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Nikita Titov 9f2ab37162
community[patch]: don't try to parse json in case of errored response (#18317) 
Related issue: #13896.

In case Ollama is behind a proxy, proxy error responses cannot be
viewed. You aren't even able to check response code.

For example, if your Ollama has basic access authentication and it's not
passed, `JSONDecodeError` will overwrite the truth response error.

<details>
<summary><b>Log now:</b></summary>

```
{
	"name": "JSONDecodeError",
	"message": "Expecting value: line 1 column 1 (char 0)",
	"stack": "---------------------------------------------------------------------------
JSONDecodeError                           Traceback (most recent call last)
File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/requests/models.py:971, in Response.json(self, **kwargs)
    970 try:
--> 971     return complexjson.loads(self.text, **kwargs)
    972 except JSONDecodeError as e:
    973     # Catch JSON-related errors and raise as requests.JSONDecodeError
    974     # This aliases json.JSONDecodeError and simplejson.JSONDecodeError

File /opt/miniforge3/envs/.gpt/lib/python3.10/json/__init__.py:346, in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
    343 if (cls is None and object_hook is None and
    344         parse_int is None and parse_float is None and
    345         parse_constant is None and object_pairs_hook is None and not kw):
--> 346     return _default_decoder.decode(s)
    347 if cls is None:

File /opt/miniforge3/envs/.gpt/lib/python3.10/json/decoder.py:337, in JSONDecoder.decode(self, s, _w)
    333 \"\"\"Return the Python representation of ``s`` (a ``str`` instance
    334 containing a JSON document).
    335 
    336 \"\"\"
--> 337 obj, end = self.raw_decode(s, idx=_w(s, 0).end())
    338 end = _w(s, end).end()

File /opt/miniforge3/envs/.gpt/lib/python3.10/json/decoder.py:355, in JSONDecoder.raw_decode(self, s, idx)
    354 except StopIteration as err:
--> 355     raise JSONDecodeError(\"Expecting value\", s, err.value) from None
    356 return obj, end

JSONDecodeError: Expecting value: line 1 column 1 (char 0)

During handling of the above exception, another exception occurred:

JSONDecodeError                           Traceback (most recent call last)
Cell In[3], line 1
----> 1 print(translate_func().invoke('text'))

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/runnables/base.py:2053, in RunnableSequence.invoke(self, input, config)
   2051 try:
   2052     for i, step in enumerate(self.steps):
-> 2053         input = step.invoke(
   2054             input,
   2055             # mark each step as a child run
   2056             patch_config(
   2057                 config, callbacks=run_manager.get_child(f\"seq:step:{i+1}\")
   2058             ),
   2059         )
   2060 # finish the root run
   2061 except BaseException as e:

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:165, in BaseChatModel.invoke(self, input, config, stop, **kwargs)
    154 def invoke(
    155     self,
    156     input: LanguageModelInput,
   (...)
    160     **kwargs: Any,
    161 ) -> BaseMessage:
    162     config = ensure_config(config)
    163     return cast(
    164         ChatGeneration,
--> 165         self.generate_prompt(
    166             [self._convert_input(input)],
    167             stop=stop,
    168             callbacks=config.get(\"callbacks\"),
    169             tags=config.get(\"tags\"),
    170             metadata=config.get(\"metadata\"),
    171             run_name=config.get(\"run_name\"),
    172             **kwargs,
    173         ).generations[0][0],
    174     ).message

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:543, in BaseChatModel.generate_prompt(self, prompts, stop, callbacks, **kwargs)
    535 def generate_prompt(
    536     self,
    537     prompts: List[PromptValue],
   (...)
    540     **kwargs: Any,
    541 ) -> LLMResult:
    542     prompt_messages = [p.to_messages() for p in prompts]
--> 543     return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:407, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)
    405         if run_managers:
    406             run_managers[i].on_llm_error(e, response=LLMResult(generations=[]))
--> 407         raise e
    408 flattened_outputs = [
    409     LLMResult(generations=[res.generations], llm_output=res.llm_output)
    410     for res in results
    411 ]
    412 llm_output = self._combine_llm_outputs([res.llm_output for res in results])

