你好@selvabharathG!我在这里协助处理你在等待人工维护者时可能遇到的任何错误、问题或贡献。让我们一起解决这个问题！
要排查数据处理管道在处理PDF文件时没有错误但无法将记录加载到Astra DB中的问题，你可以按照以下步骤操作：

1. 检查文件类型支持:确保你的文件加载组件支持PDF文件类型。FileComponent类支持特定的文本文件类型，对于不支持的文件类型会引发错误。请验证parse_text_file_to_record函数是否正确处理PDF文件。
2. 验证数据解析:确保parse_text_file_to_record函数正确解析PDF文件并返回一个有效的Record对象。该函数使用parse_pdf_to_text从PDF中提取文本。当解析成功时，预期的输出是一个包含提取的文本和元数据的Data对象，包括文件路径1(https://github.com/logspace-ai/langflow/blob/dev/src/backend/base/langflow/base/data/utils.py)。
3. 检查静默错误:如果silent_errors设置为True,文件加载过程中的错误将不会引发异常，这可能导致管道在不加载任何数据的情况下继续进行。将silent_errors设置为False以捕获并调试文件加载过程中的任何错误。
4. 检查管道配置:确保管道配置正确地将加载的Record传递给负责将数据插入Astra DB的后续组件。验证组件之间的数据流是否正确设置。
5. 检查Astra DB配置:确保Astra DB配置(如API端点、批处理大小和并发设置)正确设置并符合数据插入的要求。
6. 日志记录机制:利用管道内可用的日志记录机制跟踪数据流并确定进程可能失败的位置。日志页面提供了工作区中所有组件执行的详细记录，包括遇到的错误。
   通过遵循这些步骤，你应该能够识别并解决阻止记录加载到Astra DB的问题。

• 要继续与 Dosu 交谈，请提及 @dosu。*

你好，@selvabharathG。我刚刚尝试了1.0.13版本，对我来说是可以正常工作的。也许在处理过程中出现了一些问题。
你可以尝试使用调试日志激活langflow运行吗？
你应该添加一行这样的代码：

export LANGFLOW_LOG_LEVEL=debug
langflow run

[07/30/24 09:26:29] DEBUG    2024-07-30 09:26:29 - DEBUG    - <string> - Adding 50 documents to the Vector Store.

你好，@selvabharathG。我刚刚尝试了1.0.13版本，对我来说是可以正常工作的。可能是在处理过程中出现了一些问题。你可以尝试在运行langflow时激活调试日志。

你应该添加一行这样的代码：

export LANGFLOW_LOG_LEVEL=debug
langflow run

感谢@nicoloboschi的回复，请问哪一个流程需要添加这行代码？

AstraDB节点在索引数据时应该记录此信息。这当然取决于PDF的大小。

@nicoloboschi 抱歉，我是完全新手，请问我应该在哪里添加这行代码？将完整的节点代码粘贴在这里
from langchain_core.vectorstores import VectorStore
 from loguru import logger
from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.helpers import docs_to_data
 from langflow.inputs import DictInput, FloatInput
 from langflow.io import (
 BoolInput,
 DataInput,
 DropdownInput,
 HandleInput,
 IntInput,
 MultilineInput,
 SecretStrInput,
 StrInput,
 )
 from langflow.schema import Data
class AstraVectorStoreComponent(LCVectorStoreComponent):
 display_name: str = "Astra DB"
 description: str = "使用Astra DB实现的具有搜索功能的向量存储"
 documentation: str = " [https://python.langchain.com/docs/integrations/vectorstores/astradb](https://python.langchain.com/docs/integrations/vectorstores/astradb) "
 name = "AstraDB"
 icon: str = "AstraDB"

