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423 | class VectaraIndex(BaseManagedIndex):
"""
Vectara索引。
Vectara索引实现了一个使用Vectara作为后端的托管索引。
Vectara在后端执行传统索引中的许多功能：
- 将文档分解为块（节点）
- 为每个块（节点）创建嵌入
- 执行对查询的前k个最相似节点的搜索
- 可选地对前k个节点进行摘要
Args:
show_progress（布尔值）：是否显示tqdm进度条。默认为False。"""
def __init__(
self,
show_progress: bool = False,
nodes: Optional[Sequence[BaseNode]] = None,
vectara_customer_id: Optional[str] = None,
vectara_corpus_id: Optional[str] = None,
vectara_api_key: Optional[str] = None,
use_core_api: bool = False,
parallelize_ingest: bool = False,
**kwargs: Any,
) -> None:
"""初始化Vectara API。"""
self.parallelize_ingest = parallelize_ingest
index_struct = VectaraIndexStruct(
index_id=str(vectara_corpus_id),
summary="Vectara Index",
)
super().__init__(
show_progress=show_progress,
index_struct=index_struct,
**kwargs,
)
self._vectara_customer_id = vectara_customer_id or os.environ.get(
"VECTARA_CUSTOMER_ID"
)
self._vectara_corpus_id = vectara_corpus_id or str(
os.environ.get("VECTARA_CORPUS_ID")
)
self._vectara_api_key = vectara_api_key or os.environ.get("VECTARA_API_KEY")
if (
self._vectara_customer_id is None
or self._vectara_corpus_id is None
or self._vectara_api_key is None
):
_logger.warning(
"Can't find Vectara credentials, customer_id or corpus_id in "
"environment."
)
raise ValueError("Missing Vectara credentials")
else:
_logger.debug(f"Using corpus id {self._vectara_corpus_id}")
# setup requests session with max 3 retries and 90s timeout
# for calling Vectara API
self._session = requests.Session() # to reuse connections
adapter = requests.adapters.HTTPAdapter(max_retries=3)
self._session.mount("https://", adapter)
self.vectara_api_timeout = 90
self.use_core_api = use_core_api
self.doc_ids: List[str] = []
# if nodes is specified, consider each node as a single document
# and use _build_index_from_nodes() to add them to the index
if nodes is not None:
self._build_index_from_nodes(nodes, use_core_api)
def _build_index_from_nodes(
self, nodes: Sequence[BaseNode], use_core_api: bool = False
) -> IndexDict:
docs = [
Document(
text=node.get_content(metadata_mode=MetadataMode.NONE),
metadata=node.metadata, # type: ignore
id_=node.id_, # type: ignore
)
for node in nodes
]
self.add_documents(docs, use_core_api)
return self.index_struct
def _get_corpus_id(self, corpus_id: str) -> str:
"""获取用于索引的语料库ID。
如果提供了corpus_id，则检查它是否是有效的语料库ID。
如果没有，则使用列表中的第一个语料库ID。
"""
if corpus_id is not None:
if corpus_id in self._vectara_corpus_id.split(","):
return corpus_id
return self._vectara_corpus_id.split(",")[0]
def _get_post_headers(self) -> dict:
"""返回应附加到每个POST请求的标头。"""
return {
"x-api-key": self._vectara_api_key,
"customer-id": self._vectara_customer_id,
"Content-Type": "application/json",
"X-Source": "llama_index",
}
def _delete_doc(self, doc_id: str, corpus_id: Optional[str] = None) -> bool:
"""从Vectara语料库中删除文档。
Args:
url（str）：要删除的页面的URL。
doc_id（str）：要删除的文档的ID。
corpus_id（str）：要从中删除文档的语料库ID。
Returns:
bool：如果删除成功则为True，否则为False。
"""
valid_corpus_id = self._get_corpus_id(corpus_id)
body = {
"customerId": self._vectara_customer_id,
"corpusId": valid_corpus_id,
"documentId": doc_id,
}
response = self._session.post(
"https://api.vectara.io/v1/delete-doc",
data=json.dumps(body),
verify=True,
