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628 lines
21 KiB
Python
628 lines
21 KiB
Python
from __future__ import annotations
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import logging
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import uuid
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from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Set, Tuple, Type
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import numpy as np
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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.vectorstores import VectorStore
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from langchain_community.vectorstores.utils import maximal_marginal_relevance
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if TYPE_CHECKING:
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import awadb
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logger = logging.getLogger()
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DEFAULT_TOPN = 4
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class AwaDB(VectorStore):
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"""`AwaDB` vector store."""
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_DEFAULT_TABLE_NAME = "langchain_awadb"
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def __init__(
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self,
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table_name: str = _DEFAULT_TABLE_NAME,
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embedding: Optional[Embeddings] = None,
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log_and_data_dir: Optional[str] = None,
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client: Optional[awadb.Client] = None,
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**kwargs: Any,
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) -> None:
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"""Initialize with AwaDB client.
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If table_name is not specified,
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a random table name of `_DEFAULT_TABLE_NAME + last segment of uuid`
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would be created automatically.
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Args:
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table_name: Name of the table created, default _DEFAULT_TABLE_NAME.
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embedding: Optional Embeddings initially set.
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log_and_data_dir: Optional the root directory of log and data.
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client: Optional AwaDB client.
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kwargs: Any possible extend parameters in the future.
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Returns:
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None.
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"""
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try:
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import awadb
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except ImportError:
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raise ImportError(
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"Could not import awadb python package. "
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"Please install it with `pip install awadb`."
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)
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if client is not None:
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self.awadb_client = client
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else:
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if log_and_data_dir is not None:
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self.awadb_client = awadb.Client(log_and_data_dir)
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else:
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self.awadb_client = awadb.Client()
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if table_name == self._DEFAULT_TABLE_NAME:
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table_name += "_"
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table_name += str(uuid.uuid4()).split("-")[-1]
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self.awadb_client.Create(table_name)
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self.table2embeddings: dict[str, Embeddings] = {}
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if embedding is not None:
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self.table2embeddings[table_name] = embedding
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self.using_table_name = table_name
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@property
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def embeddings(self) -> Optional[Embeddings]:
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if self.using_table_name in self.table2embeddings:
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return self.table2embeddings[self.using_table_name]
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return None
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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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is_duplicate_texts: Optional[bool] = 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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metadatas: Optional list of metadatas associated with the texts.
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is_duplicate_texts: Optional whether to duplicate texts. Defaults to True.
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kwargs: any possible extend parameters in the future.
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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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if self.awadb_client is None:
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raise ValueError("AwaDB client is None!!!")
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embeddings = None
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if self.using_table_name in self.table2embeddings:
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embeddings = self.table2embeddings[self.using_table_name].embed_documents(
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list(texts)
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)
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return self.awadb_client.AddTexts(
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"embedding_text",
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"text_embedding",
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texts,
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embeddings,
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metadatas,
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is_duplicate_texts,
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)
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def load_local(
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self,
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table_name: str,
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**kwargs: Any,
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) -> bool:
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"""Load the local specified table.
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Args:
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table_name: Table name
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kwargs: Any possible extend parameters in the future.
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Returns:
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Success or failure of loading the local specified table
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"""
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if self.awadb_client is None:
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raise ValueError("AwaDB client is None!!!")
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return self.awadb_client.Load(table_name)
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def similarity_search(
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self,
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query: str,
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k: int = DEFAULT_TOPN,
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text_in_page_content: Optional[str] = None,
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meta_filter: Optional[dict] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs most similar to query.
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Args:
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query: Text query.
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k: The maximum number of documents to return.
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text_in_page_content: Filter by the text in page_content of Document.
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meta_filter (Optional[dict]): Filter by metadata. Defaults to None.
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E.g. `{"color" : "red", "price": 4.20}`. Optional.
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E.g. `{"max_price" : 15.66, "min_price": 4.20}`
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`price` is the metadata field, means range filter(4.20<'price'<15.66).
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E.g. `{"maxe_price" : 15.66, "mine_price": 4.20}`
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`price` is the metadata field, means range filter(4.20<='price'<=15.66).
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kwargs: Any possible extend parameters in the future.
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Returns:
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Returns the k most similar documents to the specified text query.
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"""
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if self.awadb_client is None:
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raise ValueError("AwaDB client is None!!!")
