Default Configuration Mode (using YAML/JSON)

The default configuration mode may be configured by using a settings.yml or settings.json file in the data project root. If a .env file is present along with this config file, then it will be loaded, and the environment variables defined therein will be available for token replacements in your configuration document using ${ENV_VAR} syntax. We initialize with YML by default in graphrag init but you may use the equivalent JSON form if preferred.

Many of these config values have defaults. Rather than replicate them here, please refer to the constants in the code directly.

For example:

# .env
GRAPHRAG_API_KEY=some_api_key

# settings.yml
llm: 
  api_key: ${GRAPHRAG_API_KEY}

Config Sections

Language Model Setup

models

This is a dict of model configurations. The dict key is used to reference this configuration elsewhere when a model instance is desired. In this way, you can specify as many different models as you need, and reference them differentially in the workflow steps.

For example:

models:
  default_chat_model:
    api_key: ${GRAPHRAG_API_KEY}
    type: openai_chat
    model: gpt-4o
    model_supports_json: true
  default_embedding_model:
    api_key: ${GRAPHRAG_API_KEY}
    type: openai_embedding
    model: text-embedding-ada-002

Fields

api_key str - The OpenAI API key to use.
auth_type api_key|azure_managed_identity - Indicate how you want to authenticate requests.
type chat|embedding|openai_chat|azure_openai_chat|openai_embedding|azure_openai_embedding|mock_chat|mock_embeddings - The type of LLM to use.
model_provider str|None - The model provider to use, e.g., openai, azure, anthropic, etc. Required when type == chat|embedding. When type == chat|embedding, LiteLLM is used under the hood which has support for calling 100+ models. View LiteLLm basic usage for details on how models are called (The model_provider is the portion prior to / while the model is the portion following the /). View Language Model Selection for more details and examples on using LiteLLM.
model str - The model name.
encoding_model str - The text encoding model to use. Default is to use the encoding model aligned with the language model (i.e., it is retrieved from tiktoken if unset).
api_base str - The API base url to use.
api_version str - The API version.
deployment_name str - The deployment name to use (Azure).
organization str - The client organization.
proxy str - The proxy URL to use.
audience str - (Azure OpenAI only) The URI of the target Azure resource/service for which a managed identity token is requested. Used if api_key is not defined. Default=https://cognitiveservices.azure.com/.default
model_supports_json bool - Whether the model supports JSON-mode output.
request_timeout float - The per-request timeout.
tokens_per_minute int - Set a leaky-bucket throttle on tokens-per-minute.
requests_per_minute int - Set a leaky-bucket throttle on requests-per-minute.
retry_strategy str - Retry strategy to use, "native" is the default and uses the strategy built into the OpenAI SDK. Other allowable values include "exponential_backoff", "random_wait", and "incremental_wait".
max_retries int - The maximum number of retries to use.
max_retry_wait float - The maximum backoff time.
concurrent_requests int The number of open requests to allow at once.
async_mode asyncio|threaded The async mode to use. Either asyncio or threaded.
responses list[str] - If this model type is mock, this is a list of response strings to return.
n int - The number of completions to generate.
max_tokens int - The maximum number of output tokens. Not valid for o-series models.
temperature float - The temperature to use. Not valid for o-series models.
top_p float - The top-p value to use. Not valid for o-series models.
frequency_penalty float - Frequency penalty for token generation. Not valid for o-series models.
presence_penalty float - Frequency penalty for token generation. Not valid for o-series models.
max_completion_tokens int - Max number of tokens to consume for chat completion. Must be large enough to include an unknown amount for "reasoning" by the model. o-series models only.
reasoning_effort low|medium|high - Amount of "thought" for the model to expend reasoning about a response. o-series models only.

Input Files and Chunking

input

Our pipeline can ingest .csv, .txt, or .json data from an input location. See the inputs page for more details and examples.

