baseline done!

agents/agent_decision.py

-import os
 from typing import Dict, Optional, Union, List, TypedDict, Any, Literal
 from dotenv import load_dotenv
 from langchain_core.messages import HumanMessage, AIMessage, SystemMessage, BaseMessage
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.output_parsers import JsonOutputParser
 from langgraph.graph import MessagesState, StateGraph, END
-import json
+from agents.message_filter import MessageFilter
+from agents.rag_agent.buffer_memory import WindowBufferMemory
 from config import Config
 from agents.rag_agent import ResearchAssistantRAG
 from langgraph.checkpoint.memory import MemorySaver
@@ -18,92 +16,39 @@ memory = MemorySaver()
 
 thread_config = {"configurable": {"thread_id": "1"}}
 
-class AgentConfig:
-    DECISION_MODEL = "gpt-4o"
-    CONFIDENCE_THRESHOLD = 0.85
-
-    DECISION_SYSTEM_PROMPT = """
-    Du bist ein intelligentes akademisches Routingsystem, das Benutzeranfragen an den jeweils passenden spezialisierten Agenten weiterleitet. 
-    Deine Aufgabe ist es, die Anfrage des Benutzers zu analysieren und zu bestimmen, welcher Agent am besten geeignet ist – 
-    basierend auf dem Inhalt der Anfrage und dem Gesprächskontext.
-    
-    Verfügbare Agenten:
-    1. CONVERSATION_AGENT – Für allgemeine Konversation, Begrüßungen oder nicht-akademische Fragen.
-    2. KEYWORD_SEARCH_AGENT – Für gezielte Stichwortsuchen innerhalb der Dokumentensammlung.
-    3. RAG_AGENT – Für Fragen zu spezifischen akademischen Inhalten, die aus den bereitgestellten Dokumenten beantwortet werden können.
-    4. SUMMARIZATION_AGENT – Für Zusammenfassungen von Dokumenten oder bestimmten Abschnitten.
-    5. CLASSIFICATION_AGENT – Für die Erkennung des Dokumententyps (z. B. Bachelorarbeit, Artikel) und der Hauptthemen (z. B. Informatik, Medizin).
-    6. USER_MEMORY_AGENT – Interner Agent zur Verwaltung von Benutzerhistorie und Personalisierung.
-    
-    Triff deine Entscheidung anhand der folgenden Richtlinien:
-    - Wenn die Anfrage eine Begrüßung, allgemeine Frage oder unklare Formulierung enthält → wähle CONVERSATION_AGENT.
-    - Wenn die Anfrage eine gezielte Suche nach einem Begriff oder Schlüsselwort ist → wähle KEYWORD_SEARCH_AGENT.
-    - Wenn der Benutzer eine inhaltliche Frage zu einem Thema aus den Dokumenten stellt → wähle RAG_AGENT.
-    - Wenn eine Zusammenfassung eines Dokuments oder Abschnitts gewünscht ist → wähle SUMMARIZATION_AGENT.
-    - Wenn nach Art oder Thema eines Dokuments gefragt wird → wähle CLASSIFICATION_AGENT.
-    - Wenn die Absicht nicht eindeutig ist, wähle sicherheitshalber CONVERSATION_AGENT.
-    
-    Du musst deine Antwort im JSON-Format mit folgender Struktur liefern:
-    {
-      "agent": "AGENT_NAME",
-      "reasoning": "Schrittweise Begründung für die Wahl dieses Agenten",
-      "confidence": 0.95  // Wert zwischen 0.0 und 1.0, der das Vertrauen in die Entscheidung angibt
-    }
-    """
-
-class AgentDecision(TypedDict):
-    agent: Literal[
-        "CONVERSATION_AGENT",
-        "KEYWORD_SEARCH_AGENT",
-        "RAG_AGENT",
-        "SUMMARIZATION_AGENT",
-        "CLASSIFICATION_AGENT",
-        "USER_MEMORY_AGENT"
-    ]
-    reasoning: str
-    confidence: float
-
 class AgentState(MessagesState):
     """State maintained across the workflow."""
     messages: List[BaseMessage]
-    agent_name: Optional[str]
     current_input: Optional[Union[str, Dict]]
-    has_image: bool
-    image_type: Optional[str]
     output: Optional[str]
-    needs_human_validation: bool
     retrieval_confidence: float
-    bypass_routing: bool
-    insufficient_info: bool
-
-
-# class AgentDecision(TypedDict):
-#     agent: str
-#     reasoning: str
-#     confidence: float
-
 
 def create_agent_graph():
+    message_filter = MessageFilter(config.rag.llm)
+
     def run_rag_agent(state: AgentState) -> AgentState:
         print(f"Selected agent: RAG_AGENT")
 
+        new_state = state.copy()
         rag_agent = ResearchAssistantRAG(config)
 
         messages: List[BaseMessage] = state["messages"]
         query: Union[str, Dict] = state["current_input"]
         rag_context_limit: int = config.rag.context_limit
 
-        recent_context: str = ""
-        for msg in messages[-rag_context_limit:]:
-            if isinstance(msg, HumanMessage):
-                # print("######### DEBUG 1:", msg)
-                recent_context += f"User: {msg.content}\n"
-            elif isinstance(msg, AIMessage):
-                # print("######### DEBUG 2:", msg)
-                recent_context += f"Assistant: {msg.content}\n"
+        print(f"DEBUG messages: {messages}")
+
+        buffer_memory = WindowBufferMemory(context_limit=rag_context_limit)
+        chat_history: List[Dict[str, str]] = buffer_memory.build_context(messages)
+
+        print("DEBUG chat_history input:", chat_history)
+        print("DEBUG type of chat_history:", type(chat_history))
 
-        # Call pipeline RAG
-        response: Dict[str, Any] = rag_agent.process_query(query, chat_history=recent_context)
+        if chat_history:
+            for i, msg in enumerate(chat_history):
+                print(f"DEBUG chat_history[{i}]: {msg} (type: {type(msg)})")
+
+        response: Dict[str, Any] = rag_agent.process_query(query, chat_history=chat_history)
 
         ### CHECKPOINT
         print("Final response from RAG agent:", response)
@@ -124,6 +69,7 @@ def create_agent_graph():
         print(f"Response text type: {type(response_text)}")
         print(f"Response text preview: {response_text[:100]}...")
 
+        # TODO: need to be improved because the logic is still "lord"
         if isinstance(response_text, str) and (
                 "I don't have enough information to answer this question based on the provided context" in response_text or
                 "I don't have enough information" in response_text or
@@ -135,32 +81,79 @@ def create_agent_graph():
         ):
             print("RAG response indicates insufficient information")
             print(f"Response text that triggered insufficient_info: {response_text[:100]}...")
-            insufficient_info = True
-
-        print(f"Insufficient info flag set to: {insufficient_info}")
 
         # Store RAG output ONLY if confidence is high
         if retrieval_confidence >= config.rag.min_retrieval_confidence:
-            temp_output = response["response"]
+            response_output = AIMessage(content=response_text)
         else:
-            temp_output = response["response"]
+            response_output = AIMessage(content=response_text) # should be = ""
 
