partners: add lint docstrings for chroma module (#23249)

Description: add lint docstrings for chroma module Issue: the issue #23188 @baskaryan test: ruff check passed. ![image](https://github.com/langchain-ai/langchain/assets/76683249/5e168a0c-32d0-464f-8ddb-110233918019) --------- Co-authored-by: gongwn1 <gongwn1@lenovo.com>
2 weeks ago · f9aea3db07
parent 9eda8f2fe8
commit f9aea3db07
5 changed files with 168 additions and 22 deletions
--- a/libs/partners/chroma/langchain_chroma/init.py
+++ b/libs/partners/chroma/langchain_chroma/init.py
@ -1,3 +1,7 @@
+"""This is the langchain_chroma package.
+
+It contains the Chroma class for handling various tasks.
+"""
 from langchain_chroma.vectorstores import Chroma

 __all__ = [
--- a/libs/partners/chroma/langchain_chroma/vectorstores.py
+++ b/libs/partners/chroma/langchain_chroma/vectorstores.py
@ -1,3 +1,7 @@
+"""This is the langchain_chroma.vectorstores module.
+
+It contains the Chroma class which is a vector store for handling various tasks.
+"""
 from __future__ import annotations

 import base64
@ -98,7 +102,6 @@ def maximal_marginal_relevance(
    Returns:
        List of indices of embeddings selected by maximal marginal relevance.
    """
-
    if min(k, len(embedding_list)) <= 0:
        return []
    if query_embedding.ndim == 1:
@ -159,7 +162,7 @@ class Chroma(VectorStore):
        Args:
            collection_name: Name of the collection to create.
            embedding_function: Embedding class object. Used to embed texts.
-            persist_director: Directory to persist the collection.
+            persist_directory: Directory to persist the collection.
            client_settings: Chroma client settings
            collection_metadata: Collection configurations.
            client: Chroma client. Documentation:
@ -223,6 +226,7 @@ class Chroma(VectorStore):

    @property
    def embeddings(self) -> Optional[Embeddings]:
+        """Access the query embedding object."""
        return self._embedding_function

    @xor_args(("query_texts", "query_embeddings"))
@ -245,6 +249,7 @@ class Chroma(VectorStore):
                    e.g. {"color" : "red", "price": 4.20}.
            where_document: dict used to filter by the documents.
                    E.g. {$contains: {"text": "hello"}}.
+            **kwargs: Additional keyword arguments to pass to Chroma collection query.

        Returns:
            List of `n_results` nearest neighbor embeddings for provided
@ -280,6 +285,7 @@ class Chroma(VectorStore):
            metadatas: Optional list of metadatas.
                    When querying, you can filter on this metadata.
            ids: Optional list of IDs.
+            **kwargs: Additional keyword arguments to pass.

        Returns:
            List of IDs of the added images.
@ -367,6 +373,7 @@ class Chroma(VectorStore):
            metadatas: Optional list of metadatas.
                    When querying, you can filter on this metadata.
            ids: Optional list of IDs.
+            **kwargs: Additional keyword arguments.

        Returns:
            List of IDs of the added texts.
@ -374,7 +381,6 @@ class Chroma(VectorStore):
        Raises:
            ValueError: When metadata is incorrect.
        """
-
        if ids is None:
            ids = [str(uuid.uuid4()) for _ in texts]
        embeddings = None
@ -449,6 +455,7 @@ class Chroma(VectorStore):
            query: Query text to search for.
            k: Number of results to return. Defaults to 4.
            filter: Filter by metadata. Defaults to None.
+            **kwargs: Additional keyword arguments to pass to Chroma collection query.

        Returns:
            List of documents most similar to the query text.
@ -474,6 +481,7 @@ class Chroma(VectorStore):
            filter: Filter by metadata. Defaults to None.
            where_document: dict used to filter by the documents.
                    E.g. {$contains: {"text": "hello"}}.
+            **kwargs: Additional keyword arguments to pass to Chroma collection query.

        Returns:
            List of Documents most similar to the query vector.
@ -495,8 +503,7 @@ class Chroma(VectorStore):
        where_document: Optional[Dict[str, str]] = None,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
-        """
-        Return docs most similar to embedding vector and similarity score.
+        """Return docs most similar to embedding vector and similarity score.

