| import json |
| import chromadb |
| from agentic_workflow.config import LLM_MODEL |
| from langchain_ollama import ChatOllama |
| from langchain_core.messages import HumanMessage, SystemMessage, AIMessage |
|
|
| llm = ChatOllama(model=LLM_MODEL, temperature=0) |
|
|
| def generate_llm_response(query: str, context: list[str], history: list[dict], suggestion: str = "") -> str: |
| system_parts = ["You are a helpful assistant."] |
| if suggestion: |
| system_parts.append(f"Improvement hint from previous attempt: {suggestion}") |
|
|
| messages = [SystemMessage(content=" ".join(system_parts))] |
|
|
| |
| for turn in history: |
| if turn["role"] == "user": |
| messages.append(HumanMessage(content=turn["content"])) |
| elif turn["role"] == "assistant": |
| messages.append(AIMessage(content=turn["content"])) |
|
|
| |
| user_content = query |
| if context: |
| user_content = "Context:\n" + "\n".join(context) + "\n\nQuestion: " + query |
|
|
| messages.append(HumanMessage(content=user_content)) |
|
|
| response = llm.invoke(messages) |
| return response.content |
|
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