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.gitattributes

@@ -33,4 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-*json filter=lfs diff=lfs merge=lfs -text
+*.json filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1 @@
+.env

app.py

@@ -1,13 +1,19 @@
 from pathlib import Path
+import os
+import openai
+openai.api_key = os.getenv("OAI_KEY")
+from llama_index.llms.openai import OpenAI
+from llama_index.embeddings.openai import OpenAIEmbedding
+import nest_asyncio
+
+nest_asyncio.apply()
+
+
 
 from llama_index.core import(SimpleDirectoryReader,
                             VectorStoreIndex, StorageContext,
                             Settings,set_global_tokenizer)
-from llama_index.llms.llama_cpp import LlamaCPP
-from llama_index.llms.llama_cpp.llama_utils import (
-    messages_to_prompt,
-    completion_to_prompt,
-)
+
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 from transformers import AutoTokenizer, BitsAndBytesConfig
 from llama_index.llms.huggingface import HuggingFaceLLM
@@ -17,17 +23,15 @@ import sys
 import streamlit as st
 import os
 from llama_index.core import load_index_from_storage
-default_bnb_config = BitsAndBytesConfig(
-                                                load_in_4bit=True,
-                                                bnb_4bit_quant_type='nf4',
-                                                bnb_4bit_use_double_quant=True,
-                                                bnb_4bit_compute_dtype=torch.bfloat16
-                                            )
+
+
+Settings.llm = OpenAI(model="gpt-3.5-turbo-instruct", temperature=0.2)
+Settings.embed_model = OpenAIEmbedding(
+    model="text-embedding-3-large", embed_batch_size=100
+)
+
 logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
 logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
-set_global_tokenizer(
-    AutoTokenizer.from_pretrained("NousResearch/Llama-2-13b-chat-hf").encode
-)
 
 
 def getDocs(doc_path="./data/"):
@@ -35,52 +39,29 @@ def getDocs(doc_path="./data/"):
     return documents
 
 
-def getVectorIndex(docs):
+def getVectorIndex():
     Settings.chunk_size = 512
     index_set = {}
-
-    if(os.path.isdir(f"./storage/book_data")):
-        storage_context = StorageContext.from_defaults(persist_dir=f"./storage/book_data")
+    if os.path.isdir(f"./storage/open_ai_embedding_data_large"):
+        print("Index already exists")
+        storage_context = StorageContext.from_defaults(
+        persist_dir=f"./storage/open_ai_embedding_data_large"
+        )
         cur_index = load_index_from_storage(
-            storage_context,embed_model = getEmbedModel()
+            storage_context,
         )
     else:
+        print("Index does not exist, creating new index")
+        docs = getDocs()
         storage_context = StorageContext.from_defaults()
-        cur_index = VectorStoreIndex.from_documents(docs, embed_model = getEmbedModel(), storage_context=storage_context)
-        storage_context.persist(persist_dir=f"./storage/book_data")
+        cur_index = VectorStoreIndex.from_documents(docs, storage_context=storage_context)
+        storage_context.persist(persist_dir=f"./storage/open_ai_embedding_data_large")
     return cur_index
 
-
-def getLLM():
-
-    model_path = "NousResearch/Llama-2-13b-chat-hf"
-    # model_path = "NousResearch/Llama-2-7b-chat-hf"
-
-    llm = HuggingFaceLLM(
-    context_window=3900,
-    max_new_tokens=256,
-    # generate_kwargs={"temperature": 0.25, "do_sample": False},
-    tokenizer_name=model_path,
-    model_name=model_path,
-    device_map=0,
-    tokenizer_kwargs={"max_length": 2048},
-    # uncomment this if using CUDA to reduce memory usage
-    model_kwargs={"torch_dtype": torch.float16,
-    "quantization_config": default_bnb_config,
-    }
-    )
-    return llm
-
-
 def getQueryEngine(index):
-    query_engine = index.as_chat_engine(llm=getLLM())
+    query_engine = index.as_chat_engine()
     return query_engine
 
-def getEmbedModel():
-    embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
-    return embed_model
-
-
 
 
 
@@ -101,14 +82,17 @@ if "messages" not in st.session_state.keys(): # Initialize the chat messages his
 
 @st.cache_resource(show_spinner=False)
 def load_data():
-    index = getVectorIndex(getDocs())
+    index = getVectorIndex()
     return index
-    query_engine = getQueryEngine(index)
-
+import time
+s_time = time.time()
 index = load_data()
+e_time = time.time()
+
+print(f"It took {e_time - s_time} to load index")
 
 if "chat_engine" not in st.session_state.keys(): # Initialize the chat engine
-        st.session_state.chat_engine = index.as_chat_engine(llm=getLLM(),chat_mode="condense_question", verbose=True)
+        st.session_state.chat_engine = index.as_chat_engine(chat_mode="condense_plus_context", verbose=True)
 
 if prompt := st.chat_input("Your question"): # Prompt for user input and save to chat history
     st.session_state.messages.append({"role": "user", "content": prompt})

