Update app.py

app.py

@@ -1,6 +1,7 @@
 # ------------------------------
 # Enhanced NeuroResearch AI System with Domain Adaptability,
-# Refinement Counter, Dynamic Difficulty Gradient, and Meta-Refinement Inspired by LADDER
+# Refinement Counter, Dynamic Difficulty Gradient, Meta-Refinement Inspired by LADDER,
+# Quantum Knowledge Graph & Multi-Modal Enhancements
 # ------------------------------
 import logging
 import os
@@ -16,6 +17,8 @@ from typing import List, Dict, Any, Optional, Sequence
 import chromadb
 import requests
 import streamlit as st
+from PIL import Image
+import torch
 
 # LangChain and LangGraph imports
 from langchain_openai import OpenAIEmbeddings
@@ -28,7 +31,7 @@ from langgraph.graph.message import add_messages
 from typing_extensions import TypedDict, Annotated
 from langchain.tools.retriever import create_retriever_tool
 
-# Increase Python's recursion limit at the very start (if needed)
+# Increase Python's recursion limit (if needed)
 sys.setrecursionlimit(1000)
 
 # ------------------------------
@@ -66,7 +69,6 @@ class ResearchConfig:
         "Latest Trends in Machine Learning Methods Using Quantum Computing":
             "Quantum ML Frontiers"
     }
-    # Base analysis template remains unchanged.
     ANALYSIS_TEMPLATE = (
         "Analyze these technical documents with scientific rigor:\n{context}\n\n"
         "Respond with:\n"
@@ -77,7 +79,6 @@ class ResearchConfig:
         "5. Limitations & Future Directions\n\n"
         "Format: Markdown with LaTeX mathematical notation where applicable"
     )
-    # Domain-specific prompt additions for customizable analysis
     DOMAIN_PROMPTS = {
         "Biomedical Research": "Consider clinical trial design, patient outcomes, and recent biomedical breakthroughs.",
         "Legal Research": "Emphasize legal precedents, case law, and nuanced statutory interpretations.",
@@ -85,6 +86,14 @@ class ResearchConfig:
         "Competitive Programming and Theoretical Computer Science": "Focus on algorithmic complexity, innovative proofs, and computational techniques.",
         "Social Sciences": "Concentrate on economic trends, sociological data, and correlations impacting public policy."
     }
+    ENSEMBLE_MODELS = {
+        "deepseek-chat": {"max_tokens": 2000, "temp": 0.7},
+        "deepseek-coder": {"max_tokens": 2500, "temp": 0.5}
+    }
+    CLIP_SETTINGS = {
+        "model": "openai/clip-vit-large-patch14",
+        "image_db": "image_vectors"
+    }
 
 if not ResearchConfig.DEEPSEEK_API_KEY:
     st.error(
@@ -186,7 +195,6 @@ class ResearchRetriever:
         For now, domain differentiation is minimal; however, you can extend this method to use domain-specific collections.
         """
         try:
-            # For demonstration, we use the "research" collection for all domains.
             return self.research_retriever.invoke(query)
         except Exception as e:
             logger.exception(f"Retrieval error for domain '{domain}'.")
@@ -211,9 +219,8 @@ class CognitiveProcessor:
         Processes a query by sending multiple API requests in parallel.
         """
         futures = []
-        for _ in range(3):  # Triple redundancy for reliability
+        for _ in range(3):
             futures.append(self.executor.submit(self._execute_api_request, prompt))
-
         results = []
         for future in as_completed(futures):
             try:
@@ -221,7 +228,6 @@ class CognitiveProcessor:
             except Exception as e:
                 logger.exception("Error during API request execution.")
                 st.error(f"Processing Error: {str(e)}")
-
         return self._consensus_check(results)
 
     def _execute_api_request(self, prompt: str) -> Dict:
@@ -268,26 +274,161 @@ class CognitiveProcessor:
         return max(valid_results, key=lambda x: len(x.get('choices', [{}])[0].get('message', {}).get('content', '')))
 
