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import streamlit as st
from llama_index.core import VectorStoreIndex
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
import chromadb
from llama_index.vector_stores.chroma import ChromaVectorStore
st.title("Infections - retriever")
db = chromadb.PersistentClient(path="./zakazenia")
chroma_collection = db.get_or_create_collection("zalacznik_nr12")
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
# Indeks z vector store
index = VectorStoreIndex.from_vector_store(vector_store, embed_model=embed_model)
# Retriever bez LLM
retriever = index.as_retriever(similarity_top_k=1)
if "messages" not in st.session_state:
st.session_state.messages = [{"role": "assistant", "content": "Zadaj mi pytanie..."}]
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.write(message["content"])
if input := st.chat_input():
st.session_state.messages.append({"role": "user", "content": input})
with st.chat_message("user"):
st.write(input)
if st.session_state.messages[-1]["role"] != "assistant":
with st.chat_message("assistant"):
with st.spinner("Czekaj, trwa wyszukiwanie..."):
results = retriever.retrieve(input)
content = "\n\n---\n\n".join([doc.text for doc in results])
st.write(content)
st.session_state.messages.append({"role": "assistant", "content": content}) |