mirror of
https://github.com/FoundationAgents/MetaGPT.git
synced 2026-05-15 11:02:36 +02:00
Merge branch 'geekan:main' into main
This commit is contained in:
YangQianli92
GitHub
commit
9e4e32e7c7
33 changed files with 321 additions and 140 deletions
|
|
@ -166,7 +166,7 @@ class WriteCode(Action):
|
|||
if not task_doc.content:
|
||||
task_doc = project_repo.docs.task.get(filename=task_doc.filename)
|
||||
m = json.loads(task_doc.content)
|
||||
code_filenames = m.get(TASK_LIST.key, []) if use_inc else m.get(REFINED_TASK_LIST.key, [])
|
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code_filenames = m.get(TASK_LIST.key, []) if not use_inc else m.get(REFINED_TASK_LIST.key, [])
|
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codes = []
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src_file_repo = project_repo.srcs
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|
||||
|
|
|
|||
|
|
@ -27,6 +27,8 @@ ATTENTION: Use '##' to SPLIT SECTIONS, not '#'. Output format carefully referenc
|
|||
# Context
|
||||
{context}
|
||||
|
||||
-----
|
||||
|
||||
## Code to be Reviewed: {filename}
|
||||
```Code
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{code}
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|
|
@ -38,7 +40,8 @@ EXAMPLE_AND_INSTRUCTION = """
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{format_example}
|
||||
|
||||
|
||||
# Instruction: Based on the actual code situation, follow one of the "Format example". Return only 1 file under review.
|
||||
# Instruction: Based on the actual code, follow one of the "Code Review Format example".
|
||||
- Note the code filename should be `{filename}`. Return the only ONE file `{filename}` under review.
|
||||
|
||||
## Code Review: Ordered List. Based on the "Code to be Reviewed", provide key, clear, concise, and specific answer. If any answer is no, explain how to fix it step by step.
|
||||
1. Is the code implemented as per the requirements? If not, how to achieve it? Analyse it step by step.
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||||
|
|
@ -56,7 +59,9 @@ LGTM/LBTM
|
|||
"""
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||||
|
||||
FORMAT_EXAMPLE = """
|
||||
# Format example 1
|
||||
-----
|
||||
|
||||
# Code Review Format example 1
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||||
## Code Review: {filename}
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||||
1. No, we should fix the logic of class A due to ...
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||||
2. ...
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||||
|
|
@ -92,7 +97,9 @@ FORMAT_EXAMPLE = """
|
|||
## Code Review Result
|
||||
LBTM
|
||||
|
||||
# Format example 2
|
||||
-----
|
||||
|
||||
# Code Review Format example 2
|
||||
## Code Review: {filename}
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||||
1. Yes.
|
||||
2. Yes.
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||||
|
|
@ -106,10 +113,12 @@ pass
|
|||
|
||||
## Code Review Result
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||||
LGTM
|
||||
|
||||
-----
|
||||
"""
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||||
|
||||
REWRITE_CODE_TEMPLATE = """
|
||||
# Instruction: rewrite code based on the Code Review and Actions
|
||||
# Instruction: rewrite the `{filename}` based on the Code Review and Actions
|
||||
## Rewrite Code: CodeBlock. If it still has some bugs, rewrite {filename} with triple quotes. Do your utmost to optimize THIS SINGLE FILE. Return all completed codes and prohibit the return of unfinished codes.