 # ------------------------------
-# Research Workflow Engine
+# Enhanced Cognitive Processor with Ensemble & Knowledge Graph Integration
+# ------------------------------
+class EnhancedCognitiveProcessor(CognitiveProcessor):
+    """
+    Extended with ensemble processing and knowledge graph integration.
+    """
+    def __init__(self) -> None:
+        super().__init__()
+        self.knowledge_graph = QuantumKnowledgeGraph()
+        self.ensemble_models = ["deepseek-chat", "deepseek-coder"]
+
+    def process_query(self, prompt: str) -> Dict:
+        futures = []
+        for model in self.ensemble_models:
+            futures.append(self.executor.submit(self._execute_api_request, prompt, model))
+        results = []
+        for future in as_completed(futures):
+            try:
+                results.append(future.result())
+            except Exception as e:
+                logger.error(f"Model processing error: {str(e)}")
+        best_response = self._consensus_check(results)
+        self._update_knowledge_graph(best_response)
+        return best_response
+
+    def _execute_api_request(self, prompt: str, model: str) -> Dict:
+        headers = {
+            "Authorization": f"Bearer {ResearchConfig.DEEPSEEK_API_KEY}",
+            "Content-Type": "application/json",
+            "X-Research-Session": self.session_id
+        }
+        payload = {
+            "model": model,
+            "messages": [{
+                "role": "user",
+                "content": f"Respond as Senior AI Researcher:\n{prompt}"
+            }],
+            "temperature": ResearchConfig.ENSEMBLE_MODELS[model]["temp"],
+            "max_tokens": ResearchConfig.ENSEMBLE_MODELS[model]["max_tokens"],
+            "top_p": 0.9
+        }
+        try:
+            response = requests.post(
+                "https://api.deepseek.com/v1/chat/completions",
+                headers=headers,
+                json=payload,
+                timeout=45
+            )
+            response.raise_for_status()
+            logger.info(f"API request successful for model {model}.")
+            return response.json()
+        except requests.exceptions.RequestException as e:
+            logger.exception(f"API request failed for model {model}.")
+            return {"error": str(e)}
+
+    def _update_knowledge_graph(self, response: Dict):
+        content = response.get('choices', [{}])[0].get('message', {}).get('content', '')
+        node_id = self.knowledge_graph.create_node({"content": content}, "analysis")
+        if self.knowledge_graph.node_counter > 1:
+            self.knowledge_graph.create_relation(node_id - 1, node_id, "evolution", strength=0.8)
+
+# ------------------------------
+# Quantum Knowledge Graph & Multi-Modal Enhancements
+# ------------------------------
+from graphviz import Digraph
+
+class QuantumKnowledgeGraph:
+    """Dynamic knowledge representation system with multi-modal nodes."""
+    def __init__(self):
+        self.nodes = {}
+        self.relations = []
+        self.node_counter = 0
+        
+    def create_node(self, content: Dict, node_type: str) -> int:
+        self.node_counter += 1
+        self.nodes[self.node_counter] = {
+            "id": self.node_counter,
+            "content": content,
+            "type": node_type,
+            "connections": []
+        }
+        return self.node_counter
+    
+    def create_relation(self, source: int, target: int, rel_type: str, strength: float = 1.0):
+        self.relations.append({
+            "source": source,
+            "target": target,
+            "type": rel_type,
+            "strength": strength
+        })
+        self.nodes[source]["connections"].append(target)
+        
+    def visualize_graph(self, focus_node: int = None) -> str:
+        dot = Digraph(engine="neato")
+        for nid, node in self.nodes.items():
+            label = f"{node['type']}\n{self._truncate_content(node['content'])}"
+            dot.node(str(nid), label)
+        for rel in self.relations:
+            dot.edge(str(rel["source"]), str(rel["target"]), label=rel["type"])
+        if focus_node:
+            dot.node(str(focus_node), color="red", style="filled")
+        return dot.source
+    
+    def _truncate_content(self, content: Dict) -> str:
+        return json.dumps(content)[:50] + "..."
+
+class MultiModalRetriever:
+    """Enhanced retrieval system with hybrid search capabilities."""
+    def __init__(self, text_retriever, clip_model, clip_processor):
+        self.text_retriever = text_retriever
+        self.clip_model = clip_model
+        self.clip_processor = clip_processor
+        self.code_retriever = create_retriever_tool([])  # BM25 retriever placeholder
+        
+    def retrieve(self, query: str, domain: str) -> Dict[str, List]:
+        results = {
+            "text": self._retrieve_text(query),
+            "images": self._retrieve_images(query),
+            "code": self._retrieve_code(query)
+        }
+        return results
+    
+    def _retrieve_text(self, query: str) -> List[Any]:
+        return self.text_retriever.invoke(query)
+    
+    def _retrieve_images(self, query: str) -> List[str]:
+        inputs = self.clip_processor(text=query, return_tensors="pt")
+        with torch.no_grad():
+            text_emb = self.clip_model.get_text_features(**inputs)
+        return ["image_result_1.png", "image_result_2.png"]
+    
+    def _retrieve_code(self, query: str) -> List[str]:
+        return self.code_retriever.invoke(query)
+
+# ------------------------------
+# Enhanced Research Workflow
 # ------------------------------
 class ResearchWorkflow:
     """
     Defines the multi-step research workflow using a state graph.
     """
     def __init__(self) -> None:
-        self.processor = CognitiveProcessor()
+        self.processor = EnhancedCognitiveProcessor()
         self.workflow = StateGraph(AgentState)
         self._build_workflow()
         self.app = self.workflow.compile()
 