Fields

storage StorageConfig
type file|blob|cosmosdb - The storage type to use. Default=file
base_dir str - The base directory to write output artifacts to, relative to the root.
connection_string str - (blob/cosmosdb only) The Azure Storage connection string.
container_name str - (blob/cosmosdb only) The Azure Storage container name.
storage_account_blob_url str - (blob only) The storage account blob URL to use.
cosmosdb_account_blob_url str - (cosmosdb only) The CosmosDB account blob URL to use.
file_type text|csv|json - The type of input data to load. Default is text
encoding str - The encoding of the input file. Default is utf-8
file_pattern str - A regex to match input files. Default is .*\.csv$, .*\.txt$, or .*\.json$ depending on the specified file_type, but you can customize it if needed.
file_filter dict - Key/value pairs to filter. Default is None.
text_column str - (CSV/JSON only) The text column name. If unset we expect a column named text.
title_column str - (CSV/JSON only) The title column name, filename will be used if unset.
metadata list[str] - (CSV/JSON only) The additional document attributes fields to keep.

chunks

These settings configure how we parse documents into text chunks. This is necessary because very large documents may not fit into a single context window, and graph extraction accuracy can be modulated. Also note the metadata setting in the input document config, which will replicate document metadata into each chunk.

Fields

size int - The max chunk size in tokens.
overlap int - The chunk overlap in tokens.
group_by_columns list[str] - Group documents by these fields before chunking.
strategy str[tokens|sentences] - How to chunk the text.
encoding_model str - The text encoding model to use for splitting on token boundaries.
prepend_metadata bool - Determines if metadata values should be added at the beginning of each chunk. Default=False.
chunk_size_includes_metadata bool - Specifies whether the chunk size calculation should include metadata tokens. Default=False.

Outputs and Storage

output

This section controls the storage mechanism used by the pipeline used for exporting output tables.

Fields

type file|memory|blob|cosmosdb - The storage type to use. Default=file
base_dir str - The base directory to write output artifacts to, relative to the root.
connection_string str - (blob/cosmosdb only) The Azure Storage connection string.
container_name str - (blob/cosmosdb only) The Azure Storage container name.
storage_account_blob_url str - (blob only) The storage account blob URL to use.
cosmosdb_account_blob_url str - (cosmosdb only) The CosmosDB account blob URL to use.

update_index_output

The section defines a secondary storage location for running incremental indexing, to preserve your original outputs.

Fields

type file|memory|blob|cosmosdb - The storage type to use. Default=file
base_dir str - The base directory to write output artifacts to, relative to the root.
connection_string str - (blob/cosmosdb only) The Azure Storage connection string.
container_name str - (blob/cosmosdb only) The Azure Storage container name.
storage_account_blob_url str - (blob only) The storage account blob URL to use.
cosmosdb_account_blob_url str - (cosmosdb only) The CosmosDB account blob URL to use.

cache

This section controls the cache mechanism used by the pipeline. This is used to cache LLM invocation results for faster performance when re-running the indexing process.

Fields

type file|memory|blob|cosmosdb - The storage type to use. Default=file
base_dir str - The base directory to write output artifacts to, relative to the root.
connection_string str - (blob/cosmosdb only) The Azure Storage connection string.
container_name str - (blob/cosmosdb only) The Azure Storage container name.
storage_account_blob_url str - (blob only) The storage account blob URL to use.
cosmosdb_account_blob_url str - (cosmosdb only) The CosmosDB account blob URL to use.

reporting

This section controls the reporting mechanism used by the pipeline, for common events and error messages. The default is to write reports to a file in the output directory. However, you can also choose to write reports to an Azure Blob Storage container.

Fields

type file|blob - The reporting type to use. Default=file
base_dir str - The base directory to write reports to, relative to the root.
connection_string str - (blob only) The Azure Storage connection string.
container_name str - (blob only) The Azure Storage container name.
storage_account_blob_url str - The storage account blob URL to use.

vector_store

Where to put all vectors for the system. Configured for lancedb by default. This is a dict, with the key used to identify individual store parameters (e.g., for text embedding).

