feat: added reddit loader

.gitignore

@@ -75,6 +75,7 @@ target/
 
 # Jupyter Notebook
 .ipynb_checkpoints
+**/*.ipynb
 
 # IPython
 profile_default/

application/parser/remote/reddit_loader.py

@@ -0,0 +1,27 @@
+from application.parser.remote.base import BaseRemote
+from langchain_community.document_loaders import RedditPostsLoader
+
+
+class RedditPostsLoaderRemote(BaseRemote):
+    def load_data(self, inputs):
+        client_id = inputs.get("client_id")
+        client_secret = inputs.get("client_secret")
+        user_agent = inputs.get("user_agent")
+        categories = inputs.get("categories", ["new", "hot"])
+        mode = inputs.get("mode", "subreddit")
+        search_queries = inputs.get("search_queries")
+        self.loader = RedditPostsLoader(
+            client_id=client_id,
+            client_secret=client_secret,
+            user_agent=user_agent,
+            categories=categories,
+            mode=mode,
+            search_queries=search_queries,
+        )
+        documents = []
+        try:
+            documents.extend(self.loader.load())
+        except Exception as e:
+            print(f"Error processing Data: {e}")
+        print(f"Loaded {len(documents)} documents from Reddit")
+        return documents[:5]

application/parser/remote/remote_creator.py

@@ -1,13 +1,15 @@
 from application.parser.remote.sitemap_loader import SitemapLoader
 from application.parser.remote.crawler_loader import CrawlerLoader
 from application.parser.remote.web_loader import WebLoader
+from application.parser.remote.reddit_loader import RedditPostsLoaderRemote
 
 
 class RemoteCreator:
     loaders = {
-        'url': WebLoader,
-        'sitemap': SitemapLoader,
-        'crawler': CrawlerLoader
+        "url": WebLoader,
+        "sitemap": SitemapLoader,
+        "crawler": CrawlerLoader,
+        "reddit": RedditPostsLoaderRemote,
     }
 
     @classmethod
@@ -15,4 +17,4 @@ class RemoteCreator:
         loader_class = cls.loaders.get(type.lower())
         if not loader_class:
             raise ValueError(f"No LLM class found for type {type}")
-        return loader_class(*args, **kwargs)
+        return loader_class(*args, **kwargs)

application/worker.py

@@ -15,23 +15,27 @@ from application.parser.schema.base import Document
 from application.parser.token_func import group_split
 
 try:
-    nltk.download('punkt', quiet=True)
-    nltk.download('averaged_perceptron_tagger', quiet=True)
+    nltk.download("punkt", quiet=True)
+    nltk.download("averaged_perceptron_tagger", quiet=True)
 except FileExistsError:
     pass
 
 
 # Define a function to extract metadata from a given filename.
 def metadata_from_filename(title):
-    store = '/'.join(title.split('/')[1:3])
-    return {'title': title, 'store': store}
+    store = "/".join(title.split("/")[1:3])
+    return {"title": title, "store": store}
 
 
 # Define a function to generate a random string of a given length.
 def generate_random_string(length):
-    return ''.join([string.ascii_letters[i % 52] for i in range(length)])
+    return "".join([string.ascii_letters[i % 52] for i in range(length)])
+
+
+current_dir = os.path.dirname(
+    os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+)
 
-current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 # Define the main function for ingesting and processing documents.
 def ingest_worker(self, directory, formats, name_job, filename, user):
@@ -62,38 +66,52 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
     token_check = True
     min_tokens = 150
     max_tokens = 1250
-    full_path = directory + '/' + user + '/' + name_job
+    full_path = directory + "/" + user + "/" + name_job
     import sys
+
     print(full_path, file=sys.stderr)
     # check if API_URL env variable is set
-    file_data = {'name': name_job, 'file': filename, 'user': user}
-    response = requests.get(urljoin(settings.API_URL, "/api/download"), params=file_data)
+    file_data = {"name": name_job, "file": filename, "user": user}
+    response = requests.get(
+        urljoin(settings.API_URL, "/api/download"), params=file_data
+    )
     # check if file is in the response
     print(response, file=sys.stderr)
     file = response.content
 
     if not os.path.exists(full_path):
         os.makedirs(full_path)
-    with open(full_path + '/' + filename, 'wb') as f:
+    with open(full_path + "/" + filename, "wb") as f:
         f.write(file)
 
     # check if file is .zip and extract it
-    if filename.endswith('.zip'):
-        with zipfile.ZipFile(full_path + '/' + filename, 'r') as zip_ref:
+    if filename.endswith(".zip"):
+        with zipfile.ZipFile(full_path + "/" + filename, "r") as zip_ref:
             zip_ref.extractall(full_path)
-        os.remove(full_path + '/' + filename)
+        os.remove(full_path + "/" + filename)
 
-    self.update_state(state='PROGRESS', meta={'current': 1})
+    self.update_state(state="PROGRESS", meta={"current": 1})
 
-    raw_docs = SimpleDirectoryReader(input_dir=full_path, input_files=input_files, recursive=recursive,
-                                     required_exts=formats, num_files_limit=limit,
-                                     exclude_hidden=exclude, file_metadata=metadata_from_filename).load_data()
-    raw_docs = group_split(documents=raw_docs, min_tokens=min_tokens, max_tokens=max_tokens, token_check=token_check)
+    raw_docs = SimpleDirectoryReader(
+        input_dir=full_path,
+        input_files=input_files,
+        recursive=recursive,
+        required_exts=formats,
+        num_files_limit=limit,
+        exclude_hidden=exclude,
+        file_metadata=metadata_from_filename,
+    ).load_data()
+    raw_docs = group_split(
+        documents=raw_docs,
+        min_tokens=min_tokens,
+        max_tokens=max_tokens,
+        token_check=token_check,
+    )
 
     docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
 
     call_openai_api(docs, full_path, self)
-    self.update_state(state='PROGRESS', meta={'current': 100})
+    self.update_state(state="PROGRESS", meta={"current": 100})
 
     if sample:
         for i in range(min(5, len(raw_docs))):
@@ -101,70 +119,80 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
 
