Gemini support (#324)
* first cut * Update dependencies and enhance README for optional LLM providers - Bump aiohttp version from 3.11.14 to 3.11.16 - Update yarl version from 1.18.3 to 1.19.0 - Modify pyproject.toml to include optional extras for Anthropic, Groq, and Google Gemini - Revise README.md to reflect new optional LLM provider installation instructions and clarify API key requirements * Remove deprecated packages from poetry.lock and update content hash - Removed cachetools, google-auth, google-genai, pyasn1, pyasn1-modules, rsa, and websockets from the lock file. - Added new extras for anthropic, google-genai, and groq. - Updated content hash to reflect changes. * Refactor import paths for GeminiClient in README and __init__.py - Updated import statement in README.md to reflect the new module structure for GeminiClient. - Removed GeminiClient from the __all__ list in __init__.py as it is no longer directly imported. * Refactor import paths for GeminiEmbedder in README and __init__.py - Updated import statement in README.md to reflect the new module structure for GeminiEmbedder. - Removed GeminiEmbedder and GeminiEmbedderConfig from the __all__ list in __init__.py as they are no longer directly imported.
This commit is contained in:
Daniel Chalef
GitHub
parent
d3c83adb04
commit
9e78890f2e
13 changed files with 1221 additions and 962 deletions
58
README.md
58
README.md
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@ -35,8 +35,8 @@ Use Graphiti to:
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<br />
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A knowledge graph is a network of interconnected facts, such as _“Kendra loves Adidas shoes.”_ Each fact is a “triplet” represented by two entities, or
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nodes (_”Kendra”_, _“Adidas shoes”_), and their relationship, or edge (_”loves”_). Knowledge Graphs have been explored
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A knowledge graph is a network of interconnected facts, such as _"Kendra loves Adidas shoes."_ Each fact is a "triplet" represented by two entities, or
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nodes ("Kendra", "Adidas shoes"), and their relationship, or edge ("loves"). Knowledge Graphs have been explored
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extensively for information retrieval. What makes Graphiti unique is its ability to autonomously build a knowledge graph
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while handling changing relationships and maintaining historical context.
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@ -96,7 +96,7 @@ Requirements:
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Optional:
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- Anthropic or Groq API key (for alternative LLM providers)
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- Google Gemini, Anthropic, or Groq API key (for alternative LLM providers)
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> [!TIP]
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> The simplest way to install Neo4j is via [Neo4j Desktop](https://neo4j.com/download/). It provides a user-friendly
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@ -112,6 +112,22 @@ or
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poetry add graphiti-core
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```
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You can also install optional LLM providers as extras:
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```bash
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# Install with Anthropic support
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pip install graphiti-core[anthropic]
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# Install with Groq support
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pip install graphiti-core[groq]
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# Install with Google Gemini support
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pip install graphiti-core[google-genai]
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# Install with multiple providers
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pip install graphiti-core[anthropic,groq,google-genai]
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```
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## Quick Start
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> [!IMPORTANT]
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@ -211,6 +227,42 @@ graphiti = Graphiti(
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Make sure to replace the placeholder values with your actual Azure OpenAI credentials and specify the correct embedding model name that's deployed in your Azure OpenAI service.
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## Using Graphiti with Google Gemini
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Graphiti supports Google's Gemini models for both LLM inference and embeddings. To use Gemini, you'll need to configure both the LLM client and embedder with your Google API key.
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```python
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from graphiti_core import Graphiti
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from graphiti_core.llm_client.gemini_client import GeminiClient, LLMConfig
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from graphiti_core.embedder.gemini import GeminiEmbedder, GeminiEmbedderConfig
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# Google API key configuration
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api_key = "<your-google-api-key>"
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# Initialize Graphiti with Gemini clients
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graphiti = Graphiti(
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"bolt://localhost:7687",
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"neo4j",
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"password",
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llm_client=GeminiClient(
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config=LLMConfig(
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api_key=api_key,
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model="gemini-2.0-flash"
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)
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),
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embedder=GeminiEmbedder(
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config=GeminiEmbedderConfig(
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api_key=api_key,
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embedding_model="embedding-001"
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)
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)
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)
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# Now you can use Graphiti with Google Gemini
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```
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Make sure to replace the placeholder value with your actual Google API key. You can find more details in the example file at `examples/gemini_example.py`.
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## Documentation
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- [Guides and API documentation](https://help.getzep.com/graphiti).
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@ -1,4 +1,8 @@
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from .client import EmbedderClient
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from .openai import OpenAIEmbedder, OpenAIEmbedderConfig
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__all__ = ['EmbedderClient', 'OpenAIEmbedder', 'OpenAIEmbedderConfig']
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__all__ = [
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'EmbedderClient',
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'OpenAIEmbedder',
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'OpenAIEmbedderConfig',
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]
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68
graphiti_core/embedder/gemini.py
Normal file
68
graphiti_core/embedder/gemini.py
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@ -0,0 +1,68 @@
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"""
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Copyright 2024, Zep Software, Inc.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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from collections.abc import Iterable
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from google import genai # type: ignore
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from google.genai import types # type: ignore
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from pydantic import Field
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from .client import EmbedderClient, EmbedderConfig
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DEFAULT_EMBEDDING_MODEL = 'embedding-001'
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class GeminiEmbedderConfig(EmbedderConfig):
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embedding_model: str = Field(default=DEFAULT_EMBEDDING_MODEL)
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api_key: str | None = None
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class GeminiEmbedder(EmbedderClient):
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"""
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Google Gemini Embedder Client
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"""
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def __init__(self, config: GeminiEmbedderConfig | None = None):
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if config is None:
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config = GeminiEmbedderConfig()
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self.config = config
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# Configure the Gemini API
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self.client = genai.Client(
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api_key=config.api_key,
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)
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async def create(
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self, input_data: str | list[str] | Iterable[int] | Iterable[Iterable[int]]
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) -> list[float]:
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"""
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Create embeddings for the given input data using Google's Gemini embedding model.
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Args:
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input_data: The input data to create embeddings for. Can be a string, list of strings,
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or an iterable of integers or iterables of integers.
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Returns:
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A list of floats representing the embedding vector.
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"""
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# Generate embeddings
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result = await self.client.aio.models.embed_content(
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model=self.config.embedding_model or DEFAULT_EMBEDDING_MODEL,
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contents=[input_data],
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config=types.EmbedContentConfig(output_dimensionality=self.config.embedding_dim),
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)
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return result.embeddings[0].values
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@ -71,10 +71,7 @@ from graphiti_core.utils.maintenance.graph_data_operations import (
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build_indices_and_constraints,
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retrieve_episodes,
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)
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from graphiti_core.utils.maintenance.node_operations import (
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extract_nodes,
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resolve_extracted_nodes,
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)
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from graphiti_core.utils.maintenance.node_operations import extract_nodes, resolve_extracted_nodes
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from graphiti_core.utils.maintenance.temporal_operations import get_edge_contradictions
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from graphiti_core.utils.ontology_utils.entity_types_utils import validate_entity_types
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186
graphiti_core/llm_client/gemini_client.py
Normal file
186
graphiti_core/llm_client/gemini_client.py
Normal file
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@ -0,0 +1,186 @@
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"""
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Copyright 2024, Zep Software, Inc.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import json
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import logging
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import typing
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from google import genai # type: ignore
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from google.genai import types # type: ignore
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from pydantic import BaseModel
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from ..prompts.models import Message
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from .client import LLMClient
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from .config import DEFAULT_MAX_TOKENS, LLMConfig
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from .errors import RateLimitError
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logger = logging.getLogger(__name__)
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DEFAULT_MODEL = 'gemini-2.0-flash'
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class GeminiClient(LLMClient):
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"""
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GeminiClient is a client class for interacting with Google's Gemini language models.
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This class extends the LLMClient and provides methods to initialize the client
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and generate responses from the Gemini language model.
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Attributes:
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model (str): The model name to use for generating responses.
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temperature (float): The temperature to use for generating responses.
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max_tokens (int): The maximum number of tokens to generate in a response.
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Methods:
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__init__(config: LLMConfig | None = None, cache: bool = False):
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Initializes the GeminiClient with the provided configuration and cache setting.
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_generate_response(messages: list[Message]) -> dict[str, typing.Any]:
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Generates a response from the language model based on the provided messages.
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"""
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def __init__(
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self,
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config: LLMConfig | None = None,
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cache: bool = False,
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max_tokens: int = DEFAULT_MAX_TOKENS,
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):
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"""
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Initialize the GeminiClient with the provided configuration and cache setting.
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Args:
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config (LLMConfig | None): The configuration for the LLM client, including API key, model, temperature, and max tokens.
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cache (bool): Whether to use caching for responses. Defaults to False.
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"""
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if config is None:
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config = LLMConfig()
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super().__init__(config, cache)
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self.model = config.model
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# Configure the Gemini API
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self.client = genai.Client(
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api_key=config.api_key,
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)
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self.max_tokens = max_tokens
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async def _generate_response(
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self,
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messages: list[Message],
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response_model: type[BaseModel] | None = None,
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max_tokens: int = DEFAULT_MAX_TOKENS,
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) -> dict[str, typing.Any]:
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"""
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Generate a response from the Gemini language model.
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Args:
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messages (list[Message]): A list of messages to send to the language model.
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response_model (type[BaseModel] | None): An optional Pydantic model to parse the response into.
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max_tokens (int): The maximum number of tokens to generate in the response.
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Returns:
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dict[str, typing.Any]: The response from the language model.
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Raises:
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RateLimitError: If the API rate limit is exceeded.
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RefusalError: If the content is blocked by the model.
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Exception: If there is an error generating the response.
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"""
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try:
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gemini_messages: list[types.Content] = []
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# If a response model is provided, add schema for structured output
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system_prompt = ''
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if response_model is not None:
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# Get the schema from the Pydantic model
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pydantic_schema = response_model.model_json_schema()
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# Create instruction to output in the desired JSON format
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system_prompt += (
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f'Output ONLY valid JSON matching this schema: {json.dumps(pydantic_schema)}.\n'
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'Do not include any explanatory text before or after the JSON.\n\n'
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)
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# Add messages content
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# First check for a system message
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if messages and messages[0].role == 'system':
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system_prompt = f'{messages[0].content}\n\n {system_prompt}'
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messages = messages[1:]
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# Add the rest of the messages
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for m in messages:
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m.content = self._clean_input(m.content)
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gemini_messages.append(
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types.Content(role=m.role, parts=[types.Part.from_text(text=m.content)])
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)
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# Create generation config
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generation_config = types.GenerateContentConfig(
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temperature=self.temperature,
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max_output_tokens=max_tokens or self.max_tokens,
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response_mime_type='application/json' if response_model else None,
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response_schema=response_model if response_model else None,
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system_instruction=system_prompt,
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)
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# Generate content using the simple string approach
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response = await self.client.aio.models.generate_content(
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model=self.model or DEFAULT_MODEL,
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contents=gemini_messages,
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config=generation_config,
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)
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# If this was a structured output request, parse the response into the Pydantic model
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if response_model is not None:
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try:
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validated_model = response_model.model_validate(json.loads(response.text))
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# Return as a dictionary for API consistency
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return validated_model.model_dump()
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except Exception as e:
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raise Exception(f'Failed to parse structured response: {e}') from e
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# Otherwise, return the response text as a dictionary
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return {'content': response.text}
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except Exception as e:
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# Check if it's a rate limit error
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if 'rate limit' in str(e).lower() or 'quota' in str(e).lower():
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raise RateLimitError from e
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logger.error(f'Error in generating LLM response: {e}')
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raise
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async def generate_response(
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self,
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messages: list[Message],
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response_model: type[BaseModel] | None = None,
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max_tokens: int = DEFAULT_MAX_TOKENS,
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) -> dict[str, typing.Any]:
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"""
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Generate a response from the Gemini language model.
