id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 59 127 |
|---|---|---|
f754198f078c-5 | ai_prefix: str = "AI"
llm: BaseLanguageModel
entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT
entity_summarization_prompt: BasePromptTemplate = ENTITY_SUMMARIZATION_PROMPT
entity_cache: List[str] = []
k: int = 3
chat_history_key: str = "history"
entity_store: BaseEntit... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/entity.html |
f754198f078c-6 | self.entity_cache = entities
if self.return_messages:
buffer: Any = self.buffer[-self.k * 2 :]
else:
buffer = buffer_string
return {
self.chat_history_key: buffer,
"entities": entity_summaries,
}
[docs] def save_context(self, inputs: Dic... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/entity.html |
3eae703cd074-0 | Source code for langchain.memory.buffer
from typing import Any, Dict, List, Optional
from pydantic import root_validator
from langchain.memory.chat_memory import BaseChatMemory, BaseMemory
from langchain.memory.utils import get_prompt_input_key
from langchain.schema import get_buffer_string
[docs]class ConversationBuff... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/buffer.html |
3eae703cd074-1 | def validate_chains(cls, values: Dict) -> Dict:
"""Validate that return messages is not True."""
if values.get("return_messages", False):
raise ValueError(
"return_messages must be False for ConversationStringBufferMemory"
)
return values
@property
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/buffer.html |
da535f71bcbf-0 | Source code for langchain.memory.readonly
from typing import Any, Dict, List
from langchain.schema import BaseMemory
[docs]class ReadOnlySharedMemory(BaseMemory):
"""A memory wrapper that is read-only and cannot be changed."""
memory: BaseMemory
@property
def memory_variables(self) -> List[str]:
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/readonly.html |
7bade8772e28-0 | Source code for langchain.memory.summary
from __future__ import annotations
from typing import Any, Dict, List, Type
from pydantic import BaseModel, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.memory.chat_memory import BaseChatMemory
from... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/summary.html |
7bade8772e28-1 | **kwargs: Any,
) -> ConversationSummaryMemory:
obj = cls(llm=llm, chat_memory=chat_memory, **kwargs)
for i in range(0, len(obj.chat_memory.messages), summarize_step):
obj.buffer = obj.predict_new_summary(
obj.chat_memory.messages[i : i + summarize_step], obj.buffer
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/summary.html |
7bade8772e28-2 | [docs] def clear(self) -> None:
"""Clear memory contents."""
super().clear()
self.buffer = ""
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/summary.html |
9c91c449cbfa-0 | Source code for langchain.memory.vectorstore
"""Class for a VectorStore-backed memory object."""
from typing import Any, Dict, List, Optional, Union
from pydantic import Field
from langchain.memory.chat_memory import BaseMemory
from langchain.memory.utils import get_prompt_input_key
from langchain.schema import Documen... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/vectorstore.html |
9c91c449cbfa-1 | docs = self.retriever.get_relevant_documents(query)
result: Union[List[Document], str]
if not self.return_docs:
result = "\n".join([doc.page_content for doc in docs])
else:
result = docs
return {self.memory_key: result}
def _form_documents(
self, input... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/vectorstore.html |
41f620be7ba0-0 | Source code for langchain.memory.simple
from typing import Any, Dict, List
from langchain.schema import BaseMemory
[docs]class SimpleMemory(BaseMemory):
"""Simple memory for storing context or other bits of information that shouldn't
ever change between prompts.
