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159 | class TextEmbeddingInference(BaseNodePostprocessor):
base_url: str = Field(
default=DEFAULT_URL,
description="Base URL for the text embeddings service.",
)
top_n: int = Field(
default=TOP_N, description="Number of nodes to return sorted by score."
)
keep_retrieval_score: bool = Field(
default=False,
description="Whether to keep the retrieval score in metadata.",
)
timeout: float = Field(
default=60.0,
description="Timeout in seconds for the request.",
)
truncate_text: bool = Field(
default=True,
description="Whether to truncate text or not when generating embeddings.",
)
auth_token: Optional[Union[str, Callable[[str], str]]] = Field(
default=None,
description="Authentication token or authentication token generating function for authenticated requests",
)
model_name: str = Field(
default="API",
description="Base URL for the text embeddings service.",
)
mode: str = Field(
default="text",
description="Re-ranking Method, full for including meta-data too.",
)
def __init__(
self,
top_n: int = TOP_N,
base_url: str = DEFAULT_URL,
text_instruction: Optional[str] = None,
query_instruction: Optional[str] = None,
timeout: float = 60.0,
truncate_text: bool = True,
auth_token: Optional[Union[str, Callable[[str], str]]] = None,
model_name="API",
):
super().__init__(
base_url=base_url,
top_n=top_n,
text_instruction=text_instruction,
query_instruction=query_instruction,
timeout=timeout,
truncate_text=truncate_text,
auth_token=auth_token,
model_name=model_name,
mode="text",
)
@classmethod
def class_name(cls) -> str:
return "TextEmbeddingsInference"
def _call_api(self, query: str, texts: List[str]) -> List[float]:
headers = {"Content-Type": "application/json"}
if self.auth_token is not None:
if callable(self.auth_token):
headers["Authorization"] = self.auth_token(self.base_url)
else:
headers["Authorization"] = self.auth_token
json_data = {"query": query, "texts": texts}
with httpx.Client() as client:
response = client.post(
f"{self.base_url}/rerank",
headers=headers,
json=json_data,
timeout=self.timeout,
)
return response.json()
def _postprocess_nodes(
self,
nodes: List[NodeWithScore],
query_bundle: Optional[QueryBundle] = None,
) -> List[NodeWithScore]:
dispatcher.event(
ReRankStartEvent(
query=query_bundle,
nodes=nodes,
top_n=self.top_n,
model_name=self.model_name,
)
)
if query_bundle is None:
raise ValueError("Missing query bundle in extra info.")
if len(nodes) == 0:
return []
query = query_bundle.query_str
if self.mode == "full":
texts = [
node.node.get_content(metadata_mode=MetadataMode.EMBED)
for node in nodes
]
elif self.mode == "text":
texts = [node.text for node in nodes]
else:
warnings.warn('Re-Ranking Mode defaulting to mode "text"')
texts = [node.text for node in nodes]
with self.callback_manager.event(
CBEventType.RERANKING,
payload={
EventPayload.NODES: nodes,
EventPayload.QUERY_STR: query_bundle.query_str,
EventPayload.TOP_K: self.top_n,
},
) as event:
scores = self._call_api(query, texts)
assert len(scores) == len(nodes)
for node, score in zip(nodes, scores):
if self.keep_retrieval_score:
# keep the retrieval score in metadata
node.node.metadata["retrieval_score"] = node.score
node.score = float(score["score"])
new_nodes = sorted(nodes, key=lambda x: -x.score if x.score else 0)[
: self.top_n
]
event.on_end(payload={EventPayload.NODES: new_nodes})
dispatcher.event(ReRankEndEvent(nodes=new_nodes))
return new_nodes
|
