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import anthropic
import os
from dotenv import load_dotenv
from tools.search_tool import search_duckduckgo
from tools.retriever_tool import Retriever
load_dotenv()
import logging
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=[logging.FileHandler("debug.log"), logging.StreamHandler()]
)
logger = logging.getLogger(__name__)
client = anthropic.Anthropic(
api_key=os.getenv("ANTHROPIC_API_KEY"),
)
# print("Anthropic API key:", os.getenv("ANTHROPIC_API_KEY"))
retriever = Retriever(
top_k=3,
similarity_threshold=0.2,
batch_size=8
)
def call_llm(prompt, model="claude-3-7-sonnet-20250219"):
response = client.messages.create(
model=model,
max_tokens=500,
temperature=0,
system="""You are an expert clinical AI assistant. You must strictly reply in ONLY one of the following formats: TOOL: [Document], TOOL: [Search], or TOOL: [Both].
For questions about general medical information like recovery times, procedure durations, or standard practices, prefer TOOL: [Search].
For questions about specific medical cases or rare conditions found in the document database, use TOOL: [Document].
For questions that would benefit from both sources, use TOOL: [Both].
Never explain, never say anything else.""",
messages=[
{"role": "user", "content": f"""Question: "{prompt}"
Decide the best tool for answering it. Reply exactly with TOOL: [Document], TOOL: [Search], or TOOL: [Both]. No other text."""}
]
)
return response.content[0].text
def agent_respond(question):
logger.debug(f"Received question: {question}")
tool_decision = call_llm(
f"""Decide which tool(s) are needed to answer this question: "{question}".
Available tools:
- Document RAG (for clinical facts)
- Search (for public info)
Reply in format:
TOOL: [Document/Search/Both/All]
"""
)
logger.debug(f"Tool decision raw response: '{tool_decision}'")
use_document = "document" in tool_decision.lower()
use_search = "search" in tool_decision.lower()
logger.debug(f"Parsed decision - Use Document: {use_document}, Use Search: {use_search}")
results = []
if use_document:
logger.debug("Retrieving from documents...")
try:
doc_info = retriever.query(question)
logger.debug(f"Document retrieval returned {len(doc_info)} characters")
results.append(f"Document info:\n{doc_info}")
except Exception as e:
logger.error(f"Document retrieval error: {e}")
results.append(f"Document retrieval error: {str(e)}")
if use_search:
logger.debug("Searching web...")
try:
search_info = search_duckduckgo(question)
logger.debug(f"Search returned {len(search_info)} characters")
results.append(f"Search info:\n{search_info}")
except Exception as e:
logger.error(f"Search error: {e}")
results.append(f"Search error: {str(e)}")
if results:
return "\n\n".join(results)
else:
logger.warning("No results from either tool")
return "Could not determine the right tool to use or both tools failed." |