update to llama-index v0.6.13

app.py

@@ -1,40 +1,57 @@
 import os
 import streamlit as st
-from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader, ServiceContext
-from llama_index.llm_predictor.chatgpt import ChatGPTLLMPredictor
+from llama_index import (
+    GPTVectorStoreIndex,
+    SimpleDirectoryReader,
+    ServiceContext,
+    StorageContext,
+    LLMPredictor,
+    load_index_from_storage,
+)
+from langchain.chat_models import ChatOpenAI
 
-index_name = "./index.json"
+index_name = "./saved_index"
 documents_folder = "./documents"
 
+
 @st.cache_resource
 def initialize_index(index_name, documents_folder):
-    llm_predictor = ChatGPTLLMPredictor()
+    llm_predictor = LLMPredictor(
+        llm=ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
+    )
     service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)
     if os.path.exists(index_name):
-        index = GPTSimpleVectorIndex.load_from_disk(index_name, service_context=service_context)
+        index = load_index_from_storage(
+            StorageContext.from_defaults(persist_dir=index_name),
+            service_context=service_context,
+        )
     else:
         documents = SimpleDirectoryReader(documents_folder).load_data()
-        index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
-        index.save_to_disk(index_name)
+        index = GPTVectorStoreIndex.from_documents(
+            documents, service_context=service_context
+        )
+        index.storage_context.persist(persist_dir=index_name)
 
     return index
 
 
 @st.cache_data(max_entries=200, persist=True)
 def query_index(_index, query_text):
-    response = _index.query(query_text)
+    response = _index.as_query_engine().query(query_text)
     return str(response)
 
 
 st.title("🦙 Llama Index Demo 🦙")
 st.header("Welcome to the Llama Index Streamlit Demo")
-st.write("Enter a query about Paul Graham's essays. You can check out the original essay [here](https://raw.githubusercontent.com/jerryjliu/llama_index/main/examples/paul_graham_essay/data/paul_graham_essay.txt). Your query will be answered using the essay as context, using embeddings from text-ada-002 and LLM completions from ChatGPT. You can read more about Llama Index and how this works in [our docs!](https://gpt-index.readthedocs.io/en/latest/index.html)")
+st.write(
+    "Enter a query about Paul Graham's essays. You can check out the original essay [here](https://raw.githubusercontent.com/jerryjliu/llama_index/main/examples/paul_graham_essay/data/paul_graham_essay.txt). Your query will be answered using the essay as context, using embeddings from text-ada-002 and LLM completions from gpt-3.5-turbo. You can read more about Llama Index and how this works in [our docs!](https://gpt-index.readthedocs.io/en/latest/index.html)"
+)
 
 index = None
 api_key = st.text_input("Enter your OpenAI API key here:", type="password")
 if api_key:
-    os.environ['OPENAI_API_KEY'] = api_key
-    index = initialize_index(index_name, documents_folder)    
+    os.environ["OPENAI_API_KEY"] = api_key
+    index = initialize_index(index_name, documents_folder)
 
 
 if index is None:
@@ -45,11 +62,14 @@ text = st.text_input("Query text:", value="What did the author do growing up?")
 if st.button("Run Query") and text is not None:
     response = query_index(index, text)
     st.markdown(response)
-    
+
     llm_col, embed_col = st.columns(2)
     with llm_col:
-        st.markdown(f"LLM Tokens Used: {index.service_context.llm_predictor._last_token_usage}")
-    
-    with embed_col:
-        st.markdown(f"Embedding Tokens Used: {index.service_context.embed_model._last_token_usage}")
+        st.markdown(
+            f"LLM Tokens Used: {index.service_context.llm_predictor._last_token_usage}"
+        )
 
+    with embed_col:
+        st.markdown(
+            f"Embedding Tokens Used: {index.service_context.embed_model._last_token_usage}"
+        )

requirements.txt

@@ -1,3 +1,3 @@
-langchain==0.0.123
-llama-index==0.5.1
+langchain==0.0.154
+llama-index==0.6.13
 streamlit==1.19.0

