mirror of
https://github.com/hwchase17/langchain.git
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IBM: Added WatsonxChat passing params to invoke method (#23758)
Thank you for contributing to LangChain!
- [x] **PR title**: "IBM: Added WatsonxChat to chat models preview,
update passing params to invoke method"
- [x] **PR message**:
- **Description:** Added WatsonxChat passing params to invoke method,
added integration tests
- **Dependencies:** `ibm_watsonx_ai`
- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Mateusz Szewczyk
GitHub
parent
1f5a163f42
commit
902b57d107
@ -454,7 +454,7 @@
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"\n",
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"Please note that `ChatWatsonx.bind_tools` is on beta state, so right now we only support `mistralai/mixtral-8x7b-instruct-v01` model.\n",
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"\n",
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"You should also redefine `max_new_tokens` parameter to get the entire model response. By default `max_new_tokens` is set ot 20."
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"You should also redefine `max_new_tokens` parameter to get the entire model response. By default `max_new_tokens` is set to 20."
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]
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},
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{
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@ -577,7 +577,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.13"
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"version": "3.1.undefined"
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}
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},
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"nbformat": 4,
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@ -26,6 +26,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models import LanguageModelInput
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from langchain_core.language_models.chat_models import (
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BaseChatModel,
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LangSmithParams,
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generate_from_stream,
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)
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from langchain_core.messages import (
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@ -310,8 +311,18 @@ class ChatWatsonx(BaseChatModel):
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@property
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def _llm_type(self) -> str:
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"""Return type of chat model."""
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return "watsonx-chat"
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def _get_ls_params(
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self, stop: Optional[List[str]] = None, **kwargs: Any
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) -> LangSmithParams:
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"""Get standard params for tracing."""
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params = super()._get_ls_params(stop=stop, **kwargs)
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params["ls_provider"] = "together"
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params["ls_model_name"] = self.model_id
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return params
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@property
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def lc_secrets(self) -> Dict[str, str]:
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"""A map of constructor argument names to secret ids.
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@ -457,11 +468,8 @@ Remember, even when answering to the user, you must still use this only JSON for
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if "tool_choice" in kwargs:
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del kwargs["tool_choice"]
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if "params" in kwargs:
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del kwargs["params"]
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response = self.watsonx_model.generate(
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prompt=chat_prompt, params=params, **kwargs
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prompt=chat_prompt, **(kwargs | {"params": params})
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)
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return self._create_chat_result(response)
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@ -472,11 +480,37 @@ Remember, even when answering to the user, you must still use this only JSON for
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[ChatGenerationChunk]:
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message_dicts, params = self._create_message_dicts(messages, stop)
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message_dicts, params = self._create_message_dicts(messages, stop, **kwargs)
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chat_prompt = self._create_chat_prompt(message_dicts)
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tools = kwargs.get("tools")
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if tools:
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chat_prompt = f"""[AVAILABLE_TOOLS]
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{json.dumps(tools[0], indent=2)}
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[/AVAILABLE_TOOLS]
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[INST]<<SYS>>You are Mixtral Chat function calling, an AI language model developed by
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Mistral AI. You are a cautious assistant. You carefully follow instructions. You are
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helpful and harmless and you follow ethical guidelines and promote positive behavior.
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<</SYS>>
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To use these tools you must always respond in JSON format containing `"type"` and
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`"function"` key-value pairs. Also `"function"` key-value pair always containing
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`"name"` and `"arguments"` key-value pairs.
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Between subsequent JSONs should be one blank line.
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Remember, even when answering to the user, you must still use this only JSON format!
