id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
f25fc56e5192-2 | max_iterations=max_iterations,
max_execution_time=max_execution_time,
early_stopping_method=early_stopping_method,
**(agent_executor_kwargs or {}),
) | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/agents/agent_toolkits/xorbits/base.html |
2f12915be0f3-0 | Source code for langchain_experimental.agents.agent_toolkits.csv.base
from io import IOBase
from typing import Any, List, Optional, Union
from langchain.agents.agent import AgentExecutor
from langchain.schema.language_model import BaseLanguageModel
from langchain_experimental.agents.agent_toolkits.pandas.base import (
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/agents/agent_toolkits/csv/base.html |
821f20bc50d1-0 | Source code for langchain_experimental.agents.agent_toolkits.python.base
"""Python agent."""
from typing import Any, Dict, Optional
from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.agents.openai_functions_agent.base import OpenAI... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/agents/agent_toolkits/python/base.html |
821f20bc50d1-1 | elif agent_type == AgentType.OPENAI_FUNCTIONS:
system_message = SystemMessage(content=prefix)
_prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
agent = OpenAIFunctionsAgent(
llm=llm,
prompt=_prompt,
tools=tools,
callback_m... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/agents/agent_toolkits/python/base.html |
1da039b77b0e-0 | Source code for langchain_experimental.agents.agent_toolkits.pandas.base
"""Agent for working with pandas objects."""
from typing import Any, Dict, List, Optional, Sequence, Tuple
from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/agents/agent_toolkits/pandas/base.html |
1da039b77b0e-1 | include_dfs_head = True
else:
suffix_to_use = SUFFIX_NO_DF
include_dfs_head = False
if input_variables is None:
input_variables = ["input", "agent_scratchpad", "num_dfs"]
if include_dfs_head:
input_variables += ["dfs_head"]
if prefix is None:
prefix = MULT... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/agents/agent_toolkits/pandas/base.html |
1da039b77b0e-2 | elif include_df_in_prompt:
suffix_to_use = SUFFIX_WITH_DF
include_df_head = True
else:
suffix_to_use = SUFFIX_NO_DF
include_df_head = False
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
if include_df_head:
input_variables ... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/agents/agent_toolkits/pandas/base.html |
1da039b77b0e-3 | if isinstance(df, list):
for item in df:
if not isinstance(item, pd.DataFrame):
raise ValueError(f"Expected pandas object, got {type(df)}")
return _get_multi_prompt(
df,
prefix=prefix,
suffix=suffix,
input_variables=input_variab... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/agents/agent_toolkits/pandas/base.html |
1da039b77b0e-4 | tools = [PythonAstREPLTool(locals={"df": df})]
system_message = SystemMessage(content=prefix + suffix_to_use)
prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
return prompt, tools
def _get_functions_multi_prompt(
dfs: Any,
prefix: Optional[str] = None,
suffix: Optional[... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/agents/agent_toolkits/pandas/base.html |
1da039b77b0e-5 | return prompt, tools
def _get_functions_prompt_and_tools(
df: Any,
prefix: Optional[str] = None,
suffix: Optional[str] = None,
input_variables: Optional[List[str]] = None,
include_df_in_prompt: Optional[bool] = True,
number_of_head_rows: int = 5,
) -> Tuple[BasePromptTemplate, List[PythonAstREPL... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/agents/agent_toolkits/pandas/base.html |
1da039b77b0e-6 | )
[docs]def create_pandas_dataframe_agent(
llm: BaseLanguageModel,
df: Any,
agent_type: AgentType = AgentType.ZERO_SHOT_REACT_DESCRIPTION,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: Optional[str] = None,
suffix: Optional[str] = None,
input_variables: Optional[List[str]] ... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/agents/agent_toolkits/pandas/base.html |
1da039b77b0e-7 | agent = ZeroShotAgent(
llm_chain=llm_chain,
allowed_tools=tool_names,
callback_manager=callback_manager,
**kwargs,
)
elif agent_type == AgentType.OPENAI_FUNCTIONS:
_prompt, base_tools = _get_functions_prompt_and_tools(
df,
prefi... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/agents/agent_toolkits/pandas/base.html |
97d160c91520-0 | Source code for langchain_experimental.llm_bash.bash
"""Wrapper around subprocess to run commands."""
