Merge branch 'main' into pdf-render

README.md

@@ -16,11 +16,14 @@ license: apache-2.0
 
 ## Introduction
 
-Question/Answering on scientific documents using LLMs (OpenAI, Mistral, ~~LLama2,~~ etc..).
-This application is the frontend for testing the RAG (Retrieval Augmented Generation) on scientific documents, that we are developing at NIMS.
-Differently to most of the project, we focus on scientific articles. We target only the full-text using [Grobid](https://github.com/kermitt2/grobid) that provide and cleaner results than the raw PDF2Text converter (which is comparable with most of other solutions).
+Question/Answering on scientific documents using LLMs: ChatGPT-3.5-turbo, Mistral-7b-instruct and Zephyr-7b-beta.
+The streamlit application demonstrate the implementaiton of a RAG (Retrieval Augmented Generation) on scientific documents, that we are developing at NIMS (National Institute for Materials Science), in Tsukuba, Japan.
+Differently to most of the projects, we focus on scientific articles. 
+We target only the full-text using [Grobid](https://github.com/kermitt2/grobid) that provide and cleaner results than the raw PDF2Text converter (which is comparable with most of other solutions).
 
-**NER in LLM response**: The responses from the LLMs are post-processed 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)).
+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.  
 
 **Demos**: 
  - (on HuggingFace spaces): https://lfoppiano-document-qa.hf.space/

document_qa/document_qa_engine.py

@@ -23,7 +23,13 @@ class DocumentQAEngine:
     embeddings_map_from_md5 = {}
     embeddings_map_to_md5 = {}
 
-    def __init__(self, llm, embedding_function, qa_chain_type="stuff", embeddings_root_path=None, grobid_url=None):
+    def __init__(self,
+                 llm,
+                 embedding_function,
+                 qa_chain_type="stuff",
+                 embeddings_root_path=None,
+                 grobid_url=None,
+                 ):
         self.embedding_function = embedding_function
         self.llm = llm
         self.chain = load_qa_chain(llm, chain_type=qa_chain_type)
@@ -81,14 +87,14 @@ class DocumentQAEngine:
         return self.embeddings_map_from_md5[md5]
 
     def query_document(self, query: str, doc_id, output_parser=None, context_size=4, extraction_schema=None,
-                       verbose=False) -> (
+                       verbose=False, memory=None) -> (
             Any, str):
         # self.load_embeddings(self.embeddings_root_path)
 
         if verbose:
             print(query)
 
-        response = self._run_query(doc_id, query, context_size=context_size)
+        response = self._run_query(doc_id, query, context_size=context_size, memory=memory)
         response = response['output_text'] if 'output_text' in response else response
 
         if verbose:
@@ -138,9 +144,15 @@ class DocumentQAEngine:
 
         return parsed_output
 
-    def _run_query(self, doc_id, query, context_size=4):
+    def _run_query(self, doc_id, query, memory=None, context_size=4):
         relevant_documents = self._get_context(doc_id, query, context_size)
-        return self.chain.run(input_documents=relevant_documents, question=query)
+        if memory:
+            return self.chain.run(input_documents=relevant_documents,
+                                  question=query)
+        else:
+            return self.chain.run(input_documents=relevant_documents,
+                                  question=query,
+                                  memory=memory)
         # return self.chain({"input_documents": relevant_documents, "question": prompt_chat_template}, return_only_outputs=True)
 
     def _get_context(self, doc_id, query, context_size=4):
@@ -150,6 +162,7 @@ class DocumentQAEngine:
         return relevant_documents
 
     def get_all_context_by_document(self, doc_id):
+        """Return the full context from the document"""
         db = self.embeddings_dict[doc_id]
         docs = db.get()
         return docs['documents']
@@ -161,6 +174,7 @@ class DocumentQAEngine:
         return relevant_documents
 
     def get_text_from_document(self, pdf_file_path, chunk_size=-1, perc_overlap=0.1, verbose=False):
+        """Extract text from documents using Grobid, if chunk_size is < 0 it keep each paragraph separately"""
         if verbose:
             print("File", pdf_file_path)
         filename = Path(pdf_file_path).stem
@@ -209,18 +223,17 @@ class DocumentQAEngine:
 
         if hash not in self.embeddings_dict.keys():
             self.embeddings_dict[hash] = Chroma.from_texts(texts, embedding=self.embedding_function, metadatas=metadata,
-                                                           collection_name=hash)
+                                                          collection_name=hash)
         else:
             self.embeddings_dict[hash].delete(ids=self.embeddings_dict[hash].get()['ids'])
             self.embeddings_dict[hash] = Chroma.from_texts(texts, embedding=self.embedding_function, metadatas=metadata,
                                                            collection_name=hash)
 
