fix: Resolve issue with Gemini adapter (#1494)

<!-- .github/pull_request_template.md -->

## Description
Resolve Gemini Adapter issues:
 1. resolve embedding batch issue,
2. Resolve slowness because gemini tokenizer was sending word per word
to Googles API to count tokens (using OpenAI's local tokenizer to count
tokens for Gemini now)
 3. Update deprecated library and move to instructor

## Type of Change
<!-- Please check the relevant option -->
- [x] Bug fix (non-breaking change that fixes an issue)
- [ ] New feature (non-breaking change that adds functionality)
- [ ] Breaking change (fix or feature that would cause existing
functionality to change)
- [ ] Documentation update
- [ ] Code refactoring
- [ ] Performance improvement
- [ ] Other (please specify):

## Pre-submission Checklist
<!-- Please check all boxes that apply before submitting your PR -->
- [ ] **I have tested my changes thoroughly before submitting this PR**
- [ ] **This PR contains minimal changes necessary to address the
issue/feature**
- [ ] My code follows the project's coding standards and style
guidelines
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [ ] I have added necessary documentation (if applicable)
- [ ] All new and existing tests pass
- [ ] I have searched existing PRs to ensure this change hasn't been
submitted already
- [ ] I have linked any relevant issues in the description
- [ ] My commits have clear and descriptive messages

## DCO Affirmation
I affirm that all code in every commit of this pull request conforms to
the terms of the Topoteretes Developer Certificate of Origin.

.env.template

@@ -30,6 +30,9 @@ EMBEDDING_DIMENSIONS=3072
 EMBEDDING_MAX_TOKENS=8191
 # If embedding key is not provided same key set for LLM_API_KEY will be used
 #EMBEDDING_API_KEY="your_api_key"
+# Note: OpenAI support up to 2048 elements and Gemini supports a maximum of 100 elements in an embedding batch,
+#       Cognee sets the optimal batch size for OpenAI and Gemini, but a custom size can be defined if necessary for other models
+#EMBEDDING_BATCH_SIZE=2048
 
 # If using BAML structured output these env variables will be used
 BAML_LLM_PROVIDER=openai
@@ -52,18 +55,18 @@ BAML_LLM_API_VERSION=""
 ################################################################################
 # Configure storage backend (local filesystem or S3)
 # STORAGE_BACKEND="local"  # Default: uses local filesystem
-# 
+#
 # -- To switch to S3 storage, uncomment and fill these: ---------------------
 # STORAGE_BACKEND="s3"
 # STORAGE_BUCKET_NAME="your-bucket-name"
 # AWS_REGION="us-east-1"
 # AWS_ACCESS_KEY_ID="your-access-key"
 # AWS_SECRET_ACCESS_KEY="your-secret-key"
-# 
+#
 # -- S3 Root Directories (optional) -----------------------------------------
 # DATA_ROOT_DIRECTORY="s3://your-bucket/cognee/data"
 # SYSTEM_ROOT_DIRECTORY="s3://your-bucket/cognee/system"
-# 
+#
 # -- Cache Directory (auto-configured for S3) -------------------------------
 # When STORAGE_BACKEND=s3, cache automatically uses S3: s3://BUCKET/cognee/cache
 # To override the automatic S3 cache location, uncomment:

.github/actions/cognee_setup/action.yml

@@ -41,4 +41,4 @@ runs:
             EXTRA_ARGS="$EXTRA_ARGS --extra $extra"
           done
         fi
-        uv sync --extra api --extra docs --extra evals --extra gemini --extra codegraph --extra ollama --extra dev --extra neo4j $EXTRA_ARGS
+        uv sync --extra api --extra docs --extra evals --extra codegraph --extra ollama --extra dev --extra neo4j $EXTRA_ARGS

.github/workflows/test_llms.yml

@@ -27,7 +27,7 @@ jobs:
         env:
           LLM_PROVIDER: "gemini"
           LLM_API_KEY: ${{ secrets.GEMINI_API_KEY }}
-          LLM_MODEL: "gemini/gemini-1.5-flash"
+          LLM_MODEL: "gemini/gemini-2.0-flash"
           EMBEDDING_PROVIDER: "gemini"
           EMBEDDING_API_KEY: ${{ secrets.GEMINI_API_KEY }}
           EMBEDDING_MODEL: "gemini/text-embedding-004"
@@ -83,4 +83,4 @@ jobs:
           EMBEDDING_MODEL: "openai/text-embedding-3-large"
           EMBEDDING_DIMENSIONS: "3072"
           EMBEDDING_MAX_TOKENS: "8191"
-        run: uv run python ./examples/python/simple_example.py
+        run: uv run python ./examples/python/simple_example.py

Dockerfile

@@ -31,7 +31,7 @@ COPY README.md pyproject.toml uv.lock entrypoint.sh ./
 
 # Install the project's dependencies using the lockfile and settings
 RUN --mount=type=cache,target=/root/.cache/uv \
-    uv sync --extra debug --extra api --extra postgres --extra neo4j --extra llama-index --extra gemini --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-install-project --no-dev --no-editable
+    uv sync --extra debug --extra api --extra postgres --extra neo4j --extra llama-index --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-install-project --no-dev --no-editable
 
 # Copy Alembic configuration
 COPY alembic.ini /app/alembic.ini
@@ -42,7 +42,7 @@ COPY alembic/ /app/alembic
 COPY ./cognee /app/cognee
 COPY ./distributed /app/distributed
 RUN --mount=type=cache,target=/root/.cache/uv \
-uv sync --extra debug --extra api --extra postgres --extra neo4j --extra llama-index --extra gemini --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-dev --no-editable
+uv sync --extra debug --extra api --extra postgres --extra neo4j --extra llama-index --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-dev --no-editable
 
