refactor to enable mode and model selection
Gustav von Zitzewitz
1 year ago
parent
bcd4395907
commit
56cd7e3ba5
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import streamlit as st
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from langchain.callbacks import OpenAICallbackHandler, get_openai_callback
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from langchain.chains import ConversationalRetrievalChain
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from datachad.constants import PAGE_ICON
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from datachad.database import get_vector_store
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from datachad.models import get_model
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from datachad.utils import logger
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def get_chain() -> ConversationalRetrievalChain:
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# create the langchain that will be called to generate responses
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vector_store = get_vector_store()
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retriever = vector_store.as_retriever()
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# Search params "fetch_k" and "k" define how many documents are pulled from the hub
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# and selected after the document matching to build the context
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# that is fed to the model together with your prompt
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search_kwargs = {
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"maximal_marginal_relevance": True,
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"distance_metric": "cos",
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"fetch_k": st.session_state["fetch_k"],
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"k": st.session_state["k"],
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}
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retriever.search_kwargs.update(search_kwargs)
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model = get_model()
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chain = ConversationalRetrievalChain.from_llm(
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model,
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retriever=retriever,
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chain_type="stuff",
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verbose=True,
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# we limit the maximum number of used tokens
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# to prevent running into the models token limit of 4096
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max_tokens_limit=st.session_state["max_tokens"],
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)
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return chain
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def update_chain() -> None:
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# Build chain with parameters from session state and store it back
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# Also delete chat history to not confuse the bot with old context
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try:
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st.session_state["chain"] = get_chain()
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st.session_state["chat_history"] = []
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msg = f"Data source '{st.session_state['data_source']}' is ready to go!"
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logger.info(msg)
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st.info(msg, icon=PAGE_ICON)
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except Exception as e:
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msg = f"Failed to build chain for data source '{st.session_state['data_source']}' with error: {e}"
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logger.error(msg)
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st.error(msg, icon=PAGE_ICON)
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def update_usage(cb: OpenAICallbackHandler) -> None:
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# Accumulate API call usage via callbacks
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logger.info(f"Usage: {cb}")
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callback_properties = [
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"total_tokens",
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"prompt_tokens",
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"completion_tokens",
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"total_cost",
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]
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for prop in callback_properties:
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value = getattr(cb, prop, 0)
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st.session_state["usage"].setdefault(prop, 0)
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st.session_state["usage"][prop] += value
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def generate_response(prompt: str) -> str:
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# call the chain to generate responses and add them to the chat history
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with st.spinner("Generating response"), get_openai_callback() as cb:
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response = st.session_state["chain"](
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{"question": prompt, "chat_history": st.session_state["chat_history"]}
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)
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update_usage(cb)
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logger.info(f"Response: '{response}'")
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st.session_state["chat_history"].append((prompt, response["answer"]))
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return response["answer"]
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@ -0,0 +1,51 @@
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import os
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import re
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import deeplake
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import streamlit as st
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from langchain.vectorstores import DeepLake, VectorStore
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from datachad.constants import DATA_PATH
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from datachad.loader import load_data_source
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from datachad.models import MODES, get_embeddings
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from datachad.utils import logger
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def get_dataset_path() -> str:
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# replace all non-word characters with dashes
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# to get a string that can be used to create a new dataset
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dataset_name = re.sub(r"\W+", "-", st.session_state["data_source"])
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dataset_name = re.sub(r"--+", "- ", dataset_name).strip("-")
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if st.session_state["mode"] == MODES.LOCAL:
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if not os.path.exists(DATA_PATH):
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os.makedirs(DATA_PATH)
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dataset_path = str(DATA_PATH / dataset_name)
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else:
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dataset_path = f"hub://{st.session_state['activeloop_org_name']}/{dataset_name}-{st.session_state['chunk_size']}"
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return dataset_path
