Update CHANGELOG.md and pyproject.toml for version 0.2.4

Removed redundant variables

CHANGELOG.md

@@ -1,6 +1,15 @@
 Release History
 ===============
 
+
+## 0.2.4 (2024-11-11)
+
+- General improvements.
+
+## 0.2.3 (2024-11-04)
+
+- Remove default max-tokens for OpenAI provider.
+
 ## 0.2.3 (2024-11-03)
 
 - Update default model for Amazon provider.

examples/cooking_recipe_example.py

@@ -1,5 +1,5 @@
-from pydantic import BaseModel
 from _context import simplemind as sm
+from pydantic import BaseModel
 from rich.console import Console
 from rich.panel import Panel
 from rich.text import Text

examples/discussion.py

@@ -1,11 +1,10 @@
 import time
 from typing import List, Tuple
 
+from _context import sm
 from rich.console import Console
 from rich.markdown import Markdown
 
-from _context import sm
-
 
 class MultiAIConversation:
     """Orchestrates conversations between multiple AI models."""

examples/enhanced_context.py

@@ -1,35 +1,28 @@
-from datetime import datetime
-import logging
-import sqlite3
-from typing import List
-import re
-import os
 import contextlib
-
+import logging
-import spacy
+import os
+import random
+import re
+import sqlite3
+from concurrent.futures import ThreadPoolExecutor
 from contextlib import contextmanager
-
+from datetime import datetime
-from _context import simplemind as sm
+from typing import List
 
 import nltk
-from nltk.tokenize import word_tokenize
+import spacy
-from nltk.tag import pos_tag
-
-from rich.console import Console
-from rich.panel import Panel
-from rich.markdown import Markdown
-from rich.status import Status
-
-from concurrent.futures import ThreadPoolExecutor
-import random
-
-from docopt import docopt
-
-from prompt_toolkit import PromptSession
-from prompt_toolkit.completion import Completer, Completion
-from prompt_toolkit.auto_suggest import AutoSuggestFromHistory
-
 import xerox
+from _context import simplemind as sm
+from docopt import docopt
+from nltk.tag import pos_tag
+from nltk.tokenize import word_tokenize
+from prompt_toolkit import PromptSession
+from prompt_toolkit.auto_suggest import AutoSuggestFromHistory
+from prompt_toolkit.completion import Completer, Completion
+from rich.console import Console
+from rich.markdown import Markdown
+from rich.panel import Panel
+from rich.status import Status
 
 DB_PATH = "enhanced_context.db"
 AVAILABLE_PROVIDERS = ["xai", "openai", "anthropic", "ollama"]

examples/four_way_conversation.py

@@ -1,4 +1,5 @@
 import time
+
 from _context import simplemind as sm
 
 

examples/inspiration_plugin.py

@@ -1,4 +1,5 @@
 import random
+
 from _context import simplemind as sm
 
 

examples/medicine_data.py

@@ -1,5 +1,5 @@
-from pydantic import BaseModel
 from _context import simplemind as sm
+from pydantic import BaseModel
 from rich.console import Console
 from rich.panel import Panel
 from rich.table import Table

examples/mood_detector_plugin.py

@@ -1,7 +1,7 @@
 import nltk
+from _context import simplemind as sm
 from nltk.sentiment import SentimentIntensityAnalyzer
 from rich.console import Console
-from _context import simplemind as sm
 
 nltk.download("vader_lexicon")
 

examples/sentiment_analysis.py

@@ -5,6 +5,7 @@ from pydantic import BaseModel
 
 # Note: you should probably be using textblob for this.
 
+
 class SentimentAnalysis(BaseModel):
     sentiment: Literal["positive", "negative", "neutral"]
     confidence: float

examples/web_bible_explorer.py

@@ -1,8 +1,10 @@
-from fastapi import FastAPI, Request, HTTPException
-from fastapi.templating import Jinja2Templates
-from fastapi.staticfiles import StaticFiles
-from pydantic import BaseModel
 from typing import List
+
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from pydantic import BaseModel
+
 import simplemind as sm
 
 app = FastAPI()

