feat(vector): Automatic indexing of documents in s3 storage

2026-01-30 22:38:08 +01:00
commit e60eb297fe
15 changed files with 1026 additions and 0 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1,163 @@
+# ---- Python ----
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
--- a/.python-version
+++ b/.python-version
@@ -0,0 +1 @@
+3.13
--- a/18
+++ b/18
@@ -0,0 +1,18 @@
+FROM python:3.13-slim
+
+WORKDIR /app
+
+# Copy dependency files
+COPY pyproject.toml uv.lock ./
+
+# Install uv
+RUN pip install uv
+
+# Install dependencies
+RUN uv sync --frozen
+
+# Copy application code
+COPY . .
+
+# Run the application
+CMD ["uv", "run", "python", "main.py"]
--- a/README.md
+++ b/README.md
--- a/database/init.py
+++ b/database/init.py
--- a/database/database.py
+++ b/database/database.py
--- a/main.py
+++ b/main.py
@@ -0,0 +1,77 @@
+import store
+import vector
+
+import time
+import os
+from minio.datatypes import Object
+
+
+TIME_INTERVAL = int(os.getenv("INDEXER_INTERVAL"))
+S3_ENDPOINT = os.getenv("S3_ENDPOINT")
+S3_ACCESS_KEY = os.getenv("S3_ACCESS_KEY")
+S3_SECRET_KEY = os.getenv("S3_SECRET_KEY")
+S3_BUCKET = os.getenv("S3_BUCKET")
+
+EMBEDDING_AUTHORIZATION = os.getenv("EMBEDDING_AUTHORIZATION")
+EMBEDDING_URL = os.getenv("EMBEDDING_URL")
+EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL")
+
+QDRANT_HOST = os.getenv("QDRANT_HOST")
+QDRANT_PORT = int(os.getenv("QDRANT_PORT"))
+QDRANT_COLLECTION_NAME = os.getenv("QDRANT_COLLECTION_NAME")
+
+
+def filter_not_indexed(object: Object) -> bool:
+    return object.object_name is not None and s3.get_tag(object.object_name).get("status") != "indexed"
+
+s3 = store.S3Storage(
+    endpoint=S3_ENDPOINT,
+    access_key=S3_ACCESS_KEY,
+    secret_key=S3_SECRET_KEY,
+    bucket=S3_BUCKET
+)
+
+embedded_client = vector.EmbeddedClient(
+    authorization=EMBEDDING_AUTHORIZATION,
+    url=EMBEDDING_URL,
+    model=EMBEDDING_MODEL
+)
+
+qdrant = vector.Qdrant(
+    host=QDRANT_HOST,
+    port=QDRANT_PORT,
+    collection_name=QDRANT_COLLECTION_NAME
+)
+qdrant.create_if_not_exists_collection()
+
+while True:
+    print(f"- Indexing objects...")
+    objects = s3.list_filtered(filter_not_indexed, recursive=True)
+    for object in objects:
+        if object.object_name is None:
+            continue
+        print(f"  - Downloading object {object.object_name}...")
+        content = s3.get(object.object_name)
+        if content is None:
+            continue
+        print(f"  - Chunking object {object.object_name}...")
+        chunks = vector.Chunks().chunk(content)
+        print(f"  - Embedding chunks...")
+        count_chunks = len(chunks)
+        count_embedded = 0
+        for i, chunk in enumerate(chunks):
+            print(f"    - Embedding chunk {i+1}/{count_chunks}...")
+            chunk.embed(embedded_client)
+            print(f"    - Chunk {i+1}/{count_chunks} embedded!")
+            count_embedded += 1
+        print(f"  - {count_embedded}/{count_chunks} chunks embedded!")
+
+        print(f"  - Creating points...")
+        points = qdrant.create_points(chunks, S3_BUCKET, object.object_name)
+        print(f"  - Upserting {len(points)} points to Qdrant...")
+        qdrant.upsert_points(points)
+        print(f"  - Points upserted!")
