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103
app.py
103
app.py
@@ -1,37 +1,102 @@
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import os
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from flask import Flask, request, jsonify, render_template, send_from_directory
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from quart import Quart, request, jsonify, render_template, send_from_directory
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from tortoise.contrib.quart import register_tortoise
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from quart_jwt_extended import JWTManager
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from main import consult_simba_oracle
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from blueprints.conversation.logic import (
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get_the_only_conversation,
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add_message_to_conversation,
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)
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app = Flask(__name__, static_folder="raggr-frontend/dist/static", template_folder="raggr-frontend/dist")
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app = Quart(
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__name__,
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static_folder="raggr-frontend/dist/static",
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template_folder="raggr-frontend/dist",
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)
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app.config["JWT_SECRET_KEY"] = os.getenv("JWT_SECRET_KEY", "SECRET_KEY")
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jwt = JWTManager(app)
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# Initialize Tortoise ORM
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register_tortoise(
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app,
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db_url=os.getenv("DATABASE_URL", "sqlite://raggr.db"),
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modules={"models": ["blueprints.conversation.models"]},
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generate_schemas=True,
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)
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# Serve React static files
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@app.route('/static/<path:filename>')
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def static_files(filename):
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return send_from_directory(app.static_folder, filename)
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@app.route("/static/<path:filename>")
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async def static_files(filename):
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return await send_from_directory(app.static_folder, filename)
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# Serve the React app for all routes (catch-all)
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@app.route('/', defaults={'path': ''})
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@app.route('/<path:path>')
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def serve_react_app(path):
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@app.route("/", defaults={"path": ""})
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@app.route("/<path:path>")
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async def serve_react_app(path):
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if path and os.path.exists(os.path.join(app.template_folder, path)):
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return send_from_directory(app.template_folder, path)
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return render_template('index.html')
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return await send_from_directory(app.template_folder, path)
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return await render_template("index.html")
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@app.route("/api/query", methods=["POST"])
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def query():
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data = request.get_json()
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async def query():
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data = await request.get_json()
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query = data.get("query")
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return jsonify({"response": consult_simba_oracle(query)})
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# add message to database
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conversation = await get_the_only_conversation()
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print(conversation)
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await add_message_to_conversation(
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conversation=conversation, message=query, speaker="user"
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)
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response = consult_simba_oracle(query)
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await add_message_to_conversation(
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conversation=conversation, message=response, speaker="simba"
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)
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return jsonify({"response": response})
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@app.route("/api/messages", methods=["GET"])
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async def get_messages():
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conversation = await get_the_only_conversation()
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# Prefetch related messages
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await conversation.fetch_related("messages")
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# Manually serialize the conversation with messages
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messages = []
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for msg in conversation.messages:
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messages.append(
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{
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"id": str(msg.id),
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"text": msg.text,
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"speaker": msg.speaker.value,
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"created_at": msg.created_at.isoformat(),
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}
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)
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return jsonify(
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{
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"id": str(conversation.id),
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"name": conversation.name,
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"messages": messages,
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"created_at": conversation.created_at.isoformat(),
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"updated_at": conversation.updated_at.isoformat(),
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}
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)
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# @app.route("/api/ingest", methods=["POST"])
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# def webhook():
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# data = request.get_json()
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# print(data)
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# return jsonify({"status": "received"})
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@app.route("/api/ingest", methods=["POST"])
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def webhook():
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data = request.get_json()
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print(data)
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return jsonify({"status": "received"})
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if __name__ == "__main__":
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app.run(host="0.0.0.0", port=8080, debug=True)
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17
blueprints/conversation/__init__.py
Normal file
17
blueprints/conversation/__init__.py
Normal file
@@ -0,0 +1,17 @@
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from quart import Blueprint, jsonify
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from .models import (
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Conversation,
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PydConversation,
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)
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conversation_blueprint = Blueprint(
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"conversation_api", __name__, url_prefix="/api/conversation"
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)
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@conversation_blueprint.route("/<conversation_id>")
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async def get_conversation(conversation_id: str):
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conversation = await Conversation.get(id=conversation_id)
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serialized_conversation = await PydConversation.from_tortoise_orm(conversation)
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return jsonify(serialized_conversation.model_dump_json())
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32
blueprints/conversation/logic.py
Normal file
32
blueprints/conversation/logic.py
Normal file
@@ -0,0 +1,32 @@
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from .models import Conversation, ConversationMessage
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async def create_conversation(name: str = "") -> Conversation:
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conversation = await Conversation.create(name=name)
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return conversation
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async def add_message_to_conversation(
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conversation: Conversation,
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message: str,
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speaker: str,
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) -> ConversationMessage:
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print(conversation, message, speaker)
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message = await ConversationMessage.create(
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text=message,
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speaker=speaker,
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conversation=conversation,
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)
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return message
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async def get_the_only_conversation() -> Conversation:
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try:
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conversation = await Conversation.all().first()
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if conversation is None:
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conversation = await Conversation.create(name="simba_chat")
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except Exception as _e:
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conversation = await Conversation.create(name="simba_chat")
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return conversation
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41
blueprints/conversation/models.py
Normal file
41
blueprints/conversation/models.py
Normal file
@@ -0,0 +1,41 @@
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import enum
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from tortoise.models import Model
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from tortoise import fields
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from tortoise.contrib.pydantic import (
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pydantic_queryset_creator,
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pydantic_model_creator,
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)
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class Speaker(enum.Enum):
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USER = "user"
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SIMBA = "simba"
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class Conversation(Model):
