"""Wrapper around Elasticsearch vector database."""
from __future__ import annotations
import uuid
from abc import ABC
from typing import (
TYPE_CHECKING,
Any,
Dict,
Iterable,
List,
Mapping,
Optional,
Tuple,
Union,
)
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
from langchain.vectorstores.base import VectorStore
if TYPE_CHECKING:
from elasticsearch import Elasticsearch
def _default_text_mapping(dim: int) -> Dict:
return {
"properties": {
"text": {"type": "text"},
"vector": {"type": "dense_vector", "dims": dim},
}
}
def _default_script_query(query_vector: List[float], filter: Optional[dict]) -> Dict:
if filter:
((key, value),) = filter.items()
filter = {"match": {f"metadata.{key}.keyword": f"{value}"}}
else:
filter = {"match_all": {}}
return {
"script_score": {
"query": filter,
"script": {
"source": "cosineSimilarity(params.query_vector, 'vector') + 1.0",
"params": {"query_vector": query_vector},
},
}
}
# ElasticVectorSearch is a concrete implementation of the abstract base class
# VectorStore, which defines a common interface for all vector database
# implementations. By inheriting from the ABC class, ElasticVectorSearch can be
# defined as an abstract base class itself, allowing the creation of subclasses with
# their own specific implementations. If you plan to subclass ElasticVectorSearch,
# you can inherit from it and define your own implementation of the necessary methods
# and attributes.
[docs]class ElasticVectorSearch(VectorStore, ABC):
"""Wrapper around Elasticsearch as a vector database.
To connect to an Elasticsearch instance that does not require
login credentials, pass the Elasticsearch URL and index name along with the
embedding object to the constructor.
Example:
.. code-block:: python
from langchain import ElasticVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
elastic_vector_search = ElasticVectorSearch(
elasticsearch_url="http://localhost:9200",
index_name="test_index",
embedding=embedding
)
To connect to an Elasticsearch instance that requires login credentials,
including Elastic Cloud, use the Elasticsearch URL format
https://username:password@es_host:9243. For example, to connect to Elastic
Cloud, create the Elasticsearch URL with the required authentication details and
pass it to the ElasticVectorSearch constructor as the named parameter
elasticsearch_url.
You can obtain your Elastic Cloud URL and login credentials by logging in to the
Elastic Cloud console at https://cloud.elastic.co, selecting your deployment, and
navigating to the "Deployments" page.
To obtain your Elastic Cloud password for the default "elastic" user:
1. Log in to the Elastic Cloud console at https://cloud.elastic.co
2. Go to "Security" > "Users"
3. Locate the "elastic" user and click "Edit"
4. Click "Reset password"
5. Follow the prompts to reset the password
The format for Elastic Cloud URLs is
https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.
Example:
.. code-block:: python
from langchain import ElasticVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
elastic_host = "cluster_id.region_id.gcp.cloud.es.io"
elasticsearch_url = f"https://username:password@{elastic_host}:9243"
elastic_vector_search = ElasticVectorSearch(
elasticsearch_url=elasticsearch_url,
index_name="test_index",
embedding=embedding
)
Args:
elasticsearch_url (str): The URL for the Elasticsearch instance.
index_name (str): The name of the Elasticsearch index for the embeddings.
embedding (Embeddings): An object that provides the ability to embed text.
It should be an instance of a class that subclasses the Embeddings
abstract base class, such as OpenAIEmbeddings()
Raises:
ValueError: If the elasticsearch python package is not installed.
"""
def __init__(
self,
elasticsearch_url: str,
index_name: str,
embedding: Embeddings,
*,
ssl_verify: Optional[Dict[str, Any]] = None,
):
"""Initialize with necessary components."""
try:
import elasticsearch
except ImportError:
raise ImportError(
"Could not import elasticsearch python package. "
"Please install it with `pip install elasticsearch`."