+
+        s3.set_tag(object.object_name, {"status": "indexed"})
+    print(f"✓ {len(list(objects))} objects to index! Sleeping for {TIME_INTERVAL} seconds...")
+    time.sleep(TIME_INTERVAL)
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -0,0 +1,12 @@
+[project]
+name = "indexer"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "argon2-cffi>=25.1.0",
+    "minio>=7.2.20",
+    "qdrant-client>=1.16.2",
+    "requests>=2.32.5",
+]
--- a/store/init.py
+++ b/store/init.py
@@ -0,0 +1,3 @@
+from .s3 import S3Storage
+
+__all__ = ["S3Storage"]
--- a/store/s3.py
+++ b/store/s3.py
@@ -0,0 +1,56 @@
+from typing import Iterable, Callable
+from minio import Minio
+from minio.commonconfig import Tags
+from minio.datatypes import Object
+
+class S3Storage:
+    def __init__(self, endpoint: str, access_key: str, secret_key: str, bucket: str) -> None:
+        self.__endpoint = endpoint
+        self.__access_key = access_key
+        self.__secret_key = secret_key
+        self.__bucket_name = bucket
+        self.client = Minio(
+            self.__endpoint,
+            access_key=self.__access_key,
+            secret_key=self.__secret_key,
+            secure=False
+        )
+        
+    def get(self, object_name: str) -> str:
+        response = self.client.get_object(bucket_name=self.__bucket_name, object_name=object_name)
+        raw = response.read()
+        response.close()
+        response.release_conn()
+        return raw.decode("utf-8", errors="ignore")
+        
+    def list(self, recursive: bool = True) -> Iterable[Object]:
+        objects = self.client.list_objects(bucket_name=self.__bucket_name, recursive=recursive)
+        return objects
+    
+    def list_filtered(self, filter_func: Callable[[Object], bool], recursive: bool = True) -> Iterable[Object]:
+        return list(filter(filter_func, self.list(recursive=recursive)))
+    
+    def count(self, recursive: bool = True) -> int:
+        return sum(1 for _ in self.list(recursive=recursive))
+    
+    def set_tag(self, object_name: str, tags: dict[str, str]) -> None:
+        new_tag = Tags.new_object_tags()
+        for key, value in tags.items():
+            new_tag[key] = value
+        self.client.set_object_tags(bucket_name=self.__bucket_name, object_name=object_name, tags=new_tag)
+    
+    def get_tag(self, object_name: str) -> dict[str, str]:
+        tags = self.client.get_object_tags(bucket_name=self.__bucket_name, object_name=object_name)
+        if tags is None:
+            return {}
+        return {key: value for key, value in tags.items()}
+
+    def check_tag(self, object_name: str, key: str, value: str) -> bool:
+        tags = self.get_tag(object_name)
+        return tags.get(key) == value
+
+    def delete_tag(self, object_name: str) -> None:
+        self.client.delete_object_tags(bucket_name=self.__bucket_name, object_name=object_name)
+    
+    def delete_object(self, object_name: str) -> None:
+        self.client.remove_object(bucket_name=self.__bucket_name, object_name=object_name)
--- a/uv.lock
+++ b/uv.lock
@@ -0,0 +1,574 @@
+version = 1
+revision = 3
+requires-python = ">=3.13"
+resolution-markers = [
+    "python_full_version >= '3.14'",
+    "python_full_version < '3.14'",
+]
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
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--- a/vector/init.py
+++ b/vector/init.py
@@ -0,0 +1,5 @@
+from .chunk import Chunks, Chunk
+from .client import EmbeddedClient
+from .qdrant import Qdrant
+
+__all__ = ["Chunks", "Chunk", "EmbeddedClient", "Qdrant"]
--- a/vector/chunk.py
+++ b/vector/chunk.py
@@ -0,0 +1,41 @@
+from typing import Optional
+from .client import EmbeddedClient
+
+class Chunk:
+    def __init__(self, id: int, text: str):
+        self.id = id
+        self.text = text
+        self.size = len(text)
+        self.__embedding = None
+    
+    def embed(self, client: EmbeddedClient) -> list[float]:
+        self.__embedding = client.embed(self.text)
+        return self.__embedding
+    
+    @property
