).

        Returns:
            str: A formatted string representing the prompt for documentation enhancement.

        """

        setting = SettingsManager.get_setting()
        code_info = doc_item.content
        referenced = len(doc_item.who_reference_me) > 0 and len(code_info) < 16000
        code_type = code_info["type"]
        code_name = code_info["name"]
        code_content = code_info["code_content"]
        have_return = code_info["have_return"]
        docstring = (
            code_info["md_content"][-1]
            if code_info["md_content"]
            else "Empty docstring"
        )
        file_path = doc_item.get_full_name()

        def get_referenced_prompt(doc_item: DocItem) -> str:
            if len(doc_item.reference_who) == 0:
                return ""
            prompt = [
                "As you can see, the code calls the following objects, their code and docs are as following:"
            ]
            for reference_item in doc_item.reference_who:
                instance_prompt = (
                    f"obj: {reference_item.get_full_name()}\nDocument: \n{(reference_item.md_content[-1] if len(reference_item.md_content) > 0 else 'None')}\nRaw code:```\n{(reference_item.content['code_content'] if 'code_content' in reference_item.content.keys() else '')}\n```"
                    + "=" * 10
                )
                prompt.append(instance_prompt)
            return "\n".join(prompt)

        def get_referencer_prompt(doc_item: DocItem) -> str:
            if len(doc_item.who_reference_me) == 0:
                return ""
            prompt = [
                "Also, the code has been called by the following objects, their code and docs are as following:"
            ]
            for referencer_item in doc_item.who_reference_me:
                instance_prompt = (
                    f"obj: {referencer_item.get_full_name()}\nDocument: \n{(referencer_item.md_content[-1] if len(referencer_item.md_content) > 0 else 'None')}\nRaw code:```\n{(referencer_item.content['code_content'] if 'code_content' in referencer_item.content.keys() else 'None')}\n```"
                    + "=" * 10
                )
                prompt.append(instance_prompt)
            return "\n".join(prompt)

        def get_relationship_description(referencer_content, reference_letter):
            if referencer_content and reference_letter:
                return "And please include the reference relationship with its callers and callees in the project from a functional perspective"
            elif referencer_content:
                return "And please include the relationship with its callers in the project from a functional perspective."
            elif reference_letter:
                return "And please include the relationship with its callees in the project from a functional perspective."
            else:
                return ""

        code_type_tell = "Class" if code_type == "ClassDef" else "Function"
        if referenced:
            combine_ref_situation = (
                "and combine it with its calling situation in the project,"
            )
            referencer_content = get_referencer_prompt(doc_item)
            reference_letter = get_referenced_prompt(doc_item)
            has_relationship = get_relationship_description(
                referencer_content, reference_letter
            )
        else:
            combine_ref_situation = ""
            referencer_content = ""
            reference_letter = ""
            has_relationship = ""
        if main_idea:
            return docstring_update_chat_templates.format_messages(
                combine_ref_situation=combine_ref_situation,
                file_path=file_path,
                code_type_tell=code_type_tell,
                code_name=code_name,
                main_idea=(
                    main_idea
                    if not main_idea
                    else f"You can use user-defined main idea of the project to enhance exist docstring\n{main_idea}"
                ),
                docstring=docstring,
                has_relationship=has_relationship,
                reference_letter=reference_letter,
                referencer_content=referencer_content,
                language=setting.project.language,
            )
        else:
            return chat_template.format_messages(
                combine_ref_situation=combine_ref_situation,
                file_path=file_path,
                code_type_tell=code_type_tell,
                code_name=code_name,
                code_content=code_content,
                main_idea=(
                    main_idea
                    if not main_idea
                    else f"You can use user-defined main idea of the project to enhance exist docstring\n{main_idea}"
                ),
                docstring=docstring,
                has_relationship=has_relationship,
                reference_letter=reference_letter,
                referencer_content=referencer_content,
                language=setting.project.language,
            )

    def generate_doc(self, doc_item: DocItem):
        """
        Constructs a prompt from the document item and queries a language model to generate documentation. Post-processes the response by removing code block markers.

        Args:
            doc_item: The DocItem for which to generate documentation.
        Returns:
            str: The generated documentation string, with code block markers removed.
        Raises:
            Exception: If there is an error during the LLM chat call.


