id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
2b17500a51c9-7 | output_path = gcs_output_path or self._gcs_output_path
if output_path is None:
raise ValueError(
"An output path on Google Cloud Storage should be provided."
)
processor_name = processor_name or self._processor_name
if processor_name is None:
r... | lang/api.python.langchain.com/en/latest/_modules/langchain/document_loaders/parsers/docai.html |
2b17500a51c9-8 | from google.cloud.documentai_v1 import BatchProcessMetadata
except ImportError as exc:
raise ImportError(
"documentai package not found, please install it with"
" `pip install google-cloud-documentai`"
) from exc
return [
DocAIParsingRe... | lang/api.python.langchain.com/en/latest/_modules/langchain/document_loaders/parsers/docai.html |
b6a6221eab66-0 | Source code for langchain.document_loaders.parsers.html.bs4
"""Loader that uses bs4 to load HTML files, enriching metadata with page title."""
import logging
from typing import Any, Dict, Iterator, Union
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseBlobParser
from lan... | lang/api.python.langchain.com/en/latest/_modules/langchain/document_loaders/parsers/html/bs4.html |
f4493bf6f6d6-0 | Source code for langchain.document_loaders.parsers.language.code_segmenter
from abc import ABC, abstractmethod
from typing import List
[docs]class CodeSegmenter(ABC):
"""Abstract class for the code segmenter."""
[docs] def __init__(self, code: str):
self.code = code
[docs] def is_valid(self) -> bool:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/document_loaders/parsers/language/code_segmenter.html |
19f68bdb6028-0 | Source code for langchain.document_loaders.parsers.language.python
import ast
from typing import Any, List
from langchain.document_loaders.parsers.language.code_segmenter import CodeSegmenter
[docs]class PythonSegmenter(CodeSegmenter):
"""Code segmenter for `Python`."""
[docs] def __init__(self, code: str):
... | lang/api.python.langchain.com/en/latest/_modules/langchain/document_loaders/parsers/language/python.html |
19f68bdb6028-1 | simplified_lines[line_num] = None # type: ignore
return "\n".join(line for line in simplified_lines if line is not None) | lang/api.python.langchain.com/en/latest/_modules/langchain/document_loaders/parsers/language/python.html |
4c27f3ef6a82-0 | Source code for langchain.document_loaders.parsers.language.javascript
from typing import Any, List
from langchain.document_loaders.parsers.language.code_segmenter import CodeSegmenter
[docs]class JavaScriptSegmenter(CodeSegmenter):
"""Code segmenter for JavaScript."""
[docs] def __init__(self, code: str):
... | lang/api.python.langchain.com/en/latest/_modules/langchain/document_loaders/parsers/language/javascript.html |
4c27f3ef6a82-1 | for node in tree.body:
if isinstance(
node,
(esprima.nodes.FunctionDeclaration, esprima.nodes.ClassDeclaration),
):
start = node.loc.start.line - 1
simplified_lines[start] = f"// Code for: {simplified_lines[start]}"
... | lang/api.python.langchain.com/en/latest/_modules/langchain/document_loaders/parsers/language/javascript.html |
f5eef8066bdc-0 | Source code for langchain.document_loaders.parsers.language.language_parser
from typing import Any, Dict, Iterator, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseBlobParser
from langchain.document_loaders.blob_loaders import Blob
from langchain.document_loader... | lang/api.python.langchain.com/en/latest/_modules/langchain/document_loaders/parsers/language/language_parser.html |
f5eef8066bdc-1 | "./code",
glob="**/*",
suffixes=[".py", ".js"],
parser=LanguageParser()
)
docs = loader.load()
Example instantiations to manually select the language:
.. code-block:: python
from langchain.text_splitter import Language
... | lang/api.python.langchain.com/en/latest/_modules/langchain/document_loaders/parsers/language/language_parser.html |
f5eef8066bdc-2 | )
return
if self.parser_threshold >= len(code.splitlines()):
yield Document(
page_content=code,
metadata={
"source": blob.source,
"language": language,
},
)
return
self... | lang/api.python.langchain.com/en/latest/_modules/langchain/document_loaders/parsers/language/language_parser.html |
0ec81c344b9c-0 | Source code for langchain.document_loaders.parsers.language.cobol
import re
from typing import Callable, List
from langchain.document_loaders.parsers.language.code_segmenter import CodeSegmenter
[docs]class CobolSegmenter(CodeSegmenter):
"""Code segmenter for `COBOL`."""
