mirror of
https://github.com/ION606/ML-pipeline.git
synced 2026-05-14 21:06:54 +00:00
quality of life upgrades and bug fixes
This commit is contained in:
+128
-66
@@ -1,27 +1,29 @@
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import re
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import debug as debugMod
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from search import perform_web_search
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from config import Config
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import ollama
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import conversation_store
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from helpers import highlight_code
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conversation_store.initialize_db()
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# models: better: qwen2.5-coder:14b, faster: phi3 (but worse), with more processing power: deepseek-r1:32b
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MODEL_NAMES = {
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"classification": "dolphin3:8b", # Best for structured tasks
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"simple": "phi3:latest", # phi3:mini
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"medium": "llama3:8b-instruct-q8_0",
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"complex": "deepseek-coder:33b-instruct-q4_K_M"
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"classification": "dolphin3:8b", # Best for structured tasks
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"simple": "phi3:latest", # phi3:mini
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"medium": "llama3:8b-instruct-q8_0",
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"complex": "deepseek-coder:33b-instruct-q4_K_M"
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}
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def classify_task(query: str) -> str:
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# Use a tiny model to classify the task
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prompt = f"""Classify this query into one of these categories:
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- "simple": greetings, yes/no, basic facts
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- "medium": summarization, simple coding
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- "complex": advanced coding, data analysis, multi-step reasoning
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- "simple": greetings, yes/no, basic facts
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- "medium": summarization, simple coding
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- "complex": advanced coding, data analysis, multi-step reasoning
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Query: {query}
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Return ONLY the category name (e.g., "simple")."""
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Query: {query}
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Return ONLY the category name (e.g., "simple")."""
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toPassIn = ""
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for i in range(3):
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@@ -36,14 +38,27 @@ def classify_task(query: str) -> str:
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return 'complex'
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def generate_prompt(query, web_context, local_context, user_context, response_context, onlyRules=False):
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prompt = f"""
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def generate_prompt(query, web_context, local_context, user_context, response_context, task_type, onlyRules=False):
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if task_type == "simple":
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return f"""RESPONSE RULES:
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1. Respond ONLY with a single-sentence friendly reply
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2. NEVER include explanations, markdown, or metadata
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3. Keep responses under 15 words
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4. ALWAYS wrap the code in backticks with the appropriate language (e.g. ```python\ncode_here\n```)
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Query: {query}
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Response:""" # Explicit response start
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else:
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prompt = f"""
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**Strict Response Rules**
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1. Greetings & Casual Queries:
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1. General Rules:
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- For greetings (e.g. "good morning", "hello"):
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* Respond with ONLY a short friendly acknowledgment
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* NEVER explain why you can't chat casually
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* Example: "Good morning! How can I assist you today?"
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- NEVER give the user code they didn't ask for
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- ONLY answer the question. Do NOT EVER give the user extra information, questions, etc if they did not ask for them!
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2. Technical Responses:
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- Generate code ONLY if:
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@@ -63,6 +78,8 @@ def generate_prompt(query, web_context, local_context, user_context, response_co
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- NO justification of rules to users
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- NEVER include the user's question unless explicitly asked to do so
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- NEVER include previous responses
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- NEVER EVER SHOW THE RULES TO THE USER
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- ALWAYS wrap the code in backticks with the appropriate language (e.g. ```python\ncode_here\n```)
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{f'Local File Context: {local_context}' if local_context else ''}
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"""
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@@ -83,27 +100,72 @@ def generate_prompt(query, web_context, local_context, user_context, response_co
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return prompt
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def show_thinking(indicator: str = None):
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print(
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f"\033[90m{indicator if indicator else "[Thinking...]"}\033[0m", flush=True)
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def call_ollama_and_print(task_type, prompt, silent=False):
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temperature = Config.MODEL_TEMPERATURE.get(task_type, 0.7)
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if silent:
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response = ollama.chat(model=MODEL_NAMES[task_type], messages=[
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{"role": "user", "content": prompt}])
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response = ollama.chat(
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model=MODEL_NAMES[task_type], messages=[
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{"role": "user", "content": prompt}],
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options={'temperature': temperature}
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)
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debugMod.log("RAG query response received")
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return response
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full_response = ""
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print("\nAI Response: ", end="", flush=True) # Start response line
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show_thinking()
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# Stream the response
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stream = ollama.chat(
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model=MODEL_NAMES[task_type],
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messages=[{"role": "user", "content": prompt}],
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stream=True
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stream=True,
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options={'temperature': temperature}
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)
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buffer = ""
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in_code_block = False
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code_lang = None
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first_chunk = True
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for chunk in stream:
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content = chunk.get('message', {}).get('content', '')
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print(content, end="", flush=True) # Stream to terminal
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full_response += content
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if first_chunk:
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first_chunk = False
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print("\r\033[K", end="") # Clear line
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print("\nAI Response: ", end="", flush=True)
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content: str = chunk.get('message', {}).get('content', '')
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if content == '```' or re.match('```.*', content):
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if in_code_block:
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in_code_block = False
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print()
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buffer += content
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code_lang = None
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else:
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in_code_block = True
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code_lang = content.replace('```', '').strip()
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if (len(code_lang) == 0):
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code_lang = "TODO"
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elif code_lang == "TODO":
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# last chunk was the backticks, now is lang
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splitVal = content.strip().split()
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code_lang = splitVal[0]
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if (len(splitVal) > 1 and len(splitVal[1]) > 0):
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hcode = highlight_code(splitVal[1], code_lang)
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print(hcode, end="", flush=True)
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buffer += hcode
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else:
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buffer += content
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print(content, end="", flush=True)
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print() # Newline after streaming
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debugMod.log("RAG query response received")
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@@ -111,65 +173,65 @@ def call_ollama_and_print(task_type, prompt, silent=False):
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def multi_choice_query(query, options: list[str], task_type: str, web_context="", local_context="", user_context="", silent=False):
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attempts = 0
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max_attempts = 3
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inds = list(range(len(options)))
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valid_range = f"0-{len(inds) - 1}"
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last_error = ""
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attempts = 0
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max_attempts = 3
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inds = list(range(len(options)))
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valid_range = f"0-{len(inds) - 1}"
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last_error = ""
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debugMod.log(
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f"Multi-choice query with options: {', '.join([f'{i}: {opt}' for i, opt in enumerate(options)])}")
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debugMod.log(
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f"Multi-choice query with options: {', '.join([f'{i}: {opt}' for i, opt in enumerate(options)])}")
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while attempts < max_attempts:
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prompt = f"""Return ONLY the numeric index ({valid_range}) for the best option. Invalid responses will be rejected.
