semantica/docs/reference/context.md at 3496d62335ae70210ab5b817ccca8d7d83f274eb

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title	description	icon
Context Module	Agent context graphs, decision tracking, causal chains, precedent search, policy enforcement, and multi-hop GraphRAG.	brain

semantica.context is the memory and decision layer for AI agents:

Stores facts with provenance and embedding-backed retrieval
Records decisions as first-class graph objects with full causal chains
Lets agents search their own history to stay consistent across runs
Answers complex queries via multi-hop GraphRAG traversal
Enforces versioned policies and tracks compliance exceptions

Exported Classes

Class	Role
`AgentContext`	Primary entry point: memory, retrieval, decisions, graph traversal, checkpoints
`ContextGraph`	In-memory knowledge graph with centrality, community detection, and decision tracking
`AgentMemory`	Vector-backed persistent memory: `store(text)`, `retrieve(query, max_results)`
`EntityLinker`	Link entity mentions to URIs; create typed edges between entity IDs
`ContextRetriever`	Hybrid vector + graph retrieval with min-score and graph expansion options
`DecisionRecorder`	Record decisions with embeddings, causal chains, and metadata
`PolicyEngine`	Policy management: `add_policy()`, `check_compliance()`, `get_applicable_policies()`
`CausalChainAnalyzer`	Trace how decisions influenced each other: `get_causal_chain(decision_id)`

What You Get

AgentContext — Memory, decision tracking, and graph-backed retrieval behind one API
- Conversation history and checkpoint diffing
- Persist and restore full context state to disk
ContextGraph — Thread-safe in-memory knowledge graph
- PageRank, centrality, community detection, temporal validity
- Cross-graph navigation and link traversal
AgentMemory — Embedding-backed memory with retention policy
- LRU eviction at configurable max_memory_size
- Per-conversation history isolation
DecisionRecorder — Records decisions with causal chains and confidence scores
- Temporal validity windows (valid_from / valid_until)
- Cross-system context capture on every decision
PolicyEngine — Versioned policy storage in the knowledge graph
- Compliance checking against recorded decisions
- Policy exception tracking with approver audit trail
EntityLinker — Maps entity text to stable URIs
- Creates typed links between entity IDs
- Prevents "Apple", "Apple Inc.", "AAPL" becoming separate nodes
ContextRetriever — Fuses vector similarity, graph traversal, and agent memory
- Richer context than pure vector search
- Configurable hybrid_alpha and expansion hops
CausalChainAnalyzer — Traces upstream causes and downstream effects of any decision
- Explainability paths with relationship types
- Configurable depth and direction

Quick Start

```python from semantica.context import AgentContext, ContextGraph from semantica.vector_store import VectorStore

context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=ContextGraph(advanced_analytics=True),
    decision_tracking=True,
    retention_days=90,
    max_memories=50000,
)
```

```python memory_id = context.store( "GPT-4 outperforms GPT-3.5 on reasoning benchmarks by 40%", metadata={"source": "openai_blog", "date": "2024-01"} )

results = context.retrieve("LLM benchmark comparisons", max_results=5)
for r in results:
    print("{} (score: {:.3f})".format(r["content"], r["score"]))
```

```python decision_id = context.record_decision( category="model_selection", scenario="Choose LLM for production reasoning pipeline", reasoning="GPT-4 benchmark advantage justifies 3x cost increase", outcome="selected_gpt4", confidence=0.91, entities=["gpt-4", "gpt-3.5"], decision_maker="pipeline_agent", ) ``` ```python # Search past decisions: prevents contradictory choices across runs precedents = context.find_precedents("model selection reasoning", limit=5) for p in precedents: print("[{}] {} (confidence: {:.2f})".format(p.category, p.outcome, p.confidence)) print(" Reasoning: {}".format(p.reasoning))

# Trace downstream decisions influenced by this one
chain = context.get_causal_chain(decision_id, direction="downstream", max_depth=5)
print("Downstream decisions: {}".format(len(chain)))

# Full explainability
explanation = context.trace_decision_explainability(decision_id)
print("Total connections: {}".format(explanation["total_connections"]))
```

