From 0663524fad345bd3f35fa5a42f7c429df3578096 Mon Sep 17 00:00:00 2001
From: Ben Chambers <35960+bjchambers@users.noreply.github.com>
Date: Tue, 23 Jan 2024 14:34:56 -0800
Subject: [PATCH 1/2] feat: initial pgvector indices

Also applied `ruff check --fix` and `ruff format`.
---
 .dockerignore              |    3 +-
 app/chunks/router.py       |    4 +-
 app/collections/models.py  |   18 +-
 app/collections/router.py  |   13 +-
 app/common/db.py           |   24 +-
 app/config.py              |    2 +-
 app/documents/models.py    |    3 +-
 app/documents/router.py    |   50 +-
 app/main.py                |   41 +-
 app/routes.py              |    2 +-
 migrations/0001_schema.sql |    9 +-
 poetry.lock                | 1952 +-----------------------------------
 pyproject.toml             |    1 +
 13 files changed, 133 insertions(+), 1989 deletions(-)

diff --git a/.dockerignore b/.dockerignore
index d00b206..ca72794 100644
--- a/.dockerignore
+++ b/.dockerignore
@@ -10,5 +10,4 @@
 **/__pycache__
 
 # Include (don't ignore) the migrations.
-!migrations/*.sql
-!yoyo.ini
\ No newline at end of file
+!migrations/*.sql
\ No newline at end of file
diff --git a/app/chunks/router.py b/app/chunks/router.py
index f76d279..488b168 100644
--- a/app/chunks/router.py
+++ b/app/chunks/router.py
@@ -12,7 +12,9 @@
 
 
 @router.post("/retrieve")
-async def retrieve_chunks(store: StoreDep, request: RetrieveRequest) -> RetrieveResponse:
+async def retrieve_chunks(
+    store: StoreDep, request: RetrieveRequest
+) -> RetrieveResponse:
     """Retrieve chunks based on a given query."""
 
     from llama_index.response_synthesizers import ResponseMode
diff --git a/app/collections/models.py b/app/collections/models.py
index d83fbf6..23c5df9 100644
--- a/app/collections/models.py
+++ b/app/collections/models.py
@@ -1,8 +1,21 @@
+from dataclasses import dataclass
+from enum import Enum
 
 from pydantic import BaseModel, ConfigDict, TypeAdapter
 
+
+@dataclass
+class EmbeddingDataMixin:
+    dimensions: int
+
+
+class EmbeddingModel(EmbeddingDataMixin, Enum):
+    openai_text_embedding_ada_002 = 1536
+    hf_baai_bge_small_en = 384
+
+
 class Collection(BaseModel):
-    model_config=ConfigDict(from_attributes=True)
+    model_config = ConfigDict(from_attributes=True)
 
     """A collection of indexed documents."""
     id: int
@@ -11,6 +24,7 @@ class Collection(BaseModel):
     name: str
     """The name of the collection."""
 
+
 collection_validator = TypeAdapter(Collection)
 
 
@@ -18,4 +32,4 @@ class CollectionCreate(BaseModel):
     """The request to create a collection."""
 
     name: str
-    """The name of the collection."""
\ No newline at end of file
+    """The name of the collection."""
diff --git a/app/collections/router.py b/app/collections/router.py
index 8ccf2fd..c5e0349 100644
--- a/app/collections/router.py
+++ b/app/collections/router.py
@@ -1,22 +1,25 @@
 from typing import Annotated, List
 
 from fastapi import APIRouter, Path
-from pydantic import parse_obj_as
 
+from app.collections.models import Collection, CollectionCreate
 from app.common.db import PgConnectionDep
-from app.collections.models import *
 
 router = APIRouter(prefix="/collections")
 
 
 @router.put("/")
-async def add_collection(conn: PgConnectionDep, collection: CollectionCreate) -> Collection:
+async def add_collection(
+    conn: PgConnectionDep, collection: CollectionCreate
+) -> Collection:
     """Create a collection."""
-    result = await conn.fetchrow("""
+    result = await conn.fetchrow(
+        """
         INSERT INTO collection (name) VALUES ($1)
         RETURNING id, name
     """,
-    collection.name)
+        collection.name,
+    )
     return Collection.model_validate(dict(result))
 
 
diff --git a/app/common/db.py b/app/common/db.py
index ec1c54d..a18892d 100644
--- a/app/common/db.py
+++ b/app/common/db.py
@@ -1,11 +1,10 @@
 import contextlib
-from enum import Enum
-from typing import Annotated, AsyncIterator, Optional
-from uuid import UUID
-import asyncpg
+from typing import Annotated, AsyncIterator
 
+import asyncpg
 from fastapi import Depends, Request
 
+
 @contextlib.asynccontextmanager
 async def create_pool(dsn: str) -> AsyncIterator[asyncpg.Pool]:
     """
@@ -16,17 +15,30 @@ async def create_pool(dsn: str) -> AsyncIterator[asyncpg.Pool]:
            the following format:
            `postgres://user:pass@host:port/database?option=value`.
     """
-    pool = await asyncpg.create_pool(dsn)
+
+    async def init_pool(conn: asyncpg.Connection):
+        # Need the extension before we register, so do this here.
+        await conn.execute("CREATE EXTENSION IF NOT EXISTS vector")
+
+        from pgvector.asyncpg import register_vector
+
+        await register_vector(conn)
+
+    pool = await asyncpg.create_pool(dsn, init=init_pool)
     yield pool
     pool.close()
 
