Source code for langchain_community.vectorstores.clickhouse

from __future__ import annotations

import json
import logging
from hashlib import sha1
from threading import Thread
from typing import Any, Dict, Iterable, List, Optional, Tuple, Union

from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseSettings
from langchain_core.vectorstores import VectorStore

logger = logging.getLogger()


[docs]def has_mul_sub_str(s: str, *args: Any) -> bool: """ Check if a string contains multiple substrings. Args: s: string to check. *args: substrings to check. Returns: True if all substrings are in the string, False otherwise. """ for a in args: if a not in s: return False return True
[docs]class ClickhouseSettings(BaseSettings): """`ClickHouse` client configuration. Attribute: host (str) : An URL to connect to MyScale backend. Defaults to 'localhost'. port (int) : URL port to connect with HTTP. Defaults to 8443. username (str) : Username to login. Defaults to None. password (str) : Password to login. Defaults to None. index_type (str): index type string. index_param (list): index build parameter. index_query_params(dict): index query parameters. database (str) : Database name to find the table. Defaults to 'default'. table (str) : Table name to operate on. Defaults to 'vector_table'. metric (str) : Metric to compute distance, supported are ('angular', 'euclidean', 'manhattan', 'hamming', 'dot'). Defaults to 'angular'. https://github.com/spotify/annoy/blob/main/src/annoymodule.cc#L149-L169 column_map (Dict) : Column type map to project column name onto langchain semantics. Must have keys: `text`, `id`, `vector`, must be same size to number of columns. For example: .. code-block:: python { 'id': 'text_id', 'uuid': 'global_unique_id' 'embedding': 'text_embedding', 'document': 'text_plain', 'metadata': 'metadata_dictionary_in_json', } Defaults to identity map. """ host: str = "localhost" port: int = 8123 username: Optional[str] = None password: Optional[str] = None index_type: Optional[str] = "annoy" # Annoy supports L2Distance and cosineDistance. index_param: Optional[Union[List, Dict]] = ["'L2Distance'", 100] index_query_params: Dict[str, str] = {} column_map: Dict[str, str] = { "id": "id", "uuid": "uuid", "document": "document", "embedding": "embedding", "metadata": "metadata", } database: str = "default" table: str = "langchain" metric: str = "angular" def __getitem__(self, item: str) -> Any: return getattr(self, item) class Config: env_file = ".env" env_prefix = "clickhouse_" env_file_encoding = "utf-8"
[docs]class Clickhouse(VectorStore): """`ClickHouse VectorSearch` vector store. You need a `clickhouse-connect` python package, and a valid account to connect to ClickHouse. ClickHouse can not only search with simple vector indexes, it also supports complex query with multiple conditions, constraints and even sub-queries. For more information, please visit [ClickHouse official site](https://clickhouse.com/clickhouse) """
[docs] def __init__( self, embedding: Embeddings, config: Optional[ClickhouseSettings] = None, **kwargs: Any, ) -> None: """ClickHouse Wrapper to LangChain embedding_function (Embeddings): config (ClickHouseSettings): Configuration to ClickHouse Client Other keyword arguments will pass into [clickhouse-connect](https://docs.clickhouse.com/) """ try: from clickhouse_connect import get_client except ImportError: raise ImportError( "Could not import clickhouse connect python package. " "Please install it with `pip install clickhouse-connect`." ) try: from tqdm import tqdm self.pgbar = tqdm except ImportError: # Just in case if tqdm is not installed self.pgbar = lambda x, **kwargs: x super().__init__() if config is not None: self.config = config else: self.config = ClickhouseSettings() assert self.config assert self.config.host and self.config.port assert ( self.config.column_map and self.config.database and self.config.table and self.config.metric ) for k in ["id", "embedding", "document", "metadata", "uuid"]: assert k in self.config.column_map assert self.config.metric in [ "angular", "euclidean", "manhattan", "hamming", "dot", ] # initialize the schema dim = len(embedding.embed_query("test")) index_params = ( ( ",".join([f"'{k}={v}'" for k, v in self.config.index_param.items()]) if self.config.index_param else "" ) if isinstance(self.config.index_param, Dict) else ( ",".join([str(p) for p in self.config.index_param]) if isinstance(self.config.index_param, List) else self.config.index_param ) ) self.schema = self._schema(dim, index_params) self.dim = dim self.BS = "\\" self.must_escape = ("\\", "'") self.embedding_function = embedding self.dist_order = "ASC" # Only support ConsingDistance and L2Distance # Create a connection to clickhouse self.client = get_client( host=self.config.host, port=self.config.port, username=self.config.username, password=self.config.password, **kwargs, ) # Enable JSON type self.client.command("SET allow_experimental_object_type=1") if self.config.index_type: # Enable index self.client.command( f"SET allow_experimental_{self.config.index_type}_index=1" ) self.client.command(self.schema)
