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481 lines
17 KiB
Python
481 lines
17 KiB
Python
from __future__ import annotations
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import json
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import logging
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from hashlib import sha1
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from threading import Thread
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from typing import Any, Dict, Iterable, List, Optional, Tuple
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from langchain_core.documents import Document
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseSettings
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from langchain_core.vectorstores import VectorStore
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logger = logging.getLogger()
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DEBUG = False
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class ApacheDorisSettings(BaseSettings):
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"""Apache Doris client configuration.
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Attributes:
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apache_doris_host (str) : An URL to connect to frontend.
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Defaults to 'localhost'.
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apache_doris_port (int) : URL port to connect with HTTP. Defaults to 9030.
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username (str) : Username to login. Defaults to 'root'.
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password (str) : Password to login. Defaults to None.
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database (str) : Database name to find the table. Defaults to 'default'.
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table (str) : Table name to operate on.
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Defaults to 'langchain'.
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column_map (Dict) : Column type map to project column name onto langchain
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semantics. Must have keys: `text`, `id`, `vector`,
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must be same size to number of columns. For example:
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.. code-block:: python
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{
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'id': 'text_id',
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'embedding': 'text_embedding',
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'document': 'text_plain',
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'metadata': 'metadata_dictionary_in_json',
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}
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Defaults to identity map.
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"""
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host: str = "localhost"
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port: int = 9030
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username: str = "root"
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password: str = ""
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column_map: Dict[str, str] = {
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"id": "id",
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"document": "document",
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"embedding": "embedding",
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"metadata": "metadata",
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}
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database: str = "default"
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table: str = "langchain"
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def __getitem__(self, item: str) -> Any:
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return getattr(self, item)
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class Config:
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env_file = ".env"
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env_prefix = "apache_doris_"
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env_file_encoding = "utf-8"
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class ApacheDoris(VectorStore):
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"""`Apache Doris` vector store.
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You need a `pymysql` python package, and a valid account
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to connect to Apache Doris.
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For more information, please visit
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[Apache Doris official site](https://doris.apache.org/)
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[Apache Doris github](https://github.com/apache/doris)
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"""
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def __init__(
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self,
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embedding: Embeddings,
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*,
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config: Optional[ApacheDorisSettings] = None,
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**kwargs: Any,
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) -> None:
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"""Constructor for Apache Doris.
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Args:
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embedding (Embeddings): Text embedding model.
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config (ApacheDorisSettings): Apache Doris client configuration information.
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"""
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try:
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import pymysql # type: ignore[import]
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except ImportError:
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raise ImportError(
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"Could not import pymysql python package. "
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"Please install it with `pip install pymysql`."
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)
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try:
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from tqdm import tqdm
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self.pgbar = tqdm
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except ImportError:
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# Just in case if tqdm is not installed
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self.pgbar = lambda x, **kwargs: x
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super().__init__()
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if config is not None:
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self.config = config
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else:
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self.config = ApacheDorisSettings()
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assert self.config
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assert self.config.host and self.config.port
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assert self.config.column_map and self.config.database and self.config.table
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for k in ["id", "embedding", "document", "metadata"]:
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assert k in self.config.column_map
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# initialize the schema
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dim = len(embedding.embed_query("test"))
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self.schema = f"""\
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CREATE TABLE IF NOT EXISTS {self.config.database}.{self.config.table}(
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{self.config.column_map['id']} varchar(50),
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{self.config.column_map['document']} string,
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{self.config.column_map['embedding']} array<float>,
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{self.config.column_map['metadata']} string
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) ENGINE = OLAP UNIQUE KEY(id) DISTRIBUTED BY HASH(id) \
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PROPERTIES ("replication_allocation" = "tag.location.default: 1")\
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"""
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self.dim = dim
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self.BS = "\\"
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self.must_escape = ("\\", "'")
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self._embedding = embedding
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self.dist_order = "DESC"
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_debug_output(self.config)
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# Create a connection to Apache Doris
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self.connection = pymysql.connect(
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host=self.config.host,
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port=self.config.port,
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user=self.config.username,
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password=self.config.password,
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database=self.config.database,
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**kwargs,
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)
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_debug_output(self.schema)
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_get_named_result(self.connection, self.schema)
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def escape_str(self, value: str) -> str:
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return "".join(f"{self.BS}{c}" if c in self.must_escape else c for c in value)
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@property
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def embeddings(self) -> Embeddings:
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return self._embedding
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def _build_insert_sql(self, transac: Iterable, column_names: Iterable[str]) -> str:
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ks = ",".join(column_names)
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embed_tuple_index = tuple(column_names).index(
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self.config.column_map["embedding"]
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)
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_data = []
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for n in transac:
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n = ",".join(
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[
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(
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f"'{self.escape_str(str(_n))}'"
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if idx != embed_tuple_index
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else f"array<float>{str(_n)}"
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)
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for (idx, _n) in enumerate(n)
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]
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)
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_data.append(f"({n})")
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i_str = f"""
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INSERT INTO
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{self.config.database}.{self.config.table}({ks})
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VALUES
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{','.join(_data)}
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"""
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return i_str
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def _insert(self, transac: Iterable, column_names: Iterable[str]) -> None:
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_insert_query = self._build_insert_sql(transac, column_names)
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_debug_output(_insert_query)
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_get_named_result(self.connection, _insert_query)
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def add_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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batch_size: int = 32,
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ids: Optional[Iterable[str]] = None,
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**kwargs: Any,
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) -> List[str]:
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"""Insert more texts through the embeddings and add to the VectorStore.