headers=self._get_post_headers(),
timeout=self.vectara_api_timeout,
)
if response.status_code != 200:
_logger.error(
f"Delete request failed for doc_id = {doc_id} with status code "
f"{response.status_code}, reason {response.reason}, text "
f"{response.text}"
)
return False
return True
def _index_doc(self, doc: dict, corpus_id) -> str:
request: Dict[str, Any] = {}
request["customerId"] = self._vectara_customer_id
request["corpusId"] = corpus_id
request["document"] = doc
if "parts" in doc:
api_url = "https://api.vectara.io/v1/core/index"
else:
api_url = "https://api.vectara.io/v1/index"
response = self._session.post(
headers=self._get_post_headers(),
url=api_url,
data=json.dumps(request),
timeout=self.vectara_api_timeout,
verify=True,
)
status_code = response.status_code
result = response.json()
status_str = result["status"]["code"] if "status" in result else None
if status_code == 409 and status_str and (status_str == "ALREADY_EXISTS"):
return "E_ALREADY_EXISTS"
elif status_code == 200 and status_str and (status_str == "INVALID_ARGUMENT"):
return "E_INVALID_ARGUMENT"
elif status_str and (status_str == "FORBIDDEN"):
return "E_NO_PERMISSIONS"
else:
return "E_SUCCEEDED"
def _insert(
self,
nodes: Sequence[BaseNode],
corpus_id: Optional[str] = None,
use_core_api: bool = False,
**insert_kwargs: Any,
) -> None:
"""插入一组文档（每个文档都是一个节点）。"""
def gen_hash(s: str) -> str:
hash_object = blake2b(digest_size=32)
hash_object.update(s.encode("utf-8"))
return hash_object.hexdigest()
docs = []
for node in nodes:
metadata = node.metadata.copy()
metadata["framework"] = "llama_index"
section_key = "parts" if use_core_api else "section"
text = node.get_content(metadata_mode=MetadataMode.NONE)
doc_id = gen_hash(text)
doc = {
"documentId": doc_id,
"metadataJson": json.dumps(node.metadata),
section_key: [{"text": text}],
}
docs.append(doc)
valid_corpus_id = self._get_corpus_id(corpus_id)
if self.parallelize_ingest:
with ThreadPoolExecutor() as executor:
futures = [
executor.submit(self._index_doc, doc, valid_corpus_id)
for doc in docs
]
for future in futures:
ecode = future.result()
if ecode != "E_SUCCEEDED":
_logger.error(
f"Error indexing document in Vectara with error code {ecode}"
)
self.doc_ids.extend([doc["documentId"] for doc in docs])
else:
for doc in docs:
ecode = self._index_doc(doc, valid_corpus_id)
if ecode != "E_SUCCEEDED":
_logger.error(
f"Error indexing document in Vectara with error code {ecode}"
)
self.doc_ids.append(doc["documentId"])
def add_documents(
self,
docs: Sequence[Document],
corpus_id: Optional[str],
use_core_api: bool = False,
allow_update: bool = True,
) -> None:
nodes = [
TextNode(text=doc.get_content(), metadata=doc.metadata) for doc in docs # type: ignore
]
self._insert(nodes, corpus_id, use_core_api)
def insert_file(
self,
file_path: str,
metadata: Optional[dict] = None,
corpus_id: Optional[str] = None,
**insert_kwargs: Any,
) -> Optional[str]:
"""Vectara提供了一种通过我们的API直接添加文件（二进制或文本）的方式，在其中进行预处理和分块处理，以最佳方式内部进行。
这种方法提供了一种在Llama_index中使用该API的方式。
# ruff: noqa: E501
完整的API文档：https://docs.vectara.com/docs/api-reference/indexing-apis/
file-upload/file-upload-filetypes
Args:
file_path：本地文件路径
文件可以是文本、HTML、PDF、markdown、doc/docx、ppt/pptx等。
请参阅API文档以获取完整列表
metadata：与文件关联的可选元数据列表
Returns:
与每个索引文件相关联的ID列表
