id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 59 127 |
|---|---|---|
1323debf1f02-2 | return embeddings
def _generate_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Generate embeddings using the Embaas API."""
payload = self._generate_payload(texts)
try:
return self._handle_request(payload)
except requests.exceptions.RequestException as e:
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/embaas.html |
1fff811542ca-0 | Source code for langchain.embeddings.huggingface
"""Wrapper around HuggingFace embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, Field
from langchain.embeddings.base import Embeddings
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
DEFAULT_INSTRUCT_M... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/huggingface.html |
1fff811542ca-1 | """Key word arguments to pass when calling the `encode` method of the model."""
def __init__(self, **kwargs: Any):
"""Initialize the sentence_transformer."""
super().__init__(**kwargs)
try:
import sentence_transformers
except ImportError as exc:
raise ImportEr... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/huggingface.html |
1fff811542ca-2 | To use, you should have the ``sentence_transformers``
and ``InstructorEmbedding`` python packages installed.
Example:
.. code-block:: python
from langchain.embeddings import HuggingFaceInstructEmbeddings
model_name = "hkunlp/instructor-large"
model_kwargs = {'device':... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/huggingface.html |
1fff811542ca-3 | raise ValueError("Dependencies for InstructorEmbedding not found.") from e
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a HuggingFace instruct model... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/huggingface.html |
22afd4a09680-0 | Source code for langchain.embeddings.elasticsearch
from __future__ import annotations
from typing import TYPE_CHECKING, List, Optional
from langchain.utils import get_from_env
if TYPE_CHECKING:
from elasticsearch import Elasticsearch
from elasticsearch.client import MlClient
from langchain.embeddings.base impor... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/elasticsearch.html |
22afd4a09680-1 | es_user: Optional[str] = None,
es_password: Optional[str] = None,
input_field: str = "text_field",
) -> ElasticsearchEmbeddings:
"""Instantiate embeddings from Elasticsearch credentials.
Args:
model_id (str): The model_id of the model deployed in the Elasticsearch
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/elasticsearch.html |
22afd4a09680-2 | from elasticsearch.client import MlClient
except ImportError:
raise ImportError(
"elasticsearch package not found, please install with 'pip install "
"elasticsearch'"
)
es_cloud_id = es_cloud_id or get_from_env("es_cloud_id", "ES_CLOUD_ID")
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/elasticsearch.html |
22afd4a09680-3 | Example:
.. code-block:: python
from elasticsearch import Elasticsearch
from langchain.embeddings import ElasticsearchEmbeddings
# Define the model ID and input field name (if different from default)
model_id = "your_model_id"
#... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/elasticsearch.html |
22afd4a09680-4 | list.
"""
response = self.client.infer_trained_model(
model_id=self.model_id, docs=[{self.input_field: text} for text in texts]
)
embeddings = [doc["predicted_value"] for doc in response["inference_results"]]
return embeddings
[docs] def embed_documents(self, texts... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/elasticsearch.html |
1d62e06344ee-0 | Source code for langchain.embeddings.huggingface_hub
"""Wrapper around HuggingFace Hub embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
DEFAULT_REPO_ID... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/huggingface_hub.html |
1d62e06344ee-1 | @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
huggingfacehub_api_token = get_from_dict_or_env(
values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
)
try:
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/huggingface_hub.html |
1d62e06344ee-2 | texts = [text.replace("\n", " ") for text in texts]
_model_kwargs = self.model_kwargs or {}
responses = self.client(inputs=texts, params=_model_kwargs)
return responses
[docs] def embed_query(self, text: str) -> List[float]:
"""Call out to HuggingFaceHub's embedding endpoint for embed... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/huggingface_hub.html |
c42c6d64d304-0 | Source code for langchain.embeddings.openai
"""Wrapper around OpenAI embedding models."""
from __future__ import annotations
import logging
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Sequence,
Set,
Tuple,
Union,
)
import numpy as np
from pydantic import Ba... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/openai.html |
c42c6d64d304-1 | """Use tenacity to retry the embedding call."""
retry_decorator = _create_retry_decorator(embeddings)
@retry_decorator
def _embed_with_retry(**kwargs: Any) -> Any:
return embeddings.client.create(**kwargs)
return _embed_with_retry(**kwargs)
[docs]class OpenAIEmbeddings(BaseModel, Embeddings):
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/openai.html |
c42c6d64d304-2 | embeddings = OpenAIEmbeddings(
deployment="your-embeddings-deployment-name",
model="your-embeddings-model-name",
openai_api_base="https://your-endpoint.openai.azure.com/",
openai_api_type="azure",
)
text = "This is a test query."
