add app.py

app.py

@@ -0,0 +1,137 @@
+# import streamlit as st
+# from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
+# from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+# from llama_index.core.node_parser import SentenceSplitter
+# from llama_index.core.ingestion import IngestionPipeline
+# import chromadb
+# from llama_index.vector_stores.chroma import ChromaVectorStore
+# from llama_index.llms.ollama import Ollama
+
+
+# # Ustawienia strony
+# st.title("Aplikacja z LlamaIndex")
+
+
+# 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")
+
+# # Utwórz pipeline do przetwarzania dokumentów
+# pipeline = IngestionPipeline(
+#     transformations=[
+#         SentenceSplitter(),
+#         embed_model,
+#     ],
+#     vector_store=vector_store
+# )
+
+# # Utwórz indeks
+
+# index = VectorStoreIndex.from_vector_store(vector_store, embed_model=embed_model)
+
+# # Utwórz silnik zapytań
+# llm = Ollama(model="qwen2:7b")
+# query_engine = index.as_query_engine(
+#     llm=llm,
+#     response_mode = 'compact')
+
+
+
+# # Store LLM generated responses
+# if "messages" not in st.session_state.keys():
+#     st.session_state.messages = [{"role": "assistant", "content": "Zadaj mi pytanie..."}]
+
+# # Display chat messages
+# for message in st.session_state.messages:
+#     with st.chat_message(message["role"]):
+#         st.write(message["content"])
+
+# # User-provided prompt
+# if input := st.chat_input():
+#     st.session_state.messages.append({"role": "user", "content": input})
+#     with st.chat_message("user"):
+#         st.write(input)
+
+# # Generate a new response if last message is not from assistant
+# if st.session_state.messages[-1]["role"] != "assistant":
+#     with st.chat_message("assistant"):
+#         with st.spinner("Czekaj, odpowiedź jest generowana.."):
+#             response = query_engine.query(input) 
+            
+#             st.write(response.response)
+#             for node in response.source_nodes:
+#                 st.write(node.score)
+            
+#     message = {"role": "assistant", "content": response}
+#     st.session_state.messages.append(message)
+
+
+
+import streamlit as st
+from llama_index.core import VectorStoreIndex
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.core.node_parser import SentenceSplitter
+from llama_index.core.ingestion import IngestionPipeline
+import chromadb
+from llama_index.vector_stores.chroma import ChromaVectorStore
+from llama_index.llms.ollama import Ollama
+
+# Ustawienia strony
+st.title("Aplikacja z LlamaIndex")
+
+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")
+
+# Utwórz pipeline do przetwarzania dokumentów
+pipeline = IngestionPipeline(
+    transformations=[
+        SentenceSplitter(),
+        embed_model,
+    ],
+    vector_store=vector_store
+)
+
+# Utwórz indeks
+index = VectorStoreIndex.from_vector_store(vector_store, embed_model=embed_model)
+
+# Utwórz silnik zapytań
+llm = Ollama(model="qwen2:7b")
+query_engine = index.as_query_engine(
+    llm=llm,
+    response_mode='compact')
+
+# Store LLM generated responses
+if "messages" not in st.session_state.keys():
+    st.session_state.messages = [{"role": "assistant", "content": "Zadaj mi pytanie..."}]
+
+# Display chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.write(message["content"])
+
+# User-provided prompt
+if input := st.chat_input():
+    st.session_state.messages.append({"role": "user", "content": input})
+    with st.chat_message("user"):
+        st.write(input)
+
+# Generate a new response if last message is not from assistant
+if st.session_state.messages[-1]["role"] != "assistant":
+    with st.chat_message("assistant"):
+        with st.spinner("Czekaj, odpowiedź jest generowana.."):
+            response = query_engine.query(input)
+
+            # Zbuduj treść wiadomości z odpowiedzią i score
+            content = str(response.response)  # Upewnij się, że response jest stringiem
+            if hasattr(response, 'source_nodes') and response.source_nodes:  # Sprawdź, czy source_nodes istnieje
+                # Dodaj score pierwszego węzła (jeśli istnieje)
+                content += f"\nScore: {response.source_nodes[0].score:.4f}"  # Dodaj score
+
+            st.write(content)  # Wyświetl całą treść w Streamlit
+
+    message = {"role": "assistant", "content": content}  # Zapisz całą treść w wiadomości
+    st.session_state.messages.append(message)
+