mirror of
https://github.com/arc53/DocsGPT.git
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Merge pull request #933 from siiddhantt/fix/remote-upload-issue
fix: remote upload error
This commit is contained in:
@@ -20,9 +20,12 @@ vectors_collection = db["vectors"]
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prompts_collection = db["prompts"]
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feedback_collection = db["feedback"]
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api_key_collection = db["api_keys"]
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user = Blueprint('user', __name__)
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user = Blueprint("user", __name__)
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current_dir = os.path.dirname(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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)
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current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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@user.route("/api/delete_conversation", methods=["POST"])
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def delete_conversation():
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@@ -37,21 +40,25 @@ def delete_conversation():
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return {"status": "ok"}
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@user.route("/api/delete_all_conversations", methods=["POST"])
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def delete_all_conversations():
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user_id = "local"
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conversations_collection.delete_many({"user":user_id})
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conversations_collection.delete_many({"user": user_id})
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return {"status": "ok"}
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@user.route("/api/get_conversations", methods=["get"])
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def get_conversations():
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# provides a list of conversations
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conversations = conversations_collection.find().sort("date", -1).limit(30)
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list_conversations = []
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for conversation in conversations:
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list_conversations.append({"id": str(conversation["_id"]), "name": conversation["name"]})
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list_conversations.append(
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{"id": str(conversation["_id"]), "name": conversation["name"]}
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)
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#list_conversations = [{"id": "default", "name": "default"}, {"id": "jeff", "name": "jeff"}]
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# list_conversations = [{"id": "default", "name": "default"}, {"id": "jeff", "name": "jeff"}]
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return jsonify(list_conversations)
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@@ -61,7 +68,8 @@ def get_single_conversation():
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# provides data for a conversation
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conversation_id = request.args.get("id")
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conversation = conversations_collection.find_one({"_id": ObjectId(conversation_id)})
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return jsonify(conversation['queries'])
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return jsonify(conversation["queries"])
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@user.route("/api/update_conversation_name", methods=["POST"])
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def update_conversation_name():
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@@ -69,7 +77,7 @@ def update_conversation_name():
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data = request.get_json()
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id = data["id"]
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name = data["name"]
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conversations_collection.update_one({"_id": ObjectId(id)},{"$set":{"name":name}})
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conversations_collection.update_one({"_id": ObjectId(id)}, {"$set": {"name": name}})
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return {"status": "ok"}
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@@ -80,7 +88,6 @@ def api_feedback():
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answer = data["answer"]
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feedback = data["feedback"]
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feedback_collection.insert_one(
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{
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"question": question,
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@@ -90,6 +97,7 @@ def api_feedback():
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)
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return {"status": "ok"}
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@user.route("/api/delete_by_ids", methods=["get"])
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def delete_by_ids():
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"""Delete by ID. These are the IDs in the vectorstore"""
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@@ -104,6 +112,7 @@ def delete_by_ids():
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return {"status": "ok"}
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return {"status": "error"}
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@user.route("/api/delete_old", methods=["get"])
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def delete_old():
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"""Delete old indexes."""
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@@ -119,7 +128,7 @@ def delete_old():
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if dirs_clean[0] not in ["indexes", "vectors"]:
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return {"status": "error"}
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path_clean = "/".join(dirs_clean)
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vectors_collection.delete_one({"name": dirs_clean[-1], 'user': dirs_clean[-2]})
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vectors_collection.delete_one({"name": dirs_clean[-1], "user": dirs_clean[-2]})
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if settings.VECTOR_STORE == "faiss":
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try:
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shutil.rmtree(os.path.join(current_dir, path_clean))
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@@ -130,9 +139,10 @@ def delete_old():
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settings.VECTOR_STORE, path=os.path.join(current_dir, path_clean)
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)
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vetorstore.delete_index()
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return {"status": "ok"}
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@user.route("/api/upload", methods=["POST"])
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def upload_file():
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"""Upload a file to get vectorized and indexed."""
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@@ -144,27 +154,29 @@ def upload_file():
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job_name = secure_filename(request.form["name"])
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# check if the post request has the file part
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files = request.files.getlist("file")
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if not files or all(file.filename == '' for file in files):
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if not files or all(file.filename == "" for file in files):
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return {"status": "no file name"}
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# Directory where files will be saved
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save_dir = os.path.join(current_dir, settings.UPLOAD_FOLDER, user, job_name)
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os.makedirs(save_dir, exist_ok=True)
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if len(files) > 1:
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# Multiple files; prepare them for zip
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temp_dir = os.path.join(save_dir, "temp")
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os.makedirs(temp_dir, exist_ok=True)
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for file in files:
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filename = secure_filename(file.filename)
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file.save(os.path.join(temp_dir, filename))
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# Use shutil.make_archive to zip the temp directory
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zip_path = shutil.make_archive(base_name=os.path.join(save_dir, job_name), format='zip', root_dir=temp_dir)
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zip_path = shutil.make_archive(
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base_name=os.path.join(save_dir, job_name), format="zip", root_dir=temp_dir
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)
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final_filename = os.path.basename(zip_path)
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# Clean up the temporary directory after zipping
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shutil.rmtree(temp_dir)
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else:
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@@ -173,14 +185,19 @@ def upload_file():
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final_filename = secure_filename(file.filename)
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file_path = os.path.join(save_dir, final_filename)
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file.save(file_path)
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# Call ingest with the single file or zipped file
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task = ingest.delay(settings.UPLOAD_FOLDER, [".rst", ".md", ".pdf", ".txt", ".docx",
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".csv", ".epub", ".html", ".mdx"],
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job_name, final_filename, user)
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task = ingest.delay(
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settings.UPLOAD_FOLDER,
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[".rst", ".md", ".pdf", ".txt", ".docx", ".csv", ".epub", ".html", ".mdx"],
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job_name,
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final_filename,
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user,
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)
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return {"status": "ok", "task_id": task.id}
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@user.route("/api/remote", methods=["POST"])
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def upload_remote():
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"""Upload a remote source to get vectorized and indexed."""
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@@ -193,25 +210,27 @@ def upload_remote():
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if "name" not in request.form:
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return {"status": "no name"}
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job_name = secure_filename(request.form["name"])
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# check if the post request has the file part
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if "data" not in request.form:
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print("No data")
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return {"status": "no data"}
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source_data = request.form["data"]
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if source_data:
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task = ingest_remote.delay(source_data=source_data, job_name=job_name, user=user, loader=source)
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# task id
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task = ingest_remote.delay(
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source_data=source_data, job_name=job_name, user=user, loader=source
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)
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task_id = task.id
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return {"status": "ok", "task_id": task_id}
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else:
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return {"status": "error"}
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@user.route("/api/task_status", methods=["GET"])
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def task_status():
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"""Get celery job status."""
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task_id = request.args.get("task_id")
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from application.celery import celery
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task = celery.AsyncResult(task_id)
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task_meta = task.info
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return {"status": task.status, "result": task_meta}
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@@ -253,11 +272,13 @@ def combined_json():
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}
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)
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if settings.VECTOR_STORE == "faiss":
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data_remote = requests.get("https://d3dg1063dc54p9.cloudfront.net/combined.json").json()
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data_remote = requests.get(
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"https://d3dg1063dc54p9.cloudfront.net/combined.json"
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).json()
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for index in data_remote:
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index["location"] = "remote"
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data.append(index)
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if 'duckduck_search' in settings.RETRIEVERS_ENABLED:
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if "duckduck_search" in settings.RETRIEVERS_ENABLED:
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data.append(
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{
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"name": "DuckDuckGo Search",
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@@ -271,7 +292,7 @@ def combined_json():
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"location": "custom",
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}
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)
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if 'brave_search' in settings.RETRIEVERS_ENABLED:
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if "brave_search" in settings.RETRIEVERS_ENABLED:
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data.append(
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{
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"name": "Brave Search",
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@@ -302,11 +323,11 @@ def check_docs():
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return {"status": "exists"}
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else:
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file_url = urlparse(base_path + vectorstore + "index.faiss")
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if (
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file_url.scheme in ['https'] and
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file_url.netloc == 'raw.githubusercontent.com' and
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file_url.path.startswith('/arc53/DocsHUB/main/')
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file_url.scheme in ["https"]
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and file_url.netloc == "raw.githubusercontent.com"
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and file_url.path.startswith("/arc53/DocsHUB/main/")
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):
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r = requests.get(file_url.geturl())
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if r.status_code != 200:
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@@ -325,6 +346,7 @@ def check_docs():
