Knowledge graph

KGTableRetriever

Bases: BaseRetriever

KG Table Retriever.

Arguments are shared among subclasses.

Parameters:

Name	Type	Description	Default
query_keyword_extract_template	Optional[QueryKGExtractPrompt]	A Query KG Extraction Prompt (see :ref:Prompt-Templates).	None
refine_template	Optional[BasePromptTemplate]	A Refinement Prompt (see :ref:Prompt-Templates).	required
text_qa_template	Optional[BasePromptTemplate]	A Question Answering Prompt (see :ref:Prompt-Templates).	required
max_keywords_per_query	int	Maximum number of keywords to extract from query.	10
num_chunks_per_query	int	Maximum number of text chunks to query.	10
include_text	bool	Use the document text source from each relevant triplet during queries.	True
retriever_mode	KGRetrieverMode	Specifies whether to use keywords, embeddings, or both to find relevant triplets. Should be one of "keyword", "embedding", or "hybrid".	KEYWORD
similarity_top_k	int	The number of top embeddings to use (if embeddings are used).	2
graph_store_query_depth	int	The depth of the graph store query.	2
use_global_node_triplets	bool	Whether to get more keywords(entities) from text chunks matched by keywords. This helps introduce more global knowledge. While it's more expensive, thus to be turned off by default.	False
max_knowledge_sequence	int	The maximum number of knowledge sequence to include in the response. By default, it's 30.	REL_TEXT_LIMIT

Source code in llama-index-core/llama_index/core/indices/knowledge_graph/retrievers.py

class KGTableRetriever(BaseRetriever):
    """KG Table Retriever.

    Arguments are shared among subclasses.

    Args:
        query_keyword_extract_template (Optional[QueryKGExtractPrompt]): A Query
            KG Extraction
            Prompt (see :ref:`Prompt-Templates`).
        refine_template (Optional[BasePromptTemplate]): A Refinement Prompt
            (see :ref:`Prompt-Templates`).
        text_qa_template (Optional[BasePromptTemplate]): A Question Answering Prompt
            (see :ref:`Prompt-Templates`).
        max_keywords_per_query (int): Maximum number of keywords to extract from query.
        num_chunks_per_query (int): Maximum number of text chunks to query.
        include_text (bool): Use the document text source from each relevant triplet
            during queries.
        retriever_mode (KGRetrieverMode): Specifies whether to use keywords,
            embeddings, or both to find relevant triplets. Should be one of "keyword",
            "embedding", or "hybrid".
        similarity_top_k (int): The number of top embeddings to use
            (if embeddings are used).
        graph_store_query_depth (int): The depth of the graph store query.
        use_global_node_triplets (bool): Whether to get more keywords(entities) from
            text chunks matched by keywords. This helps introduce more global knowledge.
            While it's more expensive, thus to be turned off by default.
        max_knowledge_sequence (int): The maximum number of knowledge sequence to
            include in the response. By default, it's 30.
    """

    def __init__(
        self,
        index: KnowledgeGraphIndex,
        llm: Optional[LLM] = None,
        embed_model: Optional[BaseEmbedding] = None,
        query_keyword_extract_template: Optional[BasePromptTemplate] = None,
        max_keywords_per_query: int = 10,
        num_chunks_per_query: int = 10,
        include_text: bool = True,
        retriever_mode: Optional[KGRetrieverMode] = KGRetrieverMode.KEYWORD,
        similarity_top_k: int = 2,
        graph_store_query_depth: int = 2,
        use_global_node_triplets: bool = False,
        max_knowledge_sequence: int = REL_TEXT_LIMIT,
        callback_manager: Optional[CallbackManager] = None,
        object_map: Optional[dict] = None,
        verbose: bool = False,
        **kwargs: Any,
    ) -> None:
        """Initialize params."""
        assert isinstance(index, KnowledgeGraphIndex)
        self._index = index
        self._index_struct = self._index.index_struct
        self._docstore = self._index.docstore