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:397, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)
    394 for i, m in enumerate(messages):
    395     try:
    396         results.append(
--> 397             self._generate_with_cache(
    398                 m,
    399                 stop=stop,
    400                 run_manager=run_managers[i] if run_managers else None,
    401                 **kwargs,
    402             )
    403         )
    404     except BaseException as e:
    405         if run_managers:

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:576, in BaseChatModel._generate_with_cache(self, messages, stop, run_manager, **kwargs)
    572     raise ValueError(
    573         \"Asked to cache, but no cache found at `langchain.cache`.\"
    574     )
    575 if new_arg_supported:
--> 576     return self._generate(
    577         messages, stop=stop, run_manager=run_manager, **kwargs
    578     )
    579 else:
    580     return self._generate(messages, stop=stop, **kwargs)

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_community/chat_models/ollama.py:250, in ChatOllama._generate(self, messages, stop, run_manager, **kwargs)
    226 def _generate(
    227     self,
    228     messages: List[BaseMessage],
   (...)
    231     **kwargs: Any,
    232 ) -> ChatResult:
    233     \"\"\"Call out to Ollama's generate endpoint.
    234 
    235     Args:
   (...)
    247             ])
    248     \"\"\"
--> 250     final_chunk = self._chat_stream_with_aggregation(
    251         messages,
    252         stop=stop,
    253         run_manager=run_manager,
    254         verbose=self.verbose,
    255         **kwargs,
    256     )
    257     chat_generation = ChatGeneration(
    258         message=AIMessage(content=final_chunk.text),
    259         generation_info=final_chunk.generation_info,
    260     )
    261     return ChatResult(generations=[chat_generation])

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_community/chat_models/ollama.py:183, in ChatOllama._chat_stream_with_aggregation(self, messages, stop, run_manager, verbose, **kwargs)
    174 def _chat_stream_with_aggregation(
    175     self,
    176     messages: List[BaseMessage],
   (...)
    180     **kwargs: Any,
    181 ) -> ChatGenerationChunk:
    182     final_chunk: Optional[ChatGenerationChunk] = None
--> 183     for stream_resp in self._create_chat_stream(messages, stop, **kwargs):
    184         if stream_resp:
    185             chunk = _chat_stream_response_to_chat_generation_chunk(stream_resp)

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_community/chat_models/ollama.py:156, in ChatOllama._create_chat_stream(self, messages, stop, **kwargs)
    147 def _create_chat_stream(
    148     self,
    149     messages: List[BaseMessage],
    150     stop: Optional[List[str]] = None,
    151     **kwargs: Any,
    152 ) -> Iterator[str]:
    153     payload = {
    154         \"messages\": self._convert_messages_to_ollama_messages(messages),
    155     }
--> 156     yield from self._create_stream(
    157         payload=payload, stop=stop, api_url=f\"{self.base_url}/api/chat/\", **kwargs
    158     )

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_community/llms/ollama.py:234, in _OllamaCommon._create_stream(self, api_url, payload, stop, **kwargs)
    228         raise OllamaEndpointNotFoundError(
    229             \"Ollama call failed with status code 404. \"
    230             \"Maybe your model is not found \"
    231             f\"and you should pull the model with `ollama pull {self.model}`.\"
    232         )
    233     else:
--> 234         optional_detail = response.json().get(\"error\")
    235         raise ValueError(
    236             f\"Ollama call failed with status code {response.status_code}.\"
    237             f\" Details: {optional_detail}\"
    238         )
    239 return response.iter_lines(decode_unicode=True)

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/requests/models.py:975, in Response.json(self, **kwargs)
    971     return complexjson.loads(self.text, **kwargs)
    972 except JSONDecodeError as e:
    973     # Catch JSON-related errors and raise as requests.JSONDecodeError
    974     # This aliases json.JSONDecodeError and simplejson.JSONDecodeError
--> 975     raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)

JSONDecodeError: Expecting value: line 1 column 1 (char 0)"
}
```

