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* Firsst cut at config cleanup * Reorder top nav * Add query prompts to tuning page * Remove dynamic notebook from nav * Add more thorough yml config descriptions in docs * Further clean out the config * Semver * Add new blog post * Emphasize yaml * Clarify output * Fix unit test * Fix bullet nesting
90 lines
4.1 KiB
Markdown
90 lines
4.1 KiB
Markdown
# Manual Prompt Tuning ⚙️
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The GraphRAG indexer, by default, will run with a handful of prompts that are designed to work well in the broad context of knowledge discovery.
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However, it is quite common to want to tune the prompts to better suit your specific use case.
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We provide a means for you to do this by allowing you to specify a custom prompt file, which will each use a series of token-replacements internally.
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Each of these prompts may be overridden by writing a custom prompt file in plaintext. We use token-replacements in the form of `{token_name}`, and the descriptions for the available tokens can be found below.
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## Indexing Prompts
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### Entity/Relationship Extraction
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[Prompt Source](http://github.com/microsoft/graphrag/blob/main/graphrag/prompts/index/entity_extraction.py)
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#### Tokens
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- **{input_text}** - The input text to be processed.
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- **{entity_types}** - A list of entity types
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- **{tuple_delimiter}** - A delimiter for separating values within a tuple. A single tuple is used to represent an individual entity or relationship.
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- **{record_delimiter}** - A delimiter for separating tuple instances.
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- **{completion_delimiter}** - An indicator for when generation is complete.
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### Summarize Entity/Relationship Descriptions
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[Prompt Source](http://github.com/microsoft/graphrag/blob/main/graphrag/prompts/index/summarize_descriptions.py)
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#### Tokens
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- **{entity_name}** - The name of the entity or the source/target pair of the relationship.
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- **{description_list}** - A list of descriptions for the entity or relationship.
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### Claim Extraction
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[Prompt Source](http://github.com/microsoft/graphrag/blob/main/graphrag/prompts/index/claim_extraction.py)
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#### Tokens
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- **{input_text}** - The input text to be processed.
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- **{tuple_delimiter}** - A delimiter for separating values within a tuple. A single tuple is used to represent an individual entity or relationship.
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- **{record_delimiter}** - A delimiter for separating tuple instances.
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- **{completion_delimiter}** - An indicator for when generation is complete.
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- **{entity_specs}** - A list of entity types.
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- **{claim_description}** - Description of what claims should look like. Default is: `"Any claims or facts that could be relevant to information discovery."`
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See the [configuration documentation](../config/overview.md) for details on how to change this.
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### Generate Community Reports
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[Prompt Source](http://github.com/microsoft/graphrag/blob/main/graphrag/prompts/index/community_report.py)
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#### Tokens
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- **{input_text}** - The input text to generate the report with. This will contain tables of entities and relationships.
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## Query Prompts
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### Local Search
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[Prompt Source](http://github.com/microsoft/graphrag/blob/main/graphrag/prompts/query/local_search_system_prompt.py)
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#### Tokens
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- **{response_type}** - Describe how the response should look. We default to "multiple paragraphs".
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- **{context_data}** - The data tables from GraphRAG's index.
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### Global Search
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[Mapper Prompt Source](http://github.com/microsoft/graphrag/blob/main/graphrag/prompts/query/global_search_map_system_prompt.py)
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[Reducer Prompt Source](http://github.com/microsoft/graphrag/blob/main/graphrag/prompts/query/global_search_reduce_system_prompt.py)
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[Knowledge Prompt Source](http://github.com/microsoft/graphrag/blob/main/graphrag/prompts/query/global_search_knowledge_system_prompt.py)
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Global search uses a map/reduce approach to summarization. You can tune these prompts independently. This search also includes the ability to adjust the use of general knowledge from the model's training.
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#### Tokens
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- **{response_type}** - Describe how the response should look (reducer only). We default to "multiple paragraphs".
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- **{context_data}** - The data tables from GraphRAG's index.
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### Drift Search
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[Prompt Source](http://github.com/microsoft/graphrag/blob/main/graphrag/prompts/query/drift_search_system_prompt.py)
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#### Tokens
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- **{response_type}** - Describe how the response should look. We default to "multiple paragraphs".
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- **{context_data}** - The data tables from GraphRAG's index.
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- **{community_reports}** - The most relevant community reports to include in the summarization.
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- **{query}** - The query text as injected into the context. |