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "simplemind"
-version = "0.2.2"
+version = "0.2.4"
 description = "An experimental client for AI providers that intends to replace LangChain and LangGraph for most common use cases."
 readme = "README.md"
 requires-python = ">=3.10"
@@ -10,18 +10,18 @@ dependencies = ["pydantic", "pydantic-settings", "instructor", "logfire"]
 full = [
     "openai",
     "anthropic",
-    "ollama",
     "groq",
     "google-generativeai",
     "botocore",
     "boto3"
 ]
-openai = ["openai"]
-anthropic = ["anthropic"]
-ollama = ["ollama", "openai"]
-groq = ["groq"]
-gemini = ["google-generativeai"]
 amazon = ["boto3", "botocore", "anthropic"]
+anthropic = ["anthropic"]
+gemini = ["google-generativeai"]
+groq = ["groq"]
+ollama = ["openai"]
+openai = ["openai"]
+xai = ["openai"]
 
 [build-system]
 requires = ["hatchling"]

simplemind/providers/__init__.py

@@ -1,12 +1,32 @@
 from typing import List, Type
 
 from ._base import BaseProvider
+from .amazon import Amazon
 from .anthropic import Anthropic
 from .gemini import Gemini
 from .groq import Groq
 from .ollama import Ollama
 from .openai import OpenAI
 from .xai import XAI
-from .amazon import Amazon
 
-providers: List[Type[BaseProvider]] = [Anthropic, Gemini, Groq, OpenAI, Ollama, XAI, Amazon]
+providers: List[Type[BaseProvider]] = [
+    Anthropic,
+    Gemini,
+    Groq,
+    OpenAI,
+    Ollama,
+    XAI,
+    Amazon,
+]
+
+__all__ = [
+    "Anthropic",
+    "Gemini",
+    "Groq",
+    "OpenAI",
+    "Ollama",
+    "XAI",
+    "Amazon",
+    "providers",
+    "BaseProvider",
+]

simplemind/providers/amazon.py

@@ -1,22 +1,22 @@
-from typing import Type, TypeVar, Iterator
 from functools import cached_property
+from typing import TYPE_CHECKING, Iterator, Type, TypeVar
 
 import instructor
 from pydantic import BaseModel
 
-from ._base import BaseProvider
 from ..settings import settings
+from ._base import BaseProvider
+
+if TYPE_CHECKING:
+    from ..models import Conversation, Message
 
 T = TypeVar("T", bound=BaseModel)
 
-PROVIDER_NAME = "amazon"
-DEFAULT_MODEL = "us.anthropic.claude-3-5-sonnet-20241022-v2:0"
-DEFAULT_MAX_TOKENS = 5_000
-
 
 class Amazon(BaseProvider):
-    NAME = PROVIDER_NAME
+    NAME = "amazon"
-    DEFAULT_MODEL = DEFAULT_MODEL
+    DEFAULT_MODEL = "us.anthropic.claude-3-5-sonnet-20241022-v2:0"
+    DEFAULT_MAX_TOKENS = 5_000
     supports_streaming = True
 
     def __init__(self, profile_name: str | None = None):
@@ -25,7 +25,12 @@ class Amazon(BaseProvider):
     @cached_property
     def client(self):
         """The AnthropicBedrock client."""
-        import anthropic
+        try:
+            import anthropic
+        except ImportError as exc:
+            raise ImportError(
+                "Please install the `anthropic` package: `pip install anthropic`"
+            ) from exc
 
         if not self.profile_name:
             raise ValueError("Profile name is not provided")
@@ -33,12 +38,12 @@ class Amazon(BaseProvider):
         return anthropic.AnthropicBedrock(aws_profile=self.profile_name)
 
     @cached_property
-    def structured_client(self):
+    def structured_client(self) -> instructor.Instructor:
         """A client patched with Instructor."""
 
         return instructor.from_anthropic(self.client)
 
-    def send_conversation(self, conversation: "Conversation", **kwargs):
+    def send_conversation(self, conversation: "Conversation", **kwargs) -> "Message":
         """Send a conversation to the OpenAI API."""
 
         from ..models import Message
@@ -59,7 +64,7 @@ class Amazon(BaseProvider):
             role="assistant",
             text=assistant_message.content or "",
             raw=response,
-            llm_model=conversation.llm_model or DEFAULT_MODEL,
+            llm_model=conversation.llm_model or self.DEFAULT_MODEL,
             llm_provider=PROVIDER_NAME,
         )
 
@@ -75,12 +80,12 @@ class Amazon(BaseProvider):
             messages=messages,
             model=llm_model or self.DEFAULT_MODEL,
             response_model=response_model,
-            max_tokens=DEFAULT_MAX_TOKENS,
+            max_tokens=self.DEFAULT_MAX_TOKENS,
             **kwargs,
         )
         return response
 
-    def generate_text(self, prompt, *, llm_model, **kwargs):
+    def generate_text(self, prompt: str, *, llm_model: str, **kwargs):
         messages = [
             {"role": "user", "content": prompt},
         ]
@@ -88,13 +93,15 @@ class Amazon(BaseProvider):
         response = self.client.messages.create(
             model=llm_model or self.DEFAULT_MODEL,
             messages=messages,
-            max_tokens=DEFAULT_MAX_TOKENS,
+            max_tokens=self.DEFAULT_MAX_TOKENS,
             **kwargs,
         )
 
         return response.content[0].text
 
-    def generate_stream_text(self, prompt, *, llm_model, **kwargs) -> Iterator[str]:
+    def generate_stream_text(
+        self, prompt: str, *, llm_model: str, **kwargs
+    ) -> Iterator[str]:
         """Generate streaming text using the Amazon API."""
 
         # Prepare the messages.

simplemind/providers/anthropic.py

@@ -1,5 +1,5 @@
 from functools import cached_property
-from typing import TYPE_CHECKING, Type, TypeVar, Iterator
+from typing import TYPE_CHECKING, Iterator, Type, TypeVar
 
 import instructor
 from pydantic import BaseModel
@@ -14,20 +14,15 @@ if TYPE_CHECKING:
 T = TypeVar("T", bound=BaseModel)
 
 
-PROVIDER_NAME = "anthropic"
-DEFAULT_MODEL = "claude-3-5-sonnet-20241022"
-DEFAULT_MAX_TOKENS = 1_000
-DEFAULT_KWARGS = {"max_tokens": DEFAULT_MAX_TOKENS}
-
-
 class Anthropic(BaseProvider):
-    NAME = PROVIDER_NAME
+    NAME = "anthropic"
-    DEFAULT_MODEL = DEFAULT_MODEL
+    DEFAULT_MODEL = "claude-3-5-sonnet-20241022"
-    DEFAULT_KWARGS = DEFAULT_KWARGS
+    DEFAULT_MAX_TOKENS = 1_000
+    DEFAULT_KWARGS = {"max_tokens": DEFAULT_MAX_TOKENS}
     supports_streaming = True
 
     def __init__(self, api_key: str | None = None):
-        self.api_key = api_key or settings.get_api_key(PROVIDER_NAME)
+        self.api_key = api_key or settings.get_api_key(self.NAME)
 
     @cached_property
     def client(self):
@@ -72,7 +67,7 @@ class Anthropic(BaseProvider):
             text=assistant_message,
             raw=response,
             llm_model=conversation.llm_model or self.DEFAULT_MODEL,
-            llm_provider=PROVIDER_NAME,
+            llm_provider=self.NAME,
         )
 
     @logger

simplemind/providers/gemini.py

@@ -2,7 +2,7 @@
 # IT is not currently working as desired.
 