     # get files from outputs/inputs/index.faiss and outputs/inputs/index.pkl
     # and send them to the server (provide user and name in form)
-    file_data = {'name': name_job, 'user': user}
+    file_data = {"name": name_job, "user": user}
     if settings.VECTOR_STORE == "faiss":
-        files = {'file_faiss': open(full_path + '/index.faiss', 'rb'),
-                'file_pkl': open(full_path + '/index.pkl', 'rb')}
-        response = requests.post(urljoin(settings.API_URL, "/api/upload_index"), files=files, data=file_data)
-        response = requests.get(urljoin(settings.API_URL, "/api/delete_old?path=" + full_path))
+        files = {
+            "file_faiss": open(full_path + "/index.faiss", "rb"),
+            "file_pkl": open(full_path + "/index.pkl", "rb"),
+        }
+        response = requests.post(
+            urljoin(settings.API_URL, "/api/upload_index"), files=files, data=file_data
+        )
+        response = requests.get(
+            urljoin(settings.API_URL, "/api/delete_old?path=" + full_path)
+        )
     else:
-        response = requests.post(urljoin(settings.API_URL, "/api/upload_index"), data=file_data)
+        response = requests.post(
+            urljoin(settings.API_URL, "/api/upload_index"), data=file_data
+        )
 
-    
     # delete local
     shutil.rmtree(full_path)
 
     return {
-        'directory': directory,
-        'formats': formats,
-        'name_job': name_job,
-        'filename': filename,
-        'user': user,
-        'limited': False
+        "directory": directory,
+        "formats": formats,
+        "name_job": name_job,
+        "filename": filename,
+        "user": user,
+        "limited": False,
     }
 
-def remote_worker(self, source_data, name_job, user, directory = 'temp', loader = 'url'):
+
+def remote_worker(self, source_data, name_job, user, directory="temp", loader="url"):
     # sample = False
     token_check = True
     min_tokens = 150
     max_tokens = 1250
-    full_path = directory + '/' + user + '/' + name_job
+    full_path = directory + "/" + user + "/" + name_job
 
     if not os.path.exists(full_path):
         os.makedirs(full_path)
 
-    self.update_state(state='PROGRESS', meta={'current': 1})
- 
+    self.update_state(state="PROGRESS", meta={"current": 1})
+
     # source_data {"data": [url]} for url type task just urls
- 
+
     # Use RemoteCreator to load data from URL
     remote_loader = RemoteCreator.create_loader(loader)
     raw_docs = remote_loader.load_data(source_data)
 
-    docs = group_split(documents=raw_docs, min_tokens=min_tokens, max_tokens=max_tokens, token_check=token_check)
+    docs = group_split(
+        documents=raw_docs,
+        min_tokens=min_tokens,
+        max_tokens=max_tokens,
+        token_check=token_check,
+    )
 
-    #docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
+    # docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
 
     call_openai_api(docs, full_path, self)
-    self.update_state(state='PROGRESS', meta={'current': 100})
-    
-
+    self.update_state(state="PROGRESS", meta={"current": 100})
 
     # Proceed with uploading and cleaning as in the original function
-    file_data = {'name': name_job, 'user': user}
+    file_data = {"name": name_job, "user": user}
     if settings.VECTOR_STORE == "faiss":
-        files = {'file_faiss': open(full_path + '/index.faiss', 'rb'),
-                 'file_pkl': open(full_path + '/index.pkl', 'rb')}
-        requests.post(urljoin(settings.API_URL, "/api/upload_index"), files=files, data=file_data)
+        files = {
+            "file_faiss": open(full_path + "/index.faiss", "rb"),
+            "file_pkl": open(full_path + "/index.pkl", "rb"),
+        }
+        requests.post(
+            urljoin(settings.API_URL, "/api/upload_index"), files=files, data=file_data
+        )
         requests.get(urljoin(settings.API_URL, "/api/delete_old?path=" + full_path))
     else:
         requests.post(urljoin(settings.API_URL, "/api/upload_index"), data=file_data)
 
     shutil.rmtree(full_path)
 
-    return {
-        'urls': source_data,
-        'name_job': name_job,
-        'user': user,
-        'limited': False
-    }
+    return {"urls": source_data, "name_job": name_job, "user": user, "limited": False}

frontend/src/components/Dropdown.tsx

@@ -35,10 +35,10 @@ function Dropdown({
           isOpen
             ? typeof selectedValue === 'string'
               ? 'rounded-t-xl'
-              : 'rounded-t-2xl'
+              : 'rounded-t-3xl'
             : typeof selectedValue === 'string'
             ? 'rounded-xl'
-            : 'rounded-full'
+            : 'rounded-3xl'
         }`}
       >
         {typeof selectedValue === 'string' ? (

frontend/src/upload/Upload.tsx

@@ -21,6 +21,7 @@ export default function Upload({
     { label: 'Crawler', value: 'crawler' },
     // { label: 'Sitemap', value: 'sitemap' },
     { label: 'Link', value: 'url' },
+    { label: 'Reddit', value: 'reddit' },
   ];
   const [urlType, setUrlType] = useState<{ label: string; value: string }>({
     label: 'Link',