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authorGustaf Rydholm <gustaf.rydholm@gmail.com>2024-04-24 01:10:43 +0200
committerGustaf Rydholm <gustaf.rydholm@gmail.com>2024-04-24 01:10:43 +0200
commit95305f59df84caded50286b1a57b6075e48725a8 (patch)
treec0f0157a99da6332a3c96462b0aba2bd02dfcb33
parent75be0914f6bd2cdeda1539f83b38fcbc854d5cfa (diff)
Rerank working
llama3 sucks at rag
Diffstat
-rw-r--r--README.md24
-rw-r--r--poetry.lock695
-rw-r--r--pyproject.toml1
-rw-r--r--rag/cli.py32
-rw-r--r--rag/generator/__init__.py4
-rw-r--r--rag/generator/abstract.py4
-rw-r--r--rag/generator/ollama.py10
-rw-r--r--rag/generator/prompt.py4
-rw-r--r--rag/retriever/rerank.py77
-rw-r--r--rag/retriever/retriever.py4
-rw-r--r--rag/retriever/vector.py11
-rw-r--r--rag/ui.py51
12 files changed, 841 insertions, 76 deletions
diff --git a/README.md b/README.md
index aeabf5f..718b66d 100644
--- a/README.md
+++ b/README.md
@@ -12,21 +12,27 @@ RAG with ollama (and optionally cohere) and qdrant. This is basically a glorifie
Create a .env file or set the following parameters:
```.env
-CHUNK_SIZE = <CHUNK_SIZE>
-CHUNK_OVERLAP = <CHUNK_OVERLAP>
+CHUNK_SIZE = 4096
+CHUNK_OVERLAP = 256
-ENCODER_MODEL = <ENCODER_MODEL>
-EMBEDDING_DIM = <EMBEDDING_DIM>
+ENCODER_MODEL = "nomic-embed-text"
+EMBEDDING_DIM = 768
+RETRIEVER_TOP_K = 15
+RETRIEVER_SCORE_THRESHOLD = 0.5
-GENERATOR_MODEL = <GENERATOR_MODEL>
+RERANK_MODEL = "mixedbread-ai/mxbai-rerank-large-v1"
+RERANK_TOP_K = 5
-DOCUMENT_DB_NAME = <DOCUMENT_DB_NAME>
-DOCUMENT_DB_USER = <DOCUMENT_DB_USER>
+GENERATOR_MODEL = "dolphin-llama3"
-QDRANT_URL = <QDRANT_URL>
-QDRANT_COLLECTION_NAME = <QDRANT_COLLECTION_NAME>
+DOCUMENT_DB_NAME = "rag"
+DOCUMENT_DB_USER = "aktersnurra"
+
+QDRANT_URL = "http://localhost:6333"
+QDRANT_COLLECTION_NAME = "knowledge-base"
COHERE_API_KEY = <COHERE_API_KEY> # OPTIONAL
+COHERE_RERANK_MODEL = "rerank-english-v3.0"
```
### 2. Ollama
diff --git a/poetry.lock b/poetry.lock
index 856fae7..6ab9ebb 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1397,6 +1397,17 @@ MarkupSafe = ">=2.0"
i18n = ["Babel (>=2.7)"]
[[package]]
+name = "joblib"
+version = "1.4.0"
+description = "Lightweight pipelining with Python functions"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "joblib-1.4.0-py3-none-any.whl", hash = "sha256:42942470d4062537be4d54c83511186da1fc14ba354961a2114da91efa9a4ed7"},
+ {file = "joblib-1.4.0.tar.gz", hash = "sha256:1eb0dc091919cd384490de890cb5dfd538410a6d4b3b54eef09fb8c50b409b1c"},
+]
+
+[[package]]
name = "json5"
version = "0.9.24"
description = "A Python implementation of the JSON5 data format."