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Args:
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texts: Iterable of strings to add to the VectorStore.
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ids: Optional list of ids to associate with the texts.
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batch_size: Batch size of insertion
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metadata: Optional column data to be inserted
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Returns:
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List of ids from adding the texts into the VectorStore.
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"""
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# Embed and create the documents
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ids = ids or [sha1(t.encode("utf-8")).hexdigest() for t in texts]
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colmap_ = self.config.column_map
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transac = []
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column_names = {
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colmap_["id"]: ids,
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colmap_["document"]: texts,
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colmap_["embedding"]: self._embedding.embed_documents(list(texts)),
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}
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metadatas = metadatas or [{} for _ in texts]
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column_names[colmap_["metadata"]] = map(json.dumps, metadatas)
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assert len(set(colmap_) - set(column_names)) >= 0
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keys, values = zip(*column_names.items())
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try:
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t = None
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for v in self.pgbar(
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zip(*values), desc="Inserting data...", total=len(metadatas)
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):
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assert (
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len(v[keys.index(self.config.column_map["embedding"])]) == self.dim
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)
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transac.append(v)
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if len(transac) == batch_size:
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if t:
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t.join()
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t = Thread(target=self._insert, args=[transac, keys])
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t.start()
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transac = []
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if len(transac) > 0:
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if t:
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t.join()
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self._insert(transac, keys)
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return [i for i in ids]
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except Exception as e:
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logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
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return []
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@classmethod
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def from_texts(
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cls,
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texts: List[str],
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embedding: Embeddings,
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metadatas: Optional[List[Dict[Any, Any]]] = None,
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config: Optional[ApacheDorisSettings] = None,
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text_ids: Optional[Iterable[str]] = None,
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batch_size: int = 32,
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**kwargs: Any,
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) -> ApacheDoris:
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"""Create Apache Doris wrapper with existing texts
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Args:
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embedding_function (Embeddings): Function to extract text embedding
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texts (Iterable[str]): List or tuple of strings to be added
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config (ApacheDorisSettings, Optional): Apache Doris configuration
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text_ids (Optional[Iterable], optional): IDs for the texts.
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Defaults to None.
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batch_size (int, optional): BatchSize when transmitting data to Apache
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Doris. Defaults to 32.
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metadata (List[dict], optional): metadata to texts. Defaults to None.
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Returns:
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Apache Doris Index
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"""
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ctx = cls(embedding, config=config, **kwargs)
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ctx.add_texts(texts, ids=text_ids, batch_size=batch_size, metadatas=metadatas)
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return ctx
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def __repr__(self) -> str:
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"""Text representation for Apache Doris Vector Store, prints frontends, username
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and schemas. Easy to use with `str(ApacheDoris())`
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Returns:
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repr: string to show connection info and data schema
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"""
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_repr = f"\033[92m\033[1m{self.config.database}.{self.config.table} @ "
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_repr += f"{self.config.host}:{self.config.port}\033[0m\n\n"
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_repr += f"\033[1musername: {self.config.username}\033[0m\n\nTable Schema:\n"
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width = 25
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fields = 3
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_repr += "-" * (width * fields + 1) + "\n"
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columns = ["name", "type", "key"]
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_repr += f"|\033[94m{columns[0]:24s}\033[0m|\033[96m{columns[1]:24s}"
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_repr += f"\033[0m|\033[96m{columns[2]:24s}\033[0m|\n"
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_repr += "-" * (width * fields + 1) + "\n"
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q_str = f"DESC {self.config.database}.{self.config.table}"
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_debug_output(q_str)
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rs = _get_named_result(self.connection, q_str)
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for r in rs:
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_repr += f"|\033[94m{r['Field']:24s}\033[0m|\033[96m{r['Type']:24s}"
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_repr += f"\033[0m|\033[96m{r['Key']:24s}\033[0m|\n"
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_repr += "-" * (width * fields + 1) + "\n"
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return _repr
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def _build_query_sql(
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self, q_emb: List[float], topk: int, where_str: Optional[str] = None
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) -> str:
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q_emb_str = ",".join(map(str, q_emb))
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if where_str:
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where_str = f"WHERE {where_str}"
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else:
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where_str = ""
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q_str = f"""
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SELECT {self.config.column_map['document']},
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{self.config.column_map['metadata']},
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cosine_distance(array<float>[{q_emb_str}],
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{self.config.column_map['embedding']}) as dist
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FROM {self.config.database}.{self.config.table}
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{where_str}
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ORDER BY dist {self.dist_order}
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LIMIT {topk}
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"""
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_debug_output(q_str)
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return q_str
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def similarity_search(
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self, query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any
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) -> List[Document]:
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"""Perform a similarity search with Apache Doris
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Args:
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query (str): query string
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k (int, optional): Top K neighbors to retrieve. Defaults to 4.
