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feat(RAG):add tree retriever based on document directory level (#2669)
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Aries-ckt
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36
examples/rag/doc_tree_retriever_example.py
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36
examples/rag/doc_tree_retriever_example.py
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@ -0,0 +1,36 @@
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import asyncio
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from dbgpt.model.proxy import DeepseekLLMClient
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from dbgpt.rag.knowledge.base import ChunkStrategy
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from dbgpt.rag.transformer.keyword_extractor import KeywordExtractor
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from dbgpt_ext.rag import ChunkParameters
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from dbgpt_ext.rag.knowledge import KnowledgeFactory
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from dbgpt_ext.rag.retriever.doc_tree import DocTreeRetriever
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async def main():
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knowledge = KnowledgeFactory.from_file_path("../../docs/docs/awel/awel.md")
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chunk_parameters = ChunkParameters(
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chunk_strategy=ChunkStrategy.CHUNK_BY_MARKDOWN_HEADER.name
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)
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docs = knowledge.load()
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docs = knowledge.extract(docs, chunk_parameters)
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llm_client = DeepseekLLMClient(api_key="your_api_key")
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keyword_extractor = KeywordExtractor(
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llm_client=llm_client, model_name="deepseek-chat"
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)
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# doc tree retriever retriever
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retriever = DocTreeRetriever(
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docs=docs,
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top_k=10,
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keywords_extractor=keyword_extractor,
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with_content=False,
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)
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tree_index = retriever._tree_indexes[0]
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nodes = await retriever.aretrieve("Introduce awel Operators")
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for node in nodes:
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tree_index.display_tree(node)
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if __name__ == "__main__":
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asyncio.run(main())
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@ -16,6 +16,10 @@ class Document(BaseModel):
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default_factory=dict,
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description="metadata fields",
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)
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chunks: List["Chunk"] = Field(
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default_factory=list,
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description="list of chunks",
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)
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def set_content(self, content: str) -> None:
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"""Set document content."""
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@ -164,7 +164,11 @@ class Knowledge(ABC):
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documents = self._load()
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return self._postprocess(documents)
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def extract(self, documents: List[Document]) -> List[Document]:
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def extract(
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self,
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documents: List[Document],
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chunk_parameters: Optional[Any] = None,
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) -> List[Document]:
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"""Extract knowledge from text."""
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return documents
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@ -231,3 +231,47 @@ class IndexStoreBase(ABC):
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return await blocking_func_to_async_no_executor(
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self.similar_search_with_scores, query, topk, score_threshold, filters
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)
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def full_text_search(
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self, text: str, topk: int, filters: Optional[MetadataFilters] = None
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) -> List[Chunk]:
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"""Full text search in index database.
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Args:
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text(str): The query text.
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topk(int): The number of similar documents to return.
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filters(Optional[MetadataFilters]): metadata filters.
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Return:
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List[Chunk]: The similar documents.
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"""
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raise NotImplementedError(
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"Full text search is not supported in this index store."
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)
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async def afull_text_search(
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self, text: str, topk: int, filters: Optional[MetadataFilters] = None
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) -> List[Chunk]:
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"""Similar search in index database.
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Args:
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text(str): The query text.
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topk(int): The number of similar documents to return.
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filters(Optional[MetadataFilters]): metadata filters.
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Return:
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List[Chunk]: The similar documents.
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"""
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return await blocking_func_to_async_no_executor(
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self.full_text_search, text, topk, filters
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)
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def is_support_full_text_search(self) -> bool:
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"""Support full text search.
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Args:
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collection_name(str): collection name.
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Return:
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bool: The similar documents.
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"""
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raise NotImplementedError(
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"Full text search is not supported in this index store."
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)
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@ -9,6 +9,7 @@ from dbgpt.rag.knowledge.base import (
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Knowledge,
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KnowledgeType,
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)
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from dbgpt_ext.rag import ChunkParameters
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class MarkdownKnowledge(Knowledge):
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@ -49,13 +50,30 @@ class MarkdownKnowledge(Knowledge):
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raise ValueError("file path is required")
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with open(self._path, encoding=self._encoding, errors="ignore") as f:
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markdown_text = f.read()
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metadata = {"source": self._path}
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metadata = {
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"source": self._path,
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"title": self._path.rsplit("/", 1)[-1],
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}
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if self._metadata:
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metadata.update(self._metadata) # type: ignore
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documents = [Document(content=markdown_text, metadata=metadata)]
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return documents
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return [Document.langchain2doc(lc_document) for lc_document in documents]
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def extract(
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self,
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documents: List[Document],
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chunk_parameter: Optional[ChunkParameters] = None,
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) -> List[Document]:
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"""Extract knowledge from text."""
