mirror of
https://github.com/semantica-agi/semantica.git
synced 2026-08-29 04:26:20 +00:00
Merge branch 'main' into fix/issue-888-docs-storage-backends
This commit is contained in:
Sameer Kadam
+8
-8
@@ -13,33 +13,33 @@ icon: "quote-left"
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<Tab title="BibTeX">
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```bibtex
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@software{semantica2026,
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title = {Semantica: An Open Source Framework for Semantic Layers and Knowledge Engineering},
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author = {Hawksight AI},
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title = {Semantica: Graph-Native Infrastructure for Context and Accountable AI Systems},
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author = {Semantica},
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year = {2026},
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url = {https://github.com/semantica-agi/semantica},
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version = {0.6.0},
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version = {0.6.5},
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doi = {10.5281/zenodo.XXXXXXX}
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}
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```
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</Tab>
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<Tab title="APA">
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Hawksight AI. (2026). *Semantica: An Open Source Framework for Semantic Layers and Knowledge Engineering* (Version 0.6.0) \[Computer software\]. https://github.com/semantica-agi/semantica
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Semantica. (2026). *Semantica: Graph-Native Infrastructure for Context and Accountable AI Systems* (Version 0.6.5) \[Computer software\]. https://github.com/semantica-agi/semantica
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</Tab>
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<Tab title="MLA">
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Hawksight AI. *Semantica: An Open Source Framework for Semantic Layers and Knowledge Engineering*. Version 0.6.0, GitHub, 2026, https://github.com/semantica-agi/semantica.
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Semantica. *Semantica: Graph-Native Infrastructure for Context and Accountable AI Systems*. Version 0.6.5, GitHub, 2026, https://github.com/semantica-agi/semantica.
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</Tab>
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<Tab title="Chicago">
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Hawksight AI. *Semantica: An Open Source Framework for Semantic Layers and Knowledge Engineering*. Version 0.6.0. GitHub, 2026. https://github.com/semantica-agi/semantica.
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Semantica. *Semantica: Graph-Native Infrastructure for Context and Accountable AI Systems*. Version 0.6.5. GitHub, 2026. https://github.com/semantica-agi/semantica.
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</Tab>
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<Tab title="IEEE">
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Hawksight AI, "Semantica: An Open Source Framework for Semantic Layers and Knowledge Engineering," Version 0.6.0, GitHub, 2026. \[Online\]. Available: https://github.com/semantica-agi/semantica
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Semantica, "Semantica: Graph-Native Infrastructure for Context and Accountable AI Systems," Version 0.6.5, GitHub, 2026. \[Online\]. Available: https://github.com/semantica-agi/semantica
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</Tab>
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</Tabs>
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## Acknowledgment Text
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> "This work uses Semantica (Hawksight AI, 2026), an open-source framework for semantic layer construction and knowledge engineering."
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> "This work uses Semantica (2026), an open-source graph-native infrastructure framework for context and accountable AI systems, providing Context Graphs, knowledge graphs, and full decision provenance."
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## Share Your Research
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@@ -16,6 +16,9 @@ At its core, Semantica adds a **context and accountability layer** on top of you
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- **Accountability Layer** — Provenance tracking, decision intelligence, conflict detection, and W3C PROV-O compliance make every claim in your AI stack auditable and explainable.
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- **Extension Layer** — `PluginRegistry` and `MethodRegistry` let you replace or augment any component: ingestors, extractors, reasoning engines, backends: without changing framework code.
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<Warning>
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**This is system-level explainability, not foundation-model explainability.** Semantica does not expose, reconstruct, or explain what happens *inside* the LLM/foundation model — its internal reasoning or chain-of-thought stays opaque, as it does for any external system. What Semantica explains is *outside* the model: the context and data fed in, the decision produced, its provenance, the relevant relationships, the policies applied, and the full execution trail. In short, Semantica explains and audits *what the AI system did*, not the foundation model's private internal reasoning.
