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492 lines
16 KiB
Python
492 lines
16 KiB
Python
9 months ago
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import logging
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import uuid
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from typing import (
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TYPE_CHECKING,
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Any,
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Callable,
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Dict,
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Iterable,
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List,
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Optional,
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Tuple,
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Union,
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)
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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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if TYPE_CHECKING:
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from elasticsearch import Elasticsearch
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logger = logging.getLogger(__name__)
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class BESVectorStore(VectorStore):
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"""`Baidu Elasticsearch` vector store.
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Example:
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.. code-block:: python
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from langchain_community.vectorstores import BESVectorStore
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from langchain_community.embeddings.openai import OpenAIEmbeddings
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embeddings = OpenAIEmbeddings()
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vectorstore = BESVectorStore(
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embedding=OpenAIEmbeddings(),
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index_name="langchain-demo",
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bes_url="http://localhost:9200"
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)
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Args:
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index_name: Name of the Elasticsearch index to create.
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bes_url: URL of the Baidu Elasticsearch instance to connect to.
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user: Username to use when connecting to Elasticsearch.
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password: Password to use when connecting to Elasticsearch.
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More information can be obtained from:
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https://cloud.baidu.com/doc/BES/s/8llyn0hh4
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"""
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def __init__(
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self,
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index_name: str,
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bes_url: str,
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user: Optional[str] = None,
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password: Optional[str] = None,
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embedding: Optional[Embeddings] = None,
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**kwargs: Optional[dict],
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) -> None:
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self.embedding = embedding
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self.index_name = index_name
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self.query_field = kwargs.get("query_field", "text")
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self.vector_query_field = kwargs.get("vector_query_field", "vector")
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self.space_type = kwargs.get("space_type", "cosine")
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self.index_type = kwargs.get("index_type", "linear")
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self.index_params = kwargs.get("index_params") or {}
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if bes_url is not None:
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self.client = BESVectorStore.bes_client(
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bes_url=bes_url, username=user, password=password
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)
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else:
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raise ValueError("""Please specified a bes connection url.""")
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@property
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def embeddings(self) -> Optional[Embeddings]:
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return self.embedding
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@staticmethod
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def bes_client(
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*,
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bes_url: Optional[str] = None,
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username: Optional[str] = None,
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password: Optional[str] = None,
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) -> "Elasticsearch":
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try:
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import elasticsearch
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except ImportError:
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raise ImportError(
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"Could not import elasticsearch python package. "
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"Please install it with `pip install elasticsearch`."
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)
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connection_params: Dict[str, Any] = {}
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connection_params["hosts"] = [bes_url]
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if username and password:
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connection_params["basic_auth"] = (username, password)
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es_client = elasticsearch.Elasticsearch(**connection_params)
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try:
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es_client.info()
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except Exception as e:
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logger.error(f"Error connecting to Elasticsearch: {e}")
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raise e
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return es_client
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def _create_index_if_not_exists(self, dims_length: Optional[int] = None) -> None:
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"""Create the index if it doesn't already exist.
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Args:
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dims_length: Length of the embedding vectors.
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"""
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if self.client.indices.exists(index=self.index_name):
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logger.info(f"Index {self.index_name} already exists. Skipping creation.")
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else:
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if dims_length is None:
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raise ValueError(
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"Cannot create index without specifying dims_length "
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+ "when the index doesn't already exist. "
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)
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indexMapping = self._index_mapping(dims_length=dims_length)
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logger.debug(
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f"Creating index {self.index_name} with mappings {indexMapping}"
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)
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self.client.indices.create(
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index=self.index_name,
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body={
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"settings": {"index": {"knn": True}},
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"mappings": {"properties": indexMapping},
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},
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)
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def _index_mapping(self, dims_length: Union[int, None]) -> Dict:
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"""
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Executes when the index is created.
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Args:
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dims_length: Numeric length of the embedding vectors,
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or None if not using vector-based query.
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index_params: The extra pamameters for creating index.
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Returns:
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Dict: The Elasticsearch settings and mappings for the strategy.
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"""
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if "linear" == self.index_type:
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return {
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self.vector_query_field: {
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"type": "bpack_vector",
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"dims": dims_length,
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"build_index": self.index_params.get("build_index", False),
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}
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}
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elif "hnsw" == self.index_type:
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return {
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self.vector_query_field: {
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"type": "bpack_vector",
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"dims": dims_length,
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"index_type": "hnsw",
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"space_type": self.space_type,
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"parameters": {
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"ef_construction": self.index_params.get(
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"hnsw_ef_construction", 200
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),
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"m": self.index_params.get("hnsw_m", 4),
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},
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}
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}
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else:
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return {
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self.vector_query_field: {
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"type": "bpack_vector",
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"model_id": self.index_params.get("model_id", ""),
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}
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}
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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 documents from the index.
