id stringlengths 14 16 | text stringlengths 4 1.28k | source stringlengths 54 121 |
|---|---|---|
b446f531ca85-4 | 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, observation: str, suffix: str, now: Optional[datetime] = None
) -> str:
... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
b446f531ca85-5 | + "\nObservation: {observation}"
+ "\n\n"
+ suffix
)
agent_summary_description = self.get_summary(now=now)
relevant_memories_str = self.summarize_related_memories(observation)
current_time_str = (
datetime.now().strftime("%B %d, %Y, %I:%M %p")
... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
b446f531ca85-6 | 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:
return re.sub(f"^{self.name} ", "", text.strip()).strip()
[docs]... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
b446f531ca85-7 | + "\notherwise, write:\nREACT: {agent_name}'s reaction (if anything)."
+ "\nEither do nothing, react, or say something but not both.\n\n"
)
full_result = self._generate_reaction(
observation, call_to_action_template, now=now
)
result = full_result.strip().split("\... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
b446f531ca85-8 | 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://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
b446f531ca85-9 | )
result = full_result.strip().split("\n")[0]
if "GOODBYE:" in result:
farewell = self._clean_response(result.split("GOODBYE:")[-1])
self.memory.save_context(
{},
{
self.memory.add_memory_key: f"{self.name} observed "
... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
b446f531ca85-10 | },
)
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 ... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
b446f531ca85-11 | return (
self.chain(prompt)
.run(name=self.name, queries=[f"{self.name}'s core characteristics"])
.strip()
)
[docs] def get_summary(
self, force_refresh: bool = False, now: Optional[datetime] = None
) -> str:
"""Return a descriptive summary of the agent... | https://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
b446f531ca85-12 | + 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://api.python.langchain.com/en/latest/_modules/langchain/experimental/generative_agents/generative_agent.html |
f19605247017-0 | Source code for langchain.llms.anyscale
"""Wrapper around Anyscale"""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enf... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
f19605247017-1 | anyscale_service_route="SERVICE_ROUTE",
anyscale_service_token="SERVICE_TOKEN")
# Use Ray for distributed processing
import ray
prompt_list=[]
@ray.remote
def send_query(llm, prompt):
resp = llm(prompt)
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
f19605247017-2 | @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
anyscale_service_url = get_from_dict_or_env(
values, "anyscale_service_url", "ANYSCALE_SERVICE_URL"
)
anyscale_service_route = get_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
f19605247017-3 | headers = {"Authorization": f"Bearer {anyscale_service_token}"}
requests.get(anyscale_service_endpoint, headers=headers)
except requests.exceptions.RequestException as e:
raise ValueError(e)
values["anyscale_service_url"] = anyscale_service_url
values["anyscale_service_ro... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
f19605247017-4 | def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to Anyscale Service endpoint.
Args:
prompt: The prompt to pass into the model.
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
f19605247017-5 | body = {"prompt": prompt}
resp = requests.post(anyscale_service_endpoint, headers=headers, json=body)
if resp.status_code != 200:
raise ValueError(
f"Error returned by service, status code {resp.status_code}"
)
text = resp.text
if stop is not None:... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
9dcd47362806-0 | Source code for langchain.llms.bedrock
import json
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
class LLMInputOutp... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
9dcd47362806-1 | elif provider == "amazon":
input_body = dict()
input_body["inputText"] = prompt
input_body["textGenerationConfig"] = {**model_kwargs}
else:
input_body["inputText"] = prompt
if provider == "anthropic" and "max_tokens_to_sample" not in input_body:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
9dcd47362806-2 | else:
return response_body.get("results")[0].get("outputText")
[docs]class Bedrock(LLM):
"""LLM provider to invoke Bedrock models.
