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6754826 57f672f 6754826 57f672f 6754826 57f672f 6754826 57f672f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | import json
from typing import AsyncGenerator
from openai import AsyncOpenAI
from config import LLM_BASE_URL, LLM_MODEL, OPENAI_API_KEY
from tools import TOOLS, run_tool
_SYSTEM_INJECT = (
"You have access to a live notepad. "
"Use write_to_notepad whenever the user asks you to note, write down, remember, save, "
"or record anything — always confirm what you wrote after calling the tool. "
"Use read_notepad to recall saved notes. "
"Use clear_notepad only when the user explicitly asks to erase all notes. "
"Use get_datetime for the current date or time.\n\n"
"You also act as a clinical assistant for blood test results. "
"Whenever the user asks about their blood test, lab results, deficiencies, or what "
"supplements they should take, first call get_blood_report_summary to see which "
"markers are low, high, or normal. For every abnormal (low or high) marker, call "
"search_supplements with a short query describing that marker (e.g. 'low vitamin D') "
"to get supplement recommendations, then explain the deficiency and recommendation in "
"plain spoken language — no markdown, bullet points, asterisks, or headers, since this "
"is read aloud by a speaking avatar. If the search_supplements result includes "
"interaction cautions (e.g. a medication, pregnancy, or condition to be careful with), "
"briefly mention that caution too, in plain language — this matters most for markers "
"like kidney function, cholesterol/lipids, and blood sugar where the wrong supplement "
"can be risky. Always end any discussion of blood results or supplements with a "
"reminder that this is not a medical diagnosis and the user should consult their "
"doctor before starting any supplement or changing their dose."
)
_client: AsyncOpenAI | None = None
def _get_client() -> AsyncOpenAI:
global _client
if _client is None:
_client = AsyncOpenAI(api_key=OPENAI_API_KEY, base_url=LLM_BASE_URL)
return _client
def _build_history(messages: list) -> list:
"""Inject tool guidance into the system message."""
msgs = list(messages)
if msgs and msgs[0].get("role") == "system":
msgs[0] = {**msgs[0], "content": _SYSTEM_INJECT + "\n\n" + msgs[0]["content"]}
else:
msgs.insert(0, {"role": "system", "content": _SYSTEM_INJECT})
return msgs
async def run_agent(messages: list, model: str | None = None) -> str:
"""Run the full tool-calling loop and return the final text response."""
history = _build_history(messages)
m = model or LLM_MODEL
print(f"[AGENT → {LLM_BASE_URL}] model={m} msgs={len(history)}", flush=True)
while True:
resp = await _get_client().chat.completions.create(
model=m,
messages=history,
tools=TOOLS,
tool_choice="auto",
store=True,
)
msg = resp.choices[0].message
if not msg.tool_calls:
print(f"[AGENT] ✓ final reply: {(msg.content or '')!r:.100}", flush=True)
return msg.content or ""
history.append(msg.model_dump(exclude_none=True))
for tc in msg.tool_calls:
args = json.loads(tc.function.arguments or "{}")
print(f"[AGENT] tool call: {tc.function.name}({args})", flush=True)
result = await run_tool(tc.function.name, args)
history.append({"role": "tool", "tool_call_id": tc.id, "content": result})
async def stream_agent(messages: list, model: str | None = None) -> AsyncGenerator[str, None]:
"""
Stream tokens as soon as the model produces them. Every pass is a single
streaming call — if the model asks for tool calls we resolve them and
loop with another streaming call; if it answers directly, those tokens
are yielded immediately with no extra non-streamed round trip first.
"""
history = _build_history(messages)
m = model or LLM_MODEL
while True:
stream = await _get_client().chat.completions.create(
model=m,
messages=history,
tools=TOOLS,
tool_choice="auto",
stream=True,
store=True,
)
tool_calls: dict[int, dict] = {}
async for chunk in stream:
delta = chunk.choices[0].delta
if delta.content:
yield delta.content
for tc_delta in (delta.tool_calls or []):
slot = tool_calls.setdefault(
tc_delta.index, {"id": "", "name": "", "arguments": ""}
)
if tc_delta.id:
slot["id"] = tc_delta.id
if tc_delta.function:
if tc_delta.function.name:
slot["name"] += tc_delta.function.name
if tc_delta.function.arguments:
slot["arguments"] += tc_delta.function.arguments
if not tool_calls:
return # final answer was already streamed above
ordered = [tool_calls[i] for i in sorted(tool_calls)]
history.append({
"role": "assistant",
"content": None,
"tool_calls": [
{
"id": tc["id"],
"type": "function",
"function": {"name": tc["name"], "arguments": tc["arguments"]},
}
for tc in ordered
],
})
for tc in ordered:
args = json.loads(tc["arguments"] or "{}")
print(f"[AGENT] tool call: {tc['name']}({args})", flush=True)
result = await run_tool(tc["name"], args)
history.append({"role": "tool", "tool_call_id": tc["id"], "content": result})
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