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</Warning>
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## Knowledge Graphs
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@@ -102,6 +102,7 @@
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"group": "Integrations",
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"pages": [
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"integrations/agno",
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"integrations/crewai",
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"integrations/docling",
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"integrations/snowflake",
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"integrations/databricks"
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@@ -162,7 +162,7 @@ semantica-explorer --graph my_graph.json --no-browser
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```
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<Warning>
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`--host 0.0.0.0` makes Explorer reachable on every network interface. The server has no built-in authentication. Only use this on a trusted private network.
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`--host 0.0.0.0` makes Explorer reachable on every network interface. Since v0.6.5 the Explorer API requires `SEMANTICA_API_KEY` (sent as the `X-API-Key` header) and fails closed with `503` when unconfigured; unauthenticated access is only possible when `SEMANTICA_ALLOW_ANONYMOUS=true` is set explicitly. Only use this on a trusted private network.
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</Warning>
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+11
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| API key required? | Optional: pattern extraction works with no keys |
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| Works with LangChain / LlamaIndex? | Yes: Semantica is a layer on top, not a replacement |
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| Production-ready? | Yes: 1,000+ tests, v0.5.0 ships with 12 security fixes |
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| Latest version? | **v0.6.0** (July 2026) |
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| Latest version? | **v0.6.5** (August 2026) |
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| Local LLMs? | Yes: Ollama via LiteLLM, HuggingFaceLLM for air-gapped |
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@@ -52,6 +52,16 @@ Semantica works alongside these frameworks, not against them.
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</Accordion>
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<Accordion title="Does Semantica explain an LLM's internal reasoning or chain-of-thought?" icon="triangle-exclamation">
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No. This is **system-level explainability, not foundation-model explainability**. Semantica does not expose, reconstruct, or explain what happens *inside* the LLM/foundation model — its internal reasoning or chain-of-thought stays opaque, as it does for any external system.
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What Semantica explains is *outside* the model: what context and data were used, what decision was produced, the provenance behind it, the relevant relationships, the policies applied, and the resulting decision trail.
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In short: Semantica explains and audits *what the AI system did* — not the foundation model's private internal reasoning.
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</Accordion>
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<Accordion title="Is Semantica free?" icon="tag">
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Yes: MIT licensed, no vendor lock-in, no paywalled features. Some capabilities require third-party API keys (e.g., OpenAI embeddings, Groq inference), but Semantica itself is always free and open source.
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@@ -42,7 +42,7 @@ icon: "rocket"
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Verify installation:
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```python
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import semantica
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print(semantica.__version__) # 0.6.0
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print(semantica.__version__) # 0.6.5
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```
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</Check>
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</Step>
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+5
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@@ -192,7 +192,11 @@ decision_id = context.record_decision(
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## Built for Where Mistakes Have Consequences
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Semantica was designed for domains where every decision must be explainable and every fact must be traceable:
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Semantica was designed for domains where every decision must be explainable and every fact must be traceable.
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<Warning>
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**This is system-level explainability, not foundation-model explainability.** Semantica does not expose, reconstruct, or explain what happens *inside* the LLM/foundation model — its internal reasoning or chain-of-thought stays opaque, as it does for any external system. What Semantica explains is *outside* the model: the context and data fed in, the decision produced, its provenance, the relevant relationships, the policies applied, and the full execution trail. See [Core Concepts](concepts) for the full scope note.
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</Warning>
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**Healthcare & Life Sciences**
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- Clinical decision support with full audit trails
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@@ -0,0 +1,147 @@
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---
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title: "CrewAI Integration"
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description: "Give CrewAI crews a shared semantic knowledge graph, decision intelligence, and graph-based retrieval via three drop-in components."
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icon: "users"
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---
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> Three drop-in components that bring Semantica's knowledge graph and decision intelligence into any CrewAI crew.
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## Installation
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```bash
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pip install "semantica[crewai]"
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```
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Requires `crewai >= 0.80.0`. If `crewai` is not installed, the integration still imports — every class carries the full Semantica API and degrades gracefully, but cannot be passed to a `Crew`.
