From b49a9ab171204c25e5c472b5a18dd911e20fc73a Mon Sep 17 00:00:00 2001 From: Yuhong Sun Date: Thu, 24 Oct 2024 16:45:48 -0700 Subject: [PATCH] Seeding (#2902) * checkpoint * k * k * k * fixed slack api calls * missed one --------- Co-authored-by: hagen-danswer --- backend/danswer/access/models.py | 9 + backend/danswer/configs/constants.py | 1 + backend/danswer/connectors/axero/connector.py | 2 +- .../danswer/connectors/clickup/connector.py | 2 +- .../danswer/connectors/discourse/connector.py | 2 +- .../connectors/document360/connector.py | 2 +- .../connectors/google_drive/connector.py | 2 +- backend/danswer/connectors/slack/utils.py | 2 +- .../danswer/db/connector_credential_pair.py | 5 +- backend/danswer/db/credentials.py | 7 +- backend/danswer/db/index_attempt.py | 27 + backend/danswer/indexing/embedder.py | 8 + backend/danswer/indexing/indexing_pipeline.py | 2 + backend/danswer/key_value_store/factory.py | 3 +- backend/danswer/key_value_store/interface.py | 2 + backend/danswer/seeding/__init__.py | 0 backend/danswer/seeding/initial_docs.json | 10824 ++++++++++++++++ backend/danswer/seeding/load_docs.py | 212 + backend/danswer/setup.py | 14 +- .../retry_wrapper.py | 18 +- .../google_drive/doc_sync.py | 2 +- .../google_drive/group_sync.py | 2 +- backend/scripts/document_seeding_prep.py | 240 + .../slack/slack_api_utils.py | 34 +- 24 files changed, 11372 insertions(+), 50 deletions(-) create mode 100644 backend/danswer/seeding/__init__.py create mode 100644 backend/danswer/seeding/initial_docs.json create mode 100644 backend/danswer/seeding/load_docs.py rename backend/danswer/{connectors/cross_connector_utils => utils}/retry_wrapper.py (79%) create mode 100644 backend/scripts/document_seeding_prep.py diff --git a/backend/danswer/access/models.py b/backend/danswer/access/models.py index af5a021ca9..46b9c0efa9 100644 --- a/backend/danswer/access/models.py +++ b/backend/danswer/access/models.py @@ -70,3 +70,12 @@ class DocumentAccess(ExternalAccess): user_groups=set(user_groups), is_public=is_public, ) + + +default_public_access = DocumentAccess( + external_user_emails=set(), + external_user_group_ids=set(), + user_emails=set(), + user_groups=set(), + is_public=True, +) diff --git a/backend/danswer/configs/constants.py b/backend/danswer/configs/constants.py index 6a3385b9fa..caa903af71 100644 --- a/backend/danswer/configs/constants.py +++ b/backend/danswer/configs/constants.py @@ -70,6 +70,7 @@ KV_CUSTOMER_UUID_KEY = "customer_uuid" KV_INSTANCE_DOMAIN_KEY = "instance_domain" KV_ENTERPRISE_SETTINGS_KEY = "danswer_enterprise_settings" KV_CUSTOM_ANALYTICS_SCRIPT_KEY = "__custom_analytics_script__" +KV_DOCUMENTS_SEEDED_KEY = "documents_seeded" CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT = 60 CELERY_PRIMARY_WORKER_LOCK_TIMEOUT = 120 diff --git a/backend/danswer/connectors/axero/connector.py b/backend/danswer/connectors/axero/connector.py index a4d5162b6c..000151209d 100644 --- a/backend/danswer/connectors/axero/connector.py +++ b/backend/danswer/connectors/axero/connector.py @@ -15,7 +15,6 @@ from danswer.connectors.cross_connector_utils.miscellaneous_utils import time_st from danswer.connectors.cross_connector_utils.rate_limit_wrapper import ( rate_limit_builder, ) -from danswer.connectors.cross_connector_utils.retry_wrapper import retry_builder from danswer.connectors.interfaces import GenerateDocumentsOutput from danswer.connectors.interfaces import PollConnector from danswer.connectors.interfaces import SecondsSinceUnixEpoch @@ -24,6 +23,7 @@ from danswer.connectors.models import Document from danswer.connectors.models import Section from danswer.file_processing.html_utils import parse_html_page_basic from danswer.utils.logger import setup_logger +from danswer.utils.retry_wrapper import retry_builder logger = setup_logger() diff --git a/backend/danswer/connectors/clickup/connector.py b/backend/danswer/connectors/clickup/connector.py index 78d572af41..6ef60763ba 100644 --- a/backend/danswer/connectors/clickup/connector.py +++ b/backend/danswer/connectors/clickup/connector.py @@ -10,7 +10,6 @@ from danswer.configs.constants import DocumentSource from danswer.connectors.cross_connector_utils.rate_limit_wrapper import ( rate_limit_builder, ) -from danswer.connectors.cross_connector_utils.retry_wrapper import retry_builder from danswer.connectors.interfaces import GenerateDocumentsOutput from danswer.connectors.interfaces import LoadConnector from danswer.connectors.interfaces import PollConnector @@ -19,6 +18,7 @@ from danswer.connectors.models import BasicExpertInfo from danswer.connectors.models import ConnectorMissingCredentialError from danswer.connectors.models import Document from danswer.connectors.models import Section +from danswer.utils.retry_wrapper import retry_builder CLICKUP_API_BASE_URL = "https://api.clickup.com/api/v2" diff --git a/backend/danswer/connectors/discourse/connector.py b/backend/danswer/connectors/discourse/connector.py index d74aad0f27..d1b6395a18 100644 --- a/backend/danswer/connectors/discourse/connector.py +++ b/backend/danswer/connectors/discourse/connector.py @@ -14,7 +14,6 @@ from danswer.connectors.cross_connector_utils.miscellaneous_utils import time_st from danswer.connectors.cross_connector_utils.rate_limit_wrapper import ( rate_limit_builder, ) -from danswer.connectors.cross_connector_utils.retry_wrapper import retry_builder from danswer.connectors.interfaces import GenerateDocumentsOutput from danswer.connectors.interfaces import PollConnector from danswer.connectors.interfaces import SecondsSinceUnixEpoch @@ -24,6 +23,7 @@ from danswer.connectors.models import Document from danswer.connectors.models import Section from danswer.file_processing.html_utils import parse_html_page_basic from danswer.utils.logger import setup_logger +from danswer.utils.retry_wrapper import retry_builder logger = setup_logger() diff --git a/backend/danswer/connectors/document360/connector.py b/backend/danswer/connectors/document360/connector.py index 6a9f4ba6a5..7ccf3c92e6 100644 --- a/backend/danswer/connectors/document360/connector.py +++ b/backend/danswer/connectors/document360/connector.py @@ -11,7 +11,6 @@ from danswer.configs.constants import DocumentSource from danswer.connectors.cross_connector_utils.rate_limit_wrapper import ( rate_limit_builder, ) -from danswer.connectors.cross_connector_utils.retry_wrapper import retry_builder from danswer.connectors.document360.utils import flatten_child_categories from danswer.connectors.interfaces import GenerateDocumentsOutput from danswer.connectors.interfaces import LoadConnector @@ -22,6 +21,7 @@ from danswer.connectors.models import ConnectorMissingCredentialError from danswer.connectors.models import Document from danswer.connectors.models import Section from danswer.file_processing.html_utils import parse_html_page_basic +from danswer.utils.retry_wrapper import retry_builder # Limitations and Potential Improvements # 1. The "Categories themselves contain potentially relevant information" but they're not pulled in diff --git a/backend/danswer/connectors/google_drive/connector.py