dosubot[bot]
commented
1 year ago Answer generated by a 🤖
Answer
I understand that you're encountering a TypeError: Object of type PromptTemplate is not JSON serializable when trying to use the PromptTemplate in QA. This error is occurring because Python is unable to convert the PromptTemplate object into a format that can be written as JSON. This happens when you're passing the PromptTemplate object as a parameter to the ChatOpenAI model, which then attempts to serialize it into JSON.
One potential solution to this issue is to pass the string representation of the PromptTemplate object instead of the object itself. You can achieve this by calling the render method on the PromptTemplate object with the appropriate arguments. Here's how you can modify your code:
from langchain.prompts import PromptTemplate
prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
Question: {question}
Answer in Italian:"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["question"]
)
# Render the PromptTemplate object into a string
prompt_string = PROMPT.render({"question": "Your question here"})
chain_type_kwargs = {"prompt": prompt_string}
llm = ChatOpenAI(model_name = "gpt-3.5-turbo",temperature=0,model_kwargs=chain_type_kwargs)
qa_chain = load_qa_chain(llm=llm, chain_type="stuff",verbose=True)
qa = RetrievalQA(combine_documents_chain=qa_chain, retriever=retriever.as_retriever())
query = "halo"
qa.run(query)In this code, I've called the render method on the PromptTemplate object with a dictionary that contains the question key. This replaces the {question} placeholder in the template with the value provided in the dictionary. The render method returns a string, which can be serialized into JSON without any issues.
Please note that this is one potential solution and there might be other ways to address this issue. If you're using a chat model and a ConditionalPromptSelector with a default prompt and a conditional prompt, ensure to render the ChatPromptTemplate into a string in the same way before passing it to the model.
If this solution works for you and you believe it's a bug that could impact other users, feel free to make a pull request with the fix. If the issue persists, I recommend checking if you're using the latest version of the langchain package, as the issue might have been resolved in a more recent update.
Sources
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System Info
TypeError Traceback (most recent call last) Cell In[15], line 15 12 qa = RetrievalQA(combine_documents_chain=qa_chain, retriever=retriever.as_retriever()) 14 query = "halo" ---> 15 qa.run(query)
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chains/base.py:440, in Chain.run(self, callbacks, tags, metadata, *args, **kwargs) 438 if len(args) != 1: 439 raise ValueError("
runsupports only one positional argument.") --> 440 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[ 441 _output_key 442 ] 444 if kwargs and not args: 445 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[ 446 _output_key 447 ]File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chains/base.py:243, in Chain.call(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info) 241 except (KeyboardInterrupt, Exception) as e: 242 run_manager.on_chain_error(e) --> 243 raise e 244 run_manager.on_chain_end(outputs) 245 final_outputs: Dict[str, Any] = self.prep_outputs( 246 inputs, outputs, return_only_outputs 247 )
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chains/base.py:237, in Chain.call(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info) 231 run_manager = callback_manager.on_chain_start( 232 dumpd(self), 233 inputs, 234 ) 235 try: 236 outputs = ( --> 237 self._call(inputs, run_manager=run_manager) 238 if new_arg_supported 239 else self._call(inputs) 240 ) 241 except (KeyboardInterrupt, Exception) as e: 242 run_manager.on_chain_error(e)
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chains/retrieval_qa/base.py:131, in BaseRetrievalQA._call(self, inputs, run_manager) 129 else: 130 docs = self._get_docs(question) # type: ignore[call-arg] --> 131 answer = self.combine_documents_chain.run( 132 input_documents=docs, question=question, callbacks=_run_manager.get_child() 133 ) 135 if self.return_source_documents: 136 return {self.output_key: answer, "source_documents": docs}
