When Accuracy is
Non-Negotiable

# Hybrid legal document search
results = client.query_points(
  collection_name="legal_docs",
  prefetch=[
      Prefetch(query=dense_emb, using="dense",
          limit=100),
      Prefetch(query=sparse_emb, using="sparse",
          limit=100),
  ],
  query=FusionQuery(fusion=Fusion.RRF),
  query_filter=Filter(must=[
      FieldCondition(key="jurisdiction",
          match=MatchValue("california")),
      FieldCondition("case_type",
          match=MatchValue("patent")),
  ]),
  limit=20,
)

# RAG: retrieve legal context for AI assistant
review_chunks = client.query_points(
    collection_name="case_law",
    prefetch=[
        Prefetch(query=question_emb, using="dense",
            limit=50),
        Prefetch(query=question_sparse, using="sparse",
            limit=50),
    ],
    query=FusionQuery(fusion=Fusion.RRF),
    query_filter=Filter(must=[
        FieldCondition("jurisdiction",
            match=MatchValue("australia")),
    ]),
    limit=10,
)

# Feed to LLM with grounded context
context = "\n".join([r.payload["text"]
    for r in review_chunks.points])
answer = llm.generate(
    f"Based on case law: {context}\n"
    f"Question: {user_question}"
)

# Prior art search at billion scale
results = client.query_points(
    collection_name="patents",
    query=claim_embedding,
    query_filter=Filter(must=[
        FieldCondition("grant_date",
            range=Range(lte="2015-06-01")),
        FieldCondition("technology_class",
            match=MatchAny(["H04L", "G06F"])),
    ]),
    params=SearchParams(
        quantization=QuantizationSearchParams(
            rescore=True,
            oversampling=2.0
        )
    ),
    limit=50,
)

# Claim-chart matching via Recommend API
similar = client.query_points(
    collection_name="patents",
    query=RecommendQuery(
        recommend=RecommendInput(
            positive=[claim_vector_id],
            negative=[known_irrelevant_id],
        )
    ),
    limit=25,
)