PARAGRAPH_PATTERN = re.compile(r"^[A-Z0-... | lang/api.python.langchain.com/en/latest/_modules/langchain/document_loaders/parsers/language/cobol.html |
0ec81c344b9c-1 | elements: List[str] = []
start_idx = None
inside_relevant_section = False
for i, line in enumerate(self.source_lines):
if self._is_relevant_code(line):
inside_relevant_section = True
if inside_relevant_section and (
self.PARAGRAPH_PATTERN.m... | lang/api.python.langchain.com/en/latest/_modules/langchain/document_loaders/parsers/language/cobol.html |
0ec81c344b9c-2 | # paragraph
omitted_code_added = False
if inside_relevant_section:
if is_header:
# Add header and reset the omitted code added flag
simplified_lines.append(line)
elif not omitted_code_added:
# Add omi... | lang/api.python.langchain.com/en/latest/_modules/langchain/document_loaders/parsers/language/cobol.html |
fe780c348b17-0 | Source code for langchain.load.load
import importlib
import json
import os
from typing import Any, Dict, List, Optional
from langchain.load.serializable import Serializable
[docs]class Reviver:
"""Reviver for JSON objects."""
[docs] def __init__(
self,
secrets_map: Optional[Dict[str, str]] = None... | lang/api.python.langchain.com/en/latest/_modules/langchain/load/load.html |
fe780c348b17-1 | )
if (
value.get("lc", None) == 1
and value.get("type", None) == "constructor"
and value.get("id", None) is not None
):
[*namespace, name] = value["id"]
if namespace[0] not in self.valid_namespaces:
raise ValueError(f"Invalid na... | lang/api.python.langchain.com/en/latest/_modules/langchain/load/load.html |
fe780c348b17-2 | [docs]def load(
obj: Any,
*,
secrets_map: Optional[Dict[str, str]] = None,
valid_namespaces: Optional[List[str]] = None,
) -> Any:
"""Revive a LangChain class from a JSON object. Use this if you already
have a parsed JSON object, eg. from `json.load` or `orjson.loads`.
Args:
obj: The... | lang/api.python.langchain.com/en/latest/_modules/langchain/load/load.html |
63fc20d07347-0 | Source code for langchain.load.serializable
from abc import ABC
from typing import Any, Dict, List, Literal, Optional, TypedDict, Union, cast
from langchain.pydantic_v1 import BaseModel, PrivateAttr
[docs]class BaseSerialized(TypedDict):
"""Base class for serialized objects."""
lc: int
id: List[str]
[docs]c... | lang/api.python.langchain.com/en/latest/_modules/langchain/load/serializable.html |
63fc20d07347-1 | """A map of constructor argument names to secret ids.
For example,
{"openai_api_key": "OPENAI_API_KEY"}
"""
return dict()
@property
def lc_attributes(self) -> Dict:
"""List of attribute names that should be included in the serialized kwargs.
These attributes m... | lang/api.python.langchain.com/en/latest/_modules/langchain/load/serializable.html |
63fc20d07347-2 | }
# Merge the lc_secrets and lc_attributes from every class in the MRO
for cls in [None, *self.__class__.mro()]:
# Once we get to Serializable, we're done
if cls is Serializable:
break
if cls:
deprecated_attributes = [
... | lang/api.python.langchain.com/en/latest/_modules/langchain/load/serializable.html |
63fc20d07347-3 | ) -> Dict[Any, Any]:
result = root.copy()
for path, secret_id in secrets_map.items():
[*parts, last] = path.split(".")
current = result
for part in parts:
if part not in current:
break
current[part] = current[part].copy()
current = curr... | lang/api.python.langchain.com/en/latest/_modules/langchain/load/serializable.html |
286054ffbb1c-0 | Source code for langchain.load.dump
import json
from typing import Any, Dict
from langchain.load.serializable import Serializable, to_json_not_implemented
[docs]def default(obj: Any) -> Any:
"""Return a default value for a Serializable object or
a SerializedNotImplemented object."""
