id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 49 117 |
|---|---|---|
961c25351698-94 | Tigris (class in langchain.vectorstores)
time (langchain.utilities.DuckDuckGoSearchAPIWrapper attribute)
to_typescript() (langchain.tools.APIOperation method)
token (langchain.llms.PredictionGuard attribute)
(langchain.utilities.PowerBIDataset attribute)
token_path (langchain.document_loaders.GoogleApiClient attribute)... | https://python.langchain.com/en/latest/genindex.html |
961c25351698-95 | (langchain.llms.LlamaCpp attribute)
(langchain.llms.NLPCloud attribute)
(langchain.llms.Petals attribute)
(langchain.llms.VertexAI attribute)
(langchain.retrievers.ChatGPTPluginRetriever attribute)
(langchain.retrievers.DataberryRetriever attribute)
(langchain.retrievers.PineconeHybridSearchRetriever attribute)
top_k_d... | https://python.langchain.com/en/latest/genindex.html |
961c25351698-96 | (langchain.text_splitter.TextSplitter method)
transform_input_fn (langchain.llms.Databricks attribute)
transform_output_fn (langchain.llms.Databricks attribute)
transformers (langchain.retrievers.document_compressors.DocumentCompressorPipeline attribute)
TrelloLoader (class in langchain.document_loaders)
truncate (lang... | https://python.langchain.com/en/latest/genindex.html |
961c25351698-97 | UnstructuredODTLoader (class in langchain.document_loaders)
UnstructuredPDFLoader (class in langchain.document_loaders)
UnstructuredPowerPointLoader (class in langchain.document_loaders)
UnstructuredRTFLoader (class in langchain.document_loaders)
UnstructuredURLLoader (class in langchain.document_loaders)
UnstructuredW... | https://python.langchain.com/en/latest/genindex.html |
961c25351698-98 | (langchain.llms.HuggingFaceTextGenInference class method)
(langchain.llms.HumanInputLLM class method)
(langchain.llms.LlamaCpp class method)
(langchain.llms.Modal class method)
(langchain.llms.MosaicML class method)
(langchain.llms.NLPCloud class method)
(langchain.llms.OpenAI class method)
(langchain.llms.OpenAIChat c... | https://python.langchain.com/en/latest/genindex.html |
961c25351698-99 | use_mlock (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
use_mmap (langchain.llms.LlamaCpp attribute)
use_multiplicative_presence_penalty (langchain.llms.AlephAlpha attribute)
use_query_checker (langchain.chains.SQLDatabaseChain attribute)
user... | https://python.langchain.com/en/latest/genindex.html |
961c25351698-100 | (langchain.llms.AlephAlpha attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.Anyscale attribute)
(langchain.llms.Aviary attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.Banana attribute)
(langchain.llms.Baseten attribute)
(langchain.llms.Beam attribute)
(langchain.llms.Bedrock attribute)
... | https://python.langchain.com/en/latest/genindex.html |
961c25351698-101 | (langchain.llms.SelfHostedHuggingFaceLLM attribute)
(langchain.llms.SelfHostedPipeline attribute)
(langchain.llms.StochasticAI attribute)
(langchain.llms.VertexAI attribute)
(langchain.llms.Writer attribute)
(langchain.retrievers.SelfQueryRetriever attribute)
(langchain.tools.BaseTool attribute)
(langchain.tools.Tool a... | https://python.langchain.com/en/latest/genindex.html |
961c25351698-102 | Z
zapier_description (langchain.tools.ZapierNLARunAction attribute)
ZepRetriever (class in langchain.retrievers)
ZERO_SHOT_REACT_DESCRIPTION (langchain.agents.AgentType attribute)
Zilliz (class in langchain.vectorstores)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 202... | https://python.langchain.com/en/latest/genindex.html |
a1bc0fa44abd-0 | .rst
.pdf
Welcome to LangChain
Contents
Getting Started
Modules
Use Cases
Reference Docs
Ecosystem
Additional Resources
Welcome to LangChain#
LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a l... | https://python.langchain.com/en/latest/index.html |
a1bc0fa44abd-1 | Agents: An agent is a Chain in which an LLM, given a high-level directive and a set of tools, repeatedly decides an action, executes the action and observes the outcome until the high-level directive is complete.