        """

        settings = SettingsManager.get_setting()
        if settings.project.main_idea:
            messages = self.build_prompt(doc_item, main_idea=settings.project.main_idea)
        else:
            messages = self.build_prompt(doc_item)
        try:
            response = self.llm.chat(messages)
            answer = response.message.content
            return answer.replace("```python\n", "").replace("```", "")
        except Exception as e:
            logger.error(f"Error in llamaindex chat call: {e}")
            raise

    def generate_idea(self, list_items: str):
        """
        Creates a novel idea based on the given components, adapting to configured language preferences. It communicates with a language model and records token consumption for tracking. Any issues during communication with the language model are logged and then propagated.

        Args:
            list_items: A string containing the list of items to base the idea on.

        Returns:
            str: The generated idea as a string.  If an error occurs during the LLM chat call,
                 the exception is re-raised after logging.


        """

        settings = SettingsManager.get_setting()
        messages = idea_chat_template.format_messages(
            components=list_items, language=settings.project.language
        )
        try:
            response = self.llm.chat(messages)
            logger.debug(f"LLM Prompt Tokens: {response.raw.usage.prompt_tokens}")
            logger.debug(
                f"LLM Completion Tokens: {response.raw.usage.completion_tokens}"
            )
            logger.debug(f"Total LLM Token Count: {response.raw.usage.total_tokens}")
            return response.message.content
        except Exception as e:
            logger.error(f"Error in llamaindex chat call: {e}")
            raise

    def summarize_module(self, module_desc: str):
        """
        Creates a succinct summary of a module’s purpose, informed by project configuration and language preferences. This is achieved through interaction with a large language model, with token usage tracked for monitoring.

        Args:
            module_desc: The description of the module to be summarized.

        Returns:
            str: The summary generated by the LLM.  Raises any exceptions encountered during the LLM call.


        """

        settings = SettingsManager.get_setting()
        messages = module_summary_template.format_messages(
            components=module_desc,
            main_idea=settings.project.main_idea,
            language=settings.project.language,
        )
        try:
            response = self.llm.chat(messages)
            logger.debug(f"LLM Prompt Tokens: {response.raw.usage.prompt_tokens}")
            logger.debug(
                f"LLM Completion Tokens: {response.raw.usage.completion_tokens}"
            )
            logger.debug(f"Total LLM Token Count: {response.raw.usage.total_tokens}")
            return response.message.content
        except Exception as e:
            logger.error(f"Error in llamaindex chat call: {e}")
            raise

__init__(project_manager)

Configures the chat engine with settings for the language model, including API key, base URL, timeout, model name, and temperature.

Parameters:

Name Type Description Default
project_manager

The project manager instance. This is not directly used in initialization but kept for potential future use.

 required

Returns:

Type Description

None
Source code in repo_agent/chat_engine.py 
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def __init__(self, project_manager):
    """
    Configures the chat engine with settings for the language model, including API key, base URL, timeout, model name, and temperature.

    Args:
        project_manager: The project manager instance. This is not directly used in initialization but kept for potential future use.

    Returns:
        None

    """

    setting = SettingsManager.get_setting()
    self.llm = OpenAILike(
        context_window=20000,
        api_key=setting.chat_completion.openai_api_key.get_secret_value(),
        api_base=setting.chat_completion.openai_base_url,
        timeout=setting.chat_completion.request_timeout,
        model=setting.chat_completion.model,
        temperature=setting.chat_completion.temperature,
        max_retries=1,
        is_chat_model=True,
    )

build_prompt(doc_item, main_idea='', context_length=20000)

Creates a prompt to improve code documentation by gathering information about the code’s type, name, content, docstring, and file path. It also includes details of objects that call or are called by the current code, along with their documentation and code, to provide context. A user-defined main idea can be included to focus the enhancement process.

Parameters:

Name Type Description Default
doc_item DocItem

A DocItem object containing code information and references.

 required
main_idea

An optional main idea to incorporate into the prompt.

 ''
context_length

The maximum length of the context window (default is 20000).

