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feat: Qdrant support (#1228) 

* feat: Qdrant support

* Update private_gpt/components/vector_store/vector_store_component.py
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docs/description.md
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@ -248,6 +248,34 @@ llama_new_context_with_model: total VRAM used: 4857.93 MB (model: 4095.05 MB, co
AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 0 | VSX = 0 |
```
#### Vectorstores
PrivateGPT supports [Chroma](https://www.trychroma.com/), [Qdrant](https://qdrant.tech/) as vectorstore providers. Chroma being the default.
To enable Qdrant, set the `vectorstore.database` property in the `settings.yaml` file to `qdrant` and install the `qdrant` extra.
```bash
poetry install --extras qdrant
```
By default Qdrant tries to connect to an instance at `http://localhost:3000`.
Qdrant settings can be configured by setting values to the `qdrant` propery in the `settings.yaml` file.
The available configuration options are:
| Field | Description |
|--------------|-------------|
| location | If `:memory:` - use in-memory Qdrant instance.<br>If `str` - use it as a `url` parameter.|
| url | Either host or str of 'Optional[scheme], host, Optional[port], Optional[prefix]'.<br> Eg. `http://localhost:6333` |
| port | Port of the REST API interface. Default: `6333` |
| grpc_port | Port of the gRPC interface. Default: `6334` |
| prefer_grpc | If `true` - use gRPC interface whenever possible in custom methods. |
| https | If `true` - use HTTPS(SSL) protocol.|
| api_key | API key for authentication in Qdrant Cloud.|
| prefix | If set, add `prefix` to the REST URL path.<br>Example: `service/v1` will result in `http://localhost:6333/service/v1/{qdrant-endpoint}` for REST API.|
| timeout | Timeout for REST and gRPC API requests.<br>Default: 5.0 seconds for REST and unlimited for gRPC |
| host | Host name of Qdrant service. If url and host are not set, defaults to 'localhost'.|
| path | Persistence path for QdrantLocal. Eg. `local_data/private_gpt/qdrant`|
#### Known issues and Troubleshooting
Execution of LLMs locally still has a lot of sharp edges, specially when running on non Linux platforms.

178
poetry.lock generated
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@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.7.0 and should not be changed by hand.
# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
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[[package]]
name = "pyyaml"
version = "6.0.1"
@ -3475,6 +3623,29 @@ files = [
{file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
]
[[package]]
name = "qdrant-client"
version = "1.6.4"
description = "Client library for the Qdrant vector search engine"
optional = true
python-versions = ">=3.8,<3.13"
files = [
{file = "qdrant_client-1.6.4-py3-none-any.whl", hash = "sha256:db4696978d6a62d78ff60f70b912383f1e467bda3053f732b01ddb5f93281b10"},
{file = "qdrant_client-1.6.4.tar.gz", hash = "sha256:bbd65f383b6a55a9ccf4e301250fa925179340dd90cfde9b93ce4230fd68867b"},
]
[package.dependencies]
grpcio = ">=1.41.0"
grpcio-tools = ">=1.41.0"
httpx = {version = ">=0.14.0", extras = ["http2"]}
numpy = {version = ">=1.21", markers = "python_version >= \"3.8\" and python_version < \"3.12\""}
portalocker = ">=2.7.0,<3.0.0"
pydantic = ">=1.10.8"
urllib3 = ">=1.26.14,<2.0.0"
[package.extras]
fastembed = ["fastembed (==0.1.1)"]
[[package]]
name = "referencing"
version = "0.30.2"
@ -5320,7 +5491,10 @@ files = [
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
[extras]
qdrant = ["qdrant-client"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.11,<3.12"
content-hash = "3ea28113b04a06b1dfcbe6f43cdce086b596c714a53c10eb6501ab0c5d464412"
content-hash = "a72d664bdd24534d353deb2ad98cdebb5caa1647315405260c9c9f6e64d52f5a"

72
private_gpt/components/vector_store/vector_store_component.py
View File

@ -1,7 +1,8 @@
import logging
import typing
import chromadb
from chromadb.config import Settings
from chromadb.config import Settings as ChromaSettings
from injector import inject, singleton
from llama_index import VectorStoreIndex
from llama_index.indices.vector_store import VectorIndexRetriever
@ -10,6 +11,9 @@ from llama_index.vector_stores.types import VectorStore
from private_gpt.components.vector_store.batched_chroma import BatchedChromaVectorStore
from private_gpt.open_ai.extensions.context_filter import ContextFilter
from private_gpt.paths import local_data_path
from private_gpt.settings.settings import Settings
logger = logging.getLogger(__name__)
@typing.no_type_check
@ -36,22 +40,58 @@ class VectorStoreComponent:
vector_store: VectorStore
@inject
def __init__(self) -> None:
chroma_settings = Settings(anonymized_telemetry=False)
chroma_client = chromadb.PersistentClient(
path=str((local_data_path / "chroma_db").absolute()),
settings=chroma_settings,
)
chroma_collection = chroma_client.get_or_create_collection(
"make_this_parameterizable_per_api_call"
) # TODO
def __init__(self, settings: Settings) -> None:
match settings.vectorstore.database:
case "chroma":
chroma_settings = ChromaSettings(anonymized_telemetry=False)
chroma_client = chromadb.PersistentClient(
path=str((local_data_path / "chroma_db").absolute()),
settings=chroma_settings,
)
chroma_collection = chroma_client.get_or_create_collection(
"make_this_parameterizable_per_api_call"
) # TODO
self.vector_store = typing.cast(
VectorStore,
BatchedChromaVectorStore(
chroma_client=chroma_client, chroma_collection=chroma_collection
),
)
self.vector_store = typing.cast(
VectorStore,
BatchedChromaVectorStore(
chroma_client=chroma_client, chroma_collection=chroma_collection
),
)
case "qdrant":
try:
from llama_index.vector_stores.qdrant import QdrantVectorStore
from qdrant_client import QdrantClient # type: ignore
except ImportError as e:
raise ImportError(
"'qdrant_client' is not installed."
