diff --git a/README.md b/README.md
index 90ac0e13..c9c47883 100644
--- a/README.md
+++ b/README.md
@@ -167,6 +167,7 @@ GROQ_API_KEY= <groq_api_key>
 PINECONE_API_KEY = <your_pinecone_API_KEY>
 PORT = 8000
 SEARCH_KEY = <your_Google_custom_search_engine_API_key>
+HF_TOKEN = <your_huggingface_access_token>
   ```
 
 *Run backend:*
diff --git a/backend/app/llm_config.py b/backend/app/llm_config.py
new file mode 100644
index 00000000..4cb96d2c
--- /dev/null
+++ b/backend/app/llm_config.py
@@ -0,0 +1,4 @@
+import os
+
+# Default to a stable model
+LLM_MODEL = os.getenv("LLM_MODEL", "llama-3.3-70b-versatile")
\ No newline at end of file
diff --git a/backend/app/modules/bias_detection/check_bias.py b/backend/app/modules/bias_detection/check_bias.py
index a0644529..d814fdaa 100644
--- a/backend/app/modules/bias_detection/check_bias.py
+++ b/backend/app/modules/bias_detection/check_bias.py
@@ -27,6 +27,7 @@
 from dotenv import load_dotenv
 import json
 from app.logging.logging_config import setup_logger
+from app.llm_config import LLM_MODEL
 
 logger = setup_logger(__name__)
 
@@ -61,7 +62,7 @@ def check_bias(text):
                     "content": (f"Give bias score to the following article \n\n{text}"),
                 },
             ],
-            model="gemma2-9b-it",
+            model=LLM_MODEL,
             temperature=0.3,
             max_tokens=512,
         )
diff --git a/backend/app/modules/chat/get_rag_data.py b/backend/app/modules/chat/get_rag_data.py
index 092e4587..23f9a37c 100644
--- a/backend/app/modules/chat/get_rag_data.py
+++ b/backend/app/modules/chat/get_rag_data.py
@@ -31,7 +31,7 @@
 
 load_dotenv()
 
-pc = Pinecone(os.getenv("PINECONE_API_KEY"))
+pc = Pinecone(api_key=os.getenv("PINECONE_API_KEY"))
 index = pc.Index("perspective")
 
 
diff --git a/backend/app/modules/chat/llm_processing.py b/backend/app/modules/chat/llm_processing.py
index 2d5134fa..9fe0ace3 100644
--- a/backend/app/modules/chat/llm_processing.py
+++ b/backend/app/modules/chat/llm_processing.py
@@ -27,6 +27,7 @@
 from groq import Groq
 from dotenv import load_dotenv
 from app.logging.logging_config import setup_logger
+from app.llm_config import LLM_MODEL
 
 logger = setup_logger(__name__)
 
@@ -55,7 +56,7 @@ def ask_llm(question, docs):
 """
 
     response = client.chat.completions.create(
-        model="gemma2-9b-it",
+        model=LLM_MODEL,
         messages=[
             {"role": "system", "content": "Use only the context to answer."},
             {"role": "user", "content": prompt},
diff --git a/backend/app/modules/fact_check_tool.py b/backend/app/modules/fact_check_tool.py
new file mode 100644
index 00000000..b19f6c52
--- /dev/null
+++ b/backend/app/modules/fact_check_tool.py
@@ -0,0 +1,166 @@
+"""
+fact checking tool node implementation to replace the google search
+
+"""
+
+import os
+import json
+import asyncio
+from groq import Groq
+from langchain_community.tools import DuckDuckGoSearchRun
+from app.logging.logging_config import setup_logger
+from dotenv import load_dotenv
+from app.llm_config import LLM_MODEL
+
+load_dotenv()
+
+client = Groq(api_key=os.getenv("GROQ_API_KEY"))
+search_tool = DuckDuckGoSearchRun()
+
+logger = setup_logger(__name__)
+
+async def extract_claims_node(state):
+    logger.info("--- Fact Check Step 1: Extracting Claims ---")
+    try:
+        text = state.get("cleaned_text", "")
+    
+        response = await asyncio.to_thread(
+            client.chat.completions.create,
+            messages=[
+                {
+                    "role": "system", 
+                    "content": "Extract specific, verifiable factual claims from the text. Ignore opinions. Return a simple list of strings, one per line."
+                },
+                {"role": "user", "content": text[:4000]}
+            ],
+            model=LLM_MODEL,
+            temperature=0.0
+        )
+        
+        raw_content = response.choices[0].message.content
+        
+        claims = [line.strip("- *") for line in raw_content.split("\n") if len(line.strip()) > 10]
+        
+        logger.info(f"Extracted {len(claims)} claims.")
+        return {"claims": claims}
+        
+    except Exception as e:
+        logger.error(f"Error extraction claims: {e}")
+        return {"claims": []}
+
+async def plan_searches_node(state):
+    logger.info("--- Fact Check Step 2: Planning Searches ---")
+    claims = state.get("claims", [])
+    
+    if not claims:
+        return {"search_queries": []}
+
+    claims_text = "\n".join([f"{i}. {c}" for i, c in enumerate(claims)])
+    
+    prompt = f"""
+    You are a search query generator.
+    For each claim, generate a search query to verify it.
+    
+    Output MUST be valid JSON in this format:
+    {{
+        "searches": [
+            {{"query": "search query 1", "claim_id": 0}},
+            {{"query": "search query 2", "claim_id": 1}}
+        ]
+    }}
+
+    Claims:
+    {claims_text}
+    """
+
+    try:
+        response = await asyncio.to_thread(
+            client.chat.completions.create,
+            messages=[{"role": "user", "content": prompt}],
+            model=LLM_MODEL,
+            temperature=0.0,
+            response_format={"type": "json_object"}
+        )
+        
+        plan_json = json.loads(response.choices[0].message.content)
+        queries = plan_json.get("searches", [])
+        
+        return {"search_queries": queries}
+
+    except Exception as e:
+        logger.error(f"Failed to plan searches: {e}")
+        return {"search_queries": []}
+
+async def execute_searches_node(state):
+    logger.info("--- Fact Check Step 3: Executing Parallel Searches ---")
+    queries = state.get("search_queries", [])
+    
+    if not queries:
+        return {"search_results": []}
+
+    async def run_one_search(q):
+        try:
+            query_str = q.get("query")
+            c_id = q.get("claim_id")
+            
+            res = await asyncio.to_thread(search_tool.invoke, query_str)
+            logger.info(f"Search Result for Claim {c_id}: {res[:200]}...")
+            return {"claim_id": c_id, "result": res}
+        except Exception as e:
+            return {"claim_id": q.get("claim_id"), "result": "Search failed"}
+
+    results = await asyncio.gather(*[run_one_search(q) for q in queries])
+    
+    logger.info(f"Completed {len(results)} searches.")
+    return {"search_results": results}
+
+async def verify_facts_node(state):
+    logger.info("--- Fact Check Step 4: Verifying Facts ---")
+    claims = state.get("claims", [])
+    results = state.get("search_results", [])
+    
+    if not claims:
+        return {"facts": [], "fact_check_done": True}
+
+    context = "Verify these claims based on the search results:\n"
+    for item in results:
+        c_id = item["claim_id"]
+        if c_id < len(claims):
+            context += f"\nClaim: {claims[c_id]}\nEvidence: {item['result']}\n"
+
+    try:
+        response = await asyncio.to_thread(
+            client.chat.completions.create,
+            messages=[
+                {
+                    "role": "system", 
+                    "content": "You are a strict fact checker. Return a JSON list of objects with keys: 'claim', 'status' (True/False/Unverified), and 'reason'."
+                },
+                {"role": "user", "content": context}
+            ],
+            model=LLM_MODEL,
+            temperature=0.0,
+            response_format={"type": "json_object"}
+        )
+        
+        final_verdict_str = response.choices[0].message.content
+        
+        data = json.loads(final_verdict_str)
+        
+        facts_list = []
+        if isinstance(data, dict):
+            # Look for common keys if wrapped
+            if "facts" in data:
+                facts_list = data["facts"]
+            elif "verified_claims" in data:
+                 facts_list = data["verified_claims"]
+            else:
+                facts_list = [data]
+        elif isinstance(data, list):
+            facts_list = data
+            
+        return {"facts": facts_list, "fact_check_done": True}
+
+    except Exception as e:
+        logger.error(f"Verification failed: {e}")
+        return {"facts": [], "fact_check_done": True}
\ No newline at end of file
diff --git a/backend/app/modules/facts_check/llm_processing.py b/backend/app/modules/facts_check/llm_processing.py
index dc223a85..c3ead204 100644
--- a/backend/app/modules/facts_check/llm_processing.py
+++ b/backend/app/modules/facts_check/llm_processing.py
@@ -29,6 +29,7 @@
 import json
 import re
 from app.logging.logging_config import setup_logger
+from app.llm_config import LLM_MODEL
 
 logger = setup_logger(__name__)
 
@@ -63,7 +64,7 @@ def run_claim_extractor_sdk(state):
                     ),
                 },
             ],
-            model="gemma2-9b-it",
+            model=LLM_MODEL,
             temperature=0.3,
             max_tokens=512,
         )
@@ -128,7 +129,7 @@ def run_fact_verifier_sdk(search_results):
                         ),
                     },
                 ],
-                model="gemma2-9b-it",
+                model=LLM_MODEL,
                 temperature=0.3,
                 max_tokens=256,
             )
diff --git a/backend/app/modules/langgraph_builder.py b/backend/app/modules/langgraph_builder.py
index 7729f945..b219e115 100644
--- a/backend/app/modules/langgraph_builder.py
+++ b/backend/app/modules/langgraph_builder.py
@@ -6,12 +6,12 @@
 and retry logic.
 
 Workflow:
-    1. Sentiment analysis on the cleaned text.
-    2. Fact-checking detected claims.
-    3. Generating a counter-perspective.
-    4. Judging the quality of the generated perspective.
-    5. Storing results and sending them downstream.
-    6. Error handling at any step if failures occur.
