id stringlengths 14 15 | text stringlengths 23 2.21k | source stringlengths 52 97 |
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d955ed71cf75-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/petals_example |
d955ed71cf75-2 | HUGGINGFACE_API_KEYCreate the Petals instance​You can specify different parameters such as the model name, max new tokens, temperature, etc.# this can take several minutes to download big files!llm = Petals(model_name="bigscience/bloom-petals") Downloading: 1%|� | 40.8M/7.19G [00:24<15:4... | https://python.langchain.com/docs/integrations/llms/petals_example |
99260688a203-0 | ChatGLM | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/chatglm |
99260688a203-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/chatglm |
99260688a203-2 | ChatGLM-6B and ChatGLM2-6B has the same api specs, so this example should work with both.from langchain.llms import ChatGLMfrom langchain import PromptTemplate, LLMChain# import ostemplate = """{question}"""prompt = PromptTemplate(template=template, input_variables=["question"])# default endpoint_url for a local deploy... | https://python.langchain.com/docs/integrations/llms/chatglm |
99260688a203-3 | Truellm_chain = LLMChain(prompt=prompt, llm=llm)question = "北京和上海两座åŸ�市有什么ä¸�å�Œï¼Ÿ"llm_chain.run(question) ChatGLM payload: {'prompt': '北京和上海两座åŸ�市有什么ä¸�å�Œï¼Ÿ', 'temperature': 0.1, 'history': [['我将ä»�ç¾�国到ä¸å›½æ�¥æ—…游，出行å‰�希望了解ä¸å›½çš„åŸ�市', '... | https://python.langchain.com/docs/integrations/llms/chatglm |
99260688a203-4 | '北京和上海是ä¸å›½çš„两个首都，它们在许多方é�¢éƒ½æœ‰æ‰€ä¸�å�Œã€‚\n\n北京是ä¸å›½çš„政治和文化ä¸å¿ƒï¼Œæ‹¥æœ‰æ‚ ä¹…çš„å�†å�²å’Œç�¿çƒ‚的文化。它是ä¸å›½æœ€é‡�è¦�çš„å�¤éƒ½ä¹‹ä¸€ï¼Œä¹Ÿæ˜¯ä¸å›½å�†å�²ä¸Šæœ€å��一个å°�建ç�‹æœ�的都åŸ�。北京有许多著å��çš„å�¤è¿¹å’Œæ™¯ç‚¹ï¼Œä¾‹å¦‚ç´«ç¦�... | https://python.langchain.com/docs/integrations/llms/chatglm |
99260688a203-5 | 天安门广场和长åŸ�ç‰ã€‚\n\n上海是ä¸å›½æœ€ç�°ä»£åŒ–çš„åŸ�市之一，也是ä¸å›½å•†ä¸šå’Œé‡‘è��ä¸å¿ƒã€‚上海拥有许多国际知å��çš„ä¼�业和金è��机æ�„，å�Œæ—¶ä¹Ÿæœ‰è®¸å¤šè‘—å��的景点和ç¾�食。上海的外滩是一个å�†å�²æ‚ 久的商业区，拥有许多欧å¼�建ç‘å’Œé¤�馆。\n\n除æ¤ä¹‹å¤–... | https://python.langchain.com/docs/integrations/llms/chatglm |
99260688a203-6 | 上海在交通和人å�£æ–¹é�¢ä¹Ÿæœ‰å¾ˆå¤§å·®å¼‚。北京是ä¸å›½çš„首都，人å�£ä¼—å¤šï¼Œäº¤é€šæ‹¥å µé—®é¢˜è¾ƒä¸ºä¸¥é‡�。而上海是ä¸å›½çš„商业和金è��ä¸å¿ƒï¼Œäººå�£å¯†åº¦è¾ƒä½�，交通相对较为便利。\n\n总的æ�¥è¯´ï¼ŒåŒ—京和上海是两个拥有独特é…力和特点的åŸ�市，å�¯ä»¥æ ¹æ�®è‡ªå·±ç... | https://python.langchain.com/docs/integrations/llms/chatglm |
99260688a203-7 | Œæ—¶é—´æ�¥é€‰æ‹©å‰�往其ä¸ä¸€åº§åŸ�市旅游。'PreviousCerebriumAINextClarifaiCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright | https://python.langchain.com/docs/integrations/llms/chatglm |
99260688a203-8 | © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/llms/chatglm |
161cfa46e3df-0 | Predibase | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/predibase |
161cfa46e3df-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/predibase |
161cfa46e3df-2 | wine?")print(response)Chain Call Setup​llm = Predibase( model="vicuna-13b", predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"))SequentialChain​from langchain.chains import LLMChainfrom langchain.prompts import PromptTemplate# This is an LLMChain to write a synopsis given a title of a play.template = """You ... | https://python.langchain.com/docs/integrations/llms/predibase |
161cfa46e3df-3 | replace my-finetuned-LLM with the name of your model in Predibase# response = model("Can you help categorize the following emails into positive, negative, and neutral?")PreviousPipelineAINextPrediction GuardInitial CallChain Call SetupSequentialChainFine-tuned LLM (Use your own fine-tuned LLM from Predibase)CommunityDi... | https://python.langchain.com/docs/integrations/llms/predibase |
ce47c26d5670-0 | AI21 | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/ai21 |
ce47c26d5670-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/ai21 |
