From ef5b9e40a90703c17c23f2d003045df28deb490e Mon Sep 17 00:00:00 2001 From: Abhishek Date: Sat, 17 Jan 2026 14:37:03 +0530 Subject: [PATCH] feat: knowledge base functionality for the voice agent (#120) * feat: upload file and store embedding * feat: add documents in nodes * feat: add openai embedding service --- .../dc33eef8dabe_add_document_tables.py | 194 +++++++ api/db/db_client.py | 3 + api/db/knowledge_base_client.py | 499 ++++++++++++++++++ api/db/models.py | 178 +++++++ api/requirements.txt | 3 + api/routes/knowledge_base.py | 405 ++++++++++++++ api/routes/main.py | 2 + api/routes/user.py | 6 + api/schemas/knowledge_base.py | 102 ++++ api/schemas/user_configuration.py | 2 + api/services/configuration/check_validity.py | 15 +- api/services/configuration/defaults.py | 2 + api/services/configuration/masking.py | 1 + api/services/configuration/merge.py | 2 +- api/services/configuration/registry.py | 35 +- api/services/filesystem/base.py | 13 + api/services/filesystem/minio.py | 12 + api/services/filesystem/s3.py | 11 + api/services/gen_ai/__init__.py | 15 + api/services/gen_ai/embedding/__init__.py | 12 + api/services/gen_ai/embedding/base.py | 75 +++ .../gen_ai/embedding/openai_service.py | 372 +++++++++++++ .../embedding/sentence_transformer_service.py | 350 ++++++++++++ api/services/pricing/embeddings.py | 44 ++ api/services/pricing/registry.py | 2 + api/services/workflow/dto.py | 1 + api/services/workflow/pipecat_engine.py | 61 +++ api/services/workflow/tools/knowledge_base.py | 305 +++++++++++ api/services/workflow/workflow.py | 11 +- api/tasks/arq.py | 2 + api/tasks/function_names.py | 1 + api/tasks/knowledge_base_processing.py | 311 +++++++++++ docker-compose-local.yaml | 2 +- ui/src/app/files/DocumentList.tsx | 249 +++++++++ ui/src/app/files/DocumentUpload.tsx | 220 ++++++++ ui/src/app/files/page.tsx | 104 ++++ .../workflow/[workflowId]/RenderWorkflow.tsx | 42 +- .../[workflowId]/contexts/WorkflowContext.tsx | 9 + ui/src/client/client.gen.ts | 7 +- ui/src/client/index.ts | 2 +- ui/src/client/sdk.gen.ts | 126 ++++- ui/src/client/types.gen.ts | 358 ++++++++++++- ui/src/components/ServiceConfiguration.tsx | 48 +- ui/src/components/flow/DocumentBadges.tsx | 65 +++ ui/src/components/flow/DocumentSelector.tsx | 137 +++++ ui/src/components/flow/ToolBadges.tsx | 46 +- ui/src/components/flow/ToolSelector.tsx | 50 +- ui/src/components/flow/nodes/AgentNode.tsx | 71 ++- ui/src/components/flow/nodes/StartCall.tsx | 71 ++- ui/src/components/flow/types.ts | 2 + ui/src/components/layout/AppSidebar.tsx | 6 + ui/src/context/UserConfigContext.tsx | 3 + 52 files changed, 4551 insertions(+), 114 deletions(-) create mode 100644 api/alembic/versions/dc33eef8dabe_add_document_tables.py create mode 100644 api/db/knowledge_base_client.py create mode 100644 api/routes/knowledge_base.py create mode 100644 api/schemas/knowledge_base.py create mode 100644 api/services/gen_ai/__init__.py create mode 100644 api/services/gen_ai/embedding/__init__.py create mode 100644 api/services/gen_ai/embedding/base.py create mode 100644 api/services/gen_ai/embedding/openai_service.py create mode 100644 api/services/gen_ai/embedding/sentence_transformer_service.py create mode 100644 api/services/pricing/embeddings.py create mode 100644 api/services/workflow/tools/knowledge_base.py create mode 100644 api/tasks/knowledge_base_processing.py create mode 100644 ui/src/app/files/DocumentList.tsx create mode 100644 ui/src/app/files/DocumentUpload.tsx create mode 100644 ui/src/app/files/page.tsx create mode 100644 ui/src/components/flow/DocumentBadges.tsx create mode 100644 ui/src/components/flow/DocumentSelector.tsx diff --git a/api/alembic/versions/dc33eef8dabe_add_document_tables.py b/api/alembic/versions/dc33eef8dabe_add_document_tables.py new file mode 100644 index 0000000..7ea5ebb --- /dev/null +++ b/api/alembic/versions/dc33eef8dabe_add_document_tables.py @@ -0,0 +1,194 @@ +"""add document tables + +Revision ID: dc33eef8dabe +Revises: dcb0a27d98c6 +Create Date: 2026-01-16 13:40:17.808807 + +""" + +from typing import Sequence, Union + +import sqlalchemy as sa +from alembic import op +from pgvector.sqlalchemy import Vector +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision: str = "dc33eef8dabe" +down_revision: Union[str, None] = "dcb0a27d98c6" +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + # Enable pgvector extension + op.execute("CREATE EXTENSION IF NOT EXISTS vector") + + sa.Enum( + "pending", + "processing", + "completed", + "failed", + name="document_processing_status", + ).create(op.get_bind()) + op.create_table( + "knowledge_base_documents", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("document_uuid", sa.String(length=36), nullable=False), + sa.Column("organization_id", sa.Integer(), nullable=False), + sa.Column("filename", sa.String(length=500), nullable=False), + sa.Column("file_size_bytes", sa.Integer(), nullable=True), + sa.Column("file_hash", sa.String(length=64), nullable=True), + sa.Column("mime_type", sa.String(length=100), nullable=True), + sa.Column("source_url", sa.String(), nullable=True), + sa.Column("total_chunks", sa.Integer(), nullable=False), + sa.Column( + "processing_status", + postgresql.ENUM( + "pending", + "processing", + "completed", + "failed", + name="document_processing_status", + create_type=False, + ), + server_default=sa.text("'pending'::document_processing_status"), + nullable=False, + ), + sa.Column("processing_error", sa.Text(), nullable=True), + sa.Column("docling_metadata", sa.JSON(), nullable=False), + sa.Column("custom_metadata", sa.JSON(), nullable=False), + sa.Column("created_by", sa.Integer(), nullable=False), + sa.Column("created_at", sa.DateTime(timezone=True), nullable=True), + sa.Column("updated_at", sa.DateTime(timezone=True), nullable=True), + sa.Column("is_active", sa.Boolean(), nullable=False), + sa.Column("archived_at", sa.DateTime(timezone=True), nullable=True), + sa.ForeignKeyConstraint( + ["created_by"], + ["users.id"], + ), + sa.ForeignKeyConstraint( + ["organization_id"], ["organizations.id"], ondelete="CASCADE" + ), + sa.PrimaryKeyConstraint("id"), + ) + op.create_index( + "ix_kb_documents_created_at", + "knowledge_base_documents", + ["created_at"], + unique=False, + ) + op.create_index( + "ix_kb_documents_organization_id", + "knowledge_base_documents", + ["organization_id"], + unique=False, + ) + op.create_index( + "ix_kb_documents_status", + "knowledge_base_documents", + ["processing_status"], + unique=False, + ) + op.create_index( + "ix_kb_documents_uuid", + "knowledge_base_documents", + ["document_uuid"], + unique=False, + ) + op.create_index( + op.f("ix_knowledge_base_documents_document_uuid"), + "knowledge_base_documents", + ["document_uuid"], + unique=True, + ) + op.create_table( + "knowledge_base_chunks", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("document_id", sa.Integer(), nullable=False), + sa.Column("organization_id", sa.Integer(), nullable=False), + sa.Column("chunk_text", sa.Text(), nullable=False), + sa.Column("contextualized_text", sa.Text(), nullable=True), + sa.Column("chunk_index", sa.Integer(), nullable=False), + sa.Column("chunk_metadata", sa.JSON(), nullable=False), + sa.Column("embedding_model", sa.String(length=200), nullable=False), + sa.Column("embedding_dimension", sa.Integer(), nullable=False), + sa.Column("embedding", Vector(1536), nullable=True), + sa.Column("token_count", sa.Integer(), nullable=True), + sa.Column("created_at", sa.DateTime(timezone=True), nullable=True), + sa.Column("updated_at", sa.DateTime(timezone=True), nullable=True), + sa.ForeignKeyConstraint( + ["document_id"], ["knowledge_base_documents.id"], ondelete="CASCADE" + ), + sa.ForeignKeyConstraint( + ["organization_id"], ["organizations.id"], ondelete="CASCADE" + ), + sa.PrimaryKeyConstraint("id"), + ) + op.create_index( + "ix_kb_chunks_chunk_index", + "knowledge_base_chunks", + ["chunk_index"], + unique=False, + ) + op.create_index( + "ix_kb_chunks_document_id", + "knowledge_base_chunks", + ["document_id"], + unique=False, + ) + op.create_index( + "ix_kb_chunks_embedding_ivfflat", + "knowledge_base_chunks", + ["embedding"], + unique=False, + postgresql_using="ivfflat", + postgresql_with={"lists": 100}, + postgresql_ops={"embedding": "vector_cosine_ops"}, + ) + op.create_index( + "ix_kb_chunks_organization_id", + "knowledge_base_chunks", + ["organization_id"], + unique=False, + ) + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.drop_index("ix_kb_chunks_organization_id", table_name="knowledge_base_chunks") + op.drop_index( + "ix_kb_chunks_embedding_ivfflat", + table_name="knowledge_base_chunks", + postgresql_using="ivfflat", + postgresql_with={"lists": 100}, + postgresql_ops={"embedding": "vector_cosine_ops"}, + ) + op.drop_index("ix_kb_chunks_document_id", table_name="knowledge_base_chunks") + op.drop_index("ix_kb_chunks_chunk_index", table_name="knowledge_base_chunks") + op.drop_table("knowledge_base_chunks") + op.drop_index( + op.f("ix_knowledge_base_documents_document_uuid"), + table_name="knowledge_base_documents", + ) + op.drop_index("ix_kb_documents_uuid", table_name="knowledge_base_documents") + op.drop_index("ix_kb_documents_status", table_name="knowledge_base_documents") + op.drop_index( + "ix_kb_documents_organization_id", table_name="knowledge_base_documents" + ) + op.drop_index("ix_kb_documents_created_at", table_name="knowledge_base_documents") + op.drop_table("knowledge_base_documents") + sa.Enum( + "pending", + "processing", + "completed", + "failed", + name="document_processing_status", + ).drop(op.get_bind()) + + # Note: We don't drop the vector extension as it may be used by other tables + # If you want to drop it, uncomment the following line: + # op.execute('DROP EXTENSION IF EXISTS vector') + # ### end Alembic commands ### diff --git a/api/db/db_client.py b/api/db/db_client.py index 6a6c713..8465ca9 100644 --- a/api/db/db_client.py +++ b/api/db/db_client.py @@ -3,6 +3,7 @@ from api.db.api_key_client import APIKeyClient from api.db.campaign_client import CampaignClient from api.db.embed_token_client import EmbedTokenClient from api.db.integration_client import IntegrationClient +from api.db.knowledge_base_client import KnowledgeBaseClient from api.db.looptalk_client import LoopTalkClient from api.db.organization_client import OrganizationClient from api.db.organization_configuration_client import OrganizationConfigurationClient @@ -33,6 +34,7 @@ class DBClient( AgentTriggerClient, WebhookCredentialClient, ToolClient, + KnowledgeBaseClient, ): """ Unified database client that combines all specialized database operations. @@ -54,6 +56,7 @@ class DBClient( - AgentTriggerClient: handles agent trigger operations for API-based call triggering - WebhookCredentialClient: handles webhook credential operations - ToolClient: handles tool operations for reusable HTTP API tools + - KnowledgeBaseClient: handles knowledge base document and vector search operations """ pass diff --git a/api/db/knowledge_base_client.py b/api/db/knowledge_base_client.py new file mode 100644 index 0000000..835b25a --- /dev/null +++ b/api/db/knowledge_base_client.py @@ -0,0 +1,499 @@ +"""Database client for managing knowledge base documents and chunks.""" + +import hashlib +from pathlib import Path +from typing import List, Optional + +from loguru import logger +from sqlalchemy import select +from sqlalchemy.orm import selectinload + +from api.db.base_client import BaseDBClient +from api.db.models import KnowledgeBaseChunkModel, KnowledgeBaseDocumentModel + + +class KnowledgeBaseClient(BaseDBClient): + """Client for managing knowledge base documents and vector embeddings.""" + + async def create_document( + self, + organization_id: int, + created_by: int, + filename: str, + file_size_bytes: int, + file_hash: str, + mime_type: str, + source_url: Optional[str] = None, + custom_metadata: Optional[dict] = None, + docling_metadata: Optional[dict] = None, + document_uuid: Optional[str] = None, + ) -> KnowledgeBaseDocumentModel: + """Create a new knowledge base document record. + + Args: + organization_id: ID of the organization + created_by: ID of the user uploading the document + filename: Name of the file + file_size_bytes: Size of the file in bytes + file_hash: SHA-256 hash of the file + mime_type: MIME type of the file + source_url: Optional URL if document was fetched from web + custom_metadata: Optional custom metadata dictionary + docling_metadata: Optional docling processing metadata + document_uuid: Optional UUID to use (if not provided, one will be generated) + + Returns: + The created KnowledgeBaseDocumentModel + """ + async with self.async_session() as session: + document = KnowledgeBaseDocumentModel( + organization_id=organization_id, + created_by=created_by, + filename=filename, + file_size_bytes=file_size_bytes, + file_hash=file_hash, + mime_type=mime_type, + source_url=source_url, + custom_metadata=custom_metadata or {}, + docling_metadata=docling_metadata or {}, + processing_status="pending", + total_chunks=0, + ) + + # Use provided UUID or let the model generate one + if document_uuid: + document.document_uuid = document_uuid + + session.add(document) + await session.commit() + await session.refresh(document) + + logger.info( + f"Created document '{filename}' ({document.document_uuid}) " + f"for organization {organization_id}" + ) + return document + + async def get_document_by_id( + self, + document_id: int, + ) -> Optional[KnowledgeBaseDocumentModel]: + """Get a document by its database ID. + + Args: + document_id: The database ID of the document + + Returns: + KnowledgeBaseDocumentModel if found, None otherwise + """ + async with self.async_session() as session: + query = select(KnowledgeBaseDocumentModel).where( + KnowledgeBaseDocumentModel.id == document_id + ) + + result = await session.execute(query) + return result.scalar_one_or_none() + + async def get_document_by_uuid( + self, + document_uuid: str, + organization_id: int, + ) -> Optional[KnowledgeBaseDocumentModel]: + """Get a document by its UUID, scoped to organization. + + Args: + document_uuid: The unique document UUID + organization_id: ID of the organization + + Returns: + KnowledgeBaseDocumentModel if found, None otherwise + """ + async with self.async_session() as session: + query = ( + select(KnowledgeBaseDocumentModel) + .where( + KnowledgeBaseDocumentModel.document_uuid == document_uuid, + KnowledgeBaseDocumentModel.organization_id == organization_id, + KnowledgeBaseDocumentModel.is_active == True, + ) + .options(selectinload(KnowledgeBaseDocumentModel.created_by_user)) + ) + + result = await session.execute(query) + return result.scalar_one_or_none() + + async def get_document_by_hash( + self, + file_hash: str, + organization_id: int, + ) -> Optional[KnowledgeBaseDocumentModel]: + """Check if a document with the same hash already exists. + + Returns the first matching document if multiple exist (can happen with duplicates). + + Args: + file_hash: SHA-256 hash of the file + organization_id: ID of the organization + + Returns: + KnowledgeBaseDocumentModel if found, None otherwise + """ + async with self.async_session() as session: + query = ( + select(KnowledgeBaseDocumentModel) + .where( + KnowledgeBaseDocumentModel.file_hash == file_hash, + KnowledgeBaseDocumentModel.organization_id == organization_id, + KnowledgeBaseDocumentModel.is_active == True, + ) + .order_by(KnowledgeBaseDocumentModel.created_at.asc()) + .limit(1) + ) + + result = await session.execute(query) + return result.scalars().first() + + async def get_documents_for_organization( + self, + organization_id: int, + processing_status: Optional[str] = None, + limit: int = 100, + offset: int = 0, + ) -> List[KnowledgeBaseDocumentModel]: + """Get all documents for an organization. + + Args: + organization_id: ID of the organization + processing_status: Optional filter by status + limit: Maximum number of documents to return + offset: Number of documents to skip + + Returns: + List of KnowledgeBaseDocumentModel instances + """ + async with self.async_session() as session: + query = select(KnowledgeBaseDocumentModel).where( + KnowledgeBaseDocumentModel.organization_id == organization_id, + KnowledgeBaseDocumentModel.is_active == True, + ) + + if processing_status: + query = query.where( + KnowledgeBaseDocumentModel.processing_status == processing_status + ) + + query = ( + query.order_by(KnowledgeBaseDocumentModel.created_at.desc()) + .limit(limit) + .offset(offset) + ) + + result = await session.execute(query) + return list(result.scalars().all()) + + async def update_document_metadata( + self, + document_id: int, + file_size_bytes: Optional[int] = None, + file_hash: Optional[str] = None, + mime_type: Optional[str] = None, + ) -> Optional[KnowledgeBaseDocumentModel]: + """Update document file metadata. + + Args: + document_id: ID of the document + file_size_bytes: Optional file size in bytes + file_hash: Optional SHA-256 hash of the file + mime_type: Optional MIME type + + Returns: + Updated KnowledgeBaseDocumentModel + """ + async with self.async_session() as session: + query = select(KnowledgeBaseDocumentModel).where( + KnowledgeBaseDocumentModel.id == document_id + ) + result = await session.execute(query) + document = result.scalar_one_or_none() + + if not document: + return None + + if file_size_bytes is not None: + document.file_size_bytes = file_size_bytes + if file_hash is not None: + document.file_hash = file_hash + if mime_type is not None: + document.mime_type = mime_type + + await session.commit() + await session.refresh(document) + + logger.info(f"Updated document {document_id} metadata") + return document + + async def update_document_status( + self, + document_id: int, + status: str, + error_message: Optional[str] = None, + total_chunks: Optional[int] = None, + docling_metadata: Optional[dict] = None, + ) -> Optional[KnowledgeBaseDocumentModel]: + """Update document processing status. + + Args: + document_id: ID of the document + status: New status (pending, processing, completed, failed) + error_message: Optional error message if status is failed + total_chunks: Optional total number of chunks + docling_metadata: Optional docling metadata + + Returns: + Updated KnowledgeBaseDocumentModel + """ + async with self.async_session() as session: + query = select(KnowledgeBaseDocumentModel).where( + KnowledgeBaseDocumentModel.id == document_id + ) + result = await session.execute(query) + document = result.scalar_one_or_none() + + if not document: + return None + + document.processing_status = status + if error_message: + document.processing_error = error_message + if total_chunks is not None: + document.total_chunks = total_chunks + if docling_metadata: + document.docling_metadata = docling_metadata + + await session.commit() + await session.refresh(document) + + logger.info(f"Updated document {document_id} status to {status}") + return document + + async def create_chunks_batch( + self, + chunks: List[KnowledgeBaseChunkModel], + ) -> List[KnowledgeBaseChunkModel]: + """Create multiple chunks in a batch. + + Args: + chunks: List of KnowledgeBaseChunkModel instances + + Returns: + List of created chunks with IDs + """ + async with self.async_session() as session: + session.add_all(chunks) + await session.commit() + + for chunk in chunks: + await session.refresh(chunk) + + logger.info(f"Created {len(chunks)} chunks") + return chunks + + async def get_chunks_for_document( + self, + document_id: int, + organization_id: int, + ) -> List[KnowledgeBaseChunkModel]: + """Get all chunks for a document. + + Args: + document_id: ID of the document + organization_id: ID of the organization (for authorization) + + Returns: + List of KnowledgeBaseChunkModel instances + """ + async with self.async_session() as session: + query = ( + select(KnowledgeBaseChunkModel) + .where( + KnowledgeBaseChunkModel.document_id == document_id, + KnowledgeBaseChunkModel.organization_id == organization_id, + ) + .order_by(KnowledgeBaseChunkModel.chunk_index) + ) + + result = await session.execute(query) + return list(result.scalars().all()) + + async def search_similar_chunks( + self, + query_embedding: List[float], + organization_id: int, + limit: int = 5, + document_ids: Optional[List[int]] = None, + document_uuids: Optional[List[str]] = None, + embedding_model: Optional[str] = None, + ) -> List[dict]: + """Search for similar chunks using vector similarity. + + Returns top-k most similar chunks without any similarity threshold filtering. + Filtering and reranking should be done at the application layer. + + Args: + query_embedding: The query embedding vector + organization_id: Organization ID for scoping + limit: Maximum number of results to return + document_ids: Optional list of document IDs to filter by + document_uuids: Optional list of document UUIDs to filter by + embedding_model: Optional embedding model to filter by (for dimension compatibility) + + Returns: + List of dictionaries with chunk data and similarity scores, ordered by similarity (highest first) + """ + async with self.async_session() as session: + # Get the raw connection to execute directly with asyncpg + # This avoids parameter binding issues with text() and asyncpg + connection = await session.connection() + raw_connection = await connection.get_raw_connection() + + # Build WHERE clause conditions (no similarity threshold) + where_conditions = [ + "c.organization_id = $2", + "d.is_active = true", + ] + params = [ + None, + organization_id, + limit, + ] # $1 will be embedding_str, $3 is limit + param_index = 4 # Next available parameter index + + # Add document_ids filter if provided + if document_ids: + placeholders = ", ".join( + f"${param_index + i}" for i in range(len(document_ids)) + ) + where_conditions.append(f"c.document_id IN ({placeholders})") + params.extend(document_ids) + param_index += len(document_ids) + + # Add document_uuids filter if provided + if document_uuids: + placeholders = ", ".join( + f"${param_index + i}" for i in range(len(document_uuids)) + ) + where_conditions.append(f"d.document_uuid IN ({placeholders})") + params.extend(document_uuids) + param_index += len(document_uuids) + + # Add embedding_model filter if provided (for dimension compatibility) + if embedding_model: + where_conditions.append(f"c.embedding_model = ${param_index}") + params.append(embedding_model) + param_index += 1 + + # Build the complete SQL query + where_clause = " AND ".join(where_conditions) + query_sql = f""" + SELECT + c.id, + c.document_id, + c.chunk_text, + c.contextualized_text, + c.chunk_metadata, + c.chunk_index, + d.filename, + d.document_uuid, + 1 - (c.embedding <=> $1::vector) as similarity + FROM knowledge_base_chunks c + JOIN knowledge_base_documents d ON c.document_id = d.id + WHERE {where_clause} + ORDER BY c.embedding <=> $1::vector + LIMIT $3 + """ + + # Convert embedding to string format for PostgreSQL vector type + embedding_str = "[" + ",".join(map(str, query_embedding)) + "]" + params[0] = embedding_str # Set $1 + + # Execute query directly with asyncpg + rows = await raw_connection.driver_connection.fetch( + query_sql, + *params, + ) + + # Convert asyncpg records to dictionaries + return [dict(row) for row in rows] + + async def delete_document( + self, + document_uuid: str, + organization_id: int, + ) -> bool: + """Soft delete a document by setting is_active to False. + + This will also cascade delete all chunks via the database foreign key. + + Args: + document_uuid: The unique document UUID + organization_id: ID of the organization (for authorization) + + Returns: + True if document was deleted, False if not found + """ + async with self.async_session() as session: + query = select(KnowledgeBaseDocumentModel).where( + KnowledgeBaseDocumentModel.document_uuid == document_uuid, + KnowledgeBaseDocumentModel.organization_id == organization_id, + ) + + result = await session.execute(query) + document = result.scalar_one_or_none() + + if not document: + return False + + document.is_active = False + await session.commit() + + logger.info( + f"Deleted document {document_uuid} for organization {organization_id}" + ) + return True + + @staticmethod + def compute_file_hash(file_path: str) -> str: + """Compute SHA-256 hash of a file. + + Args: + file_path: Path to the file + + Returns: + SHA-256 hash as hex string + """ + sha256_hash = hashlib.sha256() + with open(file_path, "rb") as f: + for byte_block in iter(lambda: f.read(4096), b""): + sha256_hash.update(byte_block) + return sha256_hash.hexdigest() + + @staticmethod + def get_mime_type(file_path: str) -> str: + """Get MIME type based on file extension. + + Args: + file_path: Path to the file + + Returns: + MIME type string + """ + extension = Path(file_path).suffix.lower() + mime_types = { + ".pdf": "application/pdf", + ".docx": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", + ".doc": "application/msword", + ".txt": "text/plain", + ".html": "text/html", + ".md": "text/markdown", + } + return mime_types.get(extension, "application/octet-stream") diff --git a/api/db/models.py b/api/db/models.py index c7a9489..55d0305 100644 --- a/api/db/models.py +++ b/api/db/models.py @@ -2,6 +2,7 @@ import uuid from datetime import UTC, datetime from loguru import logger +from pgvector.sqlalchemy import Vector from sqlalchemy import ( JSON, Boolean, @@ -14,6 +15,7 @@ from sqlalchemy import ( Integer, String, Table, + Text, UniqueConstraint, and_, text, @@ -890,3 +892,179 @@ class ToolModel(Base): Index("ix_tools_status", "status"), Index("ix_tools_category", "category"), ) + + +class KnowledgeBaseDocumentModel(Base): + """Model for storing document-level metadata in the knowledge base. + + Each document represents a source file (PDF, DOCX, etc.) that has been + processed and chunked for retrieval. + """ + + __tablename__ = "knowledge_base_documents" + + id = Column(Integer, primary_key=True, index=True) + + # Public identifier for API references + document_uuid = Column( + String(36), + unique=True, + nullable=False, + index=True, + default=lambda: str(uuid.uuid4()), + ) + + # Organization scoping + organization_id = Column( + Integer, ForeignKey("organizations.id", ondelete="CASCADE"), nullable=False + ) + + # Document metadata + filename = Column(String(500), nullable=False) + file_size_bytes = Column(Integer, nullable=True) + file_hash = Column(String(64), nullable=True) # SHA-256 hash for deduplication + mime_type = Column(String(100), nullable=True) + + # Processing metadata + source_url = Column(String, nullable=True) # If document was fetched from URL + total_chunks = Column(Integer, nullable=False, default=0) + processing_status = Column( + Enum( + "pending", + "processing", + "completed", + "failed", + name="document_processing_status", + ), + nullable=False, + default="pending", + server_default=text("'pending'::document_processing_status"), + ) + processing_error = Column(Text, nullable=True) + + # Docling conversion metadata + docling_metadata = Column( + JSON, nullable=False, default=dict + ) # Store docling document metadata + + # Custom metadata (user-defined tags, categories, etc.) + custom_metadata = Column(JSON, nullable=False, default=dict) + + # Audit fields + created_by = Column(Integer, ForeignKey("users.id"), nullable=False) + created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(UTC)) + updated_at = Column( + DateTime(timezone=True), + default=lambda: datetime.now(UTC), + onupdate=lambda: datetime.now(UTC), + ) + + # Soft delete + is_active = Column(Boolean, default=True, nullable=False) + archived_at = Column(DateTime(timezone=True), nullable=True) + + # Relationships + organization = relationship("OrganizationModel") + created_by_user = relationship("UserModel") + chunks = relationship( + "KnowledgeBaseChunkModel", + back_populates="document", + cascade="all, delete-orphan", + ) + + # Indexes and constraints + __table_args__ = ( + Index("ix_kb_documents_organization_id", "organization_id"), + Index("ix_kb_documents_uuid", "document_uuid"), + Index("ix_kb_documents_status", "processing_status"), + Index("ix_kb_documents_created_at", "created_at"), + ) + + +class KnowledgeBaseChunkModel(Base): + """Model for storing document chunks with vector embeddings. + + Each chunk represents a portion of a document that has been: + 1. Extracted and chunked by docling's HybridChunker + 2. Optionally contextualized with surrounding information + 3. Embedded into a vector representation for semantic search + """ + + __tablename__ = "knowledge_base_chunks" + + id = Column(Integer, primary_key=True, index=True) + + # Link to parent document + document_id = Column( + Integer, + ForeignKey("knowledge_base_documents.id", ondelete="CASCADE"), + nullable=False, + ) + + # Organization scoping (denormalized for efficient querying) + organization_id = Column( + Integer, ForeignKey("organizations.id", ondelete="CASCADE"), nullable=False + ) + + # Chunk content + chunk_text = Column(Text, nullable=False) # The actual chunk text + contextualized_text = Column( + Text, nullable=True + ) # Enriched text from chunker.contextualize() + + # Chunk positioning and metadata + chunk_index = Column(Integer, nullable=False) # Position in document (0-based) + + # Docling chunk metadata + chunk_metadata = Column( + JSON, nullable=False, default=dict + ) # Store chunk.meta if available + + # Embedding configuration + embedding_model = Column( + String(200), nullable=False + ) # e.g., "sentence-transformers/all-MiniLM-L6-v2" + embedding_dimension = Column( + Integer, nullable=False + ) # e.g., 384 for all-MiniLM-L6-v2 + + # Vector embedding (pgvector column) + # The dimension should match the embedding_dimension field + # Default: 1536 dimensions for OpenAI text-embedding-3-small + # SentenceTransformer (384-dim) also supported but stored as 384-dim vectors + embedding = Column(Vector(1536), nullable=True) + + # Token count (useful for chunking strategy analysis) + token_count = Column(Integer, nullable=True) + + # Timestamps + created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(UTC)) + updated_at = Column( + DateTime(timezone=True), + default=lambda: datetime.now(UTC), + onupdate=lambda: datetime.now(UTC), + ) + + # Relationships + document = relationship("KnowledgeBaseDocumentModel", back_populates="chunks") + organization = relationship("OrganizationModel") + + # Indexes and constraints + __table_args__ = ( + Index("ix_kb_chunks_document_id", "document_id"), + Index("ix_kb_chunks_organization_id", "organization_id"), + Index("ix_kb_chunks_chunk_index", "chunk_index"), + Index( + "ix_kb_chunks_embedding_model", "embedding_model" + ), # For filtering by model + # Vector similarity search index (using IVFFlat or HNSW) + # IVFFlat is good for datasets with 10k-1M vectors + # HNSW is better for larger datasets but uses more memory + Index( + "ix_kb_chunks_embedding_ivfflat", + "embedding", + postgresql_using="ivfflat", + postgresql_with={"lists": 100}, # Adjust based on dataset size + postgresql_ops={"embedding": "vector_cosine_ops"}, + ), + ) diff --git a/api/requirements.txt b/api/requirements.txt index da734e7..9b49c1d 100644 --- a/api/requirements.txt +++ b/api/requirements.txt @@ -13,3 +13,6 @@ python-multipart==0.0.20 sentry-sdk[fastapi]==2.38.0 sqlalchemy[asyncio]==2.0.43 msgpack==1.1.2 +docling[rapidocr]==2.68.0 +sentence-transformers==5.2.0 +pgvector==0.4.2 diff --git a/api/routes/knowledge_base.py b/api/routes/knowledge_base.py new file mode 100644 index 0000000..4553efd --- /dev/null +++ b/api/routes/knowledge_base.py @@ -0,0 +1,405 @@ +"""API routes for knowledge base operations.""" + +import uuid +from typing import Annotated, Optional + +from fastapi import APIRouter, Depends, HTTPException, Query +from loguru import logger + +from api.db import db_client +from api.schemas.knowledge_base import ( + ChunkSearchRequestSchema, + ChunkSearchResponseSchema, + DocumentListResponseSchema, + DocumentResponseSchema, + DocumentUploadRequestSchema, + DocumentUploadResponseSchema, + ProcessDocumentRequestSchema, +) +from api.services.auth.depends import get_user +from api.services.storage import storage_fs +from api.tasks.arq import enqueue_job +from api.tasks.function_names import FunctionNames + +router = APIRouter(prefix="/knowledge-base", tags=["knowledge-base"]) + + +@router.post( + "/upload-url", + response_model=DocumentUploadResponseSchema, + summary="Get presigned URL for document upload", +) +async def get_upload_url( + request: DocumentUploadRequestSchema, + user=Depends(get_user), +): + """Generate a presigned PUT URL for uploading a document. + + This endpoint: + 1. Generates a unique document UUID for organizing the S3 key + 2. Generates a presigned S3/MinIO URL for uploading the file + 3. Returns the upload URL and document metadata + + After uploading to the returned URL, call /process-document to create + the document record and trigger processing. + + Access Control: + * All authenticated users can upload documents scoped to their organization. + """ + + try: + # Generate unique document UUID for S3 organization + document_uuid = str(uuid.uuid4()) + + # Generate S3 key: knowledge_base/{org_id}/{document_uuid}/{filename} + s3_key = f"knowledge_base/{user.selected_organization_id}/{document_uuid}/{request.filename}" + + # Generate presigned PUT URL (valid for 30 minutes) + upload_url = await storage_fs.aget_presigned_put_url( + file_path=s3_key, + expiration=1800, # 30 minutes + content_type=request.mime_type, + max_size=100_000_000, # 100MB max + ) + + if not upload_url: + raise HTTPException( + status_code=500, detail="Failed to generate presigned upload URL" + ) + + logger.info( + f"Generated upload URL for document {document_uuid}, " + f"user {user.id}, org {user.selected_organization_id}" + ) + + return DocumentUploadResponseSchema( + upload_url=upload_url, + document_uuid=document_uuid, + s3_key=s3_key, + ) + + except Exception as exc: + logger.error(f"Error generating upload URL: {exc}") + raise HTTPException( + status_code=500, detail="Failed to generate upload URL" + ) from exc + + +@router.post( + "/process-document", + response_model=DocumentResponseSchema, + summary="Trigger document processing", +) +async def process_document( + request: ProcessDocumentRequestSchema, + user=Depends(get_user), +): + """Trigger asynchronous processing of an uploaded document. + + This endpoint should be called after successfully uploading a file to the presigned URL. + It will: + 1. Create a document record in the database with the specified UUID + 2. Enqueue a background task to process the document (chunking and embedding) + + The document status will be updated from 'pending' -> 'processing' -> 'completed' or 'failed'. + + Embedding Services: + * openai (default): High-quality 1536-dimensional embeddings (requires OPENAI_API_KEY) + * sentence_transformer: Free, offline-capable, 384-dimensional embeddings + + Access Control: + * Users can only process documents in their organization. + """ + + try: + # Extract filename from s3_key + filename = request.s3_key.split("/")[-1] + + # Create document record with the specific UUID from upload + document = await db_client.create_document( + organization_id=user.selected_organization_id, + created_by=user.id, + filename=filename, + file_size_bytes=0, # Will be updated by background task + file_hash="", # Will be computed by background task + mime_type="application/octet-stream", # Will be detected by background task + custom_metadata={"s3_key": request.s3_key}, + document_uuid=request.document_uuid, # Use UUID from upload + ) + + # Enqueue background task for processing + await enqueue_job( + FunctionNames.PROCESS_KNOWLEDGE_BASE_DOCUMENT, + document.id, + request.s3_key, + user.selected_organization_id, + 128, # max_tokens (default) + request.embedding_service, + ) + + logger.info( + f"Created document {request.document_uuid} (id={document.id}) and enqueued processing " + f"with {request.embedding_service} embeddings, org {user.selected_organization_id}" + ) + + return DocumentResponseSchema( + id=document.id, + document_uuid=request.document_uuid, + filename=filename, + file_size_bytes=0, + file_hash="", + mime_type="application/octet-stream", + processing_status="pending", + processing_error=None, + total_chunks=0, + custom_metadata={"s3_key": request.s3_key}, + docling_metadata={}, + source_url=None, + created_at=document.created_at, + updated_at=document.updated_at, + organization_id=user.selected_organization_id, + created_by=user.id, + is_active=True, + ) + + except HTTPException: + raise + except Exception as exc: + logger.error(f"Error processing document: {exc}") + raise HTTPException( + status_code=500, detail="Failed to process document" + ) from exc + + +@router.get( + "/documents", + response_model=DocumentListResponseSchema, + summary="List documents", +) +async def list_documents( + status: Annotated[ + Optional[str], + Query(description="Filter by processing status"), + ] = None, + limit: Annotated[int, Query(ge=1, le=100)] = 100, + offset: Annotated[int, Query(ge=0)] = 0, + user=Depends(get_user), +): + """List all documents for the user's organization. + + Access Control: + * Users can only see documents from their organization. + """ + + try: + documents = await db_client.get_documents_for_organization( + organization_id=user.selected_organization_id, + processing_status=status, + limit=limit, + offset=offset, + ) + + # Convert to response schema + document_list = [ + DocumentResponseSchema( + id=doc.id, + document_uuid=doc.document_uuid, + filename=doc.filename, + file_size_bytes=doc.file_size_bytes, + file_hash=doc.file_hash, + mime_type=doc.mime_type, + processing_status=doc.processing_status, + processing_error=doc.processing_error, + total_chunks=doc.total_chunks, + custom_metadata=doc.custom_metadata, + docling_metadata=doc.docling_metadata, + source_url=doc.source_url, + created_at=doc.created_at, + updated_at=doc.updated_at, + organization_id=doc.organization_id, + created_by=doc.created_by, + is_active=doc.is_active, + ) + for doc in documents + ] + + return DocumentListResponseSchema( + documents=document_list, + total=len(document_list), + limit=limit, + offset=offset, + ) + + except Exception as exc: + logger.error(f"Error listing documents: {exc}") + raise HTTPException(status_code=500, detail="Failed to list documents") from exc + + +@router.get( + "/documents/{document_uuid}", + response_model=DocumentResponseSchema, + summary="Get document details", +) +async def get_document( + document_uuid: str, + user=Depends(get_user), +): + """Get details of a specific document. + + Access Control: + * Users can only access documents from their organization. + """ + + try: + document = await db_client.get_document_by_uuid( + document_uuid=document_uuid, + organization_id=user.selected_organization_id, + ) + + if not document: + raise HTTPException(status_code=404, detail="Document not found") + + return DocumentResponseSchema( + id=document.id, + document_uuid=document.document_uuid, + filename=document.filename, + file_size_bytes=document.file_size_bytes, + file_hash=document.file_hash, + mime_type=document.mime_type, + processing_status=document.processing_status, + processing_error=document.processing_error, + total_chunks=document.total_chunks, + custom_metadata=document.custom_metadata, + docling_metadata=document.docling_metadata, + source_url=document.source_url, + created_at=document.created_at, + updated_at=document.updated_at, + organization_id=document.organization_id, + created_by=document.created_by, + is_active=document.is_active, + ) + + except HTTPException: + raise + except Exception as exc: + logger.error(f"Error getting document: {exc}") + raise HTTPException(status_code=500, detail="Failed to get document") from exc + + +@router.delete( + "/documents/{document_uuid}", + summary="Delete document", +) +async def delete_document( + document_uuid: str, + user=Depends(get_user), +): + """Soft delete a document and its chunks. + + Access Control: + * Users can only delete documents from their organization. + """ + + try: + success = await db_client.delete_document( + document_uuid=document_uuid, + organization_id=user.selected_organization_id, + ) + + if not success: + raise HTTPException(status_code=404, detail="Document not found") + + logger.info( + f"Deleted document {document_uuid}, " + f"user {user.id}, org {user.selected_organization_id}" + ) + + return {"success": True, "message": "Document deleted successfully"} + + except HTTPException: + raise + except Exception as exc: + logger.error(f"Error deleting document: {exc}") + raise HTTPException( + status_code=500, detail="Failed to delete document" + ) from exc + + +@router.post( + "/search", + response_model=ChunkSearchResponseSchema, + summary="Search for similar chunks", +) +async def search_chunks( + request: ChunkSearchRequestSchema, + user=Depends(get_user), +): + """Search for document chunks similar to the query. + + This endpoint uses vector similarity search to find relevant chunks. + Results are returned without threshold filtering - apply similarity + thresholds at the application layer after optional reranking. + + Access Control: + * Users can only search documents from their organization. + """ + + try: + # Import here to avoid circular dependency + from api.services.gen_ai import OpenAIEmbeddingService + + # Try to get user's embeddings configuration + user_config = await db_client.get_user_configurations(user.id) + embeddings_api_key = None + embeddings_model = None + + if user_config.embeddings: + embeddings_api_key = user_config.embeddings.api_key + embeddings_model = user_config.embeddings.model + + # Initialize embedding service with user config or fallback to env + embedding_service = OpenAIEmbeddingService( + db_client=db_client, + api_key=embeddings_api_key, + model_id=embeddings_model or "text-embedding-3-small", + ) + + # Perform search + results = await embedding_service.search_similar_chunks( + query=request.query, + organization_id=user.selected_organization_id, + limit=request.limit, + document_uuids=request.document_uuids, + ) + + # Apply similarity threshold if provided + if request.min_similarity is not None: + results = [r for r in results if r["similarity"] >= request.min_similarity] + + # Convert to response schema + from api.schemas.knowledge_base import ChunkResponseSchema + + chunks = [ + ChunkResponseSchema( + id=r["id"], + document_id=r["document_id"], + chunk_text=r["chunk_text"], + contextualized_text=r.get("contextualized_text"), + chunk_index=r["chunk_index"], + chunk_metadata=r["chunk_metadata"], + filename=r["filename"], + document_uuid=r["document_uuid"], + similarity=r["similarity"], + ) + for r in results + ] + + return ChunkSearchResponseSchema( + chunks=chunks, + query=request.query, + total_results=len(chunks), + ) + + except Exception as exc: + logger.error(f"Error searching chunks: {exc}") + raise HTTPException(status_code=500, detail="Failed to search chunks") from exc diff --git a/api/routes/main.py b/api/routes/main.py index c4ba97f..2141172 100644 --- a/api/routes/main.py +++ b/api/routes/main.py @@ -4,6 +4,7 @@ from loguru import logger from api.routes.campaign import router as campaign_router from api.routes.credentials import router as credentials_router from api.routes.integration import router as integration_router +from api.routes.knowledge_base import router as knowledge_base_router from api.routes.looptalk import router as looptalk_router from api.routes.organization import router as organization_router from api.routes.organization_usage import router as organization_usage_router @@ -43,6 +44,7 @@ router.include_router(webrtc_signaling_router) router.include_router(public_embed_router) router.include_router(public_agent_router) router.include_router(workflow_embed_router) +router.include_router(knowledge_base_router) @router.get("/health") diff --git a/api/routes/user.py b/api/routes/user.py index 0747c89..9294363 100644 --- a/api/routes/user.py +++ b/api/routes/user.py @@ -32,6 +32,7 @@ class DefaultConfigurationsResponse(TypedDict): llm: dict[str, dict] tts: dict[str, dict] stt: dict[str, dict] + embeddings: dict[str, dict] default_providers: dict[str, str] @@ -50,6 +51,10 @@ async def get_default_configurations() -> DefaultConfigurationsResponse: provider: model_cls.model_json_schema() for provider, model_cls in REGISTRY[ServiceType.STT].items() }, + "embeddings": { + provider: model_cls.model_json_schema() + for provider, model_cls in REGISTRY[ServiceType.EMBEDDINGS].items() + }, "default_providers": DEFAULT_SERVICE_PROVIDERS, } return configurations @@ -69,6 +74,7 @@ class UserConfigurationRequestResponseSchema(BaseModel): llm: dict[str, Union[str, float]] | None = None tts: dict[str, Union[str, float]] | None = None stt: dict[str, Union[str, float]] | None = None + embeddings: dict[str, Union[str, float]] | None = None test_phone_number: str | None = None timezone: str | None = None organization_pricing: dict[str, Union[float, str, bool]] | None = None diff --git a/api/schemas/knowledge_base.py b/api/schemas/knowledge_base.py new file mode 100644 index 0000000..0cb1a72 --- /dev/null +++ b/api/schemas/knowledge_base.py @@ -0,0 +1,102 @@ +"""Pydantic schemas for knowledge base operations.""" + +from datetime import datetime +from typing import Any, Dict, List, Literal, Optional + +from pydantic import BaseModel, Field + + +class DocumentUploadRequestSchema(BaseModel): + """Request schema for initiating document upload.""" + + filename: str = Field(..., description="Name of the file to upload") + mime_type: str = Field(..., description="MIME type of the file") + custom_metadata: Optional[Dict[str, Any]] = Field( + default=None, description="Optional custom metadata" + ) + + +class DocumentUploadResponseSchema(BaseModel): + """Response schema containing upload URL and document metadata.""" + + upload_url: str = Field(..., description="Signed URL for uploading the file") + document_uuid: str = Field(..., description="Unique identifier for the document") + s3_key: str = Field(..., description="S3 key where file should be uploaded") + + +class ProcessDocumentRequestSchema(BaseModel): + """Request schema for triggering document processing.""" + + document_uuid: str = Field(..., description="Document UUID to process") + s3_key: str = Field(..., description="S3 key of the uploaded file") + embedding_service: Literal["sentence_transformer", "openai"] = Field( + default="openai", + description="Embedding service to use for processing. " + "Options: 'openai' (default, 1536-dim, requires API key) or 'sentence_transformer' (free, 384-dim)", + ) + + +class DocumentResponseSchema(BaseModel): + """Response schema for document metadata.""" + + id: int + document_uuid: str + filename: str + file_size_bytes: int + file_hash: str + mime_type: str + processing_status: str # pending, processing, completed, failed + processing_error: Optional[str] = None + total_chunks: int + custom_metadata: Dict[str, Any] + docling_metadata: Dict[str, Any] + source_url: Optional[str] = None + created_at: datetime + updated_at: datetime + organization_id: int + created_by: int + is_active: bool + + +class DocumentListResponseSchema(BaseModel): + """Response schema for list of documents.""" + + documents: List[DocumentResponseSchema] + total: int + limit: int + offset: int + + +class ChunkSearchRequestSchema(BaseModel): + """Request schema for searching similar chunks.""" + + query: str = Field(..., description="Search query text") + limit: int = Field(default=5, ge=1, le=50, description="Maximum number of results") + document_uuids: Optional[List[str]] = Field( + default=None, description="Filter by specific document UUIDs" + ) + min_similarity: Optional[float] = Field( + default=None, ge=0.0, le=1.0, description="Minimum similarity threshold" + ) + + +class ChunkResponseSchema(BaseModel): + """Response schema for a document chunk.""" + + id: int + document_id: int + chunk_text: str + contextualized_text: Optional[str] + chunk_index: int + chunk_metadata: Dict[str, Any] + filename: str + document_uuid: str + similarity: float + + +class ChunkSearchResponseSchema(BaseModel): + """Response schema for chunk search results.""" + + chunks: List[ChunkResponseSchema] + query: str + total_results: int diff --git a/api/schemas/user_configuration.py b/api/schemas/user_configuration.py index 4a1fac4..9f8174b 100644 --- a/api/schemas/user_configuration.py +++ b/api/schemas/user_configuration.py @@ -3,6 +3,7 @@ from datetime import datetime from pydantic import BaseModel from api.services.configuration.registry import ( + EmbeddingsConfig, LLMConfig, STTConfig, TTSConfig, @@ -13,6 +14,7 @@ class UserConfiguration(BaseModel): llm: LLMConfig | None = None stt: STTConfig | None = None tts: TTSConfig | None = None + embeddings: EmbeddingsConfig | None = None test_phone_number: str | None = None timezone: str | None = None last_validated_at: datetime | None = None diff --git a/api/services/configuration/check_validity.py b/api/services/configuration/check_validity.py index 4d0bc6e..351aec3 100644 --- a/api/services/configuration/check_validity.py +++ b/api/services/configuration/check_validity.py @@ -48,6 +48,12 @@ class UserConfigurationValidator: status_list.extend(self._validate_service(configuration.llm, "llm")) status_list.extend(self._validate_service(configuration.stt, "stt")) status_list.extend(self._validate_service(configuration.tts, "tts")) + # Embeddings is optional - only validate if configured + status_list.extend( + self._validate_service( + configuration.embeddings, "embeddings", required=False + ) + ) if status_list: raise ValueError(status_list) @@ -55,11 +61,16 @@ class UserConfigurationValidator: return {"status": [{"model": "all", "message": "ok"}]} def _validate_service( - self, service_config: Optional[ServiceConfig], service_name: str + self, + service_config: Optional[ServiceConfig], + service_name: str, + required: bool = True, ) -> list[APIKeyStatus]: """Validate a service configuration and return any error statuses.""" if not service_config: - return [{"model": service_name, "message": "API key is missing"}] + if required: + return [{"model": service_name, "message": "API key is missing"}] + return [] # Optional service not configured is OK provider = service_config.provider api_key = service_config.api_key diff --git a/api/services/configuration/defaults.py b/api/services/configuration/defaults.py index c2058e1..a212242 100644 --- a/api/services/configuration/defaults.py +++ b/api/services/configuration/defaults.py @@ -13,6 +13,7 @@ left as ``None``. from api.services.configuration.registry import ( DeepgramSTTConfiguration, ElevenlabsTTSConfiguration, + OpenAIEmbeddingsConfiguration, OpenAILLMService, ServiceProviders, ) @@ -22,6 +23,7 @@ _DEFAULTS = { "llm": (ServiceProviders.OPENAI, OpenAILLMService), "tts": (ServiceProviders.ELEVENLABS, ElevenlabsTTSConfiguration), "stt": (ServiceProviders.DEEPGRAM, DeepgramSTTConfiguration), + "embeddings": (ServiceProviders.OPENAI, OpenAIEmbeddingsConfiguration), } # Public mapping of service name -> default provider diff --git a/api/services/configuration/masking.py b/api/services/configuration/masking.py index 4d6488d..a253431 100644 --- a/api/services/configuration/masking.py +++ b/api/services/configuration/masking.py @@ -64,6 +64,7 @@ def mask_user_config(config: UserConfiguration) -> Dict[str, Any]: "llm": _mask_service(config.llm), "tts": _mask_service(config.tts), "stt": _mask_service(config.stt), + "embeddings": _mask_service(config.embeddings), "test_phone_number": config.test_phone_number, "timezone": config.timezone, } diff --git a/api/services/configuration/merge.py b/api/services/configuration/merge.py index ac3f424..416af0b 100644 --- a/api/services/configuration/merge.py +++ b/api/services/configuration/merge.py @@ -9,7 +9,7 @@ from typing import Dict from api.schemas.user_configuration import UserConfiguration from api.services.configuration.masking import is_mask_of -SERVICE_FIELDS = ("llm", "tts", "stt") +SERVICE_FIELDS = ("llm", "tts", "stt", "embeddings") def merge_user_configurations( diff --git a/api/services/configuration/registry.py b/api/services/configuration/registry.py index 509e9e3..41a27dc 100644 --- a/api/services/configuration/registry.py +++ b/api/services/configuration/registry.py @@ -8,6 +8,7 @@ class ServiceType(Enum): LLM = auto() TTS = auto() STT = auto() + EMBEDDINGS = auto() class ServiceProviders(str, Enum): @@ -50,11 +51,16 @@ class BaseSTTConfiguration(BaseServiceConfiguration): model: str +class BaseEmbeddingsConfiguration(BaseServiceConfiguration): + model: str + + # Unified registry for all service types REGISTRY: Dict[ServiceType, Dict[str, Type[BaseServiceConfiguration]]] = { ServiceType.LLM: {}, ServiceType.TTS: {}, ServiceType.STT: {}, + ServiceType.EMBEDDINGS: {}, } T = TypeVar("T", bound=BaseServiceConfiguration) @@ -93,6 +99,10 @@ def register_stt(cls: Type[BaseSTTConfiguration]): return register_service(ServiceType.STT)(cls) +def register_embeddings(cls: Type[BaseEmbeddingsConfiguration]): + return register_service(ServiceType.EMBEDDINGS)(cls) + + ###################################################### LLM ######################################################################## # Suggested models for each provider (used for UI dropdown) @@ -436,6 +446,27 @@ STTConfig = Annotated[ Field(discriminator="provider"), ] -ServiceConfig = Annotated[ - Union[LLMConfig, TTSConfig, STTConfig], Field(discriminator="provider") +###################################################### EMBEDDINGS ######################################################################## + +OPENAI_EMBEDDING_MODELS = ["text-embedding-3-small"] + + +@register_embeddings +class OpenAIEmbeddingsConfiguration(BaseEmbeddingsConfiguration): + provider: Literal[ServiceProviders.OPENAI] = ServiceProviders.OPENAI + model: str = Field( + default="text-embedding-3-small", + json_schema_extra={"examples": OPENAI_EMBEDDING_MODELS}, + ) + api_key: str + + +EmbeddingsConfig = Annotated[ + Union[OpenAIEmbeddingsConfiguration], + Field(discriminator="provider"), +] + +ServiceConfig = Annotated[ + Union[LLMConfig, TTSConfig, STTConfig, EmbeddingsConfig], + Field(discriminator="provider"), ] diff --git a/api/services/filesystem/base.py b/api/services/filesystem/base.py index 424edb9..92fa132 100644 --- a/api/services/filesystem/base.py +++ b/api/services/filesystem/base.py @@ -85,3 +85,16 @@ class BaseFileSystem(ABC): Optional[str]: Presigned PUT URL if successful, None otherwise """ pass + + @abstractmethod + async def adownload_file(self, source_path: str, local_path: str) -> bool: + """Download a file from storage to local path. + + Args: + source_path: Path to the file in storage + local_path: Local path where file should be downloaded + + Returns: + bool: True if file was downloaded successfully, False otherwise + """ + pass diff --git a/api/services/filesystem/minio.py b/api/services/filesystem/minio.py index c149868..faedab6 100644 --- a/api/services/filesystem/minio.py +++ b/api/services/filesystem/minio.py @@ -170,3 +170,15 @@ class MinioFileSystem(BaseFileSystem): except Exception as e: logger.error(f"Error generating MinIO upload URL: {e}") return None + + async def adownload_file(self, source_path: str, local_path: str) -> bool: + """Download a file from MinIO to local path.""" + try: + + def _fget(): + self.client.fget_object(self.bucket_name, source_path, local_path) + + await asyncio.to_thread(_fget) + return True + except S3Error: + return False diff --git a/api/services/filesystem/s3.py b/api/services/filesystem/s3.py index 80383f3..5ae9918 100644 --- a/api/services/filesystem/s3.py +++ b/api/services/filesystem/s3.py @@ -126,3 +126,14 @@ class S3FileSystem(BaseFileSystem): return url except ClientError: return None + + async def adownload_file(self, source_path: str, local_path: str) -> bool: + """Download a file from S3 to local path.""" + try: + async with