from __future__ import annotations
import json
import logging
from hashlib import sha1
from threading import Thread
from typing import Any, Dict, Iterable, List, Mapping, Optional, Tuple, Union
import numpy as np
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
from pydantic_settings import BaseSettings, SettingsConfigDict
from typing_extensions import TypedDict
from langchain_community.vectorstores.utils import maximal_marginal_relevance
logger = logging.getLogger()
DEBUG = False
Metadata = Mapping[str, Union[str, int, float, bool]]
[docs]
class QueryResult(TypedDict):
ids: List[List[str]]
embeddings: List[Any]
documents: List[Document]
metadatas: Optional[List[Metadata]]
distances: Optional[List[float]]
[docs]
class ApacheDorisSettings(BaseSettings):
"""Apache Doris client configuration.
Attributes:
apache_doris_host (str) : An URL to connect to frontend.
Defaults to 'localhost'.
apache_doris_port (int) : URL port to connect with HTTP. Defaults to 9030.
username (str) : Username to login. Defaults to 'root'.
password (str) : Password to login. Defaults to None.
database (str) : Database name to find the table. Defaults to 'default'.
table (str) : Table name to operate on.
Defaults to 'langchain'.
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',
'embedding': 'text_embedding',
'document': 'text_plain',
'metadata': 'metadata_dictionary_in_json',
}
Defaults to identity map.
"""
host: str = "localhost"
port: int = 9030
username: str = "root"
password: str = ""
column_map: Dict[str, str] = {
"id": "id",
"document": "document",
"embedding": "embedding",
"metadata": "metadata",
}
database: str = "default"
table: str = "langchain"
def __getitem__(self, item: str) -> Any:
return getattr(self, item)
model_config = SettingsConfigDict(
env_file=".env",
env_file_encoding="utf-8",
env_prefix="apache_doris_",
extra="ignore",
)
[docs]
class ApacheDoris(VectorStore):
"""`Apache Doris` vector store.
You need a `pymysql` python package, and a valid account
to connect to Apache Doris.
For more information, please visit
[Apache Doris official site](https://doris.apache.org/)
[Apache Doris github](https://github.com/apache/doris)
"""
[docs]
def __init__(
self,
embedding: Embeddings,
*,
config: Optional[ApacheDorisSettings] = None,
**kwargs: Any,
) -> None:
"""Constructor for Apache Doris.
Args:
embedding (Embeddings): Text embedding model.
config (ApacheDorisSettings): Apache Doris client configuration information.
"""
try:
import pymysql # type: ignore[import-untyped]
except ImportError:
raise ImportError(
"Could not import pymysql python package. "
"Please install it with `pip install pymysql`."
)
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 = ApacheDorisSettings()
assert self.config
assert self.config.host and self.config.port
assert self.config.column_map and self.config.database and self.config.table
for k in ["id", "embedding", "document", "metadata"]:
assert k in self.config.column_map
# initialize the schema
dim = len(embedding.embed_query("test"))
self.schema = f"""\
CREATE TABLE IF NOT EXISTS {self.config.database}.{self.config.table}(
{self.config.column_map["id"]} varchar(50),
{self.config.column_map["document"]} string,
{self.config.column_map["embedding"]} array<float>,
{self.config.column_map["metadata"]} string
) ENGINE = OLAP UNIQUE KEY(id) DISTRIBUTED BY HASH(id) \
PROPERTIES ("replication_allocation" = "tag.location.default: 1")\
"""
self.dim = dim
self.BS = "\\"
self.must_escape = ("\\", "'")
self._embedding = embedding
self.dist_order = "DESC"
_debug_output(self.config)
# Create a connection to Apache Doris
self.connection = pymysql.connect(
host=self.config.host,
port=self.config.port,
user=self.config.username,
password=self.config.password,
database=self.config.database,
**kwargs,
)
_debug_output(self.schema)
_get_named_result(self.connection, self.schema)
[docs]
def escape_str(self, value: str) -> str:
return "".join(f"{self.BS}{c}" if c in self.must_escape else c for c in value)
@property
def embeddings(self) -> Embeddings:
return self._embedding
def _build_insert_sql(self, transac: Iterable, column_names: Iterable[str]) -> str:
ks = ",".join(column_names)
embed_tuple_index = tuple(column_names).index(
self.config.column_map["embedding"]
)
_data = []
for n in transac:
n = ",".join(
[
(
f"'{self.escape_str(str(_n))}'"
if idx != embed_tuple_index
else f"{str(_n)}"
)
for (idx, _n) in enumerate(n)
]
)
_data.append(f"({n})")
i_str = f"""
INSERT INTO
{self.config.database}.{self.config.table}({ks})
VALUES
{",".join(_data)}
"""
return i_str
def _insert(self, transac: Iterable, column_names: Iterable[str]) -> None:
_insert_query = self._build_insert_sql(transac, column_names)
_debug_output(_insert_query)
_get_named_result(self.connection, _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.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[ApacheDorisSettings] = None,
text_ids: Optional[Iterable[str]] = None,
batch_size: int = 32,
**kwargs: Any,
) -> ApacheDoris:
"""Create Apache Doris 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 (ApacheDorisSettings, Optional): Apache Doris configuration
text_ids (Optional[Iterable], optional): IDs for the texts.
Defaults to None.
batch_size (int, optional): BatchSize when transmitting data to Apache
Doris. Defaults to 32.
metadata (List[dict], optional): metadata to texts. Defaults to None.
