id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 54 121 |
|---|---|---|
f1cd9c5ba011-7 | lines = text.split("\n")
# Final output
lines_with_metadata: List[LineType] = []
# Content and metadata of the chunk currently being processed
current_content: List[str] = []
current_metadata: Dict[str, str] = {}
# Keep track of the nested header structure
# heade... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
f1cd9c5ba011-8 | # Push the current header to the stack
header: HeaderType = {
"level": current_header_level,
"name": name,
"data": stripped_line[len(sep) :].strip(),
}
header_stack... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
f1cd9c5ba011-9 | class Tokenizer:
chunk_overlap: int
tokens_per_chunk: int
decode: Callable[[list[int]], str]
encode: Callable[[str], List[int]]
[docs]def split_text_on_tokens(*, text: str, tokenizer: Tokenizer) -> List[str]:
"""Split incoming text and return chunks."""
splits: List[str] = []
input_ids = tok... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
f1cd9c5ba011-10 | )
if model_name is not None:
enc = tiktoken.encoding_for_model(model_name)
else:
enc = tiktoken.get_encoding(encoding_name)
self._tokenizer = enc
self._allowed_special = allowed_special
self._disallowed_special = disallowed_special
[docs] def split_text... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
f1cd9c5ba011-11 | )
self.model_name = model_name
self._model = SentenceTransformer(self.model_name)
self.tokenizer = self._model.tokenizer
self._initialize_chunk_configuration(tokens_per_chunk=tokens_per_chunk)
def _initialize_chunk_configuration(
self, *, tokens_per_chunk: Optional[int]
)... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
f1cd9c5ba011-12 | token_ids_with_start_and_end_token_ids = self.tokenizer.encode(
text,
max_length=self._max_length_equal_32_bit_integer,
truncation="do_not_truncate",
)
return token_ids_with_start_and_end_token_ids
[docs]class Language(str, Enum):
CPP = "cpp"
GO = "go"
JAV... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
f1cd9c5ba011-13 | for i, _s in enumerate(separators):
if _s == "":
separator = _s
break
if re.search(_s, text):
separator = _s
new_separators = separators[i + 1 :]
break
splits = _split_text_with_regex(text, separator, self._k... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
f1cd9c5ba011-14 | if language == Language.CPP:
return [
# Split along class definitions
"\nclass ",
# Split along function definitions
"\nvoid ",
"\nint ",
"\nfloat ",
"\ndouble ",
# Split along con... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
f1cd9c5ba011-15 | "\nfunction ",
"\nconst ",
"\nlet ",
"\nvar ",
"\nclass ",
# Split along control flow statements
"\nif ",
"\nfor ",
"\nwhile ",
"\nswitch ",
"\ncase ",
... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
f1cd9c5ba011-16 | # Now split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif language == Language.RST:
return [
# Split along section titles
"\n=+\n",
"\n-+\n",
"\n\*+... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
f1cd9c5ba011-17 | "\nobject ",
# Split along method definitions
"\ndef ",
"\nval ",
"\nvar ",
# Split along control flow statements
"\nif ",
"\nfor ",
"\nwhile ",
"\nmatch ",
"\n... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
f1cd9c5ba011-18 | "\n",
" ",
"",
]
elif language == Language.LATEX:
return [
# First, try to split along Latex sections
"\n\\\chapter{",
"\n\\\section{",
"\n\\\subsection{",
"\n\\\subsubsection{... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
f1cd9c5ba011-19 | return [
# Split along compiler informations definitions
"\npragma ",
"\nusing ",
# Split along contract definitions
"\ncontract ",
"\ninterface ",
"\nlibrary ",
# Split along method definitio... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
f1cd9c5ba011-20 | splits = self._tokenizer(text)
return self._merge_splits(splits, self._separator)
[docs]class SpacyTextSplitter(TextSplitter):
"""Implementation of splitting text that looks at sentences using Spacy."""
def __init__(
self, separator: str = "\n\n", pipeline: str = "en_core_web_sm", **kwargs: Any
... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
f1cd9c5ba011-21 | separators = self.get_separators_for_language(Language.MARKDOWN)
super().__init__(separators=separators, **kwargs)
[docs]class LatexTextSplitter(RecursiveCharacterTextSplitter):
"""Attempts to split the text along Latex-formatted layout elements."""
def __init__(self, **kwargs: Any) -> None:
"""... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
f89ccc5b6942-0 | Source code for langchain.schema
"""Common schema objects."""
