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WeaviateHybridSearchRetriever (class in langchain.retrievers) WeaviateHybridSearchRetriever.Config (class in langchain.retrievers) web_path (langchain.document_loaders.WebBaseLoader property) web_paths (langchain.document_loaders.WebBaseLoader attribute) WebBaseLoader (class in langchain.document_loaders) WhatsAppChatLoader (class in langchain.document_loaders) Wikipedia (class in langchain.docstore) WikipediaLoader (class in langchain.document_loaders) wolfram_alpha_appid (langchain.utilities.WolframAlphaAPIWrapper attribute) writer_api_key (langchain.llms.Writer attribute) writer_org_id (langchain.llms.Writer attribute) Y YoutubeLoader (class in langchain.document_loaders) Z zapier_description (langchain.tools.ZapierNLARunAction attribute) ZepRetriever (class in langchain.retrievers) ZERO_SHOT_REACT_DESCRIPTION (langchain.agents.AgentType attribute) Zilliz (class in langchain.vectorstores) By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/genindex.html

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.md .pdf Dependents Dependents# Dependents stats for hwchase17/langchain [update: 2023-05-17; only dependent repositories with Stars > 100] Repository Stars openai/openai-cookbook 35401 LAION-AI/Open-Assistant 32861 microsoft/TaskMatrix 32766 hpcaitech/ColossalAI 29560 reworkd/AgentGPT 22315 imartinez/privateGPT 17474 openai/chatgpt-retrieval-plugin 16923 mindsdb/mindsdb 16112 jerryjliu/llama_index 15407 mlflow/mlflow 14345 GaiZhenbiao/ChuanhuChatGPT 10372 databrickslabs/dolly 9919 AIGC-Audio/AudioGPT 8177 logspace-ai/langflow 6807 imClumsyPanda/langchain-ChatGLM 6087 arc53/DocsGPT 5292 e2b-dev/e2b 4622 nsarrazin/serge 4076 madawei2699/myGPTReader 3952 zauberzeug/nicegui 3952 go-skynet/LocalAI 3762 GreyDGL/PentestGPT 3388 mmabrouk/chatgpt-wrapper 3243 zilliztech/GPTCache 3189 wenda-LLM/wenda 3050 marqo-ai/marqo 2930 gkamradt/langchain-tutorials 2710 PrefectHQ/marvin 2545 project-baize/baize-chatbot 2479 whitead/paper-qa 2399 langgenius/dify

https://python.langchain.com/en/latest/dependents.html

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2479 whitead/paper-qa 2399 langgenius/dify 2344 GerevAI/gerev 2283 hwchase17/chat-langchain 2266 guangzhengli/ChatFiles 1903 Azure-Samples/azure-search-openai-demo 1884 OpenBMB/BMTools 1860 Farama-Foundation/PettingZoo 1813 OpenGVLab/Ask-Anything 1571 IntelligenzaArtificiale/Free-Auto-GPT 1480 hwchase17/notion-qa 1464 NVIDIA/NeMo-Guardrails 1419 Unstructured-IO/unstructured 1410 Kav-K/GPTDiscord 1363 paulpierre/RasaGPT 1344 StanGirard/quivr 1330 lunasec-io/lunasec 1318 vocodedev/vocode-python 1286 agiresearch/OpenAGI 1156 h2oai/h2ogpt 1141 jina-ai/thinkgpt 1106 yanqiangmiffy/Chinese-LangChain 1072 ttengwang/Caption-Anything 1064 jina-ai/dev-gpt 1057 juncongmoo/chatllama 1003 greshake/llm-security 1002 visual-openllm/visual-openllm 957 richardyc/Chrome-GPT 918 irgolic/AutoPR 886 mmz-001/knowledge_gpt 867 thomas-yanxin/LangChain-ChatGLM-Webui 850 microsoft/X-Decoder 837 peterw/Chat-with-Github-Repo 826 cirediatpl/FigmaChain 782 hashintel/hash

https://python.langchain.com/en/latest/dependents.html

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826 cirediatpl/FigmaChain 782 hashintel/hash 778 seanpixel/Teenage-AGI 773 jina-ai/langchain-serve 738 corca-ai/EVAL 737 ai-sidekick/sidekick 717 rlancemartin/auto-evaluator 703 poe-platform/api-bot-tutorial 689 SamurAIGPT/Camel-AutoGPT 666 eyurtsev/kor 608 run-llama/llama-lab 559 namuan/dr-doc-search 544 pieroit/cheshire-cat 520 griptape-ai/griptape 514 getmetal/motorhead 481 hwchase17/chat-your-data 462 langchain-ai/langchain-aiplugin 452 jina-ai/agentchain 439 SamurAIGPT/ChatGPT-Developer-Plugins 437 alexanderatallah/window.ai 433 michaelthwan/searchGPT 427 mpaepper/content-chatbot 425 mckaywrigley/repo-chat 422 whyiyhw/chatgpt-wechat 421 freddyaboulton/gradio-tools 407 jonra1993/fastapi-alembic-sqlmodel-async 395 yeagerai/yeagerai-agent 383 akshata29/chatpdf 374 OpenGVLab/InternGPT 368 ruoccofabrizio