mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
615 lines
22 KiB
Python
615 lines
22 KiB
Python
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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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def has_mul_sub_str(s: str, *args: Any) -> bool:
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"""
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Check if a string contains multiple substrings.
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Args:
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s: string to check.
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*args: substrings to check.
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Returns:
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True if all substrings are 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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class MyScaleSettings(BaseSettings):
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"""MyScale client configuration.
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Attribute:
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myscale_host (str) : An URL to connect to MyScale backend.
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Defaults to 'localhost'.
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myscale_port (int) : URL port to connect with HTTP. Defaults to 8443.
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username (str) : Username to login. Defaults to None.
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password (str) : Password to login. Defaults to None.
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index_type (str): index type string.
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index_param (dict): index build parameter.
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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 'vector_table'.
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metric (str) : Metric to compute distance,
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supported are ('L2', 'Cosine', 'IP'). Defaults to 'Cosine'.
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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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'vector': 'text_embedding',
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'text': '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 = 8443
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username: Optional[str] = None
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password: Optional[str] = None
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index_type: str = "MSTG"
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index_param: Optional[Dict[str, str]] = None
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column_map: Dict[str, str] = {
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"id": "id",
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"text": "text",
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"vector": "vector",
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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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metric: str = "Cosine"
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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 = "myscale_"
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env_file_encoding = "utf-8"
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class MyScale(VectorStore):
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"""`MyScale` vector store.
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You need a `clickhouse-connect` python package, and a valid account
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to connect to MyScale.
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MyScale can not only search with simple vector indexes.
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It also supports a complex query with multiple conditions,
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constraints and even sub-queries.
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For more information, please visit
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[myscale official site](https://docs.myscale.com/en/overview/)
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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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config: Optional[MyScaleSettings] = None,
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**kwargs: Any,
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) -> None:
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"""MyScale Wrapper to LangChain
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embedding (Embeddings):
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config (MyScaleSettings): Configuration to MyScale Client
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Other keyword arguments will pass into
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[clickhouse-connect](https://docs.myscale.com/)
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"""
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try:
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from clickhouse_connect import get_client
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except ImportError:
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raise ImportError(
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"Could not import clickhouse connect python package. "
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"Please install it with `pip install clickhouse-connect`."
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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: 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 = MyScaleSettings()
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assert self.config
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assert self.config.host and self.config.port
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assert (
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self.config.column_map
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and self.config.database
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and self.config.table
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and self.config.metric
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)
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for k in ["id", "vector", "text", "metadata"]:
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assert k in self.config.column_map
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assert self.config.metric.upper() in ["IP", "COSINE", "L2"]
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if self.config.metric in ["ip", "cosine", "l2"]:
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logger.warning(
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"Lower case metric types will be deprecated "
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"the future. Please use one of ('IP', 'Cosine', 'L2')"
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)
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# initialize the schema
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dim = len(embedding.embed_query("try this out"))
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index_params = (
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", " + ",".join([f"'{k}={v}'" for k, v in self.config.index_param.items()])
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if self.config.index_param
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else ""
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)
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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']} String,
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{self.config.column_map['text']} String,
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{self.config.column_map['vector']} Array(Float32),
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{self.config.column_map['metadata']} JSON,
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CONSTRAINT cons_vec_len CHECK length(\
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{self.config.column_map['vector']}) = {dim},
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VECTOR INDEX vidx {self.config.column_map['vector']} \
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TYPE {self.config.index_type}(\
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'metric_type={self.config.metric}'{index_params})
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) ENGINE = MergeTree ORDER BY {self.config.column_map['id']}
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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._embeddings = embedding
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self.dist_order = (
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"ASC" if self.config.metric.upper() in ["COSINE", "L2"] else "DESC"
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)
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# Create a connection to myscale
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self.client = get_client(
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host=self.config.host,
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port=self.config.port,
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username=self.config.username,
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password=self.config.password,
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**kwargs,
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)
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self.client.command("SET allow_experimental_object_type=1")
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self.client.command(schema_)
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@property
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def embeddings(self) -> Embeddings:
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return self._embeddings
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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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def _build_istr(self, transac: Iterable, column_names: Iterable[str]) -> str:
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ks = ",".join(column_names)
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_data = []
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for n in transac:
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n = ",".join([f"'{self.escape_str(str(_n))}'" for _n in n])
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_data.append(f"({n})")
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i_str = f"""
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INSERT INTO TABLE
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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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_i_str = self._build_istr(transac, column_names)
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self.client.command(_i_str)
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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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"""Run 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_["text"]: texts,
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colmap_["vector"]: map(self._embeddings.embed_query, 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 len(v[keys.index(self.config.column_map["vector"])]) == self.dim
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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: Iterable[str],
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embedding: Embeddings,
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metadatas: Optional[List[Dict[Any, Any]]] = None,
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config: Optional[MyScaleSettings] = 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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) -> MyScale:
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"""Create Myscale wrapper with existing texts
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Args:
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texts (Iterable[str]): List or tuple of strings to be added
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embedding (Embeddings): Function to extract text embedding
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config (MyScaleSettings, Optional): Myscale 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 MyScale.
