from __future__ import annotations
import uuid
import warnings
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.utils import get_from_dict_or_env
from langchain_core.vectorstores import VectorStore
from langchain_community.vectorstores.utils import maximal_marginal_relevance
IMPORT_OPENSEARCH_PY_ERROR = (
"Could not import OpenSearch. Please install it with `pip install opensearch-py`."
)
IMPORT_ASYNC_OPENSEARCH_PY_ERROR = """
Could not import AsyncOpenSearch.
Please install it with `pip install opensearch-py`."""
SCRIPT_SCORING_SEARCH = "script_scoring"
PAINLESS_SCRIPTING_SEARCH = "painless_scripting"
MATCH_ALL_QUERY = {"match_all": {}} # type: Dict
def _import_opensearch() -> Any:
"""Import OpenSearch if available, otherwise raise error."""
try:
from opensearchpy import OpenSearch
except ImportError:
raise ImportError(IMPORT_OPENSEARCH_PY_ERROR)
return OpenSearch
def _import_async_opensearch() -> Any:
"""Import AsyncOpenSearch if available, otherwise raise error."""
try:
from opensearchpy import AsyncOpenSearch
except ImportError:
raise ImportError(IMPORT_ASYNC_OPENSEARCH_PY_ERROR)
return AsyncOpenSearch
def _import_bulk() -> Any:
"""Import bulk if available, otherwise raise error."""
try:
from opensearchpy.helpers import bulk
except ImportError:
raise ImportError(IMPORT_OPENSEARCH_PY_ERROR)
return bulk
def _import_async_bulk() -> Any:
"""Import async_bulk if available, otherwise raise error."""
try:
from opensearchpy.helpers import async_bulk
except ImportError:
raise ImportError(IMPORT_ASYNC_OPENSEARCH_PY_ERROR)
return async_bulk
def _import_not_found_error() -> Any:
"""Import not found error if available, otherwise raise error."""
try:
from opensearchpy.exceptions import NotFoundError
except ImportError:
raise ImportError(IMPORT_OPENSEARCH_PY_ERROR)
return NotFoundError
def _get_opensearch_client(opensearch_url: str, **kwargs: Any) -> Any:
"""Get OpenSearch client from the opensearch_url, otherwise raise error."""
try:
opensearch = _import_opensearch()
client = opensearch(opensearch_url, **kwargs)
except ValueError as e:
raise ImportError(
f"OpenSearch client string provided is not in proper format. "
f"Got error: {e} "
)
return client
def _get_async_opensearch_client(opensearch_url: str, **kwargs: Any) -> Any:
"""Get AsyncOpenSearch client from the opensearch_url, otherwise raise error."""
try:
async_opensearch = _import_async_opensearch()
client = async_opensearch(opensearch_url, **kwargs)
except ValueError as e:
raise ImportError(
f"AsyncOpenSearch client string provided is not in proper format. "
f"Got error: {e} "
)
return client
def _validate_embeddings_and_bulk_size(embeddings_length: int, bulk_size: int) -> None:
"""Validate Embeddings Length and Bulk Size."""
if embeddings_length == 0:
raise RuntimeError("Embeddings size is zero")
if bulk_size < embeddings_length:
raise RuntimeError(
f"The embeddings count, {embeddings_length} is more than the "
f"[bulk_size], {bulk_size}. Increase the value of [bulk_size]."
)
def _validate_aoss_with_engines(is_aoss: bool, engine: str) -> None:
"""Validate AOSS with the engine."""
if is_aoss and engine != "nmslib" and engine != "faiss":
raise ValueError(
"Amazon OpenSearch Service Serverless only "
"supports `nmslib` or `faiss` engines"
)
def _is_aoss_enabled(http_auth: Any) -> bool:
"""Check if the service is http_auth is set as `aoss`."""
if (
http_auth is not None
and hasattr(http_auth, "service")
and http_auth.service == "aoss"
):
return True
return False
def _bulk_ingest_embeddings(
client: Any,
index_name: str,
embeddings: List[List[float]],
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
vector_field: str = "vector_field",
text_field: str = "text",
mapping: Optional[Dict] = None,
max_chunk_bytes: Optional[int] = 1 * 1024 * 1024,
is_aoss: bool = False,
) -> List[str]:
"""Bulk Ingest Embeddings into given index."""