if isinstance(obj, Seria... | lang/api.python.langchain.com/en/latest/_modules/langchain/load/dump.html |
f5d3cf7d2993-0 | Source code for langchain.output_parsers.json
from __future__ import annotations
import json
import re
from json import JSONDecodeError
from typing import Any, Callable, List, Optional
import jsonpatch
from langchain.schema.output_parser import (
BaseCumulativeTransformOutputParser,
OutputParserException,
)
def... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/json.html |
f5d3cf7d2993-1 | """Parse a JSON string that may be missing closing braces.
Args:
s: The JSON string to parse.
strict: Whether to use strict parsing. Defaults to False.
Returns:
The parsed JSON object as a Python dictionary.
"""
# Attempt to parse the string as-is.
try:
return json.lo... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/json.html |
f5d3cf7d2993-2 | # Close any remaining open structures in the reverse order that they were opened.
for closing_char in reversed(stack):
new_s += closing_char
# Attempt to parse the modified string as JSON.
try:
return json.loads(new_s, strict=strict)
except json.JSONDecodeError:
# If we still can... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/json.html |
f5d3cf7d2993-3 | contains the expected keys.
Args:
text: The Markdown string.
expected_keys: The expected keys in the JSON string.
Returns:
The parsed JSON object as a Python dictionary.
"""
try:
json_obj = parse_json_markdown(text)
except json.JSONDecodeError as e:
raise Outp... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/json.html |
655e7cd2af0e-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):
"""Parse the output of an LLM call using a regex."""
[docs] @classmethod
def is_lc_seria... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/regex.html |
4caa1d0d67b0-0 | Source code for langchain.output_parsers.openai_tools
import copy
import json
from typing import Any, List, Type
from langchain.pydantic_v1 import BaseModel
from langchain.schema import (
ChatGeneration,
Generation,
OutputParserException,
)
from langchain.schema.output_parser import (
BaseGenerationOutp... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/openai_tools.html |
4caa1d0d67b0-1 | [docs]class PydanticToolsParser(JsonOutputToolsParser):
"""Parse tools from OpenAI response."""
tools: List[Type[BaseModel]]
[docs] def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
results = super().parse_result(result)
name_dict = {tool.__name__: tool for to... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/openai_tools.html |
a486c9419687-0 | Source code for langchain.output_parsers.pydantic
import json
import re
from typing import Type, TypeVar
from langchain.output_parsers.format_instructions import PYDANTIC_FORMAT_INSTRUCTIONS
from langchain.pydantic_v1 import BaseModel, ValidationError
from langchain.schema import BaseOutputParser, OutputParserException... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/pydantic.html |
a486c9419687-1 | # Ensure json in context is well-formed with double quotes.
schema_str = json.dumps(reduced_schema)
return PYDANTIC_FORMAT_INSTRUCTIONS.format(schema=schema_str)
@property
def _type(self) -> str:
return "pydantic" | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/pydantic.html |
647077782fdc-0 | Source code for langchain.output_parsers.retry
from __future__ import annotations
from typing import Any, TypeVar
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import (
BaseOutputParser,
BasePromptTemplate,
OutputParserException,
PromptValue,
)
from langchain.schema.language_... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
647077782fdc-1 | def from_llm(
cls,
llm: BaseLanguageModel,
parser: BaseOutputParser[T],
prompt: BasePromptTemplate = NAIVE_RETRY_PROMPT,
max_retries: int = 1,
) -> RetryOutputParser[T]:
"""Create an OutputFixingParser from a language model and a parser.
Args:
llm:... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
647077782fdc-2 | """Parse the output of an LLM call using a wrapped parser.
Args:
completion: The chain completion to parse.
prompt_value: The prompt to use to parse the completion.
Returns:
The parsed completion.
"""
retries = 0
while retries <= self.max_retri... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
647077782fdc-3 | retry_chain: Any
"""The LLMChain to use to retry the completion."""
max_retries: int = 1
"""The maximum number of times to retry the parse."""
