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docs: add seekrflow chat model integration docs (#30596)
### **PR title** `docs: add SeekrFlow integration notebook` --- ### 💬 **PR message** - **Description:** This PR adds an integration notebook for [`[ChatSeekrFlow](https://pypi.org/project/langchain-seekrflow/)`](https://pypi.org/project/langchain-seekrflow/) under `docs/docs/integrations/chat/`. Per LangChain’s guidance, SeekrFlow has been published as a standalone OSS package (`langchain-seekrflow`) rather than as a direct community integration. This notebook ensures discoverability, demonstration, and testability of the integration within LangChain’s documentation structure. - **Issue:** N/A – this is a new integration contribution aligned with LangChain’s external package policy. - **Dependencies:** - [`[langchain-seekrflow](https://pypi.org/project/langchain-seekrflow)`](https://pypi.org/project/langchain-seekrflow) (published to PyPI) - [`[seekrai](https://pypi.org/project/seekrai/)`](https://pypi.org/project/seekrai/) (SeekrFlow client SDK) - **Twitter handle (optional):** @seekrtechnology --------- Co-authored-by: Ben Faircloth <bfaircloth@seekr.com> Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This commit is contained in:
Ben Faircloth
GitHub
parent
768e4f695a
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docs/docs/integrations/chat/seekrflow.ipynb
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docs/docs/integrations/chat/seekrflow.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "62d5a1ea",
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"metadata": {},
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"source": [
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"# ChatSeekrFlow\n",
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"\n",
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"> [Seekr](https://www.seekr.com/) provides AI-powered solutions for structured, explainable, and transparent AI interactions.\n",
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"\n",
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"This notebook provides a quick overview for getting started with Seekr [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatSeekrFlow` features and configurations, head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.seekrflow.ChatSeekrFlow.html).\n",
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"\n",
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"## Overview\n",
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"\n",
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"`ChatSeekrFlow` class wraps a chat model endpoint hosted on SeekrFlow, enabling seamless integration with LangChain applications.\n",
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"\n",
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"### Integration Details\n",
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"\n",
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"| Class | Package | Local | Serializable | Package downloads | Package latest |\n",
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"| :--- | :--- | :---: | :---: | :---: | :---: |\n",
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"| [ChatSeekrFlow](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.seekrflow.ChatSeekrFlow.html) | [seekrai](https://python.langchain.com/docs/integrations/providers/seekr/) | ❌ | beta |  |  |\n",
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"\n",
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"### Model Features\n",
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"\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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"### Supported Methods\n",
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"`ChatSeekrFlow` supports all methods of `ChatModel`, **except async APIs**.\n",
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"\n",
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"### Endpoint Requirements\n",
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"\n",
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"The serving endpoint `ChatSeekrFlow` wraps **must** have OpenAI-compatible chat input/output format. It can be used for:\n",
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"1. **Fine-tuned Seekr models**\n",
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"2. **Custom SeekrFlow models**\n",
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"3. **RAG-enabled models using Seekr's retrieval system**\n",
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"\n",
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"For async usage, please refer to `AsyncChatSeekrFlow` (coming soon).\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "93fea471",
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"metadata": {},
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"source": [
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"# Getting Started with ChatSeekrFlow in LangChain\n",
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"\n",
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"This notebook covers how to use SeekrFlow as a chat model in LangChain."
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]
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},
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{
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"cell_type": "markdown",
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"id": "2f320c17",
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"metadata": {},
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"source": [
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"## Setup\n",
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"\n",
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"Ensure you have the necessary dependencies installed:\n",
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"\n",
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"```bash\n",
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"pip install seekrai langchain langchain-community\n",
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"```\n",
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"\n",
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"You must also have an API key from Seekr to authenticate requests.\n"
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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": "911ca53c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Standard library\n",
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"import getpass\n",
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"import os\n",
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"\n",
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"# Third-party\n",
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"from langchain.prompts import ChatPromptTemplate\n",
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"from langchain.schema import HumanMessage\n",
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"from langchain_core.runnables import RunnableSequence\n",
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"\n",
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"# OSS SeekrFlow integration\n",
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"from langchain_seekrflow import ChatSeekrFlow\n",
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"from seekrai import SeekrFlow"
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]
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},
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{
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"cell_type": "markdown",
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"id": "150461cb",
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"metadata": {},
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"source": [
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"## API Key Setup\n",
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"\n",
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"You'll need to set your API key as an environment variable to authenticate requests.\n",
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"\n",
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"Run the below cell.\n",
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"\n",
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"Or manually assign it before running queries:\n",
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"\n",
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"```python\n",
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"SEEKR_API_KEY = \"your-api-key-here\"\n",
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"```\n"
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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": "38afcd6e",
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"metadata": {},
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"outputs": [],
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"source": [
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"os.environ[\"SEEKR_API_KEY\"] = getpass.getpass(\"Enter your Seekr 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": "82d83c0e",
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"metadata": {},
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"source": [
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"## Instantiation"
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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": "71b14751",
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"metadata": {},
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"outputs": [],
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"source": [
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"os.environ[\"SEEKR_API_KEY\"]\n",
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"seekr_client = SeekrFlow(api_key=SEEKR_API_KEY)\n",
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"\n",
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"llm = ChatSeekrFlow(\n",
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" client=seekr_client, model_name=\"meta-llama/Meta-Llama-3-8B-Instruct\"\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": "1046e86c",
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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": "f61a60f6",
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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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"Hello there! I'm Seekr, nice to meet you! What brings you here today? Do you have a question, or are you looking for some help with something? I'm all ears (or rather, all text)!\n"
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]
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}
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],
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"source": [