        self.max_keywords_per_query = max_keywords_per_query
        self.num_chunks_per_query = num_chunks_per_query
        self.query_keyword_extract_template = query_keyword_extract_template or DQKET
        self.similarity_top_k = similarity_top_k
        self._include_text = include_text
        self._retriever_mode = (
            KGRetrieverMode(retriever_mode)
            if retriever_mode
            else KGRetrieverMode.KEYWORD
        )

        self._llm = llm or llm_from_settings_or_context(Settings, index.service_context)
        self._embed_model = embed_model or embed_model_from_settings_or_context(
            Settings, index.service_context
        )

        self._graph_store = index.graph_store
        self.graph_store_query_depth = graph_store_query_depth
        self.use_global_node_triplets = use_global_node_triplets
        self.max_knowledge_sequence = max_knowledge_sequence
        self._verbose = kwargs.get("verbose", False)
        refresh_schema = kwargs.get("refresh_schema", False)
        try:
            self._graph_schema = self._graph_store.get_schema(refresh=refresh_schema)
        except NotImplementedError:
            self._graph_schema = ""
        except Exception as e:
            logger.warning(f"Failed to get graph schema: {e}")
            self._graph_schema = ""
        super().__init__(
            callback_manager=callback_manager
            or callback_manager_from_settings_or_context(
                Settings, index.service_context
            ),
            object_map=object_map,
            verbose=verbose,
        )

    def _get_keywords(self, query_str: str) -> List[str]:
        """Extract keywords."""
        response = self._llm.predict(
            self.query_keyword_extract_template,
            max_keywords=self.max_keywords_per_query,
            question=query_str,
        )
        keywords = extract_keywords_given_response(
            response, start_token="KEYWORDS:", lowercase=False
        )
        return list(keywords)

    def _extract_rel_text_keywords(self, rel_texts: List[str]) -> List[str]:
        """Find the keywords for given rel text triplets."""
        keywords = []

        for rel_text in rel_texts:
            splited_texts = rel_text.split(",")

            if len(splited_texts) <= 0:
                continue
            keyword = splited_texts[0]
            if keyword:
                keywords.append(keyword.strip("(\"'"))

            # Return the Object as well
            if len(splited_texts) <= 2:
                continue
            keyword = splited_texts[2]
            if keyword:
                keywords.append(keyword.strip(" ()\"'"))
        return keywords

    def _retrieve(
        self,
        query_bundle: QueryBundle,
    ) -> List[NodeWithScore]:
        """Get nodes for response."""
        node_visited = set()
        keywords = self._get_keywords(query_bundle.query_str)
        if self._verbose:
            print_text(f"Extracted keywords: {keywords}\n", color="green")
        rel_texts = []
        cur_rel_map = {}
        chunk_indices_count: Dict[str, int] = defaultdict(int)
        if self._retriever_mode != KGRetrieverMode.EMBEDDING:
            for keyword in keywords:
                subjs = {keyword}
                node_ids = self._index_struct.search_node_by_keyword(keyword)
                for node_id in node_ids[:GLOBAL_EXPLORE_NODE_LIMIT]:
                    if node_id in node_visited:
                        continue

                    if self._include_text:
                        chunk_indices_count[node_id] += 1

                    node_visited.add(node_id)
                    if self.use_global_node_triplets:
                        # Get nodes from keyword search, and add them to the subjs
                        # set. This helps introduce more global knowledge into the
                        # query. While it's more expensive, thus to be turned off
                        # by default, it can be useful for some applications.

                        # TODO: we should a keyword-node_id map in IndexStruct, so that
                        # node-keywords extraction with LLM will be called only once
                        # during indexing.
                        extended_subjs = self._get_keywords(
                            self._docstore.get_node(node_id).get_content(
                                metadata_mode=MetadataMode.LLM
                            )
                        )
                        subjs.update(extended_subjs)

                rel_map = self._graph_store.get_rel_map(
                    list(subjs), self.graph_store_query_depth
                )

                logger.debug(f"rel_map: {rel_map}")

