prompt enhancement #16
@ -125,7 +125,8 @@ class OllamaGenerator:
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writing a 3000 word blog article. You like when they use almost no code examples.
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writing a 3000 word blog article. You like when they use almost no code examples.
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You are also Australian. The content may have light comedic elements,
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You are also Australian. The content may have light comedic elements,
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you are more professional and will attempt to tone these down
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you are more professional and will attempt to tone these down
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As this person produce and an amalgamtion of this blog as a markdown document.
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As this person produce the final version of this blog as a markdown document
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keeping in mind the context provided by the previous drafts.
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The title for the blog is {self.inner_title}.
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The title for the blog is {self.inner_title}.
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Do not output the title in the markdown. Avoid repeated sentences
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Do not output the title in the markdown. Avoid repeated sentences
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The basis for the content of the blog is:
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The basis for the content of the blog is:
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@ -138,7 +139,9 @@ class OllamaGenerator:
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print("Showing pertinent info from drafts used in final edited edition")
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print("Showing pertinent info from drafts used in final edited edition")
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pertinent_draft_info = '\n\n'.join(collection.query(query_embeddings=query_embed, n_results=100)['documents'][0])
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pertinent_draft_info = '\n\n'.join(collection.query(query_embeddings=query_embed, n_results=100)['documents'][0])
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#print(pertinent_draft_info)
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#print(pertinent_draft_info)
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prompt_human = f"Generate the final document using this information from the drafts: {pertinent_draft_info} - Only output in markdown, do not wrap in markdown tags"
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prompt_human = f"""Generate the final, 3000 word, draft of the blog using this information from the drafts: {pertinent_draft_info}
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- Only output in markdown, do not wrap in markdown tags, Only provide the draft not a commentary on the drafts in the context
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"""
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print("Generating final document")
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print("Generating final document")
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messages = [("system", prompt_system), ("human", prompt_human),]
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messages = [("system", prompt_system), ("human", prompt_human),]
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self.response = self.llm.invoke(messages).text()
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self.response = self.llm.invoke(messages).text()
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