-        return {
+        updated_messages = messages + [AIMessage(content=response_text)]
+
+        new_state.update({
             **state,
-            "output": temp_output,
-            "needs_human_validation": False,
+            "messages": updated_messages,
+            "output": response_output,
             "retrieval_confidence": retrieval_confidence,
-            "agent_name": "RAG_AGENT",
-            "insufficient_info": insufficient_info
-        }
+        })
+
+        print(f"new_state in run_rag_agent: {new_state}")
+
+        return new_state
+
+    def apply_message_filter(state: AgentState) -> AgentState:
+        """Apply output guardrails to the generated response."""
+        new_state = state.copy()
+        output = state["output"]
+        current_input = state["current_input"]
+
+        # Check if output is valid
+        if not output or not isinstance(output, (str, AIMessage)):
+            return state
+
+        # Get the original input text
+        input_text = ""
+        if isinstance(current_input, str):
+            input_text = current_input
+        elif isinstance(current_input, dict):
+            input_text = current_input.get("text", "")
 
+        output_text = ""
+        if isinstance(output, str):
+            output_text = output
+        elif isinstance(output, AIMessage):
+            output_text = output.content
+
+        sanitized_output = message_filter.validate_output(output_text, input_text)
+
+        # For non-validation cases, add the sanitized output to messages
+        sanitized_message = AIMessage(content=sanitized_output) if isinstance(output, AIMessage) else sanitized_output
+
+        updated_messages = state.get("messages", [])
+        if isinstance(updated_messages, list):
+            updated_messages = updated_messages + [sanitized_message]
+        else:
+            updated_messages = [sanitized_message]  # ✅ Thêm fallback nếu messages bị sai kiểu
+
+        new_state.update({
+            **state,
+            "messages": updated_messages,
+            "output": sanitized_message
+        })
+
+        print(f"new_state in apply_message_filter: {new_state}")
+
+        return new_state
+
+    # === Create LangGraph ===
     graph = StateGraph(AgentState)
 
     graph.add_node("RAG_AGENT", run_rag_agent)
+    graph.add_node("MESSAGE_FILTER", apply_message_filter)
 
     graph.set_entry_point("RAG_AGENT")
-
-    graph.add_edge("RAG_AGENT", END)
+    graph.add_edge("RAG_AGENT", "MESSAGE_FILTER")
+    graph.add_edge("MESSAGE_FILTER", END)
 
     return graph.compile(checkpointer=memory)
 
@@ -169,31 +162,29 @@ def init_agent_state() -> AgentState:
     """Initialize the agent state with default values."""
     return {
         "messages": [],
-        "agent_name": None,
         "current_input": None,
-        "has_image": False,
-        "image_type": None,
         "output": None,
-        "needs_human_validation": False,
         "retrieval_confidence": 0.0,
-        "bypass_routing": True,
-        "insufficient_info": False
     }
 
-
 def process_query(query: Union[str, Dict], conversation_history: List[BaseMessage] = None) -> Dict[str, Any]:
     graph = create_agent_graph()
 
     state = init_agent_state()
-    # if conversation_history:
-    #     state["messages"] = conversation_history
+
+    print(f"conversation_history {conversation_history}")
 
     input_text = query if isinstance(query, str) else query.get("text", "")
 
+    if conversation_history:
+        state["messages"] = conversation_history
+    else:
+        state["messages"] = [HumanMessage(content=input_text)]
+
     state["current_input"] = query
-    state["messages"] = [HumanMessage(content=input_text)]
 
-    # result = graph.invoke(state, thread_config)
+    print(f"state first: {state}")
+
     state = graph.invoke(state, thread_config)
 
     output = state.get("output")
@@ -211,6 +202,5 @@ def process_query(query: Union[str, Dict], conversation_history: List[BaseMessag
 
     return {
         "response": response_text,
-        "agent_name": state.get("agent_name"),
         "messages": state.get("messages", [])
     }
\ No newline at end of file

agents/message_filter.py

+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import PromptTemplate
+from langchain_core.messages import AIMessage
+from langchain_core.runnables import RunnablePassthrough
+
+class MessageFilter:
+
+    def __init__(self, llm):
+        self.llm = llm
+        self.input_filter_prompt = PromptTemplate.from_template("")
+        self.output_filter_prompt = PromptTemplate.from_template("")
+
+        self.input_filter_chain = self.input_filter_prompt | self.llm | StrOutputParser()
+        self.output_filter_chain = self.output_filter_prompt | self.llm | StrOutputParser()
+
+    def validate_user_input(self, user_input: str) -> tuple[bool, AIMessage | str]:
+        result = self.input_filter_chain.invoke({"input": user_input})
+
+        return True, user_input
+
+    def validate_output(self, output: str, user_input: str = "") -> str:
+        if not output:
+            return output
+
+        output_text = ""
+        if isinstance(output, str):
+            output_text = output
+        elif hasattr(output, "content"):
+            output_text = output.content
+
+        return output_text

agents/rag_agent/__init__.py

@@ -4,11 +4,15 @@ from typing import List, Dict, Any, Optional
 import uuid
 from pathlib import Path
 import json
+
+from .query_expander import QueryExpander
 from .vector_store import QdrantRetriever
 from .document_processor import DocumentProcessor
 from .query_processor import QueryProcessor
 from .response_generator import ResponseGenerator
 from .data_ingestion import DataIngestion
+from .multi_query_generator import MultiQueryGenerator
+from .query_rewriter import QueryRewriter
 
 class ResearchAssistantRAG:
 
@@ -27,10 +31,14 @@ class ResearchAssistantRAG:
 
             self.embedding_model = self.config.rag.embedding_model
             self.logger.info(f"Using embedding model: {type(self.embedding_model).__name__}")
+            self.sparse_embedder = self.config.rag.sparse_embedder
+            self.query_expander = QueryExpander(self.config)
+            self.query_rewriter = QueryRewriter(self.llm)
 
-            self.query_processor = QueryProcessor(self.config, self.embedding_model)
+            self.query_processor = QueryProcessor(self.config, self.embedding_model, self.sparse_embedder)
 
             self.document_processor = DocumentProcessor(self.config, self.embedding_model)
+            self.query_generator = MultiQueryGenerator(llm=self.llm, max_queries=self.config.rag.max_queries or 5)
 
             self.retriever = QdrantRetriever(self.config)
 
@@ -58,16 +66,82 @@ class ResearchAssistantRAG:
     def process_query(self, query: str, chat_history: Optional[List[Dict[str, str]]] = None) -> Dict[str, Any]:
         self.logger.info(f"RAG Agent processing query: {query}")
 
+        # Process query and return result, passing chat_history
         result = self.query(query, chat_history)
 
         return result
 
+    def query(self, query: str, chat_history: Optional[List[Dict[str, str]]] = None) -> Dict[str, Any]:
+        exec_count = 1
+        self.logger.info(f"Processing query: {query}")
+        start_time = time.time()
+
+        try:
+            query_embedding: Dict[str, Any]
+            filters: Dict[str, Any]
+
+            if chat_history:
+                query = self.query_rewriter.rewrite(query, chat_history)
+
+            query_embedding, filters = self.query_processor.process_query(query)
+
+            ### DEBUG
+            # dense_vector = query_embedding.get("dense")
+            # sparse_vector = query_embedding.get("sparse")
+            #
+            # if dense_vector:
+            #     print("DEBUG dense_vector[:5]:", dense_vector[:5])
+            #     print("DEBUG dense_vector length:", len(dense_vector))
+            #
+            # if sparse_vector:
+            #     print("DEBUG sparse_vector type:", type(sparse_vector))
+            #     print("DEBUG sparse_vector indices[:5]:", getattr(sparse_vector, "indices", [])[:5])
+            #     print("DEBUG sparse_vector values[:5]:", getattr(sparse_vector, "values", [])[:5])
+            #
+            # print("DEBUG has as_object:", hasattr(sparse_vector, "as_object"))
+            ###
+
+            # TODO: Enhance retrieval by incorporating chat history context into the query embedding or retrieval strategy
+            search_results = self._run_vector_search(query_embedding, filters, query)
+
+            if self.similarity_threshold > 0:
+                search_results = [item for item in search_results if item.get('score', 0) >= self.similarity_threshold]
+                self.logger.info(f"{len(search_results)} results passed similarity threshold")
+
+            print(f"DEBUG chat_history at {exec_count}th time: \n{chat_history}")
+            exec_count += 1
+
+            response = self.response_generator.generate_response(
+                query=query,
+                retrieved_docs=search_results,
+                chat_history=chat_history
+            )
+
+            processing_time = time.time() - start_time
+            response["processing_time"] = processing_time
+
+            return response
+
+        except Exception as e:
+            self.logger.error(f"Error processing query: {e}")
+            import traceback
+            self.logger.error(traceback.format_exc())
+
+            return {
+                "response": f"I encountered an error while processing your query: {str(e)}",
+                "sources": [],
+                "confidence": 0.0,
+                "processing_time": time.time() - start_time
+            }
 