ce47c26d5670-2 | = LLMChain(prompt=prompt, llm=llm)question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"llm_chain.run(question) '\n1. What year was Justin Bieber born?\nJustin Bieber was born in 1994.\n2. What team won the Super Bowl in 1994?\nThe Dallas Cowboys won the Super Bowl in 1994.'PreviousLLMsNex... | https://python.langchain.com/docs/integrations/llms/ai21 |
c26f2592eff4-0 | DeepInfra | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/deepinfra_example |
c26f2592eff4-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/deepinfra_example |
c26f2592eff4-2 | You can print your token with deepctl auth token# get a new token: https://deepinfra.com/login?from=%2Fdashfrom getpass import getpassDEEPINFRA_API_TOKEN = getpass() ········os.environ["DEEPINFRA_API_TOKEN"] = DEEPINFRA_API_TOKENCreate the DeepInfra instance​You can also use our open source deepctl tool t... | https://python.langchain.com/docs/integrations/llms/deepinfra_example |
c26f2592eff4-3 | the north pole!\n\nStill didn't understand?\nWell, you're a failure as a teacher."PreviousDatabricksNextForefrontAIImportsSet the Environment API KeyCreate the DeepInfra instanceCreate a Prompt TemplateInitiate the LLMChainRun the LLMChainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangCha... | https://python.langchain.com/docs/integrations/llms/deepinfra_example |
68b279a657a4-0 | StochasticAI | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/stochasticai |
68b279a657a4-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/stochasticai |
68b279a657a4-2 | langchain.llms import StochasticAIfrom langchain import PromptTemplate, LLMChaintemplate = """Question: {question}Answer: Let's think step by step."""prompt = PromptTemplate(template=template, input_variables=["question"])llm = StochasticAI(api_url=YOUR_API_URL)llm_chain = LLMChain(prompt=prompt, llm=llm)question = "Wh... | https://python.langchain.com/docs/integrations/llms/stochasticai |
774a4426c282-0 | Beam | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/beam |
774a4426c282-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/beam |
774a4426c282-2 | "<Your beam client id>"beam_client_secret = "<Your beam client secret>"# Set the environment variablesos.environ["BEAM_CLIENT_ID"] = beam_client_idos.environ["BEAM_CLIENT_SECRET"] = beam_client_secret# Run the beam configure commandbeam configure --clientId={beam_client_id} --clientSecret={beam_client_secret}Install th... | https://python.langchain.com/docs/integrations/llms/beam |
6e0bd04b7fc1-0 | RELLM | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/rellm_experimental |
6e0bd04b7fc1-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/rellm_experimental |
6e0bd04b7fc1-2 | Assistant:{ "action": "Final Answer", "action_input": "The capital of Pennsylvania is Harrisburg."}Human: "What 2 + 5?"AI Assistant:{ "action": "Final Answer", "action_input": "2 + 5 = 7."}Human: 'What's the capital of Maryland?'AI Assistant:"""from transformers import pipelinefrom langchain.llms import HuggingFace... | https://python.langchain.com/docs/integrations/llms/rellm_experimental |
6e0bd04b7fc1-3 | regex=pattern, max_new_tokens=200)generated = model.predict(prompt, stop=["Human:"])print(generated) {"action": "Final Answer", "action_input": "The capital of Maryland is Baltimore." } Voila! Free of parsing errors.PreviousPromptLayer OpenAINextReplicateHugging Face BaselineRELLM LLM WrapperCommunityDisc... | https://python.langchain.com/docs/integrations/llms/rellm_experimental |
858969f36a57-0 | Tongyi Qwen | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/tongyi |
858969f36a57-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/tongyi |