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{chat_prompt}[/INST]"""
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if "tools" in kwargs:
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del kwargs["tools"]
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if "tool_choice" in kwargs:
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del kwargs["tool_choice"]
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for chunk in self.watsonx_model.generate_text_stream(
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prompt=chat_prompt, raw_response=True, params=params, **kwargs
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prompt=chat_prompt, raw_response=True, **(kwargs | {"params": params})
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):
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if not isinstance(chunk, dict):
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chunk = chunk.dict()
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@ -497,7 +531,9 @@ Remember, even when answering to the user, you must still use this only JSON for
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message=chunk, generation_info=generation_info or None
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)
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if run_manager:
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run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs)
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run_manager.on_llm_new_token(
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chunk.content, chunk=chunk, logprobs=logprobs
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)
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yield chunk
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@ -532,7 +568,9 @@ Remember, even when answering to the user, you must still use this only JSON for
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prompt += message["content"] + "\n[/INST]\n"
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else:
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prompt = ChatPromptValue(messages=convert_to_messages(messages)).to_string()
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prompt = ChatPromptValue(
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messages=convert_to_messages(messages) + [AIMessage(content="")]
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).to_string()
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return prompt
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@ -567,6 +605,13 @@ Remember, even when answering to the user, you must still use this only JSON for
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sum_of_total_generated_tokens += res["generated_token_count"]
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if "input_token_count" in res:
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sum_of_total_input_tokens += res["input_token_count"]
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total_token = sum_of_total_generated_tokens + sum_of_total_input_tokens
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if total_token and isinstance(message, AIMessage):
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message.usage_metadata = {
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"input_tokens": sum_of_total_input_tokens,
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"output_tokens": sum_of_total_generated_tokens,
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"total_tokens": total_token,
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}
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gen = ChatGeneration(
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message=message,
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generation_info=generation_info,
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@ -266,10 +266,15 @@ class WatsonxLLM(BaseLLM):
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}
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def _get_chat_params(
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self, stop: Optional[List[str]] = None
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self, stop: Optional[List[str]] = None, **kwargs: Any
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) -> Optional[Dict[str, Any]]:
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params: Optional[Dict[str, Any]] = {**self.params} if self.params else None
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params = {**self.params} if self.params else {}
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params = params | {**kwargs.get("params", {})}
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if stop is not None:
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if params and "stop_sequences" in params:
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raise ValueError(
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"`stop_sequences` found in both the input and default params."
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)
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params = (params or {}) | {"stop_sequences": stop}
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return params
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@ -355,7 +360,7 @@ class WatsonxLLM(BaseLLM):
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response = watsonx_llm.generate(["What is a molecule"])
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"""
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params = self._get_chat_params(stop=stop)
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params = self._get_chat_params(stop=stop, **kwargs)
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should_stream = stream if stream is not None else self.streaming
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if should_stream:
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if len(prompts) > 1:
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@ -378,7 +383,7 @@ class WatsonxLLM(BaseLLM):
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return LLMResult(generations=[[generation]])
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else:
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response = self.watsonx_model.generate(
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prompt=prompts, params=params, **kwargs
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prompt=prompts, **(kwargs | {"params": params})
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)
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return self._create_llm_result(response)
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@ -403,9 +408,9 @@ class WatsonxLLM(BaseLLM):
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for chunk in response:
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print(chunk, end='')
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"""
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params = self._get_chat_params(stop=stop)
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params = self._get_chat_params(stop=stop, **kwargs)
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for stream_resp in self.watsonx_model.generate_text_stream(
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prompt=prompt, raw_response=True, params=params, **kwargs
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prompt=prompt, raw_response=True, **(kwargs | {"params": params})
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):
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if not isinstance(stream_resp, dict):
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stream_resp = stream_resp.dict()
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307
libs/partners/ibm/poetry.lock
generated
307
libs/partners/ibm/poetry.lock
generated
@ -1,4 +1,4 @@
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# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
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# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
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[[package]]
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name = "annotated-types"
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@ -13,13 +13,13 @@ files = [
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[[package]]
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name = "certifi"
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version = "2024.6.2"
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version = "2024.7.4"
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description = "Python package for providing Mozilla's CA Bundle."
