autogen.agentchat.contrib.graph_rag.falkor_graph_query_engine.FalkorGraphQueryEngine

FalkorGraphQueryEngine

FalkorGraphQueryEngine(
    name: str,
    host: str = '127.0.0.1',
    port: int = 6379,
    username: str | None = None,
    password: str | None = None,
    model: ForwardRef('GenerativeModel') | None = None,
    ontology: ForwardRef('Ontology') | None = None
)

This is a wrapper for FalkorDB KnowledgeGraph.
Initialize a FalkorDB knowledge graph.
Please also refer to https://github.com/FalkorDB/GraphRAG-SDK/blob/main/graphrag_sdk/kg.py

TODO: Fix LLM API cost calculation for FalkorDB usages.

Parameters:

Name	Description
name	Type: str
host	Type: str Default: ‘127.0.0.1’
port	Type: int Default: 6379
username	Type: str | None Default: None
password	Type: str | None Default: None
model	Type: ForwardRef(‘GenerativeModel’) | None Default: None
ontology	Type: ForwardRef(‘Ontology’) | None Default: None

Instance Methods

add_records

add_records(self, new_records: list[Document]) -> bool

Parameters:

Name	Description
new_records	Type: list[Document]

connect_db

connect_db(self) -> None

Connect to an existing knowledge graph.

delete

delete(self) -> bool

Delete graph and its data from database.

init_db

init_db(self, input_doc: list[Document]) -> None

Build the knowledge graph with input documents.

Parameters:

Name	Description
input_doc	Type: list[Document]

query

query(
    self,
    question: str,
    n_results: int = 1,
    **kwargs: Any
) -> GraphStoreQueryResult

Query the knowledge graph with a question and optional message history.

Parameters:

Name	Description
question	a human input question. Type: str
n_results	number of returned results. Type: int Default: 1
**kwargs	Type: Any

Returns:

Type	Description
GraphStoreQueryResult	FalkorGraphQueryResult