-
         self.embeddings_root_path = None
 
         return hash
 
-    def create_embeddings(self, pdfs_dir_path: Path):
+    def create_embeddings(self, pdfs_dir_path: Path, chunk_size=500, perc_overlap=0.1):
         input_files = []
         for root, dirs, files in os.walk(pdfs_dir_path, followlinks=False):
             for file_ in files:
@@ -238,7 +251,8 @@ class DocumentQAEngine:
                 print(data_path, "exists. Skipping it ")
                 continue
 
-            texts, metadata, ids = self.get_text_from_document(input_file, chunk_size=500, perc_overlap=0.1)
+            texts, metadata, ids = self.get_text_from_document(input_file, chunk_size=chunk_size,
+                                                               perc_overlap=perc_overlap)
             filename = metadata[0]['filename']
 
             vector_db_document = Chroma.from_texts(texts,

pyproject.toml

@@ -3,7 +3,7 @@ requires = ["setuptools", "setuptools-scm"]
 build-backend = "setuptools.build_meta"
 
 [tool.bumpversion]
-current_version = "0.2.1"
+current_version = "0.3.0"
 commit = "true"
 tag = "true"
 tag_name = "v{new_version}"

streamlit_app.py

@@ -7,6 +7,7 @@ from tempfile import NamedTemporaryFile
 import dotenv
 from grobid_quantities.quantities import QuantitiesAPI
 from langchain.llms.huggingface_hub import HuggingFaceHub
+from langchain.memory import ConversationBufferWindowMemory
 
 dotenv.load_dotenv(override=True)
 
@@ -52,6 +53,9 @@ if 'ner_processing' not in st.session_state:
 if 'uploaded' not in st.session_state:
     st.session_state['uploaded'] = False
 
+if 'memory' not in st.session_state:
+    st.session_state['memory'] = ConversationBufferWindowMemory(k=4)
+
 if 'binary' not in st.session_state:
     st.session_state['binary'] = None
 
@@ -82,6 +86,11 @@ def new_file():
     st.session_state['loaded_embeddings'] = None
     st.session_state['doc_id'] = None
     st.session_state['uploaded'] = True
+    st.session_state['memory'].clear()
+
+
+def clear_memory():
+    st.session_state['memory'].clear()
 
 
 # @st.cache_resource
@@ -112,6 +121,7 @@ def init_qa(model, api_key=None):
     else:
         st.error("The model was not loaded properly. Try reloading. ")
         st.stop()
+        return
 
     return DocumentQAEngine(chat, embeddings, grobid_url=os.environ['GROBID_URL'])
 
@@ -183,7 +193,7 @@ with st.sidebar:
         disabled=st.session_state['doc_id'] is not None or st.session_state['uploaded'])
 
     st.markdown(
-        ":warning: Mistral and Zephyr are free to use, however requests might hit limits of the huggingface free API and fail. :warning: ")
+        ":warning: Mistral and Zephyr are **FREE** to use. Requests might fail anytime. Use at your own risk. :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:
@@ -219,6 +229,12 @@ with st.sidebar:
                         st.session_state['rqa'][model] = init_qa(model)
     # else:
     #     is_api_key_provided = st.session_state['api_key']
+
+    st.button(
+            'Reset chat memory.',
+            on_click=clear_memory(),
+            help="Clear the conversational memory. Currently implemented to retrain the 4 most recent messages.")
+
 left_column, right_column = st.columns([1, 1])
 
 with right_column:
@@ -349,7 +365,8 @@ with right_column:
         elif mode == "LLM":
             with st.spinner("Generating response..."):
                 _, text_response = st.session_state['rqa'][model].query_document(question, st.session_state.doc_id,
-                                                                                 context_size=context_size)
+                                                                             context_size=context_size,
+                                                                             memory=st.session_state.memory)
 
         if not text_response:
             st.error("Something went wrong. Contact Luca Foppiano (Foppiano.Luca@nims.co.jp) to report the issue.")
@@ -368,5 +385,11 @@ with right_column:
                 st.write(text_response)
             st.session_state.messages.append({"role": "assistant", "mode": mode, "content": text_response})
 
+        for id in range(0, len(st.session_state.messages), 2):
+            question = st.session_state.messages[id]['content']
+            if len(st.session_state.messages) > id + 1:
+                answer = st.session_state.messages[id + 1]['content']
+                st.session_state.memory.save_context({"input": question}, {"output": answer})
+
     elif st.session_state.loaded_embeddings and st.session_state.doc_id:
         play_old_messages()