 FROM python:3.12-slim-bookworm
 

cognee-mcp/pyproject.toml

@@ -8,6 +8,7 @@ requires-python = ">=3.10"
 dependencies = [
     # For local cognee repo usage remove comment bellow and add absolute path to cognee. Then run `uv sync --reinstall` in the mcp folder on local cognee changes.
     #"cognee[postgres,codegraph,gemini,huggingface,docs,neo4j] @ file:/Users/igorilic/Desktop/cognee",
+    # TODO: Remove gemini from optional dependecnies for new Cognee version after 0.3.4
     "cognee[postgres,codegraph,gemini,huggingface,docs,neo4j]==0.3.4",
     "fastmcp>=2.10.0,<3.0.0",
     "mcp>=1.12.0,<2.0.0",

cognee/infrastructure/databases/vector/embeddings/EmbeddingEngine.py

@@ -34,3 +34,12 @@ class EmbeddingEngine(Protocol):
             - int: An integer representing the number of dimensions in the embedding vector.
         """
         raise NotImplementedError()
+
+    def get_batch_size(self) -> int:
+        """
+        Return the desired batch size for embedding calls
+
+        Returns:
+
+        """
+        raise NotImplementedError()

cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py

@@ -42,11 +42,13 @@ class FastembedEmbeddingEngine(EmbeddingEngine):
         model: Optional[str] = "openai/text-embedding-3-large",
         dimensions: Optional[int] = 3072,
         max_completion_tokens: int = 512,
+        batch_size: int = 100,
     ):
         self.model = model
         self.dimensions = dimensions
         self.max_completion_tokens = max_completion_tokens
         self.tokenizer = self.get_tokenizer()
+        self.batch_size = batch_size
         # self.retry_count = 0
         self.embedding_model = TextEmbedding(model_name=model)
 
@@ -101,6 +103,15 @@ class FastembedEmbeddingEngine(EmbeddingEngine):
         """
         return self.dimensions
 
+    def get_batch_size(self) -> int:
+        """
+        Return the desired batch size for embedding calls
+
+        Returns:
+
+        """
+        return self.batch_size
+
     def get_tokenizer(self):
         """
         Instantiate and return the tokenizer used for preparing text for embedding.

cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py

@@ -58,6 +58,7 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
         endpoint: str = None,
         api_version: str = None,
         max_completion_tokens: int = 512,
+        batch_size: int = 100,
     ):
         self.api_key = api_key
         self.endpoint = endpoint
@@ -68,6 +69,7 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
         self.max_completion_tokens = max_completion_tokens
         self.tokenizer = self.get_tokenizer()
         self.retry_count = 0
+        self.batch_size = batch_size
 
         enable_mocking = os.getenv("MOCK_EMBEDDING", "false")
         if isinstance(enable_mocking, bool):
@@ -165,6 +167,15 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
         """
         return self.dimensions
 
+    def get_batch_size(self) -> int:
+        """
+        Return the desired batch size for embedding calls
+
+        Returns:
+
+        """
+        return self.batch_size
+
     def get_tokenizer(self):
         """
         Load and return the appropriate tokenizer for the specified model based on the provider.
@@ -183,9 +194,15 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
                 model=model, max_completion_tokens=self.max_completion_tokens
             )
         elif "gemini" in self.provider.lower():
-            tokenizer = GeminiTokenizer(
+            # Since Gemini tokenization needs to send an API request to get the token count we will use TikToken to
-                model=model, max_completion_tokens=self.max_completion_tokens
+            # count tokens as we calculate tokens word by word
+            tokenizer = TikTokenTokenizer(
+                model=None, max_completion_tokens=self.max_completion_tokens
             )
+            # Note: Gemini Tokenizer expects an LLM model as input and not the embedding model
+            # tokenizer = GeminiTokenizer(
+            #     llm_model=llm_model, max_completion_tokens=self.max_completion_tokens
+            # )
         elif "mistral" in self.provider.lower():
             tokenizer = MistralTokenizer(
                 model=model, max_completion_tokens=self.max_completion_tokens

cognee/infrastructure/databases/vector/embeddings/OllamaEmbeddingEngine.py

@@ -54,12 +54,14 @@ class OllamaEmbeddingEngine(EmbeddingEngine):
         max_completion_tokens: int = 512,
         endpoint: Optional[str] = "http://localhost:11434/api/embeddings",
         huggingface_tokenizer: str = "Salesforce/SFR-Embedding-Mistral",
+        batch_size: int = 100,
     ):
         self.model = model
         self.dimensions = dimensions
         self.max_completion_tokens = max_completion_tokens
         self.endpoint = endpoint
         self.huggingface_tokenizer_name = huggingface_tokenizer
+        self.batch_size = batch_size
         self.tokenizer = self.get_tokenizer()
 
         enable_mocking = os.getenv("MOCK_EMBEDDING", "false")
@@ -122,6 +124,15 @@ class OllamaEmbeddingEngine(EmbeddingEngine):
         """
         return self.dimensions
 
+    def get_batch_size(self) -> int:
+        """
+        Return the desired batch size for embedding calls
+
+        Returns:
+
+        """
+        return self.batch_size
+
     def get_tokenizer(self):
         """
         Load and return a HuggingFace tokenizer for the embedding engine.

cognee/infrastructure/databases/vector/embeddings/config.py

@@ -19,9 +19,17 @@ class EmbeddingConfig(BaseSettings):
     embedding_api_key: Optional[str] = None
     embedding_api_version: Optional[str] = None
     embedding_max_completion_tokens: Optional[int] = 8191
+    embedding_batch_size: Optional[int] = None
     huggingface_tokenizer: Optional[str] = None
     model_config = SettingsConfigDict(env_file=".env", extra="allow")
 