if not mapping:
mapping = dict()
bulk = _import_bulk()
not_found_error = _import_not_found_error()
requests = []
return_ids = []
mapping = mapping
try:
client.indices.get(index=index_name)
except not_found_error:
client.indices.create(index=index_name, body=mapping)
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
_id = ids[i] if ids else str(uuid.uuid4())
request = {
"_op_type": "index",
"_index": index_name,
vector_field: embeddings[i],
text_field: text,
"metadata": metadata,
}
if is_aoss:
request["id"] = _id
else:
request["_id"] = _id
requests.append(request)
return_ids.append(_id)
bulk(client, requests, max_chunk_bytes=max_chunk_bytes)
if not is_aoss:
client.indices.refresh(index=index_name)
return return_ids
async def _abulk_ingest_embeddings(
client: Any,
index_name: str,
embeddings: List[List[float]],
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
vector_field: str = "vector_field",
text_field: str = "text",
mapping: Optional[Dict] = None,
max_chunk_bytes: Optional[int] = 1 * 1024 * 1024,
is_aoss: bool = False,
) -> List[str]:
"""Bulk Ingest Embeddings into given index asynchronously using AsyncOpenSearch."""
if not mapping:
mapping = dict()
async_bulk = _import_async_bulk()
not_found_error = _import_not_found_error()
requests = []
return_ids = []
try:
await client.indices.get(index=index_name)
except not_found_error:
await client.indices.create(index=index_name, body=mapping)
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
_id = ids[i] if ids else str(uuid.uuid4())
request = {
"_op_type": "index",
"_index": index_name,
vector_field: embeddings[i],
text_field: text,
"metadata": metadata,
}
if is_aoss:
request["id"] = _id
else:
request["_id"] = _id
requests.append(request)
return_ids.append(_id)
await async_bulk(client, requests, max_chunk_bytes=max_chunk_bytes)
if not is_aoss:
await client.indices.refresh(index=index_name)
return return_ids
def _default_scripting_text_mapping(
dim: int,
vector_field: str = "vector_field",
) -> Dict:
"""For Painless Scripting or Script Scoring,the default mapping to create index."""
return {
"mappings": {
"properties": {
vector_field: {"type": "knn_vector", "dimension": dim},
}
}
}
def _default_text_mapping(
dim: int,
engine: str = "nmslib",
space_type: str = "l2",
ef_search: int = 512,
ef_construction: int = 512,
m: int = 16,
vector_field: str = "vector_field",
) -> Dict:
"""For Approximate k-NN Search, this is the default mapping to create index."""
return {
"settings": {"index": {"knn": True, "knn.algo_param.ef_search": ef_search}},
"mappings": {
"properties": {
vector_field: {
"type": "knn_vector",
"dimension": dim,
"method": {
"name": "hnsw",
"space_type": space_type,
"engine": engine,
"parameters": {"ef_construction": ef_construction, "m": m},
},
}
}
},
}
def _default_approximate_search_query(
query_vector: List[float],
k: int = 4,
vector_field: str = "vector_field",
score_threshold: Optional[float] = 0.0,
) -> Dict:
"""For Approximate k-NN Search, this is the default query."""
return {
"size": k,
"min_score": score_threshold,
"query": {"knn": {vector_field: {"vector": query_vector, "k": k}}},
}
def _approximate_search_query_with_boolean_filter(
query_vector: List[float],
boolean_filter: Dict,
k: int = 4,
vector_field: str = "vector_field",
subquery_clause: str = "must",
score_threshold: Optional[float] = 0.0,
) -> Dict:
"""For Approximate k-NN Search, with Boolean Filter."""
return {
"size": k,
"min_score": score_threshold,
"query": {
"bool": {
"filter": boolean_filter,
subquery_clause: [
{"knn": {vector_field: {"vector": query_vector, "k": k}}}
],
}
},
}
def _approximate_search_query_with_efficient_filter(
query_vector: List[float],
efficient_filter: Dict,
k: int = 4,
vector_field: str = "vector_field",
score_threshold: Optional[float] = 0.0,
) -> Dict:
"""For Approximate k-NN Search, with Efficient Filter for Lucene and
Faiss Engines."""
search_query = _default_approximate_search_query(
query_vector, k=k, vector_field=vector_field, score_threshold=score_threshold
)
search_query["query"]["knn"][vector_field]["filter"] = efficient_filter
return search_query
def _default_script_query(
query_vector: List[float],
k: int = 4,
space_type: str = "l2",
pre_filter: Optional[Dict] = None,
vector_field: str = "vector_field",
score_threshold: Optional[float] = 0.0,
) -> Dict:
"""For Script Scoring Search, this is the default query."""
if not pre_filter:
pre_filter = MATCH_ALL_QUERY
return {
"size": k,
"min_score": score_threshold,
"query": {
"script_score": {
"query": pre_filter,
"script": {
"source": "knn_score",
"lang": "knn",
"params": {
"field": vector_field,
"query_value": query_vector,
"space_type": space_type,
},
},
}
},
}
def __get_painless_scripting_source(
space_type: str, vector_field: str = "vector_field"
) -> str:
"""For Painless Scripting, it returns the script source based on space type."""
source_value = (
"(1.0 + " + space_type + "(params.query_value, doc['" + vector_field + "']))"
)
if space_type == "cosineSimilarity":
return source_value
else:
return "1/" + source_value
def _default_painless_scripting_query(
query_vector: List[float],
k: int = 4,
space_type: str = "l2Squared",
pre_filter: Optional[Dict] = None,
vector_field: str = "vector_field",
score_threshold: Optional[float] = 0.0,
) -> Dict:
"""For Painless Scripting Search, this is the default query."""
