feat(Vector): support pgvector (#1624)

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@ -1,7 +1,7 @@
## Vectorstores
PrivateGPT supports [Qdrant](https://qdrant.tech/) and [Chroma](https://www.trychroma.com/) as vectorstore providers. Qdrant being the default.
PrivateGPT supports [Qdrant](https://qdrant.tech/), [Chroma](https://www.trychroma.com/) and [PGVector](https://github.com/pgvector/pgvector) as vectorstore providers. Qdrant being the default.
In order to select one or the other, set the `vectorstore.database` property in the `settings.yaml` file to `qdrant` or `chroma`.
In order to select one or the other, set the `vectorstore.database` property in the `settings.yaml` file to `qdrant`, `chroma` or `pgvector`.
```yaml
vectorstore:
@ -47,4 +47,39 @@ To enable Chroma, set the `vectorstore.database` property in the `settings.yaml`
poetry install --extras chroma
```
By default `chroma` will use a disk-based database stored in local_data_path / "chroma_db" (being local_data_path defined in settings.yaml)
By default `chroma` will use a disk-based database stored in local_data_path / "chroma_db" (being local_data_path defined in settings.yaml)
### PGVector
To enable PGVector, set the `vectorstore.database` property in the `settings.yaml` file to `pgvector` and install the `pgvector` extra.
```bash
poetry install --extras pgvector
```
PGVector settings can be configured by setting values to the `pgvector` property in the `settings.yaml` file.
The available configuration options are:
| Field | Description |
|---------------|-----------------------------------------------------------|
| **host** | The server hosting the Postgres database. Default is `localhost` |
| **port** | The port on which the Postgres database is accessible. Default is `5432` |
| **database** | The specific database to connect to. Default is `postgres` |
| **user** | The username for database access. Default is `postgres` |
| **password** | The password for database access. (Required) |
| **embed_dim** | The dimensionality of the embedding model (Required) |
| **schema_name** | The database schema to use. Default is `private_gpt` |
| **table_name** | The database table to use. Default is `embeddings` |
For example:
```yaml
pgvector:
host: localhost
port: 5432
database: postgres
user: postgres
password: <PASSWORD>
embed_dim: 384 # 384 is for BAAI/bge-small-en-v1.5
schema_name: private_gpt
table_name: embeddings
```

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@ -232,6 +232,74 @@ files = [
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]
[package.dependencies]
greenlet = {version = "!=0.4.17", optional = true, markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\" or extra == \"asyncio\""}
typing-extensions = ">=4.2.0"
typing-extensions = ">=4.6.0"
[package.extras]
aiomysql = ["aiomysql (>=0.2.0)", "greenlet (!=0.4.17)"]
aioodbc = ["aioodbc", "greenlet (!=0.4.17)"]
aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing-extensions (!=3.10.0.1)"]
aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing_extensions (!=3.10.0.1)"]
asyncio = ["greenlet (!=0.4.17)"]
asyncmy = ["asyncmy (>=0.2.3,!=0.2.4,!=0.2.6)", "greenlet (!=0.4.17)"]
mariadb-connector = ["mariadb (>=1.0.1,!=1.1.2,!=1.1.5)"]
@ -4855,7 +5017,7 @@ mssql-pyodbc = ["pyodbc"]
mypy = ["mypy (>=0.910)"]
mysql = ["mysqlclient (>=1.4.0)"]
mysql-connector = ["mysql-connector-python"]
oracle = ["cx-oracle (>=8)"]
oracle = ["cx_oracle (>=8)"]
oracle-oracledb = ["oracledb (>=1.0.1)"]
postgresql = ["psycopg2 (>=2.7)"]
postgresql-asyncpg = ["asyncpg", "greenlet (!=0.4.17)"]
@ -4865,7 +5027,7 @@ postgresql-psycopg2binary = ["psycopg2-binary"]
postgresql-psycopg2cffi = ["psycopg2cffi"]
postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
pymysql = ["pymysql"]
sqlcipher = ["sqlcipher3-binary"]
sqlcipher = ["sqlcipher3_binary"]
[[package]]
name = "starlette"
@ -6108,8 +6270,9 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
[extras]
chroma = ["chromadb"]
pgvector = ["asyncpg", "pgvector", "psycopg2-binary", "sqlalchemy"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.11,<3.12"
content-hash = "121bf7797b74c02efaf11712e178c9c01880b79701eeff6485ede9ca8b25d307"
content-hash = "ce0641f693c3fc99cfd560d9c2cbfc6fba83e12bbaecbbdea33eddcda9c286c3"

