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KaifAhmad1
5cc0c7eac4
Set FastEmbed as default embedding provider in TextEmbedder. Updated dependencies in pyproject.toml. Refreshed Context Module notebook and documentation to reflect changes. Added verification tests.
51 KiB
51 KiB
Context Module
Overview
The Context Module is the core state management system of Semantica. It allows agents to maintain coherent, persistent, and structured memory across long interactions. Unlike simple RAG systems that only use vector similarity, Semantica's Context Module combines:
- FastEmbed Integration: High-performance, local embedding generation.
- Context Graph: A structured knowledge graph for reasoning about relationships.
- Hierarchical Memory: A tiered system with short-term (token-limited) and long-term (vector-backed) storage.
- Hybrid Retrieval: Combining vector search, graph traversal (GraphRAG), and keyword matching.
- Persistence: Full state serialization.
This notebook provides a technical deep dive into these components.
Documentation: API Reference
1. Setup and Configuration
We need semantica and fastembed for local, high-speed embedding generation.
In [1]:
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In [2]:
import os
import shutil
import numpy as np
from semantica.context import AgentContext, ContextGraph, AgentMemory, EntityLinker
from semantica.vector_store import VectorStorec:\Users\Mohd Kaif\AppData\Local\Programs\Python\Python311\Lib\site-packages\sentence_transformers\cross_encoder\CrossEncoder.py:13: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console) from tqdm.autonotebook import tqdm, trange
In [3]:
# 1. Initialize Vector Store
vs = VectorStore(backend="inmemory", dimension=384)
# 2. Configure FastEmbed
# We explicitly set the method to 'fastembed' and choose a lightweight, high-performance model.
if hasattr(vs, "embedder") and vs.embedder:
print("Configuring VectorStore to use FastEmbed...")
vs.embedder.set_text_model(
method="fastembed",
model_name="BAAI/bge-small-en-v1.5"
)
# 3. Verify Embedding Generation
text = "Semantica enables complex agent behaviors."
vector = vs.embed(text)
print(f"Generated embedding shape: {vector.shape}")fastembed not available. Install with: pip install fastembed. Using fallback embedding method.
Configuring VectorStore to use FastEmbed...
🧠 Semantica - 📊 Current Progress
| Status | Action | Module | Submodule | File | Time |
|---|---|---|---|---|---|
| ✅ | Semantica is embedding | 💾 embeddings | TextEmbedder | - | 0.02s |
| ✅ | Semantica is processing | 🔗 context | AgentMemory | - | 0.05s |
| ✅ | Semantica is indexing | 📊 vector_store | VectorStore | - | 0.00s |
| ✅ | Semantica is processing | 🔗 context | ContextRetriever | - | 0.05s |
Generated embedding shape: (384,)
In [4]:
kg = ContextGraph()
# Add Nodes
# Note: 'content' is what is indexed for keyword search.
kg.add_node(
node_id="user_alice",
node_type="Person",
content="Alice",
role="Lead Engineer"
)
kg.add_node(
node_id="tech_python",
node_type="Technology",
content="Python",
version="3.11"
)
kg.add_node(
node_id="project_semantica",
node_type="Project",
content="Semantica Framework"
)
# Add Edges (Relationships)
kg.add_edge(source_id="user_alice", target_id="tech_python", edge_type="USES")
kg.add_edge(source_id="tech_python", target_id="project_semantica", edge_type="POWERS")
# Traverse the Graph
print("Neighbors of Python:")
neighbors = kg.get_neighbors("tech_python")
for n in neighbors:
# The neighbor dict contains the connected node info and the relationship that led to it
print(f" - [Relationship: {n['relationship']}] -> {n['content']} ({n['type']})")Neighbors of Python: - [Relationship: POWERS] -> Semantica Framework (Project)
In [5]:
context = AgentContext(
vector_store=vs,
knowledge_graph=kg,
token_limit=500, # Max tokens in Short-Term Memory (STM)
short_term_limit=5 # Max items in STM
)
# Store a new memory
# This is automatically embedded (FastEmbed) and indexed.
context.store(
content="Alice is optimizing the graph traversal algorithms in Semantica.",
conversation_id="dev_sync_1",
user_id="alice"
)
# Retrieve Context
# 'use_graph=True' enables GraphRAG: it finds entities in the query ('Alice')
# and expands to their neighbors in the graph.
results = context.retrieve(
query="What is Alice working on?",
use_graph=True,
expand_graph=True
)
print("\n--- Hybrid Retrieval Results ---")
for res in results:
print(f"[{res['score']:.2f}] {res['content']}")--- Hybrid Retrieval Results --- [0.50] Alice is optimizing the graph traversal algorithms in Semantica. [0.20] Alice
In [6]:
print(f"STM Count (Start): {len(context.memory.short_term_memory)}")
# Fill up memory
for i in range(1, 10):
context.store(f"Log entry {i}: System status check.")
print(f"STM Count (End): {len(context.memory.short_term_memory)}")
print("\nCurrent Short-Term Memory Items:")
for item in context.memory.short_term_memory:
print(f" - {item.content}")
# Notice that earlier log entries are gone from this list,
# but they are still retrievable via search.STM Count (Start): 1 STM Count (End): 5 Current Short-Term Memory Items: - Log entry 5: System status check. - Log entry 6: System status check. - Log entry 7: System status check. - Log entry 8: System status check. - Log entry 9: System status check.
In [7]:
SAVE_PATH = "./semantica_context_state"
# 1. Save state
print(f"Saving context to {SAVE_PATH}...")
context.save(SAVE_PATH)
# 2. Initialize a fresh AgentContext
print("Initializing fresh agent...")
new_kg = ContextGraph()
new_vs = VectorStore(backend="inmemory", dimension=384)
# !!! IMPORTANT: Re-configure FastEmbed before loading !!!
if hasattr(new_vs, "embedder") and new_vs.embedder:
new_vs.embedder.set_text_model(
method="fastembed",
model_name="BAAI/bge-small-en-v1.5"
)
restored_context = AgentContext(vector_store=new_vs, knowledge_graph=new_kg)
# 3. Load state
restored_context.load(SAVE_PATH)
# 4. Verify restoration
print(f"Restored STM items: {len(restored_context.memory.short_term_memory)}")
print(f"Restored Graph nodes: {len(new_kg.nodes)}")
# Cleanup
if os.path.exists(SAVE_PATH):
shutil.rmtree(SAVE_PATH)
print("Cleanup complete.")Saving context to ./semantica_context_state... Initializing fresh agent...
fastembed not available. Install with: pip install fastembed. Using fallback embedding method.
Restored STM items: 5 Restored Graph nodes: 3 Cleanup complete.