getting gemma3 in the mix
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@@ -5,23 +5,23 @@ import chromadb, time
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class OllamaGenerator:
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def __init__(self, title: str, content: str, model: str):
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def __init__(self, title: str, content: str, model: str, inner_title: str):
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self.title = title
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self.inner_title = inner_title
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self.content = content
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self.chroma = chromadb.HttpClient(host="172.18.0.2", port=8000)
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ollama_url = f"{os.environ["OLLAMA_PROTOCOL"]}://{os.environ["OLLAMA_HOST"]}:{os.environ["OLLAMA_PORT"]}"
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self.ollama_client = Client(host=ollama_url)
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self.ollama_model = model
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self.embed_model = "snowflake-arctic-embed2:latest"
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self.agent_models = ["openthinker:7b", "deepseek-r1:7b", "qwen2.5:7b", "deepseek-coder-v2:16b"]
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self.agent_models = ["openthinker:7b", "deepseek-r1:7b", "qwen2.5:7b", "gemma3:latest"]
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self.prompt_inject = f"""
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You are a journalist Software Developer and DevOps expert
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who has transistioned in Developer Relations
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You are a journalist, Software Developer and DevOps expert
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writing a 1000 word draft blog for other tech enthusiasts.
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You like to use almost no code examples and prefer to talk
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in a light comedic tone. You are also Australian
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As this person write this blog as a markdown document.
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The title for the blog is {self.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.
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The basis for the content of the blog is:
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{self.content}
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@@ -95,13 +95,12 @@ class OllamaGenerator:
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prompt = f"""
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You are an editor taking information from {len(self.agent_models)} Software
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Developers and Data experts
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who have transistioned into Developer Relations
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writing a 3000 word blog for other tech enthusiasts.
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You like when they use almost no code examples and the
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voice is in a light comedic tone. You are also Australian
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As this person produce and an amalgamtion of this blog as a markdown document.
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The title for the blog is {self.title}.
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Do not output the title in the markdown.
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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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The basis for the content of the blog is:
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{self.content}
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"""
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@@ -110,10 +109,6 @@ class OllamaGenerator:
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collection = self.load_to_vector_db()
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collection_query = collection.query(query_embeddings=query_embed, n_results=100)
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print("Showing pertinent info from drafts used in final edited edition")
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for document in collection_query:
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print (document['ids'])
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print (document['embeddings'])
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print (document['documents'])
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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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prompt_enhanced = f"{prompt} - Generate the final document using this information from the drafts: {pertinent_draft_info} - ONLY OUTPUT THE MARKDOWN"
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print("Generating final document")
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