Interface updates, more models

README.md

@@ -41,7 +41,7 @@ The conversation is kept in memory by a buffered sliding window memory (top 4 mo
 ## Getting started
 
 - Select the model+embedding combination you want to use 
-- If using OpenAI, enter your API Key ([Open AI](https://platform.openai.com/account/api-keys)~~ or [Huggingface](https://huggingface.co/docs/hub/security-tokens))~~. 
+- If using gpt3.5-turbo, gpt4 or gpt4-turbo, enter your API Key ([Open AI](https://platform.openai.com/account/api-keys)). 
 - Upload a scientific article as a PDF document. You will see a spinner or loading indicator while the processing is in progress. 
 - Once the spinner disappears, you can proceed to ask your questions
 
@@ -77,6 +77,14 @@ Error: `streamlit: Your system has an unsupported version of sqlite3. Chroma req
 Here the [solution on Linux](https://stackoverflow.com/questions/76958817/streamlit-your-system-has-an-unsupported-version-of-sqlite3-chroma-requires-sq).
 For more information, see the [details](https://docs.trychroma.com/troubleshooting#sqlite) on Chroma website.
 
+## Disclaimer on Data, Security, and Privacy ⚠️
+
+Please read carefully:
+
+- Avoid uploading sensitive data. We temporarily store text from the uploaded PDF documents only for processing your request, and we disclaim any responsibility for subsequent use or handling of the submitted data by third-party LLMs.
+- Mistral and Zephyr are FREE to use and do not require any API, but as we leverage the free API entrypoint, there is no guarantee that all requests will go through. Use at your own risk.
+- We do not assume responsibility for how the data is utilized by the LLM end-points API.
+
 ## Development notes
 
 To release a new version: 

streamlit_app.py

@@ -19,6 +19,10 @@
 from document_qa.grobid_processors import GrobidAggregationProcessor, decorate_text_with_annotations
 from grobid_client_generic import GrobidClientGeneric
 
+OPENAI_MODELS = ['chatgpt-3.5-turbo',
+                 "gpt-4",
+                 "gpt-4-1106-preview"]
+
 if 'rqa' not in st.session_state:
     st.session_state['rqa'] = {}
 
@@ -117,17 +121,17 @@ def clear_memory():
 # @st.cache_resource
 def init_qa(model, api_key=None):
     ## For debug add: callbacks=[PromptLayerCallbackHandler(pl_tags=["langchain", "chatgpt", "document-qa"])])
-    if model == 'chatgpt-3.5-turbo':
+    if model in OPENAI_MODELS:
         st.session_state['memory'] = ConversationBufferWindowMemory(k=4)
         if api_key:
-            chat = ChatOpenAI(model_name="gpt-3.5-turbo",
+            chat = ChatOpenAI(model_name=model,
                               temperature=0,
                               openai_api_key=api_key,
                               frequency_penalty=0.1)
             embeddings = OpenAIEmbeddings(openai_api_key=api_key)
 
         else:
-            chat = ChatOpenAI(model_name="gpt-3.5-turbo",
+            chat = ChatOpenAI(model_name=model,
                               temperature=0,
                               frequency_penalty=0.1)
             embeddings = OpenAIEmbeddings()
@@ -206,20 +210,23 @@ def play_old_messages():
 # is_api_key_provided = st.session_state['api_key']
 
 with st.sidebar:
-    st.session_state['model'] = model = st.radio(
-        "Model",
-        ("chatgpt-3.5-turbo", "mistral-7b-instruct-v0.1", "zephyr-7b-beta"),
-        index=2,
-        captions=[
-            "ChatGPT 3.5 Turbo + Ada-002-text (embeddings)",
-            "Mistral-7B-Instruct-V0.1 + Sentence BERT (embeddings) :free:",
-            "Zephyr-7B-beta + Sentence BERT (embeddings) :free:"
+    st.session_state['model'] = model = st.selectbox(
+        "Model:",
+        options=[
+            "chatgpt-3.5-turbo",
+            "mistral-7b-instruct-v0.1",
+            "zephyr-7b-beta",
+            "gpt-4",
+            "gpt-4-1106-preview"
         ],
-        help="Select the LLM model and embeddings you want to use.",
-        disabled=st.session_state['doc_id'] is not None or st.session_state['uploaded'])
+        index=2,
+        placeholder="Select model",
+        help="Select the LLM model:",
+        disabled=st.session_state['doc_id'] is not None or st.session_state['uploaded']
+    )
 
     st.markdown(
-        ":warning: Mistral and Zephyr are **FREE** to use. Requests might fail anytime. Use at your own risk. :warning: ")
+        ":warning: [Usage disclaimer](https://github.com/lfoppiano/document-qa/tree/review-interface#disclaimer-on-data-security-and-privacy-%EF%B8%8F) :warning: ")
 
     if (model == 'mistral-7b-instruct-v0.1' or model == 'zephyr-7b-beta') and model not in st.session_state['api_keys']:
         if 'HUGGINGFACEHUB_API_TOKEN' not in os.environ:
@@ -238,7 +245,7 @@ def play_old_messages():
                     #     os.environ["HUGGINGFACEHUB_API_TOKEN"] = api_key
                     st.session_state['rqa'][model] = init_qa(model)
 
-    elif model == 'chatgpt-3.5-turbo' and model not in st.session_state['api_keys']:
+    elif model in OPENAI_MODELS and model not in st.session_state['api_keys']:
         if 'OPENAI_API_KEY' not in os.environ:
             api_key = st.text_input('OpenAI API Key', type="password")
             st.markdown("Get it [here](https://platform.openai.com/account/api-keys)")
@@ -297,9 +304,9 @@ def play_old_messages():
                              help="Number of chunks to consider when answering a question",
                              disabled=not uploaded_file)
 
-    st.session_state['ner_processing'] = st.checkbox("Named Entities Recognition (NER) processing on LLM response")
+    st.session_state['ner_processing'] = st.checkbox("Identify materials and properties.")
     st.markdown(
-        '**NER on LLM responses**: The responses from the LLMs are post-processed to extract <span style="color:orange">physical quantities, measurements</span> and <span style="color:green">materials</span> mentions.',
+        'The LLM responses undergo post-processing to extract <span style="color:orange">physical quantities, measurements</span>, and <span style="color:green">materials</span> mentions.',
         unsafe_allow_html=True)
 
     st.divider()