API Documentation ¶

Parameters:

args (Any)
kwargs (Any)

get_result_formatter()[source]¶

Returns the function to use to transform a neo4j.Record to a RetrieverResultItem.

Return type:: Callable[[Record], RetrieverResultItem]

default_record_formatter(record)[source]¶

Best effort to guess the node-to-text method. Inherited classes can override this method to implement custom text formatting.

Parameters:: record (Record)
Return type:: RetrieverResultItem

VectorRetriever¶

class neo4j_graphrag.retrievers.VectorRetriever(driver, index_name, embedder=None, return_properties=None, result_formatter=None, neo4j_database=None)[source]¶

Provides retrieval method using vector search over embeddings. If an embedder is provided, it needs to have the required Embedder type.

Example:

import neo4j
from neo4j_graphrag.retrievers import VectorRetriever

driver = neo4j.GraphDatabase.driver(URI, auth=AUTH)

retriever = VectorRetriever(driver, "vector-index-name", custom_embedder)
retriever.search(query_text="Find me a book about Fremen", top_k=5)

or if the vector embedding of the query text is available:

retriever.search(query_vector=..., top_k=5)

Parameters:

driver (neo4j.Driver) – The Neo4j Python driver.
index_name (str) – Vector index name.
embedder (Optional[Embedder]) – Embedder object to embed query text.
return_properties (Optional[list[str]]) – List of node properties to return.
result_formatter (Optional[Callable[[neo4j.Record], RetrieverResultItem]]) –
Provided custom function to transform a neo4j.Record to a RetrieverResultItem.

Two variables are provided in the neo4j.Record:
- node: Represents the node retrieved from the vector index search.
- score: Denotes the similarity score.
neo4j_database (Optional[str]) –
The name of the Neo4j database. If not provided, this defaults to the server’s default database (“neo4j” by default) (see reference to documentation).

Raises:

RetrieverInitializationError – If validation of the input arguments fail.

search(query_vector=None, query_text=None, top_k=5, filters=None)¶

Get the top_k nearest neighbor embeddings for either provided query_vector or query_text. See the following documentation for more details:

To query by text, an embedder must be provided when the class is instantiated. The embedder is not required if query_vector is passed.

Parameters:

query_vector (Optional[list[float]]) – The vector embeddings to get the closest neighbors of. Defaults to None.
query_text (Optional[str]) – The text to get the closest neighbors of. Defaults to None.
top_k (int) – The number of neighbors to return. Defaults to 5.
filters (Optional[dict[str, Any]]) – Filters for metadata pre-filtering. Defaults to None.

Raises:

SearchValidationError – If validation of the input arguments fail.
EmbeddingRequiredError – If no embedder is provided.

Returns:

The results of the search query as a list of neo4j.Record and an optional metadata dict

Return type:

VectorCypherRetriever¶

class neo4j_graphrag.retrievers.VectorCypherRetriever(driver, index_name, retrieval_query, embedder=None, result_formatter=None, neo4j_database=None)[source]¶

Provides retrieval method using vector similarity augmented by a Cypher query. This retriever builds on VectorRetriever. If an embedder is provided, it needs to have the required Embedder type.

Note: node is a variable from the base query that can be used in retrieval_query as seen in the example below.

The retrieval_query is additional Cypher that can allow for graph traversal after retrieving node.

Example:

import neo4j
from neo4j_graphrag.retrievers import VectorCypherRetriever

driver = neo4j.GraphDatabase.driver(URI, auth=AUTH)

retrieval_query = "MATCH (node)-[:AUTHORED_BY]->(author:Author)" "RETURN author.name"
retriever = VectorCypherRetriever(
  driver, "vector-index-name", retrieval_query, custom_embedder
)
retriever.search(query_text="Find me a book about Fremen", top_k=5)

Parameters:

driver (neo4j.Driver) – The Neo4j Python driver.
index_name (str) – Vector index name.
retrieval_query (str) – Cypher query that gets appended.
embedder (Optional[Embedder]) – Embedder object to embed query text.
result_formatter (Optional[Callable[[neo4j.Record], RetrieverResultItem]]) – Provided custom function to transform a neo4j.Record to a RetrieverResultItem.
neo4j_database (Optional[str]) –
The name of the Neo4j database. If not provided, this defaults to the server’s default database (“neo4j” by default) (see reference to documentation).

HybridRetriever¶

class neo4j_graphrag.retrievers.HybridRetriever(driver, vector_index_name, fulltext_index_name, embedder=None, return_properties=None, result_formatter=None, neo4j_database=None)[source]¶

Provides retrieval method using combination of vector search over embeddings and fulltext search. If an embedder is provided, it needs to have the required Embedder type.

Example:

import neo4j
from neo4j_graphrag.retrievers import HybridRetriever

driver = neo4j.GraphDatabase.driver(URI, auth=AUTH)

retriever = HybridRetriever(
    driver, "vector-index-name", "fulltext-index-name", custom_embedder
)
retriever.search(query_text="Find me a book about Fremen", top_k=5)

Parameters:

driver (neo4j.Driver) – The Neo4j Python driver.
vector_index_name (str) – Vector index name.
fulltext_index_name (str) – Fulltext index name.
embedder (Optional[Embedder]) – Embedder object to embed query text.
return_properties (Optional[list[str]]) – List of node properties to return.
result_formatter (Optional[Callable[[neo4j.Record], RetrieverResultItem]]) – Provided custom function to transform a neo4j.Record to a RetrieverResultItem.
neo4j_database (Optional[str]) –
The name of the Neo4j database. If not provided, this defaults to the server’s default database (“neo4j” by default) (see reference to documentation).

Two variables are provided in the neo4j.Record:
- node: Represents the node retrieved from the vector index search.
- score: Denotes the similarity score.

search(query_text, query_vector=None, top_k=5)¶

Get the top_k nearest neighbor embeddings for either provided query_vector or query_text. Both query_vector and query_text can be provided. If query_vector is provided, then it will be preferred over the embedded query_text for the vector search.

See the following documentation for more details:

To query by text, an embedder must be provided when the class is instantiated.

Parameters:

query_text (str) – The text to get the closest neighbors of.
query_vector (Optional[list[float]], optional) – The vector embeddings to get the closest neighbors of. Defaults to None.
top_k (int, optional) – The number of neighbors to return. Defaults to 5.

