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ollama: init package (#23615)
Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Isaac Francisco
GitHub
parent
f4ee3c8a22
commit
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@ -2,6 +2,7 @@
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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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@ -11,6 +12,7 @@
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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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"# ChatOllama\n",
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@ -23,6 +25,18 @@
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"\n",
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"For a complete list of supported models and model variants, see the [Ollama model library](https://github.com/jmorganca/ollama#model-library).\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](https://js.langchain.com/v0.2/docs/integrations/chat/ollama) | Package downloads | Package latest |\n",
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"| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n",
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"| [ChatOllama](https://api.python.langchain.com/en/latest/chat_models/langchain_ollama.chat_models.ChatOllama.html) | [langchain-ollama](https://api.python.langchain.com/en/latest/ollama_api_reference.html) | ✅ | ❌ | ✅ |  |  |\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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"First, follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance:\n",
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@ -40,307 +54,202 @@
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"* Specify the exact version of the model of interest as such `ollama pull vicuna:13b-v1.5-16k-q4_0` (View the [various tags for the `Vicuna`](https://ollama.ai/library/vicuna/tags) model in this instance)\n",
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"* To view all pulled models, use `ollama list`\n",
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"* To chat directly with a model from the command line, use `ollama run <name-of-model>`\n",
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"* View the [Ollama documentation](https://github.com/jmorganca/ollama) for more commands. Run `ollama help` in the terminal to see available commands too.\n",
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"\n",
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"## Usage\n",
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"\n",
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"You can see a full list of supported parameters on the [API reference page](https://api.python.langchain.com/en/latest/llms/langchain.llms.ollama.Ollama.html).\n",
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"\n",
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"If you are using a LLaMA `chat` model (e.g., `ollama pull llama3`) then you can use the `ChatOllama` interface.\n",
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"\n",
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"This includes [special tokens](https://huggingface.co/blog/llama2#how-to-prompt-llama-2) for system message and user input.\n",
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"\n",
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"## Interacting with Models \n",
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"\n",
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"Here are a few ways to interact with pulled local models\n",
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"\n",
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"#### In the terminal:\n",
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"\n",
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"* All of your local models are automatically served on `localhost:11434`\n",
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"* Run `ollama run <name-of-model>` to start interacting via the command line directly\n",
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"\n",
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"#### Via an API\n",
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"\n",
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"Send an `application/json` request to the API endpoint of Ollama to interact.\n",
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"\n",
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"```bash\n",
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"curl http://localhost:11434/api/generate -d '{\n",
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" \"model\": \"llama3\",\n",
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" \"prompt\":\"Why is the sky blue?\"\n",
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"}'\n",
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"```\n",
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"\n",
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"See the Ollama [API documentation](https://github.com/jmorganca/ollama/blob/main/docs/api.md) for all endpoints.\n",
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"\n",
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"#### Via LangChain\n",
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"\n",
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"See a typical basic example of using Ollama via the `ChatOllama` chat model in your LangChain application. \n",
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"\n",
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"View the [API Reference for ChatOllama](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.ollama.ChatOllama.html#langchain_community.chat_models.ollama.ChatOllama) for more."
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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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"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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"Why did the astronaut break up with his girlfriend?\n",
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"\n",
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"Because he needed space!\n"
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]
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}
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],
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"source": [
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"# LangChain supports many other chat models. Here, we're using Ollama\n",
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"from langchain_community.chat_models import ChatOllama\n",
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"from langchain_core.output_parsers import StrOutputParser\n",
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"\n",
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"# supports many more optional parameters. Hover on your `ChatOllama(...)`\n",
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"# class to view the latest available supported parameters\n",
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"llm = ChatOllama(model=\"llama3\")\n",
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"prompt = ChatPromptTemplate.from_template(\"Tell me a short joke about {topic}\")\n",
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"\n",
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"# using LangChain Expressive Language chain syntax\n",
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"# learn more about the LCEL on\n",
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"# /docs/concepts/#langchain-expression-language-lcel\n",
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"chain = prompt | llm | StrOutputParser()\n",
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"\n",
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"# for brevity, response is printed in terminal\n",
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"# You can use LangServe to deploy your application for\n",
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"# production\n",
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"print(chain.invoke({\"topic\": \"Space travel\"}))"
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"* View the [Ollama documentation](https://github.com/jmorganca/ollama) for more commands. Run `ollama help` in the terminal to see available commands too.\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": "72ee0c4b-9764-423a-9dbf-95129e185210",
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"metadata": {},
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"source": [
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"LCEL chains, out of the box, provide extra functionalities, such as streaming of responses, and async support"
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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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"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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"Why\n",
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" did\n",
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" the\n",
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" astronaut\n",
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" break\n",
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" up\n",
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" with\n",
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" his\n",
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" girlfriend\n",
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" before\n",
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" going\n",
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" to\n",
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" Mars\n",
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"?\n",
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"\n",
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"\n",
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"Because\n",
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" he\n",
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" needed\n",
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" space\n",
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"!\n",
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"\n"
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]
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}
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],
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"source": [
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"topic = {\"topic\": \"Space travel\"}\n",
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"\n",
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"for chunks in chain.stream(topic):\n",
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" print(chunks)"
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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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"For streaming async support, here's an example - all possible via the single chain created above."
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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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"topic = {\"topic\": \"Space travel\"}\n",
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"\n",
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"async for chunks in chain.astream(topic):\n",
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" print(chunks)"
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"# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
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"# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
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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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"Take a look at the [LangChain Expressive Language (LCEL) Interface](/docs/concepts#interface) for the other available interfaces for use when a chain is created.\n",
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"### Installation\n",
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"\n",
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"## Building from source\n",
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"\n",
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"For up to date instructions on building from source, check the Ollama documentation on [Building from Source](https://github.com/ollama/ollama?tab=readme-ov-file#building)"
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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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"## Extraction\n",
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" \n",
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"Use the latest version of Ollama and supply the [`format`](https://github.com/jmorganca/ollama/blob/main/docs/api.md#json-mode) flag. The `format` flag will force the model to produce the response in JSON.\n",
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"\n",
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"> **Note:** You can also try out the experimental [OllamaFunctions](/docs/integrations/chat/ollama_functions) wrapper for convenience."
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"The LangChain Ollama integration lives in the `langchain-ollama` 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": 4,
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"execution_count": null,
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"id": "652d6238-1f87-422a-b135-f5abbb8652fc",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.chat_models import ChatOllama\n",
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"%pip install -qU langchain-ollama"
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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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"llm = ChatOllama(model=\"llama3\", format=\"json\", temperature=0)"
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"Now we can instantiate our model object and generate chat completions:\n",
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"\n",
