feat(pipecat): voice agent integration with persistent memory

examples/agent_type_extraction.py

@@ -0,0 +1,193 @@
+"""
+Vektori — Agent-type extraction customisation examples
+=======================================================
+
+Shows how to tailor what Vektori extracts at L0 (facts) and L1 (episodes)
+for different agent personas.  The same conversation is run through three
+differently-configured Vektori instances so you can see the effect.
+
+Quick-start
+-----------
+    export OPENAI_API_KEY=...
+    python examples/agent_type_extraction.py
+
+Levels of customisation
+-----------------------
+  Level 1 — agent_type preset (zero effort):
+      Vektori(agent_type="presales")
+
+  Level 2 — domain hints on top of a preset:
+      Vektori(extraction_config=ExtractionConfig(
+          agent_type="presales",
+          focus_on=["ICP fit", "executive sponsor"],
+          ignore=["pleasantries"],
+      ))
+
+  Level 3 — prompt suffix (low effort, precise control):
+      Vektori(extraction_config=ExtractionConfig(
+          agent_type="sales",
+          facts_prompt_suffix="Always extract the exact dollar amount when pricing is mentioned.",
+      ))
+
+  Level 4 — full prompt override (escape hatch):
+      Vektori(extraction_config=ExtractionConfig(
+          custom_facts_prompt=MY_PROMPT_TEMPLATE,
+      ))
+"""
+
+import asyncio
+import json
+
+from vektori import ExtractionConfig, Vektori
+
+# ---------------------------------------------------------------------------
+# A realistic pre-sales discovery call snippet
+# ---------------------------------------------------------------------------
+PRESALES_CONVERSATION = [
+    {
+        "role": "user",
+        "content": (
+            "We're a Series B fintech, around 200 engineers. "
+            "Right now our AI agents lose context between sessions — support keeps "
+            "repeating the same questions to customers. It's killing CSAT scores."
+        ),
+    },
+    {
+        "role": "assistant",
+        "content": (
+            "That's a common friction point at your scale. What does your current "
+            "session storage look like — are you on something like Redis, or more ad hoc?"
+        ),
+    },
+    {
+        "role": "user",
+        "content": (
+            "Ad hoc, honestly. Each team rolls their own. Our CTO wants a unified "
+            "memory layer by Q3 — we've got maybe a $60–80K budget for tooling this half. "
+            "We tried Mem0 briefly but the graph retrieval wasn't granular enough."
+        ),
+    },
+    {
+        "role": "assistant",
+        "content": (
+            "Got it. So you need something that preserves the full conversation story, "
+            "not just entity triples — that's exactly what Vektori's three-layer graph "
+            "is built for. Who else is involved in the decision besides your CTO?"
+        ),
+    },
+    {
+        "role": "user",
+        "content": (
+            "Our VP Eng and the platform team lead, Aisha. She's the one who'd actually "
+            "integrate it. They're both pretty hands-on technically."
+        ),
+    },
+]
+
+# ---------------------------------------------------------------------------
+# A sales closing call
+# ---------------------------------------------------------------------------
+SALES_CONVERSATION = [
+    {
+        "role": "user",
+        "content": (
+            "We reviewed the proposal. Legal flagged the data residency clause — "
+            "they need EU hosting confirmed before we can sign."
+        ),
+    },
+    {
+        "role": "assistant",
+        "content": (
+            "Understood. Our EU region is live on AWS eu-west-1 — I'll get that "
+            "confirmed in writing by tomorrow. Are there any other open items?"
+        ),
+    },
+    {
+        "role": "user",
+        "content": (
+            "Just that. We're looking at the $48K/year enterprise tier. "
+            "If legal clears it this week we can sign by Friday the 18th."
+        ),
+    },
+    {
+        "role": "assistant",
+        "content": (
+            "Perfect. I'll loop in our legal team tonight. I'll also prep the "
+            "countersigned order form so it's ready to go the moment you get approval."
+        ),
+    },
+]
+
+
+async def run_demo():
+    print("=" * 70)
+    print("Vektori ExtractionConfig demo")
+    print("=" * 70)
+
+    # ------------------------------------------------------------------
+    # Example 1: pre-sales preset — zero effort
+    # ------------------------------------------------------------------
+    print("\n[1] agent_type='presales' — built-in preset\n")
+
+    presales_v = Vektori(agent_type="presales")
+    captured: list[dict] = []
+    await presales_v.add(
+        messages=PRESALES_CONVERSATION,
+        session_id="demo-presales-001",
+        user_id="demo-user",
+        # _capture_out is an internal debug hook used in tests
+    )
+    # Give async extraction a moment to complete for the demo
+    await asyncio.sleep(3)
+    memory = await presales_v.search("budget and decision makers", user_id="demo-user")
+    print("Facts retrieved (presales):")
+    for f in memory.get("facts", []):
+        print(f"  • {f['text']}")
+    await presales_v.close()
+
+    # ------------------------------------------------------------------
+    # Example 2: sales preset + domain hints
+    # ------------------------------------------------------------------
+    print("\n[2] agent_type='sales' + focus_on=['contract value', 'close date']\n")
+
+    sales_v = Vektori(
+        extraction_config=ExtractionConfig(
+            agent_type="sales",
+            focus_on=["contract value", "close date", "legal blockers"],
+        )
+    )
+    await sales_v.add(
+        messages=SALES_CONVERSATION,
+        session_id="demo-sales-001",
+        user_id="demo-sales-user",
+    )
+    await asyncio.sleep(3)
+    memory = await sales_v.search("deal status and blockers", user_id="demo-sales-user")
+    print("Facts retrieved (sales):")
+    for f in memory.get("facts", []):
+        print(f"  • {f['text']}")
+    await sales_v.close()
+
+    # ------------------------------------------------------------------
+    # Example 3: same sales call, general (no preset) — shows the difference
+    # ------------------------------------------------------------------
+    print("\n[3] agent_type='general' — no domain bias (baseline)\n")
+
+    general_v = Vektori()  # default, no agent_type
+    await general_v.add(
+        messages=SALES_CONVERSATION,
+        session_id="demo-general-001",
+        user_id="demo-general-user",
+    )
+    await asyncio.sleep(3)
+    memory = await general_v.search("deal status and blockers", user_id="demo-general-user")
+    print("Facts retrieved (general):")
+    for f in memory.get("facts", []):
+        print(f"  • {f['text']}")
+    await general_v.close()
+
+    print("\nDone.")
+
+
+if __name__ == "__main__":
+    asyncio.run(run_demo())