from __future__ import annotations
import platform
import re
import subprocess
from typing import TYPE_CHECKING, List, Union
from uuid import uuid4
if TYPE_CHECKING:
import pexpect
[docs]class BashProcess:
"""
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/llm_bash/bash.html |
97d160c91520-1 | self.process = None
if persistent:
self.prompt = str(uuid4())
self.process = self._initialize_persistent_process(self, self.prompt)
@staticmethod
def _lazy_import_pexpect() -> pexpect:
"""Import pexpect only when needed."""
if platform.system() == "Windows":
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/llm_bash/bash.html |
97d160c91520-2 | execute in the session
""" # noqa: E501
if isinstance(commands, str):
commands = [commands]
commands = ";".join(commands)
if self.process is not None:
return self._run_persistent(
commands,
)
else:
return self._run(... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/llm_bash/bash.html |
97d160c91520-3 | pexpect = self._lazy_import_pexpect()
if self.process is None:
raise ValueError("Process not initialized")
self.process.sendline(command)
# Clear the output with an empty string
self.process.expect(self.prompt, timeout=10)
self.process.sendline("")
try:
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/llm_bash/bash.html |
be58a027392c-0 | Source code for langchain_experimental.llm_bash.prompt
# flake8: noqa
from __future__ import annotations
import re
from typing import List
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import BaseOutputParser, OutputParserException
_PROMPT_TEMPLATE = """If someone asks you to perform a task,... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/llm_bash/prompt.html |
be58a027392c-1 | for match in pattern.finditer(t):
matched = match.group(1).strip()
if matched:
code_blocks.extend(
[line for line in matched.split("\n") if line.strip()]
)
return code_blocks
@property
def _type(self) -> str:
return "bas... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/llm_bash/prompt.html |
92038ec56221-0 | Source code for langchain_experimental.llm_bash.base
"""Chain that interprets a prompt and executes bash operations."""
from __future__ import annotations
import logging
import warnings
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/llm_bash/base.html |
92038ec56221-1 | def raise_deprecation(cls, values: Dict) -> Dict:
if "llm" in values:
warnings.warn(
"Directly instantiating an LLMBashChain with an llm is deprecated. "
"Please instantiate with llm_chain or using the from_llm class method."
)
if "llm_chain" n... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/llm_bash/base.html |
92038ec56221-2 | _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
_run_manager.on_text(inputs[self.input_key], verbose=self.verbose)
t = self.llm_chain.predict(
question=inputs[self.input_key], callbacks=_run_manager.get_child()
)
_run_manager.on_text(t, color="gree... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/llm_bash/base.html |
4a2a61623880-0 | Source code for langchain_experimental.fallacy_removal.base
"""Chain for applying removals of logical fallacies."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.ch... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/fallacy_removal/base.html |
4a2a61623880-1 | fallacy_critique_request="Tell if this answer meets criteria.",
fallacy_revision_request=\
"Give an answer that meets better criteria.",
)
],
)
fallacy_chain.run(question="How do I know if the earth is round?")
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/fallacy_removal/base.html |
4a2a61623880-2 | def input_keys(self) -> List[str]:
"""Input keys."""
return self.chain.input_keys
@property
def output_keys(self) -> List[str]:
"""Output keys."""
