openai encoder

.gitignore

+.env
+mac.env
+
+**/__pycache__

README.md

-# semantic-layer
+# Decision Layer
\ No newline at end of file

decision_layer/__init__.py

+from decision_layer.decision_layer import DecisionLayer
\ No newline at end of file

decision_layer/decision_layer.py

+from decision_layer.encoders import BaseEncoder
+from decision_layer.schema import Decision
+
+
+class DecisionLayer:
+    def __init__(self, encoder: BaseEncoder, decisions: list[Decision]):
+        self.encoder = encoder
+        self.decisions = decisions
+        if decisions:
+            pass
+
+
+    def add(self, decision: Decision):
+        pass
+        embeds = encoder(te)
\ No newline at end of file

decision_layer/encoders/__init__.py

+from decision_layer.encoders.base import BaseEncoder
+from decision_layer.encoders.cohere import CohereEncoder
+from decision_layer.encoders.huggingface import HuggingFaceEncoder
+from decision_layer.encoders.openai import OpenAIEncoder
\ No newline at end of file

decision_layer/encoders/base.py

+from pydantic import BaseModel
+
+
+class BaseEncoder(BaseModel):
+    name: str
+
+    class Config:
+        arbitrary_types_allowed = True
+
+    def __call__(self, texts: list[str]) -> list[float]:
+        pass

decision_layer/encoders/cohere.py

+from decision_layer.encoders import BaseEncoder
+
+class CohereEncoder(BaseEncoder):
+    def __init__(self, name: str):
+        super().__init__(name)
+
+    def __call__(self, texts: list[str]) -> list[float]:
+        raise NotImplementedError
\ No newline at end of file

decision_layer/encoders/huggingface.py

+from decision_layer.encoders import BaseEncoder
+
+
+class HuggingFaceEncoder(BaseEncoder):
+    def __init__(self, name: str):
+        super().__init__(name)
+
+    def __call__(self, texts: list[str]) -> list[float]:
+        raise NotImplementedError
\ No newline at end of file

decision_layer/encoders/openai.py

+import os
+
+from decision_layer.encoders import BaseEncoder
+import openai
+from time import time
+
+
+class OpenAIEncoder(BaseEncoder):
+    def __init__(self, name: str, openai_api_key: str | None = None):
+        super().__init__(name=name)
+        openai.api_key = openai_api_key or os.getenv("OPENAI_API_KEY")
+        if openai.api_key is None:
+            raise ValueError("OpenAI API key cannot be 'None'.")
+
+    def __call__(self, texts: list[str]) -> list[list[float]]:
+        """Encode a list of texts using the OpenAI API. Returns a list of
+        vector embeddings.
+        """
+        passed = False
+        # exponential backoff in case of RateLimitError
+        for j in range(5):
+            try:
+                # create embeddings
+                res = openai.Embedding.create(
+                    input=texts, engine=self.name
+                )
+                passed = True
+            except openai.error.RateLimitError:
+                time.sleep(2 ** j)
+        if not passed:
+            raise openai.error.RateLimitError
+        # get embeddings
+        embeds = [r["embedding"] for r in res["data"]]
+        return embeds
+        
\ No newline at end of file

decision_layer/schema.py

+from enum import Enum
+from pydantic import BaseModel
+from pydantic.dataclasses import dataclass
+from decision_layer.encoders import (
+    BaseEncoder,
+    HuggingFaceEncoder,
+    OpenAIEncoder,
+    CohereEncoder,
+)
+
+
+class Decision(BaseModel):
+    name: str
+    utterances: list[str]
+    description: str | None = None
+
+class EncoderType(Enum):
+    HUGGINGFACE = "huggingface"
+    OPENAI = "openai"
+    COHERE = "cohere"
+
+@dataclass
+class Encoder:
+    type: EncoderType
+    name: str
+    model: BaseEncoder
+
+    def __init__(self, type: str, name: str):
+        self.type = EncoderType(type)
+        self.name = name
+        if self.type == EncoderType.HUGGINGFACE:
+            self.model = HuggingFaceEncoder(name)
+        elif self.type == EncoderType.OPENAI:
+            self.model = OpenAIEncoder(name)
+        elif self.type == EncoderType.COHERE:
+            self.model = CohereEncoder(name)
+
+    def __call__(self, texts: list[str]) -> list[float]:
+        return self.model(texts)
+
+@dataclass
+class SemanticSpace:
+    id: str
+    decisions: list[Decision]
+    encoder: str = ""
+
+    def __init__(self, decisions: list[Decision] = []):
+        self.id = ""
+        self.decisions = decisions
+
+    def add(self, decision: Decision):
+        self.decisions.append(decision)
\ No newline at end of file

poetry.lock

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+
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