Implement prompt tuning API

.semversioner/next-release/minor-20240807023736041951.json

@@ -0,0 +1,4 @@
+{
+  "type": "minor",
+  "description": "Implement auto templating API."
+}

graphrag/prompt_tune/__main__.py

@@ -1,53 +1,48 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""The Prompt auto templating package root."""
+"""The auto templating package root."""
 
 import argparse
 import asyncio
-from enum import Enum
-
-from graphrag.prompt_tune.generator import MAX_TOKEN_COUNT
-from graphrag.prompt_tune.loader import MIN_CHUNK_SIZE
 
+from .api import DocSelectionType
 from .cli import prompt_tune
-
-
-class DocSelectionType(Enum):
-    """The type of document selection to use."""
-
-    ALL = "all"
-    RANDOM = "random"
-    TOP = "top"
-    AUTO = "auto"
-
-    def __str__(self):
-        """Return the string representation of the enum value."""
-        return self.value
-
+from .generator import MAX_TOKEN_COUNT
+from .loader import MIN_CHUNK_SIZE
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
+    parser = argparse.ArgumentParser(
+        prog="python -m graphrag.prompt_tune",
+        description="The graphrag auto templating module.",
+    )
+
+    parser.add_argument(
+        "--config",
+        help="Configuration yaml file to use when generating prompts",
+        required=True,
+        type=str,
+    )
 
     parser.add_argument(
         "--root",
-        help="The data project root. Including the config yml, json or .env",
+        help="Data project root. Default: current directory",
         required=False,
         type=str,
         default=".",
     )
 
     parser.add_argument(
         "--domain",
-        help="The domain your input data is related to. For example 'space science', 'microbiology', 'environmental news'. If left empty, the domain will be inferred from the input data.",
+        help="Domain your input data is related to. For example 'space science', 'microbiology', 'environmental news'. If not defined, the domain will be inferred from the input data.",
         required=False,
         default="",
         type=str,
     )
 
     parser.add_argument(
-        "--method",
-        help="The method to select documents, one of: all, random, top or auto",
+        "--selection-method",
+        help=f"Chunk selection method. Default: {DocSelectionType.RANDOM}",
         required=False,
         type=DocSelectionType,
         choices=list(DocSelectionType),
@@ -56,47 +51,47 @@ def __str__(self):
 
     parser.add_argument(
         "--n_subset_max",
-        help="The number of text chunks to embed when using auto selection method",
+        help="Number of text chunks to embed when using auto selection method. Default: 300",
         required=False,
         type=int,
         default=300,
     )
 
     parser.add_argument(
         "--k",
-        help="The maximum number of documents to select from each centroid when using auto selection method",
+        help="Maximum number of documents to select from each centroid when using auto selection method. Default: 15",
         required=False,
         type=int,
         default=15,
     )
 
     parser.add_argument(
         "--limit",
-        help="The limit of files to load when doing random or top selection",
+        help="Number of documents to load when doing random or top selection. Default: 15",
         type=int,
         required=False,
         default=15,
     )
 
     parser.add_argument(
         "--max-tokens",
-        help="Max token count for prompt generation",
+        help=f"Max token count for prompt generation. Default: {MAX_TOKEN_COUNT}",
         type=int,
         required=False,
         default=MAX_TOKEN_COUNT,
     )
 
     parser.add_argument(
         "--min-examples-required",
-        help="The minimum number of examples required in entity extraction prompt",
+        help="Minimum number of examples required in the entity extraction prompt. Default: 2",
         type=int,
         required=False,
         default=2,
     )
 
     parser.add_argument(
         "--chunk-size",
-        help="Max token count for prompt generation",
+        help=f"Max token count for prompt generation. Default: {MIN_CHUNK_SIZE}",
         type=int,
         required=False,
         default=MIN_CHUNK_SIZE,
@@ -120,7 +115,7 @@ def __str__(self):
 
     parser.add_argument(
         "--output",
-        help="Folder to save the generated prompts to",
+        help="Directory to save generated prompts to. Default: 'prompts'",
         type=str,
         required=False,
         default="prompts",
@@ -132,17 +127,18 @@ def __str__(self):
 
     loop.run_until_complete(
         prompt_tune(
-            args.root,
-            args.domain,
-            str(args.method),
-            args.limit,
-            args.max_tokens,
-            args.chunk_size,
-            args.language,
-            args.no_entity_types,
-            args.output,
-            args.n_subset_max,
-            args.k,
-            args.min_examples_required,
+            config=args.config,
+            root=args.root,
+            domain=args.domain,
+            selection_method=args.selection_method,
+            limit=args.limit,
+            max_tokens=args.max_tokens,
+            chunk_size=args.chunk_size,
+            language=args.language,
+            skip_entity_types=args.no_entity_types,
+            output=args.output,
+            n_subset_max=args.n_subset_max,
+            k=args.k,
+            min_examples_required=args.min_examples_required,
         )
     )

