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October 10, 2023 17:59
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| Traceback (most recent call last): | |
| File "/home/thoraxe/.pyenv/versions/3.9.16/envs/fastapi-ols-39/lib/python3.9/site-packages/uvicorn/protocols/http/h11_impl.py", line 408, in run_asgi | |
| result = await app( # type: ignore[func-returns-value] | |
| File "/home/thoraxe/.pyenv/versions/3.9.16/envs/fastapi-ols-39/lib/python3.9/site-packages/uvicorn/middleware/proxy_headers.py", line 84, in __call__ | |
| return await self.app(scope, receive, send) | |
| File "/home/thoraxe/.pyenv/versions/3.9.16/envs/fastapi-ols-39/lib/python3.9/site-packages/fastapi/applications.py", line 292, in __call__ | |
| await super().__call__(scope, receive, send) | |
| File "/home/thoraxe/.pyenv/versions/3.9.16/envs/fastapi-ols-39/lib/python3.9/site-packages/starlette/applications.py", line 122, in __call__ | |
| await self.middleware_stack(scope, receive, send) | |
| File "/home/thoraxe/.pyenv/versions/3.9.16/envs/fastapi-ols-39/lib/python3.9/site-packages/starlette/middleware/errors.py", line 184, in __call__ | |
| raise exc | |
| File "/home/thoraxe/.pyenv/versions/3.9.16/envs/fastapi-ols-39/lib/python3.9/site-packages/starlette/middleware/errors.py", line 162, in __call__ | |
| await self.app(scope, receive, _send) | |
| File "/home/thoraxe/.pyenv/versions/3.9.16/envs/fastapi-ols-39/lib/python3.9/site-packages/starlette/middleware/exceptions.py", line 79, in __call__ | |
| raise exc | |
| File "/home/thoraxe/.pyenv/versions/3.9.16/envs/fastapi-ols-39/lib/python3.9/site-packages/starlette/middleware/exceptions.py", line 68, in __call__ | |
| await self.app(scope, receive, sender) | |
| File "/home/thoraxe/.pyenv/versions/3.9.16/envs/fastapi-ols-39/lib/python3.9/site-packages/fastapi/middleware/asyncexitstack.py", line 20, in __call__ | |
| raise e | |
| File "/home/thoraxe/.pyenv/versions/3.9.16/envs/fastapi-ols-39/lib/python3.9/site-packages/fastapi/middleware/asyncexitstack.py", line 17, in __call__ | |
| await self.app(scope, receive, send) | |
| File "/home/thoraxe/.pyenv/versions/3.9.16/envs/fastapi-ols-39/lib/python3.9/site-packages/starlette/routing.py", line 718, in __call__ | |
| await route.handle(scope, receive, send) | |
| File "/home/thoraxe/.pyenv/versions/3.9.16/envs/fastapi-ols-39/lib/python3.9/site-packages/starlette/routing.py", line 276, in handle | |
| await self.app(scope, receive, send) | |
| File "/home/thoraxe/.pyenv/versions/3.9.16/envs/fastapi-ols-39/lib/python3.9/site-packages/starlette/routing.py", line 66, in app | |
| response = await func(request) | |
| File "/home/thoraxe/.pyenv/versions/3.9.16/envs/fastapi-ols-39/lib/python3.9/site-packages/fastapi/routing.py", line 273, in app | |
| raw_response = await run_endpoint_function( | |
| File "/home/thoraxe/.pyenv/versions/3.9.16/envs/fastapi-ols-39/lib/python3.9/site-packages/fastapi/routing.py", line 192, in run_endpoint_function | |
| return await run_in_threadpool(dependant.call, **values) | |
| File "/home/thoraxe/.pyenv/versions/3.9.16/envs/fastapi-ols-39/lib/python3.9/site-packages/starlette/concurrency.py", line 41, in run_in_threadpool | |
| return await anyio.to_thread.run_sync(func, *args) | |
| File "/home/thoraxe/.pyenv/versions/3.9.16/envs/fastapi-ols-39/lib/python3.9/site-packages/anyio/to_thread.py", line 33, in run_sync | |
| return await get_asynclib().run_sync_in_worker_thread( | |
| File "/home/thoraxe/.pyenv/versions/3.9.16/envs/fastapi-ols-39/lib/python3.9/site-packages/anyio/_backends/_asyncio.py", line 877, in run_sync_in_worker_thread | |
| return await future | |
| File "/home/thoraxe/.pyenv/versions/3.9.16/envs/fastapi-ols-39/lib/python3.9/site-packages/anyio/_backends/_asyncio.py", line 807, in run | |
| result = context.run(func, *args) | |
| File "/home/thoraxe/Red_Hat/openshift/llamaindex-experiments/fastapi-lightspeed-service/ols.py", line 65, in ols_request | |
| processed = task_processor.process_tasks(conversation, rag_model, task_list, llm_request.query) | |
| TypeError: process_tasks() takes 4 positional arguments but 5 were given |
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| task_processor = TaskProcessor() | |
| processed = task_processor.process_tasks(conversation, rag_model, task_list, llm_request.query) |
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| import logging | |