Raises:

SearchValidationError – If validation of the input arguments fail.
EmbeddingRequiredError – If no embedder is provided.

Returns:

The results of the search query as a list of neo4j.Record and an optional metadata dict

Return type:

HybridCypherRetriever¶

class neo4j_graphrag.retrievers.HybridCypherRetriever(driver, vector_index_name, fulltext_index_name, retrieval_query, embedder=None, result_formatter=None, neo4j_database=None)[source]¶

Provides retrieval method using combination of vector search over embeddings and fulltext search, augmented by a Cypher query. This retriever builds on HybridRetriever. If an embedder is provided, it needs to have the required Embedder type.

Note: node is a variable from the base query that can be used in retrieval_query as seen in the example below.

Example:

import neo4j
from neo4j_graphrag.retrievers import HybridCypherRetriever

driver = neo4j.GraphDatabase.driver(URI, auth=AUTH)

retrieval_query = "MATCH (node)-[:AUTHORED_BY]->(author:Author)" "RETURN author.name"
retriever = HybridCypherRetriever(
    driver, "vector-index-name", "fulltext-index-name", retrieval_query, custom_embedder
)
retriever.search(query_text="Find me a book about Fremen", top_k=5)

To query by text, an embedder must be provided when the class is instantiated.

Parameters:

driver (neo4j.Driver) – The Neo4j Python driver.
vector_index_name (str) – Vector index name.
fulltext_index_name (str) – Fulltext index name.
retrieval_query (str) – Cypher query that gets appended.
embedder (Optional[Embedder]) – Embedder object to embed query text.
result_formatter (Optional[Callable[[neo4j.Record], RetrieverResultItem]]) – Provided custom function to transform a neo4j.Record to a RetrieverResultItem.
neo4j_database (Optional[str]) –
The name of the Neo4j database. If not provided, this defaults to the server’s default database (“neo4j” by default) (see reference to documentation).

Raises:

RetrieverInitializationError – If validation of the input arguments fail.

search(query_text, query_vector=None, top_k=5, query_params=None)¶

Get the top_k nearest neighbor embeddings for either provided query_vector or query_text. Both query_vector and query_text can be provided. If query_vector is provided, then it will be preferred over the embedded query_text for the vector search.

See the following documentation for more details:

Parameters:

query_text (str) – The text to get the closest neighbors of.
query_vector (Optional[list[float]]) – The vector embeddings to get the closest neighbors of. Defaults to None.
top_k (int) – The number of neighbors to return. Defaults to 5.
query_params (Optional[dict[str, Any]]) – Parameters for the Cypher query. Defaults to None.

Raises:

SearchValidationError – If validation of the input arguments fail.
EmbeddingRequiredError – If no embedder is provided.

Returns:

The results of the search query as a list of neo4j.Record and an optional metadata dict

Return type:

Text2CypherRetriever¶

class neo4j_graphrag.retrievers.Text2CypherRetriever(driver, llm, neo4j_schema=None, examples=None, result_formatter=None, custom_prompt=None, neo4j_database=None)[source]¶

Allows for the retrieval of records from a Neo4j database using natural language. Converts a user’s natural language query to a Cypher query using an LLM, then retrieves records from a Neo4j database using the generated Cypher query.

Parameters:

driver (neo4j.Driver) – The Neo4j Python driver.
llm (neo4j_graphrag.generation.llm.LLMInterface) – LLM object to generate the Cypher query.
neo4j_schema (Optional[str]) – Neo4j schema used to generate the Cypher query.
examples (Optional[list[str], optional) – Optional user input/query pairs for the LLM to use as examples.
custom_prompt (Optional[str]) – Optional custom prompt to use instead of auto generated prompt. Will not include the neo4j_schema or examples args, if provided.
result_formatter (Optional[Callable[[neo4j.Record], RetrieverResultItem]])
neo4j_database (Optional[str])

Raises:

RetrieverInitializationError – If validation of the input arguments fail.

search(query_text, prompt_params=None)¶

Converts query_text to a Cypher query using an LLM.: Retrieve records from a Neo4j database using the generated Cypher query.

Parameters:

query_text (str) – The natural language query used to search the Neo4j database.
prompt_params (Dict[str, Any]) – additional values to inject into the custom prompt, if it is provided. Example: {‘schema’: ‘this is the graph schema’}

Raises:

SearchValidationError – If validation of the input arguments fail.
Text2CypherRetrievalError – If the LLM fails to generate a correct Cypher query.

Returns:

The results of the search query as a list of neo4j.Record and an optional metadata dict

Return type:

External Retrievers¶

This section includes retrievers that integrate with databases external to Neo4j.

WeaviateNeo4jRetriever¶

class neo4j_graphrag.retrievers.external.weaviate.weaviate.WeaviateNeo4jRetriever(driver, client, collection, id_property_external, id_property_neo4j, embedder=None, return_properties=None, retrieval_query=None, result_formatter=None, neo4j_database=None)[source]¶

Provides retrieval method using vector search over embeddings with a Weaviate database. If an embedder is provided, it needs to have the required Embedder type.

Example:

from neo4j import GraphDatabase
from neo4j_graphrag.retrievers import WeaviateNeo4jRetriever
from weaviate.connect.helpers import connect_to_local

with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
    with connect_to_local() as w_client:
        retriever = WeaviateNeo4jRetriever(
            driver=neo4j_driver,
            client=w_client,
            collection="Jeopardy",
            id_property_external="neo4j_id",
            id_property_neo4j="id"
        )

        result = retriever.search(query_text="biology", top_k=2)

Parameters:

driver (neo4j.Driver) – The Neo4j Python driver.
client (WeaviateClient) – The Weaviate client object.
collection (str) – Name of a set of Weaviate objects that share the same data structure.
id_property_external (str) – The name of the Weaviate property that has the identifier that refers to a corresponding Neo4j node id property.
id_property_neo4j (str) – The name of the Neo4j node property that’s used as the identifier for relating matches from Weaviate to Neo4j nodes.
embedder (Optional[Embedder]) – Embedder object to embed query text.
return_properties (Optional[list[str]]) – List of node properties to return.
result_formatter (Optional[Callable[[neo4j.Record], RetrieverResultItem]]) – Function to transform a neo4j.Record to a RetrieverResultItem.
neo4j_database (Optional[str]) –
The name of the Neo4j database. If not provided, this defaults to the server’s default database (“neo4j” by default) (see reference to documentation).
retrieval_query (Optional[str])

Raises:

RetrieverInitializationError – If validation of the input arguments fail.

search(query_vector=None, query_text=None, top_k=5, **kwargs)¶

Get the top_k nearest neighbor embeddings using Weaviate for either provided query_vector or query_text. Both query_vector and query_text can be provided. If query_vector is provided, then it will be preferred over the embedded query_text for the vector search. If query_text is provided, then it will check if an embedder is provided and use it to generate the query_vector. If no embedder is provided, then it will assume that the vectorizer is used in Weaviate.

