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73 lines
2.0 KiB
Python
73 lines
2.0 KiB
Python
import sys
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import os
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import glob
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import uuid
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import json
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from embedding import get_embedding
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from split_markdown import split_markdown
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from vector_store import get_client
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chroma_client = get_client()
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# 每次执行都会清理避免重复
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chroma_client.reset()
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collection = chroma_client.create_collection(name="amis")
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doc_dir = sys.argv[1]
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# 存储所有文本段用于大模型生成
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text_blocks_by_id = {}
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# embedding 缓存，虽然目前速度很快，但后续如果换成网络请求会比较慢
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embedding_cache = {}
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embedding_cache_file = os.path.join(
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os.path.dirname(__file__), 'embedding.json')
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if os.path.exists(embedding_cache_file):
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with open(embedding_cache_file, 'rb') as f:
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embedding_cache = json.load(f)
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def get_embedding_with_cache(text):
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if text in embedding_cache:
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return embedding_cache[text]
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else:
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embedding = get_embedding(text).tolist()
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embedding_cache[text] = embedding
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return embedding
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for filename in glob.iglob(doc_dir + '**/*.md', recursive=True):
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with open(filename) as f:
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content = f.read()
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md_blocks = split_markdown(content, filename)
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embeddings = []
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documents = []
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metadatas = []
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ids = []
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for block in md_blocks:
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block_id = uuid.uuid4().hex
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text_blocks_by_id[block_id] = block
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text_blocks = block.get_text_blocks()
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index = 1
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for text_block in text_blocks:
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embeddings.append(get_embedding_with_cache(text_block))
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documents.append(text_block)
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ids.append(block_id + "_" + str(index))
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metadatas.append({"source": block.file_name})
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index += 1
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collection.add(
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embeddings=embeddings,
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documents=documents,
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metadatas=metadatas,
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ids=ids
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)
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with open(os.path.join(os.path.dirname(__file__), 'text.json'), 'w') as f:
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json.dump(text_blocks_by_id, f)
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with open(embedding_cache_file, 'w') as f:
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json.dump(embedding_cache, f)
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