ValueError: The `response.text` quick accessor only works for simple (single-`Part`) text responses. This response is not simple text.Use the `result.parts` accessor or the full `result.candidates[index].content.parts` lookup instead. #170

HienBM · 2024-01-13T12:50:14Z

Description of the bug:

Can someone help me check this error? I still ran successfully yesterday with the same code

File ~/cluster-env/trident_env/lib/python3.10/site-packages/pandas/core/series.py:4630, in Series.apply(self, func, convert_dtype, args, **kwargs)
4520 def apply(
4521 self,
4522 func: AggFuncType,
(...)
4525 **kwargs,
4526 ) -> DataFrame | Series:
4527 """
4528 Invoke function on values of Series.
4529
(...)
4628 dtype: float64
4629 """
-> 4630 return SeriesApply(self, func, convert_dtype, args, kwargs).apply()

File ~/cluster-env/trident_env/lib/python3.10/site-packages/pandas/core/apply.py:1025, in SeriesApply.apply(self)
1022 return self.apply_str()
1024 # self.f is Callable
-> 1025 return self.apply_standard()

File ~/cluster-env/trident_env/lib/python3.10/site-packages/pandas/core/apply.py:1076, in SeriesApply.apply_standard(self)
1074 else:
1075 values = obj.astype(object)._values
-> 1076 mapped = lib.map_infer(
1077 values,
1078 f,
1079 convert=self.convert_dtype,
1080 )
1082 if len(mapped) and isinstance(mapped[0], ABCSeries):
1083 # GH#43986 Need to do list(mapped) in order to get treated as nested
1084 # See also GH#25959 regarding EA support
1085 return obj._constructor_expanddim(list(mapped), index=obj.index)

File ~/cluster-env/trident_env/lib/python3.10/site-packages/pandas/_libs/lib.pyx:2834, in pandas._libs.lib.map_infer()

Cell In[116], line 82, in extract_absa_with_few_shot_gemini(text)
80 response.resolve()
81 time.sleep(1)
---> 82 return list_of_dict_to_string(string_to_list_dict(response.text.lower()))

File ~/cluster-env/trident_env/lib/python3.10/site-packages/google/generativeai/types/generation_types.py:328, in BaseGenerateContentResponse.text(self)
326 parts = self.parts
327 if len(parts) != 1 or "text" not in parts[0]:
--> 328 raise ValueError(
329 "The response.text quick accessor only works for "
330 "simple (single-Part) text responses. This response is not simple text."
331 "Use the result.parts accessor or the full "
332 "result.candidates[index].content.parts lookup "
333 "instead."
334 )
335 return parts[0].text

ValueError: The response.text quick accessor only works for simple (single-Part) text responses. This response is not simple text.Use the result.parts accessor or the full result.candidates[index].content.parts lookup instead.

Actual vs expected behavior:

No response

Any other information you'd like to share?

No response

The text was updated successfully, but these errors were encountered:

falahgs · 2024-01-14T14:33:52Z

me too i have the same error especially with Gemini vision

Hert4 · 2024-01-14T15:44:47Z

You could try to delete max_output_tokens generation_config in the model if you use that

HienBM · 2024-01-15T01:18:40Z

You could try to delete the generation_config in the model if you use that

It works for me. Thanks @ydm20231608

Ki-Zhang · 2024-01-15T02:51:07Z

You could try to delete the generation_config in the model if you use that

I also encountered this problem. Where is the generation_config file that needs to be deleted? @HienBM

HienBM · 2024-01-15T03:00:24Z

Hi @Ki-Zhang ,

When you set up your model, the generation_config is in it. Try ignoring it like this.

Ki-Zhang · 2024-01-15T03:41:32Z

Thank you for your answer @HienBM
But I just used model = genai.GenerativeModel('gemini-pro-vision') to simply set up the genimi-pro-vision model. And I encountered the same problem.