Usage Patterns

Fastest setup: no knowledge graph. Best for agents that need semantic search over facts without graph traversal overhead.

```python
from semantica.context import AgentContext
from semantica.vector_store import VectorStore

# Zero-graph setup: vector memory only
context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
)

context.store("User prefers concise responses with code examples")
context.store("Project uses Python 3.11 with FastAPI and PostgreSQL")

results = context.retrieve("user coding preferences", max_results=5)
for r in results:
    print("{:.3f}  {}".format(r["score"], r["content"]))
```

<Check>
  Swap `backend="faiss"` to `backend="inmemory"` for zero-dependency local development.
</Check>

Production setup: graph + decisions + analytics. Use when you need explainability and contradiction-free decision history.

```python
from semantica.context import AgentContext, ContextGraph
from semantica.vector_store import VectorStore

context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=ContextGraph(
        advanced_analytics=True,  # PageRank, centrality, community detection
        kg_algorithms=True,       # path-finding, link prediction
    ),
    decision_tracking=True,       # requires knowledge_graph
    retention_days=90,
    max_memories=50000,
)

decision_id = context.record_decision(
    category="model_selection",
    scenario="Choose LLM for production reasoning pipeline",
    reasoning="GPT-4 benchmark advantage justifies 3x cost",
    outcome="selected_gpt4",
    confidence=0.91,
    entities=["gpt-4", "gpt-3.5"],
)

# Prevent contradictions across runs
precedents = context.find_precedents("model selection", limit=5)
```

Load a pre-built knowledge graph and answer complex questions with multi-hop graph traversal.

```python
from semantica.context import AgentContext, ContextGraph
from semantica.vector_store import VectorStore

context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=ContextGraph(advanced_analytics=True),
    hybrid_alpha=0.4,        # 0.0 = pure vector  →  1.0 = pure graph
    max_expansion_hops=3,
)

# Load a pre-built knowledge graph
context.load_graph("company_kg.json")

# Multi-hop GraphRAG retrieval
results = context.retrieve(
    "companies founded by Apple alumni",
    use_graph=True,
    max_results=10,
)
for r in results:
    print("[{:.3f}] {}".format(r["score"], r["content"]))
```

<Tip>
  Increase `max_expansion_hops` for deeper traversal at the cost of latency. Start at 2 and tune upward.
</Tip>

Add versioned compliance policies and gate every decision against them before recording.

```python
from semantica.context import AgentContext, ContextGraph, PolicyEngine
from semantica.vector_store import VectorStore

context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=ContextGraph(),
    decision_tracking=True,
)

engine = PolicyEngine(knowledge_graph=context.knowledge_graph)

engine.add_policy(
    name="data_privacy",
    description="No PII stored without user consent flag",
    version="1.2",
    effective_date="2024-01-01",
    category="privacy",
    rules={"requires_consent": True, "max_retention_days": 90},
)

decision_data = {"action": "store_user_email", "user_consent": True}
result = engine.check_compliance(decision_data, policy_names=["data_privacy"])

if result["compliant"]:
    context.record_decision(
        category="data_storage",
        scenario="Store user profile",
        outcome="stored",
        confidence=1.0,
    )
else:
    print("Blocked by policy:", result["violations"])
```

AgentContext

AgentContext is the main entry point. Wraps memory, graph, and decision tracking behind a single unified API.

Constructor Parameters

Parameter	Type	Default	Description
`vector_store`	`VectorStore`	required	Backend for embedding-based memory retrieval
`knowledge_graph`	`ContextGraph`	`None`	Enables graph-backed relationships and GraphRAG
`decision_tracking`	`bool`	`False`	Activates `DecisionRecorder`: requires `knowledge_graph` to also be set
`retention_days`	`Optional[int]`	`30`	Auto-expire memories older than N days; `None` = keep forever
`max_memories`	`int`	`10000`	Hard cap before LRU eviction
`graph_expansion`	`bool`	`True`	Auto-expands graph from stored memories
`max_expansion_hops`	`int`	`2`	Max hops for graph expansion during retrieval
`hybrid_alpha`	`float`	`0.5`	Balance between vector (`0.0`) and graph (`1.0`) retrieval
`advanced_analytics`	`bool`	`True`	Enables PageRank, centrality, and community analysis
`kg_algorithms`	`bool`	`True`	Adds path-finding and link prediction