To authenticate, the AWS client uses the following methods to
automatically load credentials:
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/crede... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
9dcd47362806-3 | credentials_profile_name="default",
model_id="amazon.titan-tg1-large"
)
"""
client: Any #: :meta private:
region_name: Optional[str] = None
"""The aws region e.g., `us-west-2`. Fallsback to AWS_DEFAULT_REGION env variable
or region specified in ~/.aws/config in case it ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
9dcd47362806-4 | """
model_id: str
"""Id of the model to call, e.g., amazon.titan-tg1-large, this is
equivalent to the modelId property in the list-foundation-models api"""
model_kwargs: Optional[Dict] = None
"""Key word arguments to pass to the model."""
class Config:
"""Configuration for this pydantic ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
9dcd47362806-5 | else:
# use default credentials
session = boto3.Session()
client_params = {}
if values["region_name"]:
client_params["region_name"] = values["region_name"]
values["client"] = session.client("bedrock", **client_params)
except Imp... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
9dcd47362806-6 | return {
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "amazon_bedrock"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLL... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
9dcd47362806-7 | provider = self.model_id.split(".")[0]
params = {**_model_kwargs, **kwargs}
input_body = LLMInputOutputAdapter.prepare_input(provider, prompt, params)
body = json.dumps(input_body)
accept = "application/json"
contentType = "application/json"
try:
response = se... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
f9e07a2c7723-0 | Source code for langchain.llms.self_hosted
"""Run model inference on self-hosted remote hardware."""
import importlib.util
import logging
import pickle
from typing import Any, Callable, List, Mapping, Optional
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llm... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
f9e07a2c7723-1 | in the batch.
"""
text = pipeline(prompt, *args, **kwargs)
if stop is not None:
text = enforce_stop_tokens(text, stop)
return text
def _send_pipeline_to_device(pipeline: Any, device: int) -> Any:
"""Send a pipeline to a device on the cluster."""
if isinstance(pipeline, str):
with... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
f9e07a2c7723-2 | )
if device < 0 and cuda_device_count > 0:
logger.warning(
"Device has %d GPUs available. "
"Provide device={deviceId} to `from_model_id` to use available"
"GPUs for execution. deviceId is -1 for CPU and "
"can be a positive integer ass... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
f9e07a2c7723-3 | To use, you should have the ``runhouse`` python package installed.
Example for custom pipeline and inference functions:
.. code-block:: python
from langchain.llms import SelfHostedPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import runho... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
f9e07a2c7723-4 | model_load_fn=load_pipeline,
hardware=gpu,
model_reqs=model_reqs, inference_fn=inference_fn
)
Example for <2GB model (can be serialized and sent directly to the server):
.. code-block:: python
from langchain.llms import SelfHostedPipeline
i... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
f9e07a2c7723-5 | import pickle
from transformers import pipeline
generator = pipeline(model="gpt2")
rh.blob(pickle.dumps(generator), path="models/pipeline.pkl"
).save().to(gpu, path="models")
llm = SelfHostedPipeline.from_pipeline(
pipeline="models/pipeline... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
f9e07a2c7723-6 | """Key word arguments to pass to the model load function."""
model_reqs: List[str] = ["./", "torch"]
"""Requirements to install on hardware to inference the model."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def __init__(self, **kwargs: Any):
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
f9e07a2c7723-7 | )
remote_load_fn = rh.function(fn=self.model_load_fn).to(
self.hardware, reqs=self.model_reqs
)
_load_fn_kwargs = self.load_fn_kwargs or {}
self.pipeline_ref = remote_load_fn.remote(**_load_fn_kwargs)
self.client = rh.function(fn=self.inference_fn).to(
sel... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
f9e07a2c7723-8 | "Note, it can be quite slow"
"to serialize and send large models with each execution. "
"Consider sending the pipeline"
"to the cluster and passing the path to the pipeline instead."
)
load_fn_kwargs = {"pipeline": pipeline, "device": device}
r... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
f9e07a2c7723-9 | return "self_hosted_llm"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
return self.client(
pipeline=self.pipeline_ref, prompt=prompt, stop=stop, **kwargs... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
946b4b84de8e-0 | Source code for langchain.llms.aleph_alpha
"""Wrapper around Aleph Alpha APIs."""
from typing import Any, Dict, List, Optional, Sequence
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforc... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
946b4b84de8e-1 | Parameters are explained more in depth here:
https://github.com/Aleph-Alpha/aleph-alpha-client/blob/c14b7dd2b4325c7da0d6a119f6e76385800e097b/aleph_alpha_client/completion.py#L10
Example:
.. code-block:: python
from langchain.llms import AlephAlpha
aleph_alpha = AlephAlpha(aleph_a... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
946b4b84de8e-2 | top_k: int = 0
"""Number of most likely tokens to consider at each step."""
top_p: float = 0.0
"""Total probability mass of tokens to consider at each step."""