Callbacks: Callbacks let you log and stream the intermediate steps of any chain, making it easy to observe,... | https://python.langchain.com/en/latest/index.html |
a1bc0fa44abd-2 | Reference Docs#
Full documentation on all methods, classes, installation methods, and integration setups for LangChain.
LangChain Installation
Reference Documentation
Ecosystem#
LangChain integrates a lot of different LLMs, systems, and products.
From the other side, many systems and products depend on LangChain.
It cr... | https://python.langchain.com/en/latest/index.html |
a1bc0fa44abd-3 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/index.html |
bc75ce6b3dde-0 | .rst
.pdf
Integrations
Contents
Integrations by Module
Dependencies
All Integrations
Integrations#
LangChain integrates with many LLMs, systems, and products.
Integrations by Module#
Integrations grouped by the core LangChain module they map to:
LLM Providers
Chat Model Providers
Text Embedding Model Providers
Docume... | https://python.langchain.com/en/latest/integrations.html |
bc75ce6b3dde-1 | Llama.cpp
MediaWikiDump
Metal
Microsoft OneDrive
Microsoft PowerPoint
Microsoft Word
Milvus
MLflow
Modal
Modern Treasury
Momento
MyScale
NLPCloud
Notion DB
Obsidian
OpenAI
OpenSearch
OpenWeatherMap
Petals
PGVector
Pinecone
PipelineAI
Prediction Guard
PromptLayer
Psychic
Qdrant
Ray Serve
Rebuff
Reddit
Redis
Replicate
Ro... | https://python.langchain.com/en/latest/integrations.html |
af52fc0a2ee1-0 | .md
.pdf
Dependents
Dependents#
Dependents stats for hwchase17/langchain
[update: 2023-06-05; only dependent repositories with Stars > 100]
Repository
Stars
openai/openai-cookbook
38024
LAION-AI/Open-Assistant
33609
microsoft/TaskMatrix
33136
hpcaitech/ColossalAI
30032
imartinez/privateGPT
28094
reworkd/AgentGPT
23430
... | https://python.langchain.com/en/latest/dependents.html |
af52fc0a2ee1-1 | 3545
gkamradt/langchain-tutorials
3404
mmabrouk/chatgpt-wrapper
3303
postgresml/postgresml
3052
marqo-ai/marqo
3014
MineDojo/Voyager
2945
PrefectHQ/marvin
2761
project-baize/baize-chatbot
2673
hwchase17/chat-langchain
2589
whitead/paper-qa
2572
Azure-Samples/azure-search-openai-demo
2366
GerevAI/gerev
2330
OpenGVLab/In... | https://python.langchain.com/en/latest/dependents.html |
af52fc0a2ee1-2 | thomas-yanxin/LangChain-ChatGLM-Webui
1182
ttengwang/Caption-Anything
1137
jina-ai/dev-gpt
1135
greshake/llm-security
1086
keephq/keep
1063
juncongmoo/chatllama
1037
richardyc/Chrome-GPT
1035
visual-openllm/visual-openllm
997
mmz-001/knowledge_gpt
995
jina-ai/langchain-serve
949
irgolic/AutoPR
936
microsoft/X-Decoder
9... | https://python.langchain.com/en/latest/dependents.html |
af52fc0a2ee1-3 | 496
microsoft/PodcastCopilot
492
debanjum/khoj
485
akshata29/chatpdf
485
langchain-ai/langchain-aiplugin
462
jina-ai/agentchain
460
alexanderatallah/window.ai
457
yeagerai/yeagerai-agent
451
mckaywrigley/repo-chat
446
michaelthwan/searchGPT
446
mpaepper/content-chatbot
441
freddyaboulton/gradio-tools
439
ruoccofabrizio... | https://python.langchain.com/en/latest/dependents.html |
af52fc0a2ee1-4 | 267
Anil-matcha/Website-to-Chatbot
266
Cheems-Seminar/grounded-segment-any-parts
260
sullivan-sean/chat-langchainjs
248
bborn/howdoi.ai
245
daveebbelaar/langchain-experiments
240
MagnivOrg/prompt-layer-library
237
ur-whitelab/exmol
234
conceptofmind/toolformer
234
recalign/RecAlign
226
OpenBMB/AgentVerse
220