 20000

Returns:

Name Type Description
str

A formatted string representing the prompt for documentation enhancement.
Source code in repo_agent/chat_engine.py 
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def build_prompt(self, doc_item: DocItem, main_idea="", context_length=20000):
    """
    Creates a prompt to improve code documentation by gathering information about the code’s type, name, content, docstring, and file path. It also includes details of objects that call or are called by the current code, along with their documentation and code, to provide context. A user-defined main idea can be included to focus the enhancement process.

    Args:
        doc_item: A DocItem object containing code information and references.
        main_idea: An optional main idea to incorporate into the prompt.
        context_length: The maximum length of the context window (default is 20000).

    Returns:
        str: A formatted string representing the prompt for documentation enhancement.

    """

    setting = SettingsManager.get_setting()
    code_info = doc_item.content
    referenced = len(doc_item.who_reference_me) > 0 and len(code_info) < 16000
    code_type = code_info["type"]
    code_name = code_info["name"]
    code_content = code_info["code_content"]
    have_return = code_info["have_return"]
    docstring = (
        code_info["md_content"][-1]
        if code_info["md_content"]
        else "Empty docstring"
    )
    file_path = doc_item.get_full_name()

    def get_referenced_prompt(doc_item: DocItem) -> str:
        if len(doc_item.reference_who) == 0:
            return ""
        prompt = [
            "As you can see, the code calls the following objects, their code and docs are as following:"
        ]
        for reference_item in doc_item.reference_who:
            instance_prompt = (
                f"obj: {reference_item.get_full_name()}\nDocument: \n{(reference_item.md_content[-1] if len(reference_item.md_content) > 0 else 'None')}\nRaw code:```\n{(reference_item.content['code_content'] if 'code_content' in reference_item.content.keys() else '')}\n```"
                + "=" * 10
            )
            prompt.append(instance_prompt)
        return "\n".join(prompt)

    def get_referencer_prompt(doc_item: DocItem) -> str:
        if len(doc_item.who_reference_me) == 0:
            return ""
        prompt = [
            "Also, the code has been called by the following objects, their code and docs are as following:"
        ]
        for referencer_item in doc_item.who_reference_me:
            instance_prompt = (
                f"obj: {referencer_item.get_full_name()}\nDocument: \n{(referencer_item.md_content[-1] if len(referencer_item.md_content) > 0 else 'None')}\nRaw code:```\n{(referencer_item.content['code_content'] if 'code_content' in referencer_item.content.keys() else 'None')}\n```"
                + "=" * 10
            )
            prompt.append(instance_prompt)
        return "\n".join(prompt)

    def get_relationship_description(referencer_content, reference_letter):
        if referencer_content and reference_letter:
            return "And please include the reference relationship with its callers and callees in the project from a functional perspective"
        elif referencer_content:
            return "And please include the relationship with its callers in the project from a functional perspective."
        elif reference_letter:
            return "And please include the relationship with its callees in the project from a functional perspective."
        else:
            return ""

    code_type_tell = "Class" if code_type == "ClassDef" else "Function"
    if referenced:
        combine_ref_situation = (
            "and combine it with its calling situation in the project,"
        )
        referencer_content = get_referencer_prompt(doc_item)
        reference_letter = get_referenced_prompt(doc_item)
        has_relationship = get_relationship_description(
            referencer_content, reference_letter
        )
    else:
        combine_ref_situation = ""
        referencer_content = ""
        reference_letter = ""
        has_relationship = ""
    if main_idea:
        return docstring_update_chat_templates.format_messages(
            combine_ref_situation=combine_ref_situation,
            file_path=file_path,
            code_type_tell=code_type_tell,
            code_name=code_name,
            main_idea=(
                main_idea
                if not main_idea
                else f"You can use user-defined main idea of the project to enhance exist docstring\n{main_idea}"
            ),
            docstring=docstring,
            has_relationship=has_relationship,
            reference_letter=reference_letter,
            referencer_content=referencer_content,
            language=setting.project.language,
        )
    else:
        return chat_template.format_messages(
            combine_ref_situation=combine_ref_situation,
            file_path=file_path,
            code_type_tell=code_type_tell,
            code_name=code_name,
            code_content=code_content,
            main_idea=(
                main_idea
                if not main_idea
                else f"You can use user-defined main idea of the project to enhance exist docstring\n{main_idea}"
            ),
            docstring=docstring,
            has_relationship=has_relationship,
            reference_letter=reference_letter,
            referencer_content=referencer_content,
            language=setting.project.language,
        )

generate_doc(doc_item)

Constructs a prompt from the document item and queries a language model to generate documentation. Post-processes the response by removing code block markers.