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def get_vector_store() -> VectorStore:
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# either load existing vector store or upload a new one to the hub
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embeddings = get_embeddings()
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dataset_path = get_dataset_path()
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if deeplake.exists(dataset_path, token=st.session_state["activeloop_token"]):
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with st.spinner("Loading vector store..."):
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logger.info(f"Dataset '{dataset_path}' exists -> loading")
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vector_store = DeepLake(
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dataset_path=dataset_path,
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read_only=True,
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embedding_function=embeddings,
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token=st.session_state["activeloop_token"],
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)
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else:
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with st.spinner("Reading, embedding and uploading data to hub..."):
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logger.info(f"Dataset '{dataset_path}' does not exist -> uploading")
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docs = load_data_source()
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vector_store = DeepLake.from_documents(
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docs,
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embeddings,
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dataset_path=dataset_path,
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token=st.session_state["activeloop_token"],
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)
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return vector_store
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@ -0,0 +1,111 @@
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from dataclasses import dataclass
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import streamlit as st
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from langchain.base_language import BaseLanguageModel
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from langchain.chat_models import ChatOpenAI
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from langchain.embeddings import HuggingFaceEmbeddings
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from langchain.embeddings.openai import Embeddings, OpenAIEmbeddings
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from langchain.llms import GPT4All, LlamaCpp
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from datachad.utils import logger
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class Enum:
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@classmethod
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def values(cls):
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return [v for k, v in cls.__dict__.items() if not k.startswith("_")]
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@classmethod
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def dict(cls):
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return {k: v for k, v in cls.__dict__.items() if not k.startswith("_")}
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@dataclass
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class Model:
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name: str
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mode: str
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embedding: str
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path: str = None # for local models only
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def __str__(self):
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return self.name
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class MODES(Enum):
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OPENAI = "OpenAI"
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LOCAL = "Local"
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class EMBEDDINGS(Enum):
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OPENAI = "openai"
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HUGGINGFACE = "all-MiniLM-L6-v2"
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class MODELS(Enum):
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GPT35TURBO = Model("gpt-3.5-turbo", MODES.OPENAI, EMBEDDINGS.OPENAI)
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GPT4 = Model("gpt-4", MODES.OPENAI, EMBEDDINGS.OPENAI)
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LLAMACPP = Model(
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"LLAMA", MODES.LOCAL, EMBEDDINGS.HUGGINGFACE, "models/llamacpp.bin"
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)
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GPT4ALL = Model(
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"GPT4All", MODES.LOCAL, EMBEDDINGS.HUGGINGFACE, "models/gpt4all.bin"
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)
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@classmethod
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def for_mode(cls, mode):
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return [v for v in cls.values() if isinstance(v, Model) and v.mode == mode]
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def get_model() -> BaseLanguageModel:
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match st.session_state["model"].name:
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case MODELS.GPT35TURBO.name:
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model = ChatOpenAI(
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model_name=st.session_state["model"].name,
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temperature=st.session_state["temperature"],
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openai_api_key=st.session_state["openai_api_key"],
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)
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case MODELS.GPT4.name:
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model = ChatOpenAI(
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model_name=st.session_state["model"].name,
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temperature=st.session_state["temperature"],
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openai_api_key=st.session_state["openai_api_key"],
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)
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case MODELS.LLAMACPP.name:
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model = LlamaCpp(
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model_path=st.session_state["model"].path,
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n_ctx=st.session_state["model_n_ctx"],
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temperature=st.session_state["temperature"],
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verbose=True,
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)
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case MODELS.GPT4ALL.name:
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model = GPT4All(
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model=st.session_state["model"].path,
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n_ctx=st.session_state["model_n_ctx"],
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backend="gptj",
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temp=st.session_state["temperature"],
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verbose=True,
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)
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# Add more models as needed
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case _default:
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msg = f"Model {st.session_state['model']} not supported!"