"To use PrivateGPT with Qdrant, install the 'qdrant' extra."
"`poetry install --extras qdrant`"
) from e
if settings.qdrant is None:
logger.info(
"Qdrant config not found. Using default settings."
"Trying to connect to Qdrant at localhost:6333."
)
client = QdrantClient()
else:
client = QdrantClient(
**settings.qdrant.model_dump(exclude_none=True)
)
self.vector_store = typing.cast(
VectorStore,
QdrantVectorStore(
client=client,
collection_name="make_this_parameterizable_per_api_call",
), # TODO
)
case _:
# Should be unreachable
# The settings validator should have caught this
raise ValueError(
f"Vectorstore database {settings.vectorstore.database} not supported"
)
@staticmethod
def get_retriever(

53
private_gpt/settings/settings.py
View File

@ -84,6 +84,10 @@ class LLMSettings(BaseModel):
mode: Literal["local", "openai", "sagemaker", "mock"]
class VectorstoreSettings(BaseModel):
database: Literal["chroma", "qdrant"]
class LocalSettings(BaseModel):
llm_hf_repo_id: str
llm_hf_model_file: str
@ -104,6 +108,53 @@ class UISettings(BaseModel):
path: str
class QdrantSettings(BaseModel):
location: str | None = Field(
None,
description=(
"If `:memory:` - use in-memory Qdrant instance.\n"
"If `str` - use it as a `url` parameter.\n"
),
)
url: str | None = Field(
None,
description=(
"Either host or str of 'Optional[scheme], host, Optional[port], Optional[prefix]'."
),
)
port: int | None = Field(6333, description="Port of the REST API interface.")
grpc_port: int | None = Field(6334, description="Port of the gRPC interface.")
prefer_grpc: bool | None = Field(
False,
description="If `true` - use gRPC interface whenever possible in custom methods.",
)
https: bool | None = Field(
None,
description="If `true` - use HTTPS(SSL) protocol.",
)
api_key: str | None = Field(
None,
description="API key for authentication in Qdrant Cloud.",
)
prefix: str | None = Field(
None,
description=(
"Prefix to add to the REST URL path."
"Example: `service/v1` will result in "
"'http://localhost:6333/service/v1/{qdrant-endpoint}' for REST API."
),
)
timeout: float | None = Field(
None,
description="Timeout for REST and gRPC API requests.",
)
host: str | None = Field(
None,
description="Host name of Qdrant service. If url and host are None, set to 'localhost'.",
)
path: str | None = Field(None, description="Persistence path for QdrantLocal.")
class Settings(BaseModel):
server: ServerSettings
data: DataSettings
@ -112,6 +163,8 @@ class Settings(BaseModel):
local: LocalSettings
sagemaker: SagemakerSettings
openai: OpenAISettings
vectorstore: VectorstoreSettings
qdrant: QdrantSettings | None = None
"""

4
pyproject.toml
View File

@ -15,6 +15,7 @@ pypdf = "^3.16.2"
llama-index = "0.8.67"
chromadb = "^0.4.13"
watchdog = "^3.0.0"
qdrant-client = {version = "^1.6.4", optional = true}
[tool.poetry.group.dev.dependencies]
black = "^22"
@ -42,6 +43,9 @@ sentence-transformers = "^2.2.2"
torch = ">=2.0.0, !=2.0.1, !=2.1.0"
transformers = "^4.34.0"
[tool.poetry.extras]
qdrant = ["qdrant-client"]
[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"

3
settings.yaml
View File

@ -23,6 +23,9 @@ ui:
llm:
mode: local
vectorstore:
database: chroma
local:
llm_hf_repo_id: TheBloke/Mistral-7B-Instruct-v0.1-GGUF
llm_hf_model_file: mistral-7b-instruct-v0.1.Q4_K_M.gguf