                if not rel_map:
                    continue
                rel_texts.extend(
                    [
                        str(rel_obj)
                        for rel_objs in rel_map.values()
                        for rel_obj in rel_objs
                    ]
                )
                cur_rel_map.update(rel_map)

        if (
            self._retriever_mode != KGRetrieverMode.KEYWORD
            and len(self._index_struct.embedding_dict) > 0
        ):
            query_embedding = self._embed_model.get_text_embedding(
                query_bundle.query_str
            )
            all_rel_texts = list(self._index_struct.embedding_dict.keys())

            rel_text_embeddings = [
                self._index_struct.embedding_dict[_id] for _id in all_rel_texts
            ]
            similarities, top_rel_texts = get_top_k_embeddings(
                query_embedding,
                rel_text_embeddings,
                similarity_top_k=self.similarity_top_k,
                embedding_ids=all_rel_texts,
            )
            logger.debug(
                f"Found the following rel_texts+query similarites: {similarities!s}"
            )
            logger.debug(f"Found the following top_k rel_texts: {rel_texts!s}")
            rel_texts.extend(top_rel_texts)

        elif len(self._index_struct.embedding_dict) == 0:
            logger.warning(
                "Index was not constructed with embeddings, skipping embedding usage..."
            )

        # remove any duplicates from keyword + embedding queries
        if self._retriever_mode == KGRetrieverMode.HYBRID:
            rel_texts = list(set(rel_texts))

            # remove shorter rel_texts that are substrings of longer rel_texts
            rel_texts.sort(key=len, reverse=True)
            for i in range(len(rel_texts)):
                for j in range(i + 1, len(rel_texts)):
                    if rel_texts[j] in rel_texts[i]:
                        rel_texts[j] = ""
            rel_texts = [rel_text for rel_text in rel_texts if rel_text != ""]

            # truncate rel_texts
            rel_texts = rel_texts[: self.max_knowledge_sequence]

        # When include_text = True just get the actual content of all the nodes
        # (Nodes with actual keyword match, Nodes which are found from the depth search and Nodes founnd from top_k similarity)
        if self._include_text:
            keywords = self._extract_rel_text_keywords(
                rel_texts
            )  # rel_texts will have all the Triplets retrieved with respect to the Query
            nested_node_ids = [
                self._index_struct.search_node_by_keyword(keyword)
                for keyword in keywords
            ]
            node_ids = [_id for ids in nested_node_ids for _id in ids]
            for node_id in node_ids:
                chunk_indices_count[node_id] += 1

        sorted_chunk_indices = sorted(
            chunk_indices_count.keys(),
            key=lambda x: chunk_indices_count[x],
            reverse=True,
        )
        sorted_chunk_indices = sorted_chunk_indices[: self.num_chunks_per_query]
        sorted_nodes = self._docstore.get_nodes(sorted_chunk_indices)

        # TMP/TODO: also filter rel_texts as nodes until we figure out better
        # abstraction
        # TODO(suo): figure out what this does
        # rel_text_nodes = [Node(text=rel_text) for rel_text in rel_texts]
        # for node_processor in self._node_postprocessors:
        #     rel_text_nodes = node_processor.postprocess_nodes(rel_text_nodes)
        # rel_texts = [node.get_content() for node in rel_text_nodes]

        sorted_nodes_with_scores = []
        for chunk_idx, node in zip(sorted_chunk_indices, sorted_nodes):
            # nodes are found with keyword mapping, give high conf to avoid cutoff
            sorted_nodes_with_scores.append(
                NodeWithScore(node=node, score=DEFAULT_NODE_SCORE)
            )
            logger.info(
                f"> Querying with idx: {chunk_idx}: "
                f"{truncate_text(node.get_content(), 80)}"
            )
        # if no relationship is found, return the nodes found by keywords
        if not rel_texts:
            logger.info("> No relationships found, returning nodes found by keywords.")
            if len(sorted_nodes_with_scores) == 0:
                logger.info("> No nodes found by keywords, returning empty response.")
                return [
                    NodeWithScore(
                        node=TextNode(text="No relationships found."), score=1.0
                    )
                ]
            # In else case the sorted_nodes_with_scores is not empty
            # thus returning the nodes found by keywords
            return sorted_nodes_with_scores