Example:

import neo4j
from neo4j_graphrag.retrievers import WeaviateNeo4jRetriever

driver = neo4j.GraphDatabase.driver(URI, auth=AUTH)

retriever = WeaviateNeo4jRetriever(
    driver=driver,
    client=weaviate_client,
    collection="Jeopardy",
    id_property_external="neo4j_id",
    id_property_neo4j="id",
)

biology_embedding = ...
retriever.search(query_vector=biology_embedding, top_k=2)

Parameters:

query_text (Optional[str]) – The text to get the closest neighbors of.
query_vector (Optional[list[float]]) – The vector embeddings to get the closest neighbors of. Defaults to None.
top_k (int) – The number of neighbors to return. Defaults to 5.
kwargs (Any)

Raises:

SearchValidationError – If validation of the input arguments fail.

Returns:

The results of the search query as a list of neo4j.Record and an optional metadata dict

Return type:

PineconeNeo4jRetriever¶

class neo4j_graphrag.retrievers.external.pinecone.pinecone.PineconeNeo4jRetriever(driver, client, index_name, id_property_neo4j, embedder=None, return_properties=None, retrieval_query=None, result_formatter=None, neo4j_database=None)[source]¶

Provides retrieval method using vector search over embeddings with a Pinecone database. If an embedder is provided, it needs to have the required Embedder type.

Example:

from neo4j import GraphDatabase
from neo4j_graphrag.retrievers import PineconeNeo4jRetriever
from pinecone import Pinecone

with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
    pc_client = Pinecone(PC_API_KEY)
    embedder = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")

    retriever = PineconeNeo4jRetriever(
        driver=neo4j_driver,
        client=pc_client,
        index_name="jeopardy",
        id_property_neo4j="id",
        embedder=embedder,
    )

    result = retriever.search(query_text="biology", top_k=2)

Parameters:

driver (neo4j.Driver) – The Neo4j Python driver.
client (Pinecone) – The Pinecone client object.
index_name (str) – The name of the Pinecone index.
id_property_neo4j (str) – The name of the Neo4j node property that’s used as the identifier for relating matches from Pinecone to Neo4j nodes.
embedder (Optional[Embedder]) – Embedder object to embed query text.
return_properties (Optional[list[str]]) – List of node properties to return.
retrieval_query (str) – Cypher query that gets appended.
result_formatter (Optional[Callable[[neo4j.Record], RetrieverResultItem]]) – Function to transform a neo4j.Record to a RetrieverResultItem.
neo4j_database (Optional[str]) –
The name of the Neo4j database. If not provided, this defaults to the server’s default database (“neo4j” by default) (see reference to documentation).

Raises:

RetrieverInitializationError – If validation of the input arguments fail.

search(query_vector=None, query_text=None, top_k=5, **kwargs)¶

Get the top_k nearest neighbor embeddings using Pinecone for either provided query_vector or query_text. Both query_vector and query_text can be provided. If query_vector is provided, then it will be preferred over the embedded query_text for the vector search. If query_text is provided, then it will check if an embedder is provided and use it to generate the query_vector.

See the following documentation for more details: - Query a vector index - db.index.vector.queryNodes() - db.index.fulltext.queryNodes()

Example:

from neo4j import GraphDatabase
from neo4j_graphrag.retrievers import PineconeNeo4jRetriever
from pinecone import Pinecone

with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
    pc_client = Pinecone(PC_API_KEY)
    retriever = PineconeNeo4jRetriever(
        driver=neo4j_driver,
        client=pc_client,
        index_name="jeopardy",
        id_property_neo4j="id"
    )
    biology_embedding = ...
    retriever.search(query_vector=biology_embedding, top_k=2)

Parameters:

query_text (str) – The text to get the closest neighbors of.
query_vector (Optional[list[float]], optional) – The vector embeddings to get the closest neighbors of. Defaults to None.
top_k (Optional[int]) – The number of neighbors to return. Defaults to 5.
kwargs (Any)

Raises:

SearchValidationError – If validation of the input arguments fail.
EmbeddingRequiredError – If no embedder is provided when using text as an input.

Returns:

The results of the search query as a list of neo4j.Record and an optional metadata dict

Return type:

QdrantNeo4jRetriever¶

class neo4j_graphrag.retrievers.external.qdrant.qdrant.QdrantNeo4jRetriever(driver, client, collection_name, id_property_neo4j, id_property_external='id', embedder=None, return_properties=None, retrieval_query=None, result_formatter=None, neo4j_database=None)[source]¶

Provides retrieval method using vector search over embeddings with a Qdrant database.

Example:

from neo4j import GraphDatabase
from neo4j_graphrag.retrievers import QdrantNeo4jRetriever
from qdrant_client import QdrantClient

with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
    client = QdrantClient()
    retriever = QdrantNeo4jRetriever(
        driver=neo4j_driver,
        client=client,
        collection_name="my_collection",
        id_property_external="neo4j_id"
    )
    embedding = ...
    retriever.search(query_vector=embedding, top_k=2)

Parameters:

driver (neo4j.Driver) – The Neo4j Python driver.
client (QdrantClient) – The Qdrant client object.
collection_name (str) – The name of the Qdrant collection to use.
id_property_neo4j (str) – The name of the Neo4j node property that’s used as the identifier for relating matches from Qdrant to Neo4j nodes.
id_property_external (str) – The name of the Qdrant payload property with identifier that refers to a corresponding Neo4j node id property.
embedder (Optional[Embedder]) – Embedder object to embed query text.
return_properties (Optional[list[str]]) – List of node properties to return.
result_formatter (Optional[Callable[[neo4j.Record], RetrieverResultItem]]) – Function to transform a neo4j.Record to a RetrieverResultItem.
neo4j_database (Optional[str]) –
The name of the Neo4j database. If not provided, this defaults to the server’s default database (“neo4j” by default) (see reference to documentation).
retrieval_query (Optional[str])

Raises:

RetrieverInitializationError – If validation of the input arguments fail.

search(query_vector=None, query_text=None, top_k=5, **kwargs)¶

Get the top_k nearest neighbour embeddings using Qdrant for either provided query_vector or query_text. If query_text is provided, then the provided embedder is used to generate the query_vector.

