.env.example

@@ -12,8 +12,22 @@ DATABASE_URL=postgres://chat_backend:chat_backend@db:5432/chat_backend
 
 # Ollama — local loopback when Ollama runs on this machine; LAN IP for GPU host.
 OLLAMA_BASE_URL=http://127.0.0.1:11434
-# OLLAMA_MODEL=llama3.2
-# OLLAMA_EMBED_MODEL=llama3.2
+# Legacy fallback (used when role-specific vars unset). Prefer the role vars.
+# OLLAMA_MODEL=gpt-oss:20b
+# OLLAMA_MODEL_THINKING=gpt-oss:20b
+# OLLAMA_MODEL_FAST=gemma4:latest
+# OLLAMA_MODEL_UTILITY=llama3.2
+# OLLAMA_EMBED_MODEL=nomic-embed-text
+# OLLAMA_NUM_CTX_THINKING=16384
+# OLLAMA_NUM_CTX_FAST=8192
+# OLLAMA_NUM_CTX_UTILITY=4096
+
+# Web search (#62) — SearxNG primary, DDGS failover. See README "SearxNG".
+ALLOW_INTERNET_ACCESS=true
+SEARCH_PROVIDER=searxng
+SEARCH_FAILOVER_PROVIDER=ddgs
+SEARXNG_BASE_URL=http://127.0.0.1:8088
+# SEARXNG_TIMEOUT_SECONDS=8
 
 # Email (SMTP2GO) — optional for local
 EMAIL_HOST=mail.smtp2go.com

.env.prod.example

@@ -32,8 +32,22 @@ WEB_PORT=8003
 
 # Ollama on GPU host (ai-server-4080). Firewall must allow 10.0.0.0/24 → :11434.
 OLLAMA_BASE_URL=http://10.0.0.128:11434
-OLLAMA_MODEL=llama3.2
-OLLAMA_EMBED_MODEL=llama3.2
+# Role-scoped models (#62). After changing OLLAMA_EMBED_MODEL, run:
+#   python manage.py reindex_embeddings
+OLLAMA_MODEL=gpt-oss:20b
+OLLAMA_MODEL_THINKING=gpt-oss:20b
+OLLAMA_MODEL_FAST=gemma4:latest
+OLLAMA_MODEL_UTILITY=llama3.2
+OLLAMA_EMBED_MODEL=nomic-embed-text
+OLLAMA_NUM_CTX_THINKING=16384
+OLLAMA_NUM_CTX_FAST=8192
+
+# Web search (#62) — self-hosted SearxNG (recommended). DDGS is automatic failover.
+ALLOW_INTERNET_ACCESS=true
+SEARCH_PROVIDER=searxng
+SEARCH_FAILOVER_PROVIDER=ddgs
+# Point at the SearxNG container/service on the LAN (see README "SearxNG").
+SEARXNG_BASE_URL=http://10.0.0.128:8088
 
 # Email (SMTP2GO)
 EMAIL_HOST=mail.smtp2go.com

README.md

@@ -87,7 +87,14 @@ with `COMPOSE_DATABASE_URL` if needed.
 | `DATABASE_URL` | SQLite fallback | yes | Shared Postgres in prod |
 | `WEB_PORT` | n/a (compose maps 8003) | `8003` | Host port for prod compose |
 | `OLLAMA_BASE_URL` | `http://127.0.0.1:11434` | yes | GPU host in prod: `http://10.0.0.128:11434` |
-| `OLLAMA_MODEL` / `OLLAMA_EMBED_MODEL` | from `DEBUG` | optional | Override model names |
+| `OLLAMA_MODEL` | `gpt-oss:20b` | optional | Legacy fallback for THINKING |
+| `OLLAMA_MODEL_THINKING` / `_FAST` / `_UTILITY` | see defaults | optional | Role-scoped chat models (#62) |
+| `OLLAMA_EMBED_MODEL` | `nomic-embed-text` | optional | Never falls back to a chat model |
+| `OLLAMA_NUM_CTX_THINKING` / `_FAST` | `16384` / `8192` | optional | Context window per role |
+| `ALLOW_INTERNET_ACCESS` | `true` | optional | Gate for live web retrieval |
+| `SEARCH_PROVIDER` | `searxng` | optional | Primary search provider (#62) |
+| `SEARCH_FAILOVER_PROVIDER` | `ddgs` | optional | Automatic failover |
+| `SEARXNG_BASE_URL` | `http://127.0.0.1:8088` | yes if using SearxNG | Self-hosted SearxNG JSON API |
 | `EMAIL_HOST_*` | empty | yes (prod/beta) | SMTP2GO |
 | `CAPTCHA_SECRET_KEY` | empty | recommended | |
 | `ENABLE_ACCOUNT_REGISTRATION` | `false` | optional | Self-serve sign-up; keep false until ready |
@@ -136,6 +143,74 @@ All clients (`ollama.Client`, `OllamaLLM`, `OllamaEmbeddings`, `ChatOllama`) use
 
 Firewall / Ollama listen on ai-server-4080 must allow `10.0.0.0/24` → `:11434`.
 
+### Role-scoped models (#62)
+
+| Role | Setting | Default | Used for |
+|------|---------|---------|----------|
+| THINKING | `OLLAMA_MODEL_THINKING` | `gpt-oss:20b` | Default chat / grounded answers |
+| FAST | `OLLAMA_MODEL_FAST` | `gemma4:latest` | FE `modelName=FAST` (smaller/faster — still grounded) |
+| UTILITY | `OLLAMA_MODEL_UTILITY` | `llama3.2` | Classify / moderate / title / grounding decision |
+| EMBED | `OLLAMA_EMBED_MODEL` | `nomic-embed-text` | Chroma embeddings |
+
+After changing `OLLAMA_EMBED_MODEL`, rebuild the vector store (dimension change):
+
+```bash
+SKIP_RAG_INIT=1 uv run python manage.py reindex_embeddings
+```
+
+### SearxNG (web search)
+
+Grounded chat uses a self-hosted **SearxNG** instance as the primary search
+provider (`SEARCH_PROVIDER=searxng`), with DuckDuckGo (`ddgs`) as automatic
+failover. Point `SEARXNG_BASE_URL` at the JSON API (no trailing path).
+
+**Recommended: run SearxNG on the GPU/infra host next to Ollama**
+(`10.0.0.128`), reachable from the chat_backend containers on the LAN.
+
+Minimal compose snippet (add to `server-infra` or run on ai-server-4080):
+
+```yaml
+services:
+  searxng:
+    image: searxng/searxng:latest
+    restart: unless-stopped
+    ports:
+      - "8080:8080"
+    volumes:
+      - ./searxng:/etc/searxng:rw
+    environment:
+      - SEARXNG_BASE_URL=http://10.0.0.128:8088/
+```
+
+In `searxng/settings.yml` (created on first start), enable the JSON format:
+
+```yaml
+search:
+  formats:
+    - html
+    - json
+```
+
+Then set in `chat_backend_prod.env` / `chat_backend_beta.env`:
+
+```text
+ALLOW_INTERNET_ACCESS=true
+SEARCH_PROVIDER=searxng
+SEARCH_FAILOVER_PROVIDER=ddgs
+SEARXNG_BASE_URL=http://10.0.0.128:8088
+```
+
+Firewall: allow `10.0.0.0/24` → `:8080` on the SearxNG host (same pattern as
+Ollama `:11434`). Verify from a backend container:
+
+```bash
+curl -sG 'http://10.0.0.128:8088/search' --data-urlencode 'q=test' -d 'format=json' | head
+```
+
+If SearxNG is down, chat still works for non-factual turns; factual turns that
+require retrieval return an explicit "couldn't reach live sources" message
+instead of hallucinating from parametric memory.
+
 ## File storage
 
 Prompt attachments and workspace documents use **`DatabaseStorage`**

llm_be/chat_backend/apps.py

@@ -3,6 +3,9 @@ from django.conf import settings
 from django.db import OperationalError, ProgrammingError
 import os
 import sys
+import logging
+
+logger = logging.getLogger(__name__)
 
 
 class ChatBackendConfig(AppConfig):
@@ -20,6 +23,7 @@ class ChatBackendConfig(AppConfig):
             "test",
             "shell",
             "check",
+            "reindex_embeddings",
         }
         if any(cmd in sys.argv for cmd in management_cmds):
             return
@@ -29,7 +33,10 @@ class ChatBackendConfig(AppConfig):
         FORCE_RELOAD = False
 
         try:
-            from .services.rag_services import AsyncRAGService
+            from .services.rag_services import (
+                AsyncRAGService,
+                EmbeddingDimensionMismatch,
+            )
             from chat_backend.models import Document
 
             if Document.objects.exists():
@@ -41,6 +48,10 @@ class ChatBackendConfig(AppConfig):
                 if FORCE_RELOAD:
                     print("Force Reload ChromaDB with existing documents...")
                     rag_service.clear_vector_store()
+        except EmbeddingDimensionMismatch as exc:
+            # Loud failure — do not silently serve with the wrong embed model.
+            logger.error("RAG embedding dimension mismatch: %s", exc)
+            raise
         except (OperationalError, ProgrammingError):
             # Database tables might not exist yet during migration
             pass

llm_be/chat_backend/consumers.py

@@ -13,15 +13,18 @@ from asgiref.sync import sync_to_async, async_to_sync
 from langchain_core.messages import HumanMessage, AIMessage
 from langchain_community.vectorstores import Chroma
 from langchain_ollama import OllamaEmbeddings
-from langchain_community.tools import DuckDuckGoSearchRun
-from chat_backend.ollama_config import ollama_embeddings_kwargs
+from chat_backend.ollama_config import (
+    ollama_embeddings_kwargs,
+    ollama_model_for_role,
+    resolve_chat_role,
+)
 from django.conf import settings as django_settings
 from langchain_core.runnables import RunnableLambda, RunnableBranch, RunnablePassthrough
 from langchain_core.tracers.context import collect_runs
 
 from .models import Conversation, Prompt, PromptMetric, DocumentWorkspace, Document, CustomUser
 from .serializers import PromptSerializer
-from .services.llm_service import AsyncLLMService
+from .services.llm_service import AsyncLLMService, build_chat_service
 from .services.rag_services import AsyncRAGService
 from .services.chat_tenant_scope import (
     ChatTenantScopeError,
@@ -35,6 +38,7 @@ from .services.title_generator import title_generator
 from .services.moderation_classifier import moderation_classifier, ModerationLabel
 from .services.prompt_classifier.prompt_classifier import PromptClassifier, PromptType
 from .services.data_analysis_service import AsyncDataAnalysisService
+from .services.grounded_chat import citations_frame, prepare_grounded_chat
 from .utils import (
     TokenUsageCollector,
     aiter_text_chunks,
@@ -52,7 +56,6 @@ from finance.services.quotas import (
 logger = logging.getLogger(__name__)
 
 CHANNEL_NAME: str = "llm_messages"
-MODEL_NAME: str = "llama3.2"
 PROMPT_CLASSIFIER = PromptClassifier()
 
 
@@ -175,7 +178,7 @@ def get_messages(conversation_id, prompt, file_string: str = None, file_type: st
 
 
 @database_sync_to_async
-def save_generated_message(conversation_id, message):
+def save_generated_message(conversation_id, message, citations=None):
     conversation = Conversation.objects.get(id=conversation_id)
 