"""
if not os.path.exists(file_path):
_logger.error(f"File {file_path} does not exist")
return None
metadata = metadata or {}
metadata["framework"] = "llama_index"
files: dict = {
"file": (file_path, open(file_path, "rb")),
"doc_metadata": json.dumps(metadata),
}
headers = self._get_post_headers()
headers.pop("Content-Type")
valid_corpus_id = self._get_corpus_id(corpus_id)
response = self._session.post(
f"https://api.vectara.io/upload?c={self._vectara_customer_id}&o={valid_corpus_id}&d=True",
files=files,
verify=True,
headers=headers,
timeout=self.vectara_api_timeout,
)
res = response.json()
if response.status_code == 409:
_logger.info(
f"File {file_path} already exists on Vectara, skipping indexing"
)
return None
elif response.status_code == 200:
quota = res["response"]["quotaConsumed"]["numChars"]
if quota == 0:
_logger.warning(
f"File Upload for {file_path} returned 0 quota consumed, please check your Vectara account quota"
)
doc_id = res["document"]["documentId"]
self.doc_ids.append(doc_id)
return doc_id
else:
_logger.info(f"Error indexing file {file_path}: {res}")
return None
def delete_ref_doc(
self, ref_doc_id: str, delete_from_docstore: bool = False, **delete_kwargs: Any
) -> None:
raise NotImplementedError(
"Vectara does not support deleting a reference document"
)
def update_ref_doc(self, document: Document, **update_kwargs: Any) -> None:
raise NotImplementedError(
"Vectara does not support updating a reference document"
)
def as_retriever(self, **kwargs: Any) -> BaseRetriever:
"""返回此托管索引的Retriever。"""
from llama_index.indices.managed.vectara.retriever import (
VectaraRetriever,
)
return VectaraRetriever(self, **kwargs)
def as_chat_engine(self, **kwargs: Any) -> BaseChatEngine:
kwargs["summary_enabled"] = True
retriever = self.as_retriever(**kwargs)
kwargs.pop("summary_enabled")
from llama_index.indices.managed.vectara.query import (
VectaraChatEngine,
)
return VectaraChatEngine.from_args(retriever, **kwargs) # type: ignore
def as_query_engine(
self, llm: Optional[LLMType] = None, **kwargs: Any
) -> BaseQueryEngine:
if kwargs.get("summary_enabled", True):
from llama_index.indices.managed.vectara.query import (
VectaraQueryEngine,
)
kwargs["summary_enabled"] = True
retriever = self.as_retriever(**kwargs)
return VectaraQueryEngine.from_args(retriever=retriever, **kwargs) # type: ignore
else:
from llama_index.core.query_engine.retriever_query_engine import (
RetrieverQueryEngine,
)
llm = (
resolve_llm(llm, callback_manager=self._callback_manager)
or Settings.llm
)
retriever = self.as_retriever(**kwargs)
response_synthesizer = get_response_synthesizer(
response_mode=ResponseMode.COMPACT,
llm=llm,
)
return RetrieverQueryEngine.from_args(
retriever=retriever,
response_synthesizer=response_synthesizer,
**kwargs,
)
@classmethod
def from_documents(
cls: Type[IndexType],
documents: Sequence[Document],
show_progress: bool = False,
callback_manager: Optional[CallbackManager] = None,
transformations: Optional[List[TransformComponent]] = None,
**kwargs: Any,
) -> IndexType:
"""从一系列文档中构建 Vectara 索引。"""
nodes = [
TextNode(text=document.get_content(), metadata=document.metadata) # type: ignore
for document in documents
]
return cls(
nodes=nodes,
show_progress=show_progress,
**kwargs,
)
|