**Set `retention_days` to avoid memory bloat.** The default of `30` prunes automatically. Compliance-critical agents may need `retention_days=None` with explicit archival via `export()`. **Persist your context between runs.** `VectorStore` does not auto-persist — passing `index_path=` to its constructor is a no-op. Call `context.save("agent_state/")` to write memory, the vector index, and the graph to disk, and `context.load("agent_state/")` on the next process to restore them. See the "Persist & Restore" tab under [Real-World Patterns](#real-world-patterns) below.

Memory Methods

Method	Returns	Description
`store(content, metadata, conversation_id, user_id)`	`str` or `Dict`	Store a fact (str → memory ID) or list of documents (list → stats dict)
`batch_store(items)`	`List[str]`	Store multiple items at once: returns list of memory IDs
`retrieve(query, max_results, min_score, use_graph, conversation_id)`	`List[Dict]`	Semantic retrieval; auto-selects GraphRAG if `knowledge_graph` is set
`forget(memory_id, conversation_id, days_old)`	`int`	Delete memories by ID, conversation, or age
`update(memory_id, content, metadata)`	`bool`	Update content or metadata of a stored memory
`get_memory(memory_id)`	`Optional[Dict]`	Fetch a specific memory by ID
`stats()`	`Dict`	Memory counts, vector store status, graph stats
`health()`	`Dict`	System health: all backends, status flags
`save(path)`	`None`	Persist full context state (memory + graph) to disk
`load(path)`	`None`	Restore context state from disk
`export(conversation_id, format)`	`str \| Dict`	Export memories as JSON or dict
`import_data(data, format)`	`int`	Import memories from JSON or dict

**`retrieve()` uses `max_results=`, not `top_k=`.** The parameter is `max_results` (default `5`). Pass `use_graph=True` to force GraphRAG or `use_graph=False` to force vector-only retrieval regardless of whether a `knowledge_graph` is configured.

Conversation Methods

# Store turns in a conversation thread
context.store("User asked about deployment options", conversation_id="conv_001")
context.store("Agent recommended Docker + Kubernetes", conversation_id="conv_001")

# Retrieve full conversation history
history = context.conversation("conv_001", max_items=50)
for turn in history:
    print("[{}] {}".format(turn["timestamp"], turn["content"]))

# Retrieve across all conversations with a query
results = context.retrieve(
    "deployment recommendations",
    conversation_id="conv_001",
    max_results=10,
)

Multi-Hop GraphRAG

Requires knowledge_graph to be set at construction: enables query_with_reasoning() for LLM-grounded multi-hop traversal:

import os
from semantica.llms import Groq

llm    = Groq(model="llama-3.3-70b-versatile", api_key=os.getenv("GROQ_API_KEY"))
result = context.query_with_reasoning(
    query="What technologies have we chosen and why?",
    llm_provider=llm,
    max_hops=2,
    max_results=10,
)

print(result["response"])
print("Confidence: {:.2f}".format(result["confidence"]))
print("Sources used: {}".format(result["num_sources"]))

Decision Methods

Method	Returns	Description
`record_decision(category, scenario, reasoning, outcome, confidence, entities, decision_maker, valid_from, valid_until)`	`str`	Record a decision; raises `RuntimeError` if `decision_tracking=False` or no `knowledge_graph`
`find_precedents(scenario, category, limit, use_hybrid_search, max_hops, as_of)`	`List[Decision]`	Find similar past decisions by semantic + structural similarity
`query_decisions(query, max_hops, use_hybrid_search)`	`List[Decision]`	Broad context-aware decision search
`get_causal_chain(decision_id, direction, max_depth)`	`List[Decision]`	Trace `"upstream"` causes or `"downstream"` effects
`trace_decision_explainability(decision_id)`	`Dict`	Full explainability: causes, effects, relationship paths
`get_policy_engine()`	`PolicyEngine`	Access the active `PolicyEngine` instance

`decision_tracking=True` requires `knowledge_graph` to also be set. Without it, `record_decision()` raises `RuntimeError`. **Use `find_precedents()` before every significant decision.** This is how the context module prevents agents from making contradictory choices across runs. Surface precedents to the LLM as context: "we chose X for similar reasons before."

