o
`+ i @ s d dl mZmZ d dlmZ d dlmZ d dlmZm Z d dl
m Z m Z d dl
mZ d dlmZ d dlmZ d d lmZmZmZ d
ed efd d
ZdZeddddd d dededee deee de d ef dd Z eddddd d!d ededee de d ef
d d Z dS )" )AnyOptional)
deprecated)BaseLanguageModel) JsonKeyOutputFunctionsParser!PydanticAttrOutputFunctionsParser)BasePromptTemplateChatPromptTemplate) BaseModel)Chain)LLMChain)_convert_schema_resolve_schema_referencesget_llm_kwargs
entity_schemareturnc C s$ dddddt | didgddS ) NZinformation_extractionz3Extracts the relevant information from the passage.objectinfoarray)typeitems)r
propertiesrequired)name description
parameters)r
)r r x/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/langchain/chains/openai_functions/extraction.py_get_extraction_function s r a< 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}
z0.1.14a LangChain has introduced a method called `with_structured_output` thatis available on ChatModels capable of tool calling.You can read more about the method here: . Please follow our extraction use case documentation for more guidelineson how to do information extraction with LLMs.. If you notice other issues, please provide feedback here:z1.0a)
from pydantic 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.")
)ZsincemessageZremoval alternativeNFschemallmprompttagsverbosec C s< t | }|p
tt}tdd}t|}t||||||dS )a Creates a chain that extracts information from a passage.
Args:
schema: The schema of the entities to extract.
llm: The language model to use.
prompt: The prompt to use for extraction.
verbose: Whether to run in verbose mode. In verbose mode, some intermediate
logs will be printed to the console. Defaults to the global `verbose` value,
accessible via `langchain.globals.get_verbose()`.
Returns:
Chain that can be used to extract information from a passage.
r )Zkey_name)r" r#
llm_kwargs
output_parserr$ r% )r r
from_template_EXTRACTION_TEMPLATEr r r ) r! r" r# r$ r% functionextraction_promptr' r& r r r create_extraction_chain/ s 7
r, pydantic_schemac
s~ G fdddt }t dr }n }t||di }t|}|p+tt }t
|dd}t |} t ||| ||dS )a@ Creates a chain that extracts information from a passage using pydantic schema.
Args:
pydantic_schema: The pydantic schema of the entities to extract.
llm: The language model to use.
prompt: The prompt to use for extraction.
verbose: Whether to run in verbose mode. In verbose mode, some intermediate
logs will be printed to the console. Defaults to the global `verbose` value,
accessible via `langchain.globals.get_verbose()`
Returns:
Chain that can be used to extract information from a passage.
c s e Zd ZU e ed< dS )z8create_extraction_chain_pydantic..PydanticSchemar N)__name__
__module__ __qualname__list__annotations__r r- r r PydanticSchema s
r4 model_json_schemaZ definitionsr )r- attr_name)r" r# r& r' r% )
r
hasattrr5 r! r getr r r( r) r r r )
r- r" r# r% r4 Z
openai_schemar* r+ r' r& r r3 r create_extraction_chain_pydantict s, 7
r9 )NNF)NF) typingr r Zlangchain_core._apir Z langchain_core.language_modelsr Z.langchain_core.output_parsers.openai_functionsr r Zlangchain_core.promptsr r Zpydanticr
Zlangchain.chains.baser Zlangchain.chains.llmr Z'langchain.chains.openai_functions.utilsr
r r dictr r) r1 strboolr, r9 r r r r sh &
" &