@@ -1980,6 +1991,23 @@ files = [
]
[[package]]
+name = "mpmath"
+version = "1.3.0"
+description = "Python library for arbitrary-precision floating-point arithmetic"
+optional = false
+python-versions = "*"
+files = [
+ {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
+ {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"},
+]
+
+[package.extras]
+develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"]
+docs = ["sphinx"]
+gmpy = ["gmpy2 (>=2.1.0a4)"]
+tests = ["pytest (>=4.6)"]
+
+[[package]]
name = "multidict"
version = "6.0.5"
description = "multidict implementation"
@@ -2181,6 +2209,24 @@ files = [
]
[[package]]
+name = "networkx"
+version = "3.3"
+description = "Python package for creating and manipulating graphs and networks"
+optional = false
+python-versions = ">=3.10"
+files = [
+ {file = "networkx-3.3-py3-none-any.whl", hash = "sha256:28575580c6ebdaf4505b22c6256a2b9de86b316dc63ba9e93abde3d78dfdbcf2"},
+ {file = "networkx-3.3.tar.gz", hash = "sha256:0c127d8b2f4865f59ae9cb8aafcd60b5c70f3241ebd66f7defad7c4ab90126c9"},
+]
+
+[package.extras]
+default = ["matplotlib (>=3.6)", "numpy (>=1.23)", "pandas (>=1.4)", "scipy (>=1.9,!=1.11.0,!=1.11.1)"]
+developer = ["changelist (==0.5)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"]
+doc = ["myst-nb (>=1.0)", "numpydoc (>=1.7)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.14)", "sphinx (>=7)", "sphinx-gallery (>=0.14)", "texext (>=0.6.7)"]
+extra = ["lxml (>=4.6)", "pydot (>=2.0)", "pygraphviz (>=1.12)", "sympy (>=1.10)"]
+test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
+
+[[package]]
name = "notebook-shim"
version = "0.2.4"
description = "A shim layer for notebook traits and config"
@@ -2243,6 +2289,147 @@ files = [
]
[[package]]
+name = "nvidia-cublas-cu12"
+version = "12.1.3.1"
+description = "CUBLAS native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl", hash = "sha256:ee53ccca76a6fc08fb9701aa95b6ceb242cdaab118c3bb152af4e579af792728"},
+ {file = "nvidia_cublas_cu12-12.1.3.1-py3-none-win_amd64.whl", hash = "sha256:2b964d60e8cf11b5e1073d179d85fa340c120e99b3067558f3cf98dd69d02906"},
+]
+
+[[package]]
+name = "nvidia-cuda-cupti-cu12"
+version = "12.1.105"
+description = "CUDA profiling tools runtime libs."
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:e54fde3983165c624cb79254ae9818a456eb6e87a7fd4d56a2352c24ee542d7e"},
+ {file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:bea8236d13a0ac7190bd2919c3e8e6ce1e402104276e6f9694479e48bb0eb2a4"},
+]
+
+[[package]]
+name = "nvidia-cuda-nvrtc-cu12"
+version = "12.1.105"
+description = "NVRTC native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:339b385f50c309763ca65456ec75e17bbefcbbf2893f462cb8b90584cd27a1c2"},
+ {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:0a98a522d9ff138b96c010a65e145dc1b4850e9ecb75a0172371793752fd46ed"},
+]
+
+[[package]]
+name = "nvidia-cuda-runtime-cu12"
+version = "12.1.105"
+description = "CUDA Runtime native Libraries"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:6e258468ddf5796e25f1dc591a31029fa317d97a0a94ed93468fc86301d61e40"},
+ {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:dfb46ef84d73fababab44cf03e3b83f80700d27ca300e537f85f636fac474344"},
+]
+
+[[package]]
+name = "nvidia-cudnn-cu12"
