mirror of
https://github.com/imartinez/privateGPT.git
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fix: chromadb max batch size (#1087)
This commit is contained in:
Iván Martínez
GitHub
parent
b46c1087e2
commit
f5a9bf4e37
82
poetry.lock
generated
82
poetry.lock
generated
@ -261,24 +261,6 @@ files = [
|
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tests = ["pytest (>=3.2.1,!=3.3.0)"]
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typecheck = ["mypy"]
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|
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[[package]]
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name = "beautifulsoup4"
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version = "4.12.2"
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description = "Screen-scraping library"
|
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python-versions = ">=3.6.0"
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[package.dependencies]
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||||
soupsieve = ">1.2"
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|
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[package.extras]
|
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html5lib = ["html5lib"]
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lxml = ["lxml"]
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[[package]]
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name = "black"
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version = "22.12.0"
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@ -643,10 +625,7 @@ files = [
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]
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[package.dependencies]
|
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numpy = [
|
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{version = ">=1.16,<2.0", markers = "python_version <= \"3.11\""},
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{version = ">=1.26.0rc1,<2.0", markers = "python_version >= \"3.12\""},
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]
|
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numpy = {version = ">=1.16,<2.0", markers = "python_version <= \"3.11\""}
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||||
|
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[package.extras]
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bokeh = ["bokeh", "selenium"]
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@ -736,13 +715,13 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
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|
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[[package]]
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||||
name = "dataclasses-json"
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python-versions = ">=3.7,<4.0"
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python-versions = ">=3.7,<3.13"
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[package.dependencies]
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@ -1140,7 +1119,6 @@ files = [
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{file = "greenlet-3.0.0-cp312-universal2-macosx_10_9_universal2.whl", hash = "sha256:553d6fb2324e7f4f0899e5ad2c427a4579ed4873f42124beba763f16032959af"},
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@ -1760,28 +1738,27 @@ test = ["httpx (>=0.24.1)", "pytest (>=7.4.0)"]
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[[package]]
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name = "llama-index"
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version = "0.8.35"
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version = "0.8.47"
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description = "Interface between LLMs and your data"
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optional = false
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python-versions = "*"
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python-versions = ">=3.8.1,<3.12"
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files = [
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{file = "llama_index-0.8.35-py3-none-any.whl", hash = "sha256:f2f1670320e75a9643b6dc96662038f777866ed543994d18f71ab54329e295ae"},
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{file = "llama_index-0.8.35.tar.gz", hash = "sha256:a8767be9d36ebd538a37e18b0c7f46bb19d9d7ec490ef7582640f75d5d9b5259"},
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[package.dependencies]
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beautifulsoup4 = "*"
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dataclasses-json = "*"
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dataclasses-json = ">=0.5.7,<0.6.0"
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fsspec = ">=2023.5.0"
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langchain = ">=0.0.293"
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nest-asyncio = "*"
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nltk = "*"
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langchain = ">=0.0.303"
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nest-asyncio = ">=1.5.8,<2.0.0"
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nltk = ">=3.8.1,<4.0.0"
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numpy = "*"
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openai = ">=0.26.4"
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pandas = "*"
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sqlalchemy = ">=2.0.15"
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SQLAlchemy = {version = ">=1.4.49", extras = ["asyncio"]}
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tenacity = ">=8.2.0,<9.0.0"
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tiktoken = "*"
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tiktoken = ">=0.3.3"
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typing-extensions = ">=4.5.0"
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typing-inspect = ">=0.8.0"
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urllib3 = "<2"
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@ -2397,10 +2374,7 @@ files = [
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]
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[package.dependencies]
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numpy = [
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{version = ">=1.23.2", markers = "python_version == \"3.11\""},
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{version = ">=1.26.0", markers = "python_version >= \"3.12\""},
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]
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numpy = {version = ">=1.23.2", markers = "python_version == \"3.11\""}
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python-dateutil = ">=2.8.2"
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pytz = ">=2020.1"
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tzdata = ">=2022.1"
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@ -3469,11 +3443,6 @@ files = [
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@ -3690,17 +3659,6 @@ files = [
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{file = "sniffio-1.3.0.tar.gz", hash = "sha256:e60305c5e5d314f5389259b7f22aaa33d8f7dee49763119234af3755c55b9101"},
|
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]
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[[package]]
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name = "soupsieve"
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version = "2.5"
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description = "A modern CSS selector implementation for Beautiful Soup."
