id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 49 117 |
|---|---|---|
d04aca0fd0ca-1 | """Whether or not to use sampling; use greedy decoding otherwise."""
max_length: Optional[int] = None
"""The maximum length of the sequence to be generated."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call
not explicitly specified.""... | https://python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
d04aca0fd0ca-2 | from petals import DistributedBloomForCausalLM
from transformers import BloomTokenizerFast
model_name = values["model_name"]
values["tokenizer"] = BloomTokenizerFast.from_pretrained(model_name)
values["client"] = DistributedBloomForCausalLM.from_pretrained(model_name)
... | https://python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
d04aca0fd0ca-3 | """Call the Petals API."""
params = self._default_params
inputs = self.tokenizer(prompt, return_tensors="pt")["input_ids"]
outputs = self.client.generate(inputs, **params)
text = self.tokenizer.decode(outputs[0])
if stop is not None:
# I believe this is required since... | https://python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
8a77fb1c3855-0 | Source code for langchain.experimental.autonomous_agents.autogpt.agent
from __future__ import annotations
from typing import List, Optional
from pydantic import ValidationError
from langchain.chains.llm import LLMChain
from langchain.chat_models.base import BaseChatModel
from langchain.experimental.autonomous_agents.au... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
8a77fb1c3855-1 | ai_role: str,
memory: VectorStoreRetriever,
tools: List[BaseTool],
llm: BaseChatModel,
human_in_the_loop: bool = False,
output_parser: Optional[BaseAutoGPTOutputParser] = None,
) -> AutoGPT:
prompt = AutoGPTPrompt(
ai_name=ai_name,
ai_role=ai_r... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
8a77fb1c3855-2 | # Get command name and arguments
action = self.output_parser.parse(assistant_reply)
tools = {t.name: t for t in self.tools}
if action.name == FINISH_NAME:
return action.args["response"]
if action.name in tools:
tool = tools[action.name]
... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/autogpt/agent.html |
fd27ab7661c6-0 | Source code for langchain.experimental.autonomous_agents.baby_agi.baby_agi
"""BabyAGI agent."""
from collections import deque
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerFo... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
fd27ab7661c6-1 | print(str(t["task_id"]) + ": " + t["task_name"])
def print_next_task(self, task: Dict) -> None:
print("\033[92m\033[1m" + "\n*****NEXT TASK*****\n" + "\033[0m\033[0m")
print(str(task["task_id"]) + ": " + task["task_name"])
def print_task_result(self, result: str) -> None:
print("\033[93m... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
fd27ab7661c6-2 | next_task_id = int(this_task_id) + 1
response = self.task_prioritization_chain.run(
task_names=", ".join(task_names),
next_task_id=str(next_task_id),
objective=objective,
)
new_tasks = response.split("\n")
prioritized_task_list = []
for task_st... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
fd27ab7661c6-3 | """Run the agent."""
objective = inputs["objective"]
first_task = inputs.get("first_task", "Make a todo list")
self.add_task({"task_id": 1, "task_name": first_task})
num_iters = 0
while True:
if self.task_list:
self.print_task_list()
# ... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
fd27ab7661c6-4 | return {}
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
vectorstore: VectorStore,
verbose: bool = False,
task_execution_chain: Optional[Chain] = None,
**kwargs: Dict[str, Any],
) -> "BabyAGI":
"""Initialize the BabyAGI Controller."""
... | https://python.langchain.com/en/latest/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html |
db2e2cea29a6-0 | Source code for langchain.experimental.generative_agents.memory
import logging
import re
from datetime import datetime
from typing import Any, Dict, List, Optional
from langchain import LLMChain
from langchain.base_language import BaseLanguageModel
from langchain.prompts import PromptTemplate
from langchain.retrievers ... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
db2e2cea29a6-1 | # output keys
relevant_memories_key: str = "relevant_memories"
relevant_memories_simple_key: str = "relevant_memories_simple"
most_recent_memories_key: str = "most_recent_memories"
now_key: str = "now"
reflecting: bool = False
def chain(self, prompt: PromptTemplate) -> LLMChain:
return L... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
db2e2cea29a6-2 | ) -> List[str]:
"""Generate 'insights' on a topic of reflection, based on pertinent memories."""
prompt = PromptTemplate.from_template(
"Statements about {topic}\n"
+ "{related_statements}\n\n"
+ "What 5 high-level insights can you infer from the above statements?"
... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
db2e2cea29a6-3 | "On the scale of 1 to 10, where 1 is purely mundane"
+ " (e.g., brushing teeth, making bed) and 10 is"
+ " extremely poignant (e.g., a break up, college"
+ " acceptance), rate the likely poignancy of the"
+ " following piece of memory. Respond with a single integer."
... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
db2e2cea29a6-4 | and not self.reflecting
):
self.reflecting = True
self.pause_to_reflect(now=now)
# Hack to clear the importance from reflection
self.aggregate_importance = 0.0
self.reflecting = False
return result
[docs] def fetch_memories(
self, ob... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
db2e2cea29a6-5 | break
consumed_tokens += self.llm.get_num_tokens(doc.page_content)
if consumed_tokens < self.max_tokens_limit:
result.append(doc)
return self.format_memories_simple(result)
@property
def memory_variables(self) -> List[str]:
"""Input keys this memory class ... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
db2e2cea29a6-6 | [docs] def clear(self) -> None:
"""Clear memory contents."""
# TODO
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/memory.html |
5c718024d5f5-0 | Source code for langchain.experimental.generative_agents.generative_agent
import re
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from pydantic import BaseModel, Field
from langchain import LLMChain
from langchain.base_language import BaseLanguageModel
from langchain.experimental.gen... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
5c718024d5f5-1 | arbitrary_types_allowed = True
# LLM-related methods
@staticmethod
def _parse_list(text: str) -> List[str]:
"""Parse a newline-separated string into a list of strings."""
lines = re.split(r"\n", text.strip())
return [re.sub(r"^\s*\d+\.\s*", "", line).strip() for line in lines]
de... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
5c718024d5f5-2 | entity_action = self._get_entity_action(observation, entity_name)
q1 = f"What is the relationship between {self.name} and {entity_name}"
q2 = f"{entity_name} is {entity_action}"
return self.chain(prompt=prompt).run(q1=q1, queries=[q1, q2]).strip()
def _generate_reaction(
self, observ... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
5c718024d5f5-3 | )
consumed_tokens = self.llm.get_num_tokens(
prompt.format(most_recent_memories="", **kwargs)
)
kwargs[self.memory.most_recent_memories_token_key] = consumed_tokens
return self.chain(prompt=prompt).run(**kwargs).strip()
def _clean_response(self, text: str) -> str:
... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
5c718024d5f5-4 | if "SAY:" in result:
said_value = self._clean_response(result.split("SAY:")[-1])
return True, f"{self.name} said {said_value}"
else:
return False, result
[docs] def generate_dialogue_response(
self, observation: str, now: Optional[datetime] = None
) -> Tuple[bo... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
5c718024d5f5-5 | )
return True, f"{self.name} said {response_text}"
else:
return False, result
######################################################
# Agent stateful' summary methods. #
# Each dialog or response prompt includes a header #
# summarizing the agent's sel... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
5c718024d5f5-6 | + f"\nInnate traits: {self.traits}"
+ f"\n{self.summary}"
)
[docs] def get_full_header(
self, force_refresh: bool = False, now: Optional[datetime] = None
) -> str:
"""Return a full header of the agent's status, summary, and current time."""
now = datetime.now() if now ... | https://python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
e16cbe7d80b8-0 | Source code for langchain.chains.mapreduce
"""Map-reduce chain.
