Configuration

Overview

The byokg-rag library provides extensive configuration options to customize query processing, retrieval strategies, and LLM (Large Language Model) behavior. Configuration occurs at multiple levels: query engine initialization, retriever setup, entity linking, and LLM parameters.

This document provides complete parameter documentation for all configurable components. Most components provide sensible defaults, allowing you to start with minimal configuration and adjust as needed for your specific use case.

Query Engine Configuration

ByoKGQueryEngine

The query engine orchestrates the entire KGQA (Knowledge Graph Question Answering) pipeline, coordinating entity linking, retrieval, and answer generation.

Constructor Parameters

Parameter	Type	Default	Description	Example
graph_store	GraphStore	Required	Graph store instance providing access to knowledge graph data	NeptuneAnalyticsGraphStore(...)
entity_linker	EntityLinker	Auto-created	Component for linking text mentions to graph entities	EntityLinker(...)
triplet_retriever	GRetriever	Auto-created when llm_generator provided	Retriever for extracting relevant triplets from the graph	AgenticRetriever(...)
path_retriever	PathRetriever	Auto-created	Retriever for finding paths between entities	PathRetriever(...)
graph_query_executor	GraphQueryRetriever	Auto-created	Executor for running structured graph queries	GraphQueryRetriever(...)
llm_generator	BaseGenerator	None	Language model for generating responses. Required for LLM-powered retrieval and response generation.	BedrockGenerator(...)
kg_linker	KGLinker	Auto-created when llm_generator provided	Linker for multi-strategy retrieval operations	KGLinker(...)
cypher_kg_linker	CypherKGLinker	None	Specialized linker for Cypher-based retrieval	CypherKGLinker(...)
direct_query_linking	bool	False	Enable direct entity linking using query embeddings	True

NOTE: When parameters are not provided, the query engine creates default instances with standard configurations where possible. Components that require an LLM (triplet_retriever, kg_linker) are only auto-created when llm_generator is explicitly provided. Without llm_generator, the engine can still perform graph-only operations (entity linking, path retrieval).

Query Method Parameters

Parameter	Type	Default	Description	Example
query	str	Required	The natural language question to answer	”Who won the Nobel Prize in Physics in 1921?”
iterations	int	2	Number of multi-strategy retrieval iterations	3
cypher_iterations	int	2	Number of Cypher query generation attempts	3
user_input	str	""	Additional instructions or context for the LLM	”Focus on recent discoveries”

Valid Ranges:

• iterations: 1-10 (higher values increase retrieval coverage but also latency)
• cypher_iterations: 1-5 (higher values allow more query refinement attempts)

Retriever Configuration

AgenticRetriever

The agentic retriever implements iterative, LLM-guided exploration of the knowledge graph.

Constructor Parameters

Parameter	Type	Default	Description	Example
llm_generator	BaseGenerator	Required	Language model for guiding exploration	BedrockGenerator(...)
graph_traversal	GTraversal	Required	Component for traversing graph structure	GTraversal(graph_store)
graph_verbalizer	TripletGVerbalizer	Required	Component for converting triplets to text	TripletGVerbalizer()
pruning_reranker	Reranker	None	Optional reranker for pruning results	BGEReranker()
max_num_relations	int	5	Maximum relations to consider per iteration	10
max_num_entities	int	3	Maximum entities to explore per iteration	5
max_num_iterations	int	3	Maximum exploration iterations	5
max_num_triplets	int	50	Maximum triplets to retain after pruning	100

Parameter Guidelines:

• Increase max_num_relations for broader exploration of relationship types
• Increase max_num_entities to explore more entity neighborhoods
• Increase max_num_iterations for complex multi-hop reasoning
• Increase max_num_triplets to retain more context (at the cost of LLM input length)

PathRetriever

The path retriever finds structured paths between entities following metapath patterns.

Constructor Parameters

Parameter	Type	Default	Description	Example
graph_traversal	GTraversal	Required	Component for traversing graph structure	GTraversal(graph_store)
path_verbalizer	PathVerbalizer	Required	Component for converting paths to text	PathVerbalizer()

The path retriever has minimal configuration. Its behavior is primarily controlled by the metapaths provided during retrieval.

GraphQueryRetriever

The graph query retriever executes structured queries (openCypher) against the graph store.

Constructor Parameters

Parameter	Type	Default	Description	Example
graph_store	GraphStore	Required	Graph store instance for query execution	NeptuneAnalyticsGraphStore(...)
block_graph_modification	bool	True	Block queries that modify the graph	True

WARNING: Setting block_graph_modification to False allows DELETE, CREATE, and other modification operations. Only disable this in controlled environments where query safety is guaranteed.

Entity Linker Configuration

KGLinker

The KG linker coordinates LLM-based entity extraction and linking for multi-strategy retrieval.

Constructor Parameters

Parameter	Type	Default	Description	Example
llm_generator	BaseGenerator	Required	Language model for entity extraction	BedrockGenerator(...)
graph_store	GraphStore	Required	Graph store for schema and entity information	NeptuneAnalyticsGraphStore(...)
max_input_tokens	int	32000	Maximum tokens allowed in user input and question	16000

The max_input_tokens parameter prevents excessively long inputs that could cause LLM errors or high costs.

CypherKGLinker

The Cypher KG linker specializes in generating and executing openCypher queries.

Constructor Parameters

Parameter	Type	Default	Description	Example
llm_generator	BaseGenerator	Required	Language model for Cypher generation	BedrockGenerator(...)
graph_store	GraphStore	Required	Graph store supporting openCypher execution	NeptuneAnalyticsGraphStore(...)
max_input_tokens	int	32000	Maximum tokens allowed in user input and question	16000

NOTE: The graph store must support openCypher query execution. Use Neptune Analytics or Neptune Database graph stores.