Parameters:

Name Type Description Default
doc_item DocItem

The DocItem for which to generate documentation.

 required
Source code in repo_agent/chat_engine.py 
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def generate_doc(self, doc_item: DocItem):
    """
    Constructs a prompt from the document item and queries a language model to generate documentation. Post-processes the response by removing code block markers.

    Args:
        doc_item: The DocItem for which to generate documentation.
    Returns:
        str: The generated documentation string, with code block markers removed.
    Raises:
        Exception: If there is an error during the LLM chat call.


    """

    settings = SettingsManager.get_setting()
    if settings.project.main_idea:
        messages = self.build_prompt(doc_item, main_idea=settings.project.main_idea)
    else:
        messages = self.build_prompt(doc_item)
    try:
        response = self.llm.chat(messages)
        answer = response.message.content
        return answer.replace("```python\n", "").replace("```", "")
    except Exception as e:
        logger.error(f"Error in llamaindex chat call: {e}")
        raise

generate_idea(list_items)

Creates a novel idea based on the given components, adapting to configured language preferences. It communicates with a language model and records token consumption for tracking. Any issues during communication with the language model are logged and then propagated.

Parameters:

Name Type Description Default
list_items str

A string containing the list of items to base the idea on.

 required

Returns:

Name Type Description
str

The generated idea as a string. If an error occurs during the LLM chat call, the exception is re-raised after logging.
Source code in repo_agent/chat_engine.py 
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def generate_idea(self, list_items: str):
    """
    Creates a novel idea based on the given components, adapting to configured language preferences. It communicates with a language model and records token consumption for tracking. Any issues during communication with the language model are logged and then propagated.

    Args:
        list_items: A string containing the list of items to base the idea on.

    Returns:
        str: The generated idea as a string.  If an error occurs during the LLM chat call,
             the exception is re-raised after logging.


    """

    settings = SettingsManager.get_setting()
    messages = idea_chat_template.format_messages(
        components=list_items, language=settings.project.language
    )
    try:
        response = self.llm.chat(messages)
        logger.debug(f"LLM Prompt Tokens: {response.raw.usage.prompt_tokens}")
        logger.debug(
            f"LLM Completion Tokens: {response.raw.usage.completion_tokens}"
        )
        logger.debug(f"Total LLM Token Count: {response.raw.usage.total_tokens}")
        return response.message.content
    except Exception as e:
        logger.error(f"Error in llamaindex chat call: {e}")
        raise

summarize_module(module_desc)

Creates a succinct summary of a module’s purpose, informed by project configuration and language preferences. This is achieved through interaction with a large language model, with token usage tracked for monitoring.

Parameters:

Name Type Description Default
module_desc str

The description of the module to be summarized.

 required

Returns:

Name Type Description
str

The summary generated by the LLM. Raises any exceptions encountered during the LLM call.
Source code in repo_agent/chat_engine.py 
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def summarize_module(self, module_desc: str):
    """
    Creates a succinct summary of a module’s purpose, informed by project configuration and language preferences. This is achieved through interaction with a large language model, with token usage tracked for monitoring.

    Args:
        module_desc: The description of the module to be summarized.

    Returns:
        str: The summary generated by the LLM.  Raises any exceptions encountered during the LLM call.


    """

    settings = SettingsManager.get_setting()
    messages = module_summary_template.format_messages(
        components=module_desc,
        main_idea=settings.project.main_idea,
        language=settings.project.language,
    )
    try:
        response = self.llm.chat(messages)
        logger.debug(f"LLM Prompt Tokens: {response.raw.usage.prompt_tokens}")
        logger.debug(
            f"LLM Completion Tokens: {response.raw.usage.completion_tokens}"
        )
        logger.debug(f"Total LLM Token Count: {response.raw.usage.total_tokens}")
        return response.message.content
    except Exception as e:
        logger.error(f"Error in llamaindex chat call: {e}")
        raise