Add requirements.txt and some code changes

ai/gen-ai-agents/custom_rag_agent/agent_state.py

@@ -40,6 +40,9 @@ class State(TypedDict):
     standalone_question: str = ""
 
     # similarity_search
+    # 30/06: modified, now they're a dict with
+    # page_content and metadata
+    # populated with docs_serializable (utils.py)
     retriever_docs: Optional[list] = []
     # reranker
     reranker_docs: Optional[list] = []

ai/gen-ai-agents/custom_rag_agent/answer_generator.py

@@ -1,7 +1,7 @@
 """
 File name: answer_generator.py
 Author: Luigi Saetta
-Date last modified: 2025-03-31
+Date last modified: 2025-04-02
 Python Version: 3.11
 
 Description:
@@ -67,10 +67,8 @@ def build_context_for_llm(self, docs: list):
 
         docs: list[Documents]
         """
-        _context = ""
-
-        for doc in docs:
-            _context += doc.page_content + "\n\n"
+        # more Pythonic
+        _context = "\n\n".join(doc["page_content"] for doc in docs)
 
         return _context
 
@@ -79,7 +77,7 @@ def invoke(self, input: State, config=None, **kwargs):
         """
         Generate the final answer
         """
-        # get the config
+        # get the model_id from config
         model_id = config["configurable"]["model_id"]
 
         if config["configurable"]["main_language"] in self.dict_languages:
@@ -102,6 +100,7 @@ def invoke(self, input: State, config=None, **kwargs):
         try:
             llm = get_llm(model_id=model_id)
 
+            # docs are returned from the reranker
             _context = self.build_context_for_llm(input["reranker_docs"])
 
             system_prompt = PromptTemplate(

ai/gen-ai-agents/custom_rag_agent/assistant_ui_langgraph.py

@@ -2,7 +2,7 @@
 File name: assistant_ui.py
 Author: Luigi Saetta
 Date created: 2024-12-04
-Date last modified: 2025-03-31
+Date last modified: 2025-07-01
 Python Version: 3.11
 
 Description:
@@ -15,7 +15,7 @@
     This code is released under the MIT License.
 
 Notes:
-    This is part of a  demo fro a RAG solution implemented
+    This is part of a  demo for a RAG solution implemented
     using LangGraph
 
 Warnings:
@@ -38,7 +38,7 @@
 from transport import http_transport
 from utils import get_console_logger
 
-# changed to better manage ENABLE_TRACING
+# changed to better manage ENABLE_TRACING (can be enabled from UI)
 import config
 
 # Constant
@@ -142,13 +142,14 @@ def register_feedback():
 
 st.sidebar.header("Options")
 
+st.sidebar.text_input(label="Region", value=config.REGION, disabled=True)
+
 # the collection used for semantic search
 st.session_state.collection_name = st.sidebar.selectbox(
     "Collection name",
     config.COLLECTION_LIST,
 )
 
-# add the choice of LLM (not used for now)
 st.session_state.main_language = st.sidebar.selectbox(
     "Select the language for the answer",
     config.LANGUAGE_LIST,
@@ -203,11 +204,11 @@ def register_feedback():
                     encoding=Encoding.V2_JSON,
                     sample_rate=100,
                 ) as span:
-                    # loop to manage streaming
                     # set the agent config
                     agent_config = {
                         "configurable": {
                             "model_id": st.session_state.model_id,
+                            "embed_model_type": config.EMBED_MODEL_TYPE,
                             "enable_reranker": st.session_state.enable_reranker,
                             "enable_tracing": config.ENABLE_TRACING,
                             "main_language": st.session_state.main_language,
@@ -219,6 +220,7 @@ def register_feedback():
                     if config.DEBUG:
                         logger.info("Agent config: %s", agent_config)
 
+                    # loop to manage streaming
                     for event in st.session_state.workflow.stream(
                         input_state,
                         config=agent_config,
@@ -248,13 +250,13 @@ def register_feedback():
                     # Stream
                     with st.chat_message(ASSISTANT):
                         response_container = st.empty()
-                        full_response = ""
+                        FULL_RESPONSE = ""
 
                         for chunk in answer_generator:
-                            full_response += chunk.content
-                            response_container.markdown(full_response + "▌")
+                            FULL_RESPONSE += chunk.content
+                            response_container.markdown(FULL_RESPONSE + "▌")
 
-                        response_container.markdown(full_response)
+                        response_container.markdown(FULL_RESPONSE)
 
                     elapsed_time = round((time.time() - time_start), 1)
                     logger.info("Elapsed time: %s sec.", elapsed_time)
@@ -268,7 +270,7 @@ def register_feedback():
 
                 # Add user/assistant message to chat history
                 add_to_chat_history(HumanMessage(content=question))
-                add_to_chat_history(AIMessage(content=full_response))
+                add_to_chat_history(AIMessage(content=FULL_RESPONSE))
 
                 # get the feedback
                 if st.session_state.get_feedback:

ai/gen-ai-agents/custom_rag_agent/bm25_search.py

@@ -57,17 +57,22 @@ def fetch_text_data(self):
                 cursor.execute(query)
 
                 while True:
-                    rows = cursor.fetchmany(self.batch_size)  # Fetch records in batches
+                    # Fetch records in batches
+                    rows = cursor.fetchmany(self.batch_size)
                     if not rows:
-                        break  # Exit loop when no more data
+                        # Exit loop when no more data
+                        break
 
                     for row in rows:
-                        lob_data = row[0]  # This is a CLOB object
+                        # This is a CLOB object
+                        lob_data = row[0]
 