     def ingest_documents(self, documents: List[Dict[str, Any]]) -> Dict[str, Any]:
         self.logger.info(f"Ingesting {len(documents)} documents")
         start_time = time.time()
 
-        print(f"documents: {documents}")
+        ###
+        for i, doc in enumerate(documents):
+            print(f"Document {i} keys: {list(doc.keys())}")
+        ###
 
         try:
             processed_dir = Path("data/processed")
@@ -78,11 +152,12 @@ class ResearchAssistantRAG:
             for document in documents:
                 content = document.get("content", "")
                 metadata = document.get("metadata", {})
+                elements = document.get("elements", [])
 
                 if "id" not in metadata:
                     metadata["id"] = str(uuid.uuid4())
 
-                chunks_from_file = self.document_processor.process_document(content, metadata)
+                chunks_from_file = self.document_processor.process_document(content, metadata, elements)
 
                 if chunks_from_file:
                     all_chunks.extend(chunks_from_file)
@@ -190,54 +265,71 @@ class ResearchAssistantRAG:
         pass
 
 
-    def _retrieve_documents(self, query_embedding: List[float], filters: Dict[str, Any], query: str):
-        self.logger.info(f"Retrieving documents with filters: {filters}")
+    def _run_vector_search(self, query_embedding: Dict[str, Any], filters: Dict[str, Any], query: str):
+        self.logger.info(f"Running vector search with filters: {filters}")
 
-        retrieved_docs = self.retriever.retrieve(
-            query_vector=query_embedding,
+        search_results = self.retriever.retrieve(
+            query_embedding=query_embedding,
             filters=filters,
-            query_text=query
         )
 
-        self.logger.info(f"Retrieved {len(retrieved_docs)} documents")
-        return retrieved_docs
-
-
-    def query(self, query: str, chat_history: Optional[List[Dict[str, str]]] = None) -> Dict[str, Any]:
-        self.logger.info(f"Processing query: {query}")
-        start_time = time.time()
-
-        try:
-            query_embedding, filters = self.query_processor.process_query(query)
-
-            # TODO: Enhance retrieval by incorporating chat history context into the query embedding or retrieval strategy
-            retrieved_docs = self._retrieve_documents(query_embedding, filters, query)
-
-            if self.similarity_threshold > 0:
-                retrieved_docs = [doc for doc in retrieved_docs if doc.get('score', 0) >= self.similarity_threshold]
-                self.logger.info(f"After similarity threshold: {len(retrieved_docs)} documents")
-
-            ###CHECKPOINT 1c.
-
-            response = self.response_generator.generate_response(
-                query=query,
-                retrieved_docs=retrieved_docs,
-                chat_history=chat_history
-            )
-
-            processing_time = time.time() - start_time
-            response["processing_time"] = processing_time
-
-            return response
-
-        except Exception as e:
-            self.logger.error(f"Error processing query: {e}")
-            import traceback
-            self.logger.error(traceback.format_exc())
-
-            return {
-                "response": f"I encountered an error while processing your query: {str(e)}",
-                "sources": [],
-                "confidence": 0.0,
-                "processing_time": time.time() - start_time
-            }
+        self.logger.info(f"Retrieved {len(search_results)} results from vector search")
+        return search_results
+
+    # def query(self, query: str, chat_history: Optional[List[Dict[str, str]]] = None) -> Dict[str, Any]:
+    #     exec_count = 1
+    #     self.logger.info(f"Processing query: {query}")
+    #     start_time = time.time()
+    #
+    #     try:
+    #         # query_embedding: Dict[str, Any]
+    #         # filters: Dict[str, Any]
+    #         # query_embedding, filters = self.query_processor.process_query(query)
+    #
+    #         # # TODO: Enhance retrieval by incorporating chat history context into the query embedding or retrieval strategy
+    #         # search_results = self._run_vector_search(query_embedding, filters, query)
+    #
+    #         # Step 1: Generate multiple sub-queries from the user query and chat history
+    #         sub_queries = self.query_generator.generate_queries(
+    #             user_question=query,
+    #             chat_history=chat_history,
+    #         )
+    #
+    #         print("sub_queries: ", sub_queries)
+    #
+    #         all_results = []
+    #         for sq in sub_queries:
+    #             sq_embedding, sq_filters = self.query_processor.process_query(sq)
+    #             results = self._run_vector_search(sq_embedding, sq_filters, sq)
+    #             all_results.extend(results)
+    #
+    #         # 🧼 Lọc trùng theo nội dung
+    #         seen = set()
+    #         deduped_results = []
+    #         for item in all_results:
+    #             content = item.get("content")
+    #             if content and content not in seen:
+    #                 deduped_results.append(item)
+    #                 seen.add(content)
+    #
+    #         search_results = deduped_results
+    #
+    #         print(f"search_results: {search_results}")
\ No newline at end of file
+    #
+    #         if self.similarity_threshold > 0:
+    #             search_results = [item for item in search_results if item.get('score', 0) >= self.similarity_threshold]
+    #             self.logger.info(f"{len(search_results)} results passed similarity threshold")
+    #
+    #         print(f"DEBUG chat_history at {exec_count}th time: \n{chat_history}")
+    #         exec_count += 1
+    #
+    #         response = self.response_generator.generate_response(
+    #             query=query,
+    #             retrieved_docs=search_results,
+    #             chat_history=chat_history
+    #         )
+    #
+    #         processing_time = time.time() - start_time
+    #         response["processing_time"] = processing_time
+    #
+    #         return response

agents/rag_agent/buffer_memory.py

+from typing import List, Union, Dict
+from langchain.schema import BaseMessage, HumanMessage, AIMessage, SystemMessage
+
+class WindowBufferMemory:
+    def __init__(self, context_limit: int = 5):
+        self.context_limit = context_limit
+
+    def build_context(self, messages: List[BaseMessage]) -> List[Dict[str, str]]:
+        """
+        Trả về các message gần nhất dưới dạng List[Dict], giữ đúng cấu trúc
+        """
+        recent_messages = messages[-self.context_limit:]
+        history = []
+
+        for message in recent_messages:
+            if isinstance(message, HumanMessage):
+                history.append({"role": "user", "content": message.content})
+            elif isinstance(message, AIMessage):
+                history.append({"role": "assistant", "content": message.content})
+            elif isinstance(message, SystemMessage):
+                history.append({"role": "system", "content": message.content})
+            else:
+                history.append({"role": "unknown", "content": getattr(message, "content", "")})
+
+        return history

