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QdrantRetrieveUserProxyAgent

class QdrantRetrieveUserProxyAgent(RetrieveUserProxyAgent)

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__init__

def __init__(name="RetrieveChatAgent",
             human_input_mode: Literal["ALWAYS", "NEVER",
                                       "TERMINATE"] = "ALWAYS",
             is_termination_msg: Optional[Callable[[dict], bool]] = None,
             retrieve_config: Optional[dict] = None,
             **kwargs)

Arguments:

  • name str - name of the agent.
  • human_input_mode str - whether to ask for human inputs every time a message is received. Possible values are “ALWAYS”, “TERMINATE”, “NEVER”.
    1. When “ALWAYS”, the agent prompts for human input every time a message is received. Under this mode, the conversation stops when the human input is “exit”, or when is_termination_msg is True and there is no human input.
    2. When “TERMINATE”, the agent only prompts for human input only when a termination message is received or the number of auto reply reaches the max_consecutive_auto_reply.
    3. When “NEVER”, the agent will never prompt for human input. Under this mode, the conversation stops when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True.
  • is_termination_msg function - a function that takes a message in the form of a dictionary and returns a boolean value indicating if this received message is a termination message. The dict can contain the following keys: “content”, “role”, “name”, “function_call”.
  • retrieve_config dict or None - config for the retrieve agent. To use default config, set to None. Otherwise, set to a dictionary with the following keys:
    • task (Optional, str): the task of the retrieve chat. Possible values are “code”, “qa” and “default”. System prompt will be different for different tasks. The default value is default, which supports both code and qa.
    • client (Optional, qdrant_client.QdrantClient(“:memory:”)): A QdrantClient instance. If not provided, an in-memory instance will be assigned. Not recommended for production. will be used. If you want to use other vector db, extend this class and override the retrieve_docs function.
    • docs_path (Optional, Union[str, List[str]]): the path to the docs directory. It can also be the path to a single file, the url to a single file or a list of directories, files and urls. Default is None, which works only if the collection is already created.
    • extra_docs (Optional, bool): when true, allows adding documents with unique IDs without overwriting existing ones; when false, it replaces existing documents using default IDs, risking collection overwrite., when set to true it enables the system to assign unique IDs starting from “length+i” for new document chunks, preventing the replacement of existing documents and facilitating the addition of more content to the collection.. By default, “extra_docs” is set to false, starting document IDs from zero. This poses a risk as new documents might overwrite existing ones, potentially causing unintended loss or alteration of data in the collection.
    • collection_name (Optional, str): the name of the collection. If key not provided, a default name autogen-docs will be used.
    • model (Optional, str): the model to use for the retrieve chat. If key not provided, a default model gpt-4 will be used.
    • chunk_token_size (Optional, int): the chunk token size for the retrieve chat. If key not provided, a default size max_tokens * 0.4 will be used.
    • context_max_tokens (Optional, int): the context max token size for the retrieve chat. If key not provided, a default size max_tokens * 0.8 will be used.
    • chunk_mode (Optional, str): the chunk mode for the retrieve chat. Possible values are “multi_lines” and “one_line”. If key not provided, a default mode multi_lines will be used.
    • must_break_at_empty_line (Optional, bool): chunk will only break at empty line if True. Default is True. If chunk_mode is “one_line”, this parameter will be ignored.
    • embedding_model (Optional, str): the embedding model to use for the retrieve chat. If key not provided, a default model BAAI/bge-small-en-v1.5 will be used. All available models can be found at https://qdrant.github.io/fastembed/examples/Supported_Models/.
    • customized_prompt (Optional, str): the customized prompt for the retrieve chat. Default is None.
    • customized_answer_prefix (Optional, str): the customized answer prefix for the retrieve chat. Default is "". If not "" and the customized_answer_prefix is not in the answer, Update Context will be triggered.
    • update_context (Optional, bool): if False, will not apply Update Context for interactive retrieval. Default is True.
    • custom_token_count_function (Optional, Callable): a custom function to count the number of tokens in a string. The function should take a string as input and return three integers (token_count, tokens_per_message, tokens_per_name). Default is None, tiktoken will be used and may not be accurate for non-OpenAI models.
    • custom_text_split_function (Optional, Callable): a custom function to split a string into a list of strings. Default is None, will use the default function in autogen.retrieve_utils.split_text_to_chunks.
    • custom_text_types (Optional, List[str]): a list of file types to be processed. Default is autogen.retrieve_utils.TEXT_FORMATS. This only applies to files under the directories in docs_path. Explicitly included files and urls will be chunked regardless of their types.
    • recursive (Optional, bool): whether to search documents recursively in the docs_path. Default is True.
    • parallel (Optional, int): How many parallel workers to use for embedding. Defaults to the number of CPU cores.
    • on_disk (Optional, bool): Whether to store the collection on disk. Default is False.
    • quantization_config: Quantization configuration. If None, quantization will be disabled.
    • hnsw_config: HNSW configuration. If None, default configuration will be used. You can find more info about the hnsw configuration options at https://qdrant.tech/documentation/concepts/indexing/#vector-index. API Reference: https://qdrant.github.io/qdrant/redoc/index.html#tag/collections/operation/create_collection
    • payload_indexing: Whether to create a payload index for the document field. Default is False. You can find more info about the payload indexing options at https://qdrant.tech/documentation/concepts/indexing/#payload-index API Reference: https://qdrant.github.io/qdrant/redoc/index.html#tag/collections/operation/create_field_index
  • **kwargs dict - other kwargs in UserProxyAgent.