+    1. Parallel analysis: sentiment analysis and fact checking tool pipeline
+       (extract_claims -> plan_searches -> execute_searches -> verify_facts)
+    2. Generating a counter-perspective.
+    3. Judging the quality of the generated perspective.
+    4. Storing results and sending.
+    5. Error handling at any step if failures occur.
 
 Core Features:
     - Uses a TypedDict (`MyState`) to define the shape of the pipeline's
@@ -31,18 +31,18 @@
 """
 
 
+from typing import List, Any
 from langgraph.graph import StateGraph
+from typing_extensions import TypedDict
+
 from app.modules.langgraph_nodes import (
     sentiment,
-    fact_check,
     generate_perspective,
     judge,
     store_and_send,
     error_handler,
 )
 
-from typing_extensions import TypedDict
-
 
 class MyState(TypedDict):
     cleaned_text: str
@@ -52,29 +52,26 @@ class MyState(TypedDict):
     score: int
     retries: int
     status: str
+    claims: List[str]
+    search_queries: List[Any]
+    search_results: List[Any]
 
 
 def build_langgraph():
     graph = StateGraph(MyState)
 
-    graph.add_node("sentiment_analysis", sentiment.run_sentiment_sdk)
-    graph.add_node("fact_checking", fact_check.run_fact_check)
+    # parallel analysis runs sentiment and fact_check tool pipeline in parallel
+    graph.add_node("parallel_analysis", sentiment.run_parallel_analysis)
+
     graph.add_node("generate_perspective", generate_perspective.generate_perspective)
     graph.add_node("judge_perspective", judge.judge_perspective)
     graph.add_node("store_and_send", store_and_send.store_and_send)
     graph.add_node("error_handler", error_handler.error_handler)
 
-    graph.set_entry_point(
-        "sentiment_analysis",
-    )
+    graph.set_entry_point("parallel_analysis")
 
     graph.add_conditional_edges(
-        "sentiment_analysis",
-        lambda x: ("error_handler" if x.get("status") == "error" else "fact_checking"),
-    )
-
-    graph.add_conditional_edges(
-        "fact_checking",
+        "parallel_analysis",
         lambda x: (
             "error_handler" if x.get("status") == "error" else "generate_perspective"
         ),
@@ -101,6 +98,7 @@ def build_langgraph():
             else "store_and_send"
         ),
     )
+
     graph.add_conditional_edges(
         "store_and_send",
         lambda x: ("error_handler" if x.get("status") == "error" else "__end__"),
diff --git a/backend/app/modules/langgraph_nodes/generate_perspective.py b/backend/app/modules/langgraph_nodes/generate_perspective.py
index be0c81f3..605b63dc 100644
--- a/backend/app/modules/langgraph_nodes/generate_perspective.py
+++ b/backend/app/modules/langgraph_nodes/generate_perspective.py
@@ -21,10 +21,11 @@
 
 
 from app.utils.prompt_templates import generation_prompt
+from app.llm_config import LLM_MODEL
 from langchain_groq import ChatGroq
 from pydantic import BaseModel, Field
 from app.logging.logging_config import setup_logger
-
+from typing import List
 logger = setup_logger(__name__)
 
 
@@ -32,11 +33,11 @@
 
 
 class PerspectiveOutput(BaseModel):
-    reasoning: str = Field(..., description="Chain-of-thought reasoning steps")
+    reasoning: List[str] = Field(description="Chain-of-thought reasoning steps", alias="reasoning_steps")
     perspective: str = Field(..., description="Generated opposite perspective")
 
 
-my_llm = "llama-3.3-70b-versatile"
+my_llm = LLM_MODEL
 
 llm = ChatGroq(model=my_llm, temperature=0.7)
 
@@ -56,17 +57,18 @@ def generate_perspective(state):
 
         if not text:
             raise ValueError("Missing or empty 'cleaned_text' in state")
-        elif not facts:
-            raise ValueError("Missing or empty 'facts' in state")
-
-        facts_str = "\n".join(
-            [
-                f"Claim: {f['original_claim']}\n"
-                "Verdict: {f['verdict']}\nExplanation: "
-                "{f['explanation']}"
-                for f in state["facts"]
-            ]
-        )
+        if not facts:
+            logger.warning("No facts found in state. Generating perspective based on text only.")
+            facts_str = "No specific claims verified."
