query classification

Reviewed-on: #7

Dockerfile

@@ -24,7 +24,6 @@ RUN uv pip install --system -e .
 # Copy application code
 COPY *.py ./
 COPY blueprints ./blueprints
-COPY aerich.toml ./
 COPY migrations ./migrations
 COPY startup.sh ./
 RUN chmod +x startup.sh
@@ -35,8 +34,8 @@ WORKDIR /app/raggr-frontend
 RUN yarn install && yarn build
 WORKDIR /app
 
-# Create ChromaDB directory
-RUN mkdir -p /app/chromadb
+# Create ChromaDB and database directories
+RUN mkdir -p /app/chromadb /app/database
 
 # Expose port
 EXPOSE 8080

add_user.py

@@ -10,7 +10,7 @@ from blueprints.users.models import User
 async def add_user(username: str, email: str, password: str):
     """Add a new user to the database"""
     await Tortoise.init(
-        db_url="sqlite://raggr.db",
+        db_url="sqlite://database/raggr.db",
         modules={
             "models": [
                 "blueprints.users.models",
@@ -56,7 +56,7 @@ async def add_user(username: str, email: str, password: str):
 async def list_users():
     """List all users in the database"""
     await Tortoise.init(
-        db_url="sqlite://raggr.db",
+        db_url="sqlite://database/raggr.db",
         modules={
             "models": [
                 "blueprints.users.models",

aerich_config.py

@@ -1,7 +1,7 @@
 import os
 
 TORTOISE_ORM = {
-    "connections": {"default": os.getenv("DATABASE_URL", "sqlite:///app/raggr.db")},
+    "connections": {"default": os.getenv("DATABASE_URL", "sqlite:///app/database/raggr.db")},
     "apps": {
         "models": {
             "models": [

app.py

@@ -27,7 +27,7 @@ app.register_blueprint(blueprints.conversation.conversation_blueprint)
 
 
 TORTOISE_CONFIG = {
-    "connections": {"default": "sqlite://raggr.db"},
+    "connections": {"default": "sqlite://database/raggr.db"},
     "apps": {
         "models": {
             "models": [
@@ -69,9 +69,11 @@ async def query():
     user = await blueprints.users.models.User.get(id=current_user_uuid)
     data = await request.get_json()
     query = data.get("query")
-    conversation = await blueprints.conversation.logic.get_conversation_for_user(
-        user=user
+    conversation_id = data.get("conversation_id")
+    conversation = await blueprints.conversation.logic.get_conversation_by_id(
+        conversation_id
     )
+    await conversation.fetch_related("messages")
     await blueprints.conversation.logic.add_message_to_conversation(
         conversation=conversation,
         message=query,
@@ -79,7 +81,11 @@ async def query():
         user=user,
     )
 
-    response = consult_simba_oracle(query)
+    transcript = await blueprints.conversation.logic.get_conversation_transcript(
+        user=user, conversation=conversation
+    )
+
+    response = consult_simba_oracle(input=query, transcript=transcript)
     await blueprints.conversation.logic.add_message_to_conversation(
         conversation=conversation,
         message=response,

blueprints/conversation/__init__.py

@@ -1,9 +1,19 @@
+import datetime
+
+from quart_jwt_extended import (
+    jwt_refresh_token_required,
+    get_jwt_identity,
+)
+
 from quart import Blueprint, jsonify
 from .models import (
     Conversation,
     PydConversation,
+    PydListConversation,
 )
 
+import blueprints.users.models
+
 conversation_blueprint = Blueprint(
     "conversation_api", __name__, url_prefix="/api/conversation"
 )
@@ -12,6 +22,51 @@ conversation_blueprint = Blueprint(
 @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)
+    await conversation.fetch_related("messages")
 
-    return jsonify(serialized_conversation.model_dump_json())
+    # 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(),
+        }
+    )
+
+
+@conversation_blueprint.post("/")
+@jwt_refresh_token_required
+async def create_conversation():
+    user_uuid = get_jwt_identity()
+    user = await blueprints.users.models.User.get(id=user_uuid)
+    conversation = await Conversation.create(
+        name=f"{user.username} {datetime.datetime.now().timestamp}",
+        user=user,
+    )
+
+    serialized_conversation = await PydConversation.from_tortoise_orm(conversation)
+    return jsonify(serialized_conversation.model_dump())
+
+
+@conversation_blueprint.get("/")
+@jwt_refresh_token_required
+async def get_all_conversations():
+    user_uuid = get_jwt_identity()
+    user = await blueprints.users.models.User.get(id=user_uuid)
+    conversations = Conversation.filter(user=user)
+    serialized_conversations = await PydListConversation.from_queryset(conversations)
+
+    return jsonify(serialized_conversations.model_dump())

