id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 49 117 |
|---|---|---|
750093febc2d-1 | if curr_buffer_length > self.max_token_limit:
pruned_memory = []
while curr_buffer_length > self.max_token_limit:
pruned_memory.append(buffer.pop(0))
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
By Harrison Chase
© Copyright 2023, ... | https://python.langchain.com/en/latest/_modules/langchain/memory/token_buffer.html |
3ff6714e80b5-0 | Source code for langchain.memory.summary_buffer
from typing import Any, Dict, List
from pydantic import root_validator
from langchain.memory.chat_memory import BaseChatMemory
from langchain.memory.summary import SummarizerMixin
from langchain.schema import BaseMessage, get_buffer_string
[docs]class ConversationSummaryB... | https://python.langchain.com/en/latest/_modules/langchain/memory/summary_buffer.html |
3ff6714e80b5-1 | if expected_keys != set(prompt_variables):
raise ValueError(
"Got unexpected prompt input variables. The prompt expects "
f"{prompt_variables}, but it should have {expected_keys}."
)
return values
[docs] def save_context(self, inputs: Dict[str, Any], ou... | https://python.langchain.com/en/latest/_modules/langchain/memory/summary_buffer.html |
6a5b491beed2-0 | Source code for langchain.memory.kg
from typing import Any, Dict, List, Type, Union
from pydantic import Field
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.graphs import NetworkxEntityGraph
from langchain.graphs.networkx_graph import KnowledgeTriple, get... | https://python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
6a5b491beed2-1 | entities = self._get_current_entities(inputs)
summary_strings = []
for entity in entities:
knowledge = self.kg.get_entity_knowledge(entity)
if knowledge:
summary = f"On {entity}: {'. '.join(knowledge)}."
summary_strings.append(summary)
cont... | https://python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
6a5b491beed2-2 | human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
output = chain.predict(
history=buffer_string,
input=input_string,
)
return get_entities(output)
def _get_current_entities(self, inputs: Dict[str, Any]) -> List[str]:
"""Get the cu... | https://python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
6a5b491beed2-3 | """Clear memory contents."""
super().clear()
self.kg.clear()
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
736f17171857-0 | 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... | https://python.langchain.com/en/latest/_modules/langchain/memory/buffer.html |
736f17171857-1 | @root_validator()
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 va... | https://python.langchain.com/en/latest/_modules/langchain/memory/buffer.html |
a739ea70fb19-0 | Source code for langchain.memory.entity
import logging
from abc import ABC, abstractmethod
from itertools import islice
from typing import Any, Dict, Iterable, List, Optional
from pydantic import Field
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.memory.... | https://python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
a739ea70fb19-1 | [docs] def set(self, key: str, value: Optional[str]) -> None:
self.store[key] = value
[docs] def delete(self, key: str) -> None:
del self.store[key]
[docs] def exists(self, key: str) -> bool:
return key in self.store
[docs] def clear(self) -> None:
return self.store.clear()
[... | https://python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
a739ea70fb19-2 | except redis.exceptions.ConnectionError as error:
logger.error(error)
self.session_id = session_id
self.key_prefix = key_prefix
self.ttl = ttl
self.recall_ttl = recall_ttl or ttl
@property
def full_key_prefix(self) -> str:
return f"{self.key_prefix}:{self.sess... | https://python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
a739ea70fb19-3 | yield batch
for keybatch in batched(
self.redis_client.scan_iter(f"{self.full_key_prefix}:*"), 500
):
self.redis_client.delete(*keybatch)
[docs]class ConversationEntityMemory(BaseChatMemory):
"""Entity extractor & summarizer to memory."""
human_prefix: str = "Human"
a... | https://python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
a739ea70fb19-4 | history=buffer_string,
input=inputs[prompt_input_key],
)
if output.strip() == "NONE":
entities = []
else:
entities = [w.strip() for w in output.split(",")]
entity_summaries = {}
for entity in entities:
entity_summaries[entity] = sel... | https://python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
a739ea70fb19-5 | """Clear memory contents."""
