Merge branch 'geekan/main' into feature/multi-llm

.github/workflows/unittest.yaml

@@ -32,7 +32,7 @@ jobs:
       run: |
         export ALLOW_OPENAI_API_CALL=0
         mkdir -p ~/.metagpt && cp tests/config2.yaml ~/.metagpt/config2.yaml
-        pytest tests/ --doctest-modules --cov=./metagpt/ --cov-report=xml:cov.xml --cov-report=html:htmlcov --durations=20 | tee unittest.txt
+        pytest tests/ --ignore=tests/metagpt/environment/android_env --ignore=tests/metagpt/ext/android_assistant --doctest-modules --cov=./metagpt/ --cov-report=xml:cov.xml --cov-report=html:htmlcov --durations=20 | tee unittest.txt
     - name: Show coverage report
       run: |
         coverage report -m

README.md

@@ -31,7 +31,7 @@ ## News
 🚀 Feb. 08, 2024: [v0.7.0](https://github.com/geekan/MetaGPT/releases/tag/v0.7.0) released, supporting assigning different LLMs to different Roles. We also introduced [Data Interpreter](https://github.com/geekan/MetaGPT/blob/main/examples/di/README.md), a powerful agent capable of solving a wide range of real-world problems.
 
 🚀 Jan. 16, 2024: Our paper [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
-](https://arxiv.org/abs/2308.00352) accepted for **oral presentation (top 1.2%)** at ICLR 2024, **ranking #1** in the LLM-based Agent category.
+](https://openreview.net/forum?id=VtmBAGCN7o) accepted for **oral presentation (top 1.2%)** at ICLR 2024, **ranking #1** in the LLM-based Agent category.
 
 🚀 Jan. 03, 2024: [v0.6.0](https://github.com/geekan/MetaGPT/releases/tag/v0.6.0) released, new features include serialization, upgraded OpenAI package and supported multiple LLM, provided [minimal example for debate](https://github.com/geekan/MetaGPT/blob/main/examples/debate_simple.py) etc.
 
@@ -166,16 +166,15 @@ ## Citation
 
 To stay updated with the latest research and development, follow [@MetaGPT_](https://twitter.com/MetaGPT_) on Twitter. 
 
-To cite [MetaGPT](https://arxiv.org/abs/2308.00352) or [Data Interpreter](https://arxiv.org/abs/2402.18679) in publications, please use the following BibTeX entries.
+To cite [MetaGPT](https://openreview.net/forum?id=VtmBAGCN7o) or [Data Interpreter](https://arxiv.org/abs/2402.18679) in publications, please use the following BibTeX entries.
 
 ```bibtex
-@misc{hong2023metagpt,
-      title={MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework}, 
-      author={Sirui Hong and Mingchen Zhuge and Jonathan Chen and Xiawu Zheng and Yuheng Cheng and Ceyao Zhang and Jinlin Wang and Zili Wang and Steven Ka Shing Yau and Zijuan Lin and Liyang Zhou and Chenyu Ran and Lingfeng Xiao and Chenglin Wu and Jürgen Schmidhuber},
-      year={2023},
-      eprint={2308.00352},
-      archivePrefix={arXiv},
-      primaryClass={cs.AI}
+@inproceedings{hong2024metagpt,
+      title={Meta{GPT}: Meta Programming for A Multi-Agent Collaborative Framework},
+      author={Sirui Hong and Mingchen Zhuge and Jonathan Chen and Xiawu Zheng and Yuheng Cheng and Jinlin Wang and Ceyao Zhang and Zili Wang and Steven Ka Shing Yau and Zijuan Lin and Liyang Zhou and Chenyu Ran and Lingfeng Xiao and Chenglin Wu and J{\"u}rgen Schmidhuber},
+      booktitle={The Twelfth International Conference on Learning Representations},
+      year={2024},
+      url={https://openreview.net/forum?id=VtmBAGCN7o}
 }
 @misc{hong2024data,
       title={Data Interpreter: An LLM Agent For Data Science}, 
@@ -185,6 +184,5 @@ ## Citation
       archivePrefix={arXiv},
       primaryClass={cs.AI}
 }
-
 ```
 