if self.return_intermediate_steps:
return ["output", "fallacy_critiques_and_revisions", "initial_output"]
return ... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/fallacy_removal/base.html |
4a2a61623880-3 | if "no fallacy critique needed" in fallacy_critique.lower():
fallacy_critiques_and_revisions.append((fallacy_critique, ""))
continue
fallacy_revision = self.fallacy_revision_chain.run(
input_prompt=input_prompt,
output_from_model=response,
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/fallacy_removal/base.html |
4a2a61623880-4 | if "Fallacy Revision request:" not in output_string:
return output_string
output_string = output_string.split("Fallacy Revision request:")[0]
if "\n\n" in output_string:
output_string = output_string.split("\n\n")[0]
return output_string | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/fallacy_removal/base.html |
42de0ca2f1f3-0 | Source code for langchain_experimental.fallacy_removal.models
"""Models for the Logical Fallacy Chain"""
from langchain_experimental.pydantic_v1 import BaseModel
[docs]class LogicalFallacy(BaseModel):
"""Class for a logical fallacy."""
fallacy_critique_request: str
fallacy_revision_request: str
name: st... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/fallacy_removal/models.html |
e0c4c8d2a194-0 | Source code for langchain_experimental.open_clip.open_clip
from typing import Any, Dict, List
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.schema.embeddings import Embeddings
[docs]class OpenCLIPEmbeddings(BaseModel, Embeddings):
model: Any
preprocess: Any
tokenizer: Any
@r... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/open_clip/open_clip.html |
e0c4c8d2a194-1 | embeddings_tensor = self.model.encode_text(tokenized_text)
# Normalize the embeddings
norm = embeddings_tensor.norm(p=2, dim=1, keepdim=True)
normalized_embeddings_tensor = embeddings_tensor.div(norm)
# Convert normalized tensor to list and add to the text_features list
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/open_clip/open_clip.html |
5072eb433e59-0 | Source code for langchain_experimental.comprehend_moderation.base_moderation_config
from typing import List, Union
from pydantic import BaseModel
[docs]class ModerationPiiConfig(BaseModel):
threshold: float = 0.5
"""Threshold for PII confidence score, defaults to 0.5 i.e. 50%"""
labels: List[str] = []
"... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/base_moderation_config.html |
5072eb433e59-1 | `[ModerationPiiConfig(), ModerationToxicityConfig(),
ModerationPromptSafetyConfig()]`
""" | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/base_moderation_config.html |
812d4341cc9b-0 | Source code for langchain_experimental.comprehend_moderation.toxicity
import asyncio
import importlib
from typing import Any, List, Optional
from langchain_experimental.comprehend_moderation.base_moderation_exceptions import (
ModerationToxicityError,
)
[docs]class ComprehendToxicity:
[docs] def __init__(
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/toxicity.html |
812d4341cc9b-1 | raise ModuleNotFoundError(
"Could not import nltk python package. "
"Please install it with `pip install nltk`."
)
except LookupError:
nltk.download("punkt")
def _split_paragraph(
self, prompt_value: str, max_size: int = 1024 * 4
) -> List[... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/toxicity.html |
812d4341cc9b-2 | if current_chunk: # Avoid appending empty chunks
chunks.append(current_chunk)
current_chunk = []
current_size = 0
current_chunk.append(sentence)
current_size += sentence_size
# Add any remaining sentences
if current_chunk:
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/toxicity.html |
812d4341cc9b-3 | toxicity_found = True
break
else:
for item in response["ResultList"]:
for label in item["Labels"]:
if (
label["Name"] in toxicity_labels
and label["Score"] >= thres... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/toxicity.html |
fd07c70e4608-0 | Source code for langchain_experimental.comprehend_moderation.prompt_safety
import asyncio
from typing import Any, Optional
from langchain_experimental.comprehend_moderation.base_moderation_exceptions import (
ModerationPromptSafetyError,
)
[docs]class ComprehendPromptSafety:
[docs] def __init__(
self,
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/prompt_safety.html |
fd07c70e4608-1 | Note:
This function checks the safety of the provided prompt text using
Comprehend's classify_document API and raises an error if unsafe
text is detected with a score above the specified threshold.