Cell In[31], line 23, in dectect_object_why(ori_img, head_pos, gaze_pos)
      8 response = model.generate_content(
      9     [
     10         "The person outlined in the blue frame is looking at what object is marked with the red circle in the picture?",
   (...)
     20     stream=True
     21 )
     22 response.resolve()
---> 23 to_markdown(response.text)
     24 return response.text

File ~/miniconda3/envs/gemini/lib/python3.9/site-packages/google/generativeai/types/generation_types.py:328, in BaseGenerateContentResponse.text(self)
    326 parts = self.parts
    327 if len(parts) != 1 or "text" not in parts[0]:
--> 328     raise ValueError(
    329         "The `response.text` quick accessor only works for "
    330         "simple (single-`Part`) text responses. This response is not simple text."
    331         "Use the `result.parts` accessor or the full "
    332         "`result.candidates[index].content.parts` lookup "
    333         "instead."
    334     )
    335 return parts[0].text

ValueError: The `response.text` quick accessor only works for simple (single-`Part`) text responses. This response is not simple text.Use the `result.parts` accessor or the full `result.candidates[index].content.parts` lookup instead.

I don't know how to solve this problem. But this problem does not occur when I use other image examples to input the model.

FareedKhan-dev · 2024-01-17T13:46:59Z

@Ki-Zhang As of January 2024, the entire list of Harm Categories can be found here. The implementation for gemini-pro or gemini-pro-vision can be carried out as follows in Python:

safety_settings = [
    {
        "category": "HARM_CATEGORY_DANGEROUS",
        "threshold": "BLOCK_NONE",
    },
    {
        "category": "HARM_CATEGORY_HARASSMENT",
        "threshold": "BLOCK_NONE",
    },
    {
        "category": "HARM_CATEGORY_HATE_SPEECH",
        "threshold": "BLOCK_NONE",
    },
    {
        "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
        "threshold": "BLOCK_NONE",
    },
    {
        "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
        "threshold": "BLOCK_NONE",
    },
]

values for each category can be found here

Threshold (Google AI Studio)	Threshold (API)	Description
Block none	BLOCK_NONE	Always show regardless of probability of unsafe content
Block few	BLOCK_ONLY_HIGH	Block when high probability of unsafe content
Block some	BLOCK_MEDIUM_AND_ABOVE	Block when medium or high probability of unsafe content
Block most	BLOCK_LOW_AND_ABOVE	Block when low, medium, or high probability of unsafe content
	HARM_BLOCK_THRESHOLD_UNSPECIFIED	Threshold is unspecified, block using default threshold

These settings can be applied as:

# For image model 
image_model.generate_content([your_image, prompt], safety_settings=safety_settings)

# For text model 
text_model.generate_content(prompt, safety_settings=safety_settings)

Additionally, make sure the image does not contain content related to openAI or chatgpt. Otherwise, it may result in an error. Screenshots taken through the default Snipping Tool on Windows might also lead to such errors.

chrbsg · 2024-01-19T15:26:55Z

So this is caused because content was blocked on the server-side? If so, the thrown exception text is terrible.

Immortalise · 2024-01-21T04:28:52Z

@Ki-Zhang As of January 2024, the entire list of Harm Categories can be found here. The implementation for gemini-pro or gemini-pro-vision can be carried out as follows in Python:
safety_settings = [
    {
        "category": "HARM_CATEGORY_DANGEROUS",
        "threshold": "BLOCK_NONE",
    },
    {
        "category": "HARM_CATEGORY_HARASSMENT",
        "threshold": "BLOCK_NONE",
    },
    {
        "category": "HARM_CATEGORY_HATE_SPEECH",
        "threshold": "BLOCK_NONE",
    },
    {
        "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
        "threshold": "BLOCK_NONE",
    },
    {
        "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
        "threshold": "BLOCK_NONE",
    },
]
values for each category can be found here