+
 def _pg_pool(request: Request) -> asyncpg.Pool:
     return request.state.pg_pool
 
+
 PgPoolDep = Annotated[asyncpg.Pool, Depends(_pg_pool)]
 
+
 async def _pg_connection(pool: PgPoolDep) -> asyncpg.Connection:
     async with pool.acquire() as connection:
         yield connection
 
-PgConnectionDep = Annotated[asyncpg.Connection, Depends(_pg_connection)]
\ No newline at end of file
+
+PgConnectionDep = Annotated[asyncpg.Connection, Depends(_pg_connection)]
diff --git a/app/config.py b/app/config.py
index 3848f9a..eab7cc8 100644
--- a/app/config.py
+++ b/app/config.py
@@ -1,7 +1,7 @@
 from typing import Any, Optional
 
 from fastapi.routing import APIRoute
-from pydantic import RedisDsn, PostgresDsn, ValidationInfo, field_validator
+from pydantic import PostgresDsn, RedisDsn, ValidationInfo, field_validator
 from pydantic_core import Url
 from pydantic_settings import BaseSettings
 
diff --git a/app/documents/models.py b/app/documents/models.py
index 227a542..f570179 100644
--- a/app/documents/models.py
+++ b/app/documents/models.py
@@ -14,6 +14,7 @@ class IngestState(Enum):
     FAILED = "failed"
     """Document failed to be ingested. See `ingest_errors` for details."""
 
+
 class Document(BaseModel):
     """Schema for documents in the SQL DB."""
 
@@ -23,4 +24,4 @@ class Document(BaseModel):
     url: str
 
     ingest_state: Optional[IngestState] = None
-    ingest_error: Optional[str] = None
\ No newline at end of file
+    ingest_error: Optional[str] = None
diff --git a/app/documents/router.py b/app/documents/router.py
index 40ca4f0..c1307f9 100644
--- a/app/documents/router.py
+++ b/app/documents/router.py
@@ -1,14 +1,12 @@
 from typing import Annotated, List
-import asyncpg
 
+import asyncpg
 from fastapi import APIRouter, BackgroundTasks, Body, HTTPException, Path, status
 from loguru import logger
-from sqlalchemy import Engine
-from sqlmodel import Session, select
-from app.collections.router import PathCollectionId
 
+from app.collections.router import PathCollectionId
 from app.common.db import PgConnectionDep, PgPoolDep
-from app.documents.models import *
+from app.documents.models import Document
 from app.ingest.extract import extract
 from app.ingest.extract.source import ExtractSource
 from app.ingest.store import Store, StoreDep
@@ -43,11 +41,15 @@ async def ingest_document(id: int, store: Store, pg_pool: asyncpg.Pool):
         nodes = await store.ingestion_pipeline.arun(documents=documents)
         logger.debug("Done. Inserted {} nodes", len(nodes))
 
-        await conn.execute("""
+        await conn.execute(
+            """
         UPDATE document
         SET ingest_state = 'ingested', ingest_error = NULL
         WHERE id = $1
-        """, id)
+        """,
+            id,
+        )
+
 
 @router.put("/")
 async def add_document(
@@ -61,37 +63,51 @@ async def add_document(
 
     row = None
     async with pg_pool.acquire() as conn:
-        row = await conn.fetchrow("""
-        INSERT INTO document (collection_id, url, ingest_state) VALUES ($1, $2, 'pending')
+        row = await conn.fetchrow(
+            """
+        INSERT INTO document (collection_id, url, ingest_state)
+        VALUES ($1, $2, 'pending')
         RETURNING id, collection_id, url, ingest_state, ingest_error
-        """, collection_id, url)
+        """,
+            collection_id,
+            url,
+        )
 
     document = Document.model_validate(dict(row))
     background.add_task(ingest_document, document.id, store, pg_pool)
     return document
 
+
 PathDocumentId = Annotated[int, Path(..., description="The document ID.")]
 
+
 @router.get("/")
-async def list_documents(collection_id: PathCollectionId, conn: PgConnectionDep) -> List[Document]:
+async def list_documents(
+    collection_id: PathCollectionId, conn: PgConnectionDep
+) -> List[Document]:
     """List documents."""
     # TODO: Test
-    results = await conn.fetch("""
+    results = await conn.fetch(
+        """
         SELECT id, collection_id, url, ingest_state, ingest_error
         FROM document WHERE collection_id = $1
-    """, collection_id)
+    """,
+        collection_id,
+    )
     return [Document.model_validate(dict(result)) for result in results]
 