"""
memories: Dict[str, Any] = dict()
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/simple.html |
44c59d235693-0 | Source code for langchain.memory.buffer_window
from typing import Any, Dict, List
from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema import BaseMessage, get_buffer_string
[docs]class ConversationBufferWindowMemory(BaseChatMemory):
"""Buffer for storing conversation memory."""
human_pr... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/buffer_window.html |
9b2f4aad14b2-0 | Source code for langchain.memory.combined
import warnings
from typing import Any, Dict, List, Set
from pydantic import validator
from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema import BaseMemory
[docs]class CombinedMemory(BaseMemory):
"""Class for combining multiple memories' data toge... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/combined.html |
9b2f4aad14b2-1 | for memory in self.memories:
memory_variables.extend(memory.memory_variables)
return memory_variables
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
"""Load all vars from sub-memories."""
memory_data: Dict[str, Any] = {}
# Collect vars fr... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/combined.html |
20edaa51834b-0 | Source code for langchain.memory.chat_message_histories.cassandra
import json
import logging
from typing import List
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
_message_to_dict,
messages_from_dict,
)
logger = logging.getLogger(__name__)
DEFAULT_KEYSPACE_NAME = "chat_history"
DEF... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/cassandra.html |
20edaa51834b-1 | OperationTimedOut,
UnresolvableContactPoints,
)
from cassandra.cluster import Cluster, PlainTextAuthProvider
except ImportError:
raise ValueError(
"Could not import cassandra-driver python package. "
"Please install it with `pip... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/cassandra.html |
20edaa51834b-2 | {self.table_name} (id UUID, session_id varchar,
history text, PRIMARY KEY ((session_id), id) );"""
)
except (OperationTimedOut, Unavailable) as error:
logger.error(
f"Unable to create cassandra \
chat message history table: {self.table_na... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/cassandra.html |
20edaa51834b-3 | logger.error("Unable to write chat history messages to cassandra")
raise error
[docs] def clear(self) -> None:
"""Clear session memory from Cassandra"""
from cassandra import OperationTimedOut, Unavailable
try:
self.session.execute(
f"DELETE FROM {self.... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/cassandra.html |
6749248172d0-0 | Source code for langchain.memory.chat_message_histories.cosmos_db
"""Azure CosmosDB Memory History."""
from __future__ import annotations
import logging
from types import TracebackType
from typing import TYPE_CHECKING, Any, List, Optional, Type
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
6749248172d0-1 | :param connection_string: The connection string to use to authenticate.
:param ttl: The time to live (in seconds) to use for documents in the container.
:param cosmos_client_kwargs: Additional kwargs to pass to the CosmosClient.
"""
self.cosmos_endpoint = cosmos_endpoint
self.cos... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
6749248172d0-2 | PartitionKey,
)
except ImportError as exc:
raise ImportError(
"You must install the azure-cosmos package to use the CosmosDBChatMessageHistory." # noqa: E501
) from exc
database = self._client.create_database_if_not_exists(self.cosmos_database)
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
6749248172d0-3 | )
except CosmosHttpResponseError:
logger.info("no session found")
return
if "messages" in item and len(item["messages"]) > 0:
self.messages = messages_from_dict(item["messages"])
[docs] def add_message(self, message: BaseMessage) -> None:
"""Add a self-crea... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
854ce6e834b8-0 | Source code for langchain.memory.chat_message_histories.in_memory
from typing import List
from pydantic import BaseModel
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
)
[docs]class ChatMessageHistory(BaseChatMessageHistory, BaseModel):
messages: List[BaseMessage] = []
[docs] def add... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/in_memory.html |
2aa1cbe92cf1-0 | Source code for langchain.memory.chat_message_histories.file
import json
import logging
from pathlib import Path
from typing import List
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
messages_from_dict,
messages_to_dict,
)
logger = logging.getLogger(__name__)
[docs]class FileChatMe... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/file.html |
74dcd0b60b09-0 | Source code for langchain.memory.chat_message_histories.redis
import json
import logging
from typing import List, Optional
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
_message_to_dict,
messages_from_dict,
)
logger = logging.getLogger(__name__)
[docs]class RedisChatMessageHistory(... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/redis.html |
