local embedding
Browse files- rag.py +3 -0
- requirements.txt +1 -1
rag.py
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@@ -13,11 +13,14 @@ df['content']=df['product']+"; "+df['purpose']+"; "+df['benefit']+"; "+df['fee']
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corpus = [row['content'] for i,row in df.iterrows()]
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'huggingface/BAAI/bge-small-en-v1.5'
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from sentence_transformers import SentenceTransformer
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# Load an extremely efficient local model for retrieval
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model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", device="cpu")
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embedder = dspy.Embedder(model.encode)
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class RecommendProduct(dspy.Signature):
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"""
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Recommend RBC financial product based on verbatim
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corpus = [row['content'] for i,row in df.iterrows()]
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'huggingface/BAAI/bge-small-en-v1.5'
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"""
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from sentence_transformers import SentenceTransformer
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# Load an extremely efficient local model for retrieval
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model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", device="cpu")
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embedder = dspy.Embedder(model.encode)
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"""
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embedder = dspy.Embedder('huggingface/BAAI/bge-small-en')
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class RecommendProduct(dspy.Signature):
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"""
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Recommend RBC financial product based on verbatim
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requirements.txt
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@@ -2,7 +2,7 @@ markdownify
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requests
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duckduckgo_search
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pandas
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sentence_transformers
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langchain
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langgraph
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litellm==1.63
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requests
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duckduckgo_search
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pandas
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#sentence_transformers
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langchain
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langgraph
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litellm==1.63
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