| import sys | |
| from string import Template | |
| from model_context import get_watsonx_predictor | |
| class TaskProcessor: | |
| def process_tasks(conversation, model, tasklist, original_query): | |
| prompt_instructions = Template( | |
| """Instructions: | |
| - You are a helpful assistant. | |
| - You are an expert in Kubernetes and OpenShift. | |
| - Respond to questions about topics other than Kubernetes and OpenShift with: "I can only answer questions about Kubernetes and OpenShift" | |
| - Refuse to participate in anything that could harm a human. | |
| - Your job is to look at the following description and provide a response. | |
| - Base your answer on the provided task and query and not on prior knowledge. | |
| TASK: | |
| ${task} | |
| QUERY: | |
| ${query} | |
| Question: | |
| Does the above query contain enough information about the task? Provide a yes or no answer with explanation. | |
| Response: | |
| """ | |
| ) | |
| logging.info(conversation + " Beginning task processing") | |
| # iterate over the tasks and figure out if we should abort and request more information | |
| # build a dictionary of stuff to use later | |
| to_do_stuff = list() | |
| for task in tasklist: | |
| logging.info(conversation + " task: " + task) | |
| # determine if we have enough information to answer the task | |
| task_query = prompt_instructions.substitute(task=task, query=original_query) | |
| logging.info(conversation + " task query: " + task_query) | |
| logging.info(conversation + " usng model: " + model) | |
| bare_llm = get_watsonx_predictor(model=model, min_new_tokens=5) | |
| response = str(bare_llm(task_query)) | |
| logging.info(conversation + " task response: " + response) | |
| # strip <|endoftext|> from the reponse | |
| clean_response = response.split("<|endoftext|>")[0] | |
| # check if the response was a yes or no answer | |
| # TODO: need to handle when this fails to return an integer | |
| response_status = int(determine_yes_no(conversation, model, clean_response)) | |
| logging.info(conversation + " response status: " + str(response_status)) | |
| if response_status == 0: | |
| logging.info( | |
| conversation | |
| + " Aborting task processing for no response - need details" | |
| ) | |
| resolution_request = str( | |
| "In trying to answer your question, we were unable to determine how to proceed." | |
| " The step we failed on was the following:\n " | |
| + task | |
| + " The failure message was:\n " | |
| + clean_response | |
| + " Please try rephrasing your request to include information that might help complete the task." | |
| ) | |
| logging.info( | |
| conversation + " resolution request: " + resolution_request | |
| ) | |
| return [response_status, resolution_request] | |
| elif response_status == 1: | |
| # we have enough information for the task, so put the task into a list for later | |
| to_do_stuff.append(task) | |
| logging.info(conversation + " Continuing task processing") | |
| else: | |
| logging.info(conversation + " Unknown response status") | |
| return [response_status, "Unknown error occurred"] | |
| return [1, to_do_stuff] | |
| def determine_yes_no(conversation, model, string): | |
| prompt_instructions = Template( | |
| """Instructions: | |
| - determine if a statement is a yes or a no | |
| - return a 1 if the statement is a yes statement | |
| - return a 0 if the statement is a no statement | |
| - return a 9 if you cannot determine if the statement is a yes or no | |
| Examples: | |
| Statement: Yes, that sounds good. | |
| Response: 1 | |
| Statement: No, I don't think that is wise. | |
| Response: 0 | |
| Statement: Apples are red. | |
| Response: 9 | |
| Statement: ${statement} | |
| Response: | |
| """ | |
| ) | |
| logging.info(conversation + " usng model: " + model) | |
| logging.info(conversation + " determining yes/no: " + string) | |
| query = prompt_instructions.substitute(statement=string) | |
| logging.info(conversation + " yes/no query: " + query) | |
| logging.info(conversation + " usng model: " + model) | |
| bare_llm = get_watsonx_predictor(model=model) | |
| response = str(bare_llm(query)) | |
| clean_response = response.split("<|endoftext|>")[0] | |
| logging.info(conversation + " yes/no response: " + clean_response) | |
| return clean_response |
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