Create app.py

app.py

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+%%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()