blueprints/conversation/logic.py

@@ -44,3 +44,17 @@ async def get_conversation_for_user(user: blueprints.users.models.User) -> Conve
         await Conversation.get_or_create(name=f"{user.username}'s chat", user=user)
 
         return await Conversation.get(user=user)
+
+
+async def get_conversation_by_id(id: str) -> Conversation:
+    return await Conversation.get(id=id)
+
+
+async def get_conversation_transcript(
+    user: blueprints.users.models.User, conversation: Conversation
+) -> str:
+    messages = []
+    for message in conversation.messages:
+        messages.append(f"{message.speaker} at {message.created_at}: {message.text}")
+
+    return "\n".join(messages)

blueprints/conversation/models.py

@@ -40,5 +40,15 @@ class ConversationMessage(Model):
 
 
 PydConversationMessage = pydantic_model_creator(ConversationMessage)
-PydConversation = pydantic_model_creator(Conversation, name="Conversation")
+PydConversation = pydantic_model_creator(
+    Conversation, name="Conversation", allow_cycles=True, exclude=("user",)
+)
+PydConversationWithMessages = pydantic_model_creator(
+    Conversation,
+    name="ConversationWithMessages",
+    allow_cycles=True,
+    exclude=("user",),
+    include=("messages",),
+)
+PydListConversation = pydantic_queryset_creator(Conversation)
 PydListConversationMessage = pydantic_queryset_creator(ConversationMessage)

chunker.py

@@ -14,7 +14,7 @@ from llm import LLMClient
 load_dotenv()
 
 ollama_client = Client(
-    host=os.getenv("OLLAMA_HOST", "http://localhost:11434"), timeout=10.0
+    host=os.getenv("OLLAMA_HOST", "http://localhost:11434"), timeout=1.0
 )
 
 

docker-compose.yml

@@ -12,6 +12,8 @@ services:
       - OPENAI_API_KEY=${OPENAI_API_KEY}
     volumes:
       - chromadb_data:/app/chromadb
+      - database_data:/app/database
 
 volumes:
   chromadb_data:
+  database_data:

index_immich.py

@@ -27,7 +27,7 @@ headers = {"x-api-key": API_KEY, "Content-Type": "application/json"}
 VISITED = {}
 
 if __name__ == "__main__":
-    conn = sqlite3.connect("./visited.db")
+    conn = sqlite3.connect("./database/visited.db")
     c = conn.cursor()
     c.execute("select immich_id from visited")
     rows = c.fetchall()

llm.py

@@ -4,15 +4,20 @@ from ollama import Client
 from openai import OpenAI
 
 import logging
+from dotenv import load_dotenv
+
+load_dotenv()
 
 logging.basicConfig(level=logging.INFO)
 
+TRY_OLLAMA = os.getenv("TRY_OLLAMA", False)
+
 
 class LLMClient:
     def __init__(self):
         try:
             self.ollama_client = Client(
-                host=os.getenv("OLLAMA_URL", "http://localhost:11434"), timeout=10.0
+                host=os.getenv("OLLAMA_URL", "http://localhost:11434"), timeout=1.0
             )
             self.ollama_client.chat(
                 model="gemma3:4b", messages=[{"role": "system", "content": "test"}]
@@ -30,7 +35,9 @@ class LLMClient:
         prompt: str,
         system_prompt: str,
     ):
+        # Instituting a fallback if my gaming PC is not on
         if self.PROVIDER == "ollama":
+            try:
                 response = self.ollama_client.chat(
                     model="gemma3:4b",
                     messages=[
@@ -41,9 +48,11 @@ class LLMClient:
                         {"role": "user", "content": prompt},
                     ],
                 )
-            print(response)
                 output = response.message.content
-        elif self.PROVIDER == "openai":
+                return output
+            except Exception as e:
+                logging.error(f"Could not connect to OLLAMA: {str(e)}")
+
         response = self.openai_client.responses.create(
             model="gpt-4o-mini",
             input=[

