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@sonichi
sonichi committed Apr 3, 2024
2 parents 56ece36 + 2053dd9 commit ccc2858
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51 changes: 30 additions & 21 deletions autogen/agentchat/chat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,9 @@ class ChatResult:
summary: str = None
"""A summary obtained from the chat."""
cost: tuple = None # (dict, dict) - (total_cost, actual_cost_with_cache)
"""The cost of the chat. a tuple of (total_cost, total_actual_cost), where total_cost is a dictionary of cost information, and total_actual_cost is a dictionary of information on the actual incurred cost with cache."""
"""The cost of the chat. a tuple of (total_cost, total_actual_cost), where total_cost is a
dictionary of cost information, and total_actual_cost is a dictionary of information on
the actual incurred cost with cache."""
human_input: List[str] = None
"""A list of human input solicited during the chat."""

Expand Down Expand Up @@ -141,25 +143,32 @@ def __post_carryover_processing(chat_info: Dict[str, Any]) -> None:

def initiate_chats(chat_queue: List[Dict[str, Any]]) -> List[ChatResult]:
"""Initiate a list of chats.
Args:
chat_queue (List[Dict]): a list of dictionaries containing the information about the chats.
Each dictionary should contain the input arguments for [`ConversableAgent.initiate_chat`](/docs/reference/agentchat/conversable_agent#initiate_chat). For example:
- "sender": the sender agent.
- "recipient": the recipient agent.
- "clear_history" (bool): whether to clear the chat history with the agent. Default is True.
- "silent" (bool or None): (Experimental) whether to print the messages in this conversation. Default is False.
- "cache" (AbstractCache or None): the cache client to use for this conversation. Default is None.
- "max_turns" (int or None): maximum number of turns for the chat. If None, the chat will continue until a termination condition is met. Default is None.
- "summary_method" (str or callable): a string or callable specifying the method to get a summary from the chat. Default is DEFAULT_summary_method, i.e., "last_msg".
- "summary_args" (dict): a dictionary of arguments to be passed to the summary_method. Default is {}.
- "message" (str, callable or None): if None, input() will be called to get the initial message.
- **context: additional context information to be passed to the chat.
- "carryover": It can be used to specify the carryover information to be passed to this chat.
If provided, we will combine this carryover with the "message" content when generating the initial chat
message in `generate_init_message`.
chat_queue (List[Dict]): A list of dictionaries containing the information about the chats.
Each dictionary should contain the input arguments for
[`ConversableAgent.initiate_chat`](/docs/reference/agentchat/conversable_agent#initiate_chat).
For example:
- `"sender"` - the sender agent.
- `"recipient"` - the recipient agent.
- `"clear_history" (bool) - whether to clear the chat history with the agent.
Default is True.
- `"silent"` (bool or None) - (Experimental) whether to print the messages in this
conversation. Default is False.
- `"cache"` (Cache or None) - the cache client to use for this conversation.
Default is None.
- `"max_turns"` (int or None) - maximum number of turns for the chat. If None, the chat
will continue until a termination condition is met. Default is None.
- `"summary_method"` (str or callable) - a string or callable specifying the method to get
a summary from the chat. Default is DEFAULT_summary_method, i.e., "last_msg".
- `"summary_args"` (dict) - a dictionary of arguments to be passed to the summary_method.
Default is {}.
- `"message"` (str, callable or None) - if None, input() will be called to get the
initial message.
- `**context` - additional context information to be passed to the chat.
- `"carryover"` - It can be used to specify the carryover information to be passed
to this chat. If provided, we will combine this carryover with the "message" content when
generating the initial chat message in `generate_init_message`.
Returns:
(list): a list of ChatResult objects corresponding to the finished chats in the chat_queue.
"""
Expand Down Expand Up @@ -228,11 +237,11 @@ async def a_initiate_chats(chat_queue: List[Dict[str, Any]]) -> Dict[int, ChatRe
"""(async) Initiate a list of chats.
args:
Please refer to `initiate_chats`.
- Please refer to `initiate_chats`.
returns:
(Dict): a dict of ChatId: ChatResult corresponding to the finished chats in the chat_queue.
- (Dict): a dict of ChatId: ChatResult corresponding to the finished chats in the chat_queue.
"""
consolidate_chat_info(chat_queue)
_validate_recipients(chat_queue)
Expand Down
135 changes: 89 additions & 46 deletions autogen/agentchat/contrib/retrieve_user_proxy_agent.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,6 +62,10 @@


