llama_index [文档]:检索策略=AsyncDenseVectorStrategy(混合模式=True)

gkl3eglg 于 4个月前发布在其他

关注(0)|答案(3)|浏览(107)

文档问题描述
elasticsearch.BadRequestError: BadRequestError(400, 'parsing_exception', '在[rank]中未知的START_OBJECT的键。'

文档链接

llama_index

来源：https://github.com/run-llama/llama_index/issues/14185

3条答案

按热度按时间

rkue9o1l1#

首先，感谢您的回复。以下是一些更详细的信息。
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-0.017330419272184372, -0.01579909399151802, 0.0051187556236982346, -0.0025322730652987957, -0.027936236932873726, -0.003676153253763914, -0.001264831400476396, 0.007177192252129316, -0.00936197116971016, 0.04181305691599846, -0.0285333264619112]}, 'query': {'bool': {'must': [{'match': {'content': {'query': '首创证券中财务报表和主要财务比率'}}}], 'filter': []}}, 'rank': {'rrf': {}}}

这是一个Python程序的错误追踪(Traceback)。程序在执行过程中遇到了一个异常，导致程序终止。异常信息如下：

Traceback (most recent call last):
  File "/root/AI/jiaxin.fan/test.py", line 272, in 
  nodes = retriever_chunk.retrieve(query)
  File "/root/anaconda3/envs/LLM_310/lib/python3.10/site-packages/llama_index/core/instrumentation/dispatcher.py", line 198, in wrapper
  result = func(*args, **kwargs)
  File "/root/anaconda3/envs/LLM_310/lib/python3.10/site-packages/llama_index/core/base/base_retriever.py", line 242, in retrieve
  nodes = self._retrieve(query_bundle)
  File "/root/anaconda3/envs/LLM_310/lib/python3.10/site-packages/llama_index/core/retrievers/recursive_retriever.py", line 206, in _retrieve
  retrieved_nodes, _ = self._retrieve_rec(query_bundle, query_id=None)
  File "/root/anaconda3/envs/LLM_310/lib/python3.10/site-packages/llama_index/core/retrievers/recursive_retriever.py", line 179, in _retrieve_rec
  nodes = obj.retrieve(query_bundle)
  File "/root/anaconda3/envs/LLM_310/lib/python3.10/site-packages/llama_index/core/instrumentation/dispatcher.py", line 198, in wrapper
  result = func(*args, **kwargs)
  File "/root/anaconda3/envs/LLM_310/lib/python3.10/site-packages/llama_index/core/indices/vector_store/retrievers/retriever.py", line 101, in _retrieve
  return self._get_nodes_with_embeddings(query_bundle)
  File "/root/anaconda3/envs/LLM_310/lib/python3.10/site-packages/llama_index/core/indices/vector_store/retrievers/retriever.py", line 177, in _get_nodes_with_embeddings
  query_result = self._vector_store.query(query, **self._kwargs)
  File "/root/anaconda3/envs/LLM_310/lib/python3.10/site-packages/llama_index/vector_stores/elasticsearch/base.py", line 413, in query
  return asyncio.get_event_loop().run_until_complete(
  File "/root/anaconda3/envs/LLM_310/lib/python3.10/site-packages/nest_asyncio.py", line 98, in run_until_complete
  return f.result()
  File "/root/anaconda3/envs/LLM_310/lib/python3.10/asyncio/futures.py", line 201, in result
  raise self._exception.with_traceback(self._exception_tb)
  File "/root/anaconda3/envs/LLM_310/lib/python3.10/asyncio/tasks.py", line 232, in __step
  result = coro.send(None)
  File "/root/anaconda3/envs/LLM_310/lib/python3.10/site-packages/llama_index/vector_stores/elasticsearch/base.py", line 453, in aquery
  hits = await self._store.search(
  File "/root/anaconda3/envs/LLM_310/lib/python3.10//site-packages//elasticsearch//helpers//vectorstore//async//vectorstore.py", line 277, in search
  response = await self.client.search(
  File "/root/anaconda3

赞(0）回复(0）举报 4个月前

r7s23pms2#

错误 BadRequestError(400, 'parsing_exception', 'Unknown key for a START_OBJECT in [rank].') 表示 Elasticsearch 无法识别查询中的 rank 字段。这种情况通常发生在查询结构不符合 Elasticsearch 的预期格式时。
要解决此问题，请确保查询结构符合 Elasticsearch 的预期格式。具体来说，如果不支持 rank 字段，则应正确定义或删除该字段。
以下是去除了 rank 字段的查询修正版本：

{
    "knn": {
        "filter": [],
        "field": "embedding",
        "query_vector": [0.020329274237155914, -0.016439935192465782, -0.01222333125770092, 0.020656481385231018, 0.015827272087335587, -0.00888893287628889, -0.01562308706343174, -0.03606059402227402, 0.04976935684680939, 0.029119601473212242, -0.002741447649896145, -0.016362575814127922, 0.0332820825278759, -0.019691595807671547, 0.012075891718268394, 0.0025564960669726133, -0.007536637596786022, 0.0033704356756061316, -0.02809642255306244, -0.02092575468122959, -0.005240238271653652, 0.025649499148130417, -0.004175655543804169, 0.025530824437737465, -0.0008253990672528744, -0.026714101433753967, 0.051027391105890274, 0.010184605605900288, 0.051661282777786255, -0.01330967340618372, -0.015519294887781143, 0.010468732565641403, -0.01525014080107212, 0.038076017051935196, 0.006049429532140493, 0.006912425626069307, -0.023785796016454697, 0.007381633389741182, 0.004440117161720991, -0.03070686198771, 0.019244011491537094, -0.003923557233065367, 0.02523256465792656, -0.06550795584917068, 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pb3s4cty3#

