Spaces:
Running
on
T4
Running
on
T4
File size: 17,166 Bytes
a267b49 44b8cfd 03c7545 a267b49 4364a50 6143b5b 15d08bb a267b49 6af775d a267b49 8b80e7e 32863e8 6143b5b e7d4e44 15d08bb a267b49 41791ed a91bbdd 32863e8 a267b49 8053dc6 0044e73 8053dc6 41791ed a267b49 4b484d7 a267b49 8053dc6 a267b49 0044e73 a267b49 41791ed e7d4e44 a267b49 41791ed a267b49 2f5d09f 41791ed a267b49 e7d4e44 a267b49 41791ed a267b49 e7d4e44 a267b49 aee31d5 a267b49 8324d7c a267b49 8324d7c 69d7f54 19a1340 69d7f54 a267b49 3a6368c 846607c 3a6368c ddbd286 846607c ddbd286 846607c ddbd286 3a6368c 846607c 2368f18 d7523dd 856a4a9 a648bb8 3a6368c b6fc313 3a6368c b6fc313 3a6368c 8f65ef0 8324d7c 19a1340 8324d7c 856a4a9 48ffedb a267b49 2e9ebb3 a91bbdd 3c8327a 41791ed 6143b5b a648bb8 3a6368c a648bb8 6143b5b 215effb a873789 41791ed a91bbdd 03c7545 8ebc68d 4b02c5c 635fbe7 4b02c5c 635fbe7 4b02c5c 635fbe7 4b02c5c 635fbe7 6143b5b 5b2e3d1 6143b5b 8053dc6 6143b5b 731b64e 846607c 6143b5b 8053dc6 828cea7 b256ccf 828cea7 8053dc6 6143b5b 41791ed 6143b5b 41791ed 6143b5b 41791ed 6143b5b 8053dc6 6143b5b 7206d43 6143b5b 41791ed 6143b5b 41791ed 6143b5b 7206d43 6143b5b 41791ed 6143b5b a5251d9 0044e73 4b02c5c 2f5d09f fc24904 8053dc6 fc24904 03c7545 165e041 8053dc6 77ed01c 03c7545 77ed01c 03c7545 77ed01c 8053dc6 77ed01c 165e041 03c7545 165e041 8053dc6 fc24904 8053dc6 fc24904 6143b5b b9c654b 6143b5b 2f5d09f 785300a 31b1908 48ffedb 31b1908 48ffedb 31b1908 1d66c65 31b1908 15d08bb f5e1bf6 15d08bb bd36946 15d08bb 95cc292 32863e8 3c8327a 32863e8 3c8327a 8795d75 942c7ba b256ccf 942c7ba caa9d03 1d66c65 69d7f54 1d66c65 caa9d03 76bbb30 1d66c65 8053dc6 95cc292 8053dc6 95cc292 31b1908 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 |
import os
import re
import nltk
import copy
import time
import joblib
import msgpack
import validators
import numpy as np
import streamlit as st
from io import BytesIO
from numpy import ndarray
from llama_cpp import Llama
from langchain_core.documents.base import Document
from scipy.sparse import csr_matrix, save_npz, load_npz, vstack
from qdrant_client import QdrantClient, models
from langchain_community.document_loaders import WikipediaLoader, WebBaseLoader
from langchain_experimental.text_splitter import SemanticChunker
from fastembed.sparse.splade_pp import supported_splade_models
from fastembed import SparseTextEmbedding, SparseEmbedding
from fastembed_ext import FastEmbedEmbeddingsLc
from huggingface_hub import hf_hub_download
from unstructured.partition.auto import partition
from qdrant_client.models import (
NamedSparseVector,
NamedVector,
SparseVector,
PointStruct,
SearchRequest,
ScoredPoint,
)
def make_points(texts: list, metadatas: list, dense: list[ndarray], sparse: list[SparseEmbedding])-> list[PointStruct]:
points = []
for idx, (text, metadata, sparse_vector, dense_vector) in enumerate(zip(texts, metadatas, sparse, dense)):
sparse_vec = SparseVector(indices=sparse_vector.indices.tolist(), values=sparse_vector.values.tolist())
point = PointStruct(
id=idx,
vector={
"text-sparse": sparse_vec,
"text-dense": dense_vector,
},
payload={
"text": text,
"metadata": metadata
}
)
points.append(point)
return points
def search(client: QdrantClient, collection_name: str, dense, sparse):
search_results = client.search_batch(
collection_name,
[
SearchRequest(
vector=NamedVector(
name="text-dense",
vector=dense,
),
limit=10
),
SearchRequest(
vector=NamedSparseVector(
name="text-sparse",
vector=SparseVector(
indices=sparse[0].indices.tolist(),
values=sparse[0].values.tolist(),
),
),
limit=10
),
],
)
return search_results
def rank_list(search_result: list[ScoredPoint]):
return [(point.id, rank + 1) for rank, point in enumerate(search_result)]
def rrf(rank_lists, alpha=60, default_rank=1000):
"""
Optimized Reciprocal Rank Fusion (RRF) using NumPy for large rank lists.