+    def model_post_init(self, __context) -> None:
+        # If embedding batch size is not defined use 2048 as default for OpenAI and 100 for all other embedding models
+        if not self.embedding_batch_size and self.embedding_provider.lower() == "openai":
+            self.embedding_batch_size = 2048
+        elif not self.embedding_batch_size:
+            self.embedding_batch_size = 100
+
     def to_dict(self) -> dict:
         """
         Serialize all embedding configuration settings to a dictionary.

cognee/infrastructure/databases/vector/embeddings/get_embedding_engine.py

@@ -31,6 +31,7 @@ def get_embedding_engine() -> EmbeddingEngine:
         config.embedding_endpoint,
         config.embedding_api_key,
         config.embedding_api_version,
+        config.embedding_batch_size,
         config.huggingface_tokenizer,
         llm_config.llm_api_key,
         llm_config.llm_provider,
@@ -46,6 +47,7 @@ def create_embedding_engine(
     embedding_endpoint,
     embedding_api_key,
     embedding_api_version,
+    embedding_batch_size,
     huggingface_tokenizer,
     llm_api_key,
     llm_provider,
@@ -84,6 +86,7 @@ def create_embedding_engine(
             model=embedding_model,
             dimensions=embedding_dimensions,
             max_completion_tokens=embedding_max_completion_tokens,
+            batch_size=embedding_batch_size,
         )
 
     if embedding_provider == "ollama":
@@ -95,6 +98,7 @@ def create_embedding_engine(
             max_completion_tokens=embedding_max_completion_tokens,
             endpoint=embedding_endpoint,
             huggingface_tokenizer=huggingface_tokenizer,
+            batch_size=embedding_batch_size,
         )
 
     from .LiteLLMEmbeddingEngine import LiteLLMEmbeddingEngine
@@ -108,4 +112,5 @@ def create_embedding_engine(
         model=embedding_model,
         dimensions=embedding_dimensions,
         max_completion_tokens=embedding_max_completion_tokens,
+        batch_size=embedding_batch_size,
     )

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/gemini/adapter.py

@@ -1,115 +1,155 @@
-import litellm
+"""Adapter for Generic API LLM provider API"""
-from pydantic import BaseModel
-from typing import Type, Optional
-from litellm import acompletion, JSONSchemaValidationError
 
-from cognee.shared.logging_utils import get_logger
+import litellm
-from cognee.modules.observability.get_observe import get_observe
+import instructor
-from cognee.infrastructure.llm.exceptions import MissingSystemPromptPathError
+from typing import Type
+from pydantic import BaseModel
+from openai import ContentFilterFinishReasonError
+from litellm.exceptions import ContentPolicyViolationError
+from instructor.core import InstructorRetryException
+
+from cognee.infrastructure.llm.exceptions import ContentPolicyFilterError
 from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import (
     LLMInterface,
 )
-from cognee.infrastructure.llm.LLMGateway import LLMGateway
 from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import (
     rate_limit_async,
     sleep_and_retry_async,
 )
 
-logger = get_logger()
-observe = get_observe()
-
 
 class GeminiAdapter(LLMInterface):
     """
-    Handles interactions with a language model API.
+    Adapter for Gemini API LLM provider.
 
-    Public methods include:
+    This class initializes the API adapter with necessary credentials and configurations for
-    - acreate_structured_output
+    interacting with the gemini LLM models. It provides methods for creating structured outputs
-    - show_prompt
+    based on user input and system prompts.
+
+    Public methods:
+    - acreate_structured_output(text_input: str, system_prompt: str, response_model:
+    Type[BaseModel]) -> BaseModel
     """
 
-    MAX_RETRIES = 5
+    name: str
+    model: str
+    api_key: str
 
     def __init__(
         self,
+        endpoint,
         api_key: str,
         model: str,
+        api_version: str,
         max_completion_tokens: int,
-        endpoint: Optional[str] = None,
+        fallback_model: str = None,
-        api_version: Optional[str] = None,
+        fallback_api_key: str = None,
-        streaming: bool = False,
+        fallback_endpoint: str = None,
-    ) -> None:
+    ):
-        self.api_key = api_key
         self.model = model
+        self.api_key = api_key
         self.endpoint = endpoint
         self.api_version = api_version
-        self.streaming = streaming
         self.max_completion_tokens = max_completion_tokens
 
-    @observe(as_type="generation")
+        self.fallback_model = fallback_model
+        self.fallback_api_key = fallback_api_key
+        self.fallback_endpoint = fallback_endpoint
+
+        self.aclient = instructor.from_litellm(litellm.acompletion, mode=instructor.Mode.JSON)
+
     @sleep_and_retry_async()
     @rate_limit_async
     async def acreate_structured_output(
         self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
     ) -> BaseModel:
         """
-        Generate structured output from the language model based on the provided input and
+        Generate a response from a user query.
-        system prompt.
 
-        This method handles retries and raises a ValueError if the request fails or the response
+        This asynchronous method sends a user query and a system prompt to a language model and
-        does not conform to the expected schema, logging errors accordingly.
+        retrieves the generated response. It handles API communication and retries up to a
+        specified limit in case of request failures.
 
         Parameters:
         -----------
 
-            - text_input (str): The user input text to generate a response for.
+            - text_input (str): The input text from the user to generate a response for.
-            - system_prompt (str): The system's prompt or context to influence the language
+            - system_prompt (str): A prompt that provides context or instructions for the
-              model's generation.
+              response generation.
-            - response_model (Type[BaseModel]): A model type indicating the expected format of
+            - response_model (Type[BaseModel]): A Pydantic model that defines the structure of
-              the response.
+              the expected response.
 
