import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Union,
)
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
if TYPE_CHECKING:
from elasticsearch import Elasticsearch
logger = logging.getLogger(__name__)
[docs]
class EcloudESVectorStore(VectorStore):
"""`ecloud Elasticsearch` vector store.
Example:
.. code-block:: python
from langchain.vectorstores import EcloudESVectorStore
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = EcloudESVectorStore(
embedding=OpenAIEmbeddings(),
index_name="langchain-demo",
es_url="http://localhost:9200"
)
Args:
index_name: Name of the Elasticsearch index to create.
es_url: URL of the ecloud Elasticsearch instance to connect to.
user: Username to use when connecting to Elasticsearch.
password: Password to use when connecting to Elasticsearch.
"""
[docs]
def __init__(
self,
index_name: str,
es_url: str,
user: Optional[str] = None,
password: Optional[str] = None,
embedding: Optional[Embeddings] = None,
**kwargs: Optional[dict],
) -> None:
self.embedding = embedding
self.index_name = index_name
self.text_field = kwargs.get("text_field", "text")
self.vector_field = kwargs.get("vector_field", "vector")
self.vector_type = kwargs.get("vector_type", "knn_dense_float_vector")
self.vector_params = kwargs.get("vector_params") or {}
self.model = self.vector_params.get("model", "")
self.index_settings = kwargs.get("index_settings") or {}
key_list = [
"text_field",
"vector_field",
"vector_type",
"vector_params",
"index_settings",
]
[kwargs.pop(key, None) for key in key_list]
if es_url is not None:
self.client = EcloudESVectorStore.es_client(
es_url=es_url, username=user, password=password, **kwargs
)
else:
raise ValueError("""Please specified a es connection url.""")
@property
def embeddings(self) -> Optional[Embeddings]:
return self.embedding
[docs]
@staticmethod
def es_client(
*,
es_url: Optional[str] = None,
username: Optional[str] = None,
password: Optional[str] = None,
**kwargs: Optional[dict],
) -> "Elasticsearch":
try:
import elasticsearch
except ImportError:
raise ImportError(
"Could not import elasticsearch python package. "
"Please install it with `pip install elasticsearch`."
)
connection_params: Dict[str, Any] = {"hosts": [es_url]}
if username and password:
connection_params["http_auth"] = (username, password)
connection_params.update(kwargs)
es_client = elasticsearch.Elasticsearch(**connection_params)
try:
es_client.info()
except Exception as e:
logger.error(f"Error connecting to Elasticsearch: {e}")
raise e
return es_client
def _create_index_if_not_exists(self, dims_length: Optional[int] = None) -> None:
"""Create the index if it doesn't already exist.
Args:
dims_length: Length of the embedding vectors.
"""
if self.client.indices.exists(index=self.index_name):
logger.info(f"Index {self.index_name} already exists. Skipping creation.")
else:
if dims_length is None:
raise ValueError(
"Cannot create index without specifying dims_length "
+ "when the index doesn't already exist. "
)
indexMapping = self._index_mapping(dims_length=dims_length)
logger.debug(
f"Creating index {self.index_name} with mappings {indexMapping}"
)
self.client.indices.create(
index=self.index_name,
body={
"settings": {"index.knn": True, **self.index_settings},
"mappings": {"properties": indexMapping},
},
)
def _index_mapping(self, dims_length: Union[int, None]) -> Dict:
"""
Executes when the index is created.
Args:
dims_length: Numeric length of the embedding vectors,
or None if not using vector-based query.
index_params: The extra pamameters for creating index.
Returns:
Dict: The Elasticsearch settings and mappings for the strategy.
"""
model = self.vector_params.get("model", "")
if "lsh" == model:
mapping: Dict[Any, Any] = {
self.vector_field: {
"type": self.vector_type,
"knn": {
"dims": dims_length,
"model": "lsh",
"similarity": self.vector_params.get("similarity", "cosine"),
"L": self.vector_params.get("L", 99),
"k": self.vector_params.get("k", 1),
},
}
}
if mapping[self.vector_field]["knn"]["similarity"] == "l2":
mapping[self.vector_field]["knn"]["w"] = self.vector_params.get("w", 3)
return mapping
elif "permutation_lsh" == model:
return {
self.vector_field: {
"type": self.vector_type,
"knn": {
"dims": dims_length,
"model": "permutation_lsh",
"k": self.vector_params.get("k", 10),
"similarity": self.vector_params.get("similarity", "cosine"),
"repeating": self.vector_params.get("repeating", True),
},
}
}
else:
return {
self.vector_field: {
"type": self.vector_type,
"knn": {"dims": dims_length},
}
}
[docs]
def delete(
self,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> Optional[bool]:
"""Delete documents from the index.