alvarosevi... | https://python.langchain.com/en/latest/dependents.html |
af52fc0a2ee1-5 | 148
chakkaradeep/pyCodeAGI
145
ccurme/yolopandas
145
shamspias/customizable-gpt-chatbot
144
realminchoi/babyagi-ui
143
PradipNichite/Youtube-Tutorials
140
gustavz/DataChad
140
Klingefjord/chatgpt-telegram
140
Jaseci-Labs/jaseci
139
handrew/browserpilot
137
jmpaz/promptlib
137
SamPink/dev-gpt
135
menloparklab/langchain-... | https://python.langchain.com/en/latest/dependents.html |
af52fc0a2ee1-6 | 111
aurelio-labs/arxiv-bot
110
fixie-ai/fixie-examples
108
miaoshouai/miaoshouai-assistant
105
flurb18/AgentOoba
103
solana-labs/chatgpt-plugin
102
Significant-Gravitas/Auto-GPT-Benchmarks
102
kaarthik108/snowChat
100
Generated by github-dependents-info
github-dependents-info --repo hwchase17/langchain --markdownfile d... | https://python.langchain.com/en/latest/dependents.html |
46379634f13f-0 | Source code for langchain.requests
"""Lightweight wrapper around requests library, with async support."""
from contextlib import asynccontextmanager
from typing import Any, AsyncGenerator, Dict, Optional
import aiohttp
import requests
from pydantic import BaseModel, Extra
class Requests(BaseModel):
"""Wrapper aroun... | https://python.langchain.com/en/latest/_modules/langchain/requests.html |
46379634f13f-1 | def delete(self, url: str, **kwargs: Any) -> requests.Response:
"""DELETE the URL and return the text."""
return requests.delete(url, headers=self.headers, **kwargs)
@asynccontextmanager
async def _arequest(
self, method: str, url: str, **kwargs: Any
) -> AsyncGenerator[aiohttp.Clien... | https://python.langchain.com/en/latest/_modules/langchain/requests.html |
46379634f13f-2 | """PATCH the URL and return the text asynchronously."""
async with self._arequest("PATCH", url, **kwargs) as response:
yield response
@asynccontextmanager
async def aput(
self, url: str, data: Dict[str, Any], **kwargs: Any
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
... | https://python.langchain.com/en/latest/_modules/langchain/requests.html |
46379634f13f-3 | """POST to the URL and return the text."""
return self.requests.post(url, data, **kwargs).text
[docs] def patch(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str:
"""PATCH the URL and return the text."""
return self.requests.patch(url, data, **kwargs).text
[docs] def put(self, ur... | https://python.langchain.com/en/latest/_modules/langchain/requests.html |
46379634f13f-4 | """PUT the URL and return the text asynchronously."""
async with self.requests.aput(url, **kwargs) as response:
return await response.text()
[docs] async def adelete(self, url: str, **kwargs: Any) -> str:
"""DELETE the URL and return the text asynchronously."""
async with self.req... | https://python.langchain.com/en/latest/_modules/langchain/requests.html |
e990308dc849-0 | Source code for langchain.text_splitter
"""Functionality for splitting text."""
from __future__ import annotations
import copy
import logging
import re
from abc import ABC, abstractmethod
from dataclasses import dataclass
from enum import Enum
from typing import (
AbstractSet,
Any,
Callable,
Collection,... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
e990308dc849-1 | chunk_overlap: int = 200,
length_function: Callable[[str], int] = len,
keep_separator: bool = False,
add_start_index: bool = False,
) -> None:
"""Create a new TextSplitter.