b/backend/danswer/connectors/google_drive/connector.py index 9f8c6fbfda..c48df5bb74 100644 --- a/backend/danswer/connectors/google_drive/connector.py +++ b/backend/danswer/connectors/google_drive/connector.py @@ -19,7 +19,6 @@ from danswer.configs.app_configs import GOOGLE_DRIVE_ONLY_ORG_PUBLIC from danswer.configs.app_configs import INDEX_BATCH_SIZE from danswer.configs.constants import DocumentSource from danswer.configs.constants import IGNORE_FOR_QA -from danswer.connectors.cross_connector_utils.retry_wrapper import retry_builder from danswer.connectors.google_drive.connector_auth import get_google_drive_creds from danswer.connectors.google_drive.constants import ( DB_CREDENTIALS_DICT_DELEGATED_USER_KEY, @@ -40,6 +39,7 @@ from danswer.file_processing.unstructured import get_unstructured_api_key from danswer.file_processing.unstructured import unstructured_to_text from danswer.utils.batching import batch_generator from danswer.utils.logger import setup_logger +from danswer.utils.retry_wrapper import retry_builder logger = setup_logger() diff --git a/backend/danswer/connectors/slack/utils.py b/backend/danswer/connectors/slack/utils.py index 20e859a6d4..78bc42a092 100644 --- a/backend/danswer/connectors/slack/utils.py +++ b/backend/danswer/connectors/slack/utils.py @@ -10,9 +10,9 @@ from slack_sdk import WebClient from slack_sdk.errors import SlackApiError from slack_sdk.web import SlackResponse -from danswer.connectors.cross_connector_utils.retry_wrapper import retry_builder from danswer.connectors.models import BasicExpertInfo from danswer.utils.logger import setup_logger +from danswer.utils.retry_wrapper import retry_builder logger = setup_logger() diff --git a/backend/danswer/db/connector_credential_pair.py b/backend/danswer/db/connector_credential_pair.py index b3e1de7647..b333dd7860 100644 --- a/backend/danswer/db/connector_credential_pair.py +++ b/backend/danswer/db/connector_credential_pair.py @@ -341,6 +341,8 @@ def add_credential_to_connector( access_type: AccessType, groups: list[int] | None, auto_sync_options: dict | None = None, + initial_status: ConnectorCredentialPairStatus = ConnectorCredentialPairStatus.ACTIVE, + last_successful_index_time: datetime | None = None, ) -> StatusResponse: connector = fetch_connector_by_id(connector_id, db_session) credential = fetch_credential_by_id(credential_id, user, db_session) @@ -384,9 +386,10 @@ def add_credential_to_connector( connector_id=connector_id, credential_id=credential_id, name=cc_pair_name, - status=ConnectorCredentialPairStatus.ACTIVE, + status=initial_status, access_type=access_type, auto_sync_options=auto_sync_options, + last_successful_index_time=last_successful_index_time, ) db_session.add(association) db_session.flush() # make sure the association has an id diff --git a/backend/danswer/db/credentials.py b/backend/danswer/db/credentials.py index 5da5099f1e..80ebe1b153 100644 --- a/backend/danswer/db/credentials.py +++ b/backend/danswer/db/credentials.py @@ -40,6 +40,8 @@ CREDENTIAL_PERMISSIONS_TO_IGNORE = { DocumentSource.MEDIAWIKI, } +PUBLIC_CREDENTIAL_ID = 0 + def _add_user_filters( stmt: Select, @@ -384,12 +386,11 @@ def delete_credential( def create_initial_public_credential(db_session: Session) -> None: - public_cred_id = 0 error_msg = ( "DB is not in a valid initial state." "There must exist an empty public credential for data connectors that do not require additional Auth." ) - first_credential = fetch_credential_by_id(public_cred_id, None, db_session) + first_credential = fetch_credential_by_id(PUBLIC_CREDENTIAL_ID, None, db_session) if first_credential is not None: if first_credential.credential_json != {} or first_credential.user is not None: @@ -397,7 +398,7 @@ def create_initial_public_credential(db_session: Session) -> None: return credential = Credential( - id=public_cred_id, + id=PUBLIC_CREDENTIAL_ID, credential_json={}, user_id=None, ) diff --git a/backend/danswer/db/index_attempt.py b/backend/danswer/db/index_attempt.py index fbbbef1bbf..45a0738794 100644 --- a/backend/danswer/db/index_attempt.py +++ b/backend/danswer/db/index_attempt.py @@ -1,5 +1,6 @@ from collections.abc import Sequence from datetime import datetime +from datetime import timedelta from datetime import timezone from sqlalchemy import and_ @@ -66,6 +67,32 @@ def create_index_attempt( return new_attempt.id +def mock_successful_index_attempt( + connector_credential_pair_id: int, + search_settings_id: int, + docs_indexed: int, + db_session: Session, +) -> int: + """Should not be used in any user triggered flows""" + db_time = func.now() + new_attempt = IndexAttempt( + connector_credential_pair_id=connector_credential_pair_id, + search_settings_id=search_settings_id, + from_beginning=True, + status=IndexingStatus.SUCCESS, + total_docs_indexed=docs_indexed, + new_docs_indexed=docs_indexed, + # Need this to be some convincing random looking value and it can't be 0 + # or the indexing rate would calculate out to infinity + time_started=db_time - timedelta(seconds=1.92), + time_updated=db_time, + ) + db_session.add(new_attempt) + db_session.commit() + + return new_attempt.id + + def get_in_progress_index_attempts( connector_id: int | None, db_session: Session, diff --git a/backend/danswer/indexing/embedder.py b/backend/danswer/indexing/embedder.py index 3e9383cb82..1c11a01b39 100644 --- a/backend/danswer/indexing/embedder.py +++ b/backend/danswer/indexing/embedder.py @@ -103,6 +103,9 @@ class DefaultIndexingEmbedder(IndexingEmbedder): self, chunks: list[DocAwareChunk], ) -> list[IndexChunk]: + """Adds embeddings to the chunks, the title and metadata suffixes are added to the chunk as well + if they exist. If there is no space for it, it would have been thrown out at the chunking step. + """ # All chunks at this point must have some non-empty content flat_chunk_texts: list[str] = [] large_chunks_present = False @@ -121,6 +124,11 @@ class DefaultIndexingEmbedder(IndexingEmbedder): flat_chunk_texts.append(chunk_text) if chunk.mini_chunk_texts: + if chunk.large_chunk_reference_ids: + # A large chunk does not contain mini chunks, if it matches the large chunk + # with a high score, then mini chunks would not be used anyway + # otherwise it should match the normal chunk + raise RuntimeError("Large chunk contains mini chunks") flat_chunk_texts.extend(chunk.mini_chunk_texts) embeddings = self.embedding_model.encode( diff --git a/backend/danswer/indexing/indexing_pipeline.py b/backend/danswer/indexing/indexing_pipeline.py index d40bd341fd..507956ff40 100644 --- a/backend/danswer/indexing/indexing_pipeline.py +++ b/backend/danswer/indexing/indexing_pipeline.py @@ -195,6 +195,8 @@ def index_doc_batch_prepare( db_session: Session, ignore_time_skip: bool = False, ) -> DocumentBatchPrepareContext | None: + """This sets up the documents in the relational DB (source of truth) for permissions, metadata, etc. + This preceeds indexing it into the actual document index.""" documents = [] for document in document_batch: empty_contents = not any(section.text.strip() for section in document.sections) diff --git a/backend/danswer/key_value_store/factory.py