mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
533 lines
19 KiB
Python
533 lines
19 KiB
Python
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import json
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import logging
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import numbers
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from hashlib import sha1
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from typing import Any, Dict, Iterable, List, Optional, Tuple
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from langchain_core.documents import Document
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from langchain_core.embeddings import Embeddings
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from langchain_core.vectorstores import VectorStore
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logger = logging.getLogger()
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class AlibabaCloudOpenSearchSettings:
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"""Alibaba Cloud Opensearch` client configuration.
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Attribute:
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endpoint (str) : The endpoint of opensearch instance, You can find it
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from the console of Alibaba Cloud OpenSearch.
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instance_id (str) : The identify of opensearch instance, You can find
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it from the console of Alibaba Cloud OpenSearch.
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username (str) : The username specified when purchasing the instance.
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password (str) : The password specified when purchasing the instance，
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After the instance is created, you can modify it on the console.
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tablename (str): The table name specified during instance configuration.
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field_name_mapping (Dict) : Using field name mapping between opensearch
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vector store and opensearch instance configuration table field names:
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{
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'id': 'The id field name map of index document.',
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'document': 'The text field name map of index document.',
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'embedding': 'In the embedding field of the opensearch instance,
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the values must be in float type and separated by separator,
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default is comma.',
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'metadata_field_x': 'Metadata field mapping includes the mapped
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field name and operator in the mapping value, separated by a comma
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between the mapped field name and the operator.',
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}
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protocol (str): Communication Protocol between SDK and Server, default is http.
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namespace (str) : The instance data will be partitioned based on the "namespace"
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field,If the namespace is enabled, you need to specify the namespace field
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name during initialization, Otherwise, the queries cannot be executed
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correctly.
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embedding_field_separator(str): Delimiter specified for writing vector
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field data, default is comma.
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output_fields: Specify the field list returned when invoking OpenSearch,
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by default it is the value list of the field mapping field.
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"""
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def __init__(
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self,
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endpoint: str,
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instance_id: str,
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username: str,
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password: str,
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table_name: str,
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field_name_mapping: Dict[str, str],
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protocol: str = "http",
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namespace: str = "",
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embedding_field_separator: str = ",",
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output_fields: Optional[List[str]] = None,
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) -> None:
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self.endpoint = endpoint
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self.instance_id = instance_id
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self.protocol = protocol
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self.username = username
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self.password = password
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self.namespace = namespace
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self.table_name = table_name
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self.opt_table_name = "_".join([self.instance_id, self.table_name])
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self.field_name_mapping = field_name_mapping
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self.embedding_field_separator = embedding_field_separator
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if output_fields is None:
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self.output_fields = [
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field.split(",")[0] for field in self.field_name_mapping.values()
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]
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self.inverse_field_name_mapping: Dict[str, str] = {}
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for key, value in self.field_name_mapping.items():
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self.inverse_field_name_mapping[value.split(",")[0]] = key
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def __getitem__(self, item: str) -> Any:
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return getattr(self, item)
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def create_metadata(fields: Dict[str, Any]) -> Dict[str, Any]:
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"""Create metadata from fields.
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Args:
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fields: The fields of the document. The fields must be a dict.
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Returns:
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metadata: The metadata of the document. The metadata must be a dict.
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"""
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metadata: Dict[str, Any] = {}
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for key, value in fields.items():
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if key == "id" or key == "document" or key == "embedding":
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continue
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metadata[key] = value
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return metadata
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class AlibabaCloudOpenSearch(VectorStore):
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"""`Alibaba Cloud OpenSearch` vector store."""
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def __init__(
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self,
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embedding: Embeddings,
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config: AlibabaCloudOpenSearchSettings,
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**kwargs: Any,
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) -> None:
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try:
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from alibabacloud_ha3engine_vector import client, models
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from alibabacloud_tea_util import models as util_models
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except ImportError:
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raise ImportError(
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"Could not import alibaba cloud opensearch python package. "
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"Please install it with `pip install alibabacloud-ha3engine-vector`."
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)
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self.config = config
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self.embedding = embedding
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self.runtime = util_models.RuntimeOptions(
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connect_timeout=5000,
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read_timeout=10000,
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autoretry=False,
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ignore_ssl=False,
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max_idle_conns=50,
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)
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self.ha3_engine_client = client.Client(
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models.Config(
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endpoint=config.endpoint,
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instance_id=config.instance_id,
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protocol=config.protocol,
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access_user_name=config.username,
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access_pass_word=config.password,
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)
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)
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self.options_headers: Dict[str, str] = {}
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def add_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> List[str]:
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"""Insert documents into the instance..
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Args:
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texts: The text segments to be inserted into the vector storage,
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should not be empty.