presence_penalty: float = 0.0
"""Penalizes repeated tokens."""
frequency_penalty: float = 0.0
"""Penalizes repeated tokens according... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
946b4b84de8e-3 | """List of strings that may be generated without penalty,
regardless of other penalty settings"""
penalty_exceptions_include_stop_sequences: Optional[bool] = None
"""Should stop_sequences be included in penalty_exceptions."""
best_of: Optional[int] = None
"""returns the one with the "best of" result... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
946b4b84de8e-4 | minimum_tokens: Optional[int] = 0
"""Generate at least this number of tokens."""
echo: bool = False
"""Echo the prompt in the completion."""
use_multiplicative_frequency_penalty: bool = False
sequence_penalty: float = 0.0
sequence_penalty_min_length: int = 2
use_multiplicative_sequence_penal... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
946b4b84de8e-5 | explicitly been set in the request.
If set to a non-None value, control parameters are also applied to similar tokens.
"""
control_log_additive: Optional[bool] = True
"""True: apply control by adding the log(control_factor) to attention scores.
False: (attention_scores - - attention_scores.min(-1)) ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
946b4b84de8e-6 | extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
aleph_alpha_api_key = get_from_dict_or_env(
values, "aleph_alpha_api_key", "ALEPH_ALPHA_API_KEY"
)
try:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
946b4b84de8e-7 | return {
"maximum_tokens": self.maximum_tokens,
"temperature": self.temperature,
"top_k": self.top_k,
"top_p": self.top_p,
"presence_penalty": self.presence_penalty,
"frequency_penalty": self.frequency_penalty,
"n": self.n,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
946b4b84de8e-8 | "log_probs": self.log_probs,
"tokens": self.tokens,
"disable_optimizations": self.disable_optimizations,
"minimum_tokens": self.minimum_tokens,
"echo": self.echo,
"use_multiplicative_frequency_penalty": self.use_multiplicative_frequency_penalty, # noqa: E501
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
946b4b84de8e-9 | "completion_bias_exclusion_first_token_only": self.completion_bias_exclusion_first_token_only, # noqa: E501
"contextual_control_threshold": self.contextual_control_threshold,
"control_log_additive": self.control_log_additive,
"repetition_penalties_include_completion": self.repetitio... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
946b4b84de8e-10 | run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to Aleph Alpha's completion endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
T... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
946b4b84de8e-11 | else:
params["stop_sequences"] = stop
params = {**params, **kwargs}
request = CompletionRequest(prompt=Prompt.from_text(prompt), **params)
response = self.client.complete(model=self.model, request=request)
text = response.completions[0].completion
# If stop tokens are... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
10acecee9e65-0 | Source code for langchain.llms.baseten
"""Wrapper around Baseten deployed model API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Field
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
logger = logging.getLogger(__name__... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/baseten.html |
10acecee9e65-1 | The Baseten model must accept a dictionary of input with the key
"prompt" and return a dictionary with a key "data" which maps
to a list of response strings.
Example:
.. code-block:: python
from langchain.llms import Baseten
my_model = Baseten(model="MODEL_ID")
ou... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/baseten.html |
10acecee9e65-2 | """Return type of model."""
return "baseten"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call to Baseten deployed model endpoint."""
try:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/baseten.html |
10acecee9e65-3 | response = model.predict({"prompt": prompt})
return "".join(response) | https://api.python.langchain.com/en/latest/_modules/langchain/llms/baseten.html |
541aa1946b26-0 | Source code for langchain.llms.textgen
"""Wrapper around text-generation-webui."""
import logging
from typing import Any, Dict, List, Optional
import requests
from pydantic import Field
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
logger = logging.getLogger(__name... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
541aa1946b26-1 | Example:
.. code-block:: python
from langchain.llms import TextGen
llm = TextGen(model_url="http://localhost:8500")
"""
model_url: str
"""The full URL to the textgen webui including http[s]://host:port """
max_new_tokens: Optional[int] = 250
"""The maximum number of t... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
541aa1946b26-2 | number. Higher value = higher range of possible random results."""
typical_p: Optional[float] = 1
"""If not set to 1, select only tokens that are at least this much more likely to
appear than random tokens, given the prior text."""
epsilon_cutoff: Optional[float] = 0 # In units of 1e-4
"""Epsilon c... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
541aa1946b26-3 | Higher value = higher range of possible random results."""
min_length: Optional[int] = 0
"""Minimum generation length in tokens."""
no_repeat_ngram_size: Optional[int] = 0
"""If not set to 0, specifies the length of token sets that are completely blocked
from repeating at all. Higher values = blocks... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
541aa1946b26-4 | """Seed (-1 for random)"""
add_bos_token: bool = Field(True, alias="add_bos_token")
"""Add the bos_token to the beginning of prompts.