b/backend/danswer/key_value_store/factory.py index 7b52fdabef..142e9031b7 100644 --- a/backend/danswer/key_value_store/factory.py +++ b/backend/danswer/key_value_store/factory.py @@ -3,5 +3,6 @@ from danswer.key_value_store.store import PgRedisKVStore def get_kv_store() -> KeyValueStore: - # this is the only one supported currently + # In the Multi Tenant case, the tenant context is picked up automatically, it does not need to be passed in + # It's read from the global thread level variable return PgRedisKVStore() diff --git a/backend/danswer/key_value_store/interface.py b/backend/danswer/key_value_store/interface.py index 190c53189d..aa815171f0 100644 --- a/backend/danswer/key_value_store/interface.py +++ b/backend/danswer/key_value_store/interface.py @@ -14,6 +14,8 @@ class KvKeyNotFoundError(Exception): class KeyValueStore: + # In the Multi Tenant case, the tenant context is picked up automatically, it does not need to be passed in + # It's read from the global thread level variable @abc.abstractmethod def store(self, key: str, val: JSON_ro, encrypt: bool = False) -> None: raise NotImplementedError diff --git a/backend/danswer/seeding/__init__.py b/backend/danswer/seeding/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/backend/danswer/seeding/initial_docs.json b/backend/danswer/seeding/initial_docs.json new file mode 100644 index 0000000000..cded36721b --- /dev/null +++ b/backend/danswer/seeding/initial_docs.json @@ -0,0 +1,10824 @@ +[ + { + "url": "https://docs.danswer.dev/more/use_cases/overview", + "title": "Use Cases Overview", + "content": "How to leverage Danswer in your organization\n\nDanswer Overview\nDanswer is the AI Assistant connected to your organization's docs, apps, and people. Danswer makes Generative AI more versatile for work by enabling new types of questions like \"What is the most common feature request we've heard from customers this month\". Whereas other AI systems have no context of your team and are generally unhelpful with work related questions, Danswer makes it possible to ask these questions in natural language and get back answers in seconds.\n\nDanswer can connect to +30 different tools and the use cases are not limited to the ones in the following pages. The highlighted use cases are for inspiration and come from feedback gathered from our users and customers.\n\n\nCommon Getting Started Questions:\n\nWhy are these docs connected in my Danswer deployment?\nAnswer: This is just an example of how connectors work in Danswer. You can connect up your own team's knowledge and you will be able to ask questions unique to your organization. Danswer will keep all of the knowledge up to date and in sync with your connected applications.\n\nIs my data being sent anywhere when I connect it up to Danswer?\nAnswer: No! Danswer is built with data security as our highest priority. We open sourced it so our users can know exactly what is going on with their data. By default all of the document processing happens within Danswer. The only time it is sent outward is for the GenAI call to generate answers.\n\nWhere is the feature for auto sync-ing document level access permissions from all connected sources?\nAnswer: This falls under the Enterprise Edition set of Danswer features built on top of the MIT/community edition. If you are on Danswer Cloud, you have access to them by default. 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Critical information is saved across a host of channels like call transcripts with prospects, engineering design docs, IT runbooks, customer support email exchanges, project management tickets, and more. As fast moving teams scale up, information gets spread out and more disorganized.\n\nSince it quickly becomes infeasible to check across every source, decisions get made on incomplete information, employee satisfaction decreases, and the most valuable members of your team are tied up with constant distractions as junior teammates are unable to unblock themselves. Danswer solves this problem by letting anyone on the team access all of the knowledge across your organization in a permissioned and secure way. Users can ask questions in natural language and get back answers and documents across all of the connected sources instantly.\n\nWhat's the real cost?\nA typical knowledge worker spends over 2 hours a week on search, but more than that, the cost of incomplete or incorrect information can be extremely high. Customer support/success that isn't able to find the reference to similar cases could cause hours or even days of delay leading to lower customer satisfaction or in the worst case - churn. An account exec not realizing that a prospect had previously mentioned a specific need could lead to lost deals. An engineer not realizing a similar feature had previously been built could result in weeks of wasted development time and tech debt with duplicate implementation. With a lack of knowledge, your whole organization is navigating in the dark - inefficient and mistake prone.", + "title_embedding": [ + -0.011060578748583794, + 0.05994883179664612, + 0.008845113217830658, + 0.011364905163645744, + 0.03147757425904274, + 0.04506697878241539, + -0.025942707434296608, + -0.011002028360962868, + -0.03507396578788757, + -0.01727098599076271, + -0.016820134595036507, + 0.04671240597963333, + 0.023456331342458725, + -0.005752791650593281, + -0.011421029455959797, + -0.04169125109910965, + 0.0652366429567337, + -0.011136278510093689, + -0.013501451350748539, + -0.006273901090025902, + 0.0012236927868798375, + -0.030771249905228615, + 0.010098040103912354, + -0.02360220067203045, + 0.006734110414981842, + 0.001379420980811119, + -0.0047225081361830235, + -0.013901330530643463, + 0.014645840041339397, + -0.02156134508550167, + -0.026707857847213745, + -0.00676271365955472, + 0.056067030876874924, + -0.0455806590616703, + -0.02762053906917572, + 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Danswer also makes it trivial to identify where knowledge is well documented and where it is lacking. Team members who are centers of knowledge can begin to effectively document their expertise since it is no longer being thrown into a black hole. All of this allows the organization to achieve higher efficiency and drive business outcomes.\n\nWith Generative AI, the entire user experience has evolved as well. For example, instead of just finding similar cases for your customer support team to reference, Danswer breaks down the issue and explains it so that even the most junior members can understand it. This in turn lets them give the most holistic and technically accurate response possible to your customers. On the other end, even the super stars of your sales team will not be able to review 10 hours of transcripts before hopping on that critical call, but Danswer can easily parse through it in mere seconds and give crucial context to help your team close.", + "title_embedding": [ + -0.011060578748583794, + 0.05994883179664612, + 0.008845113217830658, + 0.011364905163645744, + 0.03147757425904274, + 0.04506697878241539, + -0.025942707434296608, + -0.011002028360962868, + -0.03507396578788757, + -0.01727098599076271, + -0.016820134595036507, + 0.04671240597963333, + 0.023456331342458725, + -0.005752791650593281, + -0.011421029455959797, + -0.04169125109910965, + 0.0652366429567337, + -0.011136278510093689, + -0.013501451350748539, + -0.006273901090025902, + 0.0012236927868798375, + -0.030771249905228615, + 0.010098040103912354, + -0.02360220067203045, + 0.006734110414981842, + 0.001379420980811119, + -0.0047225081361830235, + -0.013901330530643463, + 0.014645840041339397, + 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reasoning capable models are helping teams to automate their work. 