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-rw-r--r--poetry.lock1852
-rw-r--r--pyproject.toml2
-rw-r--r--rag/__main__.py6
-rw-r--r--rag/cli.py43
-rw-r--r--rag/generator/prompt.py10
-rw-r--r--rag/retriever/rerank/local.py8
6 files changed, 972 insertions, 949 deletions
diff --git a/poetry.lock b/poetry.lock
index 29738b3..0d2caf0 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,91 +1,103 @@
# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
[[package]]
+name = "aiohappyeyeballs"
+version = "2.3.2"
+description = "Happy Eyeballs"
+optional = false
+python-versions = ">=3.8,<4.0"
+files = [
+ {file = "aiohappyeyeballs-2.3.2-py3-none-any.whl", hash = "sha256:903282fb08c8cfb3de356fd546b263248a477c99cb147e20a115e14ab942a4ae"},
+ {file = "aiohappyeyeballs-2.3.2.tar.gz", hash = "sha256:77e15a733090547a1f5369a1287ddfc944bd30df0eb8993f585259c34b405f4e"},
+]
+
+[[package]]
name = "aiohttp"
-version = "3.9.5"
+version = "3.10.0"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.8"
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aiosignal = ">=1.1.2"
attrs = ">=17.3.0"
frozenlist = ">=1.1.1"
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yarl = ">=1.0,<2.0"
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-speedups = ["Brotli", "aiodns", "brotlicffi"]
+speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
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name = "aiosignal"
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name = "boto3"
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description = "The AWS SDK for Python"
optional = false
python-versions = ">=3.8"
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s3transfer = ">=0.10.0,<0.11.0"
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name = "botocore"
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description = "Low-level, data-driven core of boto 3."
optional = false
python-versions = ">=3.8"
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name = "certifi"
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description = "Python package for providing Mozilla's CA Bundle."
optional = false
python-versions = ">=3.6"
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[[package]]
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description = ""
optional = false
python-versions = "<4.0,>=3.8"
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+version = "1.8.2"
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python-versions = ">=3.8"
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[[package]]
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python-versions = ">=3.10"
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parallel = ["ipyparallel"]
qtconsole = ["qtconsole"]
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uri-template = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
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python-versions = ">=3.8"
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[[package]]
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[[package]]
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[[package]]
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nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
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nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
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nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
sympy = "*"
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opt-einsum = ["opt-einsum (>=3.3)"]
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name = "transformers"
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description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
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python-versions = ">=3.8.0"
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pyyaml = ">=5.1"
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@@ -4598,41 +4609,46 @@ natten = ["natten (>=0.14.6,<0.15.0)"]
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+quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.4.4)", "urllib3 (<2.0.0)"]
ray = ["ray[tune] (>=2.7.0)"]
retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
+ruff = ["ruff (==0.4.4)"]
sagemaker = ["sagemaker (>=2.31.0)"]
sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
sigopt = ["sigopt"]
sklearn = ["scikit-learn"]
speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
-tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
-tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
+tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-timm = ["timm"]
+timm = ["timm (<=0.9.16)"]
tokenizers = ["tokenizers (>=0.19,<0.20)"]
torch = ["accelerate (>=0.21.0)", "torch"]
torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.23.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
+torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
video = ["av (==9.2.0)", "decord (==0.6.0)"]
vision = ["Pillow (>=10.0.1,<=15.0)"]
[[package]]
name = "triton"
-version = "2.3.1"
+version = "3.0.0"
description = "A language and compiler for custom Deep Learning operations"
optional = false
python-versions = "*"
files = [
- {file = "triton-2.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3c84595cbe5e546b1b290d2a58b1494df5a2ef066dd890655e5b8a8a92205c33"},
- {file = "triton-2.3.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c9d64ae33bcb3a7a18081e3a746e8cf87ca8623ca13d2c362413ce7a486f893e"},
- {file = "triton-2.3.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eaf80e8761a9e3498aa92e7bf83a085b31959c61f5e8ac14eedd018df6fccd10"},
- {file = "triton-2.3.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b13bf35a2b659af7159bf78e92798dc62d877aa991de723937329e2d382f1991"},
- {file = "triton-2.3.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:63381e35ded3304704ea867ffde3b7cfc42c16a55b3062d41e017ef510433d66"},
- {file = "triton-2.3.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d968264523c7a07911c8fb51b4e0d1b920204dae71491b1fe7b01b62a31e124"},
+ {file = "triton-3.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e1efef76935b2febc365bfadf74bcb65a6f959a9872e5bddf44cc9e0adce1e1a"},
+ {file = "triton-3.0.0-1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5ce8520437c602fb633f1324cc3871c47bee3b67acf9756c1a66309b60e3216c"},
+ {file = "triton-3.0.0-1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:34e509deb77f1c067d8640725ef00c5cbfcb2052a1a3cb6a6d343841f92624eb"},
+ {file = "triton-3.0.0-1-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:bcbf3b1c48af6a28011a5c40a5b3b9b5330530c3827716b5fbf6d7adcc1e53e9"},
+ {file = "triton-3.0.0-1-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6e5727202f7078c56f91ff13ad0c1abab14a0e7f2c87e91b12b6f64f3e8ae609"},
+ {file = "triton-3.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:39b052da883351fdf6be3d93cedae6db3b8e3988d3b09ed221bccecfa9612230"},
+ {file = "triton-3.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cd34f19a8582af96e6291d4afce25dac08cb2a5d218c599163761e8e0827208e"},
+ {file = "triton-3.0.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0d5e10de8c011adeb7c878c6ce0dd6073b14367749e34467f1cff2bde1b78253"},
+ {file = "triton-3.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e8903767951bf86ec960b4fe4e21bc970055afc65e9d57e916d79ae3c93665e3"},
+ {file = "triton-3.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:41004fb1ae9a53fcb3e970745feb87f0e3c94c6ce1ba86e95fa3b8537894bef7"},
]
[package.dependencies]
@@ -4640,8 +4656,8 @@ filelock = "*"
[package.extras]
build = ["cmake (>=3.20)", "lit"]
-tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)", "torch"]
-tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
+tests = ["autopep8", "flake8", "isort", "llnl-hatchet", "numpy", "pytest", "scipy (>=1.7.1)"]
+tutorials = ["matplotlib", "pandas", "tabulate"]
[[package]]
name = "types-python-dateutil"
@@ -4656,13 +4672,13 @@ files = [
[[package]]
name = "types-requests"
-version = "2.32.0.20240602"
+version = "2.32.0.20240712"
description = "Typing stubs for requests"
optional = false
python-versions = ">=3.8"
files = [
- {file = "types-requests-2.32.0.20240602.tar.gz", hash = "sha256:3f98d7bbd0dd94ebd10ff43a7fbe20c3b8528acace6d8efafef0b6a184793f06"},
- {file = "types_requests-2.32.0.20240602-py3-none-any.whl", hash = "sha256:ed3946063ea9fbc6b5fc0c44fa279188bae42d582cb63760be6cb4b9d06c3de8"},
+ {file = "types-requests-2.32.0.20240712.tar.gz", hash = "sha256:90c079ff05e549f6bf50e02e910210b98b8ff1ebdd18e19c873cd237737c1358"},
+ {file = "types_requests-2.32.0.20240712-py3-none-any.whl", hash = "sha256:f754283e152c752e46e70942fa2a146b5bc70393522257bb85bd1ef7e019dcc3"},
]
[package.dependencies]
@@ -4953,4 +4969,4 @@ multidict = ">=4.0"
[metadata]
lock-version = "2.0"
python-versions = "^3.11"
-content-hash = "1fd10fb293a974b16ac303f63b19409bd3c41ddec3cb601700ab567a962cd94f"