 from functools import cached_property
-from typing import TYPE_CHECKING, Type, TypeVar, Iterator
+from typing import TYPE_CHECKING, Iterator, Type, TypeVar
 
 import instructor
 from pydantic import BaseModel
@@ -17,18 +17,14 @@ if TYPE_CHECKING:
 T = TypeVar("T", bound=BaseModel)
 
 
-PROVIDER_NAME = "gemini"
-DEFAULT_MODEL = "models/gemini-1.5-flash-latest"
-
-
 class Gemini(BaseProvider):
-    NAME = PROVIDER_NAME
+    NAME = "gemini"
-    DEFAULT_MODEL = DEFAULT_MODEL
+    DEFAULT_MODEL = "models/gemini-1.5-flash-latest"
     supports_streaming = True
 
     def __init__(self, api_key: str | None = None):
-        self.api_key = api_key or settings.get_api_key(PROVIDER_NAME)
+        self.api_key = api_key or settings.get_api_key(self.NAME)
-        self.model_name = DEFAULT_MODEL
+        self.model_name = self.DEFAULT_MODEL
 
     def set_model(self, model_name: str):
         self.model_name = model_name
@@ -76,7 +72,7 @@ class Gemini(BaseProvider):
             text=response.text,
             raw=response,
             llm_model=self.model_name,
-            llm_provider=PROVIDER_NAME,
+            llm_provider=self.NAME,
         )
 
     @logger

simplemind/providers/groq.py

@@ -1,5 +1,5 @@
 from functools import cached_property
-from typing import TYPE_CHECKING, Type, TypeVar, Iterator
+from typing import TYPE_CHECKING, Iterator, Type, TypeVar
 
 import instructor
 from pydantic import BaseModel
@@ -14,20 +14,15 @@ if TYPE_CHECKING:
 T = TypeVar("T", bound=BaseModel)
 
 
-PROVIDER_NAME = "groq"
-DEFAULT_MODEL = "llama3-8b-8192"
-DEFAULT_MAX_TOKENS = 1_000
-DEFAULT_KWARGS = {"max_tokens": DEFAULT_MAX_TOKENS}
-
-
 class Groq(BaseProvider):
-    NAME = PROVIDER_NAME
+    NAME = "groq"
-    DEFAULT_MODEL = DEFAULT_MODEL
+    DEFAULT_MODEL = "llama3-8b-8192"
-    DEFAULT_KWARGS = DEFAULT_KWARGS
+    DEFAULT_MAX_TOKENS = 1_000
+    DEFAULT_KWARGS = {"max_tokens": DEFAULT_MAX_TOKENS}
     supports_streaming = True
 
     def __init__(self, api_key: str | None = None):
-        self.api_key = api_key or settings.get_api_key(PROVIDER_NAME)
+        self.api_key = api_key or settings.get_api_key(self.NAME)
 
     @cached_property
     def client(self):
@@ -75,7 +70,7 @@ class Groq(BaseProvider):
             text=assistant_message.content or "",
             raw=response,
             llm_model=conversation.llm_model or self.DEFAULT_MODEL,
-            llm_provider=PROVIDER_NAME,
+            llm_provider=self.NAME,
         )
 
     @logger

simplemind/providers/ollama.py

@@ -1,8 +1,7 @@
 from functools import cached_property
-from typing import TYPE_CHECKING, Type, TypeVar, Iterator
+from typing import TYPE_CHECKING, Iterator, Type, TypeVar
 
 import instructor
-from openai import OpenAI
 from pydantic import BaseModel
 
 from ..logging import logger
@@ -15,17 +14,11 @@ if TYPE_CHECKING:
 T = TypeVar("T", bound=BaseModel)
 