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They field requests that cover the entire surface area of the product and need to help your users find success on extremely short timelines. Because they're not the same people who designed or built the system, they often lack the depth of understanding needed - resulting in delays and escalations to other teams. Modern teams are leveraging AI to help their CS team optimize the speed and quality of these critical customer-facing interactions.\n\nThe Importance of Context\nThere are two critical components of AI copilots for customer support. The first is that the AI system needs to be connected with as much information as possible (not just support tools like Zendesk or Intercom) and that the knowledge needs to be as fresh as possible. Sometimes a fix might even be in places rarely checked by CS such as pull requests in a code repository. The second critical component is the ability of the AI system to break down difficult concepts and convoluted processes into more digestible descriptions and for your team members to be able to chat back and forth with the system to build a better understanding.\n\nDanswer takes care of both of these. 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relationships, and close more deals. Instead of searching and reading through hours of transcripts in preparation for a call, your team can now ask Danswer \"What specific features was ACME interested in seeing for the demo\". Since your team doesn't have time to read every transcript prior to a call, Danswer provides a more thorough summary because it can instantly parse hundreds of pages and distill out the relevant information. Even for fast lookups it becomes much more convenient - for example to brush up on connection building topics by asking \"What rapport building topic did we chat about in the last call with ACME\".\n\nKnow Every Product Update\nIt is impossible for Sales teams to keep up with every product update. Because of this, when a prospect has a question that the Sales team does not know, they have no choice but to rely on the Product and Engineering orgs to get an authoritative answer. Not only is this distracting to the other teams, it also slows down the time to respond to the prospect (and as we know, time is the biggest killer of deals). With Danswer, it is even possible to get answers live on call because of how fast accessing information becomes. A question like \"Have we shipped the Microsoft AD integration yet?\" can now be answered in seconds meaning that prospects can get answers while on the call instead of asynchronously and sales cycles are reduced as a result.", + "title_embedding": [ + 0.008453648537397385, + 0.049128592014312744, + 0.0009390072082169354, + -0.011420674622058868, + 0.009472657926380634, + 0.05824451148509979, + -0.04129518195986748, + -0.018892904743552208, + -0.007598293945193291, + -0.03804052621126175, + -0.003747896058484912, + 0.005537204910069704, + 0.026590371504426003, + -0.02672540210187435, + 0.02651272714138031, + -0.033856429159641266, + 0.03809495270252228, + -0.013283955864608288, + -0.00013869917893316597, + 0.020309027284383774, + -0.011650309897959232, + -0.02103874459862709, + 0.01066586747765541, + -0.057243604212999344, + 0.031903959810733795, + -0.012924387119710445, + -0.012852400541305542, + 0.01951044611632824, + 0.018149282783269882, + -0.01314238179475069, + 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Whether it is an employee asking about benefits details or how to set up the VPN for remote work, Danswer can help your team help themselves. This frees up your team to do the real impactful work of landing star candidates or improving your internal processes.\n\nAI Aided Onboarding\nOne of the periods where your team needs the most help is when they're just ramping up. Instead of feeling lost in dozens of new tools, Danswer gives them a single place where they can ask about anything in natural language. Whether it's how to set up their work environment or what their onboarding goals are, Danswer can walk them through every step with the help of Generative AI. 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sqlalchemy.orm import Session + +from danswer.access.models import default_public_access +from danswer.configs.constants import DEFAULT_BOOST +from danswer.configs.constants import DocumentSource +from danswer.configs.constants import KV_DOCUMENTS_SEEDED_KEY +from danswer.configs.model_configs import DEFAULT_DOCUMENT_ENCODER_MODEL +from danswer.connectors.models import Document +from danswer.connectors.models import IndexAttemptMetadata +from danswer.connectors.models import InputType +from danswer.connectors.models import Section +from danswer.db.connector import check_connectors_exist +from danswer.db.connector import create_connector +from danswer.db.connector_credential_pair import add_credential_to_connector +from danswer.db.credentials import PUBLIC_CREDENTIAL_ID +from danswer.db.document import check_docs_exist +from danswer.db.enums import AccessType +from danswer.db.enums import ConnectorCredentialPairStatus +from danswer.db.index_attempt import mock_successful_index_attempt +from danswer.db.search_settings import get_current_search_settings +from danswer.document_index.factory import get_default_document_index +from danswer.indexing.indexing_pipeline import index_doc_batch_prepare +from danswer.indexing.models import ChunkEmbedding +from danswer.indexing.models import DocMetadataAwareIndexChunk +from danswer.key_value_store.factory import get_kv_store +from danswer.key_value_store.interface import KvKeyNotFoundError +from danswer.server.documents.models import ConnectorBase +from danswer.utils.logger import setup_logger +from danswer.utils.retry_wrapper import retry_builder + + +logger = setup_logger() + + +def _create_indexable_chunks( + preprocessed_docs: list[dict], +) -> tuple[list[Document], list[DocMetadataAwareIndexChunk]]: + ids_to_documents = {} + chunks = [] + for preprocessed_doc in preprocessed_docs: + document = Document( + id=preprocessed_doc["url"], # For Web connector, the URL is the ID + # The section is not really used past this point since we have already done the other processing + # for the chunking and embedding. + sections=[ + Section(text=preprocessed_doc["content"], link=preprocessed_doc["url"]) + ], + source=DocumentSource.WEB, + semantic_identifier=preprocessed_doc["title"], + metadata={}, + doc_updated_at=None, + primary_owners=[], + secondary_owners=[], + ) + if preprocessed_doc["chunk_ind"] == 0: + ids_to_documents[document.id] = document + + chunk = DocMetadataAwareIndexChunk( + chunk_id=preprocessed_doc["chunk_ind"], + blurb=preprocessed_doc["content"] + .split(".", 1)[0] + .split("!", 1)[0] + .split("?", 1)[0], + content=preprocessed_doc["content"], + source_links={0: preprocessed_doc["url"]}, + section_continuation=False, + source_document=document, + title_prefix=preprocessed_doc["title"], + metadata_suffix_semantic="", + metadata_suffix_keyword="", + mini_chunk_texts=None, + large_chunk_reference_ids=[], + embeddings=ChunkEmbedding( + full_embedding=preprocessed_doc["content_embedding"], + mini_chunk_embeddings=[], + ), + title_embedding=preprocessed_doc["title_embedding"], + tenant_id=None, + access=default_public_access, + document_sets=set(), + boost=DEFAULT_BOOST, + ) + chunks.append(chunk) + + return list(ids_to_documents.values()), chunks + + +def seed_initial_documents(db_session: Session) -> None: + """ + Seed initial documents so users don't have an empty index to start + + Documents are only loaded if: + - This is the first setup (if the user deletes the docs, we don't load them again) + - The index is empty, there are no docs and no (non-default) connectors + - The user has not updated the embedding models + - If they do, then we have to actually index the website + - If the embedding model is already updated on server startup, they're not a new user + + Note that regardless of any search settings, the default documents are always loaded with + the predetermined chunk sizes and single pass embedding. + + Steps are as follows: + - Check if this needs to run + - Create the connector representing this + - Create the cc-pair (attaching the public credential) and mocking values like the last success + - Indexing the documents into Postgres + - Indexing the documents into Vespa + - Create a fake index attempt with fake times + """ + logger.info("Seeding initial documents") + + kv_store = get_kv_store() + try: + kv_store.load(KV_DOCUMENTS_SEEDED_KEY) + logger.info("Documents already seeded, skipping") + return + except KvKeyNotFoundError: + pass + + if check_docs_exist(db_session): + logger.info("Documents already exist, skipping") + return + + if check_connectors_exist(db_session): + logger.info("Connectors already exist, skipping") + return + + search_settings = get_current_search_settings(db_session) + if search_settings.model_name != DEFAULT_DOCUMENT_ENCODER_MODEL: + logger.info("Embedding model has been updated, skipping") + return + + document_index = get_default_document_index( + primary_index_name=search_settings.index_name, secondary_index_name=None + ) + + # Create a connector so the user can delete it if they want + # or reindex it with a new search model if they want + connector_data = ConnectorBase( + name="Sample Use Cases", + source=DocumentSource.WEB, + input_type=InputType.LOAD_STATE, + connector_specific_config={ + "base_url": "https://docs.danswer.dev/more/use_cases", + "web_connector_type": "recursive", + }, + refresh_freq=None, # Never refresh by default + prune_freq=None, + indexing_start=None, + ) + + connector = create_connector(db_session, connector_data) + connector_id = cast(int, connector.id) + + last_index_time = datetime.datetime.now(datetime.timezone.utc) + + result = add_credential_to_connector( + db_session=db_session, + user=None, + connector_id=connector_id, + credential_id=PUBLIC_CREDENTIAL_ID, + access_type=AccessType.PUBLIC, + cc_pair_name=connector_data.name, + groups=None, + initial_status=ConnectorCredentialPairStatus.PAUSED, + last_successful_index_time=last_index_time, + ) + cc_pair_id = cast(int, result.data) + + initial_docs_path = os.path.join( + os.getcwd(), "danswer", "seeding", "initial_docs.json" + ) + processed_docs = json.load(open(initial_docs_path)) + + docs, chunks = _create_indexable_chunks(processed_docs) + + index_doc_batch_prepare( + document_batch=docs, + index_attempt_metadata=IndexAttemptMetadata( + connector_id=connector_id, + credential_id=PUBLIC_CREDENTIAL_ID, + ), + db_session=db_session, + ignore_time_skip=True, # Doesn't actually matter here + ) + + # In this case since there are no other connectors running in the background + # and this is a fresh deployment, there is no need to grab any locks + logger.info( + "Indexing seeding documents into Vespa " + "(Vespa may take a few seconds to become ready after receiving the schema)" + ) + + # Retries here because the index may take a few seconds to become ready + # as we just sent over the Vespa schema and there is a slight delay + index_with_retries = retry_builder()(document_index.index) + index_with_retries(chunks=chunks) + + # Mock a run for the UI even though it did not actually call out to anything + mock_successful_index_attempt( + connector_credential_pair_id=cc_pair_id, + search_settings_id=search_settings.id, + docs_indexed=len(docs), + db_session=db_session, + ) + + kv_store.store(KV_DOCUMENTS_SEEDED_KEY, True) diff --git a/backend/danswer/setup.py b/backend/danswer/setup.py index 747fe80945..a27580a2b1 100644 --- a/backend/danswer/setup.py +++ b/backend/danswer/setup.py @@ -40,6 +40,7 @@ from danswer.natural_language_processing.search_nlp_models import warm_up_bi_enc from danswer.natural_language_processing.search_nlp_models import warm_up_cross_encoder from danswer.search.models import SavedSearchSettings from danswer.search.retrieval.search_runner import download_nltk_data +from danswer.seeding.load_docs import seed_initial_documents from danswer.server.manage.llm.models import LLMProviderUpsertRequest from danswer.server.settings.store import load_settings from danswer.server.settings.store import store_settings @@ -58,6 +59,12 @@ logger = setup_logger() def setup_danswer(db_session: Session) -> None: + """ + Setup Danswer for a particular tenant. In the Single Tenant case, it will set it up for the default schema + on server startup. In the MT case, it will be called when the tenant is created. + + The Tenant Service calls the tenants/create endpoint which runs this. + """ check_index_swap(db_session=db_session) search_settings = get_current_search_settings(db_session) secondary_search_settings = get_secondary_search_settings(db_session) @@ -107,7 +114,8 @@ def setup_danswer(db_session: Session) -> None: if not MULTI_TENANT: mark_reindex_flag(db_session) - # ensure Vespa is setup correctly + # Ensure Vespa is setup correctly, this step is relatively near the end because Vespa + # takes a bit of time to start up logger.notice("Verifying Document Index(s) is/are available.") document_index = get_default_document_index( primary_index_name=search_settings.index_name, @@ -139,6 +147,8 @@ def setup_danswer(db_session: Session) -> None: # update multipass indexing setting based on GPU availability update_default_multipass_indexing(db_session) + seed_initial_documents(db_session) + def translate_saved_search_settings(db_session: Session) -> None: kv_store = get_kv_store() @@ -311,11 +321,13 @@ def update_default_multipass_indexing(db_session: Session) -> None: def setup_multitenant_danswer() -> None: + # For Managed Vespa, the schema is sent over via the Vespa Console manually. if not MANAGED_VESPA: setup_vespa_multitenant(SUPPORTED_EMBEDDING_MODELS) def setup_vespa_multitenant(supported_indices: list[SupportedEmbeddingModel]) -> bool: + # This is for local testing WAIT_SECONDS = 5 VESPA_ATTEMPTS = 5 for x in range(VESPA_ATTEMPTS): diff --git a/backend/danswer/connectors/cross_connector_utils/retry_wrapper.py b/backend/danswer/utils/retry_wrapper.py similarity index 79% rename from backend/danswer/connectors/cross_connector_utils/retry_wrapper.py rename to backend/danswer/utils/retry_wrapper.py index 7312d1349f..2d6d79ca5e 100644 --- a/backend/danswer/connectors/cross_connector_utils/retry_wrapper.py +++ b/backend/danswer/utils/retry_wrapper.py @@ -22,18 +22,18 @@ def retry_builder( jitter: tuple[float, float] | float = 1, ) -> Callable[[F], F]: """Builds a generic wrapper/decorator for calls to external APIs that - may fail due to rate limiting, flakes, or other reasons. Applies expontential + may fail due to rate limiting, flakes, or other reasons. Applies exponential backoff with jitter to retry the call.""" - @retry( - tries=tries, - delay=delay, - max_delay=max_delay, - backoff=backoff, - jitter=jitter, - logger=cast(Logger, logger), - ) def retry_with_default(func: F) -> F: + @retry( + tries=tries, + delay=delay, + max_delay=max_delay, + backoff=backoff, + jitter=jitter, + logger=cast(Logger, logger), + ) def wrapped_func(*args: list, **kwargs: dict[str, Any]) -> Any: return func(*args, **kwargs) diff --git a/backend/ee/danswer/external_permissions/google_drive/doc_sync.py b/backend/ee/danswer/external_permissions/google_drive/doc_sync.py index 19dbb84532..f1e805d46d 100644 --- a/backend/ee/danswer/external_permissions/google_drive/doc_sync.py +++ b/backend/ee/danswer/external_permissions/google_drive/doc_sync.py @@ -9,7 +9,6 @@ from googleapiclient.errors import HttpError # type: ignore from sqlalchemy.orm import Session from danswer.access.models import ExternalAccess -from danswer.connectors.cross_connector_utils.retry_wrapper import retry_builder from danswer.connectors.factory import instantiate_connector from danswer.connectors.google_drive.connector_auth import ( get_google_drive_creds, @@ -19,6 +18,7 @@ from danswer.connectors.models import InputType from danswer.db.models import ConnectorCredentialPair from danswer.db.users import batch_add_non_web_user_if_not_exists__no_commit from danswer.utils.logger import setup_logger +from danswer.utils.retry_wrapper import retry_builder from ee.danswer.db.document import upsert_document_external_perms__no_commit # Google Drive APIs are quite flakey and may 500 for an diff --git a/backend/ee/danswer/external_permissions/google_drive/group_sync.py b/backend/ee/danswer/external_permissions/google_drive/group_sync.py index 7bb919d468..ab0f62a886 100644 --- a/backend/ee/danswer/external_permissions/google_drive/group_sync.py +++ b/backend/ee/danswer/external_permissions/google_drive/group_sync.py @@ -7,7 +7,6 @@ from googleapiclient.discovery import build # type: ignore from googleapiclient.errors import HttpError # type: ignore from sqlalchemy.orm import Session -from danswer.connectors.cross_connector_utils.retry_wrapper import retry_builder from danswer.connectors.google_drive.connector_auth import ( get_google_drive_creds, ) @@ -15,6 +14,7 @@ from danswer.connectors.google_drive.constants import FETCH_GROUPS_SCOPES from danswer.db.models import ConnectorCredentialPair from danswer.db.users import batch_add_non_web_user_if_not_exists__no_commit from danswer.utils.logger import setup_logger +from danswer.utils.retry_wrapper import retry_builder from ee.danswer.db.external_perm import ExternalUserGroup from ee.danswer.db.external_perm import replace_user__ext_group_for_cc_pair__no_commit diff --git a/backend/scripts/document_seeding_prep.py b/backend/scripts/document_seeding_prep.py new file mode 100644 index 0000000000..d853b68ccb --- /dev/null +++ b/backend/scripts/document_seeding_prep.py @@ -0,0 +1,240 @@ +# This script preps the documents used for initially seeding the index. It handles the embedding so that the +# documents can be added to the index with minimal processing. +import json + +from pydantic import BaseModel +from sentence_transformers import SentenceTransformer # type: ignore + + +class SeedPresaveDocument(BaseModel): + url: str + title: str + content: str + title_embedding: list[float] + content_embedding: list[float] + chunk_ind: int = 0 + + +# Be sure to use the default embedding model +model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True) +tokenizer = model.tokenizer + +# This is easier than cleaning up the crawl, needs to be updated if the sites are changed +overview_title = "Use Cases Overview" +overview = ( + "How to leverage Danswer in your organization\n\n" + "Danswer Overview\n" + "Danswer is the AI Assistant connected to your organization's docs, apps, and people. " + "Danswer makes Generative AI more versatile for work by enabling new types of questions like " + '"What is the most common feature request we\'ve heard from customers this month". ' + "Whereas other AI systems have no context of your team and are generally unhelpful with work related questions, " + "Danswer makes it possible to ask these questions in natural language and get back answers in seconds.\n\n" + "Danswer can connect to +30 different tools and the use cases are not limited to the ones in the following pages. " + "The highlighted use cases are for inspiration and come from feedback gathered from our users and customers.\n\n\n" + "Common Getting Started Questions:\n\n" + "Why are these docs connected in my Danswer deployment?\n" + "Answer: This is just an example of how connectors work in Danswer. You can connect up your own team's knowledge " + "and you will be able to ask questions unique to your organization. Danswer will keep all of the knowledge up to date " + "and in sync with your connected applications.\n\n" + "Is my data being sent anywhere when I connect it up to Danswer?\n" + "Answer: No! Danswer is built with data security as our highest priority. We open sourced it so our users can know " + "exactly what is going on with their data. By default all of the document processing happens within Danswer. " + "The only time it is sent outward is for the GenAI call to generate answers.\n\n" + "Where is the feature for auto sync-ing document level access permissions from all connected sources?\n" + "Answer: This falls under the Enterprise Edition set of Danswer features built on top of the MIT/community edition. " + "If you are on Danswer Cloud, you have access to them by default. If you're running it yourself, reach out to the " + "Danswer team to receive access." +) + +enterprise_search_title = "Enterprise Search" +enterprise_search_1 = ( + "Value of Enterprise Search with Danswer\n\n" + "What is Enterprise Search and why is it Important?