Splits up a document, sends the smaller parts to the LLM with one prompt,
then combines the results with another one.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Extra
from langchain.base_languag... | https://python.langchain.com/en/latest/_modules/langchain/chains/mapreduce.html |
e16cbe7d80b8-1 | reduce_chain = StuffDocumentsChain(llm_chain=llm_chain, callbacks=callbacks)
combine_documents_chain = MapReduceDocumentsChain(
llm_chain=llm_chain,
combine_document_chain=reduce_chain,
callbacks=callbacks,
)
return cls(
combine_documents_chain=com... | https://python.langchain.com/en/latest/_modules/langchain/chains/mapreduce.html |
3299baebe475-0 | Source code for langchain.chains.transform
"""Chain that runs an arbitrary python function."""
from typing import Callable, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
[docs]class TransformChain(Chain):
"""Chain transform chain outp... | https://python.langchain.com/en/latest/_modules/langchain/chains/transform.html |
b5fe4cebc137-0 | Source code for langchain.chains.moderation
"""Pass input through a moderation endpoint."""
from typing import Any, Dict, List, Optional
from pydantic import root_validator
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.utils import get_from_dic... | https://python.langchain.com/en/latest/_modules/langchain/chains/moderation.html |
b5fe4cebc137-1 | values,
"openai_organization",
"OPENAI_ORGANIZATION",
default="",
)
try:
import openai
openai.api_key = openai_api_key
if openai_organization:
openai.organization = openai_organization
values["client"] = ... | https://python.langchain.com/en/latest/_modules/langchain/chains/moderation.html |
bdef456eeb70-0 | Source code for langchain.chains.loading
"""Functionality for loading chains."""
import json
from pathlib import Path
from typing import Any, Union
import yaml
from langchain.chains.api.base import APIChain
from langchain.chains.base import Chain
from langchain.chains.combine_documents.map_reduce import MapReduceDocume... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
bdef456eeb70-1 | if "llm" in config:
llm_config = config.pop("llm")
llm = load_llm_from_config(llm_config)
elif "llm_path" in config:
llm = load_llm(config.pop("llm_path"))
else:
raise ValueError("One of `llm` or `llm_path` must be present.")
if "prompt" in config:
prompt_config = con... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
bdef456eeb70-2 | )
def _load_stuff_documents_chain(config: dict, **kwargs: Any) -> StuffDocumentsChain:
if "llm_chain" in config:
llm_chain_config = config.pop("llm_chain")
llm_chain = load_chain_from_config(llm_chain_config)
elif "llm_chain_path" in config:
llm_chain = load_chain(config.pop("llm_chain_p... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
bdef456eeb70-3 | if not isinstance(llm_chain, LLMChain):
raise ValueError(f"Expected LLMChain, got {llm_chain}")
if "combine_document_chain" in config:
combine_document_chain_config = config.pop("combine_document_chain")
combine_document_chain = load_chain_from_config(combine_document_chain_config)
elif ... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
bdef456eeb70-4 | # llm attribute is deprecated in favor of llm_chain, here to support old configs
elif "llm" in config:
llm_config = config.pop("llm")
llm = load_llm_from_config(llm_config)
# llm_path attribute is deprecated in favor of llm_chain_path,
# its to support old configs
elif "llm_path" in conf... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
bdef456eeb70-5 | create_draft_answer_prompt_config
)
elif "create_draft_answer_prompt_path" in config:
create_draft_answer_prompt = load_prompt(
config.pop("create_draft_answer_prompt_path")
)
if "list_assertions_prompt" in config:
list_assertions_prompt_config = config.pop("list_asse... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
bdef456eeb70-6 | llm_chain = None
if "llm_chain" in config:
llm_chain_config = config.pop("llm_chain")
llm_chain = load_chain_from_config(llm_chain_config)
elif "llm_chain_path" in config:
llm_chain = load_chain(config.pop("llm_chain_path"))
# llm attribute is deprecated in favor of llm_chain, here t... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
bdef456eeb70-7 | elif "llm_chain_path" in config:
llm_chain = load_chain(config.pop("llm_chain_path"))
else:
raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.")