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optional = false
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python-versions = ">=3.6"
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files = [
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{file = "certifi-2024.6.2-py3-none-any.whl", hash = "sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56"},
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{file = "certifi-2024.6.2.tar.gz", hash = "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516"},
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{file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
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{file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
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]
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[[package]]
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@ -223,13 +223,13 @@ ibm-cos-sdk-core = "2.13.5"
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[[package]]
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name = "ibm-watsonx-ai"
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version = "1.0.9"
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version = "1.0.10"
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description = "IBM watsonx.ai API Client"
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optional = false
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python-versions = ">=3.10"
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files = [
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{file = "ibm_watsonx_ai-1.0.9-py3-none-any.whl", hash = "sha256:3b9d42a60418430ddb37b1f9d7e98ade241089518903ebd937d77ff16b07e20b"},
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{file = "ibm_watsonx_ai-1.0.9.tar.gz", hash = "sha256:c0733a028d34ac75904812a6e3d213feffa401498a9e02a3e3d8d4d2d6d83d32"},
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{file = "ibm_watsonx_ai-1.0.10-py3-none-any.whl", hash = "sha256:6358838fc8e5a88f336a55063f3a8efee01311a1f6cab65acb5a7fbb7129670f"},
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{file = "ibm_watsonx_ai-1.0.10.tar.gz", hash = "sha256:e432396399efa342e5e41c6383cf2425890acd49e2b972eb6ea63c9f8409105f"},
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]
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[package.dependencies]
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@ -246,8 +246,8 @@ urllib3 = "*"
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[package.extras]
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fl-crypto = ["pyhelayers (==1.5.0.3)"]
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fl-crypto-rt24-1 = ["pyhelayers (==1.5.3.1)"]
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fl-rt23-1-py3-10 = ["GPUtil", "cryptography (==42.0.5)", "ddsketch (==2.0.4)", "diffprivlib (==0.5.1)", "environs (==9.5.0)", "gym", "image (==1.5.33)", "joblib (==1.1.1)", "lz4", "msgpack (==1.0.7)", "msgpack-numpy (==0.4.8)", "numcompress (==0.1.2)", "numpy (==1.23.5)", "pandas (==1.5.3)", "parse (==1.19.0)", "pathlib2 (==2.3.6)", "protobuf (==4.22.1)", "psutil", "pyYAML (==6.0.1)", "pytest (==6.2.5)", "requests (==2.31.0)", "scikit-learn (==1.1.1)", "scipy (==1.10.1)", "setproctitle", "skops (==0.9.0)", "skorch (==0.12.0)", "tabulate (==0.8.9)", "tensorflow (==2.12.0)", "torch (==2.0.1)", "websockets (==10.1)"]
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fl-rt24-1-py3-11 = ["GPUtil", "cryptography (==42.0.5)", "ddsketch (==2.0.4)", "diffprivlib (==0.5.1)", "environs (==9.5.0)", "gym", "image (==1.5.33)", "joblib (==1.3.2)", "lz4", "msgpack (==1.0.7)", "msgpack-numpy (==0.4.8)", "numcompress (==0.1.2)", "numpy (==1.26.4)", "pandas (==2.1.4)", "parse (==1.19.0)", "pathlib2 (==2.3.6)", "protobuf (==4.22.1)", "psutil", "pyYAML (==6.0.1)", "pytest (==6.2.5)", "requests (==2.31.0)", "scikit-learn (==1.3.2)", "scipy (==1.11.4)", "setproctitle", "skops (==0.9.0)", "skorch (==0.12.0)", "tabulate (==0.8.9)", "tensorflow (==2.14.1)", "torch (==2.1.2)", "websockets (==10.1)"]
