Hybrid Search — BM25 + FAISS + RRF

Section I

Notebook Summary — copy by hand

Terse cheat sheet. Keyword first, then rule. Arrows = cause/effect. Formulas verbatim. Doodles = ~10s stick sketches. Fully derivable from Section II.

═══ THE CORE PROBLEM ═══
Dense (FAISS) search  → great at MEANING, blind to exact strings
Sparse (BM25) search  → great at exact TEXT, blind to synonyms
∴ need BOTH → run in parallel → fuse results = HYBRID SEARCH

Detective analogy:
FAISS = Behavioral Profiler (motives, themes, synonyms)
  BM25  = Fingerprint Analyst (exact, microscopic matches)
  Master Agent = runs both in parallel

doodle: query → forks into [FAISS box] + [BM25 box] → both feed → [FUSE] → 1 list
                

A. The Semantic Blind Spot (FAISS / dense)#

Tokenization catastrophe
ID "INV-2023-99X" has NO single pretrained concept
  → chopped: ["INV","2023","99","X"]
  → meaning averaged: "invoice"+"year 2023"+"num 99"
  → cosine sim: 99X ≈ 99Y  (both = "2023 invoice")
  ⇒ grabs WRONG invoice, pays wrong vendor, fails SILENTLY

Cosine sim scale → bounded, tight: real scores ~0.70–0.95
doodle: two dots labelled 99X &99Y sitting on top of each other = false match
                

B. BM25 / sparse keyword search#

= the ultimate Ctrl+F → match = big score, no match = ZERO
Strength → exact IDs, SKUs, codes, error strings
Flaw → VOCABULARY GAP: search "puppy", doc says "dog" → 0 results

Built on TF-IDF:
TF  = more mentions → more relevant
  IDF = rare words rewarded, common words ("the") ≈ ignored

BM25 = TF-IDF + 2 fixes for keyword stuffing:
k₁ → SATURATION  → caps repetition reward (10x ≠ 10× better)
  b  → LENGTH NORM → punishes long rambling docs
  defaults: k₁ ≈ 1.2–2.0 , b = 0.75            [verified]
  doodle: curve rising then flattening = saturation (k₁ ceiling)
                

                    BM25 FORMULA (verbatim):
                    score(D,Q) = Σ IDF(qᵢ) · [ f(qᵢ,D)·(k₁+1) ]
                          ───────────────────────────────────
                          [ f(qᵢ,D) + k₁·(1 − b + b·|D|/avgdl) ]

 IDF(qᵢ) = ln( (N − n(qᵢ) + 0.5) / (n(qᵢ) + 0.5) + 1 )

 f=term freq in D · |D|=doc len · avgdl=avg len · N=#docs · n=#docs w/ term
                

C. The Ensembling Problem (apples vs oranges)#

Why naive add FAILS:
"add 5 miles to 30°C"
  Doc A (perfect semantic): FAISS .88 + BM25 0.0 → .44
  Doc B (random IT ticket):  FAISS .40 + BM25 12.5 → 6.45  ✗ WRONG WINS
  ⇒ BM25's big ints swallow FAISS decimals → meaning destroyed

Why Min-Max normalize FAILS:
one outlier (80.0) crushes
  99 valid docs → ~0.02 each ("curve where 1 kid scores 5000%")
                

D. Reciprocal Rank Fusion (RRF) — the equalizer#

Shift:
stop reading SCORES → read the PODIUM (rank only)
Rank is the ONLY common language between the two engines.

RRF FORMULA (verbatim):
RRF(d) = Σ over retrievers   1 / (k + rank_r(d))     k = 60

Why k=60 → flatten so #1 isn't a dictator → reward CONSENSUS
   rank1 = 1/61 = 0.0164    rank2 = 1/62 = 0.0161   (tiny gap)
   k→0 ⇒ rank1=1.0 = gold medalist dictator ✗
                

WINNER = C → won gold in NEITHER engine
  = decathlon athlete: best ALL-ROUND consensus, not a single spike
doodle: 3 lanes, C finishes 2nd+2nd but totals highest = consensus win
                

E. Production = LangChain EnsembleRetriever#

EnsembleRetriever(retrievers=[faiss,bm25], weights=[.5,.5])
auto: runs parallel async → strips raw incompatible scores
        → applies RRF (k=60) → returns 1 unified list
Senior eng knows the brutal math → so you can DEBUG when it burns.

After hybrid:
output still just a "net" → final polish =
  CROSS-ENCODER re-ranking (neural pairwise eval, slow, top-N only)
doodle: wide net catches many fish → cross-encoder = hand-picks top few
                

F. One-line decision table#

Section II

Detailed Study Notes

One numbered subsection per concept. Each opens with a bold claim, then plain-English build-up, then callouts. + marks anything I added or corrected beyond the core material.

1. The Semantic Blind Spot: smart AI fails at simple lookups #

A dense vector search understands meaning, which is exactly why it cannot reliably find an exact identifier.

Dense (semantic) search embeds every document into a vector — a long list of numbers that captures what the text is about. FAISS (Facebook AI Similarity Search) stores those vectors and, at query time, finds the documents whose vectors point in the most similar direction, measured by cosine similarity. For conceptual questions this is magic: search "money problems" and it surfaces a doc that says "budget shortfall" even though no word overlaps.

The failure appears the moment you need something exact. Imagine you need details on invoice INV-2023-99X. The embedding model has no single pretrained concept for that string, so it does two destructive things:

• It tokenizes (chops) the ID into pieces like ["INV", "2023", "99", "X"].
• It averages those meanings into roughly "an invoice, from 2023, number-ish 99."

To cosine similarity, INV-2023-99X and INV-2023-99Y now look identical — conceptually they're both "a 2023 invoice." The system confidently returns the wrong one. In a payments pipeline that means paying the wrong vendor, and the failure is silent: no error, just a wrong answer with a high confidence score.

Code: faiss_blindspot.py

2. The Revenge of Sparse Search (BM25 as the legacy savior) #

When meaning fails, fall back to literal text matching — the ultimate Ctrl+F.

Sparse keyword search doesn't know what an invoice is. It only knows whether the exact characters appear in the document. Match the literal string and you get a large relevance score; no match and you get an absolute zero. This is exactly what you want for INV-2023-99X: it either finds that precise string or it doesn't, with no semantic averaging to corrupt the result.

It's called "sparse" because each document is represented as a giant vector with one slot per vocabulary word, and almost every slot is zero — only the words actually present are non-zero. Contrast with dense vectors, where every dimension carries a value. +

The flaw is the mirror image of FAISS's: the vocabulary gap. Search for "puppy" against a document that says "dog" and you get zero results — the letters p-u-p-p-y simply aren't there. Sparse search is completely blind to synonyms.

Code: bm25_basics.py

3. TF-IDF: the foundation BM25 is built on #

Two rules decide keyword relevance: how often a word appears here, and how rare it is everywhere.

Term Frequency (TF) — the more a document mentions the keyword, the more relevant it's assumed to be. A document saying "budget" ten times beats one that says it once.

Inverse Document Frequency (IDF) — common words are penalized, rare words rewarded. In the search "the budget", the word "the" appears in nearly every document, so it carries almost no information and is effectively ignored; "budget" holds all the mathematical weight. + Intuitively, a word that shows up everywhere can't help you distinguish one document from another, so IDF down-weights it.

Code: bm25_basics.py

4. BM25: the keyword king, demystified #

BM25 is TF-IDF plus two limiters — saturation and length normalization — that have kept it powering enterprise search for 20+ years.

BM25 ("Best Matching 25") keeps the TF-IDF intuition but tames it with two free parameters. + The name comes from the Okapi information-retrieval system built at City University London in the 1980s –90s; you'll often see it written "Okapi BM25."

f(q ₐ,D) = times term appears in doc ·|D| = doc length in words ·avgdl = average doc length in the collection ·N = total docs ·n(q ₐ) = docs containing the term. + Standard IDF: ln( (N −n(q ₐ) + 0.5) / (n(q ₐ) + 0.5) + 1 ). The score sums independently over every query term.

k ₁—the saturation parameter (the spam filter)#

k ₁creates a strict ceiling on the term-frequency reward. The first mention spikes the score; by the tenth mention it has essentially flatlined. Ten mentions is not ten times better than one. This is what kills keyword stuffing: repeating "budget" 1,000 times buys you almost nothing past the first handful. + Typical default is k ₁between 1.2 and 2.0 (a common choice is 1.5).

b — the length-normalization parameter (the TL;DR enforcer)#

b punishes long, rambling documents. A concise 50-word paragraph mentioning "budget" will mathematically beat a meandering 5,000-word essay that mentions it the same number of times, because the long doc gets penalized for its length relative to avgdl. + b ranges 0–1: at b=0 length is ignored entirely; at b=1 you apply full length normalization. The standard default is b=0.75. [verified]

Then why not throw FAISS away and use BM25 for everything?#

Because of the vocabulary gap from subsection 2: BM25 is blind to synonyms. It solves exact matching and keyword stuffing perfectly, but it will still return zero for "puppy" when the document says "dog." Neither engine is sufficient alone — which is the entire setup for fusion.