+        else:
+            facts_str = "\n".join(
+                [
+                    f"Claim: {f.get('claim', f.get('original_claim', 'Unknown Claim'))}\n"
+                    f"Verdict: {f.get('status', f.get('verdict', 'Unknown Verdict'))}\n"
+                    f"Explanation: {f.get('reason', f.get('explanation', 'No explanation'))}"
+                    for f in facts
+                ]
+            )
 
         result = chain.invoke(
             {
diff --git a/backend/app/modules/langgraph_nodes/judge.py b/backend/app/modules/langgraph_nodes/judge.py
index 57100301..9613ad7f 100644
--- a/backend/app/modules/langgraph_nodes/judge.py
+++ b/backend/app/modules/langgraph_nodes/judge.py
@@ -19,12 +19,13 @@
 from langchain_groq import ChatGroq
 from langchain.schema import HumanMessage
 from app.logging.logging_config import setup_logger
+from app.llm_config import LLM_MODEL
 
 logger = setup_logger(__name__)
 
 # Init once
 groq_llm = ChatGroq(
-    model="gemma2-9b-it",
+    model=LLM_MODEL,
     temperature=0.0,
     max_tokens=10,
 )
diff --git a/backend/app/modules/langgraph_nodes/sentiment.py b/backend/app/modules/langgraph_nodes/sentiment.py
index fef1d39d..0726e061 100644
--- a/backend/app/modules/langgraph_nodes/sentiment.py
+++ b/backend/app/modules/langgraph_nodes/sentiment.py
@@ -11,13 +11,19 @@
 Functions:
     run_sentiment_sdk(state: dict) -> dict:
         Analyzes sentiment and updates the state with the result.
+    
+    run_parallel_analysis(state: dict) -> dict:
+        Runs sentiment analysis and fact-checking tool nodes in parallel.
+        Combines claims, search_queries, search_results, and facts into state.
 """
 
-
+import asyncio
 import os
 from groq import Groq
 from dotenv import load_dotenv
 from app.logging.logging_config import setup_logger
+from app.llm_config import LLM_MODEL
+from app.modules import fact_check_tool
 
 logger = setup_logger(__name__)
 
@@ -26,6 +32,67 @@
 client = Groq(api_key=os.getenv("GROQ_API_KEY"))
 
 
+async def run_parallel_analysis(state):
+    """
+    The fact-checking pipeline runs sequentially:
+        extract_claims -> plan_searches -> execute_searches -> verify_facts
+    """
+    async def run_fact_check_pipeline(state):
+        try:
+            claims_result = await fact_check_tool.extract_claims_node(state)
+            current_state = {**state, **claims_result}
+            searches_result = await fact_check_tool.plan_searches_node(current_state)
+            current_state = {**current_state, **searches_result}
+            
+            exec_result = await fact_check_tool.execute_searches_node(current_state)
+            current_state = {**current_state, **exec_result}
+            verify_result = await fact_check_tool.verify_facts_node(current_state)
+            current_state = {**current_state, **verify_result}
+            
+            return {
+                "claims": current_state.get("claims", []),
+                "search_queries": current_state.get("search_queries", []),
+                "search_results": current_state.get("search_results", []),
+                "facts": current_state.get("facts", []),
+                "status": "success"
+            }
+        except Exception as e:
+            logger.exception(f"Error in fact_check_pipeline: {e}")
+            return {
+                "status": "error",
+                "error_from": "fact_checking",
+                "message": str(e)
+            }
+
+    sentiment_task = asyncio.to_thread(run_sentiment_sdk, state)
+    fact_check_task = run_fact_check_pipeline(state)
+
+    sentiment_result, fact_check_result = await asyncio.gather(
+        sentiment_task, fact_check_task
+    )
+    if sentiment_result.get("status") == "error":
+        return {
+            "status": "error",
+            "error_from": sentiment_result.get("error_from", "sentiment_analysis"),
+            "message": sentiment_result.get("message", "Unknown error")
+        }
+    if fact_check_result.get("status") == "error":
+        return {
+            "status": "error",
+            "error_from": fact_check_result.get("error_from", "fact_checking"),
+            "message": fact_check_result.get("message", "Unknown error")
+        }
+
+    return {
+        **state,
+        "sentiment": sentiment_result.get("sentiment"),
+        "claims": fact_check_result.get("claims", []),
+        "search_queries": fact_check_result.get("search_queries", []),
+        "search_results": fact_check_result.get("search_results", []),
+        "facts": fact_check_result.get("facts", []),
+        "status": "success"
+    }
+
 def run_sentiment_sdk(state):
     try:
         text = state.get("cleaned_text")
@@ -49,7 +116,7 @@ def run_sentiment_sdk(state):
                     ),
                 },
             ],
-            model="gemma2-9b-it",
+            model=LLM_MODEL,
             temperature=0.2,
             max_tokens=3,
         )
diff --git a/backend/app/modules/pipeline.py b/backend/app/modules/pipeline.py
index 3e4a844e..1c81d57c 100644
--- a/backend/app/modules/pipeline.py
+++ b/backend/app/modules/pipeline.py
@@ -38,6 +38,7 @@
 from app.modules.langgraph_builder import build_langgraph
 from app.logging.logging_config import setup_logger
 import json
+import asyncio
 
 logger = setup_logger(__name__)
 
@@ -64,8 +65,8 @@ def run_scraper_pipeline(url: str) -> dict:
     return result
 
 
-def run_langgraph_workflow(state: dict):
+async def run_langgraph_workflow(state: dict):
     """Execute the pre-compiled LangGraph workflow."""