+
 @router.get("/{id}")
 async def get_document(
-    conn: PgConnectionDep,
-    collection_id: PathCollectionId,
-    id: PathDocumentId
+    conn: PgConnectionDep, collection_id: PathCollectionId, id: PathDocumentId
 ) -> Document:
     # TODO: Test / return not found?
     result = await conn.fetchrow(
         """
         SELECT id, collection_id, url, ingest_state, ingest_error
         FROM document WHERE id = $1 AND collection_id = $2
-        """, id, collection_id)
+        """,
+        id,
+        collection_id,
+    )
     return Document.model_validate(dict(result))
diff --git a/app/main.py b/app/main.py
index b54e018..d7f1db3 100644
--- a/app/main.py
+++ b/app/main.py
@@ -1,11 +1,10 @@
 import contextlib
 from typing import AsyncIterator, TypedDict
 
+import asyncpg
 from fastapi import FastAPI
-from sqlalchemy import Engine
-from sqlmodel import SQLModel, create_engine
-from loguru import logger
 
+from app.collections.models import EmbeddingModel
 from app.common import db
 from app.config import app_configs, settings
 from app.ingest.store import Store
@@ -14,33 +13,39 @@
 
 class State(TypedDict):
     store: Store
-    db: Engine
+    pg_pool: asyncpg.Pool
 
 
 @contextlib.asynccontextmanager
 async def lifespan(_app: FastAPI) -> AsyncIterator[State]:
     """Function creating instances used during the lifespan of the service."""
 
-    # if settings.APPLY_MIGRATIONS:
-        # from yoyo import get_backend, read_migrations
-        # backend = get_backend(settings.DB.unicode_string())
-        # migrations = read_migrations('migrations')
-        # with backend.lock():
-        #     outstanding = backend.to_apply(migrations)
-
-        #     logger.info("Applying {} migrations", len(outstanding))
-
-        #     # Apply any outstanding migrations
-        #     backend.apply_migrations(outstanding)
-
-        #     logger.info("Done applying migrations.")
-
+    # TODO: Look at https://gist.github.com/mattbillenstein/270a4d44cbdcb181ac2ed58526ae137d
+    # for simple migration scripts.
     async with db.create_pool(settings.DB.unicode_string()) as pg_pool:
         if settings.APPLY_MIGRATIONS:
             async with pg_pool.acquire() as conn:
                 with open("migrations/0001_schema.sql") as schema_file:
                     schema = schema_file.read()
                     await conn.execute(schema)
+
+                # TODO: create indices with different distance metrics and
+                # either allow configuring that, or setting a default for each
+                # embedding model? We'd need to change the `vector_cosine_ops`
+                # to `vector_{l2,ip}_ops` for l2 distance or inner-product, and
+                # use different operators when querying.
+                index_creation = [
+                    f"""
+                    CREATE INDEX IF NOT EXISTS {emb.name}_index
+                    ON embedding
+                    USING hnsw ((embedding::vector({emb.dimensions})) vector_cosine_ops)
+                    WHERE (embedding_model = '{emb.name}');
+                    """
+                    for emb in EmbeddingModel
+                ]
+                print(index_creation)
+                await conn.execute("\n\n".join(index_creation))
+
         state = {
             "store": Store(),
             "pg_pool": pg_pool,
diff --git a/app/routes.py b/app/routes.py
index ce9c5e2..0c3a64f 100644
--- a/app/routes.py
+++ b/app/routes.py
@@ -1,8 +1,8 @@
 from fastapi import APIRouter
 
+from app.chunks.router import router as chunks_router
 from app.collections.router import router as collections_router
 from app.documents.router import router as documents_router
-from app.chunks.router import router as chunks_router
 
 api_router = APIRouter(prefix="/api")
 
diff --git a/migrations/0001_schema.sql b/migrations/0001_schema.sql
index 247865f..89a26de 100644
--- a/migrations/0001_schema.sql
+++ b/migrations/0001_schema.sql
@@ -1,8 +1,13 @@
 -- Apply the base schema.
+CREATE TYPE embedding_model as ENUM (
+    'openai_text_embedding_ada_002',
+    'hf_baai_bge_small_en'
+);
 
 CREATE TABLE collection (
     id SERIAL NOT NULL,
     name VARCHAR NOT NULL,
+    text_embedding_model embedding_model NOT NULL,
 
     PRIMARY KEY (id)
 );
@@ -82,7 +87,9 @@ CREATE TYPE embedding_kind AS ENUM (
 CREATE TABLE embedding(
     id SERIAL NOT NULL,
 
-    chunk_id INTEGER,
+    embedding vector NOT NULL,
+    embedding_model embedding_model NOT NULL,
+    chunk_id INTEGER NOT NULL,
 
     key_text VARCHAR,
 
diff --git a/poetry.lock b/poetry.lock
index 390d5b1..d749d30 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -150,16 +150,6 @@ files = [
     {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
 ]
 
-[[package]]
-name = "antlr4-python3-runtime"
-version = "4.9.3"
-description = "ANTLR 4.9.3 runtime for Python 3.7"
-optional = false
-python-versions = "*"
-files = [
-    {file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"},
-]
-
 [[package]]
 name = "anyio"
 version = "4.2.0"
@@ -311,17 +301,6 @@ tests = ["attrs[tests-no-zope]", "zope-interface"]
 tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
 tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
 