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return {"status": "loaded"}
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@user.route("/api/create_prompt", methods=["POST"])
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def create_prompt():
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data = request.get_json()
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@@ -343,6 +365,7 @@ def create_prompt():
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new_id = str(resp.inserted_id)
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return {"id": new_id}
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@user.route("/api/get_prompts", methods=["GET"])
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def get_prompts():
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user = "local"
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@@ -352,30 +375,39 @@ def get_prompts():
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list_prompts.append({"id": "creative", "name": "creative", "type": "public"})
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list_prompts.append({"id": "strict", "name": "strict", "type": "public"})
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for prompt in prompts:
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list_prompts.append({"id": str(prompt["_id"]), "name": prompt["name"], "type": "private"})
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list_prompts.append(
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{"id": str(prompt["_id"]), "name": prompt["name"], "type": "private"}
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)
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return jsonify(list_prompts)
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@user.route("/api/get_single_prompt", methods=["GET"])
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def get_single_prompt():
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prompt_id = request.args.get("id")
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if prompt_id == 'default':
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with open(os.path.join(current_dir, "prompts", "chat_combine_default.txt"), "r") as f:
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if prompt_id == "default":
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with open(
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os.path.join(current_dir, "prompts", "chat_combine_default.txt"), "r"
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) as f:
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chat_combine_template = f.read()
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return jsonify({"content": chat_combine_template})
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elif prompt_id == 'creative':
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with open(os.path.join(current_dir, "prompts", "chat_combine_creative.txt"), "r") as f:
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elif prompt_id == "creative":
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with open(
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os.path.join(current_dir, "prompts", "chat_combine_creative.txt"), "r"
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) as f:
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chat_reduce_creative = f.read()
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return jsonify({"content": chat_reduce_creative})
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elif prompt_id == 'strict':
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with open(os.path.join(current_dir, "prompts", "chat_combine_strict.txt"), "r") as f:
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chat_reduce_strict = f.read()
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elif prompt_id == "strict":
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with open(
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os.path.join(current_dir, "prompts", "chat_combine_strict.txt"), "r"
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) as f:
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chat_reduce_strict = f.read()
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return jsonify({"content": chat_reduce_strict})
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prompt = prompts_collection.find_one({"_id": ObjectId(prompt_id)})
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return jsonify({"content": prompt["content"]})
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@user.route("/api/delete_prompt", methods=["POST"])
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def delete_prompt():
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data = request.get_json()
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@@ -387,6 +419,7 @@ def delete_prompt():
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)
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return {"status": "ok"}
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@user.route("/api/update_prompt", methods=["POST"])
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def update_prompt_name():
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data = request.get_json()
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@@ -396,27 +429,31 @@ def update_prompt_name():
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# check if name is null
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if name == "":
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return {"status": "error"}
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prompts_collection.update_one({"_id": ObjectId(id)},{"$set":{"name":name, "content": content}})
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prompts_collection.update_one(
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{"_id": ObjectId(id)}, {"$set": {"name": name, "content": content}}
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)
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return {"status": "ok"}
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@user.route("/api/get_api_keys", methods=["GET"])
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def get_api_keys():
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user = "local"
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keys = api_key_collection.find({"user": user})
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list_keys = []
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for key in keys:
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list_keys.append({
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"id": str(key["_id"]),
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"name": key["name"],
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"key": key["key"][:4] + "..." + key["key"][-4:],
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"source": key["source"],
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"prompt_id": key["prompt_id"],
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"chunks": key["chunks"]
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})
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list_keys.append(
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{
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"id": str(key["_id"]),
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"name": key["name"],
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"key": key["key"][:4] + "..." + key["key"][-4:],
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"source": key["source"],
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"prompt_id": key["prompt_id"],
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"chunks": key["chunks"],
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}
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)
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return jsonify(list_keys)
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@user.route("/api/create_api_key", methods=["POST"])
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def create_api_key():
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data = request.get_json()
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@@ -433,12 +470,13 @@ def create_api_key():
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"source": source,
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"user": user,
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"prompt_id": prompt_id,
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"chunks": chunks
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"chunks": chunks,
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}
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)
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new_id = str(resp.inserted_id)
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return {"id": new_id, "key": key}
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@user.route("/api/delete_api_key", methods=["POST"])
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def delete_api_key():
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data = request.get_json()
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@@ -449,4 +487,3 @@ def delete_api_key():
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}
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)
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return {"status": "ok"}
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@@ -15,7 +15,7 @@ def num_tokens_from_string(string: str, encoding_name: str) -> int:
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# Function to convert string to tokens and estimate user cost.