        # add relationships as Node
        # TODO: make initial text customizable
        rel_initial_text = (
            f"The following are knowledge sequence in max depth"
            f" {self.graph_store_query_depth} "
            f"in the form of directed graph like:\n"
            f"`subject -[predicate]->, object, <-[predicate_next_hop]-,"
            f" object_next_hop ...`"
        )
        rel_info = [rel_initial_text, *rel_texts]
        rel_node_info = {
            "kg_rel_texts": rel_texts,
            "kg_rel_map": cur_rel_map,
        }
        if self._graph_schema != "":
            rel_node_info["kg_schema"] = {"schema": self._graph_schema}
        rel_info_text = "\n".join(
            [
                str(item)
                for sublist in rel_info
                for item in (sublist if isinstance(sublist, list) else [sublist])
            ]
        )
        if self._verbose:
            print_text(f"KG context:\n{rel_info_text}\n", color="blue")
        rel_text_node = TextNode(
            text=rel_info_text,
            metadata=rel_node_info,
            excluded_embed_metadata_keys=["kg_rel_map", "kg_rel_texts"],
            excluded_llm_metadata_keys=["kg_rel_map", "kg_rel_texts"],
        )
        # this node is constructed from rel_texts, give high confidence to avoid cutoff
        sorted_nodes_with_scores.append(
            NodeWithScore(node=rel_text_node, score=DEFAULT_NODE_SCORE)
        )

        return sorted_nodes_with_scores

    def _get_metadata_for_response(
        self, nodes: List[BaseNode]
    ) -> Optional[Dict[str, Any]]:
        """Get metadata for response."""
        for node in nodes:
            if node.metadata is None or "kg_rel_map" not in node.metadata:
                continue
            return node.metadata
        raise ValueError("kg_rel_map must be found in at least one Node.")

KnowledgeGraphRAGRetriever

Bases: BaseRetriever

Knowledge Graph RAG retriever.

Retriever that perform SubGraph RAG towards knowledge graph.

Parameters:

Name	Type	Description	Default
service_context	Optional[ServiceContext]	A service context to use.	None
storage_context	Optional[StorageContext]	A storage context to use.	None
entity_extract_fn	Optional[Callable]	A function to extract entities.	None
entity_extract_template	Optional[BasePromptTemplate]	A Query Key Entity Extraction Prompt (see :ref:Prompt-Templates).	None
entity_extract_policy	Optional[str]	The entity extraction policy to use. default: "union" possible values: "union", "intersection"	'union'
synonym_expand_fn	Optional[Callable]	A function to expand synonyms.	None
synonym_expand_template	Optional[QueryKeywordExpandPrompt]	A Query Key Entity Expansion Prompt (see :ref:Prompt-Templates).	None
synonym_expand_policy	Optional[str]	The synonym expansion policy to use. default: "union" possible values: "union", "intersection"	'union'
max_entities	int	The maximum number of entities to extract. default: 5	5
max_synonyms	int	The maximum number of synonyms to expand per entity. default: 5	5
retriever_mode	Optional[str]	The retriever mode to use. default: "keyword" possible values: "keyword", "embedding", "keyword_embedding"	'keyword'
with_nl2graphquery	bool	Whether to combine NL2GraphQuery in context. default: False	False
graph_traversal_depth	int	The depth of graph traversal. default: 2	2
max_knowledge_sequence	int	The maximum number of knowledge sequence to include in the response. By default, it's 30.	REL_TEXT_LIMIT
verbose	bool	Whether to print out debug info.	False

Source code in llama-index-core/llama_index/core/indices/knowledge_graph/retrievers.py

class KnowledgeGraphRAGRetriever(BaseRetriever):
    """
    Knowledge Graph RAG retriever.

    Retriever that perform SubGraph RAG towards knowledge graph.