                         if isinstance(lob_data, oracledb.LOB):
-                            _results.append(lob_data.read())  # Read LOB content
+                            # Read LOB content
+                            _results.append(lob_data.read())
                         else:
-                            _results.append(str(lob_data))  # Fallback for non-LOB data
+                            # Fallback for non-LOB data
+                            _results.append(str(lob_data))
 
         return _results
 
@@ -116,18 +121,33 @@ def search(self, query, top_n=5):
 
 # Example Usage:
 # credential are packed in CONNECT_ARGS
-table_name = "BOOKS"
-text_column = "TEXT"
 
-# create the index
-bm25_search = BM25OracleSearch(table_name, text_column)
 
-questions = ["Chi è Luigi Saetta?", "What are the main innovation produced by GPT-4?"]
+def run_test():
+    """
+    To run a quick test.
+    """
+    table_name = "BOOKS"
+    text_column = "TEXT"
+
+    # create the index
+    bm25_search = BM25OracleSearch(table_name, text_column)
+
+    questions = [
+        "Chi è Luigi Saetta?",
+        "What are the main innovation produced by GPT-4?",
+    ]
+
+    for _question in questions:
+        results = bm25_search.search(_question, top_n=2)
+
+        # Print search results
+        for text, score in results:
+            print(f"Score: {score:.2f} - Text: {text}")
+            print("")
 
-for _question in questions:
-    results = bm25_search.search(_question, top_n=2)
 
-    # Print search results
-    for text, score in results:
-        print(f"Score: {score:.2f} - Text: {text}")
-        print("")
+#
+# Main
+#
+run_test()

ai/gen-ai-agents/custom_rag_agent/chunk_index_utils.py

@@ -24,6 +24,17 @@
 logger = get_console_logger()
 
 
+def get_chunk_header(file_path):
+    """
+    Generate an header for the chunk.
+    """
+    doc_name = remove_path_from_ref(file_path)
+    # split to remove the extension
+    doc_title = doc_name.split(".")[0]
+
+    return f"# Doc. title: {doc_title}\n", doc_name
+
+
 def get_recursive_text_splitter(chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP):
     """
     return a recursive text splitter
@@ -39,7 +50,15 @@ def get_recursive_text_splitter(chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERL
 
 def load_and_split_pdf(book_path, chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP):
     """
-    load a single book
+    Loads and splits a PDF document into chunks using a recursive character text splitter.
+
+    Args:
+        book_path (str): The file path of the PDF document.
+        chunk_size (int): Size of each text chunk.
+        chunk_overlap (int): Overlap between chunks.
+
+    Returns:
+        List[Document]: A list of LangChain Document objects with metadata.
     """
     text_splitter = get_recursive_text_splitter(chunk_size, chunk_overlap)
 
@@ -50,28 +69,33 @@ def load_and_split_pdf(book_path, chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVE
     chunk_header = ""
 
     if len(docs) > 0:
-        doc_name = remove_path_from_ref(book_path)
-        # split to remove the extension
-        doc_title = doc_name.split(".")[0]
-        chunk_header = f"# Doc. title: {doc_title}\n"
+        chunk_header, _ = get_chunk_header(book_path)
 
     # remove path from source and reduce the metadata (16/03/2025)
     for doc in docs:
         # add more context to the chunk
         doc.page_content = chunk_header + doc.page_content
         doc.metadata = {
             "source": remove_path_from_ref(book_path),
-            "page_label": doc.metadata["page_label"],
+            "page_label": doc.metadata.get("page_label", ""),
         }
 
-    logger.info("Loaded %s chunks...", len(docs))
+    logger.info("Successfully loaded and split %d chunks from %s", len(docs), book_path)
 
     return docs
 
 
 def load_and_split_docx(file_path, chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP):
     """
-    To load docx files
+    Loads and splits a docx document into chunks using a recursive character text splitter.
+
+    Args:
+        file_path (str): The file path of the document.
+        chunk_size (int): Size of each text chunk.
+        chunk_overlap (int): Overlap between chunks.
+
+    Returns:
+        List[Document]: A list of LangChain Document objects with metadata.
     """
     loader = UnstructuredLoader(file_path)
     docs = loader.load()
@@ -80,12 +104,10 @@ def load_and_split_docx(file_path, chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OV
     grouped_text = defaultdict(list)
 
     chunk_header = ""
+    doc_name = ""
 
     if len(docs) > 0:
-        doc_name = remove_path_from_ref(file_path)
-        # split to remove the extension
-        doc_title = doc_name.split(".")[0]
-        chunk_header = f"# Doc. title: {doc_title}\n"
+        chunk_header, doc_name = get_chunk_header(file_path)
 
     for doc in docs:
         # fallback to 0 if not available
@@ -115,6 +137,6 @@ def load_and_split_docx(file_path, chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OV
                 )
             )
 
-    logger.info("Loaded %s chunks...", len(final_chunks))
+    logger.info("Successfully loaded and split %d chunks from %s", len(docs), file_path)
 
     return final_chunks