chore: Pass embedding model in search utils

# Conflicts:
#	graphiti_core/search/search.py

graphiti_core/edges.py

@@ -26,7 +26,7 @@ from pydantic import BaseModel, Field
 
 from graphiti_core.errors import EdgeNotFoundError, GroupsEdgesNotFoundError
 from graphiti_core.helpers import parse_db_date
-from graphiti_core.llm_client.config import EMBEDDING_DIM
+from graphiti_core.llm_client.config import DEFAULT_EMBEDDING_MODEL, EMBEDDING_DIM
 from graphiti_core.nodes import Node
 
 logger = logging.getLogger(__name__)
@@ -171,7 +171,7 @@ class EntityEdge(Edge):
         default=None, description='datetime of when the fact stopped being true'
     )
 
-    async def generate_embedding(self, embedder, model='text-embedding-3-small'):
+    async def generate_embedding(self, embedder, model=DEFAULT_EMBEDDING_MODEL):
         start = time()
 
         text = self.fact.replace('\n', ' ')

graphiti_core/graphiti.py

@@ -318,7 +318,7 @@ class Graphiti:
             # Calculate Embeddings
 
             await asyncio.gather(
-                *[node.generate_name_embedding(embedder) for node in extracted_nodes]
+                *[node.generate_name_embedding(embedder, self.llm_client.embedding_model) for node in extracted_nodes]
             )
 
             # Resolve extracted nodes with nodes already in the graph and extract facts
@@ -346,7 +346,7 @@ class Graphiti:
             # calculate embeddings
             await asyncio.gather(
                 *[
-                    edge.generate_embedding(embedder)
+                    edge.generate_embedding(embedder, self.llm_client.embedding_model)
                     for edge in extracted_edges_with_resolved_pointers
                 ]
             )
@@ -520,8 +520,8 @@ class Graphiti:
 
             # Generate embeddings
             await asyncio.gather(
-                *[node.generate_name_embedding(embedder) for node in extracted_nodes],
-                *[edge.generate_embedding(embedder) for edge in extracted_edges],
+                *[node.generate_name_embedding(embedder, self.llm_client.embedding_model) for node in extracted_nodes],
+                *[edge.generate_embedding(embedder, self.llm_client.embedding_model) for edge in extracted_edges],
             )
 
             # Dedupe extracted nodes, compress extracted edges
@@ -571,7 +571,7 @@ class Graphiti:
 
         community_nodes, community_edges = await build_communities(self.driver, self.llm_client)
 
-        await asyncio.gather(*[node.generate_name_embedding(embedder) for node in community_nodes])
+        await asyncio.gather(*[node.generate_name_embedding(embedder, self.llm_client.embedding_model) for node in community_nodes])
 
         await asyncio.gather(*[node.save(self.driver) for node in community_nodes])
         await asyncio.gather(*[edge.save(self.driver) for edge in community_edges])
@@ -618,6 +618,7 @@ class Graphiti:
             EDGE_HYBRID_SEARCH_RRF if center_node_uuid is None else EDGE_HYBRID_SEARCH_NODE_DISTANCE
         )
         search_config.limit = num_results
+        search_config.embedding_model = self.llm_client.embedding_model
 
         edges = (
             await search(

graphiti_core/llm_client/client.py

@@ -54,6 +54,7 @@ class LLMClient(ABC):
         self.max_tokens = config.max_tokens
         self.cache_enabled = cache
         self.cache_dir = Cache(DEFAULT_CACHE_DIR)  # Create a cache directory
+        self.embedding_model = config.embedding_model
 
     @abstractmethod
     def get_embedder(self) -> typing.Any:

graphiti_core/llm_client/config.py

@@ -17,7 +17,7 @@ limitations under the License.
 EMBEDDING_DIM = 1024
 DEFAULT_MAX_TOKENS = 16384
 DEFAULT_TEMPERATURE = 0
-
+DEFAULT_EMBEDDING_MODEL = 'text-embedding-3-small'
 