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/openai.html |
c42c6d64d304-3 | extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
values["openai_api_key"] = get_from_dict_or_env(
values, "openai_api_key", "OPENAI_API_KEY"
)
values["open... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/openai.html |
c42c6d64d304-4 | )
return values
@property
def _invocation_params(self) -> Dict:
openai_args = {
"engine": self.deployment,
"request_timeout": self.request_timeout,
"headers": self.headers,
"api_key": self.openai_api_key,
"organization": self.openai_org... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/openai.html |
c42c6d64d304-5 | for i, text in enumerate(texts):
if self.model.endswith("001"):
# See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500
# replace newlines, which can negatively affect performance.
text = text.replace("\n", " ")
token = en... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/openai.html |
c42c6d64d304-6 | )[
"data"
][0]["embedding"]
else:
average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
embeddings[i] = (average / np.linalg.norm(average)).tolist()
return embeddings
def _embedding_func(self, text: str, *, engine: s... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/openai.html |
c42c6d64d304-7 | return self._get_len_safe_embeddings(texts, engine=self.deployment)
[docs] def embed_query(self, text: str) -> List[float]:
"""Call out to OpenAI's embedding endpoint for embedding query text.
Args:
text: The text to embed.
Returns:
Embedding for the text.
"""
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/openai.html |
56a492739365-0 | Source code for langchain.embeddings.cohere
"""Wrapper around Cohere embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class CohereEmbeddings(Base... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/cohere.html |
56a492739365-1 | except ImportError:
raise ValueError(
"Could not import cohere python package. "
"Please install it with `pip install cohere`."
)
return values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Call out to Cohere's embe... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/cohere.html |
66b5c35a9500-0 | Source code for langchain.embeddings.bedrock
import json
import os
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
[docs]class BedrockEmbeddings(BaseModel, Embeddings):
"""Embeddings provider to invoke Bedrock embedd... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/bedrock.html |
66b5c35a9500-1 | If not specified, the default credential profile or, if on an EC2 instance,
credentials from IMDS will be used.
See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
"""
model_id: str = "amazon.titan-e1t-medium"
"""Id of the model to call, e.g., amazon.titan-e1t-medium,... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/bedrock.html |
66b5c35a9500-2 | "profile name are valid."
) from e
return values
def _embedding_func(self, text: str) -> List[float]:
"""Call out to Bedrock embedding endpoint."""
# replace newlines, which can negatively affect performance.
text = text.replace(os.linesep, " ")
_model_kwargs = se... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/bedrock.html |
66b5c35a9500-3 | [docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a Bedrock model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
return self._embedding_func(text)
By Harrison Chase
© Copyright 20... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/bedrock.html |
4d74740bd703-0 | Source code for langchain.embeddings.fake
from typing import List
import numpy as np
from pydantic import BaseModel
from langchain.embeddings.base import Embeddings
[docs]class FakeEmbeddings(Embeddings, BaseModel):
size: int
def _get_embedding(self) -> List[float]:
return list(np.random.normal(size=sel... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/fake.html |
aaf41fb41b63-0 | Source code for langchain.embeddings.llamacpp
"""Wrapper around llama.cpp embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.embeddings.base import Embeddings
[docs]class LlamaCppEmbeddings(BaseModel, Embeddings):
"""Wrapper ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/llamacpp.html |
aaf41fb41b63-1 | use_mlock: bool = Field(False, alias="use_mlock")
"""Force system to keep model in RAM."""
n_threads: Optional[int] = Field(None, alias="n_threads")
"""Number of threads to use. If None, the number
of threads is automatically determined."""
n_batch: Optional[int] = Field(8, alias="n_batch")
"""... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/llamacpp.html |
aaf41fb41b63-2 | raise ModuleNotFoundError(
"Could not import llama-cpp-python library. "
"Please install the llama-cpp-python library to "
"use this embedding model: pip install llama-cpp-python"
)
except Exception as e:
raise ValueError(
f... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/llamacpp.html |
b61282c78106-0 | Source code for langchain.embeddings.modelscope_hub
"""Wrapper around ModelScopeHub embedding models."""