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Args:
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ids: List of ids of documents to delete
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"""
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try:
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from elasticsearch.helpers import BulkIndexError, bulk
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except ImportError:
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raise ImportError(
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"Could not import elasticsearch python package. "
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"Please install it with `pip install elasticsearch`."
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)
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body = []
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if ids is None:
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raise ValueError("ids must be provided.")
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for _id in ids:
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body.append({"_op_type": "delete", "_index": self.index_name, "_id": _id})
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if len(body) > 0:
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try:
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bulk(
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self.client,
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body,
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refresh=kwargs.get("refresh_indices", True),
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ignore_status=404,
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)
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logger.debug(f"Deleted {len(body)} texts from index")
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return True
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except BulkIndexError as e:
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logger.error(f"Error deleting texts: {e}")
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raise e
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else:
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logger.info("No documents to delete")
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return False
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def _query_body(
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self,
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query_vector: Union[List[float], None],
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filter: Optional[dict] = None,
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search_params: Dict = {},
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) -> Dict:
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query_vector_body = {"vector": query_vector, "k": search_params.get("k", 2)}
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if filter is not None and len(filter) != 0:
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query_vector_body["filter"] = filter
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if "linear" == self.index_type:
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query_vector_body["linear"] = True
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else:
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query_vector_body["ef"] = search_params.get("ef", 10)
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return {
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"size": search_params.get("size", 4),
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"query": {"knn": {self.vector_query_field: query_vector_body}},
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}
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def _search(
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self,
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query: Optional[str] = None,
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query_vector: Union[List[float], None] = None,
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filter: Optional[dict] = None,
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custom_query: Optional[Callable[[Dict, Union[str, None]], Dict]] = None,
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search_params: Dict = {},
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) -> List[Tuple[Document, float]]:
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"""Return searched documents result from BES
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Args:
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query: Text to look up documents similar to.
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query_vector: Embedding to look up documents similar to.
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filter: Array of Baidu ElasticSearch filter clauses to apply to the query.
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custom_query: Function to modify the query body before it is sent to BES.
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Returns:
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List of Documents most similar to the query and score for each
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"""
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if self.embedding and query is not None:
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query_vector = self.embedding.embed_query(query)
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query_body = self._query_body(
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query_vector=query_vector, filter=filter, search_params=search_params
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)
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if custom_query is not None:
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query_body = custom_query(query_body, query)
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logger.debug(f"Calling custom_query, Query body now: {query_body}")
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logger.debug(f"Query body: {query_body}")
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# Perform the kNN search on the BES index and return the results.
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response = self.client.search(index=self.index_name, body=query_body)
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logger.debug(f"response={response}")
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hits = [hit for hit in response["hits"]["hits"]]
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docs_and_scores = [
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(
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Document(
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page_content=hit["_source"][self.query_field],
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metadata=hit["_source"]["metadata"],
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),
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hit["_score"],
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)
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for hit in hits
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]
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return docs_and_scores
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def similarity_search(
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self,
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query: str,
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k: int = 4,
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filter: Optional[dict] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return documents most similar to query.
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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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filter: Array of Elasticsearch filter clauses to apply to the query.
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Returns:
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List of Documents most similar to the query,
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in descending order of similarity.
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"""
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results = self.similarity_search_with_score(
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query=query, k=k, filter=filter, **kwargs
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)
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return [doc for doc, _ in results]
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def similarity_search_with_score(
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self, query: str, k: int, filter: Optional[dict] = None, **kwargs: Any
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) -> List[Tuple[Document, float]]:
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"""Return documents most similar to query, along with scores.
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Args:
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query: Text to look up documents similar to.
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size: Number of Documents to return. Defaults to 4.
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filter: Array of Elasticsearch filter clauses to apply to the query.
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Returns:
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List of Documents most similar to the query and score for each
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"""
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search_params = kwargs.get("search_params") or {}
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if len(search_params) == 0 or search_params.get("size") is None:
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search_params["size"] = k
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return self._search(query=query, filter=filter, **kwargs)
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@classmethod
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def from_documents(
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cls,
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documents: List[Document],
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embedding: Optional[Embeddings] = None,
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**kwargs: Any,
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) -> "BESVectorStore":
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"""Construct BESVectorStore wrapper from documents.