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||||
```Code
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## {filename}
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|
|
@ -169,6 +178,7 @@ class WriteCodeReview(Action):
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|||
)
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cr_prompt = EXAMPLE_AND_INSTRUCTION.format(
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format_example=format_example,
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filename=self.i_context.code_doc.filename,
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)
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len1 = len(iterative_code) if iterative_code else 0
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len2 = len(self.i_context.code_doc.content) if self.i_context.code_doc.content else 0
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|
|
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|
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@ -4,7 +4,7 @@ import json
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import os
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from typing import Any, Optional, Union
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from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
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from llama_index.core import SimpleDirectoryReader
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from llama_index.core.callbacks.base import CallbackManager
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from llama_index.core.embeddings import BaseEmbedding
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from llama_index.core.embeddings.mock_embed_model import MockEmbedding
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|
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@ -63,7 +63,7 @@ class SimpleEngine(RetrieverQueryEngine):
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response_synthesizer: Optional[BaseSynthesizer] = None,
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node_postprocessors: Optional[list[BaseNodePostprocessor]] = None,
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callback_manager: Optional[CallbackManager] = None,
|
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index: Optional[BaseIndex] = None,
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transformations: Optional[list[TransformComponent]] = None,
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) -> None:
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super().__init__(
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retriever=retriever,
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|
|
@ -71,7 +71,7 @@ class SimpleEngine(RetrieverQueryEngine):
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node_postprocessors=node_postprocessors,
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callback_manager=callback_manager,
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)
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self.index = index
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self._transformations = transformations or self._default_transformations()
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@classmethod
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def from_docs(
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|
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@ -103,12 +103,17 @@ class SimpleEngine(RetrieverQueryEngine):
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documents = SimpleDirectoryReader(input_dir=input_dir, input_files=input_files).load_data()
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cls._fix_document_metadata(documents)
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|
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index = VectorStoreIndex.from_documents(
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documents=documents,
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transformations=transformations or [SentenceSplitter()],
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embed_model=cls._resolve_embed_model(embed_model, retriever_configs),
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transformations = transformations or cls._default_transformations()
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nodes = run_transformations(documents, transformations=transformations)
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|
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return cls._from_nodes(
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nodes=nodes,
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transformations=transformations,
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embed_model=embed_model,
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llm=llm,
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retriever_configs=retriever_configs,
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ranker_configs=ranker_configs,
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)
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return cls._from_index(index, llm=llm, retriever_configs=retriever_configs, ranker_configs=ranker_configs)
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@classmethod
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def from_objs(
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|
|
@ -137,12 +142,15 @@ class SimpleEngine(RetrieverQueryEngine):
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raise ValueError("In BM25RetrieverConfig, Objs must not be empty.")
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|
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nodes = [ObjectNode(text=obj.rag_key(), metadata=ObjectNode.get_obj_metadata(obj)) for obj in objs]
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index = VectorStoreIndex(
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|
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return cls._from_nodes(
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nodes=nodes,
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transformations=transformations or [SentenceSplitter()],
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embed_model=cls._resolve_embed_model(embed_model, retriever_configs),
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transformations=transformations,
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embed_model=embed_model,
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llm=llm,
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retriever_configs=retriever_configs,
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ranker_configs=ranker_configs,
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)
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return cls._from_index(index, llm=llm, retriever_configs=retriever_configs, ranker_configs=ranker_configs)
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|
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@classmethod
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def from_index(
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|
|
@ -183,7 +191,7 @@ class SimpleEngine(RetrieverQueryEngine):
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documents = SimpleDirectoryReader(input_files=input_files).load_data()
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self._fix_document_metadata(documents)
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nodes = run_transformations(documents, transformations=self.index._transformations)
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nodes = run_transformations(documents, transformations=self._transformations)
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self._save_nodes(nodes)
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def add_objs(self, objs: list[RAGObject]):
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|
|
@ -199,6 +207,29 @@ class SimpleEngine(RetrieverQueryEngine):
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self._persist(str(persist_dir), **kwargs)
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@classmethod
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def _from_nodes(
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cls,
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nodes: list[BaseNode],
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transformations: Optional[list[TransformComponent]] = None,