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"- TODO: Update model instantiation with relevant params."
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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": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_ollama import ChatOllama\n",
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"\n",
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"llm = ChatOllama(\n",
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" model=\"llama3\",\n",
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" temperature=0,\n",
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" # other params...\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": 7,
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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='{ \"morning\": \"blue\", \"noon\": \"clear blue\", \"afternoon\": \"hazy yellow\", \"evening\": \"orange-red\" }\\n\\n \\n\\n\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n ' id='run-e893700f-e2d0-4df8-ad86-17525dcee318-0'\n"
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]
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}
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],
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"source": [
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"from langchain_core.messages import HumanMessage\n",
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"\n",
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"messages = [\n",
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" HumanMessage(\n",
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" content=\"What color is the sky at different times of the day? Respond using JSON\"\n",
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" )\n",
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"]\n",
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"\n",
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"chat_model_response = llm.invoke(messages)\n",
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"print(chat_model_response)"
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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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"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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"Name: John\n",
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"Age: 35\n",
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"Likes: Pizza\n"
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]
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}
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],
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"source": [
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"import json\n",
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"\n",
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"from langchain_community.chat_models import ChatOllama\n",
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"from langchain_core.messages import HumanMessage\n",
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"from langchain_core.output_parsers import StrOutputParser\n",
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"\n",
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"json_schema = {\n",
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" \"title\": \"Person\",\n",
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" \"description\": \"Identifying information about a person.\",\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"name\": {\"title\": \"Name\", \"description\": \"The person's name\", \"type\": \"string\"},\n",
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" \"age\": {\"title\": \"Age\", \"description\": \"The person's age\", \"type\": \"integer\"},\n",
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" \"fav_food\": {\n",
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" \"title\": \"Fav Food\",\n",
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" \"description\": \"The person's favorite food\",\n",
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" \"type\": \"string\",\n",
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" },\n",
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" },\n",
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" \"required\": [\"name\", \"age\"],\n",
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"}\n",
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"\n",
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"llm = ChatOllama(model=\"llama2\")\n",
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"\n",
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"messages = [\n",
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" HumanMessage(\n",
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" content=\"Please tell me about a person using the following JSON schema:\"\n",
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" ),\n",
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" HumanMessage(content=\"{dumps}\"),\n",
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" HumanMessage(\n",
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" content=\"Now, considering the schema, tell me about a person named John who is 35 years old and loves pizza.\"\n",
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" ),\n",
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"]\n",
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"\n",
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"prompt = ChatPromptTemplate.from_messages(messages)\n",
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"dumps = json.dumps(json_schema, indent=2)\n",
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"\n",
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"chain = prompt | llm | StrOutputParser()\n",
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"\n",
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"print(chain.invoke({\"dumps\": dumps}))"
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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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"## Multi-modal\n",
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"\n",
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"Ollama has support for multi-modal LLMs, such as [bakllava](https://ollama.ai/library/bakllava) and [llava](https://ollama.ai/library/llava).\n",
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"\n",
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"Browse the full set of versions for models with `tags`, such as [Llava](https://ollama.ai/library/llava/tags).\n",
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"\n",
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"Download the desired LLM via `ollama pull bakllava`\n",
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"\n",
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"Be sure to update Ollama so that you have the most recent version to support multi-modal.\n",
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"\n",
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"Check out the typical example of how to use ChatOllama multi-modal support 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": 18,
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"id": "62e0dbc3",
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"metadata": {
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"scrolled": true
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"tags": []
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},
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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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"AIMessage(content='Je adore le programmation.\\n\\n(Note: \"programmation\" is the feminine form of the noun in French, but if you want to use the masculine form, it would be \"le programme\" instead.)' response_metadata={'model': 'llama3', 'created_at': '2024-07-04T04:20:28.138164Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1943337750, 'load_duration': 1128875, 'prompt_eval_count': 33, 'prompt_eval_duration': 322813000, 'eval_count': 43, 'eval_duration': 1618213000} id='run-ed8c17ab-7fc2-4c90-a88a-f6273b49bc78-0')\n"
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]
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}
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],
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"source": [
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"!pip install --upgrade --quiet pillow"
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"from langchain_core.messages import AIMessage\n",
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"\n",
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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 = llm.invoke(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": 1,
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"execution_count": 8,
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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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||||
"Je adore le programmation.\n",
|
||||
"\n",
|
||||
"(Note: \"programmation\" is the feminine form of the noun in French, but if you want to use the masculine form, it would be \"le programme\" instead.)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(ai_msg.content)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Chaining\n",
|
||||
"\n",
|
||||
"We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content='Ich liebe Programmieren!\\n\\n(Note: \"Ich liebe\" means \"I love\", \"Programmieren\" is the verb for \"programming\")', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T04:22:33.864132Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1310800083, 'load_duration': 1782000, 'prompt_eval_count': 16, 'prompt_eval_duration': 250199000, 'eval_count': 29, 'eval_duration': 1057192000}, id='run-cbadbe59-2de2-4ec0-a18a-b3220226c3d2-0')"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain_core.prompts import ChatPromptTemplate\n",
|
||||
"\n",
|
||||
"prompt = ChatPromptTemplate.from_messages(\n",
|
||||
" [\n",
|
||||
" (\n",
|
||||
" \"system\",\n",
|
||||
" \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
|
||||
" ),\n",
|
||||
" (\"human\", \"{input}\"),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"chain = prompt | llm\n",
|
||||
"chain.invoke(\n",
|
||||
" {\n",
|
||||
" \"input_language\": \"English\",\n",
|
||||
" \"output_language\": \"German\",\n",
|
||||
" \"input\": \"I love programming.\",\n",
|
||||
" }\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "4c5e0197",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Multi-modal\n",
|
||||
"\n",
|
||||
"Ollama has support for multi-modal LLMs, such as [bakllava](https://ollama.com/library/bakllava) and [llava](https://ollama.com/library/llava).\n",
|
||||
"\n",
|
||||
" ollama pull bakllava\n",
|
||||
"\n",
|
||||
"Be sure to update Ollama so that you have the most recent version to support multi-modal."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "36c9b1c2",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -399,7 +308,8 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 12,
|
||||
"id": "32b3ba7b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -411,8 +321,8 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain_community.chat_models import ChatOllama\n",
|
||||
"from langchain_core.messages import HumanMessage\n",
|
||||
"from langchain_ollama import ChatOllama\n",
|
||||
"\n",
|
||||
"llm = ChatOllama(model=\"bakllava\", temperature=0)\n",
|
||||
"\n",
|
||||
@ -449,20 +359,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Concurrency Features\n",
|
||||
"## API reference\n",
|
||||
"\n",
|
||||
"Ollama supports concurrency inference for a single model, and or loading multiple models simulatenously (at least [version 0.1.33](https://github.com/ollama/ollama/releases)).\n",
|
||||
"\n",
|
||||
"Start the Ollama server with:\n",
|
||||
"\n",
|
||||
"* `OLLAMA_NUM_PARALLEL`: Handle multiple requests simultaneously for a single model\n",
|
||||
"* `OLLAMA_MAX_LOADED_MODELS`: Load multiple models simultaneously\n",
|
||||
"\n",
|
||||
"Example: `OLLAMA_NUM_PARALLEL=4 OLLAMA_MAX_LOADED_MODELS=4 ollama serve`\n",
|
||||
"\n",
|
||||
"Learn more about configuring Ollama server in [the official guide](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-do-i-configure-ollama-server)."
|
||||
"For detailed documentation of all ChatOllama features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_ollama.chat_models.ChatOllama.html"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -482,9 +384,9 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.8"
|
||||
"version": "3.12.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
|
||||
@ -1,10 +1,21 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"cell_type": "raw",
|
||||
"id": "67db2992",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Ollama\n",
|
||||
"---\n",
|
||||
"sidebar_label: Ollama\n",
|
||||
"---"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "9597802c",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# OllamaLLM\n",
|
||||
"\n",
|
||||
":::caution\n",
|
||||
"You are currently on a page documenting the use of Ollama models as [text completion models](/docs/concepts/#llms). Many popular Ollama models are [chat completion models](/docs/concepts/#chat-models).\n",
|
||||
@ -12,21 +23,35 @@
|
||||
"You may be looking for [this page instead](/docs/integrations/chat/ollama/).\n",
|
||||
":::\n",
|
||||
"\n",
|
||||
"[Ollama](https://ollama.ai/) allows you to run open-source large language models, such as Llama 2, locally.\n",
|
||||
"\n",
|
||||
"Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. \n",
|
||||
"\n",
|
||||
"It optimizes setup and configuration details, including GPU usage.\n",
|
||||
"\n",
|
||||
"For a complete list of supported models and model variants, see the [Ollama model library](https://github.com/ollama/ollama#model-library).\n",
|
||||
"This page goes over how to use LangChain to interact with `Ollama` models.\n",
|
||||
"\n",
|
||||
"## Installation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "59c710c4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# install package\n",
|
||||
"%pip install -U langchain-ollama"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0ee90032",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
