Upload 2 files

app.py

@@ -0,0 +1,44 @@
+from langchain.chat_models import ChatOpenAI
+# from langchain_community.document_loaders import WebBaseLoader 
+# from langchain.text_splitter import RecursiveCharacterTextSplitter 
+import gradio as gr
+from langchain_pinecone import Pinecone
+from langchain_openai import OpenAIEmbeddings , ChatOpenAI
+from langchain.prompts import PromptTemplate 
+from langchain.chains import RetrievalQA
+import os
+
+os.environ["PINECONE_API_KEY"] = "9952de06-975b-4ca4-9908-491e8d08328a"
+os.environ["OPENAI_API_KEY"]="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"
+
+
+# create  embeddings and store in vector database (Pinecone)
+embeddings=OpenAIEmbeddings()
+#docsearch=Pinecone.from_texts([t.page_content for t in document_chunks],embeddings,index_name="index_name")
+
+
+llm = ChatOpenAI(temperature=0, model='gpt-3.5-turbo-16k')
+# If you already have embeddings stored , you can load it using below  code
+vector_search=Pinecone.from_existing_index("erginouswebsite",embeddings)
+
+template = """You are provided with a content related to the company. You will be asked any question related to the company. If you don't know ,just say I don't know the answer'.
+{context}
+Question: {question}
+Helpful Answer:"""
+QA_CHAIN_PROMPT = PromptTemplate(input_variables=["question"],template=template)
+qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=vector_search.as_retriever(),chain_type_kwargs={"prompt": QA_CHAIN_PROMPT})
+
+def predict(message, history):
+    gpt_response = qa.invoke(message)
+    return gpt_response['result']
+
+gr.ChatInterface(predict,chatbot=gr.Chatbot(height=300),
+    textbox=gr.Textbox(placeholder="Ask me a question", container=False, scale=7),
+    title="AI Assistant Chatbot",
+    description="Chatbot AI Assistant",
+    theme="soft",
+    examples=["Provide address of the company", "Solutions provided by the company", "Career opportunities in company"],
+    cache_examples=True,
+    retry_btn=None,
+    undo_btn="Delete Previous",
+    clear_btn="Clear",).launch(share=True , server_name="0.0.0.0" ,server_port=8080)

requirements.txt

@@ -0,0 +1,4 @@
+langchain_pinecone
+python-dotenv
+langchain_openai
+gradio