858969f36a57-2 | osos.environ["DASHSCOPE_API_KEY"] = DASHSCOPE_API_KEYfrom langchain.llms import Tongyifrom langchain import PromptTemplate, LLMChaintemplate = """Question: {question}Answer: Let's think step by step."""prompt = PromptTemplate(template=template, input_variables=["question"])llm = Tongyi()llm_chain = LLMChain(prompt=prom... | https://python.langchain.com/docs/integrations/llms/tongyi |
8aabdd622f56-0 | GooseAI | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/gooseai_example |
8aabdd622f56-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/gooseai_example |
8aabdd622f56-2 | = GOOSEAI_API_KEYCreate the GooseAI instance​You can specify different parameters such as the model name, max tokens generated, temperature, etc.llm = GooseAI()Create a Prompt Template​We will create a prompt template for Question and Answer.template = """Question: {question}Answer: Let's think step by step."""prom... | https://python.langchain.com/docs/integrations/llms/gooseai_example |
2822079b266b-0 | JSONFormer | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/jsonformer_experimental |
2822079b266b-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/jsonformer_experimental |
2822079b266b-2 | """Query the BigCode StarCoder model about coding questions.""" url = "https://api-inference.huggingface.co/models/bigcode/starcoder" headers = { "Authorization": f"Bearer {HF_TOKEN}", "content-type": "application/json", } payload = { "inputs": f"{query}\n\nAnswer:", "temperature... | https://python.langchain.com/docs/integrations/llms/jsonformer_experimental |
2822079b266b-3 | "What's the difference between an SVM and an LLM?"AI Assistant:{{ "action": "ask_star_coder", "action_input": {{"query": "What's the difference between SGD and an SVM?", "temperature": 1.0, "max_new_tokens": 250}}}}Observation: "SGD stands for stochastic gradient descent, while an SVM is a Support Vector Machine."BEG... | https://python.langchain.com/docs/integrations/llms/jsonformer_experimental |
2822079b266b-4 | "type": "object", "properties": ask_star_coder.args, }, },}from langchain.experimental.llms import JsonFormerjson_former = JsonFormer(json_schema=decoder_schema, pipeline=hf_model)results = json_former.predict(prompt, stop=["Observation:", "Human:"])print(results) {"action": "ask_star_coder", "a... | https://python.langchain.com/docs/integrations/llms/jsonformer_experimental |
bb5ef7a74ba4-0 | Aleph Alpha | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/aleph_alpha |
bb5ef7a74ba4-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/aleph_alpha |
bb5ef7a74ba4-2 | model="luminous-extended", maximum_tokens=20, stop_sequences=["Q:"], aleph_alpha_api_key=ALEPH_ALPHA_API_KEY,)llm_chain = LLMChain(prompt=prompt, llm=llm)question = "What is AI?"llm_chain.run(question) ' Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially c... | https://python.langchain.com/docs/integrations/llms/aleph_alpha |
a23c4d46ba88-0 | Banana | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/banana |
a23c4d46ba88-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/banana |
a23c4d46ba88-2 | {question}Answer: Let's think step by step."""prompt = PromptTemplate(template=template, input_variables=["question"])llm = Banana(model_key="YOUR_MODEL_KEY")llm_chain = LLMChain(prompt=prompt, llm=llm)question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"llm_chain.run(question)PreviousAzureM... | https://python.langchain.com/docs/integrations/llms/banana |
ff444a2dcb48-0 | Azure OpenAI | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/azure_openai_example |
ff444a2dcb48-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/azure_openai_example |
ff444a2dcb48-2 | your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource.export OPENAI_API_BASE=https://your-resource-name.openai.azure.com# The API key for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource.export OPENAI_API_KEY=<your Azure Op... | https://python.langchain.com/docs/integrations/llms/azure_openai_example |