     # add the prompt to the conversation
@@ -189,7 +192,12 @@ def save_generated_message(conversation_id, message):
     if serializer.is_valid():
         prompt_instance = serializer.save()
         prompt_instance.conversation_id = conversation.id
+        if citations:
+            prompt_instance.citations = citations
         prompt_instance = serializer.save()
+        # Ensure citations survive even if serializer omits write.
+        if citations is not None:
+            Prompt.objects.filter(pk=prompt_instance.pk).update(citations=citations)
     else:
         print(serializer.errors)
 
@@ -478,24 +486,6 @@ class ChatConsumerAgain(AsyncWebsocketConsumer):
                             }
                         return {"type": "text", "content": "Image Generation is not supported at this time, but it will be soon."}
 
-                    if prompt_type == PromptType.SEARCH:
-                        # Check modelName first - if FAST, we skip search regardless of settings
-                        if input_dict.get("model_name") == "FAST":
-                            pass # Skip search
-                        elif getattr(settings, "ALLOW_INTERNET_ACCESS", False):
-                            try:
-                                search = DuckDuckGoSearchRun()
-                                search_results = search.run(input_dict["message"])
-                                messages.append(HumanMessage(content=f"Search Results: {search_results}"))
-                            except Exception as e:
-                                logger.error(f"Search failed: {e}")
-                                # If search fails, we proceed without it, essentially falling back to general chat
-                                pass
-                        else:
-                            # If search is disabled, we could notify the user, but for now we'll just proceed
-                            # potentially adding a system message or just letting the LLM handle it with its training data
-                            pass
-
                     if prompt_type == PromptType.RAG:
                         try:
                             await enforce_feature_gate(chat_user, "rag")
@@ -520,9 +510,21 @@ class ChatConsumerAgain(AsyncWebsocketConsumer):
                              return {"type": "text", "content": "Please upload a file to perform data analysis."}
                         return service.generate_response(prompt_instance.message, decoded_file, file_type)
                     
-                    else: # GENERAL_CHAT or others
-                        service = AsyncLLMService()
-                        return service.generate_response(messages, prompt_instance.message, conversation_id)
+                    else:
+                        # GENERAL_CHAT / SEARCH / UNKNOWN — always-on grounding (#62).
+                        # FAST selects a smaller model; it no longer skips search.
+                        grounded = await prepare_grounded_chat(
+                            message=input_dict["message"],
+                            messages=messages,
+                            model_name=input_dict.get("model_name"),
+                            conversation_id=conversation_id,
+                        )
+                        if grounded.error:
+                            return grounded.error
+                        # Stash citations/model on the input for the caller.
+                        input_dict["_citations"] = grounded.citations
+                        input_dict["_resolved_model"] = grounded.model_name
+                        return grounded.generator
 
                 # --- Execution ---
                 
@@ -546,12 +548,13 @@ class ChatConsumerAgain(AsyncWebsocketConsumer):
                      #    messages = messages[:-1] + [HumanMessage(content=altered_message)]
                      # I'll add it to the input_dict if needed.
 
+                resolved_model = ollama_model_for_role(resolve_chat_role(model))
                 prompt_metric = await create_prompt_metric(
                     prompt_instance.id,
                     prompt_instance.message,
                     True if file else False,
                     file_type,
-                    MODEL_NAME,
+                    resolved_model,
                     conversation_id,
                 )
 
@@ -562,7 +565,9 @@ class ChatConsumerAgain(AsyncWebsocketConsumer):
                     "file_type": file_type,
                     "messages": messages,
                     "prompt_instance": prompt_instance,
-                    "model_name": model
+                    "model_name": model,
+                    "_citations": [],
+                    "_resolved_model": resolved_model,
                 }
 
                 # Run the pipeline steps manually to handle the async generator return type of generate_response_step
@@ -602,8 +607,22 @@ class ChatConsumerAgain(AsyncWebsocketConsumer):
                     tokens_in, tokens_out = usage.pair
 
                 await self.send("END_OF_THE_STREAM_ENDER_GAME_42")
-                
-                await save_generated_message(conversation_id, full_response)
+
+                citations = step2.get("_citations") or []
+                if citations:
+                    await self.send_json_message(json.dumps(citations_frame(citations)))
+
+                # Prefer model actually used by the grounded path when present.
+                final_model = step2.get("_resolved_model") or resolved_model
+                if final_model and final_model != prompt_metric.model_name:
+                    prompt_metric.model_name = final_model
+                    await database_sync_to_async(prompt_metric.save)(
+                        update_fields=["model_name"]
+                    )
+
+                await save_generated_message(
+                    conversation_id, full_response, citations=citations
+                )
                 await finish_prompt_metric(
                     prompt_metric,
                     len(full_response),

llm_be/chat_backend/consumers_graph.py

@@ -11,7 +11,6 @@ from asgiref.sync import sync_to_async
 from channels.generic.websocket import AsyncWebsocketConsumer
 from channels.db import database_sync_to_async
 from langchain_core.messages import HumanMessage, AIMessage, BaseMessage
-from langchain_community.tools import DuckDuckGoSearchRun
 from langgraph.graph import StateGraph, END
 
 from .models import Conversation, Prompt, PromptMetric, DocumentWorkspace, CustomUser
@@ -30,6 +29,8 @@ from .services.title_generator import title_generator
 from .services.moderation_classifier import moderation_classifier, ModerationLabel
 from .services.prompt_classifier.prompt_classifier import PromptClassifier, PromptType
 from .services.data_analysis_service import AsyncDataAnalysisService
+from .services.grounded_chat import citations_frame, prepare_grounded_chat
+from chat_backend.ollama_config import ollama_model_for_role, resolve_chat_role
 from .utils import (
     TokenUsageCollector,
     aiter_text_chunks,
@@ -43,7 +44,6 @@ from finance.services.quotas import FeatureNotAllowed, QuotaExceeded, check_gene
 logger = logging.getLogger(__name__)
 
 CHANNEL_NAME: str = "llm_messages"
-MODEL_NAME: str = "llama3.2"
 PROMPT_CLASSIFIER = PromptClassifier()
 
 # --- Database Helpers (Reused) ---
@@ -146,7 +146,7 @@ def get_messages(conversation_id, prompt, file_string: str = None, file_type: st
     return transformed_messages, prompt_instance
 
 @database_sync_to_async
-def save_generated_message(conversation_id, message):
+def save_generated_message(conversation_id, message, citations=None):
     conversation = Conversation.objects.get(id=conversation_id)
     serializer = PromptSerializer(
         data={
@@ -159,6 +159,8 @@ def save_generated_message(conversation_id, message):
         prompt_instance = serializer.save()
         prompt_instance.conversation_id = conversation.id
         prompt_instance.save()
+        if citations is not None:
+            Prompt.objects.filter(pk=prompt_instance.pk).update(citations=citations)
     else:
         print(serializer.errors)
 
@@ -221,6 +223,8 @@ class ChatState(TypedDict):
     error: Union[str, None]
     model_name: str
     chat_user: Any
+    citations: List[Dict[str, Any]]
+    resolved_model: str
 
 
 # --- LangGraph Nodes ---
@@ -277,22 +281,7 @@ async def generation_node(state: ChatState) -> ChatState:
                 }
         return {"response_generator": {"type": "text", "content": "Image Generation is not supported at this time, but it will be soon."}}
 
-    # Feature Flag: Internet Access
-    if prompt_type == PromptType.SEARCH:
-         # Check modelName first - if FAST, we skip search regardless of settings
-         if state.get("model_name") == "FAST":
-             pass
-         elif getattr(settings, "ALLOW_INTERNET_ACCESS", False):
-             try:
-                 search = DuckDuckGoSearchRun()
-                 search_results = search.run(state["message"])
-                 messages.append(HumanMessage(content=f"Search Results: {search_results}"))
-             except Exception as e:
-                 logger.error(f"Search failed: {e}")
-                 pass
-         else:
-             pass
-
+    # Feature Flag: Internet Access / always-on grounding handled below for chat.
     if prompt_type == PromptType.RAG:
         chat_user = state.get("chat_user")
         if chat_user is not None:
@@ -318,10 +307,26 @@ async def generation_node(state: ChatState) -> ChatState:
         generator = service.generate_response(prompt_instance.message, decoded_file, file_type)
         return {"response_generator": generator}
     
-    else: # GENERAL_CHAT or others
-        service = AsyncLLMService()
-        generator = service.generate_response(messages, prompt_instance.message, conversation_id)
-        return {"response_generator": generator}
+    else:
+        # GENERAL_CHAT / SEARCH / UNKNOWN — always-on grounding (#62).
+        # FAST selects a smaller model; it no longer skips search.
+        grounded = await prepare_grounded_chat(
+            message=state["message"],
+            messages=messages,
+            model_name=state.get("model_name"),
+            conversation_id=conversation_id,
+        )
+        if grounded.error:
+            return {
+                "response_generator": grounded.error,
+                "citations": [],
+                "resolved_model": grounded.model_name or "",
+            }
+        return {
+            "response_generator": grounded.generator,
+            "citations": grounded.citations,
+            "resolved_model": grounded.model_name,
+        }
 
 
 # --- LangGraph Definition ---
@@ -475,12 +480,13 @@ class ChatConsumerGraph(AsyncWebsocketConsumer):
                 if not decoded_file:
                     decoded_file, file_type = await get_conversation_file_async(conversation_id)
 
+                resolved_model = ollama_model_for_role(resolve_chat_role(model))
                 prompt_metric = await create_prompt_metric(
                     prompt_instance.id,
                     prompt_instance.message,
                     True if file else False,
                     file_type,
-                    MODEL_NAME,
+                    resolved_model,
                     conversation_id,
                 )
 
@@ -498,6 +504,8 @@ class ChatConsumerGraph(AsyncWebsocketConsumer):
                     "error": None,
                     "model_name": model,
                     "chat_user": chat_user,
+                    "citations": [],
+                    "resolved_model": resolved_model,
                 }
                 print("Initial State: ", initial_state)
 
@@ -535,8 +543,21 @@ class ChatConsumerGraph(AsyncWebsocketConsumer):
                     tokens_in, tokens_out = usage.pair
 
                 await self.send("END_OF_THE_STREAM_ENDER_GAME_42")
-                
-                await save_generated_message(conversation_id, full_response)
+
+                citations = final_state.get("citations") or []
+                if citations:
+                    await self.send_json_message(json.dumps(citations_frame(citations)))
+
+                final_model = final_state.get("resolved_model") or resolved_model
+                if final_model and final_model != prompt_metric.model_name:
+                    prompt_metric.model_name = final_model
+                    await database_sync_to_async(prompt_metric.save)(
+                        update_fields=["model_name"]
+                    )
+
+                await save_generated_message(
+                    conversation_id, full_response, citations=citations
+                )
                 await finish_prompt_metric(
                     prompt_metric,
                     len(full_response),

llm_be/chat_backend/management/commands/reindex_embeddings.py

@@ -0,0 +1,53 @@
+"""Rebuild the Chroma collection with the configured embedding model (#62).
+
+Usage:
+    python manage.py reindex_embeddings
+    python manage.py reindex_embeddings --dry-run
+"""
+
+from __future__ import annotations
+
+from django.core.management.base import BaseCommand
+
+from chat_backend.models import Document
+from chat_backend.ollama_config import ollama_embed_model
+from chat_backend.services.rag_services import AsyncRAGService
+
+
+class Command(BaseCommand):
+    help = (
+        "Drop and rebuild the Chroma vector store using OLLAMA_EMBED_MODEL, "
+        "re-ingesting every Document while preserving workspace/document metadata."
+    )
+
+    def add_arguments(self, parser):
+        parser.add_argument(
+            "--dry-run",
+            action="store_true",
+            help="Print what would be re-ingested without mutating Chroma.",
+        )
+
+    def handle(self, *args, **options):
+        dry_run = options.get("dry_run")
+        embed_model = ollama_embed_model()
+        total = Document.objects.count()
+        active = Document.objects.filter(active=True).count()
+        self.stdout.write(
+            f"OLLAMA_EMBED_MODEL={embed_model}  documents={total} "
+            f"(active={active})"
+        )
+        if dry_run:
+            self.stdout.write(self.style.WARNING("Dry run — no changes made."))
+            return
+
+        # Reset singleton so a fresh store is built under the current embed model.
+        AsyncRAGService._instance = None
+        rag = AsyncRAGService()
+        self.stdout.write("Clearing Chroma collection…")
+        rag.clear_vector_store()
+        self.stdout.write("Re-ingesting documents…")
+        rag.ingest_documents()
+        count = rag.vector_store._collection.count()
+        self.stdout.write(
+            self.style.SUCCESS(f"Reindex complete. Vector chunks now: {count}")
+        )

llm_be/chat_backend/migrations/0031_prompt_citations.py

@@ -0,0 +1,18 @@
+# Generated by Django 6.0 on 2026-08-02 14:03
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('chat_backend', '0030_drive_sync_progress_counters'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='prompt',
+            name='citations',
+            field=models.JSONField(blank=True, default=list, help_text='Structured source citations for grounded answers (#62). List of {index, title, url, published_at}.'),
+        ),
+    ]

llm_be/chat_backend/models.py

@@ -310,6 +310,14 @@ class Prompt(TimeInfoBase):
         null=True,
         help_text="file type of the file for the prompt",
     )
+    citations = models.JSONField(
+        default=list,
+        blank=True,
+        help_text=(
+            "Structured source citations for grounded answers (#62). "
+            "List of {index, title, url, published_at}."
+        ),
+    )
 
     def get_conversation_title(self):
         if self.conversation:

llm_be/chat_backend/ollama_config.py

@@ -1,25 +1,97 @@
-"""Shared Ollama client helpers — always use settings.OLLAMA_BASE_URL."""
+"""Shared Ollama client helpers — always use settings.OLLAMA_BASE_URL.
+
+Role-scoped models (#62): THINKING / FAST / UTILITY / EMBED each resolve
+independently, with ``OLLAMA_MODEL`` kept as a fallback so existing deploys
+keep working until they set the role-specific vars.
+"""
+
+from __future__ import annotations
 
 from django.conf import settings
 
+ROLE_THINKING = "thinking"
+ROLE_FAST = "fast"
+ROLE_UTILITY = "utility"
+ROLE_EMBED = "embed"
+
+_VALID_ROLES = {ROLE_THINKING, ROLE_FAST, ROLE_UTILITY, ROLE_EMBED}
+
 
 def ollama_base_url() -> str:
     return getattr(settings, "OLLAMA_BASE_URL", "http://127.0.0.1:11434")
 
 
 def ollama_model(default: str | None = None) -> str:
+    """Legacy single-model accessor. Prefer :func:`ollama_model_for_role`."""
     if default:
         return default
-    return getattr(settings, "OLLAMA_MODEL", "llama3.2")
+    return getattr(settings, "OLLAMA_MODEL", "gpt-oss:20b")
+
+
+def ollama_model_for_role(role: str) -> str:
+    """Resolve the model name for a generation role.
+
+    Lookup order: role-specific setting → ``OLLAMA_MODEL`` fallback →
+    hard-coded role default (never falls back from embed → chat model).
+    """
+    role = (role or ROLE_THINKING).lower()
+    if role not in _VALID_ROLES:
+        raise ValueError(f"Unknown Ollama role: {role!r}")
+
+    role_setting = {
+        ROLE_THINKING: "OLLAMA_MODEL_THINKING",
+        ROLE_FAST: "OLLAMA_MODEL_FAST",
+        ROLE_UTILITY: "OLLAMA_MODEL_UTILITY",
+        ROLE_EMBED: "OLLAMA_EMBED_MODEL",
+    }[role]
+    role_default = {
+        ROLE_THINKING: "gpt-oss:20b",
+        ROLE_FAST: "gemma4:latest",
+        ROLE_UTILITY: "llama3.2",
+        ROLE_EMBED: "nomic-embed-text",
+    }[role]
+
+    configured = getattr(settings, role_setting, None)
+    if configured:
+        return configured
+
+    # Embeddings must never silently fall back to a chat model (#62).
+    if role == ROLE_EMBED:
+        return role_default
+
+    legacy = getattr(settings, "OLLAMA_MODEL", None)
+    if legacy:
+        return legacy
+    return role_default
 
 
 def ollama_embed_model() -> str:
-    return getattr(settings, "OLLAMA_EMBED_MODEL", ollama_model())
+    return ollama_model_for_role(ROLE_EMBED)
 
 
-def ollama_llm_kwargs(**extra):
+def ollama_num_ctx_for_role(role: str) -> int:
+    role = (role or ROLE_THINKING).lower()
+    if role == ROLE_FAST:
+        return int(getattr(settings, "OLLAMA_NUM_CTX_FAST", 8192) or 8192)
+    if role == ROLE_UTILITY:
+        return int(getattr(settings, "OLLAMA_NUM_CTX_UTILITY", 4096) or 4096)
+    return int(getattr(settings, "OLLAMA_NUM_CTX_THINKING", 16384) or 16384)
+
+
+def resolve_chat_role(model_name: str | None) -> str:
+    """Map FE mode selector (FAST / THINKING / …) to an Ollama role."""
+    if (model_name or "").upper() == "FAST":
+        return ROLE_FAST
+    return ROLE_THINKING
+
+
+def ollama_llm_kwargs(role: str = ROLE_THINKING, **extra):
     """Keyword args for langchain_ollama.OllamaLLM / ChatOllama."""
-    kwargs = {"base_url": ollama_base_url(), "model": ollama_model()}
+    kwargs = {
+        "base_url": ollama_base_url(),
+        "model": ollama_model_for_role(role),
+        "num_ctx": ollama_num_ctx_for_role(role),
+    }
     kwargs.update(extra)
     return kwargs
 

llm_be/chat_backend/serializers.py

@@ -223,7 +223,9 @@ class PromptSerializer(serializers.ModelSerializer):
             "id",
             "tokens_in",
             "tokens_out",
+            "citations",
         )
+        read_only_fields = ("citations",)
 
     def _token_pair(self, obj):
         cache = self.context.setdefault("_prompt_token_cache", {})

llm_be/chat_backend/services/assistant_identity.py

@@ -11,3 +11,17 @@ ASSISTANT_SYSTEM_PROMPT = (
     "Your name evokes quiet, rest, silence, and stillness — "
     "respond with calm clarity; keep answers focused and uncluttered."
 )
+
+GROUNDED_ANSWER_INSTRUCTIONS = (
+    "You have been given numbered live sources. Answer ONLY from those sources. "
+    "Cite source indexes inline like [1] or [2]. "
+    "If the sources do not settle the question, say so explicitly — do not fill "
+    "gaps from memory or training data. "
+    "When sources conflict, prefer the most recent dated source. "
+    "Never state a date, number, name, or event that does not appear in the sources."
+)
+
+RETRIEVAL_FAILED_MESSAGE = (
+    "I couldn't reach live sources to answer this accurately right now. "
+    "Please try again in a moment — I won't guess from outdated training data."
+)

llm_be/chat_backend/services/base_service.py

@@ -1,20 +1,24 @@
-from abc import ABC, abstractmethod
+from abc import ABC
+
 from langchain_ollama import OllamaLLM
 from langchain_core.output_parsers import StrOutputParser
-from chat_backend.ollama_config import ollama_llm_kwargs
+
+from chat_backend.ollama_config import ROLE_UTILITY, ollama_llm_kwargs
 
 
 class BaseService(ABC):
     """Abstract base class for LLM conversation services."""
 
-    def __init__(self, temperature=0.7):
+    def __init__(self, temperature=0.7, role: str = ROLE_UTILITY, **llm_extra):
+        self.role = role
         self.llm = OllamaLLM(
             **ollama_llm_kwargs(
+                role=role,
                 temperature=temperature,
                 top_k=50,
                 top_p=0.9,
                 repeat_penalty=1.1,
-                num_ctx=4096,
+                **llm_extra,
             )
         )
         self.output_parser = StrOutputParser()

llm_be/chat_backend/services/grounded_chat.py

@@ -0,0 +1,123 @@
+"""Apply always-on grounded retrieval for a chat turn (#62).
+
+Shared by ``consumers`` and ``consumers_graph`` so both paths stay in sync.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+from typing import Any
+
+from asgiref.sync import sync_to_async
+from django.conf import settings
+
+from chat_backend.services.assistant_identity import RETRIEVAL_FAILED_MESSAGE
+from chat_backend.services.grounding_decider import (
+    GroundingDecision,
+    grounding_decider,
+)
+from chat_backend.services.llm_service import build_chat_service
+from chat_backend.services.search import (
+    SearchUnavailable,
+    format_sources_block,
+    search_and_rank,
+)
+from chat_backend.services.search.base import SearchResult
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class GroundedTurnResult:
+    """Outcome of the grounding + optional retrieval step."""
+
+    generator: Any = None
+    error: dict | None = None
+    citations: list[dict] = field(default_factory=list)
+    grounded: bool = False
+    decision: GroundingDecision | None = None
+    model_name: str = ""
+
+
+def _citations_from_results(results: list[SearchResult]) -> list[dict]:
+    return [r.to_citation(i) for i, r in enumerate(results, start=1)]
+
+
+async def prepare_grounded_chat(
+    *,
+    message: str,
+    messages: list,
+    model_name: str | None,
+    conversation_id: int,
+) -> GroundedTurnResult:
+    """Decide grounding, retrieve, and return an AsyncLLMService generator.
+
+    When retrieval is required but every provider fails, returns an ``error``
+    dict instead of falling back to parametric generation (#62 AC).
+    """
+    internet = getattr(settings, "ALLOW_INTERNET_ACCESS", False)
+    if not internet:
+        service = build_chat_service(model_name=model_name, grounded=False)
+        return GroundedTurnResult(
+            generator=service.generate_response(
+                messages, message, conversation_id
+            ),
+            model_name=service.model_name,
+        )
+
+    decision = await grounding_decider.decide_async(message)
+
+    if not decision.needs_retrieval:
+        service = build_chat_service(model_name=model_name, grounded=False)
+        return GroundedTurnResult(
+            generator=service.generate_response(
+                messages, message, conversation_id
+            ),
+            decision=decision,
+            model_name=service.model_name,
+        )
+
+    try:
+        results = await sync_to_async(search_and_rank, thread_sensitive=False)(
+            decision.queries or [message],
+            temporal=decision.temporal,
+        )
+    except SearchUnavailable as exc:
+        logger.warning(
+            "Grounded retrieval failed for %r (queries=%s): %s",
+            message,
+            decision.queries,
+            exc,
+        )
+        return GroundedTurnResult(
+            error={
+                "type": "error",
+                "code": "search_unavailable",
+                "content": RETRIEVAL_FAILED_MESSAGE,
+            },
+            decision=decision,
+            grounded=True,
+        )
+
+    sources_block = format_sources_block(results)
+    # Keep sources out of the mutable history list — AsyncLLMService injects
+    # them via {sources}. (Older code appended a HumanMessage; that double-
+    # rendered into the prompt.)
+    service = build_chat_service(
+        model_name=model_name,
+        grounded=True,
+        sources_block=sources_block,
+    )
+    return GroundedTurnResult(
+        generator=service.generate_response(messages, message, conversation_id),
+        citations=_citations_from_results(results),
+        grounded=True,
+        decision=decision,
+        model_name=service.model_name,
+    )
+
+
+def citations_frame(citations: list[dict]) -> dict:
+    """Versioned WS envelope agreed with FE progress/citations work."""
+    return {"v": 1, "type": "citations", "data": citations}