:param rank_lists: A list of rank lists. Each rank list should be a list of (item, rank) tuples.
:param alpha: The parameter alpha used in the RRF formula. Default is 60.
:param default_rank: The default rank assigned to items not present in a rank list. Default is 1000.
:return: Sorted list of items based on their RRF scores.
"""
all_items = set(item for rank_list in rank_lists for item, _ in rank_list)
item_to_index = {item: idx for idx, item in enumerate(all_items)}
rank_matrix = np.full((len(all_items), len(rank_lists)), default_rank)
for list_idx, rank_list in enumerate(rank_lists):
for item, rank in rank_list:
rank_matrix[item_to_index[item], list_idx] = rank
rrf_scores = np.sum(1.0 / (alpha + rank_matrix), axis=1)
sorted_indices = np.argsort(-rrf_scores)
sorted_items = [(list(item_to_index.keys())[idx], rrf_scores[idx]) for idx in sorted_indices]
return sorted_items
def main(query: str, client: QdrantClient, collection_name: str, llm, dense_model, sparse_model):
dense_query = list(dense_model.embed_query(query, 32))
sparse_query = list(sparse_model.embed(query, 32))
search_results = search(
client,
collection_name,
dense_query,
sparse_query
)
dense_rank_list, sparse_rank_list = rank_list(search_results[0]), rank_list(search_results[1])
rrf_rank_list = rrf([dense_rank_list, sparse_rank_list])
records_list = client.retrieve(
collection_name,
[item[0] for item in rrf_rank_list]
)
docs = [record.payload for record in records_list[:3]]
contents = [doc['text'] for doc in docs]
metadatas = [doc['metadata'] for doc in docs]
context = "\n".join(contents)
seen_values = set()
result_metadatas = "\n".join(
f'{value}'
for metadata in metadatas
for key, value in metadata.items()
if (value not in seen_values and not seen_values.add(value))
)
response = llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{
"role": "user",
"content": f"""
You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.
Question: {query}
Context: {context}
Answer:"""
}
], stop=["</s>"], temperature=0, frequency_penalty=0.2, presence_penalty=0.4, top_p=0.2)
text = response["choices"][0]["message"]['content']
print(f'TEXT: {text}')
output = llm.create_chat_completion(
messages = [
{"role": "system", "content": """Act like a professional summary writer. You have been providing summarization services for various types of documents, including academic papers, legal texts, and business reports, for over 20 years.
Your expertise includes extracting key points and important details concisely without adding unnecessary introductory phrases."""
},
{
"role": "user",
"content": f"""Write a summary of the following text delimited by triple backquotes. Ensure the summary covers the key points of the text. Do not introduce the summary with sentences like "Here is the summary:" or similar. The summary should be detailed, precise, and directly convey the essential information from the text.
```{text}```
Take a deep breath and work on this problem step-by-step."""