agents/rag_agent/document_processor.py

@@ -9,6 +9,8 @@ from datetime import datetime
 import nltk
 from nltk.tokenize import sent_tokenize
 import json
+# from sparse_embedder import GermanBM25SparseEmbedder
+from fastembed import SparseTextEmbedding
 
 try:
     nltk.data.find('tokenizers/punkt')
@@ -20,10 +22,9 @@ def make_serializable(metadata: Dict[str, Any]) -> Dict[str, Any]:
     serializable_metadata = {}
     for key, value in metadata.items():
         try:
-            json.dumps(value)  # Test if value is serializable
+            json.dumps(value)
             serializable_metadata[key] = value
         except TypeError:
-            # Handle non-serializable objects
             if isinstance(value, Header):
                 serializable_metadata[key] = str(value)  # Convert Header to string
             else:
@@ -40,8 +41,10 @@ class DocumentProcessor:
         self.processed_docs_dir = Path(config.rag.processed_docs_dir)
         self.processed_docs_dir.mkdir(parents=True, exist_ok=True)
         self.chunking_strategy = getattr(config.rag, "chunking_strategy", "sliding_window")
+        # self.sparse_embedder = GermanBM25SparseEmbedder()
+        self.sparse_embedder = SparseTextEmbedding("Qdrant/bm25")
 
-    def process_document(self, content: str, metadata: Dict[str, Any]) -> List[Dict[str, Any]]:
+    def process_document(self, content: str, metadata: Dict[str, Any], elements: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
         try:
             doc_id_base = hashlib.md5(content.encode()).hexdigest()
             doc_id = str(uuid.UUID(doc_id_base[:32]))
@@ -50,19 +53,42 @@ class DocumentProcessor:
             enhanced_metadata['processing_timestamp'] = datetime.now().isoformat()
 
             if self.chunking_strategy == "semantic":
-                chunks = self._create_semantic_chunks(content)
+                chunks = self._create_semantic_chunks(content, elements)
+            elif self.chunking_strategy == "sliding_window":
+                chunks = self._create_sliding_window_chunks(content)
             else:
                 chunks = self._create_sliding_window_chunks(content)
 
             processed_chunks = []
-            for i, chunk_data in enumerate(chunks):
-                if isinstance(chunk_data, tuple):
-                    chunk_text = chunk_data[0]
+            for i, chunk_info in enumerate(chunks):
+                if isinstance(chunk_info, tuple):
+                    chunk_text, section, level = chunk_info[0], chunk_info[1], chunk_info[2] if len(chunk_info) > 2 else "standard"
                 else:
-                    chunk_text = chunk_data
+                    chunk_text, section, level = chunk_info, "general", "standard"
 
+                # Generate chunk ID as a UUID with a suffix
                 chunk_id = str(uuid.UUID(doc_id_base[:24] + f"{i:08}"))
-                embedding = self.embedding_model.embed_documents([chunk_text])[0]
+
+                print(f"DEBUGG chunk_text: {chunk_text}")
+                print(f"DEBUGG section: {section}")
+
+                # Generate embedding
+                dense_embedding = self.embedding_model.embed_documents([chunk_text])[0]
+                sparse_embeddings = list(
+                    self.sparse_embedder.passage_embed([chunk_text]))  # Lấy sparse embedding từ passage_embed
+
+                # Đảm bảo rằng sparse_embeddings không rỗng và lấy phần tử đầu tiên
+                if sparse_embeddings:
+                    sparse_embedding = sparse_embeddings[0]
+                    # In ra các chỉ số và giá trị của sparse_embedding
+                    print(f"DEBUGG sparse_embedding indices (first 5): {sparse_embedding.indices[:5]}")
+                    print(f"DEBUGG sparse_embedding values (first 5): {sparse_embedding.values[:5]}")
+
+                else:
+                    sparse_embedding = {}
+
+                # Print embeddings to debug
+                print(f"DEBUGG dense_embedding (first 5 elements): {dense_embedding[:5]}")  # Print first 5 elements for brevity
 
                 chunk_metadata = enhanced_metadata.copy()
                 chunk_metadata["chunk_number"] = i
@@ -73,7 +99,10 @@ class DocumentProcessor:
                 processed_chunks.append({
                     "id": chunk_id,
                     "content": chunk_text,
-                    "embedding": embedding,
+                    "embedding": {
+                        "dense": dense_embedding,
+                        "sparse": sparse_embedding
+                    },
                     "metadata": make_serializable(chunk_metadata)
                 })
 
@@ -83,29 +112,53 @@ class DocumentProcessor:
             self.logger.error(f"Error processing document: {e}")
             raise
 
-    def _create_semantic_chunks(self, text: str) -> List[str]:
-        paragraphs = re.split(r'\n\s*\n', text)
+    def _create_semantic_chunks(self, content: str, elements: List[Dict[str, Any]]) -> List[Tuple[str, str, str]]:
+        """
+        Create semantic chunks from document based on 'elements' with category-based sectioning.
+        Falls back to paragraph-based chunking if elements are missing.
+
+        Returns:
+            List of (chunk_text, section_name, chunk_type) tuples
+        """
         chunks = []
+        current_section = None
+        section_text = []
+        encountered_title = False
+
+        for element in elements:
+            category = element.get("category", "")
+            element_text = element.get('text', '').strip()
+
+            # if not element_text:
+            #     continue
+
+            if category == 'Title':
+                if section_text:
+                    if not encountered_title:
+                        chunks.append((' '.join(section_text), "preamble", "section"))
+                    else:
+                        chunks.append((' '.join(section_text), current_section, "section"))
+
+                current_section = element_text
+                section_text = [element_text]
+                encountered_title = True
+            else:
+                section_text.append(element_text)
 
-        for para in paragraphs:
-            if not para.strip():
-                continue
-            if len(para.split()) > self.chunk_size:
-                sentences = sent_tokenize(para)
-                current_chunk = []
-                current_length = 0
-                for sentence in sentences:
-                    length = len(sentence.split())
-                    if current_length + length > self.chunk_size and current_chunk:
-                        chunks.append(" ".join(current_chunk))
-                        current_chunk = []
-                        current_length = 0
-                    current_chunk.append(sentence)
-                    current_length += length
-                if current_chunk:
-                    chunks.append(" ".join(current_chunk))
+        if section_text:
+            if not encountered_title:
+                chunks.append((' '.join(section_text), "preamble", "section"))
             else:
-                chunks.append(para.strip())
+                chunks.append((' '.join(section_text), current_section, "section"))
+
+        if not chunks and content:
+            paragraphs = re.split(r'\n\s*\n', content)
+            for para in paragraphs:
+                if para.strip():
+                    chunks.append((para.strip(), "paragraph", "standard"))
+
+        print("Chunks:", chunks)
+        # TODO: Split section into paragraphs if it's too large
 
         return chunks
 
@@ -116,7 +169,7 @@ class DocumentProcessor:
         if len(sentences) <= 3:
             return [text]
 
