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Code Issues Packages Projects Releases Wiki Activity

community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)

Browse Source 
Moved the following modules to new package langchain-community in a backwards compatible fashion:

```
mv langchain/langchain/adapters community/langchain_community
mv langchain/langchain/callbacks community/langchain_community/callbacks
mv langchain/langchain/chat_loaders community/langchain_community
mv langchain/langchain/chat_models community/langchain_community
mv langchain/langchain/document_loaders community/langchain_community
mv langchain/langchain/docstore community/langchain_community
mv langchain/langchain/document_transformers community/langchain_community
mv langchain/langchain/embeddings community/langchain_community
mv langchain/langchain/graphs community/langchain_community
mv langchain/langchain/llms community/langchain_community
mv langchain/langchain/memory/chat_message_histories community/langchain_community
mv langchain/langchain/retrievers community/langchain_community
mv langchain/langchain/storage community/langchain_community
mv langchain/langchain/tools community/langchain_community
mv langchain/langchain/utilities community/langchain_community
mv langchain/langchain/vectorstores community/langchain_community
mv langchain/langchain/agents/agent_toolkits community/langchain_community
mv langchain/langchain/cache.py community/langchain_community
mv langchain/langchain/adapters community/langchain_community
mv langchain/langchain/callbacks community/langchain_community/callbacks
mv langchain/langchain/chat_loaders community/langchain_community
mv langchain/langchain/chat_models community/langchain_community
mv langchain/langchain/document_loaders community/langchain_community
mv langchain/langchain/docstore community/langchain_community
mv langchain/langchain/document_transformers community/langchain_community
mv langchain/langchain/embeddings community/langchain_community
mv langchain/langchain/graphs community/langchain_community
mv langchain/langchain/llms community/langchain_community
mv langchain/langchain/memory/chat_message_histories community/langchain_community
mv langchain/langchain/retrievers community/langchain_community
mv langchain/langchain/storage community/langchain_community
mv langchain/langchain/tools community/langchain_community
mv langchain/langchain/utilities community/langchain_community
mv langchain/langchain/vectorstores community/langchain_community
mv langchain/langchain/agents/agent_toolkits community/langchain_community
mv langchain/langchain/cache.py community/langchain_community
```

Moved the following to core
```
mv langchain/langchain/utils/json_schema.py core/langchain_core/utils
mv langchain/langchain/utils/html.py core/langchain_core/utils
mv langchain/langchain/utils/strings.py core/langchain_core/utils
cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py
rm langchain/langchain/utils/env.py
```