 class LLMConfig:
     """
@@ -33,6 +33,7 @@ class LLMConfig:
         api_key: str | None = None,
         model: str | None = None,
         base_url: str | None = None,
+        embedding_model: str | None = DEFAULT_EMBEDDING_MODEL,
         temperature: float = DEFAULT_TEMPERATURE,
         max_tokens: int = DEFAULT_MAX_TOKENS,
     ):
@@ -50,9 +51,15 @@ class LLMConfig:
                 base_url (str, optional): The base URL of the LLM API service.
                                                                         Defaults to "https://api.openai.com", which is OpenAI's standard API endpoint.
                                                                         This can be changed if using a different provider or a custom endpoint.
+                embedding_model (str, optional): The specific embedding model.
+                                                                        Defaults to openai "text-embedding-3-small" model.
+                                                                        We currently only support openai embedding models, such as "text-embedding-3-small", "text-embedding-3-large", "text-embedding-ada-002".
         """
         self.base_url = base_url
         self.api_key = api_key
         self.model = model
         self.temperature = temperature
         self.max_tokens = max_tokens
+        if not embedding_model:
+            embedding_model = DEFAULT_EMBEDDING_MODEL
+        self.embedding_model = embedding_model

graphiti_core/llm_client/utils.py

@@ -18,13 +18,13 @@ import logging
 import typing
 from time import time
 
-from graphiti_core.llm_client.config import EMBEDDING_DIM
+from graphiti_core.llm_client.config import DEFAULT_EMBEDDING_MODEL, EMBEDDING_DIM
 
 logger = logging.getLogger(__name__)
 
 
 async def generate_embedding(
-    embedder: typing.Any, text: str, model: str = 'text-embedding-3-small'
+    embedder: typing.Any, text: str, model: str = DEFAULT_EMBEDDING_MODEL
 ):
     start = time()
 

graphiti_core/nodes.py

@@ -26,7 +26,7 @@ from neo4j import AsyncDriver
 from pydantic import BaseModel, Field
 
 from graphiti_core.errors import NodeNotFoundError
-from graphiti_core.llm_client.config import EMBEDDING_DIM
+from graphiti_core.llm_client.config import DEFAULT_EMBEDDING_MODEL, EMBEDDING_DIM
 
 logger = logging.getLogger(__name__)
 
@@ -212,7 +212,7 @@ class EntityNode(Node):
     name_embedding: list[float] | None = Field(default=None, description='embedding of the name')
     summary: str = Field(description='regional summary of surrounding edges', default_factory=str)
 
-    async def generate_name_embedding(self, embedder, model='text-embedding-3-small'):
+    async def generate_name_embedding(self, embedder, model=DEFAULT_EMBEDDING_MODEL):
         start = time()
         text = self.name.replace('\n', ' ')
         embedding = (await embedder.create(input=[text], model=model)).data[0].embedding
@@ -323,7 +323,7 @@ class CommunityNode(Node):
 
         return result
 
-    async def generate_name_embedding(self, embedder, model='text-embedding-3-small'):
+    async def generate_name_embedding(self, embedder, model=DEFAULT_EMBEDDING_MODEL):
         start = time()
         text = self.name.replace('\n', ' ')
         embedding = (await embedder.create(input=[text], model=model)).data[0].embedding

graphiti_core/search/search.py

@@ -23,7 +23,7 @@ from neo4j import AsyncDriver
 
 from graphiti_core.edges import EntityEdge
 from graphiti_core.errors import SearchRerankerError
-from graphiti_core.llm_client.config import EMBEDDING_DIM
+from graphiti_core.llm_client.config import DEFAULT_EMBEDDING_MODEL, EMBEDDING_DIM
 from graphiti_core.nodes import CommunityNode, EntityNode
 from graphiti_core.search.search_config import (
     DEFAULT_SEARCH_LIMIT,
@@ -68,12 +68,34 @@ async def search(
     group_ids = group_ids if group_ids else None
     edges, nodes, communities = await asyncio.gather(
         edge_search(
-            driver, embedder, query, group_ids, config.edge_config, center_node_uuid, config.limit
+            driver,
+            embedder,
+            query,
+            group_ids,
+            config.edge_config,
+            center_node_uuid,
+            config.limit,
+            config.embedding_model,
         ),
         node_search(
-            driver, embedder, query, group_ids, config.node_config, center_node_uuid, config.limit
+            driver,
+            embedder,
+            query,
+            group_ids,
+            config.node_config,
+            center_node_uuid,
+            config.limit,
+            config.embedding_model,
+        ),
+        community_search(
+            driver,
+            embedder,
+            query,
+            group_ids,
+            config.community_config,
+            config.limit,
+            config.embedding_model,
         ),
-        community_search(driver, embedder, query, group_ids, config.community_config, config.limit),
     )
 