See the following documentation for more details: - Query a vector index - db.index.vector.queryNodes() - db.index.fulltext.queryNodes()

Example:

from neo4j import GraphDatabase
from neo4j_graphrag.retrievers import QdrantNeo4jRetriever
from qdrant_client import QdrantClient

with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
    client = QdrantClient()
    retriever = QdrantNeo4jRetriever(
        driver=neo4j_driver,
        client=client,
        collection_name="my_collection",
        id_property_external="neo4j_id"
    )
    embedding = ...
    retriever.search(query_vector=embedding, top_k=2)

Parameters:

query_text (str) – The text to get the closest neighbours of.
query_vector (Optional[list[float]], optional) – The vector embeddings to get the closest neighbours of. Defaults to None.
top_k (Optional[int]) – The number of neighbours to return. Defaults to 5.
kwargs (Any) – Additional keyword arguments to pass to QdrantClient#query().

Raises:

SearchValidationError – If validation of the input arguments fail.
EmbeddingRequiredError – If no embedder is provided when using text as an input.

Returns:

The results of the search query as a list of neo4j.Record and an optional metadata dict

Return type:

Embedder¶

class neo4j_graphrag.embeddings.base.Embedder[source]¶

Interface for embedding models. An embedder passed into a retriever must implement this interface.

abstract embed_query(text)[source]¶

Embed query text.

Parameters:: text (str) – Text to convert to vector embedding
Returns:: A vector embedding.
Return type:: list[float]

SentenceTransformerEmbeddings¶

class neo4j_graphrag.embeddings.sentence_transformers.SentenceTransformerEmbeddings(model='all-MiniLM-L6-v2', *args, **kwargs)[source]¶

Parameters:

model (str)
args (Any)
kwargs (Any)

embed_query(text)[source]¶

Embed query text.

Parameters:: text (str) – Text to convert to vector embedding
Returns:: A vector embedding.
Return type:: list[float]

OpenAIEmbeddings¶

class neo4j_graphrag.embeddings.openai.OpenAIEmbeddings(model='text-embedding-ada-002', **kwargs)[source]¶

OpenAI embeddings class. This class uses the OpenAI python client to generate embeddings for text data.

Parameters:

model (str) – The name of the OpenAI embedding model to use. Defaults to “text-embedding-ada-002”.
kwargs (Any) – All other parameters will be passed to the openai.OpenAI init.

AzureOpenAIEmbeddings¶

class neo4j_graphrag.embeddings.openai.AzureOpenAIEmbeddings(model='text-embedding-ada-002', **kwargs)[source]¶

Azure OpenAI embeddings class. This class uses the Azure OpenAI python client to generate embeddings for text data.

Parameters:

model (str) – The name of the Azure OpenAI embedding model to use. Defaults to “text-embedding-ada-002”.
kwargs (Any) – All other parameters will be passed to the openai.AzureOpenAI init.

OllamaEmbeddings¶

class neo4j_graphrag.embeddings.ollama.OllamaEmbeddings(model, **kwargs)[source]¶

Ollama embeddings class. This class uses the ollama Python client to generate vector embeddings for text data.

Parameters:

model (str) – The name of the Mistral AI text embedding model to use. Defaults to “mistral-embed”.
kwargs (Any)

embed_query(text, **kwargs)[source]¶

Generate embeddings for a given query using an Ollama text embedding model.

Parameters:

text (str) – The text to generate an embedding for.
**kwargs (Any) – Additional keyword arguments to pass to the Ollama client.

Return type:

VertexAIEmbeddings¶

class neo4j_graphrag.embeddings.vertexai.VertexAIEmbeddings(model='text-embedding-004')[source]¶

Vertex AI embeddings class. This class uses the Vertex AI Python client to generate vector embeddings for text data.

Parameters:: model (str) – The name of the Vertex AI text embedding model to use. Defaults to “text-embedding-004”.

embed_query(text, task_type='RETRIEVAL_QUERY', **kwargs)[source]¶

Generate embeddings for a given query using a Vertex AI text embedding model.

Parameters:

text (str) – The text to generate an embedding for.
task_type (str) – The type of the text embedding task. Defaults to “RETRIEVAL_QUERY”. See https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api#tasktype for a full list.
**kwargs (Any) – Additional keyword arguments to pass to the Vertex AI client’s get_embeddings method.

Return type:

MistralAIEmbeddings¶

class neo4j_graphrag.embeddings.mistral.MistralAIEmbeddings(model='mistral-embed', **kwargs)[source]¶

Mistral AI embeddings class. This class uses the Mistral AI Python client to generate vector embeddings for text data.

Parameters:

model (str) – The name of the Mistral AI text embedding model to use. Defaults to “mistral-embed”.
kwargs (Any)

embed_query(text, **kwargs)[source]¶

Generate embeddings for a given query using a Mistral AI text embedding model.

Parameters:

text (str) – The text to generate an embedding for.
**kwargs (Any) – Additional keyword arguments to pass to the Mistral AI client.

Return type:

CohereEmbeddings¶

class neo4j_graphrag.embeddings.cohere.CohereEmbeddings(model='', **kwargs)[source]¶

Parameters:

model (str)
kwargs (Any)

embed_query(text, **kwargs)[source]¶

Embed query text.

Parameters:

text (str) – Text to convert to vector embedding
kwargs (Any)

Returns:

A vector embedding.

Return type:

Generation¶

LLM¶

LLMInterface¶

class neo4j_graphrag.llm.LLMInterface(model_name, model_params=None, **kwargs)[source]¶

Interface for large language models.