from __future__ import annotations
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import (
Any,
Dict,
Generic,
List,
NamedTuple,
Optional,
Sequence,
TypeVar,
Union,
)
from uuid import UUI... | https://api.python.langchain.com/en/latest/_modules/langchain/schema.html |
f89ccc5b6942-1 | """Agent's return value."""
return_values: dict
log: str
[docs]class Generation(Serializable):
"""Output of a single generation."""
text: str
"""Generated text output."""
generation_info: Optional[Dict[str, Any]] = None
"""Raw generation info response from the provider"""
"""May include ... | https://api.python.langchain.com/en/latest/_modules/langchain/schema.html |
f89ccc5b6942-2 | """Type of the message, used for serialization."""
return "system"
[docs]class FunctionMessage(BaseMessage):
name: str
@property
def type(self) -> str:
"""Type of the message, used for serialization."""
return "function"
[docs]class ChatMessage(BaseMessage):
"""Type of message wi... | https://api.python.langchain.com/en/latest/_modules/langchain/schema.html |
f89ccc5b6942-3 | Returns:
List of messages (BaseMessages).
"""
return [_message_from_dict(m) for m in messages]
[docs]class ChatGeneration(Generation):
"""Output of a single generation."""
text = ""
message: BaseMessage
@root_validator
def set_text(cls, values: Dict[str, Any]) -> Dict[str, Any]:
... | https://api.python.langchain.com/en/latest/_modules/langchain/schema.html |
f89ccc5b6942-4 | llm_output=self.llm_output,
)
)
else:
if self.llm_output is not None:
llm_output = self.llm_output.copy()
llm_output["token_usage"] = dict()
else:
llm_output = None
... | https://api.python.langchain.com/en/latest/_modules/langchain/schema.html |
f89ccc5b6942-5 | """Save the context of this model run to memory."""
[docs] @abstractmethod
def clear(self) -> None:
"""Clear memory contents."""
[docs]class BaseChatMessageHistory(ABC):
"""Base interface for chat message history
See `ChatMessageHistory` for default implementation.
"""
"""
Example:
... | https://api.python.langchain.com/en/latest/_modules/langchain/schema.html |
f89ccc5b6942-6 | raise NotImplementedError
[docs] @abstractmethod
def clear(self) -> None:
"""Remove all messages from the store"""
[docs]class Document(Serializable):
"""Interface for interacting with a document."""
page_content: str
metadata: dict = Field(default_factory=dict)
[docs]class BaseRetriever(ABC)... | https://api.python.langchain.com/en/latest/_modules/langchain/schema.html |
f89ccc5b6942-7 | """Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Args:
text: output of language model
Returns:
structured output
"""
[docs] def parse_with_prompt(self, completi... | https://api.python.langchain.com/en/latest/_modules/langchain/schema.html |
f89ccc5b6942-8 | @property
def _type(self) -> str:
return "default"
[docs] def parse(self, text: str) -> str:
return text
[docs]class OutputParserException(ValueError):
"""Exception that output parsers should raise to signify a parsing error.
This exists to differentiate parsing errors from other code or ... | https://api.python.langchain.com/en/latest/_modules/langchain/schema.html |
649767acfe47-0 | Source code for langchain.document_transformers
"""Transform documents"""
from typing import Any, Callable, List, Sequence
import numpy as np
from pydantic import BaseModel, Field
from langchain.embeddings.base import Embeddings
from langchain.math_utils import cosine_similarity
from langchain.schema import BaseDocumen... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers.html |
649767acfe47-1 | redundant_stacked = np.column_stack(redundant)
redundant_sorted = np.argsort(similarity[redundant])[::-1]
included_idxs = set(range(len(embedded_documents)))
for first_idx, second_idx in redundant_stacked[redundant_sorted]:
if first_idx in included_idxs and second_idx in included_idxs:
#... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers.html |
649767acfe47-2 | arbitrary_types_allowed = True
[docs] def transform_documents(
self, documents: Sequence[Document], **kwargs: Any
) -> Sequence[Document]:
"""Filter down documents."""
stateful_documents = get_stateful_documents(documents)
embedded_documents = _get_embeddings_from_stateful_docs(
... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers.html |
af5ade709a82-0 | Source code for langchain.vectorstores.clickhouse
"""Wrapper around open source ClickHouse VectorSearch capability."""