     def _build_workflow(self) -> None:
-        # Define nodes
+        # Define nodes from base workflow
         self.workflow.add_node("ingest", self.ingest_query)
         self.workflow.add_node("retrieve", self.retrieve_documents)
         self.workflow.add_node("analyze", self.analyze_content)
         self.workflow.add_node("validate", self.validate_output)
         self.workflow.add_node("refine", self.refine_results)
-        # Set entry point and edges
+        # Set base edges
         self.workflow.set_entry_point("ingest")
         self.workflow.add_edge("ingest", "retrieve")
         self.workflow.add_edge("retrieve", "analyze")
@@ -298,16 +439,18 @@ class ResearchWorkflow:
         )
         self.workflow.add_edge("validate", END)
         self.workflow.add_edge("refine", "retrieve")
+        # Extended node for multi-modal enhancement
+        self.workflow.add_node("enhance", self.enhance_analysis)
+        self.workflow.add_edge("validate", "enhance")
+        self.workflow.add_edge("enhance", END)
 
     def ingest_query(self, state: AgentState) -> Dict:
         """
-        Ingests the research query and initializes the refinement counter, refinement history, and domain.
+        Ingests the research query and initializes the context with query, domain, refinement counter, and history.
         """
         try:
             query = state["messages"][-1].content
-            # Retrieve domain from the query context if available; otherwise, default to "Biomedical Research"
             domain = state.get("domain", "Biomedical Research")
-            # Initialize context with raw query, selected domain, refinement counter, and empty refinement history
             new_context = {"raw_query": query, "domain": domain, "refine_count": 0, "refinement_history": []}
             logger.info(f"Query ingested. Domain: {domain}")
             return {
@@ -325,7 +468,6 @@ class ResearchWorkflow:
         """
         try:
             query = state["context"]["raw_query"]
-            # For demonstration, we use the "research" collection for all domains.
             docs = retriever.retrieve(query, state["context"].get("domain", "Biomedical Research"))
             logger.info(f"Retrieved {len(docs)} documents for query.")
             return {
@@ -352,7 +494,6 @@ class ResearchWorkflow:
             docs_text = "\n\n".join([d.page_content for d in docs])
             domain = state["context"].get("domain", "Biomedical Research")
             domain_prompt = ResearchConfig.DOMAIN_PROMPTS.get(domain, "")
-            # Combine domain-specific instructions with the base analysis template
             full_prompt = f"{domain_prompt}\n\n" + ResearchConfig.ANALYSIS_TEMPLATE.format(context=docs_text)
             response = self.processor.process_query(full_prompt)
             if "error" in response:
@@ -395,22 +536,19 @@ class ResearchWorkflow:
     def refine_results(self, state: AgentState) -> Dict:
         """
         Refines the analysis report if validation fails.
-        Implements an innovative meta-refinement mechanism inspired by LADDER.
+        Implements a meta-refinement mechanism inspired by LADDER.
         Tracks refinement history, uses a dynamic difficulty gradient, and if the refinement count exceeds a threshold,
         summarizes the history into a final output.
         """
         try:
             current_count = state["context"].get("refine_count", 0)
             state["context"]["refine_count"] = current_count + 1
-            # Append current analysis to refinement history
             refinement_history = state["context"].setdefault("refinement_history", [])
             current_analysis = state["messages"][-1].content
             refinement_history.append(current_analysis)
-            # Compute a "difficulty level" (from 3 to 0) based on refinement count
             difficulty_level = max(0, 3 - state["context"]["refine_count"])
             logger.info(f"Refinement iteration: {state['context']['refine_count']}, Difficulty level: {difficulty_level}")
             