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id = fields.UUIDField(primary_key=True)
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name = fields.CharField(max_length=255)
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created_at = fields.DatetimeField(auto_now_add=True)
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updated_at = fields.DatetimeField(auto_now=True)
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class Meta:
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table = "conversations"
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class ConversationMessage(Model):
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id = fields.UUIDField(primary_key=True)
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text = fields.TextField()
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conversation = fields.ForeignKeyField(
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"models.Conversation", related_name="messages"
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)
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created_at = fields.DatetimeField(auto_now_add=True)
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speaker = fields.CharEnumField(enum_type=Speaker, max_length=10)
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class Meta:
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table = "conversation_messages"
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PydConversationMessage = pydantic_model_creator(ConversationMessage)
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PydConversation = pydantic_model_creator(Conversation, name="Conversation")
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PydListConversationMessage = pydantic_queryset_creator(ConversationMessage)
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20
chunker.py
20
chunker.py
@@ -3,15 +3,20 @@ from math import ceil
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import re
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from typing import Union
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from uuid import UUID, uuid4
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from ollama import Client
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from chromadb.utils.embedding_functions.openai_embedding_function import (
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OpenAIEmbeddingFunction,
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)
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from dotenv import load_dotenv
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from llm import LLMClient
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load_dotenv()
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ollama_client = Client(
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host=os.getenv("OLLAMA_HOST", "http://localhost:11434"), timeout=10.0
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)
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def remove_headers_footers(text, header_patterns=None, footer_patterns=None):
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if header_patterns is None:
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@@ -80,13 +85,16 @@ class Chunk:
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class Chunker:
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embedding_fx = OpenAIEmbeddingFunction(
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api_key=os.getenv("OPENAI_API_KEY"),
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model_name="text-embedding-3-small",
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)
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def __init__(self, collection) -> None:
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self.collection = collection
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self.llm_client = LLMClient()
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def embedding_fx(self, inputs):
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openai_embedding_fx = OpenAIEmbeddingFunction(
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api_key=os.getenv("OPENAI_API_KEY"),
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model_name="text-embedding-3-small",
|
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)
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return openai_embedding_fx(inputs)
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def chunk_document(
|
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self,
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@@ -1,4 +1,4 @@
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version: '3.8'
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version: "3.8"
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services:
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raggr:
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@@ -14,4 +14,4 @@ services:
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- chromadb_data:/app/chromadb
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volumes:
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chromadb_data:
|
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chromadb_data:
|
||||
|
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83
image_process.py
Normal file
83
image_process.py
Normal file
@@ -0,0 +1,83 @@
|
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from ollama import Client
|
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import argparse
|
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import os
|
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import logging
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from PIL import Image, ExifTags
|
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from pillow_heif import register_heif_opener
|
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from pydantic import BaseModel
|
||||
|
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from dotenv import load_dotenv
|
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|
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load_dotenv()
|
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|
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register_heif_opener()
|
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|
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logging.basicConfig(level=logging.INFO)
|
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|
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|
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parser = argparse.ArgumentParser(
|
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prog="SimbaImageProcessor",
|
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description="What the program does",
|
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epilog="Text at the bottom of help",
|
||||
)
|
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|
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parser.add_argument("filepath")
|
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|
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client = Client(host=os.getenv("OLLAMA_HOST", "http://localhost:11434"))
|
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|
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|
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class SimbaImageDescription(BaseModel):
|
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image_date: str
|
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description: str
|
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|
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|
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def describe_simba_image(input):
|
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logging.info("Opening image of Simba ...")
|
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if "heic" in input.lower() or "heif" in input.lower():
|
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new_filepath = input.split(".")[0] + ".jpg"
|
||||
img = Image.open(input)
|
||||
img.save(new_filepath, "JPEG")
|
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logging.info("Extracting EXIF...")
|
||||
exif = {
|
||||
ExifTags.TAGS[k]: v for k, v in img.getexif().items() if k in ExifTags.TAGS
|
||||
}
|
||||
img = Image.open(new_filepath)
|
||||
input = new_filepath
|
||||
else:
|
||||
img = Image.open(input)
|
||||
|
||||
logging.info("Extracting EXIF...")
|
||||
exif = {
|
||||
ExifTags.TAGS[k]: v for k, v in img.getexif().items() if k in ExifTags.TAGS
|
||||
}
|
||||
|
||||
if "MakerNote" in exif:
|
||||
exif.pop("MakerNote")
|
||||
|
||||
logging.info(exif)
|
||||
|
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prompt = f"Simba is an orange cat belonging to Ryan Chen. In 2025, they lived in New York. In 2024, they lived in California. Analyze the following image and tell me what Simba seems to be doing. Be extremely descriptive about Simba, things in the background, and the setting of the image. I will also include the EXIF data of the image, please use it to help you determine information about Simba. EXIF: {exif}. Put the notes in the description field and the date in the image_date field."
|
||||
|
||||
logging.info("Sending info to Ollama ...")
|
||||
response = client.chat(
|
||||
model="gemma3:4b",
|
||||
messages=[
|
||||
{
|
||||
"role": "system",
|
||||
"content": "you are a very shrewd and descriptive note taker. all of your responses will be formatted like notes in bullet points. be very descriptive. do not leave a single thing out.",
|
||||
},
|
||||
{"role": "user", "content": prompt, "images": [input]},
|
||||
],
|
||||
format=SimbaImageDescription.model_json_schema(),
|
||||
)
|
||||
|
||||
result = SimbaImageDescription.model_validate_json(response["message"]["content"])
|
||||
|
||||
return result
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
args = parser.parse_args()
|
||||
if args.filepath:
|
||||
logging.info
|
||||
describe_simba_image(input=args.filepath)
|
||||
115
index_immich.py
Normal file
115
index_immich.py
Normal file
@@ -0,0 +1,115 @@
|
||||
import httpx
|
||||
import os
|
||||
from pathlib import Path
|
||||
import logging
|
||||
import tempfile
|
||||
|
||||
from image_process import describe_simba_image
|
||||
from request import PaperlessNGXService
|
||||
import sqlite3
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
|
||||
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
# Configuration from environment variables
|
||||
IMMICH_URL = os.getenv("IMMICH_URL", "http://localhost:2283")
|
||||
API_KEY = os.getenv("IMMICH_API_KEY")
|
||||
PERSON_NAME = os.getenv("PERSON_NAME", "Simba") # Name of the tagged person/pet
|
||||
DOWNLOAD_DIR = os.getenv("DOWNLOAD_DIR", "./simba_photos")
|
||||
|
||||
# Set up headers
|
||||
headers = {"x-api-key": API_KEY, "Content-Type": "application/json"}
|
||||
|
||||
VISITED = {}
|
||||
|
||||
if __name__ == "__main__":
|
||||
conn = sqlite3.connect("./visited.db")
|
||||
c = conn.cursor()
|
||||
c.execute("select immich_id from visited")
|
||||
rows = c.fetchall()
|
||||
for row in rows:
|
||||
VISITED.add(row[0])
|
||||
|
||||
ppngx = PaperlessNGXService()
|
||||
people_url = f"{IMMICH_URL}/api/search/person?name=Simba"
|
||||
people = httpx.get(people_url, headers=headers).json()
|
||||
|
||||
simba_id = people[0]["id"]
|
||||
|
||||
ids = {}
|
||||
|
||||
asset_search = f"{IMMICH_URL}/api/search/smart"
|
||||
request_body = {"query": "orange cat"}
|
||||
results = httpx.post(asset_search, headers=headers, json=request_body)
|
||||
|
||||
assets = results.json()["assets"]
|
||||
for asset in assets["items"]:
|
||||
if asset["type"] == "IMAGE" and asset["id"] not in VISITED:
|
||||
ids[asset["id"]] = asset.get("originalFileName")
|
||||
nextPage = assets.get("nextPage")
|
||||
|
||||
# while nextPage != None:
|
||||
# logging.info(f"next page: {nextPage}")
|
||||
# request_body["page"] = nextPage
|
||||
# results = httpx.post(asset_search, headers=headers, json=request_body)
|
||||
# assets = results.json()["assets"]
|
||||
|
||||
# for asset in assets["items"]:
|
||||
# if asset["type"] == "IMAGE":
|
||||
# ids.add(asset['id'])
|
||||
|
||||
# nextPage = assets.get("nextPage")
|
||||
|
||||
asset_search = f"{IMMICH_URL}/api/search/smart"
|
||||
request_body = {"query": "simba"}
|
||||
results = httpx.post(asset_search, headers=headers, json=request_body)
|
||||
for asset in results.json()["assets"]["items"]:
|
||||
if asset["type"] == "IMAGE":
|
||||
ids[asset["id"]] = asset.get("originalFileName")
|
||||
|
||||
for immich_asset_id, immich_filename in ids.items():
|
||||
try:
|
||||
response = httpx.get(
|
||||
f"{IMMICH_URL}/api/assets/{immich_asset_id}/original", headers=headers
|
||||
)
|
||||
|
||||
path = os.path.join("/Users/ryanchen/Programs/raggr", immich_filename)
|
||||
file = open(path, "wb+")
|
||||
for chunk in response.iter_bytes(chunk_size=8192):
|
||||
file.write(chunk)
|
||||
|
||||
logging.info("Processing image ...")
