feat(rec): Add data ingestion; add gemini demo

.gitignore

@@ -1,3 +1,4 @@
 *__pycache__*
 .venv
-.env
+.env
+data

Dockerfile

@@ -27,12 +27,13 @@ USER user
 
 # Environment variables
 ENV HOME=/home/user \
-PATH=/home/user/.local/bin:$PATH
+    PATH=/home/user/.local/bin:$PATH
 
 # Setup application directory
 WORKDIR $HOME/app
 ADD --chown=user ./prompts $HOME/app/prompts
+ADD --chown=user ./ingest.py $HOME/app/ingest.py
 ADD --chown=user ./app.py $HOME/app/app.py
 
 # Run the application
-CMD ["uv", "run", "--no-cache", "app.py"]
+CMD ["/bin/sh", "-c", "uv run --no-cache ingest.py && uv run --no-cache app.py"]

app.py

@@ -1,27 +1,32 @@
+import json
 import os
 import random
-import json
-
 from pathlib import Path
 
 import gradio as gr
+from google import genai
+from google.genai import types
 from pydantic import BaseModel
 from vllm import LLM, SamplingParams
 from vllm.sampling_params import GuidedDecodingParams
 
 
+HF_TOKEN = os.getenv("HF_TOKEN")
+
 VLLM_MODEL_NAME = os.getenv("VLLM_MODEL_NAME")
 VLLM_GPU_MEMORY_UTILIZATION = float(os.getenv("VLLM_GPU_MEMORY_UTILIZATION"))
 VLLM_MAX_SEQ_LEN = int(os.getenv("VLLM_MAX_SEQ_LEN"))
-HF_TOKEN = os.getenv("HF_TOKEN")
 VLLM_DTYPE = os.getenv("VLLM_DTYPE")
 
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+
 # -------------------------------- HELPERS -------------------------------------
 def load_prompt(path: Path) -> str:
     with path.open("r") as file:
         prompt = file.read()
     return prompt
 
+
 # --------------------------------  Data Models  -------------------------------
 class StructuredQueryRewriteResponse(BaseModel):
     general: str | None
@@ -30,10 +35,16 @@ class StructuredQueryRewriteResponse(BaseModel):
     technical: str | None
     curiosity: str | None
 
+
 class QueryRewrite(BaseModel):
     rewrites: list[str] | None = None
     structured: StructuredQueryRewriteResponse | None = None
 
+
+class APIGenreRecommendationResponse(BaseModel):
+    genres: list[str]
+
+
 # --------------------------------  VLLM  --------------------------------------
 local_llm = LLM(
     model=VLLM_MODEL_NAME,
@@ -59,8 +70,25 @@ vllm_system_prompt = (
 )
 vllm_prompt = load_prompt(Path("./prompts/local.txt"))
 
+# --------------------------------  GEMINI  ------------------------------------
+gemini_config = types.GenerateContentConfig(
+    response_mime_type="application/json",
+    response_schema=APIGenreRecommendationResponse,
+    temperature=0.7,
+    max_output_tokens=1024,
+    system_instruction=("You are a helpful music genre recommendation assistant."),
+)
+gemini_llm = genai.Client(
+    api_key=GEMINI_API_KEY,
+    http_options={"api_version": "v1alpha"},
+)
+gemini_prompt = load_prompt(Path("./prompts/api.txt"))
+
+
+# ---------------------------- RETRIEVAL ---------------------------------------
+
 
-# Dummy model functions for demonstration
+# ----------------------- GENERATE RECOMMENDATIONS -----------------------------
 def recommend_sadaimrec(query: str):
     prompt = vllm_prompt.format(query=query)
     messages = [
@@ -79,18 +107,25 @@ def recommend_sadaimrec(query: str):
     return f"SADAIMREC: response to '{rewrite.model_dump_json(indent=4)}'"
 
 
-def recommend_chatgpt(query: str):
-    return f"CHATGPT: response to '{query}'"
+def recommend_gemini(query: str):
+    prompt = gemini_prompt.format(query=query)
+    response = gemini_llm.models.generate_content(
+        model="gemini-2.0-flash",
+        contents=prompt,
+        config=gemini_config,
+    )
+    parsed_content: APIGenreRecommendationResponse = response.parsed
+    return f"CHATGPT: response to '{parsed_content.model_dump_json(indent=4)}'"
 