llm_be/chat_backend/services/grounding_decider.py

@@ -0,0 +1,203 @@
+"""Grounding decision: retrieval-on-unless-unnecessary (#62).
+
+Fails open — parse/timeout/exception ⇒ needs_retrieval=True. A deterministic
+temporal-marker pre-pass forces retrieval regardless of the model.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import re
+from dataclasses import dataclass, field
+
+from langchain_core.prompts import ChatPromptTemplate
+
+from chat_backend.ollama_config import ROLE_UTILITY
+from chat_backend.services.base_service import BaseService
+
+logger = logging.getLogger(__name__)
+
+# Years at/after common small-model cutoffs force live retrieval.
+_TRAINING_CUTOFF_YEAR = 2024
+
+_TEMPORAL_PATTERNS = (
+    r"\blatest\b",
+    r"\bcurrent\b",
+    r"\btoday\b",
+    r"\bnow\b",
+    r"\bthis year\b",
+    r"\bthis week\b",
+    r"\bthis month\b",
+    r"\bbreaking\b",
+    r"\bright now\b",
+    r"\bas of\b",
+    r"\bdid\b.+\byet\b",
+    r"\bhave\b.+\byet\b",
+    r"\bwho won\b",
+    r"\bstock price\b",
+    r"\bweather\b",
+    rf"\b(?:19|20)\d{{2}}\b",  # any year mention — keep broad; model still helps
+)
+
+_TEMPORAL_RE = re.compile("|".join(_TEMPORAL_PATTERNS), re.IGNORECASE)
+_YEAR_RE = re.compile(r"\b((?:19|20)\d{2})\b")
+
+
+@dataclass
+class GroundingDecision:
+    needs_retrieval: bool
+    reason: str = ""
+    queries: list[str] = field(default_factory=list)
+    temporal: bool = False
+    source: str = "model"  # prepass | model | fail_open
+
+
+class GroundingDecider(BaseService):
+    def __init__(self):
+        super().__init__(temperature=0.0, role=ROLE_UTILITY)
+        self.prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You decide whether a user message needs live web retrieval.
+Return ONLY compact JSON with keys:
+  needs_retrieval (boolean),
+  reason (short string),
+  queries (array of 1-3 focused search queries).
+
+Bias TOWARD retrieval. Set needs_retrieval=true for ANY question about:
+- a real person, organisation, product, price, event, date, or statistic
+- anything that can change over time or after a model training cutoff
+- news, sports, celebrity, politics, weather, stock prices
+
+Set needs_retrieval=false ONLY when the message is fully self-contained:
+creative writing, math, code, chit-chat, or a pure follow-up on text already
+in the conversation that needs no external facts.
+
+When needs_retrieval=true, produce focused search queries (not the raw user
+message). Example: "did Taylor Swift get married" →
+["Taylor Swift Travis Kelce wedding date", "Taylor Swift married 2026"].
+""",
+                ),
+                ("human", "{prompt}"),
+            ]
+        )
+        self.chain = self.prompt | self.llm
+
+    def temporal_prepass(self, prompt: str) -> GroundingDecision | None:
+        text = (prompt or "").strip()
+        if not text:
+            return GroundingDecision(
+                needs_retrieval=False,
+                reason="empty prompt",
+                source="prepass",
+            )
+        year_hits = [int(y) for y in _YEAR_RE.findall(text)]
+        forces = bool(_TEMPORAL_RE.search(text)) or any(
+            y >= _TRAINING_CUTOFF_YEAR for y in year_hits
+        )
+        if not forces:
+            return None
+        return GroundingDecision(
+            needs_retrieval=True,
+            reason="temporal marker / post-cutoff year",
+            queries=[text],
+            temporal=True,
+            source="prepass",
+        )
+
+    def _parse(self, raw: str, fallback_query: str) -> GroundingDecision:
+        text = (raw or "").strip()
+        # Strip markdown fences if the small model wraps JSON.
+        if text.startswith("```"):
+            text = re.sub(r"^```(?:json)?\s*", "", text)
+            text = re.sub(r"\s*```$", "", text)
+        try:
+            start = text.find("{")
+            end = text.rfind("}")
+            if start < 0 or end < 0:
+                raise ValueError("no JSON object")
+            payload = json.loads(text[start : end + 1])
+        except (ValueError, json.JSONDecodeError) as exc:
+            logger.warning("GroundingDecider parse fail (%s); failing open", exc)
+            return GroundingDecision(
+                needs_retrieval=True,
+                reason=f"unparseable decision: {exc}",
+                queries=[fallback_query],
+                temporal=True,
+                source="fail_open",
+            )
+
+        needs = bool(payload.get("needs_retrieval", True))
+        queries = payload.get("queries") or []
+        if not isinstance(queries, list):
+            queries = [str(queries)]
+        queries = [str(q).strip() for q in queries if str(q).strip()][:3]
+        if needs and not queries:
+            queries = [fallback_query]
+        return GroundingDecision(
+            needs_retrieval=needs,
+            reason=str(payload.get("reason") or ""),
+            queries=queries,
+            temporal=needs,
+            source="model",
+        )
+
+    async def decide_async(self, prompt: str) -> GroundingDecision:
+        pre = self.temporal_prepass(prompt)
+        # Even on temporal prepass, ask the model for better queries when possible.
+        try:
+            raw = await self.chain.ainvoke({"prompt": prompt})
+            if hasattr(raw, "content"):
+                raw = raw.content
+            decision = self._parse(str(raw), prompt)
+        except Exception as exc:
+            logger.warning("GroundingDecider LLM failed (%s); failing open", exc)
+            decision = GroundingDecision(
+                needs_retrieval=True,
+                reason=f"llm error: {exc}",
+                queries=[prompt],
+                temporal=True,
+                source="fail_open",
+            )
+
+        if pre and pre.needs_retrieval:
+            # Pre-pass wins on needs_retrieval; keep model queries when present.
+            return GroundingDecision(
+                needs_retrieval=True,
+                reason=pre.reason,
+                queries=decision.queries or pre.queries,
+                temporal=True,
+                source="prepass",
+            )
+        return decision
+
+    def decide(self, prompt: str) -> GroundingDecision:
+        pre = self.temporal_prepass(prompt)
+        try:
+            raw = self.chain.invoke({"prompt": prompt})
+            if hasattr(raw, "content"):
+                raw = raw.content
+            decision = self._parse(str(raw), prompt)
+        except Exception as exc:
+            logger.warning("GroundingDecider LLM failed (%s); failing open", exc)
+            decision = GroundingDecision(
+                needs_retrieval=True,
+                reason=f"llm error: {exc}",
+                queries=[prompt],
+                temporal=True,
+                source="fail_open",
+            )
+        if pre and pre.needs_retrieval:
+            return GroundingDecision(
+                needs_retrieval=True,
+                reason=pre.reason,
+                queries=decision.queries or pre.queries,
+                temporal=True,
+                source="prepass",
+            )
+        return decision
+
+
+grounding_decider = GroundingDecider()

llm_be/chat_backend/services/llm_service.py

@@ -1,28 +1,51 @@
 from abc import ABC, abstractmethod
 from typing import AsyncGenerator, Generator, Optional
 
-# from langchain_community.llms import Ollama
 from langchain_ollama import OllamaLLM
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
 from django.conf import settings
 
 from chat_backend.models import Conversation, Prompt
-from chat_backend.ollama_config import ollama_llm_kwargs
-from chat_backend.services.assistant_identity import ASSISTANT_SYSTEM_PROMPT
+from chat_backend.ollama_config import (
+    ROLE_FAST,
+    ROLE_THINKING,
+    ollama_llm_kwargs,
+    ollama_model_for_role,
+    ollama_num_ctx_for_role,
+    resolve_chat_role,
+)
+from chat_backend.services.assistant_identity import (
+    ASSISTANT_SYSTEM_PROMPT,
+    GROUNDED_ANSWER_INSTRUCTIONS,
+)
+from chat_backend.services.prompt_budget import (
+    estimate_tokens,
+    format_history,
+    window_history,
+)
 
 
 class LLMService(ABC):
     """Abstract base class for LLM conversation services."""
 
-    def __init__(self):
+    def __init__(
+        self,
+        role: str = ROLE_THINKING,
+        temperature: float = 0.7,
+        grounded: bool = False,
+    ):
+        self.role = role
+        self.grounded = grounded
+        self.model_name = ollama_model_for_role(role)
+        self.num_ctx = ollama_num_ctx_for_role(role)
         self.llm = OllamaLLM(
             **ollama_llm_kwargs(
-                temperature=0.7,
+                role=role,
+                temperature=temperature,
                 top_k=50,
                 top_p=0.9,
                 repeat_penalty=1.1,
-                num_ctx=4096,
             )
         )
         self.output_parser = StrOutputParser()
@@ -45,8 +68,8 @@ class LLMService(ABC):
 class SyncLLMService(LLMService):
     """Synchronous LLM conversation service."""
 
-    def __init__(self):
-        super().__init__()
+    def __init__(self, role: str = ROLE_THINKING, temperature: float = 0.7):
+        super().__init__(role=role, temperature=temperature)
         self._setup_chain()
 
     def _setup_chain(self):
@@ -85,36 +108,49 @@ class SyncLLMService(LLMService):
 class AsyncLLMService(LLMService):
     """Asynchronous LLM conversation service."""
 
-    def __init__(self):
-        super().__init__()
+    def __init__(
+        self,
+        role: str = ROLE_THINKING,
+        temperature: float = 0.7,
+        grounded: bool = False,
+        sources_block: str = "",
+    ):
+        super().__init__(role=role, temperature=temperature, grounded=grounded)
+        self.sources_block = sources_block or ""
         self._setup_chain()
 
     def _setup_chain(self):
-        """Setup the conversation chain."""
-        template = f"""{ASSISTANT_SYSTEM_PROMPT}
+        """Single history window + optional grounded sources (#62 Phase 3)."""
+        grounded_block = ""
+        if self.grounded:
+            grounded_block = (
+                f"\n\n{GROUNDED_ANSWER_INSTRUCTIONS}\n\n"
+                f"Live sources:\n{{sources}}\n"
+            )
 
-        Continue this conversation while maintaining context by providing a single helpful response. 
-        Current context: {{context}}
-        
-        Last 3 messages:
-        {{recent_history}}
-        
-        Latest message: {{query}}
-        
-        Instructions:
-        - Carefully maintain all established context
-        - If referencing previous elements (like stories), preserve all details
-        - When asked to modify something, identify what's being modified
-        
-        Response:"""
+        template = f"""{ASSISTANT_SYSTEM_PROMPT}
+{grounded_block}
+Continue this conversation while maintaining context by providing a single helpful response.
+
+Conversation history:
+{{history}}
+
+Latest message: {{query}}
+
+Instructions:
+- Carefully maintain all established context
+- If referencing previous elements (like stories), preserve all details
+- When asked to modify something, identify what's being modified
+
+Response:"""
 
         self.prompt = ChatPromptTemplate.from_template(template)
 
         self.conversation_chain = (
             {
-                "context":lambda x: x["conversation"],
-                "recent_history":lambda x: x['recent_conversation'],
+                "history": lambda x: x["history"],
                 "query": lambda x: x["query"],
+                "sources": lambda x: x.get("sources", ""),
             }
             | self.prompt
             | self.llm
@@ -122,39 +158,73 @@ class AsyncLLMService(LLMService):
             # final GenerationChunk.generation_info; the parser would drop it.
         )
 