return MapRerankDocumentsChain(llm_chain=llm_chain, **config)
def _load_pal_chain(config: dict, **kwargs: Any) -> PALChain:
... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
bdef456eeb70-8 | if llm_chain:
return PALChain(llm_chain=llm_chain, prompt=prompt, **config)
else:
return PALChain(llm=llm, prompt=prompt, **config)
def _load_refine_documents_chain(config: dict, **kwargs: Any) -> RefineDocumentsChain:
if "initial_llm_chain" in config:
initial_llm_chain_config = config.p... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
bdef456eeb70-9 | refine_llm_chain=refine_llm_chain,
document_prompt=document_prompt,
**config,
)
def _load_qa_with_sources_chain(config: dict, **kwargs: Any) -> QAWithSourcesChain:
if "combine_documents_chain" in config:
combine_documents_chain_config = config.pop("combine_documents_chain")
combi... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
bdef456eeb70-10 | config: dict, **kwargs: Any
) -> VectorDBQAWithSourcesChain:
if "vectorstore" in kwargs:
vectorstore = kwargs.pop("vectorstore")
else:
raise ValueError("`vectorstore` must be present.")
if "combine_documents_chain" in config:
combine_documents_chain_config = config.pop("combine_docum... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
bdef456eeb70-11 | vectorstore=vectorstore,
**config,
)
def _load_api_chain(config: dict, **kwargs: Any) -> APIChain:
if "api_request_chain" in config:
api_request_chain_config = config.pop("api_request_chain")
api_request_chain = load_chain_from_config(api_request_chain_config)
elif "api_request_chain... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
bdef456eeb70-12 | elif "llm_chain_path" in config:
llm_chain = load_chain(config.pop("llm_chain_path"))
else:
raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.")
if "requests_wrapper" in kwargs:
requests_wrapper = kwargs.pop("requests_wrapper")
return LLMRequestsChain(
... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
bdef456eeb70-13 | }
def load_chain_from_config(config: dict, **kwargs: Any) -> Chain:
"""Load chain from Config Dict."""
if "_type" not in config:
raise ValueError("Must specify a chain Type in config")
config_type = config.pop("_type")
if config_type not in type_to_loader_dict:
raise ValueError(f"Loading... | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
bdef456eeb70-14 | config["verbose"] = kwargs.pop("verbose")
if "memory" in kwargs:
config["memory"] = kwargs.pop("memory")
# Load the chain from the config now.
return load_chain_from_config(config, **kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/chains/loading.html |
4fbf4a02103b-0 | Source code for langchain.chains.llm_requests
"""Chain that hits a URL and then uses an LLM to parse results."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForChainRun
from langc... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_requests.html |
4fbf4a02103b-1 | :meta private:
"""
return [self.output_key]
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
try:
from bs4 import BeautifulSoup # noqa: F401
except ImportError:
... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_requests.html |
b6a3cc115d2f-0 | Source code for langchain.chains.llm
"""Chain that just formats a prompt and calls an LLM."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
from pydantic import Extra
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
b6a3cc115d2f-1 | def output_keys(self) -> List[str]:
"""Will always return text key.
:meta private:
"""
return [self.output_key]
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
response = self.... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
b6a3cc115d2f-2 | """Prepare prompts from inputs."""
stop = None
if "stop" in input_list[0]:
stop = input_list[0]["stop"]
prompts = []
for inputs in input_list:
selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}
prompt = self.prompt.format_prompt(**se... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
b6a3cc115d2f-3 | await run_manager.on_text(_text, end="\n", verbose=self.verbose)
if "stop" in inputs and inputs["stop"] != stop:
raise ValueError(
"If `stop` is present in any inputs, should be present in all."
)
prompts.append(prompt)
return prompts, ... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
b6a3cc115d2f-4 | except (KeyboardInterrupt, Exception) as e:
await run_manager.on_chain_error(e)
raise e
outputs = self.create_outputs(response)
await run_manager.on_chain_end({"outputs": outputs})
return outputs
[docs] def create_outputs(self, response: LLMResult) -> List[Dict[str, st... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
b6a3cc115d2f-5 | Returns:
Completion from LLM.
