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#4
by
awinml
- opened
- app.py +11 -1
- requirements.txt +1 -0
- utils/__pycache__/__init__.cpython-38.pyc +0 -0
- utils/__pycache__/entity_extraction.cpython-38.pyc +0 -0
- utils/__pycache__/models.cpython-38.pyc +0 -0
- utils/__pycache__/prompts.cpython-38.pyc +0 -0
- utils/__pycache__/retriever.cpython-38.pyc +0 -0
- utils/__pycache__/transcript_retrieval.cpython-38.pyc +0 -0
- utils/__pycache__/vector_index.cpython-38.pyc +0 -0
- utils/models.py +8 -0
app.py
CHANGED
@@ -24,6 +24,7 @@ from utils.models import (
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get_flan_t5_model,
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get_mpnet_embedding_model,
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get_sgpt_embedding_model,
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get_spacy_model,
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get_splade_sparse_embedding_model,
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get_t5_model,
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@@ -247,7 +248,7 @@ with st.sidebar:
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# Choose encoder model
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-
encoder_models_choice = ["MPNET", "SGPT", "Hybrid MPNET - SPLADE"]
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with st.sidebar:
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encoder_model = st.selectbox("Select Encoder Model", encoder_models_choice)
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@@ -281,6 +282,15 @@ elif encoder_model == "SGPT":
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pinecone_index = pinecone.Index(pinecone_index_name)
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retriever_model = get_sgpt_embedding_model()
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elif encoder_model == "Hybrid MPNET - SPLADE":
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pinecone.init(
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api_key=st.secrets["pinecone_hybrid_splade_mpnet"],
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get_flan_t5_model,
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get_mpnet_embedding_model,
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get_sgpt_embedding_model,
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+
get_instructor_embedding_model,
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get_spacy_model,
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get_splade_sparse_embedding_model,
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get_t5_model,
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# Choose encoder model
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encoder_models_choice = ["MPNET", "Instructor", "SGPT", "Hybrid MPNET - SPLADE"]
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with st.sidebar:
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encoder_model = st.selectbox("Select Encoder Model", encoder_models_choice)
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pinecone_index = pinecone.Index(pinecone_index_name)
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retriever_model = get_sgpt_embedding_model()
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elif encoder_model == "Instructor":
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# Connect to pinecone environment
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pinecone.init(
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api_key=st.secrets["pinecone_instructor"], environment="us-west4-gcp-free"
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)
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pinecone_index_name = "week13-instructor-xl"
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pinecone_index = pinecone.Index(pinecone_index_name)
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retriever_model = get_instructor_embedding_model()
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elif encoder_model == "Hybrid MPNET - SPLADE":
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pinecone.init(
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api_key=st.secrets["pinecone_hybrid_splade_mpnet"],
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requirements.txt
CHANGED
@@ -11,3 +11,4 @@ transformers
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streamlit
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streamlit-scrollable-textbox
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openai
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streamlit
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streamlit-scrollable-textbox
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openai
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+
InstructorEmbedding
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utils/__pycache__/__init__.cpython-38.pyc
ADDED
Binary file (181 Bytes). View file
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utils/__pycache__/entity_extraction.cpython-38.pyc
ADDED
Binary file (4.04 kB). View file
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utils/__pycache__/models.cpython-38.pyc
ADDED
Binary file (4.28 kB). View file
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utils/__pycache__/prompts.cpython-38.pyc
ADDED
Binary file (16.1 kB). View file
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utils/__pycache__/retriever.cpython-38.pyc
ADDED
Binary file (4.27 kB). View file
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utils/__pycache__/transcript_retrieval.cpython-38.pyc
ADDED
Binary file (658 Bytes). View file
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utils/__pycache__/vector_index.cpython-38.pyc
ADDED
Binary file (1.77 kB). View file
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utils/models.py
CHANGED
@@ -9,6 +9,7 @@ import spacy_transformers
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import streamlit_scrollable_textbox as stx
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import torch
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from sentence_transformers import SentenceTransformer
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from tqdm import tqdm
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from transformers import (
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AutoModelForMaskedLM,
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@@ -95,6 +96,13 @@ def get_sgpt_embedding_model():
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return model
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@st.experimental_memo
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def save_key(api_key):
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return api_key
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import streamlit_scrollable_textbox as stx
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import torch
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from sentence_transformers import SentenceTransformer
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from InstructorEmbedding import INSTRUCTOR
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from tqdm import tqdm
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from transformers import (
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AutoModelForMaskedLM,
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return model
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@st.experimental_singleton
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def get_instructor_embedding_model():
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model = INSTRUCTOR("hkunlp/instructor-large")
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return model
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@st.experimental_memo
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def save_key(api_key):
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return api_key
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