Args:
ids: List of ids of documents to delete
"""
try:
from elasticsearch.helpers import BulkIndexError, bulk
except ImportError:
raise ImportError(
"Could not import elasticsearch python package. "
"Please install it with `pip install elasticsearch`."
)
body = []
if ids is None:
raise ValueError("ids must be provided.")
for _id in ids:
body.append({"_op_type": "delete", "_index": self.index_name, "_id": _id})
if len(body) > 0:
try:
bulk(
self.client,
body,
refresh=kwargs.get("refresh_indices", True),
ignore_status=404,
)
logger.debug(f"Deleted {len(body)} texts from index")
return True
except BulkIndexError as e:
logger.error(f"Error deleting texts: {e}")
raise e
else:
logger.info("No documents to delete")
return False
def _query_body(
self,
query_vector: Union[List[float], None],
filter: Optional[dict] = None,
search_params: Dict = {},
) -> Dict:
query_vector_body = {
"field": search_params.get("vector_field", self.vector_field)
}
if self.vector_type == "knn_dense_float_vector":
query_vector_body["vec"] = {"values": query_vector}
specific_params = self.get_dense_specific_model_similarity_params(
search_params
)
query_vector_body.update(specific_params)
else:
query_vector_body["vec"] = {
"true_indices": query_vector,
"total_indices": len(query_vector) if query_vector is not None else 0,
}
specific_params = self.get_sparse_specific_model_similarity_params(
search_params
)
query_vector_body.update(specific_params)
query_vector_body = {"knn_nearest_neighbors": query_vector_body}
if filter is not None and len(filter) != 0:
query_vector_body = {
"function_score": {"query": filter, "functions": [query_vector_body]}
}
return {
"size": search_params.get("size", 4),
"query": query_vector_body,
}
[docs]
@staticmethod
def get_dense_specific_model_similarity_params(
search_params: Dict[str, Any],
) -> Dict:
model = search_params.get("model", "exact")
similarity = search_params.get("similarity", "cosine")
specific_params = {"model": model, "similarity": similarity}
if not model == "exact":
if model not in ("lsh", "permutation_lsh"):
raise ValueError(
f"vector type knn_dense_float_vector doesn't support model {model}"
)
if similarity not in ("cosine", "l2"):
raise ValueError(f"model exact doesn't support similarity {similarity}")
specific_params["candidates"] = search_params.get(
"candidates", search_params.get("size", 4)
)
if model == "lsh" and similarity == "l2":
specific_params["probes"] = search_params.get("probes", 0)
else:
if similarity not in ("cosine", "l2"):
raise ValueError(f"model exact don't support similarity {similarity}")
return specific_params
[docs]
@staticmethod
def get_sparse_specific_model_similarity_params(
search_params: Dict[str, Any],
) -> Dict:
model = search_params.get("model", "exact")
similarity = search_params.get("similarity", "hamming")
specific_params = {"model": model, "similarity": similarity}
if not model == "exact":
if model not in ("lsh",):
raise ValueError(
f"vector type knn_dense_float_vector doesn't support model {model}"
)
if similarity not in ("hamming", "jaccard"):
raise ValueError(f"model exact doesn't support similarity {similarity}")
specific_params["candidates"] = search_params.get(
"candidates", search_params.get("size", 4)
)
else:
if similarity not in ("hamming", "jaccard"):
raise ValueError(f"model exact don't support similarity {similarity}")
return specific_params
def _search(
self,
query: Optional[str] = None,
query_vector: Union[List[float], None] = None,
filter: Optional[dict] = None,
custom_query: Optional[Callable[[Dict, Union[str, None]], Dict]] = None,
search_params: Dict = {},
) -> List[Tuple[Document, float]]:
"""Return searched documents result from ecloud ES
Args:
query: Text to look up documents similar to.
query_vector: Embedding to look up documents similar to.
filter: Array of ecloud ElasticSearch filter clauses to apply to the query.
custom_query: Function to modify the query body before it is sent to ES.
Returns:
List of Documents most similar to the query and score for each
"""
if self.embedding and query is not None:
query_vector = self.embedding.embed_query(query)
query_body = self._query_body(
query_vector=query_vector, filter=filter, search_params=search_params
)
if custom_query is not None:
query_body = custom_query(query_body, query)
logger.debug(f"Calling custom_query, Query body now: {query_body}")
logger.debug(f"Query body: {query_body}")
# Perform the kNN search on the ES index and return the results.
response = self.client.search(index=self.index_name, body=query_body)
logger.debug(f"response={response}")
hits = [hit for hit in response["hits"]["hits"]]
docs_and_scores = [
(
Document(
page_content=hit["_source"][
search_params.get("text_field", self.text_field)
],
metadata=hit["_source"]["metadata"],
),
hit["_score"],
)
for hit in hits
]
return docs_and_scores
[docs]
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
**kwargs: Any,
) -> List[Document]:
"""Return documents most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Array of Elasticsearch filter clauses to apply to the query.