}
], stop=["</s>"], temperature=0.7, max_tokens=3000)
answer = output['choices'][0]['message']['content']
answer_with_metadatas = f"{answer}\n\nSource(s) :\n{result_metadatas}"
print(f'OUTPUT: {output}')
return answer, answer_with_metadatas
@st.cache_resource
def load_models_and_documents():
print('load')
supported_splade_models[0] = {
"model": "prithivida/Splade_PP_en_v2",
"vocab_size": 30522,
"description": "Implementation of SPLADE++ Model for English v2",
"size_in_GB": 0.532,
"sources": {
"hf": "devve1/Splade_PP_en_v2_onnx"
},
"model_file": "model.onnx"
}
with st.spinner('Load models...'):
llm = Llama.from_pretrained(
repo_id="MaziyarPanahi/Llama-3-8B-Instruct-32k-v0.1-GGUF",
filename="*Q8_0.gguf",
verbose=False,
chat_format="chatml",
n_ctx=16000,
n_gpu_layers=32
)
provider = ['CPUExecutionProvider']
dense_model = FastEmbedEmbeddingsLc(
model_name='mixedbread-ai/mxbai-embed-large-v1',
providers=provider,
cache_dir=os.getenv('HF_HOME'),
batch_size=32
)
sparse_model = SparseTextEmbedding(
'prithivida/Splade_PP_en_v2',
cache_dir=os.getenv('HF_HOME'),
providers=provider
)
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
client = QdrantClient(':memory:')
collection_name = 'collection_demo'
client.create_collection(
collection_name,
{
"text-dense": models.VectorParams(
size=1024,
distance=models.Distance.COSINE,
on_disk=False,
quantization_config=models.BinaryQuantization(
binary=models.BinaryQuantizationConfig(
always_ram=True
)
)
)
},
{
"text-sparse": models.SparseVectorParams(
index=models.SparseIndexParams(
on_disk=False
)
)
},
2,
optimizers_config=models.OptimizersConfigDiff(
indexing_threshold=0
),
hnsw_config=models.HnswConfigDiff(
on_disk=False,
m=16,
ef_construct=100
)
)
with st.spinner('Parse and chunk documents...'):
name = 'action_rpg'
embeddings_path = os.path.join(os.getenv('HF_HOME'), 'embeddings')
texts_path = os.path.join(embeddings_path, name + '_texts.msgpack')
metadatas_path = os.path.join(embeddings_path, name + '_metadatas.msgpack')
dense_path = os.path.join(embeddings_path, name + '_dense.npz')
sparse_path = os.path.join(embeddings_path, name + '_sparse.npz')
if not os.path.exists(embeddings_path):
os.mkdir(embeddings_path)
docs_1 = WikipediaLoader(query='Action-RPG').load()
docs_2 = WikipediaLoader(query='Real-time strategy').load()
docs_3 = WikipediaLoader(query='First-person shooter').load()
docs_4 = WikipediaLoader(query='Multiplayer online battle arena').load()
docs_5 = WikipediaLoader(query='List of video game genres').load()
docs = docs_1 + docs_2 + docs_3 + docs_4 + docs_5
texts, metadatas = [], []
for doc in docs:
texts.append(doc.page_content)
del doc.metadata['title']
del doc.metadata['summary']
metadatas.append(doc.metadata)
docs_texts, docs_metadatas, dense_embeddings, sparse_embeddings = chunk_documents(texts, metadatas, dense_model, sparse_model)
with open(texts_path, "wb") as outfile_texts:
packed_texts = msgpack.packb(docs_texts, use_bin_type=True)
outfile_texts.write(packed_texts)
with open(metadatas_path, "wb") as outfile_metadatas:
packed_metadatas = msgpack.packb(docs_metadatas, use_bin_type=True)
outfile_metadatas.write(packed_metadatas)
np.savez_compressed(dense_path, *dense_embeddings)
max_index = max(np.max(embedding.indices) for embedding in sparse_embeddings)
sparse_matrices = []
for embedding in sparse_embeddings:
data = embedding.values
indices = embedding.indices
indptr = np.array([0, len(data)])
matrix = csr_matrix((data, indices, indptr), shape=(1, max_index + 1))
sparse_matrices.append(matrix)
combined_sparse_matrix = vstack(sparse_matrices)
save_npz(sparse_path, combined_sparse_matrix)
else:
with open(texts_path, "rb") as data_file_texts:
byte_data_texts = data_file_texts.read()
with open(metadatas_path, "rb") as data_file_metadatas:
byte_data_metadatas = data_file_metadatas.read()
docs_texts = msgpack.unpackb(byte_data_texts, raw=False)
docs_metadatas = msgpack.unpackb(byte_data_metadatas, raw=False)
dense_embeddings = list(np.load(dense_path).values())
