Source code for langchain.chains.openai_functions.extraction
from typing import Any, List, Optional
from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.output_parsers.openai_functions import (
JsonKeyOutputFunctionsParser,
PydanticAttrOutputFunctionsParser,
)
from langchain_core.prompts import BasePromptTemplate, ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import (
_convert_schema,
_resolve_schema_references,
get_llm_kwargs,
)
def _get_extraction_function(entity_schema: dict) -> dict:
return {
"name": "information_extraction",
"description": "Extracts the relevant information from the passage.",
"parameters": {
"type": "object",
"properties": {
"info": {"type": "array", "items": _convert_schema(entity_schema)}
},
"required": ["info"],
},
}
_EXTRACTION_TEMPLATE = """Extract and save the relevant entities mentioned \
in the following passage together with their properties.
Only extract the properties mentioned in the 'information_extraction' function.
If a property is not present and is not required in the function parameters, do not include it in the output.
Passage:
{input}
""" # noqa: E501
[docs]@deprecated(
since="0.1.14",
message=(
"LangChain has introduced a method called `with_structured_output` that"
"is available on ChatModels capable of tool calling."
"You can read more about the method here: "
"https://python.langchain.com/docs/modules/model_io/chat/structured_output/"
"Please follow our extraction use case documentation for more guidelines"
"on how to do information extraction with LLMs."
"https://python.langchain.com/docs/use_cases/extraction/."
"If you notice other issues, please provide "
"feedback here:"
"https://github.com/langchain-ai/langchain/discussions/18154"
),
removal="0.3.0",
alternative=(
"""
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_anthropic import ChatAnthropic
class Joke(BaseModel):
setup: str = Field(description="The setup of the joke")
punchline: str = Field(description="The punchline to the joke")
# Or any other chat model that supports tools.
# Please reference to to the documentation of structured_output
# to see an up to date list of which models support
# with_structured_output.
model = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
structured_llm = model.with_structured_output(Joke)
structured_llm.invoke("Tell me a joke about cats.
Make sure to call the Joke function.")
"""
),
)
def create_extraction_chain(
schema: dict,
llm: BaseLanguageModel,
prompt: Optional[BasePromptTemplate] = None,
tags: Optional[List[str]] = None,
verbose: bool = False,
) -> Chain:
"""创建一个从段落中提取信息的链。
参数：
schema: 要提取的实体的模式。
llm: 要使用的语言模型。
prompt: 用于提取的提示。
verbose: 是否以详细模式运行。在详细模式下，一些中间日志将打印到控制台。默认为全局的 `verbose` 值，可通过 `langchain.globals.get_verbose()` 访问。
返回：
可用于从段落中提取信息的链。
"""
function = _get_extraction_function(schema)
extraction_prompt = prompt or ChatPromptTemplate.from_template(_EXTRACTION_TEMPLATE)
output_parser = JsonKeyOutputFunctionsParser(key_name="info")
llm_kwargs = get_llm_kwargs(function)
chain = LLMChain(
llm=llm,
prompt=extraction_prompt,
llm_kwargs=llm_kwargs,
output_parser=output_parser,
tags=tags,
verbose=verbose,
)
return chain
[docs]@deprecated(
since="0.1.14",
message=(
"LangChain has introduced a method called `with_structured_output` that"
"is available on ChatModels capable of tool calling."
"You can read more about the method here: "
"https://python.langchain.com/docs/modules/model_io/chat/structured_output/"
"Please follow our extraction use case documentation for more guidelines"
"on how to do information extraction with LLMs."
"https://python.langchain.com/docs/use_cases/extraction/."
"If you notice other issues, please provide "
"feedback here:"
"https://github.com/langchain-ai/langchain/discussions/18154"
),
removal="0.3.0",
alternative=(
"""
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_anthropic import ChatAnthropic
class Joke(BaseModel):
setup: str = Field(description="The setup of the joke")
punchline: str = Field(description="The punchline to the joke")
# Or any other chat model that supports tools.
# Please reference to to the documentation of structured_output
# to see an up to date list of which models support
# with_structured_output.
model = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
structured_llm = model.with_structured_output(Joke)
structured_llm.invoke("Tell me a joke about cats.
Make sure to call the Joke function.")
"""
),
)
def create_extraction_chain_pydantic(
pydantic_schema: Any,
llm: BaseLanguageModel,
prompt: Optional[BasePromptTemplate] = None,
verbose: bool = False,
) -> Chain:
"""创建一个链条，使用pydantic模式从一个段落中提取信息。
参数:
pydantic_schema: 要提取的实体的pydantic模式。
llm: 要使用的语言模型。
prompt: 用于提取的提示。
verbose: 是否以详细模式运行。在详细模式下，一些中间日志将打印到控制台。默认为全局`verbose`值，可通过`langchain.globals.get_verbose()`访问。
返回:
可用于从段落中提取信息的链条。
"""
class PydanticSchema(BaseModel):
info: List[pydantic_schema] # type: ignore
openai_schema = pydantic_schema.schema()
openai_schema = _resolve_schema_references(
openai_schema, openai_schema.get("definitions", {})
)
function = _get_extraction_function(openai_schema)
extraction_prompt = prompt or ChatPromptTemplate.from_template(_EXTRACTION_TEMPLATE)
output_parser = PydanticAttrOutputFunctionsParser(
pydantic_schema=PydanticSchema, attr_name="info"
)
llm_kwargs = get_llm_kwargs(function)
chain = LLMChain(
llm=llm,
prompt=extraction_prompt,
llm_kwargs=llm_kwargs,
output_parser=output_parser,
verbose=verbose,
)
return chain