self.session.client( + "s3", region_name=self.region_name + ) as s3_client: + await s3_client.download_file(self.bucket_name, source_path, local_path) + return True + except ClientError: + return False diff --git a/api/services/gen_ai/__init__.py b/api/services/gen_ai/__init__.py new file mode 100644 index 0000000..684a7f7 --- /dev/null +++ b/api/services/gen_ai/__init__.py @@ -0,0 +1,15 @@ +"""Generative AI services for embeddings and document processing.""" + +from .embedding import ( + BaseEmbeddingService, + EmbeddingAPIKeyNotConfiguredError, + OpenAIEmbeddingService, + SentenceTransformerEmbeddingService, +) + +__all__ = [ + "BaseEmbeddingService", + "EmbeddingAPIKeyNotConfiguredError", + "SentenceTransformerEmbeddingService", + "OpenAIEmbeddingService", +] diff --git a/api/services/gen_ai/embedding/__init__.py b/api/services/gen_ai/embedding/__init__.py new file mode 100644 index 0000000..c47c66a --- /dev/null +++ b/api/services/gen_ai/embedding/__init__.py @@ -0,0 +1,12 @@ +"""Embedding services for document processing and retrieval.""" + +from .base import BaseEmbeddingService +from .openai_service import EmbeddingAPIKeyNotConfiguredError, OpenAIEmbeddingService +from .sentence_transformer_service import SentenceTransformerEmbeddingService + +__all__ = [ + "BaseEmbeddingService", + "EmbeddingAPIKeyNotConfiguredError", + "SentenceTransformerEmbeddingService", + "OpenAIEmbeddingService", +] diff --git a/api/services/gen_ai/embedding/base.py b/api/services/gen_ai/embedding/base.py new file mode 100644 index 0000000..b750577 --- /dev/null +++ b/api/services/gen_ai/embedding/base.py @@ -0,0 +1,75 @@ +"""Base class for embedding services.""" + +from abc import ABC, abstractmethod +from typing import Any, Dict, List, Optional + + +class BaseEmbeddingService(ABC): + """Abstract base class for embedding services. + + All embedding services (SentenceTransformer, OpenAI, etc.) should inherit from this class + and implement the required methods. + """ + + @abstractmethod + def get_model_id(self) -> str: + """Return the model identifier. + + Returns: + String identifier for the model (e.g., 'sentence-transformers/all-MiniLM-L6-v2') + """ + pass + + @abstractmethod + def get_embedding_dimension(self) -> int: + """Return the embedding dimension. + + Returns: + Integer dimension of the embedding vectors + """ + pass + + @abstractmethod + async def embed_texts(self, texts: List[str]) -> List[List[float]]: + """Embed a batch of texts. + + Args: + texts: List of text strings to embed + + Returns: + List of embedding vectors (each vector is a list of floats) + """ + pass + + @abstractmethod + async def embed_query(self, query: str) -> List[float]: + """Embed a single query text. + + Args: + query: Query text to embed + + Returns: + Embedding vector as list of floats + """ + pass + + @abstractmethod + async def search_similar_chunks( + self, + query: str, + organization_id: int, + limit: int = 5, + document_uuids: Optional[List[str]] = None, + ) -> List[Dict[str, Any]]: + """Search for similar chunks using vector similarity. + + Args: + query: Search query text + organization_id: Organization ID for scoping + limit: Maximum number of results to return + document_uuids: Optional list of document UUIDs to filter by + + Returns: + List of dictionaries containing chunk data and similarity scores + """ + pass diff --git a/api/services/gen_ai/embedding/openai_service.py b/api/services/gen_ai/embedding/openai_service.py new file mode 100644 index 0000000..ec4598b --- /dev/null +++ b/api/services/gen_ai/embedding/openai_service.py @@ -0,0 +1,372 @@ +"""OpenAI embedding service. + +This module provides document processing capabilities using: +- OpenAI's text-embedding-3-small for embeddings (1536 dimensions) +- Docling for document conversion and chunking +- pgvector for vector similarity search +""" + +import os +from pathlib import Path +from typing import Any, Dict, List, Optional + +from docling.chunking import HybridChunker +from docling.document_converter import DocumentConverter +from docling_core.transforms.chunker.tokenizer.huggingface import HuggingFaceTokenizer +from loguru import logger +from openai import AsyncOpenAI +from transformers import AutoTokenizer + +from api.db.db_client import DBClient +from api.db.models import KnowledgeBaseChunkModel + +from .base import BaseEmbeddingService + +# Model configuration +DEFAULT_MODEL_ID = "text-embedding-3-small" +EMBEDDING_DIMENSION = 1536 # Dimension for text-embedding-3-small + +# For chunking, we'll use the same tokenizer as SentenceTransformer +# since OpenAI uses similar tokenization +TOKENIZER_MODEL = "sentence-transformers/all-MiniLM-L6-v2" + + +class EmbeddingAPIKeyNotConfiguredError(Exception): + """Raised when OpenAI API key is not configured for embeddings.""" + + def __init__(self): + super().__init__( + "OpenAI API key not configured. Please set your API key in " + "Model Configurations > Embedding to use document processing." + ) + + +class OpenAIEmbeddingService(BaseEmbeddingService): + """Embedding service using OpenAI's text-embedding-3-small.""" + + def __init__( + self, + db_client: DBClient, + api_key: Optional[str] = None, + model_id: str = DEFAULT_MODEL_ID, + max_tokens: int = 512, + ): + """Initialize the OpenAI embedding service. + + Args: + db_client: Database client for storing documents and chunks + api_key: OpenAI API key. If not provided, the client will not be + initialized and operations will fail with a clear error. + model_id: OpenAI embedding model ID (default: text-embedding-3-small) + max_tokens: Maximum number of tokens per chunk (default: 512) + """ + self.db = db_client + self.model_id = model_id + self.max_tokens = max_tokens + + # Only initialize OpenAI client if API key is provided + self._api_key_configured = bool(api_key) + if self._api_key_configured: + self.client = AsyncOpenAI(api_key=api_key) + logger.info(f"OpenAI embedding service initialized with model: {model_id}") + else: + self.client = None + logger.warning( + "OpenAI embedding service initialized without API key. " + "Operations will fail until API key is configured in Model Configurations." + ) + + # Initialize tokenizer for chunking + # We use a HuggingFace tokenizer for consistent chunking + logger.info( + f"Loading tokenizer for chunking: {TOKENIZER_MODEL} with max_tokens={max_tokens}" + ) + try: + self.tokenizer = HuggingFaceTokenizer( + tokenizer=AutoTokenizer.from_pretrained( + TOKENIZER_MODEL, + local_files_only=True, + ), + max_tokens=max_tokens, + ) + logger.info("Loaded tokenizer from cache") + except Exception as e: + logger.warning(f"Tokenizer not in cache, downloading: {e}") + self.tokenizer = HuggingFaceTokenizer( + tokenizer=AutoTokenizer.from_pretrained(TOKENIZER_MODEL), + max_tokens=max_tokens, + ) + logger.info("Tokenizer downloaded and cached") + + # Initialize chunker + logger.info(f"Initializing HybridChunker with max_tokens={max_tokens}") + self.chunker = HybridChunker(tokenizer=self.tokenizer) + + # Initialize document converter + self.converter = DocumentConverter() + + def get_model_id(self) -> str: + """Return the model identifier.""" + return self.model_id + + def get_embedding_dimension(self) -> int: + """Return the embedding dimension.""" + return EMBEDDING_DIMENSION + + def _ensure_api_key_configured(self): + """Check if API key is configured and raise error if not.""" + if not self._api_key_configured or self.client is None: + raise EmbeddingAPIKeyNotConfiguredError() + + async def embed_texts(self, texts: List[str]) -> List[List[float]]: + """Embed a batch of texts using OpenAI API. + + Args: + texts: List of text strings to embed + + Returns: + List of embedding vectors (each vector is a list of floats) + + Raises: + EmbeddingAPIKeyNotConfiguredError: If API key is not configured + """ + self._ensure_api_key_configured() + + try: + # OpenAI API call + response = await self.client.embeddings.create( + input=texts, + model=self.model_id, + ) + + # Extract embeddings from response + embeddings = [item.embedding for item in response.data] + return embeddings + + except Exception as e: + logger.error(f"Error generating OpenAI embeddings: {e}") + raise + + async def embed_query(self, query: str) -> List[float]: + """Embed a single query text using OpenAI API. + + Args: + query: Query text to embed + + Returns: + Embedding vector as list of floats + + Raises: + EmbeddingAPIKeyNotConfiguredError: If API key is not configured + """ + self._ensure_api_key_configured() + embeddings = await self.embed_texts([query]) + return embeddings[0] + + async def search_similar_chunks( + self, + query: str, + organization_id: int, + limit: int = 5, + document_uuids: Optional[List[str]] = None, + ) -> List[Dict[str, Any]]: + """Search for similar chunks using vector similarity. + + Args: + query: Search query text + organization_id: Organization ID for scoping + limit: Maximum number of results to return + document_uuids: Optional list of document UUIDs to filter by + + Returns: + List of dictionaries with chunk data and similarity scores + + Raises: + EmbeddingAPIKeyNotConfiguredError: If API key is not configured + """ + self._ensure_api_key_configured() + + # Generate query embedding + query_embedding = await self.embed_query(query) + + # Perform vector similarity search + results = await self.db.search_similar_chunks( + query_embedding=query_embedding, + organization_id=organization_id, + limit=limit, + document_uuids=document_uuids, + embedding_model=self.model_id, + ) + + return results + + async def process_document( + self, + file_path: str, + organization_id: int, + created_by: int, + custom_metadata: dict = None, + ): + """Process a document: convert, chunk, embed, and store in database. + + Args: + file_path: Path to the document file + organization_id: Organization ID for scoping + created_by: User ID who uploaded the document + custom_metadata: Optional custom metadata dictionary + + Returns: + The created document record + """ + try: + # Extract file metadata + filename = Path(file_path).name + file_hash = self.db.compute_file_hash(file_path) + file_size = os.path.getsize(file_path) + mime_type = self.db.get_mime_type(file_path) + + # Check if document already exists + existing_doc = await self.db.get_document_by_hash( + file_hash, organization_id + ) + if existing_doc: + logger.info(f"Document already exists: {filename} (hash: {file_hash})") + return existing_doc + + # Create document record + doc_record = await self.db.create_document( + organization_id=organization_id, + created_by=created_by, + filename=filename, + file_size_bytes=file_size, + file_hash=file_hash, + mime_type=mime_type, + custom_metadata=custom_metadata or {}, + ) + + logger.info(f"Processing document with OpenAI embeddings: {filename}") + + # Update status to processing + await self.db.update_document_status(doc_record.id, "processing") + + # Step 1: Convert document using docling + logger.info("Converting document with docling...") + conversion_result = self.converter.convert(file_path) + doc = conversion_result.document + + # Store docling metadata + docling_metadata = { + "num_pages": len(doc.pages) if hasattr(doc, "pages") else None, + "document_type": type(doc).__name__, + } + + # Step 2: Chunk the document + logger.info(f"Chunking document with max_tokens={self.max_tokens}...") + chunks = list(self.chunker.chunk(dl_doc=doc)) + total_chunks = len(chunks) + + logger.info(f"Generated {total_chunks} chunks") + + # Step 3: Process each chunk + chunk_texts = [] + chunk_records = [] + token_counts = [] + + for i, chunk in enumerate(chunks): + # Get chunk text + chunk_text = chunk.text + + # Get contextualized text + contextualized_text = self.chunker.contextualize(chunk=chunk) + + # Calculate token count + text_to_tokenize = ( + contextualized_text if contextualized_text else chunk_text + ) + token_count = len( + self.tokenizer.tokenizer.encode( + text_to_tokenize, add_special_tokens=False + ) + ) + token_counts.append(token_count) + + # Prepare chunk metadata + chunk_metadata = {} + if hasattr(chunk, "meta") and chunk.meta: + chunk_metadata = { + "doc_items": ( + [str(item) for item in chunk.meta.doc_items] + if hasattr(chunk.meta, "doc_items") + else [] + ), + "headings": ( + chunk.meta.headings + if hasattr(chunk.meta, "headings") + else [] + ), + } + + # Create chunk record (without embedding yet) + chunk_record = KnowledgeBaseChunkModel( + document_id=doc_record.id, + organization_id=organization_id, + chunk_text=chunk_text, + contextualized_text=contextualized_text, + chunk_index=i, + chunk_metadata=chunk_metadata, + embedding_model=self.model_id, + embedding_dimension=EMBEDDING_DIMENSION, + token_count=token_count, + ) + + chunk_records.append(chunk_record) + chunk_texts.append(text_to_tokenize) + + # Log chunk statistics + if token_counts: + avg_tokens = sum(token_counts) / len(token_counts) + min_tokens = min(token_counts) + max_tokens = max(token_counts) + logger.info("Chunk token statistics:") + logger.info(f" - Average: {avg_tokens:.1f} tokens") + logger.info(f" - Min: {min_tokens} tokens") + logger.info(f" - Max: {max_tokens} tokens") + + # Step 4: Generate embeddings using OpenAI API + logger.info(f"Generating embeddings using OpenAI ({self.model_id})...") + embeddings = await self.embed_texts(chunk_texts) + + # Step 5: Attach embeddings to chunk records + for chunk_record, embedding in zip(chunk_records, embeddings): + chunk_record.embedding = embedding + + # Step 6: Save all chunks in batch + logger.info("Storing chunks in database...") + await self.db.create_chunks_batch(chunk_records) + + # Update document status to completed + await self.db.update_document_status( + doc_record.id, + "completed", + total_chunks=total_chunks, + docling_metadata=docling_metadata, + ) + + logger.info(f"Successfully processed document: {filename}") + logger.info(f" - Total chunks: {total_chunks}") + logger.info(f" - Embedding model: {self.model_id}") + logger.info(f" - Document ID: {doc_record.id}") + logger.info(f" - Document UUID: {doc_record.document_uuid}") + + return doc_record + + except Exception as e: + logger.error(f"Error processing document with OpenAI: {e}") + + # Update document status to failed if it exists + if "doc_record" in locals(): + await self.db.update_document_status( + doc_record.id, "failed", error_message=str(e) + ) + + raise diff --git a/api/services/gen_ai/embedding/sentence_transformer_service.py b/api/services/gen_ai/embedding/sentence_transformer_service.py new file mode 100644 index 0000000..f2773a2 --- /dev/null +++ b/api/services/gen_ai/embedding/sentence_transformer_service.py @@ -0,0 +1,350 @@ +"""Sentence Transformer embedding service. + +This module provides document processing capabilities using: +- Sentence-transformers for embeddings (all-MiniLM-L6-v2) +- Docling for document conversion and chunking +- pgvector for vector similarity search + +Setup for offline usage: +1. First run: Downloads and caches models to ~/.cache/sentence_transformers +2. Subsequent runs: Uses cached models (no internet needed) +3. For fully offline mode: Set TRANSFORMERS_OFFLINE=1 and HF_HUB_OFFLINE=1 +""" + +import os +from pathlib import Path +from typing import Any, Dict, List, Optional + +from docling.chunking import HybridChunker +from docling.document_converter import DocumentConverter +from docling_core.transforms.chunker.tokenizer.huggingface import HuggingFaceTokenizer +from loguru import logger +from sentence_transformers import SentenceTransformer +from transformers import AutoTokenizer + +from api.db.db_client import DBClient +from api.db.models import KnowledgeBaseChunkModel + +from .base import BaseEmbeddingService + +# Set environment variables for model caching +os.environ.setdefault("TRANSFORMERS_OFFLINE", "0") +os.environ.setdefault("HF_HUB_OFFLINE", "0") +os.environ.setdefault( + "SENTENCE_TRANSFORMERS_HOME", os.path.expanduser("~/.cache/sentence_transformers") +) + +# Model configuration +DEFAULT_MODEL_ID = "sentence-transformers/all-MiniLM-L6-v2" +EMBEDDING_DIMENSION = 384 # Dimension for all-MiniLM-L6-v2 + + +class SentenceTransformerEmbeddingService(BaseEmbeddingService): + """Embedding service using Sentence Transformers.""" + + def __init__( + self, + db_client: DBClient, + model_id: str = DEFAULT_MODEL_ID, + max_tokens: int = 512, + ): + """Initialize the Sentence Transformer embedding service. + + Args: + db_client: Database client for storing documents and chunks + model_id: Sentence-transformers model ID (default: all-MiniLM-L6-v2) + max_tokens: Maximum number of tokens per chunk (default: 512) + Note: This applies to the contextualized text (with headings/captions) + """ + self.db = db_client + self.model_id = model_id + self.max_tokens = max_tokens + + # Initialize embedding model + logger.info(f"Loading embedding model: {model_id}") + try: + # Try to load from cache first (local_files_only=True) + self.embedding_model = SentenceTransformer( + model_id, + cache_folder=os.environ.get("SENTENCE_TRANSFORMERS_HOME"), + local_files_only=True, + ) + logger.info("Loaded model from cache") + except Exception as e: + logger.warning(f"Model not in cache, downloading: {e}") + # If not in cache, download it (this will cache it for next time) + self.embedding_model = SentenceTransformer( + model_id, + cache_folder=os.environ.get("SENTENCE_TRANSFORMERS_HOME"), + ) + logger.info("Model downloaded and cached") + + # Initialize tokenizer for chunking with max_tokens + logger.info(f"Loading tokenizer: {model_id} with max_tokens={max_tokens}") + try: + # Try to load from cache first + self.tokenizer = HuggingFaceTokenizer( + tokenizer=AutoTokenizer.from_pretrained( + model_id, + local_files_only=True, + ), + max_tokens=max_tokens, + ) + logger.info("Loaded tokenizer from cache") + except Exception as e: + logger.warning(f"Tokenizer not in cache, downloading: {e}") + # If not in cache, download it + self.tokenizer = HuggingFaceTokenizer( + tokenizer=AutoTokenizer.from_pretrained(model_id), + max_tokens=max_tokens, + ) + logger.info("Tokenizer downloaded and cached") + + # Initialize chunker + logger.info(f"Initializing HybridChunker with max_tokens={max_tokens}") + self.chunker = HybridChunker(tokenizer=self.tokenizer) + + # Initialize document converter + self.converter = DocumentConverter() + + def get_model_id(self) -> str: + """Return the model identifier.""" + return self.model_id + + def get_embedding_dimension(self) -> int: + """Return the embedding dimension.""" + return EMBEDDING_DIMENSION + + async def embed_texts(self, texts: List[str]) -> List[List[float]]: + """Embed a batch of texts. + + Args: + texts: List of text strings to embed + + Returns: + List of embedding vectors (each vector is a list of floats) + """ + embeddings = self.embedding_model.encode( + texts, + show_progress_bar=False, + convert_to_numpy=True, + ) + return [embedding.tolist() for embedding in embeddings] + + async def embed_query(self, query: str) -> List[float]: + """Embed a single query text. + + Args: + query: Query text to embed + + Returns: + Embedding vector as list of floats + """ + embedding = self.embedding_model.encode([query])[0] + return embedding.tolist() + + async def search_similar_chunks( + self, + query: str, + organization_id: int, + limit: int = 5, + document_uuids: Optional[List[str]] = None, + ) -> List[Dict[str, Any]]: + """Search for similar chunks using vector similarity. + + Returns top-k most similar chunks without any threshold filtering. + Apply similarity thresholds and reranking at the application layer. + + Args: + query: Search query text + organization_id: Organization ID for scoping + limit: Maximum number of results to return + document_uuids: Optional list of document UUIDs to filter by + + Returns: + List of dictionaries with chunk data and similarity scores + """ + # Generate query embedding + query_embedding = await self.embed_query(query) + + # Perform vector similarity search + results = await self.db.search_similar_chunks( + query_embedding=query_embedding, + organization_id=organization_id, + limit=limit, + document_uuids=document_uuids, + embedding_model=self.model_id, + ) + + return results + + async def process_document( + self, + file_path: str, + organization_id: int, + created_by: int, + custom_metadata: dict = None, + ): + """Process a document: convert, chunk, embed, and store in database. + + Args: + file_path: Path to the document file + organization_id: Organization ID for scoping + created_by: User ID who uploaded the document + custom_metadata: Optional custom metadata dictionary + + Returns: + The created document record + """ + try: + # Extract file metadata + filename = Path(file_path).name + file_hash = self.db.compute_file_hash(file_path) + file_size = os.path.getsize(file_path) + mime_type = self.db.get_mime_type(file_path) + + # Check if document already exists + existing_doc = await self.db.get_document_by_hash( + file_hash, organization_id + ) + if existing_doc: + logger.info(f"Document already exists: {filename} (hash: {file_hash})") + return existing_doc + + # Create document record + doc_record = await self.db.create_document( + organization_id=organization_id, + created_by=created_by, + filename=filename, + file_size_bytes=file_size, + file_hash=file_hash, + mime_type=mime_type, + custom_metadata=custom_metadata or {}, + ) + + logger.info(f"Processing document: {filename}") + + # Update status to processing + await self.db.update_document_status(doc_record.id, "processing") + + # Step 1: Convert document using docling + logger.info("Converting document with docling...") + conversion_result = self.converter.convert(file_path) + doc = conversion_result.document + + # Store docling metadata + docling_metadata = { + "num_pages": len(doc.pages) if hasattr(doc, "pages") else None, + "document_type": type(doc).__name__, + } + + # Step 2: Chunk the document + logger.info(f"Chunking document with max_tokens={self.max_tokens}...") + chunks = list(self.chunker.chunk(dl_doc=doc)) + total_chunks = len(chunks) + + logger.info(f"Generated {total_chunks} chunks") + + # Step 3: Process each chunk + chunk_texts = [] + chunk_records = [] + token_counts = [] + + for i, chunk in enumerate(chunks): + # Get chunk text + chunk_text = chunk.text + + # Get contextualized text (enriched with surrounding context) + contextualized_text = self.chunker.contextualize(chunk=chunk) + + # Calculate actual token count using the tokenizer + text_to_tokenize = ( + contextualized_text if contextualized_text else chunk_text + ) + token_count = len( + self.tokenizer.tokenizer.encode( + text_to_tokenize, add_special_tokens=False + ) + ) + token_counts.append(token_count) + + # Prepare chunk metadata + chunk_metadata = {} + if hasattr(chunk, "meta") and chunk.meta: + chunk_metadata = { + "doc_items": ( + [str(item) for item in chunk.meta.doc_items] + if hasattr(chunk.meta, "doc_items") + else [] + ), + "headings": ( + chunk.meta.headings + if hasattr(chunk.meta, "headings") + else [] + ), + } + + # Create chunk record (without embedding yet) + chunk_record = KnowledgeBaseChunkModel( + document_id=doc_record.id, + organization_id=organization_id, + chunk_text=chunk_text, + contextualized_text=contextualized_text, + chunk_index=i, + chunk_metadata=chunk_metadata, + embedding_model=self.model_id, + embedding_dimension=EMBEDDING_DIMENSION, + token_count=token_count, + ) + + chunk_records.append(chunk_record) + # Use contextualized text for embedding if available + chunk_texts.append(text_to_tokenize) + + # Log chunk statistics + if token_counts: + avg_tokens = sum(token_counts) / len(token_counts) + min_tokens = min(token_counts) + max_tokens = max(token_counts) + logger.info("Chunk token statistics:") + logger.info(f" - Average: {avg_tokens:.1f} tokens") + logger.info(f" - Min: {min_tokens} tokens") + logger.info(f" - Max: {max_tokens} tokens") + + # Step 4: Generate embeddings in batch + logger.info("Generating embeddings...") + embeddings = await self.embed_texts(chunk_texts) + + # Step 5: Attach embeddings to chunk records + for chunk_record, embedding in zip(chunk_records, embeddings): + chunk_record.embedding = embedding + + # Step 6: Save all chunks in batch + logger.info("Storing chunks in database...") + await self.db.create_chunks_batch(chunk_records) + + # Update document status to completed + await self.db.update_document_status( + doc_record.id, + "completed", + total_chunks=total_chunks, + docling_metadata=docling_metadata, + ) + + logger.info(f"Successfully processed document: {filename}") + logger.info(f" - Total chunks: {total_chunks}") + logger.info(f" - Document ID: {doc_record.id}") + logger.info(f" - Document UUID: {doc_record.document_uuid}") + + return doc_record + + except Exception as e: + logger.error(f"Error processing document: {e}") + + # Update document status to failed if it exists + if "doc_record" in locals(): + await self.db.update_document_status( + doc_record.id, "failed", error_message=str(e) + ) + + raise diff --git a/api/services/pricing/embeddings.py b/api/services/pricing/embeddings.py new file mode 100644 index 0000000..a58a8ca --- /dev/null +++ b/api/services/pricing/embeddings.py @@ -0,0 +1,44 @@ +""" +Embeddings pricing models for different providers. + +Prices are per token for embedding models. +""" + +from decimal import Decimal +from typing import Dict + +from api.services.configuration.registry import ServiceProviders + +from .models import PricingModel + + +class EmbeddingPricingModel(PricingModel): + """Pricing model for token-based embedding services.""" + + def __init__(self, token_price: Decimal): + """Initialize with price per token. + + Args: + token_price: Cost per token for embedding + """ + self.token_price = token_price + + def calculate_cost(self, token_count: int) -> Decimal: + """Calculate cost for embedding token usage.""" + return Decimal(token_count) * self.token_price + + +# Embeddings pricing registry +EMBEDDINGS_PRICING: Dict[str, Dict[str, EmbeddingPricingModel]] = { + ServiceProviders.OPENAI: { + "text-embedding-3-small": EmbeddingPricingModel( + token_price=Decimal("0.02") / 1_000_000, # $0.02 per 1M tokens + ), + "text-embedding-3-large": EmbeddingPricingModel( + token_price=Decimal("0.13") / 1_000_000, # $0.13 per 1M tokens + ), + "text-embedding-ada-002": EmbeddingPricingModel( + token_price=Decimal("0.10") / 1_000_000, # $0.10 per 1M tokens (legacy) + ), + }, +} diff --git a/api/services/pricing/registry.py b/api/services/pricing/registry.py index 198c8ee..294a94a 100644 --- a/api/services/pricing/registry.py +++ b/api/services/pricing/registry.py @@ -4,6 +4,7 @@ Main pricing registry that combines all service type pricing models. from typing import Dict +from .embeddings import EMBEDDINGS_PRICING from .llm import LLM_PRICING from .stt import STT_PRICING from .tts import TTS_PRICING @@ -13,4 +14,5 @@ PRICING_REGISTRY: Dict = { "llm": LLM_PRICING, "tts": TTS_PRICING, "stt": STT_PRICING, + "embeddings": EMBEDDINGS_PRICING, } diff --git a/api/services/workflow/dto.py b/api/services/workflow/dto.py index 75b648e..5dd5e20 100644 --- a/api/services/workflow/dto.py +++ b/api/services/workflow/dto.py @@ -58,6 +58,7 @@ class NodeDataDTO(BaseModel): delayed_start: bool = False delayed_start_duration: Optional[float] = None tool_uuids: Optional[List[str]] = None + document_uuids: Optional[List[str]] = None trigger_path: Optional[str] = None # Webhook node specific fields enabled: bool = True diff --git a/api/services/workflow/pipecat_engine.py b/api/services/workflow/pipecat_engine.py index 6ebc2ca..0ab36de 100644 --- a/api/services/workflow/pipecat_engine.py +++ b/api/services/workflow/pipecat_engine.py @@ -41,6 +41,10 @@ from api.services.workflow.pipecat_engine_variable_extractor import ( VariableExtractionManager, ) from api.services.workflow.tools.calculator import get_calculator_tools, safe_calculator +from api.services.workflow.tools.knowledge_base import ( + get_knowledge_base_tool, + retrieve_from_knowledge_base, +) from api.services.workflow.tools.timezone