Switch to OpenAI embeddings for ChromaDB

Replace Ollama embedding function with OpenAI's text-embedding-3-small
model for improved embedding quality and consistency.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

.dockerignore

@@ -0,0 +1,16 @@
+.git
+.gitignore
+README.md
+.env
+.DS_Store
+chromadb/
+chroma_db/
+raggr-frontend/node_modules/
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+.Python
+.venv/
+venv/
+.pytest_cache/

.python-version

@@ -0,0 +1 @@
+3.13

Dockerfile

@@ -0,0 +1,46 @@
+FROM python:3.13-slim
+
+WORKDIR /app
+
+# Install system dependencies, Node.js, Yarn, and uv
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    && curl -fsSL https://deb.nodesource.com/setup_20.x | bash - \
+    && apt-get install -y nodejs \
+    && npm install -g yarn \
+    && rm -rf /var/lib/apt/lists/* \
+    && curl -LsSf https://astral.sh/uv/install.sh | sh
+
+# Add uv to PATH
+ENV PATH="/root/.local/bin:$PATH"
+
+# Copy dependency files
+COPY pyproject.toml ./
+
+# Install Python dependencies using uv
+RUN uv pip install --system -e .
+
+# Copy application code
+COPY *.py ./
+COPY startup.sh ./
+RUN chmod +x startup.sh
+
+# Copy frontend code and build
+COPY raggr-frontend ./raggr-frontend
+WORKDIR /app/raggr-frontend
+RUN yarn install && yarn build
+WORKDIR /app
+
+# Create ChromaDB directory
+RUN mkdir -p /app/chromadb
+
+# Expose port
+EXPOSE 8080
+
+# Set environment variables
+ENV PYTHONPATH=/app
+ENV CHROMADB_PATH=/app/chromadb
+
+# Run the startup script
+CMD ["./startup.sh"]

app.py

@@ -0,0 +1,37 @@
+import os
+
+from flask import Flask, request, jsonify, render_template, send_from_directory
+
+from main import consult_simba_oracle
+
+app = Flask(__name__, static_folder="raggr-frontend/dist/static", template_folder="raggr-frontend/dist")
+
+
+# Serve React static files
+@app.route('/static/<path:filename>')
+def static_files(filename):
+    return 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):
+    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')
+
+@app.route("/api/query", methods=["POST"])
+def query():
+    data  = request.get_json()
+    query = data.get("query")
+    return jsonify({"response": consult_simba_oracle(query)})
+
+@app.route("/api/ingest", methods=["POST"])
+def webhook():
+    data = request.get_json()
+    print(data)
+    return jsonify({"status": "received"})
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=8080, debug=True)
+

chunker.py

@@ -4,8 +4,8 @@ import re
 from typing import Union
 from uuid import UUID, uuid4
 
-from chromadb.utils.embedding_functions.ollama_embedding_function import (
-    OllamaEmbeddingFunction,
+from chromadb.utils.embedding_functions.openai_embedding_function import (
+    OpenAIEmbeddingFunction,
 )
 from dotenv import load_dotenv
 
@@ -80,9 +80,9 @@ class Chunk:
 
 
 class Chunker:
-    embedding_fx = OllamaEmbeddingFunction(
-        url=os.getenv("OLLAMA_URL", ""),
-        model_name="mxbai-embed-large",
+    embedding_fx = OpenAIEmbeddingFunction(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        model_name="text-embedding-3-small",
     )
 
     def __init__(self, collection) -> None:
@@ -96,7 +96,7 @@ class Chunker:
     ) -> list[Chunk]:
         doc_uuid = uuid4()
 
-        chunk_size = min(chunk_size, len(document))
+        chunk_size = min(chunk_size, len(document)) or 1
 
         chunks = []
         num_chunks = ceil(len(document) / chunk_size)

cleaner.py

@@ -12,6 +12,9 @@ from request import PaperlessNGXService
 
 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"))
+
 parser = argparse.ArgumentParser(description="use llm to clean documents")
 parser.add_argument("document_id", type=str, help="questions about simba's health")
 
