feat: add memory_chroma example for ChromaMemoryService

Author: ayman3000

contributing/samples/memory_chroma/README.md

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+# ChromaDB Memory Service Example
+
+This example demonstrates using `ChromaMemoryService` for semantic memory search
+with embeddings generated by Ollama.
+
+## Prerequisites
+
+1. **Ollama Server Running**
+   ```bash
+   ollama serve
+   ```
+
+2. **Embedding Model Pulled**
+   ```bash
+   ollama pull nomic-embed-text
+   ```
+
+3. **Dependencies Installed**
+   ```bash
+   pip install chromadb
+   # Or with uv:
+   uv pip install chromadb
+   ```
+
+## Running the Example
+
+```bash
+cd contributing/samples/memory_chroma
+python main.py
+```
+
+## What This Demo Does
+
+1. **Session 1**: Creates memories by having a conversation with the agent
+   - User introduces themselves as "Jack"
+   - User mentions they like badminton
+   - User mentions what they ate recently
+
+2. **Memory Storage**: The session is saved to ChromaDB with semantic embeddings
+   - Data persists to `./chroma_db` directory
+   - Embeddings are generated using Ollama's `nomic-embed-text` model
+
+3. **Session 2**: Queries the memories using semantic search
+   - User asks about their hobbies (agent should recall "badminton")
+   - User asks about what they ate (agent should recall "burger")
+
+## Key Differences from InMemoryMemoryService
+
+| Feature | InMemory | ChromaDB |
+|---------|----------|----------|
+| Search Type | Keyword matching | **Semantic similarity** |
+| Persistence | No (lost on restart) | **Yes (disk)** |
+| Synonyms | No | **Yes** |
+| Performance | Fast | Fast (with HNSW index) |
+
+## Customization
+
+You can change the embedding model by modifying the `OllamaEmbeddingProvider`:
+
+```python
+embedding_provider = OllamaEmbeddingProvider(
+    model="mxbai-embed-large",  # Higher quality but slower
+    host="http://remote-server:11434",  # Remote Ollama server
+)
+```

contributing/samples/memory_chroma/__init__.py

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+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Sample package for ChromaMemoryService demonstration."""

contributing/samples/memory_chroma/agent.py

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+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Agent definition for ChromaMemoryService demo."""
+
+from datetime import datetime
+
+from google.adk import Agent
+from google.adk.agents.callback_context import CallbackContext
+from google.adk.tools.load_memory_tool import load_memory_tool
+from google.adk.tools.preload_memory_tool import preload_memory_tool
+
+
+def update_current_time(callback_context: CallbackContext):
+  callback_context.state["_time"] = datetime.now().isoformat()
+
+
+root_agent = Agent(
+    model="gemini-2.0-flash-001",
+    name="chroma_memory_agent",
+    description="Agent with ChromaDB-backed semantic memory.",
+    before_agent_callback=update_current_time,
+    instruction="""\
+You are an agent that helps users answer questions.
+You have access to a semantic memory system that stores past conversations.
+Use the memory tools to recall information from previous sessions.
+
+Current time: {_time}
+""",
+    tools=[
+        load_memory_tool,
+        preload_memory_tool,
+    ],
+)

contributing/samples/memory_chroma/main.py

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+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Demo script for ChromaMemoryService with OllamaEmbeddingProvider.
+
+This example demonstrates using ChromaDB for semantic memory search
+with embeddings generated by Ollama.
+
+Prerequisites:
+    1. Ollama server running: `ollama serve`
+    2. Embedding model pulled: `ollama pull nomic-embed-text`
+    3. Dependencies installed: `pip install chromadb`
+
+Usage:
+    python main.py
+"""
+
+import asyncio
+from datetime import datetime
+from datetime import timedelta
+from typing import cast
+
+import agent
+from dotenv import load_dotenv
+from google.adk.cli.utils import logs
+from google.adk.memory import ChromaMemoryService
+from google.adk.memory import OllamaEmbeddingProvider
+from google.adk.runners import InMemoryRunner
+from google.adk.sessions.session import Session
+from google.genai import types
+
+load_dotenv(override=True)
+logs.log_to_tmp_folder()
+
+
+async def main():
+  app_name = "my_app"
+  user_id_1 = "user1"
+
+  # Initialize the ChromaMemoryService with Ollama embeddings
+  embedding_provider = OllamaEmbeddingProvider(
+      model="nomic-embed-text",  # Or another embedding model you have
+  )
+  memory_service = ChromaMemoryService(
+      embedding_provider=embedding_provider,
+      collection_name="demo_memory",
+      persist_directory="./chroma_db",  # Persist to disk
+  )
+
+  runner = InMemoryRunner(
+      app_name=app_name,
+      agent=agent.root_agent,
+      memory_service=memory_service,
+  )
+
+  async def run_prompt(session: Session, new_message: str) -> Session:
+    content = types.Content(
+        role="user", parts=[types.Part.from_text(text=new_message)]
+    )
+    print("** User says:", content.model_dump(exclude_none=True))
+    async for event in runner.run_async(
+        user_id=user_id_1,
+        session_id=session.id,
+        new_message=content,
+    ):
+      if not event.content or not event.content.parts:
+        continue
+      if event.content.parts[0].text:
+        print(f"** {event.author}: {event.content.parts[0].text}")
+      elif event.content.parts[0].function_call:
+        print(
+            f"** {event.author}: fc /"
+            f" {event.content.parts[0].function_call.name} /"
+            f" {event.content.parts[0].function_call.args}\n"
+        )
+      elif event.content.parts[0].function_response:
+        print(
+            f"** {event.author}: fr /"
+            f" {event.content.parts[0].function_response.name} /"
+            f" {event.content.parts[0].function_response.response}\n"
+        )
+
+    return cast(
+        Session,
+        await runner.session_service.get_session(
+            app_name=app_name, user_id=user_id_1, session_id=session.id
+        ),
+    )
+
+  # Session 1: Create memories
+  session_1 = await runner.session_service.create_session(
+      app_name=app_name, user_id=user_id_1
+  )
+
+  print(f"----Session to create memory: {session_1.id} ----------------------")
+  session_1 = await run_prompt(session_1, "Hi")
+  session_1 = await run_prompt(session_1, "My name is Jack")
+  session_1 = await run_prompt(session_1, "I like badminton.")
+  session_1 = await run_prompt(
+      session_1,
+      f"I ate a burger on {(datetime.now() - timedelta(days=1)).date()}.",
+  )
+  session_1 = await run_prompt(
+      session_1,
+      f"I ate a banana on {(datetime.now() - timedelta(days=2)).date()}.",
+  )
+
+  print("Saving session to ChromaDB memory service...")
+  await memory_service.add_session_to_memory(session_1)
+  print("Session saved! Data persisted to ./chroma_db")
+  print("-------------------------------------------------------------------")
+
+  # Session 2: Query memories using semantic search
+  session_2 = await runner.session_service.create_session(
+      app_name=app_name, user_id=user_id_1
+  )
+  print(f"----Session to use memory: {session_2.id} ----------------------")
+  session_2 = await run_prompt(session_2, "Hi")
+  session_2 = await run_prompt(session_2, "What do I like to do?")
+  # Expected: The agent should recall "badminton" from semantic search
+  session_2 = await run_prompt(session_2, "When did I say that?")
+  session_2 = await run_prompt(session_2, "What did I eat yesterday?")
+  # Expected: The agent should recall "burger" from semantic search
+  print("-------------------------------------------------------------------")
+
+
+if __name__ == "__main__":
+  asyncio.run(main())