# Copyright (c) "Neo4j"
# Neo4j Sweden AB [https://neo4j.com]
# #
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# #
# https://www.apache.org/licenses/LICENSE-2.0
# #
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import logging
import warnings
from typing import Any, Optional
from pydantic import ValidationError
from neo4j_genai.exceptions import (
RagInitializationError,
SearchValidationError,
)
from neo4j_genai.generation.prompts import RagTemplate
from neo4j_genai.generation.types import RagInitModel, RagResultModel, RagSearchModel
from neo4j_genai.llm import LLMInterface
from neo4j_genai.retrievers.base import Retriever
from neo4j_genai.types import RetrieverResult
logger = logging.getLogger(__name__)
[docs]
class GraphRAG:
"""Performs a GraphRAG search using a specific retriever
and LLM.
Example:
.. code-block:: python
import neo4j
from neo4j_genai.retrievers import VectorRetriever
from neo4j_genai.llm.openai_llm import OpenAILLM
from neo4j_genai.generation import GraphRAG
driver = neo4j.GraphDatabase.driver(URI, auth=AUTH)
retriever = VectorRetriever(driver, "vector-index-name", custom_embedder)
llm = OpenAILLM()
graph_rag = GraphRAG(retriever, llm)
graph_rag.search(query_text="Find me a book about Fremen")
Args:
retriever (Retriever): The retriever used to find relevant context to pass to the LLM.
llm (LLMInterface): The LLM used to generate the answer.
prompt_template (RagTemplate): The prompt template that will be formatted with context and user question and passed to the LLM.
Raises:
RagInitializationError: If validation of the input arguments fail.
"""
def __init__(
self,
retriever: Retriever,
llm: LLMInterface,
prompt_template: RagTemplate = RagTemplate(),
):
try:
validated_data = RagInitModel(
retriever=retriever,
llm=llm,
prompt_template=prompt_template,
)
except ValidationError as e:
raise RagInitializationError(e.errors())
self.retriever = validated_data.retriever
self.llm = validated_data.llm
self.prompt_template = validated_data.prompt_template
[docs]
def search(
self,
query_text: str = "",
examples: str = "",
retriever_config: Optional[dict[str, Any]] = None,
return_context: bool = False,
query: Optional[str] = None,
) -> RagResultModel:
"""This method performs a full RAG search:
1. Retrieval: context retrieval
2. Augmentation: prompt formatting
3. Generation: answer generation with LLM
Args:
query_text (str): The user question
examples (str): Examples added to the LLM prompt.
retriever_config (Optional[dict]): Parameters passed to the retriever
search method; e.g.: top_k
return_context (bool): Whether to append the retriever result to the final result (default: False)
query (Optional[str]): The user question. Will be deprecated in favor of query_text.
Returns:
RagResultModel: The LLM-generated answer
"""
try:
if query is not None:
if query_text:
warnings.warn(
"Both 'query' and 'query_text' are provided, 'query_text' will be used.",
DeprecationWarning,
stacklevel=2,
)
elif isinstance(query, str):
warnings.warn(
"'query' is deprecated and will be removed in a future version, please use 'query_text' instead.",
DeprecationWarning,
stacklevel=2,
)
query_text = query
validated_data = RagSearchModel(
query_text=query_text,
examples=examples,
retriever_config=retriever_config or {},
return_context=return_context,
)
except ValidationError as e:
raise SearchValidationError(e.errors())
query_text = validated_data.query_text
retriever_result: RetrieverResult = self.retriever.search(
query_text=query_text, **validated_data.retriever_config
)
context = "\n".join(item.content for item in retriever_result.items)
prompt = self.prompt_template.format(
query_text=query_text, context=context, examples=validated_data.examples
)
logger.debug(f"RAG: retriever_result={retriever_result}")
logger.debug(f"RAG: prompt={prompt}")
answer = self.llm.invoke(prompt)
result: dict[str, Any] = {"answer": answer.content}
if return_context:
result["retriever_result"] = retriever_result
return RagResultModel(**result)