def _schema(self, dim: int, index_params: Optional[str] = "") -> str: """Create table schema :param dim: dimension of embeddings :param index_params: parameters used for index This function returns a `CREATE TABLE` statement based on the value of `self.config.index_type`. If an index type is specified that index will be created, otherwise no index will be created. In the case of there being no index, a linear scan will be performed when the embedding field is queried. """ if self.config.index_type: return f"""\ CREATE TABLE IF NOT EXISTS {self.config.database}.{self.config.table}( {self.config.column_map['id']} Nullable(String), {self.config.column_map['document']} Nullable(String), {self.config.column_map['embedding']} Array(Float32), {self.config.column_map['metadata']} JSON, {self.config.column_map['uuid']} UUID DEFAULT generateUUIDv4(), CONSTRAINT cons_vec_len CHECK length( {self.config.column_map['embedding']}) = {dim}, INDEX vec_idx {self.config.column_map['embedding']} TYPE \ {self.config.index_type}({index_params}) GRANULARITY 1000 ) ENGINE = MergeTree ORDER BY uuid SETTINGS index_granularity = 8192\ """ else: return f"""\ CREATE TABLE IF NOT EXISTS {self.config.database}.{self.config.table}( {self.config.column_map['id']} Nullable(String), {self.config.column_map['document']} Nullable(String), {self.config.column_map['embedding']} Array(Float32), {self.config.column_map['metadata']} JSON, {self.config.column_map['uuid']} UUID DEFAULT generateUUIDv4(), CONSTRAINT cons_vec_len CHECK length({ self.config.column_map['embedding']}) = {dim} ) ENGINE = MergeTree ORDER BY uuid """ @property def embeddings(self) -> Embeddings: """Provides access to the embedding mechanism used by the Clickhouse instance. This property allows direct access to the embedding function or model being used by the Clickhouse instance to convert text documents into embedding vectors for vector similarity search. Returns: The `Embeddings` instance associated with this Clickhouse instance. """ return self.embedding_function
[docs] def escape_str(self, value: str) -> str: """Escape special characters in a string for Clickhouse SQL queries. This method is used internally to prepare strings for safe insertion into SQL queries by escaping special characters that might otherwise interfere with the query syntax. Args: value: The string to be escaped. Returns: The escaped string, safe for insertion into SQL queries. """ return "".join(f"{self.BS}{c}" if c in self.must_escape else c for c in value)
def _build_insert_sql(self, transac: Iterable, column_names: Iterable[str]) -> str: """Construct an SQL query for inserting data into the Clickhouse database. This method formats and constructs an SQL `INSERT` query string using the provided transaction data and column names. It is utilized internally during the process of batch insertion of documents and their embeddings into the database. Args: transac: iterable of tuples, representing a row of data to be inserted. column_names: iterable of strings representing the names of the columns into which data will be inserted. Returns: A string containing the constructed SQL `INSERT` query. """ ks = ",".join(column_names) _data = [] for n in transac: n = ",".join([f"'{self.escape_str(str(_n))}'" for _n in n]) _data.append(f"({n})") i_str = f""" INSERT INTO TABLE {self.config.database}.{self.config.table}({ks}) VALUES {','.join(_data)} """ return i_str def _insert(self, transac: Iterable, column_names: Iterable[str]) -> None: """Execute an SQL query to insert data into the Clickhouse database. This method performs the actual insertion of data into the database by executing the SQL query constructed by `_build_insert_sql`. It's a critical step in adding new documents and their associated data into the vector store. Args: transac:iterable of tuples, representing a row of data to be inserted. column_names: An iterable of strings representing the names of the columns into which data will be inserted. """ _insert_query = self._build_insert_sql(transac, column_names) self.client.command(_insert_query)
