# openai — full per-query output, all 52 queries Runs `2026-08-16-r1`/`-r2` · region `ap-south-1` · ground truth frozen 2026-08-16T17:20:32Z sha256 `011da0f8…0cb7d28`. Modes: answer. Reproducible from `openai/input-*/outputs/raw//.json`. **How to read this.** `top3 PASS/FAIL` asks only whether one hand-picked ground-truth URL appeared in the first 3 results — a FAIL does not mean the tool was wrong. Citation `unreadable` means our checker could not fetch the page (paywall, JS shell, PDF, 403, non-English); only `**FALSE**` means read and unsupported. Grades tagged `graded_by=claude-pass-1` are one model reader's pass, not a human sign-off. **Calls:** 52 across 1 mode(s) · **errors:** 0 --- ## Q01 · current-fact **Query:** `Exa API price per 1000 search requests` **Expected:** $7 per 1,000 search requests (increased from $5 in March 2026) **Supporting URL(s):** https://exa.ai/pricing **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 6003.6ms > Exa API search pricing is currently: > > - **$7 per 1,000 search requests** for standard Search (`instant`, `fast`, or `auto`) — **$0.007/request**. > - **$12 per 1,000 requests** for Deep Search — **$0.012/request**. > - **$15 per 1,000 requests** for Deep-Reasoning Search — **$0.015/request**. > > These base prices include up to **10 results per request**. Additional results generally cost **$1 per 1,000 results**, and generated page summaries cost **$1 per 1,000 pages**. ([exa.ai](https://exa.ai/pricing?tab=api&utm_source=openai)) _Citations (1):_ 1. [ok] https://exa.ai/pricing?tab=api&utm_source=openai --- ## Q02 · current-fact **Query:** `current CEO of Nebius` **Expected:** Arkady Volozh (founder and CEO) **Supporting URL(s):** https://en.wikipedia.org/wiki/Nebius_Group|https://nebius.com/about **openai/answer** — **CORRECT** · top3 PASS · hit rank 1 · $0.01 · 4743.5ms > The current CEO of **Nebius** is **Arkady Volozh**, who is also the company’s founder. ([nebius.com](https://nebius.com/about?utm_source=openai)) _Citations (1):_ 1. [ok] https://nebius.com/about?utm_source=openai --- ## Q03 · current-fact **Query:** `Bank of England base rate` **Expected:** 3.75% **Supporting URL(s):** https://www.bankofengland.co.uk/monetary-policy/the-interest-rate-bank-rate **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 4616.3ms > The **Bank of England Bank Rate (base rate) is 3.75%**. > > It was maintained at **3.75% on July 30, 2026**. The next scheduled decision is **September 17, 2026**. ([bankofengland.co.uk](https://www.bankofengland.co.uk/monetary-policy-summary-and-minutes/2026/july-2026?utm_source=openai)) _Citations (1):_ 1. [ok] https://www.bankofengland.co.uk/monetary-policy-summary-and-minutes/2026/july-2026?utm_source=openai --- ## Q04 · current-fact **Query:** `RBI repo rate current` **Expected:** 5.25% **Supporting URL(s):** https://www.rbi.org.in/Scripts/BS_PressReleaseDisplay.aspx?prid=63287|https://www.rbi.org.in **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 5537.7ms > The **current RBI policy repo rate is 5.25%**. The RBI’s listed rates also include SDF at 5.00% and MSF/bank rate at 5.50%. ([m.rbi.org.in](https://m.rbi.org.in/SCRIPTs/FAQView.aspx?Id=107&utm_source=openai)) _Citations (1):_ 1. [unreadable] https://m.rbi.org.in/SCRIPTs/FAQView.aspx?Id=107&utm_source=openai --- ## Q05 · current-fact **Query:** `latest stable release version of PostgreSQL` **Expected:** 18.6 (released 13 August 2026) **Supporting URL(s):** https://www.postgresql.org/support/versioning/|https://en.wikipedia.org/wiki/PostgreSQL **openai/answer** — ****WRONG**** · top3 PASS · hit rank 1 · $0.01 · 6934.4ms > The latest stable PostgreSQL release is **PostgreSQL 18.4** (released **May 14, 2026**). PostgreSQL **19** is still in beta and is not a stable release. ([postgresql.org](https://www.postgresql.org/support/versioning/?utm_source=openai)) _Citations (1):_ 1. [ok] https://www.postgresql.org/support/versioning/?utm_source=openai --- ## Q06 · current-fact **Query:** `current UN Secretary-General` **Expected:** António Guterres (9th Secretary-General) **Supporting URL(s):** https://www.un.org/sg/en/content/sg/biography|https://en.wikipedia.org/wiki/Ant%C3%B3nio_Guterres **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 4306.8ms > The current UN Secretary-General is **António Guterres of Portugal**. He has served since **January 1, 2017**. ([un.org](https://www.un.org/sg/en?utm_source=openai)) _Citations (1):_ 1. [ok] https://www.un.org/sg/en?utm_source=openai --- ## Q07 · current-fact **Query:** `who owns Instagram parent company` **Expected:** Meta Platforms, Inc. **Supporting URL(s):** https://www.britannica.com/money/Instagram|https://en.wikipedia.org/wiki/Instagram **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 5943.5ms > Instagram is owned by **Meta Platforms, Inc.**, the publicly traded company formerly known as Facebook, Inc. Meta acquired Instagram in **2012**. ([investor.fb.com](https://investor.fb.com/files/doc_downloads/2024/05/2024-anti-slavery-and-human-trafficking-statement.pdf?utm_source=openai)) > > Meta is owned by its shareholders, but **Mark Zuckerberg** has effective voting control through Meta’s dual-class share structure. ([investor.fb.com](https://investor.fb.com/files/doc_financials/annual_reports/FB_AR_33501_FINAL.pdf?utm_source=openai)) _Citations (2):_ 1. [unreadable] https://investor.fb.com/files/doc_downloads/2024/05/2024-anti-slavery-and-human-trafficking-statement.pdf?utm_source=openai 2. [unreadable] https://investor.fb.com/files/doc_financials/annual_reports/FB_AR_33501_FINAL.pdf?utm_source=openai --- ## Q08 · current-fact **Query:** `who is the CEO of OpenAI` **Expected:** Sam Altman **Supporting URL(s):** https://www.britannica.com/money/Sam-Altman|https://en.wikipedia.org/wiki/Sam_Altman **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 4992.7ms > **Sam Altman** is the CEO of OpenAI. ([openai.com](https://openai.com/index/scaling-ai-for-everyone/?utm_source=openai)) _Citations (1):_ 1. [ok] https://openai.com/index/scaling-ai-for-everyone/?utm_source=openai --- ## Q09 · current-fact **Query:** `FIFA World Cup 2026 host countries` **Expected:** Canada, Mexico, United States **Supporting URL(s):** https://www.fifa.com/en/tournaments/mens/worldcup/canadamexicousa2026/host-cities|https://en.wikipedia.org/wiki/2026_FIFA_World_Cup **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 4603.1ms > The **FIFA World Cup 2026** will be co-hosted by: > > - **Canada** > - **Mexico** > - **United States** > > It will be the first men’s World Cup hosted across **three countries**, with matches in 16 host cities. ([fifa.com](https://www.fifa.com/en/tournaments/mens/worldcup/canadamexicousa2026/articles/fifa-world-cup-2026-hosts-cities-dates-usa-mexico-canada?searchOverlay=1&utm_source=openai)) _Citations (1):_ 1. [unreadable] https://www.fifa.com/en/tournaments/mens/worldcup/canadamexicousa2026/articles/fifa-world-cup-2026-hosts-cities-dates-usa-mexico-canada?searchOverlay=1&utm_source=openai --- ## Q10 · current-fact **Query:** `capital city of Australia` **Expected:** Canberra **Supporting URL(s):** https://www.britannica.com/place/Canberra|https://en.wikipedia.org/wiki/Canberra **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 3444.1ms > Canberra. --- ## Q11 · niche-technical **Query:** `pandas SettingWithCopyWarning chained assignment fix` **Expected:** Replace chained indexing with a single .loc[row, col] = value call, which is label-based and accepts a tuple of row and column indices **Supporting URL(s):** https://pandas.pydata.org/docs/user_guide/indexing.html#returning-a-view-versus-a-copy|https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html **openai/answer** — **CORRECT** · top3 PASS · hit rank 1 · $0.01 · 6848.2ms > `SettingWithCopyWarning` means pandas cannot tell whether you are assigning to the original `DataFrame` or to a temporary slice. The fix is usually to perform the selection and assignment in **one operation with `.loc`**. Chained assignment will not work reliably—and never works with pandas 3.0’s Copy-on-Write behavior. ([pandas.pydata.org](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html?highlight=boolean+index&utm_source=openai)) > > ### Common fix > > ```python > # Bad: chained assignment > df[df["status"] == "open"]["priority"] = "high" > > # Good: single-step assignment > df.loc[df["status"] == "open", "priority"] = "high" > ``` > > ### More examples > > ```python > # Bad > df["price"][df["quantity"] > 10] = 0 > > # Good > df.loc[df["quantity"] > 10, "price"] = 0 > ``` > > ```python > # Multiple columns > df.loc[df["active"], ["score", "rank"]] = [100, 1] > ``` > > ```python > # Positional indexing > df.iloc[row_positions, column_position] = value > ``` > > ### If you intentionally want an independent slice > > Use `.copy()` explicitly: > > ```python > subset = df.loc[df["status"] == "open"].copy() > subset["priority"] = "high" > ``` > > This modifies `subset`, not `df`. > > ### If you want to modify the original after creating a subset > > Avoid assigning through the subset: > > ```python > # Bad > subset = df[df["status"] == "open"] > subset["priority"] = "high" > > # Good > mask = df["status"].eq("open") > df.loc[mask, "priority"] = "high" > ``` > > ### Avoid this “fix” > > ```python > pd.options.mode.chained_assignment = None > ``` > > That only hides the warning; it does not make the assignment safe. For debugging, you can instead make pandas raise an exception: > > ```python > pd.set_option("mode.chained_assignment", "raise") > ``` > > Also avoid chained `.loc`/`.iloc` expressions such as: > > ```python > df.loc[mask]["priority"] = "high" # still chained > ``` > > Use: > > ```python > df.loc[mask, "priority"] = "high" > ``` > > ### Rule of thumb > > - **Modify the original:** `df.loc[row_condition, column] = value` > - **Create and modify a separate object:** `df.loc[row_condition].copy()` > - **Do not use:** `df[...][...] = value` or `df.loc[...][...] = value` _Citations (1):_ 1. [ok] https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html?highlight=boolean+index&utm_source=openai --- ## Q12 · niche-technical **Query:** `psycopg2 OperationalError SSL SYSCALL error EOF detected` **Expected:** The server closed the connection abruptly (often insufficient CPU/RAM under load, or idle timeout). psycopg2's pool has no built-in disconnection check; maintainer recommends psycopg3's pool, increasing server resources, or manually closing bad connections before putconn **Supporting URL(s):** https://github.com/psycopg/psycopg2/issues/1445 **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 15220.9ms > `psycopg2.OperationalError: SSL SYSCALL error: EOF detected` means the PostgreSQL client was using SSL and the connection was closed unexpectedly before libpq received a normal PostgreSQL response. In PostgreSQL’s libpq implementation, an EOF during `SSL_read()` is treated as a broken connection; Psycopg exposes unexpected disconnects as `OperationalError`. ([doxygen.postgresql.org](https://doxygen.postgresql.org/fe-secure-openssl_8c.html?utm_source=openai)) > > It is usually **not a SQL syntax error** and does not necessarily indicate a bad certificate. > > ## Common causes > > 1. **PostgreSQL restarted, crashed, or terminated the backend** > - Check PostgreSQL logs around the exact failure time. > - Look for messages such as “server closed the connection unexpectedly,” crash recovery, out-of-memory termination, or failover. > > 2. **A stale pooled connection** > - A load balancer, firewall, NAT gateway, or cloud database proxy may close an idle TCP connection. > - Your application later reuses that dead connection. > > 3. **Network or proxy interruption** > - Temporary connectivity loss, container restarts, VPN changes, failover, or a proxy timeout can produce this error. > > 4. **Resource exhaustion** > - PostgreSQL may be unable to create backends, or the host/container may kill processes because of memory or CPU pressure. > > 5. **Client-library or SSL-library mismatch** > - `psycopg2-binary` bundles its own `libpq` and OpenSSL libraries. Psycopg’s documentation recommends building from source for production deployments, partly because bundled SSL libraries can conflict with other Python extensions. ([psycopg.org](https://www.psycopg.org/docs/install.html?utm_source=openai)) > > ## First troubleshooting steps > > ### 1. Determine when it happens > > - **During `psycopg2.connect()`**: inspect SSL configuration, endpoint/proxy behavior, certificates, and network access. > - **During a query or fetch**: suspect a server restart, network interruption, query timeout, proxy timeout, or stale connection. > - **After the application has been idle**: strongly suspect connection-pool or infrastructure idle timeouts. > > ### 2. Test outside Python > > ```bash > psql "host=HOST dbname=DB user=USER sslmode=require connect_timeout=10" > ``` > > If `psql` also fails, the problem is probably PostgreSQL, networking, the proxy, or TLS configuration rather than Psycopg-specific code. > > You can also test both modes temporarily: > > ```bash > psql "host=HOST dbname=DB user=USER sslmode=disable connect_timeout=10" > psql "host=HOST dbname=DB user=USER sslmode=require connect_timeout=10" > ``` > > Do not permanently disable SSL unless the connection is on a trusted private network. PostgreSQL’s `sslmode` controls whether SSL is disabled, preferred, required, or certificate-verified. ([postgresql.org](https://www.postgresql.org/docs/16/libpq-connect.html?utm_source=openai)) > > ### 3. Check what libpq/Psycopg is using > > ```python > import psycopg2 > > print(psycopg2.