from __future__ import annotations
import json
import logging
from hashlib import sha1
from threading import Thread
from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
from pydantic im... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
af5ade709a82-1 | Defaults to 'vector_table'.
metric (str) : Metric to compute distance,
supported are ('angular', 'euclidean', 'manhattan', 'hamming',
'dot'). Defaults to 'angular'.
https://github.com/spotify/annoy/blob/main/src/annoymodule.cc#L149-L169
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
af5ade709a82-2 | return getattr(self, item)
class Config:
env_file = ".env"
env_prefix = "clickhouse_"
env_file_encoding = "utf-8"
[docs]class Clickhouse(VectorStore):
"""Wrapper around ClickHouse vector database
You need a `clickhouse-connect` python package, and a valid account
to connect to Cl... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
af5ade709a82-3 | assert self.config
assert self.config.host and self.config.port
assert (
self.config.column_map
and self.config.database
and self.config.table
and self.config.metric
)
for k in ["id", "embedding", "document", "metadata", "uuid"]:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
af5ade709a82-4 | """
self.dim = dim
self.BS = "\\"
self.must_escape = ("\\", "'")
self.embedding_function = embedding
self.dist_order = "ASC" # Only support ConsingDistance and L2Distance
# Create a connection to clickhouse
self.client = get_client(
host=self.config.h... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
af5ade709a82-5 | [docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
batch_size: int = 32,
ids: Optional[Iterable[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Insert more texts through the embeddings and add to the VectorStore.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
af5ade709a82-6 | transac.append(v)
if len(transac) == batch_size:
if t:
t.join()
t = Thread(target=self._insert, args=[transac, keys])
t.start()
transac = []
if len(transac) > 0:
if t:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
af5ade709a82-7 | Returns:
ClickHouse Index
"""
ctx = cls(embedding, config, **kwargs)
ctx.add_texts(texts, ids=text_ids, batch_size=batch_size, metadatas=metadatas)
return ctx
def __repr__(self) -> str:
"""Text representation for ClickHouse Vector Store, prints backends, username
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
af5ade709a82-8 | else:
where_str = ""
settings_strs = []
if self.config.index_query_params:
for k in self.config.index_query_params:
settings_strs.append(f"SETTING {k}={self.config.index_query_params[k]}")
q_str = f"""
SELECT {self.config.column_map['document']... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
af5ade709a82-9 | self,
embedding: List[float],
k: int = 4,
where_str: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Perform a similarity search with ClickHouse by vectors
Args:
query (str): query string
k (int, optional): Top K neighbors to retri... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
af5ade709a82-10 | Args:
query (str): query string
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional): where condition string.
Defaults to None.
NOTE: Please do not let end-user to fill this and... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
0b748921a2ca-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.embeddings.base import Embeddings
from langchain.schema import Document
from langchain.vectorstores.base import V... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
0b748921a2ca-1 | 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",
}
def __init__(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
0b748921a2ca-2 | 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 ImportError:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
0b748921a2ca-3 | self.config.datasource_name, field_name_map["id"], push_request
)
json_response = json.loads(push_response.body)
if json_response["status"] == "OK":
return [
push_doc["fields"][field_name_map["id"]]
for p... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
0b748921a2ca-4 | )
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.__setitem__("fields", add_doc_fields)
add_doc.__se... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
0b748921a2ca-5 | 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,
) -> Dict[str, Any]:
def generate_embedding_query() -> str:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
0b748921a2ca-6 | 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}"'
def search_data(single_query_str: str) -> Dict[str, Any]:
search_q... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
0b748921a2ca-7 | self, json_result: Dict[str, Any]
) -> List[Tuple[Document, float]]:
items = json_result["result"]["items"]
query_result_list: List[Tuple[Document, float]] = []
for item in items:
fields = item["fields"]
query_result_list.append(
(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/alibabacloud_opensearch.html |
0b748921a2ca-8 | 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 |
026424a18e61-0 | Source code for langchain.vectorstores.rocksetdb
"""Wrapper around Rockset vector database."""
from __future__ import annotations
import logging
from enum import Enum
from typing import Any, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
026424a18e61-1 | client: Any,
embeddings: Embeddings,
collection_name: str,
text_key: str,
embedding_key: str,
):
"""Initialize with Rockset client.
Args:
client: Rockset client object
collection: Rockset collection to insert docs / query
embeddings... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
026424a18e61-2 | """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.
batch_si... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
026424a18e61-3 | ) -> Rockset:
"""Create Rockset wrapper with existing texts.
