File size: 2,630 Bytes
1fac52c | 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 | """Use Carbon-X as a tool for an Inspect react() agent.
Serve Carbon-X first: vllm serve HuggingFaceBio/Carbon-X --trust-remote-code
Then run: python inspect_agent.py
"""
from inspect_ai import Task, eval, task
from inspect_ai.agent import as_solver, react
from inspect_ai.dataset import Sample
from inspect_ai.scorer import includes
from inspect_ai.solver import solver
from inspect_ai.tool import ToolError, tool
from inspect_ai.util import store
from openai import AsyncOpenAI
carbon = AsyncOpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
@tool
def ask_carbon():
async def execute(question: str, sequences: list[str], context: str = "") -> str:
"""Ask Carbon-X, a DNA language model, about this task's DNA sequences. Its answers are predictions.
Args:
question: One self-contained question.
sequences: Names of the sequences to send, as given in the task.
context: Known facts, such as the gene, positions or alleles.
"""
dna = store().get("sequences")
if missing := [name for name in sequences if name not in dna]:
raise ToolError(f"Unknown sequence names: {missing}. Available: {list(dna)}")
parts = [context, question] if context else [question]
parts += [f"{name}: <dna>{dna[name]}</dna>" for name in sequences]
response = await carbon.chat.completions.create(
model="HuggingFaceBio/Carbon-X",
messages=[{"role": "user", "content": "\n\n".join(parts)}],
max_tokens=4096,
extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)
return response.choices[0].message.content
return execute
@solver
def load_sequences():
async def solve(state, generate):
store().set("sequences", state.metadata["sequences"]) # the tool reads DNA from here
return state
return solve
@task
def carbon_agent():
sample = Sample(
input="Sequence `variant` differs from `reference` at position 20 (A>T). Is the variant benign or pathogenic?",
target="pathogenic",
metadata={"sequences": {
"reference": "ATGGTGCATCTGACTCCTGAGGAGAAGTCTGCCGTTACTGCCCTGTGGGGCAAGG",
"variant": "ATGGTGCATCTGACTCCTGTGGAGAAGTCTGCCGTTACTGCCCTGTGGGGCAAGG",
}},
)
agent = react(prompt="Consult Carbon-X with the ask_carbon tool before you answer.", tools=[ask_carbon()])
return Task(dataset=[sample], solver=[load_sequences(), as_solver(agent)], scorer=includes())
eval(carbon_agent(), model="openai/gpt-5") # any Inspect model with tool calling as the agent
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