"First, follow [these instructions](https://github.com/ollama/ollama) to set up and run a local Ollama instance:\n",
|
||||
"First, follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance:\n",
|
||||
"\n",
|
||||
"* [Download](https://ollama.ai/download) and install Ollama onto the available supported platforms (including Windows Subsystem for Linux)\n",
|
||||
"* Fetch available LLM model via `ollama pull <name-of-model>`\n",
|
||||
" * View a list of available models via the [model library](https://ollama.ai/library) and pull to use locally with the command `ollama pull llama3`\n",
|
||||
" * View a list of available models via the [model library](https://ollama.ai/library)\n",
|
||||
" * e.g., `ollama pull llama3`\n",
|
||||
"* This will download the default tagged version of the model. Typically, the default points to the latest, smallest sized-parameter model.\n",
|
||||
"\n",
|
||||
"> On Mac, the models will be download to `~/.ollama/models`\n",
|
||||
@ -34,194 +59,67 @@
|
||||
"> On Linux (or WSL), the models will be stored at `/usr/share/ollama/.ollama/models`\n",
|
||||
"\n",
|
||||
"* Specify the exact version of the model of interest as such `ollama pull vicuna:13b-v1.5-16k-q4_0` (View the [various tags for the `Vicuna`](https://ollama.ai/library/vicuna/tags) model in this instance)\n",
|
||||
"* To view all pulled models on your local instance, use `ollama list`\n",
|
||||
"* To view all pulled models, use `ollama list`\n",
|
||||
"* To chat directly with a model from the command line, use `ollama run <name-of-model>`\n",
|
||||
"* View the [Ollama documentation](https://github.com/ollama/ollama) for more commands. \n",
|
||||
"* Run `ollama help` in the terminal to see available commands too.\n",
|
||||
"* View the [Ollama documentation](https://github.com/jmorganca/ollama) for more commands. Run `ollama help` in the terminal to see available commands too.\n",
|
||||
"\n",
|
||||
"## Usage\n",
|
||||
"\n",
|
||||
"You can see a full list of supported parameters on the [API reference page](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.ollama.Ollama.html).\n",
|
||||
"\n",
|
||||
"If you are using a LLaMA `chat` model (e.g., `ollama pull llama3`) then you can use the `ChatOllama` [interface](https://python.langchain.com/v0.2/docs/integrations/chat/ollama/).\n",
|
||||
"\n",
|
||||
"This includes [special tokens](https://ollama.com/library/llama3) for system message and user input.\n",
|
||||
"\n",
|
||||
"## Interacting with Models \n",
|
||||
"\n",
|
||||
"Here are a few ways to interact with pulled local models\n",
|
||||
"\n",
|
||||
"#### In the terminal:\n",
|
||||
"\n",
|
||||
"* All of your local models are automatically served on `localhost:11434`\n",
|
||||
"* Run `ollama run <name-of-model>` to start interacting via the command line directly\n",
|
||||
"\n",
|
||||
"#### Via the API\n",
|
||||
"\n",
|
||||
"Send an `application/json` request to the API endpoint of Ollama to interact.\n",
|
||||
"\n",
|
||||
"```bash\n",
|
||||
"curl http://localhost:11434/api/generate -d '{\n",
|
||||
" \"model\": \"llama3\",\n",
|
||||
" \"prompt\":\"Why is the sky blue?\"\n",
|
||||
"}'\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"See the Ollama [API documentation](https://github.com/ollama/ollama/blob/main/docs/api.md) for all endpoints.\n",
|
||||
"\n",
|
||||
"#### via LangChain\n",
|
||||
"\n",
|
||||
"See a typical basic example of using [Ollama chat model](https://python.langchain.com/v0.2/docs/integrations/chat/ollama/) in your LangChain application."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!pip install langchain-community"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"\"Here's one:\\n\\nWhy don't scientists trust atoms?\\n\\nBecause they make up everything!\\n\\nHope that made you smile! Do you want to hear another one?\""
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain_community.llms import Ollama\n",
|
||||
"\n",
|
||||
"llm = Ollama(\n",
|
||||
" model=\"llama3\"\n",
|
||||
") # assuming you have Ollama installed and have llama3 model pulled with `ollama pull llama3 `\n",
|
||||
"\n",
|
||||
"llm.invoke(\"Tell me a joke\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To stream tokens, use the `.stream(...)` method:"
|
||||
"## Usage"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"id": "035dea0f",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"S\n",
|
||||
"ure\n",
|
||||
",\n",
|
||||
" here\n",
|
||||
"'\n",
|
||||
"s\n",
|
||||
" one\n",
|
||||
":\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Why\n",
|
||||
" don\n",
|
||||
"'\n",
|
||||
"t\n",
|
||||
" scient\n",
|
||||
"ists\n",
|
||||
" trust\n",
|
||||
" atoms\n",
|
||||
"?\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"B\n",
|
||||
"ecause\n",
|
||||
" they\n",
|
||||
" make\n",
|
||||
" up\n",
|
||||
" everything\n",
|
||||
"!\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"I\n",
|
||||
" hope\n",
|
||||
" you\n",
|
||||
" found\n",
|
||||
" that\n",
|
||||
" am\n",
|
||||
"using\n",
|
||||
"!\n",
|
||||
" Do\n",
|
||||
" you\n",
|
||||
" want\n",
|
||||
" to\n",
|
||||
" hear\n",
|
||||
" another\n",
|
||||
" one\n",
|
||||
"?\n",
|
||||
"\n"
|
||||
]
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'A great start!\\n\\nLangChain is a type of AI model that uses language processing techniques to generate human-like text based on input prompts or chains of reasoning. In other words, it can have a conversation with humans, understanding the context and responding accordingly.\\n\\nHere\\'s a possible breakdown:\\n\\n* \"Lang\" likely refers to its focus on natural language processing (NLP) and linguistic analysis.\\n* \"Chain\" suggests that LangChain is designed to generate text in response to a series of connected ideas or prompts, rather than simply generating random text.\\n\\nSo, what do you think LangChain\\'s capabilities might be?'"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"query = \"Tell me a joke\"\n",
|
||||
"from langchain_core.prompts import ChatPromptTemplate\n",
|
||||
"from langchain_ollama.llms import OllamaLLM\n",
|
||||
"\n",
|
||||
"for chunks in llm.stream(query):\n",
|
||||
" print(chunks)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To learn more about the LangChain Expressive Language and the available methods on an LLM, see the [LCEL Interface](/docs/concepts#interface)"
|
||||
"template = \"\"\"Question: {question}\n",
|
||||
"\n",
|
||||
"Answer: Let's think step by step.\"\"\"\n",
|
||||
"\n",
|
||||
"prompt = ChatPromptTemplate.from_template(template)\n",
|
||||
"\n",
|
||||
"model = OllamaLLM(model=\"llama3\")\n",
|
||||
"\n",
|
||||
"chain = prompt | model\n",
|
||||
"\n",
|
||||
"chain.invoke({\"question\": \"What is LangChain?\"})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e2d85456",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Multi-modal\n",
|
||||
"\n",
|
||||
"Ollama has support for multi-modal LLMs, such as [bakllava](https://ollama.ai/library/bakllava) and [llava](https://ollama.ai/library/llava).\n",
|
||||
"Ollama has support for multi-modal LLMs, such as [bakllava](https://ollama.com/library/bakllava) and [llava](https://ollama.com/library/llava).\n",
|
||||
"\n",
|
||||
"`ollama pull bakllava`\n",
|
||||
" ollama pull bakllava\n",
|
||||
"\n",
|
||||
"Be sure to update Ollama so that you have the most recent version to support multi-modal."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_community.llms import Ollama\n",
|
||||
"\n",
|
||||
"bakllava = Ollama(model=\"bakllava\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "4043e202",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -279,7 +177,8 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 4,
|
||||
"id": "79aaf863",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -288,38 +187,24 @@
|
||||
"'90%'"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"llm_with_image_context = bakllava.bind(images=[image_b64])\n",
|
||||
"from langchain_ollama import OllamaLLM\n",
|
||||
"\n",
|
||||
"llm = OllamaLLM(model=\"bakllava\")\n",
|
||||
"\n",
|
||||
"llm_with_image_context = llm.bind(images=[image_b64])\n",
|
||||
"llm_with_image_context.invoke(\"What is the dollar based gross retention rate:\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Concurrency Features\n",
|
||||
"\n",
|
||||
"Ollama supports concurrency inference for a single model, and or loading multiple models simulatenously (at least [version 0.1.33](https://github.com/ollama/ollama/releases)).\n",
|
||||
"\n",
|
||||
"Start the Ollama server with:\n",
|
||||
"\n",
|
||||
"* `OLLAMA_NUM_PARALLEL`: Handle multiple requests simultaneously for a single model\n",
|
||||
"* `OLLAMA_MAX_LOADED_MODELS`: Load multiple models simultaneously\n",
|
||||
"\n",
|
||||
"Example: `OLLAMA_NUM_PARALLEL=4 OLLAMA_MAX_LOADED_MODELS=4 ollama serve`\n",
|
||||
"\n",
|
||||
"Learn more about configuring Ollama server in [the official guide](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-do-i-configure-ollama-server)."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"display_name": "Python 3.11.1 64-bit",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
@ -333,9 +218,14 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.8"
|
||||
"version": "3.12.3"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
1
libs/partners/ollama/.gitignore
vendored
Normal file
1
libs/partners/ollama/.gitignore
vendored
Normal file
@ -0,0 +1 @@
|
||||
__pycache__
|
||||
21
libs/partners/ollama/LICENSE
Normal file
21
libs/partners/ollama/LICENSE
Normal file
@ -0,0 +1,21 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2024 LangChain, Inc.
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
64
libs/partners/ollama/Makefile
Normal file
64
libs/partners/ollama/Makefile
Normal file
@ -0,0 +1,64 @@
|
||||
.PHONY: all format lint test tests integration_tests docker_tests help extended_tests
|
||||
|
||||
# Default target executed when no arguments are given to make.
|
||||
all: help
|
||||
|
||||
# Define a variable for the test file path.
|
||||
TEST_FILE ?= tests/unit_tests/
|
||||
integration_test: TEST_FILE = tests/integration_tests/
|
||||
# note: leaving out integration_tests (with s) command to skip release testing for now
|
||||
# TODO(erick) configure ollama server to run in CI, in separate repo
|
||||
|
||||
|
||||
# unit tests are run with the --disable-socket flag to prevent network calls
|
||||
test tests:
|
||||
poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
|
||||
|
||||
# integration tests are run without the --disable-socket flag to allow network calls
|
||||
integration_test integration_tests:
|
||||
poetry run pytest $(TEST_FILE)
|
||||
|
||||
######################
|
||||
# LINTING AND FORMATTING
|
||||
######################
|
||||
|
||||
# Define a variable for Python and notebook files.
|
||||
PYTHON_FILES=.
|
||||
MYPY_CACHE=.mypy_cache
|
||||
lint format: PYTHON_FILES=.
|
||||
lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/ollama --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
|
||||
lint_package: PYTHON_FILES=langchain_ollama
|
||||
lint_tests: PYTHON_FILES=tests
|
||||
lint_tests: MYPY_CACHE=.mypy_cache_test
|
||||
|
||||
lint lint_diff lint_package lint_tests:
|
||||
poetry run ruff .
|
||||
poetry run ruff format $(PYTHON_FILES) --diff
|
||||
poetry run ruff --select I $(PYTHON_FILES)
|
||||
mkdir -p $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
|
||||
|
||||
format format_diff:
|
||||
poetry run ruff format $(PYTHON_FILES)
|
||||
poetry run ruff --select I --fix $(PYTHON_FILES)
|
||||
|
||||
spell_check:
|
||||
poetry run codespell --toml pyproject.toml
|
||||
|
||||
spell_fix:
|
||||
poetry run codespell --toml pyproject.toml -w
|
||||
|
||||
check_imports: $(shell find langchain_ollama -name '*.py')
|
||||
poetry run python ./scripts/check_imports.py $^
|
||||
|
||||
######################
|
||||
# HELP
|
||||
######################
|
||||
|
||||
help:
|
||||
@echo '----'
|
||||
@echo 'check_imports - check imports'
|
||||
@echo 'format - run code formatters'
|
||||
@echo 'lint - run linters'
|
||||
@echo 'test - run unit tests'
|
||||
@echo 'tests - run unit tests'
|
||||
@echo 'test TEST_FILE=<test_file> - run all tests in file'
|
||||
44
libs/partners/ollama/README.md
Normal file
44
libs/partners/ollama/README.md
Normal file
@ -0,0 +1,44 @@
|
||||
# langchain-ollama
|
||||
|
||||
This package contains the LangChain integration with Ollama
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
pip install -U langchain-ollama
|
||||
```
|
||||
|
||||
You will also need to run the Ollama server locally.
|
||||
You can download it [here](https://ollama.com/download).
|
||||
|
||||
## Chat Models
|
||||
|
||||
`ChatOllama` class exposes chat models from Ollama.
|
||||
|
||||
```python
|
||||
from langchain_ollama import ChatOllama
|
||||
|
||||
llm = ChatOllama(model="llama3-groq-tool-use")
|
||||
llm.invoke("Sing a ballad of LangChain.")
|
||||
```
|
||||
|
||||
## Embeddings
|
||||
|
||||
`OllamaEmbeddings` class exposes embeddings from Ollama.
|
||||
|
||||
```python
|
||||
from langchain_ollama import OllamaEmbeddings
|
||||
|
||||
embeddings = OllamaEmbeddings(model="llama3")
|
||||
embeddings.embed_query("What is the meaning of life?")
|
||||
```
|
||||
|
||||
## LLMs
|
||||
`OllamaLLM` class exposes LLMs from Ollama.
|
||||
|
||||
```python
|
||||
from langchain_ollama import OllamaLLM
|
||||
|
||||
llm = OllamaLLM(model="llama3")
|
||||
llm.invoke("The meaning of life is")
|
||||
```
|
||||
19
libs/partners/ollama/langchain_ollama/__init__.py
Normal file
19
libs/partners/ollama/langchain_ollama/__init__.py
Normal file
@ -0,0 +1,19 @@
|
||||
from importlib import metadata
|
||||
|
||||
from langchain_ollama.chat_models import ChatOllama
|
||||
from langchain_ollama.embeddings import OllamaEmbeddings
|
||||
from langchain_ollama.llms import OllamaLLM
|
||||
|
||||
try:
|
||||
__version__ = metadata.version(__package__)
|
||||
except metadata.PackageNotFoundError:
|
||||
# Case where package metadata is not available.
|
||||
__version__ = ""
|
||||
del metadata # optional, avoids polluting the results of dir(__package__)
|
||||
|
||||
__all__ = [
|
||||
"ChatOllama",
|
||||
"OllamaLLM",
|
||||
"OllamaEmbeddings",
|
||||
"__version__",
|
||||
]
|
||||
719
libs/partners/ollama/langchain_ollama/chat_models.py
Normal file
719
libs/partners/ollama/langchain_ollama/chat_models.py
Normal file
@ -0,0 +1,719 @@
|
||||
"""Ollama chat models."""