_cached_vectorstore: VectorStore | None = None

inputs = [
StrInput(
name="collection_name",
display_name="Collection Name",
info="The name of the collection within Astra DB where the vectors will be stored.",
required=True,
),
SecretStrInput(
name="token",
display_name="Astra DB Application Token",
info="Authentication token for accessing Astra DB.",
value="ASTRA_DB_APPLICATION_TOKEN",
required=True,
),
SecretStrInput(
name="api_endpoint",
display_name="API Endpoint",
info="API endpoint URL for the Astra DB service.",
value="ASTRA_DB_API_ENDPOINT",
required=True,
),
MultilineInput(
name="search_input",
display_name="Search Input",
),
DataInput(
name="ingest_data",
display_name="Ingest Data",
is_list=True,
),
StrInput(
name="namespace",
display_name="Namespace",
info="Optional namespace within Astra DB to use for the collection.",
advanced=True,
),
DropdownInput(
name="metric",
display_name="Metric",
info="Optional distance metric for vector comparisons in the vector store.",
options=["cosine", "dot_product", "euclidean"],
advanced=True,
),
IntInput(
name="batch_size",
display_name="Batch Size",
info="Optional number of data to process in a single batch.",
advanced=True,
),
IntInput(
name="bulk_insert_batch_concurrency",
display_name="Bulk Insert Batch Concurrency",
info="Optional concurrency level for bulk insert operations.",
advanced=True,
),
IntInput(
name="bulk_insert_overwrite_concurrency",
display_name="Bulk Insert Overwrite Concurrency",
info="Optional concurrency level for bulk insert operations that overwrite existing data.",
advanced=True,
),
IntInput(
name="bulk_delete_concurrency",
display_name="Bulk Delete Concurrency",
info="Optional concurrency level for bulk delete operations.",
advanced=True,
),
DropdownInput(
name="setup_mode",
display_name="Setup Mode",
info="Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.",
options=["Sync", "Async", "Off"],
advanced=True,
value="Sync",
),
BoolInput(
name="pre_delete_collection",
display_name="Pre Delete Collection",
info="Boolean flag to determine whether to delete the collection before creating a new one.",
advanced=True,
),
StrInput(
name="metadata_indexing_include",
display_name="Metadata Indexing Include",
info="Optional list of metadata fields to include in the indexing.",
advanced=True,
),
HandleInput(
name="embedding",
display_name="Embedding or Astra Vectorize",
input_types=["Embeddings", "dict"],
info="Allows either an embedding model or an Astra Vectorize configuration.", # TODO: This should be optional, but need to refactor langchain-astradb first.
),
StrInput(
name="metadata_indexing_exclude",
display_name="Metadata Indexing Exclude",
info="Optional list of metadata fields to exclude from the indexing.",
advanced=True,
),
StrInput(
name="collection_indexing_policy",
display_name="Collection Indexing Policy",
info="Optional dictionary defining the indexing policy for the collection.",
advanced=True,
),
IntInput(
name="number_of_results",
display_name="Number of Results",
info="Number of results to return.",
advanced=True,
value=4,
),
DropdownInput(
name="search_type",
display_name="Search Type",
info="Search type to use",
options=["Similarity", "Similarity with score threshold", "MMR (Max Marginal Relevance)"],
value="Similarity",
advanced=True,
),
FloatInput(
name="search_score_threshold",
display_name="Search Score Threshold",
info="Minimum similarity score threshold for search results. (when using 'Similarity with score threshold')",
value=0,
advanced=True,
),
DictInput(
name="search_filter",
display_name="Search Metadata Filter",
info="Optional dictionary of filters to apply to the search query.",
advanced=True,
is_list=True,
),
]

def _build_vector_store(self):
# cache the vector store to avoid re-initializing and ingest data again
if self._cached_vectorstore:
return self._cached_vectorstore

try:
    from langchain_astradb import AstraDBVectorStore
    from langchain_astradb.utils.astradb import SetupMode
except ImportError:
    raise ImportError(
        "Could not import langchain Astra DB integration package. "
        "Please install it with `pip install langchain-astradb`."
    )

try:
    if not self.setup_mode:
        self.setup_mode = self._inputs["setup_mode"].options[0]

    setup_mode_value = SetupMode[self.setup_mode.upper()]
except KeyError:
    raise ValueError(f"Invalid setup mode: {self.setup_mode}")

if not isinstance(self.embedding, dict):
    embedding_dict = {"embedding": self.embedding}
else:
    from astrapy.info import CollectionVectorServiceOptions

    dict_options = self.embedding.get("collection_vector_service_options", {})
    dict_options["authentication"] = {
        k: v for k, v in dict_options.get("authentication", {}).items() if k and v
    }
    dict_options["parameters"] = {k: v for k, v in dict_options.get("parameters", {}).items() if k and v}
    embedding_dict = {
        "collection_vector_service_options": CollectionVectorServiceOptions.from_dict(dict_options)
    }
    collection_embedding_api_key = self.embedding.get("collection_embedding_api_key")
    if collection_embedding_api_key:
        embedding_dict["collection_embedding_api_key"] = collection_embedding_api_key