Code: bm25_saturation.py

5. The Ensembling Problem: adding miles to Celsius #

FAISS and BM25 produce scores on incompatible scales, so you cannot simply add, weight, or normalize them together.

FAISS is a bounded sandbox: cosine similarity is normalized and predictable, with real scores hovering tightly between 0.70 and 0.95. BM25 is the unbounded wild west: it's an ever-growing probabilistic summation with no ceiling, so a rare keyword hit might score 45.8 or sail past 150. The dilemma: how do you add 5 miles to 30 °C?

Why naive addition fails#

BM25's large integers completely swallow FAISS's tiny decimals. Averaging the two destroys the semantic signal:

The genuinely relevant Doc A loses to an irrelevant ticket purely because BM25's scale dwarfs FAISS's.

Why min-max normalization also fails#

The obvious fix —"just rescale BM25 to 0–1" — breaks on outliers. Min-max scaling divides by the maximum, so a single freak document scoring 80.0 crushes 99 perfectly valid documents into microscopic decimals near 0.02. It's like grading on a curve where one student somehow scores 5000%: everyone else's real differences get flattened into noise. + The problem is that BM25 scores are skewed with long tails, so the max is a terrible reference point.

Code: naive_fusion_fail.py

6. Reciprocal Rank Fusion (RRF): the great equalizer #

Stop comparing scores. Compare positions. Rank is the one universal language both engines speak.

The paradigm shift: we can't add, weight, or normalize the raw scores — so we throw them away and look only at the podium. A document's positional rank (1st, 2nd, 3rd …) means the same thing whether it came from FAISS or BM25. RRF builds a unified score from the inverse of each rank.

Sum over every retriever r the document appears in; rankr (d) is its position in that retriever's list (1 = best); k is a smoothing constant.

The magic of k = 60#

In industry k is almost always hardcoded to 60. The reason is flattening. If k=0, the #1 spot scores a massive 1.0 and becomes a dictator — one engine's gold medalist single-handedly decides the result, destroying consensus. Adding 60 compresses the gaps:

• rank 1 → 1/(60+1) = 0.0164
• rank 2 → 1/(60+2) = 0.0161
• rank 10 → 1/(60+10) ≈0.0143 +

The gap between rank 1 and rank 2 is tiny, so being ranked highly by both engines (consensus) beats being ranked #1 by just one (a lucky spike). + k=60 isn't sacred — it comes from the original 2009 Cormack et al. paper and is tunable — but it's the robust default everyone copies.

The RRF simulation — the decathlon winner#

Query: "Q4 Revenue Project Alpha." Note that the winning document never won gold in either individual engine:

Document C wins despite placing 3rd and 4th. Like a decathlon athlete who wins no single event, its high overall consensus across both engines proves it's the most well-rounded answer. That's precisely the document a hybrid system should surface.

Code: rrf_fusion.py

7. Production: LangChain's EnsembleRetriever #

You learn the brutal math so you can debug; in production you let LangChain abstract it away.

Senior engineers understand RRF from first principles so that when the pipeline burns down, they can diagnose why a wrong document surfaced. But for shipping code, you wrap a FAISS retriever and a BM25 retriever in LangChain's EnsembleRetriever. Under the hood it:

• Runs both retrievers' searches in parallel (async).
• Strips out the wildly incompatible raw scores.
• Applies the k=60 RRF calculation and returns one unified, ranked list.

You pass weights=[0.5, 0.5] to keep the two engines balanced and let RRF do the fusing. + Weights here bias which retriever's ranks count for more within the RRF sum — they don't reintroduce the raw-score scaling problem.

Code: ensemble_retriever.py

8. The pinnacle: cross-encoder re-ranking #

Hybrid search is a wide net; a cross-encoder is the precision pick that follows.

Even a perfect hybrid+RRF result is still just a net — a good-but-coarse candidate set. The final quality step is a cross-encoder: a neural model that takes the query and each candidate document together as one input and outputs a precise relevance score.

