id stringlengths 14 15 | text stringlengths 44 2.47k | source stringlengths 61 181 |
|---|---|---|
87442ab14498-0 | Source code for langchain.vectorstores.annoy
from __future__ import annotations
import os
import pickle
import uuid
from configparser import ConfigParser
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.base import Docstore
from ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
87442ab14498-1 | self.embedding_function = embedding_function
self.index = index
self.metric = metric
self.docstore = docstore
self.index_to_docstore_id = index_to_docstore_id
@property
def embeddings(self) -> Optional[Embeddings]:
# TODO: Accept embedding object directly
return N... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
87442ab14498-2 | """Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
search_k: inspect up to search_k nodes which defaults
to n_trees * n if not provided
Returns:
List of ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
87442ab14498-3 | Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
search_k: inspect up to search_k nodes which defaults
to n_trees * n if not provided
Returns:
List of Documents most similar to the query and score ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
87442ab14498-4 | to n_trees * n if not provided
Returns:
List of Documents most similar to the embedding.
"""
docs_and_scores = self.similarity_search_with_score_by_index(
docstore_index, k, search_k
)
return [doc for doc, _ in docs_and_scores]
[docs] def similarity_sea... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
87442ab14498-5 | 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 ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
87442ab14498-6 | 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.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among th... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
87442ab14498-7 | index.build(trees, n_jobs=n_jobs)
documents = []
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
documents.append(Document(page_content=text, metadata=metadata))
index_to_id = {i: str(uuid.uuid4()) for i in range(len(documents))}
docs... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
87442ab14498-8 | .. code-block:: python
from langchain.vectorstores import Annoy
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
index = Annoy.from_texts(texts, embeddings)
"""
embeddings = embedding.embed_documents(texts)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
87442ab14498-9 | embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
db = Annoy.from_embeddings(text_embedding_pairs, embeddings)
"""
texts = [t[0] for t in text_embeddings]
em... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
87442ab14498-10 | Args:
folder_path: folder path to load index, docstore,
and index_to_docstore_id from.
embeddings: Embeddings to use when generating queries.
"""
path = Path(folder_path)
# load index separately since it is not picklable
annoy = dependable_annoy_im... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
a051b217fef7-0 | Source code for langchain.vectorstores.vectara
from __future__ import annotations
import json
import logging
import os
from hashlib import md5
from typing import Any, Iterable, List, Optional, Tuple, Type
import requests
from langchain.pydantic_v1 import Field
from langchain.schema import Document
from langchain.schema... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
a051b217fef7-1 | or self._vectara_corpus_id is None
or self._vectara_api_key is None
):
logger.warning(
"Can't find Vectara credentials, customer_id or corpus_id in "
"environment."
)
else:
logger.debug(f"Using corpus id {self._vectara_corpu... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
a051b217fef7-2 | headers=self._get_post_headers(),
timeout=self.vectara_api_timeout,
)
if response.status_code != 200:
logger.error(
f"Delete request failed for doc_id = {doc_id} with status code "
f"{response.status_code}, reason {response.reason}, text "
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
a051b217fef7-3 | pre-processing and chunking occurs internally in an optimal way
This method provides a way to use that API in LangChain
Args:
files_list: Iterable of strings, each representing a local file path.
Files could be text, HTML, PDF, markdown, doc/docx, ppt/pptx, etc.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
a051b217fef7-4 | doc_ids.append(doc_id)
else:
logger.info(f"Error indexing file {file}: {response.json()}")
return doc_ids
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
doc_metadata: Optional[dict] = None,
**kwargs... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
a051b217fef7-5 | ],
}
success_str = self._index_doc(doc)
if success_str == "E_ALREADY_EXISTS":
self._delete_doc(doc_id)
self._index_doc(doc)
elif success_str == "E_NO_PERMISSIONS":
print(
"""No permissions to add document to Vectara.