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## Components at a Glance
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- **SemanticaKGTool** — `Agent(tools=[…])`: 5 KG construction/query actions: extract entities, extract relations, add to graph, query graph, find related.
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- **SemanticaDecisionTool** — `Agent(tools=[…])`: 5 decision intelligence actions: record decisions, find precedents, trace causal chains, analyze impact, check policies.
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- **SemanticaKnowledgeSource** — `Crew(knowledge_sources=[…])`: Serializes a `ContextGraph` into CrewAI knowledge storage so every agent gets retrieval access to the graph.
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## Component Details
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<Tabs>
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<Tab title="SemanticaKGTool">
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Lets agents actively **build and query** a shared `ContextGraph` mid-reasoning.
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```python
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from crewai import Agent, Crew, Task
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from semantica.context import ContextGraph
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from integrations.crewai import SemanticaKGTool
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graph = ContextGraph()
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analyst = Agent(
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role="Knowledge Analyst",
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goal="Build and explore a knowledge graph from documents",
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backstory="You map entities and relationships into a shared graph.",
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tools=[SemanticaKGTool(graph=graph)],
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)
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crew = Crew(
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agents=[analyst],
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tasks=[Task(
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description="Extract and link key entities from the brief",
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expected_output="JSON",
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agent=analyst,
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)],
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)
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crew.kickoff()
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```
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| Tool | Description |
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| :------ | :------------- |
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| `extract_entities` | Extract named entities from `text` |
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| `extract_relations` | Extract relationships between entities in `text` |
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| `add_to_graph` | Extract entities/relations from `text` and add them to the shared graph |
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| `query_graph` | Keyword-search the graph by node id, type, and content using `query` |
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| `find_related` | Find concepts related to `entity` within `hops` hops |
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All actions return JSON so agents get parseable results.
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**Sharing a graph:** the tool reads/writes whatever `graph` you pass in. When no `graph` is given, a fresh in-memory `ContextGraph()` is created (and a warning is logged) — two tool instances that each auto-create their own graph do **not** share knowledge. Pass the same `ContextGraph` to every agent that must share state.
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</Tab>
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<Tab title="SemanticaDecisionTool">
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Exposes Semantica's decision intelligence as a native CrewAI tool, backed by `AgentContext`.
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```python
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from crewai import Agent, Crew, Task
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from integrations.crewai import SemanticaDecisionTool
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planner = Agent(
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role="Decision Planner",
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goal="Make grounded, precedented decisions",
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backstory="You record decisions and validate them against policy.",
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tools=[SemanticaDecisionTool()],
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)
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crew = Crew(agents=[planner], tasks=[...])
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```
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When no `AgentContext` is passed, one is created in-memory with `decision_tracking=True` and its own `ContextGraph`, so decision actions work out of the box (a warning is logged — pass the same `AgentContext` to every agent that must share decision state). Missing optional fields in `record_decision` fall back to `category="general"`, `reasoning="agent decision"`, and `outcome="recorded"`. `find_precedents` returns up to `max_precedents` results. If a knowledge graph cannot trace causality, `trace_causal_chain` returns an explicit error rather than substituting similarity-based results.
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| Tool | Description |
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| :------ | :------------- |
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| `record_decision` | Record a decision with reasoning, outcome, and confidence |
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| `find_precedents` | Search for similar past decisions |
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| `trace_causal_chain` | Trace the causal chain from a decision |
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| `analyze_impact` | Assess downstream influence of a decision |
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| `check_policy` | Validate a proposed decision against policy rules |
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</Tab>
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<Tab title="SemanticaKnowledgeSource">
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Gives **every agent in the crew** retrieval access to a `ContextGraph`.
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```python
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from crewai import Agent, Crew, Task
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from semantica.context import ContextGraph
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from integrations.crewai import SemanticaKnowledgeSource
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graph = ContextGraph()
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graph.add_node(node_id="privacy", node_type="policy", content="...")