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where_str (Optional[str], optional): where condition string.
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Defaults to None.
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NOTE: Please do not let end-user to fill this and always be aware
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of SQL injection. When dealing with metadatas, remember to
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use `{self.metadata_column}.attribute` instead of `attribute`
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alone. The default name for it is `metadata`.
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Returns:
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List[Document]: List of Documents
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"""
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return self.similarity_search_by_vector(
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self._embedding.embed_query(query), k, where_str, **kwargs
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)
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def similarity_search_by_vector(
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self,
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embedding: List[float],
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k: int = 4,
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where_str: Optional[str] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Perform a similarity search with Apache Doris by vectors
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Args:
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query (str): query string
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k (int, optional): Top K neighbors to retrieve. Defaults to 4.
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where_str (Optional[str], optional): where condition string.
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Defaults to None.
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NOTE: Please do not let end-user to fill this and always be aware
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of SQL injection. When dealing with metadatas, remember to
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use `{self.metadata_column}.attribute` instead of `attribute`
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alone. The default name for it is `metadata`.
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Returns:
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List[Document]: List of (Document, similarity)
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"""
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q_str = self._build_query_sql(embedding, k, where_str)
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try:
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return [
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Document(
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page_content=r[self.config.column_map["document"]],
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metadata=json.loads(r[self.config.column_map["metadata"]]),
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)
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for r in _get_named_result(self.connection, q_str)
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]
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except Exception as e:
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logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
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return []
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def similarity_search_with_relevance_scores(
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self, query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any
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) -> List[Tuple[Document, float]]:
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"""Perform a similarity search with Apache Doris
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Args:
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query (str): query string
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k (int, optional): Top K neighbors to retrieve. Defaults to 4.
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where_str (Optional[str], optional): where condition string.
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Defaults to None.
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NOTE: Please do not let end-user to fill this and always be aware
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of SQL injection. When dealing with metadatas, remember to
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use `{self.metadata_column}.attribute` instead of `attribute`
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alone. The default name for it is `metadata`.
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Returns:
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List[Document]: List of documents
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"""
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q_str = self._build_query_sql(self._embedding.embed_query(query), k, where_str)
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try:
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return [
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(
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Document(
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page_content=r[self.config.column_map["document"]],
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metadata=json.loads(r[self.config.column_map["metadata"]]),
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),
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r["dist"],
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)
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for r in _get_named_result(self.connection, q_str)
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]
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except Exception as e:
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logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
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return []
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def drop(self) -> None:
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"""
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Helper function: Drop data
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"""
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_get_named_result(
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self.connection,
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f"DROP TABLE IF EXISTS {self.config.database}.{self.config.table}",
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)
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@property
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def metadata_column(self) -> str:
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return self.config.column_map["metadata"]
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def _has_mul_sub_str(s: str, *args: Any) -> bool:
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"""Check if a string has multiple substrings.
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Args:
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s: The string to check
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*args: The substrings to check for in the string
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Returns:
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bool: True if all substrings are present in the string, False otherwise
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"""
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for a in args:
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if a not in s:
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return False
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return True
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def _debug_output(s: Any) -> None:
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"""Print a debug message if DEBUG is True.
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Args:
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s: The message to print
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"""
|
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if DEBUG:
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print(s) # noqa: T201
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def _get_named_result(connection: Any, query: str) -> List[dict[str, Any]]:
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"""Get a named result from a query.
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|
Args:
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connection: The connection to the database
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query: The query to execute
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Returns:
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List[dict[str, Any]]: The result of the query
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"""
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cursor = connection.cursor()
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cursor.execute(query)
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columns = cursor.description
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result = []
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for value in cursor.fetchall():
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r = {}
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for idx, datum in enumerate(value):
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k = columns[idx][0]
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r[k] = datum
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result.append(r)
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_debug_output(result)
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cursor.close()
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return result
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