-    async def _format_history(self, conversation: list) -> str:
-        """Async version of format conversation history."""
-        # prompts = list(
-        #     await Prompt.objects.filter(conversation_id=conversation_id)
-        #     .order_by("created")
-            
-        # )
-        # return "\n".join(
-        #     f"{'User' if prompt.is_user else 'AI'}: {prompt.text}" for prompt in prompts
-        # )
-        return "\n".join([f"{"User" if prompt.type=="human" else "AI"}: {prompt.text}" for prompt in conversation])
-
-    async def _get_recent_messages(self, conversation: list) -> str:
-        """Async version of format conversation history."""
-
-        # prompts = list(
-        #     await Prompt.objects.filter(conversation_id=conversation_id)
-        #     .order_by("created")
-        #     [-6:]
-        # )
-        # return "\n".join(
-        #     f"{'User' if prompt.is_user else 'AI'}: {prompt.text}" for prompt in prompts
-        # )
-        return "\n".join([f"{"User" if prompt.type=="human" else "AI"}: {prompt.text}" for prompt in conversation])
-
     async def generate_response(
-        self, conversation: Conversation, query: str, conversation_id: int, **kwargs
+        self,
+        conversation,
+        query: str,
+        conversation_id: int,
+        **kwargs,
     ) -> AsyncGenerator[str, None]:
-        """Generate response with async streaming support."""
+        """Generate response with async streaming support.
+
+        ``conversation`` is the LangChain message list for this turn (not a
+        Django Conversation row). History is serialised exactly once and
+        trimmed oldest-first under the role's ``num_ctx`` budget. The sources
+        block (when grounded) is never trimmed.
+        """
+        sources = self.sources_block or kwargs.get("sources_block", "") or ""
+        reserved = (
+            estimate_tokens(ASSISTANT_SYSTEM_PROMPT)
+            + estimate_tokens(GROUNDED_ANSWER_INSTRUCTIONS if self.grounded else "")
+            + estimate_tokens(sources)
+            + estimate_tokens(query)
+            + 256  # response headroom / instructions
+        )
+        # Leave ~40% of ctx for the completion.
+        history_budget = max(512, int(self.num_ctx * 0.55) - reserved)
+        # Drop any prior "Search Results:" blobs from history — sources are
+        # passed separately now so we don't double-inject.
+        clean = [
+            m
+            for m in conversation
+            if not (
+                getattr(m, "type", "") == "human"
+                and str(getattr(m, "content", "")).startswith("Search Results:")
+            )
+            and not (
+                getattr(m, "type", "") == "human"
+                and str(getattr(m, "content", "")).startswith("Live sources:")
+            )
+        ]
+        # Exclude the latest user turn from history (it's in {query}).
+        prior = clean[:-1] if clean else []
+        windowed = window_history(
+            prior, budget_tokens=history_budget, reserved_tokens=0
+        )
+        history_text = format_history(windowed)
+
         chain_input = {
-            "query": query, 
-            "conversation": await self._format_history(conversation),
-            "recent_conversation": await self._get_recent_messages(conversation[-6:])}
+            "query": query,
+            "history": history_text,
+            "sources": sources,
+        }
 
         async for chunk in self.conversation_chain.astream(chain_input):
             yield chunk
+
+
+def build_chat_service(
+    *,
+    model_name: str | None,
+    grounded: bool = False,
+    sources_block: str = "",
+) -> AsyncLLMService:
+    """Factory: FE mode → role, factual turns → low temperature."""
+    role = resolve_chat_role(model_name)
+    temperature = 0.3 if grounded else 0.7
+    return AsyncLLMService(
+        role=role,
+        temperature=temperature,
+        grounded=grounded,
+        sources_block=sources_block,
+    )

llm_be/chat_backend/services/moderation_classifier.py

@@ -3,7 +3,7 @@ from typing import Dict, Any
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_ollama import OllamaLLM
 from chat_backend.services.base_service import BaseService
-from chat_backend.ollama_config import ollama_llm_kwargs
+from chat_backend.ollama_config import ROLE_UTILITY, ollama_llm_kwargs
 
 
 class ModerationLabel(Enum):
@@ -18,9 +18,10 @@ class ModerationClassifier(BaseService):
     """
 
     def __init__(self):
-        super().__init__(temperature=0.1)
+        super().__init__(temperature=0.1, role=ROLE_UTILITY)
         self.llm = OllamaLLM(
             **ollama_llm_kwargs(
+                role=ROLE_UTILITY,
                 temperature=0.1,  # Very low for strict moderation
                 top_k=10,
                 num_ctx=2048,

llm_be/chat_backend/services/prompt_budget.py

@@ -0,0 +1,63 @@
+"""Prompt budgeting helpers (#62 Phase 3).
+
+Approximate token counts with a chars/4 heuristic. Trim oldest history first;
+never truncate the system prompt or the retrieved-sources block.
+"""
+
+from __future__ import annotations
+
+from typing import Sequence
+
+
+def estimate_tokens(text: str) -> int:
+    if not text:
+        return 0
+    return max(1, (len(text) + 3) // 4)
+
+
+def window_history(
+    messages: Sequence,
+    *,
+    budget_tokens: int,
+    reserved_tokens: int = 0,
+    max_messages: int = 24,
+) -> list:
+    """Keep the newest messages that fit under ``budget_tokens - reserved``.
+
+    ``messages`` are LangChain BaseMessage-like objects (``.type``, ``.text`` /
+    ``.content``).
+    """
+    if not messages:
+        return []
+    usable = max(0, budget_tokens - reserved_tokens)
+    selected: list = []
+    used = 0
+    for message in reversed(list(messages)[-max_messages:]):
+        text = getattr(message, "text", None)
+        if callable(text):
+            # property that looks callable in some versions — read content
+            text = getattr(message, "content", "")
+        if text is None:
+            text = getattr(message, "content", "") or ""
+        cost = estimate_tokens(str(text)) + 4  # role overhead
+        if selected and used + cost > usable:
+            break
+        selected.append(message)
+        used += cost
+    selected.reverse()
+    return selected
+
+
+def format_message_line(message) -> str:
+    role = "User" if getattr(message, "type", "") == "human" else "AI"
+    text = getattr(message, "text", None)
+    if text is None or (callable(text) and not isinstance(text, str)):
+        text = getattr(message, "content", "") or ""
+    # BaseMessage.text is a property returning str; prefer it when string.
+    if not isinstance(text, str):
+        text = getattr(message, "content", "") or ""
+    return f"{role}: {text}"
+
+
+def format_history(messages: Sequence) -> str:
+    return "\n".join(format_message_line(m) for m in messages)

llm_be/chat_backend/services/rag_services.py

@@ -25,7 +25,14 @@ from chat_backend.models import Conversation, Prompt, DocumentWorkspace, Documen
 from pathlib import Path
 from chat_backend.services.base_service import BaseService
 from chat_backend.services.assistant_identity import ASSISTANT_SYSTEM_PROMPT
-from chat_backend.ollama_config import ollama_embeddings_kwargs
+from chat_backend.ollama_config import ollama_embed_model, ollama_embeddings_kwargs
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class EmbeddingDimensionMismatch(RuntimeError):
+    """Persisted Chroma collection dim != configured embedding model (#62)."""
 
 
 @database_sync_to_async
@@ -54,6 +61,7 @@ class RAGService(BaseService):
             chunk_size=1000, chunk_overlap=200
         )
         self.vector_store = self._initialize_vector_store()
+        self._assert_embedding_dimensions()
 
         # Supported file types and their loaders
         self.loader_mapping = {
@@ -75,6 +83,34 @@ class RAGService(BaseService):
         )
         return vector_store
 
+    def _assert_embedding_dimensions(self) -> None:
+        """Refuse mismatched collections loudly (#62)."""
+        try:
+            collection = self.vector_store._collection
+            count = collection.count()
+        except Exception as exc:
+            logger.warning("Could not inspect Chroma collection: %s", exc)
+            return
+        if not count:
+            return
+        try:
+            peek = collection.peek(limit=1)
+            embeddings = peek.get("embeddings") if isinstance(peek, dict) else None
+            if not embeddings:
+                return
+            stored_dim = len(embeddings[0])
+            probe = self.embedding_model.embed_query("dimension-check")
+            expected_dim = len(probe)
+        except Exception as exc:
+            logger.warning("Embedding dimension probe failed: %s", exc)
+            return
+        if stored_dim != expected_dim:
+            raise EmbeddingDimensionMismatch(
+                f"Chroma collection embedding dim is {stored_dim} but "
+                f"OLLAMA_EMBED_MODEL={ollama_embed_model()!r} produces "
+                f"{expected_dim}. Run: python manage.py reindex_embeddings"
+            )
+
     def clear_vector_store(self):
         """Clear all vectors from the store"""
         self.vector_store.delete_collection()

llm_be/chat_backend/services/search/__init__.py

@@ -0,0 +1,22 @@
+"""Structured web-search providers (#62).
+
+Providers return typed :class:`SearchResult` rows — never a flat concatenated
+string. The facade in :mod:`chat_backend.services.search.service` picks the
+configured primary provider and fails over to the secondary.
+"""
+
+from chat_backend.services.search.base import SearchResult
+from chat_backend.services.search.service import (
+    SearchUnavailable,
+    format_sources_block,
+    get_search_service,
+    search_and_rank,
+)
+
+__all__ = [
+    "SearchResult",
+    "SearchUnavailable",
+    "format_sources_block",
+    "get_search_service",
+    "search_and_rank",
+]

llm_be/chat_backend/services/search/base.py

@@ -0,0 +1,36 @@
+"""Search provider protocol and result dataclass."""
+
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass
+from typing import Optional, Protocol, runtime_checkable
+
+
+@dataclass(frozen=True)
+class SearchResult:
+    title: str
+    url: str
+    snippet: str
+    published_at: Optional[str] = None
+    rank: int = 0
+    provider: str = ""
+
+    def to_citation(self, index: int) -> dict:
+        return {
+            "index": index,
+            "title": self.title,
+            "url": self.url,
+            "published_at": self.published_at,
+        }
+
+    def to_dict(self) -> dict:
+        return asdict(self)
+
+
+@runtime_checkable
+class SearchProvider(Protocol):
+    name: str
+
+    def search(self, query: str, *, max_results: int = 8) -> list[SearchResult]:
+        """Return structured results for ``query``. Raise on hard failure."""
+        ...