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metadatas: Metadata information.
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Returns:
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id_list: List of document IDs.
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"""
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def _upsert(push_doc_list: List[Dict]) -> List[str]:
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if push_doc_list is None or len(push_doc_list) == 0:
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return []
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try:
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push_request = models.PushDocumentsRequest(
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self.options_headers, push_doc_list
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)
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push_response = self.ha3_engine_client.push_documents(
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self.config.opt_table_name, field_name_map["id"], push_request
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)
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json_response = json.loads(push_response.body)
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if json_response["status"] == "OK":
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return [
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push_doc["fields"][field_name_map["id"]]
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for push_doc in push_doc_list
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]
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return []
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except Exception as e:
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logger.error(
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f"add doc to endpoint:{self.config.endpoint} "
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f"instance_id:{self.config.instance_id} failed.",
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e,
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)
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raise e
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from alibabacloud_ha3engine_vector import models
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id_list = [sha1(t.encode("utf-8")).hexdigest() for t in texts]
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embeddings = self.embedding.embed_documents(list(texts))
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metadatas = metadatas or [{} for _ in texts]
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field_name_map = self.config.field_name_mapping
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add_doc_list = []
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text_list = list(texts)
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for idx, doc_id in enumerate(id_list):
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embedding = embeddings[idx] if idx < len(embeddings) else None
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metadata = metadatas[idx] if idx < len(metadatas) else None
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text = text_list[idx] if idx < len(text_list) else None
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add_doc: Dict[str, Any] = dict()
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add_doc_fields: Dict[str, Any] = dict()
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add_doc_fields.__setitem__(field_name_map["id"], doc_id)
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add_doc_fields.__setitem__(field_name_map["document"], text)
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if embedding is not None:
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add_doc_fields.__setitem__(
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field_name_map["embedding"],
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self.config.embedding_field_separator.join(
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str(unit) for unit in embedding
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),
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)
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if metadata is not None:
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for md_key, md_value in metadata.items():
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add_doc_fields.__setitem__(
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field_name_map[md_key].split(",")[0], md_value
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)
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add_doc.__setitem__("fields", add_doc_fields)
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add_doc.__setitem__("cmd", "add")
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add_doc_list.append(add_doc)
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return _upsert(add_doc_list)
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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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search_filter: Optional[Dict[str, Any]] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Perform similarity retrieval based on text.
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Args:
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query: Vectorize text for retrieval.，should not be empty.
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k: top n.
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search_filter: Additional filtering conditions.
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Returns:
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document_list: List of documents.
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"""
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embedding = self.embedding.embed_query(query)
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return self.create_results(
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self.inner_embedding_query(
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embedding=embedding, search_filter=search_filter, k=k
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)
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)
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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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search_filter: Optional[dict] = None,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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"""Perform similarity retrieval based on text with scores.
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Args:
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query: Vectorize text for retrieval.，should not be empty.
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k: top n.
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search_filter: Additional filtering conditions.
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Returns:
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document_list: List of documents.
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"""
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embedding: List[float] = self.embedding.embed_query(query)
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return self.create_results_with_score(
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self.inner_embedding_query(
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embedding=embedding, search_filter=search_filter, k=k
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)
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)
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def similarity_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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search_filter: Optional[dict] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Perform retrieval directly using vectors.
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Args:
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embedding: vectors.
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k: top n.
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search_filter: Additional filtering conditions.
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Returns:
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document_list: List of documents.
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"""
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return self.create_results(
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self.inner_embedding_query(
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embedding=embedding, search_filter=search_filter, k=k
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)
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)
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def inner_embedding_query(
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self,
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embedding: List[float],
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search_filter: Optional[Dict[str, Any]] = None,
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k: int = 4,
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) -> Dict[str, Any]:
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def generate_filter_query() -> str:
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if search_filter is None:
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return ""
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filter_clause = " AND ".join(
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[
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create_filter(md_key, md_value)
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for md_key, md_value in search_filter.items()
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]
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)
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return filter_clause
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def create_filter(md_key: str, md_value: Any) -> str:
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md_filter_expr = self.config.field_name_mapping[md_key]
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if md_filter_expr is None:
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return ""
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expr = md_filter_expr.split(",")
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if len(expr) != 2:
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logger.error(
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f"filter {md_filter_expr} express is not correct, "
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f"must contain mapping field and operator."