Args:
chunk_size: Maximum size of chunks to return
chunk_overlap: Overlap in ch... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
e990308dc849-2 | metadata["start_index"] = index
new_doc = Document(page_content=chunk, metadata=metadata)
documents.append(new_doc)
return documents
[docs] def split_documents(self, documents: Iterable[Document]) -> List[Document]:
"""Split documents."""
texts, metadatas = [],... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
e990308dc849-3 | docs.append(doc)
# Keep on popping if:
# - we have a larger chunk than in the chunk overlap
# - or if we still have any chunks and the length is long
while total > self._chunk_overlap or (
total + _len + (separator_l... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
e990308dc849-4 | def from_tiktoken_encoder(
cls: Type[TS],
encoding_name: str = "gpt2",
model_name: Optional[str] = None,
allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
disallowed_special: Union[Literal["all"], Collection[str]] = "all",
**kwargs: Any,
) -> TS:
... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
e990308dc849-5 | return self.split_documents(list(documents))
[docs] async def atransform_documents(
self, documents: Sequence[Document], **kwargs: Any
) -> Sequence[Document]:
"""Asynchronously transform a sequence of documents by splitting them."""
raise NotImplementedError
[docs]class CharacterTextSpli... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
e990308dc849-6 | chunk_ids = input_ids[start_idx:cur_idx]
while start_idx < len(input_ids):
splits.append(tokenizer.decode(chunk_ids))
start_idx += tokenizer.tokens_per_chunk - tokenizer.chunk_overlap
cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids))
chunk_ids = input_ids[start_id... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
e990308dc849-7 | allowed_special=self._allowed_special,
disallowed_special=self._disallowed_special,
)
tokenizer = Tokenizer(
chunk_overlap=self._chunk_overlap,
tokens_per_chunk=self._chunk_size,
decode=self._tokenizer.decode,
encode=_encode,
)
... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
e990308dc849-8 | else:
self.tokens_per_chunk = tokens_per_chunk
if self.tokens_per_chunk > self.maximum_tokens_per_chunk:
raise ValueError(
f"The token limit of the models '{self.model_name}'"
f" is: {self.maximum_tokens_per_chunk}."
f" Argument tokens_per_... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
e990308dc849-9 | RUBY = "ruby"
RUST = "rust"
SCALA = "scala"
SWIFT = "swift"
MARKDOWN = "markdown"
LATEX = "latex"
HTML = "html"
[docs]class RecursiveCharacterTextSplitter(TextSplitter):
"""Implementation of splitting text that looks at characters.
Recursively tries to split by different characters to fi... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
e990308dc849-10 | _good_splits.append(s)
else:
if _good_splits:
merged_text = self._merge_splits(_good_splits, _separator)
final_chunks.extend(merged_text)
_good_splits = []
if not new_separators:
final_chunks.appe... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
e990308dc849-11 | elif language == Language.GO:
return [
# Split along function definitions
"\nfunc ",
"\nvar ",
"\nconst ",
"\ntype ",
# Split along control flow statements
"\nif ",
"\nfor ",
... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
e990308dc849-12 | "\nfunction ",
# Split along class definitions
"\nclass ",
# Split along control flow statements
"\nif ",
"\nforeach ",
"\nwhile ",
"\ndo ",
"\nswitch ",
"\ncase ",
... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
e990308dc849-13 | "",
]
elif language == Language.RUBY:
return [
# Split along method definitions
"\ndef ",
"\nclass ",
# Split along control flow statements
"\nif ",
"\nunless ",
"\nwhile ",
... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
e990308dc849-14 | "\nfunc ",
# Split along class definitions
"\nclass ",
"\nstruct ",
"\nenum ",
# Split along control flow statements
"\nif ",
"\nfor ",
"\nwhile ",
"\ndo ",
"\n... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
e990308dc849-15 | "\n\\\begin{quote}",
"\n\\\begin{quotation}",
"\n\\\begin{verse}",
"\n\\\begin{verbatim}",
## Now split by math environments
"\n\\\begin{align}",
"$$",
"$",
# Now split by the normal type of l... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
e990308dc849-16 | self._tokenizer = sent_tokenize
except ImportError:
raise ImportError(
"NLTK is not installed, please install it with `pip install nltk`."
