fix: repair deferred imports to preserve module-level names for test patching (#831)

A previous commit moved SDK imports into __init__/methods and
stashed them on self, which broke @patch targets in 24 unit tests.

This fixes the approach: chunker and pdf_decoder use module-level
sentinels with global/if-None guards so imports are still deferred but
patchable. Google AI Studio reverts to standard module-level imports
since the module is only loaded when communicating with Gemini.
Keeps lazy loading on other imports.

trustgraph-flow/trustgraph/chunking/recursive/chunker.py

@@ -10,6 +10,8 @@ from prometheus_client import Histogram
 from ... schema import TextDocument, Chunk, Metadata, Triples
 from ... base import ChunkingService, ConsumerSpec, ProducerSpec
 
+RecursiveCharacterTextSplitter = None
+
 from ... provenance import (
     chunk_uri as make_chunk_uri, derived_entity_triples,
     set_graph, GRAPH_SOURCE,
@@ -41,8 +43,12 @@ class Processor(ChunkingService):
         self.default_chunk_size = chunk_size
         self.default_chunk_overlap = chunk_overlap
 
-        from langchain_text_splitters import RecursiveCharacterTextSplitter
+        global RecursiveCharacterTextSplitter
-        self.RecursiveCharacterTextSplitter = RecursiveCharacterTextSplitter
+        if RecursiveCharacterTextSplitter is None:
+            from langchain_text_splitters import (
+                RecursiveCharacterTextSplitter as _cls,
+            )
+            RecursiveCharacterTextSplitter = _cls
 
         if not hasattr(__class__, "chunk_metric"):
             __class__.chunk_metric = Histogram(
@@ -52,7 +58,7 @@ class Processor(ChunkingService):
                          2500, 4000, 6400, 10000, 16000]
             )
 
-        self.text_splitter = self.RecursiveCharacterTextSplitter(
+        self.text_splitter = RecursiveCharacterTextSplitter(
             chunk_size=chunk_size,
             chunk_overlap=chunk_overlap,
             length_function=len,
@@ -105,7 +111,7 @@ class Processor(ChunkingService):
             chunk_overlap = int(chunk_overlap)
 
         # Create text splitter with effective parameters
-        text_splitter = self.RecursiveCharacterTextSplitter(
+        text_splitter = RecursiveCharacterTextSplitter(
             chunk_size=chunk_size,
             chunk_overlap=chunk_overlap,
             length_function=len,

trustgraph-flow/trustgraph/chunking/token/chunker.py

@@ -10,6 +10,8 @@ from prometheus_client import Histogram
 from ... schema import TextDocument, Chunk, Metadata, Triples
 from ... base import ChunkingService, ConsumerSpec, ProducerSpec
 
+TokenTextSplitter = None
+
 from ... provenance import (
     chunk_uri as make_chunk_uri, derived_entity_triples,
     set_graph, GRAPH_SOURCE,
@@ -41,8 +43,10 @@ class Processor(ChunkingService):
         self.default_chunk_size = chunk_size
         self.default_chunk_overlap = chunk_overlap
 
-        from langchain_text_splitters import TokenTextSplitter
+        global TokenTextSplitter
-        self.TokenTextSplitter = TokenTextSplitter
+        if TokenTextSplitter is None:
+            from langchain_text_splitters import TokenTextSplitter as _cls
+            TokenTextSplitter = _cls
 
         if not hasattr(__class__, "chunk_metric"):
             __class__.chunk_metric = Histogram(
@@ -52,7 +56,7 @@ class Processor(ChunkingService):
                          2500, 4000, 6400, 10000, 16000]
             )
 
-        self.text_splitter = self.TokenTextSplitter(
+        self.text_splitter = TokenTextSplitter(
             encoding_name="cl100k_base",
             chunk_size=chunk_size,
             chunk_overlap=chunk_overlap,
@@ -104,7 +108,7 @@ class Processor(ChunkingService):
             chunk_overlap = int(chunk_overlap)
 
         # Create text splitter with effective parameters
-        text_splitter = self.TokenTextSplitter(
+        text_splitter = TokenTextSplitter(
             encoding_name="cl100k_base",
             chunk_size=chunk_size,
             chunk_overlap=chunk_overlap,

trustgraph-flow/trustgraph/decoding/pdf/pdf_decoder.py

@@ -15,6 +15,9 @@ from ... schema import Document, TextDocument, Metadata
 from ... schema import librarian_request_queue, librarian_response_queue
 from ... schema import Triples
 from ... base import FlowProcessor, ConsumerSpec, ProducerSpec, LibrarianClient
+
+PyPDFLoader = None
+
 from ... provenance import (
     document_uri, page_uri as make_page_uri, derived_entity_triples,
     set_graph, GRAPH_SOURCE,
@@ -128,7 +131,12 @@ class Processor(FlowProcessor):
                 fp.write(base64.b64decode(v.data))
                 fp.close()
 