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)
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return ""
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md_filter_key = expr[0].strip()
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md_filter_operator = expr[1].strip()
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if isinstance(md_value, numbers.Number):
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return f"{md_filter_key} {md_filter_operator} {md_value}"
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return f'{md_filter_key}{md_filter_operator}"{md_value}"'
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def search_data() -> Dict[str, Any]:
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request = QueryRequest(
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table_name=self.config.table_name,
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namespace=self.config.namespace,
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vector=embedding,
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include_vector=True,
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output_fields=self.config.output_fields,
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filter=generate_filter_query(),
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top_k=k,
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)
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query_result = self.ha3_engine_client.query(request)
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return json.loads(query_result.body)
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from alibabacloud_ha3engine_vector.models import QueryRequest
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try:
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json_response = search_data()
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if (
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"errorCode" in json_response
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and "errorMsg" in json_response
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and len(json_response["errorMsg"]) > 0
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):
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logger.error(
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f"query {self.config.endpoint} {self.config.instance_id} "
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f"failed:{json_response['errorMsg']}."
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)
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else:
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return json_response
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except Exception as e:
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logger.error(
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f"query instance endpoint:{self.config.endpoint} "
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f"instance_id:{self.config.instance_id} failed.",
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e,
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)
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return {}
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def create_results(self, json_result: Dict[str, Any]) -> List[Document]:
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"""Assemble documents."""
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items = json_result["result"]
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query_result_list: List[Document] = []
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for item in items:
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if (
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"fields" not in item
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or self.config.field_name_mapping["document"] not in item["fields"]
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|
|
):
|
|||
|
|
query_result_list.append(Document())
|
|||
|
|
else:
|
|||
|
|
fields = item["fields"]
|
|||
|
|
query_result_list.append(
|
|||
|
|
Document(
|
|||
|
|
page_content=fields[self.config.field_name_mapping["document"]],
|
|||
|
|
metadata=self.create_inverse_metadata(fields),
|
|||
|
|
)
|
|||
|
|
)
|
|||
|
|
return query_result_list
|
|||
|
|
|
|||
|
|
def create_inverse_metadata(self, fields: Dict[str, Any]) -> Dict[str, Any]:
|
|||
|
|
"""Create metadata from fields.
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
fields: The fields of the document. The fields must be a dict.
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
metadata: The metadata of the document. The metadata must be a dict.
|
|||
|
|
"""
|
|||
|
|
metadata: Dict[str, Any] = {}
|
|||
|
|
for key, value in fields.items():
|
|||
|
|
if key == "id" or key == "document" or key == "embedding":
|
|||
|
|
continue
|
|||
|
|
metadata[self.config.inverse_field_name_mapping[key]] = value
|
|||
|
|
return metadata
|
|||
|
|
|
|||
|
|
def create_results_with_score(
|
|||
|
|
self, json_result: Dict[str, Any]
|
|||
|
|
) -> List[Tuple[Document, float]]:
|
|||
|
|
"""Parsing the returned results with scores.
|
|||
|
|
Args:
|
|||
|
|
json_result: Results from OpenSearch query.
|
|||
|
|
Returns:
|
|||
|
|
query_result_list: Results with scores.
|
|||
|
|
"""
|
|||
|
|
items = json_result["result"]
|
|||
|
|
query_result_list: List[Tuple[Document, float]] = []
|
|||
|
|
for item in items:
|
|||
|
|
fields = item["fields"]
|
|||
|
|
query_result_list.append(
|
|||
|
|
(
|
|||
|
|
Document(
|
|||
|
|
page_content=fields[self.config.field_name_mapping["document"]],
|
|||
|
|
metadata=self.create_inverse_metadata(fields),
|
|||
|
|
),
|
|||
|
|
float(item["score"]),
|
|||
|
|
)
|
|||
|
|
)
|
|||
|
|
return query_result_list
|
|||
|
|
|
|||
|
|
def delete_documents_with_texts(self, texts: List[str]) -> bool:
|
|||
|
|
"""Delete documents based on their page content.
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
texts: List of document page content.
|
|||
|
|
Returns:
|
|||
|
|
Whether the deletion was successful or not.
|
|||
|
|
"""
|
|||
|
|
id_list = [sha1(t.encode("utf-8")).hexdigest() for t in texts]
|
|||
|
|
return self.delete_documents_with_document_id(id_list)
|
|||
|
|
|
|||
|
|
def delete_documents_with_document_id(self, id_list: List[str]) -> bool:
|
|||
|
|
"""Delete documents based on their IDs.