Example:
comprehend_client = boto3.client('comprehend')
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/prompt_safety.html |
4a4269009101-0 | Source code for langchain_experimental.comprehend_moderation.pii
import asyncio
from typing import Any, Dict, Optional
from langchain_experimental.comprehend_moderation.base_moderation_exceptions import (
ModerationPiiError,
)
[docs]class ComprehendPII:
[docs] def __init__(
self,
client: Any,
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/pii.html |
4a4269009101-1 | Returns:
str: the original prompt
Note:
- The provided client should be initialized with valid AWS credentials.
"""
pii_identified = self.client.contains_pii_entities(
Text=prompt_value, LanguageCode="en"
)
if self.callback and self.callback.pi... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/pii.html |
4a4269009101-2 | prompt_value (str): The input text to be checked for PII entities.
config (Dict[str, Any]): A configuration specifying how to handle
PII entities.
Returns:
str: The processed prompt text with redacted PII entities or raised
exceptions... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/pii.html |
4a4269009101-3 | if pii_found:
raise ModerationPiiError
else:
threshold = config.get("threshold") # type: ignore
pii_labels = config.get("labels") # type: ignore
mask_marker = config.get("mask_character") # type: ignore
pii_found = False
for entity i... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/pii.html |
575654d54896-0 | Source code for langchain_experimental.comprehend_moderation.base_moderation_callbacks
from typing import Any, Callable, Dict
[docs]class BaseModerationCallbackHandler:
[docs] def __init__(self) -> None:
if (
self._is_method_unchanged(
BaseModerationCallbackHandler.on_after_pii, s... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/base_moderation_callbacks.html |
575654d54896-1 | ) -> None:
"""Run after Prompt Safety validation is complete."""
pass
@property
def pii_callback(self) -> bool:
return (
self.on_after_pii.__func__ # type: ignore
is not BaseModerationCallbackHandler.on_after_pii
)
@property
def toxicity_callback(... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/base_moderation_callbacks.html |
ec8a65d88df2-0 | Source code for langchain_experimental.comprehend_moderation.base_moderation
import uuid
from typing import Any, Callable, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.prompts.base import StringPromptValue
from langchain.prompts.chat import ChatPromptValue
from langchain.sc... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/base_moderation.html |
ec8a65d88df2-1 | SystemMessage > HumanMessage > AIMessage and so on. However assuming
that with every chat the chain is invoked we will only check the last
message. This is assuming that all previous messages have been checked
already. Only HumanMessage and AIMessage will be checked. We can perhaps
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/base_moderation.html |
ec8a65d88df2-2 | example=message.example,
additional_kwargs=message.additional_kwargs,
)
if isinstance(message, AIMessage):
messages[self.chat_message_index] = AIMessage(
content=text,
example=message.example,
add... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/base_moderation.html |
ec8a65d88df2-3 | "toxicity": ComprehendToxicity,
"prompt_safety": ComprehendPromptSafety,
}
filters = self.config.filters # type: ignore
for _filter in filters:
filter_name = (
"pii"
if isinstance(_filter, ModerationPiiConfig)
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/base_moderation.html |
ec8a65d88df2-4 | )
raise e
except Exception as e:
raise e | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/base_moderation.html |
804156d74eea-0 | Source code for langchain_experimental.comprehend_moderation.amazon_comprehend_moderation
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain_experimental.comprehend_moderation.base_moderation import BaseM... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/amazon_comprehend_moderation.html |
804156d74eea-1 | has either access keys or role information specified.
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
"""
moderation_callback: Optional[BaseModerationCa... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/amazon_comprehend_moderation.html |
804156d74eea-2 | else:
# use default credentials
session = boto3.Session()
client_params = {}
if values.get("region_name"):
client_params["region_name"] = values["region_name"]
values["client"] = session.client("comprehend", **client_params)
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/amazon_comprehend_moderation.html |
804156d74eea-3 | """
return [self.input_key]
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
"""
Executes the moderation process on the input text and returns the processed
output.
This interna... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/amazon_comprehend_moderation.html |
721a00314321-0 | Source code for langchain_experimental.comprehend_moderation.base_moderation_exceptions
[docs]class ModerationPiiError(Exception):
"""Exception raised if PII entities are detected.
