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https://github.com/hwchase17/langchain.git
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docs: add Taiga Tool integration docs (#30042)
This PR adds documentation for the langchain-taiga Tool integration, including an example notebook at 'docs/docs/integrations/tools/taiga.ipynb' and updates to 'libs/packages.yml' to track the new package. Issue: N/A Dependencies: None Twitter handle: N/A --------- Co-authored-by: ccurme <chester.curme@gmail.com>
This commit is contained in:
Alexander Henlein
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GitHub
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commit
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docs/docs/integrations/providers/taiga.mdx
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docs/docs/integrations/providers/taiga.mdx
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# Taiga
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> [Taiga](https://docs.taiga.io/) is an open-source project management platform designed for agile teams, offering features like Kanban, Scrum, and issue tracking.
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## Installation and Setup
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Install the `langchain-taiga` package:
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```bash
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pip install langchain-taiga
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```
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You must provide a logins via environment variable so the tools can authenticate.
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```bash
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export TAIGA_URL="https://taiga.xyz.org/"
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export TAIGA_API_URL="https://taiga.xyz.org/"
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export TAIGA_USERNAME="username"
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export TAIGA_PASSWORD="pw"
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export OPENAI_API_KEY="OPENAI_API_KEY"
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```
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---
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## Tools
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See a [usage example](/docs/integrations/tools/taiga)
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---
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## Toolkit
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`TaigaToolkit` groups multiple Taiga-related tools into a single interface.
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```python
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from langchain_taiga.toolkits import TaigaToolkit
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toolkit = TaigaToolkit()
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tools = toolkit.get_tools()
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```
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---
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## Future Integrations
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Check the [Taiga Developer Docs](https://docs.taiga.io/) for more information, and watch for updates or advanced usage examples in the [langchain_taiga GitHub repo](https://github.com/Shikenso-Analytics/langchain-taiga).
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306
docs/docs/integrations/tools/taiga.ipynb
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306
docs/docs/integrations/tools/taiga.ipynb
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "10238e62-3465-4973-9279-606cbb7ccf16",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_label: Taiga\n",
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"---"
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"# Taiga\n",
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"\n",
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"This notebook provides a quick overview for getting started with Taiga tooling in [langchain_taiga](https://github.com/Shikenso-Analytics/langchain-taiga/blob/main/docs/tools.ipynb). For more details on each tool and configuration, see the docstrings in your repository or relevant doc pages.\n",
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"\n",
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"\n",
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"\n",
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"## Overview\n",
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"\n",
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"### Integration details\n",
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"\n",
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"| Class | Package | Serializable | JS support | Package latest |\n",
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"|:-----------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------| :---: |:------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------:|\n",
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"| `create_entity_tool`, `search_entities_tool`, `get_entity_by_ref_tool`, `update_entity_by_ref_tool` , `add_comment_by_ref_tool`, `add_attachment_by_ref_tool` | [langchain-taiga](https://github.com/Shikenso-Analytics/langchain-taiga) | N/A | TBD |  |\n",
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"\n",
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"### Tool features\n",
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"\n",
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"- **`create_entity_tool`**: Creates user stories, tasks and issues in Taiga.\n",
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"- **`search_entities_tool`**: Searches for user stories, tasks and issues in Taiga.\n",
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"- **`get_entity_by_ref_tool`**: Gets a user story, task or issue by reference.\n",
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"- **`update_entity_by_ref_tool`**: Updates a user story, task or issue by reference.\n",
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"- **`add_comment_by_ref_tool`**: Adds a comment to a user story, task or issue.\n",
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"- **`add_attachment_by_ref_tool`**: Adds an attachment to a user story, task or issue.\n",
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"\n",
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"## Setup\n",
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"\n",
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"The integration lives in the `langchain-taiga` package."