)
self.embedding = embedding
self.index_name = index_name
_ssl_verify = ssl_verify or {}
try:
self.client = elasticsearch.Elasticsearch(elasticsearch_url, **_ssl_verify)
except ValueError as e:
raise ValueError(
f"Your elasticsearch client string is mis-formatted. Got error: {e} "
)
@property
def embeddings(self) -> Embeddings:
return self.embedding
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
refresh_indices: bool = True,
**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.
ids: Optional list of unique IDs.
refresh_indices: bool to refresh ElasticSearch indices
Returns:
List of ids from adding the texts into the vectorstore.
"""
try:
from elasticsearch.exceptions import NotFoundError
from elasticsearch.helpers import bulk
except ImportError:
raise ImportError(
"Could not import elasticsearch python package. "
"Please install it with `pip install elasticsearch`."
)
requests = []
ids = ids or [str(uuid.uuid4()) for _ in texts]
embeddings = self.embedding.embed_documents(list(texts))
dim = len(embeddings[0])
mapping = _default_text_mapping(dim)
# check to see if the index already exists
try:
self.client.indices.get(index=self.index_name)
except NotFoundError:
# TODO would be nice to create index before embedding,
# just to save expensive steps for last
self.create_index(self.client, self.index_name, mapping)
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
request = {
"_op_type": "index",
"_index": self.index_name,
"vector": embeddings[i],
"text": text,
"metadata": metadata,
"_id": ids[i],
}
requests.append(request)
bulk(self.client, requests)
if refresh_indices:
self.client.indices.refresh(index=self.index_name)
return ids
[docs] def similarity_search(
self, query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
docs_and_scores = self.similarity_search_with_score(query, k, filter=filter)
documents = [d[0] for d in docs_and_scores]
return documents
[docs] def similarity_search_with_score(
self, query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
embedding = self.embedding.embed_query(query)
script_query = _default_script_query(embedding, filter)
response = self.client_search(
self.client, self.index_name, script_query, size=k
)
hits = [hit for hit in response["hits"]["hits"]]
docs_and_scores = [
(
Document(
page_content=hit["_source"]["text"],
metadata=hit["_source"]["metadata"],
),
hit["_score"],
)
for hit in hits
]
return docs_and_scores
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
index_name: Optional[str] = None,
refresh_indices: bool = True,
**kwargs: Any,
) -> ElasticVectorSearch:
"""Construct ElasticVectorSearch wrapper from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Creates a new index for the embeddings in the Elasticsearch instance.
3. Adds the documents to the newly created Elasticsearch index.
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain import ElasticVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
elastic_vector_search = ElasticVectorSearch.from_texts(
texts,
embeddings,
elasticsearch_url="http://localhost:9200"
)
"""
elasticsearch_url = get_from_dict_or_env(
kwargs, "elasticsearch_url", "ELASTICSEARCH_URL"
)
if "elasticsearch_url" in kwargs:
del kwargs["elasticsearch_url"]
index_name = index_name or uuid.uuid4().hex
vectorsearch = cls(elasticsearch_url, index_name, embedding, **kwargs)
vectorsearch.add_texts(
texts, metadatas=metadatas, ids=ids, refresh_indices=refresh_indices
)
return vectorsearch
[docs] def create_index(self, client: Any, index_name: str, mapping: Dict) -> None:
version_num = client.info()["version"]["number"][0]
version_num = int(version_num)
if version_num >= 8:
client.indices.create(index=index_name, mappings=mapping)
else:
client.indices.create(index=index_name, body={"mappings": mapping})
[docs] def client_search(
self, client: Any, index_name: str, script_query: Dict, size: int
) -> Any:
version_num = client.info()["version"]["number"][0]
version_num = int(version_num)
if version_num >= 8:
response = client.search(index=index_name, query=script_query, size=size)
else:
response = client.search(
index=index_name, body={"query": script_query, "size": size}
)
return response
[docs] def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None:
"""Delete by vector IDs.
Args:
ids: List of ids to delete.
"""
if ids is None:
raise ValueError("No ids provided to delete.")