-[[package]]
-name = "backoff"
-version = "2.2.1"
-description = "Function decoration for backoff and retry"
-optional = false
-python-versions = ">=3.7,<4.0"
-files = [
-    {file = "backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8"},
-    {file = "backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba"},
-]
-
 [[package]]
 name = "beautifulsoup4"
 version = "4.12.2"
@@ -415,17 +394,6 @@ files = [
 [package.dependencies]
 pycparser = "*"
 
-[[package]]
-name = "chardet"
-version = "5.2.0"
-description = "Universal encoding detector for Python 3"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "chardet-5.2.0-py3-none-any.whl", hash = "sha256:e1cf59446890a00105fe7b7912492ea04b6e6f06d4b742b2c788469e34c82970"},
-    {file = "chardet-5.2.0.tar.gz", hash = "sha256:1b3b6ff479a8c414bc3fa2c0852995695c4a026dcd6d0633b2dd092ca39c1cf7"},
-]
-
 [[package]]
 name = "charset-normalizer"
 version = "3.3.2"
@@ -550,23 +518,6 @@ files = [
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
 
-[[package]]
-name = "coloredlogs"
-version = "15.0.1"
-description = "Colored terminal output for Python's logging module"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-files = [
-    {file = "coloredlogs-15.0.1-py2.py3-none-any.whl", hash = "sha256:612ee75c546f53e92e70049c9dbfcc18c935a2b9a53b66085ce9ef6a6e5c0934"},
-    {file = "coloredlogs-15.0.1.tar.gz", hash = "sha256:7c991aa71a4577af2f82600d8f8f3a89f936baeaf9b50a9c197da014e5bf16b0"},
-]
-
-[package.dependencies]
-humanfriendly = ">=9.1"
-
-[package.extras]
-cron = ["capturer (>=2.4)"]
-
 [[package]]
 name = "comm"
 version = "0.2.1"
@@ -584,129 +535,6 @@ traitlets = ">=4"
 [package.extras]
 test = ["pytest"]
 
-[[package]]
-name = "contourpy"
-version = "1.2.0"
-description = "Python library for calculating contours of 2D quadrilateral grids"
-optional = false
-python-versions = ">=3.9"
-files = [
-    {file = "contourpy-1.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0274c1cb63625972c0c007ab14dd9ba9e199c36ae1a231ce45d725cbcbfd10a8"},
-    {file = "contourpy-1.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ab459a1cbbf18e8698399c595a01f6dcc5c138220ca3ea9e7e6126232d102bb4"},
-    {file = "contourpy-1.2.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6fdd887f17c2f4572ce548461e4f96396681212d858cae7bd52ba3310bc6f00f"},
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@@ -1247,20 +945,6 @@ testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jed
 torch = ["torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
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@@ -1291,24 +975,6 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link
 perf = ["ipython"]
 testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
 
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 [package.extras]
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@@ -2626,241 +1795,47 @@ qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
 testing = ["docopt", "pytest (<6.0.0)"]
 
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-name = "pdf2image"
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-description = "A wrapper around the pdftoppm and pdftocairo command line tools to convert PDF to a PIL Image list."
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 [package.dependencies]
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@@ -4079,48 +2640,6 @@ tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"]
 testing = ["h5py (>=3.7.0)", "huggingface_hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools_rust (>=1.5.2)"]
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-dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (<2)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
-docs = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision"]
-docs-specific = ["hf-doc-builder"]
-flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
-flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-ftfy = ["ftfy"]
-integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
-ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
-modelcreation = ["cookiecutter (==1.7.3)"]
-natten = ["natten (>=0.14.6)"]
-onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
-onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
-optuna = ["optuna"]
-quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"]
-ray = ["ray[tune] (>=2.7.0)"]
-retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
-sagemaker = ["sagemaker (>=2.31.0)"]
-sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
-serving = ["fastapi", "pydantic (<2)", "starlette", "uvicorn"]
-sigopt = ["sigopt"]
-sklearn = ["scikit-learn"]
-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic (<2)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "tensorboard", "timeout-decorator"]
-tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
-tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
-tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-timm = ["timm"]
-tokenizers = ["tokenizers (>=0.14,<0.19)"]
-torch = ["accelerate (>=0.21.0)", "torch (>=1.10,!=1.12.0)"]
-torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch (>=1.10,!=1.12.0)", "tqdm (>=4.27)"]
-video = ["av (==9.2.0)", "decord (==0.6.0)"]
-vision = ["Pillow (>=10.0.1,<=15.0)"]
-
 [[package]]
 name = "triton"
 version = "2.1.0"
@@ -4811,175 +3091,6 @@ files = [
     {file = "tzdata-2023.4.tar.gz", hash = "sha256:dd54c94f294765522c77399649b4fefd95522479a664a0cec87f41bebc6148c9"},
 ]
 