That speed cost dictates the architecture: use cheap, scalable retrieval (FAISS + BM25 + RRF) to narrow thousands of documents down to a top-N (say 20–50), then run the expensive cross-encoder only on those few to produce the final ordering. +

9. How this fits the full RAG assistant + #

Hybrid retrieval is one stage inside a larger PDF-question-answering pipeline.

The end-to-end system this material supports is an advanced RAG assistant: upload a PDF, ask questions by text or voice, get answers grounded in the document. The flow:

• Ingest: user uploads a PDF → backend extracts text, chunks it, creates embeddings, and saves a FAISS index (plus a parallel BM25 index for hybrid search).
• Query: the backend rewrites the question → retrieves similar chunks (hybrid + RRF) → re-ranks them (cross-encoder) → calls the LLM to answer only from the retrieved context.
• Respond: the frontend shows the answer plus citation-style source snippets.

★ Key Takeaways #

• Dense (FAISS) nails meaning but is structurally blind to exact identifiers (tokenization averages them together).
• Sparse (BM25) nails exact text but is blind to synonyms (the vocabulary gap). The two failures are mirror images → you need both.
• BM25 = TF-IDF + k ₁(saturation) + b (length norm). Defaults k ₁∈[1.2,2.0], b=0.75. These kill keyword stuffing and rambling docs.
• You cannot combine the scores. Naive add lets BM25's big integers swallow FAISS; min-max norm lets one outlier crush everyone. Wrong units.
• RRF fuses by rank, not score: Σ1/(k+rank), k=60. Scale-free, rewards consensus, the decathlon winner beats single-event spikes.
• Production = EnsembleRetriever (parallel + RRF abstracted); learn the math to debug it.
• Retrieve wide, then re-rank narrow: hybrid is the net, a cross-encoder is the precision pick. Recall then precision.

Section III

Code Samples — runnable

Python 3.11. Pure-standard-library where possible; the BM25 demos use a tiny hand-written implementation so they run with zero installs. Each block: filename, run command, commented code, expected output, and a note on non-obvious behavior.

1. Why FAISS confuses two invoices + (no installs) #

# Simulates how an embedding model "averages" an ID into a concept,
# making INV-2023-99X and INV-2023-99Y look identical to cosine sim.
import re, math

def fake_embed(text):
    # Toy "embedding": tokenize, then average a per-token vector.
    # Real models do this with learned weights; the AVERAGING is the point.
    toks = re.findall(r"[A-Za-z]+|\d+", text.upper())  # chops INV-2023-99X
    dims = {"INV":[1,0,0], "INVOICE":[1,0,0],
            "2023":[0,1,0], "99":[0,0,1]}
    vecs = [dims.get(t, [0,0,0]) for t in toks]
    n = len(vecs) or 1
    return [sum(v[i] for v in vecs)/n for i in range(3)]  # the average
def cosine(a, b):
    dot = sum(x*y for x,y in zip(a,b))
    na = math.sqrt(sum(x*x for x in a)); nb = math.sqrt(sum(y*y for y in b))
    return dot/(na*nb) if na and nb else 0.0

x = fake_embed("INV-2023-99X")
y = fake_embed("INV-2023-99Y")   # the X/Y trailing letter maps to [0,0,0]
print(f"vec(99X) = {x}")
print(f"vec(99Y) = {y}")
print(f"cosine similarity = {cosine(x,y):.4f}")
                    

2. BM25 from scratch — exact match wins, synonym fails #

# Minimal BM25 so it runs with no pip install. Mirrors rank_bm25's BM25Okapi.
import math
from collections import Counter

class BM25:
    def __init__(self, corpus, k1=1.5, b=0.75):   # standard defaults
        self.k1, self.b = k1, b
        self.docs = [doc.lower().split() for doc in corpus]
        self.N = len(self.docs)
        self.avgdl = sum(len(d) for d in self.docs) / self.N
        self.tf = [Counter(d) for d in self.docs]
        df = Counter()                                # document frequency
        for d in self.docs:
            for term in set(d): df[term] += 1
        # IDF with +0.5 smoothing and +1 to keep it non-negative
        self.idf = {t: math.log((self.N - n + 0.5)/(n + 0.5) + 1) for t,n in df.items()}