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Defaults to 32.
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metadata (List[dict], optional): metadata to texts. Defaults to None.
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Other keyword arguments will pass into
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[clickhouse-connect](https://clickhouse.com/docs/en/integrations/python#clickhouse-connect-driver-api)
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Returns:
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MyScale Index
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"""
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ctx = cls(embedding, 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 myscale, prints backends, username and schemas.
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Easy to use with `str(Myscale())`
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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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_repr += "-" * 51 + "\n"
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for r in self.client.query(
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f"DESC {self.config.database}.{self.config.table}"
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).named_results():
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_repr += (
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f"|\033[94m{r['name']:24s}\033[0m|\033[96m{r['type']:24s}\033[0m|\n"
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)
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_repr += "-" * 51 + "\n"
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return _repr
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def _build_qstr(
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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"PREWHERE {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['text']},
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{self.config.column_map['metadata']}, dist
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FROM {self.config.database}.{self.config.table}
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{where_str}
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ORDER BY distance({self.config.column_map['vector']}, [{q_emb_str}])
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AS dist {self.dist_order}
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LIMIT {topk}
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"""
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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 MyScale
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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._embeddings.embed_query(query), k, where_str, **kwargs
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)
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|
|
def similarity_search_by_vector(
|
||
|
|
self,
|
||
|
|
embedding: List[float],
|
||
|
|
k: int = 4,
|
||
|
|
where_str: Optional[str] = None,
|
||
|
|
**kwargs: Any,
|
||
|
|
) -> List[Document]:
|
||
|
|
"""Perform a similarity search with MyScale by vectors
|
||
|
|
|
||
|
|
Args:
|
||
|
|
query (str): query string
|
||
|
|
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
|
||
|
|
where_str (Optional[str], optional): where condition string.
|
||
|
|
Defaults to None.
|
||
|
|
|
||
|
|
NOTE: Please do not let end-user to fill this and always be aware
|
||
|
|
of SQL injection. When dealing with metadatas, remember to
|
||
|
|
use `{self.metadata_column}.attribute` instead of `attribute`
|
||
|
|
alone. The default name for it is `metadata`.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
List[Document]: List of (Document, similarity)
|
||
|
|
"""
|
||
|
|
q_str = self._build_qstr(embedding, k, where_str)
|
||
|
|
try:
|
||
|
|
return [
|
||
|
|
Document(
|
||
|
|
page_content=r[self.config.column_map["text"]],
|
||
|
|
metadata=r[self.config.column_map["metadata"]],
|
||
|
|
)
|
||
|
|
for r in self.client.query(q_str).named_results()
|
||
|
|
]
|
||
|
|
except Exception as e:
|
||
|
|
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
|
||
|
|
return []
|
||
|
|
|
||
|
|
def similarity_search_with_relevance_scores(
|
||
|
|
self, query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any
|
||
|
|
) -> List[Tuple[Document, float]]:
|
||
|
|
"""Perform a similarity search with MyScale
|
||
|
|
|
||
|
|
Args:
|
||
|
|
query (str): query string
|
||
|
|
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
|
||
|
|
where_str (Optional[str], optional): where condition string.
|
||
|
|
Defaults to None.