from typing import Any, List
from pydantic import BaseModel, Extra
from langchain.embeddings.base import Embeddings
[docs]class ModelScopeEmbeddings(BaseModel, Embeddings):
"""Wrapper around modelscope_hub embed... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/modelscope_hub.html |
b61282c78106-1 | texts = list(map(lambda x: x.replace("\n", " "), texts))
inputs = {"source_sentence": texts}
embeddings = self.embed(input=inputs)["text_embedding"]
return embeddings.tolist()
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a modelscope embedd... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/modelscope_hub.html |
47053ab09af7-0 | Source code for langchain.embeddings.minimax
"""Wrapper around MiniMax APIs."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
import requests
from pydantic import BaseModel, Extra, root_validator
from tenacity import (
before_sleep_log,
retry,
stop_... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/minimax.html |
47053ab09af7-1 | the constructor.
Example:
.. code-block:: python
from langchain.embeddings import MiniMaxEmbeddings
embeddings = MiniMaxEmbeddings()
query_text = "This is a test query."
query_result = embeddings.embed_query(query_text)
document_text = "This is a t... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/minimax.html |
47053ab09af7-2 | self,
texts: List[str],
embed_type: str,
) -> List[List[float]]:
payload = {
"model": self.model,
"type": embed_type,
"texts": texts,
}
# HTTP headers for authorization
headers = {
"Authorization": f"Bearer {self.minimax... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/minimax.html |
47053ab09af7-3 | )
return embeddings[0]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/minimax.html |
8b4bdebcb88d-0 | Source code for langchain.embeddings.self_hosted
"""Running custom embedding models on self-hosted remote hardware."""
from typing import Any, Callable, List
from pydantic import Extra
from langchain.embeddings.base import Embeddings
from langchain.llms import SelfHostedPipeline
def _embed_documents(pipeline: Any, *arg... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/self_hosted.html |
8b4bdebcb88d-1 | model_load_fn=get_pipeline,
hardware=gpu
model_reqs=["./", "torch", "transformers"],
)
Example passing in a pipeline path:
.. code-block:: python
from langchain.embeddings import SelfHostedHFEmbeddings
import runhouse as rh
from... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/self_hosted.html |
8b4bdebcb88d-2 | [docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace transformer model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
text = text.replace("\n", " ")
embeddings = self.clie... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/self_hosted.html |
13be03d534cc-0 | Source code for langchain.embeddings.aleph_alpha
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings):
"""... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/aleph_alpha.html |
13be03d534cc-1 | """Attention control parameters only apply to those tokens that have
explicitly been set in the request."""
control_log_additive: Optional[bool] = True
"""Apply controls on prompt items by adding the log(control_factor)
to attention scores."""
aleph_alpha_api_key: Optional[str] = None
"""API k... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/aleph_alpha.html |
13be03d534cc-2 | document_params = {
"prompt": Prompt.from_text(text),
"representation": SemanticRepresentation.Document,
"compress_to_size": self.compress_to_size,
"normalize": self.normalize,
"contextual_control_threshold": self.contextual_control_thresho... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/aleph_alpha.html |
13be03d534cc-3 | request=symmetric_request, model=self.model
)
return symmetric_response.embedding
[docs]class AlephAlphaSymmetricSemanticEmbedding(AlephAlphaAsymmetricSemanticEmbedding):
"""The symmetric version of the Aleph Alpha's semantic embeddings.
The main difference is that here, both the documents and
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/aleph_alpha.html |
13be03d534cc-4 | """Call out to Aleph Alpha's Document endpoint.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
document_embeddings = []
for text in texts:
document_embeddings.append(self._embed(text))
retur... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/aleph_alpha.html |
9f2ad041044a-0 | Source code for langchain.embeddings.tensorflow_hub
"""Wrapper around TensorflowHub embedding models."""
from typing import Any, List
from pydantic import BaseModel, Extra
from langchain.embeddings.base import Embeddings
DEFAULT_MODEL_URL = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
[docs]clas... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/tensorflow_hub.html |
9f2ad041044a-1 | """Compute doc embeddings using a TensorflowHub embedding model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
texts = list(map(lambda x: x.replace("\n", " "), texts))
embeddings = self.embed(texts).numpy()
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/tensorflow_hub.html |
2571249fc599-0 | Source code for langchain.embeddings.dashscope
"""Wrapper around DashScope embedding models."""
from __future__ import annotations
import logging
from typing import (
Any,
Callable,
Dict,
List,
Optional,
)
from pydantic import BaseModel, Extra, root_validator
from requests.exceptions import HTTPErro... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/dashscope.html |
2571249fc599-1 | elif resp.status_code in [400, 401]:
raise ValueError(
f"status_code: {resp.status_code} \n "
f"code: {resp.code} \n message: {resp.message}"
)
else:
raise HTTPError(
f"HTTP error occurred: status_code: {resp.status_code} \n "
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/dashscope.html |
2571249fc599-2 | class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
import dashscope
"""Validate that api key and python package exists in environment."""
values["dashscope_api_key"] = get... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/dashscope.html |
2571249fc599-3 | Embedding for the text.
"""
embedding = embed_with_retry(
self, input=text, text_type="query", model=self.model
)[0]["embedding"]
return embedding
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/dashscope.html |
9d94d312d47a-0 | Source code for langchain.vectorstores.azuresearch
"""Wrapper around Azure Cognitive Search."""
from __future__ import annotations
import base64
import json
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
)
im... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/azuresearch.html |
9d94d312d47a-1 | from azure.core.credentials import AzureKeyCredential
from azure.core.exceptions import ResourceNotFoundError
from azure.identity import DefaultAzureCredential
from azure.search.documents import SearchClient
from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents.ind... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/azuresearch.html |
9d94d312d47a-2 | algorithm_configurations=[
VectorSearchAlgorithmConfiguration(
name="default",
kind="hnsw",
hnsw_parameters={
"m": 4,
"efConstruction": 400,
"efSearch": 500,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/azuresearch.html |
9d94d312d47a-3 | azure_search_endpoint,
azure_search_key,
index_name,
embedding_function,
semantic_configuration_name,
)
self.search_type = search_type
self.semantic_configuration_name = semantic_configuration_name
self.semantic_query_language = semantic_qu... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/azuresearch.html |
9d94d312d47a-4 | raise Exception(response)
# Reset data
data = []
# Considering case where data is an exact multiple of batch-size entries
if len(data) == 0:
return ids
# Upload data to index
response = self.client.upload_documents(documents=data)
# Che... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/azuresearch.html |
9d94d312d47a-5 | query, k=k, filters=kwargs.get("filters", None)
)
return [doc for doc, _ in docs_and_scores]
[docs] def vector_search_with_score(
self, query: str, k: int = 4, filters: Optional[str] = None
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/azuresearch.html |
9d94d312d47a-6 | Returns:
List[Document]: A list of documents that are most similar to the query text.
"""
docs_and_scores = self.hybrid_search_with_score(
query, k=k, filters=kwargs.get("filters", None)
)
return [doc for doc, _ in docs_and_scores]
[docs] def hybrid_search_with... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/azuresearch.html |
9d94d312d47a-7 | ) -> List[Document]:
"""
Returns the most similar indexed documents to the query text.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
Returns:
List[Document]: A list of d... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/azuresearch.html |
9d94d312d47a-8 | query_answer="extractive",
top=k,
)
# Get Semantic Answers
semantic_answers = results.get_answers()
semantic_answers_dict = {}
for semantic_answer in semantic_answers:
semantic_answers_dict[semantic_answer.key] = {
"text": semantic_answer.t... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/azuresearch.html |
9d94d312d47a-9 | azure_search_key,
index_name,
embedding.embed_query,
)
azure_search.add_texts(texts, metadatas, **kwargs)
return azure_search
class AzureSearchVectorStoreRetriever(BaseRetriever, BaseModel):
vectorstore: AzureSearch
search_type: str = "hybrid"
k: int = 4
c... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/azuresearch.html |
34c8fa439699-0 | Source code for langchain.vectorstores.analyticdb
"""VectorStore wrapper around a Postgres/PGVector database."""
from __future__ import annotations
import logging
import uuid
from typing import Any, Dict, Iterable, List, Optional, Tuple
import sqlalchemy
from sqlalchemy import REAL, Index
from sqlalchemy.dialects.postg... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/analyticdb.html |
34c8fa439699-1 | """
Get or create a collection.
Returns [Collection, bool] where the bool is True if the collection was created.
"""
created = False
collection = cls.get_by_name(session, name)
if collection:
return collection, created
collection = cls(name=name, cmeta... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/analyticdb.html |
34c8fa439699-2 | """
VectorStore implementation using AnalyticDB.
