save edge update (#721)

graphiti_core/embedder/gemini.py

@@ -37,7 +37,7 @@ from .client import EmbedderClient, EmbedderConfig
 
 logger = logging.getLogger(__name__)
 
-DEFAULT_EMBEDDING_MODEL = 'text-embedding-001' # gemini-embedding-001 or text-embedding-005
+DEFAULT_EMBEDDING_MODEL = 'text-embedding-001'  # gemini-embedding-001 or text-embedding-005
 
 DEFAULT_BATCH_SIZE = 100
 
@@ -78,7 +78,7 @@ class GeminiEmbedder(EmbedderClient):
 
         if batch_size is None and self.config.embedding_model == 'gemini-embedding-001':
             # Gemini API has a limit on the number of instances per request
-            #https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api
+            # https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api
             self.batch_size = 1
         elif batch_size is None:
             self.batch_size = DEFAULT_BATCH_SIZE
@@ -113,32 +113,34 @@ class GeminiEmbedder(EmbedderClient):
     async def create_batch(self, input_data_list: list[str]) -> list[list[float]]:
         """
         Create embeddings for a batch of input data using Google's Gemini embedding model.
-        
+
         This method handles batching to respect the Gemini API's limits on the number
         of instances that can be processed in a single request.
-        
+
         Args:
             input_data_list: A list of strings to create embeddings for.
-            
+
         Returns:
             A list of embedding vectors (each vector is a list of floats).
         """
         if not input_data_list:
             return []
-        
+
         batch_size = self.batch_size
         all_embeddings = []
-        
+
         # Process inputs in batches
         for i in range(0, len(input_data_list), batch_size):
-            batch = input_data_list[i:i + batch_size]
+            batch = input_data_list[i : i + batch_size]
-            
+
             try:
                 # Generate embeddings for this batch
                 result = await self.client.aio.models.embed_content(
                     model=self.config.embedding_model or DEFAULT_EMBEDDING_MODEL,
                     contents=batch,  # type: ignore[arg-type]  # mypy fails on broad union type
-                    config=types.EmbedContentConfig(output_dimensionality=self.config.embedding_dim),
+                    config=types.EmbedContentConfig(
+                        output_dimensionality=self.config.embedding_dim
+                    ),
                 )
 
                 if not result.embeddings or len(result.embeddings) == 0:
@@ -149,29 +151,33 @@ class GeminiEmbedder(EmbedderClient):
                     if not embedding.values:
                         raise ValueError('Empty embedding values returned')
                     all_embeddings.append(embedding.values)
-                    
+
             except Exception as e:
                 # If batch processing fails, fall back to individual processing
-                logger.warning(f"Batch embedding failed for batch {i//batch_size + 1}, falling back to individual processing: {e}")
+                logger.warning(
-                
+                    f'Batch embedding failed for batch {i // batch_size + 1}, falling back to individual processing: {e}'
+                )
+
                 for item in batch:
                     try:
                         # Process each item individually
                         result = await self.client.aio.models.embed_content(
                             model=self.config.embedding_model or DEFAULT_EMBEDDING_MODEL,
                             contents=[item],  # type: ignore[arg-type]  # mypy fails on broad union type
-                            config=types.EmbedContentConfig(output_dimensionality=self.config.embedding_dim),
+                            config=types.EmbedContentConfig(
+                                output_dimensionality=self.config.embedding_dim
+                            ),
                         )
-                        
+
                         if not result.embeddings or len(result.embeddings) == 0:
                             raise ValueError('No embeddings returned from Gemini API')
                         if not result.embeddings[0].values:
                             raise ValueError('Empty embedding values returned')
-                            
+
                         all_embeddings.append(result.embeddings[0].values)
-                        
+
                     except Exception as individual_error:
-                        logger.error(f"Failed to embed individual item: {individual_error}")
+                        logger.error(f'Failed to embed individual item: {individual_error}')
                         raise individual_error
-        
+
         return all_embeddings

graphiti_core/llm_client/client.py

@@ -172,13 +172,13 @@ class LLMClient(ABC):
         """
         Log the full input messages, the raw output (if any), and the exception for debugging failed generations.
         """
-        log = ""
+        log = ''
-        log += f"Input messages: {json.dumps([m.model_dump() for m in messages], indent=2)}\n"
+        log += f'Input messages: {json.dumps([m.model_dump() for m in messages], indent=2)}\n'
         if output is not None:
             if len(output) > 4000:
-                log += f"Raw output: {output[:2000]}... (truncated) ...{output[-2000:]}\n"
+                log += f'Raw output: {output[:2000]}... (truncated) ...{output[-2000:]}\n'
             else:
-                log += f"Raw output: {output}\n"
+                log += f'Raw output: {output}\n'
         else:
-            log += "No raw output available"
+            log += 'No raw output available'
         return log

