Create app.py

app.py

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+# import libraries
+import os
+from dotenv import load_dotenv
+
+import pinecone
+from langchain.document_loaders import PyPDFDirectoryLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain_pinecone import PineconeVectorStore
+from langchain.prompts import PromptTemplate
+from langchain.chains.question_answering import load_qa_chain
+from ctransformers import CTransformers
+
+load_dotenv()
+
+embeddings = HuggingFaceBgeEmbeddings(model_name = 'sentence-transformers/all-MiniLM-L6-v2',
+                                          model_kwargs = {'device':'cpu'})
+
+os.environ['PINECONE_API_KEY'] = 'afb0bb4d-3c15-461b-91a4-fb12fb1f25f2'
+index_name = 'harisonvecot'
+
+vectorstore = PineconeVectorStore(index_name=index_name,embedding=embeddings)
+
+# Create the vector index from documents
+def create_index(documents):
+    vectorstore.add_documents(documents)
+
+# Retrieve query from Pinecone
+def retrieve_query(query, k=2):
+    matching_results = vectorstore.similarity_search(query, k=k)
+    return matching_results
+
+# Custom prompt template
+custom_prompt_template = '''
+use the following pieces of information to answer the user's questions.
+If you don't know the answer, please just say that you don't know the answer, don't try to make up an answer.
+Content : {context}
+Question : {question}
+only return the helpful answer below and nothing else.
+'''
+
+def set_custom_prompt():
+    prompt = PromptTemplate(template=custom_prompt_template, input_variables=['context', 'question'])
+    return prompt
+
+# Load LLM model
+llm_model = CTransformers(model_name='TheBloke/Llama-2-7B-Chat-GGML',
+                          model_type = 'llama',
+                          max_new_token = 512,
+                          temperature=0.5)
+
+# Create retrieval QA chain
+def retrieval_qa_chain():
+    prompt = set_custom_prompt()
+    chain = load_qa_chain(llm_model, chain_type='stuff', prompt=prompt)
+    return chain
+
+# Search answers from Vector DB
+def retrieve_answer(query):
+    doc_search = retrieve_query(query)
+    chain = retrieval_qa_chain()
+    response = chain.run(input_documents=doc_search, question=query)
+    return response
+
+queries = st.text_input('write a medical questions ?')
+# Example usage
+submit = st.button('submit')
+    # Read and process documents
+#     doc = read_doc('documents/')
+#     documents = chunk_data(docs=doc)
+#     create_index(documents)
+if submit :
+    if queries :
+    # Query and get answer
+    #our_query = 'What is cause of Eczema?'
+        answer = retrieve_answer(queries)
+        st.write(answer)