84 lines
1.9 KiB
Python
84 lines
1.9 KiB
Python
import ollama
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from uuid import uuid4, UUID
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from request import PaperlessNGXService
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from math import ceil
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import chromadb
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client = chromadb.EphemeralClient()
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collection = client.create_collection(name="docs")
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class Chunk:
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def __init__(
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self,
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text: str,
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size: int,
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document_id: UUID,
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chunk_id: int,
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embedding,
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):
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self.text = text
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self.size = size
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self.document_id = document_id
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self.chunk_id = chunk_id
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self.embedding = embedding
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class Chunker:
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def __init__(self) -> None:
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pass
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def chunk_document(self, document: str, chunk_size: int = 300) -> list[Chunk]:
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doc_uuid = uuid4()
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chunks = []
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num_chunks = ceil(len(document) / chunk_size)
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document_length = len(document)
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for i in range(num_chunks):
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curr_pos = i * num_chunks
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to_pos = (
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curr_pos + num_chunks
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if curr_pos + num_chunks < document_length
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else document_length
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)
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text_chunk = document[curr_pos:to_pos]
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collection.add(
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ids=[str(doc_uuid) + ":" + str(i)],
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documents=[text_chunk],
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)
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return chunks
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# Setup
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# Step 1: Get the text
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ppngx = PaperlessNGXService()
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docs = ppngx.get_data()
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texts = [doc["content"] for doc in docs]
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# Step 2: Create chunks
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chunker = Chunker()
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print(f"chunking {len(texts)} documents")
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for text in texts:
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chunker.chunk_document(document=text)
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# Ask
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input = "How many teeth has Simba had removed?"
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response = ollama.embed(model="mxbai-embed-large", input=input)
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results = collection.query(query_texts=[input], n_results=1)
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print(results)
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# Generate
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output = ollama.generate(
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model="gemma3n:e4b",
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prompt=f"Using this data: {results}. Respond to this prompt: {input}",
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)
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print(output["response"])
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