if not pre_filter:
pre_filter = MATCH_ALL_QUERY
source = __get_painless_scripting_source(space_type, vector_field=vector_field)
return {
"size": k,
"min_score": score_threshold,
"query": {
"script_score": {
"query": pre_filter,
"script": {
"source": source,
"params": {
"field": vector_field,
"query_value": query_vector,
},
},
}
},
}
[docs]class OpenSearchVectorSearch(VectorStore):
"""`Amazon OpenSearch Vector Engine` vector store.
Example:
.. code-block:: python
from langchain_community.vectorstores import OpenSearchVectorSearch
opensearch_vector_search = OpenSearchVectorSearch(
"http://localhost:9200",
"embeddings",
embedding_function
)
"""
[docs] def __init__(
self,
opensearch_url: str,
index_name: str,
embedding_function: Embeddings,
**kwargs: Any,
):
"""Initialize with necessary components."""
self.embedding_function = embedding_function
self.index_name = index_name
http_auth = kwargs.get("http_auth")
self.is_aoss = _is_aoss_enabled(http_auth=http_auth)
self.client = _get_opensearch_client(opensearch_url, **kwargs)
self.async_client = _get_async_opensearch_client(opensearch_url, **kwargs)
self.engine = kwargs.get("engine")
@property
def embeddings(self) -> Embeddings:
return self.embedding_function
def __add(
self,
texts: Iterable[str],
embeddings: List[List[float]],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
bulk_size: int = 500,
**kwargs: Any,
) -> List[str]:
_validate_embeddings_and_bulk_size(len(embeddings), bulk_size)
index_name = kwargs.get("index_name", self.index_name)
text_field = kwargs.get("text_field", "text")
dim = len(embeddings[0])
engine = kwargs.get("engine", "nmslib")
space_type = kwargs.get("space_type", "l2")
ef_search = kwargs.get("ef_search", 512)
ef_construction = kwargs.get("ef_construction", 512)
m = kwargs.get("m", 16)
vector_field = kwargs.get("vector_field", "vector_field")
max_chunk_bytes = kwargs.get("max_chunk_bytes", 1 * 1024 * 1024)
_validate_aoss_with_engines(self.is_aoss, engine)
mapping = _default_text_mapping(
dim, engine, space_type, ef_search, ef_construction, m, vector_field
)
return _bulk_ingest_embeddings(
self.client,
index_name,
embeddings,
texts,
metadatas=metadatas,
ids=ids,
vector_field=vector_field,
text_field=text_field,
mapping=mapping,
max_chunk_bytes=max_chunk_bytes,
is_aoss=self.is_aoss,
)
async def __aadd(
self,
texts: Iterable[str],
embeddings: List[List[float]],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
bulk_size: int = 500,
**kwargs: Any,
) -> List[str]:
_validate_embeddings_and_bulk_size(len(embeddings), bulk_size)
index_name = kwargs.get("index_name", self.index_name)
text_field = kwargs.get("text_field", "text")
dim = len(embeddings[0])
engine = kwargs.get("engine", "nmslib")
space_type = kwargs.get("space_type", "l2")
ef_search = kwargs.get("ef_search", 512)
ef_construction = kwargs.get("ef_construction", 512)
m = kwargs.get("m", 16)
vector_field = kwargs.get("vector_field", "vector_field")
max_chunk_bytes = kwargs.get("max_chunk_bytes", 1 * 1024 * 1024)
_validate_aoss_with_engines(self.is_aoss, engine)
mapping = _default_text_mapping(
dim, engine, space_type, ef_search, ef_construction, m, vector_field
)
return await _abulk_ingest_embeddings(
self.async_client,
index_name,
embeddings,
texts,
metadatas=metadatas,
ids=ids,
vector_field=vector_field,
text_field=text_field,
mapping=mapping,
max_chunk_bytes=max_chunk_bytes,
is_aoss=self.is_aoss,
)
[docs] def delete_index(self, index_name: Optional[str] = None) -> Optional[bool]:
"""Deletes a given index from vectorstore."""
if index_name is None:
if self.index_name is None:
raise ValueError("index_name must be provided.")
index_name = self.index_name
try:
self.client.indices.delete(index=index_name)
return True
except Exception as e:
raise e
[docs] def index_exists(self, index_name: Optional[str] = None) -> Optional[bool]:
"""If given index present in vectorstore, returns True else False."""
if index_name is None:
if self.index_name is None:
raise ValueError("index_name must be provided.")
index_name = self.index_name
return self.client.indices.exists(index=index_name)
[docs] def create_index(
self,
dimension: int,
index_name: Optional[str] = uuid.uuid4().hex,
**kwargs: Any,
) -> Optional[str]:
"""Create a new Index with given arguments"""
is_appx_search = kwargs.get("is_appx_search", True)
vector_field = kwargs.get("vector_field", "vector_field")
kwargs.get("text_field", "text")
http_auth = kwargs.get("http_auth")
is_aoss = _is_aoss_enabled(http_auth=http_auth)
if is_aoss and not is_appx_search:
raise ValueError(
"Amazon OpenSearch Service Serverless only "
"supports `approximate_search`"
)
if is_appx_search:
engine = kwargs.get("engine", "nmslib")
space_type = kwargs.get("space_type", "l2")
ef_search = kwargs.get("ef_search", 512)
ef_construction = kwargs.get("ef_construction", 512)
m = kwargs.get("m", 16)
_validate_aoss_with_engines(is_aoss, engine)
mapping = _default_text_mapping(
dimension,
engine,
space_type,
ef_search,
ef_construction,
m,
vector_field,
)
else:
mapping = _default_scripting_text_mapping(dimension)
if self.index_exists(index_name):
raise RuntimeError(f"The index, {index_name} already exists.")
self.client.indices.create(index=index_name, body=mapping)
return index_name
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
bulk_size: int = 500,
**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 ids to associate with the texts.
bulk_size: Bulk API request count; Default: 500
Returns:
List of ids from adding the texts into the vectorstore.
Optional Args:
vector_field: Document field embeddings are stored in. Defaults to
"vector_field".
text_field: Document field the text of the document is stored in. Defaults
to "text".
"""
embeddings = self.embedding_function.embed_documents(list(texts))
return self.__add(
texts,
embeddings,
metadatas=metadatas,
ids=ids,
bulk_size=bulk_size,
**kwargs,
)
[docs] async def aadd_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
bulk_size: int = 500,
**kwargs: Any,
) -> List[str]:
"""
Asynchronously run more texts through the embeddings
and add to the vectorstore.
"""
embeddings = await self.embedding_function.aembed_documents(list(texts))
return await self.__aadd(
texts,
embeddings,
metadatas=metadatas,
ids=ids,
bulk_size=bulk_size,
**kwargs,
)
[docs] def add_embeddings(
self,
text_embeddings: Iterable[Tuple[str, List[float]]],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
bulk_size: int = 500,
**kwargs: Any,
) -> List[str]:
"""Add the given texts and embeddings to the vectorstore.
Args:
text_embeddings: Iterable pairs of string and embedding to
add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of ids to associate with the texts.
bulk_size: Bulk API request count; Default: 500
Returns:
List of ids from adding the texts into the vectorstore.
Optional Args:
vector_field: Document field embeddings are stored in. Defaults to
"vector_field".
text_field: Document field the text of the document is stored in. Defaults
to "text".
"""
texts, embeddings = zip(*text_embeddings)
return self.__add(
list(texts),
list(embeddings),
metadatas=metadatas,
ids=ids,
bulk_size=bulk_size,
**kwargs,
)
[docs] def delete(
self,
ids: Optional[List[str]] = None,
refresh_indices: Optional[bool] = True,
**kwargs: Any,
) -> Optional[bool]:
"""Delete documents from the Opensearch index.
Args:
ids: List of ids of documents to delete.
refresh_indices: Whether to refresh the index
after deleting documents. Defaults to True.
"""
bulk = _import_bulk()
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=refresh_indices, ignore_status=404)
return True
except Exception as e:
raise e
else:
return False
[docs] async def adelete(
self, ids: Optional[List[str]] = None, **kwargs: Any
) -> Optional[bool]:
"""Asynchronously delete by vector ID or other criteria.
Args:
ids: List of ids to delete.
**kwargs: Other keyword arguments that subclasses might use.
Returns:
Optional[bool]: True if deletion is successful,
False otherwise, None if not implemented.
"""
if ids is None:
raise ValueError("No ids provided to delete.")
actions = [{"delete": {"_index": self.index_name, "_id": id_}} for id_ in ids]
response = await self.async_client.bulk(body=actions, **kwargs)
return not any(
item.get("delete", {}).get("error") for item in response["items"]
)
@staticmethod
def _identity_fn(score: float) -> float:
return score
def _select_relevance_score_fn(self) -> Callable[[float], float]:
"""
The 'correct' relevance function
may differ depending on a few things, including:
- the distance / similarity metric used by the VectorStore
- the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
- embedding dimensionality
- etc.
Vectorstores should define their own selection based method of relevance.
"""
return self._identity_fn
[docs] def similarity_search(
self,
query: str,
k: int = 4,
score_threshold: Optional[float] = 0.0,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
By default, supports Approximate Search.
Also supports Script Scoring and Painless Scripting.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
score_threshold: Specify a score threshold to return only documents
above the threshold. Defaults to 0.0.
Returns:
List of Documents most similar to the query.
Optional Args:
vector_field: Document field embeddings are stored in. Defaults to
"vector_field".
text_field: Document field the text of the document is stored in. Defaults
to "text".
metadata_field: Document field that metadata is stored in. Defaults to
"metadata".
Can be set to a special value "*" to include the entire document.
Optional Args for Approximate Search:
search_type: "approximate_search"; default: "approximate_search"
boolean_filter: A Boolean filter is a post filter consists of a Boolean
query that contains a k-NN query and a filter.
subquery_clause: Query clause on the knn vector field; default: "must"
lucene_filter: the Lucene algorithm decides whether to perform an exact
k-NN search with pre-filtering or an approximate search with modified
post-filtering. (deprecated, use `efficient_filter`)
efficient_filter: the Lucene Engine or Faiss Engine decides whether to
perform an exact k-NN search with pre-filtering or an approximate search
with modified post-filtering.
Optional Args for Script Scoring Search:
search_type: "script_scoring"; default: "approximate_search"
space_type: "l2", "l1", "linf", "cosinesimil", "innerproduct",
"hammingbit"; default: "l2"
pre_filter: script_score query to pre-filter documents before identifying
nearest neighbors; default: {"match_all": {}}
Optional Args for Painless Scripting Search:
search_type: "painless_scripting"; default: "approximate_search"
space_type: "l2Squared", "l1Norm", "cosineSimilarity"; default: "l2Squared"
pre_filter: script_score query to pre-filter documents before identifying
nearest neighbors; default: {"match_all": {}}
"""
docs_with_scores = self.similarity_search_with_score(
query, k, score_threshold, **kwargs
)
return [doc[0] for doc in docs_with_scores]
[docs] def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
score_threshold: Optional[float] = 0.0,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to the embedding vector."""
docs_with_scores = self.similarity_search_with_score_by_vector(
embedding, k, score_threshold, **kwargs
)
return [doc[0] for doc in docs_with_scores]
[docs] def similarity_search_with_score(
self,
query: str,
k: int = 4,
score_threshold: Optional[float] = 0.0,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and it's scores most similar to query.
By default, supports Approximate Search.
Also supports Script Scoring and Painless Scripting.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
score_threshold: Specify a score threshold to return only documents
above the threshold. Defaults to 0.0.
Returns:
List of Documents along with its scores most similar to the query.
Optional Args:
same as `similarity_search`
"""
embedding = self.embedding_function.embed_query(query)
return self.similarity_search_with_score_by_vector(
embedding, k, score_threshold, **kwargs
)
[docs] def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
score_threshold: Optional[float] = 0.0,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and it's scores most similar to the embedding vector.
By default, supports Approximate Search.
Also supports Script Scoring and Painless Scripting.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
score_threshold: Specify a score threshold to return only documents
above the threshold. Defaults to 0.0.
Returns:
List of Documents along with its scores most similar to the query.
Optional Args:
same as `similarity_search`
"""
text_field = kwargs.get("text_field", "text")
metadata_field = kwargs.get("metadata_field", "metadata")
hits = self._raw_similarity_search_with_score_by_vector(
embedding=embedding, k=k, score_threshold=score_threshold, **kwargs
)
documents_with_scores = [
(
Document(
page_content=hit["_source"][text_field],
metadata=(
hit["_source"]
if metadata_field == "*" or metadata_field not in hit["_source"]
else hit["_source"][metadata_field]
),
),
hit["_score"],
)
for hit in hits
]
return documents_with_scores
def _raw_similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
score_threshold: Optional[float] = 0.0,
**kwargs: Any,
) -> List[dict]:
"""Return raw opensearch documents (dict) including vectors,
scores most similar to the embedding vector.
By default, supports Approximate Search.
Also supports Script Scoring and Painless Scripting.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
score_threshold: Specify a score threshold to return only documents
above the threshold. Defaults to 0.0.
Returns:
List of dict with its scores most similar to the embedding.
Optional Args:
same as `similarity_search`
"""
search_type = kwargs.get("search_type", "approximate_search")
vector_field = kwargs.get("vector_field", "vector_field")
index_name = kwargs.get("index_name", self.index_name)
filter = kwargs.get("filter", {})
if (
self.is_aoss
and search_type != "approximate_search"
and search_type != SCRIPT_SCORING_SEARCH
):
raise ValueError(
"Amazon OpenSearch Service Serverless only "
"supports `approximate_search` and `script_scoring`"
)
if search_type == "approximate_search":
boolean_filter = kwargs.get("boolean_filter", {})
subquery_clause = kwargs.get("subquery_clause", "must")
efficient_filter = kwargs.get("efficient_filter", {})
# `lucene_filter` is deprecated, added for Backwards Compatibility
lucene_filter = kwargs.get("lucene_filter", {})
if boolean_filter != {} and efficient_filter != {}:
raise ValueError(
"Both `boolean_filter` and `efficient_filter` are provided which "
"is invalid"
)
if lucene_filter != {} and efficient_filter != {}:
raise ValueError(
"Both `lucene_filter` and `efficient_filter` are provided which "
"is invalid. `lucene_filter` is deprecated"
)
if lucene_filter != {} and boolean_filter != {}:
raise ValueError(
"Both `lucene_filter` and `boolean_filter` are provided which "
"is invalid. `lucene_filter` is deprecated"
)
if (
efficient_filter == {}
and boolean_filter == {}
and lucene_filter == {}
and filter != {}
):
if self.engine in ["faiss", "lucene"]:
efficient_filter = filter
else:
boolean_filter = filter
if boolean_filter != {}:
search_query = _approximate_search_query_with_boolean_filter(
embedding,
boolean_filter,
k=k,
vector_field=vector_field,
subquery_clause=subquery_clause,
score_threshold=score_threshold,
)
elif efficient_filter != {}:
search_query = _approximate_search_query_with_efficient_filter(
embedding,
efficient_filter,
k=k,
vector_field=vector_field,
score_threshold=score_threshold,
)
elif lucene_filter != {}:
warnings.warn(
"`lucene_filter` is deprecated. Please use the keyword argument"
" `efficient_filter`"
)
search_query = _approximate_search_query_with_efficient_filter(
embedding,
lucene_filter,
k=k,
vector_field=vector_field,
score_threshold=score_threshold,
)
else:
search_query = _default_approximate_search_query(
embedding,
k=k,
vector_field=vector_field,
score_threshold=score_threshold,
)
elif search_type == SCRIPT_SCORING_SEARCH:
space_type = kwargs.get("space_type", "l2")
pre_filter = kwargs.get("pre_filter", MATCH_ALL_QUERY)
search_query = _default_script_query(
embedding,
k,
space_type,
pre_filter,
vector_field,
score_threshold=score_threshold,
)
elif search_type == PAINLESS_SCRIPTING_SEARCH:
space_type = kwargs.get("space_type", "l2Squared")
pre_filter = kwargs.get("pre_filter", MATCH_ALL_QUERY)
search_query = _default_painless_scripting_query(
embedding,
k,
space_type,
pre_filter,
vector_field,
score_threshold=score_threshold,
)
else:
raise ValueError("Invalid `search_type` provided as an argument")
response = self.client.search(index=index_name, body=search_query)
return [hit for hit in response["hits"]["hits"]]
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> list[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
vector_field = kwargs.get("vector_field", "vector_field")
text_field = kwargs.get("text_field", "text")
metadata_field = kwargs.get("metadata_field", "metadata")
# Get embedding of the user query
embedding = self.embedding_function.embed_query(query)
# Do ANN/KNN search to get top fetch_k results where fetch_k >= k
results = self._raw_similarity_search_with_score_by_vector(
embedding, fetch_k, **kwargs
)
embeddings = [result["_source"][vector_field] for result in results]
# Rerank top k results using MMR, (mmr_selected is a list of indices)
mmr_selected = maximal_marginal_relevance(
np.array(embedding), embeddings, k=k, lambda_mult=lambda_mult
)
return [
Document(
page_content=results[i]["_source"][text_field],
metadata=results[i]["_source"][metadata_field],
)
for i in mmr_selected
]
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
bulk_size: int = 500,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> OpenSearchVectorSearch:
"""Construct OpenSearchVectorSearch wrapper from raw texts.
Example:
.. code-block:: python
from langchain_community.vectorstores import OpenSearchVectorSearch
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
opensearch_vector_search = OpenSearchVectorSearch.from_texts(
texts,
embeddings,
opensearch_url="http://localhost:9200"
)
OpenSearch by default supports Approximate Search powered by nmslib, faiss
and lucene engines recommended for large datasets. Also supports brute force
search through Script Scoring and Painless Scripting.
Optional Args:
vector_field: Document field embeddings are stored in. Defaults to
"vector_field".
text_field: Document field the text of the document is stored in. Defaults
to "text".
Optional Keyword Args for Approximate Search:
engine: "nmslib", "faiss", "lucene"; default: "nmslib"
space_type: "l2", "l1", "cosinesimil", "linf", "innerproduct"; default: "l2"
ef_search: Size of the dynamic list used during k-NN searches. Higher values
lead to more accurate but slower searches; default: 512
ef_construction: Size of the dynamic list used during k-NN graph creation.
Higher values lead to more accurate graph but slower indexing speed;
default: 512
m: Number of bidirectional links created for each new element. Large impact
on memory consumption. Between 2 and 100; default: 16
Keyword Args for Script Scoring or Painless Scripting:
is_appx_search: False
"""
embeddings = embedding.embed_documents(texts)
return cls.from_embeddings(
embeddings,
texts,
embedding,
metadatas=metadatas,
bulk_size=bulk_size,
ids=ids,
**kwargs,
)
[docs] @classmethod
async def afrom_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
bulk_size: int = 500,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> OpenSearchVectorSearch:
"""Asynchronously construct OpenSearchVectorSearch wrapper from raw texts.
Example:
.. code-block:: python
from langchain_community.vectorstores import OpenSearchVectorSearch
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
opensearch_vector_search = await OpenSearchVectorSearch.afrom_texts(
texts,
embeddings,
opensearch_url="http://localhost:9200"
)
OpenSearch by default supports Approximate Search powered by nmslib, faiss
and lucene engines recommended for large datasets. Also supports brute force
search through Script Scoring and Painless Scripting.
Optional Args:
vector_field: Document field embeddings are stored in. Defaults to
"vector_field".
text_field: Document field the text of the document is stored in. Defaults
to "text".
Optional Keyword Args for Approximate Search:
engine: "nmslib", "faiss", "lucene"; default: "nmslib"
space_type: "l2", "l1", "cosinesimil", "linf", "innerproduct"; default: "l2"
ef_search: Size of the dynamic list used during k-NN searches. Higher values
lead to more accurate but slower searches; default: 512
ef_construction: Size of the dynamic list used during k-NN graph creation.
Higher values lead to more accurate graph but slower indexing speed;
default: 512
m: Number of bidirectional links created for each new element. Large impact
on memory consumption. Between 2 and 100; default: 16
Keyword Args for Script Scoring or Painless Scripting:
is_appx_search: False
"""
embeddings = await embedding.aembed_documents(texts)
return await cls.afrom_embeddings(
embeddings,
texts,
embedding,
metadatas=metadatas,
bulk_size=bulk_size,
ids=ids,
**kwargs,
)
[docs] @classmethod
def from_embeddings(
cls,
embeddings: List[List[float]],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
bulk_size: int = 500,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> OpenSearchVectorSearch:
"""Construct OpenSearchVectorSearch wrapper from pre-vectorized embeddings.
Example:
.. code-block:: python
from langchain_community.vectorstores import OpenSearchVectorSearch
from langchain_community.embeddings import OpenAIEmbeddings
embedder = OpenAIEmbeddings()
embeddings = embedder.embed_documents(["foo", "bar"])
opensearch_vector_search = OpenSearchVectorSearch.from_embeddings(
embeddings,
texts,
embedder,
opensearch_url="http://localhost:9200"
)
OpenSearch by default supports Approximate Search powered by nmslib, faiss
and lucene engines recommended for large datasets. Also supports brute force
search through Script Scoring and Painless Scripting.
Optional Args:
vector_field: Document field embeddings are stored in. Defaults to
"vector_field".
text_field: Document field the text of the document is stored in. Defaults
to "text".
Optional Keyword Args for Approximate Search:
engine: "nmslib", "faiss", "lucene"; default: "nmslib"
space_type: "l2", "l1", "cosinesimil", "linf", "innerproduct"; default: "l2"
ef_search: Size of the dynamic list used during k-NN searches. Higher values
lead to more accurate but slower searches; default: 512
ef_construction: Size of the dynamic list used during k-NN graph creation.
Higher values lead to more accurate graph but slower indexing speed;
default: 512
m: Number of bidirectional links created for each new element. Large impact
on memory consumption. Between 2 and 100; default: 16
Keyword Args for Script Scoring or Painless Scripting:
is_appx_search: False
"""
opensearch_url = get_from_dict_or_env(
kwargs, "opensearch_url", "OPENSEARCH_URL"
)
# List of arguments that needs to be removed from kwargs
# before passing kwargs to get opensearch client
keys_list = [
"opensearch_url",
"index_name",
"is_appx_search",
"vector_field",
"text_field",
"engine",
"space_type",
"ef_search",
"ef_construction",
"m",
"max_chunk_bytes",
"is_aoss",
]
_validate_embeddings_and_bulk_size(len(embeddings), bulk_size)
dim = len(embeddings[0])
# Get the index name from either from kwargs or ENV Variable
# before falling back to random generation
index_name = get_from_dict_or_env(
kwargs, "index_name", "OPENSEARCH_INDEX_NAME", default=uuid.uuid4().hex
)
is_appx_search = kwargs.get("is_appx_search", True)
vector_field = kwargs.get("vector_field", "vector_field")
text_field = kwargs.get("text_field", "text")
max_chunk_bytes = kwargs.get("max_chunk_bytes", 1 * 1024 * 1024)
http_auth = kwargs.get("http_auth")
is_aoss = _is_aoss_enabled(http_auth=http_auth)
engine = None
if is_aoss and not is_appx_search:
raise ValueError(
"Amazon OpenSearch Service Serverless only "
"supports `approximate_search`"
)
if is_appx_search:
engine = kwargs.get("engine", "nmslib")
space_type = kwargs.get("space_type", "l2")
ef_search = kwargs.get("ef_search", 512)
ef_construction = kwargs.get("ef_construction", 512)
m = kwargs.get("m", 16)
_validate_aoss_with_engines(is_aoss, engine)
mapping = _default_text_mapping(
dim, engine, space_type, ef_search, ef_construction, m, vector_field
)
else:
mapping = _default_scripting_text_mapping(dim)
[kwargs.pop(key, None) for key in keys_list]
client = _get_opensearch_client(opensearch_url, **kwargs)
_bulk_ingest_embeddings(
client,
index_name,
embeddings,
texts,
ids=ids,
metadatas=metadatas,
vector_field=vector_field,
text_field=text_field,
mapping=mapping,
max_chunk_bytes=max_chunk_bytes,
is_aoss=is_aoss,
)
kwargs["engine"] = engine
return cls(opensearch_url, index_name, embedding, **kwargs)
[docs] @classmethod
async def afrom_embeddings(
cls,
embeddings: List[List[float]],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
bulk_size: int = 500,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> OpenSearchVectorSearch:
"""Asynchronously construct OpenSearchVectorSearch wrapper from pre-vectorized
embeddings.