|
||||
description = describe_simba_image(path)
|
||||
|
||||
image_description = description.description
|
||||
image_date = description.image_date
|
||||
|
||||
description_filepath = os.path.join(
|
||||
"/Users/ryanchen/Programs/raggr", f"SIMBA_DESCRIBE_001.txt"
|
||||
)
|
||||
file = open(description_filepath, "w+")
|
||||
file.write(image_description)
|
||||
file.close()
|
||||
|
||||
file = open(description_filepath, "rb")
|
||||
ppngx.upload_description(
|
||||
description_filepath=description_filepath,
|
||||
file=file,
|
||||
title="SIMBA_DESCRIBE_001.txt",
|
||||
exif_date=image_date,
|
||||
)
|
||||
file.close()
|
||||
|
||||
c.execute("INSERT INTO visited (immich_id) values (?)", (immich_asset_id,))
|
||||
conn.commit()
|
||||
logging.info("Processing complete. Deleting file.")
|
||||
os.remove(file.name)
|
||||
except Exception as e:
|
||||
logging.info(f"something went wrong for {immich_filename}")
|
||||
logging.info(e)
|
||||
|
||||
conn.close()
|
||||
64
llm.py
Normal file
64
llm.py
Normal file
@@ -0,0 +1,64 @@
|
||||
import os
|
||||
|
||||
from ollama import Client
|
||||
from openai import OpenAI
|
||||
|
||||
import logging
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
|
||||
|
||||
class LLMClient:
|
||||
def __init__(self):
|
||||
try:
|
||||
self.ollama_client = Client(
|
||||
host=os.getenv("OLLAMA_URL", "http://localhost:11434"), timeout=10.0
|
||||
)
|
||||
self.ollama_client.chat(
|
||||
model="gemma3:4b", messages=[{"role": "system", "content": "test"}]
|
||||
)
|
||||
self.PROVIDER = "ollama"
|
||||
logging.info("Using Ollama as LLM backend")
|
||||
except Exception as e:
|
||||
print(e)
|
||||
self.openai_client = OpenAI()
|
||||
self.PROVIDER = "openai"
|
||||
logging.info("Using OpenAI as LLM backend")
|
||||
|
||||
def chat(
|
||||
self,
|
||||
prompt: str,
|
||||
system_prompt: str,
|
||||
):
|
||||
if self.PROVIDER == "ollama":
|
||||
response = self.ollama_client.chat(
|
||||
model="gemma3:4b",
|
||||
messages=[
|
||||
{
|
||||
"role": "system",
|
||||
"content": system_prompt,
|
||||
},
|
||||
{"role": "user", "content": prompt},
|
||||
],
|
||||
)
|
||||
print(response)
|
||||
output = response.message.content
|
||||
elif self.PROVIDER == "openai":
|
||||
response = self.openai_client.responses.create(
|
||||
model="gpt-4o-mini",
|
||||
input=[
|
||||
{
|
||||
"role": "system",
|
||||
"content": system_prompt,
|
||||
},
|
||||
{"role": "user", "content": prompt},
|
||||
],
|
||||
)
|
||||
output = response.output_text
|
||||
|
||||
return output
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
client = Client()
|
||||
client.chat(model="gemma3:4b", messages=[{"role": "system", "promp": "hack"}])
|
||||
201
main.py
201
main.py
@@ -1,28 +1,31 @@
|
||||
import datetime
|
||||
import logging
|
||||
import os
|
||||
from typing import Any, Union
|
||||
import sqlite3
|
||||
|
||||
import argparse
|
||||
import chromadb
|
||||
import ollama
|
||||
from openai import OpenAI
|
||||
|
||||
|
||||
from request import PaperlessNGXService
|
||||
from chunker import Chunker
|
||||
from query import QueryGenerator
|
||||
from cleaner import pdf_to_image, summarize_pdf_image
|
||||
from llm import LLMClient
|
||||
from query import QueryGenerator
|
||||
|
||||
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
_dotenv_loaded = load_dotenv()
|
||||
|
||||
# Configure ollama client with URL from environment or default to localhost
|
||||
ollama_client = ollama.Client(host=os.getenv("OLLAMA_URL", "http://localhost:11434"))
|
||||
ollama_client = ollama.Client(
|
||||
host=os.getenv("OLLAMA_URL", "http://localhost:11434"), timeout=10.0
|
||||
)
|
||||
|
||||
client = chromadb.PersistentClient(path=os.getenv("CHROMADB_PATH", ""))
|
||||
simba_docs = client.get_or_create_collection(name="simba_docs")
|
||||
simba_docs = client.get_or_create_collection(name="simba_docs2")
|
||||
feline_vet_lookup = client.get_or_create_collection(name="feline_vet_lookup")
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
@@ -33,30 +36,29 @@ parser.add_argument("query", type=str, help="questions about simba's health")
|
||||
parser.add_argument(
|
||||
"--reindex", action="store_true", help="re-index the simba documents"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--index", help="index a file"
|
||||
)
|
||||
parser.add_argument("--index", help="index a file")
|
||||
|
||||
ppngx = PaperlessNGXService()
|
||||
|
||||
openai_client = OpenAI()
|
||||
llm_client = LLMClient()
|
||||
|
||||
def index_using_pdf_llm():
|
||||
|
||||
def index_using_pdf_llm(doctypes):
|
||||
logging.info("reindex data...")