import ( convert_time, get_current_time, @@ -290,6 +294,48 @@ class PipecatEngine: self.llm.register_function("get_current_time", get_current_time_func) self.llm.register_function("convert_time", convert_time_func) + async def _register_knowledge_base_function( + self, document_uuids: list[str] + ) -> None: + """Register knowledge base retrieval function with the LLM. + + Args: + document_uuids: List of document UUIDs to filter the search by + """ + logger.debug( + f"Registering knowledge base retrieval function with {len(document_uuids)} document(s)" + ) + + async def retrieve_kb_func(function_call_params: FunctionCallParams) -> None: + logger.info("LLM Function Call EXECUTED: retrieve_from_knowledge_base") + logger.info(f"Arguments: {function_call_params.arguments}") + try: + query = function_call_params.arguments.get("query", "") + organization_id = await self._get_organization_id() + + if not organization_id: + raise ValueError( + "Organization ID not available for knowledge base retrieval" + ) + + result = await retrieve_from_knowledge_base( + query=query, + organization_id=organization_id, + document_uuids=document_uuids, + limit=3, # Return top 3 most relevant chunks + ) + + await function_call_params.result_callback(result) + + except Exception as e: + logger.error(f"Knowledge base retrieval failed: {e}") + await function_call_params.result_callback( + {"error": str(e), "chunks": [], "query": query, "total_results": 0} + ) + + # Register the function with the LLM + self.llm.register_function("retrieve_from_knowledge_base", retrieve_kb_func) + async def _perform_variable_extraction_if_needed( self, previous_node: Optional[Node] ) -> None: @@ -346,6 +392,10 @@ class PipecatEngine: if node.tool_uuids and self._custom_tool_manager: await self._custom_tool_manager.register_handlers(node.tool_uuids) + # Register knowledge base retrieval handler if node has documents + if node.document_uuids: + await self._register_knowledge_base_function(node.document_uuids) + # Set up system message and functions ( system_message, @@ -575,6 +625,17 @@ class PipecatEngine: # Add built-in function schemas (calculator and timezone tools) functions.extend(self.builtin_function_schemas) + # Add knowledge base retrieval tool if node has documents + if node.document_uuids: + kb_tool_def = get_knowledge_base_tool(node.document_uuids) + kb_schema = get_function_schema( + kb_tool_def["function"]["name"], + kb_tool_def["function"]["description"], + properties=kb_tool_def["function"]["parameters"].get("properties", {}), + required=kb_tool_def["function"]["parameters"].get("required", []), + ) + functions.append(kb_schema) + # Add custom tools from node.tool_uuids if node.tool_uuids and self._custom_tool_manager: custom_tool_schemas = await self._custom_tool_manager.get_tool_schemas( diff --git a/api/services/workflow/tools/knowledge_base.py b/api/services/workflow/tools/knowledge_base.py new file mode 100644 index 0000000..9732619 --- /dev/null +++ b/api/services/workflow/tools/knowledge_base.py @@ -0,0 +1,305 @@ +"""Knowledge Base retrieval tool for workflow execution. + +This module provides vector similarity search capabilities for retrieving +relevant information from the knowledge base during conversations. + +Implements OpenTelemetry tracing for observability in Langfuse. +""" + +import json +from typing import Any, Dict, List, Optional + +from loguru import logger +from opentelemetry import trace + +from api.db import db_client +from api.services.gen_ai import OpenAIEmbeddingService +from api.services.pipecat.tracing_config import is_tracing_enabled +from pipecat.utils.tracing.context_registry import ( + get_current_conversation_context, + get_current_turn_context, +) + + +async def retrieve_from_knowledge_base( + query: str, + organization_id: int, + document_uuids: Optional[List[str]] = None, + limit: int = 3, + embeddings_api_key: Optional[str] = None, + embeddings_model: Optional[str] = None, +) -> Dict[str, Any]: + """Retrieve relevant information from the knowledge base using vector similarity search. + + Uses OpenAI text-embedding-3-small for embeddings by default. This provides + high-quality 1536-dimensional embeddings for accurate retrieval. + + This function includes OpenTelemetry tracing for Langfuse observability. + + Args: + query: The search query to find relevant information + organization_id: Organization ID for scoping the search + document_uuids: Optional list of document UUIDs to filter by + limit: Maximum number of chunks to return (default: 3) + embeddings_api_key: Optional API key for embedding service + embeddings_model: Optional model ID for embedding service + + Returns: + Dictionary containing: + - chunks: List of relevant text chunks with metadata + - query: The original query + - total_results: Number of results returned + """ + # Create span for retrieval operation if tracing is enabled + if is_tracing_enabled(): + try: + # Get parent context from turn or conversation + turn_context = get_current_turn_context() + conversation_context = get_current_conversation_context() + parent_context = turn_context or conversation_context + + # Get tracer + tracer = trace.get_tracer("pipecat") + except Exception as e: + logger.debug(f"Failed to setup tracing context: {e}") + # Fall back to non-traced execution + return await _perform_retrieval( + query, + organization_id, + document_uuids, + limit, + embeddings_api_key, + embeddings_model, + ) + + # Create span with parent context + if parent_context: + with tracer.start_as_current_span( + "knowledge_base_retrieval", context=parent_context + ) as span: + try: + # Mark trace as public for Langfuse + span.set_attribute("langfuse.trace.public", True) + + # Add operation metadata + span.set_attribute( + "gen_ai.operation.name", "knowledge_base_retrieval" + ) + span.set_attribute("retrieval.query", query) + span.set_attribute("retrieval.limit", limit) + span.set_attribute("retrieval.organization_id", organization_id) + + # Add document filter info + if document_uuids: + span.set_attribute( + "retrieval.document_count", len(document_uuids) + ) + span.set_attribute( + "retrieval.document_uuids", json.dumps(document_uuids) + ) + + # Perform the actual retrieval + result = await _perform_retrieval( + query, + organization_id, + document_uuids, + limit, + embeddings_api_key, + embeddings_model, + ) + + # Add result metadata to span + span.set_attribute( + "retrieval.results_count", result["total_results"] + ) + + if result.get("error"): + span.set_attribute("retrieval.error", result["error"]) + span.set_status( + trace.Status(trace.StatusCode.ERROR, result["error"]) + ) + else: + # Add similarity scores + if result["chunks"]: + similarities = [ + chunk["similarity"] for chunk in result["chunks"] + ] + span.set_attribute( + "retrieval.avg_similarity", + round(sum(similarities) / len(similarities), 4), + ) + span.set_attribute( + "retrieval.max_similarity", max(similarities) + ) + span.set_attribute( + "retrieval.min_similarity", min(similarities) + ) + + # Add retrieved documents info + filenames = list( + set(chunk["filename"] for chunk in result["chunks"]) + ) + span.set_attribute( + "retrieval.source_files", json.dumps(filenames) + ) + + # Add output as JSON for Langfuse + output_data = { + "query": query, + "chunks_retrieved": len(result["chunks"]), + "chunks": [ + { + "text": chunk["text"][:200] + "..." + if len(chunk["text"]) > 200 + else chunk["text"], + "filename": chunk["filename"], + "similarity": chunk["similarity"], + } + for chunk in result["chunks"] + ], + } + span.set_attribute("output", json.dumps(output_data)) + + return result + + except Exception as e: + logger.error(f"Error in traced retrieval: {e}") + span.record_exception(e) + span.set_status(trace.Status(trace.StatusCode.ERROR, str(e))) + raise + else: + # No parent context - perform retrieval without tracing + logger.debug( + "No parent context available for knowledge base retrieval tracing" + ) + return await _perform_retrieval( + query, + organization_id, + document_uuids, + limit, + embeddings_api_key, + embeddings_model, + ) + else: + # Tracing is disabled - perform retrieval without tracing + return await _perform_retrieval( + query, + organization_id, + document_uuids, + limit, + embeddings_api_key, + embeddings_model, + ) + + +async def _perform_retrieval( + query: str, + organization_id: int, + document_uuids: Optional[List[str]], + limit: int, + embeddings_api_key: Optional[str] = None, + embeddings_model: Optional[str] = None, +) -> Dict[str, Any]: + """Internal function to perform the actual retrieval operation. + + Separated from tracing logic for cleaner code organization. + Uses OpenAI embeddings by default for high-quality retrieval. + """ + try: + # Create a new embedding service instance + # Uses OpenAI text-embedding-3-small by default, or user-provided config + embedding_service = OpenAIEmbeddingService( + db_client=db_client, + max_tokens=128, # This is only used for chunking, not for retrieval + api_key=embeddings_api_key, + model_id=embeddings_model or "text-embedding-3-small", + ) + + # Perform vector similarity search + results = await embedding_service.search_similar_chunks( + query=query, + organization_id=organization_id, + limit=limit, + document_uuids=document_uuids, + ) + + # Format results for LLM consumption + chunks = [] + for result in results: + chunk_info = { + "text": result.get("contextualized_text") or result.get("chunk_text"), + "filename": result.get("filename"), + "similarity": round(result.get("similarity", 0), 4), + "chunk_index": result.get("chunk_index"), + } + chunks.append(chunk_info) + + logger.info( + f"Knowledge base retrieval: query='{query}', " + f"results={len(chunks)}, " + f"document_filter={document_uuids}" + ) + + return { + "chunks": chunks, + "query": query, + "total_results": len(chunks), + } + + except Exception as e: + logger.error(f"Error retrieving from knowledge base: {e}") + return { + "error": str(e), + "chunks": [], + "query": query, + "total_results": 0, + } + + +def get_knowledge_base_tool( + document_uuids: Optional[List[str]] = None, +) -> Dict[str, Any]: + """Get knowledge base retrieval tool definition for LLM function calling. + + Args: + document_uuids: Optional list of document UUIDs to include in description + + Returns: + Tool definition compatible with LLM function calling + """ + # Build description based on whether specific documents are filtered + if document_uuids and len(document_uuids) > 0: + description = ( + "Retrieve relevant information from specific documents in the knowledge base. " + "Use this tool when you need to look up facts, policies, procedures, or any information " + "that might be stored in the available documents. The search will only look in the " + f"documents associated with this conversation step ({len(document_uuids)} document(s) available)." + ) + else: + description = ( + "Retrieve relevant information from the knowledge base. " + "Use this tool when you need to look up facts, policies, procedures, or any information " + "that might be stored in the knowledge base documents." + ) + + return { + "type": "function", + "function": { + "name": "retrieve_from_knowledge_base", + "description": description, + "parameters": { + "type": "object", + "properties": { + "query": { + "type": "string", + "description": ( + "The search query to find relevant information. " + "Be specific and use natural language. " + "Example: 'What is the refund policy for canceled orders?'" + ), + } + }, + "required": ["query"], + }, + }, + } diff --git a/api/services/workflow/workflow.py b/api/services/workflow/workflow.py index 4f68e77..cf4a3f0 100644 --- a/api/services/workflow/workflow.py +++ b/api/services/workflow/workflow.py @@ -48,6 +48,7 @@ class Node: self.delayed_start = data.delayed_start self.delayed_start_duration = data.delayed_start_duration self.tool_uuids = data.tool_uuids + self.document_uuids = data.document_uuids self.data = data @@ -189,16 +190,6 @@ class WorkflowGraph: in_d, out_d = in_deg[n.id], out_deg[n.id] match n.node_type: - case NodeType.startNode: - if in_d != 0 or out_d < 1: - errors.append( - WorkflowError( - kind=ItemKind.node, - id=n.id, - field=None, - message=f"StartNode must have at least 1 outgoing edge", - ) - ) case NodeType.endNode: if in_d < 1 or out_d != 0: errors.append( diff --git a/api/tasks/arq.py b/api/tasks/arq.py index 8c570c4..8e8f58c 100644 --- a/api/tasks/arq.py +++ b/api/tasks/arq.py @@ -46,6 +46,7 @@ from api.tasks.campaign_tasks import ( process_campaign_batch, sync_campaign_source, ) +from api.tasks.knowledge_base_processing import process_knowledge_base_document from api.tasks.run_integrations import run_integrations_post_workflow_run from api.tasks.s3_upload import ( upload_audio_to_s3, @@ -64,6 +65,7 @@ class WorkerSettings: sync_campaign_source, process_campaign_batch, monitor_campaign_progress, + process_knowledge_base_document, ] cron_jobs = [] redis_settings = REDIS_SETTINGS diff --git a/api/tasks/function_names.py b/api/tasks/function_names.py index d527769..c3e7486 100644 --- a/api/tasks/function_names.py +++ b/api/tasks/function_names.py @@ -7,3 +7,4 @@ class FunctionNames: SYNC_CAMPAIGN_SOURCE = "sync_campaign_source" PROCESS_CAMPAIGN_BATCH = "process_campaign_batch" MONITOR_CAMPAIGN_PROGRESS = "monitor_campaign_progress" + PROCESS_KNOWLEDGE_BASE_DOCUMENT = "process_knowledge_base_document" diff --git a/api/tasks/knowledge_base_processing.py b/api/tasks/knowledge_base_processing.py new file mode 100644 index 0000000..7bef401 --- /dev/null +++ b/api/tasks/knowledge_base_processing.py @@ -0,0 +1,311 @@ +"""ARQ background task for processing knowledge base documents.""" + +import os +import tempfile +from typing import Literal + +from docling.chunking import HybridChunker +from docling.document_converter import DocumentConverter +from docling_core.transforms.chunker.tokenizer.huggingface import HuggingFaceTokenizer +from loguru import logger +from transformers import AutoTokenizer + +from api.db import db_client +from api.db.models import KnowledgeBaseChunkModel +from api.services.gen_ai import ( + OpenAIEmbeddingService, + SentenceTransformerEmbeddingService, +) +from api.services.storage import storage_fs + +# For tokenization/chunking - use SentenceTransformer tokenizer as baseline +TOKENIZER_MODEL = "sentence-transformers/all-MiniLM-L6-v2" + + +async def process_knowledge_base_document( + ctx, + document_id: int, + s3_key: str, + organization_id: int, + max_tokens: int = 128, + embedding_service: Literal["sentence_transformer", "openai"] = "openai", +): + """Process a knowledge base document: download, chunk, embed, and store. + + Args: + ctx: ARQ context + document_id: Database ID of the document + s3_key: S3 key where the file is stored + organization_id: Organization ID + max_tokens: Maximum number of tokens per chunk (default: 128) + embedding_service: Embedding service to use (default: "openai") + - "openai": Use OpenAI text-embedding-3-small (1536-dim, requires API key) + - "sentence_transformer": Use SentenceTransformer (all-MiniLM-L6-v2, 384-dim, free) + """ + logger.info( + f"Starting knowledge base document processing for document_id={document_id}, " + f"s3_key={s3_key}, organization_id={organization_id}" + ) + + temp_file_path = None + + try: + # Update status to processing + await db_client.update_document_status(document_id, "processing") + + # Extract file extension from S3 key + filename = s3_key.split("/")[-1] + file_extension = ( + os.path.splitext(filename)[1] or ".bin" + ) # Default to .bin if no extension + + # Create temp file for download with correct extension + temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=file_extension) + temp_file_path = temp_file.name + temp_file.close() + + # Download file from S3 + logger.info(f"Downloading file from S3: {s3_key}") + download_success = await storage_fs.adownload_file(s3_key, temp_file_path) + + if not download_success: + raise Exception(f"Failed to download file from S3: {s3_key}") + + if not os.path.exists(temp_file_path): + raise FileNotFoundError(f"Downloaded file not found: {temp_file_path}") + + file_size = os.path.getsize(temp_file_path) + logger.info(f"Downloaded file size: {file_size} bytes") + + # Compute file hash and get mime type + file_hash = db_client.compute_file_hash(temp_file_path) + mime_type = db_client.get_mime_type(temp_file_path) + filename = s3_key.split("/")[-1] + + # Get document record + document = await db_client.get_document_by_id(document_id) + if not document: + raise Exception(f"Document {document_id} not found") + + # Check if a document with this hash already exists (reject duplicates) + existing_doc = await db_client.get_document_by_hash(file_hash, organization_id) + if existing_doc and existing_doc.id != document_id: + error_message = ( + f"This file is a duplicate of '{existing_doc.filename}'. " + f"Please delete the duplicate files and consolidate them into a single unique file before uploading." + ) + logger.warning( + f"Duplicate document detected: {document_id} is duplicate of {existing_doc.id} " + f"({existing_doc.filename})" + ) + # Update file metadata + await db_client.update_document_metadata( + document_id, + file_size_bytes=file_size, + file_hash=file_hash, + mime_type=mime_type, + ) + # Mark as failed with duplicate error message + await db_client.update_document_status( + document_id, + "failed", + error_message=error_message, + docling_metadata={ + "duplicate_of": existing_doc.document_uuid, + "duplicate_filename": existing_doc.filename, + }, + ) + return + + # Update document with file metadata + await db_client.update_document_metadata( + document_id, + file_size_bytes=file_size, + file_hash=file_hash, + mime_type=mime_type, + ) + + # Initialize the embedding service based on the parameter + if embedding_service == "openai": + logger.info( + f"Initializing OpenAI embedding service with max_tokens={max_tokens}" + ) + # Try to get user's embeddings configuration + embeddings_api_key = None + embeddings_model = None + if document.created_by: + user_config = await db_client.get_user_configurations( + document.created_by + ) + if user_config.embeddings: + embeddings_api_key = user_config.embeddings.api_key + embeddings_model = user_config.embeddings.model + logger.info( + f"Using user embeddings config: model={embeddings_model}" + ) + + # Check if API key is configured + if not embeddings_api_key: + error_message = ( + "OpenAI API key not configured. Please set your API key in " + "Model Configurations > Embedding to process documents." + ) + logger.warning(f"Document {document_id}: {error_message}") + await db_client.update_document_status( + document_id, "failed", error_message=error_message + ) + return + + service = OpenAIEmbeddingService( + db_client=db_client, + max_tokens=max_tokens, + api_key=embeddings_api_key, + model_id=embeddings_model or "text-embedding-3-small", + ) + elif embedding_service == "sentence_transformer": + logger.info( + f"Initializing SentenceTransformer embedding service with max_tokens={max_tokens}" + ) + service = SentenceTransformerEmbeddingService( + db_client=db_client, + max_tokens=max_tokens, + ) + else: + raise ValueError( + f"Invalid embedding_service: {embedding_service}. " + f"Must be 'sentence_transformer' or 'openai'" + ) + + # Step 1: Convert document with docling + logger.info("Converting document with docling") + converter = DocumentConverter() + conversion_result = converter.convert(temp_file_path) + doc = conversion_result.document + + # Store docling metadata + docling_metadata = { + "num_pages": len(doc.pages) if hasattr(doc, "pages") else None, + "document_type": type(doc).__name__, + } + + # Step 2: Initialize tokenizer for chunking + logger.info( + f"Loading tokenizer: {TOKENIZER_MODEL} with max_tokens={max_tokens}" + ) + tokenizer = HuggingFaceTokenizer( + tokenizer=AutoTokenizer.from_pretrained(TOKENIZER_MODEL), + max_tokens=max_tokens, + ) + + # Step 3: Initialize chunker + logger.info(f"Initializing HybridChunker with max_tokens={max_tokens}") + chunker = HybridChunker(tokenizer=tokenizer) + + # Step 4: Chunk the document + logger.info(f"Chunking document with max_tokens={max_tokens}") + chunks = list(chunker.chunk(dl_doc=doc)) + total_chunks = len(chunks) + logger.info(f"Generated {total_chunks} chunks") + + # Step 5: Process each chunk + chunk_texts = [] + chunk_records = [] + token_counts = [] + + for i, chunk in enumerate(chunks): + chunk_text = chunk.text + contextualized_text = chunker.contextualize(chunk=chunk) + + # Calculate token count + text_to_tokenize = ( + contextualized_text if contextualized_text else chunk_text + ) + token_count = len( + tokenizer.tokenizer.encode(text_to_tokenize, add_special_tokens=False) + ) + token_counts.append(token_count) + + # Prepare chunk metadata + chunk_metadata = {} + if hasattr(chunk, "meta") and chunk.meta: + chunk_metadata = { + "doc_items": ( + [str(item) for item in chunk.meta.doc_items] + if hasattr(chunk.meta, "doc_items") + else [] + ), + "headings": ( + chunk.meta.headings if hasattr(chunk.meta, "headings") else [] + ), + } + + # Create chunk record (without embedding yet) + chunk_record = KnowledgeBaseChunkModel( + document_id=document_id, + organization_id=organization_id, + chunk_text=chunk_text, + contextualized_text=contextualized_text, + chunk_index=i, + chunk_metadata=chunk_metadata, + embedding_model=service.get_model_id(), + embedding_dimension=service.get_embedding_dimension(), + token_count=token_count, + ) + + chunk_records.append(chunk_record) + chunk_texts.append(text_to_tokenize) + + # Log chunk statistics + if token_counts: + avg_tokens = sum(token_counts) / len(token_counts) + min_tokens = min(token_counts) + max_tokens_actual = max(token_counts) + logger.info("Chunk token statistics:") + logger.info(f" - Average: {avg_tokens:.1f} tokens") + logger.info(f" - Min: {min_tokens} tokens") + logger.info(f" - Max: {max_tokens_actual} tokens") + + # Step 6: Generate embeddings using the embedding service + logger.info(f"Generating embeddings using {embedding_service}") + embeddings = await service.embed_texts(chunk_texts) + + # Step 7: Attach embeddings to chunk records + for chunk_record, embedding in zip(chunk_records, embeddings): + chunk_record.embedding = embedding + + # Step 8: Save chunks in database + logger.info("Storing chunks in database") + await db_client.create_chunks_batch(chunk_records) + + # Step 9: Update document status to completed + await db_client.update_document_status( + document_id, + "completed", + total_chunks=total_chunks, + docling_metadata=docling_metadata, + ) + + logger.info( + f"Successfully processed knowledge base document {document_id}. " + f"Total chunks: {total_chunks}" + ) + + except Exception as e: + logger.error( + f"Error processing knowledge base document {document_id}: {e}", + exc_info=True, + ) + # Update document status to failed + await db_client.update_document_status( + document_id, "failed", error_message=str(e) + ) + raise + + finally: + # Clean up temp file + if temp_file_path and os.path.exists(temp_file_path): + try: + os.remove(temp_file_path) + logger.debug(f"Cleaned up temp file: {temp_file_path}") + except Exception as e: + logger.warning(f"Failed to clean up temp file {temp_file_path}: {e}") diff --git a/docker-compose-local.yaml b/docker-compose-local.yaml index 225e751..7c3bae3 100644 --- a/docker-compose-local.yaml +++ b/docker-compose-local.yaml @@ -1,6 +1,6 @@ services: postgres: - image: postgres:17 + image: pgvector/pgvector:pg17 environment: POSTGRES_USER: postgres POSTGRES_PASSWORD: postgres diff --git a/ui/src/app/files/DocumentList.tsx b/ui/src/app/files/DocumentList.tsx new file mode 100644 index 0000000..fd66c32 --- /dev/null +++ b/ui/src/app/files/DocumentList.tsx @@ -0,0 +1,249 @@ +'use client'; + +import { FileText, RefreshCw, Search, Trash2 } from 'lucide-react'; +import { useCallback, useEffect, useState } from 'react'; +import { toast } from 'sonner'; + +import { + deleteDocumentApiV1KnowledgeBaseDocumentsDocumentUuidDelete, + listDocumentsApiV1KnowledgeBaseDocumentsGet, +} from '@/client/sdk.gen'; +import type { DocumentResponseSchema } from '@/client/types.gen'; +import { Badge } from '@/components/ui/badge'; +import { Button } from '@/components/ui/button'; +import { Input } from '@/components/ui/input'; +import { Skeleton } from '@/components/ui/skeleton'; +import logger from '@/lib/logger'; + +interface DocumentListProps { + accessToken: string; + refreshTrigger: number; +} + +export default function DocumentList({ accessToken, refreshTrigger }: DocumentListProps) { + const [documents, setDocuments] = useState([]); + const [isLoading, setIsLoading] = useState(true); + const [searchQuery, setSearchQuery] = useState(''); + const [error, setError] = useState(null); + + const fetchDocuments = useCallback(async () => { + if (!accessToken) return; + + try { + setIsLoading(true); + setError(null); + + const response = await listDocumentsApiV1KnowledgeBaseDocumentsGet({ + headers: { + 'Authorization': `Bearer ${accessToken}`, + }, + query: { + limit: 100, + offset: 0, + }, + }); + + if (response.error || !response.data) { + throw new Error('Failed to fetch documents'); + } + + setDocuments(response.data.documents); + } catch (err) { + setError(err instanceof Error ? err.message : 'Failed to fetch documents'); + logger.error('Error fetching documents:', err); + } finally { + setIsLoading(false); + } + }, [accessToken]); + + // Fetch documents on mount and when refreshTrigger changes + useEffect(() => { + fetchDocuments(); + }, [fetchDocuments, refreshTrigger]); + + // Poll for documents that are processing + useEffect(() => { + const processingDocs = documents.filter( + (doc) => doc.processing_status === 'processing' || doc.processing_status === 'pending' + ); + + if (processingDocs.length === 0) return; + + const pollInterval = setInterval(() => { + logger.info(`Polling for ${processingDocs.length} processing documents...`); + fetchDocuments(); + }, 5000); // Poll every 5 seconds + + return () => clearInterval(pollInterval); + }, [documents, fetchDocuments]); + + const handleDelete = async (documentUuid: string, filename: string) => { + if (!confirm(`Are you sure you want to delete "${filename}"?`)) return; + + try { + const response = await deleteDocumentApiV1KnowledgeBaseDocumentsDocumentUuidDelete({ + path: { + document_uuid: documentUuid, + }, + headers: { + 'Authorization': `Bearer ${accessToken}`, + }, + }); + + if (response.error) { + throw new Error('Failed to delete document'); + } + + toast.success(`Deleted "${filename}"`); + fetchDocuments(); + } catch (err) { + toast.error(err instanceof Error ? err.message : 'Failed to delete document'); + logger.error('Error deleting document:', err); + } + }; + + const getStatusBadge = (status: string) => { + switch (status) { + case 'completed': + return Completed; + case 'processing': + return ( + + Processing + + ); + case 'pending': + return Pending; + case 'failed': + return Failed; + default: + return {status}; + } + }; + + const formatFileSize = (bytes: number): string => { + if (bytes === 0) return '0 B'; + const k = 1024; + const sizes = ['B', 'KB', 'MB', 'GB']; + const i = Math.floor(Math.log(bytes) / Math.log(k)); + return `${parseFloat((bytes / Math.pow(k, i)).toFixed(2))} ${sizes[i]}`; + }; + + const formatDate = (dateString: string): string => { + const date = new Date(dateString); + return date.toLocaleDateString() + ' ' + date.toLocaleTimeString(); + }; + + const filteredDocuments = documents.filter((doc) => + doc.filename.toLowerCase().includes(searchQuery.toLowerCase()) + ); + + if (isLoading && documents.length === 0) { + return ( +
+ {[1, 2, 3].map((i) => ( +
+
+ + +
+ +
+ ))} +
+ ); + } + + if (error) { + return ( +
+ {error} +
+ ); + } + + return ( +
+ {/* Search and Refresh */} +
+
+ + setSearchQuery(e.target.value)} + className="pl-10" + /> +
+ +
+ + {/* Document List */} + {filteredDocuments.length === 0 ? ( +
+ +