Attributes:
message -- explanation of the error
"""
def __init__(
self, message: str = "The prompt contains... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/comprehend_moderation/base_moderation_exceptions.html |
642b24749d67-0 | Source code for langchain_experimental.smart_llm.base
"""Chain for applying self-critique using the SmartGPT workflow."""
from typing import Any, Dict, List, Optional, Tuple, Type
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chai... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/smart_llm/base.html |
642b24749d67-1 | often don't.
Finally, a SmartLLMChain assumes that each underlying LLM outputs exactly 1 result.
"""
[docs] class SmartLLMChainHistory:
question: str = ""
ideas: List[str] = []
critique: str = ""
@property
def n_ideas(self) -> int:
return len(self.ideas)
[d... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/smart_llm/base.html |
642b24749d67-2 | llm: Optional[BaseLanguageModel] = None
"""LLM to use for each steps, if no specific llm for that step is given. """
n_ideas: int = 3
"""Number of ideas to generate in idea step"""
return_intermediate_steps: bool = False
"""Whether to return ideas and critique, in addition to resolution."""
hist... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/smart_llm/base.html |
642b24749d67-3 | "Either critique_llm or llm needs to be given. Pass llm, "
"if you want to use the same llm for all steps, or pass "
"ideation_llm, critique_llm and resolver_llm if you want "
"to use different llms for each step."
)
if not llm and not resolver_llm:
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/smart_llm/base.html |
642b24749d67-4 | stop = None
if "stop" in inputs:
stop = inputs["stop"]
selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}
prompt = self.prompt.format_prompt(**selected_inputs)
_colored_text = get_colored_text(prompt.to_string(), "green")
_text = "Prompt after forma... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/smart_llm/base.html |
642b24749d67-5 | if len(result.generations) != 1:
raise ValueError(
f"In SmartLLM the LLM result in step {step} is not "
"exactly 1 element. This should never happen"
)
if len(result.generations[0]) != 1:
raise ValueError(
f"In SmartLLM the LLM ... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/smart_llm/base.html |
642b24749d67-6 | " by step way to be sure we have all the errors:",
),
]
)
if stage == "critique":
return role_strings
role_strings.extend(
[
(AIMessagePromptTemplate, "Critique: {critique}"),
(
HumanMessagePr... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/smart_llm/base.html |
642b24749d67-7 | """Generate n_ideas ideas as response to user prompt."""
llm = self.ideation_llm if self.ideation_llm else self.llm
prompt = self.ideation_prompt().format_prompt(
**self.history.ideation_prompt_inputs()
)
callbacks = run_manager.get_child() if run_manager else None
if... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/smart_llm/base.html |
642b24749d67-8 | )
_colored_text = get_colored_text(critique, "yellow")
_text = "Critique:\n" + _colored_text
if run_manager:
run_manager.on_text(_text, end="\n", verbose=self.verbose)
return critique
else:
raise ValueError("llm is none, which should ne... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/smart_llm/base.html |
10cde80c6649-0 | Source code for langchain_experimental.chat_models.llm_wrapper
"""Generic Wrapper for chat LLMs, with sample implementations
for Llama-2-chat, Llama-2-instruct and Vicuna models.
