diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 000000000..c283cf25e
Binary files /dev/null and b/.DS_Store differ
diff --git a/copilot-widget/index.html b/copilot-widget/index.html
index c9285d408..ed2f1c497 100644
--- a/copilot-widget/index.html
+++ b/copilot-widget/index.html
@@ -75,7 +75,7 @@ <h2>
     </style>
     <div id="opencopilot-root"></div>
     <script>
-      const token = "EvWRh4cqyGIgct4c";
+      const token = "NtITS4Z07ZrdctTN";
       const apiUrl = "http://localhost:8888/backend";
       const socketUrl = "http://localhost:8888";
     </script>
diff --git a/copilot-widget/package.json b/copilot-widget/package.json
index 778805e5a..9e2b57dde 100644
--- a/copilot-widget/package.json
+++ b/copilot-widget/package.json
@@ -1,7 +1,7 @@
 {
   "name": "@openchatai/copilot-widget",
   "private": false,
-  "version": "2.5.3",
+  "version": "2.5.4",
   "type": "module",
   "scripts": {
     "dev": "vite",
diff --git a/copilot-widget/scripts/update-lib.sh b/copilot-widget/scripts/update-lib.sh
old mode 100644
new mode 100755
diff --git a/dashboard/package.json b/dashboard/package.json
index 92655ae0f..82ad3d60b 100644
--- a/dashboard/package.json
+++ b/dashboard/package.json
@@ -13,7 +13,7 @@
   "dependencies": {
     "@hookform/resolvers": "^3.3.1",
     "@kbox-labs/react-echarts": "^1.0.3",
-    "@openchatai/copilot-widget": "^2.5.3",
+    "@openchatai/copilot-widget": "^2.5.4",
     "@radix-ui/react-accordion": "^1.1.2",
     "@radix-ui/react-alert-dialog": "^1.0.5",
     "@radix-ui/react-avatar": "^1.0.4",
diff --git a/dashboard/pnpm-lock.yaml b/dashboard/pnpm-lock.yaml
index 95f7f42b0..6f70a07c8 100644
--- a/dashboard/pnpm-lock.yaml
+++ b/dashboard/pnpm-lock.yaml
@@ -12,8 +12,8 @@ dependencies:
     specifier: ^1.0.3
     version: 1.0.3(echarts@5.4.3)(react@18.2.0)
   '@openchatai/copilot-widget':
-    specifier: ^2.5.3
-    version: 2.5.3(react@18.2.0)
+    specifier: ^2.5.4
+    version: 2.5.4(react@18.2.0)
   '@radix-ui/react-accordion':
     specifier: ^1.1.2
     version: 1.1.2(@types/react-dom@18.2.13)(@types/react@18.2.28)(react-dom@18.2.0)(react@18.2.0)
@@ -706,8 +706,8 @@ packages:
       '@nodelib/fs.scandir': 2.1.5
       fastq: 1.15.0
 
