Handle missing imports with a context manager and a wraper for classes and objects

autogen/agentchat/contrib/capabilities/text_compressors.py

@@ -4,15 +4,12 @@
 #
 # Portions derived from  https://github.com/microsoft/autogen are under the MIT License.
 # SPDX-License-Identifier: MIT
-from typing import Any, Optional, Protocol
+from typing import Any, Protocol
 
-IMPORT_ERROR: Optional[Exception] = None
-try:
+from ....import_utils import optional_import_block, require_optional_import
+
+with optional_import_block() as result:
     import llmlingua
-except ImportError:
-    IMPORT_ERROR = ImportError("LLMLingua is not installed. Please install it with `pip install autogen[long-context]`")
-    PromptCompressor = object
-else:
     from llmlingua import PromptCompressor
 
 
@@ -27,6 +24,7 @@ def compress_text(self, text: str, **compression_params) -> dict[str, Any]:
         ...
 
 
+@require_optional_import("llmlingua", "long-context")
 class LLMLingua:
     """Compresses text messages using LLMLingua for improved efficiency in processing and response generation.
 
@@ -55,9 +53,6 @@ def __init__(
         Raises:
             ImportError: If the llmlingua library is not installed.
         """
-        if IMPORT_ERROR:
-            raise IMPORT_ERROR
-
         self._prompt_compressor = PromptCompressor(**prompt_compressor_kwargs)
 
         assert isinstance(self._prompt_compressor, llmlingua.PromptCompressor)

autogen/agentchat/contrib/llamaindex_conversable_agent.py

@@ -10,15 +10,18 @@
 from autogen.agentchat import Agent, ConversableAgent
 from autogen.agentchat.contrib.vectordb.utils import get_logger
 
+from ...import_utils import optional_import_block, require_optional_import
+
 logger = get_logger(__name__)
 
-try:
+with optional_import_block() as result:
     from llama_index.core.agent.runner.base import AgentRunner
     from llama_index.core.base.llms.types import ChatMessage
     from llama_index.core.chat_engine.types import AgentChatResponse
     from pydantic import BaseModel
     from pydantic import __version__ as pydantic_version
 
+if result.is_successful:
     # let's Avoid: AttributeError: type object 'Config' has no attribute 'copy'
     # check for v1 like in autogen/_pydantic.py
     is_pydantic_v1 = pydantic_version.startswith("1.")
@@ -35,11 +38,8 @@ class Config:
     # Added to mitigate PydanticSchemaGenerationError
     BaseModel.model_config = Config
 
-except ImportError as e:
-    logger.fatal("Failed to import llama-index. Try running 'pip install llama-index'")
-    raise e
-
 
+@require_optional_import("llama_index", "neo4j")
 class LLamaIndexConversableAgent(ConversableAgent):
     def __init__(
         self,

autogen/agentchat/contrib/math_user_proxy_agent.py

@@ -16,6 +16,11 @@
 from autogen.code_utils import UNKNOWN, execute_code, extract_code, infer_lang
 from autogen.math_utils import get_answer
 
+from ...import_utils import optional_import_block, require_optional_import
+
+with optional_import_block() as result:
+    import wolframalpha
+
 PROMPTS = {
     # default
     "default": """Let's use Python to solve a math problem.
@@ -402,16 +407,12 @@ class Config:
 
     @root_validator(skip_on_failure=True)
     @classmethod
+    @require_optional_import("wolframalpha", "mathchat")
     def validate_environment(cls, values: dict) -> dict:
         """Validate that api key and python package exists in environment."""
         wolfram_alpha_appid = get_from_dict_or_env(values, "wolfram_alpha_appid", "WOLFRAM_ALPHA_APPID")
         values["wolfram_alpha_appid"] = wolfram_alpha_appid
 
-        try:
-            import wolframalpha
-
-        except ImportError as e:
-            raise ImportError("wolframalpha is not installed. Please install it with `pip install wolframalpha`") from e
         client = wolframalpha.Client(wolfram_alpha_appid)
         values["wolfram_client"] = client
 

autogen/agentchat/contrib/qdrant_retrieve_user_proxy_agent.py

@@ -15,17 +15,17 @@
 )
 from autogen.retrieve_utils import TEXT_FORMATS, get_files_from_dir, split_files_to_chunks
 
+from ...import_utils import optional_import_block, require_optional_import
+
 logger = get_logger(__name__)
 
-try:
+with optional_import_block():
     import fastembed  # noqa: F401
     from qdrant_client import QdrantClient, models
     from qdrant_client.fastembed_common import QueryResponse
-except ImportError as e:
-    logger.fatal("Failed to import qdrant_client with fastembed. Try running 'pip install qdrant_client[fastembed]'")
-    raise e
 