    def score(self, query, i):
        q = query.lower().split(); s = 0.0; dl = len(self.docs[i])
        for term in q:
            if term not in self.idf: continue
            # vocab gap -> contributes 0
            f = self.tf[i][term]
            num = f * (self.k1 + 1)
            den = f + self.k1 * (1 - self.b + self.b * dl/self.avgdl)
            s += self.idf[term] * (num/den)
        return s

corpus = [
    "the q4 budget report invoice INV-2023-99X",   # doc 0: exact ID
    "a happy dog ran across the field",             # doc 1: 'dog'
    "annual financial budget summary",             # doc 2
    ]
bm = BM25(corpus)
for qi, q in enumerate(["INV-2023-99X", "puppy"]):
    print(f"query={q!r}")
    for i in range(len(corpus)):
        print(f"  doc{i}: {bm.score(q,i):.3f}")
                    

3. Saturation & length norm in action (k ₁and b) #

from bm25_basics import BM25

# Same word repeated 1x vs 10x vs 100x -> watch the score FLATLINE (saturation).
reps = ["budget", "budget " *10, "budget " *100]
bm = BM25(reps)
print("--- saturation (k1=1.5) ---")
for i,r in enumerate(["1x","10x","100x"]):
    print(f"  {r:>4} 'budget' -> {bm.score('budget', i):.4f}")

# Short vs long doc, same single mention -> long doc PUNISHED (length norm, b).
docs = ["budget", "budget " + "filler " *200]
bm2 = BM25(docs)
print("--- length normalization (b=0.75) ---")
print(f"  short doc -> {bm2.score('budget',0):.4f}")
print(f"  long  doc -> {bm2.score('budget',1):.4f}")
                    

4. The naive fusion catastrophe #

# Shows BOTH failure modes: naive averaging, and min-max normalization.
docs = {
    "A perfect-semantic": {"faiss": 0.88, "bm25": 0.0},
    "B random-ticket":    {"faiss": 0.40, "bm25": 12.5},
    "C also-relevant":    {"faiss": 0.80, "bm25": 1.2},
    "D outlier-spam":     {"faiss": 0.10, "bm25": 80.0},  # the curve-wrecker
    }

print("=== naive average ===")
for d,s in sorted(docs.items(), key=lambda kv: -(kv[1]["faiss"]+kv[1]["bm25"])/2):
    print(f"  {(s['faiss']+s['bm25'])/2:7.3f}  {d}")

print("=== min-max normalized then averaged ===")
bm = [s["bm25"] for s in docs.values()]
lo, hi = min(bm), max(bm)                       # hi = 80 outlier
def norm(v): return (v-lo)/(hi-lo) if hi >lo else 0.0
for d,s in sorted(docs.items(),
        key=lambda kv: -(kv[1]["faiss"]+norm(kv[1]["bm25"]))/2):
    print(f"  {(s['faiss']+norm(s['bm25']))/2:7.3f}  {d}")
                    

5. RRF done right (the decathlon winner) #

# Reciprocal Rank Fusion: ignore scores, fuse by RANK. RRF(d)=sum 1/(k+rank).
def rrf(ranked_lists, k=60):
    scores = {}
    for lst in ranked_lists:                  # one list per retriever
        for rank, doc in enumerate(lst, start=1):  # rank 1 = best
            scores[doc] = scores.get(doc, 0) + 1/(k + rank)
    return sorted(scores.items(), key=lambda kv: -kv[1])

# Each list is already ordered best -> worst by that engine.
faiss_ranked = ["A", "C", "E", "D", "B"]   # A=#1, C=#2, ...
bm25_ranked  = ["B", "C", "F", "D", "G"]   # B=#1, C=#2, ...
for doc, sc in rrf([faiss_ranked, bm25_ranked], k=60):
    print(f"  {sc:.5f}  {doc}")
                    

6. Production hybrid retriever (LangChain) #

# Real pipeline. Requires: langchain, langchain-community, faiss-cpu,
# rank_bm25, and an embeddings provider. See the Lab for install + keys.
from langchain_community.vectorstores import FAISS
from langchain_community.retrievers import BM25Retriever
from langchain.retrievers import EnsembleRetriever
from langchain_community.embeddings import HuggingFaceEmbeddings

docs = [
    "Q4 revenue for Project Alpha: income pushbacks delayed booking.",
    "IT ticket #4471: Project Alpha laptop revenue dashboard down.",
    "Financial report - Q4 Alpha income recognized this quarter.",
]