-  /@openchatai/copilot-widget@2.5.3(react@18.2.0):
-    resolution: {integrity: sha512-pgPAGlIXZgNP7iSNs3jkkz+rq/E7zuUnqBwhYteS1lDjms2QrOIDtwmOyDxeLBYDZGg/YQvfaG8xl4gzNTk6/g==}
+  /@openchatai/copilot-widget@2.5.4(react@18.2.0):
+    resolution: {integrity: sha512-ZW7RDsMbSX/f50ZTQj0GMzD3+vA8LxcGAH0wuEDEmkvgFdJ/BNtZGtXV4yonPZ38ePcxehE7zeHHHUSRxE9YUw==}
     peerDependencies:
       react: ^18.x
     dependencies:
diff --git a/dashboard/public/pilot.js b/dashboard/public/pilot.js
index a95b5cfe1..89f0eba0b 100644
--- a/dashboard/public/pilot.js
+++ b/dashboard/public/pilot.js
@@ -69,7 +69,7 @@ Error generating stack: `+i.message+`
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tP=jR(eP);function On(...e){return tP(_R(e))}/**
  * @license lucide-react v0.298.0 - ISC
  *
  * This source code is licensed under the ISC license.
diff --git a/llm-server/models/repository/action_repo.py b/llm-server/models/repository/action_repo.py
index 90d2a115d..42ca14215 100644
--- a/llm-server/models/repository/action_repo.py
+++ b/llm-server/models/repository/action_repo.py
@@ -88,7 +88,7 @@ def update_action(action_id: str, data: ActionDTO) -> Action:
         action.name = data.name
         action.description = data.description
         action.operation_id = data.operation_id
-        action.base_uri = data.api_endpoint
+        action.api_endpoint = data.api_endpoint
         action.request_type = data.request_type
         action.payload = data.payload
         action.updated_at = datetime.datetime.utcnow()
diff --git a/llm-server/requirements.txt b/llm-server/requirements.txt
index e29ccff1d..e323e2d5d 100644
--- a/llm-server/requirements.txt
+++ b/llm-server/requirements.txt
@@ -169,4 +169,5 @@ wrapt==1.16.0
 wsproto==1.2.0
 yarl==1.9.4
 zipp==3.17.0
-aioredis==2.0.1
\ No newline at end of file
+aioredis==2.0.1
+scrapingbee==2.0.1
\ No newline at end of file
diff --git a/llm-server/routes/action/action_controller.py b/llm-server/routes/action/action_controller.py
index eab9d748e..e2cf996e4 100644
--- a/llm-server/routes/action/action_controller.py
+++ b/llm-server/routes/action/action_controller.py
@@ -99,7 +99,6 @@ def add_action(chatbot_id):
 @action.route("/bot/<string:chatbot_id>/action/<string:action_id>", methods=["PATCH"])
 def update_single_action(chatbot_id: str, action_id: str):
     action_dto = ActionDTO(bot_id=chatbot_id, **request.get_json())
-
     # Todo make sure either both or non go in
     saved_action = update_action(action_id, action_dto)
     action_vector_service.update_action_by_operation_id(action_dto)
diff --git a/llm-server/routes/flow/api_info.py b/llm-server/routes/flow/api_info.py
index 76104bdeb..4cd32fc48 100644
--- a/llm-server/routes/flow/api_info.py
+++ b/llm-server/routes/flow/api_info.py
@@ -3,15 +3,15 @@
 
 class ApiInfo:
     def __init__(
-            self,
-            endpoint: Optional[str],
-            method: Optional[str],
-            path_params: Any,
-            query_params: Any,
-            body_schema: Any,
+        self,
+        endpoint: Optional[str],
+        method: Optional[str],
+        path_params: Any,
+        query_params: Any,
+        body_schema: Any,
     ) -> None:
         self.endpoint = endpoint
         self.method = method
-        self.path_params: Any = {"properties": path_params}
-        self.query_params: Any = {"properties": query_params}
+        self.path_params: Any = path_params
+        self.query_params: Any = query_params
         self.body_schema = body_schema
diff --git a/llm-server/routes/flow/generate_openapi_payload.py b/llm-server/routes/flow/generate_openapi_payload.py
index 4334bf577..3c21be578 100644
--- a/llm-server/routes/flow/generate_openapi_payload.py
+++ b/llm-server/routes/flow/generate_openapi_payload.py
@@ -52,7 +52,7 @@ async def generate_api_payload(
         body_schema=body_schema,
     )
 
-    if api_info.path_params["properties"]:
+    if api_info.path_params:
         api_info.path_params = await gen_params_from_schema(
             json.dumps(api_info.path_params, separators=(",", ":")),
             text,
@@ -60,7 +60,7 @@ async def generate_api_payload(
             current_state,
         )
 
-    if api_info.query_params["properties"]:
+    if api_info.query_params:
         api_info.query_params = await gen_params_from_schema(
             json.dumps(api_info.query_params, separators=(",", ":")),
             text,
diff --git a/llm-server/routes/flow/utils/process_conversation_step.py b/llm-server/routes/flow/utils/process_conversation_step.py
index d8a2280db..3d58d671d 100644
--- a/llm-server/routes/flow/utils/process_conversation_step.py
+++ b/llm-server/routes/flow/utils/process_conversation_step.py
@@ -111,7 +111,9 @@ def is_it_informative_or_actionable(
     messages.extend(chat_history[-6:])
     messages.append(HumanMessage(content=current_message))
     messages.append(
-        HumanMessage(content="Return the corresponding json for the last user input")
+        HumanMessage(
+            content="Return the corresponding json for the last user input, without any commentary."
+        )
     )
 