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
parser: BaseOutputParser[T],
prompt: BasePromptTemplate = NAIVE... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
647077782fdc-4 | )
raise OutputParserException("Failed to parse")
[docs] async def aparse_with_prompt(self, completion: str, prompt_value: PromptValue) -> T:
retries = 0
while retries <= self.max_retries:
try:
return await self.parser.aparse(completion)
except OutputPar... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
43ad5a9e46de-0 | Source code for langchain.output_parsers.list
from __future__ import annotations
import re
from abc import abstractmethod
from typing import List
from langchain.schema import BaseOutputParser
[docs]class ListOutputParser(BaseOutputParser[List[str]]):
"""Parse the output of an LLM call to a list."""
@property
... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/list.html |
43ad5a9e46de-1 | """Parse the output of an LLM call."""
pattern = r"\d+\.\s([^\n]+)"
# Extract the text of each item
matches = re.findall(pattern, text)
return matches
@property
def _type(self) -> str:
return "numbered-list"
[docs]class MarkdownListOutputParser(ListOutputParser):
"""P... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/list.html |
19cf5e080868-0 | Source code for langchain.output_parsers.rail_parser
from __future__ import annotations
from typing import Any, Callable, Dict, Optional
from langchain.schema import BaseOutputParser
[docs]class GuardrailsOutputParser(BaseOutputParser):
"""Parse the output of an LLM call using Guardrails."""
guard: Any
"""T... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/rail_parser.html |
19cf5e080868-1 | guard=Guard.from_rail(rail_file, num_reasks=num_reasks),
api=api,
args=args,
kwargs=kwargs,
)
[docs] @classmethod
def from_rail_string(
cls,
rail_str: str,
num_reasks: int = 1,
api: Optional[Callable] = None,
*args: Any,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/rail_parser.html |
19cf5e080868-2 | return self.guard.raw_prompt.format_instructions
[docs] def parse(self, text: str) -> Dict:
return self.guard.parse(text, llm_api=self.api, *self.args, **self.kwargs) | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/rail_parser.html |
c8b0466305da-0 | Source code for langchain.output_parsers.combining
from __future__ import annotations
from typing import Any, Dict, List
from langchain.pydantic_v1 import root_validator
from langchain.schema import BaseOutputParser
[docs]class CombiningOutputParser(BaseOutputParser):
"""Combine multiple output parsers into one."""... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/combining.html |
c8b0466305da-1 | """Parse the output of an LLM call."""
texts = text.split("\n\n")
output = dict()
for txt, parser in zip(texts, self.parsers):
output.update(parser.parse(txt.strip()))
return output | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/combining.html |
e94a034fefef-0 | Source code for langchain.output_parsers.fix
from __future__ import annotations
from typing import Any, TypeVar
from langchain.output_parsers.prompts import NAIVE_FIX_PROMPT
from langchain.schema import BaseOutputParser, BasePromptTemplate, OutputParserException
from langchain.schema.language_model import BaseLanguageM... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/fix.html |
e94a034fefef-1 | chain = LLMChain(llm=llm, prompt=prompt)
return cls(parser=parser, retry_chain=chain, max_retries=max_retries)
[docs] def parse(self, completion: str) -> T:
retries = 0
while retries <= self.max_retries:
try:
return self.parser.parse(completion)
except ... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/fix.html |
9878f8e9a7dc-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_... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/datetime.html |
9878f8e9a7dc-1 | return datetime.strptime(response.strip(), self.format)
except ValueError as e:
raise OutputParserException(
f"Could not parse datetime string: {response}"
) from e
@property
def _type(self) -> str:
return "datetime" | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/datetime.html |
8eaa9a5ed301-0 | Source code for langchain.output_parsers.loading
from langchain.output_parsers.regex import RegexParser
[docs]def load_output_parser(config: dict) -> dict:
"""Load an output parser.
Args:
config: config dict
Returns:
config dict with output parser loaded
"""
if "output_parsers" in co... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/loading.html |
d6f64ad6e9fb-0 | Source code for langchain.output_parsers.xml
import re
import xml.etree.ElementTree as ET
from typing import Any, Dict, List, Optional
from langchain.output_parsers.format_instructions import XML_FORMAT_INSTRUCTIONS
from langchain.schema import BaseOutputParser
[docs]class XMLOutputParser(BaseOutputParser):
"""Pars... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/xml.html |
d6f64ad6e9fb-1 | return result
@property
def _type(self) -> str:
return "xml" | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/xml.html |
75817d9fe0fb-0 | Source code for langchain.output_parsers.boolean
from langchain.schema import BaseOutputParser
[docs]class BooleanOutputParser(BaseOutputParser[bool]):
"""Parse the output of an LLM call to a boolean."""
true_val: str = "YES"
"""The string value that should be parsed as True."""
false_val: str = "NO"
... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/boolean.html |
a409ab1ca16b-0 | Source code for langchain.output_parsers.openai_functions
import copy
import json
from typing import Any, Dict, List, Optional, Type, Union
import jsonpatch
from langchain.output_parsers.json import parse_partial_json
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.schema import (
ChatGen... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/openai_functions.html |
a409ab1ca16b-1 | """
args_only: bool = True
"""Whether to only return the arguments to the function call."""
@property
def _type(self) -> str:
return "json_functions"
def _diff(self, prev: Optional[Any], next: Any) -> Any:
return jsonpatch.make_patch(prev, next).patch
[docs] def parse_result(self,... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/openai_functions.html |
a409ab1ca16b-2 | )
else:
try:
return {
**function_call,
"arguments": json.loads(
function_call["arguments"], strict=self.strict
),
}
... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/openai_functions.html |
a409ab1ca16b-3 | schema, BaseModel
)
elif values["args_only"] and isinstance(schema, Dict):
raise ValueError(
"If multiple pydantic schemas are provided then args_only should be"
" False."
)
return values
[docs] def parse_result(self, result: List[Ge... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/openai_functions.html |
9af3a9ca8517-0 | Source code for langchain.output_parsers.enum
from enum import Enum
from typing import Any, Dict, List, Type
from langchain.pydantic_v1 import root_validator
from langchain.schema import BaseOutputParser, OutputParserException
[docs]class EnumOutputParser(BaseOutputParser):
"""Parse an output that is one of a set o... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/enum.html |
098eb2df87f9-0 | 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):
"""Parse the output of an LLM call into a Dictionary using a regex."""
regex_pattern: st... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/regex_dict.html |
098eb2df87f9-1 | continue
else:
result[output_key] = matches[0]
return result | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/regex_dict.html |
9d45a9be3596-0 | Source code for langchain.output_parsers.structured
from __future__ import annotations
from typing import Any, List
from langchain.output_parsers.format_instructions import (
STRUCTURED_FORMAT_INSTRUCTIONS,
STRUCTURED_FORMAT_SIMPLE_INSTRUCTIONS,
)
from langchain.output_parsers.json import parse_and_check_json_m... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/structured.html |
9d45a9be3596-1 | response_schemas = [
ResponseSchema(
name="foo",
description="a list of strings",
type="List[string]"
),
ResponseSchema(
name="bar",
description="a string",
type="string"
... | lang/api.python.langchain.com/en/latest/_modules/langchain/output_parsers/structured.html |
95788910ea65-0 | Source code for langchain.embeddings.huggingface_hub
from typing import Any, Dict, List, Optional
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
DEFAULT_REPO_ID = "sentence-transformers/all-mpnet-base... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
95788910ea65-1 | @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:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
95788910ea65-2 | 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... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
33d7c1cb349f-0 | Source code for langchain.embeddings.octoai_embeddings
from typing import Any, Dict, List, Mapping, Optional
from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
DEFAULT_EMBED_INSTRUCTION = "Represen... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/octoai_embeddings.html |
33d7c1cb349f-1 | )
values["endpoint_url"] = get_from_dict_or_env(
values, "endpoint_url", "ENDPOINT_URL"
)
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Return the identifying parameters."""
return {
"endpoint_url": self.endpoint_ur... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/octoai_embeddings.html |
33d7c1cb349f-2 | text = text.replace("\n", " ")
return self._compute_embeddings([text], self.query_instruction)[0] | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/octoai_embeddings.html |
8812135dd122-0 | Source code for langchain.embeddings.jina
import os
from typing import Any, Dict, List, Optional
import requests
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class JinaEmbeddings(BaseModel, Embedding... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/jina.html |
8812135dd122-1 | headers={"Authorization": jina_auth_token},
)
if resp.status_code == 401:
raise ValueError(
"The given Jina auth token is invalid. "
"Please check your Jina auth token."
