Create app.py

app.py

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+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Pinecone 
+from sentence_transformers import SentenceTransformer
+from langchain.chains.question_answering import load_qa_chain
+import pinecone
+import os
+from langchain.llms import LlamaCpp
+from langchain.callbacks.manager import CallbackManager
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from huggingface_hub import hf_hub_download
+import streamlit as st
+from streamlit_chat import message
+from dotenv import load_dotenv
+
+load_dotenv()
+# from langchain.chains.question_answering import load_qa_chain
+# from langchain import Document
+# from langchain.document_loaders import DirectoryLoader
+
+# loader = OnlinePDFLoader("food.pdf")
+loader = PyPDFLoader("Ramesh_kumar_Resume.pdf")
+data = loader.load()
+# data
+text_splitter=RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=0)
+docs=text_splitter.split_documents(data)
+len(docs)
+
+
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = ""
+PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY', '')
+PINECONE_API_ENV = os.environ.get('PINECONE_API_ENV', 'gcp-starter')
+os.environ['PINECONE_API_KEY'] = PINECONE_API_KEY
+
+embeddings=HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
+
+# initialize pinecone
+pinecone.Pinecone(
+    api_key=PINECONE_API_KEY,  # find at app.pinecone.io
+    environment=PINECONE_API_ENV  # next to api key in console
+)
+index_name = "testindex" # put in the name of your pinecone index here
+
+
+docsearch= Pinecone.from_texts([t.page_content for t in docs],embeddings, index_name=index_name)
+
+query="what languages ramesh know?"
+
+docs=docsearch.similarity_search(query,k=1)
+
+# docs
+
+# !CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python --force-reinstall --upgrade --no-cache-dir --verbose
+
+
+
+# Callbacks support token-wise streaming
+callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+# Verbose is required to pass to the callback manager
+
+model_name_or_path = "TheBloke/Llama-2-7B-Chat-GGUF"
+filename = "llama-2-7b-chat.Q4_K_M.gguf" # the model is in bin format
+model_path = hf_hub_download(repo_id=model_name_or_path, filename=filename)
+
+n_gpu_layers = 40  # Change this value based on your model and your GPU VRAM pool.
+n_batch = 256  # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.
+
+# Loading model,
+
+
+query=st.text_input("Ask questions:")
+
+
+
+
+
+if query:
+            search=docsearch.similarity_search(query)
+ 
+            llm = LlamaCpp(
+            model_path=model_path,
+            max_tokens=256,
+            # n_gpu_layers=n_gpu_layers,
+            # n_batch=n_batch,
+            callback_manager=callback_manager,
+            n_ctx=1024,
+            verbose=True,
+            )
+
+            chain=load_qa_chain(llm, chain_type="stuff")
+            with get_openai_callback() as cb:
+                response = chain.run(input_documents=search, question=query)
+                print(cb)
+            # st.write(response)
+
+            st.session_state['messages'].append({"role": "user", "content": query})
+            st.session_state['messages'].append({"role": "assistant", "content": response})
+
+        response_container = st.container()
+        # container for text box
+        container = st.container()
+
+        with container:
+            if query:
+                output = response
+                st.session_state['past'].append(query)
+                st.session_state['generated'].append(output)
+
+        if st.session_state['generated']:
+            with response_container:
+                for i in range(len(st.session_state['generated'])):
+                    message(st.session_state["past"][i], is_user=True, key=str(i) + '_user')
+                    message(st.session_state["generated"][i], key=str(i))