Merge branch 'release/v0.6' of https://github.com/danswer-ai/danswer …

…into fixes/user-group-fk

.github/workflows/pr-python-checks.yml

@@ -3,7 +3,9 @@ name: Python Checks
 on:
   merge_group:
   pull_request:
-    branches: [ main ]
+    branches:
+      - main
+      - 'release/**'
 
 jobs:
   mypy-check:

.github/workflows/pr-python-tests.yml

@@ -3,7 +3,9 @@ name: Python Unit Tests
 on:
   merge_group:
   pull_request:
-    branches: [ main ]
+    branches:
+      - main
+      - 'release/**'
 
 jobs:
   backend-check:

.github/workflows/run-it.yml

@@ -6,7 +6,9 @@ concurrency:
 on:
   merge_group:
   pull_request:
-    branches: [ main ]
+    branches:
+      - main
+      - 'release/**'
 
 env:
   OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}

CONTRIBUTING_MACOS.md

@@ -28,4 +28,6 @@ MacOS will likely require you to remove some quarantine attributes on some of th
 After installing pre-commit, run the following command:
 ```bash
 sudo xattr -r -d com.apple.quarantine ~/.cache/pre-commit
-```
+```
+
+doc version 0.1

backend/danswer/background/indexing/run_indexing.py

@@ -29,6 +29,7 @@
 from danswer.db.models import IndexModelStatus
 from danswer.document_index.factory import get_default_document_index
 from danswer.indexing.embedder import DefaultIndexingEmbedder
+from danswer.indexing.indexing_heartbeat import IndexingHeartbeat
 from danswer.indexing.indexing_pipeline import build_indexing_pipeline
 from danswer.utils.logger import IndexAttemptSingleton
 from danswer.utils.logger import setup_logger
@@ -103,15 +104,24 @@ def _run_indexing(
     )
 
     embedding_model = DefaultIndexingEmbedder.from_db_search_settings(
-        search_settings=search_settings
+        search_settings=search_settings,
+        heartbeat=IndexingHeartbeat(
+            index_attempt_id=index_attempt.id,
+            db_session=db_session,
+            # let the world know we're still making progress after
+            # every 10 batches
+            freq=10,
+        ),
     )
 
     indexing_pipeline = build_indexing_pipeline(
         attempt_id=index_attempt.id,
         embedder=embedding_model,
         document_index=document_index,
-        ignore_time_skip=index_attempt.from_beginning
-        or (search_settings.status == IndexModelStatus.FUTURE),
+        ignore_time_skip=(
+            index_attempt.from_beginning
+            or (search_settings.status == IndexModelStatus.FUTURE)
+        ),
         db_session=db_session,
     )
 

backend/danswer/configs/app_configs.py

@@ -247,6 +247,10 @@
     for ignored_tag in os.environ.get("JIRA_CONNECTOR_LABELS_TO_SKIP", "").split(",")
     if ignored_tag
 ]
+# Maximum size for Jira tickets in bytes (default: 100KB)
+JIRA_CONNECTOR_MAX_TICKET_SIZE = int(
+    os.environ.get("JIRA_CONNECTOR_MAX_TICKET_SIZE", 100 * 1024)
+)
 
 GONG_CONNECTOR_START_TIME = os.environ.get("GONG_CONNECTOR_START_TIME")
 

backend/danswer/connectors/danswer_jira/connector.py

@@ -9,6 +9,7 @@
 
 from danswer.configs.app_configs import INDEX_BATCH_SIZE
 from danswer.configs.app_configs import JIRA_CONNECTOR_LABELS_TO_SKIP
+from danswer.configs.app_configs import JIRA_CONNECTOR_MAX_TICKET_SIZE
 from danswer.configs.constants import DocumentSource
 from danswer.connectors.cross_connector_utils.miscellaneous_utils import time_str_to_utc
 from danswer.connectors.interfaces import GenerateDocumentsOutput
@@ -134,10 +135,18 @@ def fetch_jira_issues_batch(
             else extract_text_from_adf(jira.raw["fields"]["description"])
         )
         comments = _get_comment_strs(jira, comment_email_blacklist)
-        semantic_rep = f"{description}\n" + "\n".join(
+        ticket_content = f"{description}\n" + "\n".join(