-[[package]]
-name = "unstructured"
-version = "0.11.8"
-description = "A library that prepares raw documents for downstream ML tasks."
-optional = false
-python-versions = ">=3.7.0"
-files = [
-    {file = "unstructured-0.11.8-py3-none-any.whl", hash = "sha256:71e8d135a723d8c692a0a43683e3dca4a9b3e0fd8a1d255f57689591ed3860c0"},
-    {file = "unstructured-0.11.8.tar.gz", hash = "sha256:dba8b2d54fdc781acef6c9590510e1f55e7467abaaf00403031331903d415f07"},
-]
-
-[package.dependencies]
-backoff = "*"
-beautifulsoup4 = "*"
-chardet = "*"
-dataclasses-json = "*"
-emoji = "*"
-filetype = "*"
-langdetect = "*"
-lxml = "*"
-markdown = {version = "*", optional = true, markers = "extra == \"all-docs\""}
-msg-parser = {version = "*", optional = true, markers = "extra == \"all-docs\""}
-networkx = {version = "*", optional = true, markers = "extra == \"all-docs\""}
-nltk = "*"
-numpy = "*"
-onnx = {version = "*", optional = true, markers = "extra == \"all-docs\""}
-openpyxl = {version = "*", optional = true, markers = "extra == \"all-docs\""}
-pandas = {version = "*", optional = true, markers = "extra == \"all-docs\""}
-pdf2image = {version = "*", optional = true, markers = "extra == \"all-docs\""}
-"pdfminer.six" = {version = "*", optional = true, markers = "extra == \"all-docs\""}
-pikepdf = {version = "*", optional = true, markers = "extra == \"all-docs\""}
-pypandoc = {version = "*", optional = true, markers = "extra == \"all-docs\""}
-pypdf = {version = "*", optional = true, markers = "extra == \"all-docs\""}
-python-docx = {version = ">=1.1.0", optional = true, markers = "extra == \"all-docs\""}
-python-iso639 = "*"
-python-magic = "*"
-python-pptx = {version = "<=0.6.23", optional = true, markers = "extra == \"all-docs\""}
-rapidfuzz = "*"
-requests = "*"
-tabulate = "*"
-typing-extensions = "*"
-unstructured-client = "*"
-unstructured-inference = {version = "0.7.18", optional = true, markers = "extra == \"all-docs\""}
-"unstructured.pytesseract" = {version = ">=0.3.12", optional = true, markers = "extra == \"all-docs\""}
-wrapt = "*"
-xlrd = {version = "*", optional = true, markers = "extra == \"all-docs\""}
-
-[package.extras]
-airtable = ["pyairtable"]
-all-docs = ["markdown", "msg-parser", "networkx", "onnx", "openpyxl", "pandas", "pdf2image", "pdfminer.six", "pikepdf", "pypandoc", "pypdf", "python-docx (>=1.1.0)", "python-pptx (<=0.6.23)", "unstructured-inference (==0.7.18)", "unstructured.pytesseract (>=0.3.12)", "xlrd"]
-azure = ["adlfs", "fsspec (==2023.9.1)"]
-azure-cognitive-search = ["azure-search-documents"]
-bedrock = ["boto3", "langchain"]
-biomed = ["bs4"]
-box = ["boxfs", "fsspec (==2023.9.1)"]
-chroma = ["chromadb"]
-confluence = ["atlassian-python-api"]
-csv = ["pandas"]
-delta-table = ["deltalake", "fsspec (==2023.9.1)"]
-discord = ["discord-py"]
-doc = ["python-docx (>=1.1.0)"]
-docx = ["python-docx (>=1.1.0)"]
-dropbox = ["dropboxdrivefs", "fsspec (==2023.9.1)"]
-elasticsearch = ["elasticsearch"]
-embed-huggingface = ["huggingface", "langchain", "sentence-transformers"]
-epub = ["pypandoc"]
-gcs = ["bs4", "fsspec (==2023.9.1)", "gcsfs"]
-github = ["pygithub (>1.58.0)"]
-gitlab = ["python-gitlab"]
-google-drive = ["google-api-python-client"]
-hubspot = ["hubspot-api-client", "urllib3 (>=1.26.17)"]
-huggingface = ["langdetect", "sacremoses", "sentencepiece", "torch", "transformers"]
-image = ["onnx", "pdf2image", "pdfminer.six", "pikepdf", "pypdf", "unstructured-inference (==0.7.18)", "unstructured.pytesseract (>=0.3.12)"]
-jira = ["atlassian-python-api"]
-local-inference = ["markdown", "msg-parser", "networkx", "onnx", "openpyxl", "pandas", "pdf2image", "pdfminer.six", "pikepdf", "pypandoc", "pypdf", "python-docx (>=1.1.0)", "python-pptx (<=0.6.23)", "unstructured-inference (==0.7.18)", "unstructured.pytesseract (>=0.3.12)", "xlrd"]
-md = ["markdown"]
-mongodb = ["pymongo"]
-msg = ["msg-parser"]
-notion = ["htmlBuilder", "notion-client"]
-odt = ["pypandoc", "python-docx (>=1.1.0)"]
-onedrive = ["Office365-REST-Python-Client (<2.4.3)", "bs4", "msal"]
-openai = ["langchain", "openai", "tiktoken"]
-org = ["pypandoc"]
-outlook = ["Office365-REST-Python-Client (<2.4.3)", "msal"]
-paddleocr = ["unstructured.paddleocr (==2.6.1.3)"]
-pdf = ["onnx", "pdf2image", "pdfminer.six", "pikepdf", "pypdf", "unstructured-inference (==0.7.18)", "unstructured.pytesseract (>=0.3.12)"]
-pinecone = ["pinecone-client"]
-ppt = ["python-pptx (<=0.6.23)"]
-pptx = ["python-pptx (<=0.6.23)"]
-qdrant = ["qdrant-client"]
-reddit = ["praw"]
-rst = ["pypandoc"]
-rtf = ["pypandoc"]
-s3 = ["fsspec (==2023.9.1)", "s3fs"]
-salesforce = ["simple-salesforce"]
-sftp = ["fsspec", "paramiko"]
-sharepoint = ["Office365-REST-Python-Client (<2.4.3)", "msal"]
-slack = ["slack-sdk"]
-tsv = ["pandas"]
-weaviate = ["weaviate-client"]
-wikipedia = ["wikipedia"]
-xlsx = ["networkx", "openpyxl", "pandas", "xlrd"]
-
-[[package]]
-name = "unstructured-client"
-version = "0.15.2"
-description = "Python Client SDK for Unstructured API"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "unstructured-client-0.15.2.tar.gz", hash = "sha256:47a80d90abf214c0b695812ec5ef982d08b8ed7ffa84bc76cd208f8ddec9ba4c"},
-    {file = "unstructured_client-0.15.2-py3-none-any.whl", hash = "sha256:0476ecc86a26709c768c4b03116e446646b192942e5dc6620a0e3845e0e28efc"},
-]
-
-[package.dependencies]
-certifi = ">=2023.7.22"
-charset-normalizer = ">=3.2.0"
-dataclasses-json = ">=0.6.1"
-idna = ">=3.4"
-jsonpath-python = ">=1.0.6"
-marshmallow = ">=3.19.0"
-mypy-extensions = ">=1.0.0"
-packaging = ">=23.1"
-python-dateutil = ">=2.8.2"
-requests = ">=2.31.0"
-six = ">=1.16.0"
-typing-extensions = ">=4.7.1"
-typing-inspect = ">=0.9.0"
-urllib3 = ">=1.26.18"
-
-[package.extras]
-dev = ["pylint (==2.16.2)"]
-
-[[package]]
-name = "unstructured-inference"
-version = "0.7.18"
-description = "A library for performing inference using trained models."
-optional = false
-python-versions = ">=3.7.0"
-files = [
-    {file = "unstructured_inference-0.7.18-py3-none-any.whl", hash = "sha256:86e1e5bfab0c1729d7309eb847cde743c4a32924ef1bdeb9043f99e06ec9fd9f"},
-    {file = "unstructured_inference-0.7.18.tar.gz", hash = "sha256:ad4d4c94cb40525456b5857348004b077bc0b27d6acce3ccb1a12b66017d5d26"},
-]
-
-[package.dependencies]
-huggingface-hub = "*"
-layoutparser = {version = "*", extras = ["layoutmodels", "tesseract"]}
-onnx = "*"
-onnxruntime = "<1.16"
-opencv-python = "!=4.7.0.68"
-python-multipart = "*"
-rapidfuzz = "*"
-transformers = ">=4.25.1"
-
-[[package]]
-name = "unstructured-pytesseract"
-version = "0.3.12"
-description = "Python-tesseract is a python wrapper for Google's Tesseract-OCR"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "unstructured.pytesseract-0.3.12-py3-none-any.whl", hash = "sha256:6ed42530fc697bb08d1ae4884cc517ee808620c1c1414efe8d5d90334da068d3"},
-    {file = "unstructured.pytesseract-0.3.12.tar.gz", hash = "sha256:751a21d67b1f109036bf4daf796d3e04631697a355efd650f3373412b249de2e"},
-]
-
-[package.dependencies]
-packaging = ">=21.3"
-Pillow = ">=8.0.0"
-
 [[package]]
 name = "urllib3"
 version = "2.1.0"
@@ -5118,33 +3229,6 @@ files = [
     {file = "wrapt-1.16.0.tar.gz", hash = "sha256:5f370f952971e7d17c7d1ead40e49f32345a7f7a5373571ef44d800d06b1899d"},
 ]
 
