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82f34f82f2
* Update pre-commit black to latest release * Reformat Python files for new black version
113 lines
3.5 KiB
Python
113 lines
3.5 KiB
Python
import json
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import os
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PROMPT_GATEWAY_ENDPOINT = os.getenv(
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"PROMPT_GATEWAY_ENDPOINT", "http://localhost:10000/v1/chat/completions"
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)
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PROMPT_GATEWAY_PATH = os.getenv("PROMPT_GATEWAY_PATH", "/v1/chat/completions")
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MODEL_SERVER_FUNC_PATH = os.getenv("MODEL_SERVER_FUNC_PATH", "/function_calling")
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LLM_GATEWAY_ENDPOINT = os.getenv(
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"LLM_GATEWAY_ENDPOINT", "http://localhost:12000/v1/chat/completions"
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)
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ARCH_STATE_HEADER = "x-arch-state"
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PREFILL_LIST = [
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"May",
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"Could",
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"Sure",
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"Definitely",
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"Certainly",
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"Of course",
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"Can",
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]
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TEST_CASE_FIXTURES = {
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"SIMPLE": {
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"input": {
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"messages": [
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{
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"role": "user",
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"content": "how is the weather in seattle for next 2 days",
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}
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]
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},
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"model_server_response": {
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"id": 0,
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"object": "chat_completion",
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"created": "",
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"choices": [
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{
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"id": 0,
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"message": {
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"role": "",
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"content": "",
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"tool_call_id": "",
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"tool_calls": [
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{
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"id": "call_6009",
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"type": "function",
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"function": {
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"name": "get_current_weather",
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"arguments": {
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"location": "Seattle, WA",
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"days": "2",
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},
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},
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}
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],
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},
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"finish_reason": "stop",
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}
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],
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"model": "Arch-Function",
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"metadata": {"intent_latency": "455.092", "function_latency": "312.744"},
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},
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"api_server_response": [
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{
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"date": "2024-12-12",
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"temperature": {"min": 72, "max": 90},
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"units": "Farenheit",
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"query_time": "2024-12-12 22:06:30.420319+00:00",
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},
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{
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"date": "2024-12-13",
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"temperature": {"min": 52, "max": 70},
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"units": "Farenheit",
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"query_time": "2024-12-12 22:06:30.420349+00:00",
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},
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],
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}
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}
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def get_data_chunks(stream, n=1):
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chunks = []
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for chunk in stream.iter_lines():
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if chunk:
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chunk = chunk.decode("utf-8")
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chunk_data_id = chunk[0:6]
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assert chunk_data_id == "data: "
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chunk_data = chunk[6:]
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chunk_data = chunk_data.strip()
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chunks.append(chunk_data)
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if len(chunks) >= n:
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break
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return chunks
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def get_plano_messages(response_json):
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plano_messages = []
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if response_json and "metadata" in response_json:
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# load plano_state from metadata
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plano_state_str = response_json.get("metadata", {}).get(ARCH_STATE_HEADER, "{}")
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# parse plano_state into json object
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plano_state = json.loads(plano_state_str)
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# load messages from plano_state
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plano_messages_str = plano_state.get("messages", "[]")
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# parse messages into json object
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plano_messages = json.loads(plano_messages_str)
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# append messages from plano gateway to history
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return plano_messages
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return []
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