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import re
from nexa.gguf.llama.llama import Llama
from txtai import Embeddings
from modules.util import TimeIt
from pathlib import Path
from modules.util import url_to_filename, load_file_from_url
from shared import path_manager, settings
import modules.async_worker as worker
import json
def llama_names():
names = []
folder_path = Path("llamas")
for path in folder_path.rglob("*"):
if path.suffix.lower() in [".txt"]:
f = open(path, "r", encoding='utf-8')
name = f.readline().strip()
names.append((name, str(path)))
names.sort(key=lambda x: x[0].casefold())
return names
def run_llama(system_file, prompt):
name = None
sys_pat = "system:.*\n\n"
system = re.match(sys_pat, prompt, flags=re.M|re.I)
if system is not None: # Llama system-prompt provided in the ui-prompt
name = "Llama"
system_prompt = re.sub("^[^:]*: *", "", system.group(0), flags=re.M|re.I)
prompt = re.sub(sys_pat, "", prompt)
else:
try:
file = open(system_file, "r", encoding='utf-8')
name = name if name is not None else file.readline().strip()
system_prompt = file.read().strip()
except:
print(f"LLAMA ERROR: Could not open file {system_file}")
return prompt
llama = pipeline()
llama.load_base_model()
with TimeIt(""):
print(f"# System:\n{system_prompt.strip()}\n")
print(f"# User:\n{prompt.strip()}\n")
print(f"# {name}: (Thinking...)")
try:
res = llama.llm.create_chat_completion(
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt}
],
repeat_penalty = 1.18,
)["choices"][0]["message"]["content"]
except Exception as e:
print(f"LLAMA ERROR: {e}")
res = prompt
print(f"{res.strip()}\n")
llama.llm._stack.close()
llama.llm.close()
return res
class pipeline:
pipeline_type = ["llama"]
llm = None
embeddings = None
embeddings_hash = ""
def parse_gen_data(self, gen_data):
return gen_data
def load_base_model(self):
localfile = settings.default_settings.get("llama_localfile", None)
repo = settings.default_settings.get("llama_repo", "hugging-quants/Llama-3.2-3B-Instruct-Q8_0-GGUF")
file = settings.default_settings.get("llama_file", "*q8_0.gguf")
with TimeIt("Load LLM"):
if localfile is None:
print(f"Loading {repo}")
self.llm = Llama.from_pretrained(
repo_id=repo,
filename=file,
verbose=False,
n_ctx=4096,
n_gpu_layers=-1,
offload_kqv=True,
flash_attn=True,
)
else:
llm_path = path_manager.get_folder_file_path(
"llm",
localfile,
default = Path(path_manager.model_paths["llm_path"]) / localfile
)
print(f"Loading {localfile}")
self.llm = Llama(
model_path=str(llm_path),
verbose=False,
n_ctx=4096,
n_gpu_layers=-1,
offload_kqv=True,
flash_attn=True,
)
self.embeddings = None
def index_source(self, source):
if self.embeddings == None:
self.embeddings = Embeddings(content=True)
self.embeddings.initindex(reindex=True)
match source[0]:
case "url":
print(f"Read {source[1]}")
filename = load_file_from_url(
source[1],
model_dir="cache/embeds",
progress=True,
file_name=url_to_filename(source[1]),
)
file = open(filename, "r")
data = file.read()
file.close()
if source[1].endswith(".md"):
data = data.split("\n#")
elif source[1].endswith(".txt"):
data = data.split("\n\n")
case "text":
data = source[1]
case _:
print("WARNING: Unknown embedding type {source[0]}")
return
if data:
self.embeddings.upsert(data)
def process(self, gen_data):
worker.add_result(
gen_data["task_id"],
"preview",
gen_data["history"]
)
if self.llm == None:
self.load_base_model()
# load embeds?
# FIXME should dump the entire gen_data["embed"] to index_source() and have it sort it out
embed = json.loads(gen_data['embed'])
if self.embeddings_hash != str(embed):
self.embeddings_hash = str(embed)
self.embeddings = None
if embed:
if not self.embeddings: # If chatbot has embeddings to index, check that we have them.
for source in embed:
self.index_source(source)
else:
self.embeddings = None
system_prompt = gen_data["system"]
h = gen_data["history"]
if self.embeddings:
q = h[-1]["content"]
context = "This some context that will help you answer the question:\n"
for data in self.embeddings.search(q, limit=3):
#if data["score"] >= 0.5:
context += data["text"] + "\n\n"
system_prompt += context
chat = [{"role": "system", "content": system_prompt}] + h[-3 if len(h) > 3 else -len(h):] # Keep just the last 3 messages
print(f"Thinking...")
with TimeIt("LLM thinking"):
response = self.llm.create_chat_completion(
messages = chat,
max_tokens=1024,
stream=True,
)
#["choices"][0]["message"]["content"]
text = ""
for chunk in response:
delta = chunk['choices'][0]['delta']
if 'content' in delta:
tokens = delta['content']
for token in tokens:
text += token
worker.add_result(
gen_data["task_id"],
"preview",
gen_data["history"] + [{"role": "assistant", "content": text}]
)
return text
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