+content-hash = "04ba910abe77853a11d609a12cb877dbc2557177839061fbac6e82039a7e581b"
diff --git a/pyproject.toml b/pyproject.toml
index fab44a5..8f326cb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,7 +8,7 @@ packages = [{include = "rag"}]
[tool.poetry.dependencies]
python = "^3.11"
-ollama = "^0.1.8"
+ollama = "^0.3.1"
langchain-community = "^0.0.31"
langchain = "^0.1.14"
jupyterlab-vim = "^4.1.3"
diff --git a/rag/__main__.py b/rag/__main__.py
new file mode 100644
index 0000000..be85a2e
--- /dev/null
+++ b/rag/__main__.py
@@ -0,0 +1,6 @@
+from dotenv import load_dotenv
+
+from rag.cli import cli
+
+load_dotenv()
+cli()
diff --git a/rag/cli.py b/rag/cli.py
index 070427d..b047255 100644
--- a/rag/cli.py
+++ b/rag/cli.py
@@ -5,10 +5,8 @@ from dotenv import load_dotenv
from loguru import logger as log
from tqdm import tqdm
-from rag.generator import get_generator
from rag.generator.prompt import Prompt
from rag.model import Rag
-from rag.retriever.rerank import get_reranker
from rag.retriever.retriever import Retriever
@@ -51,13 +49,6 @@ def upload(directory: str, verbose: int):
@click.command()
@click.option(
- "-q",
- "--query",
- prompt_required=False,
- help="The query for rag",
- prompt="Enter your query",
-)
-@click.option(
"-c",
"--client",
type=click.Choice(["local", "cohere"], case_sensitive=False),
@@ -65,21 +56,29 @@ def upload(directory: str, verbose: int):
help="Generator and rerank model",
)
@click.option("-v", "--verbose", count=True)
-def rag(query: str, client: str, verbose: int):
+def rag(client: str, verbose: int):
configure_logging(verbose)
rag = Rag(client)
- documents = rag.retrieve(query)
- prompt = Prompt(query, documents, client)
- print("Answer: ")
- for chunk in rag.generate(prompt):
- print(chunk, end="", flush=True)
-
- print("\n\n")
- for i, doc in enumerate(prompt.documents):
- print(f"### Document {i}")
- print(f"**Title: {doc.title}**")
- print(doc.text)
- print("---")
+ while True:
+ query = input("Query: ")
+ documents = rag.retrieve(query)
+ prompt = Prompt(query, documents, client)
+ print("Answer: ")
+ response = ""
+ for chunk in rag.generate(prompt):
+ print(chunk, end="", flush=True)
+ response += chunk
+
+ rag.add_message(rag.bot, response)
+
+ show_context = input("Display context? [y/n] ").lower() == "y"
+ print("\n\n")
+ if show_context:
+ for i, doc in enumerate(prompt.documents):
+ print(f"### Document {i}")
+ print(f"**Title: {doc.title}**")
+ print(doc.text)
+ print("---")
@click.command()
diff --git a/rag/generator/prompt.py b/rag/generator/prompt.py
index cedf610..75966e8 100644
--- a/rag/generator/prompt.py
+++ b/rag/generator/prompt.py
@@ -29,11 +29,13 @@ class Prompt:
return f"{self.query}\n\n{ANSWER_INSTRUCTION}"
else:
return (
- "Context information is below.\n"
+ "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n"
+ "Using the information contained in the context, give a comprehensive answer to the question.\n"
+ "If the answer cannot be deduced from the context, do not give an answer.\n\n"
+ "Context:\n"
"---\n"
f"{self.__context(self.documents)}\n\n"
"---\n"
- f"{ANSWER_INSTRUCTION}"
- f"Query: {self.query.strip()}\n\n"
- "Answer:"
+ f"Question: {self.query}<|eot_id|>\n"
+ "<|start_header_id|>assistant<|end_header_id|>\n\n"
)
diff --git a/rag/retriever/rerank/local.py b/rag/retriever/rerank/local.py
index fd42c2c..231d50a 100644
--- a/rag/retriever/rerank/local.py
+++ b/rag/retriever/rerank/local.py
@@ -2,16 +2,16 @@ import os
from typing import List
from loguru import logger as log
-from rag.message import Message
-from rag.retriever.vector import Document
from sentence_transformers import CrossEncoder
+from rag.message import Message
from rag.retriever.rerank.abstract import AbstractReranker
+from rag.retriever.vector import Document
class Reranker(metaclass=AbstractReranker):
def __init__(self) -> None:
- self.model = CrossEncoder(os.environ["RERANK_MODEL"])
+ self.model = CrossEncoder(os.environ["RERANK_MODEL"], device="cpu")
self.top_k = int(os.environ["RERANK_TOP_K"])
self.relevance_threshold = float(os.environ["RETRIEVER_RELEVANCE_THRESHOLD"])
@@ -33,7 +33,7 @@ class Reranker(metaclass=AbstractReranker):
def rerank_messages(self, query: str, messages: List[Message]) -> List[Message]:
results = self.model.rank(
query=query,
- documents=[m.message for m in messages],
+ documents=[m.content for m in messages],
return_documents=False,
top_k=self.top_k,
)