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22195c1 5b5c80a 22195c1 e15864e 22195c1 5b5c80a 22195c1 e15864e 22195c1 a009989 22195c1 5b5c80a 22195c1 5b5c80a 22195c1 e15864e 22195c1 a009989 22195c1 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 | from __future__ import annotations
import datetime
import json
from dataclasses import asdict
from typing import Generator
import anthropic
from rich.panel import Panel
from rich.text import Text
from beacon_logging import get_logger
from config import INTAKE_MODEL
from llm import cached_system, cached_tools
from models import PatientProfile, geocode_zip
from prompts import INTAKE_SYSTEM, lookup_disease_profile
from tools import INTAKE_TOOLS
from translations import LANGUAGE_DIRECTIVE
from _console import console
_logger = get_logger("agents.intake")
def _months_from_date(date_str: str) -> int:
"""Convert YYYY-MM string to elapsed months from today. Returns 0 on parse failure."""
try:
year, month = int(date_str[:4]), int(date_str[5:7])
today = datetime.date.today()
return (today.year - year) * 12 + (today.month - month)
except (ValueError, IndexError):
return 0
def _resolve_months(data: dict, date_key: str, months_key: str) -> int:
"""Prefer date string for exact computation; fall back to LLM-supplied integer."""
if data.get(date_key):
return _months_from_date(data[date_key])
return data.get(months_key) or 0
def run_intake_agent(client: anthropic.Anthropic) -> PatientProfile:
today = datetime.date.today().strftime("%B %d, %Y")
console.print()
console.print(Panel(
Text("Beacon — Rare Disease Clinical Trial Finder", justify="center", style="bold cyan"),
border_style="cyan",
padding=(1, 4),
))
messages: list[anthropic.types.MessageParam] = [
{"role": "user", "content": "Please begin."}
]
while True:
response = client.messages.create(
model=INTAKE_MODEL,
max_tokens=1024,
system=cached_system(f"Today's date is {today}.\n\n" + INTAKE_SYSTEM),
tools=cached_tools(INTAKE_TOOLS),
messages=messages,
)
text = next((b.text for b in response.content if b.type == "text"), "")
if text:
console.print(f"\n[bold cyan]Beacon:[/bold cyan] {text}")
identify_block = next(
(b for b in response.content if b.type == "tool_use" and b.name == "identify_disease"),
None,
)
submit_block = next(
(b for b in response.content if b.type == "tool_use" and b.name == "submit_profile"),
None,
)
if identify_block:
disease = lookup_disease_profile(identify_block.input["standardized_name"])
tool_result = json.dumps({
"benchmarks_to_collect": disease["benchmarks"] if disease else [],
"message": (
f"Collect these benchmarks for {disease['full_name']}"
if disease else "Disease not in registry — skip benchmark questions."
),
})
messages.append({"role": "assistant", "content": response.content})
messages.append({"role": "user", "content": [{
"type": "tool_result",
"tool_use_id": identify_block.id,
"content": tool_result,
}]})
continue
if submit_block:
data = submit_block.input
try:
with console.status("[cyan]Geocoding location…[/cyan]", spinner="dots"):
lat, lon = geocode_zip(data["zip_code"], data.get("country_code", "US"))
except Exception as exc:
console.print(f"[yellow]Warning:[/yellow] Geocoding failed ({exc}) — coordinates set to 0,0.")
lat, lon = 0.0, 0.0
profile = PatientProfile(
disease=data["disease"],
age=data["age"],
onset_months=_resolve_months(data, "onset_date", "onset_months"),
diagnosis_months=_resolve_months(data, "diagnosis_date", "diagnosis_months"),
benchmarks=data.get("benchmarks") or {},
zip_code=data["zip_code"],
country_code=data.get("country_code", "US"),
lat=lat,
lon=lon,
radius_miles=data.get("radius_miles", 20),
phases=data.get("phases") or [],
include_eap=data.get("include_eap", False),
include_observational=data.get("include_observational", False),
)
_logger.info(
"Patient intake complete (CLI)",
extra={"data": {"intake_summary": asdict(profile)}},
)
return profile
messages.append({"role": "assistant", "content": response.content})
user_input = input("\nYou: ").strip() or "(no response)"
messages.append({"role": "user", "content": user_input})
def intake_greeting(client: anthropic.Anthropic, lang: str = "en") -> tuple[str, list]:
"""Run the opening intake turn (blocking). Returns (greeting_text, initial_messages)."""