-    result = _LANGGRAPH_WORKFLOW.invoke(state)
+    result = await _LANGGRAPH_WORKFLOW.ainvoke(state)
     logger.info("LangGraph workflow executed successfully.")
     return result
diff --git a/backend/app/modules/vector_store/chunk_rag_data.py b/backend/app/modules/vector_store/chunk_rag_data.py
index 7e2a32eb..00b40d12 100644
--- a/backend/app/modules/vector_store/chunk_rag_data.py
+++ b/backend/app/modules/vector_store/chunk_rag_data.py
@@ -32,71 +32,79 @@
 
 def chunk_rag_data(data):
     try:
-        # Validate required top-level fields
         required_fields = ["cleaned_text", "perspective", "facts"]
         for field in required_fields:
             if field not in data:
-                raise ValueError(f"Missing required field: {field}")
+                if field == "facts":
+                    data["facts"] = []
+                else:
+                    raise ValueError(f"Missing required field: {field}")
 
         if not isinstance(data["facts"], list):
-            raise ValueError("Facts must be a list")
-
-        # Validate perspective structure
-        perspective_data = data["perspective"]
-        if hasattr(perspective_data, "dict"):
-            perspective_data = perspective_data.dict()
+            logger.warning("Facts is not a list. Treating as empty.")
+            data["facts"] = []
 
         article_id = generate_id(data["cleaned_text"])
         chunks = []
 
-        # Add counter-perspective chunk
-        perspective_obj = data["perspective"]
-
-        # Optional safety check
+        perspective_data = data["perspective"]
+        
+        if hasattr(perspective_data, "dict"):
+            p_data = perspective_data.dict()
+        elif hasattr(perspective_data, "model_dump"):
+            p_data = perspective_data.model_dump()
+        elif isinstance(perspective_data, dict):
+            p_data = perspective_data
+        else:
+            p_data = {
+                "perspective": getattr(perspective_data, "perspective", ""),
+                "reasoning": getattr(perspective_data, "reasoning", "")
+            }
 
-        if not (
-            hasattr(perspective_obj, "perspective")
-            and hasattr(perspective_obj, "reasoning")
-        ):
-            raise ValueError("Perspective object missing required fields")
+        p_text = p_data.get("perspective", "")
+        raw_reasoning = p_data.get("reasoning", "")
+        if isinstance(raw_reasoning, list):
+            p_reason = "\n".join(raw_reasoning)
+        else:
+            p_reason = str(raw_reasoning)
 
-        chunks.append(
-            {
+        if p_text:
+            chunks.append({
                 "id": f"{article_id}-perspective",
-                "text": perspective_obj.perspective,
+                "text": p_text,
                 "metadata": {
                     "type": "counter-perspective",
-                    "reasoning": perspective_obj.reasoning,
+                    "reasoning": p_reason,
                     "article_id": article_id,
                 },
-            }
-        )
+            })
 
-        # Add each fact as a separate chunk
         for i, fact in enumerate(data["facts"]):
-            fact_fields = ["original_claim", "verdict", "explanation", "source_link"]
-            for field in fact_fields:
-                if field not in fact:
-                    raise ValueError(
-                        f"Missing required fact field: {field} in fact index {i}"
-                    )
-
-            chunks.append(
-                {
-                    "id": f"{article_id}-fact-{i}",
-                    "text": fact["original_claim"],
-                    "metadata": {
-                        "type": "fact",
-                        "verdict": fact["verdict"],
-                        "explanation": fact["explanation"],
-                        "source_link": fact["source_link"],
-                        "article_id": article_id,
-                    },
-                }
-            )
+            claim_text = fact.get("claim", fact.get("original_claim", ""))
+            verdict = fact.get("status", fact.get("verdict", "Unverified"))
+            explanation = fact.get("reason", fact.get("explanation", "No explanation provided"))
+            source = fact.get("source_link", "N/A")
+
+            # SKIP invalid facts instead of Crashing
+            if not claim_text:
+                logger.warning(f"Skipping fact index {i}: Missing claim text.")
+                continue
+
+            chunks.append({
+                "id": f"{article_id}-fact-{i}",
+                "text": claim_text,
+                "metadata": {
+                    "type": "fact",
+                    "verdict": verdict,
+                    "explanation": explanation,
+                    "source_link": source,
+                    "article_id": article_id,
+                },
+            })
 
+        logger.info(f"Generated {len(chunks)} chunks for storage.")