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researcher = Agent(
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role="Policy Researcher",
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goal="Answer questions from the knowledge base",
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backstory="You retrieve from graph knowledge to answer accurately.",
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)
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crew = Crew(
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agents=[researcher],
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tasks=[...],
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knowledge_sources=[SemanticaKnowledgeSource(graph=graph)],
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)
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```
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On kickoff the graph's nodes and edges are serialized, chunked, and stored through CrewAI's knowledge pipeline.
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> **Embedder required:** storing chunks goes through CrewAI's knowledge pipeline, which needs an embedder to be configured. Set `Crew(embedder=...)` (or provide the default credentials CrewAI falls back to, e.g. `OPENAI_API_KEY`). If no working embedder is configured, storage fails, an ERROR is logged, and agents will retrieve **nothing** — the crew still runs, but its knowledge queries return empty.
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**Compatibility:** CrewAI's `BaseKnowledgeSource` contract changed between `0.80.x` and current releases (`load_content()` → `validate_content()`/`aadd()`). `SemanticaKnowledgeSource` implements both legacy and current methods, so it works across `crewai>=0.80.0`.
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</Tab>
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</Tabs>
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## Checkpoints & Serialization
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CrewAI serializes tools and knowledge sources to JSON for checkpointing/resume. Live Semantica state (`ContextGraph`, `AgentContext`, extractors) is **excluded from that serialization** — a restored tool/source comes back with a fresh in-memory `ContextGraph` and logs a warning. Until you re-attach the live graph/context, the restored objects answer queries against an **empty** graph, so re-wire them after resuming (e.g. `restored_tool.graph = live_graph`) before agents continue.
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## API Reference
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```python
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from integrations.crewai import (
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SemanticaKGTool, # BaseTool: KG construction/query actions
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SemanticaDecisionTool, # BaseTool: decision intelligence actions
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SemanticaKnowledgeSource, # BaseKnowledgeSource: graph → crew knowledge
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CREWAI_AVAILABLE, # bool: True if crewai is installed
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)
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```
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All three classes are usable without `crewai` installed: they carry the full Semantica API and degrade gracefully.
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## See Also
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- [Context Module](../reference/context) — AgentContext and ContextGraph backing the integration.
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- [Semantic Extraction](../reference/semantic_extract) — NERExtractor / RelationExtractor used by SemanticaKGTool.
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- [LLMs](../reference/llms) — Configure LLM providers for your crew's agents.
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- [Vector Store](../reference/vector_store) — Vector backend used by SemanticaDecisionTool.
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@@ -203,6 +203,13 @@ export_lpg(graph, "import.cypher", method="cypher")
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exporter = SemanticNetworkYAMLExporter()
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exporter.export(graph, "graph.yaml")
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```
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The YAML exporters read `entities`/`relationships`/`triplets` (with
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`nodes`/`edges` accepted as aliases, so `ContextGraph.to_dict()` exports
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directly). A non-empty mapping supplying none of them raises
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`ValidationError` rather than writing a file with every collection empty,
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as does one whose collection value is not a list of records
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(`{"entities": "abc"}`).
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</Tab>
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<Tab title="Graph DB Import">
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**LPGExporter** writes Cypher `CREATE` statements for Neo4j and Memgraph:
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@@ -236,6 +243,12 @@ export_lpg(graph, "import.cypher", method="cypher")
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Both exporters write to a file and return `None`.
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`LPGExporter`, `ArangoAQLExporter`, and `Neo4jCSVExporter` resolve mapping
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payloads on the same terms as the YAML exporters above, so an unrecognized
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or malformed mapping is rejected instead of exported as an empty graph.
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`Neo4jCSVExporter` still reads graph *objects* off their
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`nodes`/`entities` and `edges`/`relationships` attributes.
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<Warning>
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**`ArangoAQLExporter.export()` and `LPGExporter.export()` write to a file and return `None`.** They do not return the AQL/Cypher string. Write to a file and read it back if you need the string.
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</Warning>
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Reference in New Issue
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