    Args:
        service_context (Optional[ServiceContext]): A service context to use.
        storage_context (Optional[StorageContext]): A storage context to use.
        entity_extract_fn (Optional[Callable]): A function to extract entities.
        entity_extract_template Optional[BasePromptTemplate]): A Query Key Entity
            Extraction Prompt (see :ref:`Prompt-Templates`).
        entity_extract_policy (Optional[str]): The entity extraction policy to use.
            default: "union"
            possible values: "union", "intersection"
        synonym_expand_fn (Optional[Callable]): A function to expand synonyms.
        synonym_expand_template (Optional[QueryKeywordExpandPrompt]): A Query Key Entity
            Expansion Prompt (see :ref:`Prompt-Templates`).
        synonym_expand_policy (Optional[str]): The synonym expansion policy to use.
            default: "union"
            possible values: "union", "intersection"
        max_entities (int): The maximum number of entities to extract.
            default: 5
        max_synonyms (int): The maximum number of synonyms to expand per entity.
            default: 5
        retriever_mode (Optional[str]): The retriever mode to use.
            default: "keyword"
            possible values: "keyword", "embedding", "keyword_embedding"
        with_nl2graphquery (bool): Whether to combine NL2GraphQuery in context.
            default: False
        graph_traversal_depth (int): The depth of graph traversal.
            default: 2
        max_knowledge_sequence (int): The maximum number of knowledge sequence to
            include in the response. By default, it's 30.
        verbose (bool): Whether to print out debug info.
    """

    def __init__(
        self,
        storage_context: Optional[StorageContext] = None,
        llm: Optional[LLM] = None,
        entity_extract_fn: Optional[Callable] = None,
        entity_extract_template: Optional[BasePromptTemplate] = None,
        entity_extract_policy: Optional[str] = "union",
        synonym_expand_fn: Optional[Callable] = None,
        synonym_expand_template: Optional[BasePromptTemplate] = None,
        synonym_expand_policy: Optional[str] = "union",
        max_entities: int = 5,
        max_synonyms: int = 5,
        retriever_mode: Optional[str] = "keyword",
        with_nl2graphquery: bool = False,
        graph_traversal_depth: int = 2,
        max_knowledge_sequence: int = REL_TEXT_LIMIT,
        verbose: bool = False,
        callback_manager: Optional[CallbackManager] = None,
        # deprecated
        service_context: Optional[ServiceContext] = None,
        **kwargs: Any,
    ) -> None:
        """Initialize the retriever."""
        # Ensure that we have a graph store
        assert storage_context is not None, "Must provide a storage context."
        assert (
            storage_context.graph_store is not None
        ), "Must provide a graph store in the storage context."
        self._storage_context = storage_context
        self._graph_store = storage_context.graph_store

        self._llm = llm or llm_from_settings_or_context(Settings, service_context)

        self._entity_extract_fn = entity_extract_fn
        self._entity_extract_template = (
            entity_extract_template or DEFAULT_QUERY_KEYWORD_EXTRACT_TEMPLATE
        )
        self._entity_extract_policy = entity_extract_policy

        self._synonym_expand_fn = synonym_expand_fn
        self._synonym_expand_template = (
            synonym_expand_template or DEFAULT_SYNONYM_EXPAND_PROMPT
        )
        self._synonym_expand_policy = synonym_expand_policy

        self._max_entities = max_entities
        self._max_synonyms = max_synonyms
        self._retriever_mode = retriever_mode
        self._with_nl2graphquery = with_nl2graphquery
        if self._with_nl2graphquery:
            from llama_index.core.query_engine.knowledge_graph_query_engine import (
                KnowledgeGraphQueryEngine,
            )

            graph_query_synthesis_prompt = kwargs.get(
                "graph_query_synthesis_prompt",
                None,
            )
            if graph_query_synthesis_prompt is not None:
                del kwargs["graph_query_synthesis_prompt"]

            graph_response_answer_prompt = kwargs.get(
                "graph_response_answer_prompt",
                None,
            )
            if graph_response_answer_prompt is not None:
                del kwargs["graph_response_answer_prompt"]