"""
from typing import Any, List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerFo... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/chat_models/llm_wrapper.html |
10cde80c6649-1 | messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
llm_input = self._to_chat_prompt(messages)
llm_result = self.llm._generate(
prompts=[llm_input], stop=stop, run_m... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/chat_models/llm_wrapper.html |
10cde80c6649-2 | raise ValueError("last message must be a HumanMessage")
prompt_parts = []
if self.usr_0_beg is None:
self.usr_0_beg = self.usr_n_beg
if self.usr_0_end is None:
self.usr_0_end = self.usr_n_end
prompt_parts.append(self.sys_beg + messages[0].content + self.sys_end)
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/chat_models/llm_wrapper.html |
10cde80c6649-3 | return "llama-2-chat"
sys_beg: str = "<s>[INST] <<SYS>>\n"
sys_end: str = "\n<</SYS>>\n\n"
ai_n_beg: str = " "
ai_n_end: str = " </s>"
usr_n_beg: str = "<s>[INST] "
usr_n_end: str = " [/INST]"
usr_0_beg: str = ""
usr_0_end: str = " [/INST]"
[docs]class Orca(ChatWrapper):
@property
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/chat_models/llm_wrapper.html |
9ba6cce9ece6-0 | Source code for langchain_experimental.rl_chain.model_repository
import datetime
import glob
import logging
import os
import shutil
from pathlib import Path
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
import vowpal_wabbit_next as vw
logger = logging.getLogger(__name__)
[docs]class ModelRepositor... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/model_repository.html |
9ba6cce9ece6-1 | try:
import vowpal_wabbit_next as vw
except ImportError as e:
raise ImportError(
"Unable to import vowpal_wabbit_next, please install with "
"`pip install vowpal_wabbit_next`."
) from e
model_data = None
if self.model_path.exist... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/model_repository.html |
77a14ba3132e-0 | Source code for langchain_experimental.rl_chain.pick_best_chain
from __future__ import annotations
import logging
from typing import Any, Dict, List, Optional, Tuple, Type, Union
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chain... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/pick_best_chain.html |
77a14ba3132e-1 | Attributes:
model name (Any, optional): The type of embeddings to be used for feature representation. Defaults to BERT SentenceTransformer.
""" # noqa E501
[docs] def __init__(
self, auto_embed: bool, model: Optional[Any] = None, *args: Any, **kwargs: Any
):
super().__init__(*args, *... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/pick_best_chain.html |
77a14ba3132e-2 | else None
)
if to_select_from
else None
)
if not context_emb or not action_embs:
raise ValueError(
"Context and to_select_from must be provided in the inputs dictionary"
)
return context_emb, action_embs
[docs] def ge... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/pick_best_chain.html |
77a14ba3132e-3 | for j, action_key in enumerate(action_embeddings.keys()):
indexed_dot_product[context_key][action_key] = dot_product_matrix[i, j]
return indexed_dot_product
[docs] def format_auto_embed_on(self, event: PickBestEvent) -> str:
chosen_action, cost, prob = self.get_label(event)
co... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/pick_best_chain.html |
77a14ba3132e-4 | for elem in elements:
shared.append(f"{elem}")
nsc.append(f"{ns}={elem}")
nsc_str = " ".join(nsc)
shared.append(f"|@ {nsc_str}")
return "shared " + " ".join(shared) + "\n" + "\n".join(action_lines)
[docs] def format_auto_embed_off(self, ... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/pick_best_chain.html |
77a14ba3132e-5 | return self.format_auto_embed_on(event)
else:
return self.format_auto_embed_off(event)
[docs]class PickBestRandomPolicy(base.Policy[PickBestEvent]):
[docs] def __init__(self, feature_embedder: base.Embedder, **kwargs: Any):
self.feature_embedder = feature_embedder
[docs] def predict(se... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/pick_best_chain.html |
77a14ba3132e-6 | 5. The internal Vowpal Wabbit model is updated with the `BasedOn` input, the chosen `ToSelectFrom` action, and the resulting score from the scorer.
