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fix: reorder extraction prompts for LLM prompt caching #1873

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fix: reorder extraction prompts for LLM prompt caching #1873

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48 changes: 24 additions & 24 deletions crawl4ai/prompts.py
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Original file line number Diff line number Diff line change
@@ -1,12 +1,4 @@
PROMPT_EXTRACT_BLOCKS = """Here is the URL of the webpage:
<url>{URL}</url>

And here is the cleaned HTML content of that webpage:
<html>
{HTML}
</html>

Your task is to break down this HTML content into semantically relevant blocks, and for each block, generate a JSON object with the following keys:
PROMPT_EXTRACT_BLOCKS = """Your task is to break down the HTML content (provided below) into semantically relevant blocks, and for each block, generate a JSON object with the following keys:

- index: an integer representing the index of the block in the content
- content: a list of strings containing the text content of the block
Expand Down Expand Up @@ -46,17 +38,17 @@
}]
</blocks>

Remember, the output should be a complete, parsable JSON wrapped in <blocks> tags, with no omissions or errors. The JSON objects should semantically break down the content into relevant blocks, maintaining the original order."""

PROMPT_EXTRACT_BLOCKS_WITH_INSTRUCTION = """Here is the URL of the webpage:
Here is the URL of the webpage:
<url>{URL}</url>

And here is the cleaned HTML content of that webpage:
<html>
{HTML}
</html>

Your task is to break down this HTML content into semantically relevant blocks, following the provided user's REQUEST, and for each block, generate a JSON object with the following keys:
Remember, the output should be a complete, parsable JSON wrapped in <blocks> tags, with no omissions or errors. The JSON objects should semantically break down the content into relevant blocks, maintaining the original order."""

PROMPT_EXTRACT_BLOCKS_WITH_INSTRUCTION = """Your task is to break down the HTML content (provided below) into semantically relevant blocks, following the provided user's REQUEST, and for each block, generate a JSON object with the following keys:

- index: an integer representing the index of the block in the content
- content: a list of strings containing the text content of the block
Expand Down Expand Up @@ -103,16 +95,17 @@

**Make sure to follow the user instruction to extract blocks aligin with the instruction.**

Remember, the output should be a complete, parsable JSON wrapped in <blocks> tags, with no omissions or errors. The JSON objects should semantically break down the content into relevant blocks, maintaining the original order."""

PROMPT_EXTRACT_SCHEMA_WITH_INSTRUCTION = """Here is the content from the URL:
Here is the URL of the webpage:
<url>{URL}</url>

<url_content>
And here is the cleaned HTML content of that webpage:
<html>
{HTML}
</url_content>
</html>

Remember, the output should be a complete, parsable JSON wrapped in <blocks> tags, with no omissions or errors. The JSON objects should semantically break down the content into relevant blocks, maintaining the original order."""

The user has made the following request for what information to extract from the above content:
PROMPT_EXTRACT_SCHEMA_WITH_INSTRUCTION = """The user has made the following request for what information to extract from the above content:

<user_request>
{REQUEST}
Expand All @@ -139,17 +132,17 @@
- Do not miss closing </blocks> tag at the end of the JSON output.
- Do not generate the Python code show me how to do the task, this is your task to extract the information and return it in JSON format.

Result
Output the final list of JSON objects, wrapped in <blocks>...</blocks> XML tags. Make sure to close the tag properly."""

PROMPT_EXTRACT_INFERRED_SCHEMA = """Here is the content from the URL:
Here is the content from the URL:
<url>{URL}</url>

<url_content>
{HTML}
</url_content>

Please carefully read the URL content and the user's request. Analyze the page structure and infer the most appropriate JSON schema based on the content and request.
Result
Output the final list of JSON objects, wrapped in <blocks>...</blocks> XML tags. Make sure to close the tag properly."""

PROMPT_EXTRACT_INFERRED_SCHEMA = """Please carefully read the URL content and the user's request. Analyze the page structure and infer the most appropriate JSON schema based on the content and request.

Extraction Strategy:
1. First, determine if the page contains repetitive items (like multiple products, articles, etc.) or a single content item (like a single article or page).
Expand Down Expand Up @@ -197,6 +190,13 @@
DO NOT ADD ANY PRE OR POST COMMENTS. JUST RETURN THE JSON OBJECTS INSIDE <blocks>...</blocks> TAGS.

CRITICAL: The content inside the <blocks> tags MUST be a direct array of JSON objects (starting with '[' and ending with ']'), not a dictionary/object containing an array. For example, use <blocks>[{...}, {...}]</blocks> instead of <blocks>{"items": [{...}, {...}]}</blocks>. This is essential for proper parsing.

Here is the content from the URL:
<url>{URL}</url>

<url_content>
{HTML}
</url_content>
"""

PROMPT_FILTER_CONTENT = """Your task is to filter and convert HTML content into clean, focused markdown that's optimized for use with LLMs and information retrieval systems.
Expand Down