self.chat_memory.clear()
self.entity_cache.clear()
self.entity_store.clear()
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
86de2a679383-0 | 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... | https://python.langchain.com/en/latest/_modules/langchain/memory/combined.html |
86de2a679383-1 | 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... | https://python.langchain.com/en/latest/_modules/langchain/memory/combined.html |
23cc6abc17da-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... | https://python.langchain.com/en/latest/_modules/langchain/memory/summary.html |
23cc6abc17da-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
... | https://python.langchain.com/en/latest/_modules/langchain/memory/summary.html |
23cc6abc17da-2 | [docs] def clear(self) -> None:
"""Clear memory contents."""
super().clear()
self.buffer = ""
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/_modules/langchain/memory/summary.html |
dbd2149e3fe6-0 | 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()
... | https://python.langchain.com/en/latest/_modules/langchain/memory/simple.html |
8f595ae4e331-0 | 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]:
... | https://python.langchain.com/en/latest/_modules/langchain/memory/readonly.html |
7fc35556bed0-0 | Source code for langchain.memory.chat_message_histories.dynamodb
import logging
from typing import List
from langchain.schema import (
BaseChatMessageHistory,
BaseMessage,
_message_to_dict,
messages_from_dict,
messages_to_dict,
)
logger = logging.getLogger(__name__)
[docs]class DynamoDBChatMessageHi... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/dynamodb.html |
7fc35556bed0-1 | [docs] def add_message(self, message: BaseMessage) -> None:
"""Append the message to the record in DynamoDB"""
from botocore.exceptions import ClientError
messages = messages_to_dict(self.messages)
_message = _message_to_dict(message)
messages.append(_message)
try:
... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/dynamodb.html |
5f8079b8fd88-0 | 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... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/momento.html |
5f8079b8fd88-1 | 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 ... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/momento.html |
5f8079b8fd88-2 | 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 ... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/momento.html |
5f8079b8fd88-3 | 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... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/momento.html |
fa4c8e6259b7-0 | 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(... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/redis.html |
fa4c8e6259b7-1 | """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... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/redis.html |
36940b00086d-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... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/postgres.html |
36940b00086d-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... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/postgres.html |
1d7089b669b5-0 | 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... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cassandra.html |
1d7089b669b5-1 | OperationTimedOut,
UnresolvableContactPoints,
)
from cassandra.cluster import Cluster, PlainTextAuthProvider
except ImportError:
raise ValueError(
"Could not import cassandra-driver python package. "
"Please install it with `pip... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cassandra.html |
1d7089b669b5-2 | {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... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cassandra.html |
1d7089b669b5-3 | 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.... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cassandra.html |
0ec29e48504b-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... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/file.html |
d57ad94a38b6-0 | 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,... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
d57ad94a38b6-1 | :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... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
d57ad94a38b6-2 | 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)
... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
d57ad94a38b6-3 | )
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... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
222b7c01bf77-0 | 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... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/mongodb.html |
222b7c01bf77-1 | 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... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/mongodb.html |
0bc80d5468d1-0 | 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... | https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/in_memory.html |
4750eb0690db-0 | Source code for langchain.chat_models.azure_openai
"""Azure OpenAI chat wrapper."""
from __future__ import annotations
import logging
from typing import Any, Dict, Mapping
from pydantic import root_validator
from langchain.chat_models.openai import ChatOpenAI
from langchain.schema import ChatResult
from langchain.utils... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
4750eb0690db-1 | openai_api_base: str = ""
openai_api_version: str = ""
openai_api_key: str = ""
openai_organization: str = ""
openai_proxy: str = ""
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
openai... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
4750eb0690db-2 | openai.organization = openai_organization
if openai_proxy:
openai.proxy = {"http": openai_proxy, "https": openai_proxy} # type: ignore[assignment] # noqa: E501
except ImportError:
raise ImportError(
"Could not import openai python package. "
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
4750eb0690db-3 | if res.get("finish_reason", None) == "content_filter":
raise ValueError(
"Azure has not provided the response due to a content"
" filter being triggered"
)
return super()._create_chat_result(response)
By Harrison Chase
© Copyrigh... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
8aad21762acf-0 | Source code for langchain.chat_models.google_palm
"""Wrapper around Google's PaLM Chat API."""