6. The final response is returned.
Expected input dictionary format:
- At least one variable encapsulated within `BasedOn` to serve as the selection cri... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/pick_best_chain.html |
77a14ba3132e-7 | if feature_embedder:
if "auto_embed" in kwargs:
logger.warning(
"auto_embed will take no effect when explicit feature_embedder is provided" # noqa E501
)
# turning auto_embed off for cli setting below
auto_embed = False
els... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/pick_best_chain.html |
77a14ba3132e-8 | )
if len(list(actions.values())) > 1:
raise ValueError(
"Only one variable using 'ToSelectFrom' can be provided in the inputs for the PickBest chain. Please provide only one variable containing a list to select from." # noqa E501
)
if not context:
rai... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/pick_best_chain.html |
77a14ba3132e-9 | self, llm_response: str, event: PickBestEvent
) -> Tuple[Dict[str, Any], PickBestEvent]:
next_chain_inputs = event.inputs.copy()
# only one key, value pair in event.to_select_from
value = next(iter(event.to_select_from.values()))
v = (
value[event.selected.index]
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/pick_best_chain.html |
77a14ba3132e-10 | if selection_scorer is SENTINEL:
selection_scorer = base.AutoSelectionScorer(llm=llm_chain.llm)
return PickBest(
llm_chain=llm_chain,
prompt=prompt,
selection_scorer=selection_scorer,
**kwargs,
) | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/pick_best_chain.html |
284dc91a75da-0 | Source code for langchain_experimental.rl_chain.base
from __future__ import annotations
import logging
import os
from abc import ABC, abstractmethod
from typing import (
TYPE_CHECKING,
Any,
Dict,
Generic,
List,
Optional,
Tuple,
Type,
TypeVar,
Union,
)
from langchain.callbacks.man... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/base.html |
284dc91a75da-1 | [docs]def ToSelectFrom(anything: Any) -> _ToSelectFrom:
if not isinstance(anything, list):
raise ValueError("ToSelectFrom must be a list to select from")
return _ToSelectFrom(anything)
class _Embed:
def __init__(self, value: Any, keep: bool = False):
self.value = value
self.keep = ke... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/base.html |
284dc91a75da-2 | return [parser.parse_line(line) for line in input_str.split("\n")]
[docs]def get_based_on_and_to_select_from(inputs: Dict[str, Any]) -> Tuple[Dict, Dict]:
to_select_from = {
k: inputs[k].value
for k in inputs.keys()
if isinstance(inputs[k], _ToSelectFrom)
}
if not to_select_from:
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/base.html |
284dc91a75da-3 | [docs]class Event(Generic[TSelected], ABC):
inputs: Dict[str, Any]
selected: Optional[TSelected]
[docs] def __init__(self, inputs: Dict[str, Any], selected: Optional[TSelected] = None):
self.inputs = inputs
self.selected = selected
TEvent = TypeVar("TEvent", bound=Event)
[docs]class Policy(Ge... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/base.html |
284dc91a75da-4 | parse_lines(text_parser, self.feature_embedder.format(event))
)
[docs] def learn(self, event: TEvent) -> None:
import vowpal_wabbit_next as vw
vw_ex = self.feature_embedder.format(event)
text_parser = vw.TextFormatParser(self.workspace)
multi_ex = parse_lines(text_parser, vw_e... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/base.html |
284dc91a75da-5 | return SystemMessagePromptTemplate.from_template(
"PLEASE RESPOND ONLY WITH A SINGLE FLOAT AND NO OTHER TEXT EXPLANATION\n \
You are a strict judge that is called on to rank a response based on \
given criteria. You must respond with your ranking by providing a \
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/base.html |
284dc91a75da-6 | )
values["prompt"] = prompt
values["llm_chain"] = LLMChain(llm=llm, prompt=prompt)
return values
[docs] def score_response(
self, inputs: Dict[str, Any], llm_response: str, event: Event
) -> float:
ranking = self.llm_chain.predict(llm_response=llm_response, **inputs)
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/base.html |
284dc91a75da-7 | - model_save_dir (str, optional): Directory for saving the VW model. Default is the current directory.
- reset_model (bool): If set to True, the model starts training from scratch. Default is False.
- vw_cmd (List[str], optional): Command line arguments for the VW model.
- policy (Type[VwPolicy]... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/base.html |
284dc91a75da-8 | selected_input_key = "rl_chain_selected"
selected_based_on_input_key = "rl_chain_selected_based_on"
metrics: Optional[Union[MetricsTrackerRollingWindow, MetricsTrackerAverage]] = None
def __init__(
self,
feature_embedder: Embedder,
model_save_dir: str = "./",
reset_model: boo... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/base.html |
284dc91a75da-9 | extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return []
@property
def output_keys(self) -> List[str]:
"""Expect output key.
