agentscope.rag package

Submodules

Module contents

Import RAG related modules in the package.

class Knowledge(knowledge_id: str, emb_model: Any | None = None, knowledge_config: dict | None = None, model: ModelWrapperBase | None = None, **kwargs: Any)[source]

Bases: ABC

Base class for RAG, CANNOT be instantiated directly

classmethod build_knowledge_instance(knowledge_id: str, knowledge_config: dict | None = None, **kwargs: Any) → Knowledge[source]

A constructor to build a knowledge base instance.

Parameters:

  • knowledge_id (str) – The id of the knowledge instance.

  • knowledge_config (dict) – The configuration to the knowledge instance.

Returns:

a Knowledge instance

Return type:

Knowledge

classmethod default_config(**kwargs: Any) → dict[source]

Return a default config for a knowledge class.

Parameters:

kwargs (Any) – Parameters for config

Returns:

a default config of the knowledge class

Return type:

dict

post_processing(retrieved_docs: list[str], prompt: str, **kwargs: Any) → Any[source]

A default solution for post-processing function, generates answer based on the retrieved documents.

Parameters:

  • retrieved_docs (list[str]) – List of retrieved documents

  • prompt (str) – Prompt for LLM generating answer with the retrieved documents

Returns:

A synthesized answer from LLM with retrieved documents

Return type:

Any

Example

self.postprocessing_model(prompt.format(retrieved_docs))

abstract retrieve(query: Any, similarity_top_k: int | None = None, to_list_strs: bool = False, **kwargs: Any) → list[RetrievedChunk | str][source]

Retrieve list of content from database (vector stored index) to memory

Parameters:

  • query (Any) – Query for retrieval

  • similarity_top_k (int) – The number of most similar data returned by the retriever.

  • to_list_strs (bool) – Whether return a list of str

Returns:

Return a list with retrieved documents (in strings)

knowledge_type: str = 'base_knowledge'

A string to identify a knowledge base class

class KnowledgeBank(configs: dict | str | list | None = None, new_knowledge_types: type[Knowledge] | list | None = None)[source]

Bases: object

KnowledgeBank enables 1) provide an easy and fast way to initialize the Knowledge object; 2) make Knowledge object reusable and sharable for multiple agents.

add_data_as_knowledge(knowledge_id: str, knowledge_type: str, knowledge_config: dict | None = None, **kwargs: Any) → None[source]

Transform data in a directory to be ready to work with RAG.

Parameters:

  • knowledge_id (str) – User-defined unique id for the knowledge

  • knowledge_type (str) – Type of the knowledge to register, e.g., “llamaindex_knowledge”

  • knowledge_config (dict) –

    For LlamaIndexKnowledge (knowledge_type=”llamaindex_knowledge”) the following are required: emb_model_config_name (str):

    Name of the embedding model config

    model_config_name (Optional[str]):

    Name of the LLM config for potential post-processing or query rewrite

    data_dirs_and_types (dict[str, list[str]]):

    Dictionary of data paths (keys) to the data types (file extensions) for knowledgebase (e.g., [“.md”, “.py”, “.html”])

    knowledge_config (optional[dict]):

    Complete indexing configuration, used for more advanced applications. Users can customize - loader, - transformations, - … Examples can refer to ../examples/conversation_with_RAG_agents/

  • kwargs (Any) – Additional keyword arguments to initialize knowledge.

equip(agent: AgentBase, knowledge_id_list: list[str] | None = None, duplicate: bool = False) → None[source]

Equip the agent with the knowledge by knowledge ids.

Parameters:

  • agent (AgentBase) – The agent to be equipped with knowledge

  • knowledge_id_list (list[str]) – The list of knowledge ids to be equipped with the agent

  • duplicate (bool) – Whether to deepcopy the knowledge object

TODO: to accommodate with distributed setting

get_knowledge(knowledge_id: str, duplicate: bool = False) → Knowledge[source]

Get a Knowledge object from the knowledge bank.

Parameters:

  • knowledge_id (str) – Unique id for the Knowledge object

  • duplicate (bool) – Whether return a copy of the Knowledge object.

Returns:

The Knowledge object defined with Llama-index

Return type:

Knowledge

register_knowledge_type(knowledge_base_class: Type[Knowledge], exist_ok: bool = True) → None[source]

Add a new knowledge base class to the knowledge bank

Parameters:

  • knowledge_base_class (Type[Knowledge]) – The knowledge class to be registered, which must inherit from Knowledge.

  • exist_ok (bool) – Whether to overwrite the existing knowledge base class with the same name.

class RetrievedChunk(score: float = 0.0, content: Any | None = None, metadata: dict | None = None, embedding: Any | None = None, hash: str | None = None)[source]

Bases: object

Retrieved content with score and meta information

score

Similarity score of this retrieved chunk

Type:

float

content

The retrieved content

Type:

Any

metadata

The meta data of this retrieved chunk, such as file path

Type:

Optional[dict]

embedding

The embedding of the chunk

Type:

Optional[Any]

hash

The hash of the retrieved content

Type:

Optional[str]

to_dict() → dict[source]

convert object to dict

content: Any = None
embedding: Any | None = None
hash: str | None = None
metadata: dict | None = None
score: float = 0.0