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],
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"id": "41616bfd02d989a6"
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},
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{
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"cell_type": "code",
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"id": "f85b4089",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-02-28T12:43:23.290414Z",
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"start_time": "2025-02-28T12:43:23.162563Z"
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}
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},
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"source": "%pip install --quiet -U langchain-taiga",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"/home/henlein/Workspace/PyCharm/langchain/.venv/bin/python: No module named pip\r\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"execution_count": 3
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},
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{
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"cell_type": "markdown",
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"id": "b15e9266",
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"metadata": {},
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"source": [
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"### Credentials\n",
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"\n",
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"This integration requires you to set `TAIGA_URL`, `TAIGA_API_URL`, `TAIGA_USERNAME`, `TAIGA_PASSWORD` and `OPENAI_API_KEY` as environment variables to authenticate with Taiga.\n",
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"\n",
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"```bash\n",
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"export TAIGA_URL=\"https://taiga.xyz.org/\"\n",
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"export TAIGA_API_URL=\"https://taiga.xyz.org/\"\n",
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"export TAIGA_USERNAME=\"username\"\n",
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"export TAIGA_PASSWORD=\"pw\"\n",
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"export OPENAI_API_KEY=\"OPENAI_API_KEY\"\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bc5ab717-fd27-4c59-b912-bdd099541478",
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"metadata": {},
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"source": [
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"It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability:"
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]
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},
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{
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"cell_type": "code",
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"id": "a6c2f136-6367-4f1f-825d-ae741e1bf281",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-02-28T12:43:23.295879Z",
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"start_time": "2025-02-28T12:43:23.293809Z"
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}
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},
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"source": [
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"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
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],
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"outputs": [],
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"execution_count": 4
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"## Instantiation\n",
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"\n",
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"Below is an example showing how to instantiate the Taiga tools in `langchain_taiga`. Adjust as needed for your specific usage."
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],
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"id": "d6eab61edeeb40a5"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": [
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"from langchain_taiga.tools.discord_read_messages import create_entity_tool\n",
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"from langchain_taiga.tools.discord_send_messages import search_entities_tool\n",
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"\n",
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"create_tool = create_entity_tool\n",
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"search_tool = search_entities_tool"
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],
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"id": "8ae97a3413cd040e"
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},
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{
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"cell_type": "markdown",
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"id": "74147a1a",
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"metadata": {},
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"source": [
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"## Invocation\n",
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"\n",
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"### Direct invocation with args\n",
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"\n",
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"Below is a simple example of calling the tool with keyword arguments in a dictionary."
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]
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},
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{
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"cell_type": "code",
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"id": "65310a8b-eb0c-4d9e-a618-4f4abe2414fc",
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"metadata": {},
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"source": [
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"from langchain_taiga.tools.taiga_tools import (\n",
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" add_attachment_by_ref_tool,\n",
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" add_comment_by_ref_tool,\n",
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" create_entity_tool,\n",
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" get_entity_by_ref_tool,\n",
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" search_entities_tool,\n",
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" update_entity_by_ref_tool,\n",
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")\n",
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"\n",
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"response = create_entity_tool.invoke(\n",
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" {\n",
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" \"project_slug\": \"slug\",\n",
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" \"entity_type\": \"us\",\n",
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" \"subject\": \"subject\",\n",
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" \"status\": \"new\",\n",
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" \"description\": \"desc\",\n",
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" \"parent_ref\": 5,\n",
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" \"assign_to\": \"user\",\n",
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" \"due_date\": \"2022-01-01\",\n",
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" \"tags\": [\"tag1\", \"tag2\"],\n",
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" }\n",
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")\n",
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"\n",
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"response = search_entities_tool.invoke(\n",
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" {\"project_slug\": \"slug\", \"query\": \"query\", \"entity_type\": \"task\"}\n",