config/examples/anthropic-claude-3-5-sonnet.yaml

@@ -2,4 +2,4 @@ llm:
   api_type: 'claude' # or anthropic
   base_url: 'https://api.anthropic.com'
   api_key: 'YOUR_API_KEY'
-  model: 'claude-3-opus-20240229'
+  model: 'claude-3-5-sonnet-20240620' # or 'claude-3-opus-20240229'

docs/README_CN.md

@@ -119,13 +119,12 @@ ## 引用
 如果您在研究论文中使用 MetaGPT 或 Data Interpreter，请引用我们的工作：
 
 ```bibtex
-@misc{hong2023metagpt,
-      title={MetaGPT: Meta Programming for Multi-Agent Collaborative Framework},
-      author={Sirui Hong and Xiawu Zheng and Jonathan Chen and Yuheng Cheng and Jinlin Wang and Ceyao Zhang and Zili Wang and Steven Ka Shing Yau and Zijuan Lin and Liyang Zhou and Chenyu Ran and Lingfeng Xiao and Chenglin Wu},
-      year={2023},
-      eprint={2308.00352},
-      archivePrefix={arXiv},
-      primaryClass={cs.AI}
+@inproceedings{hong2024metagpt,
+      title={Meta{GPT}: Meta Programming for A Multi-Agent Collaborative Framework},
+      author={Sirui Hong and Mingchen Zhuge and Jonathan Chen and Xiawu Zheng and Yuheng Cheng and Jinlin Wang and Ceyao Zhang and Zili Wang and Steven Ka Shing Yau and Zijuan Lin and Liyang Zhou and Chenyu Ran and Lingfeng Xiao and Chenglin Wu and J{\"u}rgen Schmidhuber},
+      booktitle={The Twelfth International Conference on Learning Representations},
+      year={2024},
+      url={https://openreview.net/forum?id=VtmBAGCN7o}
 }
 @misc{hong2024data,
       title={Data Interpreter: An LLM Agent For Data Science}, 

docs/README_JA.md

@@ -298,13 +298,12 @@ ## 引用
 研究論文でMetaGPTやData Interpreterを使用する場合は、以下のように当社の作業を引用してください：
 
 ```bibtex
-@misc{hong2023metagpt,
-      title={MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework}, 
-      author={Sirui Hong and Mingchen Zhuge and Jonathan Chen and Xiawu Zheng and Yuheng Cheng and Ceyao Zhang and Jinlin Wang and Zili Wang and Steven Ka Shing Yau and Zijuan Lin and Liyang Zhou and Chenyu Ran and Lingfeng Xiao and Chenglin Wu and Jürgen Schmidhuber},
-      year={2023},
-      eprint={2308.00352},
-      archivePrefix={arXiv},
-      primaryClass={cs.AI}
+@inproceedings{hong2024metagpt,
+      title={Meta{GPT}: Meta Programming for A Multi-Agent Collaborative Framework},
+      author={Sirui Hong and Mingchen Zhuge and Jonathan Chen and Xiawu Zheng and Yuheng Cheng and Jinlin Wang and Ceyao Zhang and Zili Wang and Steven Ka Shing Yau and Zijuan Lin and Liyang Zhou and Chenyu Ran and Lingfeng Xiao and Chenglin Wu and J{\"u}rgen Schmidhuber},
+      booktitle={The Twelfth International Conference on Learning Representations},
+      year={2024},
+      url={https://openreview.net/forum?id=VtmBAGCN7o}
 }
 @misc{hong2024data,
       title={Data Interpreter: An LLM Agent For Data Science}, 

examples/ui_with_chainlit/.gitignore

@@ -0,0 +1,3 @@
+*.chainlit
+chainlit.md
+.files

examples/ui_with_chainlit/README.md

@@ -0,0 +1,34 @@
+# MetaGPT in UI with Chainlit! 🤖
+
+- MetaGPT functionality in UI using Chainlit.
+- It also takes a **one line requirement** as input and outputs **user stories / competitive analysis / requirements / data structures / APIs / documents, etc.**, But `everything in UI`.
+
+## Install Chainlit
+
+- Setup initial MetaGPT config from [Main](../../README.md).
+
+```bash
+pip install chainlit
+```
+
+## Usage
+
+```bash
+chainlit run app.py
+```
+
+- Now go to: http://localhost:8000
+
+- Select,
+  - `Create a 2048 game`
+  - `Write a cli Blackjack Game`
+  - `Type your own message...`
+
+- It will run a metagpt software company.
+
+## To Setup with own application
+
+- We can change `Environment.run`, `Team.run`, `Role.run`, `Role._act`, `Action.run`.
+- In this code, changed `Environment.run`, as it was easier to do.
+- We will need to change `metagpt.logs.set_llm_stream_logfunc` to stream messages in UI with Chainlit Message.
+- To use at some other place we need to call `chainlit.Message(content="").send()` with content.