LLM Configuration

BedrockGenerator

The Bedrock generator provides access to foundation models through Amazon Bedrock.

Constructor Parameters

Parameter	Type	Default	Description	Example
model_name	str	”anthropic.claude-sonnet-4-6”	Bedrock model identifier	”anthropic.claude-sonnet-4-6”
region_name	str	”us-east-1”	AWS region for Bedrock service	”us-east-1”
max_tokens	int	4096	Maximum tokens to generate in responses	8192
max_retries	int	10	Maximum retry attempts for failed requests	5
prefill	bool	False	Enable response prefilling (advanced)	False
inference_config	dict	None	Custom inference configuration	{"temperature": 0.7}
reasoning_config	dict	None	Reasoning configuration for supported models	None

Supported Models:

The following models are compatible with byokg-rag. For the latest model availability and lifecycle status, see the Amazon Bedrock model lifecycle documentation.

Active models (recommended):

• Claude Sonnet 4.6: anthropic.claude-sonnet-4-6
• Claude Sonnet 4.5: anthropic.claude-sonnet-4-5-20250929-v1:0
• Claude Sonnet 4: anthropic.claude-sonnet-4-20250514-v1:0
• Claude Opus 4.6: anthropic.claude-opus-4-6-v1
• Claude Opus 4.5: anthropic.claude-opus-4-5-20251101-v1:0
• Claude Opus 4.1: anthropic.claude-opus-4-1-20250805-v1:0
• Claude Haiku 4.5: anthropic.claude-haiku-4-5-20251001-v1:0

Legacy models (available only to users who have actively used them in the last 15 days; new users are blocked):

• Claude 3.7 Sonnet: anthropic.claude-3-7-sonnet-20250219-v1:0 (EOL: Apr 28, 2026)
• Claude 3.5 Sonnet v2: anthropic.claude-3-5-sonnet-20241022-v2:0 (EOL: Jul 30, 2026)
• Claude 3.5 Sonnet: anthropic.claude-3-5-sonnet-20240620-v1:0 (EOL: Jul 30, 2026)
• Claude 3.5 Haiku: anthropic.claude-3-5-haiku-20241022-v1:0 (EOL: Jun 19, 2026)
• Claude 3 Haiku: anthropic.claude-3-haiku-20240307-v1:0 (EOL: Sep 10, 2026)

TIP: Claude Sonnet 4.6 provides the best balance of performance and cost for most KGQA applications.

Inference Configuration:

The inference_config parameter accepts a dictionary with Bedrock inference parameters:

inference_config = {
    "temperature": 0.7,      # Controls randomness (0.0-1.0)
    "topP": 0.9,             # Nucleus sampling threshold
    "maxTokens": 4096        # Maximum tokens to generate
}

Complete Configuration Example

This example shows a fully configured query engine with custom components:

from graphrag_toolkit.byokg_rag.graphstore import NeptuneAnalyticsGraphStore
from graphrag_toolkit.byokg_rag.llm import BedrockGenerator
from graphrag_toolkit.byokg_rag.graph_connectors import KGLinker
from graphrag_toolkit.byokg_rag.graph_retrievers import (
    AgenticRetriever,
    PathRetriever,
    GraphQueryRetriever,
    EntityLinker,
    GTraversal,
    TripletGVerbalizer,
    PathVerbalizer
)
from graphrag_toolkit.byokg_rag.indexing import FuzzyStringIndex
from graphrag_toolkit.byokg_rag.byokg_query_engine import ByoKGQueryEngine

# Step 1: Set up graph store
graph_store = NeptuneAnalyticsGraphStore(
    graph_identifier="<graph-id>",
    region="<region>"
)

# Step 2: Set up LLM
llm_generator = BedrockGenerator(
    model_name="anthropic.claude-sonnet-4-6",
    region_name="us-east-1",
    max_tokens=4096,
    max_retries=10
)

# Step 3: Set up entity linking
fuzzy_index = FuzzyStringIndex()
fuzzy_index.add(graph_store.nodes())
entity_matcher = fuzzy_index.as_entity_matcher()
entity_linker = EntityLinker(entity_matcher)

# Step 4: Set up retrievers
graph_traversal = GTraversal(graph_store)
triplet_verbalizer = TripletGVerbalizer()
path_verbalizer = PathVerbalizer()

triplet_retriever = AgenticRetriever(
    llm_generator=llm_generator,
    graph_traversal=graph_traversal,
    graph_verbalizer=triplet_verbalizer,
    max_num_relations=5,
    max_num_entities=3,
    max_num_iterations=3,
    max_num_triplets=50
)

path_retriever = PathRetriever(
    graph_traversal=graph_traversal,
    path_verbalizer=path_verbalizer
)

graph_query_executor = GraphQueryRetriever(
    graph_store=graph_store,
    block_graph_modification=True
)

# Step 5: Set up KG linker
kg_linker = KGLinker(
    llm_generator=llm_generator,
    graph_store=graph_store,
    max_input_tokens=32000
)

# Step 6: Create query engine
query_engine = ByoKGQueryEngine(
    graph_store=graph_store,
    entity_linker=entity_linker,
    triplet_retriever=triplet_retriever,
    path_retriever=path_retriever,
    graph_query_executor=graph_query_executor,
    llm_generator=llm_generator,
    kg_linker=kg_linker,
    direct_query_linking=False
)

# Step 7: Execute query
context = query_engine.query(
    query="Who won the Nobel Prize in Physics in 1921?",
    iterations=2,
    user_input=""
)

print("Retrieved context:")
for item in context:
    print(f"  - {item}")

This example demonstrates explicit configuration of all components. In practice, you can rely on defaults for most parameters and only customize what you need.