PabloVD commited on
Commit
fdb4410
·
1 Parent(s): 192d160

SentenceTransformers as separate instance

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Files changed (3) hide show
  1. README.md +0 -1
  2. requirements.txt +0 -1
  3. worker.py +5 -2
README.md CHANGED
@@ -9,7 +9,6 @@ app_file: app.py
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  pinned: false
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  license: mit
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  short_description: Chatbot assistant for the CAMELS simulations documentation
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- python_version: 3.11
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  ---
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  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
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  pinned: false
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  license: mit
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  short_description: Chatbot assistant for the CAMELS simulations documentation
 
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  ---
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  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
requirements.txt CHANGED
@@ -2,4 +2,3 @@ langchain
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  langchain-community
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  langchain-huggingface
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  chromadb
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- InstructorEmbedding
 
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  langchain-community
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  langchain-huggingface
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  chromadb
 
worker.py CHANGED
@@ -5,7 +5,7 @@ from langchain_community.document_loaders import PyPDFLoader
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  from langchain.text_splitter import RecursiveCharacterTextSplitter
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  from langchain_community.vectorstores import Chroma
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  from langchain_huggingface import HuggingFaceEndpoint
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-
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  import pip
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  def install(package):
@@ -49,8 +49,11 @@ def init_llm():
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  #Initialize embeddings using a pre-trained model to represent the text data.
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  embedddings_model = "sentence-transformers/multi-qa-distilbert-cos-v1"
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  # embedddings_model = "sentence-transformers/all-MiniLM-L6-v2"
 
 
 
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  embeddings = HuggingFaceInstructEmbeddings(
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- model_name=embedddings_model,
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  model_kwargs={"device": DEVICE}
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  )
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  from langchain.text_splitter import RecursiveCharacterTextSplitter
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  from langchain_community.vectorstores import Chroma
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  from langchain_huggingface import HuggingFaceEndpoint
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+ from sentence_transformers import SentenceTransformer # Use SentenceTransformer module to use Hugging face Model
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  import pip
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  def install(package):
 
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  #Initialize embeddings using a pre-trained model to represent the text data.
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  embedddings_model = "sentence-transformers/multi-qa-distilbert-cos-v1"
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  # embedddings_model = "sentence-transformers/all-MiniLM-L6-v2"
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+
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+ emb_model = SentenceTransformer(embedddings_model)
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+
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  embeddings = HuggingFaceInstructEmbeddings(
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+ model_name=emb_model,
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  model_kwargs={"device": DEVICE}
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  )
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