cleanup and update documentation

README.md

@@ -23,7 +23,7 @@ We target only the full-text using [Grobid](https://github.com/kermitt2/grobid)
 
 Additionally, this frontend provides the visualisation of named entities on LLM responses to extract <span stype="color:yellow">physical quantities, measurements</span> (with [grobid-quantities](https://github.com/kermitt2/grobid-quantities)) and <span stype="color:blue">materials</span> mentions (with [grobid-superconductors](https://github.com/lfoppiano/grobid-superconductors)).
 
-The conversation is backed up by a sliding window memory (top 4 more recent messages) that help refers to information previously discussed in the chat.  
+The conversation is kept in memory up by a buffered sliding window memory (top 4 more recent messages) and the messages are injected in the context as "previous messages".   
 
 **Demos**: 
  - (on HuggingFace spaces): https://lfoppiano-document-qa.hf.space/

document_qa/document_qa_engine.py

@@ -41,10 +41,6 @@ def __init__(self,
                  ):
         self.embedding_function = embedding_function
         self.llm = llm
-        # if memory:
-        #     prompt = self.default_prompts[qa_chain_type].PROMPT_SELECTOR.get_prompt(llm)
-        #     self.chain = load_qa_chain(llm, chain_type=qa_chain_type, prompt=prompt, memory=memory)
-        # else:
         self.memory = memory
         self.chain = load_qa_chain(llm, chain_type=qa_chain_type)
 
@@ -161,7 +157,7 @@ def _parse_json(self, response, output_parser):
     def _run_query(self, doc_id, query, context_size=4):
         relevant_documents = self._get_context(doc_id, query, context_size)
         response = self.chain.run(input_documents=relevant_documents,
-                              question=query)
+                                  question=query)
 
         if self.memory:
             self.memory.save_context({"input": query}, {"output": response})
@@ -172,7 +168,9 @@ def _get_context(self, doc_id, query, context_size=4):
         retriever = db.as_retriever(search_kwargs={"k": context_size})
         relevant_documents = retriever.get_relevant_documents(query)
         if self.memory and len(self.memory.buffer_as_messages) > 0:
-            relevant_documents.append(Document(page_content="Previous conversation:\n{}\n\n".format(self.memory.buffer_as_str)))
+            relevant_documents.append(
+                Document(page_content="Previous conversation:\n{}\n\n".format(self.memory.buffer_as_str))
+            )
         return relevant_documents
 
     def get_all_context_by_document(self, doc_id):

streamlit_app.py

@@ -5,7 +5,6 @@
 
 import dotenv
 from grobid_quantities.quantities import QuantitiesAPI
-from langchain.callbacks import PromptLayerCallbackHandler
 from langchain.llms.huggingface_hub import HuggingFaceHub
 from langchain.memory import ConversationBufferWindowMemory