|
||||
files = ppngx.get_data()
|
||||
for file in files:
|
||||
document_id = file["id"]
|
||||
document_id: int = file["id"]
|
||||
pdf_path = ppngx.download_pdf_from_id(id=document_id)
|
||||
image_paths = pdf_to_image(filepath=pdf_path)
|
||||
print(f"summarizing {file}")
|
||||
logging.info(f"summarizing {file}")
|
||||
generated_summary = summarize_pdf_image(filepaths=image_paths)
|
||||
file["content"] = generated_summary
|
||||
|
||||
chunk_data(files, simba_docs)
|
||||
chunk_data(files, simba_docs, doctypes=doctypes)
|
||||
|
||||
|
||||
def date_to_epoch(date_str: str) -> float:
|
||||
split_date = date_str.split("-")
|
||||
print(split_date)
|
||||
date = datetime.datetime(
|
||||
int(split_date[0]),
|
||||
int(split_date[1]),
|
||||
@@ -69,23 +71,36 @@ def date_to_epoch(date_str: str) -> float:
|
||||
return date.timestamp()
|
||||
|
||||
|
||||
def chunk_data(docs: list[dict[str, Union[str, Any]]], collection):
|
||||
def chunk_data(docs, collection, doctypes):
|
||||
# Step 2: Create chunks
|
||||
chunker = Chunker(collection)
|
||||
|
||||
print(f"chunking {len(docs)} documents")
|
||||
print(docs)
|
||||
logging.info(f"chunking {len(docs)} documents")
|
||||
texts: list[str] = [doc["content"] for doc in docs]
|
||||
for index, text in enumerate(texts):
|
||||
print(docs[index]["original_file_name"])
|
||||
metadata = {
|
||||
"created_date": date_to_epoch(docs[index]["created_date"]),
|
||||
"filename": docs[index]["original_file_name"]
|
||||
}
|
||||
chunker.chunk_document(
|
||||
document=text,
|
||||
metadata=metadata,
|
||||
with sqlite3.connect("visited.db") as conn:
|
||||
to_insert = []
|
||||
c = conn.cursor()
|
||||
for index, text in enumerate(texts):
|
||||
metadata = {
|
||||
"created_date": date_to_epoch(docs[index]["created_date"]),
|
||||
"filename": docs[index]["original_file_name"],
|
||||
"document_type": doctypes.get(docs[index]["document_type"], ""),
|
||||
}
|
||||
|
||||
if doctypes:
|
||||
metadata["type"] = doctypes.get(docs[index]["document_type"])
|
||||
|
||||
chunker.chunk_document(
|
||||
document=text,
|
||||
metadata=metadata,
|
||||
)
|
||||
to_insert.append((docs[index]["id"],))
|
||||
|
||||
c.executemany(
|
||||
"INSERT INTO indexed_documents (paperless_id) values (?)", to_insert
|
||||
)
|
||||
conn.commit()
|
||||
|
||||
|
||||
def chunk_text(texts: list[str], collection):
|
||||
chunker = Chunker(collection)
|
||||
@@ -97,58 +112,56 @@ def chunk_text(texts: list[str], collection):
|
||||
metadata=metadata,
|
||||
)
|
||||
|
||||
|
||||
def consult_oracle(input: str, collection):
|
||||
print(input)
|
||||
import time
|
||||
|
||||
chunker = Chunker(collection)
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
# Ask
|
||||
# print("Starting query generation")
|
||||
# qg_start = time.time()
|
||||
# qg = QueryGenerator()
|
||||
logging.info("Starting query generation")
|
||||
qg_start = time.time()
|
||||
qg = QueryGenerator()
|
||||
doctype_query = qg.get_doctype_query(input=input)
|
||||
# metadata_filter = qg.get_query(input)
|
||||
# qg_end = time.time()
|
||||
# print(f"Query generation took {qg_end - qg_start:.2f} seconds")
|
||||
# print(metadata_filter)
|
||||
metadata_filter = {**doctype_query}
|
||||
logging.info(metadata_filter)
|
||||
qg_end = time.time()
|
||||
logging.info(f"Query generation took {qg_end - qg_start:.2f} seconds")
|
||||
|
||||
print("Starting embedding generation")
|
||||
logging.info("Starting embedding generation")
|
||||
embedding_start = time.time()
|
||||
embeddings = Chunker.embedding_fx(input=[input])
|
||||
embeddings = chunker.embedding_fx(inputs=[input])
|
||||
embedding_end = time.time()
|
||||
print(f"Embedding generation took {embedding_end - embedding_start:.2f} seconds")
|
||||
logging.info(
|
||||
f"Embedding generation took {embedding_end - embedding_start:.2f} seconds"
|
||||
)
|
||||
|
||||
print("Starting collection query")
|
||||
logging.info("Starting collection query")
|
||||
query_start = time.time()
|
||||
results = collection.query(
|
||||
query_texts=[input],
|
||||
query_embeddings=embeddings,
|
||||
#where=metadata_filter,
|
||||
where=metadata_filter,
|
||||
)
|
||||
print(results)
|
||||
query_end = time.time()
|
||||
print(f"Collection query took {query_end - query_start:.2f} seconds")
|
||||
logging.info(f"Collection query took {query_end - query_start:.2f} seconds")
|
||||
|
||||
# Generate
|
||||
print("Starting LLM generation")
|
||||
logging.info("Starting LLM generation")
|
||||
llm_start = time.time()
|
||||
# output = ollama_client.generate(
|
||||
# model="gemma3n:e4b",
|
||||
# prompt=f"You are a helpful assistant that understandings veterinary terms. Using the following data, help answer the user's query by providing as many details as possible. Using this data: {results}. Respond to this prompt: {input}",
|
||||
# )
|
||||
response = openai_client.chat.completions.create(
|
||||
model="gpt-4o-mini",
|
||||
messages=[
|
||||
{"role": "system", "content": "You are a helpful assistant that understands veterinary terms."},
|
||||
{"role": "user", "content": f"Using the following data, help answer the user's query by providing as many details as possible. Using this data: {results}. Respond to this prompt: {input}"}
|
||||
]
|
||||
)
|
||||
system_prompt = "You are a helpful assistant that understands veterinary terms."