|
||||
|
||||
from typing import (
|
||||
Any,
|
||||
AsyncIterator,
|
||||
Callable,
|
||||
Dict,
|
||||
Iterator,
|
||||
List,
|
||||
Literal,
|
||||
Mapping,
|
||||
Optional,
|
||||
Sequence,
|
||||
Type,
|
||||
Union,
|
||||
cast,
|
||||
)
|
||||
from uuid import uuid4
|
||||
|
||||
import ollama
|
||||
from langchain_core.callbacks import (
|
||||
CallbackManagerForLLMRun,
|
||||
)
|
||||
from langchain_core.callbacks.manager import AsyncCallbackManagerForLLMRun
|
||||
from langchain_core.language_models import LanguageModelInput
|
||||
from langchain_core.language_models.chat_models import BaseChatModel, LangSmithParams
|
||||
from langchain_core.messages import (
|
||||
AIMessage,
|
||||
AIMessageChunk,
|
||||
BaseMessage,
|
||||
HumanMessage,
|
||||
SystemMessage,
|
||||
ToolCall,
|
||||
ToolMessage,
|
||||
)
|
||||
from langchain_core.messages.ai import UsageMetadata
|
||||
from langchain_core.messages.tool import tool_call
|
||||
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
|
||||
from langchain_core.pydantic_v1 import BaseModel
|
||||
from langchain_core.runnables import Runnable
|
||||
from langchain_core.tools import BaseTool
|
||||
from langchain_core.utils.function_calling import convert_to_openai_tool
|
||||
from ollama import AsyncClient, Message, Options
|
||||
|
||||
|
||||
def _get_usage_metadata_from_generation_info(
|
||||
generation_info: Optional[Mapping[str, Any]],
|
||||
) -> Optional[UsageMetadata]:
|
||||
"""Get usage metadata from ollama generation info mapping."""
|
||||
if generation_info is None:
|
||||
return None
|
||||
input_tokens: Optional[int] = generation_info.get("prompt_eval_count")
|
||||
output_tokens: Optional[int] = generation_info.get("eval_count")
|
||||
if input_tokens is not None and output_tokens is not None:
|
||||
return UsageMetadata(
|
||||
input_tokens=input_tokens,
|
||||
output_tokens=output_tokens,
|
||||
total_tokens=input_tokens + output_tokens,
|
||||
)
|
||||
return None
|
||||
|
||||
|
||||
def _get_tool_calls_from_response(
|
||||
response: Mapping[str, Any],
|
||||
) -> List[ToolCall]:
|
||||
"""Get tool calls from ollama response."""
|
||||
tool_calls = []
|
||||
if "message" in response:
|
||||
if "tool_calls" in response["message"]:
|
||||
for tc in response["message"]["tool_calls"]:
|
||||
tool_calls.append(
|
||||
tool_call(
|
||||
id=str(uuid4()),
|
||||
name=tc["function"]["name"],
|
||||
args=tc["function"]["arguments"],
|
||||
)
|
||||
)
|
||||
return tool_calls
|
||||
|
||||
|
||||
def _lc_tool_call_to_openai_tool_call(tool_call: ToolCall) -> dict:
|
||||
return {
|
||||
"type": "function",
|
||||
"id": tool_call["id"],
|
||||
"function": {
|
||||
"name": tool_call["name"],
|
||||
"arguments": tool_call["args"],
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
class ChatOllama(BaseChatModel):
|
||||
"""Ollama chat model integration.
|
||||
|
||||
Setup:
|
||||
Install ``langchain-ollama`` and download any models you want to use from ollama.
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
ollama pull mistral:v0.3
|
||||
pip install -U langchain-ollama
|
||||
|
||||
Key init args — completion params:
|
||||
model: str
|
||||
Name of Ollama model to use.
|
||||
temperature: float
|
||||
Sampling temperature. Ranges from 0.0 to 1.0.
|
||||
num_predict: Optional[int]
|
||||
Max number of tokens to generate.
|
||||
|
||||
See full list of supported init args and their descriptions in the params section.
|
||||
|
||||
Instantiate:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_ollama import ChatOllama
|
||||
|
||||
llm = ChatOllama(
|
||||
model = "llama3",
|
||||
temperature = 0.8,
|
||||
num_predict = 256,
|
||||
# other params ...
|
||||
)
|
||||
|
||||
Invoke:
|
||||
.. code-block:: python
|
||||
|
||||
messages = [
|
||||
("system", "You are a helpful translator. Translate the user sentence to French."),
|
||||
("human", "I love programming."),
|
||||
]
|
||||
llm.invoke(messages)
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
AIMessage(content='J'adore le programmation. (Note: "programming" can also refer to the act of writing code, so if you meant that, I could translate it as "J'adore programmer". But since you didn\'t specify, I assumed you were talking about the activity itself, which is what "le programmation" usually refers to.)', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:37:50.182604Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 3576619666, 'load_duration': 788524916, 'prompt_eval_count': 32, 'prompt_eval_duration': 128125000, 'eval_count': 71, 'eval_duration': 2656556000}, id='run-ba48f958-6402-41a5-b461-5e250a4ebd36-0')
|
||||
|
||||
Stream:
|
||||
.. code-block:: python
|
||||
|
||||
messages = [
|
||||
("human", "Return the words Hello World!"),
|
||||
]
|
||||
for chunk in llm.stream(messages):
|
||||
print(chunk)
|
||||
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
content='Hello' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
|
||||
content=' World' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
|
||||
content='!' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
|
||||
content='' response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:39:42.274449Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 411875125, 'load_duration': 1898166, 'prompt_eval_count': 14, 'prompt_eval_duration': 297320000, 'eval_count': 4, 'eval_duration': 111099000} id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
|
||||
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
stream = llm.stream(messages)
|
||||
full = next(stream)
|
||||
for chunk in stream:
|
||||
full += chunk
|
||||
full
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
AIMessageChunk(content='Je adore le programmation.(Note: "programmation" is the formal way to say "programming" in French, but informally, people might use the phrase "le développement logiciel" or simply "le code")', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:38:54.933154Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1977300042, 'load_duration': 1345709, 'prompt_eval_duration': 159343000, 'eval_count': 47, 'eval_duration': 1815123000}, id='run-3c81a3ed-3e79-4dd3-a796-04064d804890')
|
||||
|
||||
Async:
|
||||
.. code-block:: python
|
||||
|
||||
messages = [
|
||||
("human", "Hello how are you!"),
|
||||
]
|
||||
await llm.ainvoke(messages)
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
AIMessage(content="Hi there! I'm just an AI, so I don't have feelings or emotions like humans do. But I'm functioning properly and ready to help with any questions or tasks you may have! How can I assist you today?", response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:52:08.165478Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 2138492875, 'load_duration': 1364000, 'prompt_eval_count': 10, 'prompt_eval_duration': 297081000, 'eval_count': 47, 'eval_duration': 1838524000}, id='run-29c510ae-49a4-4cdd-8f23-b972bfab1c49-0')
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
messages = [
|
||||
("human", "Say hello world!"),
|
||||
]
|
||||
async for chunk in llm.astream(messages):
|
||||
print(chunk.content)
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
HEL
|
||||
LO
|
||||
WORLD
|
||||
!
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
messages = [
|
||||
("human", "Say hello world!"),
|
||||
("human","Say goodbye world!")
|
||||
]
|
||||
await llm.abatch(messages)
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
[AIMessage(content='HELLO, WORLD!', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:55:07.315396Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1696745458, 'load_duration': 1505000, 'prompt_eval_count': 8, 'prompt_eval_duration': 111627000, 'eval_count': 6, 'eval_duration': 185181000}, id='run-da6c7562-e25a-4a44-987a-2c83cd8c2686-0'),
|
||||
AIMessage(content="It's been a blast chatting with you! Say goodbye to the world for me, and don't forget to come back and visit us again soon!", response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:55:07.018076Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1399391083, 'load_duration': 1187417, 'prompt_eval_count': 20, 'prompt_eval_duration': 230349000, 'eval_count': 31, 'eval_duration': 1166047000}, id='run-96cad530-6f3e-4cf9-86b4-e0f8abba4cdb-0')]
|
||||
|
||||
JSON mode:
|
||||
.. code-block:: python
|
||||
|
||||
|
||||
json_llm = ChatOllama(format="json")
|
||||
messages = [
|
||||
("human", "Return a query for the weather in a random location and time of day with two keys: location and time_of_day. Respond using JSON only."),
|
||||
]
|
||||
llm.invoke(messages).content
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
'{"location": "Pune, India", "time_of_day": "morning"}'
|
||||
|
||||
Tool Calling:
|
||||
.. warning::
|
||||
Ollama currently does not support streaming for tools
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_ollama import ChatOllama
|
||||
from langchain_core.pydantic_v1 import BaseModel, Field
|
||||
|
||||
class Multiply(BaseModel):
|
||||
a: int = Field(..., description="First integer")
|
||||
b: int = Field(..., description="Second integer")
|
||||
|
||||
ans = await chat.invoke("What is 45*67")
|
||||
ans.tool_calls
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
[{'name': 'Multiply',
|
||||
'args': {'a': 45, 'b': 67},
|
||||
'id': '420c3f3b-df10-4188-945f-eb3abdb40622',
|
||||
'type': 'tool_call'}]
|
||||
""" # noqa: E501
|
||||
|
||||
model: str
|
||||
"""Model name to use."""
|
||||
|
||||
mirostat: Optional[int] = None
|
||||
"""Enable Mirostat sampling for controlling perplexity.
|
||||
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)"""
|
||||
|
||||
mirostat_eta: Optional[float] = None
|
||||
"""Influences how quickly the algorithm responds to feedback
|
||||
from the generated text. A lower learning rate will result in
|
||||
slower adjustments, while a higher learning rate will make
|
||||
the algorithm more responsive. (Default: 0.1)"""
|
||||
|
||||
mirostat_tau: Optional[float] = None
|
||||
"""Controls the balance between coherence and diversity
|
||||
of the output. A lower value will result in more focused and
|
||||
coherent text. (Default: 5.0)"""
|
||||
|
||||
num_ctx: Optional[int] = None
|
||||
"""Sets the size of the context window used to generate the
|
||||
next token. (Default: 2048) """
|
||||
|
||||
num_gpu: Optional[int] = None
|
||||
"""The number of GPUs to use. On macOS it defaults to 1 to
|
||||
enable metal support, 0 to disable."""
|
||||
|
||||
num_thread: Optional[int] = None
|
||||
"""Sets the number of threads to use during computation.
|
||||
By default, Ollama will detect this for optimal performance.
|
||||
It is recommended to set this value to the number of physical
|
||||
CPU cores your system has (as opposed to the logical number of cores)."""
|
||||
|
||||
num_predict: Optional[int] = None
|
||||
"""Maximum number of tokens to predict when generating text.