             [f"Comment: {comment}" for comment in comments if comment]
         )
 
+        # Check ticket size
+        if len(ticket_content.encode("utf-8")) > JIRA_CONNECTOR_MAX_TICKET_SIZE:
+            logger.info(
+                f"Skipping {jira.key} because it exceeds the maximum size of "
+                f"{JIRA_CONNECTOR_MAX_TICKET_SIZE} bytes."
+            )
+            continue
+
         page_url = f"{jira_client.client_info()}/browse/{jira.key}"
 
         people = set()
@@ -180,7 +189,7 @@ def fetch_jira_issues_batch(
         doc_batch.append(
             Document(
                 id=page_url,
-                sections=[Section(link=page_url, text=semantic_rep)],
+                sections=[Section(link=page_url, text=ticket_content)],
                 source=DocumentSource.JIRA,
                 semantic_identifier=jira.fields.summary,
                 doc_updated_at=time_str_to_utc(jira.fields.updated),

backend/danswer/indexing/chunker.py

@@ -10,6 +10,7 @@
     get_metadata_keys_to_ignore,
 )
 from danswer.connectors.models import Document
+from danswer.indexing.indexing_heartbeat import Heartbeat
 from danswer.indexing.models import DocAwareChunk
 from danswer.natural_language_processing.utils import BaseTokenizer
 from danswer.utils.logger import setup_logger
@@ -123,6 +124,7 @@ def __init__(
         chunk_token_limit: int = DOC_EMBEDDING_CONTEXT_SIZE,
         chunk_overlap: int = CHUNK_OVERLAP,
         mini_chunk_size: int = MINI_CHUNK_SIZE,
+        heartbeat: Heartbeat | None = None,
     ) -> None:
         from llama_index.text_splitter import SentenceSplitter
 
@@ -131,6 +133,7 @@ def __init__(
         self.enable_multipass = enable_multipass
         self.enable_large_chunks = enable_large_chunks
         self.tokenizer = tokenizer
+        self.heartbeat = heartbeat
 
         self.blurb_splitter = SentenceSplitter(
             tokenizer=tokenizer.tokenize,
@@ -255,7 +258,7 @@ def _create_chunk(
         # If the chunk does not have any useable content, it will not be indexed
         return chunks
 
-    def chunk(self, document: Document) -> list[DocAwareChunk]:
+    def _handle_single_document(self, document: Document) -> list[DocAwareChunk]:
         # Specifically for reproducing an issue with gmail
         if document.source == DocumentSource.GMAIL:
             logger.debug(f"Chunking {document.semantic_identifier}")
@@ -302,3 +305,13 @@ def chunk(self, document: Document) -> list[DocAwareChunk]:
             normal_chunks.extend(large_chunks)
 
         return normal_chunks
+
+    def chunk(self, documents: list[Document]) -> list[DocAwareChunk]:
+        final_chunks: list[DocAwareChunk] = []
+        for document in documents:
+            final_chunks.extend(self._handle_single_document(document))
+
+            if self.heartbeat:
+                self.heartbeat.heartbeat()
+
+        return final_chunks

backend/danswer/indexing/embedder.py

@@ -1,12 +1,8 @@
 from abc import ABC
 from abc import abstractmethod
 
-from sqlalchemy.orm import Session
-
-from danswer.db.models import IndexModelStatus
 from danswer.db.models import SearchSettings
-from danswer.db.search_settings import get_current_search_settings
-from danswer.db.search_settings import get_secondary_search_settings
+from danswer.indexing.indexing_heartbeat import Heartbeat
 from danswer.indexing.models import ChunkEmbedding
 from danswer.indexing.models import DocAwareChunk
 from danswer.indexing.models import IndexChunk
@@ -24,6 +20,9 @@
 
 
 class IndexingEmbedder(ABC):
+    """Converts chunks into chunks with embeddings. Note that one chunk may have
