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Visits accumulate as passages. When a question arrives after new visits, the graph is
extended over them (OpenIE, embeddings, fact and synonymy edges), so memory at a checkpoint
covers exactly the visits up to it and accumulates across the whole stream. If the extension
fails, the visits stay pending for the next question and the current question is returned as an
error. Answers come from HippoRAG's own reader (retrieve, then qa), with the clinician answering
prompt as the question.
"""
from __future__ import annotations
import json
import logging
import os
import re
import shutil
import time
import traceback
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Optional
from .base import ENC, BaseAgent, MemoryBuildResult, MemoryContext, as_list
from .engine_bridge import (EngineChat, EngineEmbedder, add_engine_source, stub_optional_imports,
read_openie_docs)
from ..llm_client import USAGE, create_llm_client, extract_json_object
from ..prompts import ANSWER_NOCTX
logger = logging.getLogger(__name__)
_VISIT_HEADER_RE = re.compile(r"=== Visit v(\d+) · ")
_VISIT_START = "=== Visit v"
_READER_FRAMING = "Wikipedia Title: \n\n"
"""Text HippoRAG's reader puts around each passage; charged to every passage when fitting the budget."""
def _graph_nodes(hipporag) -> set:
"""Node names already in the graph."""
g = hipporag.graph
return set(g.vs["name"]) if "name" in g.vs.attribute_names() else set()
def _wrap_fact_edges(hipporag) -> None:
"""Pass only new chunks to fact-edge construction and register entity links for earlier chunks."""
from hipporag.utils.misc_utils import compute_mdhash_id
orig_add = hipporag.add_fact_edges
def add_fact_edges(chunk_ids, chunk_triples):
existing = _graph_nodes(hipporag)
new = [(k, t) for k, t in zip(chunk_ids, chunk_triples) if k not in existing]
orig_add([k for k, _ in new], [t for _, t in new])
for k, t in zip(chunk_ids, chunk_triples):
if k in existing:
for tr in t:
tr = tuple(tr)
for ent in (compute_mdhash_id(content=tr[0], prefix="entity-"),
compute_mdhash_id(content=tr[2], prefix="entity-")):
hipporag.ent_node_to_chunk_ids[ent] = hipporag.ent_node_to_chunk_ids.get(
ent, set()).union({k})
hipporag.add_fact_edges = add_fact_edges
def _wrap_synonymy_edges(hipporag) -> None:
"""Build synonymy edges over all nodes, dropping pairs that already exist between earlier nodes."""
orig_syn = hipporag.add_synonymy_edges
def add_synonymy_edges():
existing = _graph_nodes(hipporag)
before = dict(hipporag.node_to_node_stats)
orig_syn()
if existing:
stats = hipporag.node_to_node_stats
for edge in [e for e, w in stats.items()
if (e not in before or before[e] != w)
and e[0] in existing and e[1] in existing]:
del stats[edge]
hipporag.add_synonymy_edges = add_synonymy_edges
def _install_ner_array_fallback(hipporag) -> None:
"""Accept a bare JSON array as a NER response, in addition to {"named_entities": [...]}."""
orig_ner = hipporag.openie.ner
def ner(chunk_key, passage):
out = orig_ner(chunk_key=chunk_key, passage=passage)
if not out.unique_entities and out.response:
txt = out.response.strip()
if txt.startswith("```"):
txt = txt.strip("`").removeprefix("json").strip()
try:
arr = json.loads(txt)
if isinstance(arr, list) and all(isinstance(x, str) for x in arr):
out.unique_entities = list(dict.fromkeys(arr))
except Exception:
pass
return out
hipporag.openie.ner = ner
def _unwrap_answer(text: str) -> str:
"""Return the `answer` field if `text` is the JSON object the answering prompt requests."""
s = (text or "").strip()
if not s or "{" not in s:
return s
try:
try:
obj = json.loads(s, strict=False)
except Exception:
obj = extract_json_object(s)
if isinstance(obj, dict) and "answer" in obj:
return str(obj["answer"]).strip()
except Exception:
pass
return s
def _visit_passages(texts: list[str]) -> list[str]:
"""One passage per visit, from its header on; the memorize-template sentence in front of the first visit
becomes one more passage (the store keeps it once, because passages are deduplicated)."""
passages = []
for text in texts:
body = text.strip()
if body:
at = body.find(_VISIT_START)
passages.append(body[at:] if at > 0 else body)
lead = texts[0].find(_VISIT_START)
if lead > 0:
passages.append(texts[0][:lead].strip())
return passages
def _fit_passages(passages, budget: int, min_tail: int) -> list[str]:
"""Passages in rank order while they fit `budget` tokens (each also charged the reader's framing).
