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The question I cannot figure out is this: we have a langchain method with_structured_output() which create a runnable llm to have a structured output as a pydantic class. How I can combine this inside a Chain I have to use for example to have a retrieval, when this chains seem to need runnable which give string type as output?
Here my code:
`from dotenv import load_dotenv
load_dotenv()
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain_openai.chat_models import AzureChatOpenAI
from langchain_openai import AzureOpenAIEmbeddings
from langchain.chains import create_retrieval_chain
from langchain.chainsbine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from typing import Optional
from pydantic import BaseModel, Field
# Load from local storage
persisted_vectorstore = FAISS.load_local("faiss_index", embedding_function, allow_dangerous_deserialization=True)
# Pydantic
class TechnicalDescription(BaseModel):
"""Technical description requested from the user."""
architecture: str = Field(description="The architecture of the object requested")
functionality: str = Field(description="The functionality of the object requested")
complexity_rating: Optional[int] = Field(
default=None, description="How complex the object requested is, from 1 to 10"
)
system_prompt = (
"Usa il contesto fornito per rispondere alla domanda. "
"Se non sai la risposta, rispondi con 'non lo so' "
"Sii conciso nella risposta. "
"Context: {context}"
)
prompt = ChatPromptTemplate.from_messages(
[
("system", system_prompt),
("human", "{input}"),
]
)
llm = AzureChatOpenAI(azure_endpoint=os.getenv("ENDPOINT_GPT"),
api_version=os.getenv("OPENAI_API_VERSION_GPT"),
api_key=os.getenv("OPENAI_API_KEY_GPT"),
model=os.getenv("OPENAI_API_MODEL_DEPLOYMENT_NAME_GPT"),
temperature=0,
max_tokens=4096)
structured_llm = llm.with_structured_output(TechnicalDescription)
retriever=persisted_vectorstore.as_retriever()
question_answer_chain = create_stuff_documents_chain(structured_llm, prompt)
chain = create_retrieval_chain(retriever, question_answer_chain)
chain.invoke({"input": "What is an LLM?"})`
and here the error I receive:
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
Cell In[25], line 4
2 question_answer_chain = create_stuff_documents_chain(structured_llm, prompt)
3 chain = create_retrieval_chain(retriever, question_answer_chain)
----> 4 chain.invoke({"input": "What is an LLM?"})
File c:\Users\gabriele.volpi\OneDrive - Accenture\Documents\GenerativeAI\InternalTraining\.venv\Lib\site-packages\langchain_core\runnables\base.py:5352, in RunnableBindingBase.invoke(self, input, config, **kwargs)
5346 def invoke(
5347 self,
5348 input: Input,
5349 config: Optional[RunnableConfig] = None,
5350 **kwargs: Optional[Any],
5351 ) -> Output:
-> 5352 return self.bound.invoke(
5353 input,
5354 self._merge_configs(config),
5355 **{**self.kwargs, **kwargs},
5356 )
File c:\Users\gabriele.volpi\OneDrive - Accenture\Documents\GenerativeAI\InternalTraining\.venv\Lib\site-packages\langchain_core\runnables\base.py:3016, in RunnableSequence.invoke(self, input, config, **kwargs)
3014 input = context.run(step.invoke, input, config, **kwargs)
3015 else:
-> 3016 input = context.run(step.invoke, input, config)
3017 # finish the root run
3018 except BaseException as e:
File c:\Users\gabriele.volpi\OneDrive - Accenture\Documents\GenerativeAI\InternalTraining\.venv\Lib\site-packages\langchain_core\runnables\passthrough.py:494, in RunnableAssign.invoke(self, input, config, **kwargs)
488 def invoke(
489 self,
490 input: dict[str, Any],
491 config: Optional[RunnableConfig] = None,
492 **kwargs: Any,
493 ) -> dict[str, Any]:
--> 494 return self._call_with_config(self._invoke, input, config, **kwargs)
File c:\Users\gabriele.volpi\OneDrive - Accenture\Documents\GenerativeAI\InternalTraining\.venv\Lib\site-packages\langchain_core\runnables\base.py:1914, in Runnable._call_with_config(self, func, input, config, run_type, serialized, **kwargs)
1910 context = copy_context()
1911 context.run(_set_config_context, child_config)
1912 output = cast(
1913 Output,
-> 1914 context.run(
1915 call_func_with_variable_args, # type: ignore[arg-type]
1916 func, # type: ignore[arg-type]
1917 input, # type: ignore[arg-type]
1918 config,
1919 run_manager,
1920 **kwargs,
1921 ),
1922 )
1923 except BaseException as e:
1924 run_manager.on_chain_error(e)
File c:\Users\gabriele.volpi\OneDrive - Accenture\Documents\GenerativeAI\InternalTraining\.venv\Lib\site-packages\langchain_core\runnables\config.py:396, in call_func_with_variable_args(func, input, config, run_manager, **kwargs)
394 if run_manager is not None and accepts_run_manager(func):
395 kwargs["run_manager"] = run_manager
--> 396 return func(input, **kwargs)
File c:\Users\gabriele.volpi\OneDrive - Accenture\Documents\GenerativeAI\InternalTraining\.venv\Lib\site-packages\langchain_core\runnables\passthrough.py:481, in RunnableAssign._invoke(self, input, run_manager, config, **kwargs)
476 msg = "The input to RunnablePassthrough.assign() must be a dict."
