update

multipurpose_chatbot/demos/langchain_web_search.py

@@ -144,6 +144,7 @@ class AnyEnginePipeline(BaseLLM):
  # List to hold all results
  text_generations: List[str] = []
  stop_strings = stop
+ print(f'Pipeline run: {len(prompts)}')
  for i in range(0, len(prompts), self.batch_size):
  batch_prompts = prompts[i : i + self.batch_size]
  responses = []
@@ -156,7 +157,6 @@ class AnyEnginePipeline(BaseLLM):
  text = text[len(prompt):]
  if stop is not None and any(x in text for x in stop):
  text = text[:text.index(stop[0])]
- # print(f">>{text}")
  text_generations.append(text)
  return LLMResult(
  generations=[[Generation(text=text)] for text in text_generations]
@@ -456,8 +456,7 @@ Let's begin! Below is the question from the user.
 def create_web_search_engine(model_engine=None):
  # from langchain_community.tools.tavily_search import TavilySearchResults
  if model_engine is None:
- from ..globals import MODEL_ENGINE
- model_engine = MODEL_ENGINE
+ raise ValueError(f'model_engine empty')
 
  from langchain_core.utils.function_calling import (
  convert_to_openai_function,
@@ -472,8 +471,6 @@ def create_web_search_engine(model_engine=None):
 
  tools = [NewTavilySearchResults(max_results=1)]
  formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
- # tools = load_tools(["llm-math"], llm=web_search_llm)
- # formatted_tools = render_text_description_and_args(tools)
  prompt_template = ChatPromptTemplate.from_messages(
  [
  # (
@@ -510,249 +507,3 @@ def create_web_search_engine(model_engine=None):
 
 
 
