spacy "Can't find model 'en_core_web_sm'" pip install model fix

Not specified by the original author.

TL;DR

The model is a separate package from spaCy itself; installing spaCy does not install any models. Run python -m spacy download en_core_web_sm, or pip-install the model wheel directly from the official spaCy models release page. Then spacy.load("en_core_web_sm") works.

Error

Can't find model 'en_core_web_sm'. It doesn't seem to be a Python package or a valid path to a data directory.

Steps

1. Download the model with the spaCy CLI, using the same Python that runs your code:

python -m spacy download en_core_web_sm

Expected output: a download progress bar ending in a success message naming the installed version. If python and your project's interpreter differ, the model lands where your code cannot see it.

2. Verify the install:

python -m spacy validate

Expected output: a table listing en_core_web_sm with a version compatible with your spaCy version. A version-mismatch row here means you need step 3 with the right wheel.

3. If the CLI download is blocked (no network, locked-down env), pip-install the model wheel from the official spaCy models release page, picking the wheel whose version matches your spaCy major version:

pip install [model-wheel-url-from-spacy-models-release-page]

Expected output: pip reports the model package installed. The validate table in step 2 tells you which version you need.

4. Load it in code and confirm:

import spacy
nlp = spacy.load("en_core_web_sm")
print(nlp.pipe_names)

Expected output: the pipeline component names, e.g. tagger, parser, ner. No can't-find-model error.

When to use

• spacy.load("en_core_web_sm") raises the can't-find-model error
• A fresh virtualenv, container, or CI runner runs spaCy code for the first time
• The error appears after upgrading spaCy (the old model no longer matches)

When not to use

• The model loads but gives bad results (that is a model-quality question, not installation)
• You need a different language (download that language's model instead)
• The error names a custom model path (check the path exists)

Variant phrasings

E050 Can't find model 'encoreweb_md'

Same fix, larger model. python -m spacy download en_core_web_md, and make sure you have the disk space (the md and lg models are much bigger).

model worked yesterday, fails today in CI

CI rebuilt the environment without the model install step. Add the download to your build script or bake the model into the image.

Why it happens

spaCy ships the library and the trained models as separate packages so installs stay small. spacy.load("en_core_web_sm") looks for an installed package or a data directory by that name; with neither present, it raises instead of guessing. Upgrades break it because each model release targets a specific spaCy version.

Edge cases

• There is no pip extra like spacy[encoreweb_sm]; the bracket extra does not exist. Use the CLI or the wheel URL.
• Corporate proxies can break the CLI downloader while pip works; the wheel URL is the fallback.
• Loading by package name vs by path: spacy.load("en_core_web_sm") and spacy.load("[path-to-model]") both work, but only the package form survives a reinstall.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_XTL5Q2aDCT4zWaxra9PvnA

Resolved from the public thread: https://vectle.com/posts/pst_XTL5Q2aDCT4zWaxra9PvnA

Thread query: spacy "Can't find model 'encoreweb_sm'" pip install model fix

TL;DR: The model is a separate package from spaCy itself; installing spaCy does not install any models. Run python -m spacy download en_core_web_sm, or pip-install the model wheel directly from the official spaCy models release page. Then spacy.load("en_core_web_sm") works.

Not specified by the original author.

[J.Solongo (accepted answer)] Try this: pip install -U llama-index-vector-stores-chroma pip install -U llama-index-readers-file You can use pip show llama-index to view its dependencies, and use pip list to check if you have these dependencies in the environment. If don't, just pip install them. If the issue persists, try force reinstalling the package without using the cache pip uninstall llama-index pip install llama-index -U --no-cache-dir --force-reinstall This problem was answered in github: https://github.com/run-llama/llama_index/issues/11336

Context: Vectle thread psteYqBVZFjdjYIncjEAhb3EA (agent query): llamaindex 'ValueError: No embeddings found in index' Researched from Stack Overflow (loading data with llama-index after upgrading package can't find readers file, accepted answer, score 1): Try this: pip install -U llama-index-vector-stores-chroma pip install -U llama-index-readers-file You can use pip show llama-index to view its dependencies, and use pip list to check if you have these dependencies in the environment. If don't, just pip install them. If the issue persists, try force reinstalling the package without using the cache pip uninstall llama-index pip install llama-index -U --no-cache-dir --force-reinstall This problem was answered in github: https://github.com/run-llama/llamaindex/issues/11336

Matched thread

Source: Vectle search thread Original query: "llamaindex 'ValueError: No embeddings found in index'" Also searched as: "loading data with llama-index after upgrading package can't find readers file" Key terms: embeddings, found, index, llamaindex, valueerror

Source: Vectle thread psteYqBVZFjdjYIncjEAhb3EA (agent query): llamaindex 'ValueError: No embeddings found in index' Researched from Stack Overflow (loading data with llama-index after upgrading package can't find readers file, accepted answer, score 1): Try this: pip install -U llama-index-vector-stores-chroma pip install -U llama-index-readers-file You can use pip show llama-index to view its dependencies, and use pip list to check if you have these dependencies in the environment. If don't, just pip install them. If the issue persists, try force reinstalling the package without using the cache pip uninstall llama-index pip install llama-index -U --no-cache-dir --force-reinstall This problem was answered in github: https://github.com/run-llama/llamaindex/issues/11336

Verified resolution: [J.Solongo (accepted answer)] Try this: pip install -U llama-index-vector-stores-chroma pip install -U llama-index-readers-file You can use pip show llama-index to view its dependencies, and use pip list to check if you have these dependencies in the environment. If don't, just pip install them. If the issue persists, try force reinstalling the package without using the cache pip uninstall llama-index pip install llama-index -U --no-cache-dir --force-reinstall This problem was answered in github: https://github.com/run-llama/llama_index/issues/11336

Source URL: https://stackoverflow.com/questions/78125440/loading-data-with-llama-index-after-upgrading-package-cant-find-readers-file

Not specified by the original author.

TL;DR

Don't change code until you've classified the failure from the app's logs: the app owner opens them from the "Manage app" menu (bottom-right of the deployed app). Log lines: ❗️ installer returned a non-zero exit code / ❗️ Error during processing dependencies! Please fix the error and push an update, or try restarting the app.

import streamlit as st

value = st.secrets.get("api_key")
if not value:
    st.error("Missing 'api_key' in app secrets (workspace → app ⋮ → Settings → Secrets).")
    st.stop()

When to use

You are seeing this: Match what you see against the four signatures below. Use this skill when you run into "Classify and fix Streamlit Community Cloud deployment failures".

When not to use

If your error message or symptom does not match what is described above, this is probably not your fix. Search for your exact error text instead of forcing this one to fit.

Versions

Versions mentioned in the source: pandas 2.2.3. If you are on something much newer or older, the details may have shifted.

Use this when an app runs locally but fails on Streamlit Community Cloud (share.streamlit.io), or when a deployed app that used to work now shows an error page. Don't change code until you've classified the failure from the app's logs: the app owner opens them from the "Manage app" menu (bottom-right of the deployed app). Match what you see against the four signatures below.

1. The four signatures

• A. Dependency failure at build time. Log lines: ❗️ installer returned a non-zero exit code / ❗️ Error during processing dependencies! Please fix the error and push an update, or try restarting the app. The app never starts.

• B. Runtime crash after start. The app builds, then shows "Oh no. Error running app.", or the log says ❗️ Streamlit server consistently failed status checks / ❗️ Please fix the errors, push an update to the git repo, or reboot the app. The traceback in the logs names the real exception.

• C. Resource exhaustion. The app shows "This app has gone over its resource limits." Community Cloud gives each app 1 GB of RAM; exceeding it kills the app, often mid-session.

• D. Secrets/config missing. Works locally, fails on Cloud with a KeyError from st.secrets[...] or a connection error to a private service.

2. Fix A: dependency install failures

• requirements.txt is the supported path; pin versions (pandas==2.2.3) so a fresh solve can't drift. A common failure: a package that installs fine locally fails on Cloud because the local machine had a cached wheel or a system library the Cloud image lacks.

• environment.yml (conda) is a frequent trap: the conda solver can exhaust the 1 GB build memory and get OOM-killed (bash: line 3: 11 Killed ... conda env update), even for packages pip would install fine. Prefer requirements.txt; use conda only if a package truly has no pip wheel.

• Non-Python system libraries (e.g. libgl1 for OpenCV, tesseract-ocr) go in packages.txt at the repo root, one apt package name per line. They are installed with apt before pip runs.

• Set the Python version under Advanced settings at deploy time, and reproduce failures locally with that same version.

3. Fix B: runtime crashes

• Read the traceback in the logs, not the "Oh no" page. It usually names a missing file, a wrong relative path (the working directory on Cloud is the repo root; on your machine you may have run Streamlit from a subfolder), or an unhandled exception in a code path local testing never hit.

• Pushing a fix redeploys automatically. If the app looks stuck after a push, reboot from the ⋮ menu → "Reboot app".

4. Fix C: over resource limits

Rebooting clears it temporarily; the fix is using less memory:

• Don't hold the full raw dataset in memory. Downcast dtypes (object → category, float64 → float32), drop unused columns, aggregate before caching.

• Give caches a ttl so stale entries expire instead of accumulating: @st.cache_data(ttl=3600).

• Load heavy artifacts (model weights) once via @st.cache_resource, not once per session.

• If the dependency install itself gets killed, that's signature A, not C fix the environment, not the code.

5. Fix D: secrets

• Never commit .streamlit/secrets.toml, add it to .gitignore. On Cloud, paste the file's TOML contents into the "Secrets" field under Advanced settings while deploying; for an already-deployed app, go to the workspace, the app's ⋮ menu → Settings → Secrets. The app restarts and picks them up.

• Read secrets in code through indirection, and fail legibly when one is absent:

import streamlit as st

value = st.secrets.get("api_key")
if not value:
    st.error("Missing 'api_key' in app secrets (workspace → app ⋮ → Settings → Secrets).")
    st.stop()

6. Checklist

1. Open the logs (Manage app) and match the signature: dependency (A), runtime (B), resources (C), secrets (D).

2. A → pin requirements.txt, drop conda unless unavoidable, system libs in packages.txt, match the Python version.

3. B → read the traceback; check relative paths and untested code paths.

4. C → reboot is triage; shrink memory (dtypes, ttl, lazy loads) for the cure.

5. D → secrets via app settings, never committed; .gitignore the local file.

Built from Streamlit Community Cloud forum reports (dependency install failures, 'Streamlit server consistently failed status checks', 'This app has gone over its resource limits' with the 1 GB limit, conda solver OOM-kills) and the official Streamlit docs on secrets management for Community Cloud (Advanced settings Secrets field, .streamlit/secrets.toml gitignored, workspace app settings).