-[[package]]
-name = "xlrd"
-version = "2.0.1"
-description = "Library for developers to extract data from Microsoft Excel (tm) .xls spreadsheet files"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
-files = [
-    {file = "xlrd-2.0.1-py2.py3-none-any.whl", hash = "sha256:6a33ee89877bd9abc1158129f6e94be74e2679636b8a205b43b85206c3f0bbdd"},
-    {file = "xlrd-2.0.1.tar.gz", hash = "sha256:f72f148f54442c6b056bf931dbc34f986fd0c3b0b6b5a58d013c9aef274d0c88"},
-]
-
-[package.extras]
-build = ["twine", "wheel"]
-docs = ["sphinx"]
-test = ["pytest", "pytest-cov"]
-
-[[package]]
-name = "xlsxwriter"
-version = "3.1.9"
-description = "A Python module for creating Excel XLSX files."
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "XlsxWriter-3.1.9-py3-none-any.whl", hash = "sha256:b61c1a0c786f82644936c0936ec96ee96cd3afb9440094232f7faef9b38689f0"},
-    {file = "XlsxWriter-3.1.9.tar.gz", hash = "sha256:de810bf328c6a4550f4ffd6b0b34972aeb7ffcf40f3d285a0413734f9b63a929"},
-]
-
 [[package]]
 name = "yarl"
 version = "1.9.4"
@@ -5287,4 +3371,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
-content-hash = "00a6dc6f81282aa336da14c1b035105ee85258b285b6f28f1e4d44882963b78a"
+content-hash = "d90641731d70d83f73caee7aacb5aaeec595f9a94dd43bab9bf8134e56d84e22"
diff --git a/pyproject.toml b/pyproject.toml
index d558f5b..d04eaa0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,6 +19,7 @@ accelerate = "^0.26.1"
 safetensors = "^0.4.1"
 sqlmodel = "^0.0.14"
 asyncpg = "^0.29.0"
+pgvector = "^0.2.4"
 