</details>


<details>

<summary><b>Log after a fix:</b></summary>

```
{
	"name": "ValueError",
	"message": "Ollama call failed with status code 401. Details: <html>\r
<head><title>401 Authorization Required</title></head>\r
<body>\r
<center><h1>401 Authorization Required</h1></center>\r
<hr><center>nginx/1.18.0 (Ubuntu)</center>\r
</body>\r
</html>\r
",
	"stack": "---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[2], line 1
----> 1 print(translate_func().invoke('text'))

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/runnables/base.py:2053, in RunnableSequence.invoke(self, input, config)
   2051 try:
   2052     for i, step in enumerate(self.steps):
-> 2053         input = step.invoke(
   2054             input,
   2055             # mark each step as a child run
   2056             patch_config(
   2057                 config, callbacks=run_manager.get_child(f\"seq:step:{i+1}\")
   2058             ),
   2059         )
   2060 # finish the root run
   2061 except BaseException as e:

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:165, in BaseChatModel.invoke(self, input, config, stop, **kwargs)
    154 def invoke(
    155     self,
    156     input: LanguageModelInput,
   (...)
    160     **kwargs: Any,
    161 ) -> BaseMessage:
    162     config = ensure_config(config)
    163     return cast(
    164         ChatGeneration,
--> 165         self.generate_prompt(
    166             [self._convert_input(input)],
    167             stop=stop,
    168             callbacks=config.get(\"callbacks\"),
    169             tags=config.get(\"tags\"),
    170             metadata=config.get(\"metadata\"),
    171             run_name=config.get(\"run_name\"),
    172             **kwargs,
    173         ).generations[0][0],
    174     ).message

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:543, in BaseChatModel.generate_prompt(self, prompts, stop, callbacks, **kwargs)
    535 def generate_prompt(
    536     self,
    537     prompts: List[PromptValue],
   (...)
    540     **kwargs: Any,
    541 ) -> LLMResult:
    542     prompt_messages = [p.to_messages() for p in prompts]
--> 543     return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:407, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)
    405         if run_managers:
    406             run_managers[i].on_llm_error(e, response=LLMResult(generations=[]))
--> 407         raise e
    408 flattened_outputs = [
    409     LLMResult(generations=[res.generations], llm_output=res.llm_output)
    410     for res in results
    411 ]
    412 llm_output = self._combine_llm_outputs([res.llm_output for res in results])

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:397, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)
    394 for i, m in enumerate(messages):
    395     try:
    396         results.append(
--> 397             self._generate_with_cache(
    398                 m,
    399                 stop=stop,
    400                 run_manager=run_managers[i] if run_managers else None,
    401                 **kwargs,
    402             )
    403         )
    404     except BaseException as e:
    405         if run_managers:

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:576, in BaseChatModel._generate_with_cache(self, messages, stop, run_manager, **kwargs)
    572     raise ValueError(
    573         \"Asked to cache, but no cache found at `langchain.cache`.\"
    574     )
    575 if new_arg_supported:
--> 576     return self._generate(
    577         messages, stop=stop, run_manager=run_manager, **kwargs
    578     )
    579 else:
    580     return self._generate(messages, stop=stop, **kwargs)

File /opt/miniforge3/envs/.gpt/lib/python3.10/site-packages/langchain_community/chat_models/ollama.py:250, in ChatOllama._generate(self, messages, stop, run_manager, **kwargs)