        Args:
            embedding (List[float]): Embedding to look up documents similar to.
@ -504,6 +511,7 @@ class Chroma(VectorStore):
            filter: Filter by metadata. Defaults to None.
            where_document: dict used to filter by the documents.
                    E.g. {$contains: {"text": "hello"}}.
+            **kwargs: Additional keyword arguments to pass to Chroma collection query.

        Returns:
            List of documents most similar to the query text and relevance score
@ -534,6 +542,7 @@ class Chroma(VectorStore):
            filter: Filter by metadata. Defaults to None.
            where_document: dict used to filter by the documents.
                    E.g. {$contains: {"text": "hello"}}.
+            **kwargs: Additional keyword arguments to pass to Chroma collection query.

        Returns:
            List of documents most similar to the query text and
@ -574,7 +583,6 @@ class Chroma(VectorStore):
        Raises:
            ValueError: If the distance metric is not supported.
        """
-
        if self.override_relevance_score_fn:
            return self.override_relevance_score_fn

@ -623,11 +631,13 @@ class Chroma(VectorStore):
                to maximum diversity and 1 to minimum diversity.
                Defaults to 0.5.
            filter: Filter by metadata. Defaults to None.
+            where_document: dict used to filter by the documents.
+                    E.g. {$contains: {"text": "hello"}}.
+            **kwargs: Additional keyword arguments to pass to Chroma collection query.

        Returns:
            List of Documents selected by maximal marginal relevance.
        """
-
        results = self.__query_collection(
            query_embeddings=embedding,
            n_results=fetch_k,
@ -659,6 +669,7 @@ class Chroma(VectorStore):
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs selected using the maximal marginal relevance.
+
        Maximal marginal relevance optimizes for similarity to query AND diversity
        among selected documents.

@ -673,6 +684,7 @@ class Chroma(VectorStore):
            filter: Filter by metadata. Defaults to None.
            where_document: dict used to filter by the documents.
                    E.g. {$contains: {"text": "hello"}}.
+            **kwargs: Additional keyword arguments to pass to Chroma collection query.

        Returns:
            List of Documents selected by maximal marginal relevance.
@ -701,8 +713,10 @@ class Chroma(VectorStore):
        self._chroma_collection = None

    def reset_collection(self) -> None:
-        """Resets the collection by deleting the collection
-        and recreating an empty one."""
+        """Resets the collection.
+
+        Resets the collection by deleting the collection and recreating an empty one.
+        """
        self.delete_collection()
        self.__ensure_collection()

@ -827,9 +841,12 @@ class Chroma(VectorStore):
            embedding: Embedding function. Defaults to None.
            metadatas: List of metadatas. Defaults to None.
            ids: List of document IDs. Defaults to None.
-            client_settings: Chroma client settings
+            client_settings: Chroma client settings.
+            client: Chroma client. Documentation:
+                    https://docs.trychroma.com/reference/js-client#class:-chromaclient
            collection_metadata: Collection configurations.
                                                  Defaults to None.
+            **kwargs: Additional keyword arguments to initialize a Chroma client.

        Returns:
            Chroma: Chroma vectorstore.
@ -889,9 +906,12 @@ class Chroma(VectorStore):
            ids : List of document IDs. Defaults to None.
            documents: List of documents to add to the vectorstore.
            embedding: Embedding function. Defaults to None.
-            client_settings: Chroma client settings
+            client_settings: Chroma client settings.
+            client: Chroma client. Documentation:
+                    https://docs.trychroma.com/reference/js-client#class:-chromaclient
            collection_metadata: Collection configurations.
                                                  Defaults to None.
+            **kwargs: Additional keyword arguments to initialize a Chroma client.

        Returns:
            Chroma: Chroma vectorstore.
@ -916,5 +936,6 @@ class Chroma(VectorStore):

        Args:
            ids: List of ids to delete.
+            **kwargs: Additional keyword arguments.
        """
        self._collection.delete(ids=ids)
--- a/libs/partners/chroma/poetry.lock
+++ b/libs/partners/chroma/poetry.lock
@ -1288,7 +1288,7 @@ adal = ["adal (>=1.0.2)"]

 [[package]]
 name = "langchain"
-version = "0.2.3"
+version = "0.2.5"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@ -1298,7 +1298,7 @@ develop = true
 [package.dependencies]
 aiohttp = "^3.8.3"
 async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""}
-langchain-core = "^0.2.0"
+langchain-core = "^0.2.7"
 langchain-text-splitters = "^0.2.0"
 langsmith = "^0.1.17"
 numpy = [
@ -1309,7 +1309,7 @@ pydantic = ">=1,<3"
 PyYAML = ">=5.3"
 requests = "^2"
 SQLAlchemy = ">=1.4,<3"
-tenacity = "^8.1.0"
+tenacity = "^8.1.0,!=8.4.0"