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embedding = None
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if self.using_table_name in self.table2embeddings:
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embedding = self.table2embeddings[self.using_table_name].embed_query(query)
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else:
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from awadb import AwaEmbedding
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embedding = AwaEmbedding().Embedding(query)
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not_include_fields: Set[str] = {"text_embedding", "_id", "score"}
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return self.similarity_search_by_vector(
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embedding,
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k,
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text_in_page_content=text_in_page_content,
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meta_filter=meta_filter,
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not_include_fields_in_metadata=not_include_fields,
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)
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def similarity_search_with_score(
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self,
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query: str,
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k: int = DEFAULT_TOPN,
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text_in_page_content: Optional[str] = None,
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meta_filter: Optional[dict] = None,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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"""The most k similar documents and scores of the specified query.
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Args:
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query: Text query.
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k: The k most similar documents to the text query.
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text_in_page_content: Filter by the text in page_content of Document.
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meta_filter: Filter by metadata. Defaults to None.
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kwargs: Any possible extend parameters in the future.
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Returns:
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The k most similar documents to the specified text query.
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0 is dissimilar, 1 is the most similar.
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"""
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if self.awadb_client is None:
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raise ValueError("AwaDB client is None!!!")
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embedding = None
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if self.using_table_name in self.table2embeddings:
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embedding = self.table2embeddings[self.using_table_name].embed_query(query)
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else:
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from awadb import AwaEmbedding
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embedding = AwaEmbedding().Embedding(query)
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results: List[Tuple[Document, float]] = []
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not_include_fields: Set[str] = {"text_embedding", "_id"}
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retrieval_docs = self.similarity_search_by_vector(
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embedding,
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k,
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text_in_page_content=text_in_page_content,
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meta_filter=meta_filter,
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not_include_fields_in_metadata=not_include_fields,
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)
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for doc in retrieval_docs:
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score = doc.metadata["score"]
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del doc.metadata["score"]
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doc_tuple = (doc, score)
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results.append(doc_tuple)
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return results
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def _similarity_search_with_relevance_scores(
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self,
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query: str,
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k: int = 4,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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return self.similarity_search_with_score(query, k, **kwargs)
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def similarity_search_by_vector(
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self,
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embedding: Optional[List[float]] = None,
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k: int = DEFAULT_TOPN,
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text_in_page_content: Optional[str] = None,
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meta_filter: Optional[dict] = None,
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not_include_fields_in_metadata: Optional[Set[str]] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs most similar to embedding vector.
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Args:
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embedding: Embedding to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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text_in_page_content: Filter by the text in page_content of Document.
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meta_filter: Filter by metadata. Defaults to None.
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not_incude_fields_in_metadata: Not include meta fields of each document.
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Returns:
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List of Documents which are the most similar to the query vector.
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"""
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if self.awadb_client is None:
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raise ValueError("AwaDB client is None!!!")
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results: List[Document] = []
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if embedding is None:
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return results
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show_results = self.awadb_client.Search(
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embedding,
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k,
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text_in_page_content=text_in_page_content,
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meta_filter=meta_filter,
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not_include_fields=not_include_fields_in_metadata,
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)
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if show_results.__len__() == 0:
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return results
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for item_detail in show_results[0]["ResultItems"]:
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content = ""
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meta_data = {}
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for item_key in item_detail:
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if item_key == "embedding_text":
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content = item_detail[item_key]
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continue
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elif not_include_fields_in_metadata is not None:
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if item_key in not_include_fields_in_metadata:
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continue
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meta_data[item_key] = item_detail[item_key]
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results.append(Document(page_content=content, metadata=meta_data))
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return results
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def max_marginal_relevance_search(
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self,
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query: str,
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k: int = 4,
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fetch_k: int = 20,
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lambda_mult: float = 0.5,
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text_in_page_content: Optional[str] = None,
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meta_filter: Optional[dict] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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Maximal marginal relevance optimizes for similarity to query AND diversity
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among selected documents.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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fetch_k: Number of Documents to fetch to pass to MMR algorithm.
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lambda_mult: Number between 0 and 1 that determines the degree
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of diversity among the results with 0 corresponding
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to maximum diversity and 1 to minimum diversity.
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Defaults to 0.5.
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text_in_page_content: Filter by the text in page_content of Document.
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meta_filter (Optional[dict]): Filter by metadata. Defaults to None.
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Returns:
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List of Documents selected by maximal marginal relevance.
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"""
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if self.awadb_client is None:
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raise ValueError("AwaDB client is None!!!")