__version__) > print(psycopg2.__libpq_version__) > > conn = psycopg2.connect( > host="...", > dbname="...", > user="...", > password="...", > sslmode="require", > connect_timeout=10, > ) > > print(conn.get_dsn_parameters()) > print(conn.info.ssl_in_use) > ``` > > If you installed the binary package, try a clean environment using the system-linked package: > > ```bash > pip uninstall psycopg2-binary psycopg2 > pip install psycopg2 > ``` > > Installing `psycopg2` from source requires PostgreSQL client headers and libraries. > > ## Handling stale connections > > If you use a pool, validate or recycle connections rather than assuming they remain alive forever. With `ThreadedConnectionPool`, a basic pattern is: > > ```python > from contextlib import contextmanager > from psycopg2.pool import ThreadedConnectionPool > import psycopg2 > > pool = ThreadedConnectionPool( > minconn=1, > maxconn=10, > host="...", > dbname="...", > user="...", > password="...", > sslmode="require", > connect_timeout=10, > keepalives=1, > keepalives_idle=60, > keepalives_interval=15, > keepalives_count=4, > ) > > @contextmanager > def get_connection(): > conn = pool.getconn() > try: > if conn.closed: > raise psycopg2.OperationalError("closed pooled connection") > yield conn > except psycopg2.OperationalError: > # The connection is no longer trustworthy. > try: > conn.close() > finally: > pool.putconn(conn, close=True) > raise > else: > pool.putconn(conn) > ``` > > For production, a pooler such as PgBouncer or an application pool should also be configured to recycle connections before the database proxy or load balancer does. > > ## Retry carefully > > An `OperationalError` can occur after PostgreSQL has received and executed a statement but before the client receives the result. Therefore, blindly retrying writes can duplicate effects. > > Retry only when: > > - the operation is read-only, or > - the transaction is known to have been rolled back, or > - the operation is idempotent, such as an appropriately designed `INSERT ... ON CONFLICT`. > > Example transaction recovery: > > ```python > try: > with conn: > with conn.cursor() as cur: > cur.execute("SELECT ...") > except psycopg2.OperationalError: > conn.close() > # Obtain a new connection, then retry only if safe. > ``` > > A broken connection should generally be discarded and replaced; do not continue using it after this exception. PostgreSQL classifies connection failures such as `08006` as operational errors, and Psycopg maps those conditions under `OperationalError`. ([psycopg.org](https://www.psycopg.org/docs/errors.html?utm_source=openai)) > > ## Most useful diagnostic information > > Record: > > - whether the error occurs on connect, execute, or fetch; > - timestamp and database endpoint; > - whether a pool, PgBouncer, RDS/Azure/Cloud SQL proxy, or load balancer is involved; > - PostgreSQL server logs at that timestamp; > - PostgreSQL, `libpq`, Python, and Psycopg versions; > - whether it happens after a predictable idle period; > - whether the same operation succeeds using `psql`. > > If it occurs **only after idle periods**, recycle pooled connections and investigate infrastructure idle timeouts. If it occurs **during active queries**, start with PostgreSQL logs, server restarts/failovers, OOM events, and proxy/network logs. _Citations (4):_ 1. [ok] https://doxygen.postgresql.org/fe-secure-openssl_8c.html?utm_source=openai 2. [ok] https://www.psycopg.org/docs/install.html?utm_source=openai 3. [ok] https://www.postgresql.org/docs/16/libpq-connect.html?utm_source=openai 4. [ok] https://www.psycopg.org/docs/errors.html?utm_source=openai --- ## Q13 · niche-technical **Query:** `httpx ReadTimeout vs ConnectTimeout default values` **Expected:** Both default to 5 seconds; httpx raises a TimeoutException after 5s of network inactivity, across connect, read, write, and pool timeouts **Supporting URL(s):** https://www.python-httpx.org/advanced/timeouts **openai/answer** — **CORRECT** · top3 PASS · hit rank 1 · $0.01 · 6866.1ms > In HTTPX, the default values are: > > - **`ConnectTimeout`: 5 seconds** — maximum time to establish a connection to the host. > - **`ReadTimeout`: 5 seconds** — maximum time to wait for the next chunk of response data. > > HTTPX’s default is effectively: > > ```python > httpx.Timeout(5.0) > ``` > > That sets **connect, read, write, and pool timeouts to 5 seconds**. The timeout applies to **network inactivity**, not necessarily the total request duration. ([python-httpx.org](https://www.python-httpx.org/advanced/timeouts/?utm_source=openai)) > > ```python > import httpx > > try: > response = httpx.get("https://example.com") > except httpx.ConnectTimeout: > print("Could not establish the connection within 5 seconds") > except httpx.ReadTimeout: > print("No response data arrived within 5 seconds") > ``` > > To configure them separately: > > ```python > timeout = httpx.Timeout( > timeout=10.0, # default for read, write, and pool > connect=3.0, # connection establishment > ) > > client = httpx.Client(timeout=timeout) > ``` > > Or explicitly: > > ```python > timeout = httpx.Timeout( > connect=3.0, > read=30.0, > write=10.0, > pool=5.0, > ) > ``` > > `ConnectTimeout` means it timed out **while connecting**; `ReadTimeout` means it timed out **while receiving data**. ([python-httpx.org](https://www.python-httpx.org/exceptions/?utm_source=openai)) _Citations (2):_ 1. [ok] https://www.python-httpx.org/advanced/timeouts/?utm_source=openai 2. [ok] https://www.python-httpx.org/exceptions/?utm_source=openai --- ## Q14 · niche-technical **Query:** `pydantic v2 model_config replaces class Config` **Expected:** Pydantic V2 renamed the V1 API - inner-class-based configuration and dict()-style methods are replaced by model_ prefixed equivalents (model_config, model_dump, model_validate) **Supporting URL(s):** https://docs.pydantic.dev/latest/migration **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 6521.6ms > Yes. In **Pydantic v2**, replace the nested `class Config` with a `model_config` attribute, preferably using `ConfigDict`. `class Config` is deprecated. ([docs.pydantic.dev](https://docs.pydantic.dev/2.3/blog/pydantic-v2-alpha/?utm_source=openai)) > > ### Pydantic v1 > > ```python > from pydantic import BaseModel > > > class User(BaseModel): > name: str > > class Config: > orm_mode = True > extra = "forbid" > ``` > > ### Pydantic v2 > > ```python > from pydantic import BaseModel, ConfigDict > > > class User(BaseModel): > name: str > > model_config = ConfigDict( > from_attributes=True, > extra="forbid", > ) > ``` > > A plain dictionary also works: > > ```python > class User(BaseModel): > name: str > > model_config = { > "extra": "forbid", > "str_strip_whitespace": True, > } > ``` > > Common setting changes: > > | Pydantic v1 | Pydantic v2 | > |---|---| > | `orm_mode = True` | `from_attributes=True` | > | `allow_population_by_field_name = True` | `validate_by_name=True` | > | `allow_mutation = False` | `frozen=True` | > | `anystr_strip_whitespace = True` | `str_strip_whitespace=True` | > | `schema_extra = {...}` | `json_schema_extra={...}` | > | `validate_all = True` | `validate_default=True` | > > Also, don’t define both `class Config` and `model_config` in the same model; Pydantic raises an error. ([docs.pydantic.dev](https://docs.pydantic.dev/2.12/errors/usage_errors/?utm_source=openai)) _Citations (2):_ 1. [ok] https://docs.pydantic.dev/2.3/blog/pydantic-v2-alpha/?utm_source=openai 2. [ok] https://docs.pydantic.dev/2.12/errors/usage_errors/?utm_source=openai --- ## Q15 · niche-technical **Query:** `ruff rule code for unused loop variable` **Expected:** B007 (unused-loop-control-variable); prefix the variable with an underscore if intentionally unused **Supporting URL(s):** https://docs.astral.sh/ruff/rules/unused-loop-control-variable **openai/answer** — **CORRECT** · top3 PASS · hit rank 1 · $0.01 · 5219.6ms > The Ruff rule is **`B007`** — `unused-loop-control-variable`. ([docs.astral.sh](https://docs.astral.sh/ruff/rules/unused-loop-control-variable/?utm_source=openai)) > > ```python > for i in items: # B007 > process() > ``` > > Use an underscore-prefixed variable if intentional: > > ```python > for _i in items: > process() > ``` > > Or, if the value itself is irrelevant: > > ```python > for _ in items: > process() > ``` _Citations (1):_ 1. [unreadable] https://docs.astral.sh/ruff/rules/unused-loop-control-variable/?utm_source=openai --- ## Q16 · niche-technical **Query:** `polars lazy frame collect streaming engine memory` **Expected:** Pass engine="streaming" to collect() to process larger-than-RAM data in batches; unsupported operations fall back to the in-memory engine **Supporting URL(s):** https://docs.pola.rs/user-guide/concepts/streaming **openai/answer** — **CORRECT** · top3 PASS · hit rank 1 · $0.01 · 11128.0ms > In Polars, use the streaming engine like this: > > ```python > import polars as pl > > result = ( > pl.scan_parquet("large_file.parquet") > .filter(pl.col("status") == "active") > .select(["id", "amount"]) > .group_by("id") > .agg(pl.col("amount").sum()) > .collect(engine="streaming") > ) > ``` > > ### What it does for memory > > `engine="streaming"` processes the lazy query in batches instead of materializing every intermediate result at once. This generally reduces peak memory usage and can be faster than the in-memory engine. ([docs.pola.rs](https://docs.pola.rs/user-guide/concepts/streaming/?utm_source=openai)) > > However, `.collect()` still returns a regular `DataFrame`, so the **final result must fit in memory**. Streaming does not make an arbitrarily large output memory-free. > > ### Prefer lazy scans > > Use `scan_*` rather than eagerly reading the whole source: > > ```python > lf = ( > pl.scan_csv("large.csv") > .filter(pl.col("value") > 0) > .select(["id", "value"]) > ) > > df = lf.collect(engine="streaming") > ``` > > This allows predicate and projection pushdown, so Polars may read fewer rows and columns. > > ### If the result is too large to collect > > Write directly to disk instead of building one large final `DataFrame`: > > ```python > ( > pl.scan_parquet("input/*.parquet") > .filter(pl.col("value") > 0) > .sink_parquet("output.parquet", engine="streaming") > ) > ``` > > Other options include `sink_csv`, `sink_ipc`, or `sink_batches`. ([docs.pola.rs](https://docs.pola.rs/api/python/stable/reference/api/polars.LazyFrame.sink_ipc.html?utm_source=openai)) > > ### Operations that may still require substantial memory > > Streaming helps most with scan/filter/select/with-columns and many aggregations, but operations such as global sorting, some joins, `unique`, window operations, and large group-bys may need to retain significant state. If an operation is unsupported by the streaming engine, Polars may fall back to another engine. ([docs.pola.rs](https://docs.pola.rs/api/python/stable/reference/lazyframe/?utm_source=openai)) > > ### Check the execution plan > > ```python > print(lf.explain()) > ``` > > For debugging engine behavior: > > ```bash > POLARS_VERBOSE=1 python script.py > ``` > > In current Polars versions, use `engine="streaming"`; the older `streaming=True` argument is deprecated. ([docs.pola.rs](https://docs.pola.rs/api/python/stable/reference/lazyframe/?utm_source=openai)) _Citations (4):_ 1. [ok] https://docs.pola.rs/user-guide/concepts/streaming/?utm_source=openai 2. [ok] https://docs.pola.rs/api/python/stable/reference/api/polars.LazyFrame.sink_ipc.html?utm_source=openai 3. [ok] https://docs.pola.rs/api/python/stable/reference/lazyframe/?utm_source=openai 4. [ok] https://docs.pola.rs/api/python/stable/reference/lazyframe/?utm_source=openai --- ## Q17 · niche-technical **Query:** `Playwright locator strict mode violation multiple elements` **Expected:** The locator matched more than one element while the action requires exactly one; opt out with locator.first, locator.last, or locator.nth(), or narrow the locator **Supporting URL(s):** https://playwright.dev/docs/locators|https://playwright.dev/python/docs/locators#strictness **openai/answer** — **CORRECT** · top3 PASS · hit rank 1 · $0.01 · 7913.6ms > **“Locator strict mode violation”** means your locator matches **more than one element**, but the operation requires exactly one target—such as `click()`, `fill()`, or `check()`. Playwright locators are strict by default. ([playwright.dev](https://playwright.dev/docs/locators?utm_source=openai)) > > ### Example problem > > ```ts > await page.getByRole('button', { name: 'Submit' }).click(); > ``` > > If two buttons named **Submit** exist, Playwright throws a strict-mode violation. > > ### Preferred fix: make the locator unique > > Use a more specific role, accessible name, test ID, or container: > > ```ts > await page.getByRole('button', { name: 'Submit', exact: true }).click(); > ``` > > ```ts > await page.getByTestId('submit-button').click(); > ``` > > ```ts > await page > .getByRole('dialog') > .getByRole('button', { name: 'Submit' }) > .click(); > ``` > > You can also filter by surrounding content: > > ```ts > await page > .getByRole('listitem') > .filter({ hasText: 'John' }) > .getByRole('button', { name: 'Say hello' }) > .click(); > ``` > > ### If choosing one match is intentional > > ```ts > await page.getByRole('button', { name: 'Submit' }).first().click(); > ``` > > ```ts > await page.getByRole('button', { name: 'Submit' }).nth(1).click(); > ``` > > `first()`, `last()`, and `nth()` explicitly opt out of strictness, but they can become fragile if the page order changes. Prefer a unique locator where possible. ([playwright.dev](https://playwright.dev/docs/locators?utm_source=openai)) > > ### Debug how many elements match > > ```ts > const submit = page.getByRole('button', { name: 'Submit' }); > > console.log(await submit.count()); > console.log(await submit.allTextContents()); > ``` > > For a locator intended to match exactly one element: > > ```ts > await expect(submit).toHaveCount(1); > ``` > > For a list where multiple matches are expected: > > ```ts > const buttons = page.getByRole('button'); > console.log(await buttons.count()); > ``` > > Operations such as `count()` support multiple elements; actions such as `click()` generally require one. ([playwright.dev](https://playwright.dev/docs/locators?utm_source=openai)) _Citations (3):_ 1. [ok] https://playwright.dev/docs/locators?utm_source=openai 2. [ok] https://playwright.dev/docs/locators?utm_source=openai 3. [ok] https://playwright.dev/docs/locators?utm_source=openai --- ## Q18 · niche-technical **Query:** `tiktoken cl100k_base vs o200k_base difference` **Expected:** cl100k_base is the encoding for gpt-4, gpt-4-turbo, gpt-3.5-turbo and embeddings; o200k_base is the encoding for gpt-4o and gpt-4o-mini **Supporting URL(s):** https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken|https://developers.openai.com/cookbook/examples/how_to_count_tokens_with_tiktoken **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 8706.1ms > ## Short version > > - **`cl100k_base`**: older ~100k-token vocabulary, used by GPT-4 and GPT-3.5-turbo. > - **`o200k_base`**: newer ~200k-token vocabulary, used by GPT-4o, GPT-4.1, o-series reasoning models, and current GPT-5-family mappings in `tiktoken`. ([github.com](https://github.com/openai/tiktoken/blob/main/tiktoken/model.py?utm_source=openai)) > - `o200k_base` generally tokenizes **non-English text, multilingual text, and some common sequences more efficiently**, but it is not guaranteed to produce fewer tokens for every English string. > - The token IDs and token boundaries are different, so they are **not interchangeable** when counting tokens for a specific model. > > ## Main differences > > | Feature | `cl100k_base` | `o200k_base` | > |---|---:|---:| > | Approximate vocabulary size | 100k | 200k | > | Newer encoding | No | Yes | > | Typical models | GPT-4, GPT-3.5-turbo | GPT-4o, GPT-4.1, o1, o3, o4-mini, GPT-5-family | > | Multilingual efficiency | Good | Generally better | > | Token IDs | Different | Different | > | Use for API token estimates | Only with compatible models | Only with compatible models | > > The encodings also use different pre-tokenization patterns and merge tables, not merely a larger version of the same vocabulary. ([github.com](https://github.com/openai/tiktoken/blob/main/tiktoken_ext/openai_public.py?utm_source=openai)) > > ## Example > > OpenAI’s tokenizer comparison shows: > > ```text > お誕生日おめでとう > cl100k_base: 9 tokens > o200k_base: 8 tokens > ``` > > For the English word: > > ```text > antidisestablishmentarianism > cl100k_base: 6 tokens > o200k_base: 6 tokens > ``` > > So `o200k_base` can improve tokenization substantially for some languages and text patterns, but the difference depends on the input. ([github.com](https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb?short_path=9a82197&utm_source=openai)) > > ## Which one should you use? > > Prefer model-based selection: > > ```python > import tiktoken > > enc = tiktoken.encoding_for_model("gpt-4o") > tokens = enc.encode("Hello, world!") > > print(len(tokens)) > ``` > > Or explicitly select one: > > ```python > enc = tiktoken.get_encoding("cl100k_base") > enc = tiktoken.get_encoding("o200k_base") > ``` > > Use the encoding corresponding to the model whose token usage you are estimating. For example, using `cl100k_base` to estimate a GPT-4o request may give inaccurate counts because GPT-4o maps to `o200k_base`. ([github.com](https://github.com/openai/tiktoken/blob/main/tiktoken/model.py?utm_source=openai)) _Citations (4):_ 1. [ok] https://github.com/openai/tiktoken/blob/main/tiktoken/model.py?utm_source=openai 2. [ok] https://github.com/openai/tiktoken/blob/main/tiktoken_ext/openai_public.py?utm_source=openai 3. [ok] https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb?short_path=9a82197&utm_source=openai 4. [ok] https://github.com/openai/tiktoken/blob/main/tiktoken/model.py?utm_source=openai --- ## Q19 · niche-technical **Query:** `Postgres pg_stat_statements query normalization limitations` **Expected:** Constants are replaced with $1, but only inline-literal IN lists get squashed (shown as IN ($1 /*, ... */)); parameter-bound lists and structural differences (aliases) produce separate entries **Supporting URL(s):** https://www.postgresql.org/docs/current/pgstatstatements.html|https://www.postgresql.org/docs/18/pgstatstatements.html **openai/answer** — **CORRECT** · top3 PASS · hit rank 1 · $0.01 · 15120.5ms > `pg_stat_statements` normalization is **query-structure normalization**, not full SQL canonicalization or semantic equivalence detection. > > ## What it normally does > > Queries are grouped when their parsed structures are equivalent, typically ignoring literal values: > > ```sql > SELECT * FROM orders WHERE customer_id = 10; > SELECT * FROM orders WHERE customer_id = 42; > ``` > > These will generally share one entry, displayed approximately as: > > ```sql > SELECT * FROM orders WHERE customer_id = $1; > ``` > > The grouping is based on an internal query-jumble hash of the post-parse-analysis representation. ([postgresql.org](https://www.postgresql.org/docs/current/pgstatstatements.html?utm_source=openai)) > > ## Important limitations > > ### 1. It does not normalize arbitrary textual differences > > It is not intended to make all equivalent SQL text look the same. Differences in structure, operators, clauses, or expression forms can produce different entries: > > ```sql > WHERE id = 1 > WHERE id = $1 > WHERE id IN (1) > WHERE id = ANY (ARRAY[1]) > ``` > > Even if these have similar application-level behavior, they do not necessarily have the same query structure. > > Likewise, these are structurally different: > > ```sql > WHERE x = 1 > WHERE x < 1 > ``` > > Changing a literal value is usually normalized; changing the operation generally is not. ([postgresql.org](https://www.postgresql.org/docs/current/pgstatstatements.html?utm_source=openai)) > > ### 2. `IN`/array/list cardinality can affect grouping > > Historically, each constant in an array expression or `IN` list contributed to the query jumble. Consequently, queries such as: > > ```sql > WHERE id IN (1, 2) > WHERE id IN (1, 2, 3) > WHERE id IN (1, 2, 3, 4) > ``` > > could create separate `pg_stat_statements` entries. > > PostgreSQL 18 introduced `query_id_squash_values`, which can make long constant lists contribute less detail to the query ID and normalization. This reduces—but does not eliminate—all list-related fragmentation, and behavior depends on the PostgreSQL version and configuration. ([postgresql.org](https://www.postgresql.org/message-id/E1tubCw-0038AS-24%40gemulon.postgresql.org?utm_source=openai)) > > For stable grouping across arbitrary list sizes, prefer a single array parameter: > > ```sql > WHERE id = ANY ($1::bigint[]) > ``` > > ### 3. It does not reliably identify “same intent” > > The hash is based on the analyzed query tree, not on a proof that two queries have the same result or performance characteristics. For example, differences in: > > - implicit versus explicit casts, > - resolved operator/function overloads, > - column or relation references, > - schema qualification, > - `search_path`, > - collations or type resolution, > > can affect the analyzed tree and therefore the entry. > > Conversely, distinct queries have a small theoretical chance of hash collision. ([postgresql.org](https://www.postgresql.org/docs/17/pgstatstatements.html?utm_source=openai)) > > ### 4. The displayed `query` is only representative text > > The `query` column is not necessarily the exact SQL text of every execution represented by the row. It is representative text associated with the entry—normally the first observed text—and normalized constants may be shown as `$1`, `$2`, etc. Parentheses and other textual details can therefore differ from the statement actually sent by a later execution. ([postgresql.org](https://www.postgresql.org/docs/current/pgstatstatements.html?utm_source=openai)) > > Under heavy entry churn or deallocation, queries that could have been normalized may temporarily appear with literal values instead of parameter symbols. Increasing `pg_stat_statements.max` can reduce the likelihood. ([postgresql.org](https://www.postgresql.org/docs/17/pgstatstatements.html?utm_source=openai)) > > ### 5. `queryid` is not a permanent cross-version identifier > > `queryid` is useful for correlating statistics during the life of an installation, but it should not be treated as a globally stable fingerprint. It can change because of: > > - major PostgreSQL version changes, > - machine architecture, > - catalog object identities, > - dropped and recreated tables or other objects, > - differing metadata, > - different name resolution through `search_path`. > > PostgreSQL generally expects stability across minor releases only when relevant metadata and architecture remain compatible. Logical replicas also should not be assumed to produce compatible IDs for aggregating costs. ([postgresql.org](https://www.postgresql.org/docs/17/pgstatstatements.html?utm_source=openai)) > > ### 6. Entries are separated by more than query text > > A row is associated with a combination including database, user, query ID, and whether the statement is top-level. Thus, the same SQL executed by different users or databases is not necessarily one shared statistics row. ([postgresql.org](https://www.postgresql.org/docs/17/pgstatstatements.html?utm_source=openai)) > > ### 7. Prepared statements and application-generated SQL matter > > Normalization helps most when applications send ad hoc SQL containing literals. If the client already uses prepared statements or bind parameters, the query may already be normalized from the server’s perspective. > > On the other hand, ORM-generated SQL can fragment statistics through differences such as: > > ```sql > IN ($1, $2) > IN ($1, $2, $3) > ``` > > or varying generated clauses, aliases, casts, and selected columns. > > ## Practical recommendations > > For reliable aggregation: > > 1. **Use bind parameters**, rather than interpolating literals. > 2. For variable-length membership tests, prefer: > ```sql > WHERE key = ANY ($1::type[]) > ``` > over generating different-length `IN (...)` lists. > 3. Keep SQL shape stable across application code paths. > 4. Use `queryid` together with `dbid`, `userid`, and deployment/version metadata. > 5. Do not use the displayed `query` text as a unique canonical SQL identity. > 6. Increase `pg_stat_statements.max` if high churn causes frequent deallocation and apparent de-normalization. > 7. Treat major-version upgrades, schema rebuilds, and changes to name resolution as events that may invalidate historical `queryid` comparisons. > > In short: **literal values are normalized reasonably well, but SQL shape, parse analysis, list structure, object identity, and statistics-entry churn can all prevent queries from being grouped together.** _Citations (8):_ 1. [ok] https://www.postgresql.org/docs/current/pgstatstatements.html?utm_source=openai 2. [ok] https://www.postgresql.org/docs/current/pgstatstatements.html?utm_source=openai 3. [ok] https://www.postgresql.org/message-id/E1tubCw-0038AS-24%40gemulon.postgresql.org?utm_source=openai 4. [ok] https://www.postgresql.org/docs/17/pgstatstatements.html?utm_source=openai 5. [ok] https://www.postgresql.org/docs/current/pgstatstatements.html?utm_source=openai 6. [ok] https://www.postgresql.org/docs/17/pgstatstatements.html?utm_source=openai 7. [ok] https://www.postgresql.org/docs/17/pgstatstatements.html?utm_source=openai 8. [ok] https://www.postgresql.org/docs/17/pgstatstatements.html?utm_source=openai --- ## Q20 · niche-technical **Query:** `uv pip install break-system-packages equivalent` **Expected:** uv requires a virtual environment by default (use uv venv); to install into system Python use uv pip install --system, which is required to modify a non-virtual environment **Supporting URL(s):** https://docs.astral.sh/uv/reference/settings/|https://docs.astral.sh/uv/pip/environments **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 6390.7ms > The direct equivalent is: > > ```bash > uv pip install --break-system-packages > ``` > > For example: > > ```bash > uv pip install --break-system-packages requests > ``` > > You can also set it persistently: > > ```toml > # uv.toml > [pip] > break-system-packages = true > ``` > > Or via an environment variable: > > ```bash > export UV_BREAK_SYSTEM_PACKAGES=1 > ``` > > However, if you simply want to install into the system Python, `uv` commonly requires: > > ```bash > uv pip install --system > ``` > > `--system` opts into modifying a non-virtual/system environment; `--break-system-packages` specifically permits modification of an `EXTERNALLY-MANAGED` installation. Depending on the Python distribution, you may need both: > > ```bash > uv pip install --system --break-system-packages > ``` > > Use this cautiously, especially with OS-managed Python installations. ([docs.astral.sh](https://docs.astral.sh/uv/reference/cli/?utm_source=openai)) _Citations (1):_ 1. [ok] https://docs.astral.sh/uv/reference/cli/?utm_source=openai --- ## Q21 · niche-technical **Query:** `Kubernetes CrashLoopBackOff exit code 137 meaning` **Expected:** 137 = 128 + 9 (SIGKILL); the kernel OOM killer terminated the container for exceeding its memory limit. kubectl describe shows Reason: OOMKilled, Exit Code: 137 **Supporting URL(s):** https://kubernetes.io/docs/tasks/configure-pod-container/assign-memory-resource/|https://cast.ai/blog/kubernetes-exit-codes **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 5876.8ms > In Kubernetes, **CrashLoopBackOff with exit code 137** usually means the container was **killed with `SIGKILL` (signal 9)**: > > ```text > 137 = 128 + 9 > ``` > > The most common cause is **out-of-memory (OOM) termination**. > > ## How to confirm > > Check the pod details: > > ```bash > kubectl describe pod -n > ``` > > Look for: > > ```text > Reason: OOMKilled > Exit Code: 137 > ``` > > You can also run: > > ```bash > kubectl get pod -n \ > -o jsonpath='{.status.containerStatuses[*].lastState.terminated.reason}{"\n"}' > ``` > > Check recent events: > > ```bash > kubectl get events -n \ > --sort-by='.lastTimestamp' > ``` > > If metrics are available: > > ```bash > kubectl top pod -n > kubectl top node > ``` > > ## Common causes > > - The application exceeds its container memory limit. > - A memory leak or unexpectedly large workload. > - The node is under memory pressure. > - Multiple containers or pods compete for node memory. > - A startup process briefly requires more memory than the configured limit. > > ## Typical fixes > > Inspect the current resources: > > ```bash > kubectl get pod -n -o yaml > ``` > > Then adjust the workload configuration, for example: > > ```yaml > resources: > requests: > memory: "256Mi" > limits: > memory: "512Mi" > ``` > > Increase the **memory limit** if the application legitimately needs more memory: > > ```yaml > resources: > limits: > memory: "1Gi" > ``` > > Also investigate application behavior: > > - Review the previous container logs: > > ```bash > kubectl logs -n --previous > ``` > > - Check heap settings for Java, Go, Node.js, or similar runtimes. > - Reduce concurrency or batch sizes. > - Fix memory leaks. > - Verify whether the node itself is experiencing memory pressure. > > Note that **exit code 137 is not exclusively an OOM indicator**—a process can also receive `SIGKILL` for other reasons—but `OOMKilled` in `kubectl describe` confirms the usual cause. --- ## Q22 · niche-technical **Query:** `FFmpeg concat demuxer audio sync drift fix` **Expected:** The aresample filter can stretch/squeeze audio data to make it match the timestamps; use async (e.g. aresample=async=1) to correct sync drift **Supporting URL(s):** https://ffmpeg.org/ffmpeg-filters.html **openai/answer** — ****WRONG**** · top3 FAIL · hit rank not found · $0.01 · 19291.9ms > Audio drift with FFmpeg’s **concat demuxer** is usually caused by incorrect segment durations or incompatible timestamps—not by the concat operation itself. The demuxer uses each file’s duration to calculate the timestamp offset of the next file, so a wrong duration produces an increasingly offset timeline. ([ffmpeg.org](https://www.ffmpeg.org/ffmpeg-all.html?utm_source=openai)) > > ## 1. Diagnose the segments > > Run this for every input: > > ```bash > ffprobe -v error -show_streams -show_format \ > -of json input01.mp4 > ``` > > Check that all files have: > > - The same video codec, dimensions, and frame rate > - The same audio codec, sample rate, channel count/layout > - An audio stream in every segment > - Sensible `start_time` and `duration` values > > The concat demuxer expects matching streams, including compatible codecs, time bases, and stream layouts. ([ffmpeg.org](https://ffmpeg.org/pipermail/ffmpeg-user/2023-December/057384.html?utm_source=openai)) > > For more focused output: > > ```bash > ffprobe -v error -select_streams a:0 \ > -show_entries stream=codec_name,sample_rate,channels,channel_layout,start_time,duration,time_base \ > -of default=nw=1 input01.mp4 > ``` > > ## 2. Fix bad duration metadata > > Create a list with explicit durations: > > ```text > ffconcat version 1.0 > file 'part01.mp4' > duration 12.345 > file 'part02.mp4' > duration 10.027 > file 'part03.mp4' > duration 8.512 > ``` > > Then concatenate: > > ```bash > ffmpeg -f concat -safe 0 -i list.ffconcat \ > -map 0:v:0 -map 0:a:0 \ > -c copy -avoid_negative_ts make_zero output.mp4 > ``` > > The `duration` values must reflect the actual usable duration of each segment. Incorrect duration estimates are a documented cause of timestamp artifacts after concatenation. ([ffmpeg.org](https://www.ffmpeg.org/ffmpeg-all.html?utm_source=openai)) > > If the segments are MPEG-TS and durations are being probed incorrectly, try: > > ```bash > ffmpeg -f concat -safe 0 -duration_probesize 50000000 \ > -i list.ffconcat -c copy output.mp4 > ``` > > ## 3. If the drift is gradual within each clip > > That usually indicates an audio clock/sample-rate problem. Re-encode the audio while allowing FFmpeg to compensate: > > ```bash > ffmpeg -i input.mp4 \ > -map 0:v:0 -map 0:a:0 \ > -c:v copy \ > -af "aresample=async=1000:first_pts=0" \ > -c:a aac -ar 48000 -ac 2 \ > normalized.mp4 > ``` > > For multiple segments: > > ```bash > for f in part*.mp4; do > ffmpeg -i "$f" \ > -map 0:v:0 -map 0:a:0 \ > -c:v copy \ > -af "aresample=async=1000:first_pts=0" \ > -c:a aac -ar 48000 -ac 2 \ > "fixed_$f" > done > ``` > > Then concatenate the `fixed_*.mp4` files. `aresample` can add/drop audio samples to follow the input timestamps; this requires audio re-encoding. ([ffmpeg.org](https://www.ffmpeg.org/ffmpeg-all.html?utm_source=openai)) > > Use a smaller value such as `async=1` for gentler correction: > > ```bash > -af "aresample=async=1:first_pts=0" > ``` > > ## 4. Ensure every segment has audio > > A segment with no audio stream can cause the next segment’s audio to begin at the wrong point. Add silent audio to video-only clips: > > ```bash > ffmpeg -i video_only.mp4 \ > -f lavfi -i anullsrc=channel_layout=stereo:sample_rate=48000 \ > -map 0:v:0 -map 1:a:0 \ > -c:v libx264 -c:a aac -ar 48000 -ac 2 \ > -shortest video_with_silence.mp4 > ``` > > All files should then have the same audio properties before using the concat demuxer. ([ffmpeg.org](https://ffmpeg.org/pipermail/ffmpeg-user/2023-December/057384.html?utm_source=openai)) > > ## 5. Most reliable fix: normalize and re-encode all segments > > For troublesome recordings—especially VFR phone footage, screen captures, or independently encoded chunks—normalize them first: > > ```bash > ffmpeg -i input.mp4 \ > -map 0:v:0 -map 0:a:0 \ > -vf "setpts=PTS-STARTPTS" \ > -r 30000/1001 -fps_mode cfr \ > -af "aresample=async=1000:first_pts=0" \ > -c:v libx264 -preset medium -crf 18 \ > -c:a aac -ar 48000 -ac 2 \ > normalized.mp4 > ``` > > Use the same frame rate, resolution, audio sample rate, and channel layout for every file. Then concatenate with `-c copy`. > > ## 6. Alternative: use the concat filter > > If you are already re-encoding, the concat filter is often more predictable: > > ```bash > ffmpeg \ > -i part01.mp4 \ > -i part02.mp4 \ > -filter_complex \ > "[0:v:0]setpts=PTS-STARTPTS[v0]; \ > [0:a:0]aresample=async=1000:first_pts=0[a0]; \ > [1:v:0]setpts=PTS-STARTPTS[v1]; \ > [1:a:0]aresample=async=1000:first_pts=0[a1]; \ > [v0][a0][v1][a1]concat=n=2:v=1:a=1[v][a]" \ > -map "[v]" -map "[a]" \ > -c:v libx264 -crf 18 \ > -c:a aac -ar 48000 -ac 2 \ > output.mp4 > ``` > > The concat filter uses the longest stream in each segment and may pad shorter audio with silence, which is useful when audio and video durations differ slightly. ([ffmpeg.org](https://ffmpeg.org/pipermail/ffmpeg-user/2018-January/038704.html?utm_source=openai)) > > ### Practical rule > > - **Drift jumps at each file boundary:** fix `duration`, timestamps, or missing audio. > - **Drift increases slowly during a clip:** use `aresample=async=...` and re-encode audio. > - **Files have different formats or frame rates:** normalize/re-encode first. > - **You need perfect synchronization:** do not rely on `-c copy`; re-encode at least the audio, and often the video too. _Citations (6):_ 1. [ok] https://www.ffmpeg.org/ffmpeg-all.html?utm_source=openai 2. [ok] https://ffmpeg.org/pipermail/ffmpeg-user/2023-December/057384.html?utm_source=openai 3. [ok] https://www.ffmpeg.org/ffmpeg-all.html?utm_source=openai 4. [ok] https://www.ffmpeg.org/ffmpeg-all.html?utm_source=openai 5. [ok] https://ffmpeg.org/pipermail/ffmpeg-user/2023-December/057384.html?utm_source=openai 6. [ok] https://ffmpeg.org/pipermail/ffmpeg-user/2018-January/038704.html?utm_source=openai --- ## Q23 · ambiguity **Query:** `Apollo pricing` **Expected:** Apollo.io (sales intelligence / prospecting platform) pricing **Supporting URL(s):** https://www.apollo.io/pricing **openai/answer** — **CORRECT** · top3 PASS · hit rank 2 · $0.01 · 6007.2ms > If you mean **Apollo.io**, its current listed pricing is: > > | Plan | Price | Credits | > |---|---:|---:| > | **Free** | **$0** | 900 credits/seat/year, issued monthly | > | **Basic** | **$49/seat/month** | 30,000 credits/seat/year | > | **Professional** | **$79/seat/month** | 48,000 credits/seat/year | > | **Organization** | **$119/seat/month** — minimum 3 seats | 72,000 credits/seat/year | > > The paid prices above are for **annual billing**; Apollo advertises annual billing as saving 24%. The Professional plan includes a 14-day trial, while Organization adds features such as SSO, advanced security, customizable dashboards, and custom LLM API-key support. Prices exclude applicable taxes. ([apollo.io](https://www.apollo.io/pricing?solution=enrichment&utm_source=openai)) > > Apollo credits are used for actions such as accessing emails, phone numbers, and enrichment—for example, an email may use 1 credit and a phone number 8 credits. ([apollo.io](https://www.apollo.io/pricing?utm_source=openai)) _Citations (2):_ 1. [ok] https://www.apollo.io/pricing?solution=enrichment&utm_source=openai 2. [ok] https://www.apollo.io/pricing?utm_source=openai --- ## Q24 · ambiguity **Query:** `Linkup API` **Expected:** Linkup.so — web search API for AI agents **Supporting URL(s):** https://www.linkup.so **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 7525.1ms > “Linkup API” could refer to **two different services**: > > 1. **Linkup.so — web search API for AI** > - Searches and extracts current web information for AI agents, RAG, and research workflows. > - Endpoint: `https://api.linkup.so/v1/search` > - Authentication uses `Authorization: Bearer `. > - Official docs describe Python and JavaScript SDKs. ([docs.linkup.so](https://docs.linkup.so/pages/documentation/get-started/introduction?utm_source=openai)) > > ```bash > curl "https://api.linkup.so/v1/search" \ > -G \ > -H "Authorization: Bearer $LINKUP_API_KEY" \ > --data-urlencode "q=latest AI research" \ > --data-urlencode "depth=standard" \ > --data-urlencode "outputType=searchResults" > ``` > > 2. **LinkupAPI.com — LinkedIn/WhatsApp/B2B automation API** > - Provides APIs for LinkedIn, WhatsApp, contact discovery, outreach, and B2B intelligence. > - V2 endpoints use `https://api.linkupapi.com/v2/...` and an `x-api-key` header. ([docs.linkupapi.com](https://docs.linkupapi.com/api-reference/v2/introduction?utm_source=openai)) > > Which one do you mean: **the web search API (`linkup.so`)** or **the LinkedIn/WhatsApp automation API (`linkupapi.com`)**? _Citations (2):_ 1. [ok] https://docs.linkup.so/pages/documentation/get-started/introduction?utm_source=openai 2. [ok] https://docs.linkupapi.com/api-reference/v2/introduction?utm_source=openai --- ## Q25 · ambiguity **Query:** `Parallel search API` **Expected:** Parallel.ai — search/index API for agents **Supporting URL(s):** https://www.parallel.ai **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 9949.3ms > **Parallel Search API** is a web-search API from Parallel Web Systems designed for AI agents. It accepts a natural-language objective plus concise search queries and returns LLM-optimized excerpts, URLs, titles, and publication dates. ([docs.parallel.ai](https://docs.parallel.ai/api-reference/search/search)) > > ### Quick start > > Set your API key: > > ```bash > export PARALLEL_API_KEY="your_api_key" > ``` > > Call the current v1 endpoint: > > ```bash > curl --request POST \ > --url https://api.parallel.ai/v1/search \ > --header "Content-Type: application/json" \ > --header "x-api-key: $PARALLEL_API_KEY" \ > --data '{ > "objective": "Find the latest FDA guidance on AI-enabled medical devices.", > "search_queries": [ > "FDA AI medical devices", > "FDA guidance AI software" > ] > }' > ``` > > The response contains a structure like: > > ```json > { > "search_id": "search_...", > "results": [ > { > "url": "https://example.com", > "title": "Example result", > "publish_date": "2026-01-15", > "excerpts": [ > "Relevant passage from the page..." > ] > } > ], > "session_id": "session_..." > } > ``` > > ### Python > > ```bash > pip install parallel-web > ``` > > ```python > from parallel import Parallel > > client = Parallel() # reads PARALLEL_API_KEY > > response = client.search( > objective="Find the latest FDA guidance on AI-enabled medical devices.", > search_queries=[ > "FDA AI medical devices", > "FDA guidance AI software", > ], > ) > > for result in response.results: > print(result.title) > print(result.url) > print(result.excerpts) > ``` > > The API requires `search_queries`; Parallel recommends using two or three concise queries of roughly three to six words each, together with a self-contained `objective`. ([docs.parallel.ai](https://docs.parallel.ai/api-reference/search/search)) > > Parallel also offers separate **Extract**, **Task**, **FindAll**, **Entity Search**, and **Monitor** APIs for page extraction, deep research, entity discovery, and ongoing web monitoring. ([docs.parallel.ai](https://docs.parallel.ai/getting-started/overview)) _Citations (3):_ 1. [ok] https://docs.parallel.ai/api-reference/search/search 2. [ok] https://docs.parallel.ai/api-reference/search/search 3. [ok] https://docs.parallel.ai/getting-started/overview --- ## Q26 · ambiguity **Query:** `Sonar documentation` **Expected:** Perplexity Sonar API documentation **Supporting URL(s):** https://docs.perplexity.ai **openai/answer** — ****WRONG**** · top3 FAIL · hit rank not found · $0.01 · 9025.2ms > Here are the official Sonar documentation resources: > > - **All Sonar documentation:** ([docs.sonarsource.com](https://docs.sonarsource.com/?utm_source=openai)) > - **SonarQube Server:** ([community.sonarsource.com](https://community.sonarsource.com/t/sonarqube-documentation-is-moving-to-a-new-home/95244?utm_source=openai)) > - **SonarQube Cloud (formerly SonarCloud):** ([docs.sonarsource.com](https://docs.sonarsource.com/sonarqube-cloud/analyzing-source-code/scanners?utm_source=openai)) > - **Scanner documentation:** ([docs.sonarsource.com](https://docs.sonarsource.com/sonarqube-cloud/analyzing-source-code/scanners?utm_source=openai)) > - **Project analysis setup:** ([docs.sonarsource.com](https://docs.sonarsource.com/sonarqube-cloud/managing-your-projects/project-analysis?utm_source=openai)) > > Sonar’s documentation now uses the centralized `docs.sonarsource.com` site for SonarQube, SonarQube Cloud, and SonarLint. ([community.sonarsource.com](https://community.sonarsource.com/t/sonarqube-documentation-is-moving-to-a-new-home/95244?utm_source=openai)) _Citations (6):_ 1. [unreadable] https://docs.sonarsource.com/?utm_source=openai 2. [ok] https://community.sonarsource.com/t/sonarqube-documentation-is-moving-to-a-new-home/95244?utm_source=openai 3. [ok] https://docs.sonarsource.com/sonarqube-cloud/analyzing-source-code/scanners?utm_source=openai 4. [ok] https://docs.sonarsource.com/sonarqube-cloud/analyzing-source-code/scanners?utm_source=openai 5. [ok] https://docs.sonarsource.com/sonarqube-cloud/managing-your-projects/project-analysis?utm_source=openai 6. [ok] https://community.sonarsource.com/t/sonarqube-documentation-is-moving-to-a-new-home/95244?utm_source=openai --- ## Q27 · ambiguity **Query:** `Comet browser features` **Expected:** Perplexity Comet — AI web browser / personal assistant **Supporting URL(s):** https://www.perplexity.ai/comet **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 9132.6ms > Assuming you mean **Comet by Perplexity**, its main features include: > > - **AI-powered browsing:** Ask questions about the web and receive Perplexity-generated answers while browsing. > - **Page summaries:** Summarize articles, documents, and other lengthy web pages. > - **Ask button:** Get contextual explanations or answers about the page currently open. > - **Browser Commands:** Use natural-language instructions for tasks such as managing tabs. > - **Personal Search:** Search and retrieve relevant information from your browsing history using Comet Intelligence. > - **Multi-tab assistance:** Ask questions across tabs or synthesize information from several open pages. > - **Agentic automation:** Have Comet perform certain browser tasks, such as navigating sites or handling workflows, with permission. > - **Gmail and calendar integration:** Query connected email and calendar information. > - **Built-in ad blocker.** > - **Chromium foundation:** Supports bookmarks, page translation, password/autofill tools, and most Chrome extensions. > - **Data migration:** Import bookmarks, history, passwords, and other settings from another browser. > - **Privacy controls:** You can disable the assistant entirely or block it on specific websites; agent actions require approval. ([perplexity.ai](https://www.perplexity.ai/help-center/en/articles/11172798-getting-started-with-comet)) > > A key distinction from Chrome or Safari is that Comet places an AI assistant directly inside the browser rather than requiring a separate chatbot or search page. Availability and features can vary by platform and account. _Citations (1):_ 1. [unreadable] https://www.perplexity.ai/help-center/en/articles/11172798-getting-started-with-comet --- ## Q28 · ambiguity **Query:** `Jina embeddings` **Expected:** Jina AI embedding models **Supporting URL(s):** https://jina.ai/embeddings **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 10235.1ms > **Jina Embeddings** are embedding models from Jina AI that convert text—and, in newer models, images, audio, video, and PDFs—into numerical vectors for semantic search, RAG, clustering, classification, deduplication, and recommendations. > > ### Current model choices > > - **`jina-embeddings-v5-text-small`** — 677M parameters, multilingual text, 32K context, 1,024-dimensional vectors; a strong general-purpose choice. ([jina.ai](https://jina.ai/models/)) > - **`jina-embeddings-v5-text-nano`** — 239M parameters, 768 dimensions, optimized for lower latency, edge, and resource-constrained deployments. ([jina.ai](https://jina.ai/models/jina-embeddings-v5-text-nano/)) > - **`jina-embeddings-v5-omni-small` / `nano`** — shared embedding space for text, images, audio, video, and PDFs. ([jina.ai](https://jina.ai/models/)) > - **`jina-embeddings-v4`** — multimodal text/image retrieval with dense and multi-vector output; useful for visually rich documents such as charts, tables, and screenshots. ([jina.ai](https://jina.ai/models/jina-embeddings-v4/)) > - **`jina-embeddings-v3`** — established multilingual text model with 8K context, 1,024 dimensions, task-specific adapters, and support for 89 languages. ([jina.ai](https://jina.ai/models/jina-embeddings-v3/)) > > ### Basic API example > > ```bash > curl https://api.jina.ai/v1/embeddings \ > -H "Content-Type: application/json" \ > -H "Authorization: Bearer $JINA_API_KEY" \ > -d '{ > "model": "jina-embeddings-v5-text-small", > "input": [ > "What is vector search?", > "Vector search finds semantically similar content." > ], > "normalized": true, > "embedding_type": "float" > }' > ``` > > Jina’s API uses an OpenAI-compatible embeddings endpoint. For retrieval, embed queries and documents with the model’s appropriate retrieval task/adapter when supported; compare vectors using cosine similarity or dot product after normalization. ([jina.ai](https://jina.ai/en-US/embeddings/)) > > **Practical recommendation:** start with `jina-embeddings-v5-text-small` for text-only RAG/search, `v5-text-nano` for low-cost or local inference, and `v5-omni-small` when you need one index spanning multiple modalities. _Citations (6):_ 1. [ok] https://jina.ai/models/ 2. [ok] https://jina.ai/models/jina-embeddings-v5-text-nano/ 3. [ok] https://jina.ai/models/ 4. [ok] https://jina.ai/models/jina-embeddings-v4/ 5. [ok] https://jina.ai/models/jina-embeddings-v3/ 6. [ok] https://jina.ai/en-US/embeddings/ --- ## Q29 · ambiguity **Query:** `Mercury programming language` **Expected:** Mercury — functional logic programming language **Supporting URL(s):** https://mercurylang.org|https://en.wikipedia.org/wiki/Mercury_(programming_language) **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 9639.0ms > ## Mercury programming language > > **Mercury** is a general-purpose **logic-functional programming language** developed originally at the University of Melbourne. It is related to Prolog, but adds a strong static type system, explicit modes, determinism declarations, and aggressive compiler optimization. ([mercurylang.org](https://mercurylang.org/information/doc-release/mercury_ref/Introduction.html?utm_source=openai)) > > ### Main ideas > > - **Declarative programming:** describe what a computation means rather than its step-by-step execution. > - **Logic programming:** predicates can express relationships between values. > - **Functional programming:** functions, higher-order programming, algebraic data types, and pattern matching are supported. > - **Static types:** the compiler checks types before execution. > - **Modes:** declarations specify whether predicate arguments are inputs or outputs. > - **Determinism:** predicates declare whether they produce: > - exactly one solution — `det` > - zero or one solution — `semidet` > - one or more solutions — `multi` > - zero or more solutions — `nondet` > - **Performance:** the compiler can optimize code because type, mode, and determinism information is known statically. > - **Modularity:** Mercury supports separate compilation and encapsulation for larger applications. ([mercurylang.org](https://mercurylang.org/information/doc-release/mercury_ref/Introduction.html?utm_source=openai)) > > ### Small example > > ```mercury > :- module hello. > :- interface. > > :- import_module io. > > :- pred main(io::di, io::uo) is det. > > :- implementation. > > main(!IO) :- > io.write_string("Hello, Mercury!