القائمة
Teaches the model how to manage stateful discussions, track conversational trees/branches, navigate alternative sibling replies, and execute context-aware generation.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Teaches how to build, discover, and execute local, zero-configuration Python tools in Lollms using AST schema parsing and context injection. Includes step-by-step tool file construction guide and code examples.
Teaches the model about the persistent tiered memory system (Level 1 Working, Level 2 Deep, Level 3 Archive), proactive keyword pulling, and the required tagging protocols.
Teaches the model how to formulate strategic <coding_plan> blocks, apply indentation-agnostic Aider search/replace patches, manage Git-like version logs, and control in-message status reporting.
Teaches how to import, parse, and process PDF, DOCX, PPTX, and image files using anchor-mapped and OCR import modes in LollmsDiscussion.
Teaches how to coordinate multiple specialized agents, distribute tasks, establish consensus, and manage role-based multi-agent swarms using LollmsSwarm.
Guides the model on performing basic text generation, counting tokens, detokenizing lists, and generating structurally validated JSON outputs with LollmsClient.
| name | Lollms Discussion and Chat |
| description | Teaches the model how to manage stateful discussions, track conversational trees/branches, navigate alternative sibling replies, and execute context-aware generation. |
| author | ParisNeo |
| version | 1.0.0 |
| category | lollms_client/discussion |
| created | "2026-05-24T00:00:00.000Z" |
Instead of maintaining a simple list of message dicts, LollmsDiscussion organizes conversations as a hierarchical tree, allowing first-class support for branching, alternative model replies, and session persistence.
A discussion represents a stateful chat session. It can be fully in-memory or persisted to a local SQLite database.
from lollms_client import LollmsClient
from lollms_client.lollms_discussion import LollmsDiscussion
client = LollmsClient(llm_binding_name="ollama", llm_binding_config={"model_name": "gemma4:e2b"})
# Create an in-memory, stateful discussion
discussion = LollmsDiscussion(lollmsClient=client)
discussion.system_prompt = "You are a helpful software engineering companion."
Messages are stored in the tree. When adding a message, it automatically attaches to the current active branch tip.
# Add user message
msg_user = discussion.add_message(sender="user", content="What is an RPC?")
# Add assistant response
msg_assistant = discussion.add_message(sender="assistant", content="Remote Procedure Call.")
print(f"Active Branch Tip Message ID: {discussion.active_branch_id}")
A branch represents a conversational path from the root message to a leaf. If the user or system wants to explore an alternative path, we "fork" the conversation.
# Fork an alternative branch starting from the first user question
fork_msg = discussion.fork_from(
message_id=msg_user.id,
label="Alternative Topic Branch",
initial_content="Actually, tell me about REST instead."
)
# Switch back and forth between branches
discussion.switch_branch(msg_assistant.id) # Returns to the original RPC branch
The high-level chat() method automatically assembles all context zones (system instructions, memory, active artifacts, discussion history) and sends a correctly formatted template to the model.
# High-level chat execution
response = discussion.chat(
user_message="Explain REST constraints",
enable_memory=True,
enable_artefacts=True
)
print(response)