This is intended as a quicker way to get started.
"""
# Sanitize imputs
assert client is not None, "Rockset Client cannot be None"
assert collection_name, "Collection name cannot be empty"
assert text_ke... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
026424a18e61-4 | k (int, optional): Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional): Metadata filters supplied as a
SQL `where` condition string. Defaults to None.
eg. "price<=70.0 AND brand='Nintendo'"
NOTE: Please do not let end-user to fill this ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
026424a18e61-5 | """Accepts a query_embedding (vector), and returns documents with
similar embeddings."""
docs_and_scores = self.similarity_search_by_vector_with_relevance_scores(
embedding, k, distance_func, where_str, **kwargs
)
return [doc for doc, _ in docs_and_scores]
[docs] def simil... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
026424a18e61-6 | self._text_key, type(v)
)
page_content = v
elif k == "dist":
assert isinstance(
v, float
), "Computed distance between vectors must of type `float`. \
But found {}".format(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
026424a18e61-7 | collection=self._collection_name, data=batch
)
return [doc_status._id for doc_status in add_doc_res.data]
[docs] def delete_texts(self, ids: List[str]) -> None:
"""Delete a list of docs from the Rockset collection"""
try:
from rockset.models import DeleteDocumentsRequestDa... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/rocksetdb.html |
0245ad0cd55d-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,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
0245ad0cd55d-1 | )
[docs] async def aadd_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore."""
raise NotImplementedError
[docs] def add_documents(self, doc... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
0245ad0cd55d-2 | if search_type == "similarity":
return self.similarity_search(query, **kwargs)
elif search_type == "mmr":
return self.max_marginal_relevance_search(query, **kwargs)
else:
raise ValueError(
f"search_type of {search_type} not allowed. Expected "
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
0245ad0cd55d-3 | k: Number of Documents to return. Defaults to 4.
**kwargs: kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns:
List of Tuples ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
0245ad0cd55d-4 | raise NotImplementedError
[docs] async def asimilarity_search_with_relevance_scores(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query."""
# This is a temporary workaround to make the similarity search
# asynchronou... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
0245ad0cd55d-5 | self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to embedding vector."""
# This is a temporary workaround to make the similarity search
# asynchronous. The proper solution is to make the similarity search
# asynchronous in the v... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
0245ad0cd55d-6 | lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance."""
# This is a temporary workaround to make the similarity search
# asynchronous. The proper solution is to make the similarity search
# asynchronous in... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
0245ad0cd55d-7 | k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance."""
raise NotImplementedError
[docs] @classmethod
def from_documents(
cls: Type[VST],
documents: Li... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
0245ad0cd55d-8 | cls: Type[VST],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> VST:
"""Return VectorStore initialized from texts and embeddings."""
raise NotImplementedError
[docs] def as_retriever(self, **kwargs: Any) -> Vecto... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
0245ad0cd55d-9 | def get_relevant_documents(self, query: str) -> List[Document]:
if self.search_type == "similarity":
docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
elif self.search_type == "similarity_score_threshold":
docs_and_similarities = (
self.vector... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
0245ad0cd55d-10 | """Add documents to vectorstore."""
return self.vectorstore.add_documents(documents, **kwargs)
async def aadd_documents(
self, documents: List[Document], **kwargs: Any
) -> List[str]:
"""Add documents to vectorstore."""
return await self.vectorstore.aadd_documents(documents, **kw... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/base.html |
42d60dfd50ce-0 | Source code for langchain.vectorstores.awadb
"""Wrapper around AwaDB for embedding vectors"""
from __future__ import annotations
import logging
import uuid
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Type
from langchain.docstore.document import Document
from langchain.embeddings.base import ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
42d60dfd50ce-1 | self.table2embeddings: dict[str, Embeddings] = {}
if embedding_model is not None:
self.table2embeddings[table_name] = embedding_model
self.using_table_name = table_name
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
42d60dfd50ce-2 | [docs] def similarity_search(
self,
query: str,
k: int = DEFAULT_TOPN,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query."""
if self.awadb_client is None:
raise ValueError("AwaDB client is None!!!")