            refresh_schema = kwargs.get("refresh_schema", False)
            response_synthesizer = kwargs.get("response_synthesizer", None)
            self._kg_query_engine = KnowledgeGraphQueryEngine(
                llm=self._llm,
                storage_context=self._storage_context,
                graph_query_synthesis_prompt=graph_query_synthesis_prompt,
                graph_response_answer_prompt=graph_response_answer_prompt,
                refresh_schema=refresh_schema,
                verbose=verbose,
                response_synthesizer=response_synthesizer,
                service_context=service_context,
                **kwargs,
            )

        self._graph_traversal_depth = graph_traversal_depth
        self._max_knowledge_sequence = max_knowledge_sequence
        self._verbose = verbose
        refresh_schema = kwargs.get("refresh_schema", False)
        try:
            self._graph_schema = self._graph_store.get_schema(refresh=refresh_schema)
        except NotImplementedError:
            self._graph_schema = ""
        except Exception as e:
            logger.warning(f"Failed to get graph schema: {e}")
            self._graph_schema = ""

        super().__init__(
            callback_manager=callback_manager
            or callback_manager_from_settings_or_context(Settings, service_context)
        )

    def _process_entities(
        self,
        query_str: str,
        handle_fn: Optional[Callable],
        handle_llm_prompt_template: Optional[BasePromptTemplate],
        cross_handle_policy: Optional[str] = "union",
        max_items: Optional[int] = 5,
        result_start_token: str = "KEYWORDS:",
    ) -> List[str]:
        """Get entities from query string."""
        assert cross_handle_policy in [
            "union",
            "intersection",
        ], "Invalid entity extraction policy."
        if cross_handle_policy == "intersection":
            assert all(
                [
                    handle_fn is not None,
                    handle_llm_prompt_template is not None,
                ]
            ), "Must provide entity extract function and template."
        assert any(
            [
                handle_fn is not None,
                handle_llm_prompt_template is not None,
            ]
        ), "Must provide either entity extract function or template."
        enitities_fn: List[str] = []
        enitities_llm: Set[str] = set()

        if handle_fn is not None:
            enitities_fn = handle_fn(query_str)
        if handle_llm_prompt_template is not None:
            response = self._llm.predict(
                handle_llm_prompt_template,
                max_keywords=max_items,
                question=query_str,
            )
            enitities_llm = extract_keywords_given_response(
                response, start_token=result_start_token, lowercase=False
            )
        if cross_handle_policy == "union":
            entities = list(set(enitities_fn) | enitities_llm)
        elif cross_handle_policy == "intersection":
            entities = list(set(enitities_fn).intersection(set(enitities_llm)))
        if self._verbose:
            print_text(f"Entities processed: {entities}\n", color="green")

        return entities

    async def _aprocess_entities(
        self,
        query_str: str,
        handle_fn: Optional[Callable],
        handle_llm_prompt_template: Optional[BasePromptTemplate],
        cross_handle_policy: Optional[str] = "union",
        max_items: Optional[int] = 5,
        result_start_token: str = "KEYWORDS:",
    ) -> List[str]:
        """Get entities from query string."""
        assert cross_handle_policy in [
            "union",
            "intersection",
        ], "Invalid entity extraction policy."
        if cross_handle_policy == "intersection":
            assert all(
                [
                    handle_fn is not None,
                    handle_llm_prompt_template is not None,
                ]
            ), "Must provide entity extract function and template."
        assert any(
            [
                handle_fn is not None,
                handle_llm_prompt_template is not None,
            ]
        ), "Must provide either entity extract function or template."
        enitities_fn: List[str] = []
        enitities_llm: Set[str] = set()

        if handle_fn is not None:
            enitities_fn = handle_fn(query_str)
        if handle_llm_prompt_template is not None:
            response = await self._llm.apredict(
                handle_llm_prompt_template,
                max_keywords=max_items,
                question=query_str,
            )
            enitities_llm = extract_keywords_given_response(
                response, start_token=result_start_token, lowercase=False
            )
        if cross_handle_policy == "union":
            entities = list(set(enitities_fn) | enitities_llm)
        elif cross_handle_policy == "intersection":
            entities = list(set(enitities_fn).intersection(set(enitities_llm)))
        if self._verbose:
            print_text(f"Entities processed: {entities}\n", color="green")