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fl-rt23-1-py3-10 = ["GPUtil", "cryptography (==42.0.5)", "ddsketch (==2.0.4)", "diffprivlib (==0.5.1)", "environs (==9.5.0)", "gym", "image (==1.5.33)", "joblib (==1.1.1)", "lz4", "msgpack (==1.0.7)", "msgpack-numpy (==0.4.8)", "numcompress (==0.1.2)", "numpy (==1.23.5)", "pandas (==1.5.3)", "parse (==1.19.0)", "pathlib2 (==2.3.6)", "protobuf (==4.22.1)", "psutil", "pyYAML (==6.0.1)", "pytest (==6.2.5)", "requests (==2.32.3)", "scikit-learn (==1.1.1)", "scipy (==1.10.1)", "setproctitle", "skops (==0.9.0)", "skorch (==0.12.0)", "tabulate (==0.8.9)", "tensorflow (==2.12.0)", "torch (==2.0.1)", "websockets (==10.1)"]
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fl-rt24-1-py3-11 = ["GPUtil", "cryptography (==42.0.5)", "ddsketch (==2.0.4)", "diffprivlib (==0.5.1)", "environs (==9.5.0)", "gym", "image (==1.5.33)", "joblib (==1.3.2)", "lz4", "msgpack (==1.0.7)", "msgpack-numpy (==0.4.8)", "numcompress (==0.1.2)", "numpy (==1.26.4)", "pandas (==2.1.4)", "parse (==1.19.0)", "pathlib2 (==2.3.6)", "protobuf (==4.22.1)", "psutil", "pyYAML (==6.0.1)", "pytest (==6.2.5)", "requests (==2.32.3)", "scikit-learn (==1.3.2)", "scipy (==1.11.4)", "setproctitle", "skops (==0.9.0)", "skorch (==0.12.0)", "tabulate (==0.8.9)", "tensorflow (==2.14.1)", "torch (==2.1.2)", "websockets (==10.1)"]
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[[package]]
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name = "idna"
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@ -352,13 +352,13 @@ url = "../../core"
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[[package]]
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name = "langsmith"
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version = "0.1.82"
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version = "0.1.83"
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description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
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optional = false
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python-versions = "<4.0,>=3.8.1"
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files = [
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{file = "langsmith-0.1.82-py3-none-any.whl", hash = "sha256:9b3653e7d316036b0c60bf0bc3e280662d660f485a4ebd8e5c9d84f9831ae79c"},
|
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{file = "langsmith-0.1.82.tar.gz", hash = "sha256:c02e2bbc488c10c13b52c69d271eb40bd38da078d37b6ae7ae04a18bd48140be"},
|
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{file = "langsmith-0.1.83-py3-none-any.whl", hash = "sha256:f54d8cd8479b648b6339f3f735d19292c3516d080f680933ecdca3eab4b67ed3"},
|
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{file = "langsmith-0.1.83.tar.gz", hash = "sha256:5cdd947212c8ad19adb992c06471c860185a777daa6859bb47150f90daf64bf3"},
|
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]
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[package.dependencies]
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@ -488,57 +488,62 @@ files = [
|
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[[package]]
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name = "orjson"
|
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version = "3.10.5"
|
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version = "3.10.6"
|
||||
description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
|
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optional = false
|
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python-versions = ">=3.8"
|
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files = [
|
||||
{file = "orjson-3.10.5-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:545d493c1f560d5ccfc134803ceb8955a14c3fcb47bbb4b2fee0232646d0b932"},