)
self._separator = separator
[docs] def split_text(self, text: str) -> List[str]:
"""Split incoming text and retu... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
e990308dc849-17 | """Initialize a PythonCodeTextSplitter."""
separators = self.get_separators_for_language(Language.PYTHON)
super().__init__(separators=separators, **kwargs)
[docs]class MarkdownTextSplitter(RecursiveCharacterTextSplitter):
"""Attempts to split the text along Markdown-formatted headings."""
def __... | https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
e98f17c4398f-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://python.langchain.com/en/latest/_modules/langchain/document_transformers.html |
e98f17c4398f-1 | for first_idx, second_idx in redundant_stacked[redundant_sorted]:
if first_idx in included_idxs and second_idx in included_idxs:
# Default to dropping the second document of any highly similar pair.
included_idxs.remove(second_idx)
return list(sorted(included_idxs))
def _get_embeddin... | https://python.langchain.com/en/latest/_modules/langchain/document_transformers.html |
e98f17c4398f-2 | """Filter down documents."""
stateful_documents = get_stateful_documents(documents)
embedded_documents = _get_embeddings_from_stateful_docs(
self.embeddings, stateful_documents
)
included_idxs = _filter_similar_embeddings(
embedded_documents, self.similarity_fn, s... | https://python.langchain.com/en/latest/_modules/langchain/document_transformers.html |
e52585dff09b-0 | Source code for langchain.output_parsers.retry
from __future__ import annotations
from typing import TypeVar
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.prompt import PromptTemplate
from lang... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
e52585dff09b-1 | chain = LLMChain(llm=llm, prompt=prompt)
return cls(parser=parser, retry_chain=chain)
[docs] def parse_with_prompt(self, completion: str, prompt_value: PromptValue) -> T:
try:
parsed_completion = self.parser.parse(completion)
except OutputParserException:
new_completio... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
e52585dff09b-2 | ) -> RetryWithErrorOutputParser[T]:
chain = LLMChain(llm=llm, prompt=prompt)
return cls(parser=parser, retry_chain=chain)
[docs] def parse_with_prompt(self, completion: str, prompt_value: PromptValue) -> T:
try:
parsed_completion = self.parser.parse(completion)
except Outp... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
ca6a23bf3350-0 | Source code for langchain.output_parsers.pydantic
import json
import re
from typing import Type, TypeVar
from pydantic import BaseModel, ValidationError
from langchain.output_parsers.format_instructions import PYDANTIC_FORMAT_INSTRUCTIONS
from langchain.schema import BaseOutputParser, OutputParserException
T = TypeVar(... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/pydantic.html |
ca6a23bf3350-1 | @property
def _type(self) -> str:
return "pydantic"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/pydantic.html |
5370a30a3424-0 | Source code for langchain.output_parsers.rail_parser
from __future__ import annotations
from typing import Any, Dict
from langchain.schema import BaseOutputParser
[docs]class GuardrailsOutputParser(BaseOutputParser):
guard: Any
@property
def _type(self) -> str:
return "guardrails"
[docs] @classme... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/rail_parser.html |
8b82b3f2757f-0 | Source code for langchain.output_parsers.regex
from __future__ import annotations
import re
from typing import Dict, List, Optional
from langchain.schema import BaseOutputParser
[docs]class RegexParser(BaseOutputParser):
"""Class to parse the output into a dictionary."""
regex: str
output_keys: List[str]
... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/regex.html |
ea7943981c46-0 | Source code for langchain.output_parsers.datetime
import random
from datetime import datetime, timedelta
from typing import List
from langchain.schema import BaseOutputParser, OutputParserException
from langchain.utils import comma_list
def _generate_random_datetime_strings(
pattern: str,
n: int = 3,
start_... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/datetime.html |
ea7943981c46-1 | ) from e
@property
def _type(self) -> str:
return "datetime"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/datetime.html |
0d268a24a711-0 | Source code for langchain.output_parsers.list
from __future__ import annotations
from abc import abstractmethod
from typing import List
from langchain.schema import BaseOutputParser
[docs]class ListOutputParser(BaseOutputParser):
"""Class to parse the output of an LLM call to a list."""