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encoding = tiktoken.get_encoding(encoding_name)
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num_tokens = len(encoding.encode(string))
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total_price = ((num_tokens / 1000) * 0.0004)
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total_price = (num_tokens / 1000) * 0.0004
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return num_tokens, total_price
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@@ -26,13 +26,13 @@ def store_add_texts_with_retry(store, i):
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|
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def call_openai_api(docs, folder_name, task_status):
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# Function to create a vector store from the documents and save it to disk.
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# Function to create a vector store from the documents and save it to disk
|
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# create output folder if it doesn't exist
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if not os.path.exists(f"{folder_name}"):
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os.makedirs(f"{folder_name}")
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from tqdm import tqdm
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c1 = 0
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if settings.VECTOR_STORE == "faiss":
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docs_init = [docs[0]]
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@@ -40,25 +40,32 @@ def call_openai_api(docs, folder_name, task_status):
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store = VectorCreator.create_vectorstore(
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settings.VECTOR_STORE,
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docs_init = docs_init,
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docs_init=docs_init,
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path=f"{folder_name}",
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embeddings_key=os.getenv("EMBEDDINGS_KEY")
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embeddings_key=os.getenv("EMBEDDINGS_KEY"),
|
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)
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else:
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store = VectorCreator.create_vectorstore(
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settings.VECTOR_STORE,
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path=f"{folder_name}",
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embeddings_key=os.getenv("EMBEDDINGS_KEY")
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embeddings_key=os.getenv("EMBEDDINGS_KEY"),
|
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)
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# Uncomment for MPNet embeddings
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# model_name = "sentence-transformers/all-mpnet-base-v2"
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# hf = HuggingFaceEmbeddings(model_name=model_name)
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# store = FAISS.from_documents(docs_test, hf)
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s1 = len(docs)
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for i in tqdm(docs, desc="Embedding 🦖", unit="docs", total=len(docs),
|
||||
bar_format='{l_bar}{bar}| Time Left: {remaining}'):
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for i in tqdm(
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||||
docs,
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||||
desc="Embedding 🦖",
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||||
unit="docs",
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||||
total=len(docs),
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||||
bar_format="{l_bar}{bar}| Time Left: {remaining}",
|
||||
):
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||||
try:
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||||
task_status.update_state(state='PROGRESS', meta={'current': int((c1 / s1) * 100)})
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||||
task_status.update_state(
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state="PROGRESS", meta={"current": int((c1 / s1) * 100)}
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||||
)
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store_add_texts_with_retry(store, i)
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except Exception as e:
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||||
print(e)
|
||||
@@ -80,7 +87,9 @@ def get_user_permission(docs, folder_name):
|
||||
for doc in docs:
|
||||
docs_content += doc.page_content
|
||||
|
||||
tokens, total_price = num_tokens_from_string(string=docs_content, encoding_name="cl100k_base")
|
||||
tokens, total_price = num_tokens_from_string(
|
||||
string=docs_content, encoding_name="cl100k_base"
|
||||
)
