188 lines
9.6 KiB
Plaintext
188 lines
9.6 KiB
Plaintext
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## Vectorstores
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PrivateGPT supports [Qdrant](https://qdrant.tech/), [Milvus](https://milvus.io/), [Chroma](https://www.trychroma.com/), [PGVector](https://github.com/pgvector/pgvector) and [ClickHouse](https://github.com/ClickHouse/ClickHouse) as vectorstore providers. Qdrant being the default.
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In order to select one or the other, set the `vectorstore.database` property in the `settings.yaml` file to `qdrant`, `milvus`, `chroma`, `postgres` and `clickhouse`.
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```yaml
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vectorstore:
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database: qdrant
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```
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### Qdrant configuration
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To enable Qdrant, set the `vectorstore.database` property in the `settings.yaml` file to `qdrant`.
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Qdrant settings can be configured by setting values to the `qdrant` property in the `settings.yaml` file.
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The available configuration options are:
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| Field | Description |
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|--------------|-------------|
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| location | If `:memory:` - use in-memory Qdrant instance. If `str` - use it as a `url` parameter.|
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| url | Either host or str of 'Optional[scheme], host, Optional[port], Optional[prefix]'. Eg. `http://localhost:6333` |
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| port | Port of the REST API interface. Default: `6333` |
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| grpc_port | Port of the gRPC interface. Default: `6334` |
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| prefer_grpc | If `true` - use gRPC interface whenever possible in custom methods. |
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| https | If `true` - use HTTPS(SSL) protocol.|
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| api_key | API key for authentication in Qdrant Cloud.|
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| prefix | If set, add `prefix` to the REST URL path. Example: `service/v1` will result in `http://localhost:6333/service/v1/{qdrant-endpoint}` for REST API.|
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| timeout | Timeout for REST and gRPC API requests. Default: 5.0 seconds for REST and unlimited for gRPC |
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| host | Host name of Qdrant service. If url and host are not set, defaults to 'localhost'.|
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| path | Persistence path for QdrantLocal. Eg. `local_data/private_gpt/qdrant`|
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| force_disable_check_same_thread | Force disable check_same_thread for QdrantLocal sqlite connection, defaults to True.|
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By default Qdrant tries to connect to an instance of Qdrant server at `http://localhost:3000`.
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To obtain a local setup (disk-based database) without running a Qdrant server, configure the `qdrant.path` value in settings.yaml:
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```yaml
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qdrant:
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path: local_data/private_gpt/qdrant
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```
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### Milvus configuration
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To enable Milvus, set the `vectorstore.database` property in the `settings.yaml` file to `milvus` and install the `milvus` extra.
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```bash
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poetry install --extras vector-stores-milvus
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```
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The available configuration options are:
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| Field | Description |
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|--------------|-------------|
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| uri | Default is set to "local_data/private_gpt/milvus/milvus_local.db" as a local file; you can also set up a more performant Milvus server on docker or k8s e.g.http://localhost:19530, as your uri; To use Zilliz Cloud, adjust the uri and token to Endpoint and Api key in Zilliz Cloud.|
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| token | Pair with Milvus server on docker or k8s or zilliz cloud api key.|
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| collection_name | The name of the collection, set to default "milvus_db".|
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| overwrite | Overwrite the data in collection if it existed, set to default as True. |
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To obtain a local setup (disk-based database) without running a Milvus server, configure the uri value in settings.yaml, to store in local_data/private_gpt/milvus/milvus_local.db.
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### Chroma configuration
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To enable Chroma, set the `vectorstore.database` property in the `settings.yaml` file to `chroma` and install the `chroma` extra.
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```bash
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poetry install --extras chroma
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```
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By default `chroma` will use a disk-based database stored in local_data_path / "chroma_db" (being local_data_path defined in settings.yaml)
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### PGVector
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To use the PGVector store a [postgreSQL](https://www.postgresql.org/) database with the PGVector extension must be used.
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To enable PGVector, set the `vectorstore.database` property in the `settings.yaml` file to `postgres` and install the `vector-stores-postgres` extra.