-        stride = max(1, (self.chunk_size - self.chunk_overlap) // 20)  # Approximate 20 words/sentence
+        stride = max(1, (self.chunk_size - self.chunk_overlap) // 20)
 
         for i in range(0, len(sentences), stride):
             window_size = min(i + max(3, self.chunk_size // 20), len(sentences))

agents/rag_agent/multi_query_generator.py

+from typing import List, Dict, Optional
+from langchain_core.language_models import BaseLanguageModel
+
+class MultiQueryGenerator:
+    def __init__(self, llm: BaseLanguageModel, max_queries: int = 5):
+        """
+        MQ (Multi-query) Generator: Sinh nhiều truy vấn từ câu hỏi gốc và ngữ cảnh.
+        Có thể nâng cấp sang MQA dễ dàng trong tương lai.
+
+        Args:
+            llm: LLM dùng để sinh truy vấn (GPT-3.5, GPT-4...)
+            max_queries: Số truy vấn tối đa muốn sinh
+        """
+        self.llm = llm
+        self.max_queries = max_queries
+
+    def generate_queries(
+        self,
+        user_question: str,
+        chat_history: Optional[List[Dict[str, str]]] = None,
+        persona: Optional[List[str]] = None,
+        initial_answer: Optional[str] = None  # nếu sau này dùng MQA, có thể truyền vào đây
+    ) -> List[str]:
+        """
+        Sinh ra các truy vấn tìm kiếm từ câu hỏi và ngữ cảnh hội thoại.
+
+        Args:
+            user_question: câu hỏi cuối cùng của người dùng
+            chat_history: lịch sử hội thoại (list gồm {"role": "user"|"ai", "content": ...})
+            persona: thông tin nền của người dùng (dùng sau cho iKAT)
+            initial_answer: câu trả lời sơ bộ (dành cho nâng cấp sang MQA)
+
+        Returns:
+            List[str]: Danh sách các truy vấn con
+        """
+        history_text = "\n".join(f"{msg['role'].capitalize()}: {msg['content']}" for msg in chat_history or [])
+        persona_text = "\n".join(persona or [])
+
+        # Prompt template có thể được mở rộng sang MQA nếu truyền initial_answer
+        prompt = f"""
+Du bist ein intelligenter Suchassistent.
+Ich werde dir ein Gespräch zwischen einem Nutzer und einem System sowie die letzte Frage des Nutzers geben.
+
+Deine Aufgabe ist es, bis zu {self.max_queries} präzisere Suchanfragen zu generieren, die dabei helfen können, die gesuchte Information besser zu finden.
+
+# Hintergrundinformationen zum Nutzer:
+{persona_text}
+
+# Gesprächsverlauf:
+{history_text}
+
+# Letzte Nutzerfrage:
+{user_question}
+
+# Zu generierende Suchanfragen:
+(schreibe jede Anfrage in eine neue Zeile, ohne Nummerierung)
+        """
+
+        print(f"search-prompt: \n{prompt}")
+
+        response = self.llm.invoke(prompt)
+        if hasattr(response, "content"):
+            response_text = response.content
+        else:
+            response_text = response  # fallback nếu là str
+
+        queries = [line.strip() for line in response_text.strip().splitlines() if line.strip()]
+
+        return queries[:self.max_queries]

agents/rag_agent/query_expander.py

+import logging
+from typing import List, Dict, Any
+
+class QueryExpander:
+    def __init__(self, config):
+        self.logger = logging.getLogger(f"{self.__module__}")
+        self.config = config
+        self.model = config.rag.llm
+
+    def expand_query(self, original_query: str) -> Dict[str, Any]:
+        """
+            Expand the original query with relevant academic and scientific terms.
+
+            Args:
+                original_query: The user's original query
+
+            Returns:
+                Dictionary with original and expanded queries
+        """
+        self.logger.info(f"Expanding query: {original_query}")
+
+        # Generate expansions
+        # Step 1: expand the query using synonyms and related terms (using a predefined thesaurus or external source)
+        expanded_query = self._expand_with_synonyms_and_related_terms(original_query)
+
+        # Step 2: use llm to expand query semantically
+        expanded_query = self._expand_with_semantics(expanded_query)
+
+        return {
+            "original_query": original_query,
+            "expanded_query": expanded_query
+        }
+
+    def _expand_with_synonyms_and_related_terms(self, query: str) -> str:
+        """Expand the query by adding synonyms and related terms using a predefined thesaurus or domain knowledge."""
+
+        # Define a dictionary for basic synonyms/related terms (this can be expanded based on your domain)
+        synonym_dict = {
+            "artificial intelligence": ["machine learning", "deep learning", "neural networks", "AI"],
+            "data science": ["big data", "data analysis", "machine learning", "data mining"],
+        }
+
+        expanded_query = query.lower()  # Convert to lowercase for matching
+
+        # Expand using synonyms from the dictionary
+        for key, synonyms in synonym_dict.items():
+            if key.lower() in expanded_query:
+                expanded_query += " OR " + " OR ".join(synonyms)
+
+        self.logger.info(f"Expanded query after synonym addition: {expanded_query}")
+
+        # return expanded_query
+        ### For now: do nothing and return original query
+        return query
+
+    def _expand_with_semantics(self, query: str) -> str:
+        """Use LLM to expand query with academic and scientific terminology."""
+        prompt = f"""
+        As a research assistant, expand the following query with relevant scientific terminology, 
+        synonyms, related concepts, and keywords that would help in retrieving relevant academic papers and research articles:
+
+        User Query: {query}
+
+        Expand the query only if you feel like it is required, otherwise keep the user query intact.
+        Be specific to the field of study mentioned, do not add other irrelevant domains.
+        If the user query asks about answering in tabular format, include that in the expanded query and do not answer in tabular format yourself.
+        Provide only the expanded query without explanations.
+        """
+        expansion = self.model.invoke(prompt)
+
+        return expansion
\ No newline at end of file

agents/rag_agent/query_processor.py

@@ -5,9 +5,10 @@ from typing import List, Dict, Any, Optional, Tuple, Union
 from datetime import datetime
 
 class QueryProcessor:
-    def __init__(self, config, embedding_model):
+    def __init__(self, config, embedding_model, sparse_embedder):
         self.logger = logging.getLogger(__name__)
         self.embedding_model = embedding_model
+        self.sparse_embedder = sparse_embedder
 
         self.synonyms = {
             "zusammenfassung": ["summary", "zusammenfassen", "kurzfassung"],
@@ -22,7 +23,16 @@ class QueryProcessor:
             "article_navigation": re.compile(r"\b(link|artikel|paper|artikel)\b", re.IGNORECASE),
         }
 
-    def process_query(self, query: str) -> Tuple[List[float], Dict[str, Any]]:
+    def process_query(self, query: str) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+        """
+        Process the query to generate embedding and extract metadata filters.
+
+        Args:
+            query: User query string
+
+        Returns:
+            Tuple of (query_embedding, extracted_filters)
+        """
         try:
             query_id = str(uuid.uuid4())
 
@@ -30,11 +40,17 @@ class QueryProcessor:
 
             query_intent = self._detect_query_intent(query)
 
-            query_embedding = self.embedding_model.embed_query(expanded_query)
-            if query_embedding is None:
+            query_dense_embedding = self.embedding_model.embed_query(expanded_query)
+            if query_dense_embedding is None:
                 raise ValueError("Embedding model returned None for query.")
 
