ce9ef3ca79
* Add support for non-ASCII characters in LLM prompts - Add ensure_ascii parameter to Graphiti class (default: True) - Create to_prompt_json helper function for consistent JSON serialization - Update all prompt files to use new helper function - Preserve Korean/Japanese/Chinese characters when ensure_ascii=False - Maintain backward compatibility with existing behavior Fixes issue where non-ASCII characters were escaped as unicode sequences in prompts, making them unreadable in LLM logs and potentially affecting model understanding. * Remove unused json imports after replacing with to_prompt_json helper - Fix ruff lint errors (F401) for unused json imports - All prompt files now use to_prompt_json helper instead of json.dumps - Maintains clean code style and passes lint checks * Fix ensure_ascii propagation to all LLM calls - Add ensure_ascii parameter to maintenance operation functions that were missing it - Update function signatures in node_operations, community_operations, temporal_operations, and edge_operations - Ensure all llm_client.generate_response calls receive proper ensure_ascii context - Fix hardcoded ensure_ascii: True values that prevented non-ASCII character preservation - Maintain backward compatibility with default ensure_ascii=True - Complete the fix for issue #804 ensuring Korean/Japanese/Chinese characters are properly handled in LLM prompts
74 lines
2.8 KiB
Python
74 lines
2.8 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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from graphiti_core.edges import EntityEdge
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from graphiti_core.prompts.prompt_helpers import to_prompt_json
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from graphiti_core.search.search_config import SearchResults
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def format_edge_date_range(edge: EntityEdge) -> str:
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# return f"{datetime(edge.valid_at).strftime('%Y-%m-%d %H:%M:%S') if edge.valid_at else 'date unknown'} - {(edge.invalid_at.strftime('%Y-%m-%d %H:%M:%S') if edge.invalid_at else 'present')}"
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return f'{edge.valid_at if edge.valid_at else "date unknown"} - {(edge.invalid_at if edge.invalid_at else "present")}'
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def search_results_to_context_string(
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search_results: SearchResults, ensure_ascii: bool = True
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) -> str:
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"""Reformats a set of SearchResults into a single string to pass directly to an LLM as context"""
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fact_json = [
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{
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'fact': edge.fact,
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'valid_at': str(edge.valid_at),
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'invalid_at': str(edge.invalid_at or 'Present'),
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}
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for edge in search_results.edges
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]
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entity_json = [
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{'entity_name': node.name, 'summary': node.summary} for node in search_results.nodes
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]
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episode_json = [
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{
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'source_description': episode.source_description,
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'content': episode.content,
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}
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for episode in search_results.episodes
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]
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community_json = [
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{'community_name': community.name, 'summary': community.summary}
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for community in search_results.communities
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]
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context_string = f"""
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FACTS and ENTITIES represent relevant context to the current conversation.
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COMMUNITIES represent a cluster of closely related entities.
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These are the most relevant facts and their valid and invalid dates. Facts are considered valid
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between their valid_at and invalid_at dates. Facts with an invalid_at date of "Present" are considered valid.
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<FACTS>
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{to_prompt_json(fact_json, ensure_ascii=ensure_ascii, indent=12)}
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</FACTS>
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<ENTITIES>
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{to_prompt_json(entity_json, ensure_ascii=ensure_ascii, indent=12)}
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</ENTITIES>
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<EPISODES>
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{to_prompt_json(episode_json, ensure_ascii=ensure_ascii, indent=12)}
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</EPISODES>
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<COMMUNITIES>
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{to_prompt_json(community_json, ensure_ascii=ensure_ascii, indent=12)}
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</COMMUNITIES>
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"""
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return context_string
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