diff options
Diffstat (limited to 'autogpts/autogpt/autogpt/memory/vector/memory_item.py')
| -rw-r--r-- | autogpts/autogpt/autogpt/memory/vector/memory_item.py | 164 |
1 files changed, 94 insertions, 70 deletions
diff --git a/autogpts/autogpt/autogpt/memory/vector/memory_item.py b/autogpts/autogpt/autogpt/memory/vector/memory_item.py index 7ca8c7880..8d03d0209 100644 --- a/autogpts/autogpt/autogpt/memory/vector/memory_item.py +++ b/autogpts/autogpt/autogpt/memory/vector/memory_item.py @@ -9,7 +9,11 @@ import numpy as np from pydantic import BaseModel from autogpt.config import Config -from autogpt.core.resource.model_providers import ChatMessage +from autogpt.core.resource.model_providers import ( + ChatMessage, + ChatModelProvider, + EmbeddingModelProvider, +) from autogpt.processing.text import chunk_content, split_text, summarize_text from .utils import Embedding, get_embedding @@ -19,7 +23,6 @@ logger = logging.getLogger(__name__) MemoryDocType = Literal["webpage", "text_file", "code_file", "agent_history"] -# FIXME: implement validators instead of allowing arbitrary types class MemoryItem(BaseModel, arbitrary_types_allowed=True): """Memory object containing raw content as well as embeddings""" @@ -34,8 +37,56 @@ class MemoryItem(BaseModel, arbitrary_types_allowed=True): def relevance_for(self, query: str, e_query: Embedding | None = None): return MemoryItemRelevance.of(self, query, e_query) - @staticmethod - def from_text( + def dump(self, calculate_length=False) -> str: + n_chunks = len(self.e_chunks) + return f""" +=============== MemoryItem =============== +Size: {n_chunks} chunks +Metadata: {json.dumps(self.metadata, indent=2)} +---------------- SUMMARY ----------------- +{self.summary} +------------------ RAW ------------------- +{self.raw_content} +========================================== +""" + + def __eq__(self, other: MemoryItem): + return ( + self.raw_content == other.raw_content + and self.chunks == other.chunks + and self.chunk_summaries == other.chunk_summaries + # Embeddings can either be list[float] or np.ndarray[float32], + # and for comparison they must be of the same type + and np.array_equal( + self.e_summary + if isinstance(self.e_summary, np.ndarray) + else np.array(self.e_summary, dtype=np.float32), + other.e_summary + if isinstance(other.e_summary, np.ndarray) + else np.array(other.e_summary, dtype=np.float32), + ) + and np.array_equal( + self.e_chunks + if isinstance(self.e_chunks[0], np.ndarray) + else [np.array(c, dtype=np.float32) for c in self.e_chunks], + other.e_chunks + if isinstance(other.e_chunks[0], np.ndarray) + else [np.array(c, dtype=np.float32) for c in other.e_chunks], + ) + ) + + +class MemoryItemFactory: + def __init__( + self, + llm_provider: ChatModelProvider, + embedding_provider: EmbeddingModelProvider, + ): + self.llm_provider = llm_provider + self.embedding_provider = embedding_provider + + async def from_text( + self, text: str, source_type: MemoryDocType, config: Config, @@ -52,9 +103,19 @@ class MemoryItem(BaseModel, arbitrary_types_allowed=True): chunks = [ chunk for chunk, _ in ( - split_text(text, config.embedding_model, config) + split_text( + text=text, + config=config, + max_chunk_length=1000, # arbitrary, but shorter ~= better + tokenizer=self.llm_provider.get_tokenizer(config.fast_llm), + ) if source_type != "code_file" - else chunk_content(text, config.embedding_model) + # TODO: chunk code based on structure/outline + else chunk_content( + content=text, + max_chunk_length=1000, + tokenizer=self.llm_provider.get_tokenizer(config.fast_llm), + ) ) ] logger.debug("Chunks: " + str(chunks)) @@ -62,34 +123,38 @@ class MemoryItem(BaseModel, arbitrary_types_allowed=True): chunk_summaries = [ summary for summary, _ in [ - summarize_text( - text_chunk, - config, + await summarize_text( + text=text_chunk, instruction=how_to_summarize, question=question_for_summary, + llm_provider=self.llm_provider, + config=config, ) for text_chunk in chunks ] ] logger.debug("Chunk summaries: " + str(chunk_summaries)) - e_chunks = get_embedding(chunks, config) + e_chunks = get_embedding(chunks, config, self.embedding_provider) summary = ( chunk_summaries[0] if len(chunks) == 1 - else summarize_text( - "\n\n".join(chunk_summaries), - config, - instruction=how_to_summarize, - question=question_for_summary, + else ( + await summarize_text( + text="\n\n".join(chunk_summaries), + instruction=how_to_summarize, + question=question_for_summary, + llm_provider=self.llm_provider, + config=config, + ) )[0] ) logger.debug("Total summary: " + summary) # TODO: investigate search performance of weighted average vs summary # e_average = np.average(e_chunks, axis=0, weights=[len(c) for c in chunks]) - e_summary = get_embedding(summary, config) + e_summary = get_embedding(summary, config, self.embedding_provider) metadata["source_type"] = source_type @@ -103,17 +168,14 @@ class MemoryItem(BaseModel, arbitrary_types_allowed=True): metadata=metadata, ) - @staticmethod - def from_text_file(content: str, path: str, config: Config): - return MemoryItem.from_text(content, "text_file", config, {"location": path}) + def from_text_file(self, content: str, path: str, config: Config): + return self.from_text(content, "text_file", config, {"location": path}) - @staticmethod - def from_code_file(content: str, path: str): + def from_code_file(self, content: str, path: str): # TODO: implement tailored code memories - return MemoryItem.from_text(content, "code_file", {"location": path}) + return self.from_text(content, "code_file", {"location": path}) - @staticmethod - def from_ai_action(ai_message: ChatMessage, result_message: ChatMessage): + def from_ai_action(self, ai_message: ChatMessage, result_message: ChatMessage): # The result_message contains either user feedback # or the result of the command specified in ai_message @@ -138,17 +200,20 @@ class MemoryItem(BaseModel, arbitrary_types_allowed=True): f"Human Feedback: {user_input}" ) - return MemoryItem.from_text( + return self.from_text( text=memory_content, source_type="agent_history", - how_to_summarize="if possible, also make clear the link between the command in the assistant's response and the command result. Do not mention the human feedback if there is none", + how_to_summarize=( + "if possible, also make clear the link between the command in the" + " assistant's response and the command result. " + "Do not mention the human feedback if there is none.", + ), ) - @staticmethod def from_webpage( - content: str, url: str, config: Config, question: str | None = None + self, content: str, url: str, config: Config, question: str | None = None ): - return MemoryItem.from_text( + return self.from_text( text=content, source_type="webpage", config=config, @@ -156,47 +221,6 @@ class MemoryItem(BaseModel, arbitrary_types_allowed=True): question_for_summary=question, ) - def dump(self, calculate_length=False) -> str: - if calculate_length: - token_length = self.llm_provider.count_tokens( - self.raw_content, Config().embedding_model - ) - return f""" -=============== MemoryItem =============== -Size: {f'{token_length} tokens in ' if calculate_length else ''}{len(self.e_chunks)} chunks -Metadata: {json.dumps(self.metadata, indent=2)} ----------------- SUMMARY ----------------- -{self.summary} ------------------- RAW ------------------- -{self.raw_content} -========================================== -""" - - def __eq__(self, other: MemoryItem): - return ( - self.raw_content == other.raw_content - and self.chunks == other.chunks - and self.chunk_summaries == other.chunk_summaries - # Embeddings can either be list[float] or np.ndarray[float32], - # and for comparison they must be of the same type - and np.array_equal( - self.e_summary - if isinstance(self.e_summary, np.ndarray) - else np.array(self.e_summary, dtype=np.float32), - other.e_summary - if isinstance(other.e_summary, np.ndarray) - else np.array(other.e_summary, dtype=np.float32), - ) - and np.array_equal( - self.e_chunks - if isinstance(self.e_chunks[0], np.ndarray) - else [np.array(c, dtype=np.float32) for c in self.e_chunks], - other.e_chunks - if isinstance(other.e_chunks[0], np.ndarray) - else [np.array(c, dtype=np.float32) for c in other.e_chunks], - ) - ) - class MemoryItemRelevance(BaseModel): """ |