+ {searchQuery + ? 'No documents match your search' + : 'No documents uploaded yet'} +

+
+ ) : ( +
+ {filteredDocuments.map((doc) => ( +
+
+
+ +
+
+
+ {doc.filename} + {getStatusBadge(doc.processing_status)} +
+
+ {formatFileSize(doc.file_size_bytes)} + {doc.processing_status === 'completed' && ( + {doc.total_chunks} chunks + )} + {formatDate(doc.created_at)} +
+ {doc.processing_error && ( +

+ Error: {doc.processing_error} +

+ )} + {doc.docling_metadata && + typeof doc.docling_metadata === 'object' && + 'duplicate_of' in doc.docling_metadata && ( +

+ Duplicate of another document +

+ )} +
+
+ +
+ ))} +
+ )} +
+ ); +} diff --git a/ui/src/app/files/DocumentUpload.tsx b/ui/src/app/files/DocumentUpload.tsx new file mode 100644 index 0000000..3117bfd --- /dev/null +++ b/ui/src/app/files/DocumentUpload.tsx @@ -0,0 +1,220 @@ +'use client'; + +import { Upload } from 'lucide-react'; +import { useRef, useState } from 'react'; +import { toast } from 'sonner'; + +import { + getUploadUrlApiV1KnowledgeBaseUploadUrlPost, + processDocumentApiV1KnowledgeBaseProcessDocumentPost, +} from '@/client/sdk.gen'; +import type { DocumentUploadResponseSchema } from '@/client/types.gen'; +import { Button } from '@/components/ui/button'; +import { Progress } from '@/components/ui/progress'; +import logger from '@/lib/logger'; + +interface DocumentUploadProps { + accessToken: string; + onUploadSuccess: () => void; +} + +const MAX_FILE_SIZE = 100 * 1024 * 1024; // 100MB +const ACCEPTED_FILE_TYPES = ['.pdf', '.docx', '.doc', '.txt']; + +export default function DocumentUpload({ accessToken, onUploadSuccess }: DocumentUploadProps) { + const [uploading, setUploading] = useState(false); + const [uploadProgress, setUploadProgress] = useState(0); + const [dragActive, setDragActive] = useState(false); + const fileInputRef = useRef(null); + + const validateFile = (file: File): boolean => { + // Validate file type + const fileExtension = '.' + file.name.split('.').pop()?.toLowerCase(); + if (!ACCEPTED_FILE_TYPES.includes(fileExtension)) { + toast.error(`Please select a supported file type: ${ACCEPTED_FILE_TYPES.join(', ')}`); + return false; + } + + // Validate file size + if (file.size > MAX_FILE_SIZE) { + toast.error('File size must be less than 100MB'); + return false; + } + + return true; + }; + + const uploadFile = async (file: File) => { + if (!validateFile(file)) return; + + setUploading(true); + setUploadProgress(0); + + try { + // Step 1: Request presigned upload URL + logger.info('Requesting presigned upload URL for:', file.name); + const uploadUrlResponse = await getUploadUrlApiV1KnowledgeBaseUploadUrlPost({ + body: { + filename: file.name, + mime_type: file.type || 'application/octet-stream', + custom_metadata: { + original_filename: file.name, + uploaded_at: new Date().toISOString(), + }, + }, + headers: { + 'Authorization': `Bearer ${accessToken}`, + }, + }); + + if (uploadUrlResponse.error || !uploadUrlResponse.data) { + throw new Error('Failed to get upload URL'); + } + + const uploadData: DocumentUploadResponseSchema = uploadUrlResponse.data; + logger.info('Received presigned URL, uploading file...'); + + setUploadProgress(25); + + // Step 2: Upload file directly to S3/MinIO using PUT + const uploadResponse = await fetch(uploadData.upload_url, { + method: 'PUT', + body: file, + headers: { + 'Content-Type': file.type || 'application/octet-stream', + }, + }); + + if (!uploadResponse.ok) { + throw new Error('Failed to upload file to storage'); + } + + setUploadProgress(75); + logger.info('File uploaded successfully, triggering processing...'); + + // Step 3: Trigger document processing + const processResponse = await processDocumentApiV1KnowledgeBaseProcessDocumentPost({ + body: { + document_uuid: uploadData.document_uuid, + s3_key: uploadData.s3_key, + }, + headers: { + 'Authorization': `Bearer ${accessToken}`, + }, + }); + + if (processResponse.error) { + throw new Error('Failed to trigger processing'); + } + + setUploadProgress(100); + logger.info('Document processing triggered successfully'); + + toast.success(`File uploaded: ${file.name}. Processing started.`); + onUploadSuccess(); + } catch (error) { + logger.error('Error uploading document:', error); + toast.error(error instanceof Error ? error.message : 'Failed to upload document'); + } finally { + setUploading(false); + setUploadProgress(0); + // Reset file input + if (fileInputRef.current) { + fileInputRef.current.value = ''; + } + } + }; + + const handleFileSelect = async (event: React.ChangeEvent) => { + const file = event.target.files?.[0]; + if (file) { + await uploadFile(file); + } + }; + + const handleDrag = (e: React.DragEvent) => { + e.preventDefault(); + e.stopPropagation(); + if (e.type === 'dragenter' || e.type === 'dragover') { + setDragActive(true); + } else if (e.type === 'dragleave') { + setDragActive(false); + } + }; + + const handleDrop = async (e: React.DragEvent) => { + e.preventDefault(); + e.stopPropagation(); + setDragActive(false); + + const file = e.dataTransfer.files?.[0]; + if (file) { + await uploadFile(file); + } + }; + + const handleButtonClick = () => { + fileInputRef.current?.click(); + }; + + return ( +
+ + + {/* Drag and Drop Area */} +
+ +

+ {uploading ? 'Uploading...' : 'Drop your document here'} +

+

+ or click to browse +

+

+ Supported formats: {ACCEPTED_FILE_TYPES.join(', ')} (Max 100MB) +

+
+ + {/* Upload Progress */} + {uploading && ( +
+
+ Uploading... + {uploadProgress}% +
+ +
+ )} + + {/* Manual Upload Button */} +
+ +
+
+ ); +} diff --git a/ui/src/app/files/page.tsx b/ui/src/app/files/page.tsx new file mode 100644 index 0000000..dd389e1 --- /dev/null +++ b/ui/src/app/files/page.tsx @@ -0,0 +1,104 @@ +"use client"; + +import { useCallback, useEffect, useState } from "react"; + +import { Card, CardContent, CardDescription, CardHeader, CardTitle } from "@/components/ui/card"; +import { Skeleton } from "@/components/ui/skeleton"; +import { Tabs, TabsContent, TabsList, TabsTrigger } from "@/components/ui/tabs"; +import { useAuth } from "@/lib/auth"; + +import DocumentList from "./DocumentList"; +import DocumentUpload from "./DocumentUpload"; + +export default function FilesPage() { + const { user, getAccessToken, redirectToLogin, loading } = useAuth(); + const [refreshKey, setRefreshKey] = useState(0); + const [accessToken, setAccessToken] = useState(''); + + // Redirect if not authenticated + useEffect(() => { + if (!loading && !user) { + redirectToLogin(); + } + }, [loading, user, redirectToLogin]); + + // Get access token + const fetchAccessToken = useCallback(async () => { + if (user) { + const token = await getAccessToken(); + setAccessToken(token); + } + }, [user, getAccessToken]); + + useEffect(() => { + fetchAccessToken(); + }, [fetchAccessToken]); + + const handleUploadSuccess = () => { + // Trigger refresh of document list + setRefreshKey(prev => prev + 1); + }; + + if (loading || !user || !accessToken) { + return ( +
+
+ + +
+
+ ); + } + + return ( +
+
+

