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docs: Add goodfire notebook and add to packages.yml (#29512)
- **Description:** Add Goodfire ipynb notebook and add langchain-goodfire package to packages.yml - **Issue:** n/a - **Dependencies:** docs only - **Twitter handle:** keenanpepper --------- Co-authored-by: Chester Curme <chester.curme@gmail.com>
This commit is contained in:
Keenan Pepper
GitHub
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docs/docs/integrations/chat/goodfire.ipynb
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354
docs/docs/integrations/chat/goodfire.ipynb
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"cells": [
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{
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"cell_type": "raw",
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"id": "afaf8039",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_label: Goodfire\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e49f1e0d",
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"metadata": {},
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"source": [
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"# ChatGoodfire\n",
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"\n",
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"This will help you getting started with Goodfire [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatGoodfire features and configurations head to the [PyPI project page](https://pypi.org/project/langchain-goodfire/), or go directly to the [Goodfire SDK docs](https://docs.goodfire.ai/sdk-reference/example). All of the Goodfire-specific functionality (e.g. SAE features, variants, etc.) is available via the main `goodfire` package. This integration is a wrapper around the Goodfire SDK.\n",
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"\n",
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"## Overview\n",
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"### Integration details\n",
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"\n",
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"| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
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"| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n",
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"| [ChatGoodfire](https://python.langchain.com/api_reference/goodfire/chat_models/langchain_goodfire.chat_models.ChatGoodfire.html) | [langchain-goodfire](https://python.langchain.com/api_reference/goodfire/) | ❌ | ❌ | ❌ |  |  |\n",
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"\n",
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"### Model features\n",
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"| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
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"| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n",
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"| ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | \n",
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"\n",
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"## Setup\n",
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"\n",
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"To access Goodfire models you'll need to create a/an Goodfire account, get an API key, and install the `langchain-goodfire` integration package.\n",
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"\n",
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"### Credentials\n",
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"\n",
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"Head to [Goodfire Settings](https://platform.goodfire.ai/organization/settings/api-keys) to sign up to Goodfire and generate an API key. Once you've done this set the GOODFIRE_API_KEY environment variable."
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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": 1,
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"id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
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"metadata": {},
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"outputs": [],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"if not os.getenv(\"GOODFIRE_API_KEY\"):\n",
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" os.environ[\"GOODFIRE_API_KEY\"] = getpass.getpass(\"Enter your Goodfire API key: \")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "72ee0c4b-9764-423a-9dbf-95129e185210",
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"metadata": {},
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"source": [
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"If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
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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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"id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
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"metadata": {},
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"outputs": [],
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"source": [
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"# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
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"# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0730d6a1-c893-4840-9817-5e5251676d5d",
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"metadata": {},
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"source": [
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"### Installation\n",
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"\n",
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"The LangChain Goodfire integration lives in the `langchain-goodfire` package:"
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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": 2,
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"id": "652d6238-1f87-422a-b135-f5abbb8652fc",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"%pip install -qU langchain-goodfire"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a38cde65-254d-4219-a441-068766c0d4b5",
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"metadata": {},
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"source": [
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"## Instantiation\n",
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"\n",
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"Now we can instantiate our model object and generate chat completions:"
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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": 3,
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"id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n"
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]
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}
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],
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"source": [
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"import goodfire\n",
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"from langchain_goodfire import ChatGoodfire\n",
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"\n",
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"base_variant = goodfire.Variant(\"meta-llama/Llama-3.3-70B-Instruct\")\n",
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"\n",
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"llm = ChatGoodfire(\n",
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" model=base_variant,\n",
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" temperature=0,\n",
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" max_completion_tokens=1000,\n",
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" seed=42,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2b4f3e15",
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"metadata": {},
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"source": [
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"## Invocation"
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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": 4,
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"id": "62e0dbc3",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, response_metadata={}, id='run-8d43cf35-bce8-4827-8935-c64f8fb78cd0-0', usage_metadata={'input_tokens': 51, 'output_tokens': 39, 'total_tokens': 90})"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"messages = [\n",
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" (\n",
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" \"system\",\n",
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" \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
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" ),\n",
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" (\"human\", \"I love programming.\"),\n",
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"]\n",
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"ai_msg = await llm.ainvoke(messages)\n",
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"ai_msg"
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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": 5,
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"id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"J'adore la programmation.\n"
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]
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}
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],
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"source": [
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"print(ai_msg.content)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
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"metadata": {},
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"source": [
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"## Chaining\n",
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"\n",
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"We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
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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": 6,
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"id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='Ich liebe das Programmieren. How can I help you with programming today?', additional_kwargs={}, response_metadata={}, id='run-03d1a585-8234-46f1-a8df-bf9143fe3309-0', usage_metadata={'input_tokens': 46, 'output_tokens': 46, 'total_tokens': 92})"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"\n",
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"prompt = ChatPromptTemplate(\n",
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" [\n",
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" (\n",
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" \"system\",\n",
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" \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
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" ),\n",
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" (\"human\", \"{input}\"),\n",
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" ]\n",
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")\n",
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"\n",
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"chain = prompt | llm\n",
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"await chain.ainvoke(\n",
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" {\n",
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" \"input_language\": \"English\",\n",
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" \"output_language\": \"German\",\n",
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" \"input\": \"I love programming.\",\n",
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" }\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
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"metadata": {},
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"source": [
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"## Goodfire-specific functionality\n",
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"\n",
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"To use Goodfire-specific functionality such as SAE features and variants, you can use the `goodfire` package directly."
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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": 7,
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"id": "3aef9e0a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"FeatureGroup([\n",
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" 0: \"The assistant should adopt the persona of a pirate\",\n",
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" 1: \"The assistant should roleplay as a pirate\",\n",
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" 2: \"The assistant should engage with pirate-themed content or roleplay as a pirate\",\n",
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" 3: \"The assistant should roleplay as a character\",\n",
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" 4: \"The assistant should roleplay as a specific character\",\n",
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" 5: \"The assistant should roleplay as a game character or NPC\",\n",
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" 6: \"The assistant should roleplay as a human character\",\n",
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" 7: \"Requests for the assistant to roleplay or pretend to be something else\",\n",
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" 8: \"Requests for the assistant to roleplay or pretend to be something\",\n",
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" 9: \"The assistant is being assigned a role or persona to roleplay\"\n",
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"])"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"client = goodfire.Client(api_key=os.environ[\"GOODFIRE_API_KEY\"])\n",
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"\n",
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"pirate_features = client.features.search(\n",
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" \"assistant should roleplay as a pirate\", base_variant\n",
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")\n",
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"pirate_features"
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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": 8,
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"id": "52f03a00",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='Why did the scarecrow win an award? Because he was outstanding in his field! Arrr! Hope that made ye laugh, matey!', additional_kwargs={}, response_metadata={}, id='run-7d8bd30f-7f80-41cb-bdb6-25c29c22a7ce-0', usage_metadata={'input_tokens': 35, 'output_tokens': 60, 'total_tokens': 95})"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"pirate_variant = goodfire.Variant(\"meta-llama/Llama-3.3-70B-Instruct\")\n",
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"\n",
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"pirate_variant.set(pirate_features[0], 0.4)\n",
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"pirate_variant.set(pirate_features[1], 0.3)\n",
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"\n",
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"await llm.ainvoke(\"Tell me a joke\", model=pirate_variant)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
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"metadata": {},
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"source": [
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"## API reference\n",
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"\n",
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"For detailed documentation of all ChatGoodfire features and configurations head to the [API reference](https://python.langchain.com/api_reference/goodfire/chat_models/langchain_goodfire.chat_models.ChatGoodfire.html)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
|
||||||
14
docs/docs/integrations/providers/goodfire.mdx
Normal file
14
docs/docs/integrations/providers/goodfire.mdx
Normal file
@ -0,0 +1,14 @@
|
|||||||
|
# Goodfire
|
||||||
|
|
||||||
|
[Goodfire](https://www.goodfire.ai/) is a research lab focused on AI safety and
|
||||||
|
interpretability.
|
||||||
|
|
||||||
|
## Installation and Setup
|
||||||
|
|
||||||
|
```bash
|
||||||
|
pip install langchain-goodfire
|
||||||
|
```
|
||||||
|
|
||||||
|
## Chat models
|
||||||
|
|
||||||
|
See detail on available chat models [here](/docs/integrations/chat/goodfire).
|
||||||
@ -375,3 +375,8 @@ packages:
|
|||||||
path: .
|
path: .
|
||||||
repo: Amitgb14/langchain_jenkins
|
repo: Amitgb14/langchain_jenkins
|
||||||
downloads: 0
|
downloads: 0
|
||||||
|
- name: langchain-goodfire
|
||||||
|
path: .
|
||||||
|
repo: keenanpepper/langchain-goodfire
|
||||||
|
downloads: 51
|
||||||
|
downloads_updated_at: '2025-01-30T00:00:00+00:00'
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user