Added conversation history

Logging
Metadata filtering
2025-10-23 22:28:41 -04:00 · 2025-10-16 22:43:14 -04:00 · 2025-10-16 22:36:21 -04:00 · 2025-10-14 22:13:01 -04:00 · 2025-10-14 20:06:32 -04:00 · 2025-10-10 23:28:41 -04:00
16 changed files with 993 additions and 237 deletions
--- a/app.py
+++ b/app.py
@@ -1,43 +1,101 @@
 import os

-from flask import Flask, request, jsonify, render_template, send_from_directory
+from quart import Quart, request, jsonify, render_template, send_from_directory
+from tortoise.contrib.quart import register_tortoise
+
+from quart_jwt_extended import JWTManager

 from main import consult_simba_oracle
+from blueprints.conversation.logic import (
+    get_the_only_conversation,
+    add_message_to_conversation,
+)

-app = Flask(
+app = Quart(
    __name__,
    static_folder="raggr-frontend/dist/static",
    template_folder="raggr-frontend/dist",
 )

+app.config["JWT_SECRET_KEY"] = os.getenv("JWT_SECRET_KEY", "SECRET_KEY")
+jwt = JWTManager(app)
+
+# Initialize Tortoise ORM
+register_tortoise(
+    app,
+    db_url=os.getenv("DATABASE_URL", "sqlite://raggr.db"),
+    modules={"models": ["blueprints.conversation.models"]},
+    generate_schemas=True,
+)
+

 # Serve React static files
@app.route("/static/<path:filename>")
-def static_files(filename):
-    return send_from_directory(app.static_folder, filename)
+async def static_files(filename):
+    return await send_from_directory(app.static_folder, filename)


 # Serve the React app for all routes (catch-all)
@app.route("/", defaults={"path": ""})
@app.route("/<path:path>")
-def serve_react_app(path):
+async def serve_react_app(path):
    if path and os.path.exists(os.path.join(app.template_folder, path)):
-        return send_from_directory(app.template_folder, path)
-    return render_template("index.html")
+        return await send_from_directory(app.template_folder, path)
+    return await render_template("index.html")


@app.route("/api/query", methods=["POST"])
-def query():
-    data = request.get_json()
+async def query():
+    data = await request.get_json()
    query = data.get("query")
-    return jsonify({"response": consult_simba_oracle(query)})
+    # add message to database
+    conversation = await get_the_only_conversation()
+    print(conversation)
+    await add_message_to_conversation(
+        conversation=conversation, message=query, speaker="user"
+    )
+
+    response = consult_simba_oracle(query)
+    await add_message_to_conversation(
+        conversation=conversation, message=response, speaker="simba"
+    )
+    return jsonify({"response": response})


-@app.route("/api/ingest", methods=["POST"])
-def webhook():
-    data = request.get_json()
-    print(data)
-    return jsonify({"status": "received"})
+@app.route("/api/messages", methods=["GET"])
+async def get_messages():
+    conversation = await get_the_only_conversation()
+    # Prefetch related messages
+    await conversation.fetch_related("messages")
+
+    # Manually serialize the conversation with messages
+    messages = []
+    for msg in conversation.messages:
+        messages.append(
+            {
+                "id": str(msg.id),
+                "text": msg.text,
+                "speaker": msg.speaker.value,
+                "created_at": msg.created_at.isoformat(),
+            }
+        )
+
+    return jsonify(
+        {
+            "id": str(conversation.id),
+            "name": conversation.name,
+            "messages": messages,
+            "created_at": conversation.created_at.isoformat(),
+            "updated_at": conversation.updated_at.isoformat(),
+        }
+    )
+
+
+# @app.route("/api/ingest", methods=["POST"])
+# def webhook():
+# data = request.get_json()
+# print(data)
+# return jsonify({"status": "received"})