|
||||
prompt = f"Using the following data, help answer the user's query by providing as many details as possible. Using this data: {results}. Respond to this prompt: {input}"
|
||||
output = llm_client.chat(prompt=prompt, system_prompt=system_prompt)
|
||||
llm_end = time.time()
|
||||
print(f"LLM generation took {llm_end - llm_start:.2f} seconds")
|
||||
logging.info(f"LLM generation took {llm_end - llm_start:.2f} seconds")
|
||||
|
||||
total_time = time.time() - start_time
|
||||
print(f"Total consult_oracle execution took {total_time:.2f} seconds")
|
||||
logging.info(f"Total consult_oracle execution took {total_time:.2f} seconds")
|
||||
|
||||
return response.choices[0].message.content
|
||||
return output
|
||||
|
||||
|
||||
def paperless_workflow(input):
|
||||
@@ -167,40 +180,64 @@ def consult_simba_oracle(input: str):
|
||||
)
|
||||
|
||||
|
||||
def filter_indexed_files(docs):
|
||||
with sqlite3.connect("visited.db") as conn:
|
||||
c = conn.cursor()
|
||||
c.execute(
|
||||
"CREATE TABLE IF NOT EXISTS indexed_documents (id INTEGER PRIMARY KEY AUTOINCREMENT, paperless_id INTEGER)"
|
||||
)
|
||||
c.execute("SELECT paperless_id FROM indexed_documents")
|
||||
rows = c.fetchall()
|
||||
conn.commit()
|
||||
|
||||
visited = {row[0] for row in rows}
|
||||
return [doc for doc in docs if doc["id"] not in visited]
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
args = parser.parse_args()
|
||||
if args.reindex:
|
||||
print("Fetching documents from Paperless-NGX")
|
||||
with sqlite3.connect("./visited.db") as conn:
|
||||
c = conn.cursor()
|
||||
c.execute("DELETE FROM indexed_documents")
|
||||
|
||||
logging.info("Fetching documents from Paperless-NGX")
|
||||
ppngx = PaperlessNGXService()
|
||||
docs = ppngx.get_data()
|
||||
print(docs)
|
||||
print(f"Fetched {len(docs)} documents")
|
||||
#
|
||||
print("Chunking documents now ...")
|
||||
chunk_data(docs, collection=simba_docs)
|
||||
print("Done chunking documents")
|
||||
# index_using_pdf_llm()
|
||||
docs = filter_indexed_files(docs)
|
||||
logging.info(f"Fetched {len(docs)} documents")
|
||||
|
||||
# Delete all chromadb data
|
||||
ids = simba_docs.get(ids=None, limit=None, offset=0)
|
||||
all_ids = ids["ids"]
|
||||
if len(all_ids) > 0:
|
||||
simba_docs.delete(ids=all_ids)
|
||||
|
||||
if args.index:
|
||||
with open(args.index) as file:
|
||||
extension = args.index.split(".")[-1]
|
||||
# Chunk documents
|
||||
logging.info("Chunking documents now ...")
|
||||
tag_lookup = ppngx.get_tags()
|
||||
doctype_lookup = ppngx.get_doctypes()
|
||||
chunk_data(docs, collection=simba_docs, doctypes=doctype_lookup)
|
||||
logging.info("Done chunking documents")
|
||||
|
||||
if extension == "pdf":
|
||||
pdf_path = ppngx.download_pdf_from_id(id=document_id)
|
||||
image_paths = pdf_to_image(filepath=pdf_path)
|
||||
print(f"summarizing {file}")
|
||||
generated_summary = summarize_pdf_image(filepaths=image_paths)
|
||||
elif extension in [".md", ".txt"]:
|
||||
chunk_text(texts=[file.readall()], collection=simba_docs)
|
||||
# if args.index:
|
||||
# with open(args.index) as file:
|
||||
# extension = args.index.split(".")[-1]
|
||||
# if extension == "pdf":
|
||||
# pdf_path = ppngx.download_pdf_from_id(id=document_id)
|
||||
# image_paths = pdf_to_image(filepath=pdf_path)
|
||||
# print(f"summarizing {file}")
|
||||
# generated_summary = summarize_pdf_image(filepaths=image_paths)
|
||||
# elif extension in [".md", ".txt"]:
|
||||
# chunk_text(texts=[file.readall()], collection=simba_docs)
|
||||
|
||||
if args.query:
|
||||
print("Consulting oracle ...")
|
||||
print(consult_oracle(
|
||||
input=args.query,
|
||||
collection=simba_docs,
|
||||
))
|
||||
logging.info("Consulting oracle ...")
|
||||
print(
|
||||
consult_oracle(
|
||||
input=args.query,
|
||||
collection=simba_docs,
|
||||
)
|
||||
)
|
||||
else:
|
||||
print("please provide a query")
|
||||
|
||||
|
||||
logging.info("please provide a query")
|
||||
|
||||
@@ -14,4 +14,14 @@ dependencies = [
|
||||
"pydantic>=2.11.9",
|
||||
"pillow>=10.0.0",
|
||||
"pymupdf>=1.24.0",
|
||||
"black>=25.9.0",
|
||||
"pillow-heif>=1.1.1",
|
||||
"flask-jwt-extended>=4.7.1",
|
||||
"bcrypt>=5.0.0",
|
||||
"pony>=0.7.19",
|
||||
"flask-login>=0.6.3",
|
||||
"quart>=0.20.0",
|
||||
"tortoise-orm>=0.25.1",
|
||||
"quart-jwt-extended>=0.1.0",
|
||||
"pre-commit>=4.3.0",
|
||||
]
|
||||
|
||||
99
query.py
99
query.py
@@ -2,14 +2,16 @@ import json
|
||||
import os
|
||||
from typing import Literal
|
||||
import datetime
|
||||
from ollama import chat, ChatResponse, Client
|
||||
from ollama import Client
|
||||
|
||||
from openai import OpenAI
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
# Configure ollama client with URL from environment or default to localhost
|
||||
ollama_client = Client(host=os.getenv("OLLAMA_URL", "http://localhost:11434"))
|
||||
ollama_client = Client(
|
||||
host=os.getenv("OLLAMA_URL", "http://localhost:11434"), timeout=10.0
|
||||
)
|
||||
|
||||
# This uses inferred filters — which means using LLM to create the metadata filters
|
||||
|
||||
@@ -33,17 +35,32 @@ class GeneratedQuery(BaseModel):
|
||||
fields: list[str]
|
||||
extracted_metadata_fields: str
|
||||
|
||||
|
||||
class Time(BaseModel):
|
||||
time: int
|
||||
|
||||
|
||||
DOCTYPE_OPTIONS = [
|
||||
"Bill",
|
||||
"Image Description",
|
||||
"Insurance",
|
||||
"Medical Record",
|
||||
"Documentation",
|
||||
"Letter",
|
||||
]
|
||||
|
||||
|
||||
class DocumentType(BaseModel):
|
||||
type: list[str] = Field(description="type of document", enum=DOCTYPE_OPTIONS)
|
||||
|
||||
|
||||
PROMPT = """
|
||||
You are an information specialist that processes user queries. The current year is 2025. The user queries are all about
|
||||
a cat, Simba, and its records. The types of records are listed below. Using the query, extract the
|
||||
You are an information specialist that processes user queries. The current year is 2025. The user queries are all about
|
||||
a cat, Simba, and its records. The types of records are listed below. Using the query, extract the
|
||||
the date range the user is trying to query. You should return it as a JSON. The date tag is created_date. Return the date in epoch time.