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embed_model: BaseEmbedding = None,
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llm: LLM = None,
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retriever_configs: list[BaseRetrieverConfig] = None,
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ranker_configs: list[BaseRankerConfig] = None,
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) -> "SimpleEngine":
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embed_model = cls._resolve_embed_model(embed_model, retriever_configs)
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llm = llm or get_rag_llm()
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retriever = get_retriever(configs=retriever_configs, nodes=nodes, embed_model=embed_model)
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rankers = get_rankers(configs=ranker_configs, llm=llm) # Default []
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|
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return cls(
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retriever=retriever,
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node_postprocessors=rankers,
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response_synthesizer=get_response_synthesizer(llm=llm),
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transformations=transformations,
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)
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|
||||
@classmethod
|
||||
def _from_index(
|
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cls,
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|
|
@ -208,6 +239,7 @@ class SimpleEngine(RetrieverQueryEngine):
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|||
ranker_configs: list[BaseRankerConfig] = None,
|
||||
) -> "SimpleEngine":
|
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llm = llm or get_rag_llm()
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|
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retriever = get_retriever(configs=retriever_configs, index=index) # Default index.as_retriever
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rankers = get_rankers(configs=ranker_configs, llm=llm) # Default []
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|
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|
|
@ -215,7 +247,6 @@ class SimpleEngine(RetrieverQueryEngine):
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retriever=retriever,
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node_postprocessors=rankers,
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response_synthesizer=get_response_synthesizer(llm=llm),
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index=index,
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)
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def _ensure_retriever_modifiable(self):
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|
|
@ -266,3 +297,7 @@ class SimpleEngine(RetrieverQueryEngine):
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return MockEmbedding(embed_dim=1)
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return embed_model or get_rag_embedding()
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@staticmethod
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def _default_transformations():
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return [SentenceSplitter()]
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|
|
|
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|
|
@ -36,19 +36,26 @@ class ConfigBasedFactory(GenericFactory):
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|||
"""Designed to get objects based on object type."""
|
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def get_instance(self, key: Any, **kwargs) -> Any:
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"""Key is config, such as a pydantic model.
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"""Get instance by the type of key.
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||||
|
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Call func by the type of key, and the key will be passed to func.
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Key is config, such as a pydantic model, call func by the type of key, and the key will be passed to func.
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Raise Exception if key not found.
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"""
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creator = self._creators.get(type(key))
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if creator:
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return creator(key, **kwargs)
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|
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self._raise_for_key(key)
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|
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def _raise_for_key(self, key: Any):
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raise ValueError(f"Unknown config: `{type(key)}`, {key}")
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|
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@staticmethod
|
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def _val_from_config_or_kwargs(key: str, config: object = None, **kwargs) -> Any:
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"""It prioritizes the configuration object's value unless it is None, in which case it looks into kwargs."""
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"""It prioritizes the configuration object's value unless it is None, in which case it looks into kwargs.
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Return None if not found.
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"""
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if config is not None and hasattr(config, key):
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val = getattr(config, key)
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if val is not None:
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|
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@ -57,6 +64,4 @@ class ConfigBasedFactory(GenericFactory):
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if key in kwargs:
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return kwargs[key]
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|
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raise KeyError(
|
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f"The key '{key}' is required but not provided in either configuration object or keyword arguments."
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)
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return None
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|
|
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|
|
@ -1,10 +1,13 @@
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"""RAG Retriever Factory."""
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import copy
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from functools import wraps
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import chromadb
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import faiss
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from llama_index.core import StorageContext, VectorStoreIndex
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from llama_index.core.embeddings import BaseEmbedding
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from llama_index.core.schema import BaseNode
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from llama_index.core.vector_stores.types import BasePydanticVectorStore
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from llama_index.vector_stores.chroma import ChromaVectorStore
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from llama_index.vector_stores.elasticsearch import ElasticsearchStore
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|
|
@ -24,10 +27,25 @@ from metagpt.rag.schema import (
|
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ElasticsearchKeywordRetrieverConfig,
|
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ElasticsearchRetrieverConfig,
|
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FAISSRetrieverConfig,
|
||||
IndexRetrieverConfig,
|
||||
)
|
||||
|
||||
|
||||
def get_or_build_index(build_index_func):
|
||||
"""Decorator to get or build an index.
|
||||
|
||||
Get index using `_extract_index` method, if not found, using build_index_func.