    226 def _generate(
    227     self,
    228     messages: List[BaseMessage],
   (...)
    231     **kwargs: Any,
    232 ) -> ChatResult:
    233     \"\"\"Call out to Ollama's generate endpoint.
    234 
    235     Args:
   (...)
    247             ])
    248     \"\"\"
--> 250     final_chunk = self._chat_stream_with_aggregation(
    251         messages,
    252         stop=stop,
    253         run_manager=run_manager,
    254         verbose=self.verbose,
    255         **kwargs,
    256     )
    257     chat_generation = ChatGeneration(
    258         message=AIMessage(content=final_chunk.text),
    259         generation_info=final_chunk.generation_info,
    260     )
    261     return ChatResult(generations=[chat_generation])

File /storage/gpt-project/Repos/repo_nikita/gpt_lib/langchain/ollama.py:328, in ChatOllamaCustom._chat_stream_with_aggregation(self, messages, stop, run_manager, verbose, **kwargs)
    319 def _chat_stream_with_aggregation(
    320     self,
    321     messages: List[BaseMessage],
   (...)
    325     **kwargs: Any,
    326 ) -> ChatGenerationChunk:
    327     final_chunk: Optional[ChatGenerationChunk] = None
--> 328     for stream_resp in self._create_chat_stream(messages, stop, **kwargs):
    329         if stream_resp:
    330             chunk = _chat_stream_response_to_chat_generation_chunk(stream_resp)

File /storage/gpt-project/Repos/repo_nikita/gpt_lib/langchain/ollama.py:301, in ChatOllamaCustom._create_chat_stream(self, messages, stop, **kwargs)
    292 def _create_chat_stream(
    293     self,
    294     messages: List[BaseMessage],
    295     stop: Optional[List[str]] = None,
    296     **kwargs: Any,
    297 ) -> Iterator[str]:
    298     payload = {
    299         \"messages\": self._convert_messages_to_ollama_messages(messages),
    300     }
--> 301     yield from self._create_stream(
    302         payload=payload, stop=stop, api_url=f\"{self.base_url}/api/chat\", **kwargs
    303     )

File /storage/gpt-project/Repos/repo_nikita/gpt_lib/langchain/ollama.py:134, in _OllamaCommonCustom._create_stream(self, api_url, payload, stop, **kwargs)
    132     else:
    133         optional_detail = response.text
--> 134         raise ValueError(
    135             f\"Ollama call failed with status code {response.status_code}.\"
    136             f\" Details: {optional_detail}\"
    137         )
    138 return response.iter_lines(decode_unicode=True)

ValueError: Ollama call failed with status code 401. Details: <html>\r
<head><title>401 Authorization Required</title></head>\r
<body>\r
<center><h1>401 Authorization Required</h1></center>\r
<hr><center>nginx/1.18.0 (Ubuntu)</center>\r
</body>\r
</html>\r
"
}
```

</details>

The same is true for timeout errors or when you simply mistyped in
`base_url` arg and get response from some other service, for instance.

Real Ollama errors are still clearly readable:

```
ValueError: Ollama call failed with status code 400. Details: {"error":"invalid options: unknown_option"}
```

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2024-03-01 12:17:29 -08:00
..
cli infra: make .gitignore consistent with standard python gitignore (#16828) 2024-02-12 19:43:41 -08:00
community community[patch]: don't try to parse json in case of errored response (#18317) 2024-03-01 12:17:29 -08:00
core infra: fake model invoke callback prior to yielding token (#18286) 2024-03-01 11:46:18 -08:00
experimental text-splitters[minor], langchain[minor], community[patch], templates, docs: langchain-text-splitters 0.0.1 (#18346) 2024-02-29 18:33:21 -08:00
langchain community: Use default load() implementation in doc loaders (#18385) 2024-03-01 14:46:52 -05:00
partners docs: update pinecone README to use PineconeVectorStore (#18170) 2024-03-01 12:12:52 -08:00
text-splitters docs: text splitters readme (#18359) 2024-03-01 03:00:42 +00:00