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add oai v1 cookbook (#12961)
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@ -38,6 +38,7 @@ Notebook | Description
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[multiagent_bidding.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multiagent_bidding.ipynb) | Implement a multi-agent simulation where agents bid to speak, with the highest bidder speaking next, demonstrated through a fictitious presidential debate example.
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[myscale_vector_sql.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/myscale_vector_sql.ipynb) | Access and interact with the myscale integrated vector database, which can enhance the performance of language model (llm) applications.
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[openai_functions_retrieval_qa....](https://github.com/langchain-ai/langchain/tree/master/cookbook/openai_functions_retrieval_qa.ipynb) | Structure response output in a question-answering system by incorporating openai functions into a retrieval pipeline.
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[openai_v1_cookbook.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/openai_v1_cookbook.ipynb) | Explore new functionality released alongside the V1 release of the OpenAI Python library.
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[petting_zoo.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/petting_zoo.ipynb) | Create multi-agent simulations with simulated environments using the petting zoo library.
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[plan_and_execute_agent.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/plan_and_execute_agent.ipynb) | Create plan-and-execute agents that accomplish objectives by planning tasks with a language model (llm) and executing them with a separate agent.
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[press_releases.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/press_releases.ipynb) | Retrieve and query company press release data powered by [Kay.ai](https://kay.ai).
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184
cookbook/openai_v1_cookbook.ipynb
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184
cookbook/openai_v1_cookbook.ipynb
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@ -0,0 +1,184 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f970f757-ec76-4bf0-90cd-a2fb68b945e3",
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"metadata": {},
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"source": [
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"# Exploring OpenAI V1 functionality\n",
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"\n",
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"On 11.06.23 OpenAI released a number of new features, and along with it bumped their Python SDK to 1.0.0. This notebook shows off the new features and how to use them with LangChain."
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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": "ee897729-263a-4073-898f-bb4cf01ed829",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install \"openai>=1\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "71c34763-d1e7-4b9a-a9d7-3e4cc0dfc2c4",
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"metadata": {},
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"source": [
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"## [JSON mode](https://platform.openai.com/docs/guides/text-generation/json-mode)\n",
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"\n",
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"Constrain the model to only generate valid JSON. Note that you must include a system message with instructions to use JSON for this mode to work.\n",
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"\n",
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"Only works with certain models. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "c3e067ce-7a43-47a7-bc89-41f1de4cf136",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.schema.messages import HumanMessage, SystemMessage"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "db6072c4-f3f3-415d-872b-71ea9f3c02bb",
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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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" \"companies\": [\n",
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" {\n",
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" \"name\": \"Google\",\n",
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" \"origin\": \"USA\"\n",
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" },\n",
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" {\n",
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" \"name\": \"Deepmind\",\n",
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" \"origin\": \"UK\"\n",
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" }\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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"chat = ChatOpenAI(model=\"gpt-3.5-turbo-1106\").bind(\n",
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" response_format={\"type\": \"json_object\"}\n",
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")\n",
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"\n",
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"output = chat.invoke(\n",
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" [\n",
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" SystemMessage(\n",
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" content=\"Extract the 'name' and 'origin' of any companies mentioned in the following statement. Return a JSON list.\"\n",
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" ),\n",
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" HumanMessage(\n",
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" content=\"Google was founded in the USA, while Deepmind was founded in the UK\"\n",
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" ),\n",
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" ]\n",
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")\n",
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"print(output.content)"
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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": 9,
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"id": "08e00ccf-b991-4249-846b-9500a0ccbfa0",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'companies': [{'name': 'Google', 'origin': 'USA'},\n",
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" {'name': 'Deepmind', 'origin': 'UK'}]}"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import json\n",
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"\n",
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"json.loads(output.content)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "aa9a94d9-4319-4ab7-a979-c475ce6b5f50",
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"metadata": {},
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"source": [
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"## [System fingerprint](https://platform.openai.com/docs/guides/text-generation/reproducible-outputs)\n",
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"\n",
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"OpenAI sometimes changes model configurations in a way that impacts outputs. Whenever this happens, the system_fingerprint associated with a generation will change."