477 raise ValueError(msg) # noqa: TRY004
479 return {
480 **input,
--> 481 **self.mapper.invoke(
482 input,
483 patch_config(config, callbacks=run_manager.get_child()),
484 **kwargs,
485 ),
486 }
File c:\Users\gabriele.volpi\OneDrive - Accenture\Documents\GenerativeAI\InternalTraining\.venv\Lib\site-packages\langchain_core\runnables\base.py:3721, in RunnableParallel.invoke(self, input, config, **kwargs)
3716 with get_executor_for_config(config) as executor:
3717 futures = [
3718 executor.submit(_invoke_step, step, input, config, key)
3719 for key, step in steps.items()
3720 ]
-> 3721 output = {key: future.result() for key, future in zip(steps, futures)}
3722 # finish the root run
3723 except BaseException as e:
File c:\Users\gabriele.volpi\OneDrive - Accenture\Documents\GenerativeAI\InternalTraining\.venv\Lib\site-packages\langchain_core\runnables\base.py:3721, in <dictcomp>(.0)
3716 with get_executor_for_config(config) as executor:
3717 futures = [
3718 executor.submit(_invoke_step, step, input, config, key)
3719 for key, step in steps.items()
3720 ]
-> 3721 output = {key: future.result() for key, future in zip(steps, futures)}
3722 # finish the root run
3723 except BaseException as e:
File ~\AppData\Local\Programs\Python\Python311\Lib\concurrent\futures\_base.py:456, in Future.result(self, timeout)
454 raise CancelledError()
455 elif self._state == FINISHED:
--> 456 return self.__get_result()
457 else:
458 raise TimeoutError()
File ~\AppData\Local\Programs\Python\Python311\Lib\concurrent\futures\_base.py:401, in Future.__get_result(self)
399 if self._exception:
400 try:
--> 401 raise self._exception
402 finally:
403 # Break a reference cycle with the exception in self._exception
404 self = None
File ~\AppData\Local\Programs\Python\Python311\Lib\concurrent\futures\thread.py:58, in _WorkItem.run(self)
55 return
57 try:
---> 58 result = self.fn(*self.args, **self.kwargs)
59 except BaseException as exc:
60 self.future.set_exception(exc)
File c:\Users\gabriele.volpi\OneDrive - Accenture\Documents\GenerativeAI\InternalTraining\.venv\Lib\site-packages\langchain_core\runnables\base.py:3705, in RunnableParallel.invoke.<locals>._invoke_step(step, input, config, key)
3703 context = copy_context()
3704 context.run(_set_config_context, child_config)
-> 3705 return context.run(
3706 step.invoke,
3707 input,
3708 child_config,
3709 )
File c:\Users\gabriele.volpi\OneDrive - Accenture\Documents\GenerativeAI\InternalTraining\.venv\Lib\site-packages\langchain_core\runnables\base.py:5352, in RunnableBindingBase.invoke(self, input, config, **kwargs)
5346 def invoke(
5347 self,
5348 input: Input,
5349 config: Optional[RunnableConfig] = None,
5350 **kwargs: Optional[Any],
5351 ) -> Output:
-> 5352 return self.bound.invoke(
5353 input,
5354 self._merge_configs(config),
5355 **{**self.kwargs, **kwargs},
5356 )
File c:\Users\gabriele.volpi\OneDrive - Accenture\Documents\GenerativeAI\InternalTraining\.venv\Lib\site-packages\langchain_core\runnables\base.py:3016, in RunnableSequence.invoke(self, input, config, **kwargs)
3014 input = context.run(step.invoke, input, config, **kwargs)
3015 else:
-> 3016 input = context.run(step.invoke, input, config)
3017 # finish the root run
3018 except BaseException as e:
File c:\Users\gabriele.volpi\OneDrive - Accenture\Documents\GenerativeAI\InternalTraining\.venv\Lib\site-packages\langchain_core\output_parsers\base.py:202, in BaseOutputParser.invoke(self, input, config, **kwargs)
193 return self._call_with_config(
194 lambda inner_input: self.parse_result(
195 [ChatGeneration(message=inner_input)]
(...)