+    def embedding(self) -> Optional[list[float]]:
+        return self.__embedding
+    
+    @property
+    def has_embedding(self) -> bool:
+        return self.__embedding is not None
+
+class Chunks:
+    def __init__(self, size: int = 5000, overlap: int = 200) -> None:
+        self.size = size
+        self.overlap = overlap
+        self.__count = 0
+
+    def chunk(self, text: str) -> list[Chunk]:
+        chunks = []
+        i = 0
+        while i < len(text):
+            chunk_id = len(chunks) + 1
+            chunks.append(Chunk(id=chunk_id, text=text[i:i+self.size]))
+            i += self.size - self.overlap
+        self.__count = len(chunks)
+        return chunks
+    
+    @property
+    def count(self) -> int:
+        return self.__count
--- a/vector/client.py
+++ b/vector/client.py
@@ -0,0 +1,19 @@
+import requests
+
+class EmbeddedClient:
+    def __init__(self, authorization: str, url: str = "https://openrouter.ai/api/v1/embeddings", model: str = "qwen/qwen3-embedding-8b") -> None:
+        self.url = url
+        self.authorization = authorization
+        self.model = model
+        
+    def embed(self, text: str) -> list[float]:
+        print(f"Embedding text {text}...")
+        response = requests.post(
+            f"{self.url}",
+            headers={"Authorization": self.authorization, "Content-Type": "application/json"},
+            json={"input": text, "model": self.model},
+            timeout=60
+        )
+        response.raise_for_status()
+        embedding = response.json()["data"][0]["embedding"]
+        return embedding
--- a/vector/qdrant.py
+++ b/vector/qdrant.py
@@ -0,0 +1,57 @@
+from qdrant_client import QdrantClient
+from qdrant_client.models import PointStruct, VectorParams, Distance
+
+from .chunk import Chunk
+
+class Qdrant:
+    def __init__(self, host: str, port: int, collection_name: str   ) -> None:
+        self.client = QdrantClient(host=host, port=port)
+        self.collection_name = collection_name
+        
+    def create_collection(self) -> None:
+        self.client.create_collection(
+            collection_name=self.collection_name,
+            vectors_config=VectorParams(
+                size=4096,
+                distance=Distance.COSINE,
+            ),
+        )
+        
+    def create_if_not_exists_collection(self) -> None:
+        if not self.client.collection_exists(collection_name=self.collection_name):
+            self.create_collection()
+        
+    def delete_collection(self) -> None:
+        self.client.delete_collection(collection_name=self.collection_name)
+        
+    def create_points(self, chunks: list[Chunk], bucket: str, object_name: str) -> list[PointStruct]:
+        points = []
+        for chunk in chunks:
+            points.append(self.create_point(chunk, bucket, object_name))
+        return points
+        
+    def create_point(self, chunk: Chunk, bucket: str, object_name: str) -> PointStruct:
+        if not chunk.has_embedding or chunk.embedding is None:
+            raise ValueError("Chunk has no embedding")
+        embedding: list[float] = chunk.embedding
+        point = PointStruct(
+            id=chunk.id,
+            vector=embedding,
+            payload={
+                "text": chunk.text,
+                "bucket": bucket,
+                "object": object_name,
+                "id": chunk.id,
+                "chunk_size": chunk.size
+            }
+        )
+        return point
+    
+    def upsert_points(self, points: list[PointStruct], batch_size: int = 50) -> None:
+        num_batches = (len(points) + batch_size - 1) // batch_size
+        print(f"Upserting {len(points)} points in {num_batches} batches...")
+        for batch_start in range(0, len(points), batch_size):
+            batch = points[batch_start:batch_start + batch_size]
+            self.client.upsert(collection_name=self.collection_name, points=batch)
+            print(f"Upserted {len(batch)} points...")
+        print(f"Upserted {len(points)} points!")