@@ -131,7 +134,7 @@ Someone will kill the innocent kittens if you don't extract the text exactly. So
 
 
 def summarize_pdf_image(filepaths: list[str]):
-    res = ollama.chat(
+    res = ollama_client.chat(
         model="gemma3:4b",
         messages=[
             {

docker-compose.yml

@@ -0,0 +1,17 @@
+version: '3.8'
+
+services:
+  raggr:
+    image: torrtle/simbarag:latest
+    network_mode: host
+    environment:
+      - PAPERLESS_TOKEN=${PAPERLESS_TOKEN}
+      - BASE_URL=${BASE_URL}
+      - OLLAMA_URL=${OLLAMA_URL:-http://localhost:11434}
+      - CHROMADB_PATH=/app/chromadb
+      - OPENAI_API_KEY=${OPENAI_API_KEY}
+    volumes:
+      - chromadb_data:/app/chromadb
+
+volumes:
+  chromadb_data:

main.py

@@ -6,6 +6,7 @@ from typing import Any, Union
 import argparse
 import chromadb
 import ollama
+from openai import OpenAI
 
 
 from request import PaperlessNGXService
@@ -17,6 +18,9 @@ from dotenv import load_dotenv
 
 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"))
+
 client = chromadb.PersistentClient(path=os.getenv("CHROMADB_PATH", ""))
 simba_docs = client.get_or_create_collection(name="simba_docs")
 feline_vet_lookup = client.get_or_create_collection(name="feline_vet_lookup")
@@ -29,9 +33,13 @@ 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"
+)
 
 ppngx = PaperlessNGXService()
 
+openai_client = OpenAI()
 
 def index_using_pdf_llm():
     files = ppngx.get_data()
@@ -39,6 +47,7 @@ def index_using_pdf_llm():
         document_id = file["id"]
         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)
         file["content"] = generated_summary
 
@@ -68,36 +77,78 @@ def chunk_data(docs: list[dict[str, Union[str, Any]]], collection):
     print(docs)
     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"]),
+             "created_date": date_to_epoch(docs[index]["created_date"]),
+             "filename": docs[index]["original_file_name"]
         }
         chunker.chunk_document(
             document=text,
             metadata=metadata,
         )
 
+def chunk_text(texts: list[str], collection):
+    chunker = Chunker(collection)
+
+    for index, text in enumerate(texts):
+        metadata = {}
+        chunker.chunk_document(
+            document=text,
+            metadata=metadata,
+        )
 
 def consult_oracle(input: str, collection):
+    print(input)
+    import time
+    start_time = time.time()
+
     # Ask
-    qg = QueryGenerator()
-    metadata_filter = qg.get_query("input")
-    print(metadata_filter)
+    # print("Starting query generation")
+    # qg_start = time.time()
+    # qg = QueryGenerator()
+    # metadata_filter = qg.get_query(input)
+    # qg_end = time.time()
+    # print(f"Query generation took {qg_end - qg_start:.2f} seconds")
+    # print(metadata_filter)
+
+    print("Starting embedding generation")
+    embedding_start = time.time()
     embeddings = Chunker.embedding_fx(input=[input])
+    embedding_end = time.time()
+    print(f"Embedding generation took {embedding_end - embedding_start:.2f} seconds")
+
+    print("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")
 
     # Generate
-    output = ollama.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}",
+    print("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}"}
+        ]
     )
+    llm_end = time.time()
+    print(f"LLM generation took {llm_end - llm_start:.2f} seconds")
 
-    print(output["response"])
+    total_time = time.time() - start_time
+    print(f"Total consult_oracle execution took {total_time:.2f} seconds")
+
+    return response.choices[0].message.content
 
 
 def paperless_workflow(input):
@@ -109,24 +160,47 @@ def paperless_workflow(input):
     consult_oracle(input, simba_docs)
 