AnalyticDB is a distributed full PostgresSQL syntax cloud-native database.
- `connection_string` is a postgres connection string.
- `embedding_function` any embedding function implementing
`langchain.embeddings.base.Embeddings` interface.
- `c... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/analyticdb.html |
34c8fa439699-3 | engine = sqlalchemy.create_engine(self.connection_string)
conn = engine.connect()
return conn
[docs] def create_tables_if_not_exists(self) -> None:
Base.metadata.create_all(self._conn)
[docs] def drop_tables(self) -> None:
Base.metadata.drop_all(self._conn)
[docs] def create_col... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/analyticdb.html |
34c8fa439699-4 | """
if ids is None:
ids = [str(uuid.uuid1()) for _ in texts]
embeddings = self.embedding_function.embed_documents(list(texts))
if not metadatas:
metadatas = [{} for _ in texts]
with Session(self._conn) as session:
collection = self.get_collection(sessi... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/analyticdb.html |
34c8fa439699-5 | self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
) -> 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.
filter... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/analyticdb.html |
34c8fa439699-6 | )
.filter(filter_by)
.order_by(EmbeddingStore.embedding.op("<->")(embedding))
.join(
CollectionStore,
EmbeddingStore.collection_id == CollectionStore.uuid,
)
.limit(k)
.all()
)
docs = [
(
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/analyticdb.html |
34c8fa439699-7 | pre_delete_collection: bool = False,
**kwargs: Any,
) -> AnalyticDB:
"""
Return VectorStore initialized from texts and embeddings.
Postgres connection string is required
Either pass it as a parameter
or set the PGVECTOR_CONNECTION_STRING environment variable.
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/analyticdb.html |
34c8fa439699-8 | or set the PGVECTOR_CONNECTION_STRING environment variable.
"""
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
connection_string = cls.get_connection_string(kwargs)
kwargs["connection_string"] = connection_string
return cls.from_te... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/analyticdb.html |
2b11536e38cf-0 | Source code for langchain.vectorstores.base
"""Interface for vector stores."""
from __future__ import annotations
import asyncio
import warnings
from abc import ABC, abstractmethod
from functools import partial
from typing import (
Any,
ClassVar,
Collection,
Dict,
Iterable,
List,
Optional,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/base.html |
2b11536e38cf-1 | """Run more documents through the embeddings and add to the vectorstore.
Args:
documents (List[Document]: Documents to add to the vectorstore.
Returns:
List[str]: List of IDs of the added texts.
"""
# TODO: Handle the case where the user doesn't provide ids on the... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/base.html |
2b11536e38cf-2 | )
[docs] async def asearch(
self, query: str, search_type: str, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query using specified search type."""
if search_type == "similarity":
return await self.asimilarity_search(query, **kwargs)
elif search_typ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/base.html |
2b11536e38cf-3 | query, k=k, **kwargs
)
if any(
similarity < 0.0 or similarity > 1.0
for _, similarity in docs_and_similarities
):
warnings.warn(
"Relevance scores must be between"
f" 0 and 1, got {docs_and_similarities}"
)
s... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/base.html |
2b11536e38cf-4 | return await asyncio.get_event_loop().run_in_executor(None, func)
[docs] async def asimilarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query."""
# This is a temporary workaround to make the similarity search
# asynchr... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/base.html |
2b11536e38cf-5 | 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
amon... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/base.html |
2b11536e38cf-6 | [docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
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 marg... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/base.html |
2b11536e38cf-7 | texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
[docs] @classmethod
async def afrom_documents(
cls: Type[VST],
documents: List[Document],
embedding: Embeddings,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/base.html |
2b11536e38cf-8 | vectorstore: VectorStore
search_type: str = "similarity"
search_kwargs: dict = Field(default_factory=dict)
allowed_search_types: ClassVar[Collection[str]] = (
"similarity",
"similarity_score_threshold",
"mmr",
)
class Config:
"""Configuration for this pydantic object.... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/base.html |
2b11536e38cf-9 | docs = self.vectorstore.max_marginal_relevance_search(
query, **self.search_kwargs
)
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
async def aget_relevant_documents(self, query: str) -> List[Document]:
if self.se... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/base.html |
27adabc26c75-0 | Source code for langchain.vectorstores.faiss
"""Wrapper around FAISS vector database."""
from __future__ import annotations
import math
import os
import pickle
import uuid
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.base imp... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
27adabc26c75-1 | return faiss
def _default_relevance_score_fn(score: float) -> float:
"""Return a similarity score on a scale [0, 1]."""
