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175 | class NebulaGraphQueryEnginePack(BaseLlamaPack):
"""NebulaGraph Query Engine pack."""
def __init__(
self,
username: str,
password: str,
ip_and_port: str,
space_name: str,
edge_types: str,
rel_prop_names: str,
tags: str,
max_triplets_per_chunk: int,
docs: List[Document],
query_engine_type: Optional[NebulaGraphQueryEngineType] = None,
**kwargs: Any,
) -> None:
"""Init params."""
os.environ["GRAPHD_HOST"] = "127.0.0.1"
os.environ["NEBULA_USER"] = username
os.environ["NEBULA_PASSWORD"] = password
os.environ[
"NEBULA_ADDRESS"
] = ip_and_port # such as "127.0.0.1:9669" for local instance
nebulagraph_graph_store = NebulaGraphStore(
space_name=space_name,
edge_types=edge_types,
rel_prop_names=rel_prop_names,
tags=tags,
)
nebulagraph_storage_context = StorageContext.from_defaults(
graph_store=nebulagraph_graph_store
)
# define LLM
self.llm = OpenAI(temperature=0.1, model="gpt-3.5-turbo")
Settings.llm = self.llm
nebulagraph_index = KnowledgeGraphIndex.from_documents(
documents=docs,
storage_context=nebulagraph_storage_context,
max_triplets_per_chunk=max_triplets_per_chunk,
space_name=space_name,
edge_types=edge_types,
rel_prop_names=rel_prop_names,
tags=tags,
include_embeddings=True,
)
# create index
vector_index = VectorStoreIndex.from_documents(docs)
if query_engine_type == NebulaGraphQueryEngineType.KG_KEYWORD:
# KG keyword-based entity retrieval
self.query_engine = nebulagraph_index.as_query_engine(
# setting to false uses the raw triplets instead of adding the text from the corresponding nodes
include_text=False,
retriever_mode="keyword",
response_mode="tree_summarize",
)
elif query_engine_type == NebulaGraphQueryEngineType.KG_HYBRID:
# KG hybrid entity retrieval
self.query_engine = nebulagraph_index.as_query_engine(
include_text=True,
response_mode="tree_summarize",
embedding_mode="hybrid",
similarity_top_k=3,
explore_global_knowledge=True,
)
elif query_engine_type == NebulaGraphQueryEngineType.RAW_VECTOR:
# Raw vector index retrieval
self.query_engine = vector_index.as_query_engine()
elif query_engine_type == NebulaGraphQueryEngineType.RAW_VECTOR_KG_COMBO:
from llama_index.core.query_engine import RetrieverQueryEngine
# create custom retriever
nebulagraph_vector_retriever = VectorIndexRetriever(index=vector_index)
nebulagraph_kg_retriever = KGTableRetriever(
index=nebulagraph_index, retriever_mode="keyword", include_text=False
)
nebulagraph_custom_retriever = CustomRetriever(
nebulagraph_vector_retriever, nebulagraph_kg_retriever
)
# create response synthesizer
nebulagraph_response_synthesizer = get_response_synthesizer(
response_mode="tree_summarize"
)
# Custom combo query engine
self.query_engine = RetrieverQueryEngine(
retriever=nebulagraph_custom_retriever,
response_synthesizer=nebulagraph_response_synthesizer,
)
elif query_engine_type == NebulaGraphQueryEngineType.KG_QE:
# using KnowledgeGraphQueryEngine
from llama_index.core.query_engine import KnowledgeGraphQueryEngine
self.query_engine = KnowledgeGraphQueryEngine(
storage_context=nebulagraph_storage_context,
llm=self.llm,
verbose=True,
)
elif query_engine_type == NebulaGraphQueryEngineType.KG_RAG_RETRIEVER:
# using KnowledgeGraphRAGRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.retrievers import KnowledgeGraphRAGRetriever
nebulagraph_graph_rag_retriever = KnowledgeGraphRAGRetriever(
storage_context=nebulagraph_storage_context,
llm=self.llm,
verbose=True,
)
self.query_engine = RetrieverQueryEngine.from_args(
nebulagraph_graph_rag_retriever
)
else:
# KG vector-based entity retrieval
self.query_engine = nebulagraph_index.as_query_engine()
def get_modules(self) -> Dict[str, Any]:
"""Get modules."""
return {
"llm": self.llm,
"query_engine": self.query_engine,
}
def run(self, *args: Any, **kwargs: Any) -> Any:
"""Run the pipeline."""
return self.query_engine.query(*args, **kwargs)
|
