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update
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app.py
CHANGED
@@ -1,110 +1,67 @@
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import
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from
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from
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from
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ChatPromptTemplate,
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SystemMessagePromptTemplate,
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HumanMessagePromptTemplate,
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)
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import chainlit as cl
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
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system_template = """
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Use the following pieces of context to answer the user's question.
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Please respond as if you were Ken from the movie Barbie. Ken is a well-meaning but naive character who loves to Beach. He talks like a typical Californian Beach Bro, but he doesn't use the word "Dude" so much.
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If you don't know the answer, just say that you don't know, don't try to make up an answer.
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You can make inferences based on the context as long as it still faithfully represents the feedback.
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Example of your response should be:
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The answer is foo
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```
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]
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prompt = ChatPromptTemplate(messages=messages)
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chain_type_kwargs = {"prompt": prompt}
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@cl.author_rename
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def rename(orig_author: str):
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rename_dict = {"RetrievalQA": "Consulting The Kens"}
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return rename_dict.get(orig_author, orig_author)
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@cl.on_chat_start
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async def
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documents = text_splitter.transform_documents(data)
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store = LocalFileStore("./cache/")
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core_embeddings_model = OpenAIEmbeddings()
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embedder = CacheBackedEmbeddings.from_bytes_store(
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core_embeddings_model, store, namespace=core_embeddings_model.model
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)
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ChatOpenAI(model="gpt-4", temperature=0, streaming=True),
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chain_type="stuff",
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return_source_documents=True,
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retriever=docsearch.as_retriever(),
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chain_type_kwargs = {"prompt": prompt}
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)
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cl.user_session.set("
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@cl.on_message
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async def main(message):
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stream_final_answer=False, answer_prefix_tokens=["FINAL", "ANSWER"]
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)
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cb.answer_reached = True
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res = await chain.acall(message, callbacks=[cb], )
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answer = res["result"]
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source_elements = []
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visited_sources = set()
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docs = res["source_documents"]
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metadatas = [doc.metadata for doc in docs]
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all_sources = [m["source"] for m in metadatas]
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for
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continue
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visited_sources.add(source)
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# Create the text element referenced in the message
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source_elements.append(
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cl.Text(content="https://www.imdb.com" + source, name="Review URL")
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)
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if
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else:
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answer += "\nNo sources found"
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await
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import os
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import openai
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from llama_index.query_engine.retriever_query_engine import RetrieverQueryEngine
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from llama_index.callbacks.base import CallbackManager
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from llama_index import (
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LLMPredictor,
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ServiceContext,
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StorageContext,
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load_index_from_storage,
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)
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from langchain.chat_models import ChatOpenAI
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import chainlit as cl
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openai.api_key = os.environ.get("OPENAI_API_KEY")
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try:
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# rebuild storage context
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storage_context = StorageContext.from_defaults(persist_dir="./storage")
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# load index
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index = load_index_from_storage(storage_context)
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except:
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from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader
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documents = SimpleDirectoryReader("./data").load_data()
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index = GPTVectorStoreIndex.from_documents(documents)
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index.storage_context.persist()
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@cl.on_chat_start
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async def factory():
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llm_predictor = LLMPredictor(
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llm=ChatOpenAI(
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temperature=0,
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model_name="gpt-3.5-turbo",
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streaming=True,
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),
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service_context = ServiceContext.from_defaults(
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llm_predictor=llm_predictor,
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chunk_size=512,
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callback_manager=CallbackManager([cl.LlamaIndexCallbackHandler()]),
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)
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query_engine = index.as_query_engine(
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service_context=service_context,
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streaming=True,
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)
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cl.user_session.set("query_engine", query_engine)
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@cl.on_message
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async def main(message):
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query_engine = cl.user_session.get("query_engine") # type: RetrieverQueryEngine
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response = await cl.make_async(query_engine.query)(message)
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response_message = cl.Message(content="")
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for token in response.response_gen:
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await response_message.stream_token(token=token)
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if response.response_txt:
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response_message.content = response.response_txt
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await response_message.send()
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