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
id_list: List of document IDs.
|
|||
|
|
Returns:
|
|||
|
|
Whether the deletion was successful or not.
|
|||
|
|
"""
|
|||
|
|
if id_list is None or len(id_list) == 0:
|
|||
|
|
return True
|
|||
|
|
|
|||
|
|
from alibabacloud_ha3engine_vector import models
|
|||
|
|
|
|||
|
|
delete_doc_list = []
|
|||
|
|
for doc_id in id_list:
|
|||
|
|
delete_doc_list.append(
|
|||
|
|
{
|
|||
|
|
"fields": {self.config.field_name_mapping["id"]: doc_id},
|
|||
|
|
"cmd": "delete",
|
|||
|
|
}
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
delete_request = models.PushDocumentsRequest(
|
|||
|
|
self.options_headers, delete_doc_list
|
|||
|
|
)
|
|||
|
|
try:
|
|||
|
|
delete_response = self.ha3_engine_client.push_documents(
|
|||
|
|
self.config.opt_table_name,
|
|||
|
|
self.config.field_name_mapping["id"],
|
|||
|
|
delete_request,
|
|||
|
|
)
|
|||
|
|
json_response = json.loads(delete_response.body)
|
|||
|
|
return json_response["status"] == "OK"
|
|||
|
|
except Exception as e:
|
|||
|
|
logger.error(
|
|||
|
|
f"delete doc from :{self.config.endpoint} "
|
|||
|
|
f"instance_id:{self.config.instance_id} failed.",
|
|||
|
|
e,
|
|||
|
|
)
|
|||
|
|
raise e
|
|||
|
|
|
|||
|
|
@classmethod
|
|||
|
|
def from_texts(
|
|||
|
|
cls,
|
|||
|
|
texts: List[str],
|
|||
|
|
embedding: Embeddings,
|
|||
|
|
metadatas: Optional[List[dict]] = None,
|
|||
|
|
config: Optional[AlibabaCloudOpenSearchSettings] = None,
|
|||
|
|
**kwargs: Any,
|
|||
|
|
) -> "AlibabaCloudOpenSearch":
|
|||
|
|
"""Create alibaba cloud opensearch vector store instance.
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
texts: The text segments to be inserted into the vector storage,
|
|||
|
|
should not be empty.
|
|||
|
|
embedding: Embedding function, Embedding function.
|
|||
|
|
config: Alibaba OpenSearch instance configuration.
|
|||
|
|
metadatas: Metadata information.
|
|||
|
|
Returns:
|
|||
|
|
AlibabaCloudOpenSearch: Alibaba cloud opensearch vector store instance.
|
|||
|
|
"""
|
|||
|
|
if texts is None or len(texts) == 0:
|
|||
|
|
raise Exception("the inserted text segments, should not be empty.")
|
|||
|
|
|
|||
|
|
if embedding is None:
|
|||
|
|
raise Exception("the embeddings should not be empty.")
|
|||
|
|
|
|||
|
|
if config is None:
|
|||
|
|
raise Exception("config should not be none.")
|
|||
|
|
|
|||
|
|
ctx = cls(embedding, config, **kwargs)
|
|||
|
|
ctx.add_texts(texts=texts, metadatas=metadatas)
|
|||
|
|
return ctx
|
|||
|
|
|
|||
|
|
@classmethod
|
|||
|
|
def from_documents(
|
|||
|
|
cls,
|
|||
|
|
documents: List[Document],
|
|||
|
|
embedding: Embeddings,
|
|||
|
|
config: Optional[AlibabaCloudOpenSearchSettings] = None,
|
|||
|
|
**kwargs: Any,
|
|||
|
|
) -> "AlibabaCloudOpenSearch":
|
|||
|
|
"""Create alibaba cloud opensearch vector store instance.
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
documents: Documents to be inserted into the vector storage,
|
|||
|
|
should not be empty.
|
|||
|
|
embedding: Embedding function, Embedding function.
|
|||
|
|
config: Alibaba OpenSearch instance configuration.
|
|||
|
|
ids: Specify the ID for the inserted document. If left empty, the ID will be
|
|||
|
|
automatically generated based on the text content.
|
|||
|
|
Returns:
|
|||
|
|
AlibabaCloudOpenSearch: Alibaba cloud opensearch vector store instance.
|
|||
|
|
"""
|
|||
|
|
if documents is None or len(documents) == 0:
|
|||
|
|
raise Exception("the inserted documents, should not be empty.")
|
|||
|
|
|
|||
|
|
if embedding is None:
|
|||
|
|
raise Exception("the embeddings should not be empty.")
|
|||
|
|
|
|||
|
|
if config is None:
|
|||
|
|
raise Exception("config can't be none")
|
|||
|
|
|
|||
|
|
texts = [d.page_content for d in documents]
|
|||
|
|
metadatas = [d.metadata for d in documents]
|
|||
|
|
return cls.from_texts(
|
|||
|
|
texts=texts,
|
|||
|
|
embedding=embedding,
|
|||
|
|
metadatas=metadatas,
|
|||
|
|
config=config,
|
|||
|
|
**kwargs,
|
|||
|
|
)
|