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||||
{file = "orjson-3.10.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f4324929c2dd917598212bfd554757feca3e5e0fa60da08be11b4aa8b90013c1"},
|
||||
{file = "orjson-3.10.5-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8c13ca5e2ddded0ce6a927ea5a9f27cae77eee4c75547b4297252cb20c4d30e6"},
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{file = "orjson-3.10.5-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b6c8e30adfa52c025f042a87f450a6b9ea29649d828e0fec4858ed5e6caecf63"},
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{file = "orjson-3.10.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:338fd4f071b242f26e9ca802f443edc588fa4ab60bfa81f38beaedf42eda226c"},
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||||
{file = "orjson-3.10.5-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:6970ed7a3126cfed873c5d21ece1cd5d6f83ca6c9afb71bbae21a0b034588d96"},
|
||||
{file = "orjson-3.10.5-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:235dadefb793ad12f7fa11e98a480db1f7c6469ff9e3da5e73c7809c700d746b"},
|
||||
{file = "orjson-3.10.5-cp310-none-win32.whl", hash = "sha256:be79e2393679eda6a590638abda16d167754393f5d0850dcbca2d0c3735cebe2"},
|
||||
{file = "orjson-3.10.5-cp310-none-win_amd64.whl", hash = "sha256:c4a65310ccb5c9910c47b078ba78e2787cb3878cdded1702ac3d0da71ddc5228"},
|
||||
{file = "orjson-3.10.5-cp311-cp311-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:cdf7365063e80899ae3a697def1277c17a7df7ccfc979990a403dfe77bb54d40"},
|
||||
{file = "orjson-3.10.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6b68742c469745d0e6ca5724506858f75e2f1e5b59a4315861f9e2b1df77775a"},
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[[package]]
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||||
@ -637,109 +642,122 @@ testing = ["pytest", "pytest-benchmark"]
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[[package]]
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||||
name = "pydantic"
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version = "2.7.4"
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version = "2.8.2"
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{file = "pydantic_core-2.20.1-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:4dd484681c15e6b9a977c785a345d3e378d72678fd5f1f3c0509608da24f2ac0"},
|
||||
{file = "pydantic_core-2.20.1-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:f6d6cff3538391e8486a431569b77921adfcdef14eb18fbf19b7c0a5294d4e6a"},
|
||||
{file = "pydantic_core-2.20.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:a6d511cc297ff0883bc3708b465ff82d7560193169a8b93260f74ecb0a5e08a7"},
|
||||
{file = "pydantic_core-2.20.1.tar.gz", hash = "sha256:26ca695eeee5f9f1aeeb211ffc12f10bcb6f71e2989988fda61dabd65db878d4"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -867,7 +885,6 @@ files = [
|
||||
{file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
|
||||
|
||||
@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.poetry]
|
||||
name = "langchain-ibm"
|
||||
version = "0.1.8"
|
||||
version = "0.1.9"
|
||||
description = "An integration package connecting IBM watsonx.ai and LangChain"
|
||||
authors = [ "IBM",]
|
||||
readme = "README.md"
|
||||
|
||||
@ -52,6 +52,34 @@ def test_01a_generate_chat_with_invoke_params() -> None:
|
||||
assert response
|
||||
|
||||
|
||||
def test_01b_generate_chat_with_invoke_params() -> None:
|
||||
from ibm_watsonx_ai.metanames import GenTextParamsMetaNames
|
||||
|
||||
params_1 = {
|
||||
GenTextParamsMetaNames.MIN_NEW_TOKENS: 1,
|
||||
GenTextParamsMetaNames.MAX_NEW_TOKENS: 10,
|
||||
}
|
||||
params_2 = {
|
||||
GenTextParamsMetaNames.MIN_NEW_TOKENS: 1,
|
||||
GenTextParamsMetaNames.MAX_NEW_TOKENS: 10,
|
||||
}
|
||||
chat = ChatWatsonx(
|
||||
model_id=MODEL_ID,
|
||||
url=URL, # type: ignore[arg-type]
|
||||
project_id=WX_PROJECT_ID,
|
||||
params=params_1, # type: ignore[arg-type]
|
||||
)
|
||||
messages = [
|
||||