Checkpoint Methods

Ideal for auditing reasoning loops: take a snapshot before and after a pass to see exactly what changed:

# Take a named snapshot of the current graph state
context.checkpoint("before_inference")

# ... run reasoning, record decisions ...

context.checkpoint("after_inference")

# See exactly what was added/removed
diff = context.diff_checkpoints("before_inference", "after_inference")
print("Decisions added: {}".format(len(diff["decisions_added"])))
print("Relationships added: {}".format(len(diff["relationships_added"])))

# Persist a checkpoint to disk via TemporalVersionManager
context.flush_checkpoint("after_inference")

ContextGraph

ContextGraph is the knowledge graph backing AgentContext. Can also be used standalone for relationship modelling without the full context layer.

from semantica.context import ContextGraph

graph = ContextGraph(advanced_analytics=True)

# Build the graph
graph.add_node("Python",  "language",  properties={"paradigm": "multi-paradigm"})
graph.add_node("FastAPI", "framework", properties={"language": "Python"})
graph.add_edge("Python", "FastAPI", "enables")

# Record and query decisions directly on the graph
decision_id = graph.record_decision(
    category="technology_choice",
    scenario="Web API framework selection",
    reasoning="FastAPI's async support and auto-docs match our requirements",
    outcome="selected_fastapi",
    confidence=0.92,
    entities=["Python", "FastAPI"],
)

similar = graph.find_precedents_by_scenario("web framework", limit=3)
stats   = graph.stats()
print("Nodes: {}, Edges: {}".format(stats["node_count"], stats["edge_count"]))

Constructor Options

Parameter	Type	Default	Description
`advanced_analytics`	`bool`	`True`	PageRank, betweenness centrality
`centrality_analysis`	`bool`	`True`	Full centrality suite
`community_detection`	`bool`	`True`	Louvain community clustering
`node_embeddings`	`bool`	`True`	Node2Vec embeddings for structural similarity

ContextGraph: Full Method Reference

Method	Returns	Description
`add_node(node_id, node_type, properties, valid_from, valid_until)`	`None`	Add a node; supports temporal validity windows
`add_edge(source_id, target_id, edge_type, weight, properties)`	`None`	Add a directed edge with optional weight
`add_nodes(nodes)`	`int`	Bulk-add from a list of dicts; returns count added
`add_edges(edges)`	`int`	Bulk-add edges; returns count added
`get_neighbors(node_id, hops)`	`List[Dict]`	BFS neighbors up to given depth
`get_neighbor_distances(node_id, hops)`	`List[Dict]`	Neighbors with confidence-decay scoring
`find_node(node_id)`	`Optional[Dict]`	Look up a single node by ID
`find_nodes(node_type, skip, limit)`	`List[Dict]`	Filter nodes by type with pagination
`find_active_nodes(node_type, at_time)`	`List[Dict]`	Nodes that are valid at a given timestamp
`find_edges(edge_type, skip, limit)`	`List[Dict]`	Filter edges by type with pagination
`record_decision(category, scenario, reasoning, outcome, confidence, entities, decision_maker)`	`str`	Add decision node with causal edges
`find_precedents_by_scenario(scenario, category, limit, use_semantic_search, as_of)`	`List[Dict]`	Semantically similar past scenarios
`query(query, skip, limit)`	`List[Dict]`	Full-text search over node content
`stats()`	`Dict`	Node/edge counts, type breakdowns, graph density
`density()`	`float`	Graph density score
`save_to_file(path)`	`None`	Persist graph to JSON
`load_from_file(path)`	`None`	Load graph from JSON
`build_from_conversations(conversations, link_entities)`	`Dict`	Build graph from conversation data
`link_graph(other_graph, source_node_id, target_node_id, link_type)`	`str`	Create cross-graph navigation link; returns `link_id`
`navigate_to(link_id)`	`Tuple`	Follow a cross-graph link to `(target_graph, target_node_id)`
`cross_graph_path(source_node_id, target_graph, target_node_id, max_hops)`	`Dict`	Shortest path across linked graphs
`clear()`	`None`	Reset graph state and all indexes