     results = SearchResults(
@@ -97,6 +119,7 @@ async def edge_search(
     config: EdgeSearchConfig | None,
     center_node_uuid: str | None = None,
     limit=DEFAULT_SEARCH_LIMIT,
+    embedding_model: str | None = None,
 ) -> list[EntityEdge]:
     if config is None:
         return []
@@ -109,7 +132,7 @@ async def edge_search(
 
     if EdgeSearchMethod.cosine_similarity in config.search_methods:
         search_vector = (
-            (await embedder.create(input=[query], model='text-embedding-3-small'))
+            (await embedder.create(input=[query], model=embedding_model or DEFAULT_EMBEDDING_MODEL))
             .data[0]
             .embedding[:EMBEDDING_DIM]
         )
@@ -165,6 +188,7 @@ async def node_search(
     config: NodeSearchConfig | None,
     center_node_uuid: str | None = None,
     limit=DEFAULT_SEARCH_LIMIT,
+    embedding_model: str | None = None,
 ) -> list[EntityNode]:
     if config is None:
         return []
@@ -177,7 +201,7 @@ async def node_search(
 
     if NodeSearchMethod.cosine_similarity in config.search_methods:
         search_vector = (
-            (await embedder.create(input=[query], model='text-embedding-3-small'))
+            (await embedder.create(input=[query], model=embedding_model or DEFAULT_EMBEDDING_MODEL))
             .data[0]
             .embedding[:EMBEDDING_DIM]
         )
@@ -217,6 +241,7 @@ async def community_search(
     group_ids: list[str] | None,
     config: CommunitySearchConfig | None,
     limit=DEFAULT_SEARCH_LIMIT,
+    embedding_model: str | None = None,
 ) -> list[CommunityNode]:
     if config is None:
         return []
@@ -229,7 +254,7 @@ async def community_search(
 
     if CommunitySearchMethod.cosine_similarity in config.search_methods:
         search_vector = (
-            (await embedder.create(input=[query], model='text-embedding-3-small'))
+            (await embedder.create(input=[query], model=embedding_model or DEFAULT_EMBEDDING_MODEL))
             .data[0]
             .embedding[:EMBEDDING_DIM]
         )

graphiti_core/search/search_config.py

@@ -74,6 +74,7 @@ class SearchConfig(BaseModel):
     edge_config: EdgeSearchConfig | None = Field(default=None)
     node_config: NodeSearchConfig | None = Field(default=None)
     community_config: CommunitySearchConfig | None = Field(default=None)
+    embedding_model: str | None = Field(default=None)
     limit: int = Field(default=DEFAULT_SEARCH_LIMIT)
 
 

graphiti_core/utils/maintenance/community_operations.py

@@ -305,6 +305,6 @@ async def update_community(
         community_edge = (build_community_edges([entity], community, datetime.now()))[0]
         await community_edge.save(driver)
 
-    await community.generate_name_embedding(embedder)
+    await community.generate_name_embedding(embedder, llm_client.embedding_model)
 
     await community.save(driver)

server/graph_service/config.py

@@ -10,6 +10,7 @@ class Settings(BaseSettings):
     openai_api_key: str
     openai_base_url: str | None = Field(None)
     model_name: str | None = Field(None)
+    embedding_model_name: str | None = Field(None)
     neo4j_uri: str
     neo4j_user: str
     neo4j_password: str

server/graph_service/zep_graphiti.py

@@ -25,7 +25,7 @@ class ZepGraphiti(Graphiti):
             group_id=group_id,
             summary=summary,
         )
-        await new_node.generate_name_embedding(self.llm_client.get_embedder())
+        await new_node.generate_name_embedding(self.llm_client.get_embedder(), self.llm_client.embedding_model)
         await new_node.save(self.driver)
         return new_node
 
@@ -83,6 +83,8 @@ async def get_graphiti(settings: ZepEnvDep):
         client.llm_client.config.api_key = settings.openai_api_key
     if settings.model_name is not None:
         client.llm_client.model = settings.model_name
+    if settings.embedding_model_name is not None:
+        client.llm_client.embedding_model = settings.embedding_model_name
     try:
         yield client
     finally:

tests/test_graphiti_int.py

@@ -145,7 +145,7 @@ async def test_graph_integration():
         invalid_at=now,
     )
 
-    await entity_edge.generate_embedding(embedder)
+    await entity_edge.generate_embedding(embedder, client.llm_client.embedding_model)
 
     nodes = [episode, alice_node, bob_node]
     edges = [episodic_edge_1, episodic_edge_2, entity_edge]