1\. Handling Non-Streaming Responses

Developer Guide: LLM Response Cleaning and Formatting

This guide provides a deep dive into how WilmerAI processes and cleans the raw output from a Large Language Model (LLM) before it is returned to the user. This process is essential for removing diagnostic tags, boilerplate prefixes, and other unwanted artifacts from the final response.

The core principle is that all text cleaning occurs after the raw, unmodified response is received from the backing LLM via the llmapis layer. The logic is implemented in parallel for non-streaming and streaming responses to ensure functionally identical outcomes.

1. Handling Non-Streaming Responses

For a complete, non-streaming response, the entire block of text is processed at once in a sequential series of cleaning steps.

Location of Logic

• Primary Function: post_process_llm_output in Middleware/utilities/streaming_utils.py.
• Calling Context: This function is typically invoked by a higher-level workflow processor after a non-streaming node has executed.

Order of Operations

The post_process_llm_output function applies cleaning rules in a precise order. The sequence is as follows:

1. Thinking Tag Removal: The text is first passed to remove_thinking_from_text. This function's behavior depends on the endpoint configuration:
   • Standard Mode: It looks for a startThinkTag within an initial openingTagGracePeriod (e.g., the first 100 characters). If found, it then searches for the corresponding endThinkTag. If both are found, the entire block ( including tags) is removed. If the opening tag isn't in the grace period or the closing tag is missing, the * original text is returned unmodified*.
   • expectOnlyClosingThinkTag Mode: If this flag is True, the function searches for the endThinkTag only. If found, it discards everything before it and returns only the content that follows. If the tag is not found, it returns an empty string.
2. Leading Whitespace Stripped: The result is left-stripped to prepare it for prefix matching.
3. Workflow-Level Custom Prefixes: It checks for prefixes defined by removeCustomTextFromResponseStart and responseStartTextToRemove in the workflow node's config. The responseStartTextToRemove value is an array of strings. The function iterates through this array and removes the first prefix that matches the beginning of the response.
4. Endpoint-Level Custom Prefixes: It performs the same array-based iteration for prefixes defined by removeCustomTextFromResponseStartEndpointWide and responseStartTextToRemoveEndpointWide in the endpoint's config.
5. Timestamp Text: It removes the hardcoded [Sent less than a minute ago] prefix if the node's addDiscussionIdTimestampsForLLM flag is True.
6. "Assistant:" Prefix: It removes the Assistant: prefix based on a composite check of global settings ( get_is_chat_complete_add_user_assistant() and get_is_chat_complete_add_missing_assistant()).
7. Final Trim: If trimBeginningAndEndLineBreaks is True in the endpoint config, a final .strip() is applied to remove any leading or trailing whitespace and line breaks.

2. Handling Streaming Responses

Cleaning a streaming response is more complex, as it must be done chunk-by-chunk. This process is orchestrated by the $StreamingResponseHandler$ class, which uses a stateful helper for tag removal.

Location of Logic

• Primary Class: $StreamingResponseHandler$ in Middleware/workflows/streaming/response_handler.py.
• Key Dependency: It relies on the $StreamingThinkRemover$ class from Middleware/utilities/streaming_utils.py for stateful tag removal.

The Two-Stage Cleaning Process

$StreamingResponseHandler$ uses a two-stage process to clean the stream:

Stage 1: Thinking Tag Removal (Continuous)

Every delta chunk from the LLM is immediately passed to an instance of $StreamingThinkRemover$. This stateful class uses an internal buffer to find and remove thinking blocks in real-time.

• Standard Mode: The remover buffers initial chunks up to the openingTagGracePeriod limit. If the startThinkTag appears within this window, it enters a "thinking" state and discards all subsequent text until the endThinkTag is found. If the grace period passes without an opening tag, the remover stops looking and passes all future text through untouched. If the stream ends with an unterminated think block, the remover flushes its buffer, returning the opening tag and the buffered content.
• expectOnlyClosingThinkTag Mode: The remover buffers and discards all text until it finds the endThinkTag. Once the tag is found, it discards the buffer and begins yielding all subsequent text.

The rest of the system only sees the "clean" stream output by $StreamingThinkRemover$.

Stage 2: Prefix Removal (One-Time at Start)

The $StreamingResponseHandler$ takes the cleaned stream from Stage 1 and performs a one-time buffering operation to handle all other prefix removals.

1. Buffering: It collects the initial chunks from $StreamingThinkRemover$ into its own _prefix_buffer until a sufficient amount of text is gathered to check for prefixes. No text is yielded to the client during this phase.
2. Processing: Once the buffer is ready, the _strip_prefixes_from_buffer method is called once. This method applies the exact same ordered logic as the non-streaming post_process_llm_output function, including iterating through the arrays of custom prefixes.
3. Streaming: The cleaned text from the buffer is yielded to the client. From this point on, the prefix buffer is disabled, and all subsequent chunks from $StreamingThinkRemover$ are yielded directly to the client without delay.

3. How to Add a New Rule

To add a new hardcoded prefix removal rule, you must add it to both the non-streaming and streaming logic to ensure consistent behavior.

1. Non-Streaming: Add your logic to the sequence in post_process_llm_output in Middleware/utilities/streaming_utils.py.
2. Streaming: Add the identical logic to the _strip_prefixes_from_buffer method in Middleware/workflows/streaming/response_handler.py.

Example: Remove a [DIAGNOSTIC]: prefix.

# In both post_process_llm_output and _strip_prefixes_from_buffer...

# ... (existing custom text and timestamp removals) ...

# NEW RULE: Add it here in the sequence
if content.startswith("[DIAGNOSTIC]:"):
    content = content[len("[DIAGNOSTIC]:"):].lstrip()

# ... (existing "Assistant:" prefix removal) ...

4. Summary of Logic Locations