+version = "8.9.2.26"
+description = "cuDNN runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl", hash = "sha256:5ccb288774fdfb07a7e7025ffec286971c06d8d7b4fb162525334616d7629ff9"},
+]
+
+[package.dependencies]
+nvidia-cublas-cu12 = "*"
+
+[[package]]
+name = "nvidia-cufft-cu12"
+version = "11.0.2.54"
+description = "CUFFT native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl", hash = "sha256:794e3948a1aa71fd817c3775866943936774d1c14e7628c74f6f7417224cdf56"},
+ {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-win_amd64.whl", hash = "sha256:d9ac353f78ff89951da4af698f80870b1534ed69993f10a4cf1d96f21357e253"},
+]
+
+[[package]]
+name = "nvidia-curand-cu12"
+version = "10.3.2.106"
+description = "CURAND native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:9d264c5036dde4e64f1de8c50ae753237c12e0b1348738169cd0f8a536c0e1e0"},
+ {file = "nvidia_curand_cu12-10.3.2.106-py3-none-win_amd64.whl", hash = "sha256:75b6b0c574c0037839121317e17fd01f8a69fd2ef8e25853d826fec30bdba74a"},
+]
+
+[[package]]
+name = "nvidia-cusolver-cu12"
+version = "11.4.5.107"
+description = "CUDA solver native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl", hash = "sha256:8a7ec542f0412294b15072fa7dab71d31334014a69f953004ea7a118206fe0dd"},
+ {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-win_amd64.whl", hash = "sha256:74e0c3a24c78612192a74fcd90dd117f1cf21dea4822e66d89e8ea80e3cd2da5"},
+]
+
+[package.dependencies]
+nvidia-cublas-cu12 = "*"
+nvidia-cusparse-cu12 = "*"
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-cusparse-cu12"
+version = "12.1.0.106"
+description = "CUSPARSE native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:f3b50f42cf363f86ab21f720998517a659a48131e8d538dc02f8768237bd884c"},
+ {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-win_amd64.whl", hash = "sha256:b798237e81b9719373e8fae8d4f091b70a0cf09d9d85c95a557e11df2d8e9a5a"},
+]
+
+[package.dependencies]
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-nccl-cu12"
+version = "2.19.3"
+description = "NVIDIA Collective Communication Library (NCCL) Runtime"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl", hash = "sha256:a9734707a2c96443331c1e48c717024aa6678a0e2a4cb66b2c364d18cee6b48d"},
+]
+
+[[package]]
+name = "nvidia-nvjitlink-cu12"
+version = "12.4.127"
+description = "Nvidia JIT LTO Library"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:06b3b9b25bf3f8af351d664978ca26a16d2c5127dbd53c0497e28d1fb9611d57"},
+ {file = "nvidia_nvjitlink_cu12-12.4.127-py3-none-win_amd64.whl", hash = "sha256:fd9020c501d27d135f983c6d3e244b197a7ccad769e34df53a42e276b0e25fa1"},
+]
+
+[[package]]
+name = "nvidia-nvtx-cu12"
+version = "12.1.105"
+description = "NVIDIA Tools Extension"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:dc21cf308ca5691e7c04d962e213f8a4aa9bbfa23d95412f452254c2caeb09e5"},
+ {file = "nvidia_nvtx_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:65f4d98982b31b60026e0e6de73fbdfc09d08a96f4656dd3665ca616a11e1e82"},
+]
+
+[[package]]
name = "ollama"
version = "0.1.8"
description = "The official Python client for Ollama."
@@ -3209,6 +3396,108 @@ attrs = ">=22.2.0"
rpds-py = ">=0.7.0"
[[package]]
+name = "regex"
+version = "2024.4.16"
+description = "Alternative regular expression module, to replace re."