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Mapping, Optional
from pydantic import BaseModel, root_validator
from tenacity import (
before_sleep_log,
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
8aad21762acf-1 | raise ChatGooglePalmError("ChatResponse must have at least one candidate.")
generations: List[ChatGeneration] = []
for candidate in response.candidates:
author = candidate.get("author")
if author is None:
raise ChatGooglePalmError(f"ChatResponse must have an author: {candidate}")
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
8aad21762acf-2 | raise ChatGooglePalmError("System message must be first input message.")
context = input_message.content
elif isinstance(input_message, HumanMessage) and input_message.example:
if messages:
raise ChatGooglePalmError(
"Message examples must come before ... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
8aad21762acf-3 | return genai.types.MessagePromptDict(
context=context,
examples=examples,
messages=messages,
)
def _create_retry_decorator() -> Callable[[Any], Any]:
"""Returns a tenacity retry decorator, preconfigured to handle PaLM exceptions"""
import google.api_core.exceptions
multiplier = 2... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
8aad21762acf-4 | return await llm.client.chat_async(**kwargs)
return await _achat_with_retry(**kwargs)
[docs]class ChatGooglePalm(BaseChatModel, BaseModel):
"""Wrapper around Google's PaLM Chat API.
To use you must have the google.generativeai Python package installed and
either:
1. The ``GOOGLE_API_KEY``` envir... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
8aad21762acf-5 | """Validate api key, python package exists, temperature, top_p, and top_k."""
google_api_key = get_from_dict_or_env(
values, "google_api_key", "GOOGLE_API_KEY"
)
try:
import google.generativeai as genai
genai.configure(api_key=google_api_key)
except Im... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
8aad21762acf-6 | candidate_count=self.n,
)
return _response_to_result(response, stop)
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
) -> ChatResult:
prompt = _messages... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
0e39010ab693-0 | Source code for langchain.chat_models.promptlayer_openai
"""PromptLayer wrapper."""
import datetime
from typing import Any, List, Mapping, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.chat_models import ChatOpenAI
from langchain.sch... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/promptlayer_openai.html |
0e39010ab693-1 | ) -> ChatResult:
"""Call ChatOpenAI generate and then call PromptLayer API to log the request."""
from promptlayer.utils import get_api_key, promptlayer_api_request
request_start_time = datetime.datetime.now().timestamp()
generated_responses = super()._generate(messages, stop, run_manage... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/promptlayer_openai.html |
0e39010ab693-2 | generated_responses = await super()._agenerate(messages, stop, run_manager)
request_end_time = datetime.datetime.now().timestamp()
message_dicts, params = super()._create_message_dicts(messages, stop)
for i, generation in enumerate(generated_responses.generations):
response_dict, par... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/promptlayer_openai.html |
d064cd504b92-0 | Source code for langchain.chat_models.vertexai
"""Wrapper around Google VertexAI chat-based models."""
from dataclasses import dataclass, field
from typing import Dict, List, Optional
from pydantic import root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForL... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/vertexai.html |
d064cd504b92-1 | """
if not history:
return _ChatHistory()
first_message = history[0]
system_message = first_message if isinstance(first_message, SystemMessage) else None
chat_history = _ChatHistory(system_message=system_message)
messages_left = history[1:] if system_message else history
if len(messages_... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/vertexai.html |
d064cd504b92-2 | ) -> ChatResult:
"""Generate next turn in the conversation.
Args:
messages: The history of the conversation as a list of messages.
stop: The list of stop words (optional).
run_manager: The Callbackmanager for LLM run, it's not used at the moment.
