From 82c06d519c4733b3c3a1ce4a627dfdfe2e3b53ed Mon Sep 17 00:00:00 2001
From: Smart2332 <omribenshoham2@gmail.com>
Date: Sun, 26 May 2024 17:08:44 +0300
Subject: [PATCH] Fixed the right file
---
semantic_router/encoders/zure.py | 9 +-
semantic_router/llms/zure.py | 139 +++++++++----------------------
2 files changed, 47 insertions(+), 101 deletions(-)
diff --git a/semantic_router/encoders/zure.py b/semantic_router/encoders/zure.py
index df2bf858..7215e7f5 100644
--- a/semantic_router/encoders/zure.py
+++ b/semantic_router/encoders/zure.py
@@ -1,8 +1,9 @@
import os
from time import sleep
-from typing import List, Optional
+from typing import List, Optional, Union
import openai
+from openai._types import NotGiven
from openai import OpenAIError
from openai.types import CreateEmbeddingResponse
@@ -13,6 +14,7 @@ from semantic_router.utils.logger import logger
class AzureOpenAIEncoder(BaseEncoder):
client: Optional[openai.AzureOpenAI] = None
+ dimensions: Union[int, NotGiven] = NotGiven()
type: str = "azure"
api_key: Optional[str] = None
deployment_name: Optional[str] = None
@@ -28,6 +30,7 @@ class AzureOpenAIEncoder(BaseEncoder):
api_version: Optional[str] = None,
model: Optional[str] = None, # TODO we should change to `name` JB
score_threshold: float = 0.82,
+ dimensions: Union[int, NotGiven] = NotGiven(),
):
name = deployment_name
if name is None:
@@ -38,6 +41,8 @@ class AzureOpenAIEncoder(BaseEncoder):
self.azure_endpoint = azure_endpoint
self.api_version = api_version
self.model = model
+ # set dimensions to support openai embed 3 dimensions param
+ self.dimensions = dimensions
if self.api_key is None:
self.api_key = os.getenv("AZURE_OPENAI_API_KEY")
if self.api_key is None:
@@ -89,7 +94,7 @@ class AzureOpenAIEncoder(BaseEncoder):
for j in range(3):
try:
embeds = self.client.embeddings.create(
- input=docs, model=str(self.model)
+ input=docs, model=str(self.model), dimensions=self.dimensions,
)
if embeds.data:
break
diff --git a/semantic_router/llms/zure.py b/semantic_router/llms/zure.py
index 7215e7f5..26b7901f 100644
--- a/semantic_router/llms/zure.py
+++ b/semantic_router/llms/zure.py
@@ -1,121 +1,62 @@
import os
-from time import sleep
-from typing import List, Optional, Union
+from typing import List, Optional
import openai
-from openai._types import NotGiven
-from openai import OpenAIError
-from openai.types import CreateEmbeddingResponse
-from semantic_router.encoders import BaseEncoder
+from semantic_router.llms import BaseLLM
+from semantic_router.schema import Message
from semantic_router.utils.defaults import EncoderDefault
from semantic_router.utils.logger import logger
-class AzureOpenAIEncoder(BaseEncoder):
- client: Optional[openai.AzureOpenAI] = None
- dimensions: Union[int, NotGiven] = NotGiven()
- type: str = "azure"
- api_key: Optional[str] = None
- deployment_name: Optional[str] = None
- azure_endpoint: Optional[str] = None
- api_version: Optional[str] = None
- model: Optional[str] = None
+class AzureOpenAILLM(BaseLLM):
+ client: Optional[openai.AzureOpenAI]
+ temperature: Optional[float]
+ max_tokens: Optional[int]
def __init__(
self,
- api_key: Optional[str] = None,
- deployment_name: Optional[str] = None,
+ name: Optional[str] = None,
+ openai_api_key: Optional[str] = None,
azure_endpoint: Optional[str] = None,
- api_version: Optional[str] = None,
- model: Optional[str] = None, # TODO we should change to `name` JB
- score_threshold: float = 0.82,
- dimensions: Union[int, NotGiven] = NotGiven(),
+ temperature: float = 0.01,
+ max_tokens: int = 200,
+ api_version="2023-07-01-preview",
):
- name = deployment_name
if name is None:
- name = EncoderDefault.AZURE.value["embedding_model"]
- super().__init__(name=name, score_threshold=score_threshold)
- self.api_key = api_key
- self.deployment_name = deployment_name
- self.azure_endpoint = azure_endpoint
- self.api_version = api_version
- self.model = model
- # set dimensions to support openai embed 3 dimensions param
- self.dimensions = dimensions
- if self.api_key is None:
- self.api_key = os.getenv("AZURE_OPENAI_API_KEY")
- if self.api_key is None:
- raise ValueError("No Azure OpenAI API key provided.")
- if self.deployment_name is None:
- self.deployment_name = EncoderDefault.AZURE.value["deployment_name"]
- # deployment_name may still be None, but it is optional in the API
- if self.azure_endpoint is None:
- self.azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
- if self.azure_endpoint is None:
- raise ValueError("No Azure OpenAI endpoint provided.")
- if self.api_version is None:
- self.api_version = os.getenv("AZURE_OPENAI_API_VERSION")
- if self.api_version is None:
- raise ValueError("No Azure OpenAI API version provided.")
- if self.model is None:
- self.model = os.getenv("AZURE_OPENAI_MODEL")
- if self.model is None:
- raise ValueError("No Azure OpenAI model provided.")
- assert (
- self.api_key is not None
- and self.azure_endpoint is not None
- and self.api_version is not None
- and self.model is not None
- )
-
+ name = EncoderDefault.AZURE.value["language_model"]
+ super().__init__(name=name)
+ api_key = openai_api_key or os.getenv("AZURE_OPENAI_API_KEY")
+ if api_key is None:
+ raise ValueError("AzureOpenAI API key cannot be 'None'.")
+ azure_endpoint = azure_endpoint or os.getenv("AZURE_OPENAI_ENDPOINT")
+ if azure_endpoint is None:
+ raise ValueError("Azure endpoint API key cannot be 'None'.")
try:
self.client = openai.AzureOpenAI(
- azure_deployment=(
- str(self.deployment_name) if self.deployment_name else None
- ),
- api_key=str(self.api_key),
- azure_endpoint=str(self.azure_endpoint),
- api_version=str(self.api_version),
- # _strict_response_validation=True,
+ api_key=api_key, azure_endpoint=azure_endpoint, api_version=api_version
)
except Exception as e:
- raise ValueError(
- f"OpenAI API client failed to initialize. Error: {e}"
- ) from e
+ raise ValueError(f"AzureOpenAI API client failed to initialize. Error: {e}")
+ self.temperature = temperature
+ self.max_tokens = max_tokens
- def __call__(self, docs: List[str]) -> List[List[float]]:
+ def __call__(self, messages: List[Message]) -> str:
if self.client is None:
- raise ValueError("OpenAI client is not initialized.")
- embeds = None
- error_message = ""
-
- # Exponential backoff
- for j in range(3):
- try:
- embeds = self.client.embeddings.create(
- input=docs, model=str(self.model), dimensions=self.dimensions,
- )
- if embeds.data:
- break
- except OpenAIError as e:
- # print full traceback
- import traceback
-
- traceback.print_exc()
- sleep(2**j)
- error_message = str(e)
- logger.warning(f"Retrying in {2**j} seconds...")
- except Exception as e:
- logger.error(f"Azure OpenAI API call failed. Error: {error_message}")
- raise ValueError(f"Azure OpenAI API call failed. Error: {e}") from e
+ raise ValueError("AzureOpenAI client is not initialized.")
+ try:
+ completion = self.client.chat.completions.create(
+ model=self.name,
+ messages=[m.to_openai() for m in messages],
+ temperature=self.temperature,
+ max_tokens=self.max_tokens,
+ )
- if (
- not embeds
- or not isinstance(embeds, CreateEmbeddingResponse)
- or not embeds.data
- ):
- raise ValueError(f"No embeddings returned. Error: {error_message}")
+ output = completion.choices[0].message.content
- embeddings = [embeds_obj.embedding for embeds_obj in embeds.data]
- return embeddings
+ if not output:
+ raise Exception("No output generated")
+ return output
+ except Exception as e:
+ logger.error(f"LLM error: {e}")
+ raise Exception(f"LLM error: {e}") from e
--
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