|
||
|
|
|
||
|
|
NOTE: Please do not let end-user to fill this and always be aware
|
||
|
|
of SQL injection. When dealing with metadatas, remember to
|
||
|
|
use `{self.metadata_column}.attribute` instead of `attribute`
|
||
|
|
alone. The default name for it is `metadata`.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
List[Document]: List of documents most similar to the query text
|
||
|
|
and cosine distance in float for each.
|
||
|
|
Lower score represents more similarity.
|
||
|
|
"""
|
||
|
|
q_str = self._build_qstr(self._embeddings.embed_query(query), k, where_str)
|
||
|
|
try:
|
||
|
|
return [
|
||
|
|
(
|
||
|
|
Document(
|
||
|
|
page_content=r[self.config.column_map["text"]],
|
||
|
|
metadata=r[self.config.column_map["metadata"]],
|
||
|
|
),
|
||
|
|
r["dist"],
|
||
|
|
)
|
||
|
|
for r in self.client.query(q_str).named_results()
|
||
|
|
]
|
||
|
|
except Exception as e:
|
||
|
|
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
|
||
|
|
return []
|
||
|
|
|
||
|
|
def drop(self) -> None:
|
||
|
|
"""
|
||
|
|
Helper function: Drop data
|
||
|
|
"""
|
||
|
|
self.client.command(
|
||
|
|
f"DROP TABLE IF EXISTS {self.config.database}.{self.config.table}"
|
||
|
|
)
|
||
|
|
|
||
|
|
def delete(
|
||
|
|
self,
|
||
|
|
ids: Optional[List[str]] = None,
|
||
|
|
where_str: Optional[str] = None,
|
||
|
|
**kwargs: Any,
|
||
|
|
) -> Optional[bool]:
|
||
|
|
"""Delete by vector ID or other criteria.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
ids: List of ids to delete.
|
||
|
|
**kwargs: Other keyword arguments that subclasses might use.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Optional[bool]: True if deletion is successful,
|
||
|
|
False otherwise, None if not implemented.
|
||
|
|
"""
|
||
|
|
assert not (
|
||
|
|
ids is None and where_str is None
|
||
|
|
), "You need to specify where to be deleted! Either with `ids` or `where_str`"
|
||
|
|
conds = []
|
||
|
|
if ids:
|
||
|
|
conds.extend([f"{self.config.column_map['id']} = '{id}'" for id in ids])
|
||
|
|
if where_str:
|
||
|
|
conds.append(where_str)
|
||
|
|
assert len(conds) > 0
|
||
|
|
where_str_final = " AND ".join(conds)
|
||
|
|
qstr = (
|
||
|
|
f"DELETE FROM {self.config.database}.{self.config.table} "
|
||
|
|
f"WHERE {where_str_final}"
|
||
|
|
)
|
||
|
|
try:
|
||
|
|
self.client.command(qstr)
|
||
|
|
return True
|
||
|
|
except Exception as e:
|
||
|
|
logger.error(str(e))
|
||
|
|
return False
|
||
|
|
|
||
|
|
@property
|
||
|
|
def metadata_column(self) -> str:
|
||
|
|
return self.config.column_map["metadata"]
|
||
|
|
|
||
|
|
|
||
|
|
class MyScaleWithoutJSON(MyScale):
|
||
|
|
"""MyScale vector store without metadata column
|
||
|
|
|
||
|
|
This is super handy if you are working to a SQL-native table
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
embedding: Embeddings,
|
||
|
|
config: Optional[MyScaleSettings] = None,
|
||
|
|
must_have_cols: List[str] = [],
|
||
|
|
**kwargs: Any,
|
||
|
|
) -> None:
|
||
|
|
"""Building a myscale vector store without metadata column
|
||
|
|
|
||
|
|
embedding (Embeddings): embedding model
|
||
|
|
config (MyScaleSettings): Configuration to MyScale Client
|
||
|
|
must_have_cols (List[str]): column names to be included in query
|
||
|
|
Other keyword arguments will pass into
|
||
|
|
[clickhouse-connect](https://docs.myscale.com/)
|
||
|
|
"""
|
||
|
|
super().__init__(embedding, config, **kwargs)
|
||
|
|
self.must_have_cols: List[str] = must_have_cols
|
||
|
|
|
||