你好！我是一个机器人，可以帮助你解决任何错误、问题或贡献，同时等待人类维护者。
错误 elasticsearch.BadRequestError: BadRequestError(400, 'parsing_exception', 'Unknown key for a START_OBJECT in [rank].' 表明 Elasticsearch 查询中 rank 字段的处理方式存在问题。这可能与 AsyncDenseVectorStrategy 中的混合模式有关。
要解决此问题，请确保 rank 字段正确Map并被 Elasticsearch 识别。以下是潜在的修复方法：

确保在 Elasticsearch 索引中正确Map rank 字段。
修改代码以正确处理 rank 字段。
检查并可能修改以下代码片段：

top_k_nodes = []
top_k_ids = []
top_k_scores = []
for hit in hits:
    source = hit["_source"]
    metadata = source.get("metadata", None)
    text = source.get(self.text_field, None)
    node_id = hit["_id"]

    try:
        node = metadata_dict_to_node(metadata)
        node.text = text
    except Exception:
        # Legacy support for old metadata format
        logger.warning(
            f"Could not parse metadata from hit {hit['_source']['metadata']}"
        )
        node_info = source.get("node_info")
        relationships = source.get("relationships", {})
        start_char_idx = None
        end_char_idx = None
        if isinstance(node_info, dict):
            start_char_idx = node_info.get("start", None)
            end_char_idx = node_info.get("end", None)

        node = TextNode(
            text=text,
            metadata=metadata,
            id_=node_id,
            start_char_idx=start_char_idx,
            end_char_idx=end_char_idx,
            relationships=relationships,
        )
    top_k_nodes.append(node)
    top_k_ids.append(node_id)
    top_k_scores.append(hit.get("_rank", hit["_score"]))

if (
    isinstance(self.retrieval_strategy, AsyncDenseVectorStrategy)
    and self.retrieval_strategy.hybrid
):
    total_rank = sum(top_k_scores)
    top_k_scores = [total_rank - rank / total_rank for rank in top_k_scores]

return VectorStoreQueryResult(
    nodes=top_k_nodes,
    ids=top_k_ids,
    similarities=_to_llama_similarities(top_k_scores),
)

确保 _rank 字段正确Map并被 Elasticsearch 识别。如果 _rank 不是有效的，请使用不同的字段或调整分数计算。
此外，确保模式与检索策略相匹配。对于 AsyncDenseVectorStrategy(hybrid=True),模式应该是 VectorStoreQueryMode.HYBRID。以下是一个测试用例，以确保模式与检索策略相匹配：

def test_mode_must_match_retrieval_strategy() -> None:
    # DEFAULT mode should never raise any exception
    mode = VectorStoreQueryMode.DEFAULT
    retrieval_strategy = AsyncBM25Strategy()
    _mode_must_match_retrieval_strategy(mode, retrieval_strategy)

    # AsyncSparseVectorStrategy with mode SPARSE should not raise any exception
    mode = VectorStoreQueryMode.SPARSE
    retrieval_strategy = AsyncSparseVectorStrategy()
    _mode_must_match_retrieval_strategy(mode, retrieval_strategy)

    # AsyncBM25Strategy with TEXT_SEARCH should not raise any exception
    mode = VectorStoreQueryMode.TEXT_SEARCH
    retrieval_strategy = AsyncBM25Strategy()
    _mode_must_match_retrieval_strategy(mode, retrieval_strategy)

    # AsyncDenseVectorStrategy(hybrid=True) with mode HYBRID should not raise any exception
    mode = VectorStoreQueryMode.HYBRID
    retrieval_strategy = AsyncDenseVectorStrategy(hybrid=True)
    _mode_must_match_retrieval_strategy(mode, retrieval_strategy)

    # unknown mode should raise NotImplementedError
    for mode in [
        VectorStoreQueryMode.SEMANTIC_HYBRID,
        VectorStoreQueryMode.SVM,
        VectorStoreQueryMode.LOGISTIC_REGRESSION,
        VectorStoreQueryMode.LINEAR_REGRESSION,
        VectorStoreQueryMode.MMR,
    ]:
        retrieval_strategy = AsyncDenseVectorStrategy()
        with pytest.raises(NotImplementedError):
            _mode_must_match_retrieval_strategy(mode, retrieval_strategy)

    # if mode is SPARSE and strategy is not AsyncSparseVectorStrategy, should raise ValueError
    mode = VectorStoreQueryMode.SPARSE
    retrieval_strategy = AsyncDenseVectorStrategy()
    with pytest.raises(ValueError):
        _mode_must_match_retrieval_strategy(mode, retrieval_strategy)

    # if mode is HYBRID and strategy is not AsyncDenseVectorStrategy, should raise ValueError
    mode = VectorStoreQueryMode.HYBRID
    retrieval_strategy = AsyncSparseVectorStrategy()
    with pytest.raises(ValueError):
        _mode_must_match_retrieval_strategy(mode, retrieval_strategy)

    # if mode is HYBRID and strategy is AsyncDenseVectorStrategy but hybrid is not enabled, should raise ValueError
    mode = VectorStoreQueryMode.HYBRID
    retrieval_strategy = AsyncDenseVectorStrategy(hybrid=False)
    with pytest.raises(ValueError):
        _mode_must_match_retrieval_strategy(mode, retrieval_strategy)

确保你的模式和检索策略正确对齐，以避免此类错误 1(https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/vector_stores/llama-index-vector-stores-elasticsearch/llama_index/vector_stores/elasticsearch/base.py)2(https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/vector_stores/llama-index-vector-stores-elasticsearch/tests/test_vector_stores_elasticsearch.py)。

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我来回答

llama_index [文档]:检索策略=AsyncDenseVectorStrategy(混合模式=True)

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