sparse_embeddings = []
loaded_sparse_matrix = load_npz(sparse_path)
for i in range(loaded_sparse_matrix.shape[0]):
row = loaded_sparse_matrix.getrow(i)
values = row.data
indices = row.indices
embedding = SparseEmbedding(values, indices)
sparse_embeddings.append(embedding)
with st.spinner('Save documents...'):
client.upsert(
collection_name,
make_points(
docs_texts,
docs_metadatas,
dense_embeddings,
sparse_embeddings
)
)
client.update_collection(
collection_name=collection_name,
optimizer_config=models.OptimizersConfigDiff(indexing_threshold=20000)
)
return client, collection_name, llm, dense_model, sparse_model
def chunk_documents(texts, metadatas, dense_model, sparse_model):
text_splitter = SemanticChunker(
dense_model,
breakpoint_threshold_type='standard_deviation'
)
_metadatas = metadatas or [{}] * len(texts)
documents = []
metadatas_docs = []
def create_document(text: str, i: int, _metadatas: list):
index = -1
for chunk in text_splitter.split_text(text):
metadata = copy.deepcopy(_metadatas[i])
if text_splitter._add_start_index:
index = text.find(chunk, index + 1)
metadata['start_index'] = index
documents.append(chunk)
metadatas_docs.append(metadata)
joblib.Parallel(n_jobs=joblib.cpu_count(), verbose=1, require='sharedmem')(
joblib.delayed(create_document)(text, i, _metadatas) for i, text in enumerate(texts))
dense_embeddings = dense_model.embed_documents(documents, 32)
sparse_embeddings = list(sparse_model.embed(documents, 32))
return documents, metadatas_docs, dense_embeddings, sparse_embeddings
if __name__ == '__main__':
st.set_page_config(page_title="Video Game Assistant",
layout="wide"
)
st.title("Video Game Assistant")
client, collection_name, llm, dense_model, sparse_model = load_models_and_documents()
if "messages" not in st.session_state:
st.session_state.messages = []
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if prompt := st.chat_input("Message Video Game Assistant"):
st.chat_message("user").markdown(prompt)
st.session_state.messages.append({"role": "user", "content": prompt})
ai_response, ai_response_with_metadatas = main(prompt, client, collection_name, llm, dense_model, sparse_model)
with st.chat_message("assistant"):
message_placeholder = st.empty()
full_response = ""
for chunk in re.split(r'(\s+)', ai_response_with_metadatas):
full_response += chunk + " "
time.sleep(0.01)
message_placeholder.markdown(full_response + "▌")
st.session_state.messages.append({"role": "assistant", "content": full_response})
url = st.sidebar.text_input("Scrape an URL link :")
print(type(url))
print(url)
if validators.url(url):
docs = WebBaseLoader(url).load()
print(f'WebBaseLoader: {docs[0].metadata}')
texts, metadatas = [], []
for doc in docs:
texts.append(doc.page_content)
del doc.metadata['title']
del doc.metadata['summary']
metadatas.append(doc.metadata)
texts, metadatas, dense_embeddings, sparse_embeddings = chunk_documents(texts, metadatas, dense_model, sparse_model)
client.upsert(
collection_name,
make_points(
texts,
metadatas,
dense_embeddings,
sparse_embeddings
)
)
st.sidebar.title("Upload your file")
uploaded_files = st.sidebar.file_uploader("Choose a file", accept_multiple_files=True, type=['docx', 'doc', 'odt', 'pptx', 'ppt', 'xlsx', 'csv', 'tsv', 'eml', 'msg', 'rtf', 'epub', 'html', 'xml', 'pdf', 'png', 'jpg', 'heic','txt'])
print(f'uploaded-files : {uploaded_files}')
for uploaded_file in uploaded_files:
print('count')
elements = partition(file=uploaded_file,
strategy='hi_res',
skip_infer_table_types=['png', 'pdf', 'jpg', 'xls', 'xlsx', 'heic'],
hi_res_model_name='yolox',
include_page_breaks=True
)
metadata_dict = {"source": uploaded_file.name}
texts, metadatas = [], []
for elem in elements:
texts.append(elem.text)
metadatas.append(metadata_dict)
texts, metadatas, dense_embeddings, sparse_embeddings = chunk_documents(texts, metadatas, dense_model, sparse_model)
client.upsert(
collection_name,
make_points(
texts,
metadatas,
dense_embeddings,
sparse_embeddings
)
) |