         return chunks
 
     except Exception as e:
         logger.exception(f"Failed to chunk the data: {e}")
-        raise
+        return []
\ No newline at end of file
diff --git a/backend/app/routes/routes.py b/backend/app/routes/routes.py
index 6988f5e8..f9f01c7b 100644
--- a/backend/app/routes/routes.py
+++ b/backend/app/routes/routes.py
@@ -71,7 +71,7 @@ async def bias_detection(request: URlRequest):
 async def run_pipelines(request: URlRequest):
     article_text = await asyncio.to_thread(run_scraper_pipeline, (request.url))
     logger.debug(f"Scraper output: {json.dumps(article_text, indent=2, ensure_ascii=False)}")
-    data = await asyncio.to_thread(run_langgraph_workflow, (article_text))
+    data = await run_langgraph_workflow(article_text)
     return data
 
 
diff --git a/backend/pyproject.toml b/backend/pyproject.toml
index 70037f72..deb32a55 100644
--- a/backend/pyproject.toml
+++ b/backend/pyproject.toml
@@ -16,7 +16,7 @@ dependencies = [
     "langchain-groq>=0.3.2",
     "langgraph>=0.4.8",
     "logging>=0.4.9.6",
-    "newspaper3k>=0.2.8",
+    "newspaper4k>=0.9.4.1",
     "nltk>=3.9.1",
     "pinecone>=7.3.0",
     "rake-nltk>=1.0.6",
diff --git a/backend/uv.lock b/backend/uv.lock
index fc1e19b5..138a1f1e 100644
--- a/backend/uv.lock
+++ b/backend/uv.lock
@@ -1,6 +1,10 @@
 version = 1
 revision = 2
 requires-python = ">=3.13"
+resolution-markers = [
+    "python_full_version >= '3.14'",
+    "python_full_version < '3.14'",
+]
 
 [[package]]
 name = "aiohappyeyeballs"
@@ -113,7 +117,7 @@ dependencies = [
     { name = "langchain-groq" },
     { name = "langgraph" },
     { name = "logging" },
-    { name = "newspaper3k" },
+    { name = "newspaper4k" },
     { name = "nltk" },
     { name = "pinecone" },
     { name = "rake-nltk" },
@@ -137,7 +141,7 @@ requires-dist = [
     { name = "langchain-groq", specifier = ">=0.3.2" },
     { name = "langgraph", specifier = ">=0.4.8" },
     { name = "logging", specifier = ">=0.4.9.6" },
-    { name = "newspaper3k", specifier = ">=0.2.8" },
+    { name = "newspaper4k", specifier = ">=0.9.4.1" },
     { name = "nltk", specifier = ">=3.9.1" },
     { name = "pinecone", specifier = ">=7.3.0" },
     { name = "rake-nltk", specifier = ">=1.0.6" },
@@ -161,6 +165,34 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/50/cd/30110dc0ffcf3b131156077b90e9f60ed75711223f306da4db08eff8403b/beautifulsoup4-4.13.4-py3-none-any.whl", hash = "sha256:9bbbb14bfde9d79f38b8cd5f8c7c85f4b8f2523190ebed90e950a8dea4cb1c4b", size = 187285, upload-time = "2025-04-15T17:05:12.221Z" },
 ]
 
+[[package]]
+name = "brotli"
+version = "1.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f7/16/c92ca344d646e71a43b8bb353f0a6490d7f6e06210f8554c8f874e454285/brotli-1.2.0.tar.gz", hash = "sha256:e310f77e41941c13340a95976fe66a8a95b01e783d430eeaf7a2f87e0a57dd0a", size = 7388632, upload-time = "2025-11-05T18:39:42.86Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6c/d4/4ad5432ac98c73096159d9ce7ffeb82d151c2ac84adcc6168e476bb54674/brotli-1.2.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:9e5825ba2c9998375530504578fd4d5d1059d09621a02065d1b6bfc41a8e05ab", size = 861523, upload-time = "2025-11-05T18:38:34.67Z" },
+    { url = "https://files.pythonhosted.org/packages/91/9f/9cc5bd03ee68a85dc4bc89114f7067c056a3c14b3d95f171918c088bf88d/brotli-1.2.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:0cf8c3b8ba93d496b2fae778039e2f5ecc7cff99df84df337ca31d8f2252896c", size = 444289, upload-time = "2025-11-05T18:38:35.6Z" },
+    { url = "https://files.pythonhosted.org/packages/2e/b6/fe84227c56a865d16a6614e2c4722864b380cb14b13f3e6bef441e73a85a/brotli-1.2.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c8565e3cdc1808b1a34714b553b262c5de5fbda202285782173ec137fd13709f", size = 1528076, upload-time = "2025-11-05T18:38:36.639Z" },
+    { url = "https://files.pythonhosted.org/packages/55/de/de4ae0aaca06c790371cf6e7ee93a024f6b4bb0568727da8c3de112e726c/brotli-1.2.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:26e8d3ecb0ee458a9804f47f21b74845cc823fd1bb19f02272be70774f56e2a6", size = 1626880, upload-time = "2025-11-05T18:38:37.623Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/16/a1b22cbea436642e071adcaf8d4b350a2ad02f5e0ad0da879a1be16188a0/brotli-1.2.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:67a91c5187e1eec76a61625c77a6c8c785650f5b576ca732bd33ef58b0dff49c", size = 1419737, upload-time = "2025-11-05T18:38:38.729Z" },