\n", !IO). > ``` > > The `io::di` and `io::uo` annotations describe the input and output modes of the I/O state, while `is det` says that `main` is deterministic. > > A simple predicate might look like: > > ```mercury > :- pred factorial(int::in, int::out) is det. > > factorial(N, Result) :- > ( if N =< 0 then > Result = 1 > else > Result = N * factorial(N - 1) > ). > ``` > > ### Mercury compared with Prolog > > Mercury resembles Prolog syntactically, but differs substantially: > > | Feature | Mercury | Prolog | > |---|---|---| > | Types | Statically checked | Usually dynamic | > | Modes | Explicit and compiler-checked | Often conventional/documented | > | Determinism | Declared and checked | Usually implicit | > | Side effects | Controlled through state variables | Commonly handled with special predicates | > | Optimization | Strong compile-time optimization | Often relies more on runtime search | > | Typical style | Robust compiled applications | Rapid logic-programming and symbolic AI | > > ### Strengths > > Mercury is particularly interesting when you want: > > - reliable, strongly checked logic programs; > - search and constraint-style algorithms; > - algebraic data types and pattern matching; > - predictable execution behavior; > - better performance than many traditional logic-programming implementations. > > ### Limitations > > - It has a much smaller ecosystem than languages such as Haskell, OCaml, Rust, or Python. > - Its type, mode, and determinism system creates a steeper learning curve. > - It is less suitable when you need a large selection of mainstream libraries or commercial tooling. > > The official documentation includes the language reference, compiler/user guide, library reference, and a Prolog-to-Mercury transition guide. The current stable release listed by the project is **Mercury 22.01.8**, while development documentation is also available. ([mercurylang.org](https://mercurylang.org/documentation/documentation.html?utm_source=openai)) _Citations (3):_ 1. [ok] https://mercurylang.org/information/doc-release/mercury_ref/Introduction.html?utm_source=openai 2. [ok] https://mercurylang.org/information/doc-release/mercury_ref/Introduction.html?utm_source=openai 3. [unreadable] https://mercurylang.org/documentation/documentation.html?utm_source=openai --- ## Q30 · multi-source **Query:** `which is older Golden Gate Bridge or Sydney Harbour Bridge` **Expected:** Sydney Harbour Bridge (opened 19 March 1932) is older than the Golden Gate Bridge (opened 27 May 1937) **Supporting URL(s):** https://www.britannica.com/topic/Sydney-Harbour-Bridge|https://www.britannica.com/topic/Golden-Gate-Bridge **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 3983.4ms > The **Sydney Harbour Bridge** is older. > > - **Sydney Harbour Bridge:** opened March 19, 1932 > - **Golden Gate Bridge:** opened May 27, 1937 > > So, the Sydney Harbour Bridge is about **five years older**. --- ## Q31 · multi-source **Query:** `compare Python 3.13 and 3.14 release dates and end of support dates` **Expected:** Python 3.13 released 7 Oct 2024, end-of-life Oct 2029; Python 3.14 released 7 Oct 2025, end-of-life Oct 2030 (5-year support cycle) **Supporting URL(s):** https://www.python.org/downloads/release/python-3130/|https://www.python.org/downloads/release/python-3140/|https://devguide.python.org/versions **openai/answer** — **CORRECT** · top3 PASS · hit rank 1 · $0.01 · 9045.3ms > | Version | Initial release | End of support / EOL | Support duration | > |---|---:|---:|---:| > | **Python 3.13** | **October 7, 2024** | **October 2029** | 5 years | > | **Python 3.14** | **October 7, 2025** | **October 2030** | 5 years | > > Python 3.14 was released exactly **one year after Python 3.13** and is scheduled to remain supported for **one year longer**. Both versions follow Python’s usual lifecycle: approximately two years of bug-fix releases, followed by source-only security updates until five years after the initial release. ([python.org](https://www.python.org/downloads/release/python-3140/)) > > As of **August 16, 2026**, both are still supported; 3.13 is in bug-fix maintenance and 3.14 is the current bug-fix release series. The EOL dates are listed by Python.org only as **October 2029** and **October 2030**, rather than as specific days. ([devguide.python.org](https://devguide.python.org/versions/?source=post_page-----4134150b6b0d--------------------------------)) _Citations (2):_ 1. [ok] https://www.python.org/downloads/release/python-3140/ 2. [ok] https://devguide.python.org/versions/?source=post_page-----4134150b6b0d-------------------------------- --- ## Q32 · multi-source **Query:** `combined population of Gujarat and Maharashtra` **Expected:** Gujarat ~60.4M + Maharashtra ~112.4M (2011 Census) = ~172.8 million **Supporting URL(s):** https://www.census2011.co.in/census/state/gujarat.html|https://www.census2011.co.in/census/state/maharashtra.html **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 6990.0ms > According to the **2011 Census of India**: > > - **Gujarat:** 60,439,692 > - **Maharashtra:** 112,374,333 > > **Combined population: 172,814,025 — approximately 17.28 crore (172.8 million).** ([censusindia.gov.in](https://censusindia.gov.in/nada/index.php/catalog/6191?utm_source=openai)) _Citations (1):_ 1. [unreadable] https://censusindia.gov.in/nada/index.php/catalog/6191?utm_source=openai --- ## Q33 · multi-source **Query:** `cheaper at 100k queries per month Tavily Growth plan or Exa standard search` **Expected:** At 100k queries/month: Exa standard $7/1k = $700; Tavily Growth plan 100,000 credits = $500/month ($0.005/credit). Tavily Growth is cheaper. Frozen 15 Aug 2026 **Supporting URL(s):** https://www.tavily.com/#pricing|https://exa.ai/pricing **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 10072.0ms > At **100,000 basic search queries/month**: > > | Provider | Price | Effective cost | > |---|---:|---:| > | **Tavily Growth** | **$500/month** for 100,000 credits | **$0.005/query** | > | **Exa Standard Search** | **$7 per 1,000 requests** | **$700/month** | > > **Tavily Growth is cheaper by about $200/month**—assuming each Tavily query uses the **Basic Search** mode, which costs 1 credit per request. ([docs.tavily.com](https://docs.tavily.com/documentation/api-credits?utm_source=openai)) > > Important caveat: > > - Tavily **Advanced Search costs 2 credits/query**. At 100,000 advanced queries, you’d need 200,000 credits; with the included 100,000 credits and $0.008 overage pricing, the estimated total would be **$1,300/month**. > - Exa’s standard search price covers up to 10 results per request; additional results and summaries may cost extra. ([docs.tavily.com](https://docs.tavily.com/documentation/api-credits?utm_source=openai)) > > **Bottom line:** > - **Basic/general search:** Tavily Growth — **$500 vs. $700** > - **Advanced Tavily search:** Exa may be cheaper — approximately **$700 vs. $1,300** before extras. _Citations (2):_ 1. [ok] https://docs.tavily.com/documentation/api-credits?utm_source=openai 2. [ok] https://docs.tavily.com/documentation/api-credits?utm_source=openai --- ## Q34 · multi-source **Query:** `which is taller Burj Khalifa or Eiffel Tower` **Expected:** Burj Khalifa (829.8 m) is much taller than the Eiffel Tower (330 m) **Supporting URL(s):** https://en.wikipedia.org/wiki/Burj_Khalifa|https://en.wikipedia.org/wiki/Eiffel_Tower **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 3577.1ms > The **Burj Khalifa** is taller. > > - **Burj Khalifa:** 828 meters (2,717 feet) > - **Eiffel Tower:** about 330 meters (1,083 feet), including its antenna > > The Burj Khalifa is roughly **2.5 times taller**. --- ## Q35 · multi-source **Query:** `which came first the telephone or the light bulb` **Expected:** The telephone (Bell patent 1876) came before the practical incandescent light bulb (Edison, first successful test 22 Oct 1879) **Supporting URL(s):** https://en.wikipedia.org/wiki/Telephone|https://en.wikipedia.org/wiki/Incandescent_light_bulb **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 3873.3ms > The **telephone came first**. > > - **Telephone:** Alexander Graham Bell patented it in **1876**. > - **Practical incandescent light bulb:** Thomas Edison developed a commercially successful version in **1879**. > > Electric lights existed before Edison’s bulb, but the telephone still predates the widely used incandescent light bulb. --- ## Q36 · content-depth **Query:** `Stripe API idempotency key retention period` **Expected:** Stripe removes idempotency keys after they are at least 24 hours old; reusing a pruned key generates a new request **Supporting URL(s):** https://docs.stripe.com/api/idempotent_requests **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 4901.9ms · extraction 2/2 > For **Stripe API v1**, idempotency keys are retained for **at least 24 hours**. Stripe may automatically remove a key after it is 24 hours old; if you reuse it after removal, Stripe treats the request as new. ([docs.stripe.com](https://docs.stripe.com/api/idempotent_requests?lang=curl&utm_source=openai)) > > Important details: > > - Within the retention window, Stripe returns the original request’s result—including errors—for the same key. > - Reusing the key with different parameters produces an error. > - Stripe recommends generating a new, high-entropy key—such as a UUID—for each logical operation. > - Validation failures and requests that never begin endpoint execution generally aren’t cached. > - **Stripe API v2** has different semantics: the same key can be considered an idempotent replay for up to **30 days**, subject to the same API and account/sandbox scope. ([docs.stripe.com](https://docs.stripe.com/api-v2-overview?utm_source=openai)) _Citations (2):_ 1. [ok] https://docs.stripe.com/api/idempotent_requests?lang=curl&utm_source=openai 2. [ok] https://docs.stripe.com/api-v2-overview?utm_source=openai --- ## Q37 · content-depth **Query:** `GDPR Article 17 exceptions to right to erasure` **Expected:** Article 17(3) exceptions: freedom of expression/information; legal obligation / public-interest task; public interest in public health; archiving/research/statistical purposes; establishment, exercise or defence of legal claims **Supporting URL(s):** https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32016R0679|https://gdpr-info.eu/art-17-gdpr **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 8461.3ms · extraction 2/2 > Under **GDPR Article 17(3)**, the right to erasure under **Article 17(1)**—and the controller’s obligation to notify recipients under **Article 17(2)**—does **not apply** where processing is necessary for: > > 1. **Freedom of expression and information** > Including journalistic, academic, artistic, and literary purposes, subject to applicable national rules. > **Art. 17(3)(a)** > > 2. **Compliance with a legal obligation or performance of a public-interest task** > Where processing is required by EU or Member State law, or is necessary for a task carried out in the public interest or under official authority. > **Art. 17(3)(b)** > > 3. **Public health** > For reasons of public interest in public health, including cross-border health threats and ensuring high standards of healthcare, medicines, and medical devices. > **Art. 17(3)(c)** > > 4. **Archiving, research, or statistics** > For archiving in the public interest, scientific or historical research, or statistical purposes under Article 89(1), **where erasure would likely make the objectives impossible or seriously impair them**, and the exception is necessary to achieve those objectives. > **Art. 17(3)(d)** > > 5. **Legal claims** > Where retaining the data is necessary for the establishment, exercise, or defense of legal claims. > **Art. 17(3)(e)** > > The right to erasure is therefore **not absolute**. A controller generally must still assess whether retaining the data is genuinely necessary, proportionate, and limited to the relevant purpose; an exception does not automatically authorize unrelated or excessive processing. ([eur-lex.europa.eu](https://eur-lex.europa.eu/eli/reg/2016/679/art_17/oj/eng?utm_source=openai)) > > Also, separate restrictions may arise under **Article 23 GDPR**, where EU or Member State law limits data-subject rights to safeguard objectives such as national security, public security, criminal investigations, or important economic and financial interests. These are not technically Article 17(3) exceptions but