 
+@require_optional_import(["fastembed", "qdrant_client"], "retrievechat-qdrant")
 class QdrantRetrieveUserProxyAgent(RetrieveUserProxyAgent):
     def __init__(
         self,
@@ -158,6 +158,7 @@ def retrieve_docs(self, problem: str, n_results: int = 20, search_string: str =
         self._results = results
 
 
+@require_optional_import(["fastembed", "qdrant_client"], "retrievechat-qdrant")
 def create_qdrant_from_dir(
     dir_path: str,
     max_tokens: int = 4000,
@@ -263,6 +264,7 @@ def create_qdrant_from_dir(
         )
 
 
+@require_optional_import("qdrant_client", "retrievechat-qdrant")
 def query_qdrant(
     query_texts: list[str],
     n_results: int = 10,

autogen/agentchat/contrib/reasoning_agent.py

@@ -7,6 +7,7 @@
 import warnings
 from typing import Optional
 
+from ...import_utils import optional_import_block
 from ..agent import Agent
 from ..assistant_agent import AssistantAgent
 
@@ -169,9 +170,10 @@ def visualize_tree(root: ThinkNode) -> None:
     Args:
         root (ThinkNode): The root node of the tree.
     """
-    try:
+    with optional_import_block() as result:
         from graphviz import Digraph
-    except ImportError:
+
+    if not result.is_successful:
         print("Please install graphviz: pip install graphviz")
         return
 

autogen/agentchat/contrib/retrieve_user_proxy_agent.py

@@ -12,10 +12,6 @@
 
 from IPython import get_ipython
 
-try:
-    import chromadb
-except ImportError as e:
-    raise ImportError(f"{e}. You can try `pip install autogen[retrievechat]`, or install `chromadb` manually.")
 from autogen.agentchat import UserProxyAgent
 from autogen.agentchat.agent import Agent
 from autogen.agentchat.contrib.vectordb.base import Document, QueryResults, VectorDB, VectorDBFactory
@@ -35,6 +31,10 @@
 from autogen.token_count_utils import count_token
 
 from ...formatting_utils import colored
+from ...import_utils import optional_import_block, require_optional_import
+
+with optional_import_block():
+    import chromadb
 
 logger = get_logger(__name__)
 
@@ -91,6 +91,7 @@
 UPDATE_CONTEXT_IN_PROMPT = "you should reply exactly `UPDATE CONTEXT`"
 
 
+@require_optional_import("chromadb", "retrievechat")
 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

autogen/agentchat/contrib/vectordb/chromadb.py

@@ -7,24 +7,26 @@
 import os
 from typing import Callable
 
+from ....import_utils import optional_import_block, require_optional_import
 from .base import Document, ItemID, QueryResults, VectorDB
 from .utils import chroma_results_to_query_results, filter_results_by_distance, get_logger
 
-try:
+with optional_import_block() as result:
     import chromadb
-
-    if chromadb.__version__ < "0.4.15":
-        raise ImportError("Please upgrade chromadb to version 0.4.15 or later.")
     import chromadb.errors
     import chromadb.utils.embedding_functions as ef
     from chromadb.api.models.Collection import Collection
-except ImportError:
-    raise ImportError("Please install chromadb: `pip install chromadb`")
+
+if result.is_successful:
+    if chromadb.__version__ < "0.4.15":
+        raise ImportError("Please upgrade chromadb to version 0.4.15 or later.")
+
 
 CHROMADB_MAX_BATCH_SIZE = os.environ.get("CHROMADB_MAX_BATCH_SIZE", 40000)
 logger = get_logger(__name__)
 
 
+@require_optional_import("chromadb", "retrievechat")
 class ChromaVectorDB(VectorDB):
     """A vector database that uses ChromaDB as the backend."""
 

autogen/agentchat/contrib/vectordb/pgvectordb.py

@@ -12,24 +12,20 @@
 import numpy as np
 from sentence_transformers import SentenceTransformer
 
+from ....import_utils import optional_import_block, require_optional_import
 from .base import Document, ItemID, QueryResults, VectorDB
 from .utils import get_logger
 
-try:
+with optional_import_block():
     import pgvector  # noqa: F401
-    from pgvector.psycopg import register_vector
-except ImportError:
-    raise ImportError("Please install pgvector: `pip install pgvector`")
-
-try:
     import psycopg
-except ImportError:
-    raise ImportError("Please install pgvector: `pip install psycopg`")
+    from pgvector.psycopg import register_vector
 
 PGVECTOR_MAX_BATCH_SIZE = os.environ.get("PGVECTOR_MAX_BATCH_SIZE", 40000)
 logger = get_logger(__name__)
 
 
+@require_optional_import("psycopg", "retrievechat-pgvector")
 class Collection:
     """A Collection object for PGVector.
 