Threshold (Google AI Studio) Threshold (API) Description
Block none BLOCK_NONE Always show regardless of probability of unsafe content
Block few BLOCK_ONLY_HIGH Block when high probability of unsafe content
Block some BLOCK_MEDIUM_AND_ABOVE Block when medium or high probability of unsafe content
Block most BLOCK_LOW_AND_ABOVE Block when low, medium, or high probability of unsafe content
HARM_BLOCK_THRESHOLD_UNSPECIFIED Threshold is unspecified, block using default threshold
These settings can be applied as:
# For image model 
image_model.generate_content([your_image, prompt], safety_settings=safety_settings)

# For text model 
text_model.generate_content(prompt, safety_settings=safety_settings)
Additionally, make sure the image does not contain content related to openAI or chatgpt. Otherwise, it may result in an error. Screenshots taken through the default Snipping Tool on Windows might also lead to such errors.

Thanks for provding this! However, the safety settings does not work for me, instead, changing temperature from 0 to 0.7 works. The generated contents may be blocked since I found my input question is about black people (from MMLU dataset).

zengsihang · 2024-01-30T06:17:00Z

@Ki-Zhang As of January 2024, the entire list of Harm Categories can be found here. The implementation for gemini-pro or gemini-pro-vision can be carried out as follows in Python:
safety_settings = [
    {
        "category": "HARM_CATEGORY_DANGEROUS",
        "threshold": "BLOCK_NONE",
    },
    {
        "category": "HARM_CATEGORY_HARASSMENT",
        "threshold": "BLOCK_NONE",
    },
    {
        "category": "HARM_CATEGORY_HATE_SPEECH",
        "threshold": "BLOCK_NONE",
    },
    {
        "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
        "threshold": "BLOCK_NONE",
    },
    {
        "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
        "threshold": "BLOCK_NONE",
    },
]
values for each category can be found here

Threshold (Google AI Studio) Threshold (API) Description
Block none BLOCK_NONE Always show regardless of probability of unsafe content
Block few BLOCK_ONLY_HIGH Block when high probability of unsafe content
Block some BLOCK_MEDIUM_AND_ABOVE Block when medium or high probability of unsafe content
Block most BLOCK_LOW_AND_ABOVE Block when low, medium, or high probability of unsafe content
HARM_BLOCK_THRESHOLD_UNSPECIFIED Threshold is unspecified, block using default threshold
These settings can be applied as:
# For image model 
image_model.generate_content([your_image, prompt], safety_settings=safety_settings)

# For text model 
text_model.generate_content(prompt, safety_settings=safety_settings)
Additionally, make sure the image does not contain content related to openAI or chatgpt. Otherwise, it may result in an error. Screenshots taken through the default Snipping Tool on Windows might also lead to such errors.

Thanks for the information. However, both the safety settings and temperature don't work for me. The contents are in the biomedical domain, and all other cases are successfully generated except for one. I don't know why it fails to generate for that one.

HienBM · 2024-02-09T11:59:12Z

Try set up

for candidate in response.candidates:
            return [part.text for part in candidate.content.parts]

instead of
response.text

It worked for me

EMichaelC · 2024-02-12T09:01:17Z

This is probably happening because you are getting a finish_reason of Recitation for your chosen candidate:

finish_reason
// This field may be populated with recitation information for any text
// included in the content. These are passages that are "recited" from
// copyrighted material in the foundational LLM's training data.

So you may need to simply choose another candidate or run again for a new result that doesn't infringe on copyright.

shrijayan · 2024-02-21T16:48:57Z

You could try to delete the generation_config in the model if you use that

This also works for me but why may be the reason for that?

saramirabi · 2024-03-05T04:41:01Z

You could try to delete the generation_config in the model if you use that

This also works for me but why may be the reason for that?

_ have the same issue and I wonder what is the reason?_

Hert4 · 2024-03-05T05:50:48Z

This problem happened when ```max_output_tokens''' was too small for the response so no need to delete generation_config. That my experience

HienBM · 2024-03-05T06:17:16Z

I think the main reason is the model doesn't return any text in sometimes, based on the explanation of @MarkDaoust in
#196 (comment)

I still get this error with my code even when I delete generation_config.