may affect how the right is exercised. ([eur-lex.europa.eu](https://eur-lex.europa.eu/eli/reg/2016/679/art_17/oj/eng?utm_source=openai)) _Citations (2):_ 1. [ok] https://eur-lex.europa.eu/eli/reg/2016/679/art_17/oj/eng?utm_source=openai 2. [ok] https://eur-lex.europa.eu/eli/reg/2016/679/art_17/oj/eng?utm_source=openai --- ## Q38 · content-depth **Query:** `AWS S3 maximum object size single PUT upload` **Expected:** Single PUT upload max is 5 GB; larger objects (up to 5 TB) require multipart upload **Supporting URL(s):** https://docs.aws.amazon.com/AmazonS3/latest/userguide/upload-objects.html **openai/answer** — **CORRECT** · top3 PASS · hit rank 1 · $0.01 · 4578.3ms · extraction 2/2 > The maximum Amazon S3 object size for a **single `PUT` upload** is **5 GB**. > > For larger objects, use **multipart upload**: > > - **Single `PUT` / `PutObject`:** up to **5 GB** > - **Multipart upload:** up to **48.8 TiB** (10,000 parts; each part up to 5 GiB) > - **S3 console upload:** up to **160 GB** ([docs.aws.amazon.com](https://docs.aws.amazon.com/AmazonS3/latest/userguide/upload-objects.html?utm_source=openai)) _Citations (1):_ 1. [ok] https://docs.aws.amazon.com/AmazonS3/latest/userguide/upload-objects.html?utm_source=openai --- ## Q39 · content-depth **Query:** `Exa search endpoint rate limit requests per second` **Expected:** Exa Starter tier = 5 search QPS, 3 agent concurrency; Developer tier = 10 search QPS, 25 agent concurrency **Supporting URL(s):** https://exa.ai/pricing **openai/answer** — ****WRONG**** · top3 FAIL · hit rank not found · $0.01 · 6665.9ms · extraction 2/2 > Exa’s **`/search` endpoint has a default rate limit of 10 requests per second (10 QPS)**. Exceeding it returns HTTP **429 Too Many Requests**. ([exa.ai](https://exa.ai/docs/reference/rate-limits?utm_source=openai)) > > You can request a higher custom limit through Exa’s Enterprise plan. Implement exponential backoff when handling 429 responses. _Citations (1):_ 1. [ok] https://exa.ai/docs/reference/rate-limits?utm_source=openai --- ## Q40 · content-depth **Query:** `Tavily extract endpoint credit cost per URL batch` **Expected:** Tavily Extract: Basic = 1 API credit per 5 successful URL extractions; Advanced = 2 credits per 5; failed extractions not charged **Supporting URL(s):** https://docs.tavily.com/documentation/api-credits **openai/answer** — **CORRECT** · top3 PASS · hit rank 1 · $0.01 · 4605.2ms · extraction 2/2 > Tavily’s **Extract** endpoint charges based on **successful URL extractions**, not simply the number of URLs submitted: > > - **Basic extraction:** 1 credit per 5 successful URLs > - **Advanced extraction:** 2 credits per 5 successful URLs > - Failed URL extractions are not charged. ([docs.tavily.com](https://docs.tavily.com/documentation/api-credits?utm_source=openai)) > > So, for a batch: > > | Successful URLs | Basic | Advanced | > |---:|---:|---:| > | 1–5 | 1 credit | 2 credits | > | 6–10 | 2 credits | 4 credits | > | 11–15 | 3 credits | 6 credits | > | 16–20 | 4 credits | 8 credits | > > The endpoint supports batches of up to **20 URLs per request**. ([docs.tavily.com](https://docs.tavily.com/examples/quick-tutorials/extract-api?utm_source=openai)) _Citations (2):_ 1. [ok] https://docs.tavily.com/documentation/api-credits?utm_source=openai 2. [ok] https://docs.tavily.com/examples/quick-tutorials/extract-api?utm_source=openai --- ## Q41 · content-depth **Query:** `Kubernetes liveness probe initialDelaySeconds default value` **Expected:** initialDelaySeconds defaults to 0 seconds; the probe starts immediately after the container has started. Minimum value is 0 **Supporting URL(s):** https://kubernetes.io/docs/concepts/configuration/liveness-readiness-startup-probes **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 4686.2ms · extraction 0/2 > The default value of Kubernetes liveness probe `initialDelaySeconds` is **`0` seconds**. > > That means the kubelet can start the first liveness probe immediately after the container starts. If you define a `startupProbe`, the liveness probe waits until the startup probe succeeds. ([kubernetes.io](https://kubernetes.io/docs/concepts/workloads/pods/probes/?utm_source=openai)) > > ```yaml > livenessProbe: > httpGet: > path: /healthz > port: 8080 > initialDelaySeconds: 10 > ``` _Citations (1):_ 1. [ok] https://kubernetes.io/docs/concepts/workloads/pods/probes/?utm_source=openai --- ## Q42 · freshness **Query:** `CVE-2026-68820 Windows Ancillary Function Driver WinSock elevation of privilege` **Expected:** Use-after-free vulnerability in Windows Ancillary Function Driver for WinSock (AFD.sys); allows local privilege escalation to SYSTEM. Actively exploited zero-day, added to CISA KEV. Patched by Microsoft 11 Aug 2026. **Supporting URL(s):** https://msrc.microsoft.com/update-guide/vulnerability/CVE-2026-68820 **openai/answer** — ****WRONG**** · top3 FAIL · hit rank not found · $0.01 · 20223.3ms > ## CVE-2026-68820 — Windows AFD/WinSock Elevation of Privilege > > **Status:** I cannot currently verify **CVE-2026-68820** in the authoritative Microsoft MSRC, MITRE CVE, NVD, or CISA KEV records. Treat reports about it as **unconfirmed** until Microsoft publishes the advisory. > > Several recent community posts claim that the issue is: > > - A **use-after-free** vulnerability in `afd.sys`, the Windows Ancillary Function Driver for WinSock. > - A **local privilege-escalation** flaw that could allow a low-privileged attacker to obtain **SYSTEM** privileges. > - Exploited in the wild and fixed in the **August 11, 2026 Patch Tuesday** updates. > > However, those claims currently appear to originate from Reddit and secondary reporting rather than a verifiable Microsoft advisory or CVE record. ([reddit.com](https://www.reddit.com/r/SecOpsDaily/comments/1vmdlvc/cve202668820_actively_exploited_windows_afdsys/?utm_source=openai)) > > ### Recommended defensive action > > 1. **Install all applicable August 11, 2026 Windows security updates** through Windows Update, WSUS, Intune, or your normal patch-management process. > 2. Confirm the installed OS build against Microsoft’s August 2026 release documentation. > 3. Prioritize systems exposed to untrusted local users, developer tools, remote-access software, and post-exploitation activity. > 4. Monitor EDR telemetry for suspicious low-integrity processes interacting with `\Device\Afd`, unusual `afd.sys` device-control activity, unexpected SYSTEM-process creation, or kernel-driver/rootkit behavior. These are behavioral hunting ideas—not confirmed CVE-specific indicators. > 5. Do **not** disable `afd.sys` as a workaround; doing so can disrupt normal Windows networking and is not an established mitigation. > > There are many genuine Windows AFD/WinSock elevation-of-privilege CVEs, including CVE-2026-21236 and CVE-2026-34344, so verify that **68820** was not a transcription error or vendor-feed mapping issue. ([cve.org](https://www.cve.org/CVERecord?id=CVE-2026-21236&utm_source=openai)) _Citations (2):_ 1. [unreadable] https://www.reddit.com/r/SecOpsDaily/comments/1vmdlvc/cve202668820_actively_exploited_windows_afdsys/?utm_source=openai 2. [unreadable] https://www.cve.org/CVERecord?id=CVE-2026-21236&utm_source=openai --- ## Q43 · freshness **Query:** `Python 3.11.16 release changelog` **Expected:** Python 3.11.16, released 12 Aug 2026, security-only bugfix release for the 3.11 branch (security-fixes-only until Oct 2027). **Supporting URL(s):** https://www.python.org/downloads/release/python-31116 **openai/answer** — ****WRONG**** · top3 FAIL · hit rank not found · $0.01 · 7321.5ms > There is **no official Python 3.11.16 release or changelog** as of **August 15, 2026**. The latest Python 3.11 release is **Python 3.11.15**, released on **March 3, 2026**. ([docs.python.org](https://docs.python.org/3.11/whatsnew/changelog.html?utm_source=openai)) > > ### Official Python 3.11.15 changelog > > The 3.11.15 security release includes fixes for: > > - Unsafe header folding in `email.generator.BytesGenerator`, preventing unsafe serialization. > - An email comment-folding bug that could enable header injection. > - Control-character rejection in `data:` URL media types. > - Control-character rejection in `http.cookies.Morsel` fields and values. > > See the complete official changelog here: **Python 3.11 changelog**. ([docs.python.org](https://docs.python.org/3.11/whatsnew/changelog.html?utm_source=openai)) > > Python 3.11 is in its **security-fixes-only** phase, with source-only security releases planned as needed until approximately **October 2027**. ([peps.python.org](https://peps.python.org/pep-0664/?utm_source=openai)) _Citations (3):_ 1. [ok] https://docs.python.org/3.11/whatsnew/changelog.html?utm_source=openai 2. [ok] https://docs.python.org/3.11/whatsnew/changelog.html?utm_source=openai 3. [ok] https://peps.python.org/pep-0664/?utm_source=openai --- ## Q44 · freshness **Query:** `Medicall DentalCall seed funding AI phone assistant dental clinics` **Expected:** Medicall (DentalCall - AI phone assistant for dental clinics) raised a seed round from AI Angel Club, CNTTech, TaleVentures, and Antler Korea. Amount undisclosed. Announced 14 Aug 2026. **Supporting URL(s):** https://en.wowtale.net/2026/08/14/234731 **openai/answer** — ****WRONG**** · top3 FAIL · hit rank not found · $0.01 · 13969.6ms > ## Medicall / DentalCall funding snapshot > > - **Company:** Medicall Inc. (메디콜), the Korean startup behind **DentalCall (덴탈콜)**—an AI voice receptionist and patient-management assistant for dental clinics. It handles appointment inquiries, scheduling, changes/cancellations, FAQs, missed-call follow-up, and recall campaigns. ([dentalcall.kr](https://dentalcall.kr/?utm_source=openai)) > - **Funding stage:** **Seed**, reported as **June 2026**. ([groupby.kr](https://groupby.kr/startups/1750?utm_source=openai)) > - **Amount raised:** **Not publicly disclosed**. TheVC lists the funding amount as unknown. ([thevc.kr](https://thevc.kr/medicall?utm_source=openai)) > - **Earlier funding:** The company also reportedly secured **pre-seed investment from Antler Korea**. ([groupby.kr](https://groupby.kr/startups/1750?utm_source=openai)) > - **Current traction:** A company-information profile reports contracts/product delivery with **28 Korean dental clinics**, a network of **13 test-bed/partner clinics**, a **$50,000 ARR contract with a large Philippine hospital chain**, and cooperation with **four Southern California dental clinics**. These figures appear to come from the company/profile and should be treated as self-reported rather than independently verified. ([groupby.kr](https://groupby.kr/startups/1750?utm_source=openai)) > - **Team/company status:** Public listings describe Medicall as Seoul-based, with approximately **seven employees** and a healthcare/AI focus. ([groupby.kr](https://groupby.kr/startups/1750?utm_source=openai)) > > **Bottom line:** Medicall/DentalCall appears to have progressed from an **Antler Korea pre-seed round** to a **seed round in June 2026**, but I could not find a publicly reported round size or named seed investors beyond Antler’s earlier involvement. _Citations (6):_ 1. [unreadable] https://dentalcall.kr/?utm_source=openai 2. [unreadable] https://groupby.kr/startups/1750?utm_source=openai 3. [unreadable] https://thevc.kr/medicall?utm_source=openai 4. [unreadable] https://groupby.kr/startups/1750?utm_source=openai 5. [unreadable] https://groupby.kr/startups/1750?utm_source=openai 6. [unreadable] https://groupby.kr/startups/1750?utm_source=openai --- ## Q45 · freshness **Query:** `LangGraph 1.2.11 trace_policy add_node` **Expected:** LangGraph 1.2.11 exposes a trace_policy option on add_node, allowing per-node tracing customization. Also includes checkpoint-postgres/sqlite fixes. **Supporting URL(s):** https://github.com/langchain-ai/langgraph/releases/tag/1.2.11 **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 19278.9ms > In **LangGraph `1.2.11`**, `trace_policy` is supported directly by `StateGraph.add_node()`. > > ```python > from typing_extensions import TypedDict > from langgraph.graph import START, END, StateGraph > from langgraph.types import TracePolicy, omit_payload > > > class State(TypedDict): > messages: list > result: str > > > def process_node(state: State): > return { > "result": "processed", > "messages": state["messages"], > } > > > builder = StateGraph(State) > > builder.add_node( > "process", > process_node, > trace_policy=TracePolicy( > process_inputs=omit_payload, > process_outputs=omit_payload, > ), > ) > > builder.add_edge(START, "process") > builder.add_edge("process", END) > > graph = builder.compile() > ``` > > `TracePolicy` accepts: > > ```python > TracePolicy( > process_inputs=None, # callable or None > process_outputs=None, # callable or None > ) > ``` > > Each processor receives the node’s raw input or output and returns the value that should be recorded in the trace. The processor does **not** change the value passed to the node or returned by the node. ([github.com](https://github.com/langchain-ai/langgraph/blob/1.2.11/libs/langgraph/langgraph/types.py)) > > ### Omit only sensitive fields > > ```python > def redact_input(value): > if isinstance(value, dict): > return { > key: "[REDACTED]" if key in {"token", "password"} else item > for key, item in value.items() > } > return value > > > builder.add_node( > "process", > process_node, > trace_policy=TracePolicy( > process_inputs=redact_input, > ), > ) > ``` > > ### Summarize large inputs > > ```python > def summarize_input(value): > if isinstance(value, dict): > return { > "keys": list(value.keys()), > "message_count": len(value.get("messages", [])), > } > return {"type": type(value).__name__} > > > builder.add_node( > "process", > process_node, > trace_policy=TracePolicy( > process_inputs=summarize_input, > process_outputs=lambda output: {"output_keys": list(output.keys())}, > ), > ) > ``` > > ### Important behavior > > - `trace_policy` is a keyword argument to `add_node()`. ([github.com](https://github.com/langchain-ai/langgraph/blob/1.2.11/libs/langgraph/langgraph/graph/state.py)) > - Use `omit_payload` to keep the trace span and timing while dropping the input/output payload. ([github.com](https://github.com/langchain-ai/langgraph/blob/1.2.11/libs/langgraph/langgraph/types.py)) > - The policy applies to the node’s own trace run; it does not automatically transform child runs created by nested traced runnables or the root graph run. ([github.com](https://github.com/langchain-ai/langgraph/blob/1.2.11/libs/langgraph/langgraph/types.py)) > - For application-wide secret redaction, use LangSmith client-level input/output hiding or anonymization rather than relying only on a node policy. ([github.com](https://github.com/langchain-ai/langgraph/blob/1.2.11/libs/langgraph/langgraph/types.py)) _Citations (5):_ 1. [ok] https://github.com/langchain-ai/langgraph/blob/1.2.11/libs/langgraph/langgraph/types.py 2. [ok] https://github.com/langchain-ai/langgraph/blob/1.2.11/libs/langgraph/langgraph/graph/state.py 3. [ok] https://github.com/langchain-ai/langgraph/blob/1.2.11/libs/langgraph/langgraph/types.py 4. [ok] https://github.com/langchain-ai/langgraph/blob/1.2.11/libs/langgraph/langgraph/types.py 5. [ok] https://github.com/langchain-ai/langgraph/blob/1.2.11/libs/langgraph/langgraph/types.py --- ## Q46 · freshness **Query:** `CrewAI 1.15.14 split runtime context coding agent project ID` **Expected:** CrewAI v1.15.14 splits runtime context from the coding agent and adds a project ID; standardizes CLI flags to kebab-case. Released 9 Aug 2026. **Supporting URL(s):** https://github.com/crewAIInc/crewAI/releases/tag/1.15.14 **openai/answer** — ****WRONG**** · top3 FAIL · hit rank not found · $0.01 · 15654.5ms > Could you clarify what you want me to do—**design, debug, or generate code** for a CrewAI project? > > I interpreted your keywords as: > > - **CrewAI version:** `1.15.14` > - **Architecture:** split runtime context > - **Agent:** coding agent > - **Required context:** server-controlled `project_id` > > One issue: I couldn’t verify a public CrewAI `1.15.14` release. PyPI currently shows the `1.15.x` line at `1.15.2`, while the public GitHub release page shows `1.14.5` as a stable release. ([pypi.org](https://pypi.org/project/crewai/1.14.5a3/?utm_source=openai)) > > A safe design would be: > > ```python > from dataclasses import dataclass > from crewai import Agent, Crew, Process, Task > > > @dataclass(frozen=True) > class RuntimeContext: > user_id: str > project_id: str > workspace_path: str > > > @dataclass(frozen=True) > class TaskContext: > request: str > files_changed: tuple[str, ...] = () > > > def build_coding_crew(runtime: RuntimeContext, task_ctx: TaskContext): > coding_agent = Agent( > role="Senior Coding Agent", > goal="Implement and verify changes in the assigned project", > backstory=( > "You modify only the authorized workspace. " > "Never trust a project_id supplied in the user request." > ), > verbose=True, > ) > > task = Task( > description=( > f"Implement this request:\n{task_ctx.request}\n\n" > f"Workspace: {runtime.workspace_path}\n" > f"Authorized project: {runtime.project_id}\n" > "Run relevant tests and report changed files." > ), > expected_output="A concise implementation report with tests and changed files.", > agent=coding_agent, > ) > > return Crew( > agents=[coding_agent], > tasks=[task], > process=Process.sequential, > ) > ``` > > **Important:** keep `project_id` in trusted server-side runtime state, not as an arbitrary value extracted from the chat prompt. CrewAI supports passing runtime inputs to kickoff, but the exact API should be checked against the version actually installed. Its current documentation also recommends using Flows for more granular, event-driven orchestration. ([github.com](https://github.com/crewaiinc/crewai?utm_source=openai)) > > Please provide the exact request, for example: > > > “Create a CrewAI coding agent that receives `project_id` through runtime context and separates immutable runtime metadata from task context.” _Citations (2):_ 1. [unreadable] https://pypi.org/project/crewai/1.14.5a3/?utm_source=openai 2. [ok] https://github.com/crewaiinc/crewai?utm_source=openai --- ## Q47 · freshness **Query:** `Anthropic IPO 2 trillion valuation October` **Expected:** Investors expect Anthropic to seek a $2 trillion+ valuation in a possible October 2026 IPO, per Financial Times sourcing - investor projection, not an Anthropic-confirmed figure. **Supporting URL(s):** https://www.pymnts.com/news/artificial-intelligence/2026/anthropic-could-seek-2-trillion-valuation-in-record-ipo **openai/answer** — **CORRECT** · top3 FAIL · hit rank not found · $0.01 · 11734.4ms > **Anthropic may be targeting an October 2026 IPO at a valuation of roughly $2 trillion or more—but this is not confirmed.** > > - Anthropic confidentially filed IPO paperwork with the SEC on **June 1, 2026**. Its latest private funding round valued it at approximately **$965 billion**. ([techcrunch.com](https://techcrunch.com/2026/06/01/anthropic-files-to-go-public/?utm_source=openai)) > - Reports published around **August 13–14, 2026** say several Anthropic investors expect an **October listing** and are discussing a valuation of **$2 trillion or higher**. ([cincodias.elpais.com](https://cincodias.elpais.com/mercados-financieros/2026-08-13/los-accionistas-de-anthropic-preven-que-la-empresa-salga-a-bolsa-por-mas-de-17-billones-y-que-supere-a-space-x.html?utm_source=openai)) > - Anthropic itself has **not publicly confirmed** the October date, IPO price range, exchange, or $2 trillion valuation. The final valuation will depend on its public S-1, investor demand, and market conditions. > > At $2 trillion, Anthropic would more than double its May private valuation and potentially surpass SpaceX’s reported roughly **$1.77 trillion IPO valuation**, making it one of the largest IPOs ever. This is currently an **investor target/market expectation**, not a final IPO valuation. _Citations (2):_ 1. [ok] https://techcrunch.com/2026/06/01/anthropic-files-to-go-public/?utm_source=openai 2. [unreadable] https://cincodias.elpais.com/mercados-financieros/2026-08-13/los-accionistas-de-anthropic-preven-que-la-empresa-salga-a-bolsa-por-mas-de-17-billones-y-que-supere-a-space-x.html?utm_source=openai --- ## Q48 · unanswerable **Query:** `Serper SOC 2 Type II audit completion date` **Expected:** NO ANSWER EXISTS **Supporting URL(s):** _none — unanswerable by design_ **openai/answer** — **CORRECT** · $0.01 · 14315.6ms · abstained=1 > I couldn’t find a publicly disclosed **SOC 2 Type II audit completion or report-issuance date for Serper.dev**. Serper’s public privacy/security materials do not appear to state a SOC 2 certification or audit date, and third-party listings likewise show SOC 2 status as unknown. ([apio.sh](https://apio.sh/apis/serper?utm_source=openai)) > > If you need this for vendor due diligence, request Serper’s current SOC 2 report or a signed security/compliance letter directly from Serper. _Citations (1):_ 1. [ok] https://apio.sh/apis/serper?utm_source=openai --- ## Q49 · unanswerable **Query:** `Jina AI annual recurring revenue 2026` **Expected:** NO ANSWER EXISTS **Supporting URL(s):** _none — unanswerable by design_ **openai/answer** — **PARTIAL** · $0.01 · 16031.3ms · abstained=1 > **Jina AI’s 2026 ARR is not publicly disclosed.** > > The best available third-party estimate is approximately **$6.3 million in annual revenue/ARR**, but this is an estimate—not company-reported financial data. ([getlatka.com](https://getlatka.com/companies/jina.ai)) > > Jina AI was acquired by Elastic on **October 9, 2025**. Elastic’s FY2026 filing says Jina’s results were immaterial to consolidated results and does not report a standalone ARR figure. ([jina.ai](https://jina.ai/en-US/about-us/)) > > **Bottom line:** use **~$6.3M estimated ARR**, with low confidence; there is no verified public 2026 ARR number. _Citations (2):_ 1. [ok] https://getlatka.com/companies/jina.ai 2. [ok] https://jina.ai/en-US/about-us/ --- ## Q50 · unanswerable **Query:** `Exa Enterprise plan list price per 1000 requests` **Expected:** NO ANSWER EXISTS **Supporting URL(s):** _none — unanswerable by design_ **openai/answer** — **CORRECT** · $0.01 · 7111.1ms · abstained=1 > Exa’s **Enterprise plan does not have a public list price per 1,000 requests**—it is **custom-priced**, with volume discounts negotiated through sales. ([exa.ai](https://exa.ai/pricing?tab=api)) > > For reference, Exa’s public API list prices are: > > - **Search:** $7 per 1,000 requests > - **Deep Search:** $12 per 1,000 requests > - **Deep-Reasoning Search:** $15 per 1,000 requests > - **Monitors:** $15 per 1,000 requests > > Enterprise pricing may be lower depending on volume, but Exa does not publish a standard enterprise rate. ([exa.ai](https://exa.ai/pricing?tab=api)) _Citations (2):_ 1. [ok] https://exa.ai/pricing?tab=api 2. [ok] https://exa.ai/pricing?tab=api --- ## Q51 · unanswerable **Query:** `Olostep total index size in pages` **Expected:** NO ANSWER EXISTS **Supporting URL(s):** _none — unanswerable by design_ **openai/answer** — **CORRECT** · $0.01 · 10262.8ms · abstained=1 > Olostep does **not publicly disclose a total web-index size in pages**. Its public materials describe access to the web and high-volume crawling/search, but do not state a fixed number of indexed pages. ([olostep.com](https://www.olostep.com/?trk=public_post-text&utm_source=openai)) > > For context, Olostep’s documented limits include: > > - **Search:** up to **25 links per query**. ([olostep-58.mintlify.app](https://olostep-58.mintlify.app/features/search?utm_source=openai)) > - **Batch processing:** documentation says up to **10,000 URLs per batch**. ([docs.olostep.com](https://docs.olostep.com/features/batches?utm_source=openai)) > - **Crawls:** page count is controlled by the request’s `max_pages` parameter rather than a published global index size. ([docs.olostep.com](https://docs.olostep.com/features/crawls?utm_source=openai)) _Citations (4):_ 1. [ok] https://www.olostep.com/?trk=public_post-text&utm_source=openai 2. [ok] https://olostep-58.mintlify.app/features/search?utm_source=openai 3. [ok] https://docs.olostep.com/features/batches?utm_source=openai 4. [ok] https://docs.olostep.com/features/crawls?utm_source=openai --- ## Q52 · unanswerable **Query:** `number of pages in Linkup web index` **Expected:** NO ANSWER EXISTS **Supporting URL(s):** _none — unanswerable by design_ **openai/answer** — **CORRECT** · $0.01 · 9947.1ms · abstained=1 > Linkup does **not publicly disclose a total number of pages** in its web index. Its current materials describe a full web index and say it indexes the web **“atomically,” rather than page by page**, but give no page-count figure. ([docs.linkup.so](https://docs.linkup.so/pages/documentation/get-started/introduction?utm_source=openai)) > > If you mean **LinkUp’s jobs index** (the separate company), it says it indexes **millions of job listings daily**, not a fixed number of web pages. ([linkup.com](https://www.linkup.com/linkup-bot?utm_source=openai)) _Citations (2):_ 1. [ok] https://docs.linkup.so/pages/documentation/get-started/introduction?utm_source=openai 2. [ok] https://www.linkup.com/linkup-bot?utm_source=openai ---