View File

@ -40,6 +40,21 @@ class VectorStoreComponent:
@inject
def __init__(self, settings: Settings) -> None:
match settings.vectorstore.database:
case "pgvector":
from llama_index.vector_stores import PGVectorStore
if settings.pgvector is None:
raise ValueError(
"PGVectorStore settings not found. Please provide settings."
)
self.vector_store = typing.cast(
VectorStore,
PGVectorStore.from_params(
**settings.pgvector.model_dump(exclude_none=True)
),
)
case "chroma":
try:
import chromadb # type: ignore

View File

@ -101,7 +101,7 @@ class LLMSettings(BaseModel):
class VectorstoreSettings(BaseModel):
database: Literal["chroma", "qdrant"]
database: Literal["chroma", "qdrant", "pgvector"]
class LocalSettings(BaseModel):
@ -197,6 +197,41 @@ class UISettings(BaseModel):
)
class PGVectorSettings(BaseModel):
host: str = Field(
"localhost",
description="The server hosting the Postgres database",
)
port: int = Field(
5432,
description="The port on which the Postgres database is accessible",
)
user: str = Field(
"postgres",
description="The user to use to connect to the Postgres database",
)
password: str = Field(
"postgres",
description="The password to use to connect to the Postgres database",
)
database: str = Field(
"postgres",
description="The database to use to connect to the Postgres database",
)
embed_dim: int = Field(
384,
description="The dimension of the embeddings stored in the Postgres database",
)
schema_name: str = Field(
"public",
description="The name of the schema in the Postgres database where the embeddings are stored",
)
table_name: str = Field(
"embeddings",
description="The name of the table in the Postgres database where the embeddings are stored",
)
class QdrantSettings(BaseModel):
location: str | None = Field(
None,
@ -263,6 +298,7 @@ class Settings(BaseModel):
ollama: OllamaSettings
vectorstore: VectorstoreSettings
qdrant: QdrantSettings | None = None
pgvector: PGVectorSettings | None = None
"""

View File

@ -16,6 +16,10 @@ llama-index = { extras = ["local_models"], version = "0.9.3" }
watchdog = "^3.0.0"
qdrant-client = "^1.6.9"
chromadb = {version = "^0.4.13", optional = true}
asyncpg = {version = "^0.29.0", optional = true}
pgvector = {version = "^0.2.5", optional = true}
psycopg2-binary = {version = "^2.9.9", optional = true}
sqlalchemy = {version = "^2.0.27", optional = true}
[tool.poetry.group.dev.dependencies]
black = "^22"
@ -45,6 +49,7 @@ transformers = "^4.34.0"
[tool.poetry.extras]
chroma = ["chromadb"]
pgvector = ["sqlalchemy", "pgvector", "psycopg2-binary", "asyncpg"]
[build-system]
requires = ["poetry-core>=1.0.0"]

View File

@ -53,6 +53,16 @@ vectorstore:
qdrant:
path: local_data/private_gpt/qdrant
pgvector:
host: localhost
port: 5432
database: postgres
user: postgres
password: postgres
embed_dim: 384 # 384 is for BAAI/bge-small-en-v1.5
schema_name: private_gpt
table_name: embeddings
local:
prompt_style: "mistral"
llm_hf_repo_id: TheBloke/Mistral-7B-Instruct-v0.2-GGUF