Removed description_length template variable to have less hypers

Author: jamesbraza

README.md

@@ -937,7 +937,6 @@ will return much faster than the first query and we'll be certain the authors ma
 | `parsing.enrichment_llm_config`              | `None`                                 | Optional configuration for `enrichment_llm`.                                                                                  |
 | `parsing.enrichment_page_radius`             | `1`                                    | Page radius for context text in enrichment.                                                                                   |
 | `parsing.enrichment_prompt`                  | `image_enrichment_prompt_template`     | Prompt template for enriching media.                                                                                          |
-| `parsing.enrichment_description_length`      | `"about 150 words"`                    | Plain text stating the desired length of an enriched media's description.                                                     |
 | `prompt.summary`                             | `summary_prompt`                       | Template for summarizing text, must contain variables matching `summary_prompt`.                                              |
 | `prompt.qa`                                  | `qa_prompt`                            | Template for QA, must contain variables matching `qa_prompt`.                                                                 |
 | `prompt.select`                              | `select_paper_prompt`                  | Template for selecting papers, must contain variables matching `select_paper_prompt`.                                         |

src/paperqa/prompts.py

@@ -181,14 +181,14 @@
     " so only use aspects relevant to accompanying image or table."
     "\n\nHere's a few failure mode with possible resolutions:"
     "\n- The media was a logo or icon, so the text is unrelated."
-    " In this case, describe the media as a logo or icon,"
+    " In this case, briefly describe the media as a logo or icon,"
     " and do not mention other unrelated surrounding text."
     "\n- The media was display type, so the text is probably unrelated."
     " The display type can be spread over several lines."
-    " In this case, describe the media as display type,"
+    " In this case, briefly describe the media as display type,"
     " and do not mention other unrelated surrounding text."
     "\n- The media is a margin box or design element, so the text is unrelated."
-    " In this case, describe the media as decorative,"
+    " In this case, briefly describe the media as decorative,"
     " and do not mention other unrelated surrounding text."
     "\n- The media came from a bad PDF read, so it's garbled."
     " In this case, describe the media as garbled, state why it's considered garbled,"
@@ -197,6 +197,6 @@
     " In this case, make sure to only detail the subfigure or subtable,"
     " not the entire figure or table."
     " Do not mention other unrelated surrounding text."
-    "\n\n{context_text}Describe the media ({description_length}),"  # Allow for empty context_text
+    "\n\n{context_text}Describe the media,"  # Allow for empty context_text
     " or if uncertain on a description please state why:"
 )

src/paperqa/settings.py

@@ -334,12 +334,6 @@ class ParsingSettings(BaseModel):
         default=media_enrichment_prompt_template,
         description="Prompt template for enriching media.",
     )
-    enrichment_description_length: str = Field(
-        default="about 150 words",
-        description=(
-            "Plain text stating the desired length of an enriched media's description."
-        ),
-    )
 
     @property
     def should_parse_and_enrich_media(self) -> tuple[bool, bool]:
@@ -1183,8 +1177,7 @@ async def enrich_single_media(
                         f"Here is the co-located text from {radius_msg}:\n\n{context_text}\n\n"
                         if context_text
                         else ""
-                    ),
-                    description_length=self.parsing.enrichment_description_length,
+                    )
                 )
                 result = await llm.call_single(
                     messages=[