# 1) Dense engine: embed docs into FAISS (the Behavioral Profiler).
emb = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
faiss_store = FAISS.from_texts(docs, emb)
faiss_ret = faiss_store.as_retriever(search_kwargs={"k": 3})

# 2) Sparse engine: build an in-memory BM25 index (the Fingerprint Analyst).
bm25_ret = BM25Retriever.from_texts(docs)
bm25_ret.k = 3

# 3) Fuse: EnsembleRetriever runs both in parallel and applies RRF (k=60).
hybrid = EnsembleRetriever(
    retrievers=[faiss_ret, bm25_ret],
    weights=[0.5, 0.5],          # balanced; let RRF decide order
    )

for d in hybrid.invoke("Q4 Alpha revenue"):
    print("-", d.page_content)
                    

Section IV

Hands-On Lab — build the dual-engine pipeline

Six graded exercises, warm-up → challenge. Each: a goal, numbered steps, a predict-then-verify prompt, and a collapsible solution. The first four run with pure standard library; the last two add real libraries.

# 1) Check Python (need 3.11+)
python --version            # -> Python 3.11.x
# 2) Make a project folder
mkdir hybrid-lab && cd hybrid-lab

# 3) Virtual env (keeps installs isolated)
python -m venv .venv
source .venv/bin/activate    # Windows: .venv\Scripts\activate
# 4) Installs —only needed for Ex5 &Ex6 (Ex1-4 are stdlib only)
pip install rank_bm25 langchain langchain-community faiss-cpu sentence-transformers

# 5) Verify the install
python -c "import rank_bm25, langchain, faiss; print('ok')"
# -> ok
                    

from faiss_blindspot import fake_embed, cosine
x  = fake_embed("INV-2023-99X")
po = fake_embed("PO-2024-12A")   # none of its tokens are in 'dims' -> [0,0,0]
print(cosine(x, po))            # -> 0.0  (orthogonal: no shared concept)
                                

from bm25_basics import BM25, corpus
bm = BM25(corpus)
print(bm.score("dog", 1))            # >0: 'dog' literally in doc1
print(bm.score("budget report", 0)) # sum of per-term contributions
                                

from bm25_basics import BM25
reps = ["budget", "budget " *10, "budget " *100]
for k1 in(0, 1.5, 10):
    bm = BM25(reps, k1=k1)
    row = [round(bm.score("budget", i), 3) for i in range(3)]
    print(f"k1={k1:>4}: 1x/10x/100x = {row}") 

from rrf_fusion import rrf
docs = {"A" :{"f" :.88,"b" :0.0}, "B" :{"f" :.40,"b" :12.5},
        "C" :{"f" :.80,"b" :1.2}, "D" :{"f" :.10,"b" :80.0}}
faiss_rank = [d for d,_ in sorted(docs.items(), key=lambda kv:-kv[1]["f"])]
bm25_rank  = [d for d,_ in sorted(docs.items(), key=lambda kv:-kv[1]["b"])]
print("faiss:", faiss_rank)   # ['A','C','B','D'] print("bm25 :", bm25_rank)   # ['D','B','C','A'] for d,s in rrf([faiss_rank, bm25_rank]): print(f"  {s:.4f} {d}") 

from ensemble_retriever import faiss_ret, bm25_ret, hybrid
q = "Q4 Alpha revenue INV-2023-99X"
print("FAISS :", [d.page_content[:30] for d in faiss_ret.invoke(q)])
print("BM25  :", [d.page_content[:30] for d in bm25_ret.invoke(q)])
print("HYBRID:", [d.page_content[:30] for d in hybrid.invoke(q)])
                                

from sentence_transformers import CrossEncoder
from ensemble_retriever import hybrid

q = "booking delays on the Alpha account, ref INV-2023-99X"
cands = hybrid.invoke(q)[:3]                       # wide net -> top 3
ce = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")
pairs  = [(q, d.page_content) for d in cands]
scores = ce.predict(pairs)                          # joint query+doc scoring
reranked = sorted(zip(scores, cands), key=lambda x: -x[0])
for s, d in reranked:
    print(f"  {s:6.2f}  {d.page_content[:50]}") 

Private study notes. + marks my own enrichments and corrections beyond the core material; everything tagged was verified against the BM25 (Okapi) and RRF (Cormack et al., 2009) standard definitions before being written down.