         Returns:
         --------
 
-            - BaseModel: Returns the generated response as an instance of the specified response
+            - BaseModel: An instance of the specified response model containing the structured
-              model.
+              output from the language model.
         """
+
         try:
-            if response_model is str:
+            return await self.aclient.chat.completions.create(
-                response_schema = {"type": "string"}
+                model=self.model,
-            else:
+                messages=[
-                response_schema = response_model
+                    {
+                        "role": "user",
+                        "content": f"""{text_input}""",
+                    },
+                    {
+                        "role": "system",
+                        "content": system_prompt,
+                    },
+                ],
+                api_key=self.api_key,
+                max_retries=5,
+                api_base=self.endpoint,
+                api_version=self.api_version,
+                response_model=response_model,
+            )
+        except (
+            ContentFilterFinishReasonError,
+            ContentPolicyViolationError,
+            InstructorRetryException,
+        ) as error:
+            if (
+                isinstance(error, InstructorRetryException)
+                and "content management policy" not in str(error).lower()
+            ):
+                raise error
 
-            messages = [
+            if not (self.fallback_model and self.fallback_api_key and self.fallback_endpoint):
-                {"role": "system", "content": system_prompt},
+                raise ContentPolicyFilterError(
-                {"role": "user", "content": text_input},
+                    f"The provided input contains content that is not aligned with our content policy: {text_input}"
-            ]
-
-            try:
-                response = await acompletion(
-                    model=f"{self.model}",
-                    messages=messages,
-                    api_key=self.api_key,
-                    max_completion_tokens=self.max_completion_tokens,
-                    temperature=0.1,
-                    response_format=response_schema,
-                    timeout=100,
-                    num_retries=self.MAX_RETRIES,
                 )
 
-                if response.choices and response.choices[0].message.content:
+            try:
-                    content = response.choices[0].message.content
+                return await self.aclient.chat.completions.create(
-                    if response_model is str:
+                    model=self.fallback_model,
-                        return content
+                    messages=[
-                    return response_model.model_validate_json(content)
+                        {
-
+                            "role": "user",
-            except litellm.exceptions.BadRequestError as e:
+                            "content": f"""{text_input}""",
-                logger.error(f"Bad request error: {str(e)}")
+                        },
-                raise ValueError(f"Invalid request: {str(e)}")
+                        {
-
+                            "role": "system",
-            raise ValueError("Failed to get valid response after retries")
+                            "content": system_prompt,
-
+                        },
-        except JSONSchemaValidationError as e:
+                    ],
-            logger.error(f"Schema validation failed: {str(e)}")
+                    max_retries=5,
-            logger.debug(f"Raw response: {e.raw_response}")
+                    api_key=self.fallback_api_key,
-            raise ValueError(f"Response failed schema validation: {str(e)}")
+                    api_base=self.fallback_endpoint,
+                    response_model=response_model,
+                )
+            except (
+                ContentFilterFinishReasonError,
+                ContentPolicyViolationError,
+                InstructorRetryException,
+            ) as error:
+                if (
+                    isinstance(error, InstructorRetryException)
+                    and "content management policy" not in str(error).lower()
+                ):
+                    raise error
+                else:
+                    raise ContentPolicyFilterError(
+                        f"The provided input contains content that is not aligned with our content policy: {text_input}"
+                    )

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py

@@ -6,7 +6,7 @@ from typing import Type
 from pydantic import BaseModel
 from openai import ContentFilterFinishReasonError
 from litellm.exceptions import ContentPolicyViolationError
-from instructor.exceptions import InstructorRetryException
+from instructor.core import InstructorRetryException
 
 from cognee.infrastructure.llm.exceptions import ContentPolicyFilterError
 from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import (
@@ -56,9 +56,7 @@ class GenericAPIAdapter(LLMInterface):
         self.fallback_api_key = fallback_api_key
         self.fallback_endpoint = fallback_endpoint
 
-        self.aclient = instructor.from_litellm(
+        self.aclient = instructor.from_litellm(litellm.acompletion, mode=instructor.Mode.JSON)
-            litellm.acompletion, mode=instructor.Mode.JSON, api_key=api_key
-        )
 
     @sleep_and_retry_async()
     @rate_limit_async
@@ -102,6 +100,7 @@ class GenericAPIAdapter(LLMInterface):
                     },
                 ],
                 max_retries=5,
+                api_key=self.api_key,
                 api_base=self.endpoint,
                 response_model=response_model,
             )

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py

@@ -143,7 +143,6 @@ def get_llm_client(raise_api_key_error: bool = True):
             max_completion_tokens=max_completion_tokens,
             endpoint=llm_config.llm_endpoint,
             api_version=llm_config.llm_api_version,
-            streaming=llm_config.llm_streaming,
         )
 
     else:

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py

@@ -5,15 +5,13 @@ from typing import Type
 from pydantic import BaseModel
 from openai import ContentFilterFinishReasonError
 from litellm.exceptions import ContentPolicyViolationError
-from instructor.exceptions import InstructorRetryException
+from instructor.core import InstructorRetryException
 
-from cognee.infrastructure.llm.LLMGateway import LLMGateway
 from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import (
     LLMInterface,
 )
 from cognee.infrastructure.llm.exceptions import (
     ContentPolicyFilterError,
-    MissingSystemPromptPathError,
 )
 from cognee.infrastructure.files.utils.open_data_file import open_data_file
 from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import (

cognee/infrastructure/llm/tokenizer/Gemini/adapter.py

@@ -3,6 +3,7 @@ from typing import List, Any
 from ..tokenizer_interface import TokenizerInterface
 