 
 # Mapping names to functions
 pipelines = {
     "sadaimrec": recommend_sadaimrec,
-    "chatgpt": recommend_chatgpt,
+    "chatgpt": recommend_gemini,
 }
 
 
-# Interface logic
+# -------------------------------------- INTERFACE -----------------------------
 def generate_responses(query):
     # Randomize model order
     pipeline_names = list(pipelines.keys())
@@ -188,4 +223,6 @@ with gr.Blocks() as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+    demo.queue(max_size=10, default_concurrency_limit=1).launch(
+        server_name="0.0.0.0", server_port=7860
+    )

ingest.py

@@ -0,0 +1,101 @@
+import os
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+from fastembed import SparseTextEmbedding, SparseEmbedding
+from sentence_transformers import SentenceTransformer
+from huggingface_hub import hf_hub_download
+from qdrant_client import QdrantClient
+from qdrant_client import models as qmodels
+
+DATA_PATH = Path(os.getenv("DATA_PATH"))
+DB_PATH = DATA_PATH / "db"
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+RECREATE_DB = bool(os.getenv("RECREATE_DB", "False").lower == "true")
+DATA_REPO = os.getenv("DATA_REPO")
+DATA_FILENAME = os.getenv("DATA_FILENAME")
+
+client = QdrantClient(path=str(DB_PATH))
+collection_name = "knowledge_cards"
+dense_model_dims = 1024
+dense_batch_size = 128
+sparse_batch_size = 256
+
+dense_encoder = SentenceTransformer(
+    model_name_or_path="mixedbread-ai/mxbai-embed-large-v1", device="cuda"
+)
+sparse_encoder = SparseTextEmbedding(model_name="Qdrant/bm25", cuda=True)
+
+
+# Utils
+def convert_serialized_sparse_embeddings(sparse_dict: dict[str, float]):
+    """Convert all dictionary keys to strings for PyArrow compatibility."""
+    return SparseEmbedding.from_dict({int(k): v for k, v in sparse_dict.items()})
+
+
+def ingest_data(chunks: list[dict[str, Any]]):
+    if client.collection_exists(collection_name) and RECREATE_DB:
+        print("Recreating collection.", flush=True)
+        client.delete_collection(collection_name)
+    elif client.collection_exists(collection_name):
+        print("Collection already exists, skipping ingestion.", flush=True)
+        return
+
+    print("Ingesting knowledge cards...", flush=True)
+    client.create_collection(
+        collection_name=collection_name,
+        vectors_config={
+            "dense": qmodels.VectorParams(
+                size=dense_model_dims,
+                distance=qmodels.Distance.COSINE,
+            )
+        },
+        sparse_vectors_config={
+            "sparse": qmodels.SparseVectorParams(modifier=qmodels.Modifier.IDF)
+        },
+    )
+
+    # Generate embeddings
+    chunk_texts = [chunk["text"] for chunk in chunks]
+    dense_vectors = list(
+        dense_encoder.encode(
+            chunk_texts,
+            batch_size=dense_batch_size,
+            normalize_embeddings=True,
+        )
+    )
+    sparse_vectors = list(
+        sparse_encoder.embed(chunk_texts, batch_size=sparse_batch_size)
+    )
+
+    # Upload to db
+    client.upload_points(
+        collection_name=collection_name,
+        points=[
+            qmodels.PointStruct(
+                id=idx,
+                payload=chunk,
+                vector={"dense": dense, "sparse": sparse.as_object()},
+            )
+            for idx, (chunk, dense, sparse) in enumerate(
+                zip(chunks, dense_vectors, sparse_vectors)
+            )
+        ],
+    )
+
+
+def ingest():
+    downloaded_path = hf_hub_download(
+        repo_id=DATA_REPO, filename=DATA_FILENAME, token=HF_TOKEN, repo_type="dataset"
+    )
+    print(f"Downloaded knowledge card dataset; path = {downloaded_path}", flush=True)
+    chunk_df = pd.read_parquet(downloaded_path)
+    chunks = chunk_df.to_dict(orient="records")
+    ingest_data(chunks=chunks)
+    print("Ingestion is finished.", flush=True)
+
+
+if __name__ == "__main__":
+    ingest()

prompts/api.txt

@@ -0,0 +1,7 @@
+# Purpose
+
+Recommend 5 genres based on the user query
+
+# Query
+
+{query}