Ch... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
a051b217fef7-6 | """
data = json.dumps(
{
"query": [
{
"query": query,
"start": 0,
"num_results": k,
"context_config": {
"sentences_before": n_sentence_conte... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
a051b217fef7-7 | doc_num = x["documentIndex"]
doc_md = {m["name"]: m["value"] for m in documents[doc_num]["metadata"]}
md.update(doc_md)
metadatas.append(md)
docs_with_score = [
(
Document(
page_content=x["text"],
metadata=md... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
a051b217fef7-8 | n_sentence_context=n_sentence_context,
**kwargs,
)
return [doc for doc, _ in docs_and_scores]
[docs] @classmethod
def from_texts(
cls: Type[Vectara],
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
a051b217fef7-9 | **kwargs: Any,
) -> Vectara:
"""Construct Vectara wrapper from raw documents.
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain.vectorstores import Vectara
vectara = Vectara.from_files(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
a051b217fef7-10 | filter: Dictionary of argument(s) to filter on metadata. For example a
filter can be "doc.rating > 3.0 and part.lang = 'deu'"} see
https://docs.vectara.com/docs/search-apis/sql/filter-overview
for more details.
n_sentence_context: number of sentences before/after the matching... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vectara.html |
ff1e17f1d826-0 | Source code for langchain.vectorstores.alibabacloud_opensearch
import json
import logging
import numbers
from hashlib import sha1
from typing import Any, Dict, Iterable, List, Optional, Tuple
from langchain.schema import Document
from langchain.schema.embeddings import Embeddings
from langchain.schema.vectorstore impor... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
ff1e17f1d826-1 | """
endpoint: str
instance_id: str
username: str
password: str
datasource_name: str
embedding_index_name: str
field_name_mapping: Dict[str, str] = {
"id": "id",
"document": "document",
"embedding": "embedding",
"metadata_field_x": "metadata_field_x,operator",
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
ff1e17f1d826-2 | [docs] def __init__(
self,
embedding: Embeddings,
config: AlibabaCloudOpenSearchSettings,
**kwargs: Any,
) -> None:
try:
from alibabacloud_ha3engine import client, models
from alibabacloud_tea_util import models as util_models
except ImportE... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
ff1e17f1d826-3 | )
push_response = self.ha3EngineClient.push_documents(
self.config.datasource_name, field_name_map["id"], push_request
)
json_response = json.loads(push_response.body)
if json_response["status"] == "OK":
return [
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
ff1e17f1d826-4 | ",".join(str(unit) for unit in embedding),
)
if metadata is not None:
for md_key, md_value in metadata.items():
add_doc_fields.__setitem__(
field_name_map[md_key].split(",")[0], md_value
)
add_doc.__s... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
ff1e17f1d826-5 | return self.create_results(
self.inner_embedding_query(
embedding=embedding, search_filter=search_filter, k=k
)
)
[docs] def inner_embedding_query(
self,
embedding: List[float],
search_filter: Optional[Dict[str, Any]] = None,
k: int = 4,... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
ff1e17f1d826-6 | )
return ""
md_filter_key = expr[0].strip()
md_filter_operator = expr[1].strip()
if isinstance(md_value, numbers.Number):
return f"{md_filter_key} {md_filter_operator} {md_value}"
return f'{md_filter_key}{md_filter_operator}"{md_value}"'
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
ff1e17f1d826-7 | metadata=create_metadata(fields),
)
)
return query_result_list
[docs] def create_results_with_score(
self, json_result: Dict[str, Any]
) -> List[Tuple[Document, float]]:
items = json_result["result"]["items"]
query_result_list: List[Tuple[Document, floa... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
ff1e17f1d826-8 | texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
return cls.from_texts(
texts=texts,
embedding=embedding,
metadatas=metadatas,
config=config,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
96331df04095-0 | Source code for langchain.vectorstores.nucliadb
import os
from typing import Any, Dict, Iterable, List, Optional, Type
from langchain.schema.document import Document
from langchain.schema.embeddings import Embeddings
from langchain.schema.vectorstore import VST, VectorStore
FIELD_TYPES = {
"f": "files",
"t": "t... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/nucliadb.html |
96331df04095-1 | if not backend:
backend = "http://localhost:8080"
self._config["BACKEND"] = f"{backend}/api/v1"
self._config["TOKEN"] = None
NucliaAuth().nucliadb(url=backend)
NucliaAuth().kb(url=self.kb_url, interactive=False)
else:
self._config["BACK... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/nucliadb.html |
96331df04095-2 | )
ids.append(id)
return ids
[docs] def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
if not ids:
return None
from nuclia.sdk import NucliaResource
factory = NucliaResource()
results: List[bool] = []
for id in id... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/nucliadb.html |
96331df04095-3 | "metadata": {
"extra": getattr(
getattr(resource, "extra", {}), "metadata", None
),
"value": value,
},
"order": paragraph.order,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/nucliadb.html |
73a052eb2561-0 | Source code for langchain.vectorstores.elasticsearch
import logging
import uuid
from abc import ABC, abstractmethod
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Literal,
Optional,
Tuple,
Union,
)
from langchain.docstore.document import Document
from la... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-1 | Returns:
Dict: The Elasticsearch query body.