\n" + "An Enterprise Search system gives team members a single place to access all of the disparate knowledge " + "of an organization. Critical information is saved across a host of channels like call transcripts with " + "prospects, engineering design docs, IT runbooks, customer support email exchanges, project management " + "tickets, and more. As fast moving teams scale up, information gets spread out and more disorganized.\n\n" + "Since it quickly becomes infeasible to check across every source, decisions get made on incomplete " + "information, employee satisfaction decreases, and the most valuable members of your team are tied up " + "with constant distractions as junior teammates are unable to unblock themselves. Danswer solves this " + "problem by letting anyone on the team access all of the knowledge across your organization in a " + "permissioned and secure way. Users can ask questions in natural language and get back answers and " + "documents across all of the connected sources instantly.\n\n" + "What's the real cost?\n" + "A typical knowledge worker spends over 2 hours a week on search, but more than that, the cost of " + "incomplete or incorrect information can be extremely high. Customer support/success that isn't able " + "to find the reference to similar cases could cause hours or even days of delay leading to lower " + "customer satisfaction or in the worst case - churn. An account exec not realizing that a prospect had " + "previously mentioned a specific need could lead to lost deals. An engineer not realizing a similar " + "feature had previously been built could result in weeks of wasted development time and tech debt with " + "duplicate implementation. With a lack of knowledge, your whole organization is navigating in the dark " + "- inefficient and mistake prone." +) + +enterprise_search_2 = ( + "More than Search\n" + "When analyzing the entire corpus of knowledge within your company is as easy as asking a question " + "in a search bar, your entire team can stay informed and up to date. Danswer also makes it trivial " + "to identify where knowledge is well documented and where it is lacking. Team members who are centers " + "of knowledge can begin to effectively document their expertise since it is no longer being thrown into " + "a black hole. All of this allows the organization to achieve higher efficiency and drive business outcomes.\n\n" + "With Generative AI, the entire user experience has evolved as well. For example, instead of just finding similar " + "cases for your customer support team to reference, Danswer breaks down the issue and explains it so that even " + "the most junior members can understand it. This in turn lets them give the most holistic and technically accurate " + "response possible to your customers. On the other end, even the super stars of your sales team will not be able " + "to review 10 hours of transcripts before hopping on that critical call, but Danswer can easily parse through it " + "in mere seconds and give crucial context to help your team close." +) + +ai_platform_title = "AI Platform" +ai_platform = ( + "Build AI Agents powered by the knowledge and workflows specific to your organization.\n\n" + "Beyond Answers\n" + "Agents enabled by generative AI and reasoning capable models are helping teams to automate their work. " + "Danswer is helping teams make it happen. Danswer provides out of the box user chat sessions, attaching custom tools, " + "handling LLM reasoning, code execution, data analysis, referencing internal knowledge, and much more.\n\n" + "Danswer as a platform is not a no-code agent builder. We are made by developers for developers and this gives your " + "team the full flexibility and power to create agents not constrained by blocks and simple logic paths.\n\n" + "Flexibility and Extensibility\n" + "Danswer is open source and completely whitebox. This not only gives transparency to what happens within the system " + "but also means that your team can directly modify the source code to suit your unique needs." +) + +customer_support_title = "Customer Support" +customer_support = ( + "Help your customer support team instantly answer any question across your entire product.\n\n" + "AI Enabled Support\n" + "Customer support agents have one of the highest breadth jobs. They field requests that cover the entire surface " + "area of the product and need to help your users find success on extremely short timelines. " + "Because they're not the same people who designed or built the system, they often lack the depth of understanding " + "needed - resulting in delays and escalations to other teams. Modern teams are leveraging AI to help their CS team " + "optimize the speed and quality of these critical customer-facing interactions.\n\n" + "The Importance of Context\n" + "There are two critical components of AI copilots for customer support. The first is that the AI system needs to be " + "connected with as much information as possible (not just support tools like Zendesk or Intercom) and that the " + "knowledge needs to be as fresh as possible. Sometimes a fix might even be in places rarely checked by CS such as " + "pull requests in a code repository. The second critical component is the ability of the AI system to break down " + "difficult concepts and convoluted processes into more digestible descriptions and for your team members to be able " + "to chat back and forth with the system to build a better understanding.\n\n" + "Danswer takes care of both of these. The system connects up to over 30+ different applications and the knowledge is " + "pulled in constantly so that the information access is always up to date." +) + +sales_title = "Sales" +sales = ( + "Keep your team up to date on every conversation and update so they can close.\n\n" + "Recall Every Detail\n" + "Being able to instantly revisit every detail of any call without reading transcripts is helping Sales teams provide " + "more tailored pitches, build stronger relationships, and close more deals. Instead of searching and reading through " + 'hours of transcripts in preparation for a call, your team can now ask Danswer "What specific features was ACME ' + "interested in seeing for the demo\". Since your team doesn't have time to read every transcript prior to a call, " + "Danswer provides a more thorough summary because it can instantly parse hundreds of pages and distill out the relevant " + "information. Even for fast lookups it becomes much more convenient - for example to brush up on connection building " + 'topics by asking "What rapport building topic did we chat about in the last call with ACME".