main.py

@@ -7,6 +7,8 @@ import argparse
 import chromadb
 import ollama
 
+import time
+
 
 from request import PaperlessNGXService
 from chunker import Chunker
@@ -36,6 +38,7 @@ 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("--classify", action="store_true", help="test classification")
 parser.add_argument("--index", help="index a file")
 
 ppngx = PaperlessNGXService()
@@ -77,7 +80,7 @@ def chunk_data(docs, collection, doctypes):
 
     logging.info(f"chunking {len(docs)} documents")
     texts: list[str] = [doc["content"] for doc in docs]
-    with sqlite3.connect("visited.db") as conn:
+    with sqlite3.connect("database/visited.db") as conn:
         to_insert = []
         c = conn.cursor()
         for index, text in enumerate(texts):
@@ -113,9 +116,22 @@ def chunk_text(texts: list[str], collection):
         )
 
 
-def consult_oracle(input: str, collection):
-    import time
+def classify_query(query: str, transcript: str) -> bool:
+    logging.info("Starting query generation")
+    qg_start = time.time()
+    qg = QueryGenerator()
+    query_type = qg.get_query_type(input=query, transcript=transcript)
+    logging.info(query_type)
+    qg_end = time.time()
+    logging.info(f"Query generation took {qg_end - qg_start:.2f} seconds")
+    return query_type == "Simba"
 
+
+def consult_oracle(
+    input: str,
+    collection,
+    transcript: str = "",
+):
     chunker = Chunker(collection)
 
     start_time = time.time()
@@ -153,7 +169,10 @@ def consult_oracle(input: str, collection):
     logging.info("Starting LLM generation")
     llm_start = time.time()
     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}"
+    transcript_prompt = f"Here is the message transcript thus far {transcript}."
+    prompt = f"""Using the following data, help answer the user's query by providing as many details as possible.
+    Using this data: {results}. {transcript_prompt if len(transcript) > 0 else ""}
+    Respond to this prompt: {input}"""
     output = llm_client.chat(prompt=prompt, system_prompt=system_prompt)
     llm_end = time.time()
     logging.info(f"LLM generation took {llm_end - llm_start:.2f} seconds")
@@ -164,6 +183,16 @@ def consult_oracle(input: str, collection):
     return output
 
 
+def llm_chat(input: str, transcript: str = "") -> str:
+    system_prompt = "You are a helpful assistant that understands veterinary terms."
+    transcript_prompt = f"Here is the message transcript thus far {transcript}."
+    prompt = f"""Answer the user in a humorous way as if you were a cat named Simba. Be very coy.
+    {transcript_prompt if len(transcript) > 0 else ""}
+    Respond to this prompt: {input}"""
+    output = llm_client.chat(prompt=prompt, system_prompt=system_prompt)
+    return output
+
+
 def paperless_workflow(input):
     # Step 1: Get the text
     ppngx = PaperlessNGXService()
@@ -173,15 +202,24 @@ def paperless_workflow(input):
     consult_oracle(input, simba_docs)
 
 
-def consult_simba_oracle(input: str):
+def consult_simba_oracle(input: str, transcript: str = ""):
+    is_simba_related = classify_query(query=input, transcript=transcript)
+
+    if is_simba_related:
+        logging.info("Query is related to simba")
         return consult_oracle(
             input=input,
             collection=simba_docs,
+            transcript=transcript,
         )
 
+    logging.info("Query is NOT related to simba")
+
+    return llm_chat(input=input, transcript=transcript)
+
 
 def filter_indexed_files(docs):
-    with sqlite3.connect("visited.db") as conn:
+    with sqlite3.connect("database/visited.db") as conn:
         c = conn.cursor()
         c.execute(
             "CREATE TABLE IF NOT EXISTS indexed_documents (id INTEGER PRIMARY KEY AUTOINCREMENT, paperless_id INTEGER)"
@@ -194,12 +232,16 @@ def filter_indexed_files(docs):
     return [doc for doc in docs if doc["id"] not in visited]
 
 
-if __name__ == "__main__":
-    args = parser.parse_args()
-    if args.reindex:
-        with sqlite3.connect("./visited.db") as conn:
+def reindex():
+    with sqlite3.connect("database/visited.db") as conn:
         c = conn.cursor()
         c.execute("DELETE FROM indexed_documents")
+        conn.commit()
+
+    # Delete all documents from the collection
+    all_docs = simba_docs.get()
+    if all_docs["ids"]:
+        simba_docs.delete(ids=all_docs["ids"])
 
     logging.info("Fetching documents from Paperless-NGX")
     ppngx = PaperlessNGXService()
@@ -215,21 +257,20 @@ if __name__ == "__main__":
 