"""
[docs] @abstractmethod
def index(
self,
dims_length: Union[int, None],
vector_query_field: str,
similarity: Union[DistanceStrategy, None],
) -> Dict:
"""
Executes when the index is created.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-2 | [docs] def __init__(
self,
query_model_id: Optional[str] = None,
hybrid: Optional[bool] = False,
):
self.query_model_id = query_model_id
self.hybrid = hybrid
[docs] def query(
self,
query_vector: Union[List[float], None],
query: Union[str, None],... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-3 | if self.hybrid:
return {
"knn": knn,
"query": {
"bool": {
"must": [
{
"match": {
text_field: {
"... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-4 | query: Union[str, None],
k: int,
fetch_k: int,
vector_query_field: str,
text_field: str,
filter: Union[List[dict], None],
similarity: Union[DistanceStrategy, None],
) -> Dict:
if similarity is DistanceStrategy.COSINE:
similarityAlgo = (
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-5 | return {
"mappings": {
"properties": {
vector_query_field: {
"type": "dense_vector",
"dims": dims_length,
"index": False,
},
}
}
}
[docs]class S... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-6 | if self.model_id:
client.ingest.put_pipeline(
id=self._get_pipeline_name(),
description="Embedding pipeline for langchain vectorstore",
processors=[
{
"inference": {
"model_id": self.model... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-7 | es_url: URL of the Elasticsearch instance to connect to.
cloud_id: Cloud ID of the Elasticsearch instance to connect to.
es_user: Username to use when connecting to Elasticsearch.
es_password: Password to use when connecting to Elasticsearch.
es_api_key: API key to use when connecting to... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-8 | from langchain.embeddings.openai import OpenAIEmbeddings
from elasticsearch import Elasticsearch
es_connection = Elasticsearch("http://localhost:9200")
vectorstore = ElasticsearchStore(
embedding=OpenAIEmbeddings(),
index_name="langchain-demo",
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-9 | [docs] def __init__(
self,
index_name: str,
*,
embedding: Optional[Embeddings] = None,
es_connection: Optional["Elasticsearch"] = None,
es_url: Optional[str] = None,
es_cloud_id: Optional[str] = None,
es_user: Optional[str] = None,
es_api_key: O... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-10 | or valid credentials for creating a new connection."""
)
[docs] @staticmethod
def get_user_agent() -> str:
from langchain import __version__
return f"langchain-py-vs/{__version__}"
[docs] @staticmethod
def connect_to_elasticsearch(
*,
es_url: Optional[str] = Non... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-11 | raise e
return es_client
@property
def embeddings(self) -> Optional[Embeddings]:
return self.embedding
[docs] def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[Document]:
"""Re... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-12 | self,
embedding: List[float],
k: int = 4,
filter: Optional[List[Dict]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return Elasticsearch documents most similar to query, along with scores.
Args:
embedding: Embedding to look up documents sim... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-13 | custom_query: Function to modify the Elasticsearch
query body before it is sent to Elasticsearch.
Returns:
List of Documents most similar to the query and score for each
"""
if fields is None:
fields = ["metadata"]
if self.query_field not ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-14 | **kwargs: Any,
) -> Optional[bool]:
"""Delete documents from the Elasticsearch index.
Args:
ids: List of ids of documents to delete.
refresh_indices: Whether to refresh the index
after deleting documents. Defaults to True.
"""
try:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-15 | """
if self.client.indices.exists(index=index_name):
logger.debug(f"Index {index_name} already exists. Skipping creation.")
else:
if dims_length is None and self.strategy.require_inference():
raise ValueError(
"Cannot create index without speci... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-16 | refresh_indices: Whether to refresh the Elasticsearch indices
after adding the texts.
create_index_if_not_exists: Whether to create the Elasticsearch
index if it doesn't already exist.