today = datetime.date.today().strftime("%B %d, %Y")
system = f"Today's date is {today}.\n\n" + LANGUAGE_DIRECTIVE[lang] + INTAKE_SYSTEM
messages: list[anthropic.types.MessageParam] = [{"role": "user", "content": "Please begin."}]
response = client.messages.create(
model=INTAKE_MODEL,
max_tokens=1024,
system=cached_system(system),
tools=cached_tools(INTAKE_TOOLS),
messages=messages,
)
text = next((b.text for b in response.content if b.type == "text"), "")
return text, messages + [{"role": "assistant", "content": response.content}]
def stream_intake_turn(
client: anthropic.Anthropic,
messages: list[anthropic.types.MessageParam],
lang: str = "en",
) -> Generator[tuple, None, None]:
"""
Stream one user turn of the intake conversation.
Yields:
("token", str) — partial text chunk
("reset_stream",) — identify_disease handled; clear token buffer
("text", str, list) — model responded with text; updated messages
("profile", PatientProfile, list) — profile submitted; updated messages
"""
today = datetime.date.today().strftime("%B %d, %Y")
system = f"Today's date is {today}.\n\n" + LANGUAGE_DIRECTIVE[lang] + INTAKE_SYSTEM
new_msgs = list(messages)
while True:
intake_text = ""
with client.messages.stream(
model=INTAKE_MODEL,
max_tokens=1024,
system=cached_system(system),
tools=cached_tools(INTAKE_TOOLS),
messages=new_msgs,
) as stream:
for chunk in stream.text_stream:
intake_text += chunk
yield ("token", chunk)
response = stream.get_final_message()
intake_text = intake_text or next(
(b.text for b in response.content if b.type == "text"), ""
)
identify_block = next(
(b for b in response.content if b.type == "tool_use" and b.name == "identify_disease"),
None,
)
submit_block = next(
(b for b in response.content if b.type == "tool_use" and b.name == "submit_profile"),
None,
)
if identify_block:
disease = lookup_disease_profile(identify_block.input["standardized_name"])
tool_result = json.dumps({
"benchmarks_to_collect": disease["benchmarks"] if disease else [],
"message": (
f"Collect these benchmarks for {disease['full_name']}"
if disease else "Disease not in registry — skip benchmark questions."
),
})
new_msgs = new_msgs + [
{"role": "assistant", "content": response.content},
{"role": "user", "content": [{
"type": "tool_result",
"tool_use_id": identify_block.id,
"content": tool_result,
}]},
]
yield ("reset_stream",)
continue
new_msgs = new_msgs + [{"role": "assistant", "content": response.content}]
if submit_block:
data = submit_block.input
try:
lat, lon = geocode_zip(data["zip_code"], data.get("country_code", "US"))
except Exception:
lat, lon = 0.0, 0.0
profile = PatientProfile(
disease=data["disease"],
age=data["age"],
onset_months=_resolve_months(data, "onset_date", "onset_months"),
diagnosis_months=_resolve_months(data, "diagnosis_date", "diagnosis_months"),
benchmarks=data.get("benchmarks") or {},
zip_code=data["zip_code"],
country_code=data.get("country_code", "US"),
lat=lat,
lon=lon,
radius_miles=data.get("radius_miles", 20),
phases=data.get("phases") or [],
include_eap=data.get("include_eap", False),
include_observational=data.get("include_observational", False),
lang=lang,
)
yield ("profile", profile, new_msgs)
return
yield ("text", intake_text, new_msgs)
return
|