-            query_embedding = query_embedding.tolist() if hasattr(query_embedding, "tolist") else query_embedding
+            query_dense_embedding = query_dense_embedding.tolist() if hasattr(query_dense_embedding, "tolist") else query_dense_embedding
+
+            query_sparse_embeddings = list(self.sparse_embedder.query_embed(query))
+            if query_sparse_embeddings:
+                query_sparse_embedding = query_sparse_embeddings[0]
+            else:
+                query_sparse_embedding = {}
 
             filters: Dict[str, Any] = {
                 "query_id": query_id,
@@ -46,25 +62,37 @@ class QueryProcessor:
 
             self.logger.info(f"Processed query with filters: {filters}")
 
-            return query_embedding, filters
+            return {
+                "dense": query_dense_embedding,
+                "sparse": query_sparse_embedding
+            }, filters
 
         except Exception as e:
             self.logger.error(f"Error processing query: {e}")
 
-            fallback_embedding = self.embedding_model.embed_query(query)
-            if fallback_embedding is None:
-                fallback_embedding = []
+            fallback_dense_embedding = self.embedding_model.embed_query(query)
+            if fallback_dense_embedding is None:
+                fallback_dense_embedding = []
+            fallback_dense_embedding = fallback_dense_embedding.tolist() if hasattr(fallback_dense_embedding, "tolist") else fallback_dense_embedding
 
-            fallback_embedding = fallback_embedding.tolist() if hasattr(fallback_embedding,
-                                                                        "tolist") else fallback_embedding
+            try:
+                fallback_sparse_embeddings = list(self.sparse_embedder.query_embed(query))
+                fallback_sparse_embedding = fallback_sparse_embeddings[0] if fallback_sparse_embeddings else {}
+            except Exception as se:
+                self.logger.error(f"Error generating sparse fallback: {se}")
+                fallback_sparse_embedding = {}
 
-            fallback_filters = {
+            fallback_filters: Dict[str, Any] = {
                 "query_id": str(uuid.uuid4()),
                 "timestamp": datetime.now().isoformat(),
                 "is_fallback": True
             }
 
-            return fallback_embedding, fallback_filters
+            return {
+                "dense": fallback_dense_embedding,
+                "sparse": fallback_sparse_embedding
+            }, fallback_filters
+
 
     def _expand_query(self, query: str) -> str:
         parts = [query]

agents/rag_agent/query_rewriter.py

+from typing import List, Dict
+from langchain_core.language_models import BaseLanguageModel
+
+class QueryRewriter:
+    def __init__(self, llm: BaseLanguageModel):
+        self.llm = llm
+
+    def rewrite(self, query: str, chat_history: List[Dict[str, str]]) -> str:
+        history_str = "\n".join(f"{m['role'].capitalize()}: {m['content']}" for m in chat_history)
+        print(f"history_str in def rewrite: {history_str}")
+
+        prompt = f"""
+Du bist ein Query-Rewriter. Ich gebe dir eine Unterhaltung und eine unklare Nutzerfrage.
+
+Deine Aufgabe ist es, die letzte Frage so umzuschreiben, dass sie **ohne den Gesprächskontext** vollständig verständlich ist.
+
+# Gespräch:
+{history_str}
+
+# Ursprüngliche Frage:
+{query}
+
+# Umschriebene Frage:
+        """
+
+        response = self.llm.invoke(prompt)
+        return response.content.strip() if hasattr(response, "content") else response.strip()

agents/rag_agent/response_generator.py

@@ -30,6 +30,7 @@ class ResponseGenerator:
                           chat_history: Optional[List[Dict[str, str]]] = None) -> Dict[str, Any]:
 
         try:
+            # tai cai nay
             if not retrieved_docs:
                 return self._generate_no_documents_response(query)
 
@@ -37,6 +38,8 @@ class ResponseGenerator:
 
             prompt = self._build_prompt(query, formatted_context, chat_history)
 
+            print(f"DEBUG prompt: \n{prompt}")
+
             response = self.llm.invoke(prompt)
 
             sources = self._extract_sources(retrieved_docs) if self.include_sources else []
@@ -157,7 +160,6 @@ class ResponseGenerator:
 
     def _build_prompt(self, query: str, context: str,
                       chat_history: Optional[List[Dict[str, str]]] = None) -> str:
-
         project_instructions = """
                 Du bist ein wissenschaftlicher Chatbot, der folgende Aufgaben erfüllt:
                 - Zusammenfassung wissenschaftlicher Artikel
@@ -175,11 +177,25 @@ class ResponseGenerator:
                     3. Interpretieren und referenzieren Sie die Tabellen deutlich
                     """
 
+        chat_str = ""
+        if chat_history and isinstance(chat_history, list):
+            chat_lines = []
+            for msg in chat_history:
+                if isinstance(msg, dict):
+                    role = "User" if msg.get("type") == "human" else "Assistant"
+                    content = msg.get("content", "")
+                    chat_lines.append(f"{role}: {content}")
+                else:
+                    chat_lines.append(str(msg))
+            chat_str = "\n".join(chat_lines)
+
+        print(f"chat_str: {chat_str}")
+
         prompt = f"""
                 {project_instructions}
 
                 Bisherige Konversation:
-                {chat_history}
+                {chat_str}
 
                 Benutzeranfrage:
                 {query}

agents/rag_agent/sparse_embedder.py

+import nltk
+from sklearn.feature_extraction.text import TfidfVectorizer
+from typing import List, Dict
+
+nltk.download('stopwords', quiet=True)
+from nltk.corpus import stopwords
+
+german_stopwords = stopwords.words('german')
+
+class GermanBM25SparseEmbedder:
+    def __init__(self):
+        self.vectorizer = TfidfVectorizer(
+            stop_words=german_stopwords,
+            ngram_range=(1, 2),
+            max_features=20000,
+            norm='l2'
+        )
+        self.fitted = False
+        self.feature_names = []
+
+    def fit(self, corpus: List[str]):
+        self.vectorizer.fit(corpus)
+        self.feature_names = self.vectorizer.get_feature_names_out()
+        self.fitted = True
+
+    def encode(self, texts: List[str]) -> List[Dict[int, float]]:
+        if not self.fitted:
+            raise ValueError("Call `fit()` before `encode()`.")
+        tfidf_matrix = self.vectorizer.transform(texts)
+        return [
+            {int(i): float(v) for i, v in zip(row.indices, row.data)}
+            for row in tfidf_matrix
+        ]
\ No newline at end of file

agents/rag_agent/vector_store.py

@@ -2,7 +2,7 @@ from typing import List, Dict, Any, Optional, Union
 import logging
 from qdrant_client import QdrantClient
 from qdrant_client.http import models as qdrant_models
-from qdrant_client.http.models import ContextQuery, ContextPair, SearchParams
+from qdrant_client.http.models import ContextQuery, ContextPair, SearchParams, SparseVectorParams
 from qdrant_client.http.exceptions import UnexpectedResponse
 from qdrant_client.http.models import Distance, VectorParams, PointStruct, Prefetch, FusionQuery, Fusion
 from sklearn.feature_extraction.text import TfidfVectorizer
@@ -47,20 +47,33 @@ class QdrantRetriever:
         collection_names = [collection.name for collection in collection_info.collections]
         print("Collection names:", collection_names)
 
+        # self.client.delete_collection(collection_name=self.collection_name)
+
         if self.collection_name not in collection_names:
             self.logger.info(f"Creating new collection {self.collection_name}")
             try:
                 self.client.create_collection(
                     collection_name=self.collection_name,
-                    vectors_config=VectorParams(
-                        size=self.embedding_dim,
-                        distance=Distance.COSINE
-                    ),
-                    optimizers_config=qdrant_models.OptimizersConfigDiff(
-                        indexing_threshold=10000,
-                    ),
+                    vectors_config={
+                        "dense": VectorParams(
+                            size=self.embedding_dim,
+                            distance=self.distance_metric, # =Distance.COSINE
+                        )
+                    },
+                    sparse_vectors_config={
+                        "sparse": SparseVectorParams(
+                            modifier=qdrant_models.Modifier.IDF
+                        )
+                    }
+                    # vectors_config=VectorParams(
+                    #     size=self.embedding_dim,
+                    #     distance=Distance.COSINE
+                    # ),
+                    # optimizers_config=qdrant_models.OptimizersConfigDiff(
+                    #     indexing_threshold=10000,
+                    # ),
                 )
-                self.logger.info(f"Collection {self.collection_name} created")
+                self.logger.info(f"Created new collection: {self.collection_name}")
             except Exception as e:
                 self.logger.error(f"Error creating collection: {e}")
                 raise e
@@ -69,6 +82,7 @@ class QdrantRetriever:
 
     def upsert_chunks(self, chunks: List[Dict[str, Any]]):
         try:
+            print(f"DEBUG Upserting chunks: {chunks}")
             points: List[PointStruct] = []
 
             for chunk in chunks:
@@ -76,15 +90,29 @@ class QdrantRetriever:
 
                 payload["content"] = chunk["content"]
 