 
+# NOTE: DEPRECATED as to count tokens you need to send an API request to Google it is too slow to use with Cognee
 class GeminiTokenizer(TokenizerInterface):
     """
     Implements a tokenizer interface for the Gemini model, managing token extraction and
@@ -16,10 +17,10 @@ class GeminiTokenizer(TokenizerInterface):
 
     def __init__(
         self,
-        model: str,
+        llm_model: str,
         max_completion_tokens: int = 3072,
     ):
-        self.model = model
+        self.llm_model = llm_model
         self.max_completion_tokens = max_completion_tokens
 
         # Get LLM API key from config
@@ -28,12 +29,11 @@ class GeminiTokenizer(TokenizerInterface):
             get_llm_config,
         )
 
-        config = get_embedding_config()
         llm_config = get_llm_config()
 
-        import google.generativeai as genai
+        from google import genai
 
-        genai.configure(api_key=config.embedding_api_key or llm_config.llm_api_key)
+        self.client = genai.Client(api_key=llm_config.llm_api_key)
 
     def extract_tokens(self, text: str) -> List[Any]:
         """
@@ -77,6 +77,7 @@ class GeminiTokenizer(TokenizerInterface):
 
             - int: The number of tokens in the given text.
         """
-        import google.generativeai as genai
 
-        return len(genai.embed_content(model=f"models/{self.model}", content=text))
+        tokens_response = self.client.models.count_tokens(model=self.llm_model, contents=text)
+
+        return tokens_response.total_tokens

cognee/tasks/storage/index_data_points.py

@@ -39,7 +39,7 @@ async def index_data_points(data_points: list[DataPoint]):
         field_name = index_name_and_field[first_occurence + 1 :]
         try:
             # In case the amount of indexable points is too large we need to send them in batches
-            batch_size = 100
+            batch_size = vector_engine.embedding_engine.get_batch_size()
             for i in range(0, len(indexable_points), batch_size):
                 batch = indexable_points[i : i + batch_size]
                 await vector_engine.index_data_points(index_name, field_name, batch)

cognee/tasks/storage/index_graph_edges.py

@@ -9,7 +9,7 @@ from cognee.modules.graph.models.EdgeType import EdgeType
 logger = get_logger(level=ERROR)
 
 
-async def index_graph_edges(batch_size: int = 1024):
+async def index_graph_edges():
     """
     Indexes graph edges by creating and managing vector indexes for relationship types.
 
@@ -72,6 +72,8 @@ async def index_graph_edges(batch_size: int = 1024):
     for index_name, indexable_points in index_points.items():
         index_name, field_name = index_name.split(".")
 
+        # Get maximum batch size for embedding model
+        batch_size = vector_engine.embedding_engine.get_batch_size()
         # We save the data in batches of {batch_size} to not put a lot of pressure on the database
         for start in range(0, len(indexable_points), batch_size):
             batch = indexable_points[start : start + batch_size]

cognee/tests/unit/infrastructure/databases/test_index_graph_edges.py

@@ -1,5 +1,5 @@
 import pytest
-from unittest.mock import AsyncMock, patch
+from unittest.mock import AsyncMock, patch, MagicMock
 from cognee.tasks.storage.index_graph_edges import index_graph_edges
 
 
@@ -16,6 +16,7 @@ async def test_index_graph_edges_success():
         ],
     )
     mock_vector_engine = AsyncMock()
+    mock_vector_engine.embedding_engine.get_batch_size = MagicMock(return_value=100)
 
     # Patch the globals of the function so that when it does:
     #   vector_engine = get_vector_engine()

poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand.
 
 [[package]]
 name = "accelerate"
@@ -878,7 +878,7 @@ description = "Extensible memoizing collections and decorators"
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\""
+markers = "extra == \"deepeval\" or extra == \"chromadb\" or extra == \"docs\""
 files = [
     {file = "cachetools-6.2.0-py3-none-any.whl", hash = "sha256:1c76a8960c0041fcc21097e357f882197c79da0dbff766e7317890a65d7d8ba6"},
     {file = "cachetools-6.2.0.tar.gz", hash = "sha256:38b328c0889450f05f5e120f56ab68c8abaf424e1275522b138ffc93253f7e32"},
@@ -2749,28 +2749,6 @@ files = [
     {file = "giturlparse-0.12.0.tar.gz", hash = "sha256:c0fff7c21acc435491b1779566e038757a205c1ffdcb47e4f81ea52ad8c3859a"},
 ]
 
-[[package]]
-name = "google-ai-generativelanguage"
-version = "0.6.15"
-description = "Google Ai Generativelanguage API client library"
-optional = true
-python-versions = ">=3.7"
-groups = ["main"]
-markers = "extra == \"gemini\""
-files = [
-    {file = "google_ai_generativelanguage-0.6.15-py3-none-any.whl", hash = "sha256:5a03ef86377aa184ffef3662ca28f19eeee158733e45d7947982eb953c6ebb6c"},
-    {file = "google_ai_generativelanguage-0.6.15.tar.gz", hash = "sha256:8f6d9dc4c12b065fe2d0289026171acea5183ebf2d0b11cefe12f3821e159ec3"},
-]
-
-[package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
-proto-plus = [
-    {version = ">=1.22.3,<2.0.0dev"},
-    {version = ">=1.25.0,<2.0.0dev", markers = "python_version >= \"3.13\""},
-]
-protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0dev"
-
 [[package]]
 name = "google-api-core"
 version = "2.25.1"
@@ -2778,7 +2756,7 @@ description = "Google API client core library"
 optional = true
 python-versions = ">=3.7"
 groups = ["main"]
-markers = "extra == \"gemini\" or extra == \"docs\""
+markers = "extra == \"docs\""
 files = [
     {file = "google_api_core-2.25.1-py3-none-any.whl", hash = "sha256:8a2a56c1fef82987a524371f99f3bd0143702fecc670c72e600c1cda6bf8dbb7"},
     {file = "google_api_core-2.25.1.tar.gz", hash = "sha256:d2aaa0b13c78c61cb3f4282c464c046e45fbd75755683c9c525e6e8f7ed0a5e8"},
@@ -2808,26 +2786,6 @@ grpc = ["grpcio (>=1.33.2,<2.0.0)", "grpcio (>=1.49.1,<2.0.0) ; python_version >
 grpcgcp = ["grpcio-gcp (>=0.2.2,<1.0.0)"]
 grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.0)"]
 