 
-PROVIDER_NAME = "ollama"
-DEFAULT_MODEL = "llama3.2"
-DEFAULT_TIMEOUT = 60
-DEFAULT_KWARGS = {}
-
-
 class Ollama(BaseProvider):
-    NAME = PROVIDER_NAME
+    NAME = "ollama"
-    DEFAULT_MODEL = DEFAULT_MODEL
+    DEFAULT_MODEL = "llama3.2"
-    DEFAULT_KWARGS = DEFAULT_KWARGS
+    DEFAULT_TIMEOUT = 60
-    TIMEOUT = DEFAULT_TIMEOUT
+    DEFAULT_KWARGS = {}
     supports_streaming = True
 
     def __init__(self, host_url: str | None = None):
@@ -37,21 +30,18 @@ class Ollama(BaseProvider):
         if not self.host_url:
             raise ValueError("No ollama host url provided")
         try:
-            import ollama as ol
+            import openai
         except ImportError as exc:
             raise ImportError(
-                "Please install the `ollama` package: `pip install ollama`"
+                "Please install the `openai` package: `pip install openai`"
             ) from exc
-        return ol.Client(timeout=self.TIMEOUT, host=self.host_url)
+        return openai.OpenAI(base_url=f"{self.host_url}/v1", api_key="ollama")
 
     @cached_property
     def structured_client(self) -> instructor.Instructor:
         """A client patched with Instructor."""
         return instructor.from_openai(
-            OpenAI(
+            self.client,
-                base_url=f"{self.host_url}/v1",
-                api_key="ollama",
-            ),
             mode=instructor.Mode.JSON,
         )
 
@@ -64,7 +54,7 @@ class Ollama(BaseProvider):
             {"role": msg.role, "content": msg.text} for msg in conversation.messages
         ]
         response = self.client.chat(
-            model=conversation.llm_model or DEFAULT_MODEL,
+            model=conversation.llm_model or self.DEFAULT_MODEL,
             messages=messages,
             **{**self.DEFAULT_KWARGS, **kwargs},
         )
@@ -76,7 +66,7 @@ class Ollama(BaseProvider):
             text=assistant_message.get("content"),
             raw=response,
             llm_model=conversation.llm_model or self.DEFAULT_MODEL,
-            llm_provider=PROVIDER_NAME,
+            llm_provider=self.NAME,
         )
 
     @logger

simplemind/providers/openai.py

@@ -1,5 +1,5 @@
 from functools import cached_property
-from typing import TYPE_CHECKING, Type, TypeVar, Iterator
+from typing import TYPE_CHECKING, Iterator, Type, TypeVar
 
 import instructor
 from pydantic import BaseModel
@@ -13,20 +13,16 @@ if TYPE_CHECKING:
 
 T = TypeVar("T", bound=BaseModel)
 
-PROVIDER_NAME = "openai"
-DEFAULT_MODEL = "gpt-4o-mini"
-DEFAULT_MAX_TOKENS = None
-DEFAULT_KWARGS = {}
-
 
 class OpenAI(BaseProvider):
-    NAME = PROVIDER_NAME
+    NAME = "openai"
-    DEFAULT_MODEL = DEFAULT_MODEL
+    DEFAULT_MODEL = "gpt-4o-mini"
-    DEFAULT_KWARGS = DEFAULT_KWARGS
+    DEFAULT_MAX_TOKENS = None
+    DEFAULT_KWARGS = {}
     supports_streaming = True
 
     def __init__(self, api_key: str | None = None):
-        self.api_key = api_key or settings.get_api_key(PROVIDER_NAME)
+        self.api_key = api_key or settings.get_api_key(self.NAME)
 
     @cached_property
     def client(self):
@@ -56,7 +52,7 @@ class OpenAI(BaseProvider):
         ]
 
         response = self.client.chat.completions.create(
-            model=conversation.llm_model or DEFAULT_MODEL,
+            model=conversation.llm_model or self.DEFAULT_MODEL,
             messages=messages,
             **{**self.DEFAULT_KWARGS, **kwargs},
         )
@@ -69,8 +65,8 @@ class OpenAI(BaseProvider):
             role="assistant",
             text=assistant_message.content or "",
             raw=response,
-            llm_model=conversation.llm_model or DEFAULT_MODEL,
+            llm_model=conversation.llm_model or self.DEFAULT_MODEL,
-            llm_provider=PROVIDER_NAME,
+            llm_provider=self.NAME,
         )
 