|
||||
(Default: 128, -1 = infinite generation, -2 = fill context)"""
|
||||
|
||||
repeat_last_n: Optional[int] = None
|
||||
"""Sets how far back for the model to look back to prevent
|
||||
repetition. (Default: 64, 0 = disabled, -1 = num_ctx)"""
|
||||
|
||||
repeat_penalty: Optional[float] = None
|
||||
"""Sets how strongly to penalize repetitions. A higher value (e.g., 1.5)
|
||||
will penalize repetitions more strongly, while a lower value (e.g., 0.9)
|
||||
will be more lenient. (Default: 1.1)"""
|
||||
|
||||
temperature: Optional[float] = None
|
||||
"""The temperature of the model. Increasing the temperature will
|
||||
make the model answer more creatively. (Default: 0.8)"""
|
||||
|
||||
stop: Optional[List[str]] = None
|
||||
"""Sets the stop tokens to use."""
|
||||
|
||||
tfs_z: Optional[float] = None
|
||||
"""Tail free sampling is used to reduce the impact of less probable
|
||||
tokens from the output. A higher value (e.g., 2.0) will reduce the
|
||||
impact more, while a value of 1.0 disables this setting. (default: 1)"""
|
||||
|
||||
top_k: Optional[int] = None
|
||||
"""Reduces the probability of generating nonsense. A higher value (e.g. 100)
|
||||
will give more diverse answers, while a lower value (e.g. 10)
|
||||
will be more conservative. (Default: 40)"""
|
||||
|
||||
top_p: Optional[float] = None
|
||||
"""Works together with top-k. A higher value (e.g., 0.95) will lead
|
||||
to more diverse text, while a lower value (e.g., 0.5) will
|
||||
generate more focused and conservative text. (Default: 0.9)"""
|
||||
|
||||
format: Literal["", "json"] = ""
|
||||
"""Specify the format of the output (options: json)"""
|
||||
|
||||
keep_alive: Optional[Union[int, str]] = None
|
||||
"""How long the model will stay loaded into memory."""
|
||||
|
||||
@property
|
||||
def _default_params(self) -> Dict[str, Any]:
|
||||
"""Get the default parameters for calling Ollama."""
|
||||
return {
|
||||
"model": self.model,
|
||||
"format": self.format,
|
||||
"options": {
|
||||
"mirostat": self.mirostat,
|
||||
"mirostat_eta": self.mirostat_eta,
|
||||
"mirostat_tau": self.mirostat_tau,
|
||||
"num_ctx": self.num_ctx,
|
||||
"num_gpu": self.num_gpu,
|
||||
"num_thread": self.num_thread,
|
||||
"num_predict": self.num_predict,
|
||||
"repeat_last_n": self.repeat_last_n,
|
||||
"repeat_penalty": self.repeat_penalty,
|
||||
"temperature": self.temperature,
|
||||
"stop": self.stop,
|
||||
"tfs_z": self.tfs_z,
|
||||
"top_k": self.top_k,
|
||||
"top_p": self.top_p,
|
||||
},
|
||||
"keep_alive": self.keep_alive,
|
||||
}
|
||||
|
||||
def _convert_messages_to_ollama_messages(
|
||||
self, messages: List[BaseMessage]
|
||||
) -> Sequence[Message]:
|
||||
ollama_messages: List = []
|
||||
for message in messages:
|
||||
role = ""
|
||||
tool_call_id: Optional[str] = None
|
||||
tool_calls: Optional[List[Dict[str, Any]]] = None
|
||||
if isinstance(message, HumanMessage):
|
||||
role = "user"
|
||||
elif isinstance(message, AIMessage):
|
||||
role = "assistant"
|
||||
tool_calls = (
|
||||
[
|
||||
_lc_tool_call_to_openai_tool_call(tool_call)
|
||||
for tool_call in message.tool_calls
|
||||
]
|
||||
if message.tool_calls
|
||||
else None
|
||||
)
|
||||
elif isinstance(message, SystemMessage):
|
||||
role = "system"
|
||||
elif isinstance(message, ToolMessage):
|
||||
role = "tool"
|
||||
tool_call_id = message.tool_call_id
|
||||
else:
|
||||
raise ValueError("Received unsupported message type for Ollama.")
|
||||
|
||||
content = ""
|
||||
images = []
|
||||
if isinstance(message.content, str):
|
||||
content = message.content
|
||||
else:
|
||||
for content_part in cast(List[Dict], message.content):
|
||||
if content_part.get("type") == "text":
|
||||
content += f"\n{content_part['text']}"
|
||||
elif content_part.get("type") == "tool_use":
|
||||
continue
|
||||
elif content_part.get("type") == "image_url":
|
||||
image_url = None
|
||||
temp_image_url = content_part.get("image_url")
|
||||
if isinstance(temp_image_url, str):
|
||||
image_url = content_part["image_url"]
|
||||
elif (
|
||||
isinstance(temp_image_url, dict) and "url" in temp_image_url
|
||||
):
|
||||
image_url = temp_image_url
|
||||
else:
|
||||
raise ValueError(
|
||||
"Only string image_url or dict with string 'url' "
|
||||
"inside content parts are supported."
|
||||
)
|
||||
|
||||
image_url_components = image_url.split(",")
|
||||
# Support data:image/jpeg;base64,<image> format
|
||||
# and base64 strings
|
||||
if len(image_url_components) > 1:
|
||||
images.append(image_url_components[1])
|
||||
else:
|
||||
images.append(image_url_components[0])
|
||||
|
||||
else:
|
||||
raise ValueError(
|
||||
"Unsupported message content type. "
|
||||
"Must either have type 'text' or type 'image_url' "
|
||||
"with a string 'image_url' field."
|
||||
)
|
||||
msg = {
|
||||
"role": role,
|
||||
"content": content,
|
||||
"images": images,
|
||||
}
|
||||
if tool_call_id:
|
||||
msg["tool_call_id"] = tool_call_id
|
||||
if tool_calls:
|
||||
msg["tool_calls"] = tool_calls
|
||||
ollama_messages.append(msg)
|
||||
|
||||
return ollama_messages
|
||||
|
||||
async def _acreate_chat_stream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[Union[Mapping[str, Any], str]]:
|
||||
ollama_messages = self._convert_messages_to_ollama_messages(messages)
|
||||
|
||||
stop = stop if stop is not None else self.stop
|
||||
|
||||
params = self._default_params
|
||||
|
||||
for key in self._default_params:
|
||||
if key in kwargs:
|
||||
params[key] = kwargs[key]
|
||||
|
||||
params["options"]["stop"] = stop
|
||||
if "tools" in kwargs:
|
||||
yield await AsyncClient().chat(
|
||||
model=params["model"],
|
||||
messages=ollama_messages,
|
||||
stream=False,
|
||||
options=Options(**params["options"]),
|
||||
keep_alive=params["keep_alive"],
|
||||
format=params["format"],
|
||||
tools=kwargs["tools"],
|
||||
) # type:ignore
|
||||
else:
|
||||
async for part in await AsyncClient().chat(
|
||||
model=params["model"],
|
||||
messages=ollama_messages,
|
||||
stream=True,
|
||||
options=Options(**params["options"]),
|
||||
keep_alive=params["keep_alive"],
|
||||
format=params["format"],
|
||||
): # type:ignore
|
||||
yield part
|
||||
|
||||
def _create_chat_stream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[Union[Mapping[str, Any], str]]:
|
||||
ollama_messages = self._convert_messages_to_ollama_messages(messages)
|
||||
|
||||
stop = stop if stop is not None else self.stop
|
||||
|
||||
params = self._default_params
|
||||
|
||||
for key in self._default_params:
|
||||
if key in kwargs:
|
||||
params[key] = kwargs[key]
|
||||
|
||||
params["options"]["stop"] = stop
|
||||
if "tools" in kwargs:
|
||||
yield ollama.chat(
|
||||
model=params["model"],
|
||||
messages=ollama_messages,
|
||||
stream=False,
|
||||
options=Options(**params["options"]),
|
||||
keep_alive=params["keep_alive"],
|
||||
format=params["format"],
|
||||
tools=kwargs["tools"],
|
||||
)
|
||||
else:
|
||||
yield from ollama.chat(
|
||||
model=params["model"],
|
||||
messages=ollama_messages,
|
||||
stream=True,
|
||||
options=Options(**params["options"]),
|
||||
keep_alive=params["keep_alive"],
|
||||
format=params["format"],
|
||||
)
|
||||
|
||||
def _chat_stream_with_aggregation(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
verbose: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> ChatGenerationChunk:
|
||||
final_chunk = None
|
||||
for stream_resp in self._create_chat_stream(messages, stop, **kwargs):
|
||||
if not isinstance(stream_resp, str):
|
||||
chunk = ChatGenerationChunk(
|
||||
message=AIMessageChunk(
|
||||
content=(
|
||||
stream_resp["message"]["content"]
|
||||
if "message" in stream_resp
|
||||
and "content" in stream_resp["message"]
|
||||
else ""
|
||||
),
|
||||
usage_metadata=_get_usage_metadata_from_generation_info(
|
||||
stream_resp
|
||||
),
|
||||
tool_calls=_get_tool_calls_from_response(stream_resp),
|
||||
),
|
||||
generation_info=(
|
||||
dict(stream_resp) if stream_resp.get("done") is True else None
|
||||
),
|
||||
)
|
||||
if final_chunk is None:
|
||||
final_chunk = chunk
|
||||
else:
|
||||
final_chunk += chunk
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
chunk=chunk,
|
||||
verbose=verbose,
|
||||
)
|
||||
if final_chunk is None:
|
||||
raise ValueError("No data received from Ollama stream.")
|
||||
|
||||
return final_chunk
|
||||
|
||||
async def _achat_stream_with_aggregation(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
verbose: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> ChatGenerationChunk:
|
||||
final_chunk = None
|
||||
async for stream_resp in self._acreate_chat_stream(messages, stop, **kwargs):
|
||||
if not isinstance(stream_resp, str):
|
||||
chunk = ChatGenerationChunk(
|
||||
message=AIMessageChunk(
|
||||
content=(
|
||||
stream_resp["message"]["content"]
|
||||
if "message" in stream_resp
|
||||
and "content" in stream_resp["message"]
|
||||
else ""
|
||||
),
|
||||
usage_metadata=_get_usage_metadata_from_generation_info(
|
||||
stream_resp
|
||||
),
|
||||
tool_calls=_get_tool_calls_from_response(stream_resp),
|
||||
),
|
||||
generation_info=(
|
||||
dict(stream_resp) if stream_resp.get("done") is True else None
|
||||
),
|
||||
)
|
||||
if final_chunk is None:
|
||||
final_chunk = chunk
|
||||
else:
|
||||
final_chunk += chunk
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
chunk=chunk,
|
||||
verbose=verbose,
|
||||
)
|
||||
if final_chunk is None:
|
||||
raise ValueError("No data received from Ollama stream.")
|
||||
|
||||
return final_chunk
|
||||
|
||||
def _get_ls_params(
|
||||
self, stop: Optional[List[str]] = None, **kwargs: Any
|
||||
) -> LangSmithParams:
|
||||
"""Get standard params for tracing."""
|
||||
params = self._get_invocation_params(stop=stop, **kwargs)
|
||||
ls_params = LangSmithParams(
|
||||
ls_provider="ollama",
|
||||
ls_model_name=self.model,
|
||||
ls_model_type="chat",
|
||||
ls_temperature=params.get("temperature", self.temperature),
|
||||
)
|
||||
if ls_stop := stop or params.get("stop", None) or self.stop:
|
||||
ls_params["ls_stop"] = ls_stop
|
||||
return ls_params
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
final_chunk = self._chat_stream_with_aggregation(
|
||||
messages, stop, run_manager, verbose=self.verbose, **kwargs
|
||||
)
|
||||
generation_info = final_chunk.generation_info
|
||||
chat_generation = ChatGeneration(
|
||||
message=AIMessage(
|
||||
content=final_chunk.text,
|
||||
usage_metadata=cast(AIMessageChunk, final_chunk.message).usage_metadata,
|
||||
tool_calls=cast(AIMessageChunk, final_chunk.message).tool_calls,
|
||||
),
|
||||
generation_info=generation_info,
|
||||
)
|
||||
return ChatResult(generations=[chat_generation])
|
||||
|
||||
def _stream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[ChatGenerationChunk]:
|
||||
for stream_resp in self._create_chat_stream(messages, stop, **kwargs):
|
||||
if not isinstance(stream_resp, str):
|
||||
chunk = ChatGenerationChunk(
|
||||
message=AIMessageChunk(
|
||||
content=(
|
||||
stream_resp["message"]["content"]
|
||||
if "message" in stream_resp
|
||||
and "content" in stream_resp["message"]
|
||||
else ""
|
||||
),
|
||||
usage_metadata=_get_usage_metadata_from_generation_info(
|
||||
stream_resp
|
||||
),
|
||||
tool_calls=_get_tool_calls_from_response(stream_resp),
|
||||
),
|
||||
generation_info=(
|
||||
dict(stream_resp) if stream_resp.get("done") is True else None
|
||||
),
|
||||
)
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
verbose=self.verbose,
|
||||
)
|
||||
yield chunk
|
||||
|
||||
async def _astream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[ChatGenerationChunk]:
|
||||
async for stream_resp in self._acreate_chat_stream(messages, stop, **kwargs):
|
||||
if not isinstance(stream_resp, str):
|
||||
chunk = ChatGenerationChunk(
|
||||
message=AIMessageChunk(
|
||||
content=(
|
||||
stream_resp["message"]["content"]
|
||||
if "message" in stream_resp
|
||||
and "content" in stream_resp["message"]
|
||||
else ""
|
||||
),
|
||||
usage_metadata=_get_usage_metadata_from_generation_info(
|
||||
stream_resp
|
||||
),
|
||||
tool_calls=_get_tool_calls_from_response(stream_resp),
|
||||
),
|
||||
generation_info=(
|
||||
dict(stream_resp) if stream_resp.get("done") is True else None
|
||||
),
|
||||
)
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
verbose=self.verbose,
|
||||
)
|
||||
yield chunk
|
||||
|
||||
async def _agenerate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
final_chunk = await self._achat_stream_with_aggregation(
|
||||
messages, stop, run_manager, verbose=self.verbose, **kwargs
|
||||
)
|
||||
generation_info = final_chunk.generation_info
|
||||
chat_generation = ChatGeneration(
|
||||
message=AIMessage(
|
||||
content=final_chunk.text,
|
||||
usage_metadata=cast(AIMessageChunk, final_chunk.message).usage_metadata,
|
||||
tool_calls=cast(AIMessageChunk, final_chunk.message).tool_calls,
|
||||
),
|
||||
generation_info=generation_info,
|
||||
)
|
||||
return ChatResult(generations=[chat_generation])
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of chat model."""
|
||||
return "chat-ollama"
|
||||
|
||||
def bind_tools(
|
||||
self,
|
||||
tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
|
||||
**kwargs: Any,
|
||||
) -> Runnable[LanguageModelInput, BaseMessage]:
|
||||
formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
|
||||
return super().bind(tools=formatted_tools, **kwargs)
|
||||
51
libs/partners/ollama/langchain_ollama/embeddings.py
Normal file
51
libs/partners/ollama/langchain_ollama/embeddings.py
Normal file
@ -0,0 +1,51 @@
|
||||
from typing import List
|
||||
|
||||
import ollama
|
||||
from langchain_core.embeddings import Embeddings
|
||||
from langchain_core.pydantic_v1 import BaseModel, Extra
|
||||
from ollama import AsyncClient
|
||||
|
||||
|
||||
class OllamaEmbeddings(BaseModel, Embeddings):
|
||||
"""OllamaEmbeddings embedding model.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_ollama import OllamaEmbeddings
|
||||
|
||||
model = OllamaEmbeddings(model="llama3")
|
||||
embedder.embed_query("what is the place that jonathan worked at?")
|
||||
"""
|
||||
|
||||
model: str
|
||||
"""Model name to use."""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
|
||||
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
"""Embed search docs."""
|
||||
embedded_docs = []
|
||||
for doc in texts:
|
||||
embedded_docs.append(list(ollama.embeddings(self.model, doc)["embedding"]))
|
||||
return embedded_docs
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
"""Embed query text."""
|
||||
return self.embed_documents([text])[0]
|
||||
|
||||
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
"""Embed search docs."""
|
||||
embedded_docs = []
|
||||
for doc in texts:
|
||||
embedded_docs.append(
|
||||
list((await AsyncClient().embeddings(self.model, doc))["embedding"])
|
||||
)
|
||||
return embedded_docs
|
||||
|
||||
async def aembed_query(self, text: str) -> List[float]:
|
||||
"""Embed query text."""
|
||||
return (await self.aembed_documents([text]))[0]
|
||||
347
libs/partners/ollama/langchain_ollama/llms.py
Normal file
347
libs/partners/ollama/langchain_ollama/llms.py
Normal file
@ -0,0 +1,347 @@
|
||||
"""Ollama large language models."""
|
||||
|
||||
from typing import (
|
||||
Any,
|
||||
AsyncIterator,
|
||||
Dict,
|
||||
Iterator,
|
||||
List,
|
||||
Literal,
|
||||
Mapping,
|
||||
Optional,
|
||||
Union,
|
||||
)
|
||||
|
||||
import ollama
|
||||
from langchain_core.callbacks import (
|
||||
AsyncCallbackManagerForLLMRun,
|
||||
CallbackManagerForLLMRun,
|
||||
)
|
||||
from langchain_core.language_models import BaseLLM
|
||||
from langchain_core.outputs import GenerationChunk, LLMResult
|
||||
from ollama import AsyncClient, Options
|
||||
|
||||
|
||||
class OllamaLLM(BaseLLM):
|
||||
"""OllamaLLM large language models.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_ollama import OllamaLLM
|
||||
|
||||
model = OllamaLLM(model="llama3")
|
||||
model.invoke("Come up with 10 names for a song about parrots")
|
||||
"""
|
||||
|
||||
model: str
|
||||
"""Model name to use."""
|
||||
|
||||
mirostat: Optional[int] = None
|
||||
"""Enable Mirostat sampling for controlling perplexity.
|
||||
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)"""
|
||||
|
||||
mirostat_eta: Optional[float] = None
|
||||
"""Influences how quickly the algorithm responds to feedback
|
||||
from the generated text. A lower learning rate will result in
|
||||
slower adjustments, while a higher learning rate will make
|
||||
the algorithm more responsive. (Default: 0.1)"""
|
||||
|
||||
mirostat_tau: Optional[float] = None
|
||||
"""Controls the balance between coherence and diversity
|
||||
of the output. A lower value will result in more focused and
|
||||
coherent text. (Default: 5.0)"""
|
||||
|
||||
num_ctx: Optional[int] = None
|
||||
"""Sets the size of the context window used to generate the
|
||||
next token. (Default: 2048) """
|
||||
|
||||
num_gpu: Optional[int] = None
|
||||
"""The number of GPUs to use. On macOS it defaults to 1 to
|
||||
enable metal support, 0 to disable."""
|
||||
|
||||
num_thread: Optional[int] = None
|
||||
"""Sets the number of threads to use during computation.
|
||||
By default, Ollama will detect this for optimal performance.
|
||||
It is recommended to set this value to the number of physical
|
||||
CPU cores your system has (as opposed to the logical number of cores)."""
|
||||
|
||||
num_predict: Optional[int] = None
|
||||
"""Maximum number of tokens to predict when generating text.
|
||||
(Default: 128, -1 = infinite generation, -2 = fill context)"""
|
||||
|
||||
repeat_last_n: Optional[int] = None
|
||||
"""Sets how far back for the model to look back to prevent
|
||||
repetition. (Default: 64, 0 = disabled, -1 = num_ctx)"""
|
||||
|
||||
repeat_penalty: Optional[float] = None
|
||||
"""Sets how strongly to penalize repetitions. A higher value (e.g., 1.5)
|
||||
will penalize repetitions more strongly, while a lower value (e.g., 0.9)
|
||||
will be more lenient. (Default: 1.1)"""
|
||||
|
||||
temperature: Optional[float] = None
|
||||
"""The temperature of the model. Increasing the temperature will
|
||||
make the model answer more creatively. (Default: 0.8)"""
|
||||
|
||||
stop: Optional[List[str]] = None
|
||||
"""Sets the stop tokens to use."""
|
||||
|
||||
tfs_z: Optional[float] = None
|
||||
"""Tail free sampling is used to reduce the impact of less probable
|
||||
tokens from the output. A higher value (e.g., 2.0) will reduce the
|
||||
impact more, while a value of 1.0 disables this setting. (default: 1)"""
|
||||
|
||||
top_k: Optional[int] = None
|
||||
"""Reduces the probability of generating nonsense. A higher value (e.g. 100)
|
||||
will give more diverse answers, while a lower value (e.g. 10)
|
||||
will be more conservative. (Default: 40)"""
|
||||
|
||||
top_p: Optional[float] = None
|
||||
"""Works together with top-k. A higher value (e.g., 0.95) will lead
|
||||
to more diverse text, while a lower value (e.g., 0.5) will
|
||||
generate more focused and conservative text. (Default: 0.9)"""
|
||||
|
||||
format: Literal["", "json"] = ""
|
||||
"""Specify the format of the output (options: json)"""
|
||||
|
||||
keep_alive: Optional[Union[int, str]] = None
|
||||
"""How long the model will stay loaded into memory."""