     content = cast(str, chat(messages=messages).content)
diff --git a/llm-server/routes/search/search_service.py b/llm-server/routes/search/search_service.py
new file mode 100644
index 000000000..877130636
--- /dev/null
+++ b/llm-server/routes/search/search_service.py
@@ -0,0 +1,107 @@
+import uuid
+from shared.utils.opencopilot_utils.get_embeddings import get_embeddings
+from utils.llm_consts import VectorCollections, initialize_qdrant_client
+from qdrant_client import models
+from typing import Dict, List, Optional
+import operator
+from copy import deepcopy
+
+client = initialize_qdrant_client()
+embedding = get_embeddings()
+
+
+# Data structure (you might want to define a custom class/dataclass)
+class Item:
+    title: str
+    heading_text: str
+    heading_id: str
+
+    def __init__(self, title: str, heading_text: str, heading_id: str):
+        self.title = title
+        self.heading_text = heading_text
+        self.heading_id = heading_id
+
+
+# Function to add vectors to Qdrant
+def add_cmdbar_data(items: List[Item], metadata: Dict[str, str]) -> None:
+    points = []  # Batch of points to insert
+
+    titles = list(map(operator.attrgetter("title"), items))
+    headings = list(map(operator.attrgetter("heading_text"), items))
+
+    # this logic has to be removed, currently we are only using the html title....
+    title_embedding = None
+    if len(titles) > 3 and (titles[0] == titles[1] == titles[2] == titles[3]):
+        e = embedding.embed_query(titles[0])
+        title_embeddings = [e for _ in range(len(titles))]
+
+    else:
+        title_embeddings = embedding.embed_documents(titles)
+
+    description_embeddings = embedding.embed_documents(headings)
+    for index, item in enumerate(items):
+        title_embedding = title_embeddings[index]
+        description_embedding = description_embeddings[index]
+        _metadata = deepcopy(metadata)
+        _metadata["title"] = item.title
+        _metadata["description"] = item.heading_text or ""
+        _metadata["heading_id"] = item.heading_id or ""
+
+        points.append(
+            models.PointStruct(
+                id=uuid.uuid4().hex,  # Placeholder - See explanation below
+                payload={"metadata": _metadata},
+                vector={
+                    "description": title_embedding,
+                    "title": description_embedding,
+                },
+            )
+        )
+
+    # Perform a single batch insert
+    client.upsert(collection_name=VectorCollections.neural_search, points=points)
+
+
+# Function to search with weights
+def weighted_search(
+    query: str, title_weight: float = 0.7, description_weight: float = 0.3
+) -> List[models.ScoredPoint]:
+    query_embedding = embedding.embed_query(query)
+
+    # Search title and descriptions
+    title_results = client.search(
+        collection_name=VectorCollections.neural_search,
+        query_vector=models.NamedVector(name="title", vector=query_embedding),
+        with_payload=True,
+        with_vector=False,
+    )
+
+    description_results = client.search(
+        collection_name=VectorCollections.neural_search,
+        query_vector=models.NamedVector(name="description", vector=query_embedding),
+        with_payload=True,
+        with_vector=False,
+    )
+
+    # Build a lookup for description results
+    description_lookup = {result.id: result for result in description_results}
+
+    # Combine, weigh, and sort results
+    results: List[models.ScoredPoint] = []
+    for title_result in title_results:
+        matching_desc_result = description_lookup.get(title_result.id)
+        if matching_desc_result:
+            combined_score = (title_weight * title_result.score) + (
+                description_weight * matching_desc_result.score
+            )
+            results.append(
+                models.ScoredPoint(
+                    version=1,
+                    id=title_result.id,
+                    payload=title_result.payload,
+                    score=combined_score,
+                )
+            )
+
+    results.sort(key=lambda x: x.score, reverse=True)
+    return results
diff --git a/llm-server/shared/utils/opencopilot_utils/get_embeddings.py b/llm-server/shared/utils/opencopilot_utils/get_embeddings.py
index 64384eac2..8275acf61 100644
--- a/llm-server/shared/utils/opencopilot_utils/get_embeddings.py
+++ b/llm-server/shared/utils/opencopilot_utils/get_embeddings.py
@@ -14,7 +14,9 @@
 