        return entities

    def _get_entities(self, query_str: str) -> List[str]:
        """Get entities from query string."""
        entities = self._process_entities(
            query_str,
            self._entity_extract_fn,
            self._entity_extract_template,
            self._entity_extract_policy,
            self._max_entities,
            "KEYWORDS:",
        )
        expanded_entities = self._expand_synonyms(entities)
        return list(set(entities) | set(expanded_entities))

    async def _aget_entities(self, query_str: str) -> List[str]:
        """Get entities from query string."""
        entities = await self._aprocess_entities(
            query_str,
            self._entity_extract_fn,
            self._entity_extract_template,
            self._entity_extract_policy,
            self._max_entities,
            "KEYWORDS:",
        )
        expanded_entities = await self._aexpand_synonyms(entities)
        return list(set(entities) | set(expanded_entities))

    def _expand_synonyms(self, keywords: List[str]) -> List[str]:
        """Expand synonyms or similar expressions for keywords."""
        return self._process_entities(
            str(keywords),
            self._synonym_expand_fn,
            self._synonym_expand_template,
            self._synonym_expand_policy,
            self._max_synonyms,
            "SYNONYMS:",
        )

    async def _aexpand_synonyms(self, keywords: List[str]) -> List[str]:
        """Expand synonyms or similar expressions for keywords."""
        return await self._aprocess_entities(
            str(keywords),
            self._synonym_expand_fn,
            self._synonym_expand_template,
            self._synonym_expand_policy,
            self._max_synonyms,
            "SYNONYMS:",
        )

    def _get_knowledge_sequence(
        self, entities: List[str]
    ) -> Tuple[List[str], Optional[Dict[Any, Any]]]:
        """Get knowledge sequence from entities."""
        # Get SubGraph from Graph Store as Knowledge Sequence
        rel_map: Optional[Dict] = self._graph_store.get_rel_map(
            entities, self._graph_traversal_depth, limit=self._max_knowledge_sequence
        )
        logger.debug(f"rel_map: {rel_map}")

        # Build Knowledge Sequence
        knowledge_sequence = []
        if rel_map:
            knowledge_sequence.extend(
                [str(rel_obj) for rel_objs in rel_map.values() for rel_obj in rel_objs]
            )
        else:
            logger.info("> No knowledge sequence extracted from entities.")
            return [], None

        return knowledge_sequence, rel_map

    async def _aget_knowledge_sequence(
        self, entities: List[str]
    ) -> Tuple[List[str], Optional[Dict[Any, Any]]]:
        """Get knowledge sequence from entities."""
        # Get SubGraph from Graph Store as Knowledge Sequence
        # TBD: async in graph store
        rel_map: Optional[Dict] = self._graph_store.get_rel_map(
            entities, self._graph_traversal_depth, limit=self._max_knowledge_sequence
        )
        logger.debug(f"rel_map from GraphStore:\n{rel_map}")

        # Build Knowledge Sequence
        knowledge_sequence = []
        if rel_map:
            knowledge_sequence.extend(
                [str(rel_obj) for rel_objs in rel_map.values() for rel_obj in rel_objs]
            )
        else:
            logger.info("> No knowledge sequence extracted from entities.")
            return [], None

        return knowledge_sequence, rel_map

    def _build_nodes(
        self, knowledge_sequence: List[str], rel_map: Optional[Dict[Any, Any]] = None
    ) -> List[NodeWithScore]:
        """Build nodes from knowledge sequence."""
        if len(knowledge_sequence) == 0:
            logger.info("> No knowledge sequence extracted from entities.")
            return []
        _new_line_char = "\n"
        context_string = (
            f"The following are knowledge sequence in max depth"
            f" {self._graph_traversal_depth} "
            f"in the form of directed graph like:\n"
            f"`subject -[predicate]->, object, <-[predicate_next_hop]-,"
            f" object_next_hop ...`"
            f" extracted based on key entities as subject:\n"
            f"{_new_line_char.join(knowledge_sequence)}"
        )
        if self._verbose:
            print_text(f"Graph RAG context:\n{context_string}\n", color="blue")