But when I set up

for candidate in response.candidates:
    return [part.text for part in candidate.content.parts]

instead of
response.text

This error does not appear anymore.

Bill-A · 2024-03-29T17:25:11Z

@HienBM, Thank you for this information. It resolved my errors.

codewithdark-git · 2024-03-31T18:13:29Z

ValueError: The response.text quick accessor only works for simple (single-Part) text responses. This response is not simple text.Use the result.parts accessor or the full result.candidates[index].content.parts lookup instead.

Fix the Error Anyone check for You Code it's working for me 😎
when you work on only text

*model = genai.GenerativeModel('gemini-pro')
prompt = "What is the meaning of life?"
response = model.generate_content(prompt)
specific_answer = response.candidates[0].content.parts[0].text
    
print(specific_answer)*

and when work with image


response = model.generate_content(img)

try:
    # Check if 'candidates' list is not empty
    if response.candidates:
        # Access the first candidate's content if available
        if response.candidates[0].content.parts:
            generated_text = response.candidates[0].content.parts[0].text
            print("Generated Text:", generated_text)
        else:
            print("No generated text found in the candidate.")
    else:
        print("No candidates found in the response.")
except (AttributeError, IndexError) as e:
    print("Error:", e)

0706020994 · 2024-04-07T08:25:17Z

You could try to delete max_output_tokens generation_config in the model if you use that

thankyou very much

genicsoft · 2024-04-11T15:12:08Z

Maybe it's because multiple results are generated
When there is only one result, it is no problem to directly
respond.text
But if there are multiple results, an error will be reported. Then you need to use the first result by default.
That is
response.candidates[0].content.parts[0].text

chrbsg · 2024-04-11T17:01:18Z

Maybe it's because multiple results are generated

res.candidates[0].content.parts was an empty list in my case, and res.candidates[0].finish_reason was MAX_TOKENS.

My current diagnosis of this issue is that max_output_tokens is treated oddly by this generative-ai-python SDK. It appears that max_output_tokens is an absolute upper limit for a reply, and when this limit is reached, the SDK returns an empty reply with finish_reason=MAX_TOKENS. This is confirmed by the doc string for the error code:

MAX_TOKENS (2): The maximum number of tokens as specified in the request was reached.

So a lower max_output_tokens value will result in no text and a MAX_TOKENS error. This is different to the behaviour that most people expect, which would be to return some text when the upper limit is hit. e.g. in Vertex AI, max_output_tokens is interpreted as a length modifier, where a lower value results in shorter text responses (not no text):

MAX_OUTPUT_TOKENS: Maximum number of tokens that can be generated in the response. A token is approximately four characters. 100 tokens correspond to roughly 60-80 words. Specify a lower value for shorter responses and a higher value for potentially longer responses.

AdarshChintada · 2024-04-16T09:59:10Z

del('gemini-pro-vision'

Thank you for your answer @HienBM But I just used model = genai.GenerativeModel('gemini-pro-vision') to simply set up the genimi-pro-vision model. And I encountered the same problem.

Cell In[31], line 23, in dectect_object_why(ori_img, head_pos, gaze_pos)
      8 response = model.generate_content(
      9     [
     10         "The person outlined in the blue frame is looking at what object is marked with the red circle in the picture?",
   (...)
     20     stream=True
     21 )
     22 response.resolve()
---> 23 to_markdown(response.text)
     24 return response.text

File ~/miniconda3/envs/gemini/lib/python3.9/site-packages/google/generativeai/types/generation_types.py:328, in BaseGenerateContentResponse.text(self)
    326 parts = self.parts
    327 if len(parts) != 1 or "text" not in parts[0]:
--> 328     raise ValueError(
    329         "The `response.text` quick accessor only works for "
    330         "simple (single-`Part`) text responses. This response is not simple text."
    331         "Use the `result.parts` accessor or the full "
    332         "`result.candidates[index].content.parts` lookup "
    333         "instead."
    334     )
    335 return parts[0].text

ValueError: The `response.text` quick accessor only works for simple (single-`Part`) text responses. This response is not simple text.Use the `result.parts` accessor or the full `result.candidates[index].content.parts` lookup instead.

