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406 lines
16 KiB
Python
406 lines
16 KiB
Python
from hashlib import md5
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from typing import Any, Dict, List, Optional
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from langchain_core.utils import get_from_dict_or_env
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from langchain_community.graphs.graph_document import GraphDocument
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from langchain_community.graphs.graph_store import GraphStore
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BASE_ENTITY_LABEL = "__Entity__"
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EXCLUDED_LABELS = ["_Bloom_Perspective_", "_Bloom_Scene_"]
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EXCLUDED_RELS = ["_Bloom_HAS_SCENE_"]
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node_properties_query = """
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CALL apoc.meta.data()
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YIELD label, other, elementType, type, property
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WHERE NOT type = "RELATIONSHIP" AND elementType = "node"
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AND NOT label IN $EXCLUDED_LABELS
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WITH label AS nodeLabels, collect({property:property, type:type}) AS properties
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RETURN {labels: nodeLabels, properties: properties} AS output
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"""
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rel_properties_query = """
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CALL apoc.meta.data()
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YIELD label, other, elementType, type, property
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WHERE NOT type = "RELATIONSHIP" AND elementType = "relationship"
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AND NOT label in $EXCLUDED_LABELS
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WITH label AS nodeLabels, collect({property:property, type:type}) AS properties
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RETURN {type: nodeLabels, properties: properties} AS output
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"""
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rel_query = """
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CALL apoc.meta.data()
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YIELD label, other, elementType, type, property
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WHERE type = "RELATIONSHIP" AND elementType = "node"
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UNWIND other AS other_node
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WITH * WHERE NOT label IN $EXCLUDED_LABELS
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AND NOT other_node IN $EXCLUDED_LABELS
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RETURN {start: label, type: property, end: toString(other_node)} AS output
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"""
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include_docs_query = (
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"MERGE (d:Document {id:$document.metadata.id}) "
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"SET d.text = $document.page_content "
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"SET d += $document.metadata "
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"WITH d "
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)
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def value_sanitize(d: Any) -> Any:
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"""Sanitize the input dictionary or list.
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Sanitizes the input by removing embedding-like values,
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lists with more than 128 elements, that are mostly irrelevant for
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generating answers in a LLM context. These properties, if left in
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results, can occupy significant context space and detract from
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the LLM's performance by introducing unnecessary noise and cost.
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"""
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LIST_LIMIT = 128
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if isinstance(d, dict):
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new_dict = {}
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for key, value in d.items():
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if isinstance(value, dict):
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sanitized_value = value_sanitize(value)
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if (
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sanitized_value is not None
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): # Check if the sanitized value is not None
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new_dict[key] = sanitized_value
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elif isinstance(value, list):
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if len(value) < LIST_LIMIT:
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sanitized_value = value_sanitize(value)
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if (
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sanitized_value is not None
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): # Check if the sanitized value is not None
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new_dict[key] = sanitized_value
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# Do not include the key if the list is oversized
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else:
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new_dict[key] = value
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return new_dict
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elif isinstance(d, list):
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if len(d) < LIST_LIMIT:
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return [
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value_sanitize(item) for item in d if value_sanitize(item) is not None
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]
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else:
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return None
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else:
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return d
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def _get_node_import_query(baseEntityLabel: bool, include_source: bool) -> str:
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if baseEntityLabel:
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return (
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f"{include_docs_query if include_source else ''}"
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"UNWIND $data AS row "
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f"MERGE (source:`{BASE_ENTITY_LABEL}` {{id: row.id}}) "
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"SET source += row.properties "
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f"{'MERGE (d)-[:MENTIONS]->(source) ' if include_source else ''}"
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"WITH source, row "
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"CALL apoc.create.addLabels( source, [row.type] ) YIELD node "
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"RETURN distinct 'done' AS result"
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)
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else:
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return (
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f"{include_docs_query if include_source else ''}"
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"UNWIND $data AS row "
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"CALL apoc.merge.node([row.type], {id: row.id}, "
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"row.properties, {}) YIELD node "
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f"{'MERGE (d)-[:MENTIONS]->(node) ' if include_source else ''}"
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"RETURN distinct 'done' AS result"
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)
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def _get_rel_import_query(baseEntityLabel: bool) -> str:
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if baseEntityLabel:
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return (
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"UNWIND $data AS row "
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f"MERGE (source:`{BASE_ENTITY_LABEL}` {{id: row.source}}) "
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f"MERGE (target:`{BASE_ENTITY_LABEL}` {{id: row.target}}) "
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"WITH source, target, row "
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"CALL apoc.merge.relationship(source, row.type, "
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"{}, row.properties, target) YIELD rel "
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"RETURN distinct 'done'"
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)
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else:
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return (
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"UNWIND $data AS row "
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"CALL apoc.merge.node([row.source_label], {id: row.source},"
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"{}, {}) YIELD node as source "
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"CALL apoc.merge.node([row.target_label], {id: row.target},"
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"{}, {}) YIELD node as target "
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"CALL apoc.merge.relationship(source, row.type, "
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"{}, row.properties, target) YIELD rel "
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"RETURN distinct 'done'"
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)
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class Neo4jGraph(GraphStore):
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"""Neo4j database wrapper for various graph operations.
