From d4ebcd3e462b4ac86fdb0238464cef83f75149e6 Mon Sep 17 00:00:00 2001 From: Ismail Pelaseyed Date: Wed, 18 Oct 2023 08:02:44 +0200 Subject: [PATCH 1/3] WIP --- dist/nagato_ai-0.0.1-py3-none-any.whl | Bin 11556 -> 0 bytes dist/nagato_ai-0.0.1.tar.gz | Bin 10729 -> 0 bytes dist/nagato_ai-0.0.5-py3-none-any.whl | Bin 0 -> 10833 bytes dist/nagato_ai-0.0.5.tar.gz | Bin 0 -> 10485 bytes lib/__init__.py | 20 +- lib/models/ingest.py | 12 - lib/service/__init__.py | 48 + lib/service/embedding.py | 27 +- lib/service/finetune.py | 199 ++- lib/service/flows.py | 49 - lib/service/prompts.py | 4 +- lib/service/vectordb.py | 8 +- lib/{models => utils}/__init__.py | 0 lib/utils/logger.py | 33 + poetry.lock | 2384 ++++++++++++------------- pyproject.toml | 11 +- test/__init__.py | 0 test/mytest.py | 17 + 18 files changed, 1346 insertions(+), 1466 deletions(-) delete mode 100644 dist/nagato_ai-0.0.1-py3-none-any.whl delete mode 100644 dist/nagato_ai-0.0.1.tar.gz create mode 100644 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nodes = embedding_service.generate_chunks(documents=documents) + embedding_service.generate_embeddings(nodes=nodes, finetune_id=finetune_id) + return nodes + + +def create_finetuned_model( + provider: str, + base_model: str, + type: str, + url: str = None, + content: str = None, + webhook_url: str = None, +): + embedding_service = EmbeddingService(type=type, url=url, content=content) + documents = embedding_service.generate_documents() + nodes = embedding_service.generate_chunks(documents=documents) + finetunning_service = get_finetuning_service( + nodes=nodes, + provider=provider, + batch_size=5, + base_model=base_model, + num_questions_per_chunk=1, + ) + training_file = finetunning_service.generate_dataset() + formatted_training_file = finetunning_service.validate_dataset( + training_file=training_file + ) + finetune = finetunning_service.finetune( + training_file=formatted_training_file, webhook_url=webhook_url + ) + if provider == "OPENAI": + finetune = openai.FineTune.retrieve(id=finetune.get("id")) + finetunning_service.cleanup(training_file=finetune.get("training_file")) + return finetune diff --git a/lib/service/embedding.py b/lib/service/embedding.py index c74756f..ca41655 100644 --- a/lib/service/embedding.py +++ b/lib/service/embedding.py @@ -8,48 +8,47 @@ from numpy import ndarray from sentence_transformers import SentenceTransformer -from lib.models.ingest import IngestRequest from lib.service.vectordb import get_vector_service class EmbeddingService: - def __init__(self, payload: IngestRequest): - self.payload = payload + def __init__(self, type: str, url: str = None, content: str = None): + self.type = type + self.url = url + self.content = content def get_datasource_suffix(self) -> str: suffixes = {"TXT": ".txt", "PDF": ".pdf", "MARKDOWN": ".md"} try: - return suffixes[self.payload.type] + return suffixes[self.type] except KeyError: raise ValueError("Unsupported datasource type") - async def generate_documents(self) -> List[Document]: + def generate_documents(self) -> List[Document]: with NamedTemporaryFile( suffix=self.get_datasource_suffix(), delete=True ) as temp_file: - if self.payload.url: - content = requests.get(self.payload.url).content + if self.url: + content = requests.get(self.url).content else: - content = self.payload.content + content = self.content temp_file.write(content) temp_file.flush() reader = SimpleDirectoryReader(input_files=[temp_file.name]) docs = reader.load_data() return docs - async def generate_chunks( - self, documents: List[Document] - ) -> List[Union[Document, None]]: + def generate_chunks(self, documents: List[Document]) -> List[Union[Document, None]]: parser = SimpleNodeParser.from_defaults(chunk_size=350, chunk_overlap=20) nodes = parser.get_nodes_from_documents(documents, show_progress=True) return nodes - async def generate_embeddings( + def generate_embeddings( self, nodes: List[Union[Document, None]], finetune_id: str, ) -> List[ndarray]: - vectordb = await get_vector_service( + vectordb = get_vector_service( provider="pinecone", index_name="all-minilm-l6-v2", namespace=finetune_id, @@ -67,7 +66,7 @@ async def generate_embeddings( {**node.metadata, "content": node.text}, ) embeddings.append(embedding) - await vectordb.upsert(vectors=embeddings) + vectordb.upsert(vectors=embeddings) return embeddings # def generate_query(self): diff --git a/lib/service/finetune.py b/lib/service/finetune.py index 8523041..a0bce89 100644 --- a/lib/service/finetune.py +++ b/lib/service/finetune.py @@ -1,15 +1,20 @@ # flake8: noqa -import asyncio +import requests import json import os import uuid +import openai +import replicate +import concurrent.futures + +from tqdm import tqdm +from decouple import config from abc import ABC, abstractmethod from typing import Dict, List, Union +from concurrent.futures import ThreadPoolExecutor -import httpx -import openai -import replicate +from lib.utils.logger import logger from decouple import config from llama_index import Document @@ -21,6 +26,7 @@ openai.api_key = config("OPENAI_API_KEY") + REPLICATE_MODELS = { "LLAMA2_7B_CHAT": "meta/llama-2-7b-chat:8e6975e5ed6174911a6ff3d60540dfd4844201974602551e10e9e87ab143d81e", "LLAMA2_7B": "meta/llama-2-7b:527827021d8756c7ab79fde0abbfaac885c37a3ed5fe23c7465093f0878d55ef", @@ -36,22 +42,54 @@ class FinetuningService(ABC): - def __init__(self, nodes: List[Union[Document, None]]): + def __init__( + self, + nodes: List[Union[Document, None]], + num_questions_per_chunk: int, + batch_size: int, + ): self.nodes = nodes + self.num_questions_per_chunk = num_questions_per_chunk + self.batch_size = batch_size @abstractmethod - async def generate_dataset(self) -> str: + def generate_prompt_and_completion(self, node): pass @abstractmethod - async def validate_dataset(self, training_file: str) -> str: + def validate_dataset(self, training_file: str) -> str: pass @abstractmethod - async def finetune(self, training_file: str, base_model: str) -> Dict: + def finetune(self, training_file: str, base_model: str) -> Dict: pass - async def cleanup(self, training_file: str) -> None: + def generate_dataset(self) -> str: + training_file = f"{uuid.uuid4()}.jsonl" + total_pairs = len(self.nodes) * self.num_questions_per_chunk + with open(training_file, "w") as f: + with ThreadPoolExecutor() as executor: + progress_bar = tqdm( + total=total_pairs, desc="Generating synthetic Q&A pairs" + ) + for i in range( + 0, len(self.nodes), self.batch_size + ): # Process nodes in chunks of batch_size + batch_nodes = self.nodes[i : i + self.batch_size] + tasks = [ + executor.submit(self.generate_prompt_and_completion, node) + for node in batch_nodes + ] + for future in concurrent.futures.as_completed(tasks): + qa_pair = future.result() + json_objects = qa_pair.split("\n\n") + for json_obj in json_objects: + f.write(json_obj + "\n") + progress_bar.update(1) + progress_bar.close() + return training_file + + def cleanup(self, training_file: str) -> None: os.remove(training_file) @@ -59,49 +97,34 @@ class OpenAIFinetuningService(FinetuningService): def __init__( self, nodes: List[Union[Document, None]], - num_questions_per_chunk: int = 10, - batch_size: int = 10, + num_questions_per_chunk: int, + batch_size: int, base_model: str = "GPT_35_TURBO", ): - super().__init__(nodes=nodes) - self.num_questions_per_chunk = num_questions_per_chunk - self.batch_size = batch_size + super().