+    multiple embeddings associated with it."""
+
     def __init__(
         self,
         model_name: str,
@@ -33,6 +32,7 @@ def __init__(
         provider_type: EmbeddingProvider | None,
         api_key: str | None,
         api_url: str | None,
+        heartbeat: Heartbeat | None,
     ):
         self.model_name = model_name
         self.normalize = normalize
@@ -54,6 +54,7 @@ def __init__(
             server_host=INDEXING_MODEL_SERVER_HOST,
             server_port=INDEXING_MODEL_SERVER_PORT,
             retrim_content=True,
+            heartbeat=heartbeat,
         )
 
     @abstractmethod
@@ -74,6 +75,7 @@ def __init__(
         provider_type: EmbeddingProvider | None = None,
         api_key: str | None = None,
         api_url: str | None = None,
+        heartbeat: Heartbeat | None = None,
     ):
         super().__init__(
             model_name,
@@ -83,6 +85,7 @@ def __init__(
             provider_type,
             api_key,
             api_url,
+            heartbeat,
         )
 
     @log_function_time()
@@ -166,7 +169,7 @@ def embed_chunks(
                     title_embed_dict[title] = title_embedding
 
             new_embedded_chunk = IndexChunk(
-                **chunk.dict(),
+                **chunk.model_dump(),
                 embeddings=ChunkEmbedding(
                     full_embedding=chunk_embeddings[0],
                     mini_chunk_embeddings=chunk_embeddings[1:],
@@ -180,7 +183,7 @@ def embed_chunks(
 
     @classmethod
     def from_db_search_settings(
-        cls, search_settings: SearchSettings
+        cls, search_settings: SearchSettings, heartbeat: Heartbeat | None = None
     ) -> "DefaultIndexingEmbedder":
         return cls(
             model_name=search_settings.model_name,
@@ -190,28 +193,5 @@ def from_db_search_settings(
             provider_type=search_settings.provider_type,
             api_key=search_settings.api_key,
             api_url=search_settings.api_url,
+            heartbeat=heartbeat,
         )
-
-
-def get_embedding_model_from_search_settings(
-    db_session: Session, index_model_status: IndexModelStatus = IndexModelStatus.PRESENT
-) -> IndexingEmbedder:
-    search_settings: SearchSettings | None
-    if index_model_status == IndexModelStatus.PRESENT:
-        search_settings = get_current_search_settings(db_session)
-    elif index_model_status == IndexModelStatus.FUTURE:
-        search_settings = get_secondary_search_settings(db_session)
-        if not search_settings:
-            raise RuntimeError("No secondary index configured")
-    else:
-        raise RuntimeError("Not supporting embedding model rollbacks")
-
-    return DefaultIndexingEmbedder(
-        model_name=search_settings.model_name,
-        normalize=search_settings.normalize,
-        query_prefix=search_settings.query_prefix,
-        passage_prefix=search_settings.passage_prefix,
-        provider_type=search_settings.provider_type,
-        api_key=search_settings.api_key,
-        api_url=search_settings.api_url,
-    )

backend/danswer/indexing/indexing_heartbeat.py

@@ -0,0 +1,41 @@
+import abc
+from typing import Any
+
+from sqlalchemy import func
+from sqlalchemy.orm import Session
+
+from danswer.db.index_attempt import get_index_attempt
+from danswer.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+class Heartbeat(abc.ABC):
+    """Useful for any long-running work that goes through a bunch of items
+    and needs to occasionally give updates on progress.
+    e.g. chunking, embedding, updating vespa, etc."""
+
+    @abc.abstractmethod
+    def heartbeat(self, metadata: Any = None) -> None:
+        raise NotImplementedError
+
+
+class IndexingHeartbeat(Heartbeat):
