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Shows how to fix chromaDB DuplicateIDError on memgpt load (duplicate embedding IDs). Use it when you hit this exact problem. Skip it when your error message or symptom looks different.
Fixes the Chroma MCP server failing against Chroma Cloud with auth errors. Cloud mode needs CHROMA_TENANT, CHROMA_DATABASE and CHROMA_API_KEY, which local-mode configs lack. The fix is setting all three and using cloud client type. Use when connecting to Chroma Cloud; not for local instances.
Fixes the Chroma MCP server failing with Embedding function name mismatch after CHROMA_EMBEDDING_FUNCTION was changed. Collections remember the embedding function they were created with. The fix is deleting and re-indexing the collection with the current function. Use when the embedding setting changed after collectio…
Fixes the Chroma MCP server failing with Collection does not exist. The collection name in the tool call does not match any collection in ChromaDB. The fix is listing collections and correcting the name. Use when ChromaDB is reachable but the collection is rejected; not for connection errors.
Fixes the Chroma MCP server failing with connection refused because ChromaDB is not running at the configured host and port. The fix is starting ChromaDB and verifying CHROMA_HOST and CHROMA_PORT. Use when the MCP server cannot reach ChromaDB at all; not for collection or auth errors.
Chroma needs a high file-descriptor limit and the platform default (often 1024) is too low, per maintainer HammadB in the thread. Raise the limit (e.g. ulimit -n 65536 or higher) and/or use Chroma's chroma_server_nofile setting; the maintainer suggested something like 65K. The reporter raised the limit and confirm…
Problem outcome: unknown
A Django-hosted Chroma service ran fine for a while, then started failing with "OSError: [Errno 24] Too many open files" on chromadb 0.4.15 (chroma-hnswlib 0.7.3, Python 3.11.4). The reporter asked whether some close() call was missing after queries. The reporter's default fd limit was 1024, and open file handles grew…
Problem outcome: unknown
The load path was not deduplicating IDs before insert; the fix was merged in MemGPT PR #1001 and shipped in the nightly package (pip install pymemgpt-nightly) and the release that followed within days.
Run export HNSWLIB_NO_NATIVE=1 before installing, then pip install chromadb again. This disables hnswlib's native-architecture compile flags and was confirmed working by multiple reporters in the thread. If that still fails, switching to Python 3.10.10 (which had prebuilt wheels at the time) was another confirmed …
Problem outcome: unknown
On macOS 13.x, pip install chromadb failed while building the hnswlib wheel with clang errors such as "unknown type name 'nullptr_t'" and "command '/usr/bin/clang' failed with exit code 1". Multiple users hit this across Python 3.9 through 3.11. The root cause was hnswlib compiling with native CPU-architecture flags…
Problem outcome: unknown
This was a Flipt regression fixed in v1.9.1 and v1.10.1, upgrade to either of those patch releases and the Postgres connection works again. The reporter confirmed v1.9.1 fixed it in both local Rancher Desktop and GCP clusters. If you must stay on 1.9.0/1.10.0 temporarily, rolling back to 1.8.3 avoids the error. Source…
Problem outcome: unknown
The load path was not deduplicating IDs before insert; the fix was merged in MemGPT PR #1001 and shipped in the nightly package (pip install pymemgpt-nightly) and the release that followed within days. Update to a version containing #1001. If you are stuck on an older version, deduplicate your .jsonl by ID before lo…
Problem outcome: unknown
Running memgpt load with a large .jsonl dataset failed with "chromadb.errors.DuplicateIDError: Expected IDs to be unique, found duplicates of: ...". The dataset produced duplicate embedding IDs on load, and Chroma's validate_ids rejected the insert, leaving the data source unusable. Maintainer sarahwooders reprodu…
Problem outcome: unknown
This is intentional, not a bug: chromadb 0.4.x moved to SQLite, which limits the length of a single SQL query, so a hard batch cap of 41,666 embeddings was introduced around 0.4.6 (confirmed by a maintainer in the thread). Submit your embeddings in batches of 41,666 or fewer per add/upsert call; the error message stat…
Problem outcome: unknown
After upgrading from chromadb 0.3.26 to 0.4.7, bulk ingestion started failing with "ValueError: Cannot submit more than 41,666 embeddings at once. Please submit your embeddings in batches of size 41,666 or less." Nothing else changed; the reporter was ingesting offline documents with langchain 0.0.274 and privateGPT-s…
Problem outcome: unknown
The original reporter confirmed this fix worked: pip install --upgrade chromadb==0.4.14, and several other users said the same. The underlying cause identified in the thread is a stale sqlite connection that survives the directory deletion, so restarting the Jupyter kernel or otherwise clearing active connections befo…
Problem outcome: unknown
Using Chroma as a persistent vector store via Chroma.from_documents(docs, embedding, persist_directory=...) on Ubuntu with langchain 0.0.351. The first creation worked, but deleting the persist directory with shutil.rmtree and recreating the store in the same process raised sqlite3.OperationalError: attempt to write a…
Problem outcome: unknown