@@ -543,6 +539,7 @@ def create_collection(
         cursor.close()
 
 
+@require_optional_import(["pgvector", "psycopg"], "retrievechat-pgvector")
 class PGVectorDB(VectorDB):
     """A vector database that uses PGVector as the backend."""
 

autogen/agentchat/contrib/vectordb/qdrant.py

@@ -9,13 +9,14 @@
 from collections.abc import Sequence
 from typing import Optional, Union
 
+from ....import_utils import optional_import_block, require_optional_import
 from .base import Document, ItemID, QueryResults, VectorDB
 from .utils import get_logger
 
-try:
+with optional_import_block():
+    from fastembed import TextEmbedding
     from qdrant_client import QdrantClient, models
-except ImportError:
-    raise ImportError("Please install qdrant-client: `pip install qdrant-client`")
+
 
 logger = get_logger(__name__)
 
@@ -28,6 +29,7 @@ def __call__(self, inputs: list[str]) -> list[Embeddings]:
         raise NotImplementedError
 
 
+@require_optional_import("fastembed", "retrievechat-qdrant")
 class FastEmbedEmbeddingFunction(EmbeddingFunction):
     """Embedding function implementation using FastEmbed - https://qdrant.github.io/fastembed."""
 
@@ -57,12 +59,6 @@ def __init__(
         Raises:
             ValueError: If the model_name is not in the format `<org>/<model>` e.g. BAAI/bge-small-en-v1.5.
         """
-        try:
-            from fastembed import TextEmbedding
-        except ImportError as e:
-            raise ValueError(
-                "The 'fastembed' package is not installed. Please install it with `pip install fastembed`",
-            ) from e
         self._batch_size = batch_size
         self._parallel = parallel
         self._model = TextEmbedding(model_name=model_name, cache_dir=cache_dir, threads=threads, **kwargs)
@@ -73,6 +69,7 @@ def __call__(self, inputs: list[str]) -> list[Embeddings]:
         return [embedding.tolist() for embedding in embeddings]
 
 
+@require_optional_import("qdrant_client", "retrievechat-qdrant")
 class QdrantVectorDB(VectorDB):
     """A vector database implementation that uses Qdrant as the backend."""
 

autogen/browser_utils.py

@@ -12,27 +12,26 @@
 from typing import Any, Optional, Union
 from urllib.parse import urljoin, urlparse
 
-import markdownify
-import requests
-from bs4 import BeautifulSoup
+from .import_utils import optional_import_block, require_optional_import
+
+with optional_import_block():
+    import markdownify
+    import requests
+    from bs4 import BeautifulSoup
 
 # Optional PDF support
-IS_PDF_CAPABLE = False
-try:
+with optional_import_block() as result:
     import pdfminer
     import pdfminer.high_level
 
-    IS_PDF_CAPABLE = True
-except ModuleNotFoundError:
-    pass
+IS_PDF_CAPABLE = result.is_successful
 
 # Other optional dependencies
-try:
+with optional_import_block():
     import pathvalidate
-except ModuleNotFoundError:
-    pass
 
 
+@require_optional_import(["markdownify", "requests", "bs4", "pdfminer", "pathvalidate"], "websurfer")
 class SimpleTextBrowser:
     """(In preview) An extremely simple text-based web browser comparable to Lynx. Suitable for Agentic use."""
 

autogen/cache/cache_factory.py

@@ -8,6 +8,7 @@
 import os
 from typing import Any, Optional, Union
 
+from ..import_utils import optional_import_block
 from .abstract_cache_base import AbstractCache
 from .disk_cache import DiskCache
 
@@ -63,22 +64,23 @@ def cache_factory(
 
         """
         if redis_url:
-            try:
+            with optional_import_block() as result:
                 from .redis_cache import RedisCache
 
+            if result.is_successful:
                 return RedisCache(seed, redis_url)
-            except ImportError:
+            else:
                 logging.warning(
                     "RedisCache is not available. Checking other cache options. The last fallback is DiskCache."
                 )
 
         if cosmosdb_config:
-            try:
+            with optional_import_block() as result:
                 from .cosmos_db_cache import CosmosDBCache
 
+            if result.is_successful:
                 return CosmosDBCache.create_cache(seed, cosmosdb_config)
-
-            except ImportError:
+            else:
                 logging.warning("CosmosDBCache is not available. Fallback to DiskCache.")
 
         # Default to DiskCache if neither Redis nor Cosmos DB configurations are provided

autogen/exception_utils.py

@@ -9,6 +9,7 @@
 __all__ = [
     "AgentNameConflict",
     "InvalidCarryOverType",
+    "ModelToolNotSupportedError",
     "NoEligibleSpeaker",
     "SenderRequired",
     "UndefinedNextAgent",