     @logger

simplemind/providers/xai.py

@@ -1,5 +1,5 @@
 from functools import cached_property
-from typing import TYPE_CHECKING, Type, TypeVar, Iterator
+from typing import TYPE_CHECKING, Iterator, Type, TypeVar
 
 import instructor
 from pydantic import BaseModel
@@ -14,22 +14,17 @@ if TYPE_CHECKING:
 T = TypeVar("T", bound=BaseModel)
 
 
-PROVIDER_NAME = "xai"
-DEFAULT_MODEL = "grok-beta"
-BASE_URL = "https://api.x.ai/v1"
-DEFAULT_MAX_TOKENS = 1000
-DEFAULT_KWARGS = {"max_tokens": DEFAULT_MAX_TOKENS}
-
-
 class XAI(BaseProvider):
-    NAME = PROVIDER_NAME
+    NAME = "xai"
-    DEFAULT_MODEL = DEFAULT_MODEL
+    DEFAULT_MODEL = "grok-beta"
-    DEFAULT_KWARGS = DEFAULT_KWARGS
+    DEFAULT_MAX_TOKENS = 1000
+    DEFAULT_KWARGS = {"max_tokens": DEFAULT_MAX_TOKENS}
+    BASE_URL = "https://api.x.ai/v1"
     supports_streaming = True
     supports_structured_responses = False
 
     def __init__(self, api_key: str | None = None):
-        self.api_key = api_key or settings.get_api_key(PROVIDER_NAME)
+        self.api_key = api_key or settings.get_api_key(self.NAME)
 
     @cached_property
     def client(self):
@@ -44,7 +39,7 @@ class XAI(BaseProvider):
             ) from exc
         return oa.OpenAI(
             api_key=self.api_key,
-            base_url=BASE_URL,
+            base_url=self.BASE_URL,
         )
 
     @cached_property
@@ -76,7 +71,7 @@ class XAI(BaseProvider):
             text=assistant_message.content,
             raw=response,
             llm_model=conversation.llm_model or self.DEFAULT_MODEL,
-            llm_provider=PROVIDER_NAME,
+            llm_provider=self.NAME,
         )
 
     @logger

simplemind/settings.py

@@ -14,8 +14,9 @@ class LoggingConfig(BaseSettings):
         """Enable logging for the application."""
         # adding imports here to avoid forced dependencies
         try:
-            import logfire
             from logging import basicConfig
+
+            import logfire
         except ImportError as e:
             raise ImportError(
                 "To enable logging, please install logfire: `pip install logfire`"

tests/test_conversations.py

@@ -1,8 +1,7 @@
 import pytest
 
-from simplemind.providers import Anthropic, Gemini, OpenAI, Groq, Ollama, Amazon
-
 import simplemind as sm
+from simplemind.providers import Amazon, Anthropic, Gemini, Groq, Ollama, OpenAI
 
 
 @pytest.mark.parametrize(

tests/test_generate_data.py

@@ -1,9 +1,8 @@
 import pytest
-
-from simplemind.providers import Anthropic, Gemini, OpenAI, Groq, Ollama, Amazon
-
 from pydantic import BaseModel
 
+from simplemind.providers import Amazon, Anthropic, Gemini, Groq, Ollama, OpenAI
+
 
 class ResponseModel(BaseModel):
     result: int

tests/test_generate_text.py

@@ -1,6 +1,6 @@
 import pytest
 
-from simplemind.providers import Anthropic, Gemini, OpenAI, Groq, Ollama, Amazon
+from simplemind.providers import Amazon, Anthropic, Gemini, Groq, Ollama, OpenAI
 
 
 @pytest.mark.parametrize(