ff444a2dcb48-3 | model_name="text-davinci-002",)# Run the LLMllm("Tell me a joke") "\n\nWhy couldn't the bicycle stand up by itself? Because it was...two tired!"We can also print the LLM and see its custom print.print(llm) AzureOpenAI Params: {'deployment_name': 'text-davinci-002', 'model_name': 'text-davinci-002', 'temperatur... | https://python.langchain.com/docs/integrations/llms/azure_openai_example |
9ef6998cc538-0 | PipelineAI | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/pipelineai_example |
9ef6998cc538-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/pipelineai_example |
9ef6998cc538-2 | hours of serverless GPU compute to test different models.os.environ["PIPELINE_API_KEY"] = "YOUR_API_KEY_HERE"Create the PipelineAI instance​When instantiating PipelineAI, you need to specify the id or tag of the pipeline you want to use, e.g. pipeline_key = "public/gpt-j:base". You then have the option of passing add... | https://python.langchain.com/docs/integrations/llms/pipelineai_example |
176f630c1891-0 | Cohere | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/cohere |
176f630c1891-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/cohere |
176f630c1891-2 | = LLMChain(prompt=prompt, llm=llm)question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"llm_chain.run(question) " Let's start with the year that Justin Beiber was born. You know that he was born in 1994. We have to go back one year. 1993.\n\n1993 was the year that the Dallas Cowboys won th... | https://python.langchain.com/docs/integrations/llms/cohere |
3b84d9f5b10b-0 | octoai | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/octoai |
3b84d9f5b10b-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/octoai |
3b84d9f5b10b-2 | Token from your OctoAI account page.Paste your API key in in the code cell belowimport osos.environ["OCTOAI_API_TOKEN"] = "OCTOAI_API_TOKEN"os.environ["ENDPOINT_URL"] = "https://mpt-7b-demo-kk0powt97tmb.octoai.cloud/generate"from langchain.llms.octoai_endpoint import OctoAIEndpointfrom langchain import PromptTemplate, ... | https://python.langchain.com/docs/integrations/llms/octoai |
3b84d9f5b10b-3 | human-powered aircraft control. He may have pioneered helicopters. As a scholar, he was interested in anatomy, geology, botany, engineering, mathematics, and astronomy.\nOther painters and patrons claimed to be more talented, but Leonardo da Vinci was an incredibly productive artist, sculptor, engineer, anatomist, and ... | https://python.langchain.com/docs/integrations/llms/octoai |
9291743c093d-0 | Llama-cpp | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/llamacpp |
9291743c093d-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/llamacpp |
9291743c093d-2 | It supports several LLMs.This notebook goes over how to run llama-cpp within LangChain.Installation​There is a bunch of options how to install the llama-cpp package: only CPU usageCPU + GPU (using one of many BLAS backends)Metal GPU (MacOS with Apple Silicon Chip) CPU only installation​pip install llama-cpp-pythonI... | https://python.langchain.com/docs/integrations/llms/llamacpp |
9291743c093d-3 | is stable to install the llama-cpp-python library by compiling from the source. You can follow most of the instructions in the repository itself but there are some windows specific instructions which might be useful.Requirements to install the llama-cpp-python,gitpythoncmakeVisual Studio Community (make sure you instal... | https://python.langchain.com/docs/integrations/llms/llamacpp |
9291743c093d-4 | CallbackManager([StreamingStdOutCallbackHandler()])# Verbose is required to pass to the callback managerCPU​Llama-v2# Make sure the model path is correct for your system!llm = LlamaCpp( model_path="/Users/rlm/Desktop/Code/llama/llama-2-7b-ggml/llama-2-7b-chat.ggmlv3.q4_0.bin", input={"temperature": 0.75, "max_l... | https://python.langchain.com/docs/integrations/llms/llamacpp |