-[[package]]
-name = "google-api-python-client"
-version = "2.183.0"
-description = "Google API Client Library for Python"
-optional = true
-python-versions = ">=3.7"
-groups = ["main"]
-markers = "extra == \"gemini\""
-files = [
-    {file = "google_api_python_client-2.183.0-py3-none-any.whl", hash = "sha256:2005b6e86c27be1db1a43f43e047a0f8e004159f3cceddecb08cf1624bddba31"},
-    {file = "google_api_python_client-2.183.0.tar.gz", hash = "sha256:abae37e04fecf719388e5c02f707ed9cdf952f10b217c79a3e76c636762e3ea9"},
-]
-
-[package.dependencies]
-google-api-core = ">=1.31.5,<2.0.dev0 || >2.3.0,<3.0.0"
-google-auth = ">=1.32.0,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0"
-google-auth-httplib2 = ">=0.2.0,<1.0.0"
-httplib2 = ">=0.19.0,<1.0.0"
-uritemplate = ">=3.0.1,<5"
-
 [[package]]
 name = "google-auth"
 version = "2.41.0"
@@ -2835,7 +2793,7 @@ description = "Google Authentication Library"
 optional = true
 python-versions = ">=3.7"
 groups = ["main"]
-markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\""
+markers = "extra == \"deepeval\" or extra == \"chromadb\" or extra == \"docs\""
 files = [
     {file = "google_auth-2.41.0-py2.py3-none-any.whl", hash = "sha256:d8bed9b53ab63b7b0374656b8e1bef051f95bb14ecc0cf21ba49de7911d62e09"},
     {file = "google_auth-2.41.0.tar.gz", hash = "sha256:c9d7b534ea4a5d9813c552846797fafb080312263cd4994d6622dd50992ae101"},
@@ -2856,23 +2814,6 @@ requests = ["requests (>=2.20.0,<3.0.0)"]
 testing = ["aiohttp (<3.10.0)", "aiohttp (>=3.6.2,<4.0.0)", "aioresponses", "cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (>=38.0.3)", "cryptography (>=38.0.3)", "flask", "freezegun", "grpcio", "mock", "oauth2client", "packaging", "pyjwt (>=2.0)", "pyopenssl (<24.3.0)", "pyopenssl (>=20.0.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-localserver", "pyu2f (>=0.1.5)", "requests (>=2.20.0,<3.0.0)", "responses", "urllib3"]
 urllib3 = ["packaging", "urllib3"]
 
-[[package]]
-name = "google-auth-httplib2"
-version = "0.2.0"
-description = "Google Authentication Library: httplib2 transport"
-optional = true
-python-versions = "*"
-groups = ["main"]
-markers = "extra == \"gemini\""
-files = [
-    {file = "google-auth-httplib2-0.2.0.tar.gz", hash = "sha256:38aa7badf48f974f1eb9861794e9c0cb2a0511a4ec0679b1f886d108f5640e05"},
-    {file = "google_auth_httplib2-0.2.0-py2.py3-none-any.whl", hash = "sha256:b65a0a2123300dd71281a7bf6e64d65a0759287df52729bdd1ae2e47dc311a3d"},
-]
-
-[package.dependencies]
-google-auth = "*"
-httplib2 = ">=0.19.0"
-
 [[package]]
 name = "google-cloud-vision"
 version = "3.10.2"
@@ -2922,31 +2863,6 @@ websockets = ">=13.0.0,<15.1.0"
 aiohttp = ["aiohttp (<4.0.0)"]
 local-tokenizer = ["protobuf", "sentencepiece (>=0.2.0)"]
 