Returns:
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/vertexai.html |
d064cd504b92-3 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/_modules/langchain/chat_models/vertexai.html |
918a9c87f3a5-0 | Source code for langchain.chat_models.anthropic
from typing import Any, Dict, List, Optional
from pydantic import Extra
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.chat_models.base import BaseChatModel
from langchain.llms.anthropic import _... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
918a9c87f3a5-1 | elif isinstance(message, AIMessage):
message_text = f"{self.AI_PROMPT} {message.content}"
elif isinstance(message, SystemMessage):
message_text = f"{self.HUMAN_PROMPT} <admin>{message.content}</admin>"
else:
raise ValueError(f"Got unknown type {message}")
retu... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
918a9c87f3a5-2 | ) -> ChatResult:
prompt = self._convert_messages_to_prompt(messages)
params: Dict[str, Any] = {"prompt": prompt, **self._default_params}
if stop:
params["stop_sequences"] = stop
if self.streaming:
completion = ""
stream_resp = self.client.completion_st... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
918a9c87f3a5-3 | completion = response["completion"]
message = AIMessage(content=completion)
return ChatResult(generations=[ChatGeneration(message=message)])
[docs] def get_num_tokens(self, text: str) -> int:
"""Calculate number of tokens."""
if not self.count_tokens:
raise NameError("Plea... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
bf9859c99a11-0 | Source code for langchain.chat_models.openai
"""OpenAI chat wrapper."""
from __future__ import annotations
import logging
import sys
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
Mapping,
Optional,
Tuple,
Union,
)
from pydantic import Extra, Field, root_validator
fro... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
bf9859c99a11-1 | return retry(
reraise=True,
stop=stop_after_attempt(llm.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
bf9859c99a11-2 | elif isinstance(message, HumanMessage):
message_dict = {"role": "user", "content": message.content}
elif isinstance(message, AIMessage):
message_dict = {"role": "assistant", "content": message.content}
elif isinstance(message, SystemMessage):
message_dict = {"role": "system", "content": ... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
bf9859c99a11-3 | leave blank if not using a proxy or service emulator."""
openai_api_base: Optional[str] = None
openai_organization: Optional[str] = None
# to support explicit proxy for OpenAI
openai_proxy: Optional[str] = None
request_timeout: Optional[Union[float, Tuple[float, float]]] = None
"""Timeout for re... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
bf9859c99a11-4 | invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
if invalid_model_kwargs:
raise ValueError(
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead they were passed in as part of `model_kwargs` parameter."
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
bf9859c99a11-5 | try:
values["client"] = openai.ChatCompletion
except AttributeError:
raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely "
"due to an old version of the openai package. Try upgrading it "
"with `pip install --upgra... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
bf9859c99a11-6 | | retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
[docs] def com... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
bf9859c99a11-7 | messages=message_dicts, **params
):
role = stream_resp["choices"][0]["delta"].get("role", role)
token = stream_resp["choices"][0]["delta"].get("content", "")
inner_completion += token
if run_manager:
run_manager.on_llm_new_t... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
bf9859c99a11-8 | async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
) -> ChatResult:
message_dicts, params = self._create_message_dicts(messages, stop)
if self.streaming:
i... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
bf9859c99a11-9 | if model == "gpt-3.5-turbo":
# gpt-3.5-turbo may change over time.
# Returning num tokens assuming gpt-3.5-turbo-0301.
model = "gpt-3.5-turbo-0301"
elif model == "gpt-4":
# gpt-4 may change over time.
# Returning num tokens assuming gpt-4-0314.
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
bf9859c99a11-10 | if sys.version_info[1] <= 7:
return super().get_num_tokens_from_messages(messages)
model, encoding = self._get_encoding_model()
if model == "gpt-3.5-turbo-0301":
# every message follows <im_start>{role/name}\n{content}<im_end>\n
tokens_per_message = 4
# if... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
faee64b36615-0 | Source code for langchain.agents.initialize
"""Load agent."""
from typing import Any, Optional, Sequence
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_types import AgentType
from langchain.agents.loading import AGENT_TO_CLASS, load_agent
from langchain.base_language import BaseLanguageMod... | https://python.langchain.com/en/latest/_modules/langchain/agents/initialize.html |
faee64b36615-1 | "but at most only one should be."
)
if agent is not None:
if agent not in AGENT_TO_CLASS:
raise ValueError(
f"Got unknown agent type: {agent}. "
f"Valid types are: {AGENT_TO_CLASS.keys()}."