examples/ui_with_chainlit/app.py

@@ -0,0 +1,83 @@
+import chainlit as cl
+from init_setup import ChainlitEnv
+
+from metagpt.roles import (
+    Architect,
+    Engineer,
+    ProductManager,
+    ProjectManager,
+    QaEngineer,
+)
+from metagpt.team import Team
+
+
+# https://docs.chainlit.io/concepts/starters
+@cl.set_chat_profiles
+async def chat_profile() -> list[cl.ChatProfile]:
+    """Generates a chat profile containing starter messages which can be triggered to run MetaGPT
+
+    Returns:
+        list[chainlit.ChatProfile]: List of Chat Profile
+    """
+    return [
+        cl.ChatProfile(
+            name="MetaGPT",
+            icon="/public/MetaGPT-new-log.jpg",
+            markdown_description="It takes a **one line requirement** as input and outputs **user stories / competitive analysis / requirements / data structures / APIs / documents, etc.**, But `everything in UI`.",
+            starters=[
+                cl.Starter(
+                    label="Create a 2048 Game",
+                    message="Create a 2048 game",
+                    icon="/public/2048.jpg",
+                ),
+                cl.Starter(
+                    label="Write a cli Blackjack Game",
+                    message="Write a cli Blackjack Game",
+                    icon="/public/blackjack.jpg",
+                ),
+            ],
+        )
+    ]
+
+
+# https://docs.chainlit.io/concepts/message
+@cl.on_message
+async def startup(message: cl.Message) -> None:
+    """On Message in UI, Create a MetaGPT software company
+
+    Args:
+        message (chainlit.Message): message by chainlist
+    """
+    idea = message.content
+    company = Team(env=ChainlitEnv())
+
+    # Similar to software_company.py
+    company.hire(
+        [
+            ProductManager(),
+            Architect(),
+            ProjectManager(),
+            Engineer(n_borg=5, use_code_review=True),
+            QaEngineer(),
+        ]
+    )
+
+    company.invest(investment=3.0)
+    company.run_project(idea=idea)
+
+    await company.run(n_round=5)
+
+    workdir = company.env.context.git_repo.workdir
+    files = company.env.context.git_repo.get_files(workdir)
+    files = "\n".join([f"{workdir}/{file}" for file in files if not file.startswith(".git")])
+
+    await cl.Message(
+        content=f"""
+Codes can be found here:
+{files}
+
+---
+
+Total cost: `{company.cost_manager.total_cost}`
+"""
+    ).send()

examples/ui_with_chainlit/init_setup.py

@@ -0,0 +1,69 @@
+import asyncio
+
+import chainlit as cl
+
+from metagpt.environment import Environment
+from metagpt.logs import logger, set_llm_stream_logfunc
+from metagpt.roles import Role
+from metagpt.utils.common import any_to_name
+
+
+def log_llm_stream_chainlit(msg):
+    # Stream the message token into Chainlit UI.
+    cl.run_sync(chainlit_message.stream_token(msg))
+
+
+set_llm_stream_logfunc(func=log_llm_stream_chainlit)
+
+
+class ChainlitEnv(Environment):
+    """Chainlit Environment for UI Integration"""
+
+    async def run(self, k=1):
+        """处理一次所有信息的运行
+        Process all Role runs at once
+        """
+        for _ in range(k):
+            futures = []
+            for role in self.roles.values():
+                # Call role.run with chainlit configuration
+                future = self._chainlit_role_run(role=role)
+                futures.append(future)
+
+            await asyncio.gather(*futures)
+            logger.debug(f"is idle: {self.is_idle}")
+
+    async def _chainlit_role_run(self, role: Role) -> None:
+        """To run the role with chainlit config
+
+        Args:
+            role (Role): metagpt.role.Role
+        """
+        global chainlit_message
+        chainlit_message = cl.Message(content="")
+
+        message = await role.run()
+        # If message is from role._act() publish to UI.
+        if message is not None and message.content != "No actions taken yet":
+            # Convert a message from action node in json format
+            chainlit_message.content = await self._convert_message_to_markdownjson(message=chainlit_message.content)
+
+            # message content from which role and its action...
+            chainlit_message.content += f"---\n\nAction: `{any_to_name(message.cause_by)}` done by `{role._setting}`."
+
+            await chainlit_message.send()
+
+    # for clean view in UI
+    async def _convert_message_to_markdownjson(self, message: str) -> str:
+        """If the message is from MetaGPT Action Node output, then
+        convert it into markdown json for clear view in UI.
+
+        Args:
+            message (str): message by role._act
+
+        Returns:
+            str: message in mardown from
+        """
+        if message.startswith("[CONTENT]"):
+            return f"```json\n{message}\n```\n"
+        return message