graphiti_core/llm_client/gemini_client.py

@@ -219,14 +219,14 @@ class GeminiClient(LLMClient):
         array_match = re.search(r'\]\s*$', raw_output)
         if array_match:
             try:
-                return json.loads(raw_output[:array_match.end()])
+                return json.loads(raw_output[: array_match.end()])
             except Exception:
                 pass
         # Try to salvage a JSON object
         obj_match = re.search(r'\}\s*$', raw_output)
         if obj_match:
             try:
-                return json.loads(raw_output[:obj_match.end()])
+                return json.loads(raw_output[: obj_match.end()])
             except Exception:
                 pass
         return None
@@ -323,12 +323,14 @@ class GeminiClient(LLMClient):
                     return validated_model.model_dump()
                 except Exception as e:
                     if raw_output:
-                        logger.error("🦀 LLM generation failed parsing as JSON, will try to salvage.")
+                        logger.error(
+                            '🦀 LLM generation failed parsing as JSON, will try to salvage.'
+                        )
                         logger.error(self._get_failed_generation_log(gemini_messages, raw_output))
                         # Try to salvage
                         salvaged = self.salvage_json(raw_output)
                         if salvaged is not None:
-                            logger.warning("Salvaged partial JSON from truncated/malformed output.")
+                            logger.warning('Salvaged partial JSON from truncated/malformed output.')
                             return salvaged
                     raise Exception(f'Failed to parse structured response: {e}') from e
 
@@ -384,7 +386,11 @@ class GeminiClient(LLMClient):
                     max_tokens=max_tokens,
                     model_size=model_size,
                 )
-                last_output = response.get('content') if isinstance(response, dict) and 'content' in response else None
+                last_output = (
+                    response.get('content')
+                    if isinstance(response, dict) and 'content' in response
+                    else None
+                )
                 return response
             except RateLimitError as e:
                 # Rate limit errors should not trigger retries (fail fast)
@@ -416,7 +422,7 @@ class GeminiClient(LLMClient):
                 )
 
         # If we exit the loop without returning, all retries are exhausted
-        logger.error("🦀 LLM generation failed and retries are exhausted.")
+        logger.error('🦀 LLM generation failed and retries are exhausted.')
         logger.error(self._get_failed_generation_log(messages, last_output))
         logger.error(f'Max retries ({self.MAX_RETRIES}) exceeded. Last error: {last_error}')
-        raise last_error or Exception("Max retries exceeded")
+        raise last_error or Exception('Max retries exceeded')

graphiti_core/models/edges/edge_db_queries.py

@@ -31,9 +31,9 @@ EPISODIC_EDGE_SAVE_BULK = """
 """
 
 ENTITY_EDGE_SAVE = """
-        MATCH (source:Entity {uuid: $source_uuid}) 
+        MATCH (source:Entity {uuid: $edge_data.source_uuid}) 
-        MATCH (target:Entity {uuid: $target_uuid}) 
+        MATCH (target:Entity {uuid: $edge_data.target_uuid}) 
-        MERGE (source)-[r:RELATES_TO {uuid: $uuid}]->(target)
+        MERGE (source)-[r:RELATES_TO {uuid: $edge_data.uuid}]->(target)
         SET r = $edge_data
         WITH r CALL db.create.setRelationshipVectorProperty(r, "fact_embedding", $edge_data.fact_embedding)
         RETURN r.uuid AS uuid"""

tests/embedder/test_gemini.py

@@ -345,9 +345,9 @@ class TestGeminiEmbedderCreateBatch:
 
         # Set side_effect for embed_content to control return values for each call
         mock_gemini_client.aio.models.embed_content.side_effect = [
-            mock_batch_response,          # First call for the batch
+            mock_batch_response,  # First call for the batch
-            mock_individual_response_1,   # Second call for individual item 1
+            mock_individual_response_1,  # Second call for individual item 1
-            mock_individual_response_2    # Third call for individual item 2
+            mock_individual_response_2,  # Third call for individual item 2
         ]
 
         input_batch = ['Input 1', 'Input 2']

tests/llm_client/test_gemini_client.py

@@ -273,7 +273,7 @@ class TestGeminiClientGenerateResponse:
         messages = [Message(role='user', content='Test message')]
         with pytest.raises(Exception):  # noqa: B017
             await gemini_client.generate_response(messages, response_model=ResponseModel)
-        
+
         # Should have called generate_content MAX_RETRIES times (2 attempts total)
         assert mock_gemini_client.aio.models.generate_content.call_count == GeminiClient.MAX_RETRIES
 
@@ -344,10 +344,7 @@ class TestGeminiClientGenerateResponse:
             await gemini_client.generate_response(messages, response_model=ResponseModel)
 
         # Should have called generate_content MAX_RETRIES times (2 attempts total)
-        assert (
+        assert mock_gemini_client.aio.models.generate_content.call_count == GeminiClient.MAX_RETRIES
-            mock_gemini_client.aio.models.generate_content.call_count
-            == GeminiClient.MAX_RETRIES
-        )
 
     @pytest.mark.asyncio
     async def test_empty_response_handling(self, gemini_client, mock_gemini_client):
@@ -363,7 +360,7 @@ class TestGeminiClientGenerateResponse:
         messages = [Message(role='user', content='Test message')]
         with pytest.raises(Exception):  # noqa: B017
             await gemini_client.generate_response(messages, response_model=ResponseModel)
-        
+
         # Should have exhausted retries due to empty response (2 attempts total)
         assert mock_gemini_client.aio.models.generate_content.call_count == GeminiClient.MAX_RETRIES
 

uv.lock

@@ -746,7 +746,7 @@ wheels = [
 
 [[package]]
 name = "graphiti-core"
-version = "0.17.1"
+version = "0.17.2"
 source = { editable = "." }
 dependencies = [
     { name = "diskcache" },