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  1. app.py +10 -8
app.py CHANGED
@@ -94,8 +94,8 @@ def talk(message, history):
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  for new_text in streamer:
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  partial_text += new_text
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  yield partial_text
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- partial_text += resources
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- return partial_text
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  TITLE = "RAG"
@@ -103,15 +103,16 @@ TITLE = "RAG"
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  DESCRIPTION = """
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  A rag pipeline with a chatbot feature
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- Resources used to build this project :
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- * embedding model : https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1
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- * dataset : https://huggingface.co/datasets/not-lain/wikipedia-small-3000-embedded (used mxbai-colbert-large-v1 to create the embedding column )
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- * faiss docs : https://huggingface.co/docs/datasets/v2.18.0/en/package_reference/main_classes#datasets.Dataset.add_faiss_index
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- * chatbot : google/gemma-7b-it
 
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  If you want to support my work please click on the heart react button β€οΈπŸ€—
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- <sub><sup><sub><sup>psst, I am still open for work if please reach me out at https://not-lain.github.io/</sup></sub></sup></sub>
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  """
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  demo = gr.ChatInterface(
@@ -123,6 +124,7 @@ demo = gr.ChatInterface(
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  likeable=True,
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  layout="bubble",
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  bubble_full_width=False,
 
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  ),
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  theme="Soft",
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  examples=[["what is machine learning"]],
 
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  for new_text in streamer:
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  partial_text += new_text
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  yield partial_text
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+ # partial_text += resources
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+ # return partial_text
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  TITLE = "RAG"
 
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  DESCRIPTION = """
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  A rag pipeline with a chatbot feature
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+ Resources used to build this project :
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+
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+ embedding model : https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1
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+ dataset : https://huggingface.co/datasets/not-lain/wikipedia-small-3000-embedded (used mxbai-colbert-large-v1 to create the embedding column )
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+ faiss docs : https://huggingface.co/docs/datasets/v2.18.0/en/package_reference/main_classes#datasets.Dataset.add_faiss_index
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+ chatbot : https://huggingface.co/google/gemma-7b-it
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  If you want to support my work please click on the heart react button β€οΈπŸ€—
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+ <sub><sup><sub><sup>psst, I am still open for work, so please reach me out at https://not-lain.github.io/</sup></sub></sup></sub>
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  """
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  demo = gr.ChatInterface(
 
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  likeable=True,
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  layout="bubble",
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  bubble_full_width=False,
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+ label="RAG chatbot"
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  ),
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  theme="Soft",
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  examples=[["what is machine learning"]],