metagpt/actions/research.py

@@ -161,6 +161,8 @@ class CollectLinks(Action):
         """
         max_results = max(num_results * 2, 6)
         results = await self.search_engine.run(query, max_results=max_results, as_string=False)
+        if len(results) == 0:
+            return []
         _results = "\n".join(f"{i}: {j}" for i, j in zip(range(max_results), results))
         prompt = COLLECT_AND_RANKURLS_PROMPT.format(topic=topic, query=query, results=_results)
         logger.debug(prompt)

metagpt/const.py

@@ -1,14 +1,6 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/1 11:59
-@Author  : alexanderwu
-@File    : const.py
-@Modified By: mashenquan, 2023-11-1. According to Section 2.2.1 and 2.2.2 of RFC 116, added key definitions for
-        common properties in the Message.
-@Modified By: mashenquan, 2023-11-27. Defines file repository paths according to Section 2.2.3.4 of RFC 135.
-@Modified By: mashenquan, 2023/12/5. Add directories for code summarization..
-"""
+
 import os
 from pathlib import Path
 

metagpt/provider/bedrock/utils.py

@@ -27,6 +27,7 @@ SUPPORT_STREAM_MODELS = {
     "anthropic.claude-3-sonnet-20240229-v1:0:28k": 28000,
     "anthropic.claude-3-sonnet-20240229-v1:0:200k": 200000,
     "anthropic.claude-3-haiku-20240307-v1:0": 200000,
+    "anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
     "anthropic.claude-3-haiku-20240307-v1:0:48k": 48000,
     "anthropic.claude-3-haiku-20240307-v1:0:200k": 200000,
     # currently (2024-4-29) only available at US West (Oregon) AWS Region.

metagpt/provider/bedrock_api.py

@@ -1,5 +1,7 @@
+import asyncio
 import json
-from typing import Literal
+from functools import partial
+from typing import List, Literal
 
 import boto3
 from botocore.eventstream import EventStream
@@ -22,7 +24,6 @@ class BedrockLLM(BaseLLM):
         self.__client = self.__init_client("bedrock-runtime")
         self.__provider = get_provider(self.config.model)
         self.cost_manager = CostManager(token_costs=BEDROCK_TOKEN_COSTS)
-        logger.warning("Amazon bedrock doesn't support asynchronous now")
         if self.config.model in NOT_SUUPORT_STREAM_MODELS:
             logger.warning(f"model {self.config.model} doesn't support streaming output!")
 
@@ -64,15 +65,21 @@ class BedrockLLM(BaseLLM):
         ]
         logger.info("\n" + "\n".join(summaries))
 
-    def invoke_model(self, request_body: str) -> dict:
-        response = self.__client.invoke_model(modelId=self.config.model, body=request_body)
+    async def invoke_model(self, request_body: str) -> dict:
+        loop = asyncio.get_running_loop()
+        response = await loop.run_in_executor(
+            None, partial(self.client.invoke_model, modelId=self.config.model, body=request_body)
+        )
         usage = self._get_usage(response)
         self._update_costs(usage, self.config.model)
         response_body = self._get_response_body(response)
         return response_body
 
