Minor updates

README.md

@@ -123,7 +123,7 @@ pip install 'git+https://github.com/intellerce/[email protected]'
 
 Python ≥ 3.10. Local model experiments need [Ollama](https://ollama.com) or a vLLM/SGLang endpoint. Cloud experiments need provider API keys (`OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, etc.). A `.env` is loaded automatically by the benchmark runner; the library facade reads keys directly from `os.environ`.
 
-For SemKNN routing you additionally need a SemKNN backend reachable via HTTPS — the public package ships only the client. See [§privacy & data flow](#privacy--data-flow) and [COMMERCIAL.md](./COMMERCIAL.md) for hosted / self-host options.
+For SemKNN routing you additionally need a SemKNN backend reachable via HTTPS — the public package ships only the client. It defaults to the public hosted endpoint `https://agentcodec.intellerce.com`; override it with the `AGENTCODEC_SEMKNN_SERVER_URL` env var (or `router.server_url` in YAML) to point at your own backend. See [§privacy & data flow](#privacy--data-flow) and [COMMERCIAL.md](./COMMERCIAL.md) for hosted / self-host options.
 
 ---
 
@@ -1211,15 +1211,17 @@ Every technique below is dispatched by `agentcodec.dispatch.dispatch()` and shar
 | Ours | HARQ (ARQ retry / combine) | `harq_cc`, `harq_ir` | 2 |
 | Ours | Turbo decoding | `turbo` | 1 |
 | Ours | Fountain (rateless) | `fountain`, `fountain_soft` | 2 |
-| Ours | FEC (forward error correction) | `fec_0.75`, `fec_0.50`, `fec_0.33` | 3 |
-| Ours | Diversity combining | `diversity_sc`, `diversity_mrc`, `diversity_egc`, `diversity_sc_N`, `diversity_mrc_discrete_N`, `diversity_spatial`, `diversity_frequency`, `diversity_time`, `diversity_mrc_soft`, `diversity_mrc_discrete_N_soft` | 10 |
+| Ours | FEC (forward error correction) | `fec_0.75`, `fec_0.50`, `fec_0.33`, `fec_0.25` | 4 |
+| Ours | Diversity combining | `diversity_sc`, `diversity_mrc`, `diversity_egc`, `diversity_sc_N`, `diversity_mrc_discrete_N`, `diversity_spatial`, `diversity_frequency`, `diversity_time`, `diversity_mrc_soft`, `diversity_mrc_discrete_N_soft` | 9¹ |
 | Ours | ACM routing (adaptive coding/modulation) | `acm`, `acm_soft`, `acm_learned` | 3 |
 | Prior | Baselines | `self_consistency`, `self_refine`, `chain_of_verification`, `best_of_n`, `weighted_bon`, `cisc`, `mixture_of_agents` | 7 |
 | | **Reliability techniques** | | **28** |
 | | **+ uncoded `baseline` reference** | | **1** |
-| | **Dispatchable total** | | **29** |
+| | **Dispatchable total** | | **30** |
 
-Communication-theoretic subtotal: **21** across the six families. Prior-method baselines: **7**. That's **28 reliability techniques**; the uncoded `baseline` (single pass, no redundancy) is the reference they're all measured against, which brings the dispatch enum to **29** entries.
+¹ `diversity_spatial` is behaviorally identical to `diversity_mrc` (both = MRC combining over multiple model channels), so it's dispatchable but not counted as a distinct technique. The diversity family is really two independent axes: **what varies** (spatial = different models, frequency = different prompts, time = different temperatures) × **how branches are combined** (SC = pick best, MRC = quality-weighted synthesis, EGC = equal-weight) — any source can pair with any combiner.
+
+Communication-theoretic subtotal: **21** across the six families. Prior-method baselines: **7**. That's **28 reliability techniques**; the uncoded `baseline` (single pass, no redundancy) is the reference they're all measured against, which — plus the `diversity_spatial` alias — brings the dispatch enum to **30** entries.
 
 > **Routers live at the strategy layer, not in this list — but they're still techniques.** In communication theory, adaptive coding-modulation (ACM) *is* a technique; it's one of our six families. The library simply realizes ACM at **two layers**: the self-contained `acm` / `acm_soft` / `acm_learned` entries are dispatchable *leaf* techniques (probe difficulty → dispatch in-band) and so live in `KNOWN_TECHNIQUES`, while the `semknn` / `acm_table` / `acm_linear` *routers* — SemKNN being the flagship cost-aware one that delivers the ~56% number — **orchestrate the other techniques** and so are configured via `strategy.type` (see [§Strategies: fixed vs routed](#strategies-fixed-vs-routed) and the [router table](#routers-strategy-layer) below) rather than dispatched by name. They're techniques in the comm-theory sense; they just aren't *leaf* entries in the dispatch catalog, which is why the count of **29 dispatchable entries** doesn't include them.
 
