examples dir

examples/_context.py

@@ -0,0 +1,10 @@
+import os
+import sys
+
+# Add the parent directory to the path so we can import the module.
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+
+import simplemind as sm
+
+__all__ = ["sm"]

examples/contextual_memory.py

@@ -0,0 +1,99 @@
+from _context import sm
+
+from pydantic import BaseModel
+import openai
+import faiss
+import numpy as np
+import os
+import pickle
+
+
+class ContextualMemoryPlugin:
+    def __init__(self, api_key: str, memory_file: str = "memories.pkl", embedding_model: str = "text-embedding-ada-002"):
+        openai.api_key = api_key
+        self.memory_file = memory_file
+        self.embedding_model = embedding_model
+        self.memories = []
+        self.embeddings = None
+        self.index = None
+        self.load_memories()
+
+    def load_memories(self):
+        if os.path.exists(self.memory_file):
+            with open(self.memory_file, "rb") as f:
+                self.memories, self.embeddings = pickle.load(f)
+            self.build_faiss_index()
+        else:
+            self.memories = []
+            self.embeddings = []
+            self.index = faiss.IndexFlatL2(1536)  # Dimension for ada-002 embeddings
+
+    def save_memories(self):
+        with open(self.memory_file, "wb") as f:
+            pickle.dump((self.memories, self.embeddings), f)
+
+    def build_faiss_index(self):
+        if self.embeddings:
+            self.index = faiss.IndexFlatL2(len(self.embeddings[0]))
+            self.index.add(np.array(self.embeddings).astype('float32'))
+        else:
+            self.index = faiss.IndexFlatL2(1536)
+
+    def get_embedding(self, text: str) -> list:
+        response = openai.Embedding.create(input=text, model=self.embedding_model)
+        return response['data'][0]['embedding']
+
+    def add_memory(self, memory: str):
+        embedding = self.get_embedding(memory)
+        self.memories.append(memory)
+        self.embeddings.append(embedding)
+        self.index.add(np.array([embedding]).astype('float32'))
+        self.save_memories()
+
+    def retrieve_memories(self, query: str, top_k: int = 3) -> list:
+        if not self.index or len(self.embeddings) == 0:
+            return []
+        query_embedding = self.get_embedding(query)
+        D, I = self.index.search(np.array([query_embedding]).astype('float32'), top_k)
+        return [self.memories[i] for i in I[0] if i < len(self.memories)]
+
+    def send_hook(self, conversation: sm.Conversation):
+        # Retrieve relevant memories based on the latest user message
+        if conversation.messages:
+            last_user_message = conversation.messages[-1].text
+            relevant_memories = self.retrieve_memories(last_user_message)
+            for memory in relevant_memories:
+                conversation.add_message(role="system", text=memory)
+
+    def on_response(self, conversation: sm.Conversation, response: str):
+        # Optionally, add the AI's response to memories
+        self.add_memory(response)
+
+# Example Usage
+
+# Define a Pydantic model if needed
+class Story(BaseModel):
+    title: str
+    content: str
+
+# Initialize the conversation with the ContextualMemoryPlugin
+memory_plugin = ContextualMemoryPlugin(api_key=sm.settings.OPENAI_API_KEY)
+
+conversation = sm.create_conversation(llm_model="gpt-4o-mini", llm_provider="openai")
+conversation.add_plugin(memory_plugin)
+
+# Add user message
+conversation.add_message("user", "Tell me a story about a brave knight.")
+
+# Send the conversation and get the response
+response = conversation.send()
+print(response.text)
+
+# Optionally, retrieve structured data
+structured_response = sm.generate_data(
+    "Summarize the above story.",
+    llm_model="gpt-4o",
+    llm_provider="openai",
+    response_model=Story,
+)
+print(structured_response)

examples/generate_text.py

@@ -0,0 +1,5 @@
+from _context import sm
+
+r = sm.generate_text("Write a poem about the moon", llm_provider="openai", llm_model="gpt-3.5-turbo")
+
+print(r)

examples/requirements.txt

@@ -0,0 +1,4 @@
+numpy
+openai
+pydantic
+faiss-cpu

examples/simple_memory.py

@@ -0,0 +1,28 @@
+from _context import sm
+
+
+class SimpleMemoryPlugin:
+    def __init__(self):
+        self.memories = [
+            "the earth has fictionally beeen destroyed.",
+            "the moon is made of cheese.",
+        ]
+
+    def yield_memories(self):
+        return (m for m in self.memories)
+
+    def send_hook(self, conversation: sm.Conversation):
+        for m in self.yield_memories():
+            conversation.add_message(role="system", text=m)
+
+
+conversation = sm.create_conversation(llm_model="grok-beta", llm_provider="xai")
+conversation.add_plugin(SimpleMemoryPlugin())
+
+conversation.add_message(
+    role="user",
+    text="Write a poem about the moon",
+)
+
+r = conversation.send()
+print(r.text)

simplemind/__init__.py

@@ -1,24 +1,17 @@
 from .models import Conversation
 from .utils import find_provider
-
-
-class SimpleMind:
-
-    def structured_response(
-        self, prompt, *, llm_model=None, llm_provider=None, response_model=None
-    ):
-        provider = find_provider(llm_provider)
-
-        return provider.structured_response(
-            llm_model=llm_model, response_model=response_model, prompt=prompt
-        )
+from .settings import settings
 
 
 def create_conversation(llm_model=None, llm_provider=None):
+    """Create a new conversation."""
+
     return Conversation(llm_model=llm_model, llm_provider=llm_provider)
 
 
 def generate_data(prompt, *, llm_model=None, llm_provider=None, response_model=None):
+    """Generate structured data from a given prompt."""
+
     provider = find_provider(llm_provider)
 
     return provider.structured_response(
@@ -29,6 +22,8 @@ def generate_data(prompt, *, llm_model=None, llm_provider=None, response_model=N
 
 
 def generate_text(prompt, *, llm_model=None, llm_provider=None):
+    """Generate text from a given prompt."""
+
     provider = find_provider(llm_provider)
 
     return provider.generate_text(prompt=prompt, llm_model=llm_model)
@@ -36,9 +31,9 @@ def generate_text(prompt, *, llm_model=None, llm_provider=None):
 
 __all__ = [
     "Conversation",
-    "SimpleMind",
     "create_conversation",
     "find_provider",
     "generate_data",
     "generate_text",
+    "settings"
 ]