embedding = None
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
42d60dfd50ce-3 | retrieval_docs = self.similarity_search_by_vector(embedding, k, scores)
L2_Norm = 0.0
for score in scores:
L2_Norm = L2_Norm + score * score
L2_Norm = pow(L2_Norm, 0.5)
doc_no = 0
for doc in retrieval_docs:
doc_tuple = (doc, 1 - (scores[doc_no] / L2_Norm))... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
42d60dfd50ce-4 | L2_Norm = L2_Norm + score * score
L2_Norm = pow(L2_Norm, 0.5)
doc_no = 0
for doc in retrieval_docs:
doc_tuple = (doc, 1 - scores[doc_no] / L2_Norm)
results.append(doc_tuple)
doc_no = doc_no + 1
return results
[docs] def similarity_search_by_vector(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
42d60dfd50ce-5 | content = item_detail[item_key]
elif (
item_key == "Field@1" or item_key == "text_embedding"
): # embedding field for the document
continue
elif item_key == "score": # L2 distance
if scores is not None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
42d60dfd50ce-6 | ) -> str:
"""Get the current table."""
return self.using_table_name
[docs] @classmethod
def from_texts(
cls: Type[AwaDB],
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
table_name: str = _DEFAULT_TABLE_NAME... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
42d60dfd50ce-7 | table_name: str = _DEFAULT_TABLE_NAME,
logging_and_data_dir: Optional[str] = None,
client: Optional[awadb.Client] = None,
**kwargs: Any,
) -> AwaDB:
"""Create an AwaDB vectorstore from a list of documents.
If a logging_and_data_dir specified, the table will be persisted there... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
43606bb90b19-0 | Source code for langchain.vectorstores.milvus
"""Wrapper around the Milvus vector database."""
from __future__ import annotations
import logging
from typing import Any, Iterable, List, Optional, Tuple, Union
from uuid import uuid4
import numpy as np
from langchain.docstore.document import Document
from langchain.embedd... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
43606bb90b19-1 | The connection args used for this class comes in the form of a dict,
here are a few of the options:
address (str): The actual address of Milvus
instance. Example address: "localhost:19530"
uri (str): The uri of Milvus instance. Example uri:
"http://randomw... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
43606bb90b19-2 | Args:
embedding_function (Embeddings): Function used to embed the text.
collection_name (str): Which Milvus collection to use. Defaults to
"LangChainCollection".
connection_args (Optional[dict[str, any]]): The arguments for connection to
Milvus/Zilliz ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
43606bb90b19-3 | "RHNSW_SQ": {"metric_type": "L2", "params": {"ef": 10}},
"RHNSW_PQ": {"metric_type": "L2", "params": {"ef": 10}},
"IVF_HNSW": {"metric_type": "L2", "params": {"nprobe": 10, "ef": 10}},
"ANNOY": {"metric_type": "L2", "params": {"search_k": 10}},
"AUTOINDEX": {"metric_type"... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
43606bb90b19-4 | if drop_old and isinstance(self.col, Collection):
self.col.drop()
self.col = None
# Initialize the vector store
self._init()
def _create_connection_alias(self, connection_args: dict) -> str:
"""Create the connection to the Milvus server."""
from pymilvus impor... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
43606bb90b19-5 | and (addr["user"] == tmp_user)
):
logger.debug("Using previous connection: %s", con[0])
return con[0]
# Generate a new connection if one doesnt exist
alias = uuid4().hex
try:
connections.connect(alias=alias, **connection_args)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
43606bb90b19-6 | if dtype == DataType.UNKNOWN or dtype == DataType.NONE:
logger.error(
"Failure to create collection, unrecognized dtype for key: %s",
key,
)
raise ValueError(f"Unrecognized datatype for {key}.")
#... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
43606bb90b19-7 | for x in schema.fields:
self.fields.append(x.name)
# Since primary field is auto-id, no need to track it
self.fields.remove(self._primary_field)
def _get_index(self) -> Optional[dict[str, Any]]:
"""Return the vector index information if it exists"""
from pymil... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
43606bb90b19-8 | using=self.alias,
)
logger.debug(
"Successfully created an index on collection: %s",
self.collection_name,
)
except MilvusException as e:
logger.error(
"Failed to create an index o... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
43606bb90b19-9 | embedding and the columns are decided by the first metadata dict.
Metada keys will need to be present for all inserted values. At
the moment there is no None equivalent in Milvus.
