update dependencies, remove biblio from search space

document_qa/document_qa_engine.py

@@ -7,7 +7,6 @@
 from langchain.chains import create_extraction_chain
 from langchain.chains.question_answering import load_qa_chain, stuff_prompt, refine_prompts, map_reduce_prompt, \
     map_rerank_prompt
-from langchain.evaluation import PairwiseEmbeddingDistanceEvalChain, load_evaluator, EmbeddingDistance
 from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate
 from langchain.retrievers import MultiQueryRetriever
 from langchain.schema import Document
@@ -273,7 +272,7 @@ def query_storage_and_embeddings(self, query: str, doc_id, context_size=4) -> Li
         """
         db = self.data_storage.embeddings_dict[doc_id]
         retriever = db.as_retriever(search_kwargs={"k": context_size}, search_type="similarity_with_embeddings")
-        relevant_documents = retriever.get_relevant_documents(query)
+        relevant_documents = retriever.invoke(query)
 
         return relevant_documents
 
@@ -284,7 +283,7 @@ def analyse_query(self, query, doc_id, context_size=4):
         #     search_type="similarity_score_threshold"
         # )
         retriever = db.as_retriever(search_kwargs={"k": context_size}, search_type="similarity_with_embeddings")
-        relevant_documents = retriever.get_relevant_documents(query)
+        relevant_documents = retriever.invoke(query)
         relevant_document_coordinates = [doc.metadata['coordinates'].split(";") if 'coordinates' in doc.metadata else []
                                          for doc in
                                          relevant_documents]
@@ -338,7 +337,7 @@ def _run_query(self, doc_id, query, context_size=4) -> (List[Document], list):
     def _get_context(self, doc_id, query, context_size=4) -> (List[Document], list):
         db = self.data_storage.embeddings_dict[doc_id]
         retriever = db.as_retriever(search_kwargs={"k": context_size})
-        relevant_documents = retriever.get_relevant_documents(query)
+        relevant_documents = retriever.invoke(query)
         relevant_document_coordinates = [doc.metadata['coordinates'].split(";") if 'coordinates' in doc.metadata else []
                                          for doc in
                                          relevant_documents]
@@ -361,7 +360,7 @@ def get_full_context_by_document(self, doc_id):
     def _get_context_multiquery(self, doc_id, query, context_size=4):
         db = self.data_storage.embeddings_dict[doc_id].as_retriever(search_kwargs={"k": context_size})
         multi_query_retriever = MultiQueryRetriever.from_llm(retriever=db, llm=self.llm)
-        relevant_documents = multi_query_retriever.get_relevant_documents(query)
+        relevant_documents = multi_query_retriever.invoke(query)
         return relevant_documents
 
     def get_text_from_document(self, pdf_file_path, chunk_size=-1, perc_overlap=0.1, verbose=False):

document_qa/grobid_processors.py

@@ -148,15 +148,15 @@ def parse_grobid_xml(self, text, coordinates=False):
         soup = BeautifulSoup(text, 'xml')
         blocks_header = get_xml_nodes_header(soup, use_paragraphs=True)
 
-        passages.append({
-            "text": f"authors: {biblio['authors']}",
-            "type": passage_type,
-            "section": "<header>",
-            "subSection": "<authors>",
-            "passage_id": "hauthors",
-            "coordinates": ";".join([node['coords'] if coordinates and node.has_attr('coords') else "" for node in
-                                     blocks_header['authors']])
-        })
+        # passages.append({
+        #     "text": f"authors: {biblio['authors']}",
+        #     "type": passage_type,
+        #     "section": "<header>",
+        #     "subSection": "<authors>",
+        #     "passage_id": "hauthors",
+        #     "coordinates": ";".join([node['coords'] if coordinates and node.has_attr('coords') else "" for node in
+        #                              blocks_header['authors']])
+        # })
 
         passages.append({
             "text": self.post_process(" ".join([node.text for node in blocks_header['title']])),

requirements.txt

@@ -16,14 +16,17 @@ dateparser
 
 # LLM
 chromadb==0.4.24
-tiktoken==0.6.0
-openai==1.16.2
-langchain==0.1.14
-langchain-core==0.1.40
+tiktoken==0.7.0
+openai==1.42.0
+langchain==0.2.14
+langchain-core==0.2.34
+langchain-openai==0.1.22
+langchain-huggingface==0.0.3
+langchain-community==0.2.12
 typing-inspect==0.9.0
 typing_extensions==4.11.0
 pydantic==2.6.4
 sentence_transformers==2.6.1
-streamlit-pdf-viewer==0.0.17
+streamlit-pdf-viewer==0.0.18-dev1
 umap-learn
 plotly