74dcd0b60b09-1 | """Append the message to the record in Redis"""
self.redis_client.lpush(self.key, json.dumps(_message_to_dict(message)))
if self.ttl:
self.redis_client.expire(self.key, self.ttl)
[docs] def clear(self) -> None:
"""Clear session memory from Redis"""
self.redis_client.delete... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/redis.html |
865da7177912-0 | Source code for langchain.memory.chat_message_histories.postgres
import json
import logging
from typing import List
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
_message_to_dict,
messages_from_dict,
)
logger = logging.getLogger(__name__)
DEFAULT_CONNECTION_STRING = "postgresql://p... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/postgres.html |
865da7177912-1 | messages = messages_from_dict(items)
return messages
[docs] def add_message(self, message: BaseMessage) -> None:
"""Append the message to the record in PostgreSQL"""
from psycopg import sql
query = sql.SQL("INSERT INTO {} (session_id, message) VALUES (%s, %s);").format(
sq... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/postgres.html |
c80fabc7b629-0 | Source code for langchain.memory.chat_message_histories.dynamodb
import logging
from typing import List, Optional
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
_message_to_dict,
messages_from_dict,
messages_to_dict,
)
logger = logging.getLogger(__name__)
[docs]class DynamoDBCha... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/dynamodb.html |
c80fabc7b629-1 | except ClientError as error:
if error.response["Error"]["Code"] == "ResourceNotFoundException":
logger.warning("No record found with session id: %s", self.session_id)
else:
logger.error(error)
if response and "Item" in response:
items = respons... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/dynamodb.html |
a9683cbd94d7-0 | Source code for langchain.memory.chat_message_histories.momento
from __future__ import annotations
import json
from datetime import timedelta
from typing import TYPE_CHECKING, Any, Optional
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
_message_to_dict,
messages_from_dict,
)
from l... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/momento.html |
a9683cbd94d7-1 | Note: to instantiate the cache client passed to MomentoChatMessageHistory,
you must have a Momento account at https://gomomento.com/.
Args:
session_id (str): The session ID to use for this chat session.
cache_client (CacheClient): The Momento cache client.
cache_name ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/momento.html |
a9683cbd94d7-2 | def from_client_params(
cls,
session_id: str,
cache_name: str,
ttl: timedelta,
*,
configuration: Optional[momento.config.Configuration] = None,
auth_token: Optional[str] = None,
**kwargs: Any,
) -> MomentoChatMessageHistory:
"""Construct cache ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/momento.html |
a9683cbd94d7-3 | return []
elif isinstance(fetch_response, CacheListFetch.Error):
raise fetch_response.inner_exception
else:
raise Exception(f"Unexpected response: {fetch_response}")
[docs] def add_message(self, message: BaseMessage) -> None:
"""Store a message in the cache.
Ar... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/momento.html |
19a9baed8b87-0 | Source code for langchain.memory.chat_message_histories.mongodb
import json
import logging
from typing import List
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
_message_to_dict,
messages_from_dict,
)
logger = logging.getLogger(__name__)
DEFAULT_DBNAME = "chat_history"
DEFAULT_COLL... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/mongodb.html |
19a9baed8b87-1 | except errors.OperationFailure as error:
logger.error(error)
if cursor:
items = [json.loads(document["History"]) for document in cursor]
else:
items = []
messages = messages_from_dict(items)
return messages
[docs] def add_message(self, message: Base... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/memory/chat_message_histories/mongodb.html |
e7a97b3acca7-0 | Source code for langchain.llms.deepinfra
"""Wrapper around DeepInfra 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 im... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/deepinfra.html |
e7a97b3acca7-1 | return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
**{"model_id": self.model_id},
**{"model_kwargs": self.model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/deepinfra.html |
e7a97b3acca7-2 | if res.status_code != 200:
raise ValueError(
"Error raised by inference API HTTP code: %s, %s"
% (res.status_code, res.text)
)
try:
t = res.json()
text = t["results"][0]["generated_text"]
except requests.exceptions.JSONDecod... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/deepinfra.html |
06b7f3f65424-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/rwkv.html |
06b7f3f65424-1 | """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 ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/rwkv.html |
06b7f3f65424-2 | """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`."