Distance Intelligence (v0.5.0)

ContextGraph exposes a full Distance Intelligence API for exploring semantic neighborhoods and blending proximity into retrieval.

Full Distance Intelligence reference — distance matrices, API endpoints, embedding cache, Explorer UI — is covered in the dedicated [Distance Intelligence](distance) page. This section documents the context-layer API.

Neighbors with Distance Metadata

Pass include_distance_metadata=True to get_neighbors() to receive distance band, confidence decay, and path information alongside every neighbor:

graph = ContextGraph(advanced_analytics=True)

# ... populate graph ...

neighbors = graph.get_neighbors(
    "python",
    hops=3,
    include_distance_metadata=True,
    min_weight=0.3,   # exclude low-confidence edges
)

for n in neighbors:
    print(
        f"{n['node_id']:15s}  "
        f"band={n['distance_band']:10s}  "
        f"decay={n['confidence_decay']:.3f}  "
        f"hops={n['hop_count']}"
    )

Added field	Type	Description
`distance_band`	`str`	`"direct"` (1 hop) / `"near"` (2) / `"mid-range"` (3–4) / `"distant"` (5+)
`confidence_decay`	`float`	`edge_weight ^ hop_count` — decays with each hop
`path_to_anchor`	`List[str]`	Shortest path from anchor node to this neighbor
`hop_count`	`int`	BFS depth from anchor

Proximity-Blended Retrieval

Set proximity_weight on AgentContext to blend graph proximity into every retrieve() and find_precedents() call:

context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=ContextGraph(advanced_analytics=True),
    proximity_weight=0.3,   # 0.7×semantic + 0.3×proximity
)

# combined_score is returned alongside semantic_score and proximity_score
results = context.retrieve("web API frameworks", max_results=10)
for r in results:
    print(
        f"[{r['combined_score']:.3f}]  "
        f"semantic={r['semantic_score']:.3f}  "
        f"proximity={r['proximity_score']:.3f}  "
        f"{r['content'][:60]}"
    )

# Override weight per-call
precedents = context.find_precedents(
    "infrastructure scaling decisions",
    proximity_weight=0.5,
    limit=5,
)

`proximity_weight=0.0` disables proximity blending entirely (pure semantic). `proximity_weight=1.0` returns results ranked purely by graph proximity to the query anchor. Values between `0.2`–`0.4` work well for most production use cases.

Link multiple independent ContextGraph instances so agents can traverse across problem spaces:

domain_graph    = ContextGraph()
decision_graph  = ContextGraph()

domain_graph.add_node("microservices", "architecture", properties={"style": "distributed"})
decision_graph.add_node("deploy_k8s",  "decision",     properties={"outcome": "approved"})

link_id = domain_graph.link_graph(
    other_graph=decision_graph,
    source_node_id="microservices",
    target_node_id="deploy_k8s",
    link_type="INFORMED_BY",
)

# Follow the link at traversal time
target_graph, entry_node = domain_graph.navigate_to(link_id)

# Cross-graph pathfinding
path = domain_graph.cross_graph_path(
    source_node_id="microservices",
    target_graph=decision_graph,
    target_node_id="deploy_k8s",
    max_hops=5,
)
print("Reachable: {}, hops: {}".format(path["reachable"], path["hop_count"]))