Returns:
Apache Doris Index
"""
ctx = cls(embedding, config=config, **kwargs)
ctx.add_texts(texts, ids=text_ids, batch_size=batch_size, metadatas=metadatas)
return ctx
def __repr__(self) -> str:
"""Text representation for Apache Doris Vector Store, prints frontends, username
and schemas. Easy to use with `str(ApacheDoris())`
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"
width = 25
fields = 3
_repr += "-" * (width * fields + 1) + "\n"
columns = ["name", "type", "key"]
_repr += f"|\033[94m{columns[0]:24s}\033[0m|\033[96m{columns[1]:24s}"
_repr += f"\033[0m|\033[96m{columns[2]:24s}\033[0m|\n"
_repr += "-" * (width * fields + 1) + "\n"
q_str = f"DESC {self.config.database}.{self.config.table}"
_debug_output(q_str)
rs = _get_named_result(self.connection, q_str)
for r in rs:
_repr += f"|\033[94m{r['Field']:24s}\033[0m|\033[96m{r['Type']:24s}"
_repr += f"\033[0m|\033[96m{r['Key']:24s}\033[0m|\n"
_repr += "-" * (width * fields + 1) + "\n"
return _repr
def _build_query_sql(
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"WHERE {where_str}"
else:
where_str = ""
q_str = f"""
SELECT
id as id,
{self.config.column_map["document"]} as document,
{self.config.column_map["metadata"]} as metadata,
cosine_distance(array<float>[{q_emb_str}],
{self.config.column_map["embedding"]}) as dist,
{self.config.column_map["embedding"]} as embedding
FROM {self.config.database}.{self.config.table}
{where_str}
ORDER BY dist {self.dist_order}
LIMIT {topk}
"""
_debug_output(q_str)
return q_str
[docs]
def similarity_search(
self, query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any
) -> List[Document]:
"""Perform a similarity search with Apache Doris
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
"""
return self.similarity_search_by_vector(
self._embedding.embed_query(query), k, where_str, **kwargs
)
[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 Apache Doris 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_query_sql(embedding, k, where_str)
try:
q_r = _get_named_result(self.connection, q_str)
return [
Document(
page_content=r[self.config.column_map["document"]],
metadata=json.loads(r[self.config.column_map["metadata"]]),
)
for r in q_r
]
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 Apache Doris
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(self._embedding.embed_query(query), k, where_str)
try:
return [
(
Document(
page_content=r[self.config.column_map["document"]],
metadata=json.loads(r[self.config.column_map["metadata"]]),
),
r["dist"],
)
for r in _get_named_result(self.connection, q_str)
]
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
"""
_get_named_result(
self.connection,
f"DROP TABLE IF EXISTS {self.config.database}.{self.config.table}",
)
@property
def metadata_column(self) -> str:
return self.config.column_map["metadata"]
[docs]
def max_marginal_relevance_search_by_vector(
self,
embedding: list[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> list[Document]:
q_str = self._build_query_sql(embedding, fetch_k, None)
q_r = _get_named_result(self.connection, q_str)
results = QueryResult(
ids=[r["id"] for r in q_r],
embeddings=[
json.loads(r[self.config.column_map["embedding"]]) for r in q_r
],
documents=[r[self.config.column_map["document"]] for r in q_r],
metadatas=[json.loads(r[self.config.column_map["metadata"]]) for r in q_r],
distances=[r["dist"] for r in q_r],
)
mmr_selected = maximal_marginal_relevance(
np.array(embedding, dtype=np.float32),
results["embeddings"],
k=k,
lambda_mult=lambda_mult,
)
candidates = _results_to_docs(results)
selected_results = [r for i, r in enumerate(candidates) if i in mmr_selected]
return selected_results
[docs]
def max_marginal_relevance_search(
self,
query: str,
k: int = 5,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, str]] = None,
where_document: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
if self.embeddings is None:
raise ValueError(
"For MMR search, you must specify an embedding function oncreation."
)
embedding = self.embeddings.embed_query(query)
return self.max_marginal_relevance_search_by_vector(
embedding,
k,
fetch_k,
lambda_mult=lambda_mult,
filter=filter,
where_document=where_document,
)
def _has_mul_sub_str(s: str, *args: Any) -> bool:
"""Check if a string has multiple substrings.
Args:
s: The string to check
*args: The substrings to check for in the string
Returns:
bool: True if all substrings are present in the string, False otherwise
"""
for a in args:
if a not in s:
return False
return True
def _debug_output(s: Any) -> None:
"""Print a debug message if DEBUG is True.
Args:
s: The message to print
"""
if DEBUG:
print(s) # noqa: T201
def _get_named_result(connection: Any, query: str) -> List[dict[str, Any]]:
"""Get a named result from a query.
Args:
connection: The connection to the database
query: The query to execute
Returns:
List[dict[str, Any]]: The result of the query
"""
cursor = connection.cursor()
cursor.execute(query)
columns = cursor.description
result = []
for value in cursor.fetchall():
r = {}
for idx, datum in enumerate(value):
k = columns[idx][0]
r[k] = datum
result.append(r)
_debug_output(result)
cursor.close()
return result
def _results_to_docs(results: Any) -> List[Document]:
return [doc for doc, _ in _results_to_docs_and_scores(results)]
def _results_to_docs_and_scores(results: Any) -> List[Tuple[Document, float]]:
return [
(Document(page_content=result[0], metadata=result[1] or {}), result[2])
for result in zip(
results["documents"],
results["metadatas"],
results["distances"],
)
]