)
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/rwkv.html |
06b7f3f65424-3 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/rwkv.html |
06b7f3f65424-4 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/rwkv.html |
867e1d128899-0 | Source code for langchain.llms.sagemaker_endpoint
"""Wrapper around Sagemaker InvokeEndpoint API."""
from abc import abstractmethod
from typing import Any, Dict, Generic, List, Mapping, Optional, TypeVar, Union
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
f... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/sagemaker_endpoint.html |
867e1d128899-1 | """The MIME type of the response data returned from endpoint"""
@abstractmethod
def transform_input(self, prompt: INPUT_TYPE, model_kwargs: Dict) -> bytes:
"""Transforms the input to a format that model can accept
as the request Body. Should return bytes or seekable file
like object in t... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/sagemaker_endpoint.html |
867e1d128899-2 | )
credentials_profile_name = (
"default"
)
se = SagemakerEndpoint(
endpoint_name=endpoint_name,
region_name=region_name,
credentials_profile_name=credentials_profile_name
)
"""
client: Any #: :meta p... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/sagemaker_endpoint.html |
867e1d128899-3 | def transform_output(self, output: bytes) -> str:
response_json = json.loads(output.read().decode("utf-8"))
return response_json[0]["generated_text"]
"""
model_kwargs: Optional[Dict] = None
"""Key word arguments to pass to the model."""
endpoint_kwargs: Optional[D... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/sagemaker_endpoint.html |
867e1d128899-4 | @property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
**{"endpoint_name": self.endpoint_name},
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(s... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/sagemaker_endpoint.html |
867e1d128899-5 | text = self.content_handler.transform_output(response["Body"])
if stop is not None:
# This is a bit hacky, but I can't figure out a better way to enforce
# stop tokens when making calls to the sagemaker endpoint.
text = enforce_stop_tokens(text, stop)
return text
By H... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/sagemaker_endpoint.html |
6912a53ff031-0 | Source code for langchain.llms.petals
"""Wrapper around Petals 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.llms.utils imp... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/petals.html |
6912a53ff031-1 | """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.""... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/petals.html |
6912a53ff031-2 | 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)
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/petals.html |
6912a53ff031-3 | """Call the Petals API."""
params = self._default_params
params = {**params, **kwargs}
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:
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/petals.html |
9d38096d69b8-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/writer.html |
9d38096d69b8-1 | 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."""
base_url: Optional[str] = None
"""Base url to use, if None decides based on model name."""
class Co... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/writer.html |
9d38096d69b8-2 | """Get the identifying parameters."""
return {
**{"model_id": self.model_id, "writer_org_id": self.writer_org_id},
**self._default_params,
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "writer"
def _call(
self,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/writer.html |
9d38096d69b8-3 | # are not enforced by the model parameters
text = enforce_stop_tokens(text, stop)
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/writer.html |
1fc3dc302d95-0 | Source code for langchain.llms.mosaicml
"""Wrapper around MosaicML 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 impo... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/mosaicml.html |
1fc3dc302d95-1 | )
"""
endpoint_url: str = (
"https://models.hosted-on.mosaicml.hosting/mpt-7b-instruct/v1/predict"
)
"""Endpoint URL to use."""
inject_instruction_format: bool = False
"""Whether to inject the instruction format into the prompt."""
model_kwargs: Optional[dict] = None
"""Key word ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/mosaicml.html |
1fc3dc302d95-2 | instruction=prompt,
)
return prompt
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
is_retry: bool = False,
**kwargs: Any,
) -> str:
"""Call out to a MosaicML LLM i... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/mosaicml.html |
1fc3dc302d95-3 | raise ValueError(
f"Error raised by inference API: {parsed_response['error']}"
)
# The inference API has changed a couple of times, so we add some handling
# to be robust to multiple response formats.
if isinstance(parsed_response, dict):
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/mosaicml.html |
1fc3dc302d95-4 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/mosaicml.html |
70dcace8783d-0 | Source code for langchain.llms.replicate
"""Wrapper around Replicate 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 im... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/replicate.html |
70dcace8783d-1 | """Build extra kwargs from additional params that were passed in."""
