remove prints

backend/onyx/chat/process_message.py

@@ -43,6 +43,7 @@ from onyx.chat.prompt_builder.answer_prompt_builder import default_build_user_me
 from onyx.configs.chat_configs import CHAT_TARGET_CHUNK_PERCENTAGE
 from onyx.configs.chat_configs import DISABLE_LLM_CHOOSE_SEARCH
 from onyx.configs.chat_configs import MAX_CHUNKS_FED_TO_CHAT
+from onyx.configs.chat_configs import SELECTED_SECTIONS_MAX_WINDOW_PERCENTAGE
 from onyx.configs.constants import AGENT_SEARCH_INITIAL_KEY
 from onyx.configs.constants import BASIC_KEY
 from onyx.configs.constants import MessageType
@@ -692,8 +693,13 @@ def stream_chat_message_objects(
                 doc_identifiers=identifier_tuples,
                 document_index=document_index,
             )
+
+            # Add a maximum context size in the case of user-selected docs to prevent
+            # slight inaccuracies in context window size pruning from causing
+            # the entire query to fail
             document_pruning_config = DocumentPruningConfig(
-                is_manually_selected_docs=True
+                is_manually_selected_docs=True,
+                max_window_percentage=SELECTED_SECTIONS_MAX_WINDOW_PERCENTAGE,
             )
 
             # In case the search doc is deleted, just don't include it

backend/onyx/chat/prune_and_merge.py

@@ -312,11 +312,14 @@ def prune_sections(
     )
 
 
-def _merge_doc_chunks(chunks: list[InferenceChunk]) -> InferenceSection:
+def _merge_doc_chunks(chunks: list[InferenceChunk]) -> tuple[InferenceSection, int]:
     assert (
         len(set([chunk.document_id for chunk in chunks])) == 1
     ), "One distinct document must be passed into merge_doc_chunks"
 
+    ADJACENT_CHUNK_SEP = "\n"
+    DISTANT_CHUNK_SEP = "\n\n...\n\n"
+
     # Assuming there are no duplicates by this point
     sorted_chunks = sorted(chunks, key=lambda x: x.chunk_id)
 
@@ -324,33 +327,48 @@ def _merge_doc_chunks(chunks: list[InferenceChunk]) -> InferenceSection:
         chunks, key=lambda x: x.score if x.score is not None else float("-inf")
     )
 
+    added_chars = 0
     merged_content = []
     for i, chunk in enumerate(sorted_chunks):
         if i > 0:
             prev_chunk_id = sorted_chunks[i - 1].chunk_id
-            if chunk.chunk_id == prev_chunk_id + 1:
-                merged_content.append("\n")
-            else:
-                merged_content.append("\n\n...\n\n")
+            sep = (
+                ADJACENT_CHUNK_SEP
+                if chunk.chunk_id == prev_chunk_id + 1
+                else DISTANT_CHUNK_SEP
+            )
+            merged_content.append(sep)
+            added_chars += len(sep)
         merged_content.append(chunk.content)
 
     combined_content = "".join(merged_content)
 
-    return InferenceSection(
-        center_chunk=center_chunk,
-        chunks=sorted_chunks,
-        combined_content=combined_content,
+    return (
+        InferenceSection(
+            center_chunk=center_chunk,
+            chunks=sorted_chunks,
+            combined_content=combined_content,
+        ),
+        added_chars,
     )
 
 
 def _merge_sections(sections: list[InferenceSection]) -> list[InferenceSection]:
     docs_map: dict[str, dict[int, InferenceChunk]] = defaultdict(dict)
     doc_order: dict[str, int] = {}
+    combined_section_lengths: dict[str, int] = defaultdict(lambda: 0)
+
+    # chunk de-duping and doc ordering
     for index, section in enumerate(sections):
         if section.center_chunk.document_id not in doc_order:
             doc_order[section.center_chunk.document_id] = index
+
+        combined_section_lengths[section.center_chunk.document_id] += len(
+            section.combined_content
+        )
+
+        chunks_map = docs_map[section.center_chunk.document_id]
         for chunk in [section.center_chunk] + section.chunks:
-            chunks_map = docs_map[section.center_chunk.document_id]
             existing_chunk = chunks_map.get(chunk.chunk_id)
             if (
                 existing_chunk is None
@@ -361,8 +379,22 @@ def _merge_sections(sections: list[InferenceSection]) -> list[InferenceSection]:
                 chunks_map[chunk.chunk_id] = chunk
 
     new_sections = []
-    for section_chunks in docs_map.values():
-        new_sections.append(_merge_doc_chunks(chunks=list(section_chunks.values())))
+    for doc_id, section_chunks in docs_map.items():
+        section_chunks_list = list(section_chunks.values())
+        merged_section, added_chars = _merge_doc_chunks(chunks=section_chunks_list)
+
+        previous_length = combined_section_lengths[doc_id] + added_chars
+        # After merging, ensure the content respects the pruning done earlier. Each
+        # combined section is restricted to the sum of the lengths of the sections
+        # from the pruning step. Technically the correct approach would be to prune based
+        # on tokens AGAIN, but this is a good approximation and worth not adding the
+        # tokenization overhead. This could also be fixed if we added a way of removing
+        # chunks from sections in the pruning step; at the moment this issue largely
+        # exists because we only trim the final section's combined_content.
+        merged_section.combined_content = merged_section.combined_content[
+            :previous_length
+        ]
+        new_sections.append(merged_section)
 