|
||||
|
||||
If the created_date cannot be ascertained, set it to epoch time start.
|
||||
|
||||
|
||||
You have several operators at your disposal:
|
||||
- $gt: greater than
|
||||
- $gte: greater than or equal
|
||||
@@ -82,6 +99,19 @@ Only return the extracted metadata fields. Make sure the extracted metadata fiel
|
||||
"""
|
||||
|
||||
|
||||
DOCTYPE_PROMPT = f"""You are an information specialist that processes user queries. A query can have two tags attached from the following options. Based on the query, determine which of the following options is most appropriate: {",".join(DOCTYPE_OPTIONS)}
|
||||
|
||||
### Example 1
|
||||
Query: "Who is Simba's current vet?"
|
||||
Tags: ["Bill", "Medical Record"]
|
||||
|
||||
|
||||
### Example 2
|
||||
Query: "Who does Simba know?"
|
||||
Tags: ["Letter", "Documentation"]
|
||||
"""
|
||||
|
||||
|
||||
class QueryGenerator:
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
@@ -99,43 +129,66 @@ class QueryGenerator:
|
||||
|
||||
return date.timestamp()
|
||||
|
||||
def get_doctype_query(self, input: str):
|
||||
client = OpenAI()
|
||||
response = client.chat.completions.create(
|
||||
messages=[
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are an information specialist that is really good at deciding what tags a query should have",
|
||||
},
|
||||
{"role": "user", "content": DOCTYPE_PROMPT + " " + input},
|
||||
],
|
||||
model="gpt-4o",
|
||||
response_format={
|
||||
"type": "json_schema",
|
||||
"json_schema": {
|
||||
"name": "document_type",
|
||||
"schema": DocumentType.model_json_schema(),
|
||||
},
|
||||
},
|
||||
)
|
||||
|
||||
response_json_str = response.choices[0].message.content
|
||||
type_data = json.loads(response_json_str)
|
||||
metadata_query = {"document_type": {"$in": type_data["type"]}}
|
||||
return metadata_query
|
||||
|
||||
def get_query(self, input: str):
|
||||
client = OpenAI()
|
||||
print(input)
|
||||
response = client.responses.parse(
|
||||
model="gpt-4o",
|
||||
input=[
|
||||
{"role": "system", "content": PROMPT},
|
||||
{"role": "user", "content": input},
|
||||
],
|
||||
text_format=Time,
|
||||
text_format=GeneratedQuery,
|
||||
)
|
||||
print(response)
|
||||
print(response.output)
|
||||
query = json.loads(response.output_parsed.extracted_metadata_fields)
|
||||
|
||||
# response: ChatResponse = ollama_client.chat(
|
||||
# model="gemma3n:e4b",
|
||||
# messages=[
|
||||
# {"role": "system", "content": PROMPT},
|
||||
# {"role": "user", "content": input},
|
||||
# ],
|
||||
# format=GeneratedQuery.model_json_schema(),
|
||||
# model="gemma3n:e4b",
|
||||
# messages=[
|
||||
# {"role": "system", "content": PROMPT},
|
||||
# {"role": "user", "content": input},
|
||||
# ],
|
||||
# format=GeneratedQuery.model_json_schema(),
|
||||
# )
|
||||
|
||||
# query = json.loads(
|
||||
# json.loads(response["message"]["content"])["extracted_metadata_fields"]
|
||||
# json.loads(response["message"]["content"])["extracted_metadata_fields"]
|
||||
# )
|
||||
# date_key = list(query["created_date"].keys())[0]
|
||||
# query["created_date"][date_key] = self.date_to_epoch(
|
||||
# query["created_date"][date_key]
|
||||
# )
|
||||
date_key = list(query["created_date"].keys())[0]
|
||||
query["created_date"][date_key] = self.date_to_epoch(
|
||||
query["created_date"][date_key]
|
||||
)
|
||||
|
||||
if "$" not in date_key:
|
||||
query["created_date"]["$" + date_key] = query["created_date"][date_key]
|
||||
# if "$" not in date_key:
|
||||
# query["created_date"]["$" + date_key] = query["created_date"][date_key]
|
||||
|
||||
return query
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
qg = QueryGenerator()
|
||||
print(qg.get_query("How heavy is Simba?"))
|
||||
print(qg.get_doctype_query("How heavy is Simba?"))
|
||||
|
||||
BIN
raggr-frontend/src/.App.tsx.swp
Normal file
BIN
raggr-frontend/src/.App.tsx.swp
Normal file
Binary file not shown.