*bulk_kwargs: Additional arguments to pass ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-17 | "_id": ids[i],
}
)
else:
# the search_type doesn't require inference, so we don't need to
# embed the texts.
if create_index_if_not_exists:
self._create_index_if_not_exists(index_name=self.index_name)
for i, text... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-18 | bulk_kwargs: Optional[Dict] = None,
**kwargs: Any,
) -> "ElasticsearchStore":
"""Construct ElasticsearchStore wrapper from raw documents.
Example:
.. code-block:: python
from langchain.vectorstores import ElasticsearchStore
from langchain.embedding... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-19 | """
elasticsearchStore = ElasticsearchStore._create_cls_from_kwargs(
embedding=embedding, **kwargs
)
# Encode the provided texts and add them to the newly created index.
elasticsearchStore.add_texts(
texts, metadatas=metadatas, bulk_kwargs=bulk_kwargs
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-20 | es_api_key=es_api_key,
strategy=strategy,
distance_strategy=distance_strategy,
**optional_args,
)
[docs] @classmethod
def from_documents(
cls,
documents: List[Document],
embedding: Optional[Embeddings] = None,
bulk_kwargs: Optional[Dict]... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-21 | bulk_kwargs: Optional. Additional arguments to pass to
Elasticsearch bulk.
"""
elasticsearchStore = ElasticsearchStore._create_cls_from_kwargs(
embedding=embedding, **kwargs
)
# Encode the provided texts and add them to the newly created index.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
73a052eb2561-22 | [docs] @staticmethod
def SparseVectorRetrievalStrategy(
model_id: Optional[str] = None,
) -> "SparseRetrievalStrategy":
"""Used to perform sparse vector search via text_expansion.
Used for when you want to use ELSER model to perform document search.
At build index time, this s... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elasticsearch.html |
02d619e0dcc0-0 | Source code for langchain.vectorstores.pinecone
from __future__ import annotations
import logging
import uuid
import warnings
from typing import TYPE_CHECKING, Any, Callable, Iterable, List, Optional, Tuple, Union
import numpy as np
from langchain.docstore.document import Document
from langchain.schema.embeddings impor... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
02d619e0dcc0-1 | raise ImportError(
"Could not import pinecone python package. "
"Please install it with `pip install pinecone-client`."
)
if not isinstance(embedding, Embeddings):
warnings.warn(
"Passing in `embedding` as a Callable is deprecated. Please p... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
02d619e0dcc0-2 | namespace: Optional[str] = None,
batch_size: int = 32,
embedding_chunk_size: int = 1000,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Upsert optimization is done by chunking the embeddings and upserting them.
This... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
02d619e0dcc0-3 | for i in range(0, len(texts), embedding_chunk_size):
chunk_texts = texts[i : i + embedding_chunk_size]
chunk_ids = ids[i : i + embedding_chunk_size]
chunk_metadatas = metadatas[i : i + embedding_chunk_size]
embeddings = self._embed_documents(chunk_texts)
async... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
02d619e0dcc0-4 | embedding: List[float],
*,
k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Return pinecone documents most similar to embedding, along with scores."""
if namespace is None:
namespace = self._... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
02d619e0dcc0-5 | """
docs_and_scores = self.similarity_search_with_score(
query, k=k, filter=filter, namespace=namespace, **kwargs
)
return [doc for doc, _ in docs_and_scores]
def _select_relevance_score_fn(self) -> Callable[[float], float]:
"""
The 'correct' relevance function
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
02d619e0dcc0-6 | Args:
embedding: Embedding 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.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
02d619e0dcc0-7 | ) -> 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 ret... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
02d619e0dcc0-8 | elif len(indexes) == 0:
raise ValueError(
"No active indexes found in your Pinecone project, "
"are you sure you're using the right Pinecone API key and Environment? "
"Please double check your Pinecone dashboard."
)
else:
raise... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
02d619e0dcc0-9 | # in your Pinecone console
pinecone.init(api_key="***", environment="...")
embeddings = OpenAIEmbeddings()
pinecone = Pinecone.from_texts(
texts,
embeddings,
index_name="langchain-demo"
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
02d619e0dcc0-10 | filter: Dictionary of conditions to filter vectors to delete.