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embedding: List[float] = []
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if self.using_table_name in self.table2embeddings:
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embedding = self.table2embeddings[self.using_table_name].embed_query(query)
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else:
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from awadb import AwaEmbedding
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embedding = AwaEmbedding().Embedding(query)
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if embedding.__len__() == 0:
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return []
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results = self.max_marginal_relevance_search_by_vector(
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embedding,
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k,
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fetch_k,
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lambda_mult=lambda_mult,
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text_in_page_content=text_in_page_content,
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meta_filter=meta_filter,
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)
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return results
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def max_marginal_relevance_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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fetch_k: int = 20,
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lambda_mult: float = 0.5,
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text_in_page_content: Optional[str] = None,
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meta_filter: Optional[dict] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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Maximal marginal relevance optimizes for similarity to query AND diversity
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among selected documents.
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Args:
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embedding: Embedding to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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fetch_k: Number of Documents to fetch to pass to MMR algorithm.
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lambda_mult: Number between 0 and 1 that determines the degree
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of diversity among the results with 0 corresponding
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to maximum diversity and 1 to minimum diversity.
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Defaults to 0.5.
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text_in_page_content: Filter by the text in page_content of Document.
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meta_filter (Optional[dict]): Filter by metadata. Defaults to None.
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Returns:
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List of Documents selected by maximal marginal relevance.
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"""
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if self.awadb_client is None:
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raise ValueError("AwaDB client is None!!!")
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results: List[Document] = []
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if embedding is None:
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return results
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not_include_fields: set = {"_id", "score"}
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retrieved_docs = self.similarity_search_by_vector(
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embedding,
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fetch_k,
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text_in_page_content=text_in_page_content,
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meta_filter=meta_filter,
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not_include_fields_in_metadata=not_include_fields,
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)
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top_embeddings = []
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for doc in retrieved_docs:
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top_embeddings.append(doc.metadata["text_embedding"])
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selected_docs = maximal_marginal_relevance(
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np.array(embedding, dtype=np.float32), embedding_list=top_embeddings
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)
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for s_id in selected_docs:
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if "text_embedding" in retrieved_docs[s_id].metadata:
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del retrieved_docs[s_id].metadata["text_embedding"]
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results.append(retrieved_docs[s_id])
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return results
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def get(
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self,
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ids: Optional[List[str]] = None,
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text_in_page_content: Optional[str] = None,
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meta_filter: Optional[dict] = None,
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not_include_fields: Optional[Set[str]] = None,
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limit: Optional[int] = None,
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**kwargs: Any,
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) -> Dict[str, Document]:
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"""Return docs according ids.
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Args:
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ids: The ids of the embedding vectors.
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text_in_page_content: Filter by the text in page_content of Document.
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meta_filter: Filter by any metadata of the document.
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not_include_fields: Not pack the specified fields of each document.
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limit: The number of documents to return. Defaults to 5. Optional.
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Returns:
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Documents which satisfy the input conditions.
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"""
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if self.awadb_client is None:
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raise ValueError("AwaDB client is None!!!")
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docs_detail = self.awadb_client.Get(
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ids=ids,
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text_in_page_content=text_in_page_content,
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meta_filter=meta_filter,
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not_include_fields=not_include_fields,
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limit=limit,
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)
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results: Dict[str, Document] = {}
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for doc_detail in docs_detail:
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content = ""
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meta_info = {}
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for field in doc_detail:
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if field == "embedding_text":
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content = doc_detail[field]
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continue
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elif field == "text_embedding" or field == "_id":
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continue
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meta_info[field] = doc_detail[field]
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doc = Document(page_content=content, metadata=meta_info)
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results[doc_detail["_id"]] = doc
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return results
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def delete(
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self,
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ids: Optional[List[str]] = None,
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**kwargs: Any,
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) -> Optional[bool]:
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"""Delete the documents which have the specified ids.
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Args:
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ids: The ids of the embedding vectors.
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**kwargs: Other keyword arguments that subclasses might use.
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Returns:
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Optional[bool]: True if deletion is successful.
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False otherwise, None if not implemented.
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"""
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if self.awadb_client is None:
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raise ValueError("AwaDB client is None!!!")
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ret: Optional[bool] = None
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if ids is None or ids.__len__() == 0:
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return ret
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ret = self.awadb_client.Delete(ids)
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return ret
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def update(
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self,
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ids: List[str],
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> List[str]:
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"""Update the documents which have the specified ids.
