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app.py
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import streamlit as st
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from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
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from llama_index.embeddings.huggingface import HuggingFaceEmbedding
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from llama_index.core.node_parser import SentenceSplitter
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from llama_index.core.ingestion import IngestionPipeline
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import chromadb
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from llama_index.vector_stores.chroma import ChromaVectorStore
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from llama_index.llms.ollama import Ollama
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# Ustawienia strony
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st.title("Aplikacja z LlamaIndex")
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# Za艂aduj dokumenty
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documents = SimpleDirectoryReader('./data/').load_data()
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db = chromadb.PersistentClient(path="./ustawy")
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chroma_collection = db.get_or_create_collection("pomoc_ukrainie")
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vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
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# Utw贸rz pipeline do przetwarzania dokument贸w
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pipeline = IngestionPipeline(
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transformations=[
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SentenceSplitter(),
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HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5"),
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],
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vector_store=vector_store
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)
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# Przetw贸rz dokumenty
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nodes = pipeline.run(documents)
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# Utw贸rz indeks
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embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
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index = VectorStoreIndex.from_vector_store(vector_store, embed_model=embed_model)
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# Utw贸rz silnik zapyta艅
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llm = Ollama(model="qwen2:7b")
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query_engine = index.as_query_engine(llm=llm)
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# Wy艣wietl histori臋 wiadomo艣ci
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if "messages" not in st.session_state:
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st.session_state.messages = []
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# Wej艣cie u偶ytkownika
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if input := st.text_input("Zadaj pytanie"):
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st.session_state.messages.append({"role": "user", "content": input})
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# Wygeneruj odpowied藕
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try:
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response = query_engine.query(input)
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st.session_state.messages.append({"role": "assistant", "content": response})
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except Exception as e:
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st.error(f"B艂膮d: {e}")
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# Wy艣wietl wiadomo艣ci
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for message in st.session_state.messages:
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if message["role"] == "user":
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st.write(f"U偶ytkownik: {message['content']}")
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else:
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st.write(f"Asystent: {message['content']}")
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