The first one that does not fit is cut to the room left when that is more than `min_tail` tokens."""
framing = BaseAgent.count_tokens(_READER_FRAMING)
kept, used = [], 0
for p in passages:
text = p if isinstance(p, str) else str(p)
size = BaseAgent.count_tokens(text) + framing
if used + size <= budget:
kept.append(text)
used += size
continue
room = budget - used - framing
if room > min_tail:
kept.append(ENC.decode(ENC.encode(text)[:room]))
break
return kept
@dataclass
class _Patient:
"""Per-patient state: visits waiting for the next build and the visits already in the graph."""
texts: list = field(default_factory=list)
visits: list = field(default_factory=list)
indexed: list = field(default_factory=list)
received: int = 0
built: bool = False
engine: Any = None
class HippoRAGAgent(BaseAgent):
MEMORIZE_WITH_LLM = True
ANSWERS_INTERNALLY = True
def __init__(
self,
*,
model: str | None = None,
temperature: float,
max_tokens: int,
provider: str = "openai",
api_key: Optional[str] = None,
base_url: Optional[str] = None,
internal_base_url: Optional[str] = None,
openie_mode: str,
is_directed_graph: bool,
synonymy_edge_sim_threshold: float,
synonymy_edge_topk: int,
linking_top_k: int,
retrieval_top_k: int,
qa_top_k: int,
damping: float,
passage_node_weight: float,
force_index_from_scratch: bool,
force_openie_from_scratch: bool,
save_openie: bool,
embedding_provider: str,
embedding_model: str | None = None,
embedding_dim: Optional[int] = None,
embedding_api_key: Optional[str] = None,
embedding_base_url: Optional[str] = None,
embedding_batch_size: int | None = None,
embedding_max_seq_len: int,
max_context_tokens: int,
context_reserve_tokens: int,
min_truncated_passage_tokens: int,
working_dir: Optional[str] = None,
**kwargs,
):
super().__init__(**kwargs)
if embedding_provider not in ("openai", "api"):
raise ValueError(f"embedding_provider must be 'openai' or 'api', not {embedding_provider!r}")
base_url = base_url or internal_base_url
self.openie_mode = openie_mode
self.qa_top_k = qa_top_k
self.max_context_tokens = max_context_tokens
self.context_reserve_tokens = context_reserve_tokens
self.min_truncated_passage_tokens = min_truncated_passage_tokens
self.working_dir = working_dir or str(Path(__file__).resolve().parents[2] / "outputs" / "_hipporag_store")
self._store_cleared = False
self._engine_settings = dict(
dataset=None,
llm_name=model,
llm_base_url=base_url or os.environ.get("OPENAI_BASE_URL"),
temperature=temperature,
max_new_tokens=max_tokens,
openie_mode=openie_mode,
is_directed_graph=is_directed_graph,
synonymy_edge_sim_threshold=synonymy_edge_sim_threshold,
synonymy_edge_topk=synonymy_edge_topk,
linking_top_k=linking_top_k,
retrieval_top_k=retrieval_top_k,
qa_top_k=qa_top_k,
damping=damping,
passage_node_weight=passage_node_weight,
force_index_from_scratch=force_index_from_scratch,
force_openie_from_scratch=force_openie_from_scratch,
save_openie=save_openie,
embedding_model_name=embedding_model,
embedding_batch_size=embedding_batch_size,
embedding_max_seq_len=embedding_max_seq_len,
embedding_return_as_normalized=True,
)
client = create_llm_client(provider=provider, model=model, temperature=temperature,
max_tokens=max_tokens, api_key=api_key, base_url=base_url)
self._chat = EngineChat(client, temperature=temperature, max_tokens=max_tokens)
self._embedder = EngineEmbedder(model=embedding_model, batch_size=embedding_batch_size, dim=embedding_dim,
api_key=embedding_api_key, base_url=embedding_base_url)
self._patients: dict[Any, _Patient] = {}
self._last_build: dict = {}
add_engine_source("HippoRAG")
stub_optional_imports()
def _patient(self) -> tuple[Any, _Patient]:
key = self._context_id if self._context_id is not None else 0
if key not in self._patients:
self._patients[key] = _Patient()
return key, self._patients[key]
def _store_dir(self, key) -> str:
"""The patient's engine directory; at the first use in this run an earlier store is removed."""
path = os.path.join(self.working_dir, f"context_{key}")
if not self._store_cleared:
self._store_cleared = True
if os.path.isdir(path):
shutil.rmtree(path)
logger.info("[HippoRAG] removed the store left at %s", path)
os.makedirs(path, exist_ok=True)
return path
def _engine(self, key, state: _Patient):
"""The patient's HippoRAG instance, created on first use with the evaluation's chat and embedding clients."""