|
||||
|
||||
@property
|
||||
def _default_params(self) -> Dict[str, Any]:
|
||||
"""Get the default parameters for calling Ollama."""
|
||||
return {
|
||||
"model": self.model,
|
||||
"format": self.format,
|
||||
"options": {
|
||||
"mirostat": self.mirostat,
|
||||
"mirostat_eta": self.mirostat_eta,
|
||||
"mirostat_tau": self.mirostat_tau,
|
||||
"num_ctx": self.num_ctx,
|
||||
"num_gpu": self.num_gpu,
|
||||
"num_thread": self.num_thread,
|
||||
"num_predict": self.num_predict,
|
||||
"repeat_last_n": self.repeat_last_n,
|
||||
"repeat_penalty": self.repeat_penalty,
|
||||
"temperature": self.temperature,
|
||||
"stop": self.stop,
|
||||
"tfs_z": self.tfs_z,
|
||||
"top_k": self.top_k,
|
||||
"top_p": self.top_p,
|
||||
},
|
||||
"keep_alive": self.keep_alive,
|
||||
}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of LLM."""
|
||||
return "ollama-llm"
|
||||
|
||||
async def _acreate_generate_stream(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[Union[Mapping[str, Any], str]]:
|
||||
if self.stop is not None and stop is not None:
|
||||
raise ValueError("`stop` found in both the input and default params.")
|
||||
elif self.stop is not None:
|
||||
stop = self.stop
|
||||
|
||||
params = self._default_params
|
||||
|
||||
for key in self._default_params:
|
||||
if key in kwargs:
|
||||
params[key] = kwargs[key]
|
||||
|
||||
params["options"]["stop"] = stop
|
||||
async for part in await AsyncClient().generate(
|
||||
model=params["model"],
|
||||
prompt=prompt,
|
||||
stream=True,
|
||||
options=Options(**params["options"]),
|
||||
keep_alive=params["keep_alive"],
|
||||
format=params["format"],
|
||||
): # type: ignore
|
||||
yield part
|
||||
|
||||
def _create_generate_stream(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[Union[Mapping[str, Any], str]]:
|
||||
if self.stop is not None and stop is not None:
|
||||
raise ValueError("`stop` found in both the input and default params.")
|
||||
elif self.stop is not None:
|
||||
stop = self.stop
|
||||
|
||||
params = self._default_params
|
||||
|
||||
for key in self._default_params:
|
||||
if key in kwargs:
|
||||
params[key] = kwargs[key]
|
||||
|
||||
params["options"]["stop"] = stop
|
||||
yield from ollama.generate(
|
||||
model=params["model"],
|
||||
prompt=prompt,
|
||||
stream=True,
|
||||
options=Options(**params["options"]),
|
||||
keep_alive=params["keep_alive"],
|
||||
format=params["format"],
|
||||
)
|
||||
|
||||
async def _astream_with_aggregation(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
verbose: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> GenerationChunk:
|
||||
final_chunk = None
|
||||
async for stream_resp in self._acreate_generate_stream(prompt, stop, **kwargs):
|
||||
if not isinstance(stream_resp, str):
|
||||
chunk = GenerationChunk(
|
||||
text=stream_resp["response"] if "response" in stream_resp else "",
|
||||
generation_info=(
|
||||
dict(stream_resp) if stream_resp.get("done") is True else None
|
||||
),
|
||||
)
|
||||
if final_chunk is None:
|
||||
final_chunk = chunk
|
||||
else:
|
||||
final_chunk += chunk
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
chunk=chunk,
|
||||
verbose=verbose,
|
||||
)
|
||||
if final_chunk is None:
|
||||
raise ValueError("No data received from Ollama stream.")
|
||||
|
||||
return final_chunk
|
||||
|
||||
def _stream_with_aggregation(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
verbose: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> GenerationChunk:
|
||||
final_chunk = None
|
||||
for stream_resp in self._create_generate_stream(prompt, stop, **kwargs):
|
||||
if not isinstance(stream_resp, str):
|
||||
chunk = GenerationChunk(
|
||||
text=stream_resp["response"] if "response" in stream_resp else "",
|
||||
generation_info=(
|
||||
dict(stream_resp) if stream_resp.get("done") is True else None
|
||||
),
|
||||
)
|
||||
if final_chunk is None:
|
||||
final_chunk = chunk
|
||||
else:
|
||||
final_chunk += chunk
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
chunk=chunk,
|
||||
verbose=verbose,
|
||||
)
|
||||
if final_chunk is None:
|
||||
raise ValueError("No data received from Ollama stream.")
|
||||
|
||||
return final_chunk
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
prompts: List[str],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> LLMResult:
|
||||
generations = []
|
||||
for prompt in prompts:
|
||||
final_chunk = self._stream_with_aggregation(
|
||||
prompt,
|
||||
stop=stop,
|
||||
run_manager=run_manager,
|
||||
verbose=self.verbose,
|
||||
**kwargs,
|
||||
)
|
||||
generations.append([final_chunk])
|
||||
return LLMResult(generations=generations) # type: ignore[arg-type]
|
||||
|
||||
async def _agenerate(
|
||||
self,
|
||||
prompts: List[str],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> LLMResult:
|
||||
generations = []
|
||||
for prompt in prompts:
|
||||
final_chunk = await self._astream_with_aggregation(
|
||||
prompt,
|
||||
stop=stop,
|
||||
run_manager=run_manager,
|
||||
verbose=self.verbose,
|
||||
**kwargs,
|
||||
)
|
||||
generations.append([final_chunk])
|
||||
return LLMResult(generations=generations) # type: ignore[arg-type]
|
||||
|
||||
def _stream(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[GenerationChunk]:
|
||||
for stream_resp in self._create_generate_stream(prompt, stop, **kwargs):
|
||||
if not isinstance(stream_resp, str):
|
||||
chunk = GenerationChunk(
|
||||
text=(
|
||||
stream_resp["message"]["content"]
|
||||
if "message" in stream_resp
|
||||
else ""
|
||||
),
|
||||
generation_info=(
|
||||
dict(stream_resp) if stream_resp.get("done") is True else None
|
||||
),
|
||||
)
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
verbose=self.verbose,
|
||||
)
|
||||
yield chunk
|
||||
|
||||
async def _astream(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[GenerationChunk]:
|
||||
async for stream_resp in self._acreate_generate_stream(prompt, stop, **kwargs):
|
||||
if not isinstance(stream_resp, str):
|
||||
chunk = GenerationChunk(
|
||||
text=(
|
||||
stream_resp["message"]["content"]
|
||||
if "message" in stream_resp
|
||||
else ""
|
||||
),
|
||||
generation_info=(
|
||||
dict(stream_resp) if stream_resp.get("done") is True else None
|
||||
),
|
||||
)
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
verbose=self.verbose,
|
||||
)
|
||||
yield chunk
|
||||
0
libs/partners/ollama/langchain_ollama/py.typed
Normal file
0
libs/partners/ollama/langchain_ollama/py.typed
Normal file
879
libs/partners/ollama/poetry.lock
generated
Normal file
879
libs/partners/ollama/poetry.lock
generated
Normal file
@ -0,0 +1,879 @@
|
||||
# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.7.0"
|
||||
description = "Reusable constraint types to use with typing.Annotated"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
|
||||
{file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.4.0"
|
||||
description = "High level compatibility layer for multiple asynchronous event loop implementations"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
|
||||
{file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
|
||||
idna = ">=2.8"
|
||||
sniffio = ">=1.1"
|
||||
typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
|
||||
|
||||
[package.extras]
|
||||
doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
|
||||
test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
|
||||
trio = ["trio (>=0.23)"]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2024.7.4"
|
||||
description = "Python package for providing Mozilla's CA Bundle."
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
|
||||
{file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "charset-normalizer"
|
||||
version = "3.3.2"
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||||
description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
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||||
optional = false
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
{file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
|
||||
{file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "urllib3"
|
||||
version = "2.2.2"
|
||||
description = "HTTP library with thread-safe connection pooling, file post, and more."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
|
||||
{file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
|
||||
h2 = ["h2 (>=4,<5)"]
|
||||
socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
|
||||
zstd = ["zstandard (>=0.18.0)"]
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
content-hash = "832fbfff0734b5ca736b8175526b0f548100c280954b0323720e2e3da8430488"
|
||||
90
libs/partners/ollama/pyproject.toml
Normal file
90
libs/partners/ollama/pyproject.toml
Normal file
@ -0,0 +1,90 @@
|
||||
[tool.poetry]
|
||||
name = "langchain-ollama"
|
||||
version = "0.1.0rc0"
|
||||
description = "An integration package connecting Ollama and LangChain"
|
||||
authors = []
|
||||
readme = "README.md"
|
||||
repository = "https://github.com/langchain-ai/langchain"
|
||||
license = "MIT"
|
||||
|
||||
[tool.poetry.urls]
|
||||
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/ollama"
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.8.1,<4.0"
|
||||
ollama = ">=0.3.0,<1"
|
||||
langchain-core = "^0.2.20"
|
||||
|
||||
[tool.poetry.group.test]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.test.dependencies]
|
||||
pytest = "^7.4.3"
|
||||
pytest-asyncio = "^0.23.2"
|
||||
syrupy = "^4.0.2"
|
||||
pytest-socket = "^0.7.0"
|
||||
langchain-core = { path = "../../core", develop = true }
|
||||
langchain-standard-tests = { path = "../../standard-tests", develop = true }
|
||||
|
||||
[tool.poetry.group.codespell]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.codespell.dependencies]
|
||||
codespell = "^2.2.6"
|
||||
|
||||
[tool.poetry.group.test_integration]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.test_integration.dependencies]
|
||||
|
||||
[tool.poetry.group.lint]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.lint.dependencies]
|
||||
ruff = "^0.1.8"
|
||||
|
||||
[tool.poetry.group.typing.dependencies]
|
||||
mypy = "^1.7.1"
|
||||
langchain-core = { path = "../../core", develop = true }
|
||||
|
||||
[tool.poetry.group.dev]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
langchain-core = { path = "../../core", develop = true }
|
||||
|
||||
[tool.ruff.lint]
|
||||
select = [
|
||||
"E", # pycodestyle
|
||||
"F", # pyflakes
|
||||
"I", # isort
|
||||
"T201", # print
|
||||
]
|
||||
|
||||
[tool.mypy]
|
||||
disallow_untyped_defs = "True"
|
||||
|
||||
[tool.coverage.run]
|
||||
omit = ["tests/*"]
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core>=1.0.0"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
# --strict-markers will raise errors on unknown marks.
|
||||
# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks
|
||||
#
|
||||
# https://docs.pytest.org/en/7.1.x/reference/reference.html
|
||||
# --strict-config any warnings encountered while parsing the `pytest`
|
||||
# section of the configuration file raise errors.
|
||||
#
|
||||
# https://github.com/tophat/syrupy
|
||||
# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite.