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This method overrides the parent class method to provide a direct implementation.
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Args:
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messages (list[Message]): A list of messages to send to the language model.
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response_model (type[BaseModel] | None): An optional Pydantic model to parse the response into.
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max_tokens (int): The maximum number of tokens to generate in the response.
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Returns:
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dict[str, typing.Any]: The response from the language model.
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"""
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# Call the internal _generate_response method
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return await self._generate_response(
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messages=messages, response_model=response_model, max_tokens=max_tokens
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)
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@ -16,63 +16,27 @@ limitations under the License.
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from typing import Any, Protocol, TypedDict
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from .dedupe_edges import (
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Prompt as DedupeEdgesPrompt,
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)
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from .dedupe_edges import (
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Versions as DedupeEdgesVersions,
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)
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from .dedupe_edges import (
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versions as dedupe_edges_versions,
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)
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from .dedupe_nodes import (
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Prompt as DedupeNodesPrompt,
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)
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from .dedupe_nodes import (
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Versions as DedupeNodesVersions,
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)
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from .dedupe_nodes import (
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versions as dedupe_nodes_versions,
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)
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from .dedupe_edges import Prompt as DedupeEdgesPrompt
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from .dedupe_edges import Versions as DedupeEdgesVersions
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from .dedupe_edges import versions as dedupe_edges_versions
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from .dedupe_nodes import Prompt as DedupeNodesPrompt
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from .dedupe_nodes import Versions as DedupeNodesVersions
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from .dedupe_nodes import versions as dedupe_nodes_versions
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from .eval import Prompt as EvalPrompt
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from .eval import Versions as EvalVersions
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from .eval import versions as eval_versions
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from .extract_edge_dates import (
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Prompt as ExtractEdgeDatesPrompt,
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)
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from .extract_edge_dates import (
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Versions as ExtractEdgeDatesVersions,
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)
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from .extract_edge_dates import (
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versions as extract_edge_dates_versions,
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)
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from .extract_edges import (
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Prompt as ExtractEdgesPrompt,
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)
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from .extract_edges import (
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Versions as ExtractEdgesVersions,
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)
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from .extract_edges import (
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versions as extract_edges_versions,
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)
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from .extract_nodes import (
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Prompt as ExtractNodesPrompt,
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)
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from .extract_nodes import (
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Versions as ExtractNodesVersions,
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)
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from .extract_nodes import (
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versions as extract_nodes_versions,
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)
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from .invalidate_edges import (
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Prompt as InvalidateEdgesPrompt,
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)
