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Create langchain-oracledb Package for Oracle AI Vector Search (#3)
* Add langchain-oracledb package * Add support for oracle dsl * Add async support * Convert execute to executemany * Fix formatting issues * Fix error handling and version check * Add file level comments - better logging * Use LOB outputtypehandler * Use oracledb.DB_TYPE_JSON for JSON binding * Fix license year * Update OracleAutonomousDatabaseLoader * Use quoted identifiers
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‎.gitignore‎

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.env
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.venv*
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.idea
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.coverage

‎README.md‎

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# 🦜️🔗 LangChain 🤝 Oracle
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Welcome to the official repository for LangChain integration with [Oracle Cloud Infrastructure (OCI)](https://cloud.oracle.com/). This library provides native LangChain components for interacting with Oracle's AI services—combining support for **OCI Generative AI** and **OCI Data Science**.
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Welcome to the official repository for LangChain integration with [Oracle Cloud Infrastructure (OCI)](https://cloud.oracle.com/) and [Oracle AI Vector Search](https://www.oracle.com/database/ai-vector-search/). This project provides native LangChain components for interacting with Oracle's AI services—providing support for **OCI Generative AI**, **OCI Data Science** and **Oracle AI Vector Search**.
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## Features
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- **LLMs**: Includes LLM classes for OCI services like [Generative AI](https://cloud.oracle.com/ai-services/generative-ai) and [ModelDeployment Endpoints](https://cloud.oracle.com/ai-services/model-deployment), allowing you to leverage their language models within LangChain.
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- **Agents**: Includes Runnables to support [Oracle Generative AI Agents](https://www.oracle.com/artificial-intelligence/generative-ai/agents/), allowing you to leverage Generative AI Agents within LangChain and LangGraph.
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- **More to come**: This repository will continue to expand and offer additional components for various OCI services as development progresses.
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- **Vector Search**: Offers native integration with [Oracle AI Vector Search](https://www.oracle.com/database/ai-vector-search/) through a LangChain-compatible components. This enables pipelines that can:
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- Load the documents from various sources using `OracleDocLoader`
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- Summarize content within/outside the database using `OracleSummary`
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- Generate embeddings within/outside the database using `OracleEmbeddings`
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- Chunk according to different requirements using Advanced Oracle Capabilities from `OracleTextSplitter`
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- Store, index, and query vectors using `OracleVS`
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- **More to come**: This repository will continue to expand and offer additional components for various OCI and Oracle AI services as development progresses.
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> This project merges and replaces earlier OCI integrations from the `langchain-community` repository and unifies contributions from Oracle's GenAI and Data Science teams.
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> All integrations in this package assume that you have the credentials setup to connect with oci services.
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> This project merges and replaces earlier OCI and Oracle AI Vector Search integrations from the `langchain-community` repository and unifies contributions from Oracle teams.
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> All integrations in this package assume that you have the credentials setup to connect with oci and database services.
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---
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## Installation
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For OCI services:
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```bash
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pip install -U langchain-oci
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python -m pip install -U langchain-oci
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```
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---
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## Quick Start
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This repository includes two main integration categories:
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- [OCI Generative AI](#oci-generative-ai-examples)
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- [OCI Data Science (Model Deployment)](#oci-data-science-model-deployment-examples)
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---
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## OCI Generative AI Examples
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### 1. Use a Chat Model
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`ChatOCIGenAI` class exposes chat models from OCI Generative AI.
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```python
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from langchain_oci import ChatOCIGenAI
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llm = ChatOCIGenAI()
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llm.invoke("Sing a ballad of LangChain.")
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```
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### 2. Use a Completion Model
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`OCIGenAI` class exposes LLMs from OCI Generative AI.
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```python
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from langchain_oci import OCIGenAI
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llm = OCIGenAI()
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llm.invoke("The meaning of life is")
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```
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For Oracle AI Vector Search services:
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### 3. Use an Embedding Model
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`OCIGenAIEmbeddings` class exposes embeddings from OCI Generative AI.
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```python
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from langchain_oci import OCIGenAIEmbeddings
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embeddings = OCIGenAIEmbeddings()
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embeddings.embed_query("What is the meaning of life?")
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```
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## OCI Data Science Model Deployment Examples
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### 1. Use a Chat Model
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You may instantiate the OCI Data Science model with the generic `ChatOCIModelDeployment` or framework specific class like `ChatOCIModelDeploymentVLLM`.
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```python
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from langchain_oci.chat_models import ChatOCIModelDeployment, ChatOCIModelDeploymentVLLM
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# Create an instance of OCI Model Deployment Endpoint
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# Replace the endpoint uri with your own
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endpoint = "https://modeldeployment.<region>.oci.customer-oci.com/<ocid>/predict"
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messages = [
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(
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"system",
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"You are a helpful assistant that translates English to French. Translate the user sentence.",
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),
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("human", "I love programming."),
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]
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chat = ChatOCIModelDeployment(
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endpoint=endpoint,
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streaming=True,
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max_retries=1,
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model_kwargs={
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"temperature": 0.2,
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"max_tokens": 512,
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}, # other model params...
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default_headers={
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"route": "/v1/chat/completions",
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# other request headers ...
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},
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)
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chat.invoke(messages)
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chat_vllm = ChatOCIModelDeploymentVLLM(endpoint=endpoint)
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chat_vllm.invoke(messages)
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```
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### 2. Use a Completion Model
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You may instantiate the OCI Data Science model with `OCIModelDeploymentLLM` or `OCIModelDeploymentVLLM`.
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```python
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from langchain_oci.llms import OCIModelDeploymentLLM, OCIModelDeploymentVLLM
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# Create an instance of OCI Model Deployment Endpoint
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# Replace the endpoint uri and model name with your own
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endpoint = "https://modeldeployment.<region>.oci.customer-oci.com/<ocid>/predict"
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llm = OCIModelDeploymentLLM(
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endpoint=endpoint,
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model="odsc-llm",
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)
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llm.invoke("Who is the first president of United States?")
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vllm = OCIModelDeploymentVLLM(
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endpoint=endpoint,
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)
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vllm.invoke("Who is the first president of United States?")
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```bash
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python -m pip install -U langchain-oracledb
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```
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### 3. Use an Embedding Model
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You may instantiate the OCI Data Science model with the `OCIModelDeploymentEndpointEmbeddings`.
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```python
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from langchain_oci.embeddings import OCIModelDeploymentEndpointEmbeddings
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# Create an instance of OCI Model Deployment Endpoint
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# Replace the endpoint uri with your own
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endpoint = "https://modeldeployment.<region>.oci.customer-oci.com/<ocid>/predict"
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embeddings = OCIModelDeploymentEndpointEmbeddings(
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endpoint=endpoint,
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)
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---
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query = "Hello World!"
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embeddings.embed_query(query)
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## Quick Start
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documents = ["This is a sample document", "and here is another one"]
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embeddings.embed_documents(documents)
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```
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This repository includes three main integration categories. For detailed information, please refer to the respective libraries:
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- [OCI Generative AI](https://github.com/oracle/langchain-oracle/tree/main/libs/oci)
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- [OCI Data Science (Model Deployment)](https://github.com/oracle/langchain-oracle/tree/main/libs/oci)
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- [Oracle AI Vector Search](https://github.com/oracle/langchain-oracle/tree/main/libs/oracledb)
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## Contributing
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‎libs/oracledb/LICENSE.txt‎