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")\n",
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"\n",
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"response = get_entity_by_ref_tool.invoke(\n",
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" {\"entity_type\": \"user_story\", \"project_id\": 1, \"ref\": \"1\"}\n",
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")\n",
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"\n",
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"response = update_entity_by_ref_tool.invoke(\n",
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" {\"project_slug\": \"slug\", \"entity_ref\": 555, \"entity_type\": \"us\"}\n",
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")\n",
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"\n",
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"\n",
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"response = add_comment_by_ref_tool.invoke(\n",
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" {\"project_slug\": \"slug\", \"entity_ref\": 3, \"entity_type\": \"us\", \"comment\": \"new\"}\n",
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")\n",
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"\n",
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"response = add_attachment_by_ref_tool.invoke(\n",
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" {\n",
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" \"project_slug\": \"slug\",\n",
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" \"entity_ref\": 3,\n",
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" \"entity_type\": \"us\",\n",
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" \"attachment_url\": \"url\",\n",
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" \"content_type\": \"png\",\n",
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" \"description\": \"desc\",\n",
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" }\n",
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")"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "markdown",
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"id": "d6e73897",
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"metadata": {},
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"source": [
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"### Invocation with ToolCall\n",
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"\n",
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"If you have a model-generated `ToolCall`, pass it to `tool.invoke()` in the format shown below."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f90e33a7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# This is usually generated by a model, but we'll create a tool call directly for demo purposes.\n",
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"model_generated_tool_call = {\n",
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" \"args\": {\"project_slug\": \"slug\", \"query\": \"query\", \"entity_type\": \"task\"},\n",
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" \"id\": \"1\",\n",
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" \"name\": search_entities_tool.name,\n",
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" \"type\": \"tool_call\",\n",
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"}\n",
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"tool.invoke(model_generated_tool_call)"
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"## Chaining\n",
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"\n",
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"Below is a more complete example showing how you might integrate the `create_entity_tool` and `search_entities_tool` tools in a chain or agent with an LLM. This example assumes you have a function (like `create_react_agent`) that sets up a LangChain-style agent capable of calling tools when appropriate.\n",
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"\n",
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"\n",
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"```python\n",
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"# Example: Using Taiga Tools in an Agent\n",
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"\n",
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"from langgraph.prebuilt import create_react_agent\n",
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"from langchain_taiga.tools.taiga_tools import create_entity_tool, search_entities_tool\n",
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"\n",
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"# 1. Instantiate or configure your language model\n",
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"# (Replace with your actual LLM, e.g., ChatOpenAI(temperature=0))\n",
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"llm = ...\n",
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"\n",
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"# 2. Build an agent that has access to these tools\n",
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"agent_executor = create_react_agent(llm, [create_entity_tool, search_entities_tool])\n",
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"\n",
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"# 4. Formulate a user query that may invoke one or both tools\n",
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"example_query = \"Please create a new user story with the subject 'subject' in slug project: 'slug'\"\n",
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"\n",
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"# 5. Execute the agent in streaming mode (or however your code is structured)\n",
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"events = agent_executor.stream(\n",
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" {\"messages\": [(\"user\", example_query)]},\n",
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" stream_mode=\"values\",\n",
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")\n",
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"\n",
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"# 6. Print out the model's responses (and any tool outputs) as they arrive\n",
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"for event in events:\n",
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" event[\"messages\"][-1].pretty_print()\n",
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"```\n"
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],
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"id": "8cafefef7c8bd43e"
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"## API reference\n",
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"\n",
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"See the docstrings in:\n",
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"- [taiga_tools.py](https://github.com/Shikenso-Analytics/langchain-taiga/blob/main/langchain_taiga/tools/taiga_tools.py)\n",
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"- [toolkits.py](https://github.com/Shikenso-Analytics/langchain-taiga/blob/main/langchain_taiga/toolkits.py)\n",
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"\n",
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"for usage details, parameters, and advanced configurations."
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],
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"id": "4ac8146c"
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@@ -470,3 +470,7 @@ packages:
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repo: writer/langchain-writer
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downloads: 0
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downloads_updated_at: '2025-02-24T13:19:19.816059+00:00'
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- name: langchain-taiga
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name_title: Taiga
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path: .
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repo: Shikenso-Analytics/langchain-taiga
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