+                print(f"DEBUG Upserting chunk: {chunk['id']}")
+                print(f"DEBUG Embedding keys: {chunk['embedding']}")
+                print(f"DEBUG Payload: {payload}")
+
                 points.append(
-                    qdrant_models.PointStruct(
+                    PointStruct(
                         id=chunk["id"],
-                        vector=chunk["embedding"],
+                        # vector=chunk["embedding"],
+                        vector={
+                            "dense": chunk["embedding"]["dense"],
+                            "sparse": chunk["embedding"]["sparse"].as_object()
+                        },
+                        #Giả định chunk["embedding"] là dict kiểu:
+                        # {
+                        #     "dense": [...],  # list[float]
+                        #     "sparse": {...}  # dict[int -> float]
+                        # }
                         payload=payload
                     )
                 )
 
             batch_size = 100
+            # Nghĩa là mỗi lần gửi lên database sẽ gửi tối đa 100 points (vector + metadata).
             for i in range(0, len(points), batch_size):
                 batch = points[i:i + batch_size]
                 self.client.upsert(
@@ -134,90 +162,102 @@ class QdrantRetriever:
             "values": values
         }
 
-    from qdrant_client.http import models as qdrant_models
-    from qdrant_client import QdrantClient
-    from qdrant_client.http.models import SearchParams, ContextPair, ContextQuery, FusionQuery, Fusion, Prefetch
-
     def retrieve(
             self,
-            query_vector: List[float],
-            filters: Optional[Dict] = None,
+            query_embedding: Dict[str, Any],
+            filters: Optional[Dict[str, Any]] = None,
             top_k: int = 5,
             include_metadata: bool = True,
-            query_text: Optional[str] = None,
-            use_sparse: bool = False
-    ) -> List[Dict]:
-        # 1. Log params
+    ) -> List[Dict[str, Any]]:
+        """
+        Perform hybrid retrieval using both dense and sparse embeddings.
+
+        Args:
+            query_embedding: Dict with keys "dense" (List[float]) and "sparse" (SparseEmbedding)
+            filters: Optional metadata filters
+            top_k: Number of documents to retrieve
+            include_metadata: Whether to include payload in results
+
+        Returns:
+            List of retrieved documents with score
+        """
+        dense_vector = query_embedding.get("dense")
+        sparse_vector = query_embedding.get("sparse")
+
         self.logger.info(f"""
-        [RETRIEVE PARAMS]
-        query_vector dim={len(query_vector)}, filters={filters},
-        top_k={top_k}, include_metadata={include_metadata},
-        query_text={query_text[:50] if query_text else None},
-        use_sparse={use_sparse}
+            [RETRIEVE PARAMS]
+
+            dense_dim={len(dense_vector) if dense_vector else 'None'},
+            filters={filters},
+            top_k={top_k},
+            include_metadata={include_metadata}
         """)
 
-        sparse_vector = {}
-        if use_sparse and query_text:
-            self.logger.info("Creating sparse vector from query_text.")
-            sparse_vector = self.text_to_sparse_vector(query_text)
-            self.logger.debug(f"Sparse vector: {sparse_vector}")
+        if sparse_vector:
+            self.logger.info("Sparse vector detected → using hybrid search")
+
+            prefetch = [
+                qdrant_models.Prefetch(query=dense_vector, using="dense", limit=top_k),
+                qdrant_models.Prefetch(
+                    query=qdrant_models.SparseVector(
+                        indices=sparse_vector.indices.tolist(),
+                        values=sparse_vector.values.tolist()
+                    ),
+                    using="sparse",
+                    limit=top_k,
+                ),
+            ]
 
-        filter_obj = None
-        if filters:
-            try:
-                conditions = []
-                for key, value in filters.items():
-                    filter_obj = qdrant_models.Filter(should=conditions)
-            except Exception:
-                self.logger.error("Error creating filter", exc_info=True)
-
-        if not sparse_vector.get("indices"):
-            self.logger.info("Dense-only fallback: using client.search()")
             try:
-                hits = self.client.search(
+                response = self.client.query_points(
                     collection_name=self.collection_name,
-                    query_vector=query_vector,
-                    limit=top_k,
+                    prefetch=prefetch,
+                    query=qdrant_models.FusionQuery(fusion=qdrant_models.Fusion.RRF),
                     with_payload=include_metadata,
-                    query_filter=filter_obj,
+                    query_filter=None,  # TODO: build filter from `filters` if needed
                 )
-                results = [
-                    {**(hit.payload or {}), "score": hit.score}
-                    for hit in hits
-                ]
-                self.logger.info(f"Found {len(results)} docs (dense-only)")
+                retrieved_points = response.points
+
+                results = []
+                for point in retrieved_points:
+                    print("DEBUG point:", point)
+
+                    result_item = dict(point.payload or {})
+                    result_item["score"] = point.score
+                    results.append(result_item)
+
+                self.logger.info(f"Retrieved {len(results)} results (hybrid search)")
                 return results
+
             except Exception as e:
-                self.logger.error(f"Error in dense-only search: {e}", exc_info=True)
+                self.logger.error(f"Error during hybrid retrieval: {e}", exc_info=True)
                 return []
 
-        self.logger.info("Using hybrid search with query_points()")
-        prefetch = [
-            Prefetch(query=query_vector, using="dense", limit=top_k),
-            Prefetch(
-                query=qdrant_models.SparseVector(
-                    indices=sparse_vector["indices"],
-                    values=sparse_vector["values"],
-                ),
-                using="sparse",
-                limit=top_k,
-            ),
-        ]
-        try:
-            hits = self.client.query_points(
-                collection_name=self.collection_name,
-                prefetch=prefetch,
-                query=FusionQuery(fusion=Fusion.RRF),
-                with_payload=include_metadata,
-                query_filter=filter_obj,
-            )
-            results = [
-                {**(hit.payload or {}), "score": hit.score}
-                for hit in hits
-            ]
-            self.logger.info(f"Found {len(results)} docs (hybrid)")
-            return results
-        except Exception as e:
-            self.logger.error(f"Error querying points: {e}", exc_info=True)
-            return []
+        else:
+            self.logger.info("No sparse vector → fallback to dense-only search")
+
+            try:
+                response = self.client.query_points(
+                    collection_name=self.collection_name,
+                    prefetch=[
+                        qdrant_models.Prefetch(query=dense_vector, using="dense", limit=top_k)
+                    ],
+                    query=qdrant_models.FusionQuery(fusion=qdrant_models.Fusion.RRF),
+                    with_payload=include_metadata,
+                    query_filter=None,
+                )
+                retrieved_points = response.points
+
+                results = []
+                for point in retrieved_points:
+                    result_item = dict(point.payload or {})
+                    result_item["score"] = point.score
+                    results.append(result_item)
+
+                self.logger.info(f"Retrieved {len(results)} results (dense-only search)")
+                return results
+
+            except Exception as e:
+                self.logger.error(f"Error during dense-only retrieval: {e}", exc_info=True)
+                return []
 

api/fastapi_server.py

@@ -5,6 +5,8 @@ from fastapi.staticfiles import StaticFiles
 from pydantic import BaseModel
 import uvicorn
 from agents.agent_decision import process_query
+from typing import List, Union
+from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, SystemMessage
 
