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from langchain.docstore.document import Document
from langchain_community.retrievers import BM25Retriever
from smolagents import Tool
from faq_data import faq_entries
# Convert the FAQ data into LangChain Documents
docs = [
Document(
page_content="\n".join([
f"Category: {faq['category']}",
f"Question: {faq['question']}",
f"Answer: {faq['answer']}"
]),
metadata={"category": faq["category"]}
)
for faq in faq_entries
]
# Create the custom FAQ retriever tool
class FAQRetrieverTool(Tool):
name = "faq_retriever"
description = "Answers company FAQ questions using internal documentation."
inputs = {
"query": {
"type": "string",
"description": "The user question to answer from internal FAQ documents."
}
}
output_type = "string"
def __init__(self, docs):
self.is_initialized = True # <-- questo mancava!
self.retriever = BM25Retriever.from_documents(docs)
def forward(self, query: str):
results = self.retriever.get_relevant_documents(query)
if results:
return "\n\n".join([doc.page_content for doc in results[:3]])
else:
return "Sorry, I couldn't find a matching FAQ."
# Initialize the retriever tool
faq_tool = FAQRetrieverTool(docs)
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