Args:
texts (Iterable[str]): The texts to embed, it is assumed
that they all fit in memo... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
43606bb90b19-10 | for key, value in d.items():
if key in self.fields:
insert_dict.setdefault(key, []).append(value)
# Total insert count
vectors: list = insert_dict[self._vector_field]
total_count = len(vectors)
pks: list[str] = []
assert isinstance(self... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
43606bb90b19-11 | expr (str, optional): Filtering expression. Defaults to None.
timeout (int, optional): How long to wait before timeout error.
Defaults to None.
kwargs: Collection.search() keyword arguments.
Returns:
List[Document]: Document results for search.
"""
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
43606bb90b19-12 | return []
res = self.similarity_search_with_score_by_vector(
embedding=embedding, k=k, param=param, expr=expr, timeout=timeout, **kwargs
)
return [doc for doc, _ in res]
[docs] def similarity_search_with_score(
self,
query: str,
k: int = 4,
param: O... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
43606bb90b19-13 | res = self.similarity_search_with_score_by_vector(
embedding=embedding, k=k, param=param, expr=expr, timeout=timeout, **kwargs
)
return res
[docs] def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
param: Optional[dict] = ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
43606bb90b19-14 | # Perform the search.
res = self.col.search(
data=[embedding],
anns_field=self._vector_field,
param=param,
limit=k,
expr=expr,
output_fields=output_fields,
timeout=timeout,
**kwargs,
)
# Organize resu... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
43606bb90b19-15 | Defaults to None.
expr (str, optional): Filtering expression. Defaults to None.
timeout (int, optional): How long to wait before timeout error.
Defaults to None.
kwargs: Collection.search() keyword arguments.
Returns:
List[Document]: Document resul... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
43606bb90b19-16 | to maximum diversity and 1 to minimum diversity.
Defaults to 0.5
param (dict, optional): The search params for the specified index.
Defaults to None.
expr (str, optional): Filtering expression. Defaults to None.
timeout (int, optional): How lon... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
43606bb90b19-17 | )
# Reorganize the results from query to match search order.
vectors = {x[self._primary_field]: x[self._vector_field] for x in vectors}
ordered_result_embeddings = [vectors[x] for x in ids]
# Get the new order of results.
new_ordering = maximal_marginal_relevance(
np.... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
43606bb90b19-18 | "LangChainCollection".
connection_args (dict[str, Any], optional): Connection args to use. Defaults
to DEFAULT_MILVUS_CONNECTION.
consistency_level (str, optional): Which consistency level to use. Defaults
to "Session".
index_params (Optional[dict], op... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/milvus.html |
5d704991e9bc-0 | Source code for langchain.vectorstores.elastic_vector_search
"""Wrapper around Elasticsearch vector database."""
from __future__ import annotations
import uuid
from abc import ABC
from typing import (
TYPE_CHECKING,
Any,
Dict,
Iterable,
List,
Mapping,
Optional,
Tuple,
Union,
)
from l... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
5d704991e9bc-1 | # defined as an abstract base class itself, allowing the creation of subclasses with
# their own specific implementations. If you plan to subclass ElasticVectorSearch,
# you can inherit from it and define your own implementation of the necessary methods
# and attributes.
[docs]class ElasticVectorSearch(VectorStore, ABC... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
5d704991e9bc-2 | 4. Click "Reset password"
5. Follow the prompts to reset the password
The format for Elastic Cloud URLs is
https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.
Example:
.. code-block:: python
from langchain import ElasticVectorSearch
from langchain.embeddi... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
5d704991e9bc-3 | self.index_name = index_name
_ssl_verify = ssl_verify or {}
try:
self.client = elasticsearch.Elasticsearch(elasticsearch_url, **_ssl_verify)
except ValueError as e:
raise ValueError(
f"Your elasticsearch client string is mis-formatted. Got error: {e} "
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
5d704991e9bc-4 | # just to save expensive steps for last
self.create_index(self.client, self.index_name, mapping)
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
request = {
"_op_type": "index",
"_index": self.index_name,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
5d704991e9bc-5 | Returns:
List of Documents most similar to the query.
"""
embedding = self.embedding.embed_query(query)
script_query = _default_script_query(embedding, filter)
response = self.client_search(
self.client, self.index_name, script_query, size=k
)
hits... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
5d704991e9bc-6 | elasticsearch_url="http://localhost:9200"
)
"""
elasticsearch_url = elasticsearch_url or get_from_env(
"elasticsearch_url", "ELASTICSEARCH_URL"
)
index_name = index_name or uuid.uuid4().hex
vectorsearch = cls(elasticsearch_url, index_name, embedding, *... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
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