Returns:
List of Documents most similar to the query,
in descending order of similarity.
"""
results = self.similarity_search_with_score(
query=query, k=k, filter=filter, **kwargs
)
return [doc for doc, _ in results]
[docs]
def similarity_search_with_score(
self, query: str, k: int, filter: Optional[dict] = None, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Return documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
size: Number of Documents to return. Defaults to 4.
filter: Array of Elasticsearch filter clauses to apply to the query.
Returns:
List of Documents most similar to the query and score for each
"""
search_params: Dict[str, Any] = kwargs.get("search_params") or {}
if len(search_params) == 0:
kwargs = {"search_params": {"size": k}}
elif search_params.get("size") is None:
search_params["size"] = k
kwargs["search_params"] = search_params
return self._search(query=query, filter=filter, **kwargs)
[docs]
@classmethod
def from_documents(
cls,
documents: List[Document],
embedding: Optional[Embeddings] = None,
**kwargs: Any,
) -> "EcloudESVectorStore":
"""Construct EcloudESVectorStore wrapper from documents.
Args:
documents: List of documents to add to the Elasticsearch index.
embedding: Embedding function to use to embed the texts.
Do not provide if using a strategy
that doesn't require inference.
kwargs: create index key words arguments
"""
vectorStore = EcloudESVectorStore._es_vector_store(
embedding=embedding, **kwargs
)
# Encode the provided texts and add them to the newly created index.
vectorStore.add_documents(documents)
return vectorStore
[docs]
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[Dict[str, Any]]] = None,
**kwargs: Any,
) -> "EcloudESVectorStore":
"""Construct EcloudESVectorStore wrapper from raw documents.
Args:
texts: List of texts to add to the Elasticsearch index.
embedding: Embedding function to use to embed the texts.
metadatas: Optional list of metadatas associated with the texts.
index_name: Name of the Elasticsearch index to create.
kwargs: create index key words arguments
"""
vectorStore = cls._es_vector_store(embedding=embedding, **kwargs)
# Encode the provided texts and add them to the newly created index.
vectorStore.add_texts(texts, metadatas=metadatas, **kwargs)
return vectorStore
[docs]
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[Dict[Any, Any]]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
Returns:
List of ids from adding the texts into the vectorstore.
"""
try:
from elasticsearch.helpers import BulkIndexError, bulk
except ImportError:
raise ImportError(
"Could not import elasticsearch python package. "
"Please install it with `pip install elasticsearch`."
)
embeddings = []
create_index_if_not_exists = kwargs.get("create_index_if_not_exists", True)
ids = kwargs.get("ids", [str(uuid.uuid4()) for _ in texts])
refresh_indices = kwargs.get("refresh_indices", False)
requests = []
if self.embedding is not None:
embeddings = self.embedding.embed_documents(list(texts))
dims_length = len(embeddings[0])
if create_index_if_not_exists:
self._create_index_if_not_exists(dims_length=dims_length)
for i, (text, vector) in enumerate(zip(texts, embeddings)):
metadata = metadatas[i] if metadatas else {}
doc = {
"_op_type": "index",
"_index": self.index_name,
self.text_field: text,
"metadata": metadata,
"_id": ids[i],
}
if self.vector_type == "knn_dense_float_vector":
doc[self.vector_field] = vector
elif self.vector_type == "knn_sparse_bool_vector":
doc[self.vector_field] = {
"true_indices": vector,
"total_indices": len(vector),
}
requests.append(doc)
else:
if create_index_if_not_exists:
self._create_index_if_not_exists()
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
requests.append(
{
"_op_type": "index",
"_index": self.index_name,
self.text_field: text,
"metadata": metadata,
"_id": ids[i],
}
)
if len(requests) > 0:
try:
success, failed = bulk(
self.client, requests, stats_only=True, refresh=refresh_indices
)
logger.debug(
f"Added {success} and failed to add {failed} texts to index"
)
logger.debug(f"added texts {ids} to index")
if refresh_indices:
self.client.indices.refresh(index=self.index_name)
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 _es_vector_store(
embedding: Optional[Embeddings] = None, **kwargs: Any
) -> "EcloudESVectorStore":
index_name = kwargs.get("index_name")
if index_name is None:
raise ValueError("Please provide an index_name.")
es_url = kwargs.get("es_url")
if es_url is None:
raise ValueError("Please provided a valid es connection url")
return EcloudESVectorStore(embedding=embedding, **kwargs)