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chains/base.py:445, in Chain.run(self, callbacks, tags, metadata, *args, **kwargs) 440 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[ 441 _output_key 442 ] 444 if kwargs and not args: --> 445 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[ 446 _output_key 447 ] 449 if not kwargs and not args: 450 raise ValueError( 451 "
runsupported with either positional arguments or keyword arguments," 452 " but none were provided." 453 )File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chains/base.py:243, in Chain.call(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info) 241 except (KeyboardInterrupt, Exception) as e: 242 run_manager.on_chain_error(e) --> 243 raise e 244 run_manager.on_chain_end(outputs) 245 final_outputs: Dict[str, Any] = self.prep_outputs( 246 inputs, outputs, return_only_outputs 247 )
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chains/base.py:237, in Chain.call(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info) 231 run_manager = callback_manager.on_chain_start( 232 dumpd(self), 233 inputs, 234 ) 235 try: 236 outputs = ( --> 237 self._call(inputs, run_manager=run_manager) 238 if new_arg_supported 239 else self._call(inputs) 240 ) 241 except (KeyboardInterrupt, Exception) as e: 242 run_manager.on_chain_error(e)
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chains/combine_documents/base.py:106, in BaseCombineDocumentsChain._call(self, inputs, run_manager) 104 # Other keys are assumed to be needed for LLM prediction 105 other_keys = {k: v for k, v in inputs.items() if k != self.input_key} --> 106 output, extra_return_dict = self.combine_docs( 107 docs, callbacks=_run_manager.get_child(), **other_keys 108 ) 109 extra_return_dict[self.output_key] = output 110 return extra_return_dict
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chains/combine_documents/stuff.py:165, in StuffDocumentsChain.combine_docs(self, docs, callbacks, kwargs) 163 inputs = self._get_inputs(docs, kwargs) 164 # Call predict on the LLM. --> 165 return self.llm_chain.predict(callbacks=callbacks, **inputs), {}
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chains/llm.py:252, in LLMChain.predict(self, callbacks, kwargs) 237 def predict(self, callbacks: Callbacks = None, kwargs: Any) -> str: 238 """Format prompt with kwargs and pass to LLM. 239 240 Args: (...) 250 completion = llm.predict(adjective="funny") 251 """ --> 252 return self(kwargs, callbacks=callbacks)[self.output_key]
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chains/base.py:243, in Chain.call(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info) 241 except (KeyboardInterrupt, Exception) as e: 242 run_manager.on_chain_error(e) --> 243 raise e 244 run_manager.on_chain_end(outputs) 245 final_outputs: Dict[str, Any] = self.prep_outputs( 246 inputs, outputs, return_only_outputs 247 )
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chains/base.py:237, in Chain.call(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info) 231 run_manager = callback_manager.on_chain_start( 232 dumpd(self), 233 inputs, 234 ) 235 try: 236 outputs = ( --> 237 self._call(inputs, run_manager=run_manager) 238 if new_arg_supported 239 else self._call(inputs) 240 ) 241 except (KeyboardInterrupt, Exception) as e: 242 run_manager.on_chain_error(e)
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chains/llm.py:92, in LLMChain._call(self, inputs, run_manager) 87 def _call( 88 self, 89 inputs: Dict[str, Any], 90 run_manager: Optional[CallbackManagerForChainRun] = None, 91 ) -> Dict[str, str]: ---> 92 response = self.generate([inputs], run_manager=run_manager) 93 return self.create_outputs(response)[0]
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chains/llm.py:102, in LLMChain.generate(self, input_list, run_manager) 100 """Generate LLM result from inputs.""" 101 prompts, stop = self.prep_prompts(input_list, run_manager=run_manager) --> 102 return self.llm.generate_prompt( 103 prompts, 104 stop, 105 callbacks=run_manager.get_child() if run_manager else None, 106 **self.llm_kwargs, 107 )
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chat_models/base.py:230, in BaseChatModel.generate_prompt(self, prompts, stop, callbacks, kwargs) 222 def generate_prompt( 223 self, 224 prompts: List[PromptValue], (...) 227 kwargs: Any, 228 ) -> LLMResult: 229 prompt_messages = [p.to_messages() for p in prompts] --> 230 return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chat_models/base.py:125, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, **kwargs) 123 if run_managers: 124 run_managers[i].on_llm_error(e) --> 125 raise e 126 flattened_outputs = [ 127 LLMResult(generations=[res.generations], llm_output=res.llm_output) 128 for res in results 129 ] 130 llm_output = self._combine_llm_outputs([res.llm_output for res in results])