"""
if namespace is None:
namespace = self._namespace
if delete_all:
self._index.delete(delete_all=True, namespace=namespace, **kwargs)
elif ids is not None:
chunk_size = 1000
fo... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
c1df46f64aa8-0 | Source code for langchain.vectorstores.meilisearch
from __future__ import annotations
import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type
from langchain.docstore.document import Document
from langchain.schema.embeddings import Embeddings
from langchain.schema.vectorstore impor... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/meilisearch.html |
c1df46f64aa8-1 | To use this, you need to have `meilisearch` python package installed,
and a running Meilisearch instance.
To learn more about Meilisearch Python, refer to the in-depth
Meilisearch Python documentation: https://meilisearch.github.io/meilisearch-python/.
See the following documentation for how to run a Me... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/meilisearch.html |
c1df46f64aa8-2 | self._index_name = index_name
self._embedding = embedding
self._text_key = text_key
self._metadata_key = metadata_key
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: An... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/meilisearch.html |
c1df46f64aa8-3 | return ids
[docs] def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return meilisearch documents most similar to the query.
Args:
query (str): Query text for whic... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/meilisearch.html |
c1df46f64aa8-4 | """
_query = self._embedding.embed_query(query)
docs = self.similarity_search_by_vector_with_scores(
embedding=_query,
k=k,
filter=filter,
kwargs=kwargs,
)
return docs
[docs] def similarity_search_by_vector_with_scores(
self,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/meilisearch.html |
c1df46f64aa8-5 | **kwargs: Any,
) -> List[Document]:
"""Return meilisearch documents most similar to embedding vector.
Args:
embedding (List[float]): Embedding to look up similar documents.
k (int): Number of documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]): F... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/meilisearch.html |
c1df46f64aa8-6 | Example:
.. code-block:: python
from langchain.vectorstores import Meilisearch
from langchain.embeddings import OpenAIEmbeddings
import meilisearch
# The environment should be the one specified next to the API key
# in your Meil... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/meilisearch.html |
478f8a1e86ab-0 | Source code for langchain.vectorstores.awadb
from __future__ import annotations
import logging
import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Set, Tuple, Type
import numpy as np
from langchain.docstore.document import Document
from langchain.schema.embeddings import Embeddings
from l... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
478f8a1e86ab-1 | "Please install it with `pip install awadb`."
)
if client is not None:
self.awadb_client = client
else:
if log_and_data_dir is not None:
self.awadb_client = awadb.Client(log_and_data_dir)
else:
self.awadb_client = awadb.Clie... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
478f8a1e86ab-2 | """
if self.awadb_client is None:
raise ValueError("AwaDB client is None!!!")
embeddings = None
if self.using_table_name in self.table2embeddings:
embeddings = self.table2embeddings[self.using_table_name].embed_documents(
list(texts)
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
478f8a1e86ab-3 | E.g. `{"max_price" : 15.66, "min_price": 4.20}`
`price` is the metadata field, means range filter(4.20<'price'<15.66).
E.g. `{"maxe_price" : 15.66, "mine_price": 4.20}`
`price` is the metadata field, means range filter(4.20<='price'<=15.66).
kwargs: Any possible extend pa... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
478f8a1e86ab-4 | Args:
query: Text query.
k: The k most similar documents to the text query.
text_in_page_content: Filter by the text in page_content of Document.
meta_filter: Filter by metadata. Defaults to None.
kwargs: Any possible extend parameters in the future.
R... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
478f8a1e86ab-5 | [docs] def similarity_search_by_vector(
self,
embedding: Optional[List[float]] = None,
k: int = DEFAULT_TOPN,
text_in_page_content: Optional[str] = None,
meta_filter: Optional[dict] = None,
not_include_fields_in_metadata: Optional[Set[str]] = None,
**kwargs: An... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
478f8a1e86ab-6 | if item_key in not_include_fields_in_metadata:
continue
meta_data[item_key] = item_detail[item_key]
results.append(Document(page_content=content, metadata=meta_data))
return results
[docs] def max_marginal_relevance_search(
self,
query: str,... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
478f8a1e86ab-7 | else:
from awadb import AwaEmbedding
embedding = AwaEmbedding().Embedding(query)
if embedding.__len__() == 0:
return []
results = self.max_marginal_relevance_search_by_vector(
embedding,
k,
fetch_k,
lambda_mult=lambda_mu... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
478f8a1e86ab-8 | """
if self.awadb_client is None:
raise ValueError("AwaDB client is None!!!")
results: List[Document] = []
if embedding is None:
return results
not_include_fields: set = {"_id", "score"}
retrieved_docs = self.similarity_search_by_vector(
embedd... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
478f8a1e86ab-9 | limit: The number of documents to return. Defaults to 5. Optional.