AgentMemory

For fine-grained control over memory storage and retrieval:

from semantica.context import AgentMemory
from semantica.vector_store import VectorStore

memory = AgentMemory(
    vector_store=VectorStore(backend="faiss", dimension=768),
    max_memory_size=10000,
    retention_policy="90_days",   # or "unlimited"
)

memory_id = memory.store(
    "Critical compliance rule: all trades must be pre-approved",
    metadata={"type": "compliance"},
)

results = memory.retrieve(
    query="trade approval requirements",
    max_results=5,
    min_score=0.0,
)

memory.delete_memory(memory_id)
memory.clear_memory(conversation_id="conv_001")

history = memory.get_conversation_history(conversation_id="conv_001", max_items=100)

Parameter	Type	Default	Description
`vector_store`	`VectorStore`	required	Embedding backend for semantic retrieval
`max_memory_size`	`int`	`10000`	Max items before LRU eviction
`retention_policy`	`str`	`"unlimited"`	`"N_days"` (e.g. `"30_days"`) or `"unlimited"`

Markdown Round Trips

AgentMemory can export human-editable Markdown and import the edited files back. Each file contains one memory item, with required metadata in YAML frontmatter and the memory content in the Markdown body:

---
id: mem_compliance_rule
created_at: '2026-07-22T09:00:00+00:00'
updated_at: '2026-07-22T10:30:00+00:00'
type: compliance
tags:
- trading
- approval
---

All trades must be pre-approved.

from pathlib import Path

# A single selected memory can be returned as Markdown text.
document = memory.export(format="markdown", type="compliance")

# Export a memory set as one stable Markdown file per item.
memory.export(format="markdown", destination="memory_export/")

# New IDs create memories; existing IDs are updated in place.
count = memory.import_data(Path("memory_export/"), format="markdown")

The required frontmatter fields are id, created_at, updated_at, and either type or kind. Optional metadata can be edited at the top level. Imports reject malformed or duplicate fields before changing memory, and re-importing unchanged files is idempotent. Memory-local entities and relationships are preserved as provenance but are not applied to ContextGraph by Markdown import. Use a dedicated export directory: matching files are overwritten, but unrelated or stale Markdown files are not deleted automatically. Export refuses to overwrite symbolic links and uses atomic file replacement. Timestamp offsets are preserved in Markdown and normalized to UTC only for comparisons, so aware and local-naive records can be queried together safely. Vector-store writes are deferred until the in-memory import commits; adapter synchronization remains best-effort and logs failures.

PolicyEngine

PolicyEngine manages versioned policies stored in the knowledge graph. Policies are stored as nodes and can be linked to decisions:

from semantica.context import PolicyEngine
from semantica.context import ContextGraph
from semantica.context.decision_models import Policy, Decision
from datetime import datetime

graph  = ContextGraph()
policy = PolicyEngine(graph_store=graph)

# Create and store a policy
p = Policy(
    policy_id="policy_001",
    name="Confidence Threshold Policy",
    description="All decisions must have confidence >= 0.7",
    rules={"min_confidence": 0.7, "requires_reasoning": True},
    category="decision_quality",
    version="1.0",
    created_at=datetime.now(),
    updated_at=datetime.now(),
)
policy.add_policy(p)

# Check compliance of a specific decision
decision = Decision(
    decision_id="dec_001",
    category="loan_approval",
    scenario="First-time homebuyer",
    reasoning="Good credit score and stable employment",
    outcome="approved",
    confidence=0.94,
    timestamp=datetime.now(),
    decision_maker="loan_agent",
)
compliant = policy.check_compliance(decision, "policy_001")
print("Compliant:", compliant)

# Get applicable policies for a category
policies = policy.get_applicable_policies(category="decision_quality")
for p in policies:
    print("{} v{}".format(p.name, p.version))

EntityLinker

Maps entity text to URIs and creates typed links between entity IDs:

from semantica.context import EntityLinker

linker = EntityLinker(similarity_threshold=0.8)

# Assign a URI to an entity
uri = linker.assign_uri("apple_inc", "Apple Inc.", "ORGANIZATION")
print(uri)  # "https://semantica.dev/entity/apple_inc.#organization"