pyproject.toml

@@ -5,8 +5,12 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
+    "google-genai>=1.13.0",
     "gradio>=5.29.0",
-    "openai>=1.77.0",
+    "pandas>=2.2.3",
+    "pyarrow>=20.0.0",
     "pydantic>=2.11.4",
+    "qdrant-client[fastembed-gpu]>=1.14.2",
+    "sentence-transformers>=4.1.0",
     "vllm>=0.8.5.post1",
 ]

requirements.txt

@@ -14,6 +14,7 @@ annotated-types==0.7.0
     # via pydantic
 anyio==4.9.0
     # via
+    #   google-genai
     #   gradio
     #   httpx
     #   openai
@@ -29,7 +30,9 @@ attrs==25.3.0
 blake3==1.0.4
     # via vllm
 cachetools==5.5.2
-    # via vllm
+    # via
+    #   google-auth
+    #   vllm
 certifi==2025.4.26
     # via
     #   httpcore
@@ -47,6 +50,8 @@ cloudpickle==3.1.1
     # via
     #   outlines
     #   vllm
+coloredlogs==15.0.1
+    # via onnxruntime-gpu
 compressed-tensors==0.9.3
     # via vllm
 cupy-cuda12x==13.4.1
@@ -77,6 +82,8 @@ fastapi==0.115.12
     #   vllm
 fastapi-cli==0.0.7
     # via fastapi
+fastembed-gpu==0.6.1
+    # via qdrant-client
 fastrlock==0.8.3
     # via cupy-cuda12x
 ffmpy==0.5.0
@@ -88,6 +95,8 @@ filelock==3.18.0
     #   torch
     #   transformers
     #   vllm
+flatbuffers==25.2.10
+    # via onnxruntime-gpu
 frozenlist==1.6.0
     # via
     #   aiohttp
@@ -99,6 +108,10 @@ fsspec==2025.3.2
     #   torch
 gguf==0.16.2
     # via vllm
+google-auth==2.39.0
+    # via google-genai
+google-genai==1.13.0
+    # via sdmrec-demo (pyproject.toml)
 googleapis-common-protos==1.70.0
     # via
     #   opentelemetry-exporter-otlp-proto-grpc
@@ -110,13 +123,19 @@ gradio-client==1.10.0
 groovy==0.1.2
     # via gradio
 grpcio==1.71.0
-    # via opentelemetry-exporter-otlp-proto-grpc
+    # via
+    #   opentelemetry-exporter-otlp-proto-grpc
+    #   qdrant-client
 h11==0.16.0
     # via
     #   httpcore
     #   uvicorn
+h2==4.2.0
+    # via httpx
 hf-xet==1.1.0
     # via huggingface-hub
+hpack==4.1.0
+    # via h2
 httpcore==1.0.9
     # via httpx
 httptools==0.6.4
@@ -124,17 +143,25 @@ httptools==0.6.4
 httpx==0.28.1
     # via
     #   fastapi
+    #   google-genai
     #   gradio
     #   gradio-client
     #   openai
+    #   qdrant-client
     #   safehttpx
 huggingface-hub==0.30.2
     # via
+    #   fastembed-gpu
     #   gradio
     #   gradio-client
+    #   sentence-transformers
     #   tokenizers
     #   transformers
     #   vllm
+humanfriendly==10.0
+    # via coloredlogs
+hyperframe==6.1.0
+    # via h2
 idna==3.10
     # via
     #   anyio
@@ -159,6 +186,8 @@ jinja2==3.1.6
     #   torch
 jiter==0.9.0
     # via openai
+joblib==1.5.0
+    # via scikit-learn
 jsonschema==4.23.0
     # via
     #   mistral-common
@@ -177,6 +206,8 @@ llvmlite==0.44.0
     # via numba
 lm-format-enforcer==0.10.11
     # via vllm
+loguru==0.7.3
+    # via fastembed-gpu
 markdown-it-py==3.0.0
     # via rich
 markupsafe==3.0.2
@@ -187,6 +218,8 @@ mdurl==0.1.2
     # via markdown-it-py
 mistral-common==1.5.4
     # via vllm
+mmh3==5.1.0
+    # via fastembed-gpu
 mpmath==1.3.0
     # via sympy
 msgpack==1.1.0
@@ -210,13 +243,17 @@ numba==0.61.2
 numpy==2.2.5
     # via
     #   cupy-cuda12x
+    #   fastembed-gpu
     #   gguf
     #   gradio
     #   mistral-common
     #   numba
+    #   onnxruntime-gpu
     #   opencv-python-headless
     #   outlines
     #   pandas
+    #   qdrant-client
+    #   scikit-learn
     #   scipy
     #   torchvision
     #   transformers
@@ -256,10 +293,10 @@ nvidia-nvjitlink-cu12==12.4.127
     #   torch
 nvidia-nvtx-cu12==12.4.127
     # via torch
+onnxruntime-gpu==1.21.1
+    # via fastembed-gpu
 openai==1.77.0
-    # via
-    #   sdmrec-demo (pyproject.toml)
-    #   vllm
+    # via vllm
 opencv-python-headless==4.11.0.86
     # via
     #   mistral-common
@@ -307,18 +344,25 @@ packaging==25.0
     #   gradio-client
     #   huggingface-hub
     #   lm-format-enforcer
+    #   onnxruntime-gpu
     #   ray
     #   transformers
 pandas==2.2.3
-    # via gradio
+    # via