9291743c093d-5 | news and your jokes. While I'm the one who's really makin' a difference, with my sat llama_print_timings: load time = 358.60 ms llama_print_timings: sample time = 172.55 ms / 256 runs ( 0.67 ms per token, 1483.59 tokens per second) llama_print_timings: prompt eval time = 613.36... | https://python.langchain.com/docs/integrations/llms/llamacpp |
9291743c093d-6 | the truth to light.\nStephen Colbert:\nTruth? Ha! You think your show is about truth? Please, it's all just a joke to you.\nYou're just a fancy-pants british guy tryin' to be funny with your news and your jokes.\nWhile I'm the one who's really makin' a difference, with my sat"Llama-v1# Make sure the model path is corre... | https://python.langchain.com/docs/integrations/llms/llamacpp |
9291743c093d-7 | ms / 48 tokens ( 52.58 ms per token) llama_print_timings: eval time = 23971.57 ms / 121 runs ( 198.11 ms per token) llama_print_timings: total time = 28945.95 ms '\n\n1. First, find out when Justin Bieber was born.\n2. We know that Justin Bieber was born on March 1, 1994.\n3. Next, we ne... | https://python.langchain.com/docs/integrations/llms/llamacpp |
9291743c093d-8 | callback_manager=callback_manager, verbose=True,)llm_chain = LLMChain(prompt=prompt, llm=llm)question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"llm_chain.run(question) We are looking for an NFL team that won the Super Bowl when Justin Bieber (born March 1, 1994) was born. Fi... | https://python.langchain.com/docs/integrations/llms/llamacpp |
9291743c093d-9 | ms per token) llama_print_timings: prompt eval time = 238.04 ms / 49 tokens ( 4.86 ms per token) llama_print_timings: eval time = 10391.96 ms / 255 runs ( 40.75 ms per token) llama_print_timings: total time = 15664.80 ms " We are looking for an NFL team that won the Super Bowl whe... | https://python.langchain.com/docs/integrations/llms/llamacpp |
9291743c093d-10 | layers of the model are offloaded to your Metal GPU, in the most case, set it to 1 is enough for Metaln_batch - how many tokens are processed in parallel, default is 8, set to bigger number.f16_kv - for some reason, Metal only support True, otherwise you will get error such as Asserting on type 0 | https://python.langchain.com/docs/integrations/llms/llamacpp |
9291743c093d-11 | GGML_ASSERT: .../ggml-metal.m:706: false && "not implemented"Setting these parameters correctly will dramatically improve the evaluation speed (see wrapper code for more details).n_gpu_layers = 1 # Metal set to 1 is enough.n_batch = 512 # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon... | https://python.langchain.com/docs/integrations/llms/llamacpp |
df1bd09c9d4f-0 | Bedrock | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/bedrock |
df1bd09c9d4f-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/bedrock |
df1bd09c9d4f-2 | there!")PreviousBeamNextCerebriumAIUsing in a conversation chainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/llms/bedrock |
ef28a02df703-0 | Hugging Face Local Pipelines | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/huggingface_pipelines |
ef28a02df703-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/huggingface_pipelines |
ef28a02df703-2 | HuggingFacePipelinellm = HuggingFacePipeline.from_model_id( model_id="bigscience/bloom-1b7", task="text-generation", model_kwargs={"temperature": 0, "max_length": 64},) WARNING:root:Failed to default session, using empty session: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url... | https://python.langchain.com/docs/integrations/llms/huggingface_pipelines |
ef28a02df703-3 | Failed to establish a new connection: [Errno 61] Connection refused')) First, we need to understand what is an electroencephalogram. An electroencephalogram is a recording of brain activity. It is a recording of brain activity that is made by placing electrodes on the scalp. The electrodes are placedPreviousHugging... | https://python.langchain.com/docs/integrations/llms/huggingface_pipelines |