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from dbgpt_ext.rag.chunk_manager import ChunkManager
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chunk_manager = ChunkManager(knowledge=self, chunk_parameter=chunk_parameter)
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chunks = chunk_manager.split(documents)
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for document in documents:
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document.chunks = chunks
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return documents
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@classmethod
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def support_chunk_strategy(cls) -> List[ChunkStrategy]:
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"""Return support chunk strategy."""
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364
packages/dbgpt-ext/src/dbgpt_ext/rag/retriever/doc_tree.py
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364
packages/dbgpt-ext/src/dbgpt_ext/rag/retriever/doc_tree.py
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@ -0,0 +1,364 @@
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"""Tree-based document retriever."""
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import logging
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from concurrent.futures import Executor, ThreadPoolExecutor
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from typing import List, Optional
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from dbgpt.core import Document
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from dbgpt.rag.retriever import BaseRetriever, DefaultRanker, QueryRewrite, Ranker
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from dbgpt.rag.transformer.base import ExtractorBase
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from dbgpt.storage.vector_store.filters import MetadataFilters
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logger = logging.getLogger(__name__)
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RETRIEVER_NAME = "doc_tree_retriever"
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TITLE = "title"
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HEADER1 = "Header1"
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HEADER2 = "Header2"
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HEADER3 = "Header3"
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HEADER4 = "Header4"
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HEADER5 = "Header5"
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HEADER6 = "Header6"
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class TreeNode:
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"""TreeNode class to represent a node in the document tree."""
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def __init__(
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self, node_id: str, level_text: str, level: int, content: Optional[str] = None
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):
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"""Initialize a TreeNode."""
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self.node_id = node_id
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self.level = level # 0: title, 1: header1, 2: header2, 3: header3
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self.level_text = level_text # 0: title, 1: header1, 2: header2, 3: header3
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self.children = []
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self.content = content
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self.retriever = RETRIEVER_NAME
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def add_child(self, child_node):
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"""Add a child node to the current node."""
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self.children.append(child_node)
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class DocTreeIndex:
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def __init__(self):
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"""Initialize the document tree index."""
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self.root = TreeNode("root_id", "Root", -1)
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def add_nodes(
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self,
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node_id: str,
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title: str,
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header1: Optional[str] = None,
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header2: Optional[str] = None,
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header3: Optional[str] = None,
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header4: Optional[str] = None,
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header5: Optional[str] = None,
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header6: Optional[str] = None,
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content: Optional[str] = None,
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):
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"""Add nodes to the document tree.
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Args:
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node_id (str): The ID of the node.
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title (str): The title of the document.
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header1 (Optional[str]): The first header.
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header2 (Optional[str]): The second header.
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header3 (Optional[str]): The third header.
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header4 (Optional[str]): The fourth header.
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header5 (Optional[str]): The fifth header.
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header6 (Optional[str]): The sixth header.
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content (Optional[str]): The content of the node.
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"""
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# Assuming titles is a dictionary containing title and headers
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title_node = None
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if title:
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title_nodes = self.get_node_by_level(0)
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if not title_nodes:
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# If title already exists, do not add it again
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title_node = TreeNode(node_id, title, 0, content)
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self.root.add_child(title_node)
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else:
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title_node = title_nodes[0]
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current_node = title_node
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headers = [header1, header2, header3, header4, header5, header6]
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for level, header in enumerate(headers, start=1):
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if header:
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header_nodes = self.get_node_by_level_text(header)
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if header_nodes:
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# If header already exists, do not add it again
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current_node = header_nodes[0]
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continue
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new_header_node = TreeNode(node_id, header, level, content)
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current_node.add_child(new_header_node)
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current_node = new_header_node
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def add_nodes_with_content(
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self,
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node_id: str,
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title: str,
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header1: Optional[str] = None,
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header2: Optional[str] = None,
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header3: Optional[str] = None,
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header4: Optional[str] = None,
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header5: Optional[str] = None,
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header6: Optional[str] = None,
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):
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"""Add nodes to the document tree.