Knowledge Base Files

+

+ Upload and manage documents for your voice agents to reference. +

+
+ + + + All Files + Upload New + + + + + + Your Documents + + View and manage your uploaded documents + + + + + + + + + + + + Upload Document + + Upload a PDF or document file to add to your knowledge base + + + + + + + + +
+ ); +} diff --git a/ui/src/app/workflow/[workflowId]/RenderWorkflow.tsx b/ui/src/app/workflow/[workflowId]/RenderWorkflow.tsx index 0afdae9..4b0aa5e 100644 --- a/ui/src/app/workflow/[workflowId]/RenderWorkflow.tsx +++ b/ui/src/app/workflow/[workflowId]/RenderWorkflow.tsx @@ -7,8 +7,10 @@ import { ReactFlow, } from "@xyflow/react"; import { BrushCleaning, Maximize2, Minus, Plus, Rocket, Settings, Variable } from 'lucide-react'; -import React, { useMemo, useState } from 'react'; +import React, { useEffect, useMemo, useState } from 'react'; +import { listDocumentsApiV1KnowledgeBaseDocumentsGet, listToolsApiV1ToolsGet } from '@/client'; +import type { DocumentResponseSchema, ToolResponse } from '@/client/types.gen'; import { FlowEdge, FlowNode, NodeType } from "@/components/flow/types"; import { Button } from '@/components/ui/button'; import { Tooltip, TooltipContent, TooltipProvider, TooltipTrigger } from '@/components/ui/tooltip'; @@ -63,6 +65,8 @@ function RenderWorkflow({ initialWorkflowName, workflowId, initialFlow, initialT const [isConfigurationsDialogOpen, setIsConfigurationsDialogOpen] = useState(false); const [isEmbedDialogOpen, setIsEmbedDialogOpen] = useState(false); const [isPhoneCallDialogOpen, setIsPhoneCallDialogOpen] = useState(false); + const [documents, setDocuments] = useState(undefined); + const [tools, setTools] = useState(undefined); const { rfInstance, @@ -95,6 +99,36 @@ function RenderWorkflow({ initialWorkflowName, workflowId, initialFlow, initialT getAccessToken }); + // Fetch documents and tools once for the entire workflow + useEffect(() => { + const fetchData = async () => { + try { + const accessToken = await getAccessToken(); + + // Fetch documents + const documentsResponse = await listDocumentsApiV1KnowledgeBaseDocumentsGet({ + headers: { Authorization: `Bearer ${accessToken}` }, + query: { limit: 100 }, + }); + if (documentsResponse.data) { + setDocuments(documentsResponse.data.documents); + } + + // Fetch tools + const toolsResponse = await listToolsApiV1ToolsGet({ + headers: { Authorization: `Bearer ${accessToken}` }, + }); + if (toolsResponse.data) { + setTools(toolsResponse.data); + } + } catch (error) { + console.error('Failed to fetch documents and tools:', error); + } + }; + + fetchData(); + }, [getAccessToken]); + // Memoize defaultEdgeOptions to prevent unnecessary re-renders const defaultEdgeOptions = useMemo(() => ({ animated: true, @@ -102,7 +136,11 @@ function RenderWorkflow({ initialWorkflowName, workflowId, initialFlow, initialT }), []); // Memoize the context value to prevent unnecessary re-renders - const workflowContextValue = useMemo(() => ({ saveWorkflow }), [saveWorkflow]); + const workflowContextValue = useMemo(() => ({ + saveWorkflow, + documents, + tools + }), [saveWorkflow, documents, tools]); return ( diff --git a/ui/src/app/workflow/[workflowId]/contexts/WorkflowContext.tsx b/ui/src/app/workflow/[workflowId]/contexts/WorkflowContext.tsx index bf6d78b..89444ff 100644 --- a/ui/src/app/workflow/[workflowId]/contexts/WorkflowContext.tsx +++ b/ui/src/app/workflow/[workflowId]/contexts/WorkflowContext.tsx @@ -1,7 +1,11 @@ import { createContext, useContext } from 'react'; +import type { DocumentResponseSchema, ToolResponse } from '@/client/types.gen'; + interface WorkflowContextType { saveWorkflow: (updateWorkflowDefinition?: boolean) => Promise; + documents?: DocumentResponseSchema[]; + tools?: ToolResponse[]; } const WorkflowContext = createContext(undefined); @@ -15,3 +19,8 @@ export const useWorkflow = () => { } return context; }; + +// Optional hook that doesn't throw if context is not available +export const useWorkflowOptional = () => { + return useContext(WorkflowContext); +}; diff --git a/ui/src/client/client.gen.ts b/ui/src/client/client.gen.ts index 779eafa..cbfc632 100644 --- a/ui/src/client/client.gen.ts +++ b/ui/src/client/client.gen.ts @@ -1,9 +1,8 @@ // This file is auto-generated by @hey-api/openapi-ts -import { type ClientOptions as DefaultClientOptions, type Config, createClient, createConfig } from '@hey-api/client-fetch'; - -import { createClientConfig } from '../lib/apiClient'; import type { ClientOptions } from './types.gen'; +import { type Config, type ClientOptions as DefaultClientOptions, createClient, createConfig } from '@hey-api/client-fetch'; +import { createClientConfig } from '../lib/apiClient'; /** * The `createClientConfig()` function will be called on client initialization @@ -17,4 +16,4 @@ export type CreateClientConfig = export const client = createClient(createClientConfig(createConfig({ baseUrl: 'http://127.0.0.1:8000' -}))); +}))); \ No newline at end of file diff --git a/ui/src/client/index.ts b/ui/src/client/index.ts index 688e3c9..e64537d 100644 --- a/ui/src/client/index.ts +++ b/ui/src/client/index.ts @@ -1,3 +1,3 @@ // This file is auto-generated by @hey-api/openapi-ts -export * from './sdk.gen'; export * from './types.gen'; +export * from './sdk.gen'; \ No newline at end of file diff --git a/ui/src/client/sdk.gen.ts b/ui/src/client/sdk.gen.ts index ebb17e7..9076557 100644 --- a/ui/src/client/sdk.gen.ts +++ b/ui/src/client/sdk.gen.ts @@ -1,9 +1,8 @@ // This file is auto-generated by @hey-api/openapi-ts -import type { Client,Options as ClientOptions, TDataShape } from '@hey-api/client-fetch'; - +import type { Options as ClientOptions, TDataShape, Client } from '@hey-api/client-fetch'; +import type { InitiateCallApiV1TelephonyInitiateCallPostData, InitiateCallApiV1TelephonyInitiateCallPostError, HandleTwilioStatusCallbackApiV1TelephonyTwilioStatusCallbackWorkflowRunIdPostData, HandleTwilioStatusCallbackApiV1TelephonyTwilioStatusCallbackWorkflowRunIdPostError, HandleVonageEventsApiV1TelephonyVonageEventsWorkflowRunIdPostData, HandleVonageEventsApiV1TelephonyVonageEventsWorkflowRunIdPostError, HandleVobizHangupCallbackApiV1TelephonyVobizHangupCallbackWorkflowRunIdPostData, HandleVobizHangupCallbackApiV1TelephonyVobizHangupCallbackWorkflowRunIdPostError, HandleVobizRingCallbackApiV1TelephonyVobizRingCallbackWorkflowRunIdPostData, HandleVobizRingCallbackApiV1TelephonyVobizRingCallbackWorkflowRunIdPostError, HandleCloudonixStatusCallbackApiV1TelephonyCloudonixStatusCallbackWorkflowRunIdPostData, HandleCloudonixStatusCallbackApiV1TelephonyCloudonixStatusCallbackWorkflowRunIdPostError, HandleVobizHangupCallbackByWorkflowApiV1TelephonyVobizHangupCallbackWorkflowWorkflowIdPostData, HandleVobizHangupCallbackByWorkflowApiV1TelephonyVobizHangupCallbackWorkflowWorkflowIdPostError, HandleInboundTelephonyApiV1TelephonyInboundWorkflowIdPostData, HandleInboundTelephonyApiV1TelephonyInboundWorkflowIdPostError, HandleInboundFallbackApiV1TelephonyInboundFallbackPostData, ImpersonateApiV1SuperuserImpersonatePostData, ImpersonateApiV1SuperuserImpersonatePostResponse, ImpersonateApiV1SuperuserImpersonatePostError, GetWorkflowRunsApiV1SuperuserWorkflowRunsGetData, GetWorkflowRunsApiV1SuperuserWorkflowRunsGetResponse, GetWorkflowRunsApiV1SuperuserWorkflowRunsGetError, SetAdminCommentApiV1SuperuserWorkflowRunsRunIdCommentPostData, SetAdminCommentApiV1SuperuserWorkflowRunsRunIdCommentPostResponse, SetAdminCommentApiV1SuperuserWorkflowRunsRunIdCommentPostError, ValidateWorkflowApiV1WorkflowWorkflowIdValidatePostData, ValidateWorkflowApiV1WorkflowWorkflowIdValidatePostResponse, ValidateWorkflowApiV1WorkflowWorkflowIdValidatePostError, CreateWorkflowApiV1WorkflowCreateDefinitionPostData, CreateWorkflowApiV1WorkflowCreateDefinitionPostResponse, CreateWorkflowApiV1WorkflowCreateDefinitionPostError, CreateWorkflowFromTemplateApiV1WorkflowCreateTemplatePostData, CreateWorkflowFromTemplateApiV1WorkflowCreateTemplatePostResponse, CreateWorkflowFromTemplateApiV1WorkflowCreateTemplatePostError, GetWorkflowsApiV1WorkflowFetchGetData, GetWorkflowsApiV1WorkflowFetchGetResponse, GetWorkflowsApiV1WorkflowFetchGetError, GetWorkflowApiV1WorkflowFetchWorkflowIdGetData, GetWorkflowApiV1WorkflowFetchWorkflowIdGetResponse, GetWorkflowApiV1WorkflowFetchWorkflowIdGetError, GetWorkflowsSummaryApiV1WorkflowSummaryGetData, GetWorkflowsSummaryApiV1WorkflowSummaryGetResponse, GetWorkflowsSummaryApiV1WorkflowSummaryGetError, UpdateWorkflowStatusApiV1WorkflowWorkflowIdStatusPutData, UpdateWorkflowStatusApiV1WorkflowWorkflowIdStatusPutResponse, UpdateWorkflowStatusApiV1WorkflowWorkflowIdStatusPutError, UpdateWorkflowApiV1WorkflowWorkflowIdPutData, UpdateWorkflowApiV1WorkflowWorkflowIdPutResponse, UpdateWorkflowApiV1WorkflowWorkflowIdPutError, GetWorkflowRunsApiV1WorkflowWorkflowIdRunsGetData, GetWorkflowRunsApiV1WorkflowWorkflowIdRunsGetResponse, GetWorkflowRunsApiV1WorkflowWorkflowIdRunsGetError, CreateWorkflowRunApiV1WorkflowWorkflowIdRunsPostData, CreateWorkflowRunApiV1WorkflowWorkflowIdRunsPostResponse, CreateWorkflowRunApiV1WorkflowWorkflowIdRunsPostError, GetWorkflowRunApiV1WorkflowWorkflowIdRunsRunIdGetData, GetWorkflowRunApiV1WorkflowWorkflowIdRunsRunIdGetResponse, GetWorkflowRunApiV1WorkflowWorkflowIdRunsRunIdGetError, GetWorkflowTemplatesApiV1WorkflowTemplatesGetData, GetWorkflowTemplatesApiV1WorkflowTemplatesGetResponse, DuplicateWorkflowTemplateApiV1WorkflowTemplatesDuplicatePostData, DuplicateWorkflowTemplateApiV1WorkflowTemplatesDuplicatePostResponse, DuplicateWorkflowTemplateApiV1WorkflowTemplatesDuplicatePostError, GetDefaultConfigurationsApiV1UserConfigurationsDefaultsGetData, GetDefaultConfigurationsApiV1UserConfigurationsDefaultsGetResponse, GetAuthUserApiV1UserAuthUserGetData, GetAuthUserApiV1UserAuthUserGetResponse, GetAuthUserApiV1UserAuthUserGetError, GetUserConfigurationsApiV1UserConfigurationsUserGetData, GetUserConfigurationsApiV1UserConfigurationsUserGetResponse, GetUserConfigurationsApiV1UserConfigurationsUserGetError, UpdateUserConfigurationsApiV1UserConfigurationsUserPutData, UpdateUserConfigurationsApiV1UserConfigurationsUserPutResponse, UpdateUserConfigurationsApiV1UserConfigurationsUserPutError, ValidateUserConfigurationsApiV1UserConfigurationsUserValidateGetData, ValidateUserConfigurationsApiV1UserConfigurationsUserValidateGetResponse, ValidateUserConfigurationsApiV1UserConfigurationsUserValidateGetError, GetApiKeysApiV1UserApiKeysGetData, GetApiKeysApiV1UserApiKeysGetResponse, GetApiKeysApiV1UserApiKeysGetError, CreateApiKeyApiV1UserApiKeysPostData, CreateApiKeyApiV1UserApiKeysPostResponse, CreateApiKeyApiV1UserApiKeysPostError, ArchiveApiKeyApiV1UserApiKeysApiKeyIdDeleteData, ArchiveApiKeyApiV1UserApiKeysApiKeyIdDeleteResponse, ArchiveApiKeyApiV1UserApiKeysApiKeyIdDeleteError, ReactivateApiKeyApiV1UserApiKeysApiKeyIdReactivatePutData, ReactivateApiKeyApiV1UserApiKeysApiKeyIdReactivatePutResponse, ReactivateApiKeyApiV1UserApiKeysApiKeyIdReactivatePutError, GetVoicesApiV1UserConfigurationsVoicesProviderGetData, GetVoicesApiV1UserConfigurationsVoicesProviderGetResponse, GetVoicesApiV1UserConfigurationsVoicesProviderGetError, CreateCampaignApiV1CampaignCreatePostData, CreateCampaignApiV1CampaignCreatePostResponse, CreateCampaignApiV1CampaignCreatePostError, GetCampaignsApiV1CampaignGetData, GetCampaignsApiV1CampaignGetResponse, GetCampaignsApiV1CampaignGetError, GetCampaignApiV1CampaignCampaignIdGetData, GetCampaignApiV1CampaignCampaignIdGetResponse, GetCampaignApiV1CampaignCampaignIdGetError, StartCampaignApiV1CampaignCampaignIdStartPostData, StartCampaignApiV1CampaignCampaignIdStartPostResponse, StartCampaignApiV1CampaignCampaignIdStartPostError, PauseCampaignApiV1CampaignCampaignIdPausePostData, PauseCampaignApiV1CampaignCampaignIdPausePostResponse, PauseCampaignApiV1CampaignCampaignIdPausePostError, GetCampaignRunsApiV1CampaignCampaignIdRunsGetData, GetCampaignRunsApiV1CampaignCampaignIdRunsGetResponse, GetCampaignRunsApiV1CampaignCampaignIdRunsGetError, ResumeCampaignApiV1CampaignCampaignIdResumePostData, ResumeCampaignApiV1CampaignCampaignIdResumePostResponse, ResumeCampaignApiV1CampaignCampaignIdResumePostError, GetCampaignProgressApiV1CampaignCampaignIdProgressGetData, GetCampaignProgressApiV1CampaignCampaignIdProgressGetResponse, GetCampaignProgressApiV1CampaignCampaignIdProgressGetError, GetCampaignSourceDownloadUrlApiV1CampaignCampaignIdSourceDownloadUrlGetData, GetCampaignSourceDownloadUrlApiV1CampaignCampaignIdSourceDownloadUrlGetResponse, GetCampaignSourceDownloadUrlApiV1CampaignCampaignIdSourceDownloadUrlGetError, ListCredentialsApiV1CredentialsGetData, ListCredentialsApiV1CredentialsGetResponse, ListCredentialsApiV1CredentialsGetError, CreateCredentialApiV1CredentialsPostData, CreateCredentialApiV1CredentialsPostResponse, CreateCredentialApiV1CredentialsPostError, DeleteCredentialApiV1CredentialsCredentialUuidDeleteData, DeleteCredentialApiV1CredentialsCredentialUuidDeleteResponse, DeleteCredentialApiV1CredentialsCredentialUuidDeleteError, GetCredentialApiV1CredentialsCredentialUuidGetData, GetCredentialApiV1CredentialsCredentialUuidGetResponse, GetCredentialApiV1CredentialsCredentialUuidGetError, UpdateCredentialApiV1CredentialsCredentialUuidPutData, UpdateCredentialApiV1CredentialsCredentialUuidPutResponse, UpdateCredentialApiV1CredentialsCredentialUuidPutError, ListToolsApiV1ToolsGetData, ListToolsApiV1ToolsGetResponse, ListToolsApiV1ToolsGetError, CreateToolApiV1ToolsPostData, CreateToolApiV1ToolsPostResponse, CreateToolApiV1ToolsPostError, DeleteToolApiV1ToolsToolUuidDeleteData, DeleteToolApiV1ToolsToolUuidDeleteResponse, DeleteToolApiV1ToolsToolUuidDeleteError, GetToolApiV1ToolsToolUuidGetData, GetToolApiV1ToolsToolUuidGetResponse, GetToolApiV1ToolsToolUuidGetError, UpdateToolApiV1ToolsToolUuidPutData, UpdateToolApiV1ToolsToolUuidPutResponse, UpdateToolApiV1ToolsToolUuidPutError, UnarchiveToolApiV1ToolsToolUuidUnarchivePostData, UnarchiveToolApiV1ToolsToolUuidUnarchivePostResponse, UnarchiveToolApiV1ToolsToolUuidUnarchivePostError, GetIntegrationsApiV1IntegrationGetData, GetIntegrationsApiV1IntegrationGetResponse, GetIntegrationsApiV1IntegrationGetError, CreateSessionApiV1IntegrationSessionPostData, CreateSessionApiV1IntegrationSessionPostResponse, CreateSessionApiV1IntegrationSessionPostError, UpdateIntegrationApiV1IntegrationIntegrationIdPutData, UpdateIntegrationApiV1IntegrationIntegrationIdPutResponse, UpdateIntegrationApiV1IntegrationIntegrationIdPutError, GetIntegrationAccessTokenApiV1IntegrationIntegrationIdAccessTokenGetData, GetIntegrationAccessTokenApiV1IntegrationIntegrationIdAccessTokenGetResponse, GetIntegrationAccessTokenApiV1IntegrationIntegrationIdAccessTokenGetError, GetTelephonyConfigurationApiV1OrganizationsTelephonyConfigGetData, GetTelephonyConfigurationApiV1OrganizationsTelephonyConfigGetResponse, GetTelephonyConfigurationApiV1OrganizationsTelephonyConfigGetError, SaveTelephonyConfigurationApiV1OrganizationsTelephonyConfigPostData, SaveTelephonyConfigurationApiV1OrganizationsTelephonyConfigPostError, GetSignedUrlApiV1S3SignedUrlGetData, GetSignedUrlApiV1S3SignedUrlGetResponse, GetSignedUrlApiV1S3SignedUrlGetError, GetFileMetadataApiV1S3FileMetadataGetData, GetFileMetadataApiV1S3FileMetadataGetResponse, GetFileMetadataApiV1S3FileMetadataGetError, GetPresignedUploadUrlApiV1S3PresignedUploadUrlPostData, GetPresignedUploadUrlApiV1S3PresignedUploadUrlPostResponse, GetPresignedUploadUrlApiV1S3PresignedUploadUrlPostError, GetServiceKeysApiV1UserServiceKeysGetData, GetServiceKeysApiV1UserServiceKeysGetResponse, GetServiceKeysApiV1UserServiceKeysGetError, CreateServiceKeyApiV1UserServiceKeysPostData, CreateServiceKeyApiV1UserServiceKeysPostResponse, CreateServiceKeyApiV1UserServiceKeysPostError, ArchiveServiceKeyApiV1UserServiceKeysServiceKeyIdDeleteData, ArchiveServiceKeyApiV1UserServiceKeysServiceKeyIdDeleteError, ReactivateServiceKeyApiV1UserServiceKeysServiceKeyIdReactivatePutData, ReactivateServiceKeyApiV1UserServiceKeysServiceKeyIdReactivatePutError, ListTestSessionsApiV1LooptalkTestSessionsGetData, ListTestSessionsApiV1LooptalkTestSessionsGetResponse, ListTestSessionsApiV1LooptalkTestSessionsGetError, CreateTestSessionApiV1LooptalkTestSessionsPostData, CreateTestSessionApiV1LooptalkTestSessionsPostResponse, CreateTestSessionApiV1LooptalkTestSessionsPostError, GetTestSessionApiV1LooptalkTestSessionsTestSessionIdGetData, GetTestSessionApiV1LooptalkTestSessionsTestSessionIdGetResponse, GetTestSessionApiV1LooptalkTestSessionsTestSessionIdGetError, StartTestSessionApiV1LooptalkTestSessionsTestSessionIdStartPostData, StartTestSessionApiV1LooptalkTestSessionsTestSessionIdStartPostError, StopTestSessionApiV1LooptalkTestSessionsTestSessionIdStopPostData, StopTestSessionApiV1LooptalkTestSessionsTestSessionIdStopPostError, GetTestSessionConversationApiV1LooptalkTestSessionsTestSessionIdConversationGetData, GetTestSessionConversationApiV1LooptalkTestSessionsTestSessionIdConversationGetError, CreateLoadTestApiV1LooptalkLoadTestsPostData, CreateLoadTestApiV1LooptalkLoadTestsPostResponse, CreateLoadTestApiV1LooptalkLoadTestsPostError, GetLoadTestStatsApiV1LooptalkLoadTestsLoadTestGroupIdStatsGetData, GetLoadTestStatsApiV1LooptalkLoadTestsLoadTestGroupIdStatsGetResponse, GetLoadTestStatsApiV1LooptalkLoadTestsLoadTestGroupIdStatsGetError, GetActiveTestsApiV1LooptalkActiveTestsGetData, GetActiveTestsApiV1LooptalkActiveTestsGetError, GetCurrentPeriodUsageApiV1OrganizationsUsageCurrentPeriodGetData, GetCurrentPeriodUsageApiV1OrganizationsUsageCurrentPeriodGetResponse, GetCurrentPeriodUsageApiV1OrganizationsUsageCurrentPeriodGetError, GetUsageHistoryApiV1OrganizationsUsageRunsGetData, GetUsageHistoryApiV1OrganizationsUsageRunsGetResponse, GetUsageHistoryApiV1OrganizationsUsageRunsGetError, GetDailyUsageBreakdownApiV1OrganizationsUsageDailyBreakdownGetData, GetDailyUsageBreakdownApiV1OrganizationsUsageDailyBreakdownGetResponse, GetDailyUsageBreakdownApiV1OrganizationsUsageDailyBreakdownGetError, GetDailyReportApiV1OrganizationsReportsDailyGetData, GetDailyReportApiV1OrganizationsReportsDailyGetResponse, GetDailyReportApiV1OrganizationsReportsDailyGetError, GetWorkflowOptionsApiV1OrganizationsReportsWorkflowsGetData, GetWorkflowOptionsApiV1OrganizationsReportsWorkflowsGetResponse, GetWorkflowOptionsApiV1OrganizationsReportsWorkflowsGetError, GetDailyRunsDetailApiV1OrganizationsReportsDailyRunsGetData, GetDailyRunsDetailApiV1OrganizationsReportsDailyRunsGetResponse, GetDailyRunsDetailApiV1OrganizationsReportsDailyRunsGetError, OptionsInitApiV1PublicEmbedInitOptionsData, InitializeEmbedSessionApiV1PublicEmbedInitPostData, InitializeEmbedSessionApiV1PublicEmbedInitPostResponse, InitializeEmbedSessionApiV1PublicEmbedInitPostError, GetEmbedConfigApiV1PublicEmbedConfigTokenGetData, GetEmbedConfigApiV1PublicEmbedConfigTokenGetResponse, GetEmbedConfigApiV1PublicEmbedConfigTokenGetError, OptionsConfigApiV1PublicEmbedConfigTokenOptionsData, OptionsConfigApiV1PublicEmbedConfigTokenOptionsError, InitiateCallApiV1PublicAgentUuidPostData, InitiateCallApiV1PublicAgentUuidPostResponse, InitiateCallApiV1PublicAgentUuidPostError, DeactivateEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenDeleteData, DeactivateEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenDeleteResponse, DeactivateEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenDeleteError, GetEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenGetData, GetEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenGetResponse, GetEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenGetError, CreateOrUpdateEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenPostData, CreateOrUpdateEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenPostResponse, CreateOrUpdateEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenPostError, GetUploadUrlApiV1KnowledgeBaseUploadUrlPostData, GetUploadUrlApiV1KnowledgeBaseUploadUrlPostResponse, GetUploadUrlApiV1KnowledgeBaseUploadUrlPostError, ProcessDocumentApiV1KnowledgeBaseProcessDocumentPostData, ProcessDocumentApiV1KnowledgeBaseProcessDocumentPostResponse, ProcessDocumentApiV1KnowledgeBaseProcessDocumentPostError, ListDocumentsApiV1KnowledgeBaseDocumentsGetData, ListDocumentsApiV1KnowledgeBaseDocumentsGetResponse, ListDocumentsApiV1KnowledgeBaseDocumentsGetError, DeleteDocumentApiV1KnowledgeBaseDocumentsDocumentUuidDeleteData, DeleteDocumentApiV1KnowledgeBaseDocumentsDocumentUuidDeleteError, GetDocumentApiV1KnowledgeBaseDocumentsDocumentUuidGetData, GetDocumentApiV1KnowledgeBaseDocumentsDocumentUuidGetResponse, GetDocumentApiV1KnowledgeBaseDocumentsDocumentUuidGetError, SearchChunksApiV1KnowledgeBaseSearchPostData, SearchChunksApiV1KnowledgeBaseSearchPostResponse, SearchChunksApiV1KnowledgeBaseSearchPostError, HealthApiV1HealthGetData } from './types.gen'; import { client as _heyApiClient } from './client.gen'; -import type { ArchiveApiKeyApiV1UserApiKeysApiKeyIdDeleteData, ArchiveApiKeyApiV1UserApiKeysApiKeyIdDeleteError, ArchiveApiKeyApiV1UserApiKeysApiKeyIdDeleteResponse, ArchiveServiceKeyApiV1UserServiceKeysServiceKeyIdDeleteData, ArchiveServiceKeyApiV1UserServiceKeysServiceKeyIdDeleteError, CreateApiKeyApiV1UserApiKeysPostData, CreateApiKeyApiV1UserApiKeysPostError, CreateApiKeyApiV1UserApiKeysPostResponse, CreateCampaignApiV1CampaignCreatePostData, CreateCampaignApiV1CampaignCreatePostError, CreateCampaignApiV1CampaignCreatePostResponse, CreateCredentialApiV1CredentialsPostData, CreateCredentialApiV1CredentialsPostError, CreateCredentialApiV1CredentialsPostResponse, CreateLoadTestApiV1LooptalkLoadTestsPostData, CreateLoadTestApiV1LooptalkLoadTestsPostError, CreateLoadTestApiV1LooptalkLoadTestsPostResponse, CreateOrUpdateEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenPostData, CreateOrUpdateEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenPostError, CreateOrUpdateEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenPostResponse, CreateServiceKeyApiV1UserServiceKeysPostData, CreateServiceKeyApiV1UserServiceKeysPostError, CreateServiceKeyApiV1UserServiceKeysPostResponse, CreateSessionApiV1IntegrationSessionPostData, CreateSessionApiV1IntegrationSessionPostError, CreateSessionApiV1IntegrationSessionPostResponse, CreateTestSessionApiV1LooptalkTestSessionsPostData, CreateTestSessionApiV1LooptalkTestSessionsPostError, CreateTestSessionApiV1LooptalkTestSessionsPostResponse, CreateToolApiV1ToolsPostData, CreateToolApiV1ToolsPostError, CreateToolApiV1ToolsPostResponse, CreateWorkflowApiV1WorkflowCreateDefinitionPostData, CreateWorkflowApiV1WorkflowCreateDefinitionPostError, CreateWorkflowApiV1WorkflowCreateDefinitionPostResponse, CreateWorkflowFromTemplateApiV1WorkflowCreateTemplatePostData, CreateWorkflowFromTemplateApiV1WorkflowCreateTemplatePostError, CreateWorkflowFromTemplateApiV1WorkflowCreateTemplatePostResponse, CreateWorkflowRunApiV1WorkflowWorkflowIdRunsPostData, CreateWorkflowRunApiV1WorkflowWorkflowIdRunsPostError, CreateWorkflowRunApiV1WorkflowWorkflowIdRunsPostResponse, DeactivateEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenDeleteData, DeactivateEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenDeleteError, DeactivateEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenDeleteResponse, DeleteCredentialApiV1CredentialsCredentialUuidDeleteData, DeleteCredentialApiV1CredentialsCredentialUuidDeleteError, DeleteCredentialApiV1CredentialsCredentialUuidDeleteResponse, DeleteToolApiV1ToolsToolUuidDeleteData, DeleteToolApiV1ToolsToolUuidDeleteError, DeleteToolApiV1ToolsToolUuidDeleteResponse, DuplicateWorkflowTemplateApiV1WorkflowTemplatesDuplicatePostData, DuplicateWorkflowTemplateApiV1WorkflowTemplatesDuplicatePostError, DuplicateWorkflowTemplateApiV1WorkflowTemplatesDuplicatePostResponse, GetActiveTestsApiV1LooptalkActiveTestsGetData, GetActiveTestsApiV1LooptalkActiveTestsGetError, GetApiKeysApiV1UserApiKeysGetData, GetApiKeysApiV1UserApiKeysGetError, GetApiKeysApiV1UserApiKeysGetResponse, GetAuthUserApiV1UserAuthUserGetData, GetAuthUserApiV1UserAuthUserGetError, GetAuthUserApiV1UserAuthUserGetResponse, GetCampaignApiV1CampaignCampaignIdGetData, GetCampaignApiV1CampaignCampaignIdGetError, GetCampaignApiV1CampaignCampaignIdGetResponse, GetCampaignProgressApiV1CampaignCampaignIdProgressGetData, GetCampaignProgressApiV1CampaignCampaignIdProgressGetError, GetCampaignProgressApiV1CampaignCampaignIdProgressGetResponse, GetCampaignRunsApiV1CampaignCampaignIdRunsGetData, GetCampaignRunsApiV1CampaignCampaignIdRunsGetError, GetCampaignRunsApiV1CampaignCampaignIdRunsGetResponse, GetCampaignsApiV1CampaignGetData, GetCampaignsApiV1CampaignGetError, GetCampaignsApiV1CampaignGetResponse, GetCampaignSourceDownloadUrlApiV1CampaignCampaignIdSourceDownloadUrlGetData, GetCampaignSourceDownloadUrlApiV1CampaignCampaignIdSourceDownloadUrlGetError, GetCampaignSourceDownloadUrlApiV1CampaignCampaignIdSourceDownloadUrlGetResponse, GetCredentialApiV1CredentialsCredentialUuidGetData, GetCredentialApiV1CredentialsCredentialUuidGetError, GetCredentialApiV1CredentialsCredentialUuidGetResponse, GetCurrentPeriodUsageApiV1OrganizationsUsageCurrentPeriodGetData, GetCurrentPeriodUsageApiV1OrganizationsUsageCurrentPeriodGetError, GetCurrentPeriodUsageApiV1OrganizationsUsageCurrentPeriodGetResponse, GetDailyReportApiV1OrganizationsReportsDailyGetData, GetDailyReportApiV1OrganizationsReportsDailyGetError, GetDailyReportApiV1OrganizationsReportsDailyGetResponse, GetDailyRunsDetailApiV1OrganizationsReportsDailyRunsGetData, GetDailyRunsDetailApiV1OrganizationsReportsDailyRunsGetError, GetDailyRunsDetailApiV1OrganizationsReportsDailyRunsGetResponse, GetDailyUsageBreakdownApiV1OrganizationsUsageDailyBreakdownGetData, GetDailyUsageBreakdownApiV1OrganizationsUsageDailyBreakdownGetError, GetDailyUsageBreakdownApiV1OrganizationsUsageDailyBreakdownGetResponse, GetDefaultConfigurationsApiV1UserConfigurationsDefaultsGetData, GetDefaultConfigurationsApiV1UserConfigurationsDefaultsGetResponse, GetEmbedConfigApiV1PublicEmbedConfigTokenGetData, GetEmbedConfigApiV1PublicEmbedConfigTokenGetError, GetEmbedConfigApiV1PublicEmbedConfigTokenGetResponse, GetEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenGetData, GetEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenGetError, GetEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenGetResponse, GetFileMetadataApiV1S3FileMetadataGetData, GetFileMetadataApiV1S3FileMetadataGetError, GetFileMetadataApiV1S3FileMetadataGetResponse, GetIntegrationAccessTokenApiV1IntegrationIntegrationIdAccessTokenGetData, GetIntegrationAccessTokenApiV1IntegrationIntegrationIdAccessTokenGetError, GetIntegrationAccessTokenApiV1IntegrationIntegrationIdAccessTokenGetResponse, GetIntegrationsApiV1IntegrationGetData, GetIntegrationsApiV1IntegrationGetError, GetIntegrationsApiV1IntegrationGetResponse, GetLoadTestStatsApiV1LooptalkLoadTestsLoadTestGroupIdStatsGetData, GetLoadTestStatsApiV1LooptalkLoadTestsLoadTestGroupIdStatsGetError, GetLoadTestStatsApiV1LooptalkLoadTestsLoadTestGroupIdStatsGetResponse, GetPresignedUploadUrlApiV1S3PresignedUploadUrlPostData, GetPresignedUploadUrlApiV1S3PresignedUploadUrlPostError, GetPresignedUploadUrlApiV1S3PresignedUploadUrlPostResponse, GetServiceKeysApiV1UserServiceKeysGetData, GetServiceKeysApiV1UserServiceKeysGetError, GetServiceKeysApiV1UserServiceKeysGetResponse, GetSignedUrlApiV1S3SignedUrlGetData, GetSignedUrlApiV1S3SignedUrlGetError, GetSignedUrlApiV1S3SignedUrlGetResponse, GetTelephonyConfigurationApiV1OrganizationsTelephonyConfigGetData, GetTelephonyConfigurationApiV1OrganizationsTelephonyConfigGetError, GetTelephonyConfigurationApiV1OrganizationsTelephonyConfigGetResponse, GetTestSessionApiV1LooptalkTestSessionsTestSessionIdGetData, GetTestSessionApiV1LooptalkTestSessionsTestSessionIdGetError, GetTestSessionApiV1LooptalkTestSessionsTestSessionIdGetResponse, GetTestSessionConversationApiV1LooptalkTestSessionsTestSessionIdConversationGetData, GetTestSessionConversationApiV1LooptalkTestSessionsTestSessionIdConversationGetError, GetToolApiV1ToolsToolUuidGetData, GetToolApiV1ToolsToolUuidGetError, GetToolApiV1ToolsToolUuidGetResponse, GetUsageHistoryApiV1OrganizationsUsageRunsGetData, GetUsageHistoryApiV1OrganizationsUsageRunsGetError, GetUsageHistoryApiV1OrganizationsUsageRunsGetResponse, GetUserConfigurationsApiV1UserConfigurationsUserGetData, GetUserConfigurationsApiV1UserConfigurationsUserGetError, GetUserConfigurationsApiV1UserConfigurationsUserGetResponse, GetVoicesApiV1UserConfigurationsVoicesProviderGetData, GetVoicesApiV1UserConfigurationsVoicesProviderGetError, GetVoicesApiV1UserConfigurationsVoicesProviderGetResponse, GetWorkflowApiV1WorkflowFetchWorkflowIdGetData, GetWorkflowApiV1WorkflowFetchWorkflowIdGetError, GetWorkflowApiV1WorkflowFetchWorkflowIdGetResponse, GetWorkflowOptionsApiV1OrganizationsReportsWorkflowsGetData, GetWorkflowOptionsApiV1OrganizationsReportsWorkflowsGetError, GetWorkflowOptionsApiV1OrganizationsReportsWorkflowsGetResponse, GetWorkflowRunApiV1WorkflowWorkflowIdRunsRunIdGetData, GetWorkflowRunApiV1WorkflowWorkflowIdRunsRunIdGetError, GetWorkflowRunApiV1WorkflowWorkflowIdRunsRunIdGetResponse, GetWorkflowRunsApiV1SuperuserWorkflowRunsGetData, GetWorkflowRunsApiV1SuperuserWorkflowRunsGetError, GetWorkflowRunsApiV1SuperuserWorkflowRunsGetResponse, GetWorkflowRunsApiV1WorkflowWorkflowIdRunsGetData, GetWorkflowRunsApiV1WorkflowWorkflowIdRunsGetError, GetWorkflowRunsApiV1WorkflowWorkflowIdRunsGetResponse, GetWorkflowsApiV1WorkflowFetchGetData, GetWorkflowsApiV1WorkflowFetchGetError, GetWorkflowsApiV1WorkflowFetchGetResponse, GetWorkflowsSummaryApiV1WorkflowSummaryGetData, GetWorkflowsSummaryApiV1WorkflowSummaryGetError, GetWorkflowsSummaryApiV1WorkflowSummaryGetResponse, GetWorkflowTemplatesApiV1WorkflowTemplatesGetData, GetWorkflowTemplatesApiV1WorkflowTemplatesGetResponse, HandleCloudonixStatusCallbackApiV1TelephonyCloudonixStatusCallbackWorkflowRunIdPostData, HandleCloudonixStatusCallbackApiV1TelephonyCloudonixStatusCallbackWorkflowRunIdPostError, HandleInboundFallbackApiV1TelephonyInboundFallbackPostData, HandleInboundTelephonyApiV1TelephonyInboundWorkflowIdPostData, HandleInboundTelephonyApiV1TelephonyInboundWorkflowIdPostError, HandleTwilioStatusCallbackApiV1TelephonyTwilioStatusCallbackWorkflowRunIdPostData, HandleTwilioStatusCallbackApiV1TelephonyTwilioStatusCallbackWorkflowRunIdPostError, HandleVobizHangupCallbackApiV1TelephonyVobizHangupCallbackWorkflowRunIdPostData, HandleVobizHangupCallbackApiV1TelephonyVobizHangupCallbackWorkflowRunIdPostError, HandleVobizHangupCallbackByWorkflowApiV1TelephonyVobizHangupCallbackWorkflowWorkflowIdPostData, HandleVobizHangupCallbackByWorkflowApiV1TelephonyVobizHangupCallbackWorkflowWorkflowIdPostError, HandleVobizRingCallbackApiV1TelephonyVobizRingCallbackWorkflowRunIdPostData, HandleVobizRingCallbackApiV1TelephonyVobizRingCallbackWorkflowRunIdPostError, HandleVonageEventsApiV1TelephonyVonageEventsWorkflowRunIdPostData, HandleVonageEventsApiV1TelephonyVonageEventsWorkflowRunIdPostError, HealthApiV1HealthGetData,ImpersonateApiV1SuperuserImpersonatePostData, ImpersonateApiV1SuperuserImpersonatePostError, ImpersonateApiV1SuperuserImpersonatePostResponse, InitializeEmbedSessionApiV1PublicEmbedInitPostData, InitializeEmbedSessionApiV1PublicEmbedInitPostError, InitializeEmbedSessionApiV1PublicEmbedInitPostResponse, InitiateCallApiV1PublicAgentUuidPostData, InitiateCallApiV1PublicAgentUuidPostError, InitiateCallApiV1PublicAgentUuidPostResponse, InitiateCallApiV1TelephonyInitiateCallPostData, InitiateCallApiV1TelephonyInitiateCallPostError, ListCredentialsApiV1CredentialsGetData, ListCredentialsApiV1CredentialsGetError, ListCredentialsApiV1CredentialsGetResponse, ListTestSessionsApiV1LooptalkTestSessionsGetData, ListTestSessionsApiV1LooptalkTestSessionsGetError, ListTestSessionsApiV1LooptalkTestSessionsGetResponse, ListToolsApiV1ToolsGetData, ListToolsApiV1ToolsGetError, ListToolsApiV1ToolsGetResponse, OptionsConfigApiV1PublicEmbedConfigTokenOptionsData, OptionsConfigApiV1PublicEmbedConfigTokenOptionsError, OptionsInitApiV1PublicEmbedInitOptionsData, PauseCampaignApiV1CampaignCampaignIdPausePostData, PauseCampaignApiV1CampaignCampaignIdPausePostError, PauseCampaignApiV1CampaignCampaignIdPausePostResponse, ReactivateApiKeyApiV1UserApiKeysApiKeyIdReactivatePutData, ReactivateApiKeyApiV1UserApiKeysApiKeyIdReactivatePutError, ReactivateApiKeyApiV1UserApiKeysApiKeyIdReactivatePutResponse, ReactivateServiceKeyApiV1UserServiceKeysServiceKeyIdReactivatePutData, ReactivateServiceKeyApiV1UserServiceKeysServiceKeyIdReactivatePutError, ResumeCampaignApiV1CampaignCampaignIdResumePostData, ResumeCampaignApiV1CampaignCampaignIdResumePostError, ResumeCampaignApiV1CampaignCampaignIdResumePostResponse, SaveTelephonyConfigurationApiV1OrganizationsTelephonyConfigPostData, SaveTelephonyConfigurationApiV1OrganizationsTelephonyConfigPostError, SetAdminCommentApiV1SuperuserWorkflowRunsRunIdCommentPostData, SetAdminCommentApiV1SuperuserWorkflowRunsRunIdCommentPostError, SetAdminCommentApiV1SuperuserWorkflowRunsRunIdCommentPostResponse, StartCampaignApiV1CampaignCampaignIdStartPostData, StartCampaignApiV1CampaignCampaignIdStartPostError, StartCampaignApiV1CampaignCampaignIdStartPostResponse, StartTestSessionApiV1LooptalkTestSessionsTestSessionIdStartPostData, StartTestSessionApiV1LooptalkTestSessionsTestSessionIdStartPostError, StopTestSessionApiV1LooptalkTestSessionsTestSessionIdStopPostData, StopTestSessionApiV1LooptalkTestSessionsTestSessionIdStopPostError, UnarchiveToolApiV1ToolsToolUuidUnarchivePostData, UnarchiveToolApiV1ToolsToolUuidUnarchivePostError, UnarchiveToolApiV1ToolsToolUuidUnarchivePostResponse, UpdateCredentialApiV1CredentialsCredentialUuidPutData, UpdateCredentialApiV1CredentialsCredentialUuidPutError, UpdateCredentialApiV1CredentialsCredentialUuidPutResponse, UpdateIntegrationApiV1IntegrationIntegrationIdPutData, UpdateIntegrationApiV1IntegrationIntegrationIdPutError, UpdateIntegrationApiV1IntegrationIntegrationIdPutResponse, UpdateToolApiV1ToolsToolUuidPutData, UpdateToolApiV1ToolsToolUuidPutError, UpdateToolApiV1ToolsToolUuidPutResponse, UpdateUserConfigurationsApiV1UserConfigurationsUserPutData, UpdateUserConfigurationsApiV1UserConfigurationsUserPutError, UpdateUserConfigurationsApiV1UserConfigurationsUserPutResponse, UpdateWorkflowApiV1WorkflowWorkflowIdPutData, UpdateWorkflowApiV1WorkflowWorkflowIdPutError, UpdateWorkflowApiV1WorkflowWorkflowIdPutResponse, UpdateWorkflowStatusApiV1WorkflowWorkflowIdStatusPutData, UpdateWorkflowStatusApiV1WorkflowWorkflowIdStatusPutError, UpdateWorkflowStatusApiV1WorkflowWorkflowIdStatusPutResponse, ValidateUserConfigurationsApiV1UserConfigurationsUserValidateGetData, ValidateUserConfigurationsApiV1UserConfigurationsUserValidateGetError, ValidateUserConfigurationsApiV1UserConfigurationsUserValidateGetResponse, ValidateWorkflowApiV1WorkflowWorkflowIdValidatePostData, ValidateWorkflowApiV1WorkflowWorkflowIdValidatePostError, ValidateWorkflowApiV1WorkflowWorkflowIdValidatePostResponse } from './types.gen'; export type Options = ClientOptions & { /** @@ -1338,6 +1337,125 @@ export const createOrUpdateEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenPost = (options: Options) => { + return (options.client ?? _heyApiClient).post({ + url: '/api/v1/knowledge-base/upload-url', + ...options, + headers: { + 'Content-Type': 'application/json', + ...options?.headers + } + }); +}; + +/** + * Trigger document processing + * Trigger asynchronous processing of an uploaded document. + * + * This endpoint should be called after successfully uploading a file to the presigned URL. + * It will: + * 1. Create a document record in the database with the specified UUID + * 2. Enqueue a background task to process the document (chunking and embedding) + * + * The document status will be updated from 'pending' -> 'processing' -> 'completed' or 'failed'. + * + * Embedding Services: + * * openai (default): High-quality 1536-dimensional embeddings (requires OPENAI_API_KEY) + * * sentence_transformer: Free, offline-capable, 384-dimensional embeddings + * + * Access Control: + * * Users can only process documents in their organization. + */ +export const processDocumentApiV1KnowledgeBaseProcessDocumentPost = (options: Options) => { + return (options.client ?? _heyApiClient).post({ + url: '/api/v1/knowledge-base/process-document', + ...options, + headers: { + 'Content-Type': 'application/json', + ...options?.headers + } + }); +}; + +/** + * List documents + * List all documents for the user's organization. + * + * Access Control: + * * Users can only see documents from their organization. + */ +export const listDocumentsApiV1KnowledgeBaseDocumentsGet = (options?: Options) => { + return (options?.client ?? _heyApiClient).get({ + url: '/api/v1/knowledge-base/documents', + ...options + }); +}; + +/** + * Delete document + * Soft delete a document and its chunks. + * + * Access Control: + * * Users can only delete documents from their organization. + */ +export const deleteDocumentApiV1KnowledgeBaseDocumentsDocumentUuidDelete = (options: Options) => { + return (options.client ?? _heyApiClient).delete({ + url: '/api/v1/knowledge-base/documents/{document_uuid}', + ...options + }); +}; + +/** + * Get document details + * Get details of a specific document. + * + * Access Control: + * * Users can only access documents from their organization. + */ +export const getDocumentApiV1KnowledgeBaseDocumentsDocumentUuidGet = (options: Options) => { + return (options.client ?? _heyApiClient).get({ + url: '/api/v1/knowledge-base/documents/{document_uuid}', + ...options + }); +}; + +/** + * Search for similar chunks + * Search for document chunks similar to the query. + * + * This endpoint uses vector similarity search to find relevant chunks. + * Results are returned without threshold filtering - apply similarity + * thresholds at the application layer after optional reranking. + * + * Access Control: + * * Users can only search documents from their organization. + */ +export const searchChunksApiV1KnowledgeBaseSearchPost = (options: Options) => { + return (options.client ?? _heyApiClient).post({ + url: '/api/v1/knowledge-base/search', + ...options, + headers: { + 'Content-Type': 'application/json', + ...options?.headers + } + }); +}; + /** * Health */ @@ -1346,4 +1464,4 @@ export const healthApiV1HealthGet = (optio url: '/api/v1/health', ...options }); -}; +}; \ No newline at end of file diff --git a/ui/src/client/types.gen.ts b/ui/src/client/types.gen.ts index aee4bea..af53bd9 100644 --- a/ui/src/client/types.gen.ts +++ b/ui/src/client/types.gen.ts @@ -83,6 +83,54 @@ export type CampaignsResponse = { campaigns: Array; }; +/** + * Response schema for a document chunk. + */ +export type ChunkResponseSchema = { + id: number; + document_id: number; + chunk_text: string; + contextualized_text: string | null; + chunk_index: number; + chunk_metadata: { + [key: string]: unknown; + }; + filename: string; + document_uuid: string; + similarity: number; +}; + +/** + * Request schema for searching similar chunks. + */ +export type ChunkSearchRequestSchema = { + /** + * Search query text + */ + query: string; + /** + * Maximum number of results + */ + limit?: number; + /** + * Filter by specific document UUIDs + */ + document_uuids?: Array | null; + /** + * Minimum similarity threshold + */ + min_similarity?: number | null; +}; + +/** + * Response schema for chunk search results. + */ +export type ChunkSearchResponseSchema = { + chunks: Array; + query: string; + total_results: number; +}; + /** * Request schema for Cloudonix configuration. */ @@ -303,11 +351,91 @@ export type DefaultConfigurationsResponse = { [key: string]: unknown; }; }; + embeddings: { + [key: string]: { + [key: string]: unknown; + }; + }; default_providers: { [key: string]: string; }; }; +/** + * Response schema for list of documents. + */ +export type DocumentListResponseSchema = { + documents: Array; + total: number; + limit: number; + offset: number; +}; + +/** + * Response schema for document metadata. + */ +export type DocumentResponseSchema = { + id: number; + document_uuid: string; + filename: string; + file_size_bytes: number; + file_hash: string; + mime_type: string; + processing_status: string; + processing_error?: string | null; + total_chunks: number; + custom_metadata: { + [key: string]: unknown; + }; + docling_metadata: { + [key: string]: unknown; + }; + source_url?: string | null; + created_at: string; + updated_at: string; + organization_id: number; + created_by: number; + is_active: boolean; +}; + +/** + * Request schema for initiating document upload. + */ +export type DocumentUploadRequestSchema = { + /** + * Name of the file to upload + */ + filename: string; + /** + * MIME type of the file + */ + mime_type: string; + /** + * Optional custom metadata + */ + custom_metadata?: { + [key: string]: unknown; + } | null; +}; + +/** + * Response schema containing upload URL and document metadata. + */ +export type DocumentUploadResponseSchema = { + /** + * Signed URL for uploading the file + */ + upload_url: string; + /** + * Unique identifier for the document + */ + document_uuid: string; + /** + * S3 key where file should be uploaded + */ + s3_key: string; +}; + export type DuplicateTemplateRequest = { template_id: number; workflow_name: string; @@ -537,6 +665,24 @@ export type PresignedUploadUrlResponse = { expires_in: number; }; +/** + * Request schema for triggering document processing. + */ +export type ProcessDocumentRequestSchema = { + /** + * Document UUID to process + */ + document_uuid: string; + /** + * S3 key of the uploaded file + */ + s3_key: string; + /** + * Embedding service to use for processing. Options: 'openai' (default, 1536-dim, requires API key) or 'sentence_transformer' (free, 384-dim) + */ + embedding_service?: 'sentence_transformer' | 'openai'; +}; + export type S3SignedUrlResponse = { url: string; expires_in: number; @@ -787,6 +933,9 @@ export type UserConfigurationRequestResponseSchema = { stt?: { [key: string]: string | number; } | null; + embeddings?: { + [key: string]: string | number; + } | null; test_phone_number?: string | null; timezone?: string | null; organization_pricing?: { @@ -4126,6 +4275,213 @@ export type CreateOrUpdateEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenPostRespons export type CreateOrUpdateEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenPostResponse = CreateOrUpdateEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenPostResponses[keyof CreateOrUpdateEmbedTokenApiV1WorkflowWorkflowIdEmbedTokenPostResponses]; +export type GetUploadUrlApiV1KnowledgeBaseUploadUrlPostData = { + body: DocumentUploadRequestSchema; + headers?: { + authorization?: string | null; + 'X-API-Key'?: string | null; + }; + path?: never; + query?: never; + url: '/api/v1/knowledge-base/upload-url'; +}; + +export type GetUploadUrlApiV1KnowledgeBaseUploadUrlPostErrors = { + /** + * Not found + */ + 404: unknown; + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type GetUploadUrlApiV1KnowledgeBaseUploadUrlPostError = GetUploadUrlApiV1KnowledgeBaseUploadUrlPostErrors[keyof GetUploadUrlApiV1KnowledgeBaseUploadUrlPostErrors]; + +export type GetUploadUrlApiV1KnowledgeBaseUploadUrlPostResponses = { + /** + * Successful Response + */ + 200: DocumentUploadResponseSchema; +}; + +export type GetUploadUrlApiV1KnowledgeBaseUploadUrlPostResponse = GetUploadUrlApiV1KnowledgeBaseUploadUrlPostResponses[keyof GetUploadUrlApiV1KnowledgeBaseUploadUrlPostResponses]; + +export type ProcessDocumentApiV1KnowledgeBaseProcessDocumentPostData = { + body: ProcessDocumentRequestSchema; + headers?: { + authorization?: string | null; + 'X-API-Key'?: string | null; + }; + path?: never; + query?: never; + url: '/api/v1/knowledge-base/process-document'; +}; + +export type ProcessDocumentApiV1KnowledgeBaseProcessDocumentPostErrors = { + /** + * Not found + */ + 404: unknown; + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type ProcessDocumentApiV1KnowledgeBaseProcessDocumentPostError = ProcessDocumentApiV1KnowledgeBaseProcessDocumentPostErrors[keyof ProcessDocumentApiV1KnowledgeBaseProcessDocumentPostErrors]; + +export type ProcessDocumentApiV1KnowledgeBaseProcessDocumentPostResponses = { + /** + * Successful Response + */ + 200: DocumentResponseSchema; +}; + +export type ProcessDocumentApiV1KnowledgeBaseProcessDocumentPostResponse = ProcessDocumentApiV1KnowledgeBaseProcessDocumentPostResponses[keyof ProcessDocumentApiV1KnowledgeBaseProcessDocumentPostResponses]; + +export type ListDocumentsApiV1KnowledgeBaseDocumentsGetData = { + body?: never; + headers?: { + authorization?: string | null; + 'X-API-Key'?: string | null; + }; + path?: never; + query?: { + /** + * Filter by processing status + */ + status?: string | null; + limit?: number; + offset?: number; + }; + url: '/api/v1/knowledge-base/documents'; +}; + +export type ListDocumentsApiV1KnowledgeBaseDocumentsGetErrors = { + /** + * Not found + */ + 404: unknown; + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type ListDocumentsApiV1KnowledgeBaseDocumentsGetError = ListDocumentsApiV1KnowledgeBaseDocumentsGetErrors[keyof ListDocumentsApiV1KnowledgeBaseDocumentsGetErrors]; + +export type ListDocumentsApiV1KnowledgeBaseDocumentsGetResponses = { + /** + * Successful Response + */ + 200: DocumentListResponseSchema; +}; + +export type ListDocumentsApiV1KnowledgeBaseDocumentsGetResponse = ListDocumentsApiV1KnowledgeBaseDocumentsGetResponses[keyof ListDocumentsApiV1KnowledgeBaseDocumentsGetResponses]; + +export type DeleteDocumentApiV1KnowledgeBaseDocumentsDocumentUuidDeleteData = { + body?: never; + headers?: { + authorization?: string | null; + 'X-API-Key'?: string | null; + }; + path: { + document_uuid: string; + }; + query?: never; + url: '/api/v1/knowledge-base/documents/{document_uuid}'; +}; + +export type DeleteDocumentApiV1KnowledgeBaseDocumentsDocumentUuidDeleteErrors = { + /** + * Not found + */ + 404: unknown; + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type DeleteDocumentApiV1KnowledgeBaseDocumentsDocumentUuidDeleteError = DeleteDocumentApiV1KnowledgeBaseDocumentsDocumentUuidDeleteErrors[keyof DeleteDocumentApiV1KnowledgeBaseDocumentsDocumentUuidDeleteErrors]; + +export type DeleteDocumentApiV1KnowledgeBaseDocumentsDocumentUuidDeleteResponses = { + /** + * Successful Response + */ + 200: unknown; +}; + +export type GetDocumentApiV1KnowledgeBaseDocumentsDocumentUuidGetData = { + body?: never; + headers?: { + authorization?: string | null; + 'X-API-Key'?: string | null; + }; + path: { + document_uuid: string; + }; + query?: never; + url: '/api/v1/knowledge-base/documents/{document_uuid}'; +}; + +export type GetDocumentApiV1KnowledgeBaseDocumentsDocumentUuidGetErrors = { + /** + * Not found + */ + 404: unknown; + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type GetDocumentApiV1KnowledgeBaseDocumentsDocumentUuidGetError = GetDocumentApiV1KnowledgeBaseDocumentsDocumentUuidGetErrors[keyof GetDocumentApiV1KnowledgeBaseDocumentsDocumentUuidGetErrors]; + +export type GetDocumentApiV1KnowledgeBaseDocumentsDocumentUuidGetResponses = { + /** + * Successful Response + */ + 200: DocumentResponseSchema; +}; + +export type GetDocumentApiV1KnowledgeBaseDocumentsDocumentUuidGetResponse = GetDocumentApiV1KnowledgeBaseDocumentsDocumentUuidGetResponses[keyof GetDocumentApiV1KnowledgeBaseDocumentsDocumentUuidGetResponses]; + +export type SearchChunksApiV1KnowledgeBaseSearchPostData = { + body: ChunkSearchRequestSchema; + headers?: { + authorization?: string | null; + 'X-API-Key'?: string | null; + }; + path?: never; + query?: never; + url: '/api/v1/knowledge-base/search'; +}; + +export type SearchChunksApiV1KnowledgeBaseSearchPostErrors = { + /** + * Not found + */ + 404: unknown; + /** + * Validation Error + */ + 422: HttpValidationError; +}; + +export type SearchChunksApiV1KnowledgeBaseSearchPostError = SearchChunksApiV1KnowledgeBaseSearchPostErrors[keyof SearchChunksApiV1KnowledgeBaseSearchPostErrors]; + +export type SearchChunksApiV1KnowledgeBaseSearchPostResponses = { + /** + * Successful Response + */ + 200: ChunkSearchResponseSchema; +}; + +export type SearchChunksApiV1KnowledgeBaseSearchPostResponse = SearchChunksApiV1KnowledgeBaseSearchPostResponses[keyof SearchChunksApiV1KnowledgeBaseSearchPostResponses]; + export type HealthApiV1HealthGetData = { body?: never; path?: never; @@ -4149,4 +4505,4 @@ export type HealthApiV1HealthGetResponses = { export type ClientOptions = { baseUrl: 'http://127.0.0.1:8000' | (string & {}); -}; +}; \ No newline at end of file diff --git a/ui/src/components/ServiceConfiguration.tsx b/ui/src/components/ServiceConfiguration.tsx index c2d88ff..1b4efcf 100644 --- a/ui/src/components/ServiceConfiguration.tsx +++ b/ui/src/components/ServiceConfiguration.tsx @@ -14,7 +14,7 @@ import { Tabs, TabsContent, TabsList, TabsTrigger } from "@/components/ui/tabs"; import { VoiceSelector } from "@/components/VoiceSelector"; import { useUserConfig } from "@/context/UserConfigContext"; -type ServiceSegment = "llm" | "tts" | "stt"; +type ServiceSegment = "llm" | "tts" | "stt" | "embeddings"; interface SchemaProperty { type?: string; @@ -41,6 +41,7 @@ const TAB_CONFIG: { key: ServiceSegment; label: string }[] = [ { key: "llm", label: "LLM" }, { key: "tts", label: "Voice" }, { key: "stt", label: "Transcriber" }, + { key: "embeddings", label: "Embedding" }, ]; // Display names for language codes (Deepgram + Sarvam) @@ -109,12 +110,14 @@ export default function ServiceConfiguration() { const [schemas, setSchemas] = useState>>({ llm: {}, tts: {}, - stt: {} + stt: {}, + embeddings: {} }); const [serviceProviders, setServiceProviders] = useState>({ llm: "", tts: "", - stt: "" + stt: "", + embeddings: "" }); const [isManualModelInput, setIsManualModelInput] = useState(false); const [hasCheckedManualMode, setHasCheckedManualMode] = useState(false); @@ -136,7 +139,8 @@ export default function ServiceConfiguration() { setSchemas({ llm: response.data.llm as Record, tts: response.data.tts as Record, - stt: response.data.stt as Record + stt: response.data.stt as Record, + embeddings: response.data.embeddings as Record }); } else { console.error("Failed to fetch configurations"); @@ -147,7 +151,8 @@ export default function ServiceConfiguration() { const selectedProviders: Record = { llm: response.data.default_providers.llm, tts: response.data.default_providers.tts, - stt: response.data.default_providers.stt + stt: response.data.default_providers.stt, + embeddings: response.data.default_providers.embeddings }; const setServicePropertyValues = (service: ServiceSegment) => { @@ -173,6 +178,7 @@ export default function ServiceConfiguration() { setServicePropertyValues("llm"); setServicePropertyValues("tts"); setServicePropertyValues("stt"); + setServicePropertyValues("embeddings"); // IMPORTANT: Reset form values BEFORE changing providers // Otherwise, Radix Select sees old values that don't match new provider's enum @@ -246,7 +252,7 @@ export default function ServiceConfiguration() { setApiError(null); setIsSaving(true); - const userConfig = { + const userConfig: Record> = { llm: { provider: serviceProviders.llm, api_key: data.llm_api_key as string, @@ -259,6 +265,11 @@ export default function ServiceConfiguration() { stt: { provider: serviceProviders.stt, api_key: data.stt_api_key as string + }, + embeddings: { + provider: serviceProviders.embeddings, + api_key: data.embeddings_api_key as string, + model: data.embeddings_model as string } }; @@ -273,12 +284,25 @@ export default function ServiceConfiguration() { } }); + // Build save config - only include embeddings if api_key is provided + const saveConfig: { + llm: Record; + tts: Record; + stt: Record; + embeddings?: Record; + } = { + llm: userConfig.llm, + tts: userConfig.tts, + stt: userConfig.stt + }; + + // Only include embeddings if user has configured it (has api_key) + if (userConfig.embeddings.api_key) { + saveConfig.embeddings = userConfig.embeddings; + } + try { - await saveUserConfig({ - llm: userConfig.llm, - tts: userConfig.tts, - stt: userConfig.stt - }); + await saveUserConfig(saveConfig); setApiError(null); } catch (error: unknown) { if (error instanceof Error) { @@ -543,7 +567,7 @@ export default function ServiceConfiguration() { - + {TAB_CONFIG.map(({ key, label }) => ( {label} diff --git a/ui/src/components/flow/DocumentBadges.tsx b/ui/src/components/flow/DocumentBadges.tsx new file mode 100644 index 0000000..7069b45 --- /dev/null +++ b/ui/src/components/flow/DocumentBadges.tsx @@ -0,0 +1,65 @@ +"use client"; + +import { useCallback, useEffect, useState } from "react"; + +import { useWorkflow } from "@/app/workflow/[workflowId]/contexts/WorkflowContext"; +import type { DocumentResponseSchema } from "@/client/types.gen"; +import { Badge } from "@/components/ui/badge"; + +interface DocumentBadgesProps { + documentUuids: string[]; + onStaleUuidsDetected?: (staleUuids: string[]) => void; +} + +export const DocumentBadges = ({ documentUuids, onStaleUuidsDetected }: DocumentBadgesProps) => { + const { documents } = useWorkflow(); + const [documentNames, setDocumentNames] = useState>({}); + + const processDocuments = useCallback((docs: DocumentResponseSchema[]) => { + const nameMap: Record = {}; + const validUuids = new Set(); + + docs + .filter((doc) => documentUuids.includes(doc.document_uuid)) + .forEach((doc) => { + nameMap[doc.document_uuid] = doc.filename; + validUuids.add(doc.document_uuid); + }); + setDocumentNames(nameMap); + + // Detect stale UUIDs - this only runs when we have loaded data (not undefined) + if (onStaleUuidsDetected) { + const staleUuids = documentUuids.filter(uuid => !validUuids.has(uuid)); + if (staleUuids.length > 0) { + onStaleUuidsDetected(staleUuids); + } + } + }, [documentUuids, onStaleUuidsDetected]); + + useEffect(() => { + if (documentUuids.length > 0 && documents !== undefined) { + processDocuments(documents); + } else if (documentUuids.length === 0) { + setDocumentNames({}); + } + }, [documentUuids, documents, processDocuments]); + + if (documentUuids.length === 0) { + return <>; + } + + // Show loading while data hasn't loaded yet + if (documents === undefined) { + return Loading...; + } + + return ( + <> + {documentUuids.map((uuid) => ( + + {documentNames[uuid] || uuid} + + ))} + + ); +}; diff --git a/ui/src/components/flow/DocumentSelector.tsx b/ui/src/components/flow/DocumentSelector.tsx new file mode 100644 index 0000000..f8de6cf --- /dev/null +++ b/ui/src/components/flow/DocumentSelector.tsx @@ -0,0 +1,137 @@ +"use client"; + +import { FileText } from "lucide-react"; +import Link from "next/link"; +import { useMemo } from "react"; + +import type { DocumentResponseSchema } from "@/client/types.gen"; +import { Button } from "@/components/ui/button"; +import { Checkbox } from "@/components/ui/checkbox"; +import { Label } from "@/components/ui/label"; + +interface DocumentSelectorProps { + value: string[]; + onChange: (uuids: string[]) => void; + documents: DocumentResponseSchema[]; + disabled?: boolean; + label?: string; + description?: string; + showLabel?: boolean; +} + +export const DocumentSelector = ({ + value, + onChange, + documents, + disabled = false, + label = "Knowledge Base Documents", + description = "Select documents that the agent can reference during conversations.", + showLabel = true, +}: DocumentSelectorProps) => { + // Only show completed documents + const completedDocuments = useMemo( + () => documents.filter((doc) => doc.processing_status === "completed"), + [documents] + ); + + const handleToggle = (documentUuid: string, checked: boolean) => { + if (checked) { + onChange([...value, documentUuid]); + } else { + onChange(value.filter((uuid) => uuid !== documentUuid)); + } + }; + + const formatFileSize = (bytes: number): string => { + if (bytes === 0) return "0 Bytes"; + const k = 1024; + const sizes = ["Bytes", "KB", "MB", "GB"]; + const i = Math.floor(Math.log(bytes) / Math.log(k)); + return Math.round(bytes / Math.pow(k, i) * 100) / 100 + " " + sizes[i]; + }; + + if (completedDocuments.length === 0) { + return ( +
+ {showLabel && ( + <> + + {description && ( + + )} + + )} +
+
+ No documents available. Upload documents to the knowledge base first. +
+
+ + + +
+
+
+ ); + } + + return ( +
+ {showLabel && ( + <> + + {description && ( + + )} + + )} +
+
+ {completedDocuments.map((doc) => ( +
+ + handleToggle(doc.document_uuid, checked as boolean) + } + disabled={disabled} + /> +
+ +
+
+ ))} +
+
+
+ + {value.length} {value.length === 1 ? "document" : "documents"} selected + + + Manage Documents + +
+
+ ); +}; diff --git a/ui/src/components/flow/ToolBadges.tsx b/ui/src/components/flow/ToolBadges.tsx index d4437db..56d9ce5 100644 --- a/ui/src/components/flow/ToolBadges.tsx +++ b/ui/src/components/flow/ToolBadges.tsx @@ -2,43 +2,43 @@ import { useCallback, useEffect, useState } from "react"; -import { listToolsApiV1ToolsGet } from "@/client/sdk.gen"; +import { useWorkflow } from "@/app/workflow/[workflowId]/contexts/WorkflowContext"; import type { ToolResponse } from "@/client/types.gen"; import { Badge } from "@/components/ui/badge"; -import { useAuth } from "@/lib/auth"; interface ToolBadgesProps { toolUuids: string[]; + onStaleUuidsDetected?: (staleUuids: string[]) => void; } -export function ToolBadges({ toolUuids }: ToolBadgesProps) { - const { getAccessToken } = useAuth(); - const [tools, setTools] = useState([]); +export function ToolBadges({ toolUuids, onStaleUuidsDetected }: ToolBadgesProps) { + const { tools } = useWorkflow(); + const [selectedTools, setSelectedTools] = useState([]); - const fetchTools = useCallback(async () => { - try { - const accessToken = await getAccessToken(); - const response = await listToolsApiV1ToolsGet({ - headers: { Authorization: `Bearer ${accessToken}` }, - }); - if (response.data) { - setTools(response.data); + const processTools = useCallback((toolsData: ToolResponse[]) => { + const filtered = toolsData.filter(tool => toolUuids.includes(tool.tool_uuid)); + setSelectedTools(filtered); + + // Detect stale UUIDs - this only runs when we have loaded data (not undefined) + if (onStaleUuidsDetected) { + const validUuids = new Set(toolsData.map(tool => tool.tool_uuid)); + const staleUuids = toolUuids.filter(uuid => !validUuids.has(uuid)); + if (staleUuids.length > 0) { + onStaleUuidsDetected(staleUuids); } - } catch (error) { - console.error("Failed to fetch tools:", error); } - }, [getAccessToken]); + }, [toolUuids, onStaleUuidsDetected]); useEffect(() => { - if (toolUuids.length > 0) { - fetchTools(); + if (toolUuids.length > 0 && tools !== undefined) { + processTools(tools); + } else if (toolUuids.length === 0) { + setSelectedTools([]); } - }, [toolUuids.length, fetchTools]); + }, [toolUuids, tools, processTools]); - const selectedTools = tools.filter((tool) => toolUuids.includes(tool.tool_uuid)); - - if (selectedTools.length === 0 && toolUuids.length > 0) { - // Still loading or tools not found + // Show loading while data hasn't loaded yet + if (tools === undefined && toolUuids.length > 0) { return (
diff --git a/ui/src/components/flow/ToolSelector.tsx b/ui/src/components/flow/ToolSelector.tsx index 7162f40..cf3a100 100644 --- a/ui/src/components/flow/ToolSelector.tsx +++ b/ui/src/components/flow/ToolSelector.tsx @@ -1,20 +1,18 @@ "use client"; -import { ExternalLink, Loader2 } from "lucide-react"; +import { ExternalLink } from "lucide-react"; import Link from "next/link"; -import { useCallback, useEffect, useState } from "react"; import { renderToolIcon } from "@/app/tools/config"; -import { listToolsApiV1ToolsGet } from "@/client/sdk.gen"; import type { ToolResponse } from "@/client/types.gen"; import { Button } from "@/components/ui/button"; import { Checkbox } from "@/components/ui/checkbox"; import { Label } from "@/components/ui/label"; -import { useAuth } from "@/lib/auth"; interface ToolSelectorProps { value: string[]; onChange: (uuids: string[]) => void; + tools: ToolResponse[]; disabled?: boolean; label?: string; description?: string; @@ -24,43 +22,14 @@ interface ToolSelectorProps { export function ToolSelector({ value, onChange, + tools, disabled = false, label = "Tools", description = "Select tools that the agent can use during the conversation.", showLabel = true, }: ToolSelectorProps) { - const { getAccessToken } = useAuth(); - - const [tools, setTools] = useState([]); - const [loading, setLoading] = useState(false); - - const fetchTools = useCallback(async () => { - setLoading(true); - try { - const accessToken = await getAccessToken(); - const response = await listToolsApiV1ToolsGet({ - headers: { Authorization: `Bearer ${accessToken}` }, - query: { status: "active" }, - }); - if (response.error) { - console.error("Failed to fetch tools:", response.error); - setTools([]); - return; - } - if (response.data) { - setTools(response.data); - } - } catch (error) { - console.error("Failed to fetch tools:", error); - setTools([]); - } finally { - setLoading(false); - } - }, [getAccessToken]); - - useEffect(() => { - fetchTools(); - }, [fetchTools]); + // Filter to only show active tools + const activeTools = tools.filter((tool) => tool.status === "active"); const handleToggle = (toolUuid: string, checked: boolean) => { if (checked) { @@ -83,12 +52,7 @@ export function ToolSelector({ )} - {loading ? ( -
- - Loading tools... -
- ) : tools.length === 0 ? ( + {activeTools.length === 0 ? (