llm_be/chat_backend/services/search/ddgs_provider.py

@@ -0,0 +1,46 @@
+"""DuckDuckGo (ddgs) search provider — failover for SearxNG (#62)."""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+from chat_backend.services.search.base import SearchResult
+
+logger = logging.getLogger(__name__)
+
+
+class DDGSProvider:
+    name = "ddgs"
+
+    def search(self, query: str, *, max_results: int = 8) -> list[SearchResult]:
+        try:
+            from ddgs import DDGS
+        except ImportError as exc:  # pragma: no cover - dependency is declared
+            raise RuntimeError("ddgs package is not installed") from exc
+
+        try:
+            with DDGS() as ddgs:
+                raw: list[dict[str, Any]] = list(
+                    ddgs.text(query, max_results=max_results)
+                )
+        except Exception as exc:
+            logger.warning("DDGS search failed for %r: %s", query, exc)
+            raise RuntimeError(f"DDGS unreachable: {exc}") from exc
+
+        results: list[SearchResult] = []
+        for idx, item in enumerate(raw or []):
+            url = (item.get("href") or item.get("link") or item.get("url") or "").strip()
+            if not url:
+                continue
+            results.append(
+                SearchResult(
+                    title=(item.get("title") or url).strip(),
+                    url=url,
+                    snippet=(item.get("body") or item.get("snippet") or "").strip(),
+                    published_at=item.get("date") or item.get("published") or None,
+                    rank=idx,
+                    provider=self.name,
+                )
+            )
+        return results

llm_be/chat_backend/services/search/ranking.py

@@ -0,0 +1,128 @@
+"""Rank, dedupe, and rumour-filter search results (#62)."""
+
+from __future__ import annotations
+
+import re
+from datetime import datetime
+from typing import Iterable
+from urllib.parse import urlparse
+
+from chat_backend.services.search.base import SearchResult
+
+_RUMOUR_MARKERS = (
+    "rumor",
+    "rumour",
+    "speculation",
+    "ai-generated",
+    "ai generated",
+    "blind item",
+    "fake",
+    "allegedly",
+    "unconfirmed",
+)
+
+_DATE_FORMATS = (
+    "%Y-%m-%d",
+    "%Y-%m-%dT%H:%M:%S",
+    "%Y-%m-%dT%H:%M:%SZ",
+    "%Y-%m-%dT%H:%M:%S%z",
+    "%b %d, %Y",
+    "%B %d, %Y",
+    "%d %b %Y",
+    "%d %B %Y",
+)
+
+
+def registrable_domain(url: str) -> str:
+    host = (urlparse(url).hostname or "").lower()
+    if host.startswith("www."):
+        host = host[4:]
+    parts = host.split(".")
+    if len(parts) >= 2:
+        return ".".join(parts[-2:])
+    return host
+
+
+def parse_published_at(value: str | None) -> datetime | None:
+    if not value:
+        return None
+    text = value.strip()
+    if not text:
+        return None
+    # Prefer ISO-ish prefixes.
+    for fmt in _DATE_FORMATS:
+        try:
+            return datetime.strptime(text[: len(fmt) + 8], fmt)
+        except ValueError:
+            continue
+    iso = text.replace("Z", "+00:00")
+    try:
+        return datetime.fromisoformat(iso)
+    except ValueError:
+        return None
+
+
+def is_rumour_heavy(result: SearchResult) -> bool:
+    haystack = f"{result.title} {result.snippet}".lower()
+    hits = sum(1 for marker in _RUMOUR_MARKERS if marker in haystack)
+    return hits >= 2 or ("ai-generated" in haystack and "fake" in haystack)
+
+
+def rank_and_dedupe(
+    results: Iterable[SearchResult],
+    *,
+    temporal: bool = False,
+    max_results: int = 6,
+) -> list[SearchResult]:
+    """Deduplicate by domain, drop rumour-heavy rows when alternatives exist."""
+    seen_domains: set[str] = set()
+    kept: list[SearchResult] = []
+    rumour_bucket: list[SearchResult] = []
+
+    for result in results:
+        domain = registrable_domain(result.url)
+        if not domain or domain in seen_domains:
+            continue
+        seen_domains.add(domain)
+        if is_rumour_heavy(result):
+            rumour_bucket.append(result)
+        else:
+            kept.append(result)
+
+    # Only use rumour-heavy rows if we have nothing better.
+    if not kept and rumour_bucket:
+        kept = rumour_bucket
+
+    def sort_key(item: SearchResult):
+        published = parse_published_at(item.published_at)
+        # Prefer dated + recent when temporal; otherwise keep provider rank.
+        if temporal:
+            # Newer first; undated last.
+            stamp = published.timestamp() if published else float("-inf")
+            return (-stamp, item.rank)
+        has_date = 0 if published else 1
+        return (has_date, item.rank)
+
+    kept.sort(key=sort_key)
+    return [
+        SearchResult(
+            title=r.title,
+            url=r.url,
+            snippet=r.snippet,
+            published_at=r.published_at,
+            rank=i,
+            provider=r.provider,
+        )
+        for i, r in enumerate(kept[:max_results])
+    ]
+
+
+_HOST_RE = re.compile(r"^https?://([^/]+)", re.I)
+
+
+def display_host(url: str) -> str:
+    match = _HOST_RE.match(url or "")
+    if not match:
+        return url or ""
+    host = match.group(1).lower()
+    return host[4:] if host.startswith("www.") else host

llm_be/chat_backend/services/search/searxng.py

@@ -0,0 +1,83 @@
+"""SearxNG search provider (#62).
+
+Hits a self-hosted SearxNG instance's JSON API. Deterministic, no third-party
+rate limits, and returns per-result title/url/snippet/publishedDate.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+from urllib.parse import urljoin
+
+import requests
+from django.conf import settings
+
+from chat_backend.services.search.base import SearchResult
+
+logger = logging.getLogger(__name__)
+
+
+class SearxNGProvider:
+    name = "searxng"
+
+    def __init__(
+        self,
+        base_url: str | None = None,
+        timeout: float | None = None,
+    ):
+        self.base_url = (
+            base_url
+            or getattr(settings, "SEARXNG_BASE_URL", "http://127.0.0.1:8080")
+        ).rstrip("/")
+        self.timeout = float(
+            timeout
+            if timeout is not None
+            else getattr(settings, "SEARXNG_TIMEOUT_SECONDS", 8)
+        )
+
+    def search(self, query: str, *, max_results: int = 8) -> list[SearchResult]:
+        endpoint = urljoin(self.base_url + "/", "search")
+        params = {
+            "q": query,
+            "format": "json",
+            "language": "en",
+        }
+        try:
+            response = requests.get(
+                endpoint,
+                params=params,
+                timeout=self.timeout,
+                headers={"Accept": "application/json"},
+            )
+            response.raise_for_status()
+            payload: dict[str, Any] = response.json()
+        except requests.RequestException as exc:
+            logger.warning("SearxNG search failed for %r: %s", query, exc)
+            raise RuntimeError(f"SearxNG unreachable: {exc}") from exc
+        except ValueError as exc:
+            raise RuntimeError(f"SearxNG returned non-JSON: {exc}") from exc
+
+        results: list[SearchResult] = []
+        for idx, item in enumerate(payload.get("results") or []):
+            url = (item.get("url") or "").strip()
+            if not url:
+                continue
+            results.append(
+                SearchResult(
+                    title=(item.get("title") or url).strip(),
+                    url=url,
+                    snippet=(item.get("content") or item.get("snippet") or "").strip(),
+                    published_at=(
+                        item.get("publishedDate")
+                        or item.get("published_at")
+                        or item.get("pubdate")
+                        or None
+                    ),
+                    rank=idx,
+                    provider=self.name,
+                )
+            )
+            if len(results) >= max_results:
+                break
+        return results

llm_be/chat_backend/services/search/service.py

@@ -0,0 +1,145 @@
+"""Search facade: primary + failover providers, concurrent multi-query (#62)."""
+
+from __future__ import annotations
+
+import logging
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from typing import Iterable
+
+from django.conf import settings
+
+from chat_backend.services.search.base import SearchProvider, SearchResult
+from chat_backend.services.search.ddgs_provider import DDGSProvider
+from chat_backend.services.search.ranking import display_host, rank_and_dedupe
+from chat_backend.services.search.searxng import SearxNGProvider
+
+logger = logging.getLogger(__name__)
+
+
+class SearchUnavailable(RuntimeError):
+    """Raised when every configured provider fails for a query set."""
+
+
+def _build_provider(name: str) -> SearchProvider:
+    key = (name or "").lower()
+    if key in {"searxng", "searx"}:
+        return SearxNGProvider()
+    if key in {"ddgs", "duckduckgo", "ddg"}:
+        return DDGSProvider()
+    raise ValueError(f"Unknown search provider: {name!r}")
+
+
+class SearchService:
+    def __init__(
+        self,
+        primary: SearchProvider | None = None,
+        failover: SearchProvider | None = None,
+    ):
+        primary_name = getattr(settings, "SEARCH_PROVIDER", "searxng")
+        failover_name = getattr(settings, "SEARCH_FAILOVER_PROVIDER", "ddgs")
+        self.primary = primary or _build_provider(primary_name)
+        # Avoid wiring the same provider twice.
+        if failover is not None:
+            self.failover = failover
+        elif failover_name and failover_name.lower() != getattr(
+            self.primary, "name", ""
+        ):
+            try:
+                self.failover = _build_provider(failover_name)
+            except ValueError:
+                self.failover = None
+        else:
+            self.failover = None
+
+    def search_one(self, query: str, *, max_results: int = 8) -> list[SearchResult]:
+        errors: list[str] = []
+        for provider in (self.primary, self.failover):
+            if provider is None:
+                continue
+            try:
+                return provider.search(query, max_results=max_results)
+            except Exception as exc:
+                errors.append(f"{getattr(provider, 'name', provider)}: {exc}")
+                logger.warning(
+                    "Search provider %s failed for %r: %s",
+                    getattr(provider, "name", provider),
+                    query,
+                    exc,
+                )
+        raise SearchUnavailable(
+            f"All search providers failed for {query!r}: {'; '.join(errors)}"
+        )
+
+    def search_many(
+        self,
+        queries: Iterable[str],
+        *,
+        max_results_per_query: int = 6,
+        temporal: bool = False,
+        max_results: int = 6,
+    ) -> list[SearchResult]:
+        cleaned = [q.strip() for q in queries if q and str(q).strip()]
+        if not cleaned:
+            raise SearchUnavailable("No search queries provided")
+
+        collected: list[SearchResult] = []
+        failures = 0
+        with ThreadPoolExecutor(max_workers=min(3, len(cleaned))) as pool:
+            futures = {
+                pool.submit(
+                    self.search_one, query, max_results=max_results_per_query
+                ): query
+                for query in cleaned[:3]
+            }
+            for future in as_completed(futures):
+                query = futures[future]
+                try:
+                    collected.extend(future.result())
+                except SearchUnavailable as exc:
+                    failures += 1
+                    logger.warning("Query %r exhausted providers: %s", query, exc)
+
+        if not collected:
+            raise SearchUnavailable(
+                f"All {failures} search queries failed; no results available"
+            )
+        return rank_and_dedupe(
+            collected, temporal=temporal, max_results=max_results
+        )
+
+
+_service: SearchService | None = None
+
+
+def get_search_service() -> SearchService:
+    global _service
+    if _service is None:
+        _service = SearchService()
+    return _service
+
+
+def search_and_rank(
+    queries: Iterable[str],
+    *,
+    temporal: bool = False,
+    max_results: int = 6,
+) -> list[SearchResult]:
+    return get_search_service().search_many(
+        queries, temporal=temporal, max_results=max_results
+    )
+
+
+def format_sources_block(results: list[SearchResult]) -> str:
+    """Numbered, dated, delimited context block for the LLM prompt."""
+    if not results:
+        return ""
+    lines: list[str] = []
+    for i, result in enumerate(results, start=1):
+        host = display_host(result.url)
+        date = result.published_at or "undated"
+        lines.append(f'[{i}] "{result.title}" — {host} — {date}')
+        if result.snippet:
+            lines.append(f"    {result.snippet}")
+        lines.append(f"    URL: {result.url}")
+        lines.append("")
+    return "\n".join(lines).rstrip()

llm_be/chat_backend/services/title_generator.py

@@ -3,7 +3,7 @@ from langchain_core.prompts import ChatPromptTemplate
 # from langchain_community.llms import Ollama
 from langchain_ollama import OllamaLLM
 from typing import Optional
-from chat_backend.ollama_config import ollama_llm_kwargs
+from chat_backend.ollama_config import ROLE_UTILITY, ollama_llm_kwargs
 
 
 class TitleGenerator:
@@ -14,6 +14,7 @@ class TitleGenerator:
     def __init__(self):
         self.llm = OllamaLLM(
             **ollama_llm_kwargs(
+                role=ROLE_UTILITY,
                 temperature=0.5,  # Slightly creative but not too random
                 top_k=20,
                 num_ctx=2048,  # Shorter context needed for titles

llm_be/chat_backend/tests/test_consumers.py

@@ -422,58 +422,103 @@ class GraphNodeTestCase(TransactionTestCase):
         self.assertEqual(result["response_generator"], "generator")
 
     async def test_generation_node_defaults_to_general_chat(self):
-        with mock.patch.object(consumers_graph, "AsyncLLMService") as service:
-            service.return_value.generate_response.return_value = "generator"
+        with mock.patch(
+            "chat_backend.consumers_graph.prepare_grounded_chat"
+        ) as prepare:
+            from chat_backend.services.grounded_chat import GroundedTurnResult
 
+            prepare.return_value = GroundedTurnResult(
+                generator="generator", model_name="gpt-oss:20b"
+            )
             result = await consumers_graph.generation_node(self._state())
 
         self.assertEqual(result["response_generator"], "generator")
-        service.return_value.generate_response.assert_called_once()
+        prepare.assert_called_once()
 
     @override_settings(ALLOW_INTERNET_ACCESS=True)
-    async def test_search_prompts_append_web_results(self):
+    async def test_search_prompts_use_grounded_chat(self):
         state = self._state(prompt_type=PromptType.SEARCH)
 
-        with mock.patch.object(consumers_graph, "DuckDuckGoSearchRun") as search:
-            search.return_value.run.return_value = "top result"
-            with mock.patch.object(consumers_graph, "AsyncLLMService"):
-                await consumers_graph.generation_node(state)
+        with mock.patch(
+            "chat_backend.consumers_graph.prepare_grounded_chat"
+        ) as prepare:
+            from chat_backend.services.grounded_chat import GroundedTurnResult
 
-        self.assertIn("Search Results: top result", state["messages"][-1].content)
+            prepare.return_value = GroundedTurnResult(
+                generator="generator",
+                citations=[
+                    {
+                        "index": 1,
+                        "title": "T",
+                        "url": "https://example.com",
+                        "published_at": None,
+                    }
+                ],
+                grounded=True,
+                model_name="gpt-oss:20b",
+            )
+            result = await consumers_graph.generation_node(state)
+
+        prepare.assert_called_once()
+        self.assertEqual(result["response_generator"], "generator")
+        self.assertEqual(result["citations"][0]["url"], "https://example.com")
 
     @override_settings(ALLOW_INTERNET_ACCESS=True)
-    async def test_fast_model_skips_web_search(self):
+    async def test_fast_model_still_runs_grounding(self):
+        """FAST must not skip retrieval (#62) — it only picks a smaller model."""
         state = self._state(prompt_type=PromptType.SEARCH, model_name="FAST")
 
-        with mock.patch.object(consumers_graph, "DuckDuckGoSearchRun") as search:
-            with mock.patch.object(consumers_graph, "AsyncLLMService"):
-                await consumers_graph.generation_node(state)
+        with mock.patch(
+            "chat_backend.consumers_graph.prepare_grounded_chat"
+        ) as prepare:
+            from chat_backend.services.grounded_chat import GroundedTurnResult
 
-        search.assert_not_called()
-        self.assertEqual(len(state["messages"]), 1)
+            prepare.return_value = GroundedTurnResult(
+                generator="generator", model_name="gemma4:latest"
+            )
+            await consumers_graph.generation_node(state)
+
+        prepare.assert_called_once()
+        self.assertEqual(prepare.call_args.kwargs["model_name"], "FAST")
 
     @override_settings(ALLOW_INTERNET_ACCESS=False)
     async def test_search_is_skipped_when_internet_access_is_disabled(self):
         state = self._state(prompt_type=PromptType.SEARCH)
 
-        with mock.patch.object(consumers_graph, "DuckDuckGoSearchRun") as search:
-            with mock.patch.object(consumers_graph, "AsyncLLMService"):
-                await consumers_graph.generation_node(state)
+        with mock.patch(
+            "chat_backend.consumers_graph.prepare_grounded_chat"
+        ) as prepare:
+            from chat_backend.services.grounded_chat import GroundedTurnResult
 
-        search.assert_not_called()
+            prepare.return_value = GroundedTurnResult(
+                generator="generator", model_name="gpt-oss:20b"
+            )
+            await consumers_graph.generation_node(state)
+
+        # prepare_grounded_chat still runs; inside it skips providers when
+        # ALLOW_INTERNET_ACCESS is False.
+        prepare.assert_called_once()
 
     @override_settings(ALLOW_INTERNET_ACCESS=True)
-    async def test_search_failures_fall_back_to_plain_chat(self):
+    async def test_search_failures_surface_error_not_plain_chat(self):
         state = self._state(prompt_type=PromptType.SEARCH)
 
-        with mock.patch.object(consumers_graph, "DuckDuckGoSearchRun") as search:
-            search.return_value.run.side_effect = RuntimeError("ddg unreachable")
-            with mock.patch.object(consumers_graph, "AsyncLLMService") as service:
-                service.return_value.generate_response.return_value = "generator"
-                result = await consumers_graph.generation_node(state)
+        with mock.patch(
+            "chat_backend.consumers_graph.prepare_grounded_chat"
+        ) as prepare:
+            from chat_backend.services.grounded_chat import GroundedTurnResult
 
-        self.assertEqual(result["response_generator"], "generator")
-        self.assertEqual(len(state["messages"]), 1)
+            prepare.return_value = GroundedTurnResult(
+                error={
+                    "type": "error",
+                    "code": "search_unavailable",
+                    "content": "couldn't reach live sources",
+                },
+                grounded=True,
+            )
+            result = await consumers_graph.generation_node(state)
+
+        self.assertEqual(result["response_generator"]["code"], "search_unavailable")
 
 
 class WebSocketRoutingTestCase(TransactionTestCase):

llm_be/chat_backend/tests/test_grounding_search.py

@@ -0,0 +1,180 @@
+"""Unit tests for grounding + search layer (#62 Phases 1–3)."""
+
+from __future__ import annotations
+
+from django.test import SimpleTestCase, override_settings
+from unittest import mock
+
+from chat_backend.services.grounding_decider import GroundingDecider, GroundingDecision
+from chat_backend.services.search.base import SearchResult
+from chat_backend.services.search.ranking import (
+    is_rumour_heavy,
+    rank_and_dedupe,
+    registrable_domain,
+)
+from chat_backend.services.search.service import (
+    SearchService,
+    SearchUnavailable,
+    format_sources_block,
+)
+from chat_backend.ollama_config import (
+    ROLE_EMBED,
+    ROLE_FAST,
+    ROLE_THINKING,
+    ROLE_UTILITY,
+    ollama_model_for_role,
+    resolve_chat_role,
+)
+
+
+class OllamaConfigRoleTestCase(SimpleTestCase):
+    @override_settings(
+        OLLAMA_MODEL="legacy-model",
+        OLLAMA_MODEL_THINKING="think-model",
+        OLLAMA_MODEL_FAST="fast-model",
+        OLLAMA_MODEL_UTILITY="util-model",
+        OLLAMA_EMBED_MODEL="nomic-embed-text",
+    )
+    def test_role_resolution(self):
+        self.assertEqual(ollama_model_for_role(ROLE_THINKING), "think-model")
+        self.assertEqual(ollama_model_for_role(ROLE_FAST), "fast-model")
+        self.assertEqual(ollama_model_for_role(ROLE_UTILITY), "util-model")
+        self.assertEqual(ollama_model_for_role(ROLE_EMBED), "nomic-embed-text")
+
+    @override_settings(
+        OLLAMA_MODEL="legacy-model",
+        OLLAMA_MODEL_THINKING="",
+        OLLAMA_MODEL_FAST="",
+        OLLAMA_MODEL_UTILITY="",
+        OLLAMA_EMBED_MODEL="",
+    )
+    def test_embed_never_falls_back_to_chat_model(self):
+        # Empty embed setting → hard default, not OLLAMA_MODEL.
+        self.assertEqual(ollama_model_for_role(ROLE_EMBED), "nomic-embed-text")
+
+    def test_resolve_chat_role(self):
+        self.assertEqual(resolve_chat_role("FAST"), ROLE_FAST)
+        self.assertEqual(resolve_chat_role("THINKING"), ROLE_THINKING)
+        self.assertEqual(resolve_chat_role(None), ROLE_THINKING)
+
+
+class GroundingPrepassTestCase(SimpleTestCase):
+    def setUp(self):
+        self.decider = GroundingDecider.__new__(GroundingDecider)
+
+    def test_temporal_markers_force_retrieval(self):
+        cases = [
+            "did Taylor Swift get married yet",
+            "What is the latest news on AI?",
+            "Who won the Super Bowl this year?",
+            "current stock price of Apple",
+            "What happened in 2025?",
+        ]
+        for prompt in cases:
+            with self.subTest(prompt=prompt):
+                decision = self.decider.temporal_prepass(prompt)
+                self.assertIsNotNone(decision)
+                self.assertTrue(decision.needs_retrieval)
+
+    def test_creative_prompt_does_not_force(self):
+        decision = self.decider.temporal_prepass("Write a poem about cats")
+        self.assertIsNone(decision)
+
+    def test_parse_failure_fails_open(self):
+        decision = self.decider._parse("NOT JSON", "fallback query")
+        self.assertTrue(decision.needs_retrieval)
+        self.assertEqual(decision.source, "fail_open")
+        self.assertEqual(decision.queries, ["fallback query"])
+
+    def test_decide_fail_open_on_llm_error(self):
+        self.decider.chain = mock.Mock()
+        self.decider.chain.invoke.side_effect = RuntimeError("ollama down")
+        decision = self.decider.decide("Is the sky blue?")
+        self.assertTrue(decision.needs_retrieval)
+        self.assertEqual(decision.source, "fail_open")
+
+
+class SearchRankingTestCase(SimpleTestCase):
+    def test_dedupe_by_domain(self):
+        results = [
+            SearchResult("A", "https://www.people.com/a", "married", "2026-07-03", 0),
+            SearchResult("B", "https://people.com/b", "also", "2026-07-02", 1),
+            SearchResult("C", "https://bbc.com/c", "ok", "2026-07-01", 2),
+        ]
+        ranked = rank_and_dedupe(results, temporal=True)
+        domains = {registrable_domain(r.url) for r in ranked}
+        self.assertEqual(domains, {"people.com", "bbc.com"})
+
+    def test_rumour_heavy_dropped_when_alternatives_exist(self):
+        clean = SearchResult(
+            "Married",
+            "https://people.com/wedding",
+            "Taylor Swift and Travis Kelce married July 3",
+            "2026-07-03",
+            0,
+        )
+        poison = SearchResult(
+            "Rumors",
+            "https://gossip.com/fake",
+            "fake AI-generated photos and speculation and blind item",
+            None,
+            1,
+        )
+        self.assertTrue(is_rumour_heavy(poison))
+        ranked = rank_and_dedupe([poison, clean], temporal=True)
+        self.assertEqual(len(ranked), 1)
+        self.assertEqual(ranked[0].url, clean.url)
+
+    def test_format_sources_block_is_numbered(self):
+        block = format_sources_block(
+            [
+                SearchResult(
+                    "Title",
+                    "https://example.com/x",
+                    "Snippet here",
+                    "2026-07-03",
+                    0,
+                )
+            ]
+        )
+        self.assertIn('[1] "Title"', block)
+        self.assertIn("2026-07-03", block)
+        self.assertIn("URL: https://example.com/x", block)
+        self.assertNotIn("Search Results:", block)
+
+
+class FakeProvider:
+    def __init__(self, name, results=None, error=None):
+        self.name = name
+        self.results = results or []
+        self.error = error
+        self.calls = 0
+
+    def search(self, query, *, max_results=8):
+        self.calls += 1
+        if self.error:
+            raise self.error
+        return list(self.results)
+
+
+class SearchFailoverTestCase(SimpleTestCase):
+    def test_failover_when_primary_raises(self):
+        primary = FakeProvider("searxng", error=RuntimeError("down"))
+        failover = FakeProvider(
+            "ddgs",
+            results=[
+                SearchResult("T", "https://a.com", "s", None, 0, "ddgs"),
+            ],
+        )
+        service = SearchService(primary=primary, failover=failover)
+        results = service.search_one("q")
+        self.assertEqual(primary.calls, 1)
+        self.assertEqual(failover.calls, 1)
+        self.assertEqual(results[0].provider, "ddgs")
+
+    def test_all_providers_fail_raises(self):
+        primary = FakeProvider("searxng", error=RuntimeError("down"))
+        failover = FakeProvider("ddgs", error=RuntimeError("also down"))
+        service = SearchService(primary=primary, failover=failover)
+        with self.assertRaises(SearchUnavailable):
+            service.search_one("q")

llm_be/chat_backend/tests/test_services_llm.py

@@ -2,6 +2,7 @@ from django.test import SimpleTestCase
 from langchain_core.messages import AIMessage, HumanMessage
 
 from chat_backend.services.llm_service import AsyncLLMService, SyncLLMService
+from chat_backend.services.prompt_budget import format_history, window_history
 
 from .fakes import FakeChain
 
@@ -18,13 +19,6 @@ class AsyncLLMServiceTestCase(SimpleTestCase):
     def setUp(self):
         self.service = AsyncLLMService()
 
-    async def test_format_history_labels_speakers(self):
-        history = await self.service._format_history(
-            [HumanMessage(content="hello"), AIMessage(content="hi")]
-        )
-
-        self.assertEqual(history, "User: hello\nAI: hi")
-
     async def test_generate_response_streams_chunks(self):
         self.service.conversation_chain = FakeChain(chunks=["Hel", "lo!"])
 
@@ -37,7 +31,7 @@ class AsyncLLMServiceTestCase(SimpleTestCase):
 
         self.assertEqual("".join(chunks), "Hello!")
 
-    async def test_generate_response_sends_full_and_recent_history(self):
+    async def test_generate_response_sends_single_history_window(self):
         self.service.conversation_chain = FakeChain(chunks=["ok"])
         messages = conversation(4)  # 8 messages
 
@@ -46,19 +40,39 @@ class AsyncLLMServiceTestCase(SimpleTestCase):
 
         payload = self.service.conversation_chain.calls[0]
         self.assertEqual(payload["query"], "latest")
-        self.assertEqual(len(payload["conversation"].splitlines()), 8)
-        self.assertEqual(len(payload["recent_conversation"].splitlines()), 6)
-        self.assertTrue(payload["recent_conversation"].endswith("AI: answer 3"))
+        # Latest user turn is in {query}; history is prior turns only, once.
+        self.assertIn("history", payload)
+        self.assertNotIn("recent_conversation", payload)
+        self.assertNotIn("conversation", payload)
+        # 8 messages → drop last (query) → 7 prior lines max in window.
+        self.assertLessEqual(len(payload["history"].splitlines()), 7)
+
+    async def test_grounded_service_includes_sources(self):
+        service = AsyncLLMService(grounded=True, sources_block='[1] "T" — x.com — undated')
+        service.conversation_chain = FakeChain(chunks=["ok"])
+
+        async for _ in service.generate_response([], "q", 1):
+            pass
+
+        payload = service.conversation_chain.calls[0]
+        self.assertIn("[1]", payload["sources"])
 
 
 class SyncLLMServiceTestCase(SimpleTestCase):
-    def test_generate_response_streams_chunks(self):
-        service = SyncLLMService()
-        service.conversation_chain = FakeChain(chunks=["one ", "two"])
+    def test_constructs(self):
+        self.assertIsNotNone(SyncLLMService())
 
-        chunks = list(service.generate_response(conversation=None, query="hello"))
 
-        self.assertEqual("".join(chunks), "one two")
-        self.assertEqual(
-            service.conversation_chain.calls, [{"query": "hello", "conversation": None}]
+class PromptBudgetTestCase(SimpleTestCase):
+    def test_window_history_drops_oldest_first(self):
+        messages = [HumanMessage(content="x" * 40) for _ in range(10)]
+        kept = window_history(messages, budget_tokens=30, reserved_tokens=0)
+        self.assertLess(len(kept), 10)
+        # Newest messages survive.
+        self.assertEqual(kept[-1].content, messages[-1].content)
+
+    def test_format_history_labels_speakers(self):
+        text = format_history(
+            [HumanMessage(content="hello"), AIMessage(content="hi")]
         )
+        self.assertEqual(text, "User: hello\nAI: hi")

llm_be/chat_backend/tests/test_utils.py

@@ -137,7 +137,11 @@ class LastDayOfMonthTestCase(SimpleTestCase):
 @override_settings(
     OLLAMA_BASE_URL="http://10.0.0.128:11434",
     OLLAMA_MODEL="llama3.2",
+    OLLAMA_MODEL_THINKING="llama3.2",
+    OLLAMA_MODEL_FAST="gemma4:latest",
+    OLLAMA_MODEL_UTILITY="llama3.2",
     OLLAMA_EMBED_MODEL="nomic-embed-text",
+    OLLAMA_NUM_CTX_THINKING=16384,
 )
 class OllamaConfigTestCase(SimpleTestCase):
     def test_reads_settings(self):
@@ -179,15 +183,18 @@ class OllamaConfigFallbackTestCase(SimpleTestCase):
             del settings.OLLAMA_BASE_URL
             del settings.OLLAMA_MODEL
             del settings.OLLAMA_EMBED_MODEL
+            if hasattr(settings, "OLLAMA_MODEL_THINKING"):
+                del settings.OLLAMA_MODEL_THINKING
 
             self.assertEqual(ollama_base_url(), "http://127.0.0.1:11434")
-            self.assertEqual(ollama_model(), "llama3.2")
-            self.assertEqual(ollama_embed_model(), "llama3.2")
+            self.assertEqual(ollama_model(), "gpt-oss:20b")
+            # Embeddings never fall back to a chat model (#62).
+            self.assertEqual(ollama_embed_model(), "nomic-embed-text")
 
-    def test_embed_model_falls_back_to_chat_model(self):
+    def test_embed_model_does_not_fall_back_to_chat_model(self):
         with override_settings(OLLAMA_MODEL="llama3.2"):
             del settings.OLLAMA_EMBED_MODEL
-            self.assertEqual(ollama_embed_model(), "llama3.2")
+            self.assertEqual(ollama_embed_model(), "nomic-embed-text")
 
 
 class UserPromptGuardTestCase(SimpleTestCase):

llm_be/llm_be/settings.py

@@ -148,14 +148,23 @@ CORS_ALLOWED_ORIGINS = with_capacitor_webview_origins(
 
 # Ollama — GPU host on LAN for deployed envs; loopback for local Ollama.
 # Prod/beta control-node secret should set OLLAMA_BASE_URL=http://10.0.0.128:11434
+# Role-scoped models (#62). Dev and prod share the same defaults; override per
+# role. ``OLLAMA_MODEL`` remains a fallback for THINKING so existing secrets
+# keep working until role vars are set. Embeddings never fall back to a chat
+# model.
 OLLAMA_BASE_URL = (
     env("OLLAMA_BASE_URL", "http://127.0.0.1:11434") or "http://127.0.0.1:11434"
 )
-OLLAMA_MODEL = env(
-    "OLLAMA_MODEL",
-    "llama3.2" if not DEBUG else "gpt-oss:20b",
-) or ("llama3.2" if not DEBUG else "gpt-oss:20b")
-OLLAMA_EMBED_MODEL = env("OLLAMA_EMBED_MODEL", OLLAMA_MODEL) or OLLAMA_MODEL
+OLLAMA_MODEL = env("OLLAMA_MODEL", "gpt-oss:20b") or "gpt-oss:20b"
+OLLAMA_MODEL_THINKING = env("OLLAMA_MODEL_THINKING", "") or OLLAMA_MODEL
+OLLAMA_MODEL_FAST = env("OLLAMA_MODEL_FAST", "") or "gemma4:latest"
+OLLAMA_MODEL_UTILITY = env("OLLAMA_MODEL_UTILITY", "") or "llama3.2"
+OLLAMA_EMBED_MODEL = env("OLLAMA_EMBED_MODEL", "") or "nomic-embed-text"
+OLLAMA_NUM_CTX_THINKING = int(
+    env("OLLAMA_NUM_CTX_THINKING", "16384") or "16384"
+)
+OLLAMA_NUM_CTX_FAST = int(env("OLLAMA_NUM_CTX_FAST", "8192") or "8192")
+OLLAMA_NUM_CTX_UTILITY = int(env("OLLAMA_NUM_CTX_UTILITY", "4096") or "4096")
 
 CHROMA_PERSIST_DIRECTORY = env(
     "CHROMA_PERSIST_DIRECTORY",
@@ -307,6 +316,15 @@ os.makedirs(directory_path, exist_ok=True)
 
 ALLOW_IMAGE_GENERATION = env_bool("ALLOW_IMAGE_GENERATION", False)
 ALLOW_INTERNET_ACCESS = env_bool("ALLOW_INTERNET_ACCESS", True)
+# Web search (#62). Primary provider is SearxNG; DDGS is the automatic failover.
+SEARCH_PROVIDER = (env("SEARCH_PROVIDER", "searxng") or "searxng").lower()
+SEARCH_FAILOVER_PROVIDER = (
+    env("SEARCH_FAILOVER_PROVIDER", "ddgs") or "ddgs"
+).lower()
+SEARXNG_BASE_URL = (
+    env("SEARXNG_BASE_URL", "http://127.0.0.1:8080") or "http://127.0.0.1:8080"
+).rstrip("/")
+SEARXNG_TIMEOUT_SECONDS = float(env("SEARXNG_TIMEOUT_SECONDS", "8") or "8")
 # When True, chat turns require an active plan and respect prompt/token quotas.
 ENFORCE_SUBSCRIPTION_GATES = env_bool("ENFORCE_SUBSCRIPTION_GATES", True)
 

pyproject.toml

@@ -42,6 +42,7 @@ dependencies = [
     "pillow==12.0.0",
     "beautifulsoup4==4.14.3",
     "ddgs==9.9.3",
+    "requests>=2.32,<3",
     "httpx==0.28.1",
     "python-dateutil==2.9.0.post0",
     "pytz==2025.2",