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Steve Androulakis
2024-12-31 12:19:53 -08:00
parent c6b71b8ffa
commit 4286be2e5d
4 changed files with 72 additions and 57 deletions
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7
README.md
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@@ -2,7 +2,12 @@
Multi-Turn Chat using an Entity Workflow. The workflow runs forever unless explicitly ended. The workflow continues as new after a configurable number of chat turns to keep the prompt size small and the Temporal event history small. Each continued-as-new workflow receives a summary of the conversation history so far for context.
To run, first see `samples-python` [README.md](../../README.md), and `ollama` [README.md](../README.md) for prerequisites specific to this sample. Once set up, run the following from this directory:
## Setup
* Install [Ollama](https://ollama.com) and the Mistral model (`ollama run mistral`).
* Install and run Temporal. Follow the instructions in the [Temporal documentation](https://learn.temporal.io/getting_started/python/dev_environment/#set-up-a-local-temporal-service-for-development-with-temporal-cli) to install and run the Temporal server.
* Install the dependencies: `poetry install`
## Running the example
1. Run the worker: `poetry run python run_worker.py`
2. In another terminal run the client with a prompt.

17
activities.py
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@@ -1,19 +1,26 @@
from dataclasses import dataclass
from temporalio import activity
from ollama import chat, ChatResponse
@dataclass
class OllamaPromptInput:
prompt: str
context_instructions: str
class OllamaActivities:
@activity.defn
def prompt_ollama(self, prompt: str) -> str:
def prompt_ollama(self, input: OllamaPromptInput) -> str:
model_name = 'mistral'
messages = [
{
'role': 'system',
'content': input.context_instructions,
},
{
'role': 'user',
'content': prompt
'content': input.prompt,
}
]
# Call ollama's chat function
response: ChatResponse = chat(model=model_name, messages=messages)
# Return the model's text response
return response.message.content

2
send_message.py
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@@ -14,7 +14,7 @@ async def main(prompt):
# Sends a signal to the workflow (and starts it if needed)
await client.start_workflow(
EntityOllamaWorkflow.run,
OllamaParams(None, None),
OllamaParams(None, None), # or pass in custom summary/prompt_queue if desired
id=workflow_id,
task_queue="ollama-task-queue",
start_signal="user_prompt",