all_required_field_names = {field.alias for field in cls.__fields__.values()}
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name not in all_required_field_names:
if ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/replicate.html |
70dcace8783d-2 | import replicate as replicate_python
except ImportError:
raise ImportError(
"Could not import replicate python package. "
"Please install it with `pip install replicate`."
)
# get the model and version
model_str, version_str = self.model.sp... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/replicate.html |
8e5972ee73f8-0 | 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.... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_endpoint.html |
8e5972ee73f8-1 | huggingfacehub_api_token: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
hugging... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_endpoint.html |
8e5972ee73f8-2 | return "huggingface_endpoint"
def _call(
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: Th... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_endpoint.html |
8e5972ee73f8-3 | elif self.task == "text2text-generation":
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 ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_endpoint.html |
1fbaaa65f49e-0 | Source code for langchain.llms.self_hosted_hugging_face
"""Wrapper around HuggingFace Pipeline API to run on self-hosted remote hardware."""
import importlib.util
import logging
from typing import Any, Callable, List, Mapping, Optional
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerFo... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/self_hosted_hugging_face.html |
1fbaaa65f49e-1 | text = enforce_stop_tokens(text, stop)
return text
def _load_transformer(
model_id: str = DEFAULT_MODEL_ID,
task: str = DEFAULT_TASK,
device: int = 0,
model_kwargs: Optional[dict] = None,
) -> Any:
"""Inference function to send to the remote hardware.
Accepts a huggingface model_id and retur... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/self_hosted_hugging_face.html |
1fbaaa65f49e-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/self_hosted_hugging_face.html |
1fbaaa65f49e-3 | hf = SelfHostedHuggingFaceLLM(
model_id="google/flan-t5-large", task="text2text-generation",
hardware=gpu
)
Example passing fn that generates a pipeline (bc the pipeline is not serializable):
.. code-block:: python
from langchain.llms import SelfHosted... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/self_hosted_hugging_face.html |
1fbaaa65f49e-4 | """Function to load the model remotely on the server."""
inference_fn: Callable = _generate_text #: :meta private:
"""Inference function to send to the remote hardware."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def __init__(self, **kwargs: Any):... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/self_hosted_hugging_face.html |
1fbaaa65f49e-5 | )
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/self_hosted_hugging_face.html |
15c77f83a7b2-0 | Source code for langchain.llms.stochasticai
"""Wrapper around StochasticAI APIs."""
import logging
import time
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/stochasticai.html |
15c77f83a7b2-1 | raise ValueError(f"Found {field_name} supplied twice.")
logger.warning(
f"""{field_name} was transfered to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/stochasticai.html |
15c77f83a7b2-2 | response = StochasticAI("Tell me a joke.")
"""
params = self.model_kwargs or {}
params = {**params, **kwargs}
response_post = requests.post(
url=self.api_url,
json={"prompt": prompt, "params": params},
headers={
"apiKey": f"{self.stocha... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/stochasticai.html |
2be2f3485db7-0 | Source code for langchain.llms.beam
"""Wrapper around Beam API."""