199 run_type="parser",
200 )
201 else:
--> 202 return self._call_with_config(
203 lambda inner_input: self.parse_result([Generation(text=inner_input)]),
204 input,
205 config,
206 run_type="parser",
207 )
File c:\Users\gabriele.volpi\OneDrive - Accenture\Documents\GenerativeAI\InternalTraining\.venv\Lib\site-packages\langchain_core\runnables\base.py:1914, in Runnable._call_with_config(self, func, input, config, run_type, serialized, **kwargs)
1910 context = copy_context()
1911 context.run(_set_config_context, child_config)
1912 output = cast(
1913 Output,
-> 1914 context.run(
1915 call_func_with_variable_args, # type: ignore[arg-type]
1916 func, # type: ignore[arg-type]
1917 input, # type: ignore[arg-type]
1918 config,
1919 run_manager,
1920 **kwargs,
1921 ),
1922 )
1923 except BaseException as e:
1924 run_manager.on_chain_error(e)
File c:\Users\gabriele.volpi\OneDrive - Accenture\Documents\GenerativeAI\InternalTraining\.venv\Lib\site-packages\langchain_core\runnables\config.py:396, in call_func_with_variable_args(func, input, config, run_manager, **kwargs)
394 if run_manager is not None and accepts_run_manager(func):
395 kwargs["run_manager"] = run_manager
--> 396 return func(input, **kwargs)
File c:\Users\gabriele.volpi\OneDrive - Accenture\Documents\GenerativeAI\InternalTraining\.venv\Lib\site-packages\langchain_core\output_parsers\base.py:203, in BaseOutputParser.invoke.<locals>.<lambda>(inner_input)
193 return self._call_with_config(
194 lambda inner_input: self.parse_result(
195 [ChatGeneration(message=inner_input)]
(...)
199 run_type="parser",
200 )
201 else:
202 return self._call_with_config(
--> 203 lambda inner_input: self.parse_result([Generation(text=inner_input)]),
204 input,
205 config,
206 run_type="parser",
207 )
File c:\Users\gabriele.volpi\OneDrive - Accenture\Documents\GenerativeAI\InternalTraining\.venv\Lib\site-packages\langchain_core\load\serializable.py:125, in Serializable.__init__(self, *args, **kwargs)
123 def __init__(self, *args: Any, **kwargs: Any) -> None:
124 """"""
--> 125 super().__init__(*args, **kwargs)
File c:\Users\gabriele.volpi\OneDrive - Accenture\Documents\GenerativeAI\InternalTraining\.venv\Lib\site-packages\pydantic\main.py:214, in BaseModel.__init__(self, **data)
212 # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
213 __tracebackhide__ = True
--> 214 validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
215 if self is not validated_self:
216 warnings.warn(
217 'A custom validator is returning a value other than `self`.\n'
218 "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n"
219 'See the `model_validator` docs (/latest/concepts/validators/#model-validators) for more details.',
220 stacklevel=2,
221 )
ValidationError: 1 validation error for Generation
text
Input should be a valid string [type=string_type, input_value=TechnicalDescription(arch....', complexity_rating=8), input_type=TechnicalDescription]
For further information visit /2.10/v/string_type
I expected to receive a chain which I could invoke to have a structured output as defined
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