Parameters:

model_name (str) – The name of the language model.
model_params (Optional[dict], optional) – Additional parameters passed to the model when text is sent to it. Defaults to None.
**kwargs (Any) – Arguments passed to the model when for the class is initialised. Defaults to None.

abstract invoke(input)[source]¶

Sends a text input to the LLM and retrieves a response.

Parameters:: input (str) – Text sent to the LLM
Returns:: The response from the LLM.
Return type:: LLMResponse
Raises:: LLMGenerationError – If anything goes wrong.

abstract async ainvoke(input)[source]¶

Asynchronously sends a text input to the LLM and retrieves a response.

Parameters:: input (str) – Text sent to the LLM
Returns:: The response from the LLM.
Return type:: LLMResponse
Raises:: LLMGenerationError – If anything goes wrong.

OpenAILLM¶

class neo4j_graphrag.llm.openai_llm.OpenAILLM(model_name, model_params=None, **kwargs)[source]¶

Parameters:

model_name (str)
model_params (Optional[dict[str, Any]])
kwargs (Any)

AzureOpenAILLM¶

class neo4j_graphrag.llm.openai_llm.AzureOpenAILLM(model_name, model_params=None, **kwargs)[source]¶

Parameters:

model_name (str)
model_params (Optional[dict[str, Any]])
kwargs (Any)

OllamaLLM¶

class neo4j_graphrag.llm.ollama_llm.OllamaLLM(model_name, model_params=None, **kwargs)[source]¶

Parameters:

model_name (str)
model_params (dict[str, Any] | None)
kwargs (Any)

invoke(input)[source]¶

Sends a text input to the LLM and retrieves a response.

Parameters:: input (str) – Text sent to the LLM
Returns:: The response from the LLM.
Return type:: LLMResponse
Raises:: LLMGenerationError – If anything goes wrong.

async ainvoke(input)[source]¶

Asynchronously sends a text input to the LLM and retrieves a response.

Parameters:: input (str) – Text sent to the LLM
Returns:: The response from the LLM.
Return type:: LLMResponse
Raises:: LLMGenerationError – If anything goes wrong.

VertexAILLM¶

class neo4j_graphrag.llm.vertexai_llm.VertexAILLM(model_name='gemini-1.5-flash-001', model_params=None, **kwargs)[source]¶

Interface for large language models on Vertex AI

Parameters:

model_name (str, optional) – Name of the LLM to use. Defaults to “gemini-1.5-flash-001”.
model_params (Optional[dict], optional) – Additional parameters passed to the model when text is sent to it. Defaults to None.
**kwargs (Any) – Arguments passed to the model when for the class is initialised. Defaults to None.

Raises:

LLMGenerationError – If there’s an error generating the response from the model.

Example:

from neo4j_graphrag.llm import VertexAILLM
from vertexai.generative_models import GenerationConfig

generation_config = GenerationConfig(temperature=0.0)
llm = VertexAILLM(
    model_name="gemini-1.5-flash-001", generation_config=generation_config
)
llm.invoke("Who is the mother of Paul Atreides?")

invoke(input)[source]¶

Sends text to the LLM and returns a response.

Parameters:: input (str) – The text to send to the LLM.
Returns:: The response from the LLM.
Return type:: LLMResponse

async ainvoke(input)[source]¶

Asynchronously sends text to the LLM and returns a response.

Parameters:: input (str) – The text to send to the LLM.
Returns:: The response from the LLM.
Return type:: LLMResponse

AnthropicLLM¶

class neo4j_graphrag.llm.anthropic_llm.AnthropicLLM(model_name, model_params=None, **kwargs)[source]¶

Interface for large language models on Anthropic

Parameters:

model_name (str, optional) – Name of the LLM to use. Defaults to “gemini-1.5-flash-001”.
model_params (Optional[dict], optional) – Additional parameters passed to the model when text is sent to it. Defaults to None.
**kwargs (Any) – Arguments passed to the model when for the class is initialised. Defaults to None.

Raises:

LLMGenerationError – If there’s an error generating the response from the model.

Example:

from neo4j_graphrag.llm import AnthropicLLM

llm = AnthropicLLM(
    model_name="claude-3-opus-20240229",
    model_params={"max_tokens": 1000},
    api_key="sk...",   # can also be read from env vars
)
llm.invoke("Who is the mother of Paul Atreides?")

invoke(input)[source]¶

Sends text to the LLM and returns a response.

Parameters:: input (str) – The text to send to the LLM.
Returns:: The response from the LLM.
Return type:: LLMResponse

async ainvoke(input)[source]¶

Asynchronously sends text to the LLM and returns a response.

Parameters:: input (str) – The text to send to the LLM.
Returns:: The response from the LLM.
Return type:: LLMResponse

CohereLLM¶

class neo4j_graphrag.llm.cohere_llm.CohereLLM(model_name='', model_params=None, **kwargs)[source]¶

Interface for large language models on the Cohere platform

Parameters:

model_name (str, optional) – Name of the LLM to use. Defaults to “gemini-1.5-flash-001”.
model_params (Optional[dict], optional) – Additional parameters passed to the model when text is sent to it. Defaults to None.
**kwargs (Any) – Arguments passed to the model when for the class is initialised. Defaults to None.

Raises:

LLMGenerationError – If there’s an error generating the response from the model.

Example:

from neo4j_graphrag.llm import CohereLLM

llm = CohereLLM(api_key="...")
llm.invoke("Say something")

invoke(input)[source]¶

Sends text to the LLM and returns a response.

Parameters:: input (str) – The text to send to the LLM.
Returns:: The response from the LLM.
Return type:: LLMResponse

async ainvoke(input)[source]¶

Asynchronously sends text to the LLM and returns a response.

Parameters:: input (str) – The text to send to the LLM.
Returns:: The response from the LLM.
Return type:: LLMResponse

MistralAILLM¶

class neo4j_graphrag.llm.mistralai_llm.MistralAILLM(model_name, model_params=None, **kwargs)[source]¶

Parameters:

model_name (str)
model_params (Optional[dict[str, Any]])
kwargs (Any)

get_messages(input)[source]¶

Parameters:: input (str)
Return type:: list[AssistantMessage | SystemMessage | ToolMessage | UserMessage]

invoke(input)[source]¶

Sends a text input to the Mistral chat completion model and returns the response’s content.