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optional = false
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python-versions = ">=3.8"
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files = [
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{file = "soupsieve-2.5-py3-none-any.whl", hash = "sha256:eaa337ff55a1579b6549dc679565eac1e3d000563bcb1c8ab0d0fefbc0c2cdc7"},
|
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{file = "soupsieve-2.5.tar.gz", hash = "sha256:5663d5a7b3bfaeee0bc4372e7fc48f9cff4940b3eec54a6451cc5299f1097690"},
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]
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[[package]]
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name = "sqlalchemy"
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version = "2.0.22"
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@ -3760,7 +3718,7 @@ files = [
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]
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[package.dependencies]
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greenlet = {version = "!=0.4.17", 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\""}
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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\""}
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typing-extensions = ">=4.2.0"
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[package.extras]
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@ -4738,5 +4696,5 @@ multidict = ">=4.0"
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[metadata]
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lock-version = "2.0"
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python-versions = ">=3.11,<3.13"
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content-hash = "c1fa5accdcd9cd81430839398e16d2596b43b1c314b5d2a8a76aa05bbb83a39c"
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python-versions = ">=3.11,<3.12"
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content-hash = "56b78ce6a8a6dfbe42b490bcf4ffbf2820f10d5ce70a28c61ee4e357172dab33"
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87
private_gpt/components/vector_store/batched_chroma.py
Normal file
87
private_gpt/components/vector_store/batched_chroma.py
Normal file
@ -0,0 +1,87 @@
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from typing import Any
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from llama_index.schema import BaseNode, MetadataMode
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from llama_index.vector_stores import ChromaVectorStore
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from llama_index.vector_stores.chroma import chunk_list
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from llama_index.vector_stores.utils import node_to_metadata_dict
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class BatchedChromaVectorStore(ChromaVectorStore):
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"""Chroma vector store, batching additions to avoid reaching the max batch limit.
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In this vector store, embeddings are stored within a ChromaDB collection.
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During query time, the index uses ChromaDB to query for the top
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k most similar nodes.
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Args:
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chroma_client (from chromadb.api.API):
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API instance
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chroma_collection (chromadb.api.models.Collection.Collection):
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ChromaDB collection instance
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"""
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chroma_client: Any | None
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def __init__(
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self,
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chroma_client: Any,
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chroma_collection: Any,
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host: str | None = None,
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port: str | None = None,
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ssl: bool = False,
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headers: dict[str, str] | None = None,
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collection_kwargs: dict[Any, Any] | None = None,
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) -> None:
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super().__init__(
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chroma_collection=chroma_collection,
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host=host,
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port=port,
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ssl=ssl,
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headers=headers,
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collection_kwargs=collection_kwargs or {},
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)
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self.chroma_client = chroma_client
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def add(self, nodes: list[BaseNode]) -> list[str]:
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"""Add nodes to index, batching the insertion to avoid issues.