|
|
def _build_qstr(
|
||
|
|
self, q_emb: List[float], topk: int, where_str: Optional[str] = None
|
||
|
|
) -> str:
|
||
|
|
q_emb_str = ",".join(map(str, q_emb))
|
||
|
|
if where_str:
|
||
|
|
where_str = f"PREWHERE {where_str}"
|
||
|
|
else:
|
||
|
|
where_str = ""
|
||
|
|
|
||
|
|
q_str = f"""
|
||
|
|
SELECT {self.config.column_map['text']}, dist,
|
||
|
|
{','.join(self.must_have_cols)}
|
||
|
|
FROM {self.config.database}.{self.config.table}
|
||
|
|
{where_str}
|
||
|
|
ORDER BY distance({self.config.column_map['vector']}, [{q_emb_str}])
|
||
|
|
AS dist {self.dist_order}
|
||
|
|
LIMIT {topk}
|
||
|
|
"""
|
||
|
|
return q_str
|
||
|
|
|
||
|
|
def similarity_search_by_vector(
|
||
|
|
self,
|
||
|
|
embedding: List[float],
|
||
|
|
k: int = 4,
|
||
|
|
where_str: Optional[str] = None,
|
||
|
|
**kwargs: Any,
|
||
|
|
) -> List[Document]:
|
||
|
|
"""Perform a similarity search with MyScale by vectors
|
||
|
|
|
||
|
|
Args:
|
||
|
|
query (str): query string
|
||
|
|
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
|
||
|
|
where_str (Optional[str], optional): where condition string.
|
||
|
|
Defaults to None.
|
||
|
|
|
||
|
|
NOTE: Please do not let end-user to fill this and always be aware
|
||
|
|
of SQL injection. When dealing with metadatas, remember to
|
||
|
|
use `{self.metadata_column}.attribute` instead of `attribute`
|
||
|
|
alone. The default name for it is `metadata`.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
List[Document]: List of (Document, similarity)
|
||
|
|
"""
|
||
|
|
q_str = self._build_qstr(embedding, k, where_str)
|
||
|
|
try:
|
||
|
|
return [
|
||
|
|
Document(
|
||
|
|
page_content=r[self.config.column_map["text"]],
|
||
|
|
metadata={k: r[k] for k in self.must_have_cols},
|
||
|
|
)
|
||
|
|
for r in self.client.query(q_str).named_results()
|
||
|
|
]
|
||
|
|
except Exception as e:
|
||
|
|
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
|
||
|
|
return []
|
||
|
|
|
||
|
|
def similarity_search_with_relevance_scores(
|
||
|
|
self, query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any
|
||
|
|
) -> List[Tuple[Document, float]]:
|
||
|
|
"""Perform a similarity search with MyScale
|
||
|
|
|
||
|
|
Args:
|
||
|
|
query (str): query string
|
||
|
|
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
|
||
|
|
where_str (Optional[str], optional): where condition string.
|
||
|
|
Defaults to None.
|
||
|
|
|
||
|
|
NOTE: Please do not let end-user to fill this and always be aware
|
||
|
|
of SQL injection. When dealing with metadatas, remember to
|
||
|
|
use `{self.metadata_column}.attribute` instead of `attribute`
|
||
|
|
alone. The default name for it is `metadata`.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
List[Document]: List of documents most similar to the query text
|
||
|
|
and cosine distance in float for each.
|
||
|
|
Lower score represents more similarity.
|
||
|
|
"""
|
||
|
|
q_str = self._build_qstr(self._embeddings.embed_query(query), k, where_str)
|
||
|
|
try:
|
||
|
|
return [
|
||
|
|
(
|
||
|
|
Document(
|
||
|
|
page_content=r[self.config.column_map["text"]],
|
||
|
|
metadata={k: r[k] for k in self.must_have_cols},
|
||
|
|
),
|
||
|
|
r["dist"],
|
||
|
|
)
|
||
|
|
for r in self.client.query(q_str).named_results()
|
||
|
|
]
|
||
|
|
except Exception as e:
|
||
|
|
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
|
||
|
|
return []
|
||
|
|
|
||
|
|
@property
|
||
|
|
def metadata_column(self) -> str:
|
||
|
|
return ""
|