103
workflows.py
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@@ -6,7 +6,8 @@ from typing import Deque, List, Optional, Tuple
from temporalio import workflow
with workflow.unsafe.imports_passed_through():
from activities import OllamaActivities
# Import the updated OllamaActivities and the new dataclass
from activities import OllamaActivities, OllamaPromptInput
@dataclass
@@ -18,7 +19,6 @@ class OllamaParams:
@workflow.defn
class EntityOllamaWorkflow:
def __init__(self) -> None:
# List to store prompt history
self.conversation_history: List[Tuple[str, str]] = []
self.prompt_queue: Deque[str] = deque()
self.conversation_summary: Optional[str] = None
@@ -26,16 +26,11 @@ class EntityOllamaWorkflow:
self.chat_ended: bool = False
@workflow.run
async def run(
self,
params: OllamaParams,
) -> str:
async def run(self, params: OllamaParams) -> str:
if params and params.conversation_summary:
self.conversation_history.append(
("conversation_summary", params.conversation_summary)
)
self.conversation_summary = params.conversation_summary
if params and params.prompt_queue:
@@ -44,71 +39,79 @@ class EntityOllamaWorkflow:
while True:
workflow.logger.info("Waiting for prompts...")
# Wait for a chat message (signal) or timeout
await workflow.wait_condition(
lambda: bool(self.prompt_queue) or self.chat_ended
)
if self.prompt_queue:
# Fetch next user prompt and add to conversation history
# Get user's prompt
prompt = self.prompt_queue.popleft()
self.conversation_history.append(("user", prompt))
# Build prompt + context
context_instructions, actual_prompt = self.prompt_with_history(prompt)
workflow.logger.info("Prompt: " + prompt)
# Send prompt to Ollama
# Pass a single input object
prompt_input = OllamaPromptInput(
prompt=actual_prompt,
context_instructions=context_instructions,
)
# Call activity with one argument
response = await workflow.execute_activity_method(
OllamaActivities.prompt_ollama,
self.prompt_with_history(prompt),
prompt_input,
schedule_to_close_timeout=timedelta(seconds=20),
)
workflow.logger.info(f"{response}")
# Append the response to the conversation history
workflow.logger.info(f"Ollama response: {response}")
self.conversation_history.append(("response", response))
# Continue as new every x conversational turns to avoid event
# history size getting too large. This is also to avoid the
# prompt (with conversational history) getting too large for
# AWS Ollama.
# We summarize the chat to date and use that as input to the
# new workflow
# Continue as new after X turns
if len(self.conversation_history) >= self.continue_as_new_per_turns:
# Summarize the conversation to date using Ollama
# Summarize conversation
summary_context, summary_prompt = self.prompt_summary_with_history()
summary_input = OllamaPromptInput(
prompt=summary_prompt,
context_instructions=summary_context,
)
self.conversation_summary = await workflow.start_activity_method(
OllamaActivities.prompt_ollama,
self.prompt_summary_from_history(),
summary_input,
schedule_to_close_timeout=timedelta(seconds=20),
)
workflow.logger.info(
"Continuing as new due to %i conversational turns."
"Continuing as new after %i turns."
% self.continue_as_new_per_turns,
)
workflow.continue_as_new(
args=[
OllamaParams(
self.conversation_summary,
self.prompt_queue,
conversation_summary=self.conversation_summary,
prompt_queue=self.prompt_queue,
)
]
)
continue
# If end chat signal was sent
# Handle end of chat
if self.chat_ended:
# The workflow might be continued as new without any
# chat to summarize, so only call Ollama if there
# is more than the previous summary in the history.
if len(self.conversation_history) > 1:
# Summarize the conversation to date using Ollama
# Summarize conversation
summary_context, summary_prompt = self.prompt_summary_with_history()
summary_input = OllamaPromptInput(
prompt=summary_prompt,
context_instructions=summary_context,
)
self.conversation_summary = await workflow.start_activity_method(
OllamaActivities.prompt_ollama,
self.prompt_summary_from_history(),
summary_input,
schedule_to_close_timeout=timedelta(seconds=20),
)
@@ -116,16 +119,13 @@ class EntityOllamaWorkflow:
"Chat ended. Conversation summary:\n"
+ f"{self.conversation_summary}"
)
return f"{self.conversation_history}"
@workflow.signal
async def user_prompt(self, prompt: str) -> None:
# Chat ended but the workflow is waiting for a chat summary to be generated
if self.chat_ended:
workflow.logger.warn(f"Message dropped due to chat closed: {prompt}")
return
self.prompt_queue.append(prompt)
@workflow.signal
@@ -140,25 +140,28 @@ class EntityOllamaWorkflow:
def get_summary_from_history(self) -> Optional[str]:
return self.conversation_summary
# Helper method used in prompts to Ollama
# Helper: generate text of the entire conversation so far
def format_history(self) -> str:
return " ".join(f"{text}" for _, text in self.conversation_history)
# Create the prompt given to Ollama for each conversational turn
def prompt_with_history(self, prompt: str) -> str:
# Return (context_instructions, prompt)
def prompt_with_history(self, prompt: str) -> tuple[str, str]:
history_string = self.format_history()
return (
f"Here is the conversation history: {history_string} Please add "
+ "a few sentence response to the prompt in plain text sentences. "
+ "Don't editorialize or add metadata like response. Keep the "
+ f"text a plain explanation based on the history. Prompt: {prompt}"
context_instructions = (
f"Here is the conversation history: {history_string} "
"Please add a few sentence response in plain text sentences. "
"Don't editorialize or add metadata. "
"Keep the text a plain explanation based on the history."
)
return (context_instructions, prompt)
# Create the prompt to Ollama to summarize the conversation history
def prompt_summary_from_history(self) -> str:
# Return (context_instructions, prompt) for summarizing the conversation
def prompt_summary_with_history(self) -> tuple[str, str]:
history_string = self.format_history()
return (
"Here is the conversation history between a user and a chatbot: "
+ f"{history_string} -- Please produce a two sentence summary of "
+ "this conversation."
context_instructions = (
f"Here is the conversation history between a user and a chatbot: {history_string}"
)
actual_prompt = (
"Please produce a two sentence summary of this conversation."
)
return (context_instructions, actual_prompt)