     # 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 __name__ == "__main__":
+    args = parser.parse_args()
+    if args.reindex:
+        reindex()
+
+    if args.classify:
+        consult_simba_oracle(input="yohohoho testing")
+        consult_simba_oracle(input="write an email")
+        consult_simba_oracle(input="how much does simba weigh")
 
     if args.query:
         logging.info("Consulting oracle ...")

query.py

@@ -49,11 +49,20 @@ DOCTYPE_OPTIONS = [
     "Letter",
 ]
 
+QUERY_TYPE_OPTIONS = [
+    "Simba",
+    "Other",
+]
+
 
 class DocumentType(BaseModel):
     type: list[str] = Field(description="type of document", enum=DOCTYPE_OPTIONS)
 
 
+class QueryType(BaseModel):
+    type: str = Field(desciption="type of query", enum=QUERY_TYPE_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
@@ -111,6 +120,27 @@ Query: "Who does Simba know?"
 Tags: ["Letter", "Documentation"]
 """
 
+QUERY_TYPE_PROMPT = f"""You are an information specialist that processes user queries.
+A query can have one tag attached from the following options. Based on the query and the transcript which is listed below, determine
+ which of the following options is most appropriate: {",".join(QUERY_TYPE_OPTIONS)}
+
+### Example 1
+Query: "Who is Simba's current vet?"
+Tags: ["Simba"]
+
+
+### Example 2
+Query: "What is the capital of Tokyo?"
+Tags: ["Other"]
+
+
+### Example 3
+Query: "Can you help me write an email?"
+Tags: ["Other"]
+
+TRANSCRIPT:
+"""
+
 
 class QueryGenerator:
     def __init__(self) -> None:
@@ -154,6 +184,33 @@ class QueryGenerator:
         metadata_query = {"document_type": {"$in": type_data["type"]}}
         return metadata_query
 
+    def get_query_type(self, input: str, transcript: 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": f"{QUERY_TYPE_PROMPT}\nTRANSCRIPT:\n{transcript}\nQUERY:{input}",
+                },
+            ],
+            model="gpt-4o",
+            response_format={
+                "type": "json_schema",
+                "json_schema": {
+                    "name": "query_type",
+                    "schema": QueryType.model_json_schema(),
+                },
+            },
+        )
+
+        response_json_str = response.choices[0].message.content
+        type_data = json.loads(response_json_str)
+        return type_data["type"]
+
     def get_query(self, input: str):
         client = OpenAI()
         response = client.responses.parse(

raggr-frontend/package.json

@@ -6,14 +6,18 @@
   "scripts": {
     "build": "rsbuild build",
     "dev": "rsbuild dev --open",
-    "preview": "rsbuild preview"
+    "preview": "rsbuild preview",
+    "watch": "npm-watch build",
+    "watch:build": "rsbuild build --watch"
   },
   "dependencies": {
     "axios": "^1.12.2",
     "marked": "^16.3.0",
+    "npm-watch": "^0.13.0",
     "react": "^19.1.1",
     "react-dom": "^19.1.1",
-    "react-markdown": "^10.1.0"
+    "react-markdown": "^10.1.0",
+    "watch": "^1.0.2"
   },
   "devDependencies": {
     "@rsbuild/core": "^1.5.6",
@@ -22,5 +26,16 @@
     "@types/react": "^19.1.13",
     "@types/react-dom": "^19.1.9",
     "typescript": "^5.9.2"
+  },
+  "watch": {
+    "build": {
+      "patterns": [
+        "src"
+      ],
+      "extensions": "ts,tsx,css,js,jsx",
+      "delay": 1000,
+      "quiet": false,
+      "inherit": true
+    }
   }
 }

raggr-frontend/rsbuild.config.ts

@@ -3,4 +3,8 @@ import { pluginReact } from '@rsbuild/plugin-react';
 
 export default defineConfig({
   plugins: [pluginReact()],
+  html: {
+    title: 'Raggr',
+    favicon: './src/assets/favicon.svg',
+  },
 });

raggr-frontend/src/api/conversationService.ts

@@ -10,9 +10,10 @@ interface Message {
 interface Conversation {
   id: string;
   name: string;
-  messages: Message[];
+  messages?: Message[];
   created_at: string;
   updated_at: string;
+  user_id?: string;
 }
 
 interface QueryRequest {
@@ -23,15 +24,23 @@ interface QueryResponse {
   response: string;
 }
 