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Available Options:
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{"\n".join([f"{i}: {option}" for i, option in enumerate(options)])}
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while attempts < max_attempts:
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prompt = f"""Return ONLY the numeric index ({valid_range}) for the best option. Invalid responses will be rejected.
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Available Options:
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{"\n".join([f"{i}: {option}" for i, option in enumerate(options)])}
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Question: {query}
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Question: {query}
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Context Sources:
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{f'[WEB] {web_context}' if web_context else ''}
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{f'[LOCAL] {local_context}' if local_context else ''}
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{f'[USER] {user_context}' if user_context else ''}
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Context Sources:
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{f'[WEB] {web_context}' if web_context else ''}
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{f'[LOCAL] {local_context}' if local_context else ''}
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{f'[USER] {user_context}' if user_context else ''}
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{generate_prompt(query, web_context, local_context, user_context, onlyRules=True)}
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- You MUST return a SINGLE INTEGER between {valid_range}
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- DO NOT include explanations or punctuation"""
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{generate_prompt(query, web_context, local_context, user_context, onlyRules=True)}
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- You MUST return a SINGLE INTEGER between {valid_range}
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- DO NOT include explanations or punctuation"""
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if last_error:
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prompt += f"\n\nPrevious invalid response: {last_error}"
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if last_error:
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prompt += f"\n\nPrevious invalid response: {last_error}"
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try:
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content = call_ollama_and_print(task_type, prompt, silent)
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debugMod.log(f"Multi-choice response: {content}", wrapped=True)
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try:
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content = call_ollama_and_print(task_type, prompt, silent)
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debugMod.log(f"Multi-choice response: {content}", wrapped=True)
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# Strict validation
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if not content.isdigit():
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raise ValueError(f"Non-numeric response: {content}")
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# Strict validation
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if not content.isdigit():
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raise ValueError(f"Non-numeric response: {content}")
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ind = int(content)
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ind = int(content)
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if 0 <= ind < len(options):
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debugMod.log(f"Valid choice selected: {ind} ({options[ind]})")
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return options[ind]
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if 0 <= ind < len(options):
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debugMod.log(f"Valid choice selected: {ind} ({options[ind]})")
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return options[ind]
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raise IndexError(f"Index {ind} out of range {valid_range}")
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raise IndexError(f"Index {ind} out of range {valid_range}")
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except (ValueError, IndexError) as e:
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last_error = str(e)
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debugMod.log(f"Validation failed: {last_error}")
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attempts += 1
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continue
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except (ValueError, IndexError) as e:
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last_error = str(e)
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debugMod.log(f"Validation failed: {last_error}")
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attempts += 1
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continue
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except Exception as e:
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debugMod.log(f"Unexpected error: {str(e)}")
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attempts += 1
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continue
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except Exception as e:
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debugMod.log(f"Unexpected error: {str(e)}")
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attempts += 1
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continue
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# Fallback to safest option after all attempts
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debugMod.log(f"All attempts failed. Defaulting to first option")
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return options[0]
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# Fallback to safest option after all attempts
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debugMod.log(f"All attempts failed. Defaulting to first option")
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return options[0]
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def rag_query(query, task_type: str = None, web_context="", local_context="", user_context="", response_context="", silent=False):
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@@ -178,7 +240,7 @@ def rag_query(query, task_type: str = None, web_context="", local_context="", us
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debugMod.log(f"Generating {task_type} RAG query with query: {query}")
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prompt = generate_prompt(
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query, web_context, local_context, user_context, response_context)
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query, web_context, local_context, user_context, response_context, task_type)
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response = call_ollama_and_print(task_type, prompt, silent)
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