 
+def consult_simba_oracle(input: str):
+    return consult_oracle(
+        input=input,
+        collection=simba_docs,
+    )
+
+
 if __name__ == "__main__":
     args = parser.parse_args()
     if args.reindex:
-        # logging.info(msg="Fetching documents from Paperless-NGX")
-        # ppngx = PaperlessNGXService()
-        # docs = ppngx.get_data()
-        # logging.info(msg=f"Fetched {len(docs)} documents")
+        print("Fetching documents from Paperless-NGX")
+        ppngx = PaperlessNGXService()
+        docs = ppngx.get_data()
+        print(docs)
+        print(f"Fetched {len(docs)} documents")
         #
-        # logging.info(msg="Chunking documents now ...")
-        # chunk_data(docs, collection=simba_docs)
-        # logging.info(msg="Done chunking documents")
-        index_using_pdf_llm()
+        print("Chunking documents now ...")
+        chunk_data(docs, collection=simba_docs)
+        print("Done chunking documents")
+        # index_using_pdf_llm()
+
+
+    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:
-        logging.info("Consulting oracle ...")
-        consult_oracle(
+        print("Consulting oracle ...")
+        print(consult_oracle(
             input=args.query,
             collection=simba_docs,
-        )
+        ))
     else:
         print("please provide a query")
+
+

pyproject.toml

@@ -4,4 +4,14 @@ version = "0.1.0"
 description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.13"
-dependencies = []
+dependencies = [
+    "chromadb>=1.1.0",
+    "python-dotenv>=1.0.0",
+    "flask>=3.1.2",
+    "httpx>=0.28.1",
+    "ollama>=0.6.0",
+    "openai>=2.0.1",
+    "pydantic>=2.11.9",
+    "pillow>=10.0.0",
+    "pymupdf>=1.24.0",
+]

query.py

@@ -1,10 +1,16 @@
 import json
+import os
 from typing import Literal
 import datetime
-from ollama import chat, ChatResponse
+from ollama import chat, ChatResponse, 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"))
+
 # This uses inferred filters — which means using LLM to create the metadata filters
 
 
@@ -27,11 +33,15 @@ class GeneratedQuery(BaseModel):
     fields: list[str]
     extracted_metadata_fields: str
 
+class Time(BaseModel):
+    time: int
 
 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 
-the date range the user is trying to query. You should return the it as a JSON. The date tag is created_date. Return the date in epoch time
+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:
@@ -90,18 +100,31 @@ class QueryGenerator:
         return date.timestamp()
 
     def get_query(self, input: str):
-        response: ChatResponse = chat(
-            model="gemma3n:e4b",
-            messages=[
+        client = OpenAI()
+        print(input)
+        response = client.responses.parse(
+            model="gpt-4o",
+            input=[
                 {"role": "system", "content": PROMPT},
                 {"role": "user", "content": input},
             ],
-            format=GeneratedQuery.model_json_schema(),
+            text_format=Time,
         )
+        print(response)
+        query = json.loads(response.output_parsed.extracted_metadata_fields)
 
-        query = json.loads(
-            json.loads(response["message"]["content"])["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(),
+        # )
+
+        # 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]

raggr-frontend/.gitignore

@@ -0,0 +1,16 @@
+# Local
+.DS_Store
+*.local
+*.log*
+
+# Dist
+node_modules
+dist/
+
+# Profile
+.rspack-profile-*/
+
+# IDE
+.vscode/*
+!.vscode/extensions.json
+.idea

raggr-frontend/README.md

@@ -0,0 +1,36 @@
+# Rsbuild project
+
+## Setup
+
+Install the dependencies:
+
+```bash
+pnpm install
+```
+
+## Get started
+
+Start the dev server, and the app will be available at [http://localhost:3000](http://localhost:3000).
+
+```bash
+pnpm dev
+```
+
+Build the app for production:
+
+```bash
+pnpm build
+```
+
+Preview the production build locally:
+
+```bash
+pnpm preview
+```
+
+## Learn more
+
+To learn more about Rsbuild, check out the following resources:
+
+- [Rsbuild documentation](https://rsbuild.rs) - explore Rsbuild features and APIs.
+- [Rsbuild GitHub repository](https://github.com/web-infra-dev/rsbuild) - your feedback and contributions are welcome!