-    def invoke_model_with_response_stream(self, request_body: str) -> EventStream:
-        response = self.__client.invoke_model_with_response_stream(modelId=self.config.model, body=request_body)
+    async def invoke_model_with_response_stream(self, request_body: str) -> EventStream:
+        loop = asyncio.get_running_loop()
+        response = await loop.run_in_executor(
+            None, partial(self.client.invoke_model_with_response_stream, modelId=self.config.model, body=request_body)
+        )
         usage = self._get_usage(response)
         self._update_costs(usage, self.config.model)
         return response
@@ -97,7 +104,7 @@ class BedrockLLM(BaseLLM):
 
     async def acompletion(self, messages: list[dict]) -> dict:
         request_body = self.__provider.get_request_body(messages, self._const_kwargs)
-        response_body = self.invoke_model(request_body)
+        response_body = await self.invoke_model(request_body)
         return response_body
 
     async def _achat_completion(self, messages: list[dict], timeout=USE_CONFIG_TIMEOUT) -> dict:
@@ -111,14 +118,8 @@ class BedrockLLM(BaseLLM):
             return full_text
 
         request_body = self.__provider.get_request_body(messages, self._const_kwargs, stream=True)
-
-        response = self.invoke_model_with_response_stream(request_body)
-        collected_content = []
-        for event in response["body"]:
-            chunk_text = self.__provider.get_choice_text_from_stream(event)
-            collected_content.append(chunk_text)
-            log_llm_stream(chunk_text)
-
+        stream_response = await self.invoke_model_with_response_stream(request_body)
+        collected_content = await self._get_stream_response_body(stream_response)
         log_llm_stream("\n")
         full_text = ("".join(collected_content)).lstrip()
         return full_text
@@ -127,6 +128,18 @@ class BedrockLLM(BaseLLM):
         response_body = json.loads(response["body"].read())
         return response_body
 
+    async def _get_stream_response_body(self, stream_response) -> List[str]:
+        def collect_content() -> str:
+            collected_content = []
+            for event in stream_response["body"]:
+                chunk_text = self.__provider.get_choice_text_from_stream(event)
+                collected_content.append(chunk_text)
+                log_llm_stream(chunk_text)
+            return collected_content
+
+        loop = asyncio.get_running_loop()
+        return await loop.run_in_executor(None, collect_content)
+
     def _get_usage(self, response) -> dict[str, int]:
         headers = response.get("ResponseMetadata", {}).get("HTTPHeaders", {})
         prompt_tokens = int(headers.get("x-amzn-bedrock-input-token-count", 0))

metagpt/provider/qianfan_api.py

@@ -50,6 +50,9 @@ class QianFanLLM(BaseLLM):
         else:
             raise ValueError("Set the `access_key`&`secret_key` or `api_key`&`secret_key` first")
 
+        if self.config.base_url:
+            os.environ.setdefault("QIANFAN_BASE_URL", self.config.base_url)
+
         support_system_pairs = [
             ("ERNIE-Bot-4", "completions_pro"),  # (model, corresponding-endpoint)
             ("ERNIE-Bot-8k", "ernie_bot_8k"),

metagpt/roles/invoice_ocr_assistant.py

@@ -80,19 +80,17 @@ class InvoiceOCRAssistant(Role):
                 raise Exception("Invoice file not uploaded")
 
             resp = await todo.run(file_path)
+            actions = list(self.actions)
             if len(resp) == 1:
                 # Single file support for questioning based on OCR recognition results
-                self.set_actions([GenerateTable, ReplyQuestion])
+                actions.extend([GenerateTable, ReplyQuestion])
                 self.orc_data = resp[0]
             else:
-                self.set_actions([GenerateTable])
-
-            self.set_todo(None)
+                actions.append(GenerateTable)
+            self.set_actions(actions)
+            self.rc.max_react_loop = len(self.actions)
             content = INVOICE_OCR_SUCCESS
             resp = OCRResults(ocr_result=json.dumps(resp))
-            msg = Message(content=content, instruct_content=resp)
-            self.rc.memory.add(msg)
-            return await super().react()
         elif isinstance(todo, GenerateTable):
             ocr_results: OCRResults = msg.instruct_content
             resp = await todo.run(json.loads(ocr_results.ocr_result), self.filename)