|
||||
"""
|
||||
|
||||
@wraps(build_index_func)
|
||||
def wrapper(self, config, **kwargs):
|
||||
index = self._extract_index(config, **kwargs)
|
||||
if index is not None:
|
||||
return index
|
||||
return build_index_func(self, config, **kwargs)
|
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|
||||
return wrapper
|
||||
|
||||
|
||||
class RetrieverFactory(ConfigBasedFactory):
|
||||
"""Modify creators for dynamically instance implementation."""
|
||||
|
||||
|
|
@ -54,48 +72,79 @@ class RetrieverFactory(ConfigBasedFactory):
|
|||
return SimpleHybridRetriever(*retrievers) if len(retrievers) > 1 else retrievers[0]
|
||||
|
||||
def _create_default(self, **kwargs) -> RAGRetriever:
|
||||
return self._extract_index(**kwargs).as_retriever()
|
||||
index = self._extract_index(None, **kwargs) or self._build_default_index(**kwargs)
|
||||
|
||||
return index.as_retriever()
|
||||
|
||||
def _create_faiss_retriever(self, config: FAISSRetrieverConfig, **kwargs) -> FAISSRetriever:
|
||||
vector_store = FaissVectorStore(faiss_index=faiss.IndexFlatL2(config.dimensions))
|
||||
config.index = self._build_index_from_vector_store(config, vector_store, **kwargs)
|
||||
config.index = self._build_faiss_index(config, **kwargs)
|
||||
|
||||
return FAISSRetriever(**config.model_dump())
|
||||
|
||||
def _create_bm25_retriever(self, config: BM25RetrieverConfig, **kwargs) -> DynamicBM25Retriever:
|
||||
config.index = copy.deepcopy(self._extract_index(config, **kwargs))
|
||||
index = self._extract_index(config, **kwargs)
|
||||
nodes = list(index.docstore.docs.values()) if index else self._extract_nodes(config, **kwargs)
|
||||
|
||||
return DynamicBM25Retriever(nodes=list(config.index.docstore.docs.values()), **config.model_dump())
|
||||
return DynamicBM25Retriever(nodes=nodes, **config.model_dump())
|
||||
|
||||
def _create_chroma_retriever(self, config: ChromaRetrieverConfig, **kwargs) -> ChromaRetriever:
|
||||
db = chromadb.PersistentClient(path=str(config.persist_path))
|
||||
chroma_collection = db.get_or_create_collection(config.collection_name, metadata=config.metadata)
|
||||
|
||||
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
|
||||
config.index = self._build_index_from_vector_store(config, vector_store, **kwargs)
|
||||
config.index = self._build_chroma_index(config, **kwargs)
|
||||
|
||||
return ChromaRetriever(**config.model_dump())
|
||||
|
||||
def _create_es_retriever(self, config: ElasticsearchRetrieverConfig, **kwargs) -> ElasticsearchRetriever:
|
||||
vector_store = ElasticsearchStore(**config.store_config.model_dump())
|
||||
config.index = self._build_index_from_vector_store(config, vector_store, **kwargs)
|
||||
config.index = self._build_es_index(config, **kwargs)
|
||||
|
||||
return ElasticsearchRetriever(**config.model_dump())
|
||||
|
||||
def _extract_index(self, config: BaseRetrieverConfig = None, **kwargs) -> VectorStoreIndex:
|
||||
return self._val_from_config_or_kwargs("index", config, **kwargs)
|
||||
|
||||
def _extract_nodes(self, config: BaseRetrieverConfig = None, **kwargs) -> list[BaseNode]:
|
||||
return self._val_from_config_or_kwargs("nodes", config, **kwargs)
|
||||
|
||||
def _extract_embed_model(self, config: BaseRetrieverConfig = None, **kwargs) -> BaseEmbedding:
|
||||
return self._val_from_config_or_kwargs("embed_model", config, **kwargs)
|
||||
|
||||
def _build_default_index(self, **kwargs) -> VectorStoreIndex:
|
||||
index = VectorStoreIndex(
|
||||
nodes=self._extract_nodes(**kwargs),
|
||||
embed_model=self._extract_embed_model(**kwargs),
|
||||
)
|
||||
|
||||
return index
|
||||
|
||||
@get_or_build_index
|
||||
def _build_faiss_index(self, config: FAISSRetrieverConfig, **kwargs) -> VectorStoreIndex:
|
||||
vector_store = FaissVectorStore(faiss_index=faiss.IndexFlatL2(config.dimensions))
|
||||
|
||||
return self._build_index_from_vector_store(config, vector_store, **kwargs)
|
||||
|
||||
@get_or_build_index
|
||||
def _build_chroma_index(self, config: ChromaRetrieverConfig, **kwargs) -> VectorStoreIndex:
|
||||
db = chromadb.PersistentClient(path=str(config.persist_path))
|
||||
chroma_collection = db.get_or_create_collection(config.collection_name, metadata=config.metadata)
|
||||
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
|
||||
|
||||
return self._build_index_from_vector_store(config, vector_store, **kwargs)
|
||||
|
||||
@get_or_build_index
|
||||
def _build_es_index(self, config: ElasticsearchRetrieverConfig, **kwargs) -> VectorStoreIndex:
|
||||
vector_store = ElasticsearchStore(**config.store_config.model_dump())
|
||||
|
||||
return self._build_index_from_vector_store(config, vector_store, **kwargs)
|
||||
|
||||
def _build_index_from_vector_store(
|
||||
self, config: IndexRetrieverConfig, vector_store: BasePydanticVectorStore, **kwargs
|
||||
self, config: BaseRetrieverConfig, vector_store: BasePydanticVectorStore, **kwargs
|
||||
) -> VectorStoreIndex:
|
||||
storage_context = StorageContext.from_defaults(vector_store=vector_store)
|
||||
old_index = self._extract_index(config, **kwargs)
|
||||
new_index = VectorStoreIndex(
|
||||
nodes=list(old_index.docstore.docs.values()),
|
||||
index = VectorStoreIndex(
|
||||
nodes=self._extract_nodes(config, **kwargs),
|
||||
storage_context=storage_context,
|
||||
embed_model=old_index._embed_model,
|
||||
embed_model=self._extract_embed_model(config, **kwargs),
|
||||
)
|
||||
return new_index
|
||||
|
||||
return index
|
||||
|
||||
|
||||
get_retriever = RetrieverFactory().get_retriever
|
||||
|
|
|
|||
|
|
@ -406,7 +406,7 @@ class Role(SerializationMixin, ContextMixin, BaseModel):
|
|||
elif isinstance(response, Message):
|
||||
msg = response
|
||||
else:
|
||||
msg = Message(content=response, role=self.profile, cause_by=self.rc.todo, sent_from=self)
|
||||
msg = Message(content=response or "", role=self.profile, cause_by=self.rc.todo, sent_from=self)
|
||||
self.rc.memory.add(msg)
|
||||
|
||||
return msg
|
||||
|
|
|
|||
|
|
@ -123,9 +123,10 @@ def startup(
|
|||
|
||||
DEFAULT_CONFIG = """# Full Example: https://github.com/geekan/MetaGPT/blob/main/config/config2.example.yaml
|
||||
# Reflected Code: https://github.com/geekan/MetaGPT/blob/main/metagpt/config2.py
|
||||
# Config Docs: https://docs.deepwisdom.ai/main/en/guide/get_started/configuration.html
|
||||
llm:
|
||||
api_type: "openai" # or azure / ollama / open_llm etc. Check LLMType for more options
|
||||
model: "gpt-4-turbo-preview" # or gpt-3.5-turbo-1106 / gpt-4-1106-preview
|
||||
api_type: "openai" # or azure / ollama / groq etc.