)
elif resp.status_code == 404:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/jina.html |
8812135dd122-2 | Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
from docarray import Document, DocumentArray
embedding = self._post(docs=DocumentArray([Document(text=text)])).embeddings[0]
return list(map(float, embedding)) | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/jina.html |
7a8f81a035e7-0 | Source code for langchain.embeddings.minimax
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
import requests
from tenacity import (
before_sleep_log,
retry,
stop_after_attempt,
wait_exponential,
)
from langchain.pydantic_v1 import BaseModel, Extra... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/minimax.html |
7a8f81a035e7-1 | the constructor.
Example:
.. code-block:: python
from langchain.embeddings import MiniMaxEmbeddings
embeddings = MiniMaxEmbeddings()
query_text = "This is a test query."
query_result = embeddings.embed_query(query_text)
document_text = "This is a t... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/minimax.html |
7a8f81a035e7-2 | self,
texts: List[str],
embed_type: str,
) -> List[List[float]]:
payload = {
"model": self.model,
"type": embed_type,
"texts": texts,
}
# HTTP headers for authorization
headers = {
"Authorization": f"Bearer {self.minimax... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/minimax.html |
7fab94c26398-0 | Source code for langchain.embeddings.xinference
"""Wrapper around Xinference embedding models."""
from typing import Any, List, Optional
from langchain.schema.embeddings import Embeddings
[docs]class XinferenceEmbeddings(Embeddings):
"""Xinference embedding models.
To use, you should have the xinference library... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/xinference.html |
7fab94c26398-1 | server_url: Optional[str]
"""URL of the xinference server"""
model_uid: Optional[str]
"""UID of the launched model"""
[docs] def __init__(
self, server_url: Optional[str] = None, model_uid: Optional[str] = None
):
try:
from xinference.client import RESTfulClient
ex... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/xinference.html |
7fab94c26398-2 | embedding_res = model.create_embedding(text)
embedding = embedding_res["data"][0]["embedding"]
return list(map(float, embedding)) | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/xinference.html |
54e63e26bebb-0 | Source code for langchain.embeddings.huggingface
from typing import Any, Dict, List, Optional
import requests
from langchain.pydantic_v1 import BaseModel, Extra, Field
from langchain.schema.embeddings import Embeddings
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
DEFAULT_INSTRUCT_MODEL = "hkunlp/instr... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
54e63e26bebb-1 | Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass to the model."""
encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass when calling the `encode` method of the... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
54e63e26bebb-2 | return embeddings.tolist()
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace transformer model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
return self.embed_documents([text]... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
54e63e26bebb-3 | query_instruction: str = DEFAULT_QUERY_INSTRUCTION
"""Instruction to use for embedding query."""
def __init__(self, **kwargs: Any):
"""Initialize the sentence_transformer."""
super().__init__(**kwargs)
try:
from InstructorEmbedding import INSTRUCTOR
self.client = ... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
54e63e26bebb-4 | Example:
.. code-block:: python
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': True}
hf = HuggingFaceBgeEmbeddings(
mo... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
54e63e26bebb-5 | self.query_instruction = DEFAULT_QUERY_BGE_INSTRUCTION_ZH
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 transformer model.