+optional = false
+python-versions = ">=3.7"
+files = [
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+[package.dependencies]
+filelock = "*"
+huggingface-hub = ">=0.19.3,<1.0"
+numpy = ">=1.17"
+packaging = ">=20.0"
+pyyaml = ">=5.1"
+regex = "!=2019.12.17"
+requests = "*"
+safetensors = ">=0.4.1"
+tokenizers = ">=0.14,<0.19"
+tqdm = ">=4.27"
+
+[package.extras]
+accelerate = ["accelerate (>=0.21.0)"]
+agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch"]
+all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision"]
+audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+codecarbon = ["codecarbon (==1.2.0)"]
+deepspeed = ["accelerate (>=0.21.0)", "deepspeed (>=0.9.3)"]
+deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.21.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "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"]
+dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "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", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "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", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.14,<0.19)", "urllib3 (<2.0.0)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "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", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+docs = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision"]
+docs-specific = ["hf-doc-builder"]
+flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
+flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+ftfy = ["ftfy"]
+integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
+ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+modelcreation = ["cookiecutter (==1.7.3)"]
+natten = ["natten (>=0.14.6,<0.15.0)"]
+onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
+onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
+optuna = ["optuna"]
+quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"]
+ray = ["ray[tune] (>=2.7.0)"]
+retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
+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", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "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", "tensorboard", "timeout-decorator"]
+tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+timm = ["timm"]
+tokenizers = ["tokenizers (>=0.14,<0.19)"]
+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 (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch", "tqdm (>=4.27)"]
+video = ["av (==9.2.0)", "decord (==0.6.0)"]
+vision = ["Pillow (>=10.0.1,<=15.0)"]
+
+[[package]]
+name = "triton"
+version = "2.2.0"
+description = "A language and compiler for custom Deep Learning operations"
+optional = false
+python-versions = "*"
+files = [
+ {file = "triton-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a2294514340cfe4e8f4f9e5c66c702744c4a117d25e618bd08469d0bfed1e2e5"},
+ {file = "triton-2.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da58a152bddb62cafa9a857dd2bc1f886dbf9f9c90a2b5da82157cd2b34392b0"},
+ {file = "triton-2.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0af58716e721460a61886668b205963dc4d1e4ac20508cc3f623aef0d70283d5"},
+ {file = "triton-2.2.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e8fe46d3ab94a8103e291bd44c741cc294b91d1d81c1a2888254cbf7ff846dab"},
+ {file = "triton-2.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b8ce26093e539d727e7cf6f6f0d932b1ab0574dc02567e684377630d86723ace"},
+ {file = "triton-2.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:227cc6f357c5efcb357f3867ac2a8e7ecea2298cd4606a8ba1e931d1d5a947df"},
+]
+
+[package.dependencies]
+filelock = "*"
+
+[package.extras]
+build = ["cmake (>=3.20)", "lit"]
+tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)", "torch"]
+tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
+
+[[package]]
name = "types-python-dateutil"
version = "2.9.0.20240316"
description = "Typing stubs for python-dateutil"
@@ -4161,4 +4854,4 @@ multidict = ">=4.0"
[metadata]
lock-version = "2.0"
python-versions = "^3.11"
-content-hash = "28d2833cd03e06191ab01107031fa972a6955c76385eac763141406ff92df2c5"
+content-hash = "1fd10fb293a974b16ac303f63b19409bd3c41ddec3cb601700ab567a962cd94f"
diff --git a/pyproject.toml b/pyproject.toml
index 38986f4..fab44a5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,6 +22,7 @@ streamlit = "^1.33.0"
cohere = "^5.2.3"
tqdm = "^4.66.2"
click = "^8.1.7"
+sentence-transformers = "^2.7.0"
[build-system]
diff --git a/rag/cli.py b/rag/cli.py
index 932e2a9..b210808 100644
--- a/rag/cli.py
+++ b/rag/cli.py
@@ -8,6 +8,7 @@ from tqdm import tqdm
from rag.generator import get_generator
from rag.generator.prompt import Prompt
+from rag.retriever.rerank import get_reranker
from rag.retriever.retriever import Retriever
@@ -33,11 +34,12 @@ def upload(directory: str):
retriever.add_pdf(path=path)
-def rag(generator: str, query: str, limit):
+def rag(model: str, query: str):
retriever = Retriever()
- generator = get_generator(generator)
- documents = retriever.retrieve(query, limit=limit)
- prompt = generator.rerank(Prompt(query, documents))
+ generator = get_generator(model)
+ reranker = get_reranker(model)
+ documents = retriever.retrieve(query)
+ prompt = reranker.rerank(Prompt(query, documents))
print("Answer: ")
for chunk in generator.generate(prompt):
print(chunk, end="", flush=True)
@@ -50,6 +52,7 @@ def rag(generator: str, query: str, limit):
print("---")
+@click.command()
@click.option(
"-q",
"--query",
@@ -58,20 +61,12 @@ def rag(generator: str, query: str, limit):
prompt="Enter your query",
)
@click.option(
- "-g",
- "--generator",
- type=click.Choice(["ollama", "cohere"], case_sensitive=False),
- default="ollama",
- help="Generator client",
-)
-@click.option(
- "-l",
- "--limit",
- type=click.IntRange(1, 20, clamp=True),
- default=5,
- help="Max number of documents used in grouding",
+ "-m",
+ "--model",
+ type=click.Choice(["local", "cohere"], case_sensitive=False),
+ default="local",
+ help="Generator and rerank model",
)
-@click.command()
@click.option(
"-d",
"--directory",
@@ -90,7 +85,6 @@ def rag(generator: str, query: str, limit):
def main(
query: Optional[str],
generator: str,
- limit: int,
directory: Optional[str],
verbose: int,
):
@@ -98,7 +92,7 @@ def main(
if directory:
upload(directory)
if query:
- rag(generator, query, limit)
+ rag(generator, query)
# TODO: maybe add override for models
diff --git a/rag/generator/__init__.py b/rag/generator/__init__.py
index ba23ffc..a776231 100644
--- a/rag/generator/__init__.py
+++ b/rag/generator/__init__.py
@@ -4,11 +4,11 @@ from .abstract import AbstractGenerator
from .cohere import Cohere
from .ollama import Ollama
-MODELS = ["ollama", "cohere"]
+MODELS = ["local", "cohere"]
def get_generator(model: str) -> Type[AbstractGenerator]:
match model:
- case "ollama":
+ case "local":
return Ollama()
case "cohere":
return Cohere()
diff --git a/rag/generator/abstract.py b/rag/generator/abstract.py
index 439c1b5..1beacfb 100644
--- a/rag/generator/abstract.py
+++ b/rag/generator/abstract.py
@@ -16,7 +16,3 @@ class AbstractGenerator(type):
@abstractmethod
def generate(self, prompt: Prompt) -> Generator[Any, Any, Any]:
pass
-
- @abstractmethod
- def rerank(self, prompt: Prompt) -> Prompt:
- return prompt
diff --git a/rag/generator/ollama.py b/rag/generator/ollama.py
index b72d763..9118906 100644
--- a/rag/generator/ollama.py
+++ b/rag/generator/ollama.py
@@ -1,5 +1,5 @@
import os
-from typing import Any, Dict, Generator, List
+from typing import Any, Generator, List
import ollama
from loguru import logger as log
@@ -24,12 +24,12 @@ class Ollama(metaclass=AbstractGenerator):
def __metaprompt(self, prompt: Prompt) -> str:
metaprompt = (
- "Answer the question based only on the following context:\n"
- "<context>\n"
- f"{self.__context(prompt.documents)}\n\n"
- "</context>\n"
f"{ANSWER_INSTRUCTION}"
+ "Only the information between <results>...</results> should be used to answer the question.\n"
f"Question: {prompt.query.strip()}\n\n"
+ "<results>\n"
+ f"{self.__context(prompt.documents)}\n\n"
+ "</results>\n"
"Answer:"
)
return metaprompt
diff --git a/rag/generator/prompt.py b/rag/generator/prompt.py
index fa007db..f607122 100644
--- a/rag/generator/prompt.py
+++ b/rag/generator/prompt.py
@@ -5,8 +5,8 @@ from rag.retriever.vector import Document
ANSWER_INSTRUCTION = (
"Given the context information and not prior knowledge, answer the question."