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Copyright (c) 2025 Oracle and/or its affiliates. All rights reserved.
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The Universal Permissive License (UPL), Version 1.0
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Subject to the condition set forth below, permission is hereby granted to any
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person obtaining a copy of this software, associated documentation and/or data
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(collectively the "Software"), free of charge and under any and all copyright
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rights in the Software, and any and all patent rights owned or freely
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licensable by each licensor hereunder covering either (i) the unmodified
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Software as contributed to or provided by such licensor, or (ii) the Larger
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Works (as defined below), to deal in both
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(a) the Software, and
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(b) any piece of software and/or hardware listed in the lrgrwrks.txt file if
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one is included with the Software (each a "Larger Work" to which the Software
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is contributed by such licensors),
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without restriction, including without limitation the rights to copy, create
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derivative works of, display, perform, and distribute the Software and make,
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use, sell, offer for sale, import, export, have made, and have sold the
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Software and the Larger Work(s), and to sublicense the foregoing rights on
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either these or other terms.
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This license is subject to the following condition:
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The above copyright notice and either this complete permission notice or at
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a minimum a reference to the UPL must be included in all copies or
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substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎libs/oracledb/Makefile‎

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.PHONY: all format lint test tests integration_tests help
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# Default target executed when no arguments are given to make.
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all: help
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# Define a variable for the test file path.
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TEST_FILE ?= tests/unit_tests/
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integration_test integration_tests: TEST_FILE = tests/integration_tests/
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test tests integration_test integration_tests:
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poetry run pytest $(TEST_FILE)
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test_watch:
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poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE)
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######################
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# LINTING AND FORMATTING
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######################
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# Define a variable for Python and notebook files.
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PYTHON_FILES=.
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MYPY_CACHE=.mypy_cache
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lint format: PYTHON_FILES=.
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lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/oracledb --name-only --diff-filter=d main | grep -E '\.py$$|\.ipynb$$')
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lint_package: PYTHON_FILES=langchain_oracledb
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lint_tests: PYTHON_FILES=tests
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lint_tests: MYPY_CACHE=.mypy_cache_test
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lint lint_diff lint_package lint_tests:
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poetry run ruff .
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poetry run ruff format $(PYTHON_FILES) --diff
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poetry run ruff --select I $(PYTHON_FILES)
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mkdir -p $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
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format format_diff:
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poetry run ruff format $(PYTHON_FILES)
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poetry run ruff --select I --fix $(PYTHON_FILES)
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spell_check:
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poetry run codespell --toml pyproject.toml
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spell_fix:
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poetry run codespell --toml pyproject.toml -w
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check_imports: $(shell find langchain_oracledb -name '*.py')
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poetry run python ./scripts/check_imports.py $^
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######################
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# HELP
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######################
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help:
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@echo '----'
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@echo 'check_imports - check imports'
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@echo 'format - run code formatters'
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@echo 'lint - run linters'
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@echo 'test - run unit tests'
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@echo 'tests - run unit tests'
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@echo 'test TEST_FILE=<test_file> - run all tests in file'

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