Fields

type lancedb|azure_ai_search|cosmosdb - Type of vector store. Default=lancedb
db_uri str (only for lancedb) - The database uri. Default=storage.base_dir/lancedb
url str (only for AI Search) - AI Search endpoint
api_key str (optional - only for AI Search) - The AI Search api key to use.
audience str (only for AI Search) - Audience for managed identity token if managed identity authentication is used.
container_name str - The name of a vector container. This stores all indexes (tables) for a given dataset ingest. Default=default
database_name str - (cosmosdb only) Name of the database.
overwrite bool (only used at index creation time) - Overwrite collection if it exist. Default=True

Workflow Configurations

These settings control each individual workflow as they execute.

workflows

list[str] - This is a list of workflow names to run, in order. GraphRAG has built-in pipelines to configure this, but you can run exactly and only what you want by specifying the list here. Useful if you have done part of the processing yourself.

embed_text

By default, the GraphRAG indexer will only export embeddings required for our query methods. However, the model has embeddings defined for all plaintext fields, and these can be customized by setting the target and names fields.

Supported embeddings names are:

text_unit.text
document.text
entity.title
entity.description
relationship.description
community.title
community.summary
community.full_content

Fields

model_id str - Name of the model definition to use for text embedding.
vector_store_id str - Name of vector store definition to write to.
batch_size int - The maximum batch size to use.
batch_max_tokens int - The maximum batch # of tokens.
names list[str] - List of the embeddings names to run (must be in supported list).

extract_graph

Tune the language model-based graph extraction process.

Fields

model_id str - Name of the model definition to use for API calls.
prompt str - The prompt file to use.
entity_types list[str] - The entity types to identify.
max_gleanings int - The maximum number of gleaning cycles to use.

summarize_descriptions

Fields

model_id str - Name of the model definition to use for API calls.
prompt str - The prompt file to use.
max_length int - The maximum number of output tokens per summarization.
max_input_length int - The maximum number of tokens to collect for summarization (this will limit how many descriptions you send to be summarized for a given entity or relationship).

extract_graph_nlp

Defines settings for NLP-based graph extraction methods.

Fields

normalize_edge_weights bool - Whether to normalize the edge weights during graph construction. Default=True.
text_analyzer dict - Parameters for the NLP model.
extractor_type regex_english|syntactic_parser|cfg - Default=regex_english.
model_name str - Name of NLP model (for SpaCy-based models)
max_word_length int - Longest word to allow. Default=15.
word_delimiter str - Delimiter to split words. Default ' '.
include_named_entities bool - Whether to include named entities in noun phrases. Default=True.
exclude_nouns list[str] | None - List of nouns to exclude. If None, we use an internal stopword list.
exclude_entity_tags list[str] - List of entity tags to ignore.
exclude_pos_tags list[str] - List of part-of-speech tags to ignore.
noun_phrase_tags list[str] - List of noun phrase tags to ignore.
noun_phrase_grammars dict[str, str] - Noun phrase grammars for the model (cfg-only).

prune_graph

Parameters for manual graph pruning. This can be used to optimize the modularity of your graph clusters, by removing overly-connected or rare nodes.

Fields

min_node_freq int - The minimum node frequency to allow.
max_node_freq_std float | None - The maximum standard deviation of node frequency to allow.
min_node_degree int - The minimum node degree to allow.
max_node_degree_std float | None - The maximum standard deviation of node degree to allow.
min_edge_weight_pct float - The minimum edge weight percentile to allow.
remove_ego_nodes bool - Remove ego nodes.
lcc_only bool - Only use largest connected component.

cluster_graph

These are the settings used for Leiden hierarchical clustering of the graph to create communities.

Fields

max_cluster_size int - The maximum cluster size to export.
use_lcc bool - Whether to only use the largest connected component.
seed int - A randomization seed to provide if consistent run-to-run results are desired. We do provide a default in order to guarantee clustering stability.

extract_claims

Fields

enabled bool - Whether to enable claim extraction. Off by default, because claim prompts really need user tuning.
model_id str - Name of the model definition to use for API calls.
prompt str - The prompt file to use.
description str - Describes the types of claims we want to extract.
max_gleanings int - The maximum number of gleaning cycles to use.

community_reports

Fields

model_id str - Name of the model definition to use for API calls.
prompt str - The prompt file to use.
max_length int - The maximum number of output tokens per report.
max_input_length int - The maximum number of input tokens to use when generating reports.

embed_graph

We use node2vec to embed the graph. This is primarily used for visualization, so it is not turned on by default.