|
||||
addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5"
|
||||
# Registering custom markers.
|
||||
# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers
|
||||
markers = [
|
||||
"compile: mark placeholder test used to compile integration tests without running them",
|
||||
]
|
||||
asyncio_mode = "auto"
|
||||
17
libs/partners/ollama/scripts/check_imports.py
Normal file
17
libs/partners/ollama/scripts/check_imports.py
Normal file
@ -0,0 +1,17 @@
|
||||
import sys
|
||||
import traceback
|
||||
from importlib.machinery import SourceFileLoader
|
||||
|
||||
if __name__ == "__main__":
|
||||
files = sys.argv[1:]
|
||||
has_failure = False
|
||||
for file in files:
|
||||
try:
|
||||
SourceFileLoader("x", file).load_module()
|
||||
except Exception:
|
||||
has_faillure = True
|
||||
print(file) # noqa: T201
|
||||
traceback.print_exc()
|
||||
print() # noqa: T201
|
||||
|
||||
sys.exit(1 if has_failure else 0)
|
||||
27
libs/partners/ollama/scripts/check_pydantic.sh
Executable file
27
libs/partners/ollama/scripts/check_pydantic.sh
Executable file
@ -0,0 +1,27 @@
|
||||
#!/bin/bash
|
||||
#
|
||||
# This script searches for lines starting with "import pydantic" or "from pydantic"
|
||||
# in tracked files within a Git repository.
|
||||
#
|
||||
# Usage: ./scripts/check_pydantic.sh /path/to/repository
|
||||
|
||||
# Check if a path argument is provided
|
||||
if [ $# -ne 1 ]; then
|
||||
echo "Usage: $0 /path/to/repository"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
repository_path="$1"
|
||||
|
||||
# Search for lines matching the pattern within the specified repository
|
||||
result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic')
|
||||
|
||||
# Check if any matching lines were found
|
||||
if [ -n "$result" ]; then
|
||||
echo "ERROR: The following lines need to be updated:"
|
||||
echo "$result"
|
||||
echo "Please replace the code with an import from langchain_core.pydantic_v1."
|
||||
echo "For example, replace 'from pydantic import BaseModel'"
|
||||
echo "with 'from langchain_core.pydantic_v1 import BaseModel'"
|
||||
exit 1
|
||||
fi
|
||||
18
libs/partners/ollama/scripts/lint_imports.sh
Executable file
18
libs/partners/ollama/scripts/lint_imports.sh
Executable file
@ -0,0 +1,18 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -eu
|
||||
|
||||
# Initialize a variable to keep track of errors
|
||||
errors=0
|
||||
|
||||
# make sure not importing from langchain, langchain_experimental, or langchain_community
|
||||
git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
|
||||
git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
|
||||
git --no-pager grep '^from langchain_community\.' . && errors=$((errors+1))
|
||||
|
||||
# Decide on an exit status based on the errors
|
||||
if [ "$errors" -gt 0 ]; then
|
||||
exit 1
|
||||
else
|
||||
exit 0
|
||||
fi
|
||||
0
libs/partners/ollama/tests/__init__.py
Normal file
0
libs/partners/ollama/tests/__init__.py
Normal file
@ -0,0 +1,17 @@
|
||||
"""Test chat model integration."""
|
||||
|
||||
from typing import Type
|
||||
|
||||
from langchain_standard_tests.integration_tests import ChatModelIntegrationTests
|
||||
|
||||
from langchain_ollama.chat_models import ChatOllama
|
||||
|
||||
|
||||
class TestChatOllama(ChatModelIntegrationTests):
|
||||
@property
|
||||
def chat_model_class(self) -> Type[ChatOllama]:
|
||||
return ChatOllama
|
||||
|
||||
@property
|
||||
def chat_model_params(self) -> dict:
|
||||
return {"model": "llama3-groq-tool-use"}
|
||||
@ -0,0 +1,7 @@
|
||||
import pytest
|
||||
|
||||
|
||||
@pytest.mark.compile
|
||||
def test_placeholder() -> None:
|
||||
"""Used for compiling integration tests without running any real tests."""
|
||||
pass
|
||||
@ -0,0 +1,20 @@
|
||||
"""Test Ollama embeddings."""
|
||||
|
||||
from langchain_ollama.embeddings import OllamaEmbeddings
|
||||
|
||||
|
||||
def test_langchain_ollama_embedding_documents() -> None:
|
||||
"""Test cohere embeddings."""
|
||||
documents = ["foo bar"]
|
||||
embedding = OllamaEmbeddings(model="llama3")
|
||||
output = embedding.embed_documents(documents)
|
||||
assert len(output) == 1
|
||||
assert len(output[0]) > 0
|
||||
|
||||
|
||||
def test_langchain_ollama_embedding_query() -> None:
|
||||
"""Test cohere embeddings."""
|
||||
document = "foo bar"
|
||||
embedding = OllamaEmbeddings(model="llama3")
|
||||
output = embedding.embed_query(document)
|
||||
assert len(output) > 0
|
||||
66
libs/partners/ollama/tests/integration_tests/test_llms.py
Normal file
66
libs/partners/ollama/tests/integration_tests/test_llms.py
Normal file
@ -0,0 +1,66 @@
|
||||
"""Test OllamaLLM llm."""
|
||||
|
||||
from langchain_ollama.llms import OllamaLLM
|
||||
|
||||
MODEL_NAME = "llama3"
|
||||
|
||||
|
||||
def test_stream() -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
llm = OllamaLLM(model=MODEL_NAME)
|
||||
|
||||
for token in llm.stream("I'm Pickle Rick"):
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
async def test_astream() -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
llm = OllamaLLM(model=MODEL_NAME)
|
||||
|
||||
async for token in llm.astream("I'm Pickle Rick"):
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
async def test_abatch() -> None:
|
||||
"""Test streaming tokens from OllamaLLM."""
|
||||
llm = OllamaLLM(model=MODEL_NAME)
|
||||
|
||||
result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
|
||||
for token in result:
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
async def test_abatch_tags() -> None:
|
||||
"""Test batch tokens from OllamaLLM."""
|
||||
llm = OllamaLLM(model=MODEL_NAME)
|
||||
|
||||
result = await llm.abatch(
|
||||
["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
|
||||
)
|
||||
for token in result:
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
def test_batch() -> None:
|
||||
"""Test batch tokens from OllamaLLM."""
|
||||
llm = OllamaLLM(model=MODEL_NAME)
|
||||
|
||||
result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
|
||||
for token in result:
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
async def test_ainvoke() -> None:
|
||||
"""Test invoke tokens from OllamaLLM."""
|
||||
llm = OllamaLLM(model=MODEL_NAME)
|
||||
|
||||
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
|
||||
assert isinstance(result, str)
|
||||
|
||||
|
||||
def test_invoke() -> None:
|
||||
"""Test invoke tokens from OllamaLLM."""
|
||||
llm = OllamaLLM(model=MODEL_NAME)
|
||||
|
||||
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
|
||||
assert isinstance(result, str)
|
||||
0
libs/partners/ollama/tests/unit_tests/__init__.py
Normal file
0
libs/partners/ollama/tests/unit_tests/__init__.py
Normal file
17
libs/partners/ollama/tests/unit_tests/test_chat_models.py
Normal file
17
libs/partners/ollama/tests/unit_tests/test_chat_models.py
Normal file
@ -0,0 +1,17 @@
|
||||
"""Test chat model integration."""
|
||||
|
||||
from typing import Dict, Type
|
||||
|
||||
from langchain_standard_tests.unit_tests import ChatModelUnitTests
|
||||
|
||||
from langchain_ollama.chat_models import ChatOllama
|
||||
|
||||
|
||||
class TestChatOllama(ChatModelUnitTests):
|
||||
@property
|
||||
def chat_model_class(self) -> Type[ChatOllama]:
|
||||
return ChatOllama
|
||||
|
||||
@property
|
||||
def chat_model_params(self) -> Dict:
|
||||
return {"model": "llama3-groq-tool-use"}
|
||||
8
libs/partners/ollama/tests/unit_tests/test_embeddings.py
Normal file
8
libs/partners/ollama/tests/unit_tests/test_embeddings.py
Normal file
@ -0,0 +1,8 @@
|
||||
"""Test embedding model integration."""
|
||||
|
||||
from langchain_ollama.embeddings import OllamaEmbeddings
|
||||
|
||||
|
||||
def test_initialization() -> None:
|
||||
"""Test embedding model initialization."""
|
||||
OllamaEmbeddings(model="llama3")
|
||||
12
libs/partners/ollama/tests/unit_tests/test_imports.py
Normal file
12
libs/partners/ollama/tests/unit_tests/test_imports.py
Normal file
@ -0,0 +1,12 @@
|
||||
from langchain_ollama import __all__
|
||||
|
||||
EXPECTED_ALL = [
|
||||
"OllamaLLM",
|
||||
"ChatOllama",
|
||||
"OllamaEmbeddings",
|
||||
"__version__",
|
||||
]
|
||||
|
||||
|
||||
def test_all_imports() -> None:
|
||||
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
||||
8
libs/partners/ollama/tests/unit_tests/test_llms.py
Normal file
8
libs/partners/ollama/tests/unit_tests/test_llms.py
Normal file
@ -0,0 +1,8 @@
|
||||
"""Test Ollama Chat API wrapper."""
|
||||
|
||||
from langchain_ollama import OllamaLLM
|
||||
|
||||
|
||||
def test_initialization() -> None:
|
||||
"""Test integration initialization."""
|
||||
OllamaLLM(model="llama3")
|
||||
Loading…
Reference in New Issue
Block a user