graphrag/prompt_tune/api.py

@@ -0,0 +1,173 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""
+Auto Templating API.
+
+This API provides access to the auto templating feature of graphrag, allowing external applications
+to hook into graphrag and generate prompts from private data.
+
+WARNING: This API is under development and may undergo changes in future releases.
+Backwards compatibility is not guaranteed at this time.
+"""
+
+from datashaper import NoopVerbCallbacks
+from pydantic import PositiveInt, validate_call
+
+from graphrag.config.models.graph_rag_config import GraphRagConfig
+from graphrag.index.llm import load_llm
+from graphrag.index.progress import PrintProgressReporter
+
+from .cli import DocSelectionType
+from .generator import (
+    MAX_TOKEN_COUNT,
+    create_community_summarization_prompt,
+    create_entity_extraction_prompt,
+    create_entity_summarization_prompt,
+    detect_language,
+    generate_community_report_rating,
+    generate_community_reporter_role,
+    generate_domain,
+    generate_entity_relationship_examples,
+    generate_entity_types,
+    generate_persona,
+)
+from .loader import (
+    MIN_CHUNK_SIZE,
+    load_docs_in_chunks,
+)
+
+
+@validate_call
+async def generate_indexing_prompts(
+    config: GraphRagConfig,
+    root: str,
+    chunk_size: PositiveInt = MIN_CHUNK_SIZE,
+    limit: PositiveInt = 15,
+    selection_method: DocSelectionType = DocSelectionType.RANDOM,
+    domain: str | None = None,
+    language: str | None = None,
+    max_tokens: int = MAX_TOKEN_COUNT,
+    skip_entity_types: bool = False,
+    min_examples_required: PositiveInt = 2,
+    n_subset_max: PositiveInt = 300,
+    k: PositiveInt = 15,
+) -> tuple[str, str, str]:
+    """Generate indexing prompts.
+
+    Parameters
+    ----------
+    - config: The GraphRag configuration.
+    - output_path: The path to store the prompts.
+    - chunk_size: The chunk token size to use for input text units.
+    - limit: The limit of chunks to load.
+    - selection_method: The chunk selection method.
+    - domain: The domain to map the input documents to.
+    - language: The language to use for the prompts.
+    - max_tokens: The maximum number of tokens to use on entity extraction prompts
+    - skip_entity_types: Skip generating entity types.
+    - min_examples_required: The minimum number of examples required for entity extraction prompts.
+    - n_subset_max: The number of text chunks to embed when using auto selection method.
+    - k: The number of documents to select when using auto selection method.
+
+    Returns
+    -------
+    tuple[str, str, str]: entity extraction prompt, entity summarization prompt, community summarization prompt
+    """
+    reporter = PrintProgressReporter("")
+
+    # Retrieve documents
+    doc_list = await load_docs_in_chunks(
+        root=root,
+        config=config,
+        limit=limit,
+        select_method=selection_method,
+        reporter=reporter,
+        chunk_size=chunk_size,
+        n_subset_max=n_subset_max,
+        k=k,
+    )
+
+    # Create LLM from config
+    llm = load_llm(
+        "prompt_tuning",
+        config.llm.type,
+        NoopVerbCallbacks(),
+        None,
+        config.llm.model_dump(),
+    )
+
+    if not domain:
+        reporter.info("Generating domain...")
+        domain = await generate_domain(llm, doc_list)
+        reporter.info(f"Generated domain: {domain}")
+
+    if not language:
+        reporter.info("Detecting language...")
+        language = await detect_language(llm, doc_list)
+
+    reporter.info("Generating persona...")
+    persona = await generate_persona(llm, domain)
+
+    reporter.info("Generating community report ranking description...")
+    community_report_ranking = await generate_community_report_rating(
+        llm, domain=domain, persona=persona, docs=doc_list
+    )
+
+    entity_types = None
+    if not skip_entity_types:
+        reporter.info("Generating entity types...")
+        entity_types = await generate_entity_types(
+            llm,
+            domain=domain,
+            persona=persona,
+            docs=doc_list,
+            json_mode=config.llm.model_supports_json or False,
+        )
+
+    reporter.info("Generating entity relationship examples...")
+    examples = await generate_entity_relationship_examples(
+        llm,
+        persona=persona,
+        entity_types=entity_types,
+        docs=doc_list,
+        language=language,
+        json_mode=False,  # config.llm.model_supports_json should be used, but this prompts are used in non-json by the index engine
+    )
+
+    reporter.info("Generating entity extraction prompt...")
+    entity_extraction_prompt = create_entity_extraction_prompt(
+        entity_types=entity_types,
+        docs=doc_list,
+        examples=examples,
+        language=language,
+        json_mode=False,  # config.llm.model_supports_json should be used, but these prompts are used in non-json by the index engine
+        encoding_model=config.encoding_model,
+        max_token_count=max_tokens,
+        min_examples_required=min_examples_required,
+    )
+
+    reporter.info("Generating entity summarization prompt...")
+    entity_summarization_prompt = create_entity_summarization_prompt(
+        persona=persona,
+        language=language,
+    )
+
+    reporter.info("Generating community reporter role...")
+    community_reporter_role = await generate_community_reporter_role(
+        llm, domain=domain, persona=persona, docs=doc_list
+    )
+
+    reporter.info("Generating community summarization prompt...")
+    community_summarization_prompt = create_community_summarization_prompt(
+        persona=persona,
+        role=community_reporter_role,
+        report_rating_description=community_report_ranking,
+        language=language,
+    )
+
+    return (
+        entity_extraction_prompt,
+        entity_summarization_prompt,
+        community_summarization_prompt,
+    )