# 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 ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
27adabc26c75-2 | self._normalize_L2 = normalize_L2
def __add(
self,
texts: Iterable[str],
embeddings: Iterable[List[float]],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
if not isinstance(self.docstore, AddableMixi... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
27adabc26c75-3 | return [_id for _, _id, _ in full_info]
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
27adabc26c75-4 | ids: Optional list of unique IDs.
Returns:
List of ids from adding the texts into the vectorstore.
"""
if not isinstance(self.docstore, AddableMixin):
raise ValueError(
"If trying to add texts, the underlying docstore should support "
f"add... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
27adabc26c75-5 | docs = []
for j, i in enumerate(indices[0]):
if i == -1:
# This happens when not enough docs are returned.
continue
_id = self.index_to_docstore_id[i]
doc = self.docstore.search(_id)
if not isinstance(doc, Document):
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
27adabc26c75-6 | fetch_k=fetch_k,
**kwargs,
)
return docs
[docs] def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[Dict[str, Any]] = None,
fetch_k: int = 20,
**kwargs: Any,
) -> List[Document]:
"""Return ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
27adabc26c75-7 | fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
Returns:
List of Documents most similar to the query.
"""
docs_and_scores = self.similarity_search_with_score(
query, k, filter=filter, fetch_k=fetch_k, **kwargs
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
27adabc26c75-8 | for i in indices[0]:
if i == -1:
# This happens when not enough docs are returned.
continue
_id = self.index_to_docstore_id[i]
doc = self.docstore.search(_id)
if not isinstance(doc, Document):
rai... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
27adabc26c75-9 | **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 ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
27adabc26c75-10 | # Get id and docs from target FAISS object
full_info = []
for i, target_id in target.index_to_docstore_id.items():
doc = target.docstore.search(target_id)
if not isinstance(doc, Document):
raise ValueError("Document should be returned")
full_info.appen... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
27adabc26c75-11 | return cls(
embedding.embed_query,
index,
docstore,
index_to_id,
normalize_L2=normalize_L2,
**kwargs,
)
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Opti... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
27adabc26c75-12 | This is a user friendly interface that:
1. Embeds documents.
2. Creates an in memory docstore
3. Initializes the FAISS database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain import FAISS
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
27adabc26c75-13 | )
# save docstore and index_to_docstore_id
with open(path / "{index_name}.pkl".format(index_name=index_name), "wb") as f:
pickle.dump((self.docstore, self.index_to_docstore_id), f)
[docs] @classmethod
def load_local(
cls, folder_path: str, embeddings: Embeddings, index_name: s... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
27adabc26c75-14 | **kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and their similarity scores on a scale from 0 to 1."""
if self.relevance_score_fn is None:
raise ValueError(
"normalize_score_fn must be provided to"
" FAISS constructor to normalize scores"
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html |
d0972190eae7-0 | Source code for langchain.vectorstores.typesense
"""Wrapper around Typesense vector search"""
from __future__ import annotations
import uuid
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Union
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
fro... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/typesense.html |
d0972190eae7-1 | *,
typesense_collection_name: Optional[str] = None,
text_key: str = "text",
):
"""Initialize with Typesense client."""
try:
from typesense import Client
except ImportError:
raise ValueError(
"Could not import typesense python package. "... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/typesense.html |
d0972190eae7-2 | ]
def _create_collection(self, num_dim: int) -> None:
fields = [
{"name": "vec", "type": "float[]", "num_dim": num_dim},
{"name": f"{self._text_key}", "type": "string"},
{"name": ".*", "type": "auto"},
]
self._typesense_client.collections.create(
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/typesense.html |
d0972190eae7-3 | self,
query: str,
k: int = 4,
filter: Optional[str] = "",
) -> List[Tuple[Document, float]]:
"""Return typesense documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. De... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/typesense.html |
d0972190eae7-4 | k: Number of Documents to return. Defaults to 4.
filter: typesense filter_by expression to filter documents on
Returns:
List of Documents most similar to the query and score for each
"""
docs_and_score = self.similarity_search_with_score(query, k=k, filter=filter)
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/typesense.html |
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