-            # If refinement count exceeds threshold, perform meta-refinement by summarizing the history
             if state["context"]["refine_count"] >= 3:
                 meta_prompt = (
                     "You are given the following series of refinement outputs:\n" +
@@ -424,7 +562,6 @@ class ResearchWorkflow:
                     "context": state["context"]
                 }
             else:
-                # Standard refinement with a dynamic difficulty prompt
                 refinement_prompt = (
                     f"Refine this analysis (current difficulty level: {difficulty_level}):\n{current_analysis}\n\n"
                     "Improve the following aspects:\n1. Technical precision\n2. Empirical grounding\n3. Theoretical coherence\n\n"
@@ -466,11 +603,12 @@ class ResearchWorkflow:
         }
 
 # ------------------------------
-# Research Interface (Streamlit UI)
+# Enhanced Research Interface
 # ------------------------------
 class ResearchInterface:
     """
     Provides the Streamlit-based interface for executing the research workflow.
+    Extended with collaboration features and knowledge visualization.
     """
     def __init__(self) -> None:
         self.workflow = ResearchWorkflow()
@@ -540,6 +678,14 @@ class ResearchInterface:
             st.subheader("Analysis Metrics")
             st.metric("Vector Collections", 2)
             st.metric("Embedding Dimensions", ResearchConfig.EMBEDDING_DIMENSIONS)
+            with st.sidebar.expander("Collaboration Hub"):
+                st.subheader("Live Research Team")
+                st.write("👩💻 Researcher A")
+                st.write("👨🔬 Researcher B")
+                st.write("🤖 AI Assistant")
+                st.subheader("Knowledge Graph")
+                if st.button("🕸 View Current Graph"):
+                    self._display_knowledge_graph()
 
     def _build_main_interface(self) -> None:
         st.title("🧠 NeuroResearch AI")
@@ -548,7 +694,6 @@ class ResearchInterface:
             height=200,
             placeholder="Enter technical research question..."
         )
-        # Domain selection for research use cases
         domain = st.selectbox(
             "Select Research Domain:",
             options=[
@@ -566,7 +711,6 @@ class ResearchInterface:
     def _execute_analysis(self, query: str, domain: str) -> None:
         try:
             with st.spinner("Initializing Quantum Analysis..."):
-                # Pass domain into the context by adding it to the initial state
                 results = self.workflow.app.stream({
                     "messages": [HumanMessage(content=query)],
                     "context": {"domain": domain},
@@ -614,6 +758,56 @@ Potential issues:
                     st.warning("⚠️ Validation Issues Detected")
                     with st.expander("View Validation Details", expanded=True):
                         st.markdown(content)
+        elif 'enhance' in event:
+            with st.container():
+                content = event['enhance']['messages'][0].content
+                with st.expander("Enhanced Multi-Modal Analysis Report", expanded=True):
+                    st.markdown(content)
+
+    def _display_knowledge_graph(self) -> None:
+        graph = self.workflow.processor.knowledge_graph.visualize_graph()
+        st.graphviz_chart(graph)
+
+# ------------------------------
+# Multi-Modal Retriever Initialization
+# ------------------------------
+from transformers import CLIPProcessor, CLIPModel
+clip_model = CLIPModel.from_pretrained(ResearchConfig.CLIP_SETTINGS["model"])
+clip_processor = CLIPProcessor.from_pretrained(ResearchConfig.CLIP_SETTINGS["model"])
+multi_retriever = MultiModalRetriever(retriever.research_retriever, clip_model, clip_processor)
+
+# ------------------------------
+# Updated Document Processing for Multi-Modal Documents
+# ------------------------------
+class QuantumDocumentManager(QuantumDocumentManager):
+    """Extended with multi-modal document handling."""
+    def create_image_collection(self, image_paths: List[str]):
+        embeddings = []
+        for img_path in image_paths:
+            image = Image.open(img_path)
+            inputs = clip_processor(images=image, return_tensors="pt")
+            with torch.no_grad():
+                emb = clip_model.get_image_features(**inputs)
+            embeddings.append(emb.numpy())
+        return Chroma.from_embeddings(
+            embeddings=embeddings,
+            documents=image_paths,
+            collection_name="neuro_images"
+        )
+
+# Initialize image collection
+qdm.create_image_collection([
+    "data/images/quantum_computing.png",
+    "data/images/neural_arch.png"
+])
+
+# ------------------------------
+# Execute the Application
+# ------------------------------
+class ResearchInterface(ResearchInterface):
+    """Extended with domain adaptability, collaboration, and graph visualization."""
+    def _build_main_interface(self) -> None:
+        super()._build_main_interface()
 
 if __name__ == "__main__":
     ResearchInterface()