 if __name__ == "__main__":
--- a/blueprints/conversation/init.py
+++ b/blueprints/conversation/init.py
@@ -0,0 +1,17 @@
+from quart import Blueprint, jsonify
+from .models import (
+    Conversation,
+    PydConversation,
+)
+
+conversation_blueprint = Blueprint(
+    "conversation_api", __name__, url_prefix="/api/conversation"
+)
+
+
+@conversation_blueprint.route("/<conversation_id>")
+async def get_conversation(conversation_id: str):
+    conversation = await Conversation.get(id=conversation_id)
+    serialized_conversation = await PydConversation.from_tortoise_orm(conversation)
+
+    return jsonify(serialized_conversation.model_dump_json())
--- a/blueprints/conversation/logic.py
+++ b/blueprints/conversation/logic.py
@@ -0,0 +1,32 @@
+from .models import Conversation, ConversationMessage
+
+
+async def create_conversation(name: str = "") -> Conversation:
+    conversation = await Conversation.create(name=name)
+    return conversation
+
+
+async def add_message_to_conversation(
+    conversation: Conversation,
+    message: str,
+    speaker: str,
+) -> ConversationMessage:
+    print(conversation, message, speaker)
+    message = await ConversationMessage.create(
+        text=message,
+        speaker=speaker,
+        conversation=conversation,
+    )
+
+    return message
+
+
+async def get_the_only_conversation() -> Conversation:
+    try:
+        conversation = await Conversation.all().first()
+        if conversation is None:
+            conversation = await Conversation.create(name="simba_chat")
+    except Exception as _e:
+        conversation = await Conversation.create(name="simba_chat")
+
+    return conversation
--- a/blueprints/conversation/models.py
+++ b/blueprints/conversation/models.py
@@ -0,0 +1,41 @@
+import enum
+
+from tortoise.models import Model
+from tortoise import fields
+from tortoise.contrib.pydantic import (
+    pydantic_queryset_creator,
+    pydantic_model_creator,
+)
+
+
+class Speaker(enum.Enum):
+    USER = "user"
+    SIMBA = "simba"
+
+
+class Conversation(Model):
+    id = fields.UUIDField(primary_key=True)
+    name = fields.CharField(max_length=255)
+    created_at = fields.DatetimeField(auto_now_add=True)
+    updated_at = fields.DatetimeField(auto_now=True)
+
+    class Meta:
+        table = "conversations"
+
+
+class ConversationMessage(Model):
+    id = fields.UUIDField(primary_key=True)
+    text = fields.TextField()
+    conversation = fields.ForeignKeyField(
+        "models.Conversation", related_name="messages"
+    )
+    created_at = fields.DatetimeField(auto_now_add=True)
+    speaker = fields.CharEnumField(enum_type=Speaker, max_length=10)
+
+    class Meta:
+        table = "conversation_messages"
+
+
+PydConversationMessage = pydantic_model_creator(ConversationMessage)
+PydConversation = pydantic_model_creator(Conversation, name="Conversation")
+PydListConversationMessage = pydantic_queryset_creator(ConversationMessage)
--- a/chunker.py
+++ b/chunker.py
@@ -3,15 +3,20 @@ from math import ceil
 import re
 from typing import Union
 from uuid import UUID, uuid4
-
+from ollama import Client
 from chromadb.utils.embedding_functions.openai_embedding_function import (
    OpenAIEmbeddingFunction,
 )
 from dotenv import load_dotenv
+from llm import LLMClient


 load_dotenv()

+ollama_client = Client(
+    host=os.getenv("OLLAMA_HOST", "http://localhost:11434"), timeout=10.0
+)
+

 def remove_headers_footers(text, header_patterns=None, footer_patterns=None):
    if header_patterns is None:
@@ -80,13 +85,16 @@ class Chunk:


 class Chunker:
-    embedding_fx = OpenAIEmbeddingFunction(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        model_name="text-embedding-3-small",
-    )
-
    def __init__(self, collection) -> None:
        self.collection = collection
+        self.llm_client = LLMClient()
+
+    def embedding_fx(self, inputs):
+        openai_embedding_fx = OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            model_name="text-embedding-3-small",
+        )
+        return openai_embedding_fx(inputs)

    def chunk_document(
        self,
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -1,4 +1,4 @@
-version: '3.8'
+version: "3.8"

 services:
  raggr:
--- a/image_process.py
+++ b/image_process.py
@@ -25,22 +25,24 @@ parser.add_argument("filepath")

 client = Client(host=os.getenv("OLLAMA_HOST", "http://localhost:11434"))

+
 class SimbaImageDescription(BaseModel):
    image_date: str
    description: str