Fields

enabled bool - Whether to enable graph embeddings.
dimensions int - Number of vector dimensions to produce.
num_walks int - The node2vec number of walks.
walk_length int - The node2vec walk length.
window_size int - The node2vec window size.
iterations int - The node2vec number of iterations.
random_seed int - The node2vec random seed.
strategy dict - Fully override the embed graph strategy.

umap

Indicates whether we should run UMAP dimensionality reduction. This is used to provide an x/y coordinate to each graph node, suitable for visualization. If this is not enabled, nodes will receive a 0/0 x/y coordinate. If this is enabled, you must enable graph embedding as well.

Fields

enabled bool - Whether to enable UMAP layouts.

snapshots

Fields

embeddings bool - Export embeddings snapshots to parquet.
graphml bool - Export graph snapshots to GraphML.

Query

local_search

Fields

chat_model_id str - Name of the model definition to use for Chat Completion calls.
embedding_model_id str - Name of the model definition to use for Embedding calls.
prompt str - The prompt file to use.
text_unit_prop float - The text unit proportion.
community_prop float - The community proportion.
conversation_history_max_turns int - The conversation history maximum turns.
top_k_entities int - The top k mapped entities.
top_k_relationships int - The top k mapped relations.
max_context_tokens int - The maximum tokens to use building the request context.

global_search

Fields

chat_model_id str - Name of the model definition to use for Chat Completion calls.
map_prompt str - The mapper prompt file to use.
reduce_prompt str - The reducer prompt file to use.
knowledge_prompt str - The knowledge prompt file to use.
map_prompt str | None - The global search mapper prompt to use.
reduce_prompt str | None - The global search reducer to use.
knowledge_prompt str | None - The global search general prompt to use.
max_context_tokens int - The maximum context size to create, in tokens.
data_max_tokens int - The maximum tokens to use constructing the final response from the reduces responses.
map_max_length int - The maximum length to request for map responses, in words.
reduce_max_length int - The maximum length to request for reduce responses, in words.
dynamic_search_threshold int - Rating threshold in include a community report.
dynamic_search_keep_parent bool - Keep parent community if any of the child communities are relevant.
dynamic_search_num_repeats int - Number of times to rate the same community report.
dynamic_search_use_summary bool - Use community summary instead of full_context.
dynamic_search_max_level int - The maximum level of community hierarchy to consider if none of the processed communities are relevant.

drift_search

Fields

chat_model_id str - Name of the model definition to use for Chat Completion calls.
embedding_model_id str - Name of the model definition to use for Embedding calls.
prompt str - The prompt file to use.
reduce_prompt str - The reducer prompt file to use.
data_max_tokens int - The data llm maximum tokens.
reduce_max_tokens int - The maximum tokens for the reduce phase. Only use if a non-o-series model.
reduce_max_completion_tokens int - The maximum tokens for the reduce phase. Only use for o-series models.
concurrency int - The number of concurrent requests.
drift_k_followups int - The number of top global results to retrieve.
primer_folds int - The number of folds for search priming.
primer_llm_max_tokens int - The maximum number of tokens for the LLM in primer.
n_depth int - The number of drift search steps to take.
local_search_text_unit_prop float - The proportion of search dedicated to text units.
local_search_community_prop float - The proportion of search dedicated to community properties.
local_search_top_k_mapped_entities int - The number of top K entities to map during local search.
local_search_top_k_relationships int - The number of top K relationships to map during local search.
local_search_max_data_tokens int - The maximum context size in tokens for local search.
local_search_temperature float - The temperature to use for token generation in local search.
local_search_top_p float - The top-p value to use for token generation in local search.
local_search_n int - The number of completions to generate in local search.
local_search_llm_max_gen_tokens int - The maximum number of generated tokens for the LLM in local search. Only use if a non-o-series model.
local_search_llm_max_gen_completion_tokens int - The maximum number of generated tokens for the LLM in local search. Only use for o-series models.

basic_search

Fields

chat_model_id str - Name of the model definition to use for Chat Completion calls.
embedding_model_id str - Name of the model definition to use for Embedding calls.
prompt str - The prompt file to use.
k int | None - Number of text units to retrieve from the vector store for context building.