metagpt/roles/researcher.py

@@ -58,7 +58,9 @@ class Researcher(Role):
             )
         elif isinstance(todo, WebBrowseAndSummarize):
             links = instruct_content.links
-            todos = (todo.run(*url, query=query, system_text=research_system_text) for (query, url) in links.items())
+            todos = (
+                todo.run(*url, query=query, system_text=research_system_text) for (query, url) in links.items() if url
+            )
             if self.enable_concurrency:
                 summaries = await asyncio.gather(*todos)
             else:

metagpt/tools/search_engine_serpapi.py

@@ -87,8 +87,11 @@ class SerpAPIWrapper(BaseModel):
         get_focused = lambda x: {i: j for i, j in x.items() if i in focus}
 
         if "error" in res.keys():
-            raise ValueError(f"Got error from SerpAPI: {res['error']}")
-        if "answer_box" in res.keys() and "answer" in res["answer_box"].keys():
+            if res["error"] == "Google hasn't returned any results for this query.":
+                toret = "No good search result found"
+            else:
+                raise ValueError(f"Got error from SerpAPI: {res['error']}")
+        elif "answer_box" in res.keys() and "answer" in res["answer_box"].keys():
             toret = res["answer_box"]["answer"]
         elif "answer_box" in res.keys() and "snippet" in res["answer_box"].keys():
             toret = res["answer_box"]["snippet"]

metagpt/utils/di_graph_repository.py

@@ -23,8 +23,8 @@ from metagpt.utils.graph_repository import SPO, GraphRepository
 class DiGraphRepository(GraphRepository):
     """Graph repository based on DiGraph."""
 
-    def __init__(self, name: str, **kwargs):
-        super().__init__(name=name, **kwargs)
+    def __init__(self, name: str | Path, **kwargs):
+        super().__init__(name=str(name), **kwargs)
         self._repo = networkx.DiGraph()
 
     async def insert(self, subject: str, predicate: str, object_: str):
@@ -112,8 +112,28 @@ class DiGraphRepository(GraphRepository):
     async def load(self, pathname: str | Path):
         """Load a directed graph repository from a JSON file."""
         data = await aread(filename=pathname, encoding="utf-8")
-        m = json.loads(data)
+        self.load_json(data)
+
+    def load_json(self, val: str):
+        """
+        Loads a JSON-encoded string representing a graph structure and updates
+        the internal repository (_repo) with the parsed graph.
+
+        Args:
+            val (str): A JSON-encoded string representing a graph structure.
+
+        Returns:
+            self: Returns the instance of the class with the updated _repo attribute.
+
+        Raises:
+            TypeError: If val is not a valid JSON string or cannot be parsed into
+                       a valid graph structure.
+        """
+        if not val:
+            return self
+        m = json.loads(val)
         self._repo = networkx.node_link_graph(m)
+        return self
 
     @staticmethod
     async def load_from(pathname: str | Path) -> GraphRepository:
@@ -126,9 +146,7 @@ class DiGraphRepository(GraphRepository):
             GraphRepository: A new instance of the graph repository loaded from the specified JSON file.
         """
         pathname = Path(pathname)
-        name = pathname.with_suffix("").name
-        root = pathname.parent
-        graph = DiGraphRepository(name=name, root=root)
+        graph = DiGraphRepository(name=pathname.stem, root=pathname.parent)
         if pathname.exists():
             await graph.load(pathname=pathname)
         return graph

metagpt/utils/stream_pipe.py

@@ -11,8 +11,10 @@ from multiprocessing import Pipe
 
 
 class StreamPipe:
-    parent_conn, child_conn = Pipe()
-    finish: bool = False
+    def __init__(self, name=None):
+        self.name = name
+        self.parent_conn, self.child_conn = Pipe()
+        self.finish: bool = False
 