vector_store_kwargs = {
    **embedding_dict,
    "collection_name": self.collection_name,
    "token": self.token,
    "api_endpoint": self.api_endpoint,
    "namespace": self.namespace or None,
    "metric": self.metric or None,
    "batch_size": self.batch_size or None,
    "bulk_insert_batch_concurrency": self.bulk_insert_batch_concurrency or None,
    "bulk_insert_overwrite_concurrency": self.bulk_insert_overwrite_concurrency or None,
    "bulk_delete_concurrency": self.bulk_delete_concurrency or None,
    "setup_mode": setup_mode_value,
    "pre_delete_collection": self.pre_delete_collection or False,
}

if self.metadata_indexing_include:
    vector_store_kwargs["metadata_indexing_include"] = self.metadata_indexing_include
elif self.metadata_indexing_exclude:
    vector_store_kwargs["metadata_indexing_exclude"] = self.metadata_indexing_exclude
elif self.collection_indexing_policy:
    vector_store_kwargs["collection_indexing_policy"] = self.collection_indexing_policy

try:
    vector_store = AstraDBVectorStore(**vector_store_kwargs)
except Exception as e:
    raise ValueError(f"Error initializing AstraDBVectorStore: {str(e)}") from e

self._add_documents_to_vector_store(vector_store)

self._cached_vectorstore = vector_store

return vector_store

def _add_documents_to_vector_store(self, vector_store):
documents = []
for _input in self.ingest_data or []:
if isinstance(_input, Data):
documents.append(_input.to_lc_document())
else:
raise ValueError("Vector Store Inputs must be Data objects.")

if documents:
    logger.debug(f"Adding {len(documents)} documents to the Vector Store.")
    try:
        vector_store.add_documents(documents)
    except Exception as e:
        raise ValueError(f"Error adding documents to AstraDBVectorStore: {str(e)}") from e
else:
    logger.debug("No documents to add to the Vector Store.")

def _map_search_type(self):
if self.search_type == "Similarity with score threshold":
return "similarity_score_threshold"
elif self.search_type == "MMR (Max Marginal Relevance)":
return "mmr"
else:
return "similarity"

def _build_search_args(self):
args = {
"k": self.number_of_results,
"score_threshold": self.search_score_threshold,
}

if self.search_filter:
    clean_filter = {k: v for k, v in self.search_filter.items() if k and v}
    if len(clean_filter) > 0:
        args["filter"] = clean_filter
return args

def search_documents(self) -> list[Data]:
vector_store = self._build_vector_store()

logger.debug(f"Search input: {self.search_input}")
logger.debug(f"Search type: {self.search_type}")
logger.debug(f"Number of results: {self.number_of_results}")

if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
    try:
        search_type = self._map_search_type()
        search_args = self._build_search_args()

        docs = vector_store.search(query=self.search_input, search_type=search_type, **search_args)
    except Exception as e:
        raise ValueError(f"Error performing search in AstraDBVectorStore: {str(e)}") from e

    logger.debug(f"Retrieved documents: {len(docs)}")

    data = docs_to_data(docs)
    logger.debug(f"Converted documents to data: {len(data)}")
    self.status = data
    return data
else:
    logger.debug("No search input provided. Skipping search.")
    return []

def get_retriever_kwargs(self):
search_args = self._build_search_args()
return {
"search_type": self._map_search_type(),
"search_kwargs": search_args,
}

def build_vector_store(self):
vector_store = self._build_vector_store()
return vector_store

嘿，@selvabharathG,抱歉，也许我没有说清楚。你不需要添加任何行。如果你在运行langflow之前设置LANGFLOW_LOG_LEVEL=debug,你会在输出中看到那一行日志。

selvabharathG,只是想确认你是否解决了问题，这样我就可以关闭这个问题了。

嗨@nicoloboschi
抱歉回复晚了。我正在旅行。
我无法添加建议的方法，因为我正在尝试在DataStax在线GUI中使用这种流程。
请问还有其他方法可以识别问题吗？