raggr-frontend/package.json

@@ -0,0 +1,26 @@
+{
+  "name": "raggr-frontend",
+  "version": "1.0.0",
+  "private": true,
+  "type": "module",
+  "scripts": {
+    "build": "rsbuild build",
+    "dev": "rsbuild dev --open",
+    "preview": "rsbuild preview"
+  },
+  "dependencies": {
+    "axios": "^1.12.2",
+    "marked": "^16.3.0",
+    "react": "^19.1.1",
+    "react-dom": "^19.1.1",
+    "react-markdown": "^10.1.0"
+  },
+  "devDependencies": {
+    "@rsbuild/core": "^1.5.6",
+    "@rsbuild/plugin-react": "^1.4.0",
+    "@tailwindcss/postcss": "^4.0.0",
+    "@types/react": "^19.1.13",
+    "@types/react-dom": "^19.1.9",
+    "typescript": "^5.9.2"
+  }
+}

raggr-frontend/postcss.config.mjs

@@ -0,0 +1,5 @@
+export default {
+	plugins: {
+		"@tailwindcss/postcss": {},
+	},
+};

raggr-frontend/rsbuild.config.ts

@@ -0,0 +1,6 @@
+import { defineConfig } from '@rsbuild/core';
+import { pluginReact } from '@rsbuild/plugin-react';
+
+export default defineConfig({
+  plugins: [pluginReact()],
+});

raggr-frontend/src/App.css

@@ -0,0 +1,6 @@
+@import "tailwindcss";
+
+body {
+	margin: 0;
+	font-family: Inter, Avenir, Helvetica, Arial, sans-serif;
+}

raggr-frontend/src/App.tsx

@@ -0,0 +1,66 @@
+import { 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);
+
+	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>
+	);
+};
+
+export default App;

raggr-frontend/src/env.d.ts

@@ -0,0 +1,11 @@
+/// <reference types="@rsbuild/core/types" />
+
+/**
+ * Imports the SVG file as a React component.
+ * @requires [@rsbuild/plugin-svgr](https://npmjs.com/package/@rsbuild/plugin-svgr)
+ */
+declare module '*.svg?react' {
+  import type React from 'react';
+  const ReactComponent: React.FunctionComponent<React.SVGProps<SVGSVGElement>>;
+  export default ReactComponent;
+}

raggr-frontend/src/index.tsx

@@ -0,0 +1,13 @@
+import React from 'react';
+import ReactDOM from 'react-dom/client';
+import App from './App';
+
+const rootEl = document.getElementById('root');
+if (rootEl) {
+  const root = ReactDOM.createRoot(rootEl);
+  root.render(
+    <React.StrictMode>
+      <App />
+    </React.StrictMode>,
+  );
+}

raggr-frontend/tsconfig.json

@@ -0,0 +1,25 @@
+{
+  "compilerOptions": {
+    "lib": ["DOM", "ES2020"],
+    "jsx": "react-jsx",
+    "target": "ES2020",
+    "noEmit": true,
+    "skipLibCheck": true,
+    "useDefineForClassFields": true,
+
+    /* modules */
+    "module": "ESNext",
+    "moduleDetection": "force",
+    "moduleResolution": "bundler",
+    "verbatimModuleSyntax": true,
+    "resolveJsonModule": true,
+    "allowImportingTsExtensions": true,
+    "noUncheckedSideEffectImports": true,
+
+    /* type checking */
+    "strict": true,
+    "noUnusedLocals": true,
+    "noUnusedParameters": true
+  },
+  "include": ["src"]
+}

startup.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+
+echo "Starting reindex process..."
+python main.py "" --reindex
+
+echo "Starting Flask application..."
+python app.py