# TODO: Check if this can be done in bulk
for id in ids:
self.client.delete(index=self.index_name, id=id)
[docs]class ElasticKnnSearch(VectorStore, ABC):
"""
ElasticKnnSearch is a class for performing k-nearest neighbor
(k-NN) searches on text data using Elasticsearch.
This class is used to create an Elasticsearch index of text data that
can be searched using k-NN search. The text data is transformed into
vector embeddings using a provided embedding model, and these embeddings
are stored in the Elasticsearch index.
Attributes:
index_name (str): The name of the Elasticsearch index.
embedding (Embeddings): The embedding model to use for transforming text data
into vector embeddings.
es_connection (Elasticsearch, optional): An existing Elasticsearch connection.
es_cloud_id (str, optional): The Cloud ID of your Elasticsearch Service
deployment.
es_user (str, optional): The username for your Elasticsearch Service deployment.
es_password (str, optional): The password for your Elasticsearch Service
deployment.
vector_query_field (str, optional): The name of the field in the Elasticsearch
index that contains the vector embeddings.
query_field (str, optional): The name of the field in the Elasticsearch index
that contains the original text data.
Usage:
>>> from embeddings import Embeddings
>>> embedding = Embeddings.load('glove')
>>> es_search = ElasticKnnSearch('my_index', embedding)
>>> es_search.add_texts(['Hello world!', 'Another text'])
>>> results = es_search.knn_search('Hello')
[(Document(page_content='Hello world!', metadata={}), 0.9)]
"""
def __init__(
self,
index_name: str,
embedding: Embeddings,
es_connection: Optional["Elasticsearch"] = None,
es_cloud_id: Optional[str] = None,
es_user: Optional[str] = None,
es_password: Optional[str] = None,
vector_query_field: Optional[str] = "vector",
query_field: Optional[str] = "text",
):
try:
import elasticsearch
except ImportError:
raise ImportError(
"Could not import elasticsearch python package. "
"Please install it with `pip install elasticsearch`."
)
self.embedding = embedding
self.index_name = index_name
self.query_field = query_field
self.vector_query_field = vector_query_field
# If a pre-existing Elasticsearch connection is provided, use it.
if es_connection is not None:
self.client = es_connection
else:
# If credentials for a new Elasticsearch connection are provided,
# create a new connection.
if es_cloud_id and es_user and es_password:
self.client = elasticsearch.Elasticsearch(
cloud_id=es_cloud_id, basic_auth=(es_user, es_password)
)
else:
raise ValueError(
"""Either provide a pre-existing Elasticsearch connection, \
or valid credentials for creating a new connection."""
)
@staticmethod
def _default_knn_mapping(
dims: int, similarity: Optional[str] = "dot_product"
) -> Dict:
return {
"properties": {
"text": {"type": "text"},
"vector": {
"type": "dense_vector",
"dims": dims,
"index": True,
"similarity": similarity,
},
}
}
def _default_knn_query(
self,
query_vector: Optional[List[float]] = None,
query: Optional[str] = None,
model_id: Optional[str] = None,
k: Optional[int] = 10,
num_candidates: Optional[int] = 10,
) -> Dict:
knn: Dict = {
"field": self.vector_query_field,
"k": k,
"num_candidates": num_candidates,
}
# Case 1: `query_vector` is provided, but not `model_id` -> use query_vector
if query_vector and not model_id:
knn["query_vector"] = query_vector
# Case 2: `query` and `model_id` are provided, -> use query_vector_builder
elif query and model_id:
knn["query_vector_builder"] = {
"text_embedding": {
"model_id": model_id, # use 'model_id' argument
"model_text": query, # use 'query' argument
}
}
else:
raise ValueError(
"Either `query_vector` or `model_id` must be provided, but not both."