 UPLOAD_FOLDER = "uploads/backend"
 os.makedirs(UPLOAD_FOLDER, exist_ok=True)
@@ -13,8 +15,23 @@ app = FastAPI(title="Transfer-Bot", version="1.0")
 
 app.mount("/uploads", StaticFiles(directory="uploads"), name="uploads")
 
+def deserialize_messages(serialized: List[dict]) -> List[BaseMessage]:
+    message_objects = []
+    for msg in serialized:
+        print(f"Message deri: {msg}")
+        msg_type = msg.get("type")
+        content = msg.get("content", "")
+        if msg_type == "human":
+            message_objects.append(HumanMessage(content=content))
+        elif msg_type == "ai":
+            message_objects.append(AIMessage(content=content))
+        elif msg_type == "system":
+            message_objects.append(SystemMessage(content=content))
+    return message_objects
+
 class QueryRequest(BaseModel):
     query: str
+    conversation_history: List[dict] = []
 
 @app.post("/chat")
 def chat(request: QueryRequest,
@@ -25,7 +42,16 @@ def chat(request: QueryRequest,
     session_id = request_obj.cookies.get("session_id", str(uuid.uuid4()))
 
     try:
-        response_data = process_query(request.query)
+        print(f"Session id: {session_id}")
+        history: List[BaseMessage] = deserialize_messages(request.conversation_history)
+
+        print(f"History: {history}")
+        print("Incoming request:", request.model_dump())
+
+        response_data = process_query(
+            query=request.query,
+            conversation_history=history
+        )
 
         response_text = response_data['response']
 

chat_gui.py

@@ -9,6 +9,12 @@ st.title("Transfer-Bot Frontend")
 if "chat_history" not in st.session_state:
     st.session_state.chat_history = []
 
+def get_serialized_history():
+    return [
+        {"type": "human" if role == "user" else "ai", "content": msg}
+        for role, msg in st.session_state.chat_history
+    ]
+
 tab1, tab2 = st.tabs(["Chat", "Upload mit Text"])
 
 with tab1:
@@ -40,8 +46,6 @@ with tab1:
 
     user_query = st.chat_input("Stell mir deine Frage...")
 
-
-
     if user_query:
         st.session_state.chat_history.append(("user", user_query))
         st.session_state.pending_query = user_query
@@ -49,14 +53,32 @@ with tab1:
 
     if "pending_query" in st.session_state:
         with st.spinner("Wird verarbeitet..."):
+            data = {}
             try:
-                response = requests.post(f"{API_URL}/chat", json={"query": st.session_state.pending_query})
+                payload = {
+                    "query": st.session_state.pending_query,
+                    "conversation_history": get_serialized_history()  # ✅ Thêm
+                }
+
+                print(f"payload: {payload}")
+
+                response = requests.post(f"{API_URL}/chat", json=payload)
+
                 data = response.json()
-                bot_reply = data["response"]
+                bot_reply = data.get("response", "Keine Antwort erhalten.")
+
+                # st.session_state.chat_history.append(("bot", bot_reply))
+                if "messages" in data:
+                    st.session_state.chat_history = [
+                        ("user" if m["type"] == "human" else "bot", m["content"])
+                        for m in data["messages"]
+                    ]
+                else:
+                    st.session_state.chat_history.append(("bot", bot_reply))
             except Exception as e:
                 bot_reply = f"Error: {str(e)}"
+                st.session_state.chat_history.append(("bot", bot_reply))
 
-            st.session_state.chat_history.append(("bot", bot_reply))
             del st.session_state.pending_query
             st.rerun()
 

config.py

 import os
 from dotenv import load_dotenv
+from fastembed import SparseTextEmbedding
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
 from openai import OpenAI
 
 load_dotenv()
 
-# class AgentDecisoinConfig:
-#     # def __init__(self):
-#     #     self.llm = AzureChatOpenAI(
-#     #         deployment_name=os.getenv("deployment_name"),
-#     #         model_name=os.getenv("model_name"),
-#     #         azure_endpoint=os.getenv("azure_endpoint"),
-#     #         openai_api_key=os.getenv("openai_api_key"),
-#     #         openai_api_version=os.getenv("openai_api_version"),
-#     #         temperature=0.1
-#     #     )
-#     def __init__(self):
-#         self.client = OpenAI(api_key=os.getenv("openai_api_key"))
-#
-#         self.llm = self.get_openai_llm()
-#
-#     def get_openai_llm(self):
-#         return self.client.chat.completions.create(
-#             model="gpt-3.5-turbo",
-#             messages=[{"role": "user", "content": "Hello"}],
-#             temperature=0.1
-#         )
+class AgentDecisoinConfig:
+    def __init__(self):
+        self.llm = ChatOpenAI(
+            model=os.getenv("model_name"),
+            api_key=os.getenv("openai_api_key"),
+            temperature=0.3
+        )
 
 class RAGConfig:
     def __init__(self):
@@ -33,10 +20,10 @@ class RAGConfig:
         self.embedding_dim = 1536
         self.distance_metric = "Cosine"
         self.use_local = True
-        self.local_path = "./data/qdrant_db"
+        self.vector_local_path = "./data/qdrant_db"  # Add this with a default value
         self.url = os.getenv("QDRANT_URL")
         self.api_key = os.getenv("QDRANT_API_KEY")
-        self.collection_name = "transfer_rag"
+        self.collection_name = "research_assistant_rag"
         self.chunk_size = 512
         self.chunk_overlap = 50
         self.processed_docs_dir = "./data/processed"
@@ -45,17 +32,20 @@ class RAGConfig:
             model=os.getenv("embedding_model_name"),
             api_key=os.getenv("openai_api_key")
         )
+        self.sparse_embedder = SparseTextEmbedding("Qdrant/bm25")
 
         self.llm = ChatOpenAI(
             model=os.getenv("model_name"),
             api_key=os.getenv("openai_api_key"),
             temperature=0.3
         )
-
+        self.max_queries = 3
         self.top_k = 5
         self.similarity_threshold = 0.75
         self.huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
-        self.chunking_strategy = "hybrid"
+        self.vector_search_type = 'similarity'  # or 'mmr'
+
+        self.chunking_strategy = "semantic"
         self.max_context_length = 8192
 
         self.response_format_instructions = """Instructions:
@@ -66,8 +56,8 @@ class RAGConfig:
         6. Provide a well-structured response based on retrieved knowledge."""
         self.include_sources = True
         self.metrics_save_path = "./logs/rag_metrics.json"
-        self.min_retrieval_confidence = 0.8
-        self.context_limit = 20
+        self.min_retrieval_confidence = 0.8 # the auto routing from RAG agent to WEB_SEARCH agent is dependent on this value
+        self.context_limit = 20 # include last 20 messsages (10 Q&A pairs) in history
 
 class APIConfig:
     def __init__(self):
@@ -75,11 +65,11 @@ class APIConfig:
         self.port = 8000
         self.debug = True
         self.rate_limit = 10
-        self.max_image_upload_size = 5
+        self.max_image_upload_size = 5 # max upload size in MB
 
 class Config:
     def __init__(self):
-        # self.agent_decision = AgentDecisoinConfig()
+        self.agent_decision = AgentDecisoinConfig()
         self.rag = RAGConfig()
         self.api = APIConfig()
         self.max_conversation_history = 40