mirror of
https://github.com/hwchase17/langchain.git
synced 2025-09-19 09:16:24 +00:00
Updates after review and added Makefile targets for publishing
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@@ -8,10 +8,13 @@ RUFF := .venv/bin/ruff
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PYTEST := .venv/bin/pytest
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MYPY := .venv/bin/mypy
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PACKAGE_NAME=langchain-coherence
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DIST_DIR=dist
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install:
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@echo "🔧 Installing all dependencies..."
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uv venv
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uv pip install -e .[lint,typing,test,docs]
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uv pip install -e .[lint,typing,test,docs,publish]
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update-dev:
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@echo "🔄 Updating development dependencies..."
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@@ -51,7 +54,17 @@ test:
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clean:
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@echo "🧹 Cleaning build/test artifacts..."
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rm -rf .pytest_cache .mypy_cache .ruff_cache .venv .uv __pycache__ *.egg-info dist build
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rm -rf .pytest_cache .mypy_cache .ruff_cache __pycache__ *.egg-info dist build
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build:
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@echo "🧱 Building distribution using local virtualenv"
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$(PYTHON) -m build --no-isolation
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upload-pypi:
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@echo "🚀 Uploading to PyPI"
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$(PYTHON) -m twine upload dist/*
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publish: build upload-pypi
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help:
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@echo "🛠 Available Make targets:"
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@@ -63,6 +76,9 @@ help:
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@echo " lint - Run linter and mypy"
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@echo " fix - Autoformat and fix issues"
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@echo " test - Run all tests"
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@echo " build - Building distribution using local virtualenv"
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@echo " upload-pypi - Uploading to PyPI"
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@echo " publish - calls build, upload-pypi"
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@echo " clean - Remove temp and build files"
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@@ -378,7 +378,7 @@ class CoherenceVectorStore(VectorStore):
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async def asimilarity_search(
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self, query: str, k: int = 4, **kwargs: Any
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) -> list[Document]:
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"""Async method return docs most similar to query.
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"""Async method return list of docs most similar to query.
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Args:
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query: Input text.
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@@ -423,7 +423,7 @@ class CoherenceVectorStore(VectorStore):
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async def asimilarity_search_by_vector(
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self, embedding: list[float], k: int = 4, **kwargs: Any
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) -> list[Document]:
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"""Async method return docs most similar to passed embedding vector.
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"""Async method return list of docs most similar to passed embedding vector.
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Args:
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embedding: Input vector.
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@@ -466,7 +466,7 @@ class CoherenceVectorStore(VectorStore):
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async def asimilarity_search_with_score(
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self, query: str, k: int = 4, **kwargs: Any
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) -> list[tuple[Document, float]]:
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"""Async method return list tuple(Document, score) most similar to query.
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"""Async method return list of tuple(Document, score) most similar to query.
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Args:
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query: Input text.
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@@ -529,7 +529,7 @@ class CoherenceVectorStore(VectorStore):
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metadatas: Optional[list[dict[str, Any]]] = None,
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**kwargs: Any,
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) -> CoherenceVectorStore:
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"""Asynchronously initialize CoherenceVectorStore from texts and embeddings.
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"""Asynchronously initialize the CoherenceVectorStore from texts and embeddings.
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Args:
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texts: List of input text strings.
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@@ -539,7 +539,6 @@ class CoherenceVectorStore(VectorStore):
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- cache: Required Coherence NamedCache[str, Document] instance.
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- ids: Optional list of document IDs.
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Returns:
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CoherenceVectorStore: An initialized and populated vector store.
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@@ -32,7 +32,10 @@ docs = [
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"jupytext>=1.16",
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"nbdoc>=0.0.29",
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]
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publish = [
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"build",
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"twine"
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]
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[tool.mypy]
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strict = "True"
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disallow_untyped_defs = "True"
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2
libs/partners/coherence/uv.lock
generated
2
libs/partners/coherence/uv.lock
generated
@@ -1336,7 +1336,7 @@ wheels = [
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[[package]]
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name = "langchain-coherence"
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version = "0.0.1.dev0"
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version = "0.0.1"
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source = { virtual = "." }
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dependencies = [
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{ name = "coherence-client" },
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