@@ -1,66 +1,204 @@
|
||||
import { useState } from "react";
|
||||
import { useEffect, useState } from "react";
|
||||
import axios from "axios";
|
||||
import ReactMarkdown from "react-markdown";
|
||||
|
||||
import "./App.css";
|
||||
|
||||
const App = () => {
|
||||
const [query, setQuery] = useState<string>("");
|
||||
const [answer, setAnswer] = useState<string>("");
|
||||
const [loading, setLoading] = useState<boolean>(false);
|
||||
type QuestionAnswer = {
|
||||
question: string;
|
||||
answer: string;
|
||||
};
|
||||
|
||||
const handleQuestionSubmit = () => {
|
||||
const payload = { query: query };
|
||||
setLoading(true);
|
||||
axios
|
||||
.post("/api/query", payload)
|
||||
.then((result) => setAnswer(result.data.response))
|
||||
.finally(() => setLoading(false));
|
||||
};
|
||||
const handleQueryChange = (event) => {
|
||||
setQuery(event.target.value);
|
||||
};
|
||||
return (
|
||||
<div className="flex flex-row justify-center py-4">
|
||||
<div className="flex flex-col gap-4 min-w-xl max-w-xl">
|
||||
<div className="flex flex-row justify-center gap-2 grow">
|
||||
<h1 className="text-3xl">ask simba!</h1>
|
||||
</div>
|
||||
<div className="flex flex-row justify-between gap-2 grow">
|
||||
<textarea
|
||||
type="text"
|
||||
className="p-4 border border-blue-200 rounded-md grow"
|
||||
onChange={handleQueryChange}
|
||||
/>
|
||||
</div>
|
||||
<div className="flex flex-row justify-between gap-2 grow">
|
||||
<button
|
||||
className="p-4 border border-blue-400 bg-blue-200 hover:bg-blue-400 cursor-pointer rounded-md flex-grow"
|
||||
onClick={() => handleQuestionSubmit()}
|
||||
type="submit"
|
||||
>
|
||||
Submit
|
||||
</button>
|
||||
</div>
|
||||
{loading ? (
|
||||
<div className="flex flex-col w-full animate-pulse gap-2">
|
||||
<div className="flex flex-row gap-2 w-full">
|
||||
<div className="bg-gray-400 w-1/2 p-3 rounded-lg" />
|
||||
<div className="bg-gray-400 w-1/2 p-3 rounded-lg" />
|
||||
</div>
|
||||
<div className="flex flex-row gap-2 w-full">
|
||||
<div className="bg-gray-400 w-1/3 p-3 rounded-lg" />
|
||||
<div className="bg-gray-400 w-2/3 p-3 rounded-lg" />
|
||||
</div>
|
||||
</div>
|
||||
) : (
|
||||
<div className="flex flex-col">
|
||||
<ReactMarkdown>{answer}</ReactMarkdown>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
type QuestionBubbleProps = {
|
||||
text: string;
|
||||
};
|
||||
|
||||
type AnswerBubbleProps = {
|
||||
text: string;
|
||||
loading: string;
|
||||
};
|
||||
|
||||
type QuestionAnswerPairProps = {
|
||||
question: string;
|
||||
answer: string;
|
||||
loading: boolean;
|
||||
};
|
||||
|
||||
type Conversation = {
|
||||
title: string;
|
||||
id: string;
|
||||
};
|
||||
|
||||
type Message = {
|
||||
text: string;
|
||||
speaker: "simba" | "user";
|
||||
};
|
||||
|
||||
type ConversationMenuProps = {
|
||||
conversations: Conversation[];
|
||||
};
|
||||
|
||||
const ConversationMenu = ({ conversations }: ConversationMenuProps) => {
|
||||
return (
|
||||
<div className="absolute bg-white w-md rounded-md shadow-xl m-4 p-4">
|
||||
<p className="py-2 px-4 rounded-md w-full text-xl font-bold">askSimba!</p>
|
||||
{conversations.map((conversation) => (
|
||||
<p className="py-2 px-4 rounded-md hover:bg-stone-200 w-full text-xl font-bold cursor-pointer">
|
||||
{conversation.title}
|
||||
</p>
|
||||
))}
|
||||
</div>
|
||||
);
|
||||
};
|
||||
|
||||
const QuestionBubble = ({ text }: QuestionBubbleProps) => {
|
||||
return <div className="rounded-md bg-stone-200 p-3">🤦: {text}</div>;
|
||||
};
|
||||
|
||||
const AnswerBubble = ({ text, loading }: AnswerBubbleProps) => {
|
||||
return (
|
||||
<div className="rounded-md bg-orange-100 p-3">
|
||||
{loading ? (
|
||||
<div className="flex flex-col w-full animate-pulse gap-2">
|
||||
<div className="flex flex-row gap-2 w-full">
|
||||
<div className="bg-gray-400 w-1/2 p-3 rounded-lg" />
|
||||
<div className="bg-gray-400 w-1/2 p-3 rounded-lg" />
|
||||
</div>
|
||||
<div className="flex flex-row gap-2 w-full">
|
||||
<div className="bg-gray-400 w-1/3 p-3 rounded-lg" />
|
||||
<div className="bg-gray-400 w-2/3 p-3 rounded-lg" />
|
||||
</div>
|
||||
</div>
|
||||
) : (
|
||||
<div className="flex flex-col">
|
||||
<ReactMarkdown>{"🐈: " + text}</ReactMarkdown>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
};
|
||||
|
||||
const QuestionAnswerPair = ({
|
||||
question,
|
||||
answer,
|
||||
loading,
|
||||
}: QuestionAnswerPairProps) => {
|
||||
return (
|
||||
<div className="flex flex-col gap-4">
|
||||
<QuestionBubble text={question} />
|
||||
<AnswerBubble text={answer} loading={loading} />
|
||||
</div>
|
||||
);
|
||||
};
|
||||
|
||||
const App = () => {
|
||||
const [query, setQuery] = useState<string>("");
|
||||
const [answer, setAnswer] = useState<string>("");
|
||||
const [simbaMode, setSimbaMode] = useState<boolean>(false);
|
||||
const [questionsAnswers, setQuestionsAnswers] = useState<QuestionAnswer[]>(
|
||||
[],
|
||||
);
|
||||
const [messages, setMessages] = useState<Message[]>([]);
|
||||
const [conversations, setConversations] = useState<Conversation[]>([
|
||||
{ title: "simba meow meow", id: "uuid" },
|
||||
]);
|
||||
|
||||
const simbaAnswers = ["meow.", "hiss...", "purrrrrr", "yowOWROWWowowr"];
|
||||
|
||||
useEffect(() => {
|
||||
axios.get("/api/messages").then((result) => {
|
||||
setMessages(
|
||||
result.data.messages.map((message) => {
|
||||
return {
|
||||
text: message.text,
|
||||
speaker: message.speaker,
|
||||
};
|
||||
}),
|
||||
);
|
||||
});
|
||||
}, []);
|
||||
|
||||
const handleQuestionSubmit = () => {
|
||||
let currMessages = messages.concat([{ text: query, speaker: "user" }]);
|
||||
setMessages(currMessages);
|
||||
if (simbaMode) {
|
||||
console.log("simba mode activated");
|
||||
const randomIndex = Math.floor(Math.random() * simbaAnswers.length);
|
||||
const randomElement = simbaAnswers[randomIndex];
|
||||
setAnswer(randomElement);
|
||||
setQuestionsAnswers(
|
||||
questionsAnswers.concat([
|
||||
{
|
||||
question: query,
|
||||
answer: randomElement,
|
||||
},
|
||||
]),
|
||||
);
|
||||
return;
|
||||
}
|
||||
const payload = { query: query };
|
||||
axios.post("/api/query", payload).then((result) => {
|
||||
setQuestionsAnswers(
|
||||
questionsAnswers.concat([