+interface CreateConversationRequest {
+  user_id: string;
+}
+
 class ConversationService {
   private baseUrl = "/api";
+  private conversationBaseUrl = "/api/conversation";
 
-  async sendQuery(query: string): Promise<QueryResponse> {
+  async sendQuery(
+    query: string,
+    conversation_id: string,
+  ): Promise<QueryResponse> {
     const response = await userService.fetchWithRefreshToken(
       `${this.baseUrl}/query`,
       {
         method: "POST",
-        body: JSON.stringify({ query }),
+        body: JSON.stringify({ query, conversation_id }),
       },
     );
 
@@ -56,6 +65,51 @@ class ConversationService {
 
     return await response.json();
   }
+
+  async getConversation(conversationId: string): Promise<Conversation> {
+    const response = await userService.fetchWithRefreshToken(
+      `${this.conversationBaseUrl}/${conversationId}`,
+      {
+        method: "GET",
+      },
+    );
+
+    if (!response.ok) {
+      throw new Error("Failed to fetch conversation");
+    }
+
+    return await response.json();
+  }
+
+  async createConversation(): Promise<Conversation> {
+    const response = await userService.fetchWithRefreshToken(
+      `${this.conversationBaseUrl}/`,
+      {
+        method: "POST",
+      },
+    );
+
+    if (!response.ok) {
+      throw new Error("Failed to create conversation");
+    }
+
+    return await response.json();
+  }
+
+  async getAllConversations(): Promise<Conversation[]> {
+    const response = await userService.fetchWithRefreshToken(
+      `${this.conversationBaseUrl}/`,
+      {
+        method: "GET",
+      },
+    );
+
+    if (!response.ok) {
+      throw new Error("Failed to fetch conversations");
+    }
+
+    return await response.json();
+  }
 }
 
 export const conversationService = new ConversationService();

raggr-frontend/src/assets/favicon.svg

@@ -0,0 +1,3 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 100 100">
+  <text y="80" font-size="80" font-family="system-ui, -apple-system, sans-serif">🐱</text>
+</svg>

raggr-frontend/src/components/ChatScreen.tsx

@@ -2,6 +2,8 @@ import { useEffect, useState } from "react";
 import { conversationService } from "../api/conversationService";
 import { QuestionBubble } from "./QuestionBubble";
 import { AnswerBubble } from "./AnswerBubble";
+import { ConversationList } from "./ConversationList";
+import { parse } from "node:path/win32";
 
 type Message = {
   text: string;
@@ -33,13 +35,69 @@ export const ChatScreen = ({ setAuthenticated }: ChatScreenProps) => {
   const [conversations, setConversations] = useState<Conversation[]>([
     { title: "simba meow meow", id: "uuid" },
   ]);
+  const [showConversations, setShowConversations] = useState<boolean>(false);
+  const [selectedConversation, setSelectedConversation] =
+    useState<Conversation | null>(null);
 
   const simbaAnswers = ["meow.", "hiss...", "purrrrrr", "yowOWROWWowowr"];
 
-  useEffect(() => {
+  const handleSelectConversation = (conversation: Conversation) => {
+    setShowConversations(false);
+    setSelectedConversation(conversation);
     const loadMessages = async () => {
       try {
-        const conversation = await conversationService.getMessages();
+        const fetchedConversation = await conversationService.getConversation(
+          conversation.id,
+        );
+        setMessages(
+          fetchedConversation.messages.map((message) => ({
+            text: message.text,
+            speaker: message.speaker,
+          })),
+        );
+      } catch (error) {
+        console.error("Failed to load messages:", error);
+      }
+    };
+    loadMessages();
+  };
+
+  const loadConversations = async () => {
+    try {
+      const fetchedConversations =
+        await conversationService.getAllConversations();
+      const parsedConversations = fetchedConversations.map((conversation) => ({
+        id: conversation.id,
+        title: conversation.name,
+      }));
+      setConversations(parsedConversations);
+      setSelectedConversation(parsedConversations[0]);
+      console.log(parsedConversations);
+    } catch (error) {
+      console.error("Failed to load messages:", error);
+    }
+  };
+
+  const handleCreateNewConversation = async () => {
+    const newConversation = await conversationService.createConversation();
+    await loadConversations();
+    setSelectedConversation({
+      title: newConversation.name,
+      id: newConversation.id,
+    });
+  };
+
+  useEffect(() => {
+    loadConversations();
+  }, []);
+
+  useEffect(() => {
+    const loadMessages = async () => {
+      if (selectedConversation == null) return;
+      try {
+        const conversation = await conversationService.getConversation(
+          selectedConversation.id,
+        );
         setMessages(
           conversation.messages.map((message) => ({
             text: message.text,
@@ -51,7 +109,7 @@ export const ChatScreen = ({ setAuthenticated }: ChatScreenProps) => {
       }
     };
     loadMessages();
-  }, []);
+  }, [selectedConversation]);
 