Parameters:: input (str) – Text sent to the LLM
Returns:: The response from MistralAI.
Return type:: LLMResponse
Raises:: LLMGenerationError – If anything goes wrong.

async ainvoke(input)[source]¶

Asynchronously sends a text input to the MistralAI chat completion model and returns the response’s content.

Parameters:: input (str) – Text sent to the LLM
Returns:: The response from MistralAI.
Return type:: LLMResponse
Raises:: LLMGenerationError – If anything goes wrong.

PromptTemplate¶

class neo4j_graphrag.generation.prompts.PromptTemplate(template=None, expected_inputs=None)[source]¶

This class is used to generate a parameterized prompt. It is defined from a string (the template) using the Python format syntax (parameters between curly braces {}) and a list of required inputs. Before sending the instructions to an LLM, call the format method that will replace parameters with the provided values. If any of the expected inputs is missing, a PromptMissingInputError is raised.

Parameters:

template (Optional[str])
expected_inputs (Optional[list[str]])

DEFAULT_TEMPLATE: str = ''¶

EXPECTED_INPUTS: list[str] = []¶

format(*args, **kwargs)[source]¶

This method is used to replace parameters with the provided values. Parameters must be provided: - as kwargs - as args if using the same order as in the expected inputs

Example:

prompt_template = PromptTemplate(
    template='''Explain the following concept to {target_audience}:
    Concept: {concept}
    Answer:
    ''',
    expected_inputs=['target_audience', 'concept']
)
prompt = prompt_template.format('12 yo children', concept='graph database')
print(prompt)

# Result:
# '''Explain the following concept to 12 yo children:
# Concept: graph database
# Answer:
# '''

Parameters:

args (Any)
kwargs (Any)

Return type:

str

RagTemplate¶

class neo4j_graphrag.generation.prompts.RagTemplate(template=None, expected_inputs=None)[source]¶

Parameters:

template (Optional[str])
expected_inputs (Optional[list[str]])

DEFAULT_TEMPLATE: str = 'Answer the user question using the following context\n\nContext:\n{context}\n\nExamples:\n{examples}\n\nQuestion:\n{query_text}\n\nAnswer:\n'¶

EXPECTED_INPUTS: list[str] = ['context', 'query_text', 'examples']¶

ERExtractionTemplate¶

class neo4j_graphrag.generation.prompts.ERExtractionTemplate(template=None, expected_inputs=None)[source]¶

Parameters:

template (Optional[str])
expected_inputs (Optional[list[str]])

DEFAULT_TEMPLATE: str = '\nYou are a top-tier algorithm designed for extracting\ninformation in structured formats to build a knowledge graph.\n\nExtract the entities (nodes) and specify their type from the following text.\nAlso extract the relationships between these nodes.\n\nReturn result as JSON using the following format:\n{{"nodes": [ {{"id": "0", "label": "Person", "properties": {{"name": "John"}} }}],\n"relationships": [{{"type": "KNOWS", "start_node_id": "0", "end_node_id": "1", "properties": {{"since": "2024-08-01"}} }}] }}\n\nUse only the following nodes and relationships (if provided):\n{schema}\n\nAssign a unique ID (string) to each node, and reuse it to define relationships.\nDo respect the source and target node types for relationship and\nthe relationship direction.\n\nMake sure you adhere to the following rules to produce valid JSON objects:\n- Do not return any additional information other than the JSON in it.\n- Omit any backticks around the JSON - simply output the JSON on its own.\n- The JSON object must not wrapped into a list - it is its own JSON object.\n- Property names must be enclosed in double quotes\n\nExamples:\n{examples}\n\nInput text:\n\n{text}\n'¶

EXPECTED_INPUTS: list[str] = ['text']¶

Text2CypherTemplate¶

class neo4j_graphrag.generation.prompts.Text2CypherTemplate(template=None, expected_inputs=None)[source]¶

Parameters:

template (Optional[str])
expected_inputs (Optional[list[str]])

DEFAULT_TEMPLATE: str = '\nTask: Generate a Cypher statement for querying a Neo4j graph database from a user input.\n\nSchema:\n{schema}\n\nExamples (optional):\n{examples}\n\nInput:\n{query_text}\n\nDo not use any properties or relationships not included in the schema.\nDo not include triple backticks ``` or any additional text except the generated Cypher statement in your response.\n\nCypher query:\n'¶

EXPECTED_INPUTS: list[str] = ['query_text']¶

RAG¶

GraphRAG¶

class neo4j_graphrag.generation.graphrag.GraphRAG(retriever, llm, prompt_template=<neo4j_graphrag.generation.prompts.RagTemplate object>)[source]¶

Performs a GraphRAG search using a specific retriever and LLM.

Example:

import neo4j
from neo4j_graphrag.retrievers import VectorRetriever
from neo4j_graphrag.llm.openai_llm import OpenAILLM
from neo4j_graphrag.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")

Parameters:

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.

search(query_text='', examples='', retriever_config=None, return_context=None)[source]¶

Warning

The default value of ‘return_context’ will change from ‘False’ to ‘True’ in a future version.

This method performs a full RAG search:

Retrieval: context retrieval
Augmentation: prompt formatting
Generation: answer generation with LLM

Parameters:

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)

Returns:

The LLM-generated answer

Return type:

RagResultModel

Database Interaction¶

neo4j_graphrag.indexes.create_vector_index(driver, name, label, embedding_property, dimensions, similarity_fn, fail_if_exists=False, neo4j_database=None)[source]¶

This method constructs a Cypher query and executes it to create a new vector index in Neo4j.

See Cypher manual on creating vector indexes.

Ensure that the index name provided is unique within the database context.

Example:

from neo4j import GraphDatabase
from neo4j_graphrag.indexes import create_vector_index

URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")

INDEX_NAME = "vector-index-name"

# Connect to Neo4j database
driver = GraphDatabase.driver(URI, auth=AUTH)

# Creating the index
create_vector_index(
    driver,
    INDEX_NAME,
    label="Document",
    embedding_property="vectorProperty",
    dimensions=1536,
    similarity_fn="euclidean",
    fail_if_exists=False,
)

Parameters:

driver (neo4j.Driver) – Neo4j Python driver instance.
name (str) – The unique name of the index.
label (str) – The node label to be indexed.
embedding_property (str) – The property key of a node which contains embedding values.
dimensions (int) – Vector embedding dimension
similarity_fn (str) – case-insensitive values for the vector similarity function: euclidean or cosine.
fail_if_exists (bool) – If True raise an error if the index already exists. Defaults to False.
neo4j_database (Optional[str]) –
The name of the Neo4j database. If not provided, this defaults to the server’s default database (“neo4j” by default) (see reference to documentation).