-[[package]]
-name = "google-generativeai"
-version = "0.8.5"
-description = "Google Generative AI High level API client library and tools."
-optional = true
-python-versions = ">=3.9"
-groups = ["main"]
-markers = "extra == \"gemini\""
-files = [
-    {file = "google_generativeai-0.8.5-py3-none-any.whl", hash = "sha256:22b420817fb263f8ed520b33285f45976d5b21e904da32b80d4fd20c055123a2"},
-]
-
-[package.dependencies]
-google-ai-generativelanguage = "0.6.15"
-google-api-core = "*"
-google-api-python-client = "*"
-google-auth = ">=2.15.0"
-protobuf = "*"
-pydantic = "*"
-tqdm = "*"
-typing-extensions = "*"
-
-[package.extras]
-dev = ["Pillow", "absl-py", "black", "ipython", "nose2", "pandas", "pytype", "pyyaml"]
-
 [[package]]
 name = "googleapis-common-protos"
 version = "1.70.0"
@@ -2954,7 +2870,7 @@ description = "Common protobufs used in Google APIs"
 optional = true
 python-versions = ">=3.7"
 groups = ["main"]
-markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\""
+markers = "extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\""
 files = [
     {file = "googleapis_common_protos-1.70.0-py3-none-any.whl", hash = "sha256:b8bfcca8c25a2bb253e0e0b0adaf8c00773e5e6af6fd92397576680b807e0fd8"},
     {file = "googleapis_common_protos-1.70.0.tar.gz", hash = "sha256:0e1b44e0ea153e6594f9f394fef15193a68aaaea2d843f83e2742717ca753257"},
@@ -3104,7 +3020,7 @@ description = "HTTP/2-based RPC framework"
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\""
+markers = "extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\""
 files = [
     {file = "grpcio-1.75.1-cp310-cp310-linux_armv7l.whl", hash = "sha256:1712b5890b22547dd29f3215c5788d8fc759ce6dd0b85a6ba6e2731f2d04c088"},
     {file = "grpcio-1.75.1-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:8d04e101bba4b55cea9954e4aa71c24153ba6182481b487ff376da28d4ba46cf"},
@@ -3182,7 +3098,7 @@ description = "Status proto mapping for gRPC"
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "extra == \"gemini\" or extra == \"docs\""
+markers = "extra == \"docs\""
 files = [
     {file = "grpcio_status-1.71.2-py3-none-any.whl", hash = "sha256:803c98cb6a8b7dc6dbb785b1111aed739f241ab5e9da0bba96888aa74704cfd3"},
     {file = "grpcio_status-1.71.2.tar.gz", hash = "sha256:c7a97e176df71cdc2c179cd1847d7fc86cca5832ad12e9798d7fed6b7a1aab50"},
@@ -3374,22 +3290,6 @@ http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 trio = ["trio (>=0.22.0,<1.0)"]
 
-[[package]]
-name = "httplib2"
-version = "0.31.0"
-description = "A comprehensive HTTP client library."
-optional = true
-python-versions = ">=3.6"
-groups = ["main"]
-markers = "extra == \"gemini\""
-files = [
-    {file = "httplib2-0.31.0-py3-none-any.whl", hash = "sha256:b9cd78abea9b4e43a7714c6e0f8b6b8561a6fc1e95d5dbd367f5bf0ef35f5d24"},
-    {file = "httplib2-0.31.0.tar.gz", hash = "sha256:ac7ab497c50975147d4f7b1ade44becc7df2f8954d42b38b3d69c515f531135c"},
-]
-
-[package.dependencies]
-pyparsing = ">=3.0.4,<4"
-
 [[package]]
 name = "httptools"
 version = "0.6.4"
@@ -4071,8 +3971,6 @@ groups = ["main"]
 markers = "extra == \"dlt\""
 files = [
     {file = "jsonpath-ng-1.7.0.tar.gz", hash = "sha256:f6f5f7fd4e5ff79c785f1573b394043b39849fb2bb47bcead935d12b00beab3c"},
-    {file = "jsonpath_ng-1.7.0-py2-none-any.whl", hash = "sha256:898c93fc173f0c336784a3fa63d7434297544b7198124a68f9a3ef9597b0ae6e"},
-    {file = "jsonpath_ng-1.7.0-py3-none-any.whl", hash = "sha256:f3d7f9e848cba1b6da28c55b1c26ff915dc9e0b1ba7e752a53d6da8d5cbd00b6"},
 ]
 