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```bash
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poetry install --extras vector-stores-postgres
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```
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PGVector settings can be configured by setting values to the `postgres` property in the `settings.yaml` file.
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The available configuration options are:
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| Field | Description |
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|---------------|-----------------------------------------------------------|
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| **host** | The server hosting the Postgres database. Default is `localhost` |
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| **port** | The port on which the Postgres database is accessible. Default is `5432` |
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| **database** | The specific database to connect to. Default is `postgres` |
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| **user** | The username for database access. Default is `postgres` |
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| **password** | The password for database access. (Required) |
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| **schema_name** | The database schema to use. Default is `private_gpt` |
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For example:
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```yaml
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vectorstore:
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database: postgres
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postgres:
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host: localhost
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port: 5432
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database: postgres
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user: postgres
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password: <PASSWORD>
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schema_name: private_gpt
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```
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The following table will be created in the database
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```
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postgres=# \d private_gpt.data_embeddings
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Table "private_gpt.data_embeddings"
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Column | Type | Collation | Nullable | Default
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-----------+-------------------+-----------+----------+---------------------------------------------------------
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id | bigint | | not null | nextval('private_gpt.data_embeddings_id_seq'::regclass)
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text | character varying | | not null |
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metadata_ | json | | |
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node_id | character varying | | |
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embedding | vector(768) | | |
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Indexes:
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"data_embeddings_pkey" PRIMARY KEY, btree (id)
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postgres=#
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```
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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.
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### ClickHouse
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To utilize ClickHouse as the vector store, a [ClickHouse](https://github.com/ClickHouse/ClickHouse) database must be employed.
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To enable ClickHouse, set the `vectorstore.database` property in the `settings.yaml` file to `clickhouse` and install the `vector-stores-clickhouse` extra.
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```bash
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poetry install --extras vector-stores-clickhouse
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```
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ClickHouse settings can be configured by setting values to the `clickhouse` property in the `settings.yaml` file.
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The available configuration options are:
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| Field | Description |
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|----------------------|----------------------------------------------------------------|
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| **host** | The server hosting the ClickHouse database. Default is `localhost` |
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| **port** | The port on which the ClickHouse database is accessible. Default is `8123` |
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| **username** | The username for database access. Default is `default` |
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| **password** | The password for database access. (Optional) |
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| **database** | The specific database to connect to. Default is `__default__` |
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| **secure** | Use https/TLS for secure connection to the server. Default is `false` |
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| **interface** | The protocol used for the connection, either 'http' or 'https'. (Optional) |
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| **settings** | Specific ClickHouse server settings to be used with the session. (Optional) |
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| **connect_timeout** | Timeout in seconds for establishing a connection. (Optional) |
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| **send_receive_timeout** | Read timeout in seconds for http connection. (Optional) |
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| **verify** | Verify the server certificate in secure/https mode. (Optional) |
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| **ca_cert** | Path to Certificate Authority root certificate (.pem format). (Optional) |
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| **client_cert** | Path to TLS Client certificate (.pem format). (Optional) |
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| **client_cert_key** | Path to the private key for the TLS Client certificate. (Optional) |
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| **http_proxy** | HTTP proxy address. (Optional) |
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| **https_proxy** | HTTPS proxy address. (Optional) |
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| **server_host_name** | Server host name to be checked against the TLS certificate. (Optional) |
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For example:
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```yaml
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vectorstore:
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database: clickhouse
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clickhouse:
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host: localhost
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port: 8443
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username: admin
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password: <PASSWORD>
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database: embeddings
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secure: false
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```
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The following table will be created in the database:
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```
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clickhouse-client
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:) \d embeddings.llama_index
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Table "llama_index"
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№ | name | type | default_type | default_expression | comment | codec_expression | ttl_expression
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----|-----------|----------------------------------------------|--------------|--------------------|---------|------------------|---------------
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1 | id | String | | | | |
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2 | doc_id | String | | | | |
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3 | text | String | | | | |
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4 | vector | Array(Float32) | | | | |
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5 | node_info | Tuple(start Nullable(UInt64), end Nullable(UInt64)) | | | | |
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6 | metadata | String | | | | |
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clickhouse-client
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```
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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.
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