I don't know how to solve this problem. But this problem does not occur when I use other image examples to input the model.

@Ki-Zhang

The problem occurs not only with the image but also the prompt. I tried same image with different prompt and it works.
If you do not want to change the prompt then block the safety settings like below:

def get_gemini_response(input, image):
model = genai.GenerativeModel('gemini-pro-vision')
safe = [
{
"category": "HARM_CATEGORY_DANGEROUS",
"threshold": "BLOCK_NONE",
},
{
"category": "HARM_CATEGORY_HARASSMENT",
"threshold": "BLOCK_NONE",
},
{
"category": "HARM_CATEGORY_HATE_SPEECH",
"threshold": "BLOCK_NONE",
},
{
"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
"threshold": "BLOCK_NONE",
},
{
"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
"threshold": "BLOCK_NONE",
},
]
if input != "":
response = model.generate_content([input, image], safety_settings=safe)
else:
response = model.generate_content(image)
return response.text

I hope it definitely works. Thank you!

kishan2k2 · 2024-05-18T13:16:02Z

You could try to delete max_output_tokens generation_config in the model if you use that

It worked for me.

justarmadillo · 2024-05-20T20:38:01Z

nothing works for me, my content is medical and it considers it unsafe !
anyone found a solution ?

kishan2k2 · 2024-05-21T11:27:12Z

nothing works for me, my content is medical and it considers it unsafe ! anyone found a solution ?

I have not tried it yet, but I have heard that it might work. Try using some prompting techniques to extract the response, like giving the context before the question, like Instruction = f"You are the backend of a medical chatbot, be responsible and respond in formal tone. The users of this medical chatbot are mature and intelligent students and doctors, So respond according to the query {query}"

Pratik-Kumar-Cse · 2024-08-26T11:58:01Z

I am facing this issue how to resolve it

Invalid operation: The response.parts quick accessor requires a single candidate, but none were returned. Please check the response.prompt_feedback to determine if the prompt was blocked.

Change-Id: I8599dafc9e5084a43f2ce482644d0e9e16b61061

MarkDaoust · 2024-08-26T18:35:12Z

@Vital1162

This problem happened when ```max_output_tokens''' was too small for the response so no need to delete generation_config. That my experience

This has been fixed, now it does return the partial text.

@HienBM

Try set up

Yes, this is what the error message is trying to tell you. That something went wrong and it couldn't get a simple text result.

The one downside to this approach is that empty responses become an empty string, when maybe you want to check what went wrong, and/or re-run the request.

@Pratik-Kumar-Cse

This will update the error messages to output more information: #527. The right fix depends on what is going wrong.

* Expand error descriptions for #170 Change-Id: I8599dafc9e5084a43f2ce482644d0e9e16b61061 * Fix test failure cause by upgrade to protobuf>=5.0.0 Change-Id: I16a3c1964284d16efb48073662901454d4e4a6a1 * Format Change-Id: Iee130a7e58f2cfbc1001808ac892f119338626eb

barun-saha · 2024-11-06T07:24:06Z

I'm facing this issue today (did not occur with the same code in the last few days).