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Args:
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node_id (str): The ID of the node.
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title (str): The title of the document.
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header1 (Optional[str]): The first header.
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header2 (Optional[str]): The second header.
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header3 (Optional[str]): The third header.
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header4 (Optional[str]): The fourth header.
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header5 (Optional[str]): The fifth header.
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header6 (Optional[str]): The sixth header.
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"""
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# Assuming titles is a dictionary containing title and headers
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title_node = None
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if title:
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title_nodes = self.get_node_by_level(0)
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if not title_nodes:
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# If title already exists, do not add it again
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title_node = TreeNode(node_id, title, 0)
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self.root.add_child(title_node)
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else:
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title_node = title_nodes[0]
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current_node = title_node
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headers = [header1, header2, header3, header4, header5, header6]
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for level, header in enumerate(headers, start=1):
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if header:
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header_nodes = self.get_node_by_level_text(header)
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if header_nodes:
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# If header already exists, do not add it again
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current_node = header_nodes[0]
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continue
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new_header_node = TreeNode(node_id, header, level)
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current_node.add_child(new_header_node)
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current_node = new_header_node
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def get_node_by_level(self, level):
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"""Get nodes by level."""
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# Traverse the tree to find nodes at the specified level
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result = []
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self._traverse(self.root, level, result)
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return result
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def get_node_by_level_text(self, content):
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"""Get nodes by level."""
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# Traverse the tree to find nodes at the specified level
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result = []
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self._traverse_by_level_text(self.root, content, result)
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return result
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def get_all_children(self, node):
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"""get all children of the node."""
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# Get all children of the current node
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result = []
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self._traverse(node, node.level, result)
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return result
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def display_tree(self, node: TreeNode, prefix: Optional[str] = ""):
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"""Recursive function to display the directory structure with visual cues."""
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# Print the current node title with prefix
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if node.content:
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print(
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f"{prefix}├── {node.level_text} (node_id: {node.node_id}) "
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f"(content: {node.content})"
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)
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else:
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print(f"{prefix}├── {node.level_text} (node_id: {node.node_id})")
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# Update prefix for children
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new_prefix = prefix + "│ " # Extend the prefix for child nodes
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for i, child in enumerate(node.children):
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if i == len(node.children) - 1: # If it's the last child
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new_prefix_child = prefix + "└── "
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else:
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new_prefix_child = new_prefix
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# Recursive call for the next child node
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self.display_tree(child, new_prefix_child)
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def _traverse(self, node, level, result):
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"""Traverse the tree to find nodes at the specified level."""
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# If the current node's level matches the specified level, add it to the result
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if node.level == level:
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result.append(node)
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# Recursively traverse child nodes
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for child in node.children:
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self._traverse(child, level, result)
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def _traverse_by_level_text(self, node, level_text, result):
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"""Traverse the tree to find nodes at the specified level."""
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# If the current node's level matches the specified level, add it to the result
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if node.level_text == level_text:
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result.append(node)
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# Recursively traverse child nodes
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for child in node.children:
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self._traverse_by_level_text(child, level_text, result)
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def search_keywords(self, node, keyword) -> Optional[TreeNode]:
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# Check if the keyword matches the current node title
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if keyword.lower() == node.level_text.lower():
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logger.info(f"DocTreeIndex Match found in: {node.level_text}")
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return node
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# Recursively search in child nodes
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for child in node.children:
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result = self.search_keywords(child, keyword)
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if result:
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return result
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# Check if the keyword matches any of the child nodes
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# If no match, continue to search in all children
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return None
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class DocTreeRetriever(BaseRetriever):
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"""Doc Tree retriever."""
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def __init__(
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self,
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docs: List[Document] = None,
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top_k: Optional[int] = 10,
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query_rewrite: Optional[QueryRewrite] = None,
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rerank: Optional[Ranker] = None,
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keywords_extractor: Optional[ExtractorBase] = None,
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with_content: bool = False,
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executor: Optional[Executor] = None,
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):
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"""Create DocTreeRetriever.
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Args:
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docs (List[Document]): List of documents to initialize the tree with.