saved_index/index_store.json

@@ -0,0 +1 @@
+{"index_store/data": {"cab2dd05-1512-4926-a828-be9d7324dc45": {"__type__": "vector_store", "__data__": {"index_id": "cab2dd05-1512-4926-a828-be9d7324dc45", "summary": null, "nodes_dict": {"f3bd7381-b54c-41f3-8291-a08e68673084": "f3bd7381-b54c-41f3-8291-a08e68673084", "a44b225d-992a-4778-a942-4e49093869a1": "a44b225d-992a-4778-a942-4e49093869a1", "63bf8e58-048d-4a3d-9f57-ade12c9a1a2d": "63bf8e58-048d-4a3d-9f57-ade12c9a1a2d", "4bb75a66-54eb-48fc-b7d3-3419865620a8": "4bb75a66-54eb-48fc-b7d3-3419865620a8", "960a0805-66b8-4294-a222-d65b68af53ed": "960a0805-66b8-4294-a222-d65b68af53ed", "44246830-1b06-4370-9ed5-32d5e81f296d": "44246830-1b06-4370-9ed5-32d5e81f296d", "d1445983-9afd-4032-bd25-2dfa42c3f1f6": "d1445983-9afd-4032-bd25-2dfa42c3f1f6", "fa8fea99-ec02-466d-a0db-9918823f6d1f": "fa8fea99-ec02-466d-a0db-9918823f6d1f", "5384bc5c-c5b8-4151-8473-36edcfbecae6": "5384bc5c-c5b8-4151-8473-36edcfbecae6", "23374f34-97ba-42b0-9abb-9d3e784417a8": "23374f34-97ba-42b0-9abb-9d3e784417a8", "eadcdd6e-1590-42af-bbd2-b9c00bd6e057": "eadcdd6e-1590-42af-bbd2-b9c00bd6e057", "d8400f95-51f1-4282-aaec-92dbbb635acf": "d8400f95-51f1-4282-aaec-92dbbb635acf", "75b2baab-761e-438f-9fd8-39ccb855c8e4": "75b2baab-761e-438f-9fd8-39ccb855c8e4", "d4a45a65-3de2-45b8-b236-ec2f3ac9b21b": "d4a45a65-3de2-45b8-b236-ec2f3ac9b21b", "7ece5730-d2e6-44a8-baca-f9cc0f5afe26": "7ece5730-d2e6-44a8-baca-f9cc0f5afe26", "c1aaf2ab-5466-4424-ad44-27c58368b2e7": "c1aaf2ab-5466-4424-ad44-27c58368b2e7", "d0b1c9a3-5d8b-4f74-a17c-9a5c21d343da": "d0b1c9a3-5d8b-4f74-a17c-9a5c21d343da", "23a7c0a0-eecd-4d0c-a2fd-d7e9d1d0d0f5": "23a7c0a0-eecd-4d0c-a2fd-d7e9d1d0d0f5", "bc3f54c4-bf8d-46bb-adbd-0f692b2b215a": "bc3f54c4-bf8d-46bb-adbd-0f692b2b215a", "b87356e4-99b4-4bd2-86e9-4233e662cc62": "b87356e4-99b4-4bd2-86e9-4233e662cc62", "88937b63-2017-458e-be11-e351b5a8af99": "88937b63-2017-458e-be11-e351b5a8af99", "c00da86c-923e-4b66-92ad-6edd4034f37a": "c00da86c-923e-4b66-92ad-6edd4034f37a", "8af21ed2-e832-4019-accb-79ca6d05b5bb": "8af21ed2-e832-4019-accb-79ca6d05b5bb", "7f61ffbd-83d9-49ea-8093-1fb1532b00a1": "7f61ffbd-83d9-49ea-8093-1fb1532b00a1"}, "doc_id_dict": {"563fad24-4559-490f-9eb5-ed29ffdc9402": ["f3bd7381-b54c-41f3-8291-a08e68673084", "a44b225d-992a-4778-a942-4e49093869a1", "63bf8e58-048d-4a3d-9f57-ade12c9a1a2d", "4bb75a66-54eb-48fc-b7d3-3419865620a8", "960a0805-66b8-4294-a222-d65b68af53ed", "44246830-1b06-4370-9ed5-32d5e81f296d", "d1445983-9afd-4032-bd25-2dfa42c3f1f6", "fa8fea99-ec02-466d-a0db-9918823f6d1f", "5384bc5c-c5b8-4151-8473-36edcfbecae6", "23374f34-97ba-42b0-9abb-9d3e784417a8", "eadcdd6e-1590-42af-bbd2-b9c00bd6e057", "d8400f95-51f1-4282-aaec-92dbbb635acf", "75b2baab-761e-438f-9fd8-39ccb855c8e4", "d4a45a65-3de2-45b8-b236-ec2f3ac9b21b", "7ece5730-d2e6-44a8-baca-f9cc0f5afe26", "c1aaf2ab-5466-4424-ad44-27c58368b2e7", "d0b1c9a3-5d8b-4f74-a17c-9a5c21d343da", "23a7c0a0-eecd-4d0c-a2fd-d7e9d1d0d0f5", "bc3f54c4-bf8d-46bb-adbd-0f692b2b215a", "b87356e4-99b4-4bd2-86e9-4233e662cc62", "88937b63-2017-458e-be11-e351b5a8af99", "c00da86c-923e-4b66-92ad-6edd4034f37a", "8af21ed2-e832-4019-accb-79ca6d05b5bb", "7f61ffbd-83d9-49ea-8093-1fb1532b00a1"]}, "embeddings_dict": {}}}}}