Pinecone not returning documents after successful upload - FastAPI + LangChain integration

Support
1

Hello Pinecone Community,

We’re facing a critical issue with our production system, and would appreciate your insight.

System Context:

  • Backend: FastAPI (Python 3.11)

  • Vector DB: Pinecone (serverless, free and considering upgrade to Standard)

  • ORM: SQLAlchemy with PostgreSQL (hosted on Railway)

  • Framework: LangChain

  • LLM/Embeddings: OpenAI GPT-4o-mini, text-embedding-ada-002

How it works:

  • Users upload PDF/TXT files via FastAPI endpoint.

  • Text is extracted (PyPDF2/pdfplumber), split into chunks (RecursiveCharacterTextSplitter).

  • Chunks are embedded and upserted to Pinecone under namespace format: user_{user_id}_cond_{condo_id}.

  • Each condominium and user has an isolated namespace; there is strict separation by design.

  • Queries are routed to Pinecone via LangChain with the respective namespace.

Symptoms:

  • Upload endpoint reports success (no errors shown).

  • Chunks are successfully processed and upserted to Pinecone (confirmed by logs).

  • When querying, LangChain searches the correct namespace (confirmed by logs).

  • Query always returns empty/None, or otherwise “No documents found,” even immediately after upload.

What we’ve checked:

  • Document model strictly matches DB schema (no extraneous or missing fields).

  • Upload process and query both use the same embedding model and chunking parameters.

  • Namespace string is identical between upsert and query.

  • Pinecone’s describe_index_stats() shows the namespace being created, but sometimes the vector count is inconsistent or remains zero after upload.

  • PostgreSQL database does not report any constraint violations.

What we’ve tried:

  • Re-uploaded files with minimal and default metadata.

  • Completely cleared and recreated Pinecone namespaces and indexes.

  • Stripped any special characters from namespace names and file names.

  • Debugged LangChain pipeline with smaller texts and synthetic documents: still no results.

Questions:

  • What are the most common causes for successfully upserted vectors being “invisible” to queries (especially in serverless/Starter tier)?

  • Are there edge cases in metadata, namespace, or embedding config that would cause vectors to not be returned or to be dropped silently?

  • Could there be issues with chunking length, batch size, or API rate limits where the upsert reports no error but nothing is persisted?

  • Is there a restricted feature set on Starter tier that could block retrieval in isolation scenarios?

  • Any recommended debugging steps with Pinecone CLI/API to confirm vectors, or to test “raw” queries bypassing LangChain?

We are ready to provide code samples/logs and can migrate to Standard plan if that helps unlock advanced diagnostics/support.

Thank you for any help,
Team AlexandraLex