No tools available. @@ -102,7 +66,7 @@ export function ToolSelector({

) : (
- {tools.map((tool) => { + {activeTools.map((tool) => { const isSelected = value.includes(tool.tool_uuid); return (
- + +
+ )} + {data.document_uuids && data.document_uuids.length > 0 && ( +
+
+ + Documents: +
+
)} @@ -179,6 +223,10 @@ export const AgentNode = memo(({ data, selected, id }: AgentNodeProps) => { setAddGlobalPrompt={setAddGlobalPrompt} toolUuids={toolUuids} setToolUuids={setToolUuids} + documentUuids={documentUuids} + setDocumentUuids={setDocumentUuids} + tools={tools ?? []} + documents={documents ?? []} /> )} @@ -203,6 +251,10 @@ const AgentNodeEditForm = ({ setAddGlobalPrompt, toolUuids, setToolUuids, + documentUuids, + setDocumentUuids, + tools, + documents, }: AgentNodeEditFormProps) => { const handleVariableNameChange = (idx: number, value: string) => { const newVars = [...variables]; @@ -343,9 +395,20 @@ const AgentNodeEditForm = ({ + + {/* Documents Section */} +
+ +
); }; diff --git a/ui/src/components/flow/nodes/StartCall.tsx b/ui/src/components/flow/nodes/StartCall.tsx index 865590c..abec38d 100644 --- a/ui/src/components/flow/nodes/StartCall.tsx +++ b/ui/src/components/flow/nodes/StartCall.tsx @@ -1,8 +1,11 @@ import { NodeProps, NodeToolbar, Position } from "@xyflow/react"; -import { Edit, Play, PlusIcon, Trash2Icon, Wrench } from "lucide-react"; -import { memo, useEffect, useMemo, useState } from "react"; +import { Edit, FileText, Play, PlusIcon, Trash2Icon, Wrench } from "lucide-react"; +import { memo, useCallback, useEffect, useMemo, useState } from "react"; import { useWorkflow } from "@/app/workflow/[workflowId]/contexts/WorkflowContext"; +import type { DocumentResponseSchema, ToolResponse } from "@/client/types.gen"; +import { DocumentBadges } from "@/components/flow/DocumentBadges"; +import { DocumentSelector } from "@/components/flow/DocumentSelector"; import { ToolBadges } from "@/components/flow/ToolBadges"; import { ToolSelector } from "@/components/flow/ToolSelector"; import { ExtractionVariable, FlowNodeData } from "@/components/flow/types"; @@ -41,6 +44,10 @@ interface StartCallEditFormProps { setVariables: (vars: ExtractionVariable[]) => void; toolUuids: string[]; setToolUuids: (value: string[]) => void; + documentUuids: string[]; + setDocumentUuids: (value: string[]) => void; + tools: ToolResponse[]; + documents: DocumentResponseSchema[]; } interface StartCallNodeProps extends NodeProps { @@ -52,7 +59,7 @@ export const StartCall = memo(({ data, selected, id }: StartCallNodeProps) => { id, additionalData: { is_start: true } }); - const { saveWorkflow } = useWorkflow(); + const { saveWorkflow, tools, documents } = useWorkflow(); // Form state const [prompt, setPrompt] = useState(data.prompt ?? ""); @@ -66,6 +73,7 @@ export const StartCall = memo(({ data, selected, id }: StartCallNodeProps) => { const [extractionPrompt, setExtractionPrompt] = useState(data.extraction_prompt ?? ""); const [variables, setVariables] = useState(data.extraction_variables ?? []); const [toolUuids, setToolUuids] = useState(data.tool_uuids ?? []); + const [documentUuids, setDocumentUuids] = useState(data.document_uuids ?? []); // Compute if form has unsaved changes (only check prompt, name) const isDirty = useMemo(() => { @@ -89,6 +97,7 @@ export const StartCall = memo(({ data, selected, id }: StartCallNodeProps) => { extraction_prompt: extractionPrompt, extraction_variables: variables, tool_uuids: toolUuids.length > 0 ? toolUuids : undefined, + document_uuids: documentUuids.length > 0 ? documentUuids : undefined, }); setOpen(false); // Save the workflow after updating node data with a small delay to ensure state is updated @@ -111,6 +120,7 @@ export const StartCall = memo(({ data, selected, id }: StartCallNodeProps) => { setExtractionPrompt(data.extraction_prompt ?? ""); setVariables(data.extraction_variables ?? []); setToolUuids(data.tool_uuids ?? []); + setDocumentUuids(data.document_uuids ?? []); } setOpen(newOpen); }; @@ -129,9 +139,34 @@ export const StartCall = memo(({ data, selected, id }: StartCallNodeProps) => { setExtractionPrompt(data.extraction_prompt ?? ""); setVariables(data.extraction_variables ?? []); setToolUuids(data.tool_uuids ?? []); + setDocumentUuids(data.document_uuids ?? []); } }, [data, open]); + // Handle cleanup of stale document UUIDs + const handleStaleDocuments = useCallback((staleUuids: string[]) => { + const cleanedUuids = (data.document_uuids ?? []).filter(uuid => !staleUuids.includes(uuid)); + handleSaveNodeData({ + ...data, + document_uuids: cleanedUuids.length > 0 ? cleanedUuids : undefined, + }); + setTimeout(async () => { + await saveWorkflow(); + }, 100); + }, [data, handleSaveNodeData, saveWorkflow]); + + // Handle cleanup of stale tool UUIDs + const handleStaleTools = useCallback((staleUuids: string[]) => { + const cleanedUuids = (data.tool_uuids ?? []).filter(uuid => !staleUuids.includes(uuid)); + handleSaveNodeData({ + ...data, + tool_uuids: cleanedUuids.length > 0 ? cleanedUuids : undefined, + }); + setTimeout(async () => { + await saveWorkflow(); + }, 100); + }, [data, handleSaveNodeData, saveWorkflow]); + return ( <> { Tools: - + + + )} + {data.document_uuids && data.document_uuids.length > 0 && ( +
+
+ + Documents: +
+
)} @@ -199,6 +243,10 @@ export const StartCall = memo(({ data, selected, id }: StartCallNodeProps) => { setVariables={setVariables} toolUuids={toolUuids} setToolUuids={setToolUuids} + documentUuids={documentUuids} + setDocumentUuids={setDocumentUuids} + tools={tools ?? []} + documents={documents ?? []} /> )} @@ -229,6 +277,10 @@ const StartCallEditForm = ({ setVariables, toolUuids, setToolUuids, + documentUuids, + setDocumentUuids, + tools, + documents, }: StartCallEditFormProps) => { const handleVariableNameChange = (idx: number, value: string) => { const newVars = [...variables]; @@ -414,9 +466,20 @@ const StartCallEditForm = ({ + + {/* Documents Section */} +
+ +
); }; diff --git a/ui/src/components/flow/types.ts b/ui/src/components/flow/types.ts index a04d64c..3768716 100644 --- a/ui/src/components/flow/types.ts +++ b/ui/src/components/flow/types.ts @@ -42,6 +42,8 @@ export type FlowNodeData = { }; // Tools - array of tool UUIDs that can be invoked by this node tool_uuids?: string[]; + // Documents - array of knowledge base document UUIDs that can be referenced by this node + document_uuids?: string[]; } export type FlowNode = { diff --git a/ui/src/components/layout/AppSidebar.tsx b/ui/src/components/layout/AppSidebar.tsx index cd950de..e7c566a 100644 --- a/ui/src/components/layout/AppSidebar.tsx +++ b/ui/src/components/layout/AppSidebar.tsx @@ -6,6 +6,7 @@ import { ChevronLeft, ChevronRight, CircleDollarSign, + Database, FileText, HelpCircle, Home, @@ -114,6 +115,11 @@ export function AppSidebar() { url: "/tools", icon: Wrench, }, + { + title: "Files", + url: "/files", + icon: Database, + }, // { // title: "Integrations", // url: "/integrations", diff --git a/ui/src/context/UserConfigContext.tsx b/ui/src/context/UserConfigContext.tsx index fa88b62..feb4654 100644 --- a/ui/src/context/UserConfigContext.tsx +++ b/ui/src/context/UserConfigContext.tsx @@ -18,6 +18,9 @@ export type SaveUserConfigFunctionParams = { stt?: { [key: string]: string | number; } | null; + embeddings?: { + [key: string]: string | number; + } | null; test_phone_number?: string | null; timezone?: string | null; };