-
-
-# if LANGCHAIN_AVAILABLE:
-# class LooseReActJsonSingleInputOutputParser(ReActJsonSingleInputOutputParser):
-# def parse(self, text: str) -> AgentAction | AgentFinish:
-# try:
-# return super().parse(text)
-# except OutputParserException as e:
-# return AgentFinish({"output": text}, text)
-
-
- # class ChatHuggingfaceFromLocalPipeline(ChatHuggingFace):
- # @root_validator()
- # def validate_llm(cls, values: dict) -> dict:
- # return values
- # def _resolve_model_id(self) -> None:
- # """Resolve the model_id from the LLM's inference_server_url"""
- # self.model_id = self.llm.model_id
-
-
- # class NewHuggingfacePipeline(HuggingFacePipeline):
- # bos_token = "<bos>"
- # add_bos_token = True
-
- # @classmethod
- # def from_model_id(
- # cls,
- # model_id: str,
- # task: str,
- # backend: str = "default",
- # device: Optional[int] = -1,
- # device_map: Optional[str] = None,
- # model_kwargs: Optional[dict] = None,
- # pipeline_kwargs: Optional[dict] = None,
- # batch_size: int = 2,
- # model = None,
- # **kwargs: Any,
- # ) -> HuggingFacePipeline:
- # """Construct the pipeline object from model_id and task."""
- # try:
- # from transformers import (
- # AutoModelForCausalLM,
- # AutoModelForSeq2SeqLM,
- # AutoTokenizer,
- # )
- # from transformers import pipeline as hf_pipeline
-
- # except ImportError:
- # raise ValueError(
- # "Could not import transformers python package. "
- # "Please install it with `pip install transformers`."
- # )
-
- # _model_kwargs = model_kwargs or {}
- # tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
- # if model is None:
- # try:
- # if task == "text-generation":
- # if backend == "openvino":
- # try:
- # from optimum.intel.openvino import OVModelForCausalLM
-
- # except ImportError:
- # raise ValueError(
- # "Could not import optimum-intel python package. "
- # "Please install it with: "
- # "pip install 'optimum[openvino,nncf]' "
- # )
- # try:
- # # use local model
- # model = OVModelForCausalLM.from_pretrained(
- # model_id, **_model_kwargs
- # )
-
- # except Exception:
- # # use remote model
- # model = OVModelForCausalLM.from_pretrained(
- # model_id, export=True, **_model_kwargs
- # )
- # else:
- # model = AutoModelForCausalLM.from_pretrained(
- # model_id, **_model_kwargs
- # )
- # elif task in ("text2text-generation", "summarization", "translation"):
- # if backend == "openvino":
- # try:
- # from optimum.intel.openvino import OVModelForSeq2SeqLM
-
- # except ImportError:
- # raise ValueError(
- # "Could not import optimum-intel python package. "
- # "Please install it with: "
- # "pip install 'optimum[openvino,nncf]' "
- # )
- # try:
- # # use local model
- # model = OVModelForSeq2SeqLM.from_pretrained(
- # model_id, **_model_kwargs
- # )
-
- # except Exception:
- # # use remote model
- # model = OVModelForSeq2SeqLM.from_pretrained(
- # model_id, export=True, **_model_kwargs
- # )
- # else:
- # model = AutoModelForSeq2SeqLM.from_pretrained(
- # model_id, **_model_kwargs
- # )
- # else:
- # raise ValueError(
- # f"Got invalid task {task}, "
- # f"currently only {VALID_TASKS} are supported"
- # )
- # except ImportError as e:
- # raise ValueError(
- # f"Could not load the {task} model due to missing dependencies."
- # ) from e
- # else:
- # print(f'PIpeline skipping creation of model because model is given')
-
- # if tokenizer.pad_token is None:
- # tokenizer.pad_token_id = model.config.eos_token_id
-
- # if (
- # (
- # getattr(model, "is_loaded_in_4bit", False)
- # or getattr(model, "is_loaded_in_8bit", False)
- # )
- # and device is not None
- # and backend == "default"
- # ):
- # logger.warning(
- # f"Setting the `device` argument to None from {device} to avoid "
- # "the error caused by attempting to move the model that was already "
- # "loaded on the GPU using the Accelerate module to the same or "
- # "another device."
- # )
- # device = None
-
- # if (
- # device is not None
- # and importlib.util.find_spec("torch") is not None
- # and backend == "default"
- # ):
- # import torch
-
- # cuda_device_count = torch.cuda.device_count()
- # if device < -1 or (device >= cuda_device_count):
- # raise ValueError(
- # f"Got device=={device}, "
- # f"device is required to be within [-1, {cuda_device_count})"
- # )
- # if device_map is not None and device < 0:
- # device = None
- # if device is not None and device < 0 and cuda_device_count > 0:
- # logger.warning(
- # "Device has %d GPUs available. "
- # "Provide device={deviceId} to `from_model_id` to use available"
- # "GPUs for execution. deviceId is -1 (default) for CPU and "
- # "can be a positive integer associated with CUDA device id.",
- # cuda_device_count,
- # )
- # if device is not None and device_map is not None and backend == "openvino":
- # logger.warning("Please set device for OpenVINO through: " "'model_kwargs'")
- # if "trust_remote_code" in _model_kwargs:
- # _model_kwargs = {
- # k: v for k, v in _model_kwargs.items() if k != "trust_remote_code"
- # }
- # _pipeline_kwargs = pipeline_kwargs or {}
- # pipeline = hf_pipeline(
- # task=task,
- # model=model,
- # tokenizer=tokenizer,
- # device=device,
- # device_map=device_map,
- # batch_size=batch_size,
- # model_kwargs=_model_kwargs,
- # **_pipeline_kwargs,
- # )
- # if pipeline.task not in VALID_TASKS:
- # raise ValueError(
- # f"Got invalid task {pipeline.task}, "
- # f"currently only {VALID_TASKS} are supported"
- # )
- # return cls(
- # pipeline=pipeline,
- # model_id=model_id,
- # model_kwargs=_model_kwargs,
- # pipeline_kwargs=_pipeline_kwargs,
- # batch_size=batch_size,
- # **kwargs,
- # )
- 
- # def _generate(
- # self,
- # prompts: List[str],
- # stop: Optional[List[str]] = None,
- # run_manager: Optional[CallbackManagerForLLMRun] = None,
- # **kwargs: Any,
- # ) -> LLMResult:
- # # List to hold all results
- # text_generations: List[str] = []
- # pipeline_kwargs = kwargs.get("pipeline_kwargs", self.pipeline_kwargs)
- # pipeline_kwargs = pipeline_kwargs if len(pipeline_kwargs) > 0 else self.pipeline_kwargs
- # for i in range(0, len(prompts), self.batch_size):
- # batch_prompts = prompts[i : i + self.batch_size]
- # bos_token = self.pipeline.tokenizer.convert_ids_to_tokens(self.pipeline.tokenizer.bos_token_id)
- # for i in range(len(batch_prompts)):
- # if not batch_prompts[i].startswith(bos_token) and self.add_bos_token:
- # batch_prompts[i] = bos_token + batch_prompts[i]
- # # print(f'PROMPT: {stop=} {pipeline_kwargs=} ==================\n{batch_prompts[0]}\n==========================')
- # # Process batch of prompts
- # responses = self.pipeline(
- # batch_prompts,
- # **pipeline_kwargs,
- # )
- # # Process each response in the batch
- # for j, (prompt, response) in enumerate(zip(batch_prompts, responses)):
- # if isinstance(response, list):
- # # if model returns multiple generations, pick the top one
- # response = response[0]
- # if self.pipeline.task == "text-generation":
- # text = response["generated_text"]
- # elif self.pipeline.task == "text2text-generation":
- # text = response["generated_text"]
- # elif self.pipeline.task == "summarization":
- # text = response["summary_text"]
- # elif self.pipeline.task in "translation":
- # text = response["translation_text"]
- # else:
- # raise ValueError(
- # f"Got invalid task {self.pipeline.task}, "
- # f"currently only {VALID_TASKS} are supported"
- # )
- # # Append the processed text to results
- # if text.startswith(prompt):
- # text = text[len(prompt):]
- # if stop is not None and any(x in text for x in stop):
- # text = text[:text.index(stop[0])]
- # # print(f">>{text}")
- # text_generations.append(text)
- # return LLMResult(
- # generations=[[Generation(text=text)] for text in text_generations]
- # )
-

multipurpose_chatbot/demos/websearch_chat_interface.py

@@ -115,8 +115,8 @@ def chat_web_search_response_stream_multiturn_engine(
  if len(message) == 0:
  raise gr.Error("The message cannot be empty!")
  
+ print(f'Begin agent_invoke.')
  response_output = agent_executor.invoke({"input": message})
- print(response_output)
  response = response_output['output']
  
  full_prompt = gradio_history_to_conversation_prompt(message.strip(), history=history, system_prompt=system_prompt)
@@ -217,6 +217,10 @@ class WebSearchChatInterfaceDemo(BaseDemo):
  return demo_chat
 
 
+
+
+
+
 """
 run