Create qabot.py

qabot.py

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+from ibm_watsonx_ai.foundation_models import ModelInference
+from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams
+from ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames
+from ibm_watsonx_ai import Credentials
+from langchain_ibm import WatsonxLLM, WatsonxEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.chains import RetrievalQA
+
+import gradio as gr
+
+# You can use this section to suppress warnings generated by your code:
+def warn(*args, **kwargs):
+    pass
+import warnings
+warnings.warn = warn
+warnings.filterwarnings('ignore')
+
+## LLM
+def get_llm():
+    model_id = 'mistralai/mixtral-8x7b-instruct-v01'
+    parameters = {
+        GenParams.MAX_NEW_TOKENS: 256,
+        GenParams.TEMPERATURE: 0.5,
+    }
+    project_id = "skills-network"
+    watsonx_llm = WatsonxLLM(
+        model_id=model_id,
+        url="https://us-south.ml.cloud.ibm.com",
+        project_id=project_id,
+        params=parameters,
+    )
+    return watsonx_llm
+
+## Document loader
+def document_loader(file):
+    loader = PyPDFLoader(file.name)
+    loaded_document = loader.load()
+    return loaded_document
+
+## Text splitter
+def text_splitter(data):
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=50,
+        length_function=len,
+    )
+    chunks = text_splitter.split_documents(data)
+    return chunks
+
+## Vector db
+def vector_database(chunks):
+    embedding_model = watsonx_embedding()
+    vectordb = Chroma.from_documents(chunks, embedding_model)
+    return vectordb
+
+## Embedding model
+def watsonx_embedding():
+    embed_params = {
+        EmbedTextParamsMetaNames.TRUNCATE_INPUT_TOKENS: 3,
+        EmbedTextParamsMetaNames.RETURN_OPTIONS: {"input_text": True},
+    }
+    watsonx_embedding = WatsonxEmbeddings(
+        model_id="ibm/slate-125m-english-rtrvr",
+        url="https://us-south.ml.cloud.ibm.com",
+        project_id="skills-network",
+        params=embed_params,
+    )
+    return watsonx_embedding
+
+## Retriever
+def retriever(file):
+    splits = document_loader(file)
+    chunks = text_splitter(splits)
+    vectordb = vector_database(chunks)
+    retriever = vectordb.as_retriever()
+    return retriever
+
+## QA Chain
+def retriever_qa(file, query):
+    llm = get_llm()
+    retriever_obj = retriever(file)
+    qa = RetrievalQA.from_chain_type(llm=llm, 
+                                    chain_type="stuff", 
+                                    retriever=retriever_obj, 
+                                    return_source_documents=False)
+    response = qa.invoke(query)
+    return response['result']
+
+
+# Create Gradio interface
+rag_application = gr.Interface(
+    fn=retriever_qa,
+    allow_flagging="never",
+    inputs=[
+        gr.File(label="Upload PDF File", file_count="single", file_types=['.pdf'], type="filepath"),  # Drag and drop file upload
+        gr.Textbox(label="Input Query", lines=2, placeholder="Type your question here...")
+    ],
+    outputs=gr.Textbox(label="Output"),
+    title="RAG Chatbot",
+    description="Upload a PDF document and ask any question. The chatbot will try to answer using the provided document."
+)
+
+# Launch the app
+rag_application.launch(server_name="0.0.0.0", server_port= 7860)