 [package.dependencies]
@@ -8184,7 +8082,7 @@ description = "Beautiful, Pythonic protocol buffers"
 optional = true
 python-versions = ">=3.7"
 groups = ["main"]
-markers = "extra == \"gemini\" or extra == \"docs\""
+markers = "extra == \"docs\""
 files = [
     {file = "proto_plus-1.26.1-py3-none-any.whl", hash = "sha256:13285478c2dcf2abb829db158e1047e2f1e8d63a077d94263c2b88b043c75a66"},
     {file = "proto_plus-1.26.1.tar.gz", hash = "sha256:21a515a4c4c0088a773899e23c7bbade3d18f9c66c73edd4c7ee3816bc96a012"},
@@ -8256,7 +8154,6 @@ files = [
     {file = "psycopg2-2.9.10-cp311-cp311-win_amd64.whl", hash = "sha256:0435034157049f6846e95103bd8f5a668788dd913a7c30162ca9503fdf542cb4"},
     {file = "psycopg2-2.9.10-cp312-cp312-win32.whl", hash = "sha256:65a63d7ab0e067e2cdb3cf266de39663203d38d6a8ed97f5ca0cb315c73fe067"},
     {file = "psycopg2-2.9.10-cp312-cp312-win_amd64.whl", hash = "sha256:4a579d6243da40a7b3182e0430493dbd55950c493d8c68f4eec0b302f6bbf20e"},
-    {file = "psycopg2-2.9.10-cp313-cp313-win_amd64.whl", hash = "sha256:91fd603a2155da8d0cfcdbf8ab24a2d54bca72795b90d2a3ed2b6da8d979dee2"},
     {file = "psycopg2-2.9.10-cp39-cp39-win32.whl", hash = "sha256:9d5b3b94b79a844a986d029eee38998232451119ad653aea42bb9220a8c5066b"},
     {file = "psycopg2-2.9.10-cp39-cp39-win_amd64.whl", hash = "sha256:88138c8dedcbfa96408023ea2b0c369eda40fe5d75002c0964c78f46f11fa442"},
     {file = "psycopg2-2.9.10.tar.gz", hash = "sha256:12ec0b40b0273f95296233e8750441339298e6a572f7039da5b260e3c8b60e11"},
@@ -8318,7 +8215,6 @@ files = [
     {file = "psycopg2_binary-2.9.10-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:bb89f0a835bcfc1d42ccd5f41f04870c1b936d8507c6df12b7737febc40f0909"},
     {file = "psycopg2_binary-2.9.10-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:f0c2d907a1e102526dd2986df638343388b94c33860ff3bbe1384130828714b1"},
     {file = "psycopg2_binary-2.9.10-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:f8157bed2f51db683f31306aa497311b560f2265998122abe1dce6428bd86567"},
-    {file = "psycopg2_binary-2.9.10-cp313-cp313-win_amd64.whl", hash = "sha256:27422aa5f11fbcd9b18da48373eb67081243662f9b46e6fd07c3eb46e4535142"},
     {file = "psycopg2_binary-2.9.10-cp38-cp38-macosx_12_0_x86_64.whl", hash = "sha256:eb09aa7f9cecb45027683bb55aebaaf45a0df8bf6de68801a6afdc7947bb09d4"},
     {file = "psycopg2_binary-2.9.10-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b73d6d7f0ccdad7bc43e6d34273f70d587ef62f824d7261c4ae9b8b1b6af90e8"},
     {file = "psycopg2_binary-2.9.10-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ce5ab4bf46a211a8e924d307c1b1fcda82368586a19d0a24f8ae166f5c784864"},
@@ -8528,7 +8424,7 @@ description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs
 optional = true
 python-versions = ">=3.8"
 groups = ["main"]
-markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\""
+markers = "extra == \"deepeval\" or extra == \"chromadb\" or extra == \"docs\""
 files = [
     {file = "pyasn1-0.6.1-py3-none-any.whl", hash = "sha256:0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629"},
     {file = "pyasn1-0.6.1.tar.gz", hash = "sha256:6f580d2bdd84365380830acf45550f2511469f673cb4a5ae3857a3170128b034"},
@@ -8541,7 +8437,7 @@ description = "A collection of ASN.1-based protocols modules"
 optional = true
 python-versions = ">=3.8"
 groups = ["main"]
-markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\""
+markers = "extra == \"deepeval\" or extra == \"chromadb\" or extra == \"docs\""
 files = [
     {file = "pyasn1_modules-0.4.2-py3-none-any.whl", hash = "sha256:29253a9207ce32b64c3ac6600edc75368f98473906e8fd1043bd6b5b1de2c14a"},
     {file = "pyasn1_modules-0.4.2.tar.gz", hash = "sha256:677091de870a80aae844b1ca6134f54652fa2c8c5a52aa396440ac3106e941e6"},
@@ -9430,13 +9326,6 @@ optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "PyYAML-6.0.3-cp38-cp38-macosx_10_13_x86_64.whl", hash = "sha256:c2514fceb77bc5e7a2f7adfaa1feb2fb311607c9cb518dbc378688ec73d8292f"},
-    {file = "PyYAML-6.0.3-cp38-cp38-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9c57bb8c96f6d1808c030b1687b9b5fb476abaa47f0db9c0101f5e9f394e97f4"},
-    {file = "PyYAML-6.0.3-cp38-cp38-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:efd7b85f94a6f21e4932043973a7ba2613b059c4a000551892ac9f1d11f5baf3"},
-    {file = "PyYAML-6.0.3-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:22ba7cfcad58ef3ecddc7ed1db3409af68d023b7f940da23c6c2a1890976eda6"},
-    {file = "PyYAML-6.0.3-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:6344df0d5755a2c9a276d4473ae6b90647e216ab4757f8426893b5dd2ac3f369"},
-    {file = "PyYAML-6.0.3-cp38-cp38-win32.whl", hash = "sha256:3ff07ec89bae51176c0549bc4c63aa6202991da2d9a6129d7aef7f1407d3f295"},
-    {file = "PyYAML-6.0.3-cp38-cp38-win_amd64.whl", hash = "sha256:5cf4e27da7e3fbed4d6c3d8e797387aaad68102272f8f9752883bc32d61cb87b"},
     {file = "pyyaml-6.0.3-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:214ed4befebe12df36bcc8bc2b64b396ca31be9304b8f59e25c11cf94a4c033b"},
     {file = "pyyaml-6.0.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:02ea2dfa234451bbb8772601d7b8e426c2bfa197136796224e50e35a78777956"},
     {file = "pyyaml-6.0.3-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b30236e45cf30d2b8e7b3e85881719e98507abed1011bf463a8fa23e9c3e98a8"},
@@ -10249,7 +10138,7 @@ description = "Pure-Python RSA implementation"
 optional = true
 python-versions = "<4,>=3.6"
 groups = ["main"]
-markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\""
+markers = "extra == \"deepeval\" or extra == \"chromadb\" or extra == \"docs\""
 files = [
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@@ -12003,19 +11892,6 @@ files = [
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@@ -12882,7 +12758,6 @@ dlt = ["dlt"]
 docs = ["unstructured"]
 evals = ["gdown", "matplotlib", "pandas", "plotly", "scikit-learn"]
 falkordb = ["falkordb"]
-gemini = ["google-generativeai"]
 graphiti = ["graphiti-core"]
 groq = ["groq"]
 huggingface = ["transformers"]
@@ -12901,4 +12776,4 @@ posthog = ["posthog"]
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+content-hash = "c76267fe685339b5b5665342c81850a3e891cadaf760178bf3b04058f35b1014"

pyproject.toml

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 ]
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 ollama = ["transformers>=4.46.3,<5"]
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uv.lock

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@@ -4869,7 +4791,7 @@ name = "nvidia-cudnn-cu12"
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 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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-    { name = "nvidia-cusparse-cu12", marker = "sys_platform != 'emscripten'" },
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