 [package.source]
 type = "directory"
@ -1317,7 +1317,7 @@ url = "../../langchain"

 [[package]]
 name = "langchain-community"
-version = "0.2.4"
+version = "0.2.5"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = ">=3.8.1,<4.0"
@ -1327,8 +1327,8 @@ develop = true
 [package.dependencies]
 aiohttp = "^3.8.3"
 dataclasses-json = ">= 0.5.7, < 0.7"
-langchain = "^0.2.0"
-langchain-core = "^0.2.0"
+langchain = "^0.2.5"
+langchain-core = "^0.2.7"
 langsmith = "^0.1.0"
 numpy = [
    {version = ">=1,<2", markers = "python_version < \"3.12\""},
@ -1337,7 +1337,7 @@ numpy = [
 PyYAML = ">=5.3"
 requests = "^2"
 SQLAlchemy = ">=1.4,<3"
-tenacity = "^8.1.0"
+tenacity = "^8.1.0,!=8.4.0"

 [package.source]
 type = "directory"
@ -1345,7 +1345,7 @@ url = "../../community"

 [[package]]
 name = "langchain-core"
-version = "0.2.5"
+version = "0.2.9"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@ -1356,9 +1356,12 @@ develop = true
 jsonpatch = "^1.33"
 langsmith = "^0.1.75"
 packaging = ">=23.2,<25"
-pydantic = ">=1,<3"
+pydantic = [
+    {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
+    {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
+]
 PyYAML = ">=5.3"
-tenacity = "^8.1.0"
+tenacity = "^8.1.0,!=8.4.0"

 [package.source]
 type = "directory"
@ -1366,7 +1369,7 @@ url = "../../core"

 [[package]]
 name = "langchain-openai"
-version = "0.1.8"
+version = "0.1.9"
 description = "An integration package connecting OpenAI and LangChain"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@ -2318,6 +2321,25 @@ typing-extensions = ">=4.6.1"
 [package.extras]
 email = ["email-validator (>=2.0.0)"]

+[[package]]
+name = "pydantic"
+version = "2.7.4"
+description = "Data validation using Python type hints"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pydantic-2.7.4-py3-none-any.whl", hash = "sha256:ee8538d41ccb9c0a9ad3e0e5f07bf15ed8015b481ced539a1759d8cc89ae90d0"},
+    {file = "pydantic-2.7.4.tar.gz", hash = "sha256:0c84efd9548d545f63ac0060c1e4d39bb9b14db8b3c0652338aecc07b5adec52"},
+]
+
+[package.dependencies]
+annotated-types = ">=0.4.0"
+pydantic-core = "2.18.4"
+typing-extensions = ">=4.6.1"
+
+[package.extras]
+email = ["email-validator (>=2.0.0)"]
+
 [[package]]
 name = "pydantic-core"
 version = "2.18.2"
@ -2409,6 +2431,97 @@ files = [
 [package.dependencies]
 typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"

+[[package]]
+name = "pydantic-core"
+version = "2.18.4"
+description = "Core functionality for Pydantic validation and serialization"
+optional = false
+python-versions = ">=3.8"
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+]
+
+[package.dependencies]
+typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
+
 [[package]]
 name = "pygments"
 version = "2.18.0"
--- a/libs/partners/chroma/pyproject.toml
+++ b/libs/partners/chroma/pyproject.toml
@ -73,9 +73,16 @@ select = [
  "F",    # pyflakes
  "I",    # isort
  "T201", # print
+  "D",    # pydocstyle

 ]

+[tool.ruff.lint.pydocstyle]
+convention = "google"
+
+[tool.ruff.lint.per-file-ignores]
+"tests/**" = ["D"]  # ignore docstring checks for tests
+
 [tool.mypy]
 disallow_untyped_defs = "True"

--- a/libs/partners/chroma/scripts/check_imports.py
+++ b/libs/partners/chroma/scripts/check_imports.py
@ -1,3 +1,4 @@
+"""This module checks if the given python files can be imported without error."""
 import sys
 import traceback
 from importlib.machinery import SourceFileLoader