+    { url = "https://files.pythonhosted.org/packages/46/63/c968a97cbb3bdbf7f974ef5a6ab467a2879b82afbc5ffb65b8acbb744f95/brotli-1.2.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:4ecdb3b6dc36e6d6e14d3a1bdc6c1057c8cbf80db04031d566eb6080ce283a48", size = 1484440, upload-time = "2025-11-05T18:38:39.916Z" },
+    { url = "https://files.pythonhosted.org/packages/06/9d/102c67ea5c9fc171f423e8399e585dabea29b5bc79b05572891e70013cdd/brotli-1.2.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:3e1b35d56856f3ed326b140d3c6d9db91740f22e14b06e840fe4bb1923439a18", size = 1593313, upload-time = "2025-11-05T18:38:41.24Z" },
+    { url = "https://files.pythonhosted.org/packages/9e/4a/9526d14fa6b87bc827ba1755a8440e214ff90de03095cacd78a64abe2b7d/brotli-1.2.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:54a50a9dad16b32136b2241ddea9e4df159b41247b2ce6aac0b3276a66a8f1e5", size = 1487945, upload-time = "2025-11-05T18:38:42.277Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/e8/3fe1ffed70cbef83c5236166acaed7bb9c766509b157854c80e2f766b38c/brotli-1.2.0-cp313-cp313-win32.whl", hash = "sha256:1b1d6a4efedd53671c793be6dd760fcf2107da3a52331ad9ea429edf0902f27a", size = 334368, upload-time = "2025-11-05T18:38:43.345Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/91/e739587be970a113b37b821eae8097aac5a48e5f0eca438c22e4c7dd8648/brotli-1.2.0-cp313-cp313-win_amd64.whl", hash = "sha256:b63daa43d82f0cdabf98dee215b375b4058cce72871fd07934f179885aad16e8", size = 369116, upload-time = "2025-11-05T18:38:44.609Z" },
+    { url = "https://files.pythonhosted.org/packages/17/e1/298c2ddf786bb7347a1cd71d63a347a79e5712a7c0cba9e3c3458ebd976f/brotli-1.2.0-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:6c12dad5cd04530323e723787ff762bac749a7b256a5bece32b2243dd5c27b21", size = 863080, upload-time = "2025-11-05T18:38:45.503Z" },
+    { url = "https://files.pythonhosted.org/packages/84/0c/aac98e286ba66868b2b3b50338ffbd85a35c7122e9531a73a37a29763d38/brotli-1.2.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:3219bd9e69868e57183316ee19c84e03e8f8b5a1d1f2667e1aa8c2f91cb061ac", size = 445453, upload-time = "2025-11-05T18:38:46.433Z" },
+    { url = "https://files.pythonhosted.org/packages/ec/f1/0ca1f3f99ae300372635ab3fe2f7a79fa335fee3d874fa7f9e68575e0e62/brotli-1.2.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:963a08f3bebd8b75ac57661045402da15991468a621f014be54e50f53a58d19e", size = 1528168, upload-time = "2025-11-05T18:38:47.371Z" },
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+    { url = "https://files.pythonhosted.org/packages/f3/2f/0976d5b097ff8a22163b10617f76b2557f15f0f39d6a0fe1f02b1a53e92b/brotli-1.2.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:cf9cba6f5b78a2071ec6fb1e7bd39acf35071d90a81231d67e92d637776a6a63", size = 1419861, upload-time = "2025-11-05T18:38:49.372Z" },
+    { url = "https://files.pythonhosted.org/packages/9c/97/d76df7176a2ce7616ff94c1fb72d307c9a30d2189fe877f3dd99af00ea5a/brotli-1.2.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7547369c4392b47d30a3467fe8c3330b4f2e0f7730e45e3103d7d636678a808b", size = 1484594, upload-time = "2025-11-05T18:38:50.655Z" },
+    { url = "https://files.pythonhosted.org/packages/d3/93/14cf0b1216f43df5609f5b272050b0abd219e0b54ea80b47cef9867b45e7/brotli-1.2.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:fc1530af5c3c275b8524f2e24841cbe2599d74462455e9bae5109e9ff42e9361", size = 1593455, upload-time = "2025-11-05T18:38:51.624Z" },
+    { url = "https://files.pythonhosted.org/packages/b3/73/3183c9e41ca755713bdf2cc1d0810df742c09484e2e1ddd693bee53877c1/brotli-1.2.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:d2d085ded05278d1c7f65560aae97b3160aeb2ea2c0b3e26204856beccb60888", size = 1488164, upload-time = "2025-11-05T18:38:53.079Z" },
+    { url = "https://files.pythonhosted.org/packages/64/6a/0c78d8f3a582859236482fd9fa86a65a60328a00983006bcf6d83b7b2253/brotli-1.2.0-cp314-cp314-win32.whl", hash = "sha256:832c115a020e463c2f67664560449a7bea26b0c1fdd690352addad6d0a08714d", size = 339280, upload-time = "2025-11-05T18:38:54.02Z" },
+    { url = "https://files.pythonhosted.org/packages/f5/10/56978295c14794b2c12007b07f3e41ba26acda9257457d7085b0bb3bb90c/brotli-1.2.0-cp314-cp314-win_amd64.whl", hash = "sha256:e7c0af964e0b4e3412a0ebf341ea26ec767fa0b4cf81abb5e897c9338b5ad6a3", size = 375639, upload-time = "2025-11-05T18:38:55.67Z" },
+]
+
 [[package]]
 name = "bs4"
 version = "0.0.2"
@@ -355,27 +387,16 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/50/b3/b51f09c2ba432a576fe63758bddc81f78f0c6309d9e5c10d194313bf021e/fastapi-0.115.12-py3-none-any.whl", hash = "sha256:e94613d6c05e27be7ffebdd6ea5f388112e5e430c8f7d6494a9d1d88d43e814d", size = 95164, upload-time = "2025-03-23T22:55:42.101Z" },