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"response = llm.invoke([HumanMessage(content=\"Hello, Seekr!\")])\n",
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"print(response.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": "853b0349",
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"metadata": {},
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"source": [
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"## Chaining"
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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": "35fca3ec",
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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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"content='The translation of \"Good morning\" in French is:\\n\\n\"Bonne journée\"' additional_kwargs={} response_metadata={}\n"
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]
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}
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],
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"source": [
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"prompt = ChatPromptTemplate.from_template(\"Translate to French: {text}\")\n",
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"\n",
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"chain: RunnableSequence = prompt | llm\n",
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"result = chain.invoke({\"text\": \"Good morning\"})\n",
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"print(result)"
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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": "a7b28b8d",
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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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"\n",
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"🔹 Testing Sync `stream()` (Streaming)...\n",
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"Here is a haiku:\n",
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"\n",
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"Golden sunset fades\n",
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"Ripples on the quiet lake\n",
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"Peaceful evening sky"
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]
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}
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],
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"source": [
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"def test_stream():\n",
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" \"\"\"Test synchronous invocation in streaming mode.\"\"\"\n",
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" print(\"\\n🔹 Testing Sync `stream()` (Streaming)...\")\n",
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"\n",
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" for chunk in llm.stream([HumanMessage(content=\"Write me a haiku.\")]):\n",
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" print(chunk.content, end=\"\", flush=True)\n",
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"\n",
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"\n",
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"# ✅ Ensure streaming is enabled\n",
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"llm = ChatSeekrFlow(\n",
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" client=seekr_client,\n",
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" model_name=\"meta-llama/Meta-Llama-3-8B-Instruct\",\n",
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" streaming=True, # ✅ Enable streaming\n",
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")\n",
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"\n",
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"# ✅ Run sync streaming test\n",
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"test_stream()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b3847b34",
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"metadata": {},
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"source": [
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"## Error Handling & Debugging"
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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": "6bc38b48",
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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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"Running test: Missing Client\n",
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"✅ Expected Error: SeekrFlow client cannot be None.\n",
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"Running test: Missing Model Name\n",
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"✅ Expected Error: A valid model name must be provided.\n"
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]
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}
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],
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"source": [
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"# Define a minimal mock SeekrFlow client\n",
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"class MockSeekrClient:\n",
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" \"\"\"Mock SeekrFlow API client that mimics the real API structure.\"\"\"\n",
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"\n",
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" class MockChat:\n",
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" \"\"\"Mock Chat object with a completions method.\"\"\"\n",
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"\n",
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" class MockCompletions:\n",
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" \"\"\"Mock Completions object with a create method.\"\"\"\n",
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"\n",
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" def create(self, *args, **kwargs):\n",
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" return {\n",
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" \"choices\": [{\"message\": {\"content\": \"Mock response\"}}]\n",
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" } # Mimic API response\n",
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"\n",
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" completions = MockCompletions()\n",
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"\n",
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" chat = MockChat()\n",
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"\n",
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"\n",
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"def test_initialization_errors():\n",
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" \"\"\"Test that invalid ChatSeekrFlow initializations raise expected errors.\"\"\"\n",
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"\n",
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" test_cases = [\n",
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" {\n",
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" \"name\": \"Missing Client\",\n",
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" \"args\": {\"client\": None, \"model_name\": \"seekrflow-model\"},\n",
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" \"expected_error\": \"SeekrFlow client cannot be None.\",\n",
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" },\n",
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" {\n",
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" \"name\": \"Missing Model Name\",\n",
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" \"args\": {\"client\": MockSeekrClient(), \"model_name\": \"\"},\n",
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" \"expected_error\": \"A valid model name must be provided.\",\n",
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" },\n",
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" ]\n",
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"\n",
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" for test in test_cases:\n",
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" try:\n",
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" print(f\"Running test: {test['name']}\")\n",
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" faulty_llm = ChatSeekrFlow(**test[\"args\"])\n",
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"\n",
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" # If no error is raised, fail the test\n",
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" print(f\"❌ Test '{test['name']}' failed: No error was raised!\")\n",
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" except Exception as e:\n",
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" error_msg = str(e)\n",
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" assert test[\"expected_error\"] in error_msg, f\"Unexpected error: {error_msg}\"\n",
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" print(f\"✅ Expected Error: {error_msg}\")\n",
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"\n",
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"\n",
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"# Run test\n",
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"test_initialization_errors()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d1c9ddf3",
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"metadata": {},
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"source": [
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"## API reference"
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]
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},
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{
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"cell_type": "markdown",
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"id": "411a8bea",
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"metadata": {},
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"source": [
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"- `ChatSeekrFlow` class: [`langchain_seekrflow.ChatSeekrFlow`](https://github.com/benfaircloth/langchain-seekrflow/blob/main/langchain_seekrflow/seekrflow.py)\n",
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"- PyPI package: [`langchain-seekrflow`](https://pypi.org/project/langchain-seekrflow/)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3ef00a51",
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"metadata": {},
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"source": []
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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": "3.10.12"
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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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}
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