-            from langchain_community.document_loaders import PyPDFLoader
+            global PyPDFLoader
+            if PyPDFLoader is None:
+                from langchain_community.document_loaders import (
+                    PyPDFLoader as _cls,
+                )
+                PyPDFLoader = _cls
             loader = PyPDFLoader(temp_path)
             pages = loader.load()
 

trustgraph-vertexai/trustgraph/model/text_completion/googleaistudio/llm.py

@@ -18,6 +18,12 @@ import logging
 # Module logger
 logger = logging.getLogger(__name__)
 
+from google import genai
+from google.genai import types
+from google.genai.types import HarmCategory, HarmBlockThreshold
+from google.genai.errors import ClientError
+from google.api_core.exceptions import ResourceExhausted
+
 from .... exceptions import TooManyRequests
 from .... base import LlmService, LlmResult, LlmChunk
 
@@ -37,18 +43,6 @@ class Processor(LlmService):
         temperature = params.get("temperature", default_temperature)
         max_output = params.get("max_output", default_max_output)
 
-        from google import genai                                               
-        from google.genai import types                                          
-        from google.genai.types import HarmCategory, HarmBlockThreshold        
-        from google.genai.errors import ClientError                             
-        from google.api_core.exceptions import ResourceExhausted               
-        self.genai = genai                                                      
-        self.types = types                                                      
-        self.HarmCategory = HarmCategory                                        
-        self.HarmBlockThreshold = HarmBlockThreshold                           
-        self.ClientError = ClientError                                          
-        self.ResourceExhausted = ResourceExhausted                             
-
         if api_key is None:
             raise RuntimeError("Google AI Studio API key not specified")
 
@@ -60,7 +54,7 @@ class Processor(LlmService):
             }
         )
 
-        self.client = self.genai.Client(api_key=api_key, vertexai=False)
+        self.client = genai.Client(api_key=api_key, vertexai=False)
         self.default_model = model
         self.temperature = temperature
         self.max_output = max_output
@@ -68,23 +62,23 @@ class Processor(LlmService):
         # Cache for generation configs per model
         self.generation_configs = {}
 
-        block_level = self.HarmBlockThreshold.BLOCK_ONLY_HIGH
+        block_level = HarmBlockThreshold.BLOCK_ONLY_HIGH
 
         self.safety_settings = [
-            self.types.SafetySetting(                           
+            types.SafetySetting(                           
-                category = self.HarmCategory.HARM_CATEGORY_HATE_SPEECH,
+                category = HarmCategory.HARM_CATEGORY_HATE_SPEECH,
                 threshold = block_level,
             ),
-            self.types.SafetySetting(                           
+            types.SafetySetting(                           
-                category = self.HarmCategory.HARM_CATEGORY_HARASSMENT,
+                category = HarmCategory.HARM_CATEGORY_HARASSMENT,
                 threshold = block_level,
             ),
-            self.types.SafetySetting(                           
+            types.SafetySetting(                           
-                category = self.HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT,
+                category = HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT,
                 threshold = block_level,
             ),
-            self.types.SafetySetting(                           
+            types.SafetySetting(                           
-                category = self.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
+                category = HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
                 threshold = block_level,
             ),
             # There is a documentation conflict on whether or not
@@ -104,7 +98,7 @@ class Processor(LlmService):
 
         if cache_key not in self.generation_configs:
             logger.info(f"Creating generation config for '{model_name}' with temperature {effective_temperature}")
-            self.generation_configs[cache_key] = self.types.GenerateContentConfig( 
+            self.generation_configs[cache_key] = types.GenerateContentConfig( 
                 temperature = effective_temperature,
                 top_p = 1,
                 top_k = 40,
@@ -153,14 +147,14 @@ class Processor(LlmService):
 
             return resp
 
-        except self.ResourceExhausted as e:
+        except ResourceExhausted as e:
 
             logger.warning("Rate limit exceeded")
 
             # Leave rate limit retries to the default handler
             raise TooManyRequests()
 
-        except self.ClientError as e:                 
+        except ClientError as e:                 
             # google-genai SDK throws ClientError for 4xx errors
             if e.code == 429:
                 logger.warning(f"Rate limit exceeded (ClientError 429): {e}")
@@ -229,11 +223,11 @@ class Processor(LlmService):
 
             logger.debug("Streaming complete")
 
-        except self.ResourceExhausted:              
+        except ResourceExhausted:              
             logger.warning("Rate limit exceeded during streaming")
             raise TooManyRequests()
 
-        except self.ClientError as e:                 
+        except ClientError as e:                 
             # google-genai SDK throws ClientError for 4xx errors
             if e.code == 429:
                 logger.warning(f"Rate limit exceeded during streaming (ClientError 429): {e}")