Following the previous posts here, I have disabled max_output_tokens. But this code:

response = chat_session.send_message(
    [prompt],
    stream=True,
)
# for chunk in response:
#     print(chunk.text, end='')
for candidate in response.candidates:
    print([part.text for part in candidate.content.parts])

leads to the following error (running on Colab):

IncompleteIterationError                  Traceback (most recent call last)

[<ipython-input-20-f50ae0551a39>](https://localhost:8080/#) in <cell line: 7>()
      5 # for chunk in response:
      6 #     print(chunk.text, end='')
----> 7 for candidate in response.candidates:
      8     print([part.text for part in candidate.content.parts])
      9 # print('\n\n', response.usage_metadata)

[/usr/local/lib/python3.10/dist-packages/google/generativeai/types/generation_types.py](https://localhost:8080/#) in candidates(self)
    423         """
    424         if not self._done:
--> 425             raise IncompleteIterationError(_INCOMPLETE_ITERATION_MESSAGE)
    426         return self._result.candidates
    427 

IncompleteIterationError: Please let the response complete iteration before accessing the final accumulated
attributes (or call `response.resolve()`)

Calling response.resolve() before iterating over the response appears to prevent the error but did not try out extensively.

Ki-Zhang · 2024-11-06T07:24:39Z

您的邮件我已收到，我会尽快处理，谢谢！

HienBM added component:python sdk Issue/PR related to Python SDK type:bug Something isn't working labels Jan 13, 2024

HienBM closed this as completed Jan 15, 2024

chrbsg mentioned this issue Jan 19, 2024

Misleading ValueError raised on seemingly blocked content, chat history corrupt and session can not be continued #174

Open

HienBM mentioned this issue Feb 9, 2024

Gemini-Pro response.text Error #196

Open

MarkDaoust added a commit to MarkDaoust/generative-ai-python that referenced this issue Aug 26, 2024

Expand error descriptions for google-gemini#170

e069551

Change-Id: I8599dafc9e5084a43f2ce482644d0e9e16b61061

MarkDaoust mentioned this issue Aug 26, 2024

Expand .text error descriptions. #527

Merged

MarkDaoust reopened this Aug 26, 2024

MarkDaoust mentioned this issue Aug 26, 2024

Invalid operation: The response.parts #526

Closed

markmcd assigned MarkDaoust Aug 27, 2024

JeremyEastham mentioned this issue Aug 29, 2024

Response.text raises an exception when only a function call is returned #532

Closed

markmcd added the status:triaged Issue/PR triaged to the corresponding sub-team label Sep 10, 2024

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ValueError: The `response.text` quick accessor only works for simple (single-`Part`) text responses. This response is not simple text.Use the `result.parts` accessor or the full `result.candidates[index].content.parts` lookup instead. #170

ValueError: The `response.text` quick accessor only works for simple (single-`Part`) text responses. This response is not simple text.Use the `result.parts` accessor or the full `result.candidates[index].content.parts` lookup instead. #170

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ValueError: The response.text quick accessor only works for simple (single-Part) text responses. This response is not simple text.Use the result.parts accessor or the full result.candidates[index].content.parts lookup instead. #170

ValueError: The response.text quick accessor only works for simple (single-Part) text responses. This response is not simple text.Use the result.parts accessor or the full result.candidates[index].content.parts lookup instead. #170

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HienBM commented Jan 13, 2024

Description of the bug:

Actual vs expected behavior:

Any other information you'd like to share?

falahgs commented Jan 14, 2024

Hert4 commented Jan 14, 2024 • edited Loading

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Ki-Zhang commented Nov 6, 2024 via email

ValueError: The `response.text` quick accessor only works for simple (single-`Part`) text responses. This response is not simple text.Use the `result.parts` accessor or the full `result.candidates[index].content.parts` lookup instead. #170

ValueError: The `response.text` quick accessor only works for simple (single-`Part`) text responses. This response is not simple text.Use the `result.parts` accessor or the full `result.candidates[index].content.parts` lookup instead. #170

Hert4 commented Jan 14, 2024 •

edited

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EMichaelC commented Feb 12, 2024 •

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Hert4 commented Mar 5, 2024 •

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HienBM commented Mar 5, 2024 •

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Bill-A commented Mar 29, 2024 •

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codewithdark-git commented Mar 31, 2024 •

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genicsoft commented Apr 11, 2024 •

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AdarshChintada commented Apr 16, 2024 •

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