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Args:
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nodes: List[BaseNode]: list of nodes with embeddings
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"""
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if not self.chroma_client:
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raise ValueError("Client not initialized")
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if not self._collection:
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raise ValueError("Collection not initialized")
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max_chunk_size = self.chroma_client.max_batch_size
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node_chunks = chunk_list(nodes, max_chunk_size)
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all_ids = []
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for node_chunk in node_chunks:
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embeddings = []
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metadatas = []
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ids = []
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documents = []
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for node in node_chunk:
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embeddings.append(node.get_embedding())
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metadatas.append(
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node_to_metadata_dict(
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node, remove_text=True, flat_metadata=self.flat_metadata
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)
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)
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ids.append(node.node_id)
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documents.append(node.get_content(metadata_mode=MetadataMode.NONE))
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self._collection.add(
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embeddings=embeddings,
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ids=ids,
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metadatas=metadatas,
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documents=documents,
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)
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all_ids.extend(ids)
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return all_ids
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@ -4,9 +4,9 @@ import chromadb
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from injector import inject, singleton
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from llama_index import VectorStoreIndex
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from llama_index.indices.vector_store import VectorIndexRetriever
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from llama_index.vector_stores import ChromaVectorStore
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from llama_index.vector_stores.types import VectorStore
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from private_gpt.components.vector_store.batched_chroma import BatchedChromaVectorStore
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from private_gpt.open_ai.extensions.context_filter import ContextFilter
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from private_gpt.paths import local_data_path
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@ -36,14 +36,16 @@ class VectorStoreComponent:
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@inject
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def __init__(self) -> None:
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db = chromadb.PersistentClient(
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chroma_client = chromadb.PersistentClient(
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path=str((local_data_path / "chroma_db").absolute())
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)
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chroma_collection = db.get_or_create_collection(
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chroma_collection = chroma_client.get_or_create_collection(
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"make_this_parameterizable_per_api_call"
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) # TODO
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self.vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
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self.vector_store = BatchedChromaVectorStore(
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chroma_client=chroma_client, chroma_collection=chroma_collection
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)
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@staticmethod
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def get_retriever(
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@ -5,7 +5,7 @@ description = "Private GPT"
|
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authors = ["Zylon <hi@zylon.ai>"]
|
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[tool.poetry.dependencies]
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python = ">=3.11,<3.13"
|
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python = ">=3.11,<3.12"
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fastapi = { extras = ["all"], version = "^0.103.1" }
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loguru = "^0.7.2"
|
||||
boto3 = "^1.28.56"
|
||||
@ -13,7 +13,7 @@ injector = "^0.21.0"
|
||||
pyyaml = "^6.0.1"
|
||||
python-multipart = "^0.0.6"
|
||||
pypdf = "^3.16.2"
|
||||
llama-index = "v0.8.35"
|
||||
llama-index = "0.8.47"
|
||||
chromadb = "^0.4.13"
|
||||
watchdog = "^3.0.0"
|
||||
transformers = "^4.34.0"
|
||||
|
||||
27
tests/server/ingest/test_ingest_service.py
Normal file
27
tests/server/ingest/test_ingest_service.py
Normal file
@ -0,0 +1,27 @@
|
||||
from unittest.mock import PropertyMock, patch
|
||||
|
||||
from llama_index import Document
|
||||
|
||||
from private_gpt.server.ingest.ingest_service import IngestService
|
||||
from tests.fixtures.mock_injector import MockInjector
|
||||
|
||||
|
||||
def test_save_many_nodes(injector: MockInjector) -> None:
|
||||
"""This is a specific test for a local Chromadb Vector Database setup.
|
||||
|
||||
Extend it when we add support for other vector databases in VectorStoreComponent.
|
||||
"""
|
||||
with patch(
|
||||
"chromadb.api.segment.SegmentAPI.max_batch_size", new_callable=PropertyMock
|
||||
) as max_batch_size:
|
||||
# Make max batch size of Chromadb very small
|
||||
max_batch_size.return_value = 10
|
||||
|
||||
ingest_service = injector.get(IngestService)
|
||||
|
||||
documents = []
|
||||
for _i in range(100):
|
||||
documents.append(Document(text="This is a sentence."))
|
||||
|
||||
ingested_docs = ingest_service._save_docs(documents)
|
||||
assert len(ingested_docs) == len(documents)
|
||||
Loading…
Reference in New Issue
Block a user