|
||||
model: "gpt-4-turbo" # or gpt-3.5-turbo
|
||||
base_url: "https://api.openai.com/v1" # or forward url / other llm url
|
||||
api_key: "YOUR_API_KEY"
|
||||
"""
|
||||
|
|
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|||
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|
@ -78,7 +78,7 @@ class GitRepository:
|
|||
self._repository = Repo.init(path=Path(local_path))
|
||||
|
||||
gitignore_filename = Path(local_path) / ".gitignore"
|
||||
ignores = ["__pycache__", "*.pyc"]
|
||||
ignores = ["__pycache__", "*.pyc", ".vs"]
|
||||
with open(str(gitignore_filename), mode="w") as writer:
|
||||
writer.write("\n".join(ignores))
|
||||
self._repository.index.add([".gitignore"])
|
||||
|
|
|
|||
|
|
@ -32,9 +32,9 @@ TOKEN_COSTS = {
|
|||
"gpt-4-32k-0314": {"prompt": 0.06, "completion": 0.12},
|
||||
"gpt-4-0613": {"prompt": 0.06, "completion": 0.12},
|
||||
"gpt-4-turbo-preview": {"prompt": 0.01, "completion": 0.03},
|
||||
"gpt-4-turbo": {"prompt": 0.01, "completion": 0.03},
|
||||
"gpt-4-0125-preview": {"prompt": 0.01, "completion": 0.03},
|
||||
"gpt-4-1106-preview": {"prompt": 0.01, "completion": 0.03},
|
||||
"gpt-4-0125-preview": {"prompt": 0.01, "completion": 0.03},
|
||||
"gpt-4-turbo": {"prompt": 0.01, "completion": 0.03},
|
||||
"gpt-4-vision-preview": {"prompt": 0.01, "completion": 0.03}, # TODO add extra image price calculator
|
||||
"gpt-4-1106-vision-preview": {"prompt": 0.01, "completion": 0.03},
|
||||
"text-embedding-ada-002": {"prompt": 0.0004, "completion": 0.0},
|
||||
|
|
@ -57,6 +57,8 @@ TOKEN_COSTS = {
|
|||
"yi-34b-chat-0205": {"prompt": 0.0003, "completion": 0.0003},
|
||||
"yi-34b-chat-200k": {"prompt": 0.0017, "completion": 0.0017},
|
||||
"microsoft/wizardlm-2-8x22b": {"prompt": 0.00108, "completion": 0.00108}, # for openrouter, start
|
||||
"meta-llama/llama-3-70b-instruct": {"prompt": 0.008, "completion": 0.008},
|
||||
"llama3-70b-8192": {"prompt": 0.0059, "completion": 0.0079},
|
||||
"openai/gpt-3.5-turbo-0125": {"prompt": 0.0005, "completion": 0.0015},
|
||||
"openai/gpt-4-turbo-preview": {"prompt": 0.01, "completion": 0.03},
|
||||
}
|
||||
|
|
@ -155,8 +157,8 @@ FIREWORKS_GRADE_TOKEN_COSTS = {
|
|||
TOKEN_MAX = {
|
||||
"gpt-4-0125-preview": 128000,
|
||||
"gpt-4-turbo-preview": 128000,
|
||||
"gpt-4-turbo": 128000,
|
||||
"gpt-4-1106-preview": 128000,
|
||||
"gpt-4-turbo": 128000,
|
||||
"gpt-4-vision-preview": 128000,
|
||||
"gpt-4-1106-vision-preview": 128000,
|
||||
"gpt-4": 8192,
|
||||
|
|
@ -190,6 +192,8 @@ TOKEN_MAX = {
|
|||
"yi-34b-chat-0205": 4000,
|
||||
"yi-34b-chat-200k": 200000,
|
||||
"microsoft/wizardlm-2-8x22b": 65536,
|
||||
"meta-llama/llama-3-70b-instruct": 8192,
|
||||
"llama3-70b-8192": 8192,
|
||||
"openai/gpt-3.5-turbo-0125": 16385,
|
||||
"openai/gpt-4-turbo-preview": 128000,
|
||||
}
|
||||
|
|
@ -217,7 +221,7 @@ def count_message_tokens(messages, model="gpt-3.5-turbo-0125"):
|
|||
"gpt-4-turbo",
|
||||
"gpt-4-turbo-preview",
|
||||
"gpt-4-0125-preview",
|
||||
"gpt-4-1106-preview",
|
||||
"gpt-4-turbo",
|
||||
"gpt-4-vision-preview",
|
||||
"gpt-4-1106-vision-preview",
|
||||
}:
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue