Support run_id / token usage for prompt runs

pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "python-magic~=0.4.27",
     "python-dotenv==1.0.0",
     # LLM Triad
-    "unstract-adapters~=0.15.1",
+    "unstract-adapters~=0.16.0",
     "llama-index==0.10.28",
     "tiktoken~=0.4.0",
     "transformers==4.37.0",

src/unstract/sdk/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.26.1"
+__version__ = "0.27.0"
 
 
 def get_sdk_version():

src/unstract/sdk/audit.py

@@ -1,3 +1,5 @@
+from typing import Any
+
 import requests
 from llama_index.core.callbacks import CBEventType, TokenCountingHandler
 
@@ -26,7 +28,7 @@ def push_usage_data(
         token_counter: TokenCountingHandler = None,
         model_name: str = "",
         event_type: CBEventType = None,
-        **kwargs,
+        kwargs: dict[Any, Any] = None,
     ) -> None:
         """Pushes the usage data to the platform service.
 
@@ -84,9 +86,7 @@ def push_usage_data(
         headers = {"Authorization": f"Bearer {bearer_token}"}
 
         try:
-            response = requests.post(
-                url, headers=headers, json=data, timeout=30
-            )
+            response = requests.post(url, headers=headers, json=data, timeout=30)
             if response.status_code != 200:
                 self.stream_log(
                     log=(

src/unstract/sdk/constants.py

@@ -146,3 +146,8 @@ class ToolSettingsKey:
     EMBEDDING_ADAPTER_ID = "embeddingAdapterId"
     VECTOR_DB_ADAPTER_ID = "vectorDbAdapterId"
     X2TEXT_ADAPTER_ID = "x2TextAdapterId"
+    ADAPTER_INSTANCE_ID = "adapter_instance_id"
+    EMBEDDING_DIMENSION = "embedding_dimension"
+    RUN_ID = "run_id"
+    WORKFLOW_ID = "workflow_id"
+    EXECUTION_ID = "execution_id"

src/unstract/sdk/embedding.py

@@ -1,22 +1,44 @@
+from typing import Any
+
+from llama_index.core.base.embeddings.base import Embedding
 from llama_index.core.embeddings import BaseEmbedding
+from typing_extensions import deprecated
 from unstract.adapters.constants import Common
 from unstract.adapters.embedding import adapters
 
 from unstract.sdk.adapters import ToolAdapter
-from unstract.sdk.constants import LogLevel
-from unstract.sdk.exceptions import SdkError, ToolEmbeddingError
+from unstract.sdk.constants import LogLevel, ToolEnv
+from unstract.sdk.exceptions import EmbeddingError, SdkError
 from unstract.sdk.tool.base import BaseTool
+from unstract.sdk.utils.callback_manager import CallbackManager
+
 
+class Embedding:
+    _TEST_SNIPPET = "Hello, I am Unstract"
+    MAX_TOKENS = 1024 * 16
+    embedding_adapters = adapters
 
-class ToolEmbedding:
-    __TEST_SNIPPET = "Hello, I am Unstract"
+    def __init__(
+        self,
+        tool: BaseTool,
+        adapter_instance_id: str,
+        usage_kwargs: dict[Any, Any] = None,
+    ):
+        self._tool = tool
+        self._adapter_instance_id = adapter_instance_id
+        self._embedding_instance: BaseEmbedding = self._get_embedding()
+        self._length: int = self._get_embedding_length()
 
-    def __init__(self, tool: BaseTool):
-        self.tool = tool
-        self.max_tokens = 1024 * 16
-        self.embedding_adapters = adapters
+        self._usage_kwargs = usage_kwargs.copy()
+        self._usage_kwargs["adapter_instance_id"] = adapter_instance_id
+        platform_api_key = self._tool.get_env_or_die(ToolEnv.PLATFORM_API_KEY)
+        CallbackManager.set_callback_manager(
+            platform_api_key=platform_api_key,
+            model=self._embedding_instance,
+            kwargs=self._usage_kwargs,
+        )
 
-    def get_embedding(self, adapter_instance_id: str) -> BaseEmbedding:
+    def _get_embedding(self) -> BaseEmbedding:
         """Gets an instance of LlamaIndex's embedding object.
 
         Args:
@@ -27,7 +49,7 @@ def get_embedding(self, adapter_instance_id: str) -> BaseEmbedding:
         """
         try:
             embedding_config_data = ToolAdapter.get_adapter_config(
-                self.tool, adapter_instance_id
+                self._tool, self._adapter_instance_id
             )
             embedding_adapter_id = embedding_config_data.get(Common.ADAPTER_ID)
             if embedding_adapter_id not in self.embedding_adapters:
@@ -42,12 +64,25 @@ def get_embedding(self, adapter_instance_id: str) -> BaseEmbedding:
             embedding_adapter_class = embedding_adapter(embedding_metadata)
             return embedding_adapter_class.get_embedding_instance()
         except Exception as e:
-            self.tool.stream_log(
+            self._tool.stream_log(
                 log=f"Error getting embedding: {e}", level=LogLevel.ERROR
             )
-            raise ToolEmbeddingError(f"Error getting embedding instance: {e}") from e
+            raise EmbeddingError(f"Error getting embedding instance: {e}") from e
 
-    def get_embedding_length(self, embedding: BaseEmbedding) -> int:
-        embedding_list = embedding._get_text_embedding(self.__TEST_SNIPPET)
+    def get_query_embedding(self, query: str) -> Embedding:
+        return self._embedding_instance.get_query_embedding(query)
+
+    def _get_embedding_length(self) -> int:
+        embedding_list = self._embedding_instance._get_text_embedding(
+            self._TEST_SNIPPET
+        )
         embedding_dimension = len(embedding_list)
         return embedding_dimension
+
+    @deprecated("Use the new class Embedding")
+    def get_embedding_length(self, embedding: BaseEmbedding) -> int:
+        return self._get_embedding_length(embedding)
+
+
+# Legacy
+ToolEmbedding = Embedding

src/unstract/sdk/exceptions.py

@@ -17,15 +17,15 @@ def __init__(self, message: str = ""):
         super().__init__(message)
 
 
-class ToolLLMError(SdkError):
+class LLMError(SdkError):
     DEFAULT_MESSAGE = "Error ocurred related to LLM"
 
 
-class ToolEmbeddingError(SdkError):
+class EmbeddingError(SdkError):
     DEFAULT_MESSAGE = "Error ocurred related to embedding"
 
 
-class ToolVectorDBError(SdkError):
+class VectorDBError(SdkError):
     DEFAULT_MESSAGE = "Error ocurred related to vector DB"