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Docugami kg rag

DocugamiKgRagPack #

Bases: BaseLlamaPack

Docugami KG-RAG Pack.

用于使用自己的RAG管道进行评估的包。

Source code in llama_index/packs/docugami_kg_rag/base.py 
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class DocugamiKgRagPack(BaseLlamaPack):
    """Docugami KG-RAG Pack.

    用于使用自己的RAG管道进行评估的包。"""

    def __init__(self) -> None:
        self.docugami_client = Docugami()

    def list_docsets(self):
        """
        列出你的Docugami文档集以及它们的文档集名称和ID。
        """
        docsets_response = self.docugami_client.docsets.list()
        for idx, docset in enumerate(docsets_response.docsets, start=1):
            print(f"{idx}: {docset.name} (ID: {docset.id})")

    def index_docset(self, docset_id: str, overwrite: bool = False):
        """
        为docset构建索引并为其创建代理。
        """
        docsets_response = self.docugami_client.docsets.list()
        docset = next(
            (docset for docset in docsets_response.docsets if docset.id == docset_id),
            None,
        )

        if not docset:
            raise Exception(
                f"Docset with id {docset_id} does not exist in your workspace"
            )

        index_docset(docset_id, docset.name, overwrite)

    def build_agent_for_docset(
        self, docset_id: str, use_reports: bool = DEFAULT_USE_REPORTS
    ):
        local_state = read_all_local_index_state()

        tools: List[BaseTool] = []
        for docset_id in local_state:
            docset_state = local_state[docset_id]
            direct_retrieval_tool = get_retrieval_tool_for_docset(
                docset_id, docset_state
            )
            if direct_retrieval_tool:
                # Direct retrieval tool for each indexed docset (direct KG-RAG against semantic XML)
                tools.append(direct_retrieval_tool)

            if use_reports:
                for report in docset_state.reports:
                    # Report retrieval tool for each published report (user-curated views on semantic XML)
                    report_retrieval_tool = get_retrieval_tool_for_report(report)
                    if report_retrieval_tool:
                        tools.append(report_retrieval_tool)

        self.agent = ReActAgent.from_tools(
            tools,
            llm=LARGE_CONTEXT_INSTRUCT_LLM,
            verbose=True,
            context=ASSISTANT_SYSTEM_MESSAGE,
        )

    def get_modules(self) -> Dict[str, Any]:
        """获取模块。"""
        return {
            "agent": self.agent,
        }

    def run(self, *args: Any, **kwargs: Any) -> Any:
        """运行流水线。"""
        return self.agent.query(*args, **kwargs)

list_docsets #

list_docsets()

列出你的Docugami文档集以及它们的文档集名称和ID。

Source code in llama_index/packs/docugami_kg_rag/base.py 
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def list_docsets(self):
    """
    列出你的Docugami文档集以及它们的文档集名称和ID。
    """
    docsets_response = self.docugami_client.docsets.list()
    for idx, docset in enumerate(docsets_response.docsets, start=1):
        print(f"{idx}: {docset.name} (ID: {docset.id})")

index_docset #

index_docset(docset_id: str, overwrite: bool = False)

为docset构建索引并为其创建代理。

Source code in llama_index/packs/docugami_kg_rag/base.py 
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def index_docset(self, docset_id: str, overwrite: bool = False):
    """
    为docset构建索引并为其创建代理。
    """
    docsets_response = self.docugami_client.docsets.list()
    docset = next(
        (docset for docset in docsets_response.docsets if docset.id == docset_id),
        None,
    )

    if not docset:
        raise Exception(
            f"Docset with id {docset_id} does not exist in your workspace"
        )

    index_docset(docset_id, docset.name, overwrite)

get_modules #

get_modules() -> Dict[str, Any]

获取模块。

Source code in llama_index/packs/docugami_kg_rag/base.py 
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def get_modules(self) -> Dict[str, Any]:
    """获取模块。"""
    return {
        "agent": self.agent,
    }

run #

run(*args: Any, **kwargs: Any) -> Any

运行流水线。

Source code in llama_index/packs/docugami_kg_rag/base.py 
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def run(self, *args: Any, **kwargs: Any) -> Any:
    """运行流水线。"""
    return self.agent.query(*args, **kwargs)