import base64
import json
import logging
import subprocess
import textwrap
import time
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import Callba... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/beam.html |
2be2f3485db7-1 | max_length=50)
llm._deploy()
call_result = llm._call(input)
"""
model_name: str = ""
name: str = ""
cpu: str = ""
memory: str = ""
gpu: str = ""
python_version: str = ""
python_packages: List[str] = []
max_length: str = ""
url: str = ""
"""model endpoi... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/beam.html |
2be2f3485db7-2 | @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
beam_client_id = get_from_dict_or_env(
values, "beam_client_id", "BEAM_CLIENT_ID"
)
beam_client_secret = get_from_dict_or_env(
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/beam.html |
2be2f3485db7-3 | python_packages={python_packages},
)
app.Trigger.RestAPI(
inputs={{"prompt": beam.Types.String(), "max_length": beam.Types.String()}},
outputs={{"text": beam.Types.String()}},
handler="run.py:beam_langchain",
)
"""
)
script_name = "app.... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/beam.html |
2be2f3485db7-4 | file.write(script.format(model_name=self.model_name))
def _deploy(self) -> str:
"""Call to Beam."""
try:
import beam # type: ignore
if beam.__path__ == "":
raise ImportError
except ImportError:
raise ImportError(
"Could not... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/beam.html |
2be2f3485db7-5 | self,
prompt: str,
stop: Optional[list] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call to Beam."""
url = "https://apps.beam.cloud/" + self.app_id if self.app_id else self.url
payload = {"prompt": prompt, "max_l... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/beam.html |
aa5d199abcf6-0 | Source code for langchain.llms.huggingface_hub
"""Wrapper around HuggingFace APIs."""
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 enf... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_hub.html |
aa5d199abcf6-1 | """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(
values, "huggingfac... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_hub.html |
aa5d199abcf6-2 | 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: Optional list of... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_hub.html |
aa5d199abcf6-3 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_hub.html |
53df2fb38aff-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__... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/baseten.html |
53df2fb38aff-1 | """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:
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/baseten.html |
8682edeeb4ca-0 | Source code for langchain.llms.pipelineai
"""Wrapper around Pipeline Cloud API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from l... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/pipelineai.html |
8682edeeb4ca-1 | extra = values.get("pipeline_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"""{field_... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/pipelineai.html |
8682edeeb4ca-2 | "Please install it with `pip install pipeline-ai`."
)
client = PipelineCloud(token=self.pipeline_api_key)
params = self.pipeline_kwargs or {}
params = {**params, **kwargs}
run = client.run_pipeline(self.pipeline_key, [prompt, params])
try:
text = run.resul... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/pipelineai.html |
bec8d20e826f-0 | Source code for langchain.llms.openai
"""Wrapper around OpenAI APIs."""
from __future__ import annotations
import logging
import sys
import warnings
from typing import (
AbstractSet,
Any,
Callable,
Collection,
Dict,
Generator,
List,
Literal,
Mapping,
Optional,
Set,
Tuple,... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/openai.html |
bec8d20e826f-1 | "finish_reason"
]
response["choices"][0]["logprobs"] = stream_response["choices"][0]["logprobs"]
def _streaming_response_template() -> Dict[str, Any]:
return {
"choices": [
{
"text": "",
"finish_reason": None,
"logprobs": None,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/openai.html |
bec8d20e826f-2 | return llm.client.create(**kwargs)
return _completion_with_retry(**kwargs)
async def acompletion_with_retry(
llm: Union[BaseOpenAI, OpenAIChat], **kwargs: Any
) -> Any:
"""Use tenacity to retry the async completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
async de... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/openai.html |
bec8d20e826f-3 | """How many completions to generate for each prompt."""
best_of: int = 1
"""Generates best_of completions server-side and returns the "best"."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
openai_api_ke... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/openai.html |
bec8d20e826f-4 | warnings.warn(
"You are trying to use a chat model. This way of initializing it is "
"no longer supported. Instead, please use: "
"`from langchain.chat_models import ChatOpenAI`"
)
return OpenAIChat(**data)
return super().__new__(cls)
c... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/openai.html |
bec8d20e826f-5 | values["openai_api_key"] = get_from_dict_or_env(
values, "openai_api_key", "OPENAI_API_KEY"
)
values["openai_api_base"] = get_from_dict_or_env(
values,
"openai_api_base",
"OPENAI_API_BASE",
default="",
)
values["openai_proxy"] =... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/openai.html |
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