insightvault.services package

Submodules

insightvault.services.database module

class insightvault.services.database.AbstractDatabaseService

Bases: ABC

Abstract database service

abstract async add_documents(documents: list[Document]) → None

Add a list of documents to the database

abstract async delete_all_documents() → None

Delete all documents from the database

abstract async get_documents() → list[Document] | None

Get all documents from the database

abstract async query(query_embedding: Sequence[float], collection_name: str = 'default', filter_docs: bool = True) → list[Document]

Query the database for documents similar to the query embedding

class insightvault.services.database.ChromaDatabaseService(config: DatabaseConfig)

Bases: AbstractDatabaseService

Chroma database service

This service is used to interact with the Chroma database.

Embedding functions are not provided here, so the caller must provide them.

async add_documents(documents: list[Document], collection_name: str = 'default') → None

Add a list of documents to the database. The documents must have embeddings.

async delete_all_documents(collection_name: str = 'default') → None

Delete all documents in the database

async get_documents(collection_name: str = 'default') → list[Document] | None

List all documents in the database

async query(query_embedding: Sequence[float], collection_name: str = 'default', filter_docs: bool = True) → list[Document]

Query the database for documents similar to the query embedding

insightvault.services.embedding module

class insightvault.services.embedding.EmbeddingService(config: EmbeddingConfig)

Bases: object

Service for generating embeddings from text using sentence-transformers.

To use it, you must first call await get_client() to ensure the model is loaded.

Attributes:

config: The configuration for the embedding service client: The embedding model client loading_task: The task that loads the embedding model logger: The logger for the embedding service

async embed(texts: list[str]) → list[list[float]]

Generate embeddings for a list of texts

Args:

texts: List of text strings to embed

Returns:

List of embedding vectors (as lists of floats)

async init() → None

Initialize the embedding service

insightvault.services.llm module

class insightvault.services.llm.AbstractLLMService(model_name: str)

Bases: ABC

abstract async chat(prompt: str) → str | None

Generate a response from the model while maintaining chat history.

abstract async clear_chat_history() → None

Clear the chat history.

abstract async init() → None

Prepare the LLM service for use, such as loading model weights.

abstract async query(prompt: str) → str | None

Generate a one-off response from the model without chat history.

class insightvault.services.llm.BaseLLMService(model_name: str)

Bases: AbstractLLMService

class insightvault.services.llm.OllamaLLMService(model_name: str = 'llama3')

Bases: BaseLLMService

Ollama LLM service

async chat(prompt: str) → str | None

Generate a response from the model while maintaining chat history.

async clear_chat_history() → None

Clear the chat history.

async init() → None

Initialize the LLM service

async query(prompt: str) → str | None

Generate a one-off response from the model without chat history.

insightvault.services.prompt module

class insightvault.services.prompt.PromptService

Bases: object

Prompt service

get_prompt(prompt_type: str, context: dict[str, str] | None = None) → str

Retrieves a predefined prompt for a specific use case and injects context if needed. The context can include parameters like ‘text’, etc.

Args:

prompt_type (str): The type of prompt to retrieve. context (dict | None): The context to inject into the prompt.

Returns:

str: The prompt with the injected context.

insightvault.services.splitter module

class insightvault.services.splitter.SplitterService(config: SplitterConfig)

Bases: object

Splitter service

Attributes:

config: The configuration for the splitter

split(document: Document) → list[Document]

Split a document into chunks of a given size

Module contents