feat: embedding
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@ -12,6 +12,9 @@ class Config(TypedDict):
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llm_api_key: str
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llm_api_host: str
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llm_model: str
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embedding_api_key: str
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embedding_api_host: str
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embedding_model: str
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def _read_config() -> Config:
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@ -22,6 +25,9 @@ def _read_config() -> Config:
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"llm_api_host": os.getenv("LLM_API_HOST"),
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"llm_api_key": os.getenv("LLM_API_KEY"),
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"llm_model": os.getenv("LLM_MODEL"),
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"embedding_api_host": os.getenv("EMBEDDING_API_HOST"),
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"embedding_api_key": os.getenv("EMBEDDING_API_KEY"),
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"embedding_model": os.getenv("EMBEDDING_MODEL"),
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}
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config = _read_config()
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@ -1,8 +1,25 @@
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from src.pipeline.config import config
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import httpx
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from urllib.parse import urljoin
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async def chat_completion(messages, model=None):
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model = model or settings.LLM_MODEL
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async with httpx.AsyncClient(timeout=60) as client:
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r = await client.post(settings.VLLM_CHAT_URL, json={"model": model, "messages": messages}, headers=HEADERS)
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r.raise_for_status()
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return r.json()
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async def get_embedding(text, timeout: int = 30):
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try:
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async with httpx.AsyncClient(timeout=timeout) as client:
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url = config["embedding_api_host"]
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body = {
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"model": config["embedding_model"],
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"input": text,
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}
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {config['embedding_api_key']}",
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}
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print(url)
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print(headers)
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res = await client.post(url, headers=headers, json=body)
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res.raise_for_status()
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data = res.json()
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return data["data"][0]["embedding"]
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except Exception as e:
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print(f"get_embedding[ERROR]: {e}")
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return []
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@ -1,6 +1,6 @@
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import uuid
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from src.pipeline.core.pocket_flow import AsyncBatchNode
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from src.pipeline.core.utils import fixed_size_chunk
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import json
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import re
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@ -9,8 +9,7 @@ class ChunkDocumentsNode(AsyncBatchNode):
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return shared["documents"]
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async def exec_async(self, document):
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""" 简单切片
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:param self
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"""
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:param document: {text, file_name}
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"""
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# print(f"document: {document}")
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@ -21,16 +20,13 @@ class ChunkDocumentsNode(AsyncBatchNode):
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text = re.sub(r" +", " ", text)
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# 去除首尾空格
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text = text.strip()
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return [{"text": x, "file_name": document["file_name"]} for x in fixed_size_chunk(text)]
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return [{"text": x, "file_name": document["file_name"], "uuid": uuid.uuid4().hex} for x in fixed_size_chunk(text)]
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async def post_async(self, shared, prep_res, exec_res_list):
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all_chunks = []
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for chunks in exec_res_list:
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all_chunks.extend(chunks)
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print(f"all_chunks: {json.dumps(all_chunks, indent=2, ensure_ascii=False)}")
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shared["texts"] = all_chunks
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shared["documents"] = all_chunks
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return "default"
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@ -0,0 +1,19 @@
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from src.pipeline.core.pocket_flow import AsyncBatchNode
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from src.pipeline.llm import get_embedding
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class EmbeddingDocumentsNode(AsyncBatchNode):
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async def prep_async(self, shared):
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return shared["documents"]
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async def exec_async(self, document):
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"""
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:param document: {text, file_name}
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"""
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return {**document, "embedding": await get_embedding(document["text"])}
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async def post_async(self, shared, prep_res, exec_res_list):
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shared["documents"] = exec_res_list
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return "default"
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@ -1,9 +1,12 @@
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import pytest
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import json
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from src.pipeline.nodes.read_document_node import ReadDocumentNode
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from src.pipeline.nodes.chunk_document_node import ChunkDocumentsNode
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from src.pipeline.nodes.embedding_document_node import EmbeddingDocumentsNode
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# from src.pipeline.nodes import ReadDocumentNode, ChunkDocumentsNode
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from src.pipeline.core.pocket_flow import AsyncFlow
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@pytest.mark.asyncio
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async def test_embedding():
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@ -20,7 +23,10 @@ async def test_embedding():
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readNode = ReadDocumentNode()
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chunkNode = ChunkDocumentsNode()
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readNode >> chunkNode
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embeddingNode = EmbeddingDocumentsNode()
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readNode >> chunkNode >> embeddingNode
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flow = AsyncFlow(readNode)
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await flow.run_async(shared)
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print(json.dumps(shared["documents"], indent=4, ensure_ascii=False))
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