     format_data = {
         "id": "chatcmpl-96bVnBOOyPFZZxEoTIGbdpFcVEnur",

metagpt/utils/token_counter.py

@@ -57,7 +57,9 @@ TOKEN_COSTS = {
     "claude-2.0": {"prompt": 0.008, "completion": 0.024},
     "claude-2.1": {"prompt": 0.008, "completion": 0.024},
     "claude-3-sonnet-20240229": {"prompt": 0.003, "completion": 0.015},
+    "claude-3-5-sonnet-20240620": {"prompt": 0.003, "completion": 0.015},
     "claude-3-opus-20240229": {"prompt": 0.015, "completion": 0.075},
+    "claude-3-haiku-20240307": {"prompt": 0.00025, "completion": 0.00125},
     "yi-34b-chat-0205": {"prompt": 0.0003, "completion": 0.0003},
     "yi-34b-chat-200k": {"prompt": 0.0017, "completion": 0.0017},
     "yi-large": {"prompt": 0.0028, "completion": 0.0028},
@@ -224,6 +226,8 @@ TOKEN_MAX = {
     "claude-2.1": 200000,
     "claude-3-sonnet-20240229": 200000,
     "claude-3-opus-20240229": 200000,
+    "claude-3-5-sonnet-20240620": 200000,
+    "claude-3-haiku-20240307": 200000,
     "yi-34b-chat-0205": 4000,
     "yi-34b-chat-200k": 200000,
     "yi-large": 16385,
@@ -280,6 +284,7 @@ BEDROCK_TOKEN_COSTS = {
     "anthropic.claude-3-sonnet-20240229-v1:0": {"prompt": 0.003, "completion": 0.015},
     "anthropic.claude-3-sonnet-20240229-v1:0:28k": {"prompt": 0.003, "completion": 0.015},
     "anthropic.claude-3-sonnet-20240229-v1:0:200k": {"prompt": 0.003, "completion": 0.015},
+    "anthropic.claude-3-5-sonnet-20240620-v1:0": {"prompt": 0.003, "completion": 0.015},
     "anthropic.claude-3-haiku-20240307-v1:0": {"prompt": 0.00025, "completion": 0.00125},
     "anthropic.claude-3-haiku-20240307-v1:0:48k": {"prompt": 0.00025, "completion": 0.00125},
     "anthropic.claude-3-haiku-20240307-v1:0:200k": {"prompt": 0.00025, "completion": 0.00125},

setup.py

@@ -45,30 +45,6 @@ extras_require = {
         "llama-index-postprocessor-flag-embedding-reranker==0.1.2",
         "docx2txt==0.8",
     ],
-    "android_assistant": [
-        "pyshine==0.0.9",
-        "opencv-python==4.6.0.66",
-        "protobuf<3.20,>=3.9.2",
-        "modelscope",
-        "tensorflow==2.9.1; os_name == 'linux'",
-        "tensorflow==2.9.1; os_name == 'win32'",
-        "tensorflow-macos==2.9; os_name == 'darwin'",
-        "keras==2.9.0",
-        "torch",
-        "torchvision",
-        "transformers",
-        "opencv-python",
-        "matplotlib",
-        "pycocotools",
-        "SentencePiece",
-        "tf_slim",
-        "tf_keras",
-        "pyclipper",
-        "shapely",
-        "groundingdino-py",
-        "datasets==2.18.0",
-        "clip-openai",
-    ],
 }
 
 extras_require["test"] = [
@@ -85,6 +61,9 @@ extras_require["test"] = [
     "aioboto3~=12.4.0",
     "gradio==3.0.0",
     "grpcio-status==1.48.2",
+    "grpcio-tools==1.48.2",
+    "google-api-core==2.17.1",
+    "protobuf==3.19.6",
     "pylint==3.0.3",
     "pybrowsers",
 ]
@@ -93,7 +72,30 @@ extras_require["pyppeteer"] = [
     "pyppeteer>=1.0.2"
 ]  # pyppeteer is unmaintained and there are conflicts with dependencies
 extras_require["dev"] = (["pylint~=3.0.3", "black~=23.3.0", "isort~=5.12.0", "pre-commit~=3.6.0"],)
-
+extras_require["android_assistant"] = [
+    "pyshine==0.0.9",
+    "opencv-python==4.6.0.66",
+    "protobuf<3.20,>=3.9.2",
+    "modelscope",
+    "tensorflow==2.9.1; os_name == 'linux'",
+    "tensorflow==2.9.1; os_name == 'win32'",
+    "tensorflow-macos==2.9; os_name == 'darwin'",
+    "keras==2.9.0",
+    "torch",
+    "torchvision",
+    "transformers",
+    "opencv-python",
+    "matplotlib",
+    "pycocotools",
+    "SentencePiece",
+    "tf_slim",
+    "tf_keras",
+    "pyclipper",
+    "shapely",
+    "groundingdino-py",
+    "datasets==2.18.0",
+    "clip-openai",
+]
 
 setup(
     name="metagpt",