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from .invalidate_edges import (
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Versions as InvalidateEdgesVersions,
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)
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from .invalidate_edges import (
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versions as invalidate_edges_versions,
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)
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from .extract_edge_dates import Prompt as ExtractEdgeDatesPrompt
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from .extract_edge_dates import Versions as ExtractEdgeDatesVersions
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from .extract_edge_dates import versions as extract_edge_dates_versions
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from .extract_edges import Prompt as ExtractEdgesPrompt
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from .extract_edges import Versions as ExtractEdgesVersions
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from .extract_edges import versions as extract_edges_versions
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from .extract_nodes import Prompt as ExtractNodesPrompt
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from .extract_nodes import Versions as ExtractNodesVersions
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from .extract_nodes import versions as extract_nodes_versions
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from .invalidate_edges import Prompt as InvalidateEdgesPrompt
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from .invalidate_edges import Versions as InvalidateEdgesVersions
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from .invalidate_edges import versions as invalidate_edges_versions
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from .models import Message, PromptFunction
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from .prompt_helpers import DO_NOT_ESCAPE_UNICODE
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from .summarize_nodes import Prompt as SummarizeNodesPrompt
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|
|
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|
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@ -1,8 +1,5 @@
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from .edge_operations import build_episodic_edges, extract_edges
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from .graph_data_operations import (
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clear_data,
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retrieve_episodes,
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)
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from .graph_data_operations import clear_data, retrieve_episodes
|
||||
from .node_operations import extract_nodes
|
||||
|
||||
__all__ = [
|
||||
|
|
|
|||
|
|
@ -9,11 +9,7 @@ from graphiti_core.edges import CommunityEdge
|
|||
from graphiti_core.embedder import EmbedderClient
|
||||
from graphiti_core.helpers import DEFAULT_DATABASE, semaphore_gather
|
||||
from graphiti_core.llm_client import LLMClient
|
||||
from graphiti_core.nodes import (
|
||||
CommunityNode,
|
||||
EntityNode,
|
||||
get_community_node_from_record,
|
||||
)
|
||||
from graphiti_core.nodes import CommunityNode, EntityNode, get_community_node_from_record
|
||||
from graphiti_core.prompts import prompt_library
|
||||
from graphiti_core.prompts.summarize_nodes import Summary, SummaryDescription
|
||||
from graphiti_core.utils.datetime_utils import utc_now
|
||||
|
|
|
|||
|
|
@ -26,11 +26,7 @@ from graphiti_core.llm_client import LLMClient
|
|||
from graphiti_core.nodes import EntityNode, EpisodeType, EpisodicNode
|
||||
from graphiti_core.prompts import prompt_library
|
||||
from graphiti_core.prompts.dedupe_nodes import NodeDuplicate
|
||||
from graphiti_core.prompts.extract_nodes import (
|
||||
EntityClassification,
|
||||
ExtractedNodes,
|
||||
MissedEntities,
|
||||
)
|
||||
from graphiti_core.prompts.extract_nodes import EntityClassification, ExtractedNodes, MissedEntities
|
||||
from graphiti_core.prompts.summarize_nodes import Summary
|
||||
from graphiti_core.utils.datetime_utils import utc_now
|
||||
|
||||
|
|
|
|||
1753
poetry.lock
generated
1753
poetry.lock
generated
File diff suppressed because it is too large
Load diff
|
|
@ -21,7 +21,11 @@ openai = "^1.53.0"
|
|||
tenacity = "9.0.0"
|
||||
numpy = ">=1.0.0"
|
||||
python-dotenv = "^1.0.1"
|
||||
anthropic = "~0.49.0"
|
||||
|
||||
[tool.poetry.extras]
|
||||
anthropic = ["anthropic"]
|
||||
groq = ["groq"]
|
||||
google-genai = ["google-genai"]
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
mypy = "^1.11.1"
|
||||
|
|
|
|||
|
|
@ -1,12 +1,6 @@
|
|||
from .common import Message, Result
|
||||
from .ingest import AddEntityNodeRequest, AddMessagesRequest
|
||||
from .retrieve import (
|
||||
FactResult,
|
||||
GetMemoryRequest,
|
||||
GetMemoryResponse,
|
||||
SearchQuery,
|
||||
SearchResults,
|
||||
)
|
||||
from .retrieve import FactResult, GetMemoryRequest, GetMemoryResponse, SearchQuery, SearchResults
|
||||
|
||||
__all__ = [
|
||||
'SearchQuery',
|
||||
|
|
|
|||
|
|
@ -26,9 +26,7 @@ from graphiti_core.edges import EntityEdge, EpisodicEdge
|
|||
from graphiti_core.graphiti import Graphiti
|
||||
from graphiti_core.helpers import semaphore_gather
|
||||
from graphiti_core.nodes import EntityNode, EpisodicNode
|
||||
from graphiti_core.search.search_config_recipes import (
|
||||
COMBINED_HYBRID_SEARCH_CROSS_ENCODER,
|
||||
)
|
||||
from graphiti_core.search.search_config_recipes import COMBINED_HYBRID_SEARCH_CROSS_ENCODER
|
||||
from graphiti_core.search.search_filters import SearchFilters
|
||||
|
||||
pytestmark = pytest.mark.integration
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue