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Generalize Comment on Streaming Support for LLM Implementations and add examples (#8115)
The example provided demonstrates the usage of the HuggingFaceTextGenInference implementation with streaming enabled.
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@ -48,6 +48,36 @@
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")\n",
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"llm(\"What did foo say about bar?\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Streaming"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import HuggingFaceTextGenInference\n",
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"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
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"\n",
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"\n",
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"llm = HuggingFaceTextGenInference(\n",
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" inference_server_url=\"http://localhost:8010/\",\n",
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" max_new_tokens=512,\n",
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" top_k=10,\n",
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" top_p=0.95,\n",
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" typical_p=0.95,\n",
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" temperature=0.01,\n",
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" repetition_penalty=1.03,\n",
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" stream=True\n",
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")\n",
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"llm(\"What did foo say about bar?\", callbacks=[StreamingStdOutCallbackHandler()])"
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]
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}
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],
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"metadata": {
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@ -1,5 +1,4 @@
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Currently, we support streaming for the `OpenAI`, `ChatOpenAI`, and `ChatAnthropic` implementations. To utilize streaming, use a [`CallbackHandler`](https://github.com/hwchase17/langchain/blob/master/langchain/callbacks/base.py) that implements `on_llm_new_token`. In this example, we are using `StreamingStdOutCallbackHandler`.
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Currently, we support streaming for a broad range of LLM implementations, including but not limited to `OpenAI`, `ChatOpenAI`, `ChatAnthropic`, `Hugging Face Text Generation Inference`, and `Replicate`. This feature has been expanded to accommodate most of the models. To utilize streaming, use a [`CallbackHandler`](https://github.com/hwchase17/langchain/blob/master/langchain/callbacks/base.py) that implements `on_llm_new_token`. In this example, we are using `StreamingStdOutCallbackHandler`.
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```python
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from langchain.llms import OpenAI
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from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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