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-[[package]]
-name = "feedfinder2"
-version = "0.0.4"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
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-    { name = "requests" },
-    { name = "six" },
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-sdist = { url = "https://files.pythonhosted.org/packages/35/82/1251fefec3bb4b03fd966c7e7f7a41c9fc2bb00d823a34c13f847fd61406/feedfinder2-0.0.4.tar.gz", hash = "sha256:3701ee01a6c85f8b865a049c30ba0b4608858c803fe8e30d1d289fdbe89d0efe", size = 3297, upload-time = "2016-01-25T15:09:17.492Z" }
-
 [[package]]
 name = "feedparser"
-version = "6.0.11"
+version = "6.0.12"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "sgmllib3k" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/ff/aa/7af346ebeb42a76bf108027fe7f3328bb4e57a3a96e53e21fd9ef9dd6dd0/feedparser-6.0.11.tar.gz", hash = "sha256:c9d0407b64c6f2a065d0ebb292c2b35c01050cc0dc33757461aaabdc4c4184d5", size = 286197, upload-time = "2023-12-10T16:03:20.854Z" }
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 wheels = [
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 [[package]]
@@ -594,12 +615,6 @@ wheels = [
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-[[package]]
-name = "jieba3k"
-version = "0.35.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/a9/cb/2c8332bcdc14d33b0bedd18ae0a4981a069c3513e445120da3c3f23a8aaa/jieba3k-0.35.1.zip", hash = "sha256:980a4f2636b778d312518066be90c7697d410dd5a472385f5afced71a2db1c10", size = 7423646, upload-time = "2014-11-15T05:47:47.978Z" }
-
 [[package]]
 name = "jinja2"
 version = "3.1.6"
@@ -760,7 +775,7 @@ name = "langgraph-checkpoint"
 version = "2.0.26"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "langchain-core", marker = "python_full_version < '4.0'" },
+    { name = "langchain-core", marker = "python_full_version < '4'" },
     { name = "ormsgpack" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/c5/61/e2518ac9216a4e9f4efda3ac61595e3c9e9ac00833141c9688e8d56bd7eb/langgraph_checkpoint-2.0.26.tar.gz", hash = "sha256:2b800195532d5efb079db9754f037281225ae175f7a395523f4bf41223cbc9d6", size = 37874, upload-time = "2025-05-15T17:31:22.466Z" }
@@ -973,27 +988,26 @@ wheels = [
 ]
 
 [[package]]
-name = "newspaper3k"
-version = "0.2.8"
+name = "newspaper4k"
+version = "0.9.4.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "beautifulsoup4" },
-    { name = "cssselect" },
-    { name = "feedfinder2" },
+    { name = "brotli" },
     { name = "feedparser" },
-    { name = "jieba3k" },
-    { name = "lxml" },
+    { name = "lxml", extra = ["html-clean"] },
     { name = "nltk" },
-    { name = "pillow" },
+    { name = "pillow", version = "11.2.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
+    { name = "pillow", version = "12.0.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
     { name = "python-dateutil" },
     { name = "pyyaml" },
     { name = "requests" },
-    { name = "tinysegmenter" },
     { name = "tldextract" },
+    { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/ce/fb/8f8525be0cafa48926e85b0c06a7cb3e2a892d340b8036f8c8b1b572df1c/newspaper3k-0.2.8.tar.gz", hash = "sha256:9f1bd3e1fb48f400c715abf875cc7b0a67b7ddcd87f50c9aeeb8fcbbbd9004fb", size = 205685, upload-time = "2018-09-28T04:58:23.53Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/b7/cc/cf743a3d06b10907cec76a675fe0857445907ded0e64ec4624483c1467ce/newspaper4k-0.9.4.1.tar.gz", hash = "sha256:5b1a92dfb04d6d379f9484fad4ad44741deb9ac3d55a6c178badf2a0d4bba903", size = 3971874, upload-time = "2025-11-18T06:08:56.543Z" }
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+    { url = "https://files.pythonhosted.org/packages/63/e8/7e6c6a6e626fec2ec25f7b69fa0c446d1ca8a7fad121b61f2672e5779b76/newspaper4k-0.9.4.1-py3-none-any.whl", hash = "sha256:fab18fdb0637da0ea2452e18c5986c4af2263ba3016ff684f1c522f696bd39bd", size = 306153, upload-time = "2025-11-18T06:08:54.9Z" },
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 [[package]]
@@ -1226,6 +1240,9 @@ wheels = [
 name = "pillow"
 version = "11.2.1"
 source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version < '3.14'",
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 sdist = { url = "https://files.pythonhosted.org/packages/af/cb/bb5c01fcd2a69335b86c22142b2bccfc3464087efb7fd382eee5ffc7fdf7/pillow-11.2.1.tar.gz", hash = "sha256:a64dd61998416367b7ef979b73d3a85853ba9bec4c2925f74e588879a58716b6", size = 47026707, upload-time = "2025-04-12T17:50:03.289Z" }
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