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retrieve_docs

def retrieve_docs(problem: str, n_results: int = 20, search_string: str = "")

Arguments:

  • problem str - the problem to be solved.
  • n_results int - the number of results to be retrieved. Default is 20.
  • search_string str - only docs that contain an exact match of this string will be retrieved. Default is "".

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create_qdrant_from_dir

def create_qdrant_from_dir(
        dir_path: str,
        max_tokens: int = 4000,
        client: QdrantClient = None,
        collection_name: str = "all-my-documents",
        chunk_mode: str = "multi_lines",
        must_break_at_empty_line: bool = True,
        embedding_model: str = "BAAI/bge-small-en-v1.5",
        custom_text_split_function: Callable = None,
        custom_text_types: list[str] = TEXT_FORMATS,
        recursive: bool = True,
        extra_docs: bool = False,
        parallel: int = 0,
        on_disk: bool = False,
        quantization_config: Optional[models.QuantizationConfig] = None,
        hnsw_config: Optional[models.HnswConfigDiff] = None,
        payload_indexing: bool = False,
        qdrant_client_options: Optional[dict] = \{})

Create a Qdrant collection from all the files in a given directory, the directory can also be a single file or a url to a single file.

Arguments:

  • dir_path str - the path to the directory, file or url.
  • max_tokens Optional, int - the maximum number of tokens per chunk. Default is 4000.
  • client Optional, QdrantClient - the QdrantClient instance. Default is None.
  • collection_name Optional, str - the name of the collection. Default is “all-my-documents”.
  • chunk_mode Optional, str - the chunk mode. Default is “multi_lines”.
  • must_break_at_empty_line Optional, bool - Whether to break at empty line. Default is True.
  • embedding_model Optional, str - the embedding model to use. Default is “BAAI/bge-small-en-v1.5”. The list of all the available models can be at https://qdrant.github.io/fastembed/examples/Supported_Models/.
  • custom_text_split_function Optional, Callable - a custom function to split a string into a list of strings. Default is None, will use the default function in autogen.retrieve_utils.split_text_to_chunks.
  • custom_text_types Optional, List[str] - a list of file types to be processed. Default is TEXT_FORMATS.
  • recursive Optional, bool - whether to search documents recursively in the dir_path. Default is True.
  • extra_docs Optional, bool - whether to add more documents in the collection. Default is False
  • parallel Optional, int - How many parallel workers to use for embedding. Defaults to the number of CPU cores
  • on_disk Optional, bool - Whether to store the collection on disk. Default is False.
  • quantization_config - Quantization configuration. If None, quantization will be disabled.
  • Ref - https://qdrant.github.io/qdrant/redoc/index.html#tag/collections/operation/create_collection
  • hnsw_config - HNSW configuration. If None, default configuration will be used.
  • Ref - https://qdrant.github.io/qdrant/redoc/index.html#tag/collections/operation/create_collection
  • payload_indexing - Whether to create a payload index for the document field. Default is False.
  • qdrant_client_options - (Optional, dict): the options for instantiating the qdrant client.
  • Ref - https://github.com/qdrant/qdrant-client/blob/master/qdrant_client/qdrant_client.py#L36-L58.

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query_qdrant

def query_qdrant(
        query_texts: list[str],
        n_results: int = 10,
        client: QdrantClient = None,
        collection_name: str = "all-my-documents",
        search_string: str = "",
        embedding_model: str = "BAAI/bge-small-en-v1.5",
        qdrant_client_options: Optional[dict] = \{}
) -> list[list[QueryResponse]]

Perform a similarity search with filters on a Qdrant collection

Arguments:

  • query_texts List[str] - the query texts.
  • n_results Optional, int - the number of results to return. Default is 10.
  • client Optional, API - the QdrantClient instance. A default in-memory client will be instantiated if None.
  • collection_name Optional, str - the name of the collection. Default is “all-my-documents”.
  • search_string Optional, str - the search string. Default is "".
  • embedding_model Optional, str - the embedding model to use. Default is “all-MiniLM-L6-v2”. Will be ignored if embedding_function is not None.
  • qdrant_client_options - (Optional, dict): the options for instantiating the qdrant client. Reference: https://github.com/qdrant/qdrant-client/blob/master/qdrant_client/qdrant_client.py#L36-L58.

Returns:

  • List[List[QueryResponse]] - the query result. The format is: class QueryResponse(BaseModel, extra=“forbid”): # type: ignore
  • id - Union[str, int]
  • embedding - Optional[List[float]]
  • metadata - Dict[str, Any]
  • document - str
  • score - float
multimodal_conversable_agentreasoning_agent