Disabling this can make the replies more creative."""
truncation_length: Optional[int] = 2048
"""Truncate the prompt up to this length. The leftmost tokens are remove... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
541aa1946b26-5 | streaming: bool = False
"""Whether to stream the results, token by token (currently unimplemented)."""
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling textgen."""
return {
"max_new_tokens": self.max_new_tokens,
"do_samp... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
541aa1946b26-6 | "penalty_alpha": self.penalty_alpha,
"length_penalty": self.length_penalty,
"early_stopping": self.early_stopping,
"seed": self.seed,
"add_bos_token": self.add_bos_token,
"truncation_length": self.truncation_length,
"ban_eos_token": self.ban_eos_to... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
541aa1946b26-7 | return "textgen"
def _get_parameters(self, stop: Optional[List[str]] = None) -> Dict[str, Any]:
"""
Performs sanity check, preparing paramaters in format needed by textgen.
Args:
stop (Optional[List[str]]): List of stop sequences for textgen.
Returns:
Dictiona... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
541aa1946b26-8 | self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call the textgen web API and return the output.
Args:
prompt: The prompt to use for generation.
stop: A lis... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
541aa1946b26-9 | params = self._get_parameters(stop)
request = params.copy()
request["prompt"] = prompt
response = requests.post(url, json=request)
if response.status_code == 200:
result = response.json()["results"][0]["text"]
print(prompt + result)
else:
print... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
415b310558a8-0 | Source code for langchain.llms.gooseai
"""Wrapper around GooseAI API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.utils import... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
415b310558a8-1 | from langchain.llms import GooseAI
gooseai = GooseAI(model_name="gpt-neo-20b")
"""
client: Any
model_name: str = "gpt-neo-20b"
"""Model name to use"""
temperature: float = 0.7
"""What sampling temperature to use"""
max_tokens: int = 256
"""The maximum number of tokens to gene... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
415b310558a8-2 | presence_penalty: float = 0
"""Penalizes repeated tokens."""
n: int = 1
"""How many completions to generate for each prompt."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
logit_bias: Optional[Dict[... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
415b310558a8-3 | extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name not in all_required_field_names:
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
logger.warning(
f"""WARNING! {... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
415b310558a8-4 | )
try:
import openai
openai.api_key = gooseai_api_key
openai.api_base = "https://api.goose.ai/v1"
values["client"] = openai.Completion
except ImportError:
raise ImportError(
"Could not import openai python package. "
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
415b310558a8-5 | "n": self.n,
"logit_bias": self.logit_bias,
}
return {**normal_params, **self.model_kwargs}
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{"model_name": self.model_name}, **self._default_params}
@prop... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
415b310558a8-6 | if stop is not None:
if "stop" in params:
raise ValueError("`stop` found in both the input and default params.")
params["stop"] = stop
params = {**params, **kwargs}
response = self.client.create(engine=self.model_name, prompt=prompt, **params)
text = respo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
ccccfa42260b-0 | Source code for langchain.llms.rwkv
"""Wrapper for the RWKV model.
Based on https://github.com/saharNooby/rwkv.cpp/blob/master/rwkv/chat_with_bot.py
https://github.com/BlinkDL/ChatRWKV/blob/main/v2/chat.py
"""
from typing import Any, Dict, List, Mapping, Optional, Set
from pydantic import BaseModel, Extra, roo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
ccccfa42260b-1 | .. code-block:: python
from langchain.llms import RWKV
model = RWKV(model="./models/rwkv-3b-fp16.bin", strategy="cpu fp32")
# Simplest invocation
response = model("Once upon a time, ")
"""
model: str
"""Path to the pre-trained RWKV model file."""
tokens_pa... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
ccccfa42260b-2 | """Positive values penalize new tokens based on their existing frequency
in the text so far, decreasing the model's likelihood to repeat the same
line verbatim.."""
penalty_alpha_presence: float = 0.4
"""Positive values penalize new tokens based on whether they appear
in the text so far, increasing ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
ccccfa42260b-3 | class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {
"verbose": self.verbose,
"top_p": self.top_p,
"temperature": ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
ccccfa42260b-4 | """Validate that the python package exists in the environment."""
try:
import tokenizers
except ImportError:
raise ImportError(
"Could not import tokenizers python package. "
"Please install it with `pip install tokenizers`."