Returns:
Documents which satisfy the input conditions.
"""
if self.awadb_client is None:
raise ValueError("AwaDB client is None!!!")
docs_detail = self.awadb_client.Get(
ids=ids,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
478f8a1e86ab-10 | return ret
ret = self.awadb_client.Delete(ids)
return ret
[docs] def update(
self,
ids: List[str],
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Update the documents which have the specified ids.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
478f8a1e86ab-11 | return ret
[docs] def list_tables(
self,
**kwargs: Any,
) -> List[str]:
"""List all the tables created by the client."""
if self.awadb_client is None:
return []
return self.awadb_client.ListAllTables()
[docs] def get_current_table(
self,
**kw... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
478f8a1e86ab-12 | log_and_data_dir=log_and_data_dir,
client=client,
)
awadb_client.add_texts(texts=texts, metadatas=metadatas)
return awadb_client
[docs] @classmethod
def from_documents(
cls: Type[AwaDB],
documents: List[Document],
embedding: Optional[Embeddings] = None,... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
f42e419738b7-0 | Source code for langchain.vectorstores.pgembedding
from __future__ import annotations
import logging
import uuid
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
import sqlalchemy
from sqlalchemy import func
from sqlalchemy.dialects.postgresql import JSON, UUID
from sqlalchemy.orm import Session, dec... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
f42e419738b7-1 | 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, cmetadata=cmetadata)
session.add(collection)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
f42e419738b7-2 | - NOTE: This is not the name of the table, but the name of the collection.
The tables will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
- `distance_strategy` is the distance strategy to use. (default: EUCLIDEAN)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
f42e419738b7-3 | [docs] def create_hnsw_extension(self) -> None:
try:
with Session(self._conn) as session:
statement = sqlalchemy.text("CREATE EXTENSION IF NOT EXISTS embedding")
session.execute(statement)
session.commit()
except Exception as e:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
f42e419738b7-4 | try:
with Session(self._conn) as session:
# Create the HNSW index
session.execute(create_index_query)
session.commit()
print("HNSW extension and index created successfully.")
except Exception as e:
print(f"Failed to create HNSW ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
f42e419738b7-5 | pre_delete_collection=pre_delete_collection,
)
store.add_embeddings(
texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
)
return store
[docs] def add_embeddings(
self,
texts: List[str],
embeddings: List[List[float]],
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
f42e419738b7-6 | embedding_store = EmbeddingStore(
embedding=embedding,
document=text,
cmetadata=metadata,
custom_id=id,
)
collection.embeddings.append(embedding_store)
session.add(embedding_store)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
f42e419738b7-7 | if filter is not None:
filter_clauses = []
for key, value in filter.items():
IN = "in"
if isinstance(value, dict) and IN in map(str.lower, value):
value_case_insensitive = {
k.lower(): v for k, v ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
f42e419738b7-8 | metadata=result.EmbeddingStore.cmetadata,
),
result.distance if self.embedding_function is not None else None,
)
for result in results
]
return docs
[docs] def similarity_search_by_vector(
self,
embedding: List[float],
k:... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
f42e419738b7-9 | ids: Optional[List[str]] = None,
pre_delete_collection: bool = False,
**kwargs: Any,
) -> PGEmbedding:
texts = [t[0] for t in text_embeddings]
embeddings = [t[1] for t in text_embeddings]
return cls._initialize_from_embeddings(
texts,
embeddings,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
f42e419738b7-10 | def from_documents(
cls: Type[PGEmbedding],
documents: List[Document],
embedding: Embeddings,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
ids: Optional[List[str]] = None,
pre_delete_collection: bool = False,
**kwargs: Any,
) -> PGEmbedding:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
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