)
return knn
[docs] def similarity_search(
self, query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any
) -> List[Document]:
"""
Pass through to `knn_search`
"""
results = self.knn_search(query=query, k=k, **kwargs)
return [doc for doc, score in results]
[docs] def similarity_search_with_score(
self, query: str, k: int = 10, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Pass through to `knn_search including score`"""
return self.knn_search(query=query, k=k, **kwargs)
[docs] def knn_search(
self,
query: Optional[str] = None,
k: Optional[int] = 10,
query_vector: Optional[List[float]] = None,
model_id: Optional[str] = None,
size: Optional[int] = 10,
source: Optional[bool] = True,
fields: Optional[
Union[List[Mapping[str, Any]], Tuple[Mapping[str, Any], ...], None]
] = None,
page_content: Optional[str] = "text",
) -> List[Tuple[Document, float]]:
"""
Perform a k-NN search on the Elasticsearch index.
Args:
query (str, optional): The query text to search for.
k (int, optional): The number of nearest neighbors to return.
query_vector (List[float], optional): The query vector to search for.
model_id (str, optional): The ID of the model to use for transforming the
query text into a vector.
size (int, optional): The number of search results to return.
source (bool, optional): Whether to return the source of the search results.
fields (List[Mapping[str, Any]], optional): The fields to return in the
search results.
page_content (str, optional): The name of the field that contains the page
content.
Returns:
A list of tuples, where each tuple contains a Document object and a score.
"""
# if not source and (fields == None or page_content not in fields):
if not source and (
fields is None or not any(page_content in field for field in fields)
):
raise ValueError("If source=False `page_content` field must be in `fields`")
knn_query_body = self._default_knn_query(
query_vector=query_vector, query=query, model_id=model_id, k=k
)
# Perform the kNN search on the Elasticsearch index and return the results.
response = self.client.search(
index=self.index_name,
knn=knn_query_body,
size=size,
source=source,
fields=fields,
)
hits = [hit for hit in response["hits"]["hits"]]
docs_and_scores = [
(
Document(
page_content=hit["_source"][page_content]
if source
else hit["fields"][page_content][0],
metadata=hit["fields"] if fields else {},
),
hit["_score"],
)
for hit in hits
]
return docs_and_scores
[docs] def knn_hybrid_search(
self,
query: Optional[str] = None,
k: Optional[int] = 10,
query_vector: Optional[List[float]] = None,
model_id: Optional[str] = None,
size: Optional[int] = 10,
source: Optional[bool] = True,
knn_boost: Optional[float] = 0.9,
query_boost: Optional[float] = 0.1,
fields: Optional[
Union[List[Mapping[str, Any]], Tuple[Mapping[str, Any], ...], None]
] = None,
page_content: Optional[str] = "text",
) -> List[Tuple[Document, float]]:
"""
Perform a hybrid k-NN and text search on the Elasticsearch index.
Args:
query (str, optional): The query text to search for.
k (int, optional): The number of nearest neighbors to return.
query_vector (List[float], optional): The query vector to search for.
model_id (str, optional): The ID of the model to use for transforming the
query text into a vector.
size (int, optional): The number of search results to return.
source (bool, optional): Whether to return the source of the search results.
knn_boost (float, optional): The boost value to apply to the k-NN search
results.
query_boost (float, optional): The boost value to apply to the text search
results.
fields (List[Mapping[str, Any]], optional): The fields to return in the
search results.
page_content (str, optional): The name of the field that contains the page
content.
Returns:
A list of tuples, where each tuple contains a Document object and a score.
"""
# if not source and (fields == None or page_content not in fields):
if not source and (
fields is None or not any(page_content in field for field in fields)
):
raise ValueError("If source=False `page_content` field must be in `fields`")
knn_query_body = self._default_knn_query(
query_vector=query_vector, query=query, model_id=model_id, k=k
)
# Modify the knn_query_body to add a "boost" parameter
knn_query_body["boost"] = knn_boost
# Generate the body of the standard Elasticsearch query
match_query_body = {
"match": {self.query_field: {"query": query, "boost": query_boost}}
}
# Perform the hybrid search on the Elasticsearch index and return the results.
response = self.client.search(
index=self.index_name,
query=match_query_body,
knn=knn_query_body,
fields=fields,
size=size,
source=source,
)
hits = [hit for hit in response["hits"]["hits"]]
docs_and_scores = [
(
Document(
page_content=hit["_source"][page_content]
if source
else hit["fields"][page_content][0],
metadata=hit["fields"] if fields else {},
),
hit["_score"],
)
for hit in hits
]
return docs_and_scores
[docs] def create_knn_index(self, mapping: Dict) -> None:
"""
Create a new k-NN index in Elasticsearch.
