Fix encoding model parameter in prompt auto templating (#500)

* Fix encoding model parameter in prompt tune

* Format changes

.semversioner/next-release/patch-20240711004716103302.json

@@ -0,0 +1,4 @@
+{
+  "type": "patch",
+  "description": "Fix encoding model parameter on prompt tune"
+}

graphrag/index/utils/tokens.py

@@ -2,6 +2,7 @@
 # Licensed under the MIT License
 
 """Utilities for working with tokens."""
+
 import logging
 
 import tiktoken
@@ -17,9 +18,9 @@ def num_tokens_from_string(
     if model is not None:
         try:
             encoding = tiktoken.encoding_for_model(model)
-        except KeyError as e:
+        except KeyError:
-            log.error(f"Failed to get encoding for {model} when getting num_tokens_from_string, "
+            msg = f"Failed to get encoding for {model} when getting num_tokens_from_string. Fall back to default encoding {DEFAULT_ENCODING_NAME}"
-                      f"fall back to default encoding {DEFAULT_ENCODING_NAME}")
+            log.warning(msg)
             encoding = tiktoken.get_encoding(DEFAULT_ENCODING_NAME)
     else:
         encoding = tiktoken.get_encoding(encoding_name or DEFAULT_ENCODING_NAME)

graphrag/prompt_tune/cli.py

@@ -218,8 +218,8 @@ async def generate_indexing_prompts(
         examples=examples,
         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
-        model_name=config.llm.model,
         output_path=output_path,
+        encoding_model=config.encoding_model,
         max_token_count=max_tokens,
     )
     reporter.info(f"Generated entity extraction prompt, stored in folder {output_path}")

graphrag/prompt_tune/generator/entity_extraction_prompt.py

@@ -5,6 +5,7 @@
 
 from pathlib import Path
 
+import graphrag.config.defaults as defs
 from graphrag.index.utils.tokens import num_tokens_from_string
 from graphrag.prompt_tune.template import (
     EXAMPLE_EXTRACTION_TEMPLATE,
@@ -22,8 +23,8 @@ def create_entity_extraction_prompt(
     docs: list[str],
     examples: list[str],
     language: str,
-    model_name: str,
     max_token_count: int,
+    encoding_model: str = defs.ENCODING_MODEL,
     json_mode: bool = False,
     output_path: Path | None = None,
 ) -> str:
@@ -36,7 +37,7 @@ def create_entity_extraction_prompt(
     - docs (list[str]): The list of documents to extract entities from
     - examples (list[str]): The list of examples to use for entity extraction
     - language (str): The language of the inputs and outputs
-    - model_name (str): The name of the model to use for token counting
+    - encoding_model (str): The name of the model to use for token counting
     - max_token_count (int): The maximum number of tokens to use for the prompt
     - json_mode (bool): Whether to use JSON mode for the prompt. Default is False
     - output_path (Path | None): The path to write the prompt to. Default is None. If None, the prompt is not written to a file. Default is None.
@@ -55,8 +56,8 @@ def create_entity_extraction_prompt(
 
     tokens_left = (
         max_token_count
-        - num_tokens_from_string(prompt, model=model_name)
+        - num_tokens_from_string(prompt, model=encoding_model)
-        - num_tokens_from_string(entity_types, model=model_name)
+        - num_tokens_from_string(entity_types, model=encoding_model)
         if entity_types
         else 0
     )
@@ -76,7 +77,7 @@ def create_entity_extraction_prompt(
             )
         )
 
-        example_tokens = num_tokens_from_string(example_formatted, model=model_name)
+        example_tokens = num_tokens_from_string(example_formatted, model=encoding_model)
 
         # Squeeze in at least one example
         if i > 0 and example_tokens > tokens_left: