feat: Added max_tokens and timeout to all LLMs and few embedding adapters (#93)

* Added max_tokens and timeout to all LLMs and few embedding adapters

* Updated Open AI to OpenAI

Author: chandrasekharan-zipstack

src/unstract/sdk/adapters/embedding/azure_open_ai/README.md

@@ -1,4 +1,4 @@
-# Unstract Azure Open AI Embedding Adapter
+# Unstract Azure OpenAI Embedding Adapter
 
 This package consists of the functionalities required to adapt with Azure OpenAI Embedding 
 Version supported

src/unstract/sdk/adapters/embedding/azure_open_ai/pyproject.toml

@@ -6,7 +6,7 @@ build-backend = "pdm.backend"
 [project]
 name = "unstract-azure-open-ai-embedding"
 version = "0.0.1"
-description = "Azure Open AI Embedding"
+description = "Azure OpenAI Embedding"
 authors = [
     {name = "Zipstack Inc.", email = "[email protected]"},
 ]

src/unstract/sdk/adapters/embedding/azure_open_ai/src/azure_open_ai.py

@@ -17,6 +17,8 @@ class Constants:
     AZURE_ENDPOINT = "azure_endpoint"
     DEPLOYMENT_NAME = "deployment_name"
     API_TYPE = "azure"
+    TIMEOUT = "timeout"
+    DEFAULT_TIMEOUT = 60
 
 
 class AzureOpenAI(EmbeddingAdapter):
@@ -56,6 +58,7 @@ def get_embedding_instance(self) -> BaseEmbedding:
             embedding_batch_size = EmbeddingHelper.get_embedding_batch_size(
                 config=self.config
             )
+            timeout = int(self.config.get(Constants.TIMEOUT, Constants.DEFAULT_TIMEOUT))
             embedding: BaseEmbedding = AzureOpenAIEmbedding(
                 model=str(self.config.get(Constants.MODEL)),
                 deployment_name=str(self.config.get(Constants.DEPLOYMENT_NAME)),
@@ -64,6 +67,7 @@ def get_embedding_instance(self) -> BaseEmbedding:
                 azure_endpoint=str(self.config.get(Constants.AZURE_ENDPOINT)),
                 embed_batch_size=embedding_batch_size,
                 api_type=Constants.API_TYPE,
+                timeout=timeout,
             )
             return embedding
         except Exception as e:

src/unstract/sdk/adapters/embedding/azure_open_ai/src/static/json_schema.json

@@ -1,5 +1,5 @@
 {
-  "title": "Azure Open AI Embedding",
+  "title": "Azure OpenAI Embedding",
   "type": "object",
   "required": [
     "adapter_name",
@@ -28,13 +28,6 @@
       "default": "",
       "description": "Provide the name of the deployment you defined in Azure console"
     },
-    "embed_batch_size": {
-      "type": "number",
-      "minimum": 0,
-      "multipleOf": 1,
-      "title": "Embedding Batch Size",
-      "default": 5
-    },
     "api_key": {
       "type": "string",
       "title": "API Key",
@@ -53,6 +46,21 @@
       "default": "",
       "format": "uri",
       "description": "Provide the Azure endpoint. Example: https://<your-deployment>.openai.azure.com/"
+    },
+    "embed_batch_size": {
+      "type": "number",
+      "minimum": 0,
+      "multipleOf": 1,
+      "title": "Embedding Batch Size",
+      "default": 5
+    },
+    "timeout": {
+      "type": "number",
+      "minimum": 0,
+      "multipleOf": 1,
+      "title": "Timeout",
+      "default": 60,
+      "description": "Timeout for each request in seconds"
     }
   }
 }

src/unstract/sdk/adapters/embedding/open_ai/README.md

@@ -1 +1 @@
-# Unstract Open AI Embeddings
+# Unstract OpenAI Embeddings

src/unstract/sdk/adapters/embedding/open_ai/pyproject.toml

@@ -6,7 +6,7 @@ build-backend = "pdm.backend"
 [project]
 name = "unstract-open-ai-embedding"
 version = "0.0.1"
-description = "Open AI Embedding"
+description = "OpenAI Embedding"
 authors = [
     {name = "Zipstack Inc.", email = "[email protected]"},
 ]

src/unstract/sdk/adapters/embedding/open_ai/src/open_ai.py

@@ -15,6 +15,8 @@ class Constants:
     API_BASE_KEY = "api_base"
     ADAPTER_NAME = "adapter_name"
     API_TYPE = "openai"
+    TIMEOUT = "timeout"
+    DEFAULT_TIMEOUT = 60
 
 
 class OpenAI(EmbeddingAdapter):
@@ -51,12 +53,14 @@ def get_json_schema() -> str:
 