if state.engine is not None:
return state.engine
from hipporag.HippoRAG import HippoRAG
from hipporag.utils.config_utils import BaseConfig
config = BaseConfig(save_dir=self._store_dir(key), **self._engine_settings)
engine = HippoRAG(global_config=config, embedding_model=self._embedder)
for role in ("llm_model", "qa_llm", "extraction_llm"):
setattr(engine, role, self._chat)
_wrap_fact_edges(engine)
_wrap_synonymy_edges(engine)
if getattr(engine, "openie", None) is not None:
engine.openie.llm_model = self._chat
_install_ner_array_fallback(engine)
if getattr(engine, "rerank_filter", None) is not None:
engine.rerank_filter.llm_infer_fn = self._chat.infer
engine.embedding_model = self._embedder
for store in ("chunk_embedding_store", "entity_embedding_store", "fact_embedding_store"):
if getattr(engine, store, None) is not None:
getattr(engine, store).embedding_model = self._embedder
logger.info("[HippoRAG] engine ready for %s (openie_mode=%s, store=%s)", key, self.openie_mode,
config.save_dir)
state.engine = engine
return engine
def memorize(self, text: str, visit_number: int, date: str) -> MemoryBuildResult:
"""Accumulate one visit; the graph is extended at the next question."""
USAGE.set_phase("memorize")
t0 = time.time()
key, state = self._patient()
state.texts.append(text)
state.visits.append(visit_number)
state.received += 1
return MemoryBuildResult(
success=True, method="hipporag", action="accumulate", visit_number=visit_number,
chunk_count=state.received, added_chunks=1, time_cost=time.time() - t0,
extra={"context_id": key, "session_number": state.received, "pending_sessions": len(state.texts),
"total_accumulated_chars": sum(map(len, state.texts))})
def dump_memory(self, out_dir):
"""Per-checkpoint export: the indexed passages and a graph size summary."""
from .base import snapshot_jsonl_gz, snapshot_tree
stats = {}
for cid, state in self._patients.items():
hr = state.engine
if hr is None:
continue
try:
src = os.path.join(self.working_dir, f"context_{cid}")
if os.path.isdir(src):
stats[f"store_files_{cid}"] = snapshot_tree(src, out_dir / f"store_{cid}")
except Exception as e:
logger.error("[HippoRAG] store snapshot failed (run continues): %s", e)
rows = []
store = getattr(hr, "chunk_embedding_store", None)
if store is not None:
try:
mapping = getattr(store, "hash_id_to_row", None)
if mapping is None and hasattr(store, "get_all_ids"):
ids = list(store.get_all_ids())
mapping = store.get_rows(ids) if ids else {}
for hid, row in (mapping or {}).items():
text = str((row or {}).get("content", ""))
m = _VISIT_HEADER_RE.findall(text)
rows.append({"hash_id": str(hid),
"source_visit": int(m[0]) if m else None,
"content": text[:300]})
except Exception as e:
logger.error("[HippoRAG] passage export failed (run continues): %s", e)
if not rows:
logger.warning("[HippoRAG] passage export is empty for context %s; the "
"store snapshot still holds the passages", cid)
stats[f"passages_{cid}"] = snapshot_jsonl_gz(
out_dir / f"passages_{cid}.jsonl.gz", rows)
g = getattr(hr, "graph", None)
if g is not None:
try:
stats[f"graph_{cid}"] = {"vertices": g.vcount(), "edges": g.ecount()}
except Exception:
pass
return stats or None
def _extend_graph(self, key, state: _Patient) -> dict:
"""Index the waiting visits into the patient's graph. Returns the build summary; on failure the
waiting visits are kept, so the next question indexes them again, and the summary carries the error."""
t0 = time.time()
waiting = len(state.texts)
try:
engine = self._engine(key, state)
passages = _visit_passages(state.texts)
expected = waiting + int(state.texts[0].find(_VISIT_START) > 0)
engine.index(passages)
engine.ready_to_retrieve = False
graph = engine.get_graph_info()
openie = read_openie_docs(engine.openie_results_path)
except Exception as e:
logger.error("[HippoRAG] graph build failed: %s", e)
traceback.print_exc()
return {"success": False, "session_count": waiting, "error": str(e), "time_cost": time.time() - t0}
state.indexed.extend(state.visits)
state.texts, state.visits = [], []
state.built = True
logger.info("[HippoRAG] %d visits indexed as %d passages in %.1fs; graph %s", waiting, len(passages),
time.time() - t0, graph)
return {"success": True, "session_count": waiting, "num_documents": len(passages),
"expected_documents": expected, "graph_info": graph,
"openie_count": len(openie) if openie else 0, "time_cost": time.time() - t0}
def build_memory_context(self, question: str,
as_of: str = "", display_question: str | None = None) -> MemoryContext:
"""Index pending visits, retrieve, and answer with HippoRAG's reader. When indexing fails the question
is returned as an error ("[err graph_build]") instead of being answered from the earlier graph."""