+
 def describe_simba_image(input):
    logging.info("Opening image of Simba ...")
    if "heic" in input.lower() or "heif" in input.lower():
        new_filepath = input.split(".")[0] + ".jpg"
        img = Image.open(input)
-        img.save(new_filepath, 'JPEG')
+        img.save(new_filepath, "JPEG")
        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
+        input = new_filepath
    else:
        img = Image.open(input)

@@ -66,7 +68,7 @@ def describe_simba_image(input):
            },
            {"role": "user", "content": prompt, "images": [input]},
        ],
-        format=SimbaImageDescription.model_json_schema()
+        format=SimbaImageDescription.model_json_schema(),
    )

    result = SimbaImageDescription.model_validate_json(response["message"]["content"])
--- a/index_immich.py
+++ b/index_immich.py
@@ -6,6 +6,7 @@ import tempfile

 from image_process import describe_simba_image
 from request import PaperlessNGXService
+import sqlite3

 logging.basicConfig(level=logging.INFO)

@@ -23,8 +24,16 @@ 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()
@@ -39,7 +48,7 @@ if __name__ == "__main__":

    assets = results.json()["assets"]
    for asset in assets["items"]:
-        if asset["type"] == "IMAGE":
+        if asset["type"] == "IMAGE" and asset["id"] not in VISITED:
            ids[asset["id"]] = asset.get("originalFileName")
    nextPage = assets.get("nextPage")

@@ -58,41 +67,49 @@ if __name__ == "__main__":
    asset_search = f"{IMMICH_URL}/api/search/smart"
    request_body = {"query": "simba"}
    results = httpx.post(asset_search, headers=headers, json=request_body)
-    print(results.json()["assets"]["total"])
    for asset in results.json()["assets"]["items"]:
        if asset["type"] == "IMAGE":
            ids[asset["id"]] = asset.get("originalFileName")

-    immich_asset_id = list(ids.keys())[1]
-    immich_filename = ids.get(immich_asset_id)
-    response = httpx.get(
-        f"{IMMICH_URL}/api/assets/{immich_asset_id}/original", headers=headers
-    )
+    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)
+            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)
+            logging.info("Processing image ...")
+            description = describe_simba_image(path)

-    image_description = description.description
-    image_date = description.image_date
+            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()
+            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')
+            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()

-    ppngx.upload_description(description_filepath=description_filepath, file=file, title="SIMBA_DESCRIBE_001.txt", exif_date=image_date)
+            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)

-
-    file.close()
-
-    
-
-    logging.info("Processing complete. Deleting file.")
-    os.remove(file.name)
+    conn.close()
--- a/llm.py
+++ b/llm.py
@@ -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"}])
--- a/main.py
+++ b/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(
@@ -37,25 +40,25 @@ 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]),
@@ -68,23 +71,35 @@ 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):
@@ -99,64 +114,54 @@ def chunk_text(texts: list[str], collection):


 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):
@@ -175,34 +180,59 @@ 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")

-    if args.index:
-        with open(args.index) as file:
-            extension = args.index.split(".")[-1]
+        # 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 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)
+        # 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 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 ...")
+        logging.info("Consulting oracle ...")
        print(
            consult_oracle(
                input=args.query,
@@ -210,4 +240,4 @@ if __name__ == "__main__":
            )
        )
    else:
-        print("please provide a query")
+        logging.info("please provide a query")
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -16,4 +16,12 @@ dependencies = [
    "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",
 ]
--- a/query.py
+++ b/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

@@ -38,6 +40,20 @@ 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
@@ -45,7 +61,6 @@ the date range the user is trying to query. You should return it as a JSON. The

 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
@@ -84,6 +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
@@ -101,20 +129,43 @@ 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=[
@@ -127,17 +178,17 @@ class QueryGenerator:
        # query = json.loads(
        # 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?"))
--- a/raggr-frontend/src/.App.tsx.swp
+++ b/raggr-frontend/src/.App.tsx.swp
--- a/raggr-frontend/src/App.tsx
+++ b/raggr-frontend/src/App.tsx
@@ -1,91 +1,204 @@
-import { useState } from "react";
+import { useEffect, useState } from "react";
 import axios from "axios";
 import ReactMarkdown from "react-markdown";

 import "./App.css";