)
agent_cls = AGENT_TO_CLASS[agent]
ag... | https://python.langchain.com/en/latest/_modules/langchain/agents/initialize.html |
a2d910173a2e-0 | Source code for langchain.agents.agent
"""Chain that takes in an input and produces an action and action input."""
from __future__ import annotations
import asyncio
import json
import logging
import time
from abc import abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Sequ... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-1 | return None
[docs] @abstractmethod
def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Ste... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-2 | # `force` just returns a constant string
return AgentFinish(
{"output": "Agent stopped due to iteration limit or time limit."}, ""
)
else:
raise ValueError(
f"Got unsupported early_stopping_method `{early_stopping_method}`"
)
[docs]... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-3 | directory_path.mkdir(parents=True, exist_ok=True)
# Fetch dictionary to save
agent_dict = self.dict()
if save_path.suffix == ".json":
with open(file_path, "w") as f:
json.dump(agent_dict, f, indent=4)
elif save_path.suffix == ".yaml":
with open(fil... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-4 | **kwargs: Any,
) -> Union[List[AgentAction], AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-5 | Example:
.. code-block:: python
# If working with agent executor
agent.agent.save(file_path="path/agent.yaml")
"""
# Convert file to Path object.
if isinstance(file_path, str):
save_path = Path(file_path)
else:
save_path = file_path... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-6 | return _dict
[docs] def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has take... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-7 | }
[docs]class Agent(BaseSingleActionAgent):
"""Class responsible for calling the language model and deciding the action.
This is driven by an LLMChain. The prompt in the LLMChain MUST include
a variable called "agent_scratchpad" where the agent can put its
intermediary work.
"""
llm_chain: LLMCh... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-8 | return thoughts
[docs] def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has t... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-9 | """Create the full inputs for the LLMChain from intermediate steps."""
thoughts = self._construct_scratchpad(intermediate_steps)
new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
full_inputs = {**kwargs, **new_inputs}
return full_inputs
@property
def input_keys(self... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-10 | """Create a prompt for this class."""
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
"""Validate that appropriate tools are passed in."""
pass
@classmethod
@abstractmethod
def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
"""G... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-11 | # `force` just returns a constant string
return AgentFinish(
{"output": "Agent stopped due to iteration limit or time limit."}, ""
)
elif early_stopping_method == "generate":
# Generate does one final forward pass
thoughts = ""
for acti... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-12 | }
class ExceptionTool(BaseTool):
name = "_Exception"
description = "Exception tool"
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
return query
async def _arun(
self,
query: str,
run_manager: Opti... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-13 | tools = values["tools"]
allowed_tools = agent.get_allowed_tools()
if allowed_tools is not None:
if set(allowed_tools) != set([tool.name for tool in tools]):
raise ValueError(
f"Allowed tools ({allowed_tools}) different than "
f"provided... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-14 | :meta private:
"""
if self.return_intermediate_steps:
return self.agent.return_values + ["intermediate_steps"]
else:
return self.agent.return_values
[docs] def lookup_tool(self, name: str) -> BaseTool:
"""Lookup tool by name."""
return {tool.name: tool ... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-15 | return final_output
def _take_next_step(
self,
name_to_tool_map: Dict[str, BaseTool],
color_mapping: Dict[str, str],
inputs: Dict[str, str],
intermediate_steps: List[Tuple[AgentAction, str]],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Union[Age... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-16 | if run_manager:
run_manager.on_agent_action(output, color="green")
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
observation = ExceptionTool().run(
output.tool_input,
verbose=self.verbose,
color=None,
callba... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-17 | color=None,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
result.append((agent_action, observation))
return result
async def _atake_next_step(
self,
name_to_tool_map: Dict[str, BaseTool],
... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-18 | tool_run_kwargs = self.agent.tool_run_logging_kwargs()
observation = await ExceptionTool().arun(
output.tool_input,
verbose=self.verbose,
color=None,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-19 | agent_action.tool,
verbose=self.verbose,
color=None,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
return agent_action, observation
# Use asyncio.gather to run multiple ... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
a2d910173a2e-20 | next_step_output, intermediate_steps, run_manager=run_manager
)
intermediate_steps.extend(next_step_output)
if len(next_step_output) == 1:
next_step_action = next_step_output[0]
# See if tool should return directly
tool_return = sel... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
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