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Parameters:
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url (Optional[str]): The URL of the Neo4j database server.
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username (Optional[str]): The username for database authentication.
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password (Optional[str]): The password for database authentication.
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database (str): The name of the database to connect to. Default is 'neo4j'.
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timeout (Optional[float]): The timeout for transactions in seconds.
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Useful for terminating long-running queries.
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By default, there is no timeout set.
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sanitize (bool): A flag to indicate whether to remove lists with
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more than 128 elements from results. Useful for removing
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embedding-like properties from database responses. Default is False.
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refresh_schema (bool): A flag whether to refresh schema information
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at initialization. Default is True.
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*Security note*: Make sure that the database connection uses credentials
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that are narrowly-scoped to only include necessary permissions.
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Failure to do so may result in data corruption or loss, since the calling
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code may attempt commands that would result in deletion, mutation
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of data if appropriately prompted or reading sensitive data if such
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data is present in the database.
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The best way to guard against such negative outcomes is to (as appropriate)
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limit the permissions granted to the credentials used with this tool.
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See https://python.langchain.com/docs/security for more information.
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"""
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def __init__(
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self,
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url: Optional[str] = None,
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username: Optional[str] = None,
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password: Optional[str] = None,
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database: Optional[str] = None,
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timeout: Optional[float] = None,
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sanitize: bool = False,
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refresh_schema: bool = True,
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) -> None:
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"""Create a new Neo4j graph wrapper instance."""
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try:
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import neo4j
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except ImportError:
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raise ValueError(
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"Could not import neo4j python package. "
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"Please install it with `pip install neo4j`."
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)
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url = get_from_dict_or_env({"url": url}, "url", "NEO4J_URI")
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username = get_from_dict_or_env(
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{"username": username}, "username", "NEO4J_USERNAME"
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)
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password = get_from_dict_or_env(
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{"password": password}, "password", "NEO4J_PASSWORD"
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)
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database = get_from_dict_or_env(
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{"database": database}, "database", "NEO4J_DATABASE", "neo4j"
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)
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self._driver = neo4j.GraphDatabase.driver(url, auth=(username, password))
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self._database = database
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self.timeout = timeout
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self.sanitize = sanitize
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self.schema: str = ""
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self.structured_schema: Dict[str, Any] = {}
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# Verify connection
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try:
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self._driver.verify_connectivity()
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except neo4j.exceptions.ServiceUnavailable:
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raise ValueError(
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"Could not connect to Neo4j database. "
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"Please ensure that the url is correct"
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)
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except neo4j.exceptions.AuthError:
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raise ValueError(
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"Could not connect to Neo4j database. "
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"Please ensure that the username and password are correct"
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)
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# Set schema
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if refresh_schema:
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try:
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self.refresh_schema()
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except neo4j.exceptions.ClientError as e:
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if e.code == "Neo.ClientError.Procedure.ProcedureNotFound":
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raise ValueError(
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"Could not use APOC procedures. "
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"Please ensure the APOC plugin is installed in Neo4j and that "
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"'apoc.meta.data()' is allowed in Neo4j configuration "
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)
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raise e
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@property
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def get_schema(self) -> str:
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"""Returns the schema of the Graph"""
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return self.schema
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@property
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def get_structured_schema(self) -> Dict[str, Any]:
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"""Returns the structured schema of the Graph"""
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return self.structured_schema
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def query(self, query: str, params: dict = {}) -> List[Dict[str, Any]]:
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"""Query Neo4j database."""
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from neo4j import Query
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from neo4j.exceptions import CypherSyntaxError
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with self._driver.session(database=self._database) as session:
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try:
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data = session.run(Query(text=query, timeout=self.timeout), params)
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json_data = [r.data() for r in data]
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if self.sanitize:
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json_data = [value_sanitize(el) for el in json_data]
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return json_data
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except CypherSyntaxError as e:
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raise ValueError(f"Generated Cypher Statement is not valid\n{e}")
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def refresh_schema(self) -> None:
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"""
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Refreshes the Neo4j graph schema information.