\n\n' + "Know Every Product Update\n" + "It is impossible for Sales teams to keep up with every product update. Because of this, when a prospect has a question " + "that the Sales team does not know, they have no choice but to rely on the Product and Engineering orgs to get an " + "authoritative answer. Not only is this distracting to the other teams, it also slows down the time to respond to the " + "prospect (and as we know, time is the biggest killer of deals). With Danswer, it is even possible to get answers live " + 'on call because of how fast accessing information becomes. A question like "Have we shipped the Microsoft AD ' + 'integration yet?" can now be answered in seconds meaning that prospects can get answers while on the call instead of ' + "asynchronously and sales cycles are reduced as a result." +) + +operations_title = "Operations" +operations = ( + "Double the productivity of your Ops teams like IT, HR, etc.\n\n" + "Automatically Resolve Tickets\n" + "Modern teams are leveraging AI to auto-resolve up to 50% of tickets. Whether it is an employee asking about benefits " + "details or how to set up the VPN for remote work, Danswer can help your team help themselves. This frees up your team to " + "do the real impactful work of landing star candidates or improving your internal processes.\n\n" + "AI Aided Onboarding\n" + "One of the periods where your team needs the most help is when they're just ramping up. Instead of feeling lost in dozens " + "of new tools, Danswer gives them a single place where they can ask about anything in natural language. Whether it's how to " + "set up their work environment or what their onboarding goals are, Danswer can walk them through every step with the help " + "of Generative AI. This lets your team feel more empowered and gives time back to the more seasoned members of your team to " + "focus on moving the needle." +) + +# For simplicity, we're not adding any metadata suffix here. Generally there is none for the Web connector anyway +overview_doc = SeedPresaveDocument( + url="https://docs.danswer.dev/more/use_cases/overview", + title=overview_title, + content=overview, + title_embedding=model.encode(f"search_document: {overview_title}"), + content_embedding=model.encode(f"search_document: {overview_title}\n{overview}"), +) + +enterprise_search_doc = SeedPresaveDocument( + url="https://docs.danswer.dev/more/use_cases/enterprise_search", + title=enterprise_search_title, + content=enterprise_search_1, + title_embedding=model.encode(f"search_document: {enterprise_search_title}"), + content_embedding=model.encode( + f"search_document: {enterprise_search_title}\n{enterprise_search_1}" + ), +) + +enterprise_search_doc_2 = SeedPresaveDocument( + url="https://docs.danswer.dev/more/use_cases/enterprise_search", + title=enterprise_search_title, + content=enterprise_search_2, + title_embedding=model.encode(f"search_document: {enterprise_search_title}"), + content_embedding=model.encode( + f"search_document: {enterprise_search_title}\n{enterprise_search_2}" + ), + chunk_ind=1, +) + +ai_platform_doc = SeedPresaveDocument( + url="https://docs.danswer.dev/more/use_cases/ai_platform", + title=ai_platform_title, + content=ai_platform, + title_embedding=model.encode(f"search_document: {ai_platform_title}"), + content_embedding=model.encode( + f"search_document: {ai_platform_title}\n{ai_platform}" + ), +) + +customer_support_doc = SeedPresaveDocument( + url="https://docs.danswer.dev/more/use_cases/customer_support", + title=customer_support_title, + content=customer_support, + title_embedding=model.encode(f"search_document: {customer_support_title}"), + content_embedding=model.encode( + f"search_document: {customer_support_title}\n{customer_support}" + ), +) + +sales_doc = SeedPresaveDocument( + url="https://docs.danswer.dev/more/use_cases/sales", + title=sales_title, + content=sales, + title_embedding=model.encode(f"search_document: {sales_title}"), + content_embedding=model.encode(f"search_document: {sales_title}\n{sales}"), +) + +operations_doc = SeedPresaveDocument( + url="https://docs.danswer.dev/more/use_cases/operations", + title=operations_title, + content=operations, + title_embedding=model.encode(f"search_document: {operations_title}"), + content_embedding=model.encode( + f"search_document: {operations_title}\n{operations}" + ), +) + +documents = [ + overview_doc, + enterprise_search_doc, + enterprise_search_doc_2, + ai_platform_doc, + customer_support_doc, + sales_doc, + operations_doc, +] + +documents_dict = [doc.model_dump() for doc in documents] + +with open("./backend/danswer/seeding/initial_docs.json", "w") as json_file: + json.dump(documents_dict, json_file, indent=4) diff --git a/backend/tests/integration/connector_job_tests/slack/slack_api_utils.py b/backend/tests/integration/connector_job_tests/slack/slack_api_utils.py index db39932891..f17c421106 100644 --- a/backend/tests/integration/connector_job_tests/slack/slack_api_utils.py +++ b/backend/tests/integration/connector_job_tests/slack/slack_api_utils.py @@ -73,9 +73,7 @@ def _clear_slack_conversation_members( if member_id == admin_user_id: continue try: - make_slack_api_call_w_retries( - slack_client.conversations_kick, channel=channel_id, user=member_id - ) + slack_client.conversations_kick(channel=channel_id, user=member_id) print(f"Kicked member: {member_id}") except Exception as e: if "cant_kick_self" in str(e): @@ -83,9 +81,7 @@ def _clear_slack_conversation_members( print(f"Error kicking member: {e}") print(member_id) try: - make_slack_api_call_w_retries( - slack_client.conversations_unarchive, channel=channel_id - ) + slack_client.conversations_unarchive(channel=channel_id) channel["is_archived"] = False except Exception: # Channel is already unarchived @@ -98,11 +94,7 @@ def _add_slack_conversation_members( channel_id = _get_slack_channel_id(channel) for user_id in member_ids: try: - make_slack_api_call_w_retries( - slack_client.conversations_invite, - channel=channel_id, - users=user_id, - ) + slack_client.conversations_invite(channel=channel_id, users=user_id) except Exception as e: if "already_in_channel" in str(e): continue @@ -129,11 +121,7 @@ def _delete_slack_conversation_messages( try: if not (ts := message.get("ts")): raise ValueError("Message timestamp is missing") - make_slack_api_call_w_retries( - slack_client.chat_delete, - channel=channel_id, - ts=ts, - ) + slack_client.chat_delete(channel=channel_id, ts=ts) except Exception as e: print(f"Error deleting message: {e}") print(message) @@ -165,16 +153,12 @@ def _build_slack_channel_from_name( ) try: - channel_response = make_slack_api_call_w_retries( - slack_client.conversations_unarchive, - channel=channel_response["channel"]["id"], - ) + slack_client.conversations_unarchive(channel=channel_response["channel"]["id"]) except Exception: # Channel is already unarchived pass try: - channel_response = make_slack_api_call_w_retries( - slack_client.conversations_invite, + slack_client.conversations_invite( channel=channel_response["channel"]["id"], users=[admin_user_id], ) @@ -302,10 +286,6 @@ class SlackManager: # "done" in the channel name indicates that this channel is free to be used for a new test new_name = f"done_{str(uuid4())}" try: - make_slack_api_call_w_retries( - slack_client.conversations_rename, - channel=channel["id"], - name=new_name, - ) + slack_client.conversations_rename(channel=channel["id"], name=new_name) except SlackApiError as e: print(f"Error renaming channel {channel['id']}: {e}")