# Link entities from extracted text
entities = [
    {"id": "e1", "text": "Apple Inc.", "type": "ORGANIZATION"},
    {"id": "e2", "text": "Apple",      "type": "ORGANIZATION"},
]
linked = linker.link(text="Apple Inc. was founded by Steve Jobs.", entities=entities)
for e in linked:
    print("{} → {}  (confidence: {:.2f})".format(e.text, e.uri, e.confidence))

# Explicitly link two entity IDs (not a list: takes two IDs)
linker.link_entities(
    entity1_id="apple_inc",
    entity2_id="aapl",
    link_type="same_as",
    confidence=0.99,
)

# Build the full entity web
web = linker.build_entity_web()
print("Entities:", web["statistics"]["total_entities"])
print("Links:   ", web["statistics"]["total_links"])

**`EntityLinker.link_entities()` links two entity IDs, not a list.** Call `link_entities(entity1_id, entity2_id, link_type)` to create a typed edge between two known IDs. For linking entities extracted from text, use `link(text, entities=[...])` instead.

LinkedEntity fields returned by link():

Field	Type	Description
`entity_id`	`str`	Entity identifier
`uri`	`str`	Generated URI (e.g. `"https://semantica.dev/entity/apple_inc."`)
`text`	`str`	Surface form text
`type`	`str`	Entity type
`linked_entities`	`List[EntityLink]`	Related entity links with `source_entity_id`, `target_entity_id`, `link_type`, `confidence`
`context`	`Dict`	Entity metadata
`confidence`	`float`	Overall confidence score

ContextRetriever

Hybrid retrieval combining vector similarity, graph traversal, and memory:

from semantica.context import ContextRetriever

retriever = ContextRetriever(
    memory_store=memory,
    knowledge_graph=context_graph,
    vector_store=vector_store,
    use_graph_expansion=True,
    max_expansion_hops=2,
    hybrid_alpha=0.5,
)

results = retriever.retrieve(
    query="What decisions were made about cloud infrastructure?",
    max_results=10,
    use_graph_expansion=True,
    min_relevance_score=0.3,
)

for r in results:
    print("[{}] score={:.3f}: {}".format(r.source, r.score, r.content[:80]))

Data Structures

@dataclass
class Decision:
    decision_id:          str
    category:             str
    scenario:             str
    reasoning:            str
    outcome:              str
    confidence:           float               # 0.0 - 1.0
    timestamp:            datetime
    decision_maker:       str
    reasoning_embedding:  Optional[List[float]]  # generated embedding
    node2vec_embedding:   Optional[List[float]]  # structural embedding
    valid_from:           Optional[str]       # ISO datetime
    valid_until:          Optional[str]       # ISO datetime
    metadata:             Dict[str, Any]

@dataclass
class Precedent:
    precedent_id:        str
    source_decision_id:  str
    similarity_score:    float               # 0-1 match score
    relationship_type:   str                 # "similar_scenario" | "same_policy" | "exception_precedent"
    metadata:            Dict[str, Any]

@dataclass
class Policy:
    policy_id:    str
    name:         str
    description:  str
    rules:        Dict[str, Any]    # rule definitions
    category:     str
    version:      str               # e.g. "1.0", "2.1"
    created_at:   datetime
    updated_at:   datetime
    metadata:     Dict[str, Any]

@dataclass
class PolicyException:
    exception_id:        str
    decision_id:         str
    policy_id:           str
    reason:              str
    approver:            str
    approval_timestamp:  datetime
    justification:       str
    metadata:            Dict[str, Any]

@dataclass
class ApprovalChain:
    approval_id:       str
    decision_id:       str
    approver:          str
    approval_method:   str          # "slack_dm" | "zoom_call" | "email" | "system"
    approval_context:  str
    timestamp:         datetime
    metadata:          Dict[str, Any]

@dataclass
class LinkedEntity:
    entity_id:       str
    uri:             str
    text:            str
    type:            str
    linked_entities: List[EntityLink]
    context:         Dict[str, Any]
    confidence:      float