Example:
.. code-block:: python
completion = llm.predict(adjective="funny")
"""
return (await self.acall(kwargs, callbacks=callbacks))[self.output_key]
[docs] def predict_and_parse(
self, callbacks: Callbacks = None... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
b6a3cc115d2f-6 | if self.prompt.output_parser is not None:
return [
self.prompt.output_parser.parse(res[self.output_key]) for res in result
]
else:
return result
[docs] async def aapply_and_parse(
self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm.html |
1869b5d332c9-0 | Source code for langchain.chains.sequential
"""Chain pipeline where the outputs of one step feed directly into next."""
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)... | https://python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
1869b5d332c9-1 | overlapping_keys = set(input_variables) & set(memory_keys)
raise ValueError(
f"The the input key(s) {''.join(overlapping_keys)} are found "
f"in the Memory keys ({memory_keys}) - please use input and "
f"memory keys that don't overlap."
... | https://python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
1869b5d332c9-2 | for i, chain in enumerate(self.chains):
callbacks = _run_manager.get_child()
outputs = chain(known_values, return_only_outputs=True, callbacks=callbacks)
known_values.update(outputs)
return {k: known_values[k] for k in self.output_variables}
async def _acall(
self... | https://python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
1869b5d332c9-3 | """
return [self.output_key]
@root_validator()
def validate_chains(cls, values: Dict) -> Dict:
"""Validate that chains are all single input/output."""
for chain in values["chains"]:
if len(chain.input_keys) != 1:
raise ValueError(
"Chains u... | https://python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
1869b5d332c9-4 | ) -> Dict[str, Any]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
_input = inputs[self.input_key]
color_mapping = get_color_mapping([str(i) for i in range(len(self.chains))])
for i, chain in enumerate(self.c... | https://python.langchain.com/en/latest/_modules/langchain/chains/sequential.html |
3edb00401123-0 | Source code for langchain.chains.llm_checker.base
"""Chain for question-answering with self-verification."""
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.cal... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
3edb00401123-1 | )
chains = [
create_draft_answer_chain,
list_assertions_chain,
check_assertions_chain,
revised_answer_chain,
]
question_to_checked_assertions_chain = SequentialChain(
chains=chains,
input_variables=["question"],
output_variables=["revised_statement"],
... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
3edb00401123-2 | if "llm" in values:
warnings.warn(
"Directly instantiating an LLMCheckerChain with an llm is deprecated. "
"Please instantiate with question_to_checked_assertions_chain "
"or using the from_llm class method."
)
if (
"que... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
3edb00401123-3 | output = self.question_to_checked_assertions_chain(
{"question": question}, callbacks=_run_manager.get_child()
)
return {self.output_key: output["revised_statement"]}
@property
def _chain_type(self) -> str:
return "llm_checker_chain"
[docs] @classmethod
def from_llm(
... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
c5565a3a143a-0 | Source code for langchain.chains.sql_database.base
"""Chain for interacting with SQL Database."""
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.callbac... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
c5565a3a143a-1 | return_intermediate_steps: bool = False
"""Whether or not to return the intermediate steps along with the final answer."""
return_direct: bool = False
"""Whether or not to return the result of querying the SQL table directly."""
use_query_checker: bool = False
"""Whether or not the query checker too... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
c5565a3a143a-2 | :meta private:
"""
if not self.return_intermediate_steps:
return [self.output_key]
else:
return [self.output_key, INTERMEDIATE_STEPS_KEY]
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
c5565a3a143a-3 | result = self.database.run(sql_cmd)
intermediate_steps.append(str(result)) # output: sql exec
else:
query_checker_prompt = self.query_checker_prompt or PromptTemplate(
template=QUERY_CHECKER, input_variables=["query", "dialect"]
)
... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
c5565a3a143a-4 | llm_inputs["input"] = input_text
intermediate_steps.append(llm_inputs) # input: final answer
final_result = self.llm_chain.predict(
callbacks=_run_manager.get_child(),
**llm_inputs,
).strip()
intermediate_steps.appe... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
c5565a3a143a-5 | 2. Based on those tables, call the normal SQL database chain.
This is useful in cases where the number of tables in the database is large.
