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Taiga

This notebook provides a quick overview for getting started with Taiga tooling inlangchain_taiga. For more details on each tool and configuration, see the docstrings in your repository or relevant doc pages.

Overview

Integration details

ClassPackageSerializableJS supportPackage latest
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_toollangchain-taigaN/ATBDPyPI - Version

Tool features

  • create_entity_tool: Creates user stories, tasks and issues in Taiga.
  • search_entities_tool: Searches for user stories, tasks and issues in Taiga.
  • get_entity_by_ref_tool: Gets a user story, task or issue by reference.
  • update_entity_by_ref_tool: Updates a user story, task or issue by reference.
  • add_comment_by_ref_tool: Adds a comment to a user story, task or issue.
  • add_attachment_by_ref_tool: Adds an attachment to a user story, task or issue.

Setup

The integration lives in thelangchain-taiga package.

%pip install--quiet-U langchain-taiga
/home/henlein/Workspace/PyCharm/langchain/.venv/bin/python: No module named pip
Note: you may need to restart the kernel to use updated packages.

Credentials

This integration requires you to setTAIGA_URL,TAIGA_API_URL,TAIGA_USERNAME,TAIGA_PASSWORD andOPENAI_API_KEY as environment variables to authenticate with Taiga.

export TAIGA_URL="https://taiga.xyz.org/"
export TAIGA_API_URL="https://taiga.xyz.org/"
export TAIGA_USERNAME="username"
export TAIGA_PASSWORD="pw"
export OPENAI_API_KEY="OPENAI_API_KEY"

It's also helpful (but not needed) to set upLangSmith for best-in-class observability:

# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass()

Instantiation

Below is an example showing how to instantiate the Taiga tools inlangchain_taiga. Adjust as needed for your specific usage.

from langchain_taiga.tools.discord_read_messagesimport create_entity_tool
from langchain_taiga.tools.discord_send_messagesimport search_entities_tool

create_tool= create_entity_tool
search_tool= search_entities_tool

Invocation

Direct invocation with args

Below is a simple example of calling the tool with keyword arguments in a dictionary.

from langchain_taiga.tools.taiga_toolsimport(
add_attachment_by_ref_tool,
add_comment_by_ref_tool,
create_entity_tool,
get_entity_by_ref_tool,
search_entities_tool,
update_entity_by_ref_tool,
)

response= create_entity_tool.invoke(
{
"project_slug":"slug",
"entity_type":"us",
"subject":"subject",
"status":"new",
"description":"desc",
"parent_ref":5,
"assign_to":"user",
"due_date":"2022-01-01",
"tags":["tag1","tag2"],
}
)

response= search_entities_tool.invoke(
{"project_slug":"slug","query":"query","entity_type":"task"}
)

response= get_entity_by_ref_tool.invoke(
{"entity_type":"user_story","project_id":1,"ref":"1"}
)

response= update_entity_by_ref_tool.invoke(
{"project_slug":"slug","entity_ref":555,"entity_type":"us"}
)


response= add_comment_by_ref_tool.invoke(
{"project_slug":"slug","entity_ref":3,"entity_type":"us","comment":"new"}
)

response= add_attachment_by_ref_tool.invoke(
{
"project_slug":"slug",
"entity_ref":3,
"entity_type":"us",
"attachment_url":"url",
"content_type":"png",
"description":"desc",
}
)

Invocation with ToolCall

If you have a model-generatedToolCall, pass it totool.invoke() in the format shown below.

# This is usually generated by a model, but we'll create a tool call directly for demo purposes.
model_generated_tool_call={
"args":{"project_slug":"slug","query":"query","entity_type":"task"},
"id":"1",
"name": search_entities_tool.name,
"type":"tool_call",
}
tool.invoke(model_generated_tool_call)

Chaining

Below is a more complete example showing how you might integrate thecreate_entity_tool andsearch_entities_tool tools in a chain or agent with an LLM. This example assumes you have a function (likecreate_react_agent) that sets up a LangChain-style agent capable of calling tools when appropriate.

# Example: Using Taiga Tools in an Agent

from langgraph.prebuiltimport create_react_agent
from langchain_taiga.tools.taiga_toolsimport create_entity_tool, search_entities_tool

# 1. Instantiate or configure your language model
# (Replace with your actual LLM, e.g., ChatOpenAI(temperature=0))
llm=...

# 2. Build an agent that has access to these tools
agent_executor= create_react_agent(llm,[create_entity_tool, search_entities_tool])

# 4. Formulate a user query that may invoke one or both tools
example_query="Please create a new user story with the subject 'subject' in slug project: 'slug'"

# 5. Execute the agent in streaming mode (or however your code is structured)
events= agent_executor.stream(
{"messages":[("user", example_query)]},
stream_mode="values",
)

# 6. Print out the model's responses (and any tool outputs) as they arrive
for eventin events:
event["messages"][-1].pretty_print()
API Reference:create_react_agent

API reference

See the docstrings in:

for usage details, parameters, and advanced configurations.

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