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community[patch]: Release 0.0.2 (#14610)
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@ -1,4 +1,38 @@
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from langchain_community.tools.render import format_tool_to_openai_function
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from langchain_core.tools import BaseTool
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# For backwards compatibility
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__all__ = ["format_tool_to_openai_function"]
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from langchain_community.utils.openai_functions import (
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FunctionDescription,
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ToolDescription,
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convert_pydantic_to_openai_function,
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)
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def format_tool_to_openai_function(tool: BaseTool) -> FunctionDescription:
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"""Format tool into the OpenAI function API."""
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if tool.args_schema:
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return convert_pydantic_to_openai_function(
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tool.args_schema, name=tool.name, description=tool.description
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)
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else:
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return {
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"name": tool.name,
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"description": tool.description,
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"parameters": {
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# This is a hack to get around the fact that some tools
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# do not expose an args_schema, and expect an argument
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# which is a string.
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# And Open AI does not support an array type for the
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# parameters.
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"properties": {
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"__arg1": {"title": "__arg1", "type": "string"},
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},
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"required": ["__arg1"],
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"type": "object",
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},
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}
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def format_tool_to_openai_tool(tool: BaseTool) -> ToolDescription:
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"""Format tool into the OpenAI function API."""
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function = format_tool_to_openai_function(tool)
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return {"type": "function", "function": function}
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@ -4,8 +4,6 @@ Depending on the LLM you are using and the prompting strategy you are using,
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you may want Tools to be rendered in a different way.
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This module contains various ways to render tools.
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"""
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from typing import List
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from langchain_core.tools import BaseTool
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from langchain_community.utils.openai_functions import (
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@ -15,37 +13,6 @@ from langchain_community.utils.openai_functions import (
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)
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def render_text_description(tools: List[BaseTool]) -> str:
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"""Render the tool name and description in plain text.
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Output will be in the format of:
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.. code-block:: markdown
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search: This tool is used for search
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calculator: This tool is used for math
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"""
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return "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
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def render_text_description_and_args(tools: List[BaseTool]) -> str:
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"""Render the tool name, description, and args in plain text.
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Output will be in the format of:
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.. code-block:: markdown
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search: This tool is used for search, args: {"query": {"type": "string"}}
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calculator: This tool is used for math, \
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args: {"expression": {"type": "string"}}
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"""
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tool_strings = []
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for tool in tools:
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args_schema = str(tool.args)
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tool_strings.append(f"{tool.name}: {tool.description}, args: {args_schema}")
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return "\n".join(tool_strings)
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def format_tool_to_openai_function(tool: BaseTool) -> FunctionDescription:
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"""Format tool into the OpenAI function API."""
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if tool.args_schema:
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4
libs/community/poetry.lock
generated
4
libs/community/poetry.lock
generated
@ -3421,7 +3421,7 @@ files = [
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[[package]]
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name = "langchain-core"
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version = "0.0.13"
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version = "0.1.0"
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description = "Building applications with LLMs through composability"
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optional = false
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python-versions = ">=3.8.1,<4.0"
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@ -8485,4 +8485,4 @@ extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "as
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[metadata]
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lock-version = "2.0"
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python-versions = ">=3.8.1,<4.0"
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content-hash = "a77af01e299fdeb0849f20f2ae8a49030bd38993ff603f464c1c1f6a170d1d9b"
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content-hash = "e3bacf389a13d283c4dd29e3a673e1863826b4e98785c666fefc10cf714c2f6f"
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@ -1,6 +1,6 @@
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[tool.poetry]
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name = "langchain-community"
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version = "0.0.1"
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version = "0.0.2"
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description = "Community contributed LangChain integrations."
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authors = []
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license = "MIT"
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@ -9,7 +9,7 @@ repository = "https://github.com/langchain-ai/langchain"
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[tool.poetry.dependencies]
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python = ">=3.8.1,<4.0"
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langchain-core = ">=0.0.13,<0.1"
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langchain-core = "^0.1"
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SQLAlchemy = ">=1.4,<3"
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requests = "^2"
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PyYAML = ">=5.3"
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@ -1,13 +1,52 @@
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from langchain_community.tools.render import (
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"""Different methods for rendering Tools to be passed to LLMs.
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Depending on the LLM you are using and the prompting strategy you are using,
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you may want Tools to be rendered in a different way.
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This module contains various ways to render tools.
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"""
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from typing import List
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# For backwards compatibility
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from langchain_community.tools.convert_to_openai import (
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format_tool_to_openai_function,
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format_tool_to_openai_tool,
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render_text_description,
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render_text_description_and_args,
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)
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from langchain_core.tools import BaseTool
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__all__ = [
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"render_text_description",
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"render_text_description_and_args",
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"format_tool_to_openai_function",
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"format_tool_to_openai_tool",
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"format_tool_to_openai_function",
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]
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def render_text_description(tools: List[BaseTool]) -> str:
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"""Render the tool name and description in plain text.
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Output will be in the format of:
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.. code-block:: markdown
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search: This tool is used for search
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calculator: This tool is used for math
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"""
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return "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
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def render_text_description_and_args(tools: List[BaseTool]) -> str:
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"""Render the tool name, description, and args in plain text.
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Output will be in the format of:
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.. code-block:: markdown
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search: This tool is used for search, args: {"query": {"type": "string"}}
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calculator: This tool is used for math, \
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args: {"expression": {"type": "string"}}
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"""
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tool_strings = []
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for tool in tools:
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args_schema = str(tool.args)
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tool_strings.append(f"{tool.name}: {tool.description}, args: {args_schema}")
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return "\n".join(tool_strings)
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@ -3,7 +3,7 @@ from typing import List
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import pytest
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from langchain_core.tools import BaseTool, tool
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from langchain_community.tools.render import (
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from langchain.tools.render import (
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render_text_description,
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render_text_description_and_args,
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)
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