AegiSense AI Assistant

# Simple example: RAG query over the AegiSense knowledge base
from langchain_anthropic import ChatAnthropic
from langchain_openai import OpenAIEmbeddings
from langchain_qdrant import QdrantVectorStore
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser

llm = ChatAnthropic(model="claude-sonnet-5", temperature=0)

retriever = QdrantVectorStore.from_existing_collection(
    collection_name="aegisense_docs",
    embedding=OpenAIEmbeddings(model="text-embedding-3-small"),
    url="http://localhost:6333",
).as_retriever(search_kwargs={"k": 4})

prompt = ChatPromptTemplate.from_template(
    "Answer using only this context:\n{context}\n\nQuestion: {question}"
)

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

print(chain.invoke("How do I enable MFA for a user?"))