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| import os
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| _Mistral = None
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| try:
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| from mistralai import Mistral as _Mistral
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| except ImportError:
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| try:
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| from mistralai.client import Mistral as _Mistral
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| except ImportError:
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| try:
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| from mistralai.client import MistralClient as _OldClient
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| from mistralai.models.chat_completion import ChatMessage as _OldMsg
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| class _ChatShim:
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| def __init__(self, client):
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| self._client = client
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| def complete(self, model, messages, temperature=None,
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| max_tokens=None, tools=None):
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| msgs = [_OldMsg(role=m["role"], content=m.get("content", ""))
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| for m in messages]
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| return self._client.chat(
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| model=model, messages=msgs,
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| temperature=temperature, max_tokens=max_tokens,
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| )
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| class _MistralV0Wrapper:
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| def __init__(self, api_key):
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| self._client = _OldClient(api_key=api_key)
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| self.chat = _ChatShim(self._client)
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| _Mistral = _MistralV0Wrapper
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| except ImportError as _e:
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| raise ImportError(
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| "mistralai package is missing or an unknown version. "
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| "Tried v1 (from mistralai import Mistral), "
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| "v2 (from mistralai.client import Mistral), "
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| "and v0 (from mistralai.client import MistralClient). "
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| f"All failed. Last error: {_e}"
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| )
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| Mistral = _Mistral
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| from parameters import TEMPERATURE, MAX_TOKENS
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| from prompts import WORKFLOW_STEP1_CLARIFY, WORKFLOW_STEP2_ANSWER
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| import providers
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| BACKEND_NAME = "Workflow"
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| def get_client(api_key, provider="Mistral"):
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| """Return a provider-agnostic LLM client.
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| The factory in providers.py handles all adapter logic. Old callers that
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| pass only (api_key) still work — provider defaults to Mistral.
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| """
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| return providers.get_llm_client(provider, api_key)
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| def _llm(client, messages, provider="Mistral"):
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| model = providers.get_llm_model(provider)
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| return client.chat.complete(
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| model=model,
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| temperature=TEMPERATURE,
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| max_tokens=MAX_TOKENS,
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| messages=messages,
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| ).choices[0].message
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| def run(client, user_message, provider="Mistral"):
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| """Fixed 2-step prompt chain: clarify -> answer. No tools."""
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| steps = []
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| step1 = _llm(client, [
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| {"role": "system", "content": WORKFLOW_STEP1_CLARIFY},
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| {"role": "user", "content": user_message},
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| ], provider=provider)
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| clarified = step1.content or ""
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| steps.append({
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| "step": 1, "type": "llm_call", "tool": "clarify",
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| "args": user_message, "result": clarified,
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| })
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| step2 = _llm(client, [
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| {"role": "system", "content": WORKFLOW_STEP2_ANSWER},
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| {"role": "user", "content": clarified},
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| ], provider=provider)
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| answer = step2.content or ""
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| steps.append({
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| "step": 2, "type": "llm_call", "tool": "answer",
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| "args": clarified, "result": answer,
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| })
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| return {
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| "reply": answer,
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| "steps": steps,
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| "extracted": {"clarified_question": clarified},
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| }
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| def build_code_snippets(user_message, steps):
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| lines = [
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| "# Backend: Workflow",
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| "# Raw Mistral SDK, fixed 2-step prompt chain, no tools.",
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| f"# User message: {user_message}",
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| "",
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| "# Step 1: clarify the user message using the clarify system prompt",
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| "step1 = client.chat.complete(",
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| " model=MODEL,",
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| " messages=[",
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| " {'role': 'system', 'content': WORKFLOW_STEP1_CLARIFY},",
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| f" {{'role': 'user', 'content': {user_message!r}}},",
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| " ],",
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| ").choices[0].message",
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| "clarified = step1.content",
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| "",
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| "# Step 2: answer the clarified question using the answer system prompt",
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| "step2 = client.chat.complete(",
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| " model=MODEL,",
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| " messages=[",
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| " {'role': 'system', 'content': WORKFLOW_STEP2_ANSWER},",
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| " {'role': 'user', 'content': clarified},",
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| " ],",
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| ").choices[0].message",
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| "answer = step2.content # final reply to the user",
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| "",
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| "# ---------- actual step log ----------",
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| ]
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| for s in steps:
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| lines.append(f"# Step {s['step']} [{s['type']}] {s['tool']}")
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| lines.append(f"# input: {s['args']!r}")
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| lines.append(f"# output: {s['result']!r}")
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| return "\n".join(lines)
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|