+    def __init__(self, index_attempt_id: int, db_session: Session, freq: int):
+        self.cnt = 0
+
+        self.index_attempt_id = index_attempt_id
+        self.db_session = db_session
+        self.freq = freq
+
+    def heartbeat(self, metadata: Any = None) -> None:
+        self.cnt += 1
+        if self.cnt % self.freq == 0:
+            index_attempt = get_index_attempt(
+                db_session=self.db_session, index_attempt_id=self.index_attempt_id
+            )
+            if index_attempt:
+                index_attempt.time_updated = func.now()
+                self.db_session.commit()
+            else:
+                logger.error("Index attempt not found, this should not happen!")

backend/danswer/indexing/indexing_pipeline.py

@@ -31,6 +31,7 @@
 from danswer.document_index.interfaces import DocumentMetadata
 from danswer.indexing.chunker import Chunker
 from danswer.indexing.embedder import IndexingEmbedder
+from danswer.indexing.indexing_heartbeat import IndexingHeartbeat
 from danswer.indexing.models import DocAwareChunk
 from danswer.indexing.models import DocMetadataAwareIndexChunk
 from danswer.utils.logger import setup_logger
@@ -283,18 +284,10 @@ def index_doc_batch(
         return 0, 0
 
     logger.debug("Starting chunking")
-    chunks: list[DocAwareChunk] = []
-    for document in ctx.updatable_docs:
-        chunks.extend(chunker.chunk(document=document))
+    chunks: list[DocAwareChunk] = chunker.chunk(ctx.updatable_docs)
 
     logger.debug("Starting embedding")
-    chunks_with_embeddings = (
-        embedder.embed_chunks(
-            chunks=chunks,
-        )
-        if chunks
-        else []
-    )
+    chunks_with_embeddings = embedder.embed_chunks(chunks) if chunks else []
 
     updatable_ids = [doc.id for doc in ctx.updatable_docs]
 
@@ -406,6 +399,13 @@ def build_indexing_pipeline(
         tokenizer=embedder.embedding_model.tokenizer,
         enable_multipass=multipass,
         enable_large_chunks=enable_large_chunks,
+        # after every doc, update status in case there are a bunch of
+        # really long docs
+        heartbeat=IndexingHeartbeat(
+            index_attempt_id=attempt_id, db_session=db_session, freq=1
+        )
+        if attempt_id
+        else None,
     )
 
     return partial(

backend/danswer/natural_language_processing/search_nlp_models.py

@@ -16,6 +16,7 @@
 )
 from danswer.configs.model_configs import DOC_EMBEDDING_CONTEXT_SIZE
 from danswer.db.models import SearchSettings
+from danswer.indexing.indexing_heartbeat import Heartbeat
 from danswer.natural_language_processing.utils import get_tokenizer
 from danswer.natural_language_processing.utils import tokenizer_trim_content
 from danswer.utils.logger import setup_logger
@@ -95,6 +96,7 @@ def __init__(
         api_url: str | None,
         provider_type: EmbeddingProvider | None,
         retrim_content: bool = False,
+        heartbeat: Heartbeat | None = None,
     ) -> None:
         self.api_key = api_key
         self.provider_type = provider_type
@@ -107,6 +109,7 @@ def __init__(
         self.tokenizer = get_tokenizer(
             model_name=model_name, provider_type=provider_type
         )
+        self.heartbeat = heartbeat
 
         model_server_url = build_model_server_url(server_host, server_port)
         self.embed_server_endpoint = f"{model_server_url}/encoder/bi-encoder-embed"
@@ -166,6 +169,9 @@ def _batch_encode_texts(
 
             response = self._make_model_server_request(embed_request)
             embeddings.extend(response.embeddings)
+
+            if self.heartbeat:
+                self.heartbeat.heartbeat()
         return embeddings
 
     def encode(