[docs] def add_texts( self, texts: Iterable[str], metadatas: Optional[List[dict]] = None, batch_size: int = 32, ids: Optional[Iterable[str]] = None, **kwargs: Any, ) -> List[str]: """Insert more texts through the embeddings and add to the VectorStore. Args: texts: Iterable of strings to add to the VectorStore. ids: Optional list of ids to associate with the texts. batch_size: Batch size of insertion metadata: Optional column data to be inserted Returns: List of ids from adding the texts into the VectorStore. """ # Embed and create the documents ids = ids or [sha1(t.encode("utf-8")).hexdigest() for t in texts] colmap_ = self.config.column_map transac = [] column_names = { colmap_["id"]: ids, colmap_["document"]: texts, colmap_["embedding"]: self.embedding_function.embed_documents(list(texts)), } metadatas = metadatas or [{} for _ in texts] column_names[colmap_["metadata"]] = map(json.dumps, metadatas) assert len(set(colmap_) - set(column_names)) >= 0 keys, values = zip(*column_names.items()) try: t = None for v in self.pgbar( zip(*values), desc="Inserting data...", total=len(metadatas) ): assert ( len(v[keys.index(self.config.column_map["embedding"])]) == self.dim ) transac.append(v) if len(transac) == batch_size: if t: t.join() t = Thread(target=self._insert, args=[transac, keys]) t.start() transac = [] if len(transac) > 0: if t: t.join() self._insert(transac, keys) return [i for i in ids] except Exception as e: logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m") return []
[docs] @classmethod def from_texts( cls, texts: List[str], embedding: Embeddings, metadatas: Optional[List[Dict[Any, Any]]] = None, config: Optional[ClickhouseSettings] = None, text_ids: Optional[Iterable[str]] = None, batch_size: int = 32, **kwargs: Any, ) -> Clickhouse: """Create ClickHouse wrapper with existing texts Args: embedding_function (Embeddings): Function to extract text embedding texts (Iterable[str]): List or tuple of strings to be added config (ClickHouseSettings, Optional): ClickHouse configuration text_ids (Optional[Iterable], optional): IDs for the texts. Defaults to None. batch_size (int, optional): Batchsize when transmitting data to ClickHouse. Defaults to 32. metadata (List[dict], optional): metadata to texts. Defaults to None. Other keyword arguments will pass into [clickhouse-connect](https://clickhouse.com/docs/en/integrations/python#clickhouse-connect-driver-api) Returns: ClickHouse Index """ ctx = cls(embedding, config, **kwargs) ctx.add_texts(texts, ids=text_ids, batch_size=batch_size, metadatas=metadatas) return ctx
def __repr__(self) -> str: """Text representation for ClickHouse Vector Store, prints backends, username and schemas. Easy to use with `str(ClickHouse())` Returns: repr: string to show connection info and data schema """ _repr = f"\033[92m\033[1m{self.config.database}.{self.config.table} @ " _repr += f"{self.config.host}:{self.config.port}\033[0m\n\n" _repr += f"\033[1musername: {self.config.username}\033[0m\n\nTable Schema:\n" _repr += "-" * 51 + "\n" for r in self.client.query( f"DESC {self.config.database}.{self.config.table}" ).named_results(): _repr += ( f"|\033[94m{r['name']:24s}\033[0m|\033[96m{r['type']:24s}\033[0m|\n" ) _repr += "-" * 51 + "\n" return _repr def _build_query_sql( self, q_emb: List[float], topk: int, where_str: Optional[str] = None ) -> str: """Construct an SQL query for performing a similarity search. This internal method generates an SQL query for finding the top-k most similar vectors in the database to a given query vector.It allows for optional filtering conditions to be applied via a WHERE clause. Args: q_emb: The query vector as a list of floats. topk: The number of top similar items to retrieve. where_str: opt str representing additional WHERE conditions for the query Defaults to None. Returns: A string containing the SQL query for the similarity search. """ q_emb_str = ",".join(map(str, q_emb)) if where_str: where_str = f"PREWHERE {where_str}" else: where_str = "" settings_strs = [] if self.config.index_query_params: for k in self.config.index_query_params: settings_strs.append(f"SETTING {k}={self.config.index_query_params[k]}") q_str = f""" SELECT {self.config.column_map['document']}, {self.config.column_map['metadata']}, dist FROM {self.config.database}.{self.config.table} {where_str} ORDER BY L2Distance({self.config.column_map['embedding']}, [{q_emb_str}]) AS dist {self.dist_order} LIMIT {topk} {' '.join(settings_strs)} """ return q_str
[docs] 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 ClickHouse 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 documents """ q_str = self._build_query_sql(embedding, k, where_str) try: return [ Document( page_content=r[self.config.column_map["document"]], 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 []
[docs] 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 ClickHouse 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_query_sql( self.embedding_function.embed_query(query), k, where_str ) try: return [ ( Document( page_content=r[self.config.column_map["document"]], 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 []
[docs] def drop(self) -> None: """ Helper function: Drop data """ self.client.command( f"DROP TABLE IF EXISTS {self.config.database}.{self.config.table}" )
@property def metadata_column(self) -> str: return self.config.column_map["metadata"]