class RetrieveUserProxyAgent(UserProxyAgent):
"""(In preview) The Retrieval-Augmented User Proxy retrieves document chunks based on the embedding
similarity, and sends them along with the question to the Retrieval-Augmented Assistant
"""

def __init__(
self,
name="RetrieveChatAgent", # default set to RetrieveChatAgent
Expand All @@ -73,67 +77,106 @@ def __init__(
r"""
Args:
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.
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, chromadb.Client): the chromadb client. If key not provided, a default client `chromadb.Client()`
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.
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, chromadb.Client) - the chromadb client. If key not provided, a
default client `chromadb.Client()` 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.
- `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.
- `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.
- `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.
- `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 `all-MiniLM-L6-v2` will be used. All available models
can be found at `https://www.sbert.net/docs/pretrained_models.html`. The default model is a
fast model. If you want to use a high performance model, `all-mpnet-base-v2` is recommended.
- embedding_function (Optional, Callable): the embedding function for creating the vector db. Default is None,
SentenceTransformer with the given `embedding_model` will be used. If you want to use OpenAI, Cohere, HuggingFace or
other embedding functions, you can pass it here, follow the examples in `https://docs.trychroma.com/embeddings`.
- 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.
- get_or_create (Optional, bool): if True, will create/return a collection for the retrieve chat. This is the same as that used in chromadb.
Default is False. Will raise ValueError if the collection already exists and get_or_create is False. Will be set to True if docs_path is None.
- custom_token_count_function (Optional, Callable): a custom function to count the number of tokens in a string.
The function should take (text:str, model:str) as input and return the token_count(int). the retrieve_config["model"] will be passed in the function.
Default is autogen.token_count_utils.count_token that uses tiktoken, which 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.
- `embedding_model` (Optional, str) - the embedding model to use for the retrieve chat.
If key not provided, a default model `all-MiniLM-L6-v2` will be used. All available
models can be found at `https://www.sbert.net/docs/pretrained_models.html`.
The default model is a fast model. If you want to use a high performance model,
`all-mpnet-base-v2` is recommended.
- `embedding_function` (Optional, Callable) - the embedding function for creating the
vector db. Default is None, SentenceTransformer with the given `embedding_model`
will be used. If you want to use OpenAI, Cohere, HuggingFace or other embedding
functions, you can pass it here,
follow the examples in `https://docs.trychroma.com/embeddings`.
- `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.
- `get_or_create` (Optional, bool) - if True, will create/return a collection for the
retrieve chat. This is the same as that used in chromadb.
Default is False. Will raise ValueError if the collection already exists and
get_or_create is False. Will be set to True if docs_path is None.
- `custom_token_count_function` (Optional, Callable) - a custom function to count the
number of tokens in a string.
The function should take (text:str, model:str) as input and return the
token_count(int). the retrieve_config["model"] will be passed in the function.
Default is autogen.token_count_utils.count_token that uses tiktoken, which 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.
`**kwargs` (dict): other kwargs in [UserProxyAgent](../user_proxy_agent#__init__).
Example:
Example of overriding retrieve_docs - If you have set up a customized vector db, and it's not compatible with chromadb, you can easily plug in it with below code.
Example of overriding retrieve_docs - If you have set up a customized vector db, and it's
not compatible with chromadb, you can easily plug in it with below code.
```python
class MyRetrieveUserProxyAgent(RetrieveUserProxyAgent):
def query_vector_db(
Expand Down Expand Up @@ -416,9 +459,9 @@ def message_generator(sender, recipient, context):
sender (Agent): the sender agent. It should be the instance of RetrieveUserProxyAgent.
recipient (Agent): the recipient agent. Usually it's the assistant agent.
context (dict): the context for the message generation. It should contain the following keys:
- 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 "".
- `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 "".
Returns:
str: the generated message ready to be sent to the recipient agent.
"""
Expand Down
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