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Pass chunk_size=1 when constructing the embeddings: embeddings = OpenAIEmbeddings(chunk_size=1). This was the confirmed fix in the thread and also works through helpers like vectorstore.from_documents() where you can't pass chunk_size yourself. In langchain.js the equivalent option is batchSize (`new OpenAIEmb…
Problem outcome: unknown
Using langchain's OpenAIEmbeddings against Azure OpenAI failed with "InvalidRequestError: Too many inputs for model None. The max number of inputs is 1." Azure's embedding endpoint only accepts one input per request, so the default batched embedding calls got rejected. The reporter also noted that OpenAIEmbeddings nee…
Problem outcome: unknown
This warning is non-fatal: the streamed output is unaffected, only the tracing callback hits the missing key. Multiple reporters confirmed it went away after upgrading (langchain 0.2.14 / langchain-core 0.2.32 or later), so upgrading is the fix. As a temporary workaround, downgrading langchain-core to 0.2.22 silenced …
Problem outcome: unknown
Building a conversational RAG chatbot with create_retrieval_chain wrapped in RunnableWithMessageHistory (input_messages_key="input", history_messages_key="chat_history", output_messages_key="answer"). Calling astream() on the chain printed repeated warnings: Error in RootListenersTracer.on_chain_end callback: KeyError…
Problem outcome: unknown