 @lru_cache(maxsize=1)
 def get_embeddings():
-    embedding_provider = os.environ.get("EMBEDDING_PROVIDER", EmbeddingProvider.OPENAI.value)
+    embedding_provider = os.environ.get(
+        "EMBEDDING_PROVIDER", EmbeddingProvider.OPENAI.value
+    )
 
     if embedding_provider == EmbeddingProvider.azure.value:
         deployment = os.environ.get("AZURE_OPENAI_EMBEDDING_MODEL_NAME")
@@ -23,9 +25,9 @@ def get_embeddings():
         return OpenAIEmbeddings(
             deployment=deployment,
             client=client,
-            chunk_size=8,
+            chunk_size=1000,
         )
-    
+
     elif embedding_provider == EmbeddingProvider.openchat.value:
         logger.info("Got ollama embedding provider", provider=embedding_provider)
         return OllamaEmbeddings(base_url=f"{LOCAL_IP}:11434", model="openchat")
@@ -45,4 +47,4 @@ def get_embeddings():
         raise ValueError(
             f"Embedding service '{embedding_provider}' is not currently available. "
             f"Available services: {available_providers}"
-        )
\ No newline at end of file
+        )
diff --git a/llm-server/utils/llm_consts.py b/llm-server/utils/llm_consts.py
index f7f6fd134..23ba0e4b8 100644
--- a/llm-server/utils/llm_consts.py
+++ b/llm-server/utils/llm_consts.py
@@ -40,6 +40,7 @@ class VectorCollections:
     flows = "flows"
     actions = "actions"
     knowledgebase = "knowledgebase"
+    neural_search = "neural_search"
 
 
 class ChatStrategy:
diff --git a/llm-server/utils/vector_store_setup.py b/llm-server/utils/vector_store_setup.py
index 32a7b6d56..a9c54afe2 100644
--- a/llm-server/utils/vector_store_setup.py
+++ b/llm-server/utils/vector_store_setup.py
@@ -1,6 +1,8 @@
 from qdrant_client import QdrantClient, models
 import os
 
+import qdrant_client
+
 from utils.llm_consts import VectorCollections, initialize_qdrant_client
 
 vector_size = int(os.getenv("VECTOR_SIZE", "1536"))
@@ -24,6 +26,26 @@ def try_create_collection(name: str, vectors_config: models.VectorParams):
         print(f"{name} collection already exists, ignoring")
 
 
+# quick fix, connect to the try_create_collection function
+def try_create_neural_search_collection(name: str, vector_params: models.VectorParams):
+    try:
+        client.create_collection(
+            collection_name=name,
+            vectors_config={
+                "title": vector_params,
+                "description": vector_params,
+            },
+        )
+
+        client.create_payload_index(
+            collection_name=name,
+            field_name="metadata.bot_id",
+            field_schema=models.PayloadFieldSchema.KEYWORD,
+        )
+    except Exception:
+        print(f"{name} collection already exists, ignoring")
+
+
 vector_params = models.VectorParams(size=vector_size, distance=models.Distance.COSINE)
 
 
@@ -31,3 +53,5 @@ def init_qdrant_collections():
     try_create_collection(VectorCollections.knowledgebase, vector_params)
     try_create_collection(VectorCollections.actions, vector_params)
     try_create_collection(VectorCollections.flows, vector_params)
+    try_create_collection(VectorCollections.flows, vector_params)
+    try_create_neural_search_collection(VectorCollections.neural_search, vector_params)
diff --git a/llm-server/workers/tasks/url_parsers.py b/llm-server/workers/tasks/url_parsers.py
index a6462aac2..ce8d3f4f0 100644
--- a/llm-server/workers/tasks/url_parsers.py
+++ b/llm-server/workers/tasks/url_parsers.py
@@ -1,12 +1,11 @@
 import io
 import json
-from typing import List, Union
+from typing import List, Optional, Tuple, Union
 from PyPDF2 import PdfReader
 from enum import Enum
 
 from abc import ABC, abstractmethod
-from bs4 import BeautifulSoup
-
+from bs4 import BeautifulSoup, Tag
 from utils.get_logger import CustomLogger
 import requests
 