Raises:

ValueError – If validation of the input arguments fail.
neo4j.exceptions.ClientError – If creation of vector index fails.

Return type:

None

neo4j_graphrag.indexes.create_fulltext_index(driver, name, label, node_properties, fail_if_exists=False, neo4j_database=None)[source]¶

This method constructs a Cypher query and executes it to create a new fulltext index in Neo4j.

See Cypher manual on creating fulltext indexes.

Ensure that the index name provided is unique within the database context.

Example:

from neo4j import GraphDatabase
from neo4j_graphrag.indexes import create_fulltext_index

URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")

INDEX_NAME = "fulltext-index-name"

# Connect to Neo4j database
driver = GraphDatabase.driver(URI, auth=AUTH)

# Creating the index
create_fulltext_index(
    driver,
    INDEX_NAME,
    label="Document",
    node_properties=["vectorProperty"],
    fail_if_exists=False,
)

Parameters:

driver (neo4j.Driver) – Neo4j Python driver instance.
name (str) – The unique name of the index.
label (str) – The node label to be indexed.
node_properties (list[str]) – The node properties to create the fulltext index on.
fail_if_exists (bool) – If True raise an error if the index already exists. Defaults to False.
neo4j_database (Optional[str]) –
The name of the Neo4j database. If not provided, this defaults to the server’s default database (“neo4j” by default) (see reference to documentation).

Raises:

ValueError – If validation of the input arguments fail.
neo4j.exceptions.ClientError – If creation of fulltext index fails.

Return type:

None

neo4j_graphrag.indexes.drop_index_if_exists(driver, name, neo4j_database=None)[source]¶

This method constructs a Cypher query and executes it to drop an index in Neo4j, if the index exists. See Cypher manual on dropping vector indexes.

Example:

from neo4j import GraphDatabase
from neo4j_graphrag.indexes import drop_index_if_exists

URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")

INDEX_NAME = "fulltext-index-name"

# Connect to Neo4j database
driver = GraphDatabase.driver(URI, auth=AUTH)

# Dropping the index if it exists
drop_index_if_exists(
    driver,
    INDEX_NAME,
)

Parameters:

driver (neo4j.Driver) – Neo4j Python driver instance.
name (str) – The name of the index to delete.
neo4j_database (Optional[str]) –
The name of the Neo4j database. If not provided, this defaults to the server’s default database (“neo4j” by default) (see reference to documentation).

Raises:

neo4j.exceptions.ClientError – If dropping of index fails.

Return type:

None

neo4j_graphrag.indexes.upsert_vector(driver, node_id, embedding_property, vector, neo4j_database=None)[source]¶

This method constructs a Cypher query and executes it to upsert (insert or update) a vector property on a specific node.

Example:

from neo4j import GraphDatabase
from neo4j_graphrag.indexes import upsert_vector

URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")

# Connect to Neo4j database
driver = GraphDatabase.driver(URI, auth=AUTH)

# Upsert the vector data
upsert_vector(
    driver,
    node_id="nodeId",
    embedding_property="vectorProperty",
    vector=...,
)

Parameters:

driver (neo4j.Driver) – Neo4j Python driver instance.
node_id (int) – The element id of the node.
embedding_property (str) – The name of the property to store the vector in.
vector (list[float]) – The vector to store.
neo4j_database (Optional[str]) –
The name of the Neo4j database. If not provided, this defaults to the server’s default database (“neo4j” by default) (see reference to documentation).

Raises:

Neo4jInsertionError – If upserting of the vector fails.

Return type:

None

neo4j_graphrag.indexes.upsert_vector_on_relationship(driver, rel_id, embedding_property, vector, neo4j_database=None)[source]¶

This method constructs a Cypher query and executes it to upsert (insert or update) a vector property on a specific relationship.

Example:

from neo4j import GraphDatabase
from neo4j_graphrag.indexes import upsert_vector_on_relationship

URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")

# Connect to Neo4j database
driver = GraphDatabase.driver(URI, auth=AUTH)

# Upsert the vector data
upsert_vector_on_relationship(
    driver,
    node_id="nodeId",
    embedding_property="vectorProperty",
    vector=...,
)

Parameters:

driver (neo4j.Driver) – Neo4j Python driver instance.
rel_id (int) – The element id of the relationship.
embedding_property (str) – The name of the property to store the vector in.
vector (list[float]) – The vector to store.
neo4j_database (Optional[str]) –
The name of the Neo4j database. If not provided, this defaults to the server’s default database (“neo4j” by default) (see reference to documentation).

Raises:

Neo4jInsertionError – If upserting of the vector fails.

Return type:

None

async neo4j_graphrag.indexes.async_upsert_vector(driver, node_id, embedding_property, vector, neo4j_database=None)[source]¶

This method constructs a Cypher query and asynchronously executes it to upsert (insert or update) a vector property on a specific node.

Example:

from neo4j import AsyncGraphDatabase
from neo4j_graphrag.indexes import upsert_vector

URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")

# Connect to Neo4j database
driver = AsyncGraphDatabase.driver(URI, auth=AUTH)

# Upsert the vector data
async_upsert_vector(
    driver,
    node_id="nodeId",
    embedding_property="vectorProperty",
    vector=...,
)

Parameters:

driver (neo4j.AsyncDriver) – Neo4j Python asynchronous driver instance.
node_id (int) – The element id of the node.
embedding_property (str) – The name of the property to store the vector in.
vector (list[float]) – The vector to store.
neo4j_database (Optional[str]) –
The name of the Neo4j database. If not provided, this defaults to the server’s default database (“neo4j” by default) (see reference to documentation).

Raises:

Neo4jInsertionError – If upserting of the vector fails.