- "If the context is irrelevant to the question, answer that you do not know "
- "the answer to the question given the context and stop.\n"
+ "If the context is irrelevant to the question or empty, then do not attempt to answer "
+ "the question, just reply that you do not know based on the context provided.\n"
)
diff --git a/rag/retriever/rerank.py b/rag/retriever/rerank.py
new file mode 100644
index 0000000..08a9a27
--- /dev/null
+++ b/rag/retriever/rerank.py
@@ -0,0 +1,77 @@
+import os
+from abc import abstractmethod
+from typing import Type
+
+import cohere
+from loguru import logger as log
+from sentence_transformers import CrossEncoder
+
+from rag.generator.prompt import Prompt
+
+
+class AbstractReranker(type):
+ _instances = {}
+
+ def __call__(cls, *args, **kwargs):
+ if cls not in cls._instances:
+ instance = super().__call__(*args, **kwargs)
+ cls._instances[cls] = instance
+ return cls._instances[cls]
+
+ @abstractmethod
+ def rank(self, prompt: Prompt) -> Prompt:
+ return prompt
+
+
+class Reranker(metaclass=AbstractReranker):
+ def __init__(self) -> None:
+ self.model = CrossEncoder(os.environ["RERANK_MODEL"])
+ self.top_k = int(os.environ["RERANK_TOP_K"])
+
+ def rank(self, prompt: Prompt) -> Prompt:
+ if prompt.documents:
+ results = self.model.rank(
+ query=prompt.query,
+ documents=[d.text for d in prompt.documents],
+ return_documents=False,
+ top_k=self.top_k,
+ )
+ ranking = list(filter(lambda x: x.get("score", 0.0) > 0.5, results))
+ log.debug(
+ f"Reranking gave {len(ranking)} relevant documents of {len(prompt.documents)}"
+ )
+ prompt.documents = [
+ prompt.documents[r.get("corpus_id", 0)] for r in ranking
+ ]
+ return prompt
+
+
+class CohereReranker(metaclass=AbstractReranker):
+ def __init__(self) -> None:
+ self.client = cohere.Client(os.environ["COHERE_API_KEY"])
+ self.top_k = int(os.environ["RERANK_TOP_K"])
+
+ def rank(self, prompt: Prompt) -> Prompt:
+ if prompt.documents:
+ response = self.client.rerank(
+ model="rerank-english-v3.0",
+ query=prompt.query,
+ documents=[d.text for d in prompt.documents],
+ top_n=self.top_k,
+ )
+ ranking = list(filter(lambda x: x.relevance_score > 0.5, response.results))
+ log.debug(
+ f"Reranking gave {len(ranking)} relevant documents of {len(prompt.documents)}"
+ )
+ prompt.documents = [prompt.documents[r.index] for r in ranking]
+ return prompt
+
+
+def get_reranker(model: str) -> Type[AbstractReranker]:
+ match model:
+ case "local":
+ return Reranker()
+ case "cohere":
+ return CohereReranker()
+ case _:
+ exit(1)
diff --git a/rag/retriever/retriever.py b/rag/retriever/retriever.py
index deffae5..351cfb0 100644
--- a/rag/retriever/retriever.py
+++ b/rag/retriever/retriever.py
@@ -45,7 +45,7 @@ class Retriever:
else:
log.error("Invalid input!")
- def retrieve(self, query: str, limit: int = 5) -> List[Document]:
+ def retrieve(self, query: str) -> List[Document]:
log.debug(f"Finding documents matching query: {query}")
query_emb = self.encoder.encode_query(query)
- return self.vec_db.search(query_emb, limit)
+ return self.vec_db.search(query_emb)
diff --git a/rag/retriever/vector.py b/rag/retriever/vector.py
index b72a3c1..1a484f3 100644
--- a/rag/retriever/vector.py
+++ b/rag/retriever/vector.py
@@ -22,11 +22,12 @@ class Document:
class VectorDB:
- def __init__(self, score_threshold: float = 0.5):
+ def __init__(self):
self.dim = int(os.environ["EMBEDDING_DIM"])
self.collection_name = os.environ["QDRANT_COLLECTION_NAME"]
self.client = QdrantClient(url=os.environ["QDRANT_URL"])
- self.score_threshold = score_threshold
+ self.top_k = int(os.environ["RETRIEVER_TOP_K"])
+ self.score_threshold = float(os.environ["RETRIEVER_SCORE_THRESHOLD"])
self.__configure()
def __configure(self):
@@ -58,15 +59,15 @@ class VectorDB:
max_retries=3,
)
- def search(self, query: List[float], limit: int = 5) -> List[Document]:
+ def search(self, query: List[float]) -> List[Document]:
log.debug("Searching for vectors...")
hits = self.client.search(
collection_name=self.collection_name,
query_vector=query,
- limit=limit,
+ limit=self.top_k,
score_threshold=self.score_threshold,
)
- log.debug(f"Got {len(hits)} hits in the vector db with limit={limit}")
+ log.debug(f"Got {len(hits)} hits in the vector db with limit={self.top_k}")
return list(
map(
lambda h: Document(
diff --git a/rag/ui.py b/rag/ui.py
index 2fbf8de..f46c24d 100644
--- a/rag/ui.py
+++ b/rag/ui.py
@@ -1,5 +1,4 @@
from dataclasses import dataclass
-from enum import Enum
from typing import Dict, List
import streamlit as st
@@ -9,27 +8,18 @@ from loguru import logger as log
from rag.generator import MODELS, get_generator
from rag.generator.prompt import Prompt
+from rag.retriever.rerank import get_reranker
from rag.retriever.retriever import Retriever
from rag.retriever.vector import Document
-class Cohere(Enum):
- USER = "USER"
- BOT = "CHATBOT"
-
-
-class Ollama(Enum):
- USER = "user"
- BOT = "assistant"
-
-
@dataclass
class Message:
role: str
message: str
- def as_dict(self, client: str) -> Dict[str, str]:
- if client == "cohere":
+ def as_dict(self, model: str) -> Dict[str, str]:
+ if model == "cohere":
return {"role": self.role, "message": self.message}
else:
return {"role": self.role, "content": self.message}
@@ -38,12 +28,8 @@ class Message:
def set_chat_users():
log.debug("Setting user and bot value")
ss = st.session_state
- if ss.generator == "cohere":
- ss.user = Cohere.USER.value
- ss.bot = Cohere.BOT.value
- else:
- ss.user = Ollama.USER.value
- ss.bot = Ollama.BOT.value
+ ss.user = "user"
+ ss.bot = "assistant"
@st.cache_resource
@@ -52,13 +38,19 @@ def load_retriever():
st.session_state.retriever = Retriever()
-@st.cache_resource
-def load_generator(client: str):
+# @st.cache_resource
+def load_generator(model: str):
log.debug("Loading generator model")
- st.session_state.generator = get_generator(client)
+ st.session_state.generator = get_generator(model)
set_chat_users()
+# @st.cache_resource
+def load_reranker(model: str):
+ log.debug("Loading reranker model")
+ st.session_state.reranker = get_reranker(model)
+
+
@st.cache_data(show_spinner=False)
def upload(files):
retriever = st.session_state.retriever
@@ -95,11 +87,12 @@ def generate_chat(query: str):
retriever = ss.retriever
generator = ss.generator
+ reranker = ss.reranker
- documents = retriever.retrieve(query, limit=15)
+ documents = retriever.retrieve(query)
prompt = Prompt(query, documents)
- prompt = generator.rerank(prompt)
+ prompt = reranker.rank(prompt)
with st.chat_message(ss.bot):
response = st.write_stream(generator.generate(prompt))
@@ -137,9 +130,12 @@ def sidebar():
upload(files)
st.header("Generative Model")
- st.markdown("Select the model that will be used for generating the answer.")
- st.selectbox("Generative Model", key="client", options=MODELS)
- load_generator(st.session_state.client)
+ st.markdown(
+ "Select the model that will be used for reranking and generating the answer."
+ )
+ st.selectbox("Model", key="model", options=MODELS)
+ load_generator(st.session_state.model)
+ load_reranker(st.session_state.model)
def page():
@@ -157,6 +153,7 @@ def page():
if __name__ == "__main__":
load_dotenv()
st.title("Retrieval Augmented Generation")
+ set_chat_users()
load_retriever()
sidebar()
page()
generated by cgit v1.2.3-70-g09d2 (git 2.46.0) at 2024-09-19 22:48:38 +0000