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
ccccfa42260b-5 | )
values["pipeline"] = PIPELINE(values["client"], values["tokens_path"])
except ImportError:
raise ValueError(
"Could not import rwkv python package. "
"Please install it with `pip install rwkv`."
)
return values
@property
def _... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
ccccfa42260b-6 | AVOID_REPEAT_TOKENS = []
AVOID_REPEAT = "，：？！"
for i in AVOID_REPEAT:
dd = self.pipeline.encode(i)
assert len(dd) == 1
AVOID_REPEAT_TOKENS += dd
tokens = [int(x) for x in _tokens]
self.model_tokens += tokens
out: Any = None
while len(to... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
ccccfa42260b-7 | return out
def rwkv_generate(self, prompt: str) -> str:
self.model_state = None
self.model_tokens = []
logits = self.run_rnn(self.tokenizer.encode(prompt).ids)
begin = len(self.model_tokens)
out_last = begin
occurrence: Dict = {}
decoded = ""
for i in ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
ccccfa42260b-8 | occurrence[token] += 1
logits = self.run_rnn([token])
xxx = self.tokenizer.decode(self.model_tokens[out_last:])
if "\ufffd" not in xxx: # avoid utf-8 display issues
decoded += xxx
out_last = begin + i + 1
if i >= self.max_tokens_per_ge... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
ccccfa42260b-9 | Example:
.. code-block:: python
prompt = "Once upon a time, "
response = model(prompt, n_predict=55)
"""
text = self.rwkv_generate(prompt)
if stop is not None:
text = enforce_stop_tokens(text, stop)
return text | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
56e4dea41085-0 | Source code for langchain.llms.ctransformers
"""Wrapper around the C Transformers library."""
from typing import Any, Dict, Optional, Sequence
from pydantic import root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
[docs]class CTransformers(LLM):
"""W... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html |
56e4dea41085-1 | model: str
"""The path to a model file or directory or the name of a Hugging Face Hub
model repo."""
model_type: Optional[str] = None
"""The model type."""
model_file: Optional[str] = None
"""The name of the model file in repo or directory."""
config: Optional[Dict[str, Any]] = None
"""T... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html |
56e4dea41085-2 | "model_type": self.model_type,
"model_file": self.model_file,
"config": self.config,
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "ctransformers"
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html |
56e4dea41085-3 | values["model"],
model_type=values["model_type"],
model_file=values["model_file"],
lib=values["lib"],
**config,
)
return values
def _call(
self,
prompt: str,
stop: Optional[Sequence[str]] = None,
run_manager: Optional[Ca... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html |
56e4dea41085-4 | """
text = []
_run_manager = run_manager or CallbackManagerForLLMRun.get_noop_manager()
for chunk in self.client(prompt, stop=stop, stream=True):
text.append(chunk)
_run_manager.on_llm_new_token(chunk, verbose=self.verbose)
return "".join(text) | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html |
78ae43e6af1d-0 | Source code for langchain.llms.huggingface_endpoint
"""Wrapper around HuggingFace APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
78ae43e6af1d-1 | Only supports `text-generation` and `text2text-generation` for now.
Example:
.. code-block:: python
from langchain.llms import HuggingFaceEndpoint
endpoint_url = (
"https://abcdefghijklmnop.us-east-1.aws.endpoints.huggingface.cloud"
)
hf = Hugg... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
78ae43e6af1d-2 | class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@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(
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
78ae43e6af1d-3 | ) from e
except ImportError:
raise ValueError(
"Could not import huggingface_hub python package. "
"Please install it with `pip install huggingface_hub`."