streamlit_app.py

@@ -6,10 +6,11 @@
 import dotenv
 from grobid_quantities.quantities import QuantitiesAPI
 from langchain.memory import ConversationBufferWindowMemory
-from langchain_community.chat_models.openai import ChatOpenAI
-from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
-from langchain_community.embeddings.openai import OpenAIEmbeddings
+from langchain_community.callbacks import PromptLayerCallbackHandler
+from langchain_community.chat_models import ChatOpenAI
 from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_openai import OpenAIEmbeddings
 from streamlit_pdf_viewer import pdf_viewer
 
 from document_qa.ner_client_generic import NERClientGeneric
@@ -97,6 +98,9 @@
 if 'embeddings' not in st.session_state:
     st.session_state['embeddings'] = None
 
+if 'scroll_to_first_annotation' not in st.session_state:
+    st.session_state['scroll_to_first_annotation'] = False
+
 st.set_page_config(
     page_title="Scientific Document Insights Q/A",
     page_icon="📝",
@@ -169,7 +173,8 @@ def init_qa(model, embeddings_name=None, api_key=None):
             repo_id=OPEN_MODELS[model],
             temperature=0.01,
             max_new_tokens=4092,
-            model_kwargs={"max_length": 8192}
+            model_kwargs={"max_length": 8192},
+            callbacks=[PromptLayerCallbackHandler(pl_tags=[model, "document-qa"])]
         )
         embeddings = HuggingFaceEmbeddings(
             model_name=OPEN_EMBEDDINGS[embeddings_name])
@@ -233,8 +238,8 @@ def play_old_messages(container):
 # is_api_key_provided = st.session_state['api_key']
 
 with st.sidebar:
-    st.title("📝 Scientific Document Insights Q/A")
-    st.subheader("Upload a scientific article in PDF, ask questions, get insights.")
+    st.title("📝 Document Q/A")
+    st.markdown("Upload a scientific article in PDF, ask questions, get insights.")
     st.markdown(
         ":warning: [Usage disclaimer](https://github.com/lfoppiano/document-qa?tab=readme-ov-file#disclaimer-on-data-security-and-privacy-%EF%B8%8F) :warning: ")
 
@@ -301,14 +306,14 @@ def play_old_messages(container):
     #     help="Clear the conversational memory. Currently implemented to retrain the 4 most recent messages.",
     #     disabled=model in st.session_state['rqa'] and st.session_state['rqa'][model].memory is None)
 
-left_column, right_column = st.columns([1, 1])
+left_column, right_column = st.columns([5, 4])
 right_column = right_column.container(border=True)
 left_column = left_column.container(border=True)
 
 with right_column:
     uploaded_file = st.file_uploader(
-        "Upload an article",
-        type=("pdf", "txt"),
+        "Upload a scientific article",
+        type=("pdf"),
         on_change=new_file,
         disabled=st.session_state['model'] is not None and st.session_state['model'] not in
                  st.session_state['api_keys'],
@@ -343,6 +348,10 @@ def play_old_messages(container):
              "relevant paragraphs to the question in the paper. "
              "Question coefficient attempt to estimate how effective the question will be answered."
     )
+    st.session_state['scroll_to_first_annotation'] = st.checkbox(
+        "Scroll to context",
+        help='The PDF viewer will automatically scroll to the first relevant passage in the document.'
+    )
     st.session_state['ner_processing'] = st.checkbox(
         "Identify materials and properties.",
         help='The LLM responses undergo post-processing to extract physical quantities, measurements, and materials mentions.'
@@ -415,7 +424,6 @@ def generate_color_gradient(num_elements):
 
 with right_column:
     if st.session_state.loaded_embeddings and question and len(question) > 0 and st.session_state.doc_id:
-        # messages.chat_message("user").markdown(question)
         st.session_state.messages.append({"role": "user", "mode": mode, "content": question})
 
         for message in st.session_state.messages:
@@ -491,5 +499,6 @@ def generate_color_gradient(num_elements):
                 input=st.session_state['binary'],
                 annotation_outline_size=2,
                 annotations=st.session_state['annotations'],
-                render_text=True
+                render_text=True,
+                scroll_to_annotation=1 if (st.session_state['annotations'] and st.session_state['scroll_to_first_annotation']) else None
             )