"""
decider_chain: LLMChain
sql_chain: SQLDatabaseChain
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
return_... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
c5565a3a143a-6 | def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
_table_names = self.sql_chain.database.get_usable_table_names()
table_na... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
cf8a9c153d17-0 | Source code for langchain.chains.constitutional_ai.base
"""Chain for applying constitutional principles to the outputs of another chain."""
from typing import Any, Dict, List, Optional
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain... | https://python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
cf8a9c153d17-1 | critique_chain: LLMChain
revision_chain: LLMChain
return_intermediate_steps: bool = False
[docs] @classmethod
def get_principles(
cls, names: Optional[List[str]] = None
) -> List[ConstitutionalPrinciple]:
if names is None:
return list(PRINCIPLES.values())
else:
... | https://python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
cf8a9c153d17-2 | ) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
response = self.chain.run(
**inputs,
callbacks=_run_manager.get_child(),
)
initial_response = response
input_prompt = self.chain.prompt.format(**inputs)
... | https://python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
cf8a9c153d17-3 | _run_manager.on_text(
text=f"Applying {constitutional_principle.name}..." + "\n\n",
verbose=self.verbose,
color="green",
)
_run_manager.on_text(
text="Critique: " + critique + "\n\n",
verbose=self.verbose,
... | https://python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
e411ab45e193-0 | Source code for langchain.chains.conversational_retrieval.base
"""Chain for chatting with a vector database."""
from __future__ import annotations
import warnings
from abc import abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pydantic import Extra, Fiel... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
e411ab45e193-1 | human = "Human: " + dialogue_turn[0]
ai = "Assistant: " + dialogue_turn[1]
buffer += "\n" + "\n".join([human, ai])
else:
raise ValueError(
f"Unsupported chat history format: {type(dialogue_turn)}."
f" Full chat history: {chat_history} "
... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
e411ab45e193-2 | ) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
question = inputs["question"]
get_chat_history = self.get_chat_history or _get_chat_history
chat_history_str = get_chat_history(inputs["chat_history"])
if chat_history_str:
... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
e411ab45e193-3 | new_question = await self.question_generator.arun(
question=question, chat_history=chat_history_str, callbacks=callbacks
)
else:
new_question = question
docs = await self._aget_docs(new_question, inputs)
new_inputs = inputs.copy()
new_inputs["quest... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
e411ab45e193-4 | while token_count > self.max_tokens_limit:
num_docs -= 1
token_count -= tokens[num_docs]
return docs[:num_docs]
def _get_docs(self, question: str, inputs: Dict[str, Any]) -> List[Document]:
docs = self.retriever.get_relevant_documents(question)
return self._re... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
e411ab45e193-5 | combine_docs_chain=doc_chain,
question_generator=condense_question_chain,
**kwargs,
)
[docs]class ChatVectorDBChain(BaseConversationalRetrievalChain):
"""Chain for chatting with a vector database."""
vectorstore: VectorStore = Field(alias="vectorstore")
top_k_docs_for_context... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
e411ab45e193-6 | **kwargs: Any,
) -> BaseConversationalRetrievalChain:
"""Load chain from LLM."""
combine_docs_chain_kwargs = combine_docs_chain_kwargs or {}
doc_chain = load_qa_chain(
llm,
chain_type=chain_type,
**combine_docs_chain_kwargs,
)
condense_ques... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
af9548bb47a6-0 | Source code for langchain.chains.api.base
"""Chain that makes API calls and summarizes the responses to answer a question."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Field, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.ca... | https://python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
af9548bb47a6-1 | if set(input_vars) != expected_vars:
raise ValueError(
f"Input variables should be {expected_vars}, got {input_vars}"
)
return values
@root_validator(pre=True)
def validate_api_answer_prompt(cls, values: Dict) -> Dict:
"""Check that api answer prompt expec... | https://python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
af9548bb47a6-2 | async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
question = inputs[self.question_key]
api_url = await se... | https://python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
af9548bb47a6-3 | requests_wrapper = TextRequestsWrapper(headers=headers)
get_answer_chain = LLMChain(llm=llm, prompt=api_response_prompt)
return cls(
api_request_chain=get_request_chain,
api_answer_chain=get_answer_chain,
requests_wrapper=requests_wrapper,
api_docs=api_doc... | https://python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
991f51fa6853-0 | Source code for langchain.chains.api.openapi.chain
"""Chain that makes API calls and summarizes the responses to answer a question."""
from __future__ import annotations
import json
from typing import Any, Dict, List, NamedTuple, Optional, cast
from pydantic import BaseModel, Field
from requests import Response
from la... | https://python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
991f51fa6853-1 | """
return [self.instructions_key]
@property
def output_keys(self) -> List[str]:
"""Expect output key.