|
||||
# Here we print the number of tokens and the approx user cost with some visually appealing formatting.
|
||||
print(f"Number of Tokens = {format(tokens, ',d')}")
|
||||
print(f"Approx Cost = ${format(total_price, ',.2f')}")
|
||||
|
||||
@@ -1,22 +1,32 @@
|
||||
from application.parser.remote.base import BaseRemote
|
||||
from langchain_community.document_loaders import WebBaseLoader
|
||||
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0",
|
||||
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*"
|
||||
";q=0.8",
|
||||
"Accept-Language": "en-US,en;q=0.5",
|
||||
"Referer": "https://www.google.com/",
|
||||
"DNT": "1",
|
||||
"Connection": "keep-alive",
|
||||
"Upgrade-Insecure-Requests": "1",
|
||||
}
|
||||
|
||||
|
||||
class WebLoader(BaseRemote):
|
||||
def __init__(self):
|
||||
from langchain.document_loaders import WebBaseLoader
|
||||
self.loader = WebBaseLoader
|
||||
|
||||
def load_data(self, inputs):
|
||||
urls = inputs
|
||||
|
||||
if isinstance(urls, str):
|
||||
urls = [urls] # Convert string to list if a single URL is passed
|
||||
|
||||
urls = [urls]
|
||||
documents = []
|
||||
for url in urls:
|
||||
try:
|
||||
loader = self.loader([url]) # Process URLs one by one
|
||||
loader = self.loader([url], header_template=headers)
|
||||
documents.extend(loader.load())
|
||||
except Exception as e:
|
||||
print(f"Error processing URL {url}: {e}")
|
||||
continue # Continue with the next URL if an error occurs
|
||||
return documents
|
||||
continue
|
||||
return documents
|
||||
|
||||
@@ -36,6 +36,7 @@ current_dir = os.path.dirname(
|
||||
os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
)
|
||||
|
||||
|
||||
def extract_zip_recursive(zip_path, extract_to, current_depth=0, max_depth=5):
|
||||
"""
|
||||
Recursively extract zip files with a limit on recursion depth.
|
||||
@@ -50,7 +51,7 @@ def extract_zip_recursive(zip_path, extract_to, current_depth=0, max_depth=5):
|
||||
print(f"Reached maximum recursion depth of {max_depth}")
|
||||
return
|
||||
|
||||
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
|
||||
with zipfile.ZipFile(zip_path, "r") as zip_ref:
|
||||
zip_ref.extractall(extract_to)
|
||||
os.remove(zip_path) # Remove the zip file after extracting
|
||||
|
||||
@@ -96,7 +97,6 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
|
||||
full_path = os.path.join(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}
|
||||
@@ -114,7 +114,9 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
|
||||
|
||||
# check if file is .zip and extract it
|
||||
if filename.endswith(".zip"):
|
||||
extract_zip_recursive(os.path.join(full_path, filename), full_path, 0, recursion_depth)
|
||||
extract_zip_recursive(
|
||||
os.path.join(full_path, filename), full_path, 0, recursion_depth
|
||||
)
|
||||
|
||||
self.update_state(state="PROGRESS", meta={"current": 1})
|
||||
|
||||
@@ -176,7 +178,6 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
|
||||
|
||||
|
||||
def remote_worker(self, source_data, name_job, user, loader, directory="temp"):
|
||||
# sample = False
|
||||
token_check = True
|
||||
min_tokens = 150
|
||||
max_tokens = 1250
|
||||
@@ -184,12 +185,8 @@ def remote_worker(self, source_data, name_job, user, loader, directory="temp"):
|
||||
|
||||
if not os.path.exists(full_path):
|
||||
os.makedirs(full_path)
|
||||
|
||||
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)
|
||||
|
||||
@@ -201,7 +198,6 @@ def remote_worker(self, source_data, name_job, user, loader, directory="temp"):
|
||||
)
|
||||
|
||||
# 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})
|
||||
|
||||
|
||||
Reference in New Issue
Block a user