7f7a17c926
* Bump version from 0.9.0 to 0.9.1 in pyproject.toml and update google-genai dependency to >=0.1.0 * Bump version from 0.9.1 to 0.9.2 in pyproject.toml * Update google-genai dependency version to >=0.8.0 in pyproject.toml * loc file * Update pyproject.toml to version 0.9.3, restructure dependencies, and modify author format. Remove outdated Google API key note from README.md. * upgrade poetry and ruff * Update README.md to include installation instructions for Graphiti with Google Gemini support * fix to deps since peotry doesn't fully implement PEP 735 * Refactor string formatting in various files to use single quotes for consistency and improve readability. This includes updates in agent.ipynb, quickstart.py, multiple prompt files, and ingest.py and retrieve.py modules. * Remove optional dependencies from pyproject.toml to streamline project requirements.
113 lines
3.4 KiB
Python
113 lines
3.4 KiB
Python
"""
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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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from typing import Any, Protocol, TypedDict
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from pydantic import BaseModel, Field
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from .models import Message, PromptFunction, PromptVersion
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class Edge(BaseModel):
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relation_type: str = Field(..., description='RELATION_TYPE_IN_CAPS')
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source_entity_name: str = Field(..., description='name of the source entity')
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target_entity_name: str = Field(..., description='name of the target entity')
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fact: str = Field(..., description='extracted factual information')
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class ExtractedEdges(BaseModel):
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edges: list[Edge]
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class MissingFacts(BaseModel):
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missing_facts: list[str] = Field(..., description="facts that weren't extracted")
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class Prompt(Protocol):
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edge: PromptVersion
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reflexion: PromptVersion
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class Versions(TypedDict):
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edge: PromptFunction
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reflexion: PromptFunction
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def edge(context: dict[str, Any]) -> list[Message]:
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return [
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Message(
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role='system',
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content='You are an expert fact extractor that extracts fact triples from text.',
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),
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Message(
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role='user',
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content=f"""
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<PREVIOUS MESSAGES>
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{json.dumps([ep for ep in context['previous_episodes']], indent=2)}
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</PREVIOUS MESSAGES>
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<CURRENT MESSAGE>
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{context['episode_content']}
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</CURRENT MESSAGE>
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<ENTITIES>
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{context['nodes']}
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</ENTITIES>
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{context['custom_prompt']}
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Given the above MESSAGES and ENTITIES, extract all facts pertaining to the listed ENTITIES from the CURRENT MESSAGE.
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Guidelines:
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1. Extract facts only between the provided entities.
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2. Each fact should represent a clear relationship between two DISTINCT nodes.
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3. The relation_type should be a concise, all-caps description of the fact (e.g., LOVES, IS_FRIENDS_WITH, WORKS_FOR).
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4. Provide a more detailed fact containing all relevant information.
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5. Consider temporal aspects of relationships when relevant.
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""",
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),
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]
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def reflexion(context: dict[str, Any]) -> list[Message]:
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sys_prompt = """You are an AI assistant that determines which facts have not been extracted from the given context"""
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user_prompt = f"""
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<PREVIOUS MESSAGES>
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{json.dumps([ep for ep in context['previous_episodes']], indent=2)}
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</PREVIOUS MESSAGES>
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<CURRENT MESSAGE>
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{context['episode_content']}
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</CURRENT MESSAGE>
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<EXTRACTED ENTITIES>
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{context['nodes']}
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</EXTRACTED ENTITIES>
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<EXTRACTED FACTS>
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{context['extracted_facts']}
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</EXTRACTED FACTS>
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Given the above MESSAGES, list of EXTRACTED ENTITIES entities, and list of EXTRACTED FACTS;
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determine if any facts haven't been extracted.
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"""
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return [
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Message(role='system', content=sys_prompt),
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Message(role='user', content=user_prompt),
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]
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versions: Versions = {'edge': edge, 'reflexion': reflexion}
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