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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": "1281883c-bf8f-4665-89cd-4f33ccde69ab",
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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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"{'token_usage': {'completion_tokens': 43, 'prompt_tokens': 49, 'total_tokens': 92}, 'model_name': 'gpt-3.5-turbo-1106', 'system_fingerprint': 'fp_eeff13170a'}\n"
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]
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}
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],
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"source": [
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"chat = ChatOpenAI(model=\"gpt-3.5-turbo-1106\")\n",
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"output = chat.generate(\n",
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" [\n",
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" [\n",
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" SystemMessage(\n",
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" content=\"Extract the 'name' and 'origin' of any companies mentioned in the following statement. Return a JSON list.\"\n",
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" ),\n",
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" HumanMessage(\n",
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" content=\"Google was founded in the USA, while Deepmind was founded in the UK\"\n",
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" ),\n",
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" ]\n",
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" ]\n",
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")\n",
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"print(output.llm_output)"
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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": "5c637ba1-322d-4fc9-b97e-3afa83dc4d72",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "poetry-venv",
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"language": "python",
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"name": "poetry-venv"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -84,9 +84,7 @@
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"model_id = \"gpt2\"\n",
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"tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
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"model = AutoModelForCausalLM.from_pretrained(model_id)\n",
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"pipe = pipeline(\n",
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" \"text-generation\", model=model, tokenizer=tokenizer, max_new_tokens=10\n",
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")\n",
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"pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer, max_new_tokens=10)\n",
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"hf = HuggingFacePipeline(pipeline=pipe)"
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]
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},
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@ -39,8 +39,8 @@
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"from langchain.embeddings import OpenCLIPEmbeddings\n",
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"\n",
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"# Images\n",
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"img_path_dog='/Users/rlm/Desktop/Papers/LLaVA/dog.jpeg'\n",
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"img_path_house='/Users/rlm/Desktop/Papers/LLaVA/house.jpeg'\n",
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"img_path_dog = \"/Users/rlm/Desktop/Papers/LLaVA/dog.jpeg\"\n",
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"img_path_house = \"/Users/rlm/Desktop/Papers/LLaVA/house.jpeg\"\n",
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"\n",
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"# Load images and convert to numpy arrays\n",
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"image_np_dog = np.array(_PILImage.open(img_path_dog).convert(\"RGB\"))\n",
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@ -106,18 +106,26 @@
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"text_feat_house_np = np.array(text_feat_house[0])\n",
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"\n",
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"# Compute similarity\n",
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"similarities = np.array([\n",
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" [text_feat_dog_np @ img_feat_dog_np.T][0][0], \n",
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" [text_feat_dog_np @ img_feat_house_np.T][0][0],\n",
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" [text_feat_house_np @ img_feat_dog_np.T][0][0], \n",
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" [text_feat_house_np @ img_feat_house_np.T][0][0]\n",
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"]).reshape(2, 2)\n",
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"similarities = np.array(\n",
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" [\n",
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" [text_feat_dog_np @ img_feat_dog_np.T][0][0],\n",
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" [text_feat_dog_np @ img_feat_house_np.T][0][0],\n",
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" [text_feat_house_np @ img_feat_dog_np.T][0][0],\n",
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" [text_feat_house_np @ img_feat_house_np.T][0][0],\n",
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" ]\n",
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").reshape(2, 2)\n",
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"\n",
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"# Ensure similarities is of shape (2, 2)\n",
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"print(similarities.shape) # Expected: (2, 2)\n",
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"\n",
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"# Plot heatmap\n",
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"sns.heatmap(similarities, annot=True, cmap='viridis', xticklabels=['dog image', 'house image'], yticklabels=['dog text', 'house text'])\n"
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"sns.heatmap(\n",
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" similarities,\n",
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" annot=True,\n",
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" cmap=\"viridis\",\n",
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" xticklabels=[\"dog image\", \"house image\"],\n",
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" yticklabels=[\"dog text\", \"house text\"],\n",
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")"
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]
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}
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],
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@ -59,8 +59,8 @@
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"import os\n",
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"import getpass\n",
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"\n",
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"os.environ['QIANFAN_AK'] = getpass.getpass(\"Your Qianfan AK:\")\n",
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"os.environ['QIANFAN_SK'] = getpass.getpass(\"Your Qianfan SK:\")"
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"os.environ[\"QIANFAN_AK\"] = getpass.getpass(\"Your Qianfan AK:\")\n",
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"os.environ[\"QIANFAN_SK\"] = getpass.getpass(\"Your Qianfan SK:\")"
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]
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},
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{
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@ -85,6 +85,7 @@
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"docs = text_splitter.split_documents(documents)\n",
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"\n",
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"from langchain.embeddings import QianfanEmbeddingsEndpoint\n",
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"\n",
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"embeddings = QianfanEmbeddingsEndpoint()"
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]
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},
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@ -104,8 +105,12 @@
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"source": [
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"# Create a bes instance and index docs.\n",
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"from langchain.vectorstores import BESVectorStore\n",
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"\n",
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"bes = BESVectorStore.from_documents(\n",
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" documents=docs, embedding=embeddings, bes_url=\"your bes cluster url\", index_name=\"your vector index\"\n",
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" documents=docs,\n",
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" embedding=embeddings,\n",
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" bes_url=\"your bes cluster url\",\n",
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" index_name=\"your vector index\",\n",
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")\n",
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"bes.client.indices.refresh(index=\"your vector index\")"
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]
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@ -108,7 +108,10 @@
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"\n",
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"dingo_client = DingoDB(user=\"\", password=\"\", host=[\"127.0.0.1:13000\"])\n",
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"# First, check if our index already exists. If it doesn't, we create it\n",
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"if index_name not in dingo_client.get_index() and index_name.upper() not in dingo_client.get_index():\n",
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"if (\n",
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" index_name not in dingo_client.get_index()\n",
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" and index_name.upper() not in dingo_client.get_index()\n",
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"):\n",
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" # we create a new index, modify to your own\n",
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" dingo_client.create_index(\n",
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" index_name=index_name, dimension=1536, metric_type=\"cosine\", auto_id=False\n",
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