@@ -25,12 +24,22 @@ def __repr__(self):
 
 class ContentParser(ABC):
     @abstractmethod
-    def parse(self, content) -> List[LinkInformation]:
+    def get_url_fragments(self, content) -> List[LinkInformation]:
+        pass
+
+    @abstractmethod
+    def parse_text_content(self, content) -> str:
+        pass
+
+    @abstractmethod
+    def find_all_headings_and_highlights(
+        self, content: str
+    ) -> Tuple[str, List[Tuple[str, str]]]:
         pass
 
 
 class TextContentParser(ContentParser):
-    def parse(self, content) -> List[LinkInformation]:
+    def get_url_fragments(self, content) -> List[LinkInformation]:
         soup = BeautifulSoup(content, "lxml")
         links = soup.find_all("a")
 
@@ -57,9 +66,36 @@ def parse(self, content) -> List[LinkInformation]:
             results.append(LinkInformation("", "", text_content))
         return results
 
+    # for now i am returning only the headings and the page title. We will enhance this later to also have highlights
+    def find_all_headings_and_highlights(
+        self, content: str
+    ) -> Tuple[str, List[Tuple[str, str]]]:
+        soup = BeautifulSoup(content, "lxml")
+        title_tag = soup.title
+        title = ""
+        if title_tag is not None:
+            title = title_tag.get_text(strip=True)
+
+        headings: List[Tuple[str, str]] = []
+
+        for heading in soup.find_all(["h1", "h2", "h3", "h4", "h5", "h6"]):
+            heading_text = heading.get_text(strip=True)
+
+            # Check if the heading or one of its children has an 'id' attribute
+            id_tag = heading.find(attrs={"id": True})
+            if id_tag:
+                heading_id = id_tag["id"]
+                headings.append((heading_text, heading_id))
+
+        return title, headings
+
+    def parse_text_content(self, content) -> str:
+        text = BeautifulSoup(content, "lxml").get_text()
+        return text
+
 
 class JsonContentParser(ContentParser):
-    def parse(self, content) -> Union[LinkInformation, None]:
+    def get_url_fragments(self, content) -> Union[LinkInformation, None]:
         try:
             json_data = json.loads(content)
             # Convert JSON object to a string representation
@@ -69,9 +105,17 @@ def parse(self, content) -> Union[LinkInformation, None]:
             print(f"Failed to parse JSON content: {e}")
             return None
 
+    def find_all_headings_and_highlights(self, content: str):
+        raise NotImplementedError()
+
+    @abstractmethod
+    def parse_text_content(self, content) -> str:
+        result = self.get_url_fragments(content)
+        return result.target_text if result else ""
+
 
 class PDFContentParser(ContentParser):
-    def parse(self, content) -> Union[LinkInformation, None]:
+    def get_url_fragments(self, content) -> Union[LinkInformation, None]:
         try:
             pdf_file = PdfReader(io.BytesIO(content))
             text = ""
@@ -85,6 +129,14 @@ def parse(self, content) -> Union[LinkInformation, None]:
             print(f"Failed to parse PDF content: {e}")
             return None
 
+    def find_all_headings_and_highlights(self, content: str):
+        raise NotImplementedError()
+
+    def parse_text_content(self, content) -> str:
+        result = self.get_url_fragments(content)
+
+        return result.target_text if result else ""
+
 
 class ContentType(Enum):
     PDF = "pdf"
@@ -108,6 +160,7 @@ def get_parser(url) -> ContentParser:
         # Add more parsers as needed for different content types
         raise ValueError(f"No parser available for content type: {content_type}")
 
+
 def identify_content_type(url):
     try:
         response = requests.head(url)
diff --git a/llm-server/workers/tasks/web_crawl.py b/llm-server/workers/tasks/web_crawl.py
index eb597e0b9..37379a539 100644
--- a/llm-server/workers/tasks/web_crawl.py
+++ b/llm-server/workers/tasks/web_crawl.py
@@ -1,11 +1,11 @@
-from typing import Optional
+from typing import Set
 from urllib.parse import urlparse, urljoin
 from celery import shared_task
 
 import traceback
 
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from abc import ABC, abstractmethod
+from routes.search.search_service import add_cmdbar_data, Item
 from shared.utils.opencopilot_utils.init_vector_store import init_vector_store
 from shared.utils.opencopilot_utils.interfaces import StoreOptions
 from shared.models.opencopilot_db.website_data_sources import (
@@ -15,55 +15,49 @@
 )
 from utils.llm_consts import WEB_CRAWL_STRATEGY, max_pages_to_crawl
 from models.repository.copilot_settings import ChatbotSettingCRUD
-from workers.tasks.url_parsers import ParserFactory
+from workers.tasks.url_parsers import ParserFactory, TextContentParser
 
 from workers.utils.remove_escape_sequences import remove_escape_sequences
 from utils.get_logger import CustomLogger
-import io
-import json
-from PyPDF2 import PdfReader
 from workers.tasks.web_scraping_strategy import get_scraper
 from bs4 import BeautifulSoup
 
 logger = CustomLogger(__name__)
 
 
-def get_links(url: str, strategy=WEB_CRAWL_STRATEGY) -> list:
+def get_links(url: str, strategy=WEB_CRAWL_STRATEGY) -> Set[str]:
     if url.endswith((".jpg", ".jpeg", ".png", ".gif", ".bmp", ".mp4", ".avi", ".mkv")):
-        return []
+        return set()
 
     scraper = get_scraper(strategy)
     html = scraper.extract_data(url)
 
     if html:
         soup = BeautifulSoup(html, "lxml")
-        base_url = urlparse(url).scheme + "://" + urlparse(url).hostname
+        # base_url = urlparse(url).scheme + "://" + urlparse(url).hostname
         links = [a.get("href") for a in soup.find_all("a", href=True)]
 
         # Only apply urljoin if the link isn't already an http or https
         absolute_links = [
-            urljoin(base_url, link) if not link.startswith("http") else link
+            urljoin(url, link) if not link.startswith("http") else link
             for link in links
         ]
 
-        same_host_links = [
-            link
-            for link in absolute_links
-            if urlparse(link).hostname == urlparse(url).hostname
-        ]
+        same_host_links: Set[str] = set()
+
+        for abslink in absolute_links:
+            if abslink.startswith(url):
+                same_host_links.add(abslink.split("#")[0])
 
-        same_host_links = [
-            urlparse(link)._replace(path=urlparse(link).path.rstrip("/")).geturl()
-            for link in same_host_links
-        ]
         return same_host_links
     else:
         print("Failed to retrieve content.")
-        return []
+        return set()
 
 
-def scrape_url(url: str, strategy=WEB_CRAWL_STRATEGY) -> Optional[str]:
+def scrape_url(url: str, bot_id: str):
     try:
+        strategy = WEB_CRAWL_STRATEGY
         # for external sources always use text content parser, because we don't know the content type
         if strategy != "requests":
             parser = TextContentParser()
@@ -71,12 +65,19 @@ def scrape_url(url: str, strategy=WEB_CRAWL_STRATEGY) -> Optional[str]:
             parser = ParserFactory.get_parser(url)
 
         scraper = get_scraper(strategy)
-        response = scraper.extract_data(url)
+        content = scraper.extract_data(url)
+
+        title, headings = parser.find_all_headings_and_highlights(content)
+        items = [
+            Item(title=title, heading_text=heading_text, heading_id=heading_id)
+            for heading_text, heading_id in headings
+        ]
 
-        return parser.parse(response)
+        add_cmdbar_data(items, {"url": url, "bot_id": bot_id})
+        return parser.parse_text_content(content)
     except ValueError as e:
         # Log an error message if no parser is available for the content type
-        logger.error(str(e))
+        logger.error("SCRAPE_URL_FN", error=e)
         return None
 
 
@@ -97,7 +98,7 @@ def scrape_website(url: str, bot_id: str, max_pages: int) -> int:
             info=f"Reached max pages to crawl for chatbot {bot_id}. Stopping crawl.",
         )
 
-    visited_urls = set()
+    visited_urls: Set[str] = set()
 
     # Use a queue for breadth-first scraping
     queue = [url]
@@ -116,43 +117,37 @@ def scrape_website(url: str, bot_id: str, max_pages: int) -> int:
             ):
                 continue
 