Return type:

None

async neo4j_graphrag.indexes.async_upsert_vector_on_relationship(driver, rel_id, embedding_property, vector, neo4j_database=None)[source]¶

This method constructs a Cypher query and asynchronously executes it to upsert (insert or update) a vector property on a specific relationship.

Example:

from neo4j import AsyncGraphDatabase
from neo4j_graphrag.indexes import upsert_vector_on_relationship

URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")

# Connect to Neo4j database
driver = AsyncGraphDatabase.driver(URI, auth=AUTH)

# Upsert the vector data
async_upsert_vector_on_relationship(
    driver,
    node_id="nodeId",
    embedding_property="vectorProperty",
    vector=...,
)

Parameters:

driver (neo4j.AsyncDriver) – Neo4j Python asynchronous driver instance.
rel_id (int) – The element id of the relationship.
embedding_property (str) – The name of the property to store the vector in.
vector (list[float]) – The vector to store.
neo4j_database (Optional[str]) –
The name of the Neo4j database. If not provided, this defaults to the server’s default database (“neo4j” by default) (see reference to documentation).

Raises:

Neo4jInsertionError – If upserting of the vector fails.

Return type:

None

neo4j_graphrag.schema.get_structured_schema(driver)[source]¶

Returns the structured schema of the graph.

Returns a dict with following format:

{
    'node_props': {
        'Person': [{'property': 'id', 'type': 'INTEGER'}, {'property': 'name', 'type': 'STRING'}]
    },
    'rel_props': {
        'KNOWS': [{'property': 'fromDate', 'type': 'DATE'}]
    },
    'relationships': [
        {'start': 'Person', 'type': 'KNOWS', 'end': 'Person'}
    ],
    'metadata': {
        'constraint': [
            {'id': 7, 'name': 'person_id', 'type': 'UNIQUENESS', 'entityType': 'NODE', 'labelsOrTypes': ['Persno'], 'properties': ['id'], 'ownedIndex': 'person_id', 'propertyType': None},
        ],
        'index': [
            {'label': 'Person', 'properties': ['name'], 'size': 2, 'type': 'RANGE', 'valuesSelectivity': 1.0, 'distinctValues': 2.0},
        ]
    }
}

Note

The internal structure of the returned dict depends on the apoc.meta.data and apoc.schema.nodes procedures.

Warning

Some labels are excluded from the output schema:

The __Entity__ and __KGBuilder__ node labels which are created by the KG Builder pipeline within this package
Some labels related to Bloom internals.

Parameters:: driver (neo4j.Driver) – Neo4j Python driver instance.
Returns:: the graph schema information in a structured format.
Return type:: dict[str, Any]

neo4j_graphrag.schema.get_schema(driver)[source]¶

Returns the schema of the graph as a string with following format:

Node properties:
Person {id: INTEGER, name: STRING}
Relationship properties:
KNOWS {fromDate: DATE}
The relationships:
(:Person)-[:KNOWS]->(:Person)

Parameters:: driver (neo4j.Driver) – Neo4j Python driver instance.
Returns:: the graph schema information in a serialized format.
Return type:: str

Errors¶

neo4j_graphrag.exceptions.Neo4jGraphRagError

Neo4jGraphRagError¶

class neo4j_graphrag.exceptions.Neo4jGraphRagError[source]¶

Bases: Exception

Global exception used for the neo4j-graphrag package.

RetrieverInitializationError¶

class neo4j_graphrag.exceptions.RetrieverInitializationError(errors)[source]¶

Exception raised when initialization of a retriever fails.

Parameters:: errors (list[ErrorDetails])

SearchValidationError¶

class neo4j_graphrag.exceptions.SearchValidationError(errors)[source]¶

Exception raised for validation errors during search.

Parameters:: errors (list[ErrorDetails])

FilterValidationError¶

class neo4j_graphrag.exceptions.FilterValidationError[source]¶

Exception raised when input validation for metadata filtering fails.

EmbeddingsGenerationError¶

class neo4j_graphrag.exceptions.EmbeddingsGenerationError[source]¶

Exception raised when generation of embeddings fails

EmbeddingRequiredError¶

class neo4j_graphrag.exceptions.EmbeddingRequiredError[source]¶

Exception raised when an embedding method is required but not provided.

InvalidRetrieverResultError¶

class neo4j_graphrag.exceptions.InvalidRetrieverResultError[source]¶

Exception raised when the Retriever fails to return a result.

Neo4jIndexError¶

class neo4j_graphrag.exceptions.Neo4jIndexError[source]¶

Exception raised when handling Neo4j index fails.

Neo4jInsertionError¶

class neo4j_graphrag.exceptions.Neo4jInsertionError[source]¶

Exception raised when inserting data into the Neo4j database fails.

Neo4jVersionError¶

class neo4j_graphrag.exceptions.Neo4jVersionError[source]¶

Exception raised when Neo4j version does not meet minimum requirements.

Text2CypherRetrievalError¶

class neo4j_graphrag.exceptions.Text2CypherRetrievalError[source]¶

Exception raised when text-to-cypher retrieval fails.

SchemaFetchError¶

class neo4j_graphrag.exceptions.SchemaFetchError[source]¶

Exception raised when a Neo4jSchema cannot be fetched.

RagInitializationError¶

class neo4j_graphrag.exceptions.RagInitializationError(errors)[source]¶

Parameters:: errors (list[ErrorDetails])

PromptMissingInputError¶

class neo4j_graphrag.exceptions.PromptMissingInputError[source]¶

Exception raised when a prompt required input is missing.

LLMGenerationError¶

class neo4j_graphrag.exceptions.LLMGenerationError[source]¶

Exception raised when answer generation from LLM fails.

PipelineDefinitionError¶

class neo4j_graphrag.experimental.pipeline.exceptions.PipelineDefinitionError[source]¶

Raised when the pipeline graph is invalid

PipelineMissingDependencyError¶

class neo4j_graphrag.experimental.pipeline.exceptions.PipelineMissingDependencyError[source]¶

Raised when a task is scheduled but its dependencies are not yet done

PipelineStatusUpdateError¶

class neo4j_graphrag.experimental.pipeline.exceptions.PipelineStatusUpdateError[source]¶

Raises when trying an invalid change of state (e.g. DONE => DOING)

InvalidJSONError¶

class neo4j_graphrag.experimental.pipeline.exceptions.InvalidJSONError[source]¶