 [tool.poetry.group.dev.dependencies]
 ruff = "^0.1.11"

From 0203577353470be43707784dd4852d83dd699b98 Mon Sep 17 00:00:00 2001
From: Ben Chambers <35960+bjchambers@users.noreply.github.com>
Date: Wed, 24 Jan 2024 10:00:20 -0800
Subject: [PATCH 2/2] create per-collection indices

---
 app/collections/models.py  | 46 ++++++++++++++++++++++-------
 app/collections/router.py  | 59 ++++++++++++++++++++++++++++++++------
 app/main.py                | 18 ------------
 migrations/0001_schema.sql | 14 ++++-----
 4 files changed, 93 insertions(+), 44 deletions(-)

diff --git a/app/collections/models.py b/app/collections/models.py
index 23c5df9..fb93679 100644
--- a/app/collections/models.py
+++ b/app/collections/models.py
@@ -1,17 +1,21 @@
-from dataclasses import dataclass
 from enum import Enum
 
-from pydantic import BaseModel, ConfigDict, TypeAdapter
+from pydantic import BaseModel, ConfigDict, Field, TypeAdapter
 
 
-@dataclass
-class EmbeddingDataMixin:
-    dimensions: int
+class DistanceMetric(Enum):
+    cosine = "cosine"
+    inner_product = "ip"
+    l2 = "l2"
 
-
-class EmbeddingModel(EmbeddingDataMixin, Enum):
-    openai_text_embedding_ada_002 = 1536
-    hf_baai_bge_small_en = 384
+    def vector_ops(self) -> str:
+        match self:
+            case DistanceMetric.cosine:
+                return "vector_cosine_ops"
+            case DistanceMetric.inner_product:
+                return "vector_ip_ops"
+            case DistanceMetric.l2:
+                return "vector_l2_ops"
 
 
 class Collection(BaseModel):
@@ -24,6 +28,17 @@ class Collection(BaseModel):
     name: str
     """The name of the collection."""
 
+    text_embedding_model: str
+    """The name of the embedding model.
+
+    NOTE: Changing embedding models is not currently supported.
+    """
+
+    text_distance_metric: DistanceMetric = DistanceMetric.cosine
+    """The distance metric to use on the text embedding.
+
+    NOTE: Changing distance metrics is not currently supported."""
+
 
 collection_validator = TypeAdapter(Collection)
 
@@ -31,5 +46,16 @@ class Collection(BaseModel):
 class CollectionCreate(BaseModel):
     """The request to create a collection."""
 
-    name: str
+    name: str = Field(examples=["my_collection"])
     """The name of the collection."""
+
+    text_embedding_model: str = Field(examples=["openai:text-embedding-ada-002", "hf:BAAI/bge-small-en"])
+    """The name of the embedding model.
+
+    NOTE: Changing embedding models is not currently supported.
+    """
+
+    text_distance_metric: DistanceMetric = DistanceMetric.cosine
+    """The distance metric to use on the text embedding.
+
+    NOTE: Changing distance metrics is not currently supported."""
\ No newline at end of file
diff --git a/app/collections/router.py b/app/collections/router.py
index c5e0349..7735ab5 100644
--- a/app/collections/router.py
+++ b/app/collections/router.py
@@ -8,25 +8,59 @@
 router = APIRouter(prefix="/collections")
 