Example:
.. code-block:: python
from langchain_community.vectorstores import OpenSearchVectorSearch
from langchain_community.embeddings import OpenAIEmbeddings
embedder = OpenAIEmbeddings()
embeddings = await embedder.aembed_documents(["foo", "bar"])
opensearch_vector_search =
await OpenSearchVectorSearch.afrom_embeddings(
embeddings,
texts,
embedder,
opensearch_url="http://localhost:9200"
)
OpenSearch by default supports Approximate Search powered by nmslib, faiss
and lucene engines recommended for large datasets. Also supports brute force
search through Script Scoring and Painless Scripting.
Optional Args:
vector_field: Document field embeddings are stored in. Defaults to
"vector_field".
text_field: Document field the text of the document is stored in. Defaults
to "text".
Optional Keyword Args for Approximate Search:
engine: "nmslib", "faiss", "lucene"; default: "nmslib"
space_type: "l2", "l1", "cosinesimil", "linf", "innerproduct"; default: "l2"
ef_search: Size of the dynamic list used during k-NN searches. Higher values
lead to more accurate but slower searches; default: 512
ef_construction: Size of the dynamic list used during k-NN graph creation.
Higher values lead to more accurate graph but slower indexing speed;
default: 512
m: Number of bidirectional links created for each new element. Large impact
on memory consumption. Between 2 and 100; default: 16
Keyword Args for Script Scoring or Painless Scripting:
is_appx_search: False
"""
opensearch_url = get_from_dict_or_env(
kwargs, "opensearch_url", "OPENSEARCH_URL"
)
# List of arguments that needs to be removed from kwargs
# before passing kwargs to get opensearch client
keys_list = [
"opensearch_url",
"index_name",
"is_appx_search",
"vector_field",
"text_field",
"engine",
"space_type",
"ef_search",
"ef_construction",
"m",
"max_chunk_bytes",
"is_aoss",
]
_validate_embeddings_and_bulk_size(len(embeddings), bulk_size)
dim = len(embeddings[0])
# Get the index name from either from kwargs or ENV Variable
# before falling back to random generation
index_name = get_from_dict_or_env(
kwargs, "index_name", "OPENSEARCH_INDEX_NAME", default=uuid.uuid4().hex
)
is_appx_search = kwargs.get("is_appx_search", True)
vector_field = kwargs.get("vector_field", "vector_field")
text_field = kwargs.get("text_field", "text")
max_chunk_bytes = kwargs.get("max_chunk_bytes", 1 * 1024 * 1024)
http_auth = kwargs.get("http_auth")
is_aoss = _is_aoss_enabled(http_auth=http_auth)
engine = None
if is_aoss and not is_appx_search:
raise ValueError(
"Amazon OpenSearch Service Serverless only "
"supports `approximate_search`"
)
if is_appx_search:
engine = kwargs.get("engine", "nmslib")
space_type = kwargs.get("space_type", "l2")
ef_search = kwargs.get("ef_search", 512)
ef_construction = kwargs.get("ef_construction", 512)
m = kwargs.get("m", 16)
_validate_aoss_with_engines(is_aoss, engine)
mapping = _default_text_mapping(
dim, engine, space_type, ef_search, ef_construction, m, vector_field
)
else:
mapping = _default_scripting_text_mapping(dim)
[kwargs.pop(key, None) for key in keys_list]
client = _get_async_opensearch_client(opensearch_url, **kwargs)
await _abulk_ingest_embeddings(
client,
index_name,
embeddings,
texts,
ids=ids,
metadatas=metadatas,
vector_field=vector_field,
text_field=text_field,
mapping=mapping,
max_chunk_bytes=max_chunk_bytes,
is_aoss=is_aoss,
)
kwargs["engine"] = engine
return cls(opensearch_url, index_name, embedding, **kwargs)