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Args:
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ids: The id list of the updating embedding vector.
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texts: The texts of the updating documents.
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metadatas: The metadatas of the updating documents.
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Returns:
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the ids of the updated documents.
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"""
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if self.awadb_client is None:
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raise ValueError("AwaDB client is None!!!")
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return self.awadb_client.UpdateTexts(
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ids=ids, text_field_name="embedding_text", texts=texts, metadatas=metadatas
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)
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def create_table(
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self,
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table_name: str,
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**kwargs: Any,
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) -> bool:
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"""Create a new table."""
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|
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if self.awadb_client is None:
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return False
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ret = self.awadb_client.Create(table_name)
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if ret:
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self.using_table_name = table_name
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return ret
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def use(
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self,
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table_name: str,
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**kwargs: Any,
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) -> bool:
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"""Use the specified table. Don't know the tables, please invoke list_tables."""
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if self.awadb_client is None:
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return False
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ret = self.awadb_client.Use(table_name)
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if ret:
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self.using_table_name = table_name
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return ret
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def list_tables(
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|
self,
|
|
**kwargs: Any,
|
|
) -> List[str]:
|
|
"""List all the tables created by the client."""
|
|
|
|
if self.awadb_client is None:
|
|
return []
|
|
|
|
return self.awadb_client.ListAllTables()
|
|
|
|
def get_current_table(
|
|
self,
|
|
**kwargs: Any,
|
|
) -> str:
|
|
"""Get the current table."""
|
|
|
|
return self.using_table_name
|
|
|
|
@classmethod
|
|
def from_texts(
|
|
cls: Type[AwaDB],
|
|
texts: List[str],
|
|
embedding: Optional[Embeddings] = None,
|
|
metadatas: Optional[List[dict]] = None,
|
|
table_name: str = _DEFAULT_TABLE_NAME,
|
|
log_and_data_dir: Optional[str] = None,
|
|
client: Optional[awadb.Client] = None,
|
|
**kwargs: Any,
|
|
) -> AwaDB:
|
|
"""Create an AwaDB vectorstore from a raw documents.
|
|
|
|
Args:
|
|
texts (List[str]): List of texts to add to the table.
|
|
embedding (Optional[Embeddings]): Embedding function. Defaults to None.
|
|
metadatas (Optional[List[dict]]): List of metadatas. Defaults to None.
|
|
table_name (str): Name of the table to create.
|
|
log_and_data_dir (Optional[str]): Directory of logging and persistence.
|
|
client (Optional[awadb.Client]): AwaDB client
|
|
|
|
Returns:
|
|
AwaDB: AwaDB vectorstore.
|
|
"""
|
|
awadb_client = cls(
|
|
table_name=table_name,
|
|
embedding=embedding,
|
|
log_and_data_dir=log_and_data_dir,
|
|
client=client,
|
|
)
|
|
awadb_client.add_texts(texts=texts, metadatas=metadatas)
|
|
return awadb_client
|
|
|
|
@classmethod
|
|
def from_documents(
|
|
cls: Type[AwaDB],
|
|
documents: List[Document],
|
|
embedding: Optional[Embeddings] = None,
|
|
table_name: str = _DEFAULT_TABLE_NAME,
|
|
log_and_data_dir: Optional[str] = None,
|
|
client: Optional[awadb.Client] = None,
|
|
**kwargs: Any,
|
|
) -> AwaDB:
|
|
"""Create an AwaDB vectorstore from a list of documents.
|
|
|
|
If a log_and_data_dir specified, the table will be persisted there.
|
|
|
|
Args:
|
|
documents (List[Document]): List of documents to add to the vectorstore.
|
|
embedding (Optional[Embeddings]): Embedding function. Defaults to None.
|
|
table_name (str): Name of the table to create.
|
|
log_and_data_dir (Optional[str]): Directory to persist the table.
|
|
client (Optional[awadb.Client]): AwaDB client.
|
|
Any: Any possible parameters in the future
|
|
|
|
Returns:
|
|
AwaDB: AwaDB vectorstore.
|
|
"""
|
|
texts = [doc.page_content for doc in documents]
|
|
metadatas = [doc.metadata for doc in documents]
|
|
return cls.from_texts(
|
|
texts=texts,
|
|
embedding=embedding,
|
|
metadatas=metadatas,
|
|
table_name=table_name,
|
|
log_and_data_dir=log_and_data_dir,
|
|
client=client,
|
|
)
|