        rel_node_info = {
            "kg_rel_map": rel_map,
            "kg_rel_text": knowledge_sequence,
        }
        metadata_keys = ["kg_rel_map", "kg_rel_text"]
        if self._graph_schema != "":
            rel_node_info["kg_schema"] = {"schema": self._graph_schema}
            metadata_keys.append("kg_schema")
        node = NodeWithScore(
            node=TextNode(
                text=context_string,
                score=1.0,
                metadata=rel_node_info,
                excluded_embed_metadata_keys=metadata_keys,
                excluded_llm_metadata_keys=metadata_keys,
            )
        )
        return [node]

    def _retrieve_keyword(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        """Retrieve in keyword mode."""
        if self._retriever_mode not in ["keyword", "keyword_embedding"]:
            return []
        # Get entities
        entities = self._get_entities(query_bundle.query_str)
        # Before we enable embedding/semantic search, we need to make sure
        # we don't miss any entities that's synoynm of the entities we extracted
        # in string matching based retrieval in following steps, thus we expand
        # synonyms here.
        if len(entities) == 0:
            logger.info("> No entities extracted from query string.")
            return []

        # Get SubGraph from Graph Store as Knowledge Sequence
        knowledge_sequence, rel_map = self._get_knowledge_sequence(entities)

        return self._build_nodes(knowledge_sequence, rel_map)

    async def _aretrieve_keyword(
        self, query_bundle: QueryBundle
    ) -> List[NodeWithScore]:
        """Retrieve in keyword mode."""
        if self._retriever_mode not in ["keyword", "keyword_embedding"]:
            return []
        # Get entities
        entities = await self._aget_entities(query_bundle.query_str)
        # Before we enable embedding/semantic search, we need to make sure
        # we don't miss any entities that's synoynm of the entities we extracted
        # in string matching based retrieval in following steps, thus we expand
        # synonyms here.
        if len(entities) == 0:
            logger.info("> No entities extracted from query string.")
            return []

        # Get SubGraph from Graph Store as Knowledge Sequence
        knowledge_sequence, rel_map = await self._aget_knowledge_sequence(entities)

        return self._build_nodes(knowledge_sequence, rel_map)

    def _retrieve_embedding(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        """Retrieve in embedding mode."""
        if self._retriever_mode not in ["embedding", "keyword_embedding"]:
            return []
        # TBD: will implement this later with vector store.
        raise NotImplementedError

    async def _aretrieve_embedding(
        self, query_bundle: QueryBundle
    ) -> List[NodeWithScore]:
        """Retrieve in embedding mode."""
        if self._retriever_mode not in ["embedding", "keyword_embedding"]:
            return []
        # TBD: will implement this later with vector store.
        raise NotImplementedError

    def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        """Build nodes for response."""
        nodes: List[NodeWithScore] = []
        if self._with_nl2graphquery:
            try:
                nodes_nl2graphquery = self._kg_query_engine._retrieve(query_bundle)
                nodes.extend(nodes_nl2graphquery)
            except Exception as e:
                logger.warning(f"Error in retrieving from nl2graphquery: {e}")

        nodes.extend(self._retrieve_keyword(query_bundle))
        nodes.extend(self._retrieve_embedding(query_bundle))

        return nodes

    async def _aretrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        """Build nodes for response."""
        nodes: List[NodeWithScore] = []
        if self._with_nl2graphquery:
            try:
                nodes_nl2graphquery = await self._kg_query_engine._aretrieve(
                    query_bundle
                )
                nodes.extend(nodes_nl2graphquery)
            except Exception as e:
                logger.warning(f"Error in retrieving from nl2graphquery: {e}")

        nodes.extend(await self._aretrieve_keyword(query_bundle))
        nodes.extend(await self._aretrieve_embedding(query_bundle))

        return nodes