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137 | class LlamafileEmbedding(BaseEmbedding):
"""
Class for llamafile embeddings.
llamafile lets you distribute and run large language models with a
single file.
To get started, see: https://github.com/Mozilla-Ocho/llamafile
To use this class, you will need to first:
1. Download a llamafile.
2. Make the downloaded file executable: `chmod +x path/to/model.llamafile`
3. Start the llamafile in server mode with embeddings enabled:
`./path/to/model.llamafile --server --nobrowser --embedding`
"""
base_url: str = Field(
description="base url of the llamafile server", default="http://localhost:8080"
)
request_timeout: float = Field(
default=DEFAULT_REQUEST_TIMEOUT,
description="The timeout for making http request to llamafile API server",
)
def __init__(
self,
base_url: str = "http://localhost:8080",
callback_manager: Optional[CallbackManager] = None,
**kwargs,
) -> None:
super().__init__(
base_url=base_url,
callback_manager=callback_manager or CallbackManager([]),
**kwargs,
)
@classmethod
def class_name(cls) -> str:
return "LlamafileEmbedding"
def _get_query_embedding(self, query: str) -> Embedding:
return self._get_text_embedding(query)
async def _aget_query_embedding(self, query: str) -> Embedding:
return await self._aget_text_embedding(query)
def _get_text_embedding(self, text: str) -> Embedding:
"""
Embed the input text synchronously.
"""
request_body = {
"content": text,
}
with httpx.Client(timeout=Timeout(self.request_timeout)) as client:
response = client.post(
url=f"{self.base_url}/embedding",
headers={"Content-Type": "application/json"},
json=request_body,
)
response.encoding = "utf-8"
response.raise_for_status()
return response.json()["embedding"]
async def _aget_text_embedding(self, text: str) -> Embedding:
"""
Embed the input text asynchronously.
"""
request_body = {
"content": text,
}
async with httpx.AsyncClient(timeout=Timeout(self.request_timeout)) as client:
response = await client.post(
url=f"{self.base_url}/embedding",
headers={"Content-Type": "application/json"},
json=request_body,
)
response.encoding = "utf-8"
response.raise_for_status()
return response.json()["embedding"]
def _get_text_embeddings(self, texts: List[str]) -> List[Embedding]:
"""
Embed the input texts synchronously.
"""
request_body = {
"content": texts,
}
with httpx.Client(timeout=Timeout(self.request_timeout)) as client:
response = client.post(
url=f"{self.base_url}/embedding",
headers={"Content-Type": "application/json"},
json=request_body,
)
response.encoding = "utf-8"
response.raise_for_status()
return [output["embedding"] for output in response.json()["results"]]
async def _aget_text_embeddings(self, texts: List[str]) -> Embedding:
"""
Embed the input text asynchronously.
"""
request_body = {
"content": texts,
}
async with httpx.AsyncClient(timeout=Timeout(self.request_timeout)) as client:
response = await client.post(
url=f"{self.base_url}/embedding",
headers={"Content-Type": "application/json"},
json=request_body,
)
response.encoding = "utf-8"
response.raise_for_status()
return [output["embedding"] for output in response.json()["results"]]
|
