Spaces:
Runtime error
Runtime error
| %%capture | |
| !pip install gradio | |
| !pip install --upgrade langchain | |
| !pip install --upgrade openai | |
| !pip install --upgrade torch | |
| !pip install --upgrade transformers | |
| !pip install --upgrade rank-bm25 | |
| !pip install --upgrade accelerate | |
| !pip install --upgrade sentence_transformers | |
| !pip install --upgrade qdrant_client | |
| !pip install -U langchain_openai | |
| import os | |
| import gradio as gr | |
| from langchain.vectorstores import qdrant | |
| from langchain.embeddings import HuggingFaceBgeEmbeddings | |
| from langchain_community.vectorstores import Qdrant | |
| from qdrant_client import QdrantClient | |
| import qdrant_client | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain_core.prompts import ChatPromptTemplate | |
| from langchain.chains import LLMChain | |
| from langchain.llms import OpenAI | |
| from langchain_openai.chat_models import ChatOpenAI | |
| from langchain.prompts import PromptTemplate, ChatPromptTemplate | |
| from langchain.chains import SimpleSequentialChain | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.prompts import ChatPromptTemplate | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.chains.router import MultiPromptChain | |
| from langchain.chains import ConversationChain | |
| from langchain.chains.llm import LLMChain | |
| from langchain.prompts import PromptTemplate | |
| from langchain.chains import RetrievalQA | |
| from langchain.chains.router.embedding_router import EmbeddingRouterChain | |
| import os | |
| os.environ["OPENAI_API_KEY"] = "sk-proj-kcPWLq6XrtyJcmZP7IbAT3BlbkFJHf5I0O8ulxcd89Tm0wtn" | |
| # load the embedding model | |
| model_name = "BAAI/bge-large-en" | |
| # model_kwargs = {"device":"cpu"} | |
| encode_kwargs = {"normalize_embeddings": False} | |
| embeddings = HuggingFaceBgeEmbeddings( | |
| model_name=model_name, | |
| # model_kwargs=model_kwargs, | |
| encode_kwargs=encode_kwargs | |
| ) | |
| qdrant_key = "UVNJLKReKbTGd1F5kLVfKkDVmRYy69h7MPAzVb8zQ6hk8l3eTydKKw" | |
| qdrant_url = "https://5169b771-7376-4534-b174-d14e9ffd679b.us-east4-0.gcp.cloud.qdrant.io:6333" | |
| def get_vector_store(): | |
| client = qdrant_client.QdrantClient( | |
| qdrant_url, | |
| api_key=qdrant_key | |
| ) | |
| vector_store = Qdrant( | |
| client=client, | |
| collection_name="hybridshop", | |
| embeddings=embeddings, | |
| ) | |
| return vector_store | |
| vector_store = get_vector_store() | |
| llm = ChatOpenAI(model="gpt-3.5-turbo",temperature=0.2) | |
| memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True) | |
| conversation_chain = ConversationalRetrievalChain.from_llm( | |
| llm=llm, | |
| chain_type="stuff", | |
| retriever=vector_store.as_retriever(), | |
| memory=memory | |
| ) | |
| def predict(question): | |
| result = conversation_chain({"question": question}) | |
| answer = result["answer"] | |
| return answer | |
| interface = gr.Interface( | |
| fn=predict, | |
| inputs=["text"], | |
| outputs=["text"], | |
| examples=[ | |
| ["suggest me some smart tvs"], | |
| ["smartphones under 8gb"], | |
| ["smartphones with 8gb ram"], | |
| ["When will I go to the moon?"], | |
| ["latest phones under 20,000"], | |
| ["latest smartphones under 20,000"], | |
| ["Which monitors are compatible with Mac laptops?"] | |
| ] | |
| ) | |
| interface.launch() |