:meta private:
"""
if not self.return_intermediate_steps:
return [self.output_key]
else:
return [self.output_key, "intermediate_steps"]
... | https://python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
991f51fa6853-2 | path = self._construct_path(args)
body_params = self._extract_body_params(args)
query_params = self._extract_query_params(args)
return {
"url": path,
"data": body_params,
"params": query_params,
}
def _get_output(self, output: str, intermediate_ste... | https://python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
991f51fa6853-3 | method = getattr(self.requests, self.api_operation.method.value)
api_response: Response = method(**request_args)
if api_response.status_code != 200:
method_str = str(self.api_operation.method.value)
response_text = (
f"{api_response.status_code... | https://python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
991f51fa6853-4 | # TODO: Handle async
) -> "OpenAPIEndpointChain":
"""Create an OpenAPIEndpoint from a spec at the specified url."""
operation = APIOperation.from_openapi_url(spec_url, path, method)
return cls.from_api_operation(
operation,
requests=requests,
llm=llm,
... | https://python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
991f51fa6853-5 | requests=_requests,
param_mapping=param_mapping,
verbose=verbose,
return_intermediate_steps=return_intermediate_steps,
callbacks=callbacks,
**kwargs,
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023... | https://python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
c58bb0e82b01-0 | Source code for langchain.chains.llm_math.base
"""Chain that interprets a prompt and executes python code to do math."""
from __future__ import annotations
import math
import re
import warnings
from typing import Any, Dict, List, Optional
import numexpr
from pydantic import Extra, root_validator
from langchain.base_lan... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
c58bb0e82b01-1 | if "llm" in values:
warnings.warn(
"Directly instantiating an LLMMathChain with an llm is deprecated. "
"Please instantiate with llm_chain argument or using the from_llm "
"class method."
)
if "llm_chain" not in values and values["llm"]... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
c58bb0e82b01-2 | ) -> Dict[str, str]:
run_manager.on_text(llm_output, color="green", verbose=self.verbose)
llm_output = llm_output.strip()
text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
if text_match:
expression = text_match.group(1)
output = self._evaluate_exp... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
c58bb0e82b01-3 | elif llm_output.startswith("Answer:"):
answer = llm_output
elif "Answer:" in llm_output:
answer = "Answer: " + llm_output.split("Answer:")[-1]
else:
raise ValueError(f"unknown format from LLM: {llm_output}")
return {self.output_key: answer}
def _call(
... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
c58bb0e82b01-4 | [docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: BasePromptTemplate = PROMPT,
**kwargs: Any,
) -> LLMMathChain:
llm_chain = LLMChain(llm=llm, prompt=prompt)
return cls(llm_chain=llm_chain, **kwargs)
By Harrison Chase
© Copyright... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
0e552595d0f1-0 | Source code for langchain.chains.llm_summarization_checker.base
"""Chain for summarization with self-verification."""
from __future__ import annotations
import warnings
from pathlib import Path
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.base_language import Ba... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
0e552595d0f1-1 | verbose=verbose,
),
LLMChain(
llm=llm,
prompt=check_assertions_prompt,
output_key="checked_assertions",
verbose=verbose,
),
LLMChain(
llm=llm,
prompt=revised_summary_prompt,
... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
0e552595d0f1-2 | input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
max_checks: int = 2
"""Maximum number of times to check the assertions. Default to double-checking."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitr... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
0e552595d0f1-3 | def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
all_true = False
count = 0
output = None
original_input ... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
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