Args... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
54e63e26bebb-6 | "/pipeline"
"/feature-extraction"
f"/{self.model_name}"
)
@property
def _headers(self) -> dict:
return {"Authorization": f"Bearer {self.api_key}"}
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Get the embeddings for a list of texts... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
ae7aac2da377-0 | Source code for langchain.embeddings.johnsnowlabs
import os
import sys
from typing import Any, List
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra
[docs]class JohnSnowLabsEmbeddings(BaseModel, Embeddings):
"""JohnSnowLabs embedding models
To use, you should h... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/johnsnowlabs.html |
ae7aac2da377-1 | try:
if isinstance(model, str):
self.model = nlp.load(model)
elif isinstance(model, NLUPipeline):
self.model = model
else:
self.model = nlp.to_nlu_pipe(model)
except Exception as exc:
raise Exception("Failure loading... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/johnsnowlabs.html |
cea683f2c53a-0 | Source code for langchain.embeddings.deepinfra
from typing import Any, Dict, List, Mapping, Optional
import requests
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
DEFAULT_MODEL_ID = "sentence-transfo... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/deepinfra.html |
cea683f2c53a-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... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/deepinfra.html |
cea683f2c53a-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: ... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/deepinfra.html |
57b4ab341798-0 | Source code for langchain.embeddings.dashscope
from __future__ import annotations
import logging
from typing import (
Any,
Callable,
Dict,
List,
Optional,
)
from requests.exceptions import HTTPError
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_a... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/dashscope.html |
57b4ab341798-1 | elif resp.status_code in [400, 401]:
raise ValueError(
f"status_code: {resp.status_code} \n "
f"code: {resp.code} \n message: {resp.message}"
)
else:
raise HTTPError(
f"HTTP error occurred: status_code: {resp.status_code} \n "
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/dashscope.html |
57b4ab341798-2 | """Maximum number of retries to make when generating."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
import dashscope
"""Validate that api key and python package exists... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/dashscope.html |
57b4ab341798-3 | Returns:
Embedding for the text.
"""
embedding = embed_with_retry(
self, input=text, text_type="query", model=self.model
)[0]["embedding"]
return embedding | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/dashscope.html |
81f8620aaf77-0 | Source code for langchain.embeddings.azure_openai
"""Azure OpenAI embeddings wrapper."""
from __future__ import annotations
import os
import warnings
from typing import Dict, Optional, Union
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.pydantic_v1 import Field, root_validator
from langchain.u... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/azure_openai.html |
81f8620aaf77-1 | """A function that returns an Azure Active Directory token.
Will be invoked on every request.
"""
openai_api_version: Optional[str] = Field(default=None, alias="api_version")
"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
validate_base_url: bool = True
@root_... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/azure_openai.html |
81f8620aaf77-2 | "OPENAI_PROXY",
default="",
)
values["azure_endpoint"] = values["azure_endpoint"] or os.getenv(
"AZURE_OPENAI_ENDPOINT"
)
values["azure_ad_token"] = values["azure_ad_token"] or os.getenv(
"AZURE_OPENAI_AD_TOKEN"
)
try:
impor... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/azure_openai.html |
81f8620aaf77-3 | "and `azure_endpoint`."
)
if values["deployment"] not in values["openai_api_base"]:
warnings.warn(
"As of openai>=1.0.0, if `openai_api_base` "
"(or alias `base_url`) is specified it is expected to be... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/azure_openai.html |
81f8620aaf77-4 | values["client"] = openai.Embedding
return values
@property
def _llm_type(self) -> str:
return "azure-openai-chat" | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/azure_openai.html |
054b9abd8a46-0 | Source code for langchain.embeddings.vertexai
from typing import Dict, List
from langchain.llms.vertexai import _VertexAICommon
from langchain.pydantic_v1 import root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utilities.vertexai import raise_vertex_import_error
[docs]class VertexAIEmbed... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/vertexai.html |
054b9abd8a46-1 | """Embed a text.
Args:
text: The text to embed.
Returns:
Embedding for the text.
"""
embeddings = self.client.get_embeddings([text])
return embeddings[0].values | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/vertexai.html |
e6ce46a2da38-0 | Source code for langchain.embeddings.google_palm
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
from langchain.pydantic_v1 import... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/google_palm.html |
e6ce46a2da38-1 | return embeddings.client.generate_embeddings(*args, **kwargs)
return _embed_with_retry(*args, **kwargs)
[docs]class GooglePalmEmbeddings(BaseModel, Embeddings):
"""Google's PaLM Embeddings APIs."""
client: Any
google_api_key: Optional[str]
model_name: str = "models/embedding-gecko-001"
"""Model ... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/google_palm.html |
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