Reset uv.lock to main branch version

Change group_id parameter from str = '' to str | None = None to match
the pattern used throughout the codebase and align with the optional
nature of group_id in generate_response().

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

graphiti_core/llm_client/client.py

@@ -33,12 +33,16 @@ DEFAULT_TEMPERATURE = 0
 DEFAULT_CACHE_DIR = './llm_cache'
 
 
-def get_extraction_language_instruction() -> str:
+def get_extraction_language_instruction(group_id: str | None = None) -> str:
     """Returns instruction for language extraction behavior.
 
     Override this function to customize language extraction:
     - Return empty string to disable multilingual instructions
     - Return custom instructions for specific language requirements
+    - Use group_id to provide different instructions per group/partition
+
+    Args:
+        group_id: Optional partition identifier for the graph
 
     Returns:
         str: Language instruction to append to system messages
@@ -142,6 +146,7 @@ class LLMClient(ABC):
         response_model: type[BaseModel] | None = None,
         max_tokens: int | None = None,
         model_size: ModelSize = ModelSize.medium,
+        group_id: str | None = None,
     ) -> dict[str, typing.Any]:
         if max_tokens is None:
             max_tokens = self.max_tokens
@@ -155,7 +160,7 @@ class LLMClient(ABC):
             )
 
         # Add multilingual extraction instructions
-        messages[0].content += get_extraction_language_instruction()
+        messages[0].content += get_extraction_language_instruction(group_id)
 
         if self.cache_enabled and self.cache_dir is not None:
             cache_key = self._get_cache_key(messages)

graphiti_core/llm_client/gemini_client.py

@@ -357,6 +357,7 @@ class GeminiClient(LLMClient):
         response_model: type[BaseModel] | None = None,
         max_tokens: int | None = None,
         model_size: ModelSize = ModelSize.medium,
+        group_id: str | None = None,
     ) -> dict[str, typing.Any]:
         """
         Generate a response from the Gemini language model with retry logic and error handling.
@@ -367,6 +368,7 @@ class GeminiClient(LLMClient):
             response_model (type[BaseModel] | None): An optional Pydantic model to parse the response into.
             max_tokens (int | None): The maximum number of tokens to generate in the response.
             model_size (ModelSize): The size of the model to use (small or medium).
+            group_id (str | None): Optional partition identifier for the graph.
 
         Returns:
             dict[str, typing.Any]: The response from the language model.
@@ -376,7 +378,7 @@ class GeminiClient(LLMClient):
         last_output = None
 
         # Add multilingual extraction instructions
-        messages[0].content += get_extraction_language_instruction()
+        messages[0].content += get_extraction_language_instruction(group_id)
 
         while retry_count < self.MAX_RETRIES:
             try:

graphiti_core/llm_client/openai_base_client.py

@@ -175,6 +175,7 @@ class BaseOpenAIClient(LLMClient):
         response_model: type[BaseModel] | None = None,
         max_tokens: int | None = None,
         model_size: ModelSize = ModelSize.medium,
+        group_id: str | None = None,
     ) -> dict[str, typing.Any]:
         """Generate a response with retry logic and error handling."""
         if max_tokens is None:
@@ -184,7 +185,7 @@ class BaseOpenAIClient(LLMClient):
         last_error = None
 
         # Add multilingual extraction instructions
-        messages[0].content += get_extraction_language_instruction()
+        messages[0].content += get_extraction_language_instruction(group_id)
 
         while retry_count <= self.MAX_RETRIES:
             try:

graphiti_core/llm_client/openai_generic_client.py

@@ -120,6 +120,7 @@ class OpenAIGenericClient(LLMClient):
         response_model: type[BaseModel] | None = None,
         max_tokens: int | None = None,
         model_size: ModelSize = ModelSize.medium,
+        group_id: str | None = None,
     ) -> dict[str, typing.Any]:
         if max_tokens is None:
             max_tokens = self.max_tokens
@@ -136,7 +137,7 @@ class OpenAIGenericClient(LLMClient):
             )
 
         # Add multilingual extraction instructions
-        messages[0].content += get_extraction_language_instruction()
+        messages[0].content += get_extraction_language_instruction(group_id)
 
         while retry_count <= self.MAX_RETRIES:
             try:

graphiti_core/utils/maintenance/edge_operations.py

@@ -139,6 +139,7 @@ async def extract_edges(
             prompt_library.extract_edges.edge(context),
             response_model=ExtractedEdges,
             max_tokens=extract_edges_max_tokens,
+            group_id=group_id,
         )
         edges_data = ExtractedEdges(**llm_response).edges
 
