Add Support for Knowledge Base Retrieval Params & Preprocessing

minds/knowledge_bases/knowledge_bases.py

@@ -2,6 +2,7 @@
 
 from pydantic import BaseModel
 
+from minds.knowledge_bases.preprocessing import PreprocessingConfig
 from minds.rest_api import RestAPI
 
 
@@ -25,6 +26,8 @@ class KnowledgeBaseConfig(BaseModel):
     description: str
     vector_store_config: Optional[VectorStoreConfig] = None
     embedding_config: Optional[EmbeddingConfig] = None
+    # Params to apply to retrieval pipeline.
+    params: Optional[Dict] = None
 
 
 class KnowledgeBaseDocument(BaseModel):
@@ -39,7 +42,7 @@ def __init__(self, name, api: RestAPI):
         self.name = name
         self.api = api
 
-    def insert_from_select(self, query: str):
+    def insert_from_select(self, query: str, preprocessing_config: PreprocessingConfig = None):
         '''
         Inserts select content of a connected datasource into this knowledge base
 
@@ -48,9 +51,11 @@ def insert_from_select(self, query: str):
         update_request = {
             'query': query
         }
+        if preprocessing_config is not None:
+            update_request['preprocessing'] = preprocessing_config.model_dump()
         _ = self.api.put(f'/knowledge_bases/{self.name}', data=update_request)
 
-    def insert_documents(self, documents: List[KnowledgeBaseDocument]):
+    def insert_documents(self, documents: List[KnowledgeBaseDocument], preprocessing_config: PreprocessingConfig = None):
         '''
         Inserts documents directly into this knowledge base
 
@@ -59,9 +64,11 @@ def insert_documents(self, documents: List[KnowledgeBaseDocument]):
         update_request = {
             'rows': [d.model_dump() for d in documents]
         }
+        if preprocessing_config is not None:
+            update_request['preprocessing'] = preprocessing_config.model_dump()
         _ = self.api.put(f'/knowledge_bases/{self.name}', data=update_request)
 
-    def insert_urls(self, urls: List[str]):
+    def insert_urls(self, urls: List[str], preprocessing_config: PreprocessingConfig = None):
         '''
         Crawls URLs & inserts the retrieved webpages into this knowledge base
 
@@ -70,9 +77,11 @@ def insert_urls(self, urls: List[str]):
         update_request = {
             'urls': urls
         }
+        if preprocessing_config is not None:
+            update_request['preprocessing'] = preprocessing_config.model_dump()
         _ = self.api.put(f'/knowledge_bases/{self.name}', data=update_request)
 
-    def insert_files(self, files: List[str]):
+    def insert_files(self, files: List[str], preprocessing_config: PreprocessingConfig = None):
         '''
         Inserts files that have already been uploaded to MindsDB into this knowledge base
 
@@ -81,6 +90,8 @@ def insert_files(self, files: List[str]):
         update_request = {
             'files': files
         }
+        if preprocessing_config is not None:
+            update_request['preprocessing'] = preprocessing_config.model_dump()
         _ = self.api.put(f'/knowledge_bases/{self.name}', data=update_request)
 
 
@@ -117,6 +128,8 @@ def create(self, config: KnowledgeBaseConfig) -> KnowledgeBase:
             if config.embedding_config.params is not None:
                 embedding_data.update(config.embedding_config.params)
             create_request['embedding_model'] = embedding_data
+        if config.params is not None:
+            create_request['params'] = config.params
 
         _ = self.api.post('/knowledge_bases', data=create_request)
         return self.get(config.name)

minds/knowledge_bases/preprocessing.py

@@ -0,0 +1,78 @@
+from typing import Any, Dict, List, Literal, Optional
+
+from pydantic import BaseModel, Field, model_validator
+
+
+DEFAULT_LLM_MODEL = 'gpt-4o'
+DEFAULT_LLM_MODEL_PROVIDER = 'openai'
+
+
+class TextChunkingConfig(BaseModel):
+    '''Configuration for chunking text content before they are inserted into a knowledge base'''
+    separators: List[str] = Field(
+        default=['\n\n', '\n', ' ', ''],
+        description='List of separators to use for splitting text, in order of priority'
+    )
+    chunk_size: int = Field(
+        default=1000,
+        description='The target size of each text chunk',
+        gt=0
+    )
+    chunk_overlap: int = Field(
+        default=200,
+        description='The number of characters to overlap between chunks',
+        ge=0
+    )
+
+
+class LLMConfig(BaseModel):
+    model_name: str = Field(default=DEFAULT_LLM_MODEL, description='LLM model to use for context generation')
+    provider: str = Field(default=DEFAULT_LLM_MODEL_PROVIDER, description='LLM model provider to use for context generation')
+    params: Dict[str, Any] = Field(default={}, description='Additional parameters to pass in when initializing the LLM')
+
+
+class ContextualConfig(BaseModel):
+    '''Configuration specific to contextual preprocessing'''
+    llm_config: LLMConfig = Field(
+        default=LLMConfig(),
+        description='LLM configuration to use for context generation'
+    )
+    context_template: Optional[str] = Field(
+        default=None,
+        description='Custom template for context generation'
+    )
+    chunk_size: int = Field(
+        default=1000,
+        description='The target size of each text chunk',
+        gt=0
+    )
+    chunk_overlap: int = Field(
+        default=200,
+        description='The number of characters to overlap between chunks',
+        ge=0
+    )
+
+
+class PreprocessingConfig(BaseModel):
+    '''Complete preprocessing configuration'''
+    type: Literal['contextual', 'text_chunking'] = Field(
+        default='text_chunking',
+        description='Type of preprocessing to apply'
+    )
+    contextual_config: Optional[ContextualConfig] = Field(
+        default=None,
+        description='Configuration for contextual preprocessing'
+    )
+    text_chunking_config: Optional[TextChunkingConfig] = Field(
+        default=None,
+        description='Configuration for text chunking preprocessing'
+    )
+
+    @model_validator(mode='after')
+    def validate_config_presence(self) -> 'PreprocessingConfig':
+        '''Ensure the appropriate config is present for the chosen type'''
+        if self.type == 'contextual' and not self.contextual_config:
+            self.contextual_config = ContextualConfig()
+        if self.type == 'text_chunking' and not self.text_chunking_config:
+            self.text_chunking_config = TextChunkingConfig()
+        return self

tests/unit/test_unit.py

@@ -130,7 +130,10 @@ def test_create_knowledge_bases(self, mock_post, mock_get):
             name='test_kb',
             description='Test knowledge base',
             vector_store_config=test_vector_store_config,
-            embedding_config=test_embedding_config
+            embedding_config=test_embedding_config,
+            params={
+                'k1': 'v1'
+            }
         )
         response_mock(mock_get, test_knowledge_base_config.model_dump())
 
@@ -152,6 +155,9 @@ def test_create_knowledge_bases(self, mock_post, mock_get):
                 'provider': test_embedding_config.provider,
                 'name': test_embedding_config.model,
                 'k1': 'v1'
+            },
+            'params': {
+                'k1': 'v1'
             }
         }