+    #   sdmrec-demo (pyproject.toml)
+    #   gradio
 partial-json-parser==0.2.1.1.post5
     # via vllm
 pillow==11.2.1
     # via
+    #   fastembed-gpu
     #   gradio
     #   mistral-common
+    #   sentence-transformers
     #   torchvision
     #   vllm
+portalocker==2.10.1
+    # via qdrant-client
 prometheus-client==0.21.1
     # via
     #   prometheus-fastapi-instrumentator
@@ -332,13 +376,25 @@ propcache==0.3.1
 protobuf==4.25.7
     # via
     #   googleapis-common-protos
+    #   onnxruntime-gpu
     #   opentelemetry-proto
+    #   qdrant-client
     #   ray
     #   vllm
 psutil==7.0.0
     # via vllm
 py-cpuinfo==9.0.0
     # via vllm
+py-rust-stemmers==0.1.5
+    # via fastembed-gpu
+pyarrow==20.0.0
+    # via sdmrec-demo (pyproject.toml)
+pyasn1==0.6.1
+    # via
+    #   pyasn1-modules
+    #   rsa
+pyasn1-modules==0.4.2
+    # via google-auth
 pycountry==24.6.1
     # via outlines
 pydantic==2.11.4
@@ -346,11 +402,13 @@ pydantic==2.11.4
     #   sdmrec-demo (pyproject.toml)
     #   compressed-tensors
     #   fastapi
+    #   google-genai
     #   gradio
     #   lm-format-enforcer
     #   mistral-common
     #   openai
     #   outlines
+    #   qdrant-client
     #   vllm
     #   xgrammar
 pydantic-core==2.33.2
@@ -383,6 +441,8 @@ pyyaml==6.0.2
     #   vllm
 pyzmq==26.4.0
     # via vllm
+qdrant-client==1.14.2
+    # via sdmrec-demo (pyproject.toml)
 ray==2.45.0
     # via vllm
 referencing==0.36.2
@@ -396,6 +456,8 @@ regex==2024.11.6
     #   transformers
 requests==2.32.3
     # via
+    #   fastembed-gpu
+    #   google-genai
     #   huggingface-hub
     #   mistral-common
     #   opentelemetry-exporter-otlp-proto-http
@@ -414,16 +476,25 @@ rpds-py==0.24.0
     # via
     #   jsonschema
     #   referencing
+rsa==4.9.1
+    # via google-auth
 ruff==0.11.8
     # via gradio
 safehttpx==0.1.6
     # via gradio
 safetensors==0.5.3
     # via transformers
+scikit-learn==1.6.1
+    # via sentence-transformers
 scipy==1.15.2
-    # via vllm
+    # via
+    #   scikit-learn
+    #   sentence-transformers
+    #   vllm
 semantic-version==2.10.0
     # via gradio
+sentence-transformers==4.1.0
+    # via sdmrec-demo (pyproject.toml)
 sentencepiece==0.2.0
     # via
     #   gguf
@@ -450,7 +521,11 @@ starlette==0.46.2
     #   gradio
     #   prometheus-fastapi-instrumentator
 sympy==1.13.1
-    # via torch
+    # via
+    #   onnxruntime-gpu
+    #   torch
+threadpoolctl==3.6.0
+    # via scikit-learn
 tiktoken==0.9.0
     # via
     #   mistral-common
@@ -458,6 +533,7 @@ tiktoken==0.9.0
     #   xgrammar
 tokenizers==0.21.1
     # via
+    #   fastembed-gpu
     #   transformers
     #   vllm
 tomlkit==0.13.2
@@ -466,6 +542,7 @@ torch==2.6.0
     # via
     #   compressed-tensors
     #   outlines
+    #   sentence-transformers
     #   torchaudio
     #   torchvision
     #   vllm
@@ -477,15 +554,18 @@ torchvision==0.21.0
     # via vllm
 tqdm==4.67.1
     # via
+    #   fastembed-gpu
     #   gguf
     #   huggingface-hub
     #   openai
     #   outlines
+    #   sentence-transformers
     #   transformers
     #   vllm
 transformers==4.51.3
     # via
     #   compressed-tensors
+    #   sentence-transformers
     #   vllm
     #   xgrammar
 triton==3.2.0
@@ -500,6 +580,7 @@ typing-extensions==4.13.2
     # via
     #   anyio
     #   fastapi
+    #   google-genai
     #   gradio
     #   gradio-client
     #   huggingface-hub
@@ -511,6 +592,7 @@ typing-extensions==4.13.2
     #   pydantic-core
     #   referencing
     #   rich-toolkit
+    #   sentence-transformers
     #   torch
     #   typer
     #   typing-inspection
@@ -520,7 +602,9 @@ typing-inspection==0.4.0
 tzdata==2025.2
     # via pandas
 urllib3==2.4.0
-    # via requests
+    # via
+    #   qdrant-client
+    #   requests
 uvicorn==0.34.2
     # via
     #   fastapi
@@ -536,6 +620,7 @@ watchfiles==1.0.5
     #   vllm
 websockets==15.0.1
     # via
+    #   google-genai
     #   gradio-client
     #   uvicorn
 wrapt==1.17.2

