[#16] Replace routines in embedding_helper with a class, qdrant_colle…

…ction. Improvements to model_styles.py, and other tweaks. Begin porting demo/chat_pdf_streamlit_ui.py

demo/chat_pdf_streamlit_ui.py

@@ -48,9 +48,10 @@
 from PyPDF2 import PdfReader
 
 from ogbujipt.config import openai_emulation, openai_live, HOST_DEFAULT
-from ogbujipt.prompting.basic import context_build, pdelim
+from ogbujipt.prompting import format, CHATGPT_DELIMITERS
+from ogbujipt import oapi_choice1_text
 from ogbujipt.text_helper import text_splitter
-from ogbujipt.embedding_helper import qdrant_init_embedding_db, qdrant_add_collection
+from ogbujipt.embedding_helper import qdrant_collection
 
 # Avoid re-entrace complaints from huggingface/tokenizers
 os.environ['TOKENIZERS_PARALLELISM'] = 'false'
@@ -82,14 +83,18 @@
 throbber = 'data:image/gif;base64,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'  # noqa E501
 
 
-async def prep_pdf(pdf, knowledge_base, embedding_model, collection_name):
+async def prep_pdf(pdf, embedding_model, collection_name):
     # Streamlit treats function docstrings as magic strings for user display
     # Describe function via comments instead:
     # Converts pdf content into chunks according to chunk size & overlap
     # Vectorizes chunks for sLLM lookup
     # returns `knowledge_base`, the vector DB with indexed chunks
+
+    # Create in-memory Qdrant instance
+    knowledge_base = qdrant_collection(collection_name, embedding_model)
+
     pdf_reader = PdfReader(pdf)
-    
+
     # Collect text from pdf
     text = ''.join((page.extract_text() for page in pdf_reader.pages))
 
@@ -101,13 +106,9 @@ async def prep_pdf(pdf, knowledge_base, embedding_model, collection_name):
         separator='\n'
         )
 
+    print('\n\n'.join([ch[:100] for ch in chunks]))
     # Add a new collection for this document, and upsert the chunks into it
-    knowledge_base = qdrant_add_collection(
-        client=knowledge_base,
-        collection_name=collection_name, 
-        chunks=chunks, 
-        embedding_model=embedding_model
-        )
+    knowledge_base.add(texts=chunks)
 
     return knowledge_base
 
@@ -119,78 +120,76 @@ async def async_main(openai_api, model, LLM_TEMP):
     '''
     Oori — Ask your PDF 📄💬
     '''
-    # Define delimeters in OpenAI's style
-    openai_delimiters = {
-        pdelim.PREQUERY: '### USER:',
-        pdelim.POSTQUERY: '### ASSISTANT:',
-    }
-
-    # LLM will be downloaded from HuggingFace automatically
-    # There seem to be reentrancy issues with HuggingFace; defer import
-    from sentence_transformers import SentenceTransformer
-    embedding_model = SentenceTransformer(DOC_EMBEDDINGS_LLM)
-
-    # Create in-memory Qdrant instance
-    knowledge_base = qdrant_init_embedding_db()
-
     # create file upload box on Streamlit, set from the user's upload
     pdf = st.file_uploader("Upload a PDF", type=["pdf"], accept_multiple_files=False)
-    
+
     if pdf:
         # Show throbber, vectorize the PDF, and setup for similarity search
-        with st.empty():
-            st.image(throbber)
-            kb = await prep_pdf(pdf, knowledge_base, embedding_model, collection_name=pdf.name)
-
-            user_question = st.text_input(PDF_USER_QUESTION_PROMPT)
-
-            embedded_question = embedding_model.encode(user_question)
-
-            docs = None
-            if user_question:
-                docs = kb.search(
-                    collection_name=pdf.name,
-                    query_vector=embedded_question, 
-                    limit=K
+        placeholder = st.empty()
+        with placeholder:
+            placeholder.image(throbber)
+
+            # LLM will be downloaded from HuggingFace automatically
+            # There seem to be reentrancy issues with HuggingFace; defer import
+            from sentence_transformers import SentenceTransformer
+            embedding_model = SentenceTransformer(DOC_EMBEDDINGS_LLM)
+
+            kb = await prep_pdf(pdf, embedding_model, collection_name=pdf.name)
+            placeholder.empty()
+
+        user_question = st.text_input(PDF_USER_QUESTION_PROMPT)
+
+        # docs = None
+        while not user_question:
+            await asyncio.sleep(0.1)
+
+        embedded_question = embedding_model.encode(user_question)
+        docs = kb.db.search(
+            collection_name=kb.name,
+            query_vector=embedded_question, 
+            limit=K
+            )
+
+        print(kb.name, pdf.name, docs)
+        if docs:
+            # Collects "chunked_doc" into "gathered_chunks"
+            gathered_chunks = '\n\n'.join(
+                doc.payload['_text'] for doc in docs
+                )
+
+            # Build prompt the doc chunks as context
+            prompt = format(
+                f'Given the context, {user_question}\n\n'
+                f'Context: """\n{gathered_chunks}\n"""\n',
+                preamble='### SYSTEM:\nYou are a helpful assistant, who answers '
+                'questions directly and as briefly as possible. '
+                'If you cannot answer with the given context, just say so.\n',
+                delimiters=CHATGPT_DELIMITERS
+                )
+
+            print(prompt)
+            # Show throbber, and send LLM prompt
+            with st.empty():
+                st.image(throbber)
+                response = openai_api.Completion.create(
+                    model=model,  # Model (Required)
+                    prompt=prompt,  # Prompt (Required)
+                    temperature=LLM_TEMP,  # Temp (Default 1)
+                    max_tokens=1024  # Maximum tokens to return (Default 16)
                     )
 
-            if docs:
-                # Collects "chunked_doc" into "gathered_chunks"
-                gathered_chunks = '\n\n'.join(
-                    doc.payload['chunk_string'] for doc in docs
-                    )
+                # Response is a json-like object; extract the text
+                print('\nFull response data from LLM:\n', response)
 
-                # Build "prompt" with the context of "chunked_doc"
-                prompt = context_build(
-                    f'Given the context, {user_question}\n\n'
-                    f'Context: """\n{gathered_chunks}\n"""\n',
-                    preamble='### SYSTEM:\nYou are a helpful assistant, who answers '
-                    'questions directly and as briefly as possible. '
-                    'If you cannot answer with the given context, just say so.\n',
-                    delimiters=openai_delimiters
-                    )
+                # Response is a json-like object; 
+                # just get back the text of the response
+                response_text = oapi_choice1_text(response).strip()
+                print('\nResponse text from LLM:\n', response_text)
 
-                print(prompt)
-                # Show throbber, and send LLM prompt
-                with st.empty():
-                    st.image(throbber)
-                    response = openai_api.Completion.create(
-                        model=model,  # Model (Required)
-                        prompt=prompt,  # Prompt (Required)
-                        temperature=LLM_TEMP,  # Temp (Default 1)
-                        max_tokens=1024  # Maximum tokens to return (Default 16)
-                        )
-
-                    # Response is a json-like object; extract the text
-                    print('\nFull response data from LLM:\n', response)
-
-                    # Response is a json-like object; 
-                    # just get back the text of the response
-                    response_text = response.choices[0].text.strip()
-                    print('\nResponse text from LLM:\n', response_text)
-
-                    # Write the response text to Streamlit
-                    st.write(response_text)
+                # Write the response text to Streamlit
+                st.write(response_text)
+        else:
+            st.write('No context info found')
 
 
 def main():