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logger.error(msg)
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st.error(msg)
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exit
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return model
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def get_embeddings() -> Embeddings:
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match st.session_state["model"].embedding:
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case EMBEDDINGS.OPENAI:
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embeddings = OpenAIEmbeddings(
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disallowed_special=(), openai_api_key=st.session_state["openai_api_key"]
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)
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case EMBEDDINGS.HUGGINGFACE:
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embeddings = HuggingFaceEmbeddings(model_name=EMBEDDINGS.HUGGINGFACE)
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# Add more embeddings as needed
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case _default:
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msg = f"Embeddings {st.session_state['embeddings']} not supported!"
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logger.error(msg)
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st.error(msg)
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exit
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return embeddings
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@ -0,0 +1,104 @@
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import logging
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import os
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import sys
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import deeplake
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import openai
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import streamlit as st
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from dotenv import load_dotenv
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from datachad.constants import APP_NAME, DATA_PATH, PAGE_ICON
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# loads environment variables
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load_dotenv()
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logger = logging.getLogger(APP_NAME)
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def configure_logger(debug: int = 0) -> None:
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# boilerplate code to enable logging in the streamlit app console
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log_level = logging.DEBUG if debug == 1 else logging.INFO
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logger.setLevel(log_level)
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stream_handler = logging.StreamHandler(stream=sys.stdout)
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stream_handler.setLevel(log_level)
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formatter = logging.Formatter("%(message)s")
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stream_handler.setFormatter(formatter)
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logger.addHandler(stream_handler)
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logger.propagate = False
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configure_logger(0)
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def authenticate(
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openai_api_key: str, activeloop_token: str, activeloop_org_name: str
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) -> None:
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# Validate all credentials are set and correct
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# Check for env variables to enable local dev and deployments with shared credentials
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openai_api_key = (
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openai_api_key
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or os.environ.get("OPENAI_API_KEY")
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or st.secrets.get("OPENAI_API_KEY")
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)
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activeloop_token = (
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activeloop_token
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or os.environ.get("ACTIVELOOP_TOKEN")
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or st.secrets.get("ACTIVELOOP_TOKEN")
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)
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activeloop_org_name = (
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activeloop_org_name
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or os.environ.get("ACTIVELOOP_ORG_NAME")
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or st.secrets.get("ACTIVELOOP_ORG_NAME")
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)
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if not (openai_api_key and activeloop_token and activeloop_org_name):
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st.session_state["auth_ok"] = False
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st.error("Credentials neither set nor stored", icon=PAGE_ICON)
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return
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try:
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# Try to access openai and deeplake
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with st.spinner("Authentifying..."):
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openai.api_key = openai_api_key
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openai.Model.list()
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deeplake.exists(
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f"hub://{activeloop_org_name}/DataChad-Authentication-Check",
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token=activeloop_token,
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)
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except Exception as e:
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logger.error(f"Authentication failed with {e}")
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st.session_state["auth_ok"] = False
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st.error("Authentication failed", icon=PAGE_ICON)
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return
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# store credentials in the session state
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st.session_state["auth_ok"] = True
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st.session_state["openai_api_key"] = openai_api_key
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st.session_state["activeloop_token"] = activeloop_token
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st.session_state["activeloop_org_name"] = activeloop_org_name
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logger.info("Authentification successful!")
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def save_uploaded_file() -> str:
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# streamlit uploaded files need to be stored locally
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# before embedded and uploaded to the hub
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uploaded_file = st.session_state["uploaded_file"]
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if not os.path.exists(DATA_PATH):
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os.makedirs(DATA_PATH)
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file_path = str(DATA_PATH / uploaded_file.name)
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uploaded_file.seek(0)
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file_bytes = uploaded_file.read()
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file = open(file_path, "wb")
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file.write(file_bytes)
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file.close()
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logger.info(f"Saved: {file_path}")
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return file_path
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def delete_uploaded_file() -> None:
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# cleanup locally stored files
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file_path = DATA_PATH / st.session_state["uploaded_file"].name
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if os.path.exists(DATA_PATH):
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os.remove(file_path)
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logger.info(f"Removed: {file_path}")
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