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chat_models/base.py:115, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, kwargs) 112 for i, m in enumerate(messages): 113 try: 114 results.append( --> 115 self._generate_with_cache( 116 m, 117 stop=stop, 118 run_manager=run_managers[i] if run_managers else None, 119 kwargs, 120 ) 121 ) 122 except (KeyboardInterrupt, Exception) as e: 123 if run_managers:
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chat_models/base.py:262, in BaseChatModel._generate_with_cache(self, messages, stop, run_manager, kwargs) 258 raise ValueError( 259 "Asked to cache, but no cache found at
langchain.cache." 260 ) 261 if new_arg_supported: --> 262 return self._generate( 263 messages, stop=stop, run_manager=run_manager, kwargs 264 ) 265 else: 266 return self._generate(messages, stop=stop, **kwargs)File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chat_models/openai.py:371, in ChatOpenAI._generate(self, messages, stop, run_manager, kwargs) 363 message = _convert_dict_to_message( 364 { 365 "content": inner_completion, (...) 368 } 369 ) 370 return ChatResult(generations=[ChatGeneration(message=message)]) --> 371 response = self.completion_with_retry(messages=message_dicts, params) 372 return self._create_chat_result(response)
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chat_models/openai.py:319, in ChatOpenAI.completion_with_retry(self, kwargs) 315 @retry_decorator 316 def _completion_with_retry(kwargs: Any) -> Any: 317 return self.client.create(kwargs) --> 319 return _completion_with_retry(kwargs)
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/tenacity/init.py:289, in BaseRetrying.wraps..wrapped_f(*args, kw)
287 @functools.wraps(f)
288 def wrapped_f(*args: t.Any, *kw: t.Any) -> t.Any:
--> 289 return self(f, args, kw)
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/tenacity/init.py:379, in Retrying.call(self, fn, *args, **kwargs) 377 retry_state = RetryCallState(retry_object=self, fn=fn, args=args, kwargs=kwargs) 378 while True: --> 379 do = self.iter(retry_state=retry_state) 380 if isinstance(do, DoAttempt): 381 try:
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/tenacity/init.py:314, in BaseRetrying.iter(self, retry_state) 312 is_explicit_retry = fut.failed and isinstance(fut.exception(), TryAgain) 313 if not (is_explicit_retry or self.retry(retry_state)): --> 314 return fut.result() 316 if self.after is not None: 317 self.after(retry_state)
File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/concurrent/futures/_base.py:438, in Future.result(self, timeout) 436 raise CancelledError() 437 elif self._state == FINISHED: --> 438 return self.__get_result() 440 self._condition.wait(timeout) 442 if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:
File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/concurrent/futures/_base.py:390, in Future.__get_result(self) 388 if self._exception: 389 try: --> 390 raise self._exception 391 finally: 392 # Break a reference cycle with the exception in self._exception 393 self = None
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/tenacity/init.py:382, in Retrying.call(self, fn, *args, *kwargs) 380 if isinstance(do, DoAttempt): 381 try: --> 382 result = fn(args, **kwargs) 383 except BaseException: # noqa: B902 384 retry_state.set_exception(sys.exc_info()) # type: ignore[arg-type]
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/langchain/chat_models/openai.py:317, in ChatOpenAI.completion_with_retry.._completion_with_retry(kwargs)
315 @retry_decorator
316 def _completion_with_retry(kwargs: Any) -> Any:
--> 317 return self.client.create(**kwargs)
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/openai/api_resources/chat_completion.py:25, in ChatCompletion.create(cls, *args, *kwargs) 23 while True: 24 try: ---> 25 return super().create(args, **kwargs) 26 except TryAgain as e: 27 if timeout is not None and time.time() > start + timeout:
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py:153, in EngineAPIResource.create(cls, api_key, api_base, api_type, request_id, api_version, organization, params) 127 @classmethod 128 def create( 129 cls, (...) 136 params, 137 ): 138 ( 139 deployment_id, 140 engine, (...) 150 api_key, api_base, api_type, apiversion, organization, **params 151 ) --> 153 response, , api_key = requestor.request( 154 "post", 155 url, 156 params=params, 157 headers=headers, 158 stream=stream, 159 request_id=request_id, 160 request_timeout=request_timeout, 161 ) 163 if stream: 164 # must be an iterator 165 assert not isinstance(response, OpenAIResponse)
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/openai/api_requestor.py:288, in APIRequestor.request(self, method, url, params, headers, files, stream, request_id, request_timeout) 277 def request( 278 self, 279 method, (...) 286 request_timeout: Optional[Union[float, Tuple[float, float]]] = None, 287 ) -> Tuple[Union[OpenAIResponse, Iterator[OpenAIResponse]], bool, str]: --> 288 result = self.request_raw( 289 method.lower(), 290 url, 291 params=params, 292 supplied_headers=headers, 293 files=files, 294 stream=stream, 295 request_id=request_id, 296 request_timeout=request_timeout, 297 ) 298 resp, got_stream = self._interpret_response(result, stream) 299 return resp, got_stream, self.api_key
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/openai/api_requestor.py:581, in APIRequestor.request_raw(self, method, url, params, supplied_headers, files, stream, request_id, request_timeout) 569 def request_raw( 570 self, 571 method, (...) 579 request_timeout: Optional[Union[float, Tuple[float, float]]] = None, 580 ) -> requests.Response: --> 581 abs_url, headers, data = self._prepare_request_raw( 582 url, supplied_headers, method, params, files, request_id 583 ) 585 if not hasattr(_thread_context, "session"): 586 _thread_context.session = _make_session()
File /Volumes/Samsung_T7/Augmented-FinQA/venv/lib/python3.10/site-packages/openai/api_requestor.py:553, in APIRequestor._prepare_request_raw(self, url, supplied_headers, method, params, files, request_id) 551 data = params 552 if params and not files: --> 553 data = json.dumps(params).encode() 554 headers["Content-Type"] = "application/json" 555 else:
File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/json/init.py:231, in dumps(obj, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw) 226 # cached encoder 227 if (not skipkeys and ensure_ascii and 228 check_circular and allow_nan and 229 cls is None and indent is None and separators is None and 230 default is None and not sort_keys and not kw): --> 231 return _default_encoder.encode(obj) 232 if cls is None: 233 cls = JSONEncoder
File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/json/encoder.py:199, in JSONEncoder.encode(self, o) 195 return encode_basestring(o) 196 # This doesn't pass the iterator directly to ''.join() because the 197 # exceptions aren't as detailed. The list call should be roughly 198 # equivalent to the PySequence_Fast that ''.join() would do. --> 199 chunks = self.iterencode(o, _one_shot=True) 200 if not isinstance(chunks, (list, tuple)): 201 chunks = list(chunks)
File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/json/encoder.py:257, in JSONEncoder.iterencode(self, o, _one_shot) 252 else: 253 _iterencode = _make_iterencode( 254 markers, self.default, _encoder, self.indent, floatstr, 255 self.key_separator, self.item_separator, self.sort_keys, 256 self.skipkeys, _one_shot) --> 257 return _iterencode(o, 0)
File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/json/encoder.py:179, in JSONEncoder.default(self, o) 160 def default(self, o): 161 """Implement this method in a subclass such that it returns 162 a serializable object for
o, or calls the base implementation 163 (to raise aTypeError). (...) 177 178 """ --> 179 raise TypeError(f'Object of type {o.class.name} ' 180 f'is not JSON serializable')TypeError: Object of type PromptTemplate is not JSON serializable
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Reproduction
from langchain.prompts import PromptTemplate prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
Question: {question} Answer in Italian:""" PROMPT = PromptTemplate( template=prompt_template, input_variables=["question"] ) chain_type_kwargs = {"prompt": PROMPT} llm = ChatOpenAI(model_name = "gpt-3.5-turbo",temperature=0,model_kwargs=chain_type_kwargs) qa_chain = load_qa_chain(llm=llm, chain_type="stuff",verbose=True) qa = RetrievalQA(combine_documents_chain=qa_chain, retriever=retriever.as_retriever()) query = "halo" qa.run(query)
Expected behavior
Hope to use the PromptTemplate in QA