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"""
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from neo4j.exceptions import ClientError
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node_properties = [
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el["output"]
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for el in self.query(
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node_properties_query,
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params={"EXCLUDED_LABELS": EXCLUDED_LABELS + [BASE_ENTITY_LABEL]},
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)
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]
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rel_properties = [
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el["output"]
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for el in self.query(
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rel_properties_query, params={"EXCLUDED_LABELS": EXCLUDED_RELS}
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)
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]
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relationships = [
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el["output"]
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for el in self.query(
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rel_query,
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params={"EXCLUDED_LABELS": EXCLUDED_LABELS + [BASE_ENTITY_LABEL]},
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)
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]
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# Get constraints & indexes
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try:
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constraint = self.query("SHOW CONSTRAINTS")
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index = self.query("SHOW INDEXES YIELD *")
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except (
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ClientError
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): # Read-only user might not have access to schema information
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constraint = []
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index = []
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self.structured_schema = {
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"node_props": {el["labels"]: el["properties"] for el in node_properties},
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"rel_props": {el["type"]: el["properties"] for el in rel_properties},
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"relationships": relationships,
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"metadata": {"constraint": constraint, "index": index},
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}
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# Format node properties
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formatted_node_props = []
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for el in node_properties:
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props_str = ", ".join(
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[f"{prop['property']}: {prop['type']}" for prop in el["properties"]]
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)
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formatted_node_props.append(f"{el['labels']} {{{props_str}}}")
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# Format relationship properties
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formatted_rel_props = []
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for el in rel_properties:
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props_str = ", ".join(
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[f"{prop['property']}: {prop['type']}" for prop in el["properties"]]
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)
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formatted_rel_props.append(f"{el['type']} {{{props_str}}}")
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# Format relationships
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formatted_rels = [
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f"(:{el['start']})-[:{el['type']}]->(:{el['end']})" for el in relationships
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]
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self.schema = "\n".join(
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[
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"Node properties are the following:",
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",".join(formatted_node_props),
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"Relationship properties are the following:",
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",".join(formatted_rel_props),
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"The relationships are the following:",
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",".join(formatted_rels),
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]
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)
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def add_graph_documents(
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self,
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graph_documents: List[GraphDocument],
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include_source: bool = False,
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baseEntityLabel: bool = False,
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) -> None:
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"""
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This method constructs nodes and relationships in the graph based on the
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provided GraphDocument objects.
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Parameters:
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- graph_documents (List[GraphDocument]): A list of GraphDocument objects
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that contain the nodes and relationships to be added to the graph. Each
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GraphDocument should encapsulate the structure of part of the graph,
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including nodes, relationships, and the source document information.
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- include_source (bool, optional): If True, stores the source document
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and links it to nodes in the graph using the MENTIONS relationship.
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This is useful for tracing back the origin of data. Merges source
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documents based on the `id` property from the source document metadata
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if available; otherwise it calculates the MD5 hash of `page_content`
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for merging process. Defaults to False.
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- baseEntityLabel (bool, optional): If True, each newly created node
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gets a secondary __Entity__ label, which is indexed and improves import
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speed and performance. Defaults to False.
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"""
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if baseEntityLabel: # Check if constraint already exists
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constraint_exists = any(
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[
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el["labelsOrTypes"] == [BASE_ENTITY_LABEL]
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and el["properties"] == ["id"]
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for el in self.structured_schema.get("metadata", {}).get(
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"constraint"
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)
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]
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)
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if not constraint_exists:
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# Create constraint
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self.query(
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f"CREATE CONSTRAINT IF NOT EXISTS FOR (b:{BASE_ENTITY_LABEL}) "
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"REQUIRE b.id IS UNIQUE;"
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)
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self.refresh_schema() # Refresh constraint information
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node_import_query = _get_node_import_query(baseEntityLabel, include_source)
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rel_import_query = _get_rel_import_query(baseEntityLabel)
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for document in graph_documents:
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if not document.source.metadata.get("id"):
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document.source.metadata["id"] = md5(
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document.source.page_content.encode("utf-8")
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).hexdigest()
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# Import nodes
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self.query(
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node_import_query,
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{
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"data": [el.__dict__ for el in document.nodes],
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"document": document.source.__dict__,
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},
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)
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# Import relationships
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self.query(
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rel_import_query,
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{
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"data": [
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{
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"source": el.source.id,
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"source_label": el.source.type,
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"target": el.target.id,
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"target_label": el.target.type,
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"type": el.type.replace(" ", "_").upper(),
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"properties": el.properties,
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}
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for el in document.relationships
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]
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},
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)
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