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feat: unify settings for vector and nodestore connections to PostgreSQL (#1730)

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* Unify pgvector and postgres connection settings

* Remove local changes

* Update file pgvector->postgres
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Brett England 2024-03-15 04:55:17 -04:00 • committed by GitHub
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34
fern/docs/pages/manual/vectordb.mdx
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@ -1,7 +1,7 @@
## Vectorstores
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`, `chroma` or `pgvector`.
In order to select one or the other, set the `vectorstore.database` property in the `settings.yaml` file to `qdrant`, `chroma` or `postgres`.
```yaml
vectorstore:
@ -50,14 +50,15 @@ 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)
### PGVector
To use the PGVector store a [postgreSQL](https://www.postgresql.org/) database with the PGVector extension must be used.
To enable PGVector, set the `vectorstore.database` property in the `settings.yaml` file to `pgvector` and install the `vector-stores-postgres` extra.
To enable PGVector, set the `vectorstore.database` property in the `settings.yaml` file to `postgres` and install the `vector-stores-postgres` extra.
```bash
poetry install --extras vector-stores-postgres
```
PGVector settings can be configured by setting values to the `pgvector` property in the `settings.yaml` file.
PGVector settings can be configured by setting values to the `postgres` property in the `settings.yaml` file.
The available configuration options are:
| Field | Description |
@ -67,19 +68,36 @@ The available configuration options are:
| **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:
vectorstore:
database: postgresql
postgres:
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
```
The following table will be created in the database
```
postgres=# \d private_gpt.data_embeddings
Table "private_gpt.data_embeddings"
Column | Type | Collation | Nullable | Default
-----------+-------------------+-----------+----------+---------------------------------------------------------
id | bigint | | not null | nextval('private_gpt.data_embeddings_id_seq'::regclass)
text | character varying | | not null |
metadata_ | json | | |
node_id | character varying | | |
embedding | vector(768) | | |
Indexes:
"data_embeddings_pkey" PRIMARY KEY, btree (id)
postgres=#
```
The dimensions of the embeddings columns will be set based on the `embedding.embed_dim` value. If the embedding model changes this table may need to be dropped and recreated to avoid a dimension mismatch.