@@ -150,6 +151,7 @@ async def extract_edges(
                 prompt_library.extract_edges.reflexion(context),
                 response_model=MissingFacts,
                 max_tokens=extract_edges_max_tokens,
+                group_id=group_id,
             )
 
             missing_facts = reflexion_response.get('missing_facts', [])

graphiti_core/utils/maintenance/node_operations.py

@@ -64,6 +64,7 @@ async def extract_nodes_reflexion(
     episode: EpisodicNode,
     previous_episodes: list[EpisodicNode],
     node_names: list[str],
+    group_id: str | None = None,
 ) -> list[str]:
     # Prepare context for LLM
     context = {
@@ -73,7 +74,7 @@ async def extract_nodes_reflexion(
     }
 
     llm_response = await llm_client.generate_response(
-        prompt_library.extract_nodes.reflexion(context), MissedEntities
+        prompt_library.extract_nodes.reflexion(context), MissedEntities, group_id=group_id
     )
     missed_entities = llm_response.get('missed_entities', [])
 
@@ -129,16 +130,19 @@ async def extract_nodes(
             llm_response = await llm_client.generate_response(
                 prompt_library.extract_nodes.extract_message(context),
                 response_model=ExtractedEntities,
+                group_id=episode.group_id,
             )
         elif episode.source == EpisodeType.text:
             llm_response = await llm_client.generate_response(
                 prompt_library.extract_nodes.extract_text(context),
                 response_model=ExtractedEntities,
+                group_id=episode.group_id,
             )
         elif episode.source == EpisodeType.json:
             llm_response = await llm_client.generate_response(
                 prompt_library.extract_nodes.extract_json(context),
                 response_model=ExtractedEntities,
+                group_id=episode.group_id,
             )
 
         response_object = ExtractedEntities(**llm_response)
@@ -152,6 +156,7 @@ async def extract_nodes(
                 episode,
                 previous_episodes,
                 [entity.name for entity in extracted_entities],
+                episode.group_id,
             )
 
             entities_missed = len(missing_entities) != 0
@@ -510,6 +515,7 @@ async def extract_attributes_from_node(
                 prompt_library.extract_nodes.extract_attributes(attributes_context),
                 response_model=entity_type,
                 model_size=ModelSize.small,
+                group_id=node.group_id,
             )
         )
         if has_entity_attributes
@@ -527,6 +533,7 @@ async def extract_attributes_from_node(
             prompt_library.extract_nodes.extract_summary(summary_context),
             response_model=EntitySummary,
             model_size=ModelSize.small,
+            group_id=node.group_id,
         )
         node.summary = summary_response.get('summary', '')
 

uv.lock

@@ -803,7 +803,6 @@ anthropic = [
 ]
 dev = [
     { name = "anthropic" },
-    { name = "boto3" },
     { name = "diskcache-stubs" },
     { name = "falkordb" },
     { name = "google-genai" },
@@ -812,11 +811,9 @@ dev = [
     { name = "jupyterlab" },
     { name = "kuzu" },
     { name = "langchain-anthropic" },
-    { name = "langchain-aws" },
     { name = "langchain-openai" },
     { name = "langgraph" },
     { name = "langsmith" },
-    { name = "opensearch-py" },
     { name = "pyright" },
     { name = "pytest" },
     { name = "pytest-asyncio" },
@@ -858,7 +855,6 @@ voyageai = [
 requires-dist = [
     { name = "anthropic", marker = "extra == 'anthropic'", specifier = ">=0.49.0" },
     { name = "anthropic", marker = "extra == 'dev'", specifier = ">=0.49.0" },
-    { name = "boto3", marker = "extra == 'dev'", specifier = ">=1.39.16" },
     { name = "boto3", marker = "extra == 'neo4j-opensearch'", specifier = ">=1.39.16" },
     { name = "boto3", marker = "extra == 'neptune'", specifier = ">=1.39.16" },
     { name = "diskcache", specifier = ">=5.6.3" },
@@ -874,7 +870,6 @@ requires-dist = [
     { name = "kuzu", marker = "extra == 'dev'", specifier = ">=0.11.2" },
     { name = "kuzu", marker = "extra == 'kuzu'", specifier = ">=0.11.2" },
     { name = "langchain-anthropic", marker = "extra == 'dev'", specifier = ">=0.2.4" },
-    { name = "langchain-aws", marker = "extra == 'dev'", specifier = ">=0.2.29" },
     { name = "langchain-aws", marker = "extra == 'neptune'", specifier = ">=0.2.29" },
     { name = "langchain-openai", marker = "extra == 'dev'", specifier = ">=0.2.6" },
     { name = "langgraph", marker = "extra == 'dev'", specifier = ">=0.2.15" },
@@ -882,7 +877,6 @@ requires-dist = [
     { name = "neo4j", specifier = ">=5.26.0" },
     { name = "numpy", specifier = ">=1.0.0" },
     { name = "openai", specifier = ">=1.91.0" },
-    { name = "opensearch-py", marker = "extra == 'dev'", specifier = ">=3.0.0" },
     { name = "opensearch-py", marker = "extra == 'neo4j-opensearch'", specifier = ">=3.0.0" },
     { name = "opensearch-py", marker = "extra == 'neptune'", specifier = ">=3.0.0" },
     { name = "posthog", specifier = ">=3.0.0" },