key, state = self._patient()
if state.texts:
USAGE.set_phase("memorize")
build = self._extend_graph(key, state)
graph = build.get("graph_info") or {}
self._last_build = {
"new_documents": build.get("num_documents"),
"sessions_indexed": build.get("session_count"),
"indexed_visits_total": len(state.indexed),
"graph_nodes": graph.get("num_total_nodes"),
"graph_triples": graph.get("num_total_triples"),
"build_time_s": round(build.get("time_cost", 0.0), 1),
"build_ok": build.get("success", False),
}
if not build.get("success"):
return MemoryContext(mem="", n_tok=0, retrieved_count=0, error=True, answer="[err graph_build]",
extra={"method": "hipporag", "error": "graph_build",
"build_error": str(build.get("error"))[:300], **self._last_build})
if build["num_documents"] != build["expected_documents"]:
raise RuntimeError(f"[HippoRAG] {build['num_documents']} documents for "
f"{build['session_count']} pending visits")
USAGE.set_phase("query")
t0 = time.time()
if not state.built:
logger.warning("[HippoRAG] nothing is indexed for %s", key)
return MemoryContext(mem="", n_tok=0, retrieved_count=0, answer="[err no_data_indexed]", error=True,
extra={"method": "hipporag", "error": "no_data_indexed"})
prompt = ANSWER_NOCTX.format(as_of=as_of, qtext=display_question or question)
answer, shown, failed, cut = "", [], False, False
try:
answer, shown, cut = self._retrieve_and_read(self._engine(key, state), question, prompt)
except Exception as e:
logger.error("[HippoRAG] query failed: %s", e)
traceback.print_exc()
answer, failed = f"[err {str(e)[:60]}]", True
mem = "\n\n".join(f"[Memory {i}]\n{p['content']}" for i, p in enumerate(shown, 1))
return MemoryContext(
mem=mem,
n_tok=self.count_tokens(mem),
retrieved_count=len(shown),
retrieved_visits=sorted({int(v) for p in shown for v in _VISIT_HEADER_RE.findall(p["content"])}),
retrieved_memories=[{"visit": None, "memory": p["content"][:300], "score": p.get("score")}
for p in shown],
context_truncated=cut,
answer=_unwrap_answer(answer), error=failed,
extra={"method": "hipporag", "openie_mode": self.openie_mode,
"query_time_s": round(time.time() - t0, 2), **self._last_build},
)
def _retrieve_and_read(self, engine, question: str, prompt: str) -> tuple[str, list, bool]:
"""Retrieve with the question, fit the top passages to the context budget and let the reader answer
`prompt`. Returns the answer, the passages it read (with scores) and whether the budget cut passages."""
if not engine.ready_to_retrieve:
engine.prepare_retrieval_objects()
found = engine.retrieve(queries=[question])
try:
first = (found or [None])[0]
self._trace("query_retrieve", question=question,
docs=as_list(getattr(first, "docs", None))[:20],
scores=[float(x) for x in as_list(getattr(first, "doc_scores", None))[:20]])
except Exception:
pass
if not found:
return "", [], False
solution = found[0]
top = solution.docs[:self.qa_top_k] if solution.docs else []
cut = False
if top and self.max_context_tokens > 0:
budget = max(self.max_context_tokens - self.count_tokens(prompt) - self.context_reserve_tokens, 0)
fitted = _fit_passages(top, budget, self.min_truncated_passage_tokens)
cut = len(fitted) < len(top)
top = fitted
solution.docs = top
solution.question = prompt
read, _, _ = engine.qa([solution])
solution = read[0] if read else solution
answer = solution.answer if hasattr(solution, "answer") else str(solution)
shown = []
docs = getattr(solution, "docs", None)
if docs:
scores = getattr(solution, "doc_scores", None)
for rank, doc in enumerate(docs[:self.qa_top_k]):
entry = {"rank": rank + 1, "content": doc if isinstance(doc, str) else str(doc)}
if scores is not None and len(scores) > rank:
entry["score"] = float(scores[rank])
shown.append(entry)
return answer, shown, cut
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