("system", "You are a helpful assistant that translates English to French."),
|
||||
(
|
||||
"human",
|
||||
"Translate this sentence from English to French. I love programming.",
|
||||
),
|
||||
]
|
||||
response = chat.invoke(messages, params=params_2)
|
||||
assert response
|
||||
|
||||
|
||||
def test_02_generate_chat_with_few_inputs() -> None:
|
||||
chat = ChatWatsonx(model_id=MODEL_ID, url=URL, project_id=WX_PROJECT_ID) # type: ignore[arg-type]
|
||||
message = HumanMessage(content="Hello")
|
||||
@ -75,6 +103,19 @@ def test_05_generate_chat_with_stream() -> None:
|
||||
assert isinstance(chunk.content, str)
|
||||
|
||||
|
||||
def test_05a_generate_chat_with_stream_with_param() -> None:
|
||||
from ibm_watsonx_ai.metanames import GenTextParamsMetaNames
|
||||
|
||||
params = {
|
||||
GenTextParamsMetaNames.MIN_NEW_TOKENS: 1,
|
||||
GenTextParamsMetaNames.MAX_NEW_TOKENS: 10,
|
||||
}
|
||||
chat = ChatWatsonx(model_id=MODEL_ID, url=URL, project_id=WX_PROJECT_ID) # type: ignore[arg-type]
|
||||
response = chat.stream("What's the weather in san francisco", params=params)
|
||||
for chunk in response:
|
||||
assert isinstance(chunk.content, str)
|
||||
|
||||
|
||||
def test_10_chaining() -> None:
|
||||
chat = ChatWatsonx(model_id=MODEL_ID, url=URL, project_id=WX_PROJECT_ID) # type: ignore[arg-type]
|
||||
prompt = ChatPromptTemplate.from_messages(
|
||||
|
||||
@ -52,6 +52,45 @@ def test_watsonxllm_invoke_with_params() -> None:
|
||||
assert len(response) > 0
|
||||
|
||||
|
||||
def test_watsonxllm_invoke_with_params_2() -> None:
|
||||
parameters = {
|
||||
GenTextParamsMetaNames.DECODING_METHOD: "sample",
|
||||
GenTextParamsMetaNames.MAX_NEW_TOKENS: 10,
|
||||
GenTextParamsMetaNames.MIN_NEW_TOKENS: 5,
|
||||
}
|
||||
|
||||
watsonxllm = WatsonxLLM(
|
||||
model_id=MODEL_ID,
|
||||
url="https://us-south.ml.cloud.ibm.com", # type: ignore[arg-type]
|
||||
project_id=WX_PROJECT_ID,
|
||||
)
|
||||
response = watsonxllm.invoke("What color sunflower is?", params=parameters)
|
||||
print(f"\nResponse: {response}")
|
||||
assert isinstance(response, str)
|
||||
assert len(response) > 0
|
||||
|
||||
|
||||
def test_watsonxllm_invoke_with_params_3() -> None:
|
||||
parameters_1 = {
|
||||
GenTextParamsMetaNames.DECODING_METHOD: "sample",
|
||||
GenTextParamsMetaNames.MAX_NEW_TOKENS: 10,
|
||||
}
|
||||
parameters_2 = {
|
||||
GenTextParamsMetaNames.MIN_NEW_TOKENS: 5,
|
||||
}
|
||||
|
||||
watsonxllm = WatsonxLLM(
|
||||
model_id=MODEL_ID,
|
||||
url="https://us-south.ml.cloud.ibm.com", # type: ignore[arg-type]
|
||||
project_id=WX_PROJECT_ID,
|
||||
params=parameters_1,
|
||||
)
|
||||
response = watsonxllm.invoke("What color sunflower is?", params=parameters_2)
|
||||
print(f"\nResponse: {response}")
|
||||
assert isinstance(response, str)
|
||||
assert len(response) > 0
|
||||
|
||||
|
||||
def test_watsonxllm_generate() -> None:
|
||||
watsonxllm = WatsonxLLM(
|
||||
model_id=MODEL_ID,
|
||||
@ -66,6 +105,25 @@ def test_watsonxllm_generate() -> None:
|
||||
assert len(response_text) > 0
|
||||
|
||||
|
||||
def test_watsonxllm_generate_with_param() -> None:
|
||||
parameters = {
|
||||
GenTextParamsMetaNames.DECODING_METHOD: "sample",
|
||||
GenTextParamsMetaNames.MAX_NEW_TOKENS: 10,
|
||||
GenTextParamsMetaNames.MIN_NEW_TOKENS: 5,
|
||||
}
|
||||
watsonxllm = WatsonxLLM(
|
||||
model_id=MODEL_ID,
|
||||
url="https://us-south.ml.cloud.ibm.com", # type: ignore[arg-type]
|
||||
project_id=WX_PROJECT_ID,
|
||||
)
|
||||
response = watsonxllm.generate(["What color sunflower is?"], params=parameters)
|
||||
print(f"\nResponse: {response}")
|
||||
response_text = response.generations[0][0].text
|
||||
print(f"Response text: {response_text}")
|
||||
assert isinstance(response, LLMResult)
|
||||
assert len(response_text) > 0
|
||||
|
||||
|
||||
def test_watsonxllm_generate_with_multiple_prompts() -> None:
|
||||
watsonxllm = WatsonxLLM(
|
||||
model_id=MODEL_ID,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user