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top_k (int): top k
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query_rewrite (Optional[QueryRewrite]): query rewrite
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rerank (Ranker): rerank
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keywords_extractor (Optional[ExtractorBase]): keywords extractor
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with_content: bool: whether to include content
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executor (Optional[Executor]): executor
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Returns:
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DocTreeRetriever: BM25 retriever
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"""
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super().__init__()
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self._top_k = top_k
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self._query_rewrite = query_rewrite
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self._rerank = rerank or DefaultRanker(self._top_k)
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self._keywords_extractor = keywords_extractor
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self._with_content = with_content
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self._tree_indexes = self._initialize_doc_tree(docs)
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self._executor = executor or ThreadPoolExecutor()
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def get_tree_indexes(self):
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"""Get the tree indexes."""
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return self._tree_indexes
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def _retrieve(
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self, query: str, filters: Optional[MetadataFilters] = None
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) -> List[TreeNode]:
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"""Retrieve knowledge chunks.
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Args:
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query (str): query text
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filters: metadata filters.
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Return:
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List[Chunk]: list of chunks
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"""
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raise NotImplementedError("DocTreeRetriever does not support retrieval.")
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def _retrieve_with_score(
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self,
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query: str,
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score_threshold: float,
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filters: Optional[MetadataFilters] = None,
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) -> List[TreeNode]:
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"""Retrieve knowledge chunks with score.
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Args:
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query (str): query text
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score_threshold (float): score threshold
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filters: metadata filters.
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Return:
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List[Chunk]: list of chunks with score
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"""
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raise NotImplementedError("DocTreeRetriever does not support score retrieval.")
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async def _aretrieve(
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self, query: str, filters: Optional[MetadataFilters] = None
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) -> List[TreeNode]:
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"""Retrieve knowledge chunks.
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Args:
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query (str): query text.
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filters: metadata filters.
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Return:
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List[Chunk]: list of chunks
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"""
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keywords = [query]
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if self._keywords_extractor:
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keywords = await self._keywords_extractor.extract(query)
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all_nodes = []
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for keyword in keywords:
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for tree_index in self._tree_indexes:
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retrieve_node = tree_index.search_keywords(tree_index.root, keyword)
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if retrieve_node:
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# If a match is found, return the corresponding chunks
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all_nodes.append(retrieve_node)
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return all_nodes
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async def _aretrieve_with_score(
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self,
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query: str,
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score_threshold: float,
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filters: Optional[MetadataFilters] = None,
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) -> List[TreeNode]:
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"""Retrieve knowledge chunks with score.
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Args:
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query (str): query text
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score_threshold (float): score threshold
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filters: metadata filters.
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Return:
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List[Chunk]: list of chunks with score
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"""
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return await self._aretrieve(query, filters)
|
||||
|
||||
def _initialize_doc_tree(self, docs: List[Document]):
|
||||
"""Initialize the document tree with docs.
|
||||
|
||||
Args:
|
||||
docs (List[Document]): List of docs to initialize the tree with.
|
||||
"""
|
||||
tree_indexes = []
|
||||
for doc in docs:
|
||||
tree_index = DocTreeIndex()
|
||||
for chunk in doc.chunks:
|
||||
if not chunk.metadata.get(TITLE):
|
||||
continue
|
||||
if not self._with_content:
|
||||
tree_index.add_nodes(
|
||||
node_id=chunk.chunk_id,
|
||||
title=chunk.metadata[TITLE],
|
||||
header1=chunk.metadata.get(HEADER1),
|
||||
header2=chunk.metadata.get(HEADER2),
|
||||
header3=chunk.metadata.get(HEADER3),
|
||||
header4=chunk.metadata.get(HEADER4),
|
||||
header5=chunk.metadata.get(HEADER5),
|
||||
header6=chunk.metadata.get(HEADER6),
|
||||
)
|
||||
else:
|
||||
tree_index.add_nodes(
|
||||
node_id=chunk.chunk_id,
|
||||
title=chunk.metadata[TITLE],
|
||||
header1=chunk.metadata.get(HEADER1),
|
||||
header2=chunk.metadata.get(HEADER2),
|
||||
header3=chunk.metadata.get(HEADER3),
|
||||
header4=chunk.metadata.get(HEADER4),
|
||||
header5=chunk.metadata.get(HEADER5),
|
||||
header6=chunk.metadata.get(HEADER6),
|
||||
content=chunk.content,
|
||||
)
|
||||
tree_indexes.append(tree_index)
|
||||
return tree_indexes
|
||||
Loading…
Reference in New Issue
Block a user