mirror of
https://github.com/hwchase17/langchain
synced 2024-11-04 06:00:26 +00:00
217 lines
7.9 KiB
Python
217 lines
7.9 KiB
Python
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import logging
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from string import Template
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from typing import Any, Dict, Optional
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logger = logging.getLogger(__name__)
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rel_query = Template(
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"""
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MATCH ()-[e:`$edge_type`]->()
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WITH e limit 1
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MATCH (m)-[:`$edge_type`]->(n) WHERE id(m) == src(e) AND id(n) == dst(e)
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RETURN "(:" + tags(m)[0] + ")-[:$edge_type]->(:" + tags(n)[0] + ")" AS rels
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"""
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)
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RETRY_TIMES = 3
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class NebulaGraph:
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"""NebulaGraph wrapper for graph operations.
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NebulaGraph inherits methods from Neo4jGraph to bring ease to the user space.
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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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space: str,
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username: str = "root",
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password: str = "nebula",
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address: str = "127.0.0.1",
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port: int = 9669,
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session_pool_size: int = 30,
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) -> None:
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"""Create a new NebulaGraph wrapper instance."""
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try:
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import nebula3 # noqa: F401
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import pandas # noqa: F401
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except ImportError:
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raise ValueError(
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"Please install NebulaGraph Python client and pandas first: "
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"`pip install nebula3-python pandas`"
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)
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self.username = username
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self.password = password
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self.address = address
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self.port = port
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self.space = space
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self.session_pool_size = session_pool_size
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self.session_pool = self._get_session_pool()
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self.schema = ""
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# Set schema
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try:
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self.refresh_schema()
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except Exception as e:
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raise ValueError(f"Could not refresh schema. Error: {e}")
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def _get_session_pool(self) -> Any:
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assert all(
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[self.username, self.password, self.address, self.port, self.space]
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), (
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"Please provide all of the following parameters: "
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"username, password, address, port, space"
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)
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from nebula3.Config import SessionPoolConfig
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from nebula3.Exception import AuthFailedException, InValidHostname
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from nebula3.gclient.net.SessionPool import SessionPool
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config = SessionPoolConfig()
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config.max_size = self.session_pool_size
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try:
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session_pool = SessionPool(
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self.username,
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self.password,
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self.space,
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[(self.address, self.port)],
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)
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except InValidHostname:
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raise ValueError(
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"Could not connect to NebulaGraph database. "
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"Please ensure that the address and port are correct"
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)
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try:
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session_pool.init(config)
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except AuthFailedException:
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raise ValueError(
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"Could not connect to NebulaGraph database. "
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"Please ensure that the username and password are correct"
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)
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except RuntimeError as e:
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raise ValueError(f"Error initializing session pool. Error: {e}")
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return session_pool
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def __del__(self) -> None:
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try:
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self.session_pool.close()
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except Exception as e:
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logger.warning(f"Could not close session pool. Error: {e}")
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@property
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def get_schema(self) -> str:
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"""Returns the schema of the NebulaGraph database"""
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return self.schema
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def execute(self, query: str, params: Optional[dict] = None, retry: int = 0) -> Any:
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"""Query NebulaGraph database."""
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from nebula3.Exception import IOErrorException, NoValidSessionException
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from nebula3.fbthrift.transport.TTransport import TTransportException
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params = params or {}
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try:
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result = self.session_pool.execute_parameter(query, params)
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if not result.is_succeeded():
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logger.warning(
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f"Error executing query to NebulaGraph. "
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f"Error: {result.error_msg()}\n"
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f"Query: {query} \n"
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)
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return result
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except NoValidSessionException:
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logger.warning(
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f"No valid session found in session pool. "
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f"Please consider increasing the session pool size. "
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f"Current size: {self.session_pool_size}"
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)
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raise ValueError(
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f"No valid session found in session pool. "
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f"Please consider increasing the session pool size. "
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f"Current size: {self.session_pool_size}"
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)
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except RuntimeError as e:
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if retry < RETRY_TIMES:
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retry += 1
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logger.warning(
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f"Error executing query to NebulaGraph. "
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f"Retrying ({retry}/{RETRY_TIMES})...\n"
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f"query: {query} \n"
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f"Error: {e}"
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)
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return self.execute(query, params, retry)
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else:
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raise ValueError(f"Error executing query to NebulaGraph. Error: {e}")
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except (TTransportException, IOErrorException):
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# connection issue, try to recreate session pool
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if retry < RETRY_TIMES:
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retry += 1
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logger.warning(
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f"Connection issue with NebulaGraph. "
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f"Retrying ({retry}/{RETRY_TIMES})...\n to recreate session pool"
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)
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self.session_pool = self._get_session_pool()
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return self.execute(query, params, retry)
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def refresh_schema(self) -> None:
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"""
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Refreshes the NebulaGraph schema information.
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"""
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tags_schema, edge_types_schema, relationships = [], [], []
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for tag in self.execute("SHOW TAGS").column_values("Name"):
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tag_name = tag.cast()
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tag_schema = {"tag": tag_name, "properties": []}
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r = self.execute(f"DESCRIBE TAG `{tag_name}`")
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props, types = r.column_values("Field"), r.column_values("Type")
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for i in range(r.row_size()):
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tag_schema["properties"].append((props[i].cast(), types[i].cast()))
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tags_schema.append(tag_schema)
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for edge_type in self.execute("SHOW EDGES").column_values("Name"):
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edge_type_name = edge_type.cast()
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edge_schema = {"edge": edge_type_name, "properties": []}
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r = self.execute(f"DESCRIBE EDGE `{edge_type_name}`")
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props, types = r.column_values("Field"), r.column_values("Type")
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for i in range(r.row_size()):
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edge_schema["properties"].append((props[i].cast(), types[i].cast()))
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edge_types_schema.append(edge_schema)
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# build relationships types
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r = self.execute(
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rel_query.substitute(edge_type=edge_type_name)
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).column_values("rels")
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if len(r) > 0:
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relationships.append(r[0].cast())
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self.schema = (
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f"Node properties: {tags_schema}\n"
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f"Edge properties: {edge_types_schema}\n"
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f"Relationships: {relationships}\n"
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)
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def query(self, query: str, retry: int = 0) -> Dict[str, Any]:
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result = self.execute(query, retry=retry)
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columns = result.keys()
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d: Dict[str, list] = {}
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for col_num in range(result.col_size()):
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col_name = columns[col_num]
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col_list = result.column_values(col_name)
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d[col_name] = [x.cast() for x in col_list]
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return d
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