:meta private:
"""
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/base.html |
284dc91a75da-10 | [docs] def activate_selection_scorer(self) -> None:
"""
Activates the selection scorer, meaning that the chain will attempt to use the selection scorer to score responses.
""" # noqa: E501
self.selection_scorer_activated = True
[docs] def save_progress(self) -> None:
"""
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/base.html |
284dc91a75da-11 | self, llm_response: str, event: TEvent
) -> Tuple[Dict[str, Any], TEvent]:
...
@abstractmethod
def _call_after_scoring_before_learning(
self, event: TEvent, score: Optional[float]
) -> TEvent:
...
def _call(
self,
inputs: Dict[str, Any],
run_manager: O... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/base.html |
284dc91a75da-12 | inputs=next_chain_inputs, llm_response=output, event=event
)
except Exception as e:
logger.info(
f"The selection scorer was not able to score, \
and the chain was not able to adjust to this response, error: {e}"
)
if self.metrics an... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/base.html |
284dc91a75da-13 | if namespace is None:
raise ValueError(
"The default namespace must be provided when embedding a string or _Embed object." # noqa: E501
)
return {namespace: keep_str + encoded}
[docs]def embed_dict_type(item: Dict, model: Any) -> Dict[str, Any]:
"""Helper function to embed a diction... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/base.html |
284dc91a75da-14 | return ret_list
[docs]def embed(
to_embed: Union[Union[str, _Embed], Dict, List[Union[str, _Embed]], List[Dict]],
model: Any,
namespace: Optional[str] = None,
) -> List[Dict[str, Union[str, List[str]]]]:
"""
Embeds the actions or context using the SentenceTransformer model (or a model that has an `e... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/base.html |
5467eaf1e8be-0 | Source code for langchain_experimental.rl_chain.vw_logger
from os import PathLike
from pathlib import Path
from typing import Optional, Union
[docs]class VwLogger:
[docs] def __init__(self, path: Optional[Union[str, PathLike]]):
self.path = Path(path) if path else None
if self.path:
self.... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/vw_logger.html |
d66996ca92db-0 | Source code for langchain_experimental.rl_chain.metrics
from collections import deque
from typing import TYPE_CHECKING, Dict, List, Union
if TYPE_CHECKING:
import pandas as pd
[docs]class MetricsTrackerAverage:
[docs] def __init__(self, step: int):
self.history: List[Dict[str, Union[int, float]]] = [{"st... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/metrics.html |
d66996ca92db-1 | @property
def score(self) -> float:
return self.sum / len(self.queue) if len(self.queue) > 0 else 0
[docs] def on_decision(self) -> None:
pass
[docs] def on_feedback(self, value: float) -> None:
self.sum += value
self.queue.append(value)
self.i += 1
if len(self.... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/rl_chain/metrics.html |
4fd8ea3f9841-0 | Source code for langchain_experimental.prompt_injection_identifier.hugging_face_identifier
"""Tool for the identification of prompt injection attacks."""
from __future__ import annotations
from typing import TYPE_CHECKING
from langchain.pydantic_v1 import Field
from langchain.tools.base import BaseTool
if TYPE_CHECKING... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/prompt_injection_identifier/hugging_face_identifier.html |
aba36ae210b9-0 | Source code for langchain_experimental.llm_symbolic_math.base
"""Chain that interprets a prompt and executes python code to do symbolic math."""
from __future__ import annotations
import re
from typing import Any, Dict, List, Optional
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manage... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/llm_symbolic_math/base.html |
aba36ae210b9-1 | :meta private:
"""
return [self.output_key]
def _evaluate_expression(self, expression: str) -> str:
try:
import sympy
except ImportError as e:
raise ImportError(
"Unable to import sympy, please install it with `pip install sympy`."
... | lang/api.python.langchain.com/en/latest/_modules/langchain_experimental/llm_symbolic_math/base.html |
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