Args:
mapping (Dict): The mapping to use for the new index.
Returns:
None
"""
self.client.indices.create(index=self.index_name, mappings=mapping)
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[Dict[Any, Any]]] = None,
model_id: Optional[str] = None,
refresh_indices: bool = False,
**kwargs: Any,
) -> List[str]:
"""
Add a list of texts to the Elasticsearch index.
Args:
texts (Iterable[str]): The texts to add to the index.
metadatas (List[Dict[Any, Any]], optional): A list of metadata dictionaries
to associate with the texts.
model_id (str, optional): The ID of the model to use for transforming the
texts into vectors.
refresh_indices (bool, optional): Whether to refresh the Elasticsearch
indices after adding the texts.
**kwargs: Arbitrary keyword arguments.
Returns:
A list of IDs for the added texts.
"""
# Check if the index exists.
if not self.client.indices.exists(index=self.index_name):
dims = kwargs.get("dims")
if dims is None:
raise ValueError("ElasticKnnSearch requires 'dims' parameter")
similarity = kwargs.get("similarity")
optional_args = {}
if similarity is not None:
optional_args["similarity"] = similarity
mapping = self._default_knn_mapping(dims=dims, **optional_args)
self.create_knn_index(mapping)
embeddings = self.embedding.embed_documents(list(texts))
# body = []
body: List[Mapping[str, Any]] = []
for text, vector in zip(texts, embeddings):
body.extend(
[
{"index": {"_index": self.index_name}},
{"text": text, "vector": vector},
]
)
responses = self.client.bulk(operations=body)
ids = [
item["index"]["_id"]
for item in responses["items"]
if item["index"]["result"] == "created"
]
if refresh_indices:
self.client.indices.refresh(index=self.index_name)
return ids
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[Dict[Any, Any]]] = None,
**kwargs: Any,
) -> ElasticKnnSearch:
"""
Create a new ElasticKnnSearch instance and add a list of texts to the
Elasticsearch index.
Args:
texts (List[str]): The texts to add to the index.
embedding (Embeddings): The embedding model to use for transforming the
texts into vectors.
metadatas (List[Dict[Any, Any]], optional): A list of metadata dictionaries
to associate with the texts.
**kwargs: Arbitrary keyword arguments.
Returns:
A new ElasticKnnSearch instance.
"""
index_name = kwargs.get("index_name", str(uuid.uuid4()))
es_connection = kwargs.get("es_connection")
es_cloud_id = kwargs.get("es_cloud_id")
es_user = kwargs.get("es_user")
es_password = kwargs.get("es_password")
vector_query_field = kwargs.get("vector_query_field", "vector")
query_field = kwargs.get("query_field", "text")
model_id = kwargs.get("model_id")
dims = kwargs.get("dims")
if dims is None:
raise ValueError("ElasticKnnSearch requires 'dims' parameter")
optional_args = {}
if vector_query_field is not None:
optional_args["vector_query_field"] = vector_query_field
if query_field is not None:
optional_args["query_field"] = query_field
knnvectorsearch = cls(
index_name=index_name,
embedding=embedding,
es_connection=es_connection,
es_cloud_id=es_cloud_id,
es_user=es_user,
es_password=es_password,
**optional_args,
)
# Encode the provided texts and add them to the newly created index.
knnvectorsearch.add_texts(texts, model_id=model_id, dims=dims, **optional_args)
return knnvectorsearch