See .scripts/community_split/script_integrations.sh for all changes
This commit is contained in:
Bagatur
2023-12-11 13:53:30 -08:00
committed by GitHub
parent c0f4b95aa9
commit ed58eeb9c5
2446 changed files with 171805 additions and 137118 deletions
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258
libs/community/langchain_community/callbacks/utils.py Normal file
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import hashlib
from pathlib import Path
from typing import Any, Dict, Iterable, Tuple, Union
def import_spacy() -> Any:
"""Import the spacy python package and raise an error if it is not installed."""
try:
import spacy
except ImportError:
raise ImportError(
"This callback manager requires the `spacy` python "
"package installed. Please install it with `pip install spacy`"
)
return spacy
def import_pandas() -> Any:
"""Import the pandas python package and raise an error if it is not installed."""
try:
import pandas
except ImportError:
raise ImportError(
"This callback manager requires the `pandas` python "
"package installed. Please install it with `pip install pandas`"
)
return pandas
def import_textstat() -> Any:
"""Import the textstat python package and raise an error if it is not installed."""
try:
import textstat
except ImportError:
raise ImportError(
"This callback manager requires the `textstat` python "
"package installed. Please install it with `pip install textstat`"
)
return textstat
def _flatten_dict(
nested_dict: Dict[str, Any], parent_key: str = "", sep: str = "_"
) -> Iterable[Tuple[str, Any]]:
"""
Generator that yields flattened items from a nested dictionary for a flat dict.
Parameters:
nested_dict (dict): The nested dictionary to flatten.
parent_key (str): The prefix to prepend to the keys of the flattened dict.
sep (str): The separator to use between the parent key and the key of the
flattened dictionary.
Yields:
(str, any): A key-value pair from the flattened dictionary.
"""
for key, value in nested_dict.items():
new_key = parent_key + sep + key if parent_key else key
if isinstance(value, dict):
yield from _flatten_dict(value, new_key, sep)
else:
yield new_key, value
def flatten_dict(
nested_dict: Dict[str, Any], parent_key: str = "", sep: str = "_"
) -> Dict[str, Any]:
"""Flattens a nested dictionary into a flat dictionary.
Parameters:
nested_dict (dict): The nested dictionary to flatten.
parent_key (str): The prefix to prepend to the keys of the flattened dict.
sep (str): The separator to use between the parent key and the key of the
flattened dictionary.
Returns:
(dict): A flat dictionary.
"""
flat_dict = {k: v for k, v in _flatten_dict(nested_dict, parent_key, sep)}
return flat_dict
def hash_string(s: str) -> str:
"""Hash a string using sha1.
Parameters:
s (str): The string to hash.
Returns:
(str): The hashed string.
"""
return hashlib.sha1(s.encode("utf-8")).hexdigest()
def load_json(json_path: Union[str, Path]) -> str:
"""Load json file to a string.
Parameters:
json_path (str): The path to the json file.
Returns:
(str): The string representation of the json file.
"""
with open(json_path, "r") as f:
data = f.read()
return data
class BaseMetadataCallbackHandler:
"""This class handles the metadata and associated function states for callbacks.
Attributes:
step (int): The current step.
starts (int): The number of times the start method has been called.
ends (int): The number of times the end method has been called.
errors (int): The number of times the error method has been called.
text_ctr (int): The number of times the text method has been called.
ignore_llm_ (bool): Whether to ignore llm callbacks.
ignore_chain_ (bool): Whether to ignore chain callbacks.
ignore_agent_ (bool): Whether to ignore agent callbacks.
ignore_retriever_ (bool): Whether to ignore retriever callbacks.
always_verbose_ (bool): Whether to always be verbose.
chain_starts (int): The number of times the chain start method has been called.
chain_ends (int): The number of times the chain end method has been called.
llm_starts (int): The number of times the llm start method has been called.
llm_ends (int): The number of times the llm end method has been called.
llm_streams (int): The number of times the text method has been called.
tool_starts (int): The number of times the tool start method has been called.
tool_ends (int): The number of times the tool end method has been called.
agent_ends (int): The number of times the agent end method has been called.
on_llm_start_records (list): A list of records of the on_llm_start method.
on_llm_token_records (list): A list of records of the on_llm_token method.
on_llm_end_records (list): A list of records of the on_llm_end method.
on_chain_start_records (list): A list of records of the on_chain_start method.
on_chain_end_records (list): A list of records of the on_chain_end method.
on_tool_start_records (list): A list of records of the on_tool_start method.
on_tool_end_records (list): A list of records of the on_tool_end method.
on_agent_finish_records (list): A list of records of the on_agent_end method.
"""
def __init__(self) -> None:
self.step = 0
self.starts = 0
self.ends = 0
self.errors = 0
self.text_ctr = 0
self.ignore_llm_ = False
self.ignore_chain_ = False
self.ignore_agent_ = False
self.ignore_retriever_ = False
self.always_verbose_ = False
self.chain_starts = 0
self.chain_ends = 0
self.llm_starts = 0
self.llm_ends = 0
self.llm_streams = 0
self.tool_starts = 0
self.tool_ends = 0
self.agent_ends = 0
self.on_llm_start_records: list = []
self.on_llm_token_records: list = []
self.on_llm_end_records: list = []
self.on_chain_start_records: list = []
self.on_chain_end_records: list = []
self.on_tool_start_records: list = []
self.on_tool_end_records: list = []
self.on_text_records: list = []
self.on_agent_finish_records: list = []
self.on_agent_action_records: list = []
@property
def always_verbose(self) -> bool:
"""Whether to call verbose callbacks even if verbose is False."""
return self.always_verbose_
@property
def ignore_llm(self) -> bool:
"""Whether to ignore LLM callbacks."""
return self.ignore_llm_
@property
def ignore_chain(self) -> bool:
"""Whether to ignore chain callbacks."""
return self.ignore_chain_
@property
def ignore_agent(self) -> bool:
"""Whether to ignore agent callbacks."""
return self.ignore_agent_
def get_custom_callback_meta(self) -> Dict[str, Any]:
return {
"step": self.step,
"starts": self.starts,
"ends": self.ends,
"errors": self.errors,
"text_ctr": self.text_ctr,
"chain_starts": self.chain_starts,
"chain_ends": self.chain_ends,
"llm_starts": self.llm_starts,
"llm_ends": self.llm_ends,
"llm_streams": self.llm_streams,
"tool_starts": self.tool_starts,
"tool_ends": self.tool_ends,
"agent_ends": self.agent_ends,
}
def reset_callback_meta(self) -> None:
"""Reset the callback metadata."""
self.step = 0
self.starts = 0
self.ends = 0
self.errors = 0
self.text_ctr = 0
self.ignore_llm_ = False
self.ignore_chain_ = False
self.ignore_agent_ = False
self.always_verbose_ = False
self.chain_starts = 0
self.chain_ends = 0
self.llm_starts = 0
self.llm_ends = 0
self.llm_streams = 0
self.tool_starts = 0
self.tool_ends = 0
self.agent_ends = 0
self.on_llm_start_records = []
self.on_llm_token_records = []
self.on_llm_end_records = []
self.on_chain_start_records = []
self.on_chain_end_records = []
self.on_tool_start_records = []
self.on_tool_end_records = []
self.on_text_records = []
self.on_agent_finish_records = []
self.on_agent_action_records = []
return None