__init__( + nodes=nodes, + num_questions_per_chunk=num_questions_per_chunk, + batch_size=batch_size, + ) self.base_model = base_model - async def generate_prompt_and_completion(self, node): + def generate_prompt_and_completion(self, node): prompt = generate_qa_pair_prompt( context=node.text, num_of_qa_paris=10, format=GPT_DATA_FORMAT ) - completion = await openai.ChatCompletion.acreate( + completion = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=[{"role": "user", "content": prompt}], temperature=0, ) return completion.choices[0].message.content - async def generate_dataset(self) -> str: - training_file = f"{uuid.uuid4()}.jsonl" - with open(training_file, "w") as f: - for i in range( - 0, len(self.nodes), self.batch_size - ): # Process nodes in chunks of batch_size - tasks = [ - self.generate_prompt_and_completion(node) - for node in self.nodes[i : i + self.batch_size] - ] - qa_pairs = await asyncio.gather(*tasks) - for qa_pair in qa_pairs: - json_objects = qa_pair.split("\n\n") - for json_obj in json_objects: - f.write(json_obj + "\n") - return training_file - - async def validate_dataset(self, training_file: str) -> str: + def validate_dataset(self, training_file: str) -> str: pass - async def finetune(self, training_file: str) -> Dict: + def finetune(self, training_file: str, _webhook_url: str = None) -> Dict: file = openai.File.create(file=open(training_file, "rb"), purpose="fine-tune") - finetune = await openai.FineTuningJob.acreate( + finetune = openai.FineTuningJob.create( training_file=file.get("id"), model=OPENAI_MODELS[self.base_model] ) return {**finetune, "training_file": training_file} @@ -111,70 +134,69 @@ class ReplicateFinetuningService(FinetuningService): def __init__( self, nodes: List[Union[Document, None]], - num_questions_per_chunk: int = 1, - batch_size: int = 10, + num_questions_per_chunk: int, + batch_size: int, base_model: str = "LLAMA2_7B_CHAT", ): - super().__init__(nodes=nodes) - self.num_questions_per_chunk = num_questions_per_chunk - self.batch_size = batch_size + super().__init__( + nodes=nodes, + num_questions_per_chunk=num_questions_per_chunk, + batch_size=batch_size, + ) self.base_model = base_model - async def generate_prompt_and_completion(self, node): + def generate_prompt_and_completion(self, node): prompt = generate_qa_pair_prompt( - context=node.text, num_of_qa_paris=10, format=REPLICATE_FORMAT + context=node.text, + num_of_qa_pairs=self.num_questions_per_chunk, + format=REPLICATE_FORMAT, ) - completion = await openai.ChatCompletion.acreate( + completion = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=[{"role": "user", "content": prompt}], temperature=0, ) return completion.choices[0].message.content - async def generate_dataset(self) -> str: - training_file = f"{uuid.uuid4()}.jsonl" - with open(training_file, "w") as f: - for i in range( - 0, len(self.nodes), self.batch_size - ): # Process nodes in chunks of batch_size - tasks = [ - self.generate_prompt_and_completion(node) - for node in self.nodes[i : i + self.batch_size] - ] - qa_pairs = await asyncio.gather(*tasks) - for qa_pair in qa_pairs: - json_objects = qa_pair.split("\n\n") - for json_obj in json_objects: - f.write(json_obj + "\n") - return training_file - - async def validate_dataset(self, training_file: str) -> str: + def validate_dataset(self, training_file: str) -> str: valid_data = [] with open(training_file, "r") as f: - for line in f: - data = json.loads(line) - if "prompt" in data and "completion" in data: - valid_data.append(data) + lines = f.readlines() + total_lines = len(lines) + progress_bar = tqdm(total=total_lines, desc="Validating lines") + for i, line in enumerate(lines, start=1): + try: + data = json.loads(line) + if "prompt" in data and "completion" in data: + valid_data.append(data) + except json.JSONDecodeError: + pass + progress_bar.update(1) + progress_bar.close() + with open(training_file, "w") as f: for data in valid_data: f.write(json.dumps(data) + "\n") + return training_file - async def finetune(self, training_file: str) -> Dict: - training_file_url = await upload_replicate_dataset(training_file=training_file) - training = replicate.trainings.create( + def finetune(self, training_file: str, webhook_url: str = None) -> Dict: + training_file_url = upload_replicate_dataset(training_file=training_file) + training = replicate.Client( + api_token=config("REPLICATE_API_KEY") + ).trainings.create( version=REPLICATE_MODELS[self.base_model], input={ "train_data": training_file_url, "num_train_epochs": 6, }, destination="homanp/test", - webhook="https://api.nagato.sh/api/v1/webhook/finetune", + webhook=webhook_url, ) return {"id": training.id, "training_file": training_file} -async def get_finetuning_service( +def get_finetuning_service( nodes: List[Union[Document, None]], provider: str = "openai", base_model: str = "GPT_35_TURBO", @@ -197,22 +219,21 @@ async def get_finetuning_service( ) -async def upload_replicate_dataset(training_file: str) -> str: - headers = {"Authorization": f"Token {config('REPLICATE_API_TOKEN')}"} - async with httpx.AsyncClient() as client: - response = await client.post( - "https://dreambooth-api-experimental.replicate.com/v1/upload/data.jsonl", - headers=headers, +def upload_replicate_dataset(training_file: str) -> str: + headers = {"Authorization": f"Token {config('REPLICATE_API_KEY')}"} + upload_response = requests.post( + "https://dreambooth-api-experimental.replicate.com/v1/upload/data.jsonl", + headers=headers, + ) + upload_response_data = upload_response.json() + upload_url = upload_response_data["upload_url"] + + with open(training_file, "rb") as f: + requests.put( + upload_url, + headers={"Content-Type": "application/jsonl"}, + data=f.read(), ) - response_data = response.json() - upload_url = response_data["upload_url"] - - with open(training_file, "rb") as f: - await client.put( - upload_url, - headers={"Content-Type": "application/jsonl"}, - content=f.read(), - ) - - serving_url = response_data["serving_url"] - return serving_url + + serving_url = upload_response_data["serving_url"] + return serving_url diff --git a/lib/service/flows.py b/lib/service/flows.py deleted file mode 100644 index a708b5d..0000000 --- a/lib/service/flows.py +++ /dev/null @@ -1,49 +0,0 @@ -from typing import List, Union - -import openai -from llama_index import Document -from prefect import flow, task - -from lib.models.ingest import IngestRequest -from lib.service.embedding import EmbeddingService -from lib.service.finetune import get_finetuning_service - - -@task -async def create_vector_embeddings( - payload: IngestRequest, - finetune_id: str, -) -> List[Union[Document, None]]: - embedding_service = EmbeddingService(payload=payload) - documents = await embedding_service.generate_documents() - nodes = await embedding_service.generate_chunks(documents=documents) - await embedding_service.generate_embeddings(nodes=nodes, finetune_id=finetune_id) - return nodes - - -@task -async def create_finetuned_model(payload: IngestRequest): - embedding_service = EmbeddingService(payload=payload) - documents = await embedding_service.generate_documents() - nodes = await embedding_service.generate_chunks(documents=documents) - finetunning_service = await get_finetuning_service( - nodes=nodes, - provider=payload.provider, - batch_size=5, - base_model=payload.base_model, - ) - training_file = await finetunning_service.generate_dataset() - formatted_training_file = await finetunning_service.validate_dataset( - training_file=training_file - ) - finetune = await finetunning_service.finetune(training_file=formatted_training_file) - if payload.provider == "OPENAI": - finetune = await openai.FineTune.retrieve(id=finetune.get("id")) - await finetunning_service.cleanup(training_file=finetune.get("training_file")) - return finetune - - -@flow(name="create_finetune", description="Create a finetune", retries=0) -async def create_finetune(payload: IngestRequest): - finetune = await create_finetuned_model(payload=payload) - await create_vector_embeddings(payload=payload, finetune_id=finetune.get("id")) diff --git a/lib/service/prompts.py b/lib/service/prompts.py index f87c87d..7cfcaad 100644 --- a/lib/service/prompts.py +++ b/lib/service/prompts.py @@ -18,11 +18,11 @@ ) -def generate_qa_pair_prompt(format: str, context: str, num_of_qa_paris: int = 10): +def generate_qa_pair_prompt(format: str, context: str, num_of_qa_pairs: int = 10): prompt = ( "You are an AI assistant tasked with generating question and answer pairs" "for the given context using the given format. Only answer in the format with" - f"no other text. You should create the following number of question/answer pairs: {num_of_qa_paris}" + f"no other text. You should create the following number of question/answer pairs: {num_of_qa_pairs}" "Return the question/answer pairs as a JSONL." "Each dict in the list should have the full context provided," "a relevant question to the context and an answer to the question.\n\n" diff --git a/lib/service/vectordb.py b/lib/service/vectordb.py index 192ae72..078c3b0 100644 --- a/lib/service/vectordb.py +++ b/lib/service/vectordb.py @@ -35,12 +35,10 @@ def __init__(self, index_name: str, dimension: int, namespace: str = None): ) self.index = pinecone.Index(index_name=self.index_name) - async def upsert(self, vectors: ndarray): + def upsert(self, vectors: ndarray): self.index.upsert(vectors=vectors, namespace=self.namespace) - async def query( - self, queries: List[ndarray], top_k: int, include_metadata: bool = True - ): + def query(self, queries: List[ndarray], top_k: int, include_metadata: bool = True): return self.index.query( queries=queries, top_k=top_k, @@ -49,7 +47,7 @@ async def query( ) -async def get_vector_service( +def get_vector_service( provider: str, index_name: str, namespace: str = None, dimension: int = 384 ): services = { diff --git a/lib/models/__init__.py b/lib/utils/__init__.py similarity index 100% rename from lib/models/__init__.py rename to lib/utils/__init__.py diff --git a/lib/utils/logger.py b/lib/utils/logger.py new file mode 100644 index 0000000..ab807f1 --- /dev/null +++ b/lib/utils/logger.py @@ -0,0 +1,33 @@ +import logging + +import colorlog + + +def setup_logger(): + """Return a logger with a default ColoredFormatter.""" + formatter = colorlog.ColoredFormatter( + "%(log_color)s%(levelname)-8s%(reset)s %(blue)s%(message)s", + datefmt=None, + reset=True, + log_colors={ + "DEBUG": "cyan", + "INFO": "green", + "WARNING": "yellow", + "ERROR": "red", + "CRITICAL": "red,bg_white", + }, + secondary_log_colors={}, + style="%", + ) + + logger = colorlog.getLogger("example") + handler = logging.StreamHandler() + handler.setFormatter(formatter) + logger.addHandler(handler) + logger.setLevel(logging.DEBUG) + + return logger + + +# Set up logger +logger = setup_logger() diff --git 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hash = "sha256:3aad25d31284266bcfcfd1fd8a743f63282305a364b8d0948a43bd606acc652f"}, - {file = "websocket_client-1.6.3-py3-none-any.whl", hash = "sha256:6cfc30d051ebabb73a5fa246efdcc14c8fbebbd0330f8984ac3bb6d9edd2ad03"}, + {file = "websocket-client-1.6.4.tar.gz", hash = "sha256:b3324019b3c28572086c4a319f91d1dcd44e6e11cd340232978c684a7650d0df"}, + {file = "websocket_client-1.6.4-py3-none-any.whl", hash = "sha256:084072e0a7f5f347ef2ac3d8698a5e0b4ffbfcab607628cadabc650fc9a83a24"}, ] [package.extras] @@ -4592,4 +4434,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = "^3.9" -content-hash = "514bdad6dd0513c4aa3e4670d4215034fccc9f97b5290ea48d658982e617866f" +content-hash = "0b2e29f42899f9cfbc456e118dbbd6bd49e75ab1a256749c76acba40c89ac28e" diff --git a/pyproject.toml b/pyproject.toml index 656cee6..399e629 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "nagato-ai" -version = "0.0.1" +version = "0.0.5" description = "" authors = ["Ismail Pelaseyed"] readme = "./README.md" @@ -8,15 +8,11 @@ packages = [{include = "lib"}] [tool.poetry.dependencies] python = "^3.9" -uvicorn = {extras = ["standard"], version = "^0.22.0"} -gunicorn = "^20.1.0" python-decouple = "^3.8" pydantic = "^1.10.7" -sse-starlette = "^1.5.0" flake8 = "^6.0.0" ruff = "^0.0.265" black = "^23.3.0" -prefect = "^2.11.4" pinecone-client = "^2.2.2" unstructured = "^0.10.16" requests = "^2.31.0" @@ -28,7 +24,10 @@ pypdf = "^3.16.2" tiktoken = "^0.5.1" sentence-transformers = "^2.2.2" replicate = "^0.15.4" -wheel = "^0.41.2" +wheel = "^0.41.0" +python-dotenv = "^1.0.0" +prefect = "^2.13.7" +tqdm = "^4.66.1" [build-system] requires = ["poetry-core"] diff --git a/test/__init__.py b/test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/mytest.py b/test/mytest.py new file mode 100644 index 0000000..19a989a --- /dev/null +++ b/test/mytest.py @@ -0,0 +1,17 @@ +from lib.service import create_finetuned_model + + +# Define a function that calls the ingest function +def main(): + result = create_finetuned_model( + url="https://digitalassets.tesla.com/tesla-contents/image/upload/IR/TSLA-Q2-2023-Update.pdf", + type="PDF", + base_model="LLAMA2_7B_CHAT", + provider="REPLICATE", + webhook_url="https://webhook.site/ebe803b9-1e34-4b20-a6ca-d06356961cd1", + ) + print(result) + + +# Run the function +main() From 965fd01d1ee0fdcb6fbfb0d8bfcd7ab33b645d3a Mon Sep 17 00:00:00 2001 From: Ismail Pelaseyed Date: Wed, 18 Oct 2023 08:23:31 +0200 Subject: [PATCH 2/3] Minor tweaks --- poetry.lock | 12 +++++++++++- pyproject.toml | 1 + 2 files changed, 12 insertions(+), 1 deletion(-) diff --git a/poetry.lock b/poetry.lock index 2a1a7bf..1810a45 100644 --- a/poetry.lock +++ b/poetry.lock @@ -736,6 +736,16 @@ calendars = ["convertdate", "hijri-converter"] fasttext = ["fasttext"] langdetect = ["langdetect"] +[[package]] +name = "decouple" +version = "0.0.7" +description = "Decoupling logic" +optional = false +python-versions = ">=3.7" +files = [ + {file = "decouple-0.0.7.tar.gz", hash = "sha256:c253c1cc62b76e9720a066f334552548c6de1884bc3bdf456ddb62d61b867b09"}, +] + [[package]] name = "dnspython" version = "2.4.2" @@ -4434,4 +4444,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = "^3.9" -content-hash = "0b2e29f42899f9cfbc456e118dbbd6bd49e75ab1a256749c76acba40c89ac28e" +content-hash = "e49fb8af2c65bba9ae18d49cdcb1c275ab7f783f977e222aa945187a265dbe4f" diff --git a/pyproject.toml b/pyproject.toml index 399e629..23be279 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,6 +28,7 @@ wheel = "^0.41.0" python-dotenv = "^1.0.0" prefect = "^2.13.7" tqdm = "^4.66.1" +decouple = "^0.0.7" [build-system] requires = ["poetry-core"] From c40ee13b617b37cffbabaaeec9a8aa66fa4809c5 Mon Sep 17 00:00:00 2001 From: Ismail Pelaseyed Date: Wed, 18 Oct 2023 08:25:02 +0200 Subject: [PATCH 3/3] Small tweaks --- .env.example | 2 -- 1 file changed, 2 deletions(-) diff --git a/.env.example b/.env.example index 9a27584..9eab04b 100644 --- a/.env.example +++ b/.env.example @@ -1,5 +1,3 @@ -DATABASE_URL= -DATABASE_MIGRATION_URL= OPENAI_API_KEY= HF_API_KEY= PINECONE_API_KEY=