     def get_embedding_instance(self) -> BaseEmbedding:
         try:
+            timeout = int(self.config.get(Constants.TIMEOUT, Constants.DEFAULT_TIMEOUT))
             embedding: BaseEmbedding = OpenAIEmbedding(
                 api_key=str(self.config.get(Constants.API_KEY)),
                 api_base=str(
                     self.config.get(Constants.API_BASE_KEY, Constants.API_BASE_VALUE)
                 ),
                 api_type=Constants.API_TYPE,
+                timeout=timeout,
             )
             return embedding
         except Exception as e:

src/unstract/sdk/adapters/embedding/open_ai/src/static/json_schema.json

@@ -1,5 +1,5 @@
 {
-  "title": "Open AI Embedding",
+  "title": "OpenAI Embedding",
   "type": "object",
   "required": [
     "adapter_name",
@@ -18,18 +18,26 @@
       "default": "",
       "format": "password"
     },
+    "api_base": {
+      "type": "string",
+      "title": "API Base",
+      "format": "uri",
+      "default": "https://api.openai.com/v1/"
+    },
     "embed_batch_size": {
       "type": "number",
       "minimum": 0,
       "multipleOf": 1,
       "title": "Embed Batch Size",
       "default": 10
     },
-    "api_base": {
-      "type": "string",
-      "title": "API Base",
-      "format": "uri",
-      "default": "https://api.openai.com/v1/"
+    "timeout": {
+      "type": "number",
+      "minimum": 0,
+      "multipleOf": 1,
+      "title": "Timeout",
+      "default": 60,
+      "description": "Timeout in seconds"
     }
   }
 }

src/unstract/sdk/adapters/llm/anthropic/src/__init__.py

@@ -7,3 +7,5 @@
     "description": "Anthropic LLM adapter",
     "is_active": True,
 }
+
+__all__ = ["AnthropicLLM"]

src/unstract/sdk/adapters/llm/anthropic/src/anthropic.py

@@ -1,20 +1,22 @@
 import os
 from typing import Any
 
+from anthropic import APIError
 from llama_index.core.llms import LLM
 from llama_index.llms.anthropic import Anthropic
+from llama_index.llms.anthropic.base import DEFAULT_ANTHROPIC_MAX_TOKENS
 
-from unstract.sdk.adapters.exceptions import AdapterError
+from unstract.sdk.adapters.exceptions import AdapterError, LLMError
 from unstract.sdk.adapters.llm.constants import LLMKeys
-from unstract.sdk.adapters.llm.helper import LLMHelper
 from unstract.sdk.adapters.llm.llm_adapter import LLMAdapter
 
 
 class Constants:
     MODEL = "model"
     API_KEY = "api_key"
     TIMEOUT = "timeout"
-    MAX_RETIRES = "max_retries"
+    MAX_RETRIES = "max_retries"
+    MAX_TOKENS = "max_tokens"
 
 
 class AnthropicLLM(LLMAdapter):
@@ -50,6 +52,9 @@ def get_json_schema() -> str:
         return schema
 
     def get_llm_instance(self) -> LLM:
+        max_tokens = int(
+            self.config.get(Constants.MAX_TOKENS, DEFAULT_ANTHROPIC_MAX_TOKENS)
+        )
         try:
             llm: LLM = Anthropic(
                 model=str(self.config.get(Constants.MODEL)),
@@ -58,15 +63,32 @@ def get_llm_instance(self) -> LLM:
                     self.config.get(Constants.TIMEOUT, LLMKeys.DEFAULT_TIMEOUT)
                 ),
                 max_retries=int(
-                    self.config.get(Constants.MAX_RETIRES, LLMKeys.DEFAULT_MAX_RETRIES)
+                    self.config.get(Constants.MAX_RETRIES, LLMKeys.DEFAULT_MAX_RETRIES)
                 ),
                 temperature=0,
+                max_tokens=max_tokens,
             )
             return llm
         except Exception as e:
             raise AdapterError(str(e))
 
-    def test_connection(self) -> bool:
-        llm = self.get_llm_instance()
-        test_result: bool = LLMHelper.test_llm_instance(llm=llm)
-        return test_result
+    @staticmethod
+    def parse_llm_err(e: APIError) -> LLMError:
+        """Parse the error from Anthropic.
+
+        Helps parse errors from Anthropic and wraps with custom exception.
+
+        Args:
+            e (AnthropicAPIError): Exception from Anthropic
+
+        Returns:
+            LLMError: Error to be sent to the user
+        """
+        msg = "Error from Anthropic. "
+        if hasattr(e, "body"):
+            if isinstance(e.body, dict) and "error" in e.body:
+                err = e.body["error"]
+                msg += err.get("message", e.message)
+        else:
+            msg += e.message
+        return LLMError(msg)