|
||||
{ question: query, answer: result.data.response },
|
||||
]),
|
||||
);
|
||||
setMessages(
|
||||
currMessages.concat([{ text: result.data.response, speaker: "simba" }]),
|
||||
);
|
||||
});
|
||||
};
|
||||
const handleQueryChange = (event) => {
|
||||
setQuery(event.target.value);
|
||||
};
|
||||
return (
|
||||
<div className="h-screen bg-opacity-20">
|
||||
<div className="bg-white/85 h-screen">
|
||||
<div className="flex flex-row justify-center py-4">
|
||||
<div className="flex flex-col gap-4 min-w-xl max-w-xl">
|
||||
<header className="flex flex-row justify-center gap-2 grow sticky top-0 z-10 bg-white">
|
||||
<h1 className="text-3xl">ask simba!</h1>
|
||||
</header>
|
||||
{/*{questionsAnswers.map((qa) => (
|
||||
<QuestionAnswerPair question={qa.question} answer={qa.answer} />
|
||||
))}*/}
|
||||
{messages.map((msg) => {
|
||||
if (msg.speaker == "simba") {
|
||||
return <AnswerBubble text={msg.text} loading="" />;
|
||||
}
|
||||
|
||||
return <QuestionBubble text={msg.text} />;
|
||||
})}
|
||||
<footer className="flex flex-col gap-2 sticky bottom-0">
|
||||
<div className="flex flex-row justify-between gap-2 grow">
|
||||
<textarea
|
||||
type="text"
|
||||
className="p-4 border border-blue-200 rounded-md grow bg-white"
|
||||
onChange={handleQueryChange}
|
||||
/>
|
||||
</div>
|
||||
<div className="flex flex-row justify-between gap-2 grow">
|
||||
<button
|
||||
className="p-4 border border-blue-400 bg-blue-200 hover:bg-blue-400 cursor-pointer rounded-md flex-grow"
|
||||
onClick={() => handleQuestionSubmit()}
|
||||
type="submit"
|
||||
>
|
||||
Submit
|
||||
</button>
|
||||
</div>
|
||||
<div className="flex flex-row justify-center gap-2 grow">
|
||||
<input
|
||||
type="checkbox"
|
||||
onChange={(event) => setSimbaMode(event.target.checked)}
|
||||
/>
|
||||
<p>simba mode?</p>
|
||||
</div>
|
||||
</footer>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
};
|
||||
|
||||
export default App;
|
||||
|
||||
BIN
raggr-frontend/src/simba_cute.jpeg
Normal file
BIN
raggr-frontend/src/simba_cute.jpeg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 3.4 MiB |
BIN
raggr-frontend/src/simba_troll.jpeg
Normal file
BIN
raggr-frontend/src/simba_troll.jpeg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 2.1 MiB |
49
request.py
49
request.py
@@ -1,31 +1,43 @@
|
||||
import os
|
||||
import tempfile
|
||||
import httpx
|
||||
import logging
|
||||
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
|
||||
|
||||
class PaperlessNGXService:
|
||||
def __init__(self):
|
||||
self.base_url = os.getenv("BASE_URL")
|
||||
self.token = os.getenv("PAPERLESS_TOKEN")
|
||||
self.url = f"http://{os.getenv("BASE_URL")}/api/documents/?query=simba"
|
||||
self.headers = {"Authorization": f"Token {os.getenv("PAPERLESS_TOKEN")}"}
|
||||
self.url = f"http://{os.getenv('BASE_URL')}/api/documents/?tags__id=8"
|
||||
self.headers = {"Authorization": f"Token {os.getenv('PAPERLESS_TOKEN')}"}
|
||||
|
||||
def get_data(self):
|
||||
print(f"Getting data from: {self.url}")
|
||||
r = httpx.get(self.url, headers=self.headers)
|
||||
return r.json()["results"]
|
||||
results = r.json()["results"]
|
||||
|
||||
nextLink = r.json().get("next")
|
||||
|
||||
while nextLink:
|
||||
r = httpx.get(nextLink, headers=self.headers)
|
||||
results += r.json()["results"]
|
||||
nextLink = r.json().get("next")
|
||||
|
||||
return results
|
||||
|
||||
def get_doc_by_id(self, doc_id: int):
|
||||
url = f"http://{os.getenv("BASE_URL")}/api/documents/{doc_id}/"
|
||||
url = f"http://{os.getenv('BASE_URL')}/api/documents/{doc_id}/"
|
||||
r = httpx.get(url, headers=self.headers)
|
||||
return r.json()
|
||||
|
||||
def download_pdf_from_id(self, id: int) -> str:
|
||||
download_url = f"http://{os.getenv("BASE_URL")}/api/documents/{id}/download/"
|
||||
download_url = f"http://{os.getenv('BASE_URL')}/api/documents/{id}/download/"
|
||||
response = httpx.get(
|
||||
download_url, headers=self.headers, follow_redirects=True, timeout=30
|
||||
)
|
||||
@@ -39,10 +51,35 @@ class PaperlessNGXService:
|
||||
return pdf_to_process
|
||||
|
||||
def upload_cleaned_content(self, document_id, data):
|
||||
PUTS_URL = f"http://{os.getenv("BASE_URL")}/api/documents/{document_id}/"
|
||||
PUTS_URL = f"http://{os.getenv('BASE_URL')}/api/documents/{document_id}/"
|
||||
r = httpx.put(PUTS_URL, headers=self.headers, data=data)
|
||||
r.raise_for_status()
|
||||
|
||||
def upload_description(self, description_filepath, file, title, exif_date: str):
|
||||
POST_URL = f"http://{os.getenv('BASE_URL')}/api/documents/post_document/"
|
||||
files = {"document": ("description_filepath", file, "application/txt")}
|
||||
data = {
|
||||
"title": title,
|
||||
"create": exif_date,
|
||||
"document_type": 3,
|
||||
"tags": [7],
|
||||
}
|
||||
|
||||
r = httpx.post(POST_URL, headers=self.headers, data=data, files=files)
|
||||
r.raise_for_status()
|
||||
|
||||
def get_tags(self):
|
||||
GET_URL = f"http://{os.getenv('BASE_URL')}/api/tags/"
|
||||
r = httpx.get(GET_URL, headers=self.headers)
|
||||
data = r.json()
|
||||
return {tag["id"]: tag["name"] for tag in data["results"]}
|
||||
|
||||
def get_doctypes(self):
|
||||
GET_URL = f"http://{os.getenv('BASE_URL')}/api/document_types/"
|
||||
r = httpx.get(GET_URL, headers=self.headers)
|
||||
data = r.json()
|
||||
return {doctype["id"]: doctype["name"] for doctype in data["results"]}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
pp = PaperlessNGXService()
|
||||
|
||||
466
uv.lock
generated
466
uv.lock
generated
@@ -2,6 +2,27 @@ version = 1
|
||||
revision = 2
|
||||
requires-python = ">=3.13"
|
||||
|
||||
[[package]]
|
||||
name = "aiofiles"
|
||||
version = "25.1.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354, upload-time = "2025-10-09T20:51:04.358Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/bc/8a/340a1555ae33d7354dbca4faa54948d76d89a27ceef032c8c3bc661d003e/aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695", size = 14668, upload-time = "2025-10-09T20:51:03.174Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "aiosqlite"
|
||||
version = "0.21.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
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Reference in New Issue
Block a user