app.py.bkp

@@ -0,0 +1,162 @@
+from pathlib import Path
+
+from llama_index.core import(SimpleDirectoryReader,
+                            VectorStoreIndex, StorageContext,
+                            Settings,set_global_tokenizer)
+from llama_index.llms.llama_cpp import LlamaCPP
+from llama_index.llms.llama_cpp.llama_utils import (
+    messages_to_prompt,
+    completion_to_prompt,
+)
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from transformers import AutoTokenizer, BitsAndBytesConfig
+from llama_index.llms.huggingface import HuggingFaceLLM
+import torch
+import logging
+import sys
+import streamlit as st
+import os
+from llama_index.core import load_index_from_storage
+default_bnb_config = BitsAndBytesConfig(
+                                                load_in_4bit=True,
+                                                bnb_4bit_quant_type='nf4',
+                                                bnb_4bit_use_double_quant=True,
+                                                bnb_4bit_compute_dtype=torch.bfloat16
+                                            )
+logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
+logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
+set_global_tokenizer(
+    AutoTokenizer.from_pretrained("NousResearch/Llama-2-13b-chat-hf").encode
+)
+
+
+def getDocs(doc_path="./data/"):
+    documents = SimpleDirectoryReader(doc_path).load_data()
+    return documents
+
+
+def getVectorIndex(docs):
+    Settings.chunk_size = 512
+    index_set = {}
+
+    if(os.path.isdir(f"./storage/book_data")):
+        storage_context = StorageContext.from_defaults(persist_dir=f"./storage/book_data")
+        cur_index = load_index_from_storage(
+            storage_context,embed_model = getEmbedModel()
+        )
+    else:
+        storage_context = StorageContext.from_defaults()
+        cur_index = VectorStoreIndex.from_documents(docs, embed_model = getEmbedModel(), storage_context=storage_context)
+        storage_context.persist(persist_dir=f"./storage/book_data")
+    return cur_index
+
+
+def getLLM():
+
+    model_path = "NousResearch/Llama-2-13b-chat-hf"
+    # model_path = "NousResearch/Llama-2-7b-chat-hf"
+
+    llm = HuggingFaceLLM(
+    context_window=3900,
+    max_new_tokens=256,
+    # generate_kwargs={"temperature": 0.25, "do_sample": False},
+    tokenizer_name=model_path,
+    model_name=model_path,
+    device_map=0,
+    tokenizer_kwargs={"max_length": 2048},
+    # uncomment this if using CUDA to reduce memory usage
+    model_kwargs={"torch_dtype": torch.float16,
+    "quantization_config": default_bnb_config,
+    }
+    )
+    return llm
+
+
+def getQueryEngine(index):
+    query_engine = index.as_chat_engine(llm=getLLM())
+    return query_engine
+
+def getEmbedModel():
+    embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
+    return embed_model
+
+
+
+
+
+
+
+
+
+
+
+st.set_page_config(page_title="Project BookWorm: Your own Librarian!", page_icon="🦙", layout="centered", initial_sidebar_state="auto", menu_items=None)
+st.title("Project BookWorm: Your own Librarian!")
+st.info("Use this app to get recommendations for books that your kids will love!", icon="📃")
+         
+if "messages" not in st.session_state.keys(): # Initialize the chat messages history
+    st.session_state.messages = [
+        {"role": "assistant", "content": "Ask me a question about children's books or movies!"}
+    ]
+
+@st.cache_resource(show_spinner=False)
+def load_data():
+    index = getVectorIndex(getDocs())
+    return index
+    query_engine = getQueryEngine(index)
+
+index = load_data()
+
+if "chat_engine" not in st.session_state.keys(): # Initialize the chat engine
+        st.session_state.chat_engine = index.as_chat_engine(llm=getLLM(),chat_mode="condense_question", verbose=True)
+
+if prompt := st.chat_input("Your question"): # Prompt for user input and save to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+for message in st.session_state.messages: # Display the prior chat messages
+    with st.chat_message(message["role"]):
+        st.write(message["content"])
+
+# If last message is not from assistant, generate a new response
+if st.session_state.messages[-1]["role"] != "assistant":
+    with st.chat_message("assistant"):
+        with st.spinner("Thinking..."):
+            response = st.session_state.chat_engine.chat(prompt)
+            st.write(response.response)
+            message = {"role": "assistant", "content": response.response}
+            st.session_state.messages.append(message) # Add response to message history
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+# if __name__ == "__main__":
+
+#     index = getVectorIndex(getDocs())
+#     query_engine = getQueryEngine(index)
+#     while(True):
+#         your_request = input("Your comment: ")
+#         response = query_engine.chat(your_request)
+#         print(response)
+
+
+
+
+
+
+
+
+
+    

storage/.gitattributes

@@ -0,0 +1 @@
+*json filter=lfs diff=lfs merge=lfs -text

storage/open_ai_embedding_data/default__vector_store.json

@@ -0,0 +1,3 @@
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+size 244449202

storage/open_ai_embedding_data/docstore.json

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storage/open_ai_embedding_data/graph_store.json

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storage/open_ai_embedding_data/image__vector_store.json

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storage/open_ai_embedding_data/index_store.json

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storage/open_ai_embedding_data_large/docstore.json

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storage/open_ai_embedding_data_large/graph_store.json

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