uv.lock

@@ -339,6 +339,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
 ]
 
+[[package]]
+name = "coloredlogs"
+version = "15.0.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "humanfriendly" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/cc/c7/eed8f27100517e8c0e6b923d5f0845d0cb99763da6fdee00478f91db7325/coloredlogs-15.0.1.tar.gz", hash = "sha256:7c991aa71a4577af2f82600d8f8f3a89f936baeaf9b50a9c197da014e5bf16b0", size = 278520, upload-time = "2021-06-11T10:22:45.202Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl", hash = "sha256:612ee75c546f53e92e70049c9dbfcc18c935a2b9a53b66085ce9ef6a6e5c0934", size = 46018, upload-time = "2021-06-11T10:22:42.561Z" },
+]
+
 [[package]]
 name = "compressed-tensors"
 version = "0.9.3"
@@ -496,6 +508,27 @@ standard = [
     { name = "uvicorn", extra = ["standard"] },
 ]
 
+[[package]]
+name = "fastembed-gpu"
+version = "0.6.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "huggingface-hub" },
+    { name = "loguru" },
+    { name = "mmh3" },
+    { name = "numpy" },
+    { name = "onnxruntime-gpu" },
+    { name = "pillow" },
+    { name = "py-rust-stemmers" },
+    { name = "requests" },
+    { name = "tokenizers" },
+    { name = "tqdm" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/5e/f1/0cde735baa81827ef54c875ab1689f307bf6562f5ec12e3aeed53f002e56/fastembed_gpu-0.6.1.tar.gz", hash = "sha256:5a9ed9cc5f81c26eed9e99cd5f8d4af8ceefcf2c0c1a879432aa1fa9a72cc2dd", size = 51705, upload-time = "2025-04-10T13:48:17.65Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7f/f1/4746523eaf1273be4274244cadd72b25fd30c31c4113b61d5866388318e8/fastembed_gpu-0.6.1-py3-none-any.whl", hash = "sha256:66273814c4c69be850236663547549770c6085d6289c4e8ecfae9a4f626c72f0", size = 86839, upload-time = "2025-04-10T13:48:16.549Z" },
+]
+
 [[package]]
 name = "fastrlock"
 version = "0.8.3"
@@ -532,6 +565,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/4d/36/2a115987e2d8c300a974597416d9de88f2444426de9571f4b59b2cca3acc/filelock-3.18.0-py3-none-any.whl", hash = "sha256:c401f4f8377c4464e6db25fff06205fd89bdd83b65eb0488ed1b160f780e21de", size = 16215, upload-time = "2025-03-14T07:11:39.145Z" },
 ]
 
+[[package]]
+name = "flatbuffers"
+version = "25.2.10"
+source = { registry = "https://pypi.org/simple" }
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 source = { virtual = "." }
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