     # Sort by highest score, then by original document order
     # It is now 1 large section per doc, the center chunk being the one with the highest score

backend/onyx/configs/chat_configs.py

@@ -16,6 +16,9 @@ MAX_CHUNKS_FED_TO_CHAT = float(os.environ.get("MAX_CHUNKS_FED_TO_CHAT") or 10.0)
 # ~3k input, half for docs, half for chat history + prompts
 CHAT_TARGET_CHUNK_PERCENTAGE = 512 * 3 / 3072
 
+# Maximum percentage of the context window to fill with selected sections
+SELECTED_SECTIONS_MAX_WINDOW_PERCENTAGE = 0.8
+
 # 1 / (1 + DOC_TIME_DECAY * doc-age-in-years), set to 0 to have no decay
 # Capped in Vespa at 0.5
 DOC_TIME_DECAY = float(

backend/tests/unit/onyx/chat/test_prune_and_merge.py

@@ -4,6 +4,7 @@ from onyx.chat.prune_and_merge import _merge_sections
 from onyx.configs.constants import DocumentSource
 from onyx.context.search.models import InferenceChunk
 from onyx.context.search.models import InferenceSection
+from onyx.context.search.utils import inference_section_from_chunks
 
 
 # This large test accounts for all of the following:
@@ -111,7 +112,7 @@ Content 17
             # Sections
             [
                 # Document 1, top/middle/bot connected + disconnected section
-                InferenceSection(
+                inference_section_from_chunks(
                     center_chunk=DOC_1_TOP_CHUNK,
                     chunks=[
                         DOC_1_FILLER_1,
@@ -120,9 +121,8 @@ Content 17
                         DOC_1_MID_CHUNK,
                         DOC_1_FILLER_3,
                     ],
-                    combined_content="N/A",  # Not used
                 ),
-                InferenceSection(
+                inference_section_from_chunks(
                     center_chunk=DOC_1_MID_CHUNK,
                     chunks=[
                         DOC_1_FILLER_2,
@@ -131,9 +131,8 @@ Content 17
                         DOC_1_FILLER_3,
                         DOC_1_FILLER_4,
                     ],
-                    combined_content="N/A",
                 ),
-                InferenceSection(
+                inference_section_from_chunks(
                     center_chunk=DOC_1_BOTTOM_CHUNK,
                     chunks=[
                         DOC_1_FILLER_3,
@@ -142,9 +141,8 @@ Content 17
                         DOC_1_FILLER_5,
                         DOC_1_FILLER_6,
                     ],
-                    combined_content="N/A",
                 ),
-                InferenceSection(
+                inference_section_from_chunks(
                     center_chunk=DOC_1_DISCONNECTED,
                     chunks=[
                         DOC_1_FILLER_7,
@@ -153,9 +151,8 @@ Content 17
                         DOC_1_FILLER_9,
                         DOC_1_FILLER_10,
                     ],
-                    combined_content="N/A",
                 ),
-                InferenceSection(
+                inference_section_from_chunks(
                     center_chunk=DOC_2_TOP_CHUNK,
                     chunks=[
                         DOC_2_FILLER_1,
@@ -164,9 +161,8 @@ Content 17
                         DOC_2_FILLER_3,
                         DOC_2_BOTTOM_CHUNK,
                     ],
-                    combined_content="N/A",
                 ),
-                InferenceSection(
+                inference_section_from_chunks(
                     center_chunk=DOC_2_BOTTOM_CHUNK,
                     chunks=[
                         DOC_2_TOP_CHUNK,
@@ -175,7 +171,6 @@ Content 17
                         DOC_2_FILLER_4,
                         DOC_2_FILLER_5,
                     ],
-                    combined_content="N/A",
                 ),
             ],
             # Expected Content
@@ -204,15 +199,13 @@ def test_merge_sections(
         (
             # Sections
             [
-                InferenceSection(
+                inference_section_from_chunks(
                     center_chunk=DOC_1_TOP_CHUNK,
                     chunks=[DOC_1_TOP_CHUNK],
-                    combined_content="N/A",  # Not used
                 ),
-                InferenceSection(
+                inference_section_from_chunks(
                     center_chunk=DOC_1_MID_CHUNK,
                     chunks=[DOC_1_MID_CHUNK],
-                    combined_content="N/A",
                 ),
             ],
             # Expected Content