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174 | class Neo4jQueryEnginePack(BaseLlamaPack):
"""Neo4j Query Engine pack."""
def __init__(
self,
username: str,
password: str,
url: str,
database: str,
docs: List[Document],
query_engine_type: Optional[Neo4jQueryEngineType] = None,
**kwargs: Any,
) -> None:
"""Init params."""
neo4j_graph_store = Neo4jGraphStore(
username=username,
password=password,
url=url,
database=database,
)
neo4j_storage_context = StorageContext.from_defaults(
graph_store=neo4j_graph_store
)
# define LLM
self.llm = OpenAI(temperature=0.1, model="gpt-3.5-turbo")
self.service_context = ServiceContext.from_defaults(llm=self.llm)
neo4j_index = KnowledgeGraphIndex.from_documents(
documents=docs,
storage_context=neo4j_storage_context,
max_triplets_per_chunk=10,
service_context=self.service_context,
include_embeddings=True,
)
# create node parser to parse nodes from document
node_parser = SentenceSplitter(chunk_size=512)
# use transforms directly
nodes = node_parser(docs)
print(f"loaded nodes with {len(nodes)} nodes")
# based on the nodes and service_context, create index
vector_index = VectorStoreIndex(
nodes=nodes, service_context=self.service_context
)
if query_engine_type == Neo4jQueryEngineType.KG_KEYWORD:
# KG keyword-based entity retrieval
self.query_engine = neo4j_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 == Neo4jQueryEngineType.KG_HYBRID:
# KG hybrid entity retrieval
self.query_engine = neo4j_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 == Neo4jQueryEngineType.RAW_VECTOR:
# Raw vector index retrieval
self.query_engine = vector_index.as_query_engine()
elif query_engine_type == Neo4jQueryEngineType.RAW_VECTOR_KG_COMBO:
from llama_index.core.query_engine import RetrieverQueryEngine
# create neo4j custom retriever
neo4j_vector_retriever = VectorIndexRetriever(index=vector_index)
neo4j_kg_retriever = KGTableRetriever(
index=neo4j_index, retriever_mode="keyword", include_text=False
)
neo4j_custom_retriever = CustomRetriever(
neo4j_vector_retriever, neo4j_kg_retriever
)
# create neo4j response synthesizer
neo4j_response_synthesizer = get_response_synthesizer(
service_context=self.service_context,
response_mode="tree_summarize",
)
# Custom combo query engine
self.query_engine = RetrieverQueryEngine(
retriever=neo4j_custom_retriever,
response_synthesizer=neo4j_response_synthesizer,
)
elif query_engine_type == Neo4jQueryEngineType.KG_QE:
# using KnowledgeGraphQueryEngine
from llama_index.core.query_engine import KnowledgeGraphQueryEngine
self.query_engine = KnowledgeGraphQueryEngine(
storage_context=neo4j_storage_context,
service_context=self.service_context,
llm=self.llm,
verbose=True,
)
elif query_engine_type == Neo4jQueryEngineType.KG_RAG_RETRIEVER:
# using KnowledgeGraphRAGRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.retrievers import KnowledgeGraphRAGRetriever
neo4j_graph_rag_retriever = KnowledgeGraphRAGRetriever(
storage_context=neo4j_storage_context,
service_context=self.service_context,
llm=self.llm,
verbose=True,
)
self.query_engine = RetrieverQueryEngine.from_args(
neo4j_graph_rag_retriever, service_context=self.service_context
)
else:
# KG vector-based entity retrieval
self.query_engine = neo4j_index.as_query_engine()
def get_modules(self) -> Dict[str, Any]:
"""Get modules."""
return {
"llm": self.llm,
"service_context": self.service_context,
"query_engine": self.query_engine,
}
def run(self, *args: Any, **kwargs: Any) -> Any:
"""Run the pipeline."""
return self.query_engine.query(*args, **kwargs)
|