@dataclass
class EntityLink:
    source_entity_id:  str
    target_entity_id:  str
    link_type:         str          # "same_as" | "related_to" | "part_of"
    confidence:        float
    source:            Optional[str]
    metadata:          Dict[str, Any]

Real-World Patterns

```python from semantica.context import AgentContext, ContextGraph from semantica.vector_store import VectorStore

health_agent = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=ContextGraph(),
    decision_tracking=True,
)

health_agent.store("Patient has hypertension, type 2 diabetes")
health_agent.store("Patient allergic to penicillin: verified 2024-01")

decision_id = health_agent.record_decision(
    category="treatment_plan",
    scenario="Hypertension with comorbid diabetes",
    reasoning="ACE inhibitors are renoprotective in diabetic patients",
    outcome="prescribed_lisinopril",
    confidence=0.91,
)

precedents = health_agent.find_precedents("hypertension diabetes", limit=5)
for p in precedents:
    print("Past: {}  (confidence: {:.2f})".format(p.outcome, p.confidence))

chain = health_agent.get_causal_chain(decision_id, direction="downstream")
print("Follow-up decisions triggered: {}".format(len(chain)))
```

```python from semantica.context import AgentContext, ContextGraph, PolicyEngine from semantica.context.decision_models import Policy, Decision from semantica.vector_store import VectorStore from datetime import datetime

graph  = ContextGraph()
policy = PolicyEngine(graph_store=graph)

# Add compliance policy
p = Policy(
    policy_id="lending_policy",
    name="Lending Policy",
    description="Min confidence 0.8 for loan decisions",
    rules={"min_confidence": 0.8},
    category="loan_approval",
    version="1.0",
    created_at=datetime.now(),
    updated_at=datetime.now(),
)
policy.add_policy(p)

loan_agent = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=graph,
    decision_tracking=True,
)
loan_agent.store("Applicant: credit score 750, DTI 28%, stable employment 4yr")

# Check compliance before recording
d = Decision(
    decision_id="dec_loan_001",
    category="loan_approval",
    scenario="First-time homebuyer: 30yr fixed, 20% down",
    reasoning="Credit score above threshold, DTI within limits",
    outcome="approved_300k",
    confidence=0.94,
    timestamp=datetime.now(),
    decision_maker="loan_agent",
)
compliant = policy.check_compliance(d, "lending_policy")
if compliant:
    loan_agent.record_decision(
        category=d.category,
        scenario=d.scenario,
        reasoning=d.reasoning,
        outcome=d.outcome,
        confidence=d.confidence,
    )
```

```python from semantica.context import AgentContext, ContextGraph from semantica.vector_store import VectorStore

context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=ContextGraph(),
    decision_tracking=True,
)

context.store("Important fact learned during session")
context.record_decision(
    category="ops", scenario="Scale up", reasoning="Load > 80%",
    outcome="scaled_to_10_replicas", confidence=0.97,
)

# Persist everything
context.save("agent_state/")

# Later: restore and continue
restored = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=ContextGraph(),
    decision_tracking=True,
)
restored.load("agent_state/")

results = restored.retrieve("load scaling decisions", max_results=3)
```

Vector Store — Embedding storage backend for memory retrieval.
Knowledge Graph — Graph algorithms and analytics used inside ContextGraph.
Reasoning — Logical inference layered on top of context.
Provenance — W3C PROV-O lineage for every stored fact.
Context Module — Memory and decision tracking · Intermediate
Advanced Context Engineering — Production FAISS + Neo4j setup · Advanced

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Exported Classes

What You Get

Quick Start

Usage Patterns

AgentContext

Constructor Parameters

Memory Methods

Conversation Methods

Multi-Hop GraphRAG

Decision Methods

Checkpoint Methods

ContextGraph

Constructor Options

ContextGraph: Full Method Reference

Distance Intelligence (v0.5.0)

Neighbors with Distance Metadata

Proximity-Blended Retrieval

Cross-Graph Navigation

AgentMemory

Markdown Round Trips

PolicyEngine

EntityLinker

ContextRetriever

Data Structures

Real-World Patterns

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