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Args:
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documents: List of documents to add to the Elasticsearch index.
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embedding: Embedding function to use to embed the texts.
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Do not provide if using a strategy
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that doesn't require inference.
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kwargs: create index key words arguments
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"""
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vectorStore = BESVectorStore._bes_vector_store(embedding=embedding, **kwargs)
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# Encode the provided texts and add them to the newly created index.
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vectorStore.add_documents(documents)
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return vectorStore
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@classmethod
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def from_texts(
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cls,
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texts: List[str],
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embedding: Optional[Embeddings] = None,
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metadatas: Optional[List[Dict[str, Any]]] = None,
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**kwargs: Any,
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) -> "BESVectorStore":
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"""Construct BESVectorStore wrapper from raw documents.
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Args:
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texts: List of texts to add to the Elasticsearch index.
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embedding: Embedding function to use to embed the texts.
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metadatas: Optional list of metadatas associated with the texts.
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index_name: Name of the Elasticsearch index to create.
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kwargs: create index key words arguments
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"""
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vectorStore = BESVectorStore._bes_vector_store(embedding=embedding, **kwargs)
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# Encode the provided texts and add them to the newly created index.
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vectorStore.add_texts(texts, metadatas=metadatas, **kwargs)
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return vectorStore
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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[Any, Any]]] = 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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Returns:
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List of ids from adding the texts into the vectorstore.
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"""
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try:
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from elasticsearch.helpers import BulkIndexError, bulk
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||
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except ImportError:
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raise ImportError(
|
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|
"Could not import elasticsearch python package. "
|
||
|
"Please install it with `pip install elasticsearch`."
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)
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|
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embeddings = []
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create_index_if_not_exists = kwargs.get("create_index_if_not_exists", True)
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ids = kwargs.get("ids", [str(uuid.uuid4()) for _ in texts])
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refresh_indices = kwargs.get("refresh_indices", True)
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requests = []
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if self.embedding is not None:
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embeddings = self.embedding.embed_documents(list(texts))
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dims_length = len(embeddings[0])
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if create_index_if_not_exists:
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self._create_index_if_not_exists(dims_length=dims_length)
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for i, (text, vector) in enumerate(zip(texts, embeddings)):
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metadata = metadatas[i] if metadatas else {}
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requests.append(
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{
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"_op_type": "index",
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"_index": self.index_name,
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self.query_field: text,
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self.vector_query_field: vector,
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"metadata": metadata,
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"_id": ids[i],
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}
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)
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else:
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if create_index_if_not_exists:
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self._create_index_if_not_exists()
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for i, text in enumerate(texts):
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metadata = metadatas[i] if metadatas else {}
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requests.append(
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{
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"_op_type": "index",
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"_index": self.index_name,
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self.query_field: text,
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"metadata": metadata,
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"_id": ids[i],
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}
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)
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if len(requests) > 0:
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try:
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success, failed = bulk(
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self.client, requests, stats_only=True, refresh=refresh_indices
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)
|
||
|
logger.debug(
|
||
|
f"Added {success} and failed to add {failed} texts to index"
|
||
|
)
|
||
|
|
||
|
logger.debug(f"added texts {ids} to index")
|
||
|
return ids
|
||
|
except BulkIndexError as e:
|
||
|
logger.error(f"Error adding texts: {e}")
|
||
|
firstError = e.errors[0].get("index", {}).get("error", {})
|
||
|
logger.error(f"First error reason: {firstError.get('reason')}")
|
||
|
raise e
|
||
|
|
||
|
else:
|
||
|
logger.debug("No texts to add to index")
|
||
|
return []
|
||
|
|
||
|
@staticmethod
|
||
|
def _bes_vector_store(
|
||
|
embedding: Optional[Embeddings] = None, **kwargs: Any
|
||
|
) -> "BESVectorStore":
|
||
|
index_name = kwargs.get("index_name")
|
||
|
|
||
|
if index_name is None:
|
||
|
raise ValueError("Please provide an index_name.")
|
||
|
|
||
|
bes_url = kwargs.get("bes_url")
|
||
|
if bes_url is None:
|
||
|
raise ValueError("Please provided a valid bes connection url")
|
||
|
|
||
|
return BESVectorStore(embedding=embedding, **kwargs)
|