mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
101 lines
3.7 KiB
Python
101 lines
3.7 KiB
Python
from typing import Any, Dict, List, Mapping, Optional
|
|
|
|
from langchain_core.embeddings import Embeddings
|
|
from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator
|
|
from langchain_core.utils import get_from_dict_or_env
|
|
|
|
DEFAULT_EMBED_INSTRUCTION = "Represent this input: "
|
|
DEFAULT_QUERY_INSTRUCTION = "Represent the question for retrieving similar documents: "
|
|
|
|
|
|
class OctoAIEmbeddings(BaseModel, Embeddings):
|
|
"""OctoAI Compute Service embedding models.
|
|
|
|
The environment variable ``OCTOAI_API_TOKEN`` should be set
|
|
with your API token, or it can be passed
|
|
as a named parameter to the constructor.
|
|
"""
|
|
|
|
endpoint_url: Optional[str] = Field(None, description="Endpoint URL to use.")
|
|
model_kwargs: Optional[dict] = Field(
|
|
None, description="Keyword arguments to pass to the model."
|
|
)
|
|
octoai_api_token: Optional[str] = Field(None, description="OCTOAI API Token")
|
|
embed_instruction: str = Field(
|
|
DEFAULT_EMBED_INSTRUCTION,
|
|
description="Instruction to use for embedding documents.",
|
|
)
|
|
query_instruction: str = Field(
|
|
DEFAULT_QUERY_INSTRUCTION, description="Instruction to use for embedding query."
|
|
)
|
|
|
|
class Config:
|
|
"""Configuration for this pydantic object."""
|
|
|
|
extra = Extra.forbid
|
|
|
|
@root_validator(allow_reuse=True)
|
|
def validate_environment(cls, values: Dict) -> Dict:
|
|
"""Ensure that the API key and python package exist in environment."""
|
|
values["octoai_api_token"] = get_from_dict_or_env(
|
|
values, "octoai_api_token", "OCTOAI_API_TOKEN"
|
|
)
|
|
values["endpoint_url"] = get_from_dict_or_env(
|
|
values, "endpoint_url", "https://text.octoai.run/v1/embeddings"
|
|
)
|
|
return values
|
|
|
|
@property
|
|
def _identifying_params(self) -> Mapping[str, Any]:
|
|
"""Return the identifying parameters."""
|
|
return {
|
|
"endpoint_url": self.endpoint_url,
|
|
"model_kwargs": self.model_kwargs or {},
|
|
}
|
|
|
|
def _compute_embeddings(
|
|
self, texts: List[str], instruction: str
|
|
) -> List[List[float]]:
|
|
"""Compute embeddings using an OctoAI instruct model."""
|
|
from octoai import client
|
|
|
|
embedding = []
|
|
embeddings = []
|
|
octoai_client = client.Client(token=self.octoai_api_token)
|
|
|
|
for text in texts:
|
|
parameter_payload = {
|
|
"sentence": str([text]),
|
|
"input": str([text]),
|
|
"instruction": str([instruction]),
|
|
"model": "thenlper/gte-large",
|
|
"parameters": self.model_kwargs or {},
|
|
}
|
|
|
|
try:
|
|
resp_json = octoai_client.infer(self.endpoint_url, parameter_payload)
|
|
if "embeddings" in resp_json:
|
|
embedding = resp_json["embeddings"]
|
|
elif "data" in resp_json:
|
|
json_data = resp_json["data"]
|
|
for item in json_data:
|
|
if "embedding" in item:
|
|
embedding = item["embedding"]
|
|
|
|
except Exception as e:
|
|
raise ValueError(f"Error raised by the inference endpoint: {e}") from e
|
|
|
|
embeddings.append(embedding)
|
|
|
|
return embeddings
|
|
|
|
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
|
"""Compute document embeddings using an OctoAI instruct model."""
|
|
texts = list(map(lambda x: x.replace("\n", " "), texts))
|
|
return self._compute_embeddings(texts, self.embed_instruction)
|
|
|
|
def embed_query(self, text: str) -> List[float]:
|
|
"""Compute query embedding using an OctoAI instruct model."""
|
|
text = text.replace("\n", " ")
|
|
return self._compute_embeddings([text], self.query_instruction)[0]
|