added langchain openai support document chat

.gitignore

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+venv
+
+.env

app.py

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+# Reference https://huggingface.co/spaces/johnmuchiri/anspro1/blob/main/app.py
+# Resource https://python.langchain.com/docs/modules/chains
+
+import streamlit as st
+from langchain_community.document_loaders.pdf import PyPDFLoader
+from langchain_community.vectorstores import pinecone
+from langchain_openai import OpenAIEmbeddings, OpenAI
+from langchain.memory import ConversationBufferMemory
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.chains import ConversationalRetrievalChain, RetrievalQA
+import openai
+from dotenv import load_dotenv
+import os
+
+# import pinecone
+
+load_dotenv()
+
+# please create a streamlit app on huggingface that uses openai api
+# and langchain data framework, the user should be able to upload
+# a document and ask questions about the document, the app should
+# respond with an answer and also display where the response is
+# referenced from using some sort of visual annotation on the document
+
+# set the path where you want to save the uploaded PDF file
+SAVE_DIR = "pdf"
+
+
+def generate_response(pages, query_text, k, chain_type):
+    if pages is not None:
+        pinecone.init(
+            api_key=os.getenv("PINECONE_API_KEY"),
+            environment=os.getenv("PINECONE_ENV_NAME"),
+        )
+
+        vector_db = pinecone.Pinecone.from_documents(
+            documents=pages, embedding=OpenAIEmbeddings(), index_name="openai-index"
+        )
+
+        retriever = vector_db.as_retriever(
+            search_type="similarity", search_kwards={"k": k}
+        )
+        
+        # create a chain to answer questions
+        qa = RetrievalQA.from_chain_type(
+            llm=OpenAI(),
+            chain_type=chain_type,
+            retriever=retriever,
+            return_source_documents=True
+        )
+
+        response = qa({"query": query_text})
+        return response
+
+def visual_annotate(document, answer):
+    # Implement this function according to your specific requirements
+    # Highlight the part of the document where the answer was found
+    start = document.find(answer)
+    annotated_document = (
+        document[:start]
+        + "**"
+        + document[start : start + len(answer)]
+        + "**"
+        + document[start + len(answer) :]
+    )
+    return annotated_document
+
+
+st.set_page_config(page_title="🦜🔗 Ask the Doc App")
+st.title("Document Question Answering App")
+
+with st.sidebar.form(key="sidebar-form"):
+    st.header("Configurations")
+
+    openai_api_key = st.text_input("Enter OpenAI API key here", type="password")
+    os.environ["OPENAI_API_KEY"] = openai_api_key
+
+    pinecone_api_key = st.text_input(
+        "Enter your Pinecone environment key", type="password"
+    )
+    os.environ["PINECONE_API_KEY"] = pinecone_api_key
+
+    pinecone_env_name = st.text_input("Enter your Pinecone environment name)")
+    os.environ["PINECONE_ENV_NAME"] = pinecone_env_name
+
+    submitted = st.sidebar.form_submit_button(
+        label="Submit",
+        disabled=not (openai_api_key and pinecone_api_key and pinecone_env_name),
+    )
+
+left_column, right_column = st.columns(2)
+
+with left_column:
+    uploaded_file = st.file_uploader("Choose a pdf file", type="pdf")
+
+    if uploaded_file is not None:
+        # save the uploaded file to the specified directory
+        file_path = os.path.join(SAVE_DIR, uploaded_file.name)
+        with open(file_path, "wb") as f:
+            f.write(uploaded_file.getbuffer())
+        st.success(f"File {uploaded_file.name} is saved at path {file_path}")
+
+        loader = PyPDFLoader(file_path=file_path)
+        pages = loader.load_and_split()
+
+    query_text = st.text_input(
+        "Enter your question:", placeholder="Please provide a short summary."
+    )
+
+    chain_type = st.selectbox(
+        "chain type", ("stuff", "map_reduce", "refine", "map_rerank")
+    )
+
+    k = st.slider("Number of relevant chunks", 1, 5)
+
+    with st.spinner("Retrieving and generating a response ..."):
+        response = generate_response(
+            pages=pages,
+            query_text=query_text,
+            k=k,
+            chain_type=chain_type
+        )
+
+        with right_column:
+            st.write("Output of your question")
+
+            st.subheader("Result")
+            st.write(response['result'])
+
+            st.subheader("source_documents")
+            st.write(response['source_documents'][0])
+
+
+# with st.form("myform", clear_on_submit=True):
+#     openai_api_key = st.text_input(
+#         "OpenAI API Key", type="password", disabled=not (uploaded_file and query_text)
+#     )
+#     submitted = st.form_submit_button(
+#         "Submit", disabled=not (pages and query_text)
+#     )
+#     if submitted and openai_api_key.startswith("sk-"):
+#         with st.spinner("Calculating..."):
+#             response = generate_response(pages, openai_api_key, query_text)
+#             result.append(response)
+#             del openai_api_key
+
+# if len(result):
+#     st.info(response)
+
+# if st.button("Get Answer"):
+#     answer = get_answer(question, document)
+#     st.write(answer["answer"])
+
+#     # Visual annotation on the document
+#     annotated_document = visual_annotate(document, answer["answer"])
+#     st.markdown(annotated_document)

requirements.txt

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+transformers
+langchain
+openai
+python-dotenv
+langchain_openai
+langchain_community
+pypdf
+pinecone-client