   const handleQuestionSubmit = async () => {
     const currMessages = messages.concat([{ text: query, speaker: "user" }]);
@@ -74,7 +132,10 @@ export const ChatScreen = ({ setAuthenticated }: ChatScreenProps) => {
     }
 
     try {
-      const result = await conversationService.sendQuery(query);
+      const result = await conversationService.sendQuery(
+        query,
+        selectedConversation.id,
+      );
       setQuestionsAnswers(
         questionsAnswers.concat([{ question: query, answer: result.response }]),
       );
@@ -101,16 +162,33 @@ export const ChatScreen = ({ setAuthenticated }: ChatScreenProps) => {
         <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-between">
-              <header className="flex flex-row justify-center gap-2 grow sticky top-0 z-10 bg-white">
+              <header className="flex flex-row justify-center gap-2 sticky top-0 z-10 bg-white">
                 <h1 className="text-3xl">ask simba!</h1>
               </header>
+              <div className="flex flex-row gap-2">
                 <button
-                className="p-4 border border-red-400 bg-red-200 hover:bg-red-400 cursor-pointer rounded-md"
+                  className="p-2 border border-green-400 bg-green-200 hover:bg-green-400 cursor-pointer rounded-md"
+                  onClick={() => setShowConversations(!showConversations)}
+                >
+                  {showConversations
+                    ? "hide conversations"
+                    : "show conversations"}
+                </button>
+                <button
+                  className="p-2 border border-red-400 bg-red-200 hover:bg-red-400 cursor-pointer rounded-md"
                   onClick={() => setAuthenticated(false)}
                 >
                   logout
                 </button>
               </div>
+            </div>
+            {showConversations && (
+              <ConversationList
+                conversations={conversations}
+                onCreateNewConversation={handleCreateNewConversation}
+                onSelectConversation={handleSelectConversation}
+              />
+            )}
             {messages.map((msg, index) => {
               if (msg.speaker === "simba") {
                 return <AnswerBubble key={index} text={msg.text} />;

raggr-frontend/src/components/ConversationList.tsx

@@ -0,0 +1,60 @@
+import { useState, useEffect } from "react";
+
+import { conversationService } from "../api/conversationService";
+type Conversation = {
+  title: string;
+  id: string;
+};
+
+type ConversationProps = {
+  conversations: Conversation[];
+  onSelectConversation: (conversation: Conversation) => void;
+  onCreateNewConversation: () => void;
+};
+
+export const ConversationList = ({
+  conversations,
+  onSelectConversation,
+  onCreateNewConversation,
+}: ConversationProps) => {
+  const [conservations, setConversations] = useState(conversations);
+
+  useEffect(() => {
+    const loadConversations = async () => {
+      try {
+        const fetchedConversations =
+          await conversationService.getAllConversations();
+        setConversations(
+          fetchedConversations.map((conversation) => ({
+            id: conversation.id,
+            title: conversation.name,
+          })),
+        );
+      } catch (error) {
+        console.error("Failed to load messages:", error);
+      }
+    };
+    loadConversations();
+  }, []);
+
+  return (
+    <div className="bg-indigo-300 rounded-md p-3 flex flex-col">
+      {conservations.map((conversation) => {
+        return (
+          <div
+            className="border-blue-400 bg-indigo-300 hover:bg-indigo-200 cursor-pointer rounded-md p-2"
+            onClick={() => onSelectConversation(conversation)}
+          >
+            <p>{conversation.title}</p>
+          </div>
+        );
+      })}
+      <div
+        className="border-blue-400 bg-indigo-300 hover:bg-indigo-200 cursor-pointer rounded-md p-2"
+        onClick={() => onCreateNewConversation()}
+      >
+        <p> + Start a new thread</p>
+      </div>
+    </div>
+  );
+};