76caf237e446-0 | ForefrontAI | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/forefrontai_example |
76caf237e446-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/forefrontai_example |
76caf237e446-2 | ForefrontAI instance​You can specify different parameters such as the model endpoint url, length, temperature, etc. You must provide an endpoint url.llm = ForefrontAI(endpoint_url="YOUR ENDPOINT URL HERE")Create a Prompt Template​We will create a prompt template for Question and Answer.template = """Question: {ques... | https://python.langchain.com/docs/integrations/llms/forefrontai_example |
6eab2d2cb90d-0 | OpenAI | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/openai |
6eab2d2cb90d-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/openai |
6eab2d2cb90d-2 | OPENAI_ORGANIZATIONfrom langchain.llms import OpenAIfrom langchain import PromptTemplate, LLMChaintemplate = """Question: {question}Answer: Let's think step by step."""prompt = PromptTemplate(template=template, input_variables=["question"])llm = OpenAI()If you manually want to specify your OpenAI API key and/or organiz... | https://python.langchain.com/docs/integrations/llms/openai |
7967da7bf0c2-0 | Amazon API Gateway | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/amazon_api_gateway_example |
7967da7bf0c2-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/amazon_api_gateway_example |
7967da7bf0c2-2 | the API calls you receive and the amount of data transferred out and, with the API Gateway tiered pricing model, you can reduce your cost as your API usage scales.LLM​from langchain.llms import AmazonAPIGatewayapi_url = "https://<api_gateway_id>.execute-api.<region>.amazonaws.com/LATEST/HF"llm = AmazonAPIGateway(api_... | https://python.langchain.com/docs/integrations/llms/amazon_api_gateway_example |
7967da7bf0c2-3 | the language model, and the type of agent we want to use.agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,)# Now let's test it out!agent.run( """Write a Python script that prints "Hello, world!"""") > Entering new chain... I need to use th... | https://python.langchain.com/docs/integrations/llms/amazon_api_gateway_example |
7967da7bf0c2-4 | > Finished chain. '42.43998894277659'PreviousAleph AlphaNextAnyscaleLLMAgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/llms/amazon_api_gateway_example |
5f5eb06e5450-0 | Anyscale | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/anyscale |
5f5eb06e5450-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/anyscale |
5f5eb06e5450-2 | PromptTemplate(template=template, input_variables=["question"])llm = Anyscale()llm_chain = LLMChain(prompt=prompt, llm=llm)question = "When was George Washington president?"llm_chain.run(question)With Ray, we can distribute the queries without asyncrhonized implementation. This not only applies to Anyscale LLM model, b... | https://python.langchain.com/docs/integrations/llms/anyscale |
f3ec7e5c32e4-0 | MosaicML | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/llms/mosaicml |
f3ec7e5c32e4-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabric... | https://python.langchain.com/docs/integrations/llms/mosaicml |
f3ec7e5c32e4-2 | model_kwargs={"do_sample": False})llm_chain = LLMChain(prompt=prompt, llm=llm)question = "What is one good reason why you should train a large language model on domain specific data?"llm_chain.run(question)PreviousModalNextNLP CloudCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc... | https://python.langchain.com/docs/integrations/llms/mosaicml |
619b5efe033c-0 | Memory | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/memory/ |
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