@property
def _type(... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/list.html |
5359cc1d3772-0 | Source code for langchain.output_parsers.structured
from __future__ import annotations
from typing import Any, List
from pydantic import BaseModel
from langchain.output_parsers.format_instructions import STRUCTURED_FORMAT_INSTRUCTIONS
from langchain.output_parsers.json import parse_and_check_json_markdown
from langchai... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/structured.html |
b8734bee59e7-0 | Source code for langchain.output_parsers.regex_dict
from __future__ import annotations
import re
from typing import Dict, Optional
from langchain.schema import BaseOutputParser
[docs]class RegexDictParser(BaseOutputParser):
"""Class to parse the output into a dictionary."""
regex_pattern: str = r"{}:\s?([^.'\n'... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/regex_dict.html |
35eec0c5b588-0 | Source code for langchain.output_parsers.fix
from __future__ import annotations
from typing import TypeVar
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.output_parsers.prompts import NAIVE_FIX_PROMPT
from langchain.prompts.base import BasePromptTemplate
f... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/fix.html |
af0860406392-0 | 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 ... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
af0860406392-1 | 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")
"""... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
af0860406392-2 | 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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
b6ecaf2575c8-0 | 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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/tensorflow_hub.html |
b6ecaf2575c8-1 | """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()
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/tensorflow_hub.html |
7b11115d5237-0 | 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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
7b11115d5237-1 | 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
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
7b11115d5237-2 | 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")
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
7b11115d5237-3 | 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"
#... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
7b11115d5237-4 | 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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
47e5eeb4b1e9-0 | 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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/bedrock.html |
47e5eeb4b1e9-1 | 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,... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/bedrock.html |
47e5eeb4b1e9-2 | "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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/bedrock.html |
47e5eeb4b1e9-3 | [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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/bedrock.html |
38c1e1d83a2e-0 | Source code for langchain.embeddings.self_hosted_hugging_face
"""Wrapper around HuggingFace embedding models for self-hosted remote hardware."""
import importlib
import logging
from typing import Any, Callable, List, Optional
from langchain.embeddings.self_hosted import SelfHostedEmbeddings
DEFAULT_MODEL_NAME = "senten... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
38c1e1d83a2e-1 | if device < 0 and cuda_device_count > 0:
logger.warning(
"Device has %d GPUs available. "
"Provide device={deviceId} to `from_model_id` to use available"
"GPUs for execution. deviceId is -1 for CPU and "
"can be a positive integer associated wi... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
38c1e1d83a2e-2 | model_load_fn: Callable = load_embedding_model
"""Function to load the model remotely on the server."""
load_fn_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model load function."""
inference_fn: Callable = _embed_documents
"""Inference function to extract the embeddings."""
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
38c1e1d83a2e-3 | model_name=model_name, hardware=gpu)
"""
model_id: str = DEFAULT_INSTRUCT_MODEL
"""Model name to use."""
embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
"""Instruction to use for embedding documents."""
query_instruction: str = DEFAULT_QUERY_INSTRUCTION
"""Instruction to use for embedding... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
38c1e1d83a2e-4 | Returns:
Embeddings for the text.
"""
instruction_pair = [self.query_instruction, text]
embedding = self.client(self.pipeline_ref, [instruction_pair])[0]
return embedding.tolist()
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, ... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
64ef7e8ab507-0 | 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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
64ef7e8ab507-1 | @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:
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
64ef7e8ab507-2 | 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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
094599aa4c01-0 | 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):
"""... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
094599aa4c01-1 | """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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
094599aa4c01-2 | 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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
094599aa4c01-3 | 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
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
094599aa4c01-4 | """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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
eedc1f895dcc-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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/fake.html |
1533f89840fa-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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/cohere.html |
1533f89840fa-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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/cohere.html |
00bc5482a026-0 | 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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/modelscope_hub.html |
00bc5482a026-1 | 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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/modelscope_hub.html |
682a003d0ef2-0 | 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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
682a003d0ef2-1 | """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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
682a003d0ef2-2 | 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':... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
682a003d0ef2-3 | 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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
efe14e94b84b-0 | Source code for langchain.embeddings.deepinfra
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
DEFAULT_MODEL_ID = "sentence-transformers/clip-ViT-... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/deepinfra.html |
efe14e94b84b-1 | model_kwargs: Optional[dict] = None
"""Other model keyword args"""
deepinfra_api_token: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate tha... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/deepinfra.html |
efe14e94b84b-2 | try:
t = res.json()
embeddings = t["embeddings"]
except requests.exceptions.JSONDecodeError as e:
raise ValueError(
f"Error raised by inference API: {e}.\nResponse: {res.text}"
)
return embeddings
[docs] def embed_documents(self, texts: ... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/deepinfra.html |
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