@@ -1243,7 +1245,7 @@ SemKNN is the recommended router — see [§SemKNN](#semknn--the-recommended-rou
 | `harq_ir` | Hybrid ARQ Incremental Redundancy — retry with critic feedback. | `max_rounds` (5) |
 | `turbo` | Iterative SISO decoding — generator + critic exchange extrinsic info. | `max_iterations` (5) |
 | `fountain` | Rateless: keep generating samples until the judge is satisfied. | `max_samples` (8) |
-| `fec_0.75`, `fec_0.50`, `fec_0.33` | Forward error correction at three code rates. | `code_rate` (from name) |
+| `fec_0.75`, `fec_0.50`, `fec_0.33`, `fec_0.25` | Forward error correction at four code rates (1/2/3/4 parity calls). | `code_rate` (from name) |
 | `diversity_sc` | Selection Combining — pick the highest-scored branch. | (one branch per channel) |
 | `diversity_mrc` | Maximal Ratio Combining — quality-weighted synthesis. | (one branch per channel) |
 | `diversity_egc` | Equal Gain Combining — equal-weight consensus. | — |

agentcodec/dispatch.py

@@ -347,12 +347,15 @@ def _baseline(task: TaskItem, channel: AgentChannel, scorer: QualityScorer) -> R
     "best_of_n", "weighted_bon", "cisc", "mixture_of_agents",
     "diversity_sc", "diversity_mrc", "diversity_egc",
     "diversity_sc_N", "diversity_mrc_discrete_N",
-    "diversity_spatial", "diversity_frequency", "diversity_time",
+    # NOTE: "diversity_spatial" is intentionally omitted — it's an alias for
+    # diversity_mrc (same MRC combining; see dispatch branch above) and is not
+    # counted as a distinct public technique. It still dispatches correctly.
+    "diversity_frequency", "diversity_time",
     "diversity_mrc_soft", "diversity_mrc_discrete_N_soft",
     "harq_cc", "harq_ir",
     "turbo",
     "fountain", "fountain_soft",
-    "fec_0.75", "fec_0.50", "fec_0.33",
+    "fec_0.75", "fec_0.50", "fec_0.33", "fec_0.25",
     "acm", "acm_soft", "acm_learned",
 )
 
@@ -417,6 +420,7 @@ def _baseline(task: TaskItem, channel: AgentChannel, scorer: QualityScorer) -> R
     "fec_0.75",
     "fec_0.50",
     "fec_0.33",
+    "fec_0.25",
     "diversity_mrc_soft",
     "diversity_mrc_discrete_N_soft",
     "acm",

agentcodec/techniques/acm_learned.py

@@ -73,6 +73,7 @@
     "fec_0.75":                  {"code_rate": 0.75},
     "fec_0.50":                  {"code_rate": 0.50},
     "fec_0.33":                  {"code_rate": 0.33},
+    "fec_0.25":                  {"code_rate": 0.25},
     "diversity_sc_N":            {"num_samples": 5},
     "diversity_mrc_discrete_N":  {"num_samples": 5},
     "self_consistency":          {"num_samples": 5},

examples/00_drop_in_openai.py

@@ -146,6 +146,12 @@ def layer_3_full_module():
     client = OpenAI(
         api_key=API_KEY, base_url=BASE_URL, reliability=mod,
     )
+    # `model=` is required by the OpenAI SDK signature (this is a drop-in
+    # wrapper), but with a full ReliabilityModule injected it does NOT pick
+    # the channel models — the module's own `models=[MODEL_A, MODEL_B]` above
+    # drives the calls. Here `model` is inert: it's only stamped onto
+    # `resp.model` as a label. (It *does* matter in passthrough / preset-string
+    # layers, which is why every layer keeps the same call shape.)
     resp = client.chat.completions.create(
         model=MODEL_A,
         messages=[

setup.sh

@@ -1,8 +1,14 @@
 #!/usr/bin/env bash
 # Set up a local development environment for the public AgentCodec package.
 #
-#   ./setup.sh           # create .venv, install package + dev deps
-#   ./setup.sh --remote  # also install the remote-semknn extra (BGE encoder)
+#   ./setup.sh           # create .venv, install package + dev + provider SDKs
+#   ./setup.sh --heavy   # also install benchmark + remote-semknn (torch, etc.)
+#
+# The default install includes the openai/anthropic/ollama provider SDKs and
+# the eval extra so the full test suite and examples run without "module not
+# found" surprises. The heavy extras (torch, transformers, matplotlib, pandas,
+# scikit-learn, datasets) are opt-in via --heavy since most contributors don't
+# need them and they make a clean install much larger.
 #
 # Re-runnable; uses uv (https://docs.astral.sh/uv/) for fast installs.
 
@@ -15,10 +21,21 @@ if ! command -v uv >/dev/null 2>&1; then
     exit 1
 fi
 
-EXTRAS="dev"
-if [[ "${1:-}" == "--remote" ]]; then
-    EXTRAS="dev,remote-semknn"
-fi
+# Default: dev tooling + every provider SDK + eval stats. Lets all tests run
+# without pulling in torch/matplotlib/datasets.
+EXTRAS="dev,openai,anthropic,ollama,eval"
+case "${1:-}" in
+    --heavy)
+        # `all` bundles the provider SDKs plus remote-semknn/benchmark/eval.
+        EXTRAS="dev,all"
+        ;;
+    "")
+        ;;
+    *)
+        echo "Unknown option: $1 (expected --heavy)" >&2
+        exit 1
+        ;;
+esac
 
 # Create the venv (Python >= 3.10 per pyproject.toml).
 if [[ ! -d .venv ]]; then
@@ -29,5 +46,6 @@ fi
 uv pip install --python .venv/bin/python -e ".[${EXTRAS}]"
 
 echo
+echo "Installed extras: ${EXTRAS}"
 echo "Done. Activate with:  source .venv/bin/activate"
 echo "Run tests with:       ./.venv/bin/pytest tests/"