mirror of
https://github.com/hwchase17/langchain
synced 2024-11-04 06:00:26 +00:00
ed58eeb9c5
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
134 lines
4.0 KiB
Python
134 lines
4.0 KiB
Python
# flake8: noqa
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"""Tools for working with JSON specs."""
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from __future__ import annotations
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import json
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import re
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from pathlib import Path
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from typing import Dict, List, Optional, Union
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.callbacks import (
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AsyncCallbackManagerForToolRun,
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CallbackManagerForToolRun,
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)
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from langchain_core.tools import BaseTool
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def _parse_input(text: str) -> List[Union[str, int]]:
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"""Parse input of the form data["key1"][0]["key2"] into a list of keys."""
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_res = re.findall(r"\[.*?]", text)
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# strip the brackets and quotes, convert to int if possible
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res = [i[1:-1].replace('"', "").replace("'", "") for i in _res]
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res = [int(i) if i.isdigit() else i for i in res]
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return res
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class JsonSpec(BaseModel):
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"""Base class for JSON spec."""
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dict_: Dict
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max_value_length: int = 200
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@classmethod
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def from_file(cls, path: Path) -> JsonSpec:
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"""Create a JsonSpec from a file."""
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if not path.exists():
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raise FileNotFoundError(f"File not found: {path}")
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dict_ = json.loads(path.read_text())
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return cls(dict_=dict_)
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def keys(self, text: str) -> str:
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"""Return the keys of the dict at the given path.
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Args:
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text: Python representation of the path to the dict (e.g. data["key1"][0]["key2"]).
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"""
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try:
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items = _parse_input(text)
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val = self.dict_
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for i in items:
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if i:
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val = val[i]
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if not isinstance(val, dict):
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raise ValueError(
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f"Value at path `{text}` is not a dict, get the value directly."
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)
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return str(list(val.keys()))
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except Exception as e:
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return repr(e)
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def value(self, text: str) -> str:
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"""Return the value of the dict at the given path.
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Args:
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text: Python representation of the path to the dict (e.g. data["key1"][0]["key2"]).
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"""
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try:
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items = _parse_input(text)
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val = self.dict_
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for i in items:
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val = val[i]
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if isinstance(val, dict) and len(str(val)) > self.max_value_length:
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return "Value is a large dictionary, should explore its keys directly"
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str_val = str(val)
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if len(str_val) > self.max_value_length:
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str_val = str_val[: self.max_value_length] + "..."
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return str_val
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except Exception as e:
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return repr(e)
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class JsonListKeysTool(BaseTool):
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"""Tool for listing keys in a JSON spec."""
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name: str = "json_spec_list_keys"
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description: str = """
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Can be used to list all keys at a given path.
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Before calling this you should be SURE that the path to this exists.
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The input is a text representation of the path to the dict in Python syntax (e.g. data["key1"][0]["key2"]).
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"""
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spec: JsonSpec
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def _run(
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self,
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tool_input: str,
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run_manager: Optional[CallbackManagerForToolRun] = None,
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) -> str:
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return self.spec.keys(tool_input)
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async def _arun(
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self,
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tool_input: str,
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run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
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) -> str:
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return self._run(tool_input)
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class JsonGetValueTool(BaseTool):
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"""Tool for getting a value in a JSON spec."""
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name: str = "json_spec_get_value"
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description: str = """
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Can be used to see value in string format at a given path.
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Before calling this you should be SURE that the path to this exists.
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The input is a text representation of the path to the dict in Python syntax (e.g. data["key1"][0]["key2"]).
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"""
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spec: JsonSpec
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def _run(
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self,
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tool_input: str,
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run_manager: Optional[CallbackManagerForToolRun] = None,
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) -> str:
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return self.spec.value(tool_input)
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async def _arun(
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self,
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tool_input: str,
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run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
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) -> str:
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return self._run(tool_input)
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