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The root cause: with_fallbacks wraps the model in a Runnable that hid bind_tools, so the agent factory could not find the method. A maintainer linked the fix in PR #22139, which forwards bind_tools through fallbacks: upgrade to langchain-core>=0.2.2. For the AzureChatOpenAI variant, the fix was importing the model fro…
Wrapping a chat model with .with_fallbacks([fallback_model]) and passing the result to create_tool_calling_agent raised ValueError: This function requires a .bind_tools method be implemented on the LLM. The same error also hit AzureChatOpenAI users importing from langchain_community. The code worked fine without with_…
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…
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…
The root cause was on the Ollama server side, not in LangChain. A reporter confirmed that updating to the latest Ollama release (which shipped streaming-tool support) fixed streaming with bind_tools, and another confirmed the same behavior: ChatOpenAI pointed at a non-Ollama backend streamed fine with the same code. A…
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 …
Streaming chat responses from an Ollama model (e.g. llama3.1:70b via ChatOllama, or ChatOpenAI pointed at Ollama's /v1 endpoint) with tools bound via llm.bind_tools([...]).stream(). With tools bound, streaming produced no tokens at all and the on_llm_new_token callback never fired, so apps relying on token-by-token ou…
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…