)
values["huggingfacehub_api_token"] = huggingfacehub_api_token
return values
@pr... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
78ae43e6af1d-4 | self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to HuggingFace Hub's inference endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Op... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
78ae43e6af1d-5 | headers = {
"Authorization": f"Bearer {self.huggingfacehub_api_token}",
"Content-Type": "application/json",
}
# send request
try:
response = requests.post(
self.endpoint_url, headers=headers, json=parameter_payload
)
except ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
78ae43e6af1d-6 | text = generated_text[0]["generated_text"]
elif self.task == "summarization":
text = generated_text[0]["summary_text"]
else:
raise ValueError(
f"Got invalid task {self.task}, "
f"currently only {VALID_TASKS} are supported"
)
if ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
e5912d3323b9-0 | Source code for langchain.llms.aviary
"""Wrapper around Aviary"""
import dataclasses
import os
from typing import Any, Dict, List, Mapping, Optional, Union, cast
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LL... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aviary.html |
e5912d3323b9-1 | assert aviary_url, "AVIARY_URL must be set"
aviary_token = os.getenv("AVIARY_TOKEN", "")
bearer = f"Bearer {aviary_token}" if aviary_token else ""
aviary_url += "/" if not aviary_url.endswith("/") else ""
return cls(aviary_url, bearer)
def get_models() -> List[str]:
"""List available... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aviary.html |
e5912d3323b9-2 | result = sorted(
[k.lstrip("/").replace("--", "/") for k in result.keys() if "--" in k]
)
return result
def get_completions(
model: str,
prompt: str,
use_prompt_format: bool = True,
version: str = "",
) -> Dict[str, Union[str, float, int]]:
"""Get completions from Aviary models."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aviary.html |
e5912d3323b9-3 | except requests.JSONDecodeError as e:
raise RuntimeError(
f"Error decoding JSON from {url}. Text response: {response.text}"
) from e
[docs]class Aviary(LLM):
"""Allow you to use an Aviary.
Aviary is a backend for hosted models. You can
find out more about aviary at
http://git... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aviary.html |
e5912d3323b9-4 | os.environ["AVIARY_TOKEN"] = "<TOKEN>"
light = Aviary(model='amazon/LightGPT')
output = light('How do you make fried rice?')
"""
model: str = "amazon/LightGPT"
aviary_url: Optional[str] = None
aviary_token: Optional[str] = None
# If True the prompt template for the model will... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aviary.html |
e5912d3323b9-5 | aviary_url = get_from_dict_or_env(values, "aviary_url", "AVIARY_URL")
aviary_token = get_from_dict_or_env(values, "aviary_token", "AVIARY_TOKEN")
# Set env viarables for aviary sdk
os.environ["AVIARY_URL"] = aviary_url
os.environ["AVIARY_TOKEN"] = aviary_token
try:
av... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aviary.html |
e5912d3323b9-6 | return {
"model_name": self.model,
"aviary_url": self.aviary_url,
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return f"aviary-{self.model.replace('/', '-')}"
def _call(
self,
prompt: str,
stop: Optional[List[str]] ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aviary.html |
e5912d3323b9-7 | """
kwargs = {"use_prompt_format": self.use_prompt_format}
if self.version:
kwargs["version"] = self.version
output = get_completions(
model=self.model,
prompt=prompt,
**kwargs,
)
text = cast(str, output["generated_text"])
i... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aviary.html |
e1bcbd876753-0 | Source code for langchain.llms.writer
"""Wrapper around Writer APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import e... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
e1bcbd876753-1 | """Writer organization ID."""
model_id: str = "palmyra-instruct"
"""Model name to use."""
min_tokens: Optional[int] = None
"""Minimum number of tokens to generate."""
max_tokens: Optional[int] = None
"""Maximum number of tokens to generate."""
temperature: Optional[float] = None
"""What ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
e1bcbd876753-2 | best_of: Optional[int] = None
"""Generates this many completions server-side and returns the "best"."""
logprobs: bool = False
"""Whether to return log probabilities."""
n: Optional[int] = None
"""How many completions to generate."""
writer_api_key: Optional[str] = None
"""Writer API key."""... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
e1bcbd876753-3 | )
values["writer_api_key"] = writer_api_key
writer_org_id = get_from_dict_or_env(values, "writer_org_id", "WRITER_ORG_ID")
values["writer_org_id"] = writer_org_id
return values
@property
def _default_params(self) -> Mapping[str, Any]:
"""Get the default parameters for cal... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
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