+type QuestionAnswer = {
+  question: string;
+  answer: string;
+};
+
+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 [loading, setLoading] = useState<boolean>(false);
-	const [simbaMode, setSimbaMode] = useState<boolean>(false);
+  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"];
+  const simbaAnswers = ["meow.", "hiss...", "purrrrrr", "yowOWROWWowowr"];

-	const handleQuestionSubmit = () => {
-		if (simbaMode) {
-			console.log("simba mode activated");
-			setLoading(true);
-			const randomIndex = Math.floor(Math.random() * simbaAnswers.length);
-			const randomElement = simbaAnswers[randomIndex];
-			setAnswer(randomElement);
-			setTimeout(() => setLoading(false), 3500);
-			return;
-		}
-		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="bg-[url('./simba_cute.jpeg')] bg-cover bg-center bg-no-repeat 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">
-						<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 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>
-						{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>
-			</div>
-		</div>
-	);
+  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;
--- a/request.py
+++ b/request.py
@@ -1,23 +1,35 @@
 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.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}/"
@@ -45,17 +57,29 @@ class PaperlessNGXService:

    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')}
+        files = {"document": ("description_filepath", file, "application/txt")}
        data = {
-                "title": title,
-                "create": exif_date,
-                "document_type": 3
-                "tags": [7]
+            "title": title,
+            "create": exif_date,
+            "document_type": 3,
+            "tags": [7],
        }

-        r= httpx.post(POST_URL, headers=self.headers, data=data, files=files)
+        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()
--- a/uv.lock
+++ b/uv.lock
@@ -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" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/13/7d/8bca2bf9a247c2c5dfeec1d7a5f40db6518f88d314b8bca9da29670d2671/aiosqlite-0.21.0.tar.gz", hash = "sha256:131bb8056daa3bc875608c631c678cda73922a2d4ba8aec373b19f18c17e7aa3", size = 13454, upload-time = "2025-02-03T07:30:16.235Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f5/10/6c25ed6de94c49f88a91fa5018cb4c0f3625f31d5be9f771ebe5cc7cd506/aiosqlite-0.21.0-py3-none-any.whl", hash = "sha256:2549cf4057f95f53dcba16f2b64e8e2791d7e1adedb13197dd8ed77bb226d7d0", size = 15792, upload-time = "2025-02-03T07:30:13.6Z" },
+]
+
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
@@ -170,6 +191,15 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/e5/48/1549795ba7742c948d2ad169c1c8cdbae65bc450d6cd753d124b17c8cd32/certifi-2025.8.3-py3-none-any.whl", hash = "sha256:f6c12493cfb1b06ba2ff328595af9350c65d6644968e5d3a2ffd78699af217a5", size = 161216, upload-time = "2025-08-03T03:07:45.777Z" },
 ]

+[[package]]
+name = "cfgv"
+version = "3.4.0"
+source = { registry = "https://pypi.org/simple" }
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Author	SHA1	Message	Date
Ryan Chen	5054b4a859	Added conversation history	2025-10-23 22:28:41 -04:00
Ryan Chen	8479898cc4	Logging	2025-10-16 22:43:14 -04:00
Ryan Chen	acaf681927	Metadata filtering	2025-10-16 22:36:21 -04:00
Ryan Chen	2bbe33fedc	Starting attempt #2 at metadata filtering	2025-10-14 22:13:01 -04:00
Ryan Chen	b872750444	Only use OpenAI for embedding	2025-10-14 20:06:32 -04:00
Ryan Chen	376baccadb	message-style frontend	2025-10-10 23:28:41 -04:00
Ryan Chen	c978b1a255	Reducing startup time/cost	2025-10-08 23:21:22 -04:00
Ryan Chen	51b9932389	fixing loal llm	2025-10-08 22:52:49 -04:00
Ryan Chen	ebf39480b6	urf	2025-10-08 22:46:16 -04:00
Ryan Chen	e4a04331cb	add some more debugging	2025-10-08 21:17:45 -04:00
Ryan Chen	166ffb4c09	i only ship bugs	2025-10-08 21:13:15 -04:00
Ryan Chen	64e286e623	oops	2025-10-08 21:07:33 -04:00
Ryan Chen	c6c14729dd	interseting	2025-10-08 21:03:42 -04:00
Ryan Chen	910097d13b	data	2025-10-05 20:31:46 -04:00