-            contents = scrape_url(current_url)
-
-            for content in contents or []:
-                # Check if scraping was successful
-                if content is not None:
-                    # Process the scraped content as needed
-                    target_text = remove_escape_sequences(content.target_text)
-                    text_splitter = RecursiveCharacterTextSplitter(
-                        chunk_size=1000, chunk_overlap=200, length_function=len
-                    )
-                    docs = text_splitter.create_documents([target_text])
-                    init_vector_store(
-                        docs,
-                        StoreOptions(
-                            namespace="knowledgebase",
-                            metadata={
-                                "bot_id": bot_id,
-                                "link": current_url,
-                                "title": content.link_text,
-                                "scroll_id": content.href,
-                            },
-                        ),
-                    )
-                    create_website_data_source(
-                        chatbot_id=bot_id, url=current_url, status="SUCCESS"
-                    )
-
-                    total_pages_scraped += 1
-
-                    # Get links on the current page
-                    links = get_links(current_url)
-
-                    # Add new links to the queue
-                    queue.extend(links)
+            content = scrape_url(current_url, bot_id=bot_id)
+
+            total_pages_scraped += 1
+            visited_urls.add(current_url)
+
+            if content is not None:
+                # Process the scraped content as needed
+                target_text = remove_escape_sequences(content)
+                text_splitter = RecursiveCharacterTextSplitter(
+                    chunk_size=1000, chunk_overlap=200, length_function=len
+                )
+                docs = text_splitter.create_documents([target_text])
+                init_vector_store(
+                    docs,
+                    StoreOptions(
+                        namespace="knowledgebase",
+                        metadata={"bot_id": bot_id, "link": current_url},
+                    ),
+                )
+                create_website_data_source(
+                    chatbot_id=bot_id, url=current_url, status="SUCCESS"
+                )
+
+                # Get links on the current page
+                links = get_links(current_url)
+
+                # Add new links to the queue
+                queue.extend(links)
 
         except Exception as e:
-            logger.error(f"Error scraping {current_url}: {e}")
+            logger.error("WEB_SCRAPE_ERROR", error=e)
 
         # Mark the URL as visited
         visited_urls.add(current_url)
@@ -160,7 +155,7 @@ def scrape_website(url: str, bot_id: str, max_pages: int) -> int:
     return total_pages_scraped
 
 
-@shared_task
+@shared_task(enable_trace=True)
 def web_crawl(url, bot_id: str):
     try:
         # setting = ChatbotSettings.get_chatbot_setting(bot_id)
@@ -197,46 +192,3 @@ def resume_failed_website_scrape(website_data_source_id: str):
     url = website_data_source.url
 
     scrape_website(url, website_data_source.bot_id, max_pages_to_crawl)
-
-
-class ContentParser(ABC):
-    @abstractmethod
-    def parse(self, content):
-        pass
-
-
-class TextContentParser(ContentParser):
-    def parse(self, content):
-        soup = BeautifulSoup(content, "lxml")
-        text_content = " ".join(
-            [
-                p.text.strip()
-                for p in soup.find_all(["p", "h1", "h2", "h3", "h4", "h5", "h6"])
-            ]
-        )
-        return text_content
-
-
-class JsonContentParser(ContentParser):
-    def parse(self, content):
-        try:
-            json_data = json.loads(content)
-            # Convert JSON object to a string representation
-            return json.dumps(json_data, indent=2)
-        except json.JSONDecodeError as e:
-            logger.error(f"Failed to parse JSON content: {e}")
-            return None
-
-
-class PDFContentParser(ContentParser):
-    def parse(self, content):
-        try:
-            with io.BytesIO(content) as pdf_file:
-                reader = PdfReader(pdf_file)
-                text_content = ""
-                for page in reader.pages:
-                    text_content += page.extract_text() + "\n"
-                return text_content
-        except Exception as e:
-            logger.error(f"Failed to parse PDF content: {e}")
-            return None
diff --git a/llm-server/workers/tasks/web_scraping_strategy.py b/llm-server/workers/tasks/web_scraping_strategy.py
index 4688cb4d6..4cf357641 100644
--- a/llm-server/workers/tasks/web_scraping_strategy.py
+++ b/llm-server/workers/tasks/web_scraping_strategy.py
@@ -8,8 +8,8 @@
 
 class WebScraperStrategy(ABC):
     @abstractmethod
-    def extract_data(self, url):
-        pass
+    def extract_data(self, url) -> str:
+        return ""
 
 
 class RequestsWebScraperStrategy(WebScraperStrategy):
@@ -37,7 +37,7 @@ def extract_data(self, url):
                 "render_js": True,
             },
         )
-        return response.content
+        return response.text
 
 
 @lru_cache(maxsize=1)