 
+def get_dimensions(model: str) -> int:
+    # TODO: Consider instantiating the model and applying it to a string
+    # to determine the dimensions. This would make it easier to support
+    # new models.
+    match model:
+        case "openai:text-embedding-ada-002":
+            return 1536
+        case "hf:BAAI/bge-small-en":
+            return 384
+        case _:
+            raise ValueError(f"Unsupported model '{model}'")
+
+
 @router.put("/")
 async def add_collection(
     conn: PgConnectionDep, collection: CollectionCreate
 ) -> Collection:
     """Create a collection."""
-    result = await conn.fetchrow(
-        """
-        INSERT INTO collection (name) VALUES ($1)
-        RETURNING id, name
-    """,
-        collection.name,
-    )
+    dimensions = get_dimensions(collection.text_embedding_model)
+    async with conn.transaction():
+        result = await conn.fetchrow(
+            """
+            INSERT INTO collection (name, text_embedding_model, text_distance_metric)
+            VALUES ($1, $2, $3)
+            RETURNING id, name, text_embedding_model, text_distance_metric
+        """,
+            collection.name,
+            collection.text_embedding_model,
+            collection.text_distance_metric.value,
+        )
+
+        # Create a separate *partial* index on each collection.
+        # This allows us to define different dimensions (and vector distance) for
+        # each collection.
+        #
+        # https://github.com/pgvector/pgvector?tab=readme-ov-file#can-i-store-vectors-with-different-dimensions-in-the-same-column
+        id = result["id"]
+        vector_ops = collection.text_distance_metric.vector_ops()
+        await conn.execute(
+            f"""
+            CREATE INDEX embedding_collection_{id}_index
+            ON embedding
+            USING hnsw ((embedding::vector({dimensions})) {vector_ops})
+            WHERE collection_id = {id}
+            """
+        )
     return Collection.model_validate(dict(result))
 
 
 @router.get("/")
 async def list_collections(conn: PgConnectionDep) -> List[Collection]:
     """List collections."""
-    results = await conn.fetch("SELECT id, name FROM collection")
+    results = await conn.fetch("SELECT id, name, text_embedding_model FROM collection")
     return [Collection.model_validate(dict(result)) for result in results]
 
 
@@ -36,5 +70,12 @@ async def list_collections(conn: PgConnectionDep) -> List[Collection]:
 @router.get("/{id}")
 async def get_collection(id: PathCollectionId, conn: PgConnectionDep) -> Collection:
     """Get a specific collection."""
-    result = await conn.fetchrow("SELECT id, name FROM collection WHERE id = $1", id)
+    result = await conn.fetchrow(
+        """
+        SELECT id, name, text_embedding_model
+        FROM collection
+        WHERE id = $1
+        """,
+        id,
+    )
     return Collection.model_validate(dict(result))
diff --git a/app/main.py b/app/main.py
index d7f1db3..2a1633f 100644
--- a/app/main.py
+++ b/app/main.py
@@ -4,7 +4,6 @@
 import asyncpg
 from fastapi import FastAPI
 
-from app.collections.models import EmbeddingModel
 from app.common import db
 from app.config import app_configs, settings
 from app.ingest.store import Store
@@ -29,23 +28,6 @@ async def lifespan(_app: FastAPI) -> AsyncIterator[State]:
                     schema = schema_file.read()
                     await conn.execute(schema)
 
-                # TODO: create indices with different distance metrics and
-                # either allow configuring that, or setting a default for each
-                # embedding model? We'd need to change the `vector_cosine_ops`
-                # to `vector_{l2,ip}_ops` for l2 distance or inner-product, and
-                # use different operators when querying.
-                index_creation = [
-                    f"""
-                    CREATE INDEX IF NOT EXISTS {emb.name}_index
-                    ON embedding
-                    USING hnsw ((embedding::vector({emb.dimensions})) vector_cosine_ops)
-                    WHERE (embedding_model = '{emb.name}');
-                    """
-                    for emb in EmbeddingModel
-                ]
-                print(index_creation)
-                await conn.execute("\n\n".join(index_creation))
-
         state = {
             "store": Store(),
             "pg_pool": pg_pool,
diff --git a/migrations/0001_schema.sql b/migrations/0001_schema.sql
index 89a26de..0e69d43 100644
--- a/migrations/0001_schema.sql
+++ b/migrations/0001_schema.sql
@@ -1,15 +1,15 @@
 -- Apply the base schema.
-CREATE TYPE embedding_model as ENUM (
-    'openai_text_embedding_ada_002',
-    'hf_baai_bge_small_en'
-);
+
+CREATE TYPE distance_metric AS ENUM ('cosine', 'l2', 'ip');
 
 CREATE TABLE collection (
     id SERIAL NOT NULL,
     name VARCHAR NOT NULL,
-    text_embedding_model embedding_model NOT NULL,
+    text_embedding_model VARCHAR NOT NULL,
+    text_distance_metric distance_metric NOT NULL,
 
-    PRIMARY KEY (id)
+    PRIMARY KEY (id),
+    UNIQUE (name)
 );
 
 CREATE TYPE ingest_state AS ENUM ('pending', 'ingested', 'failed');
@@ -88,7 +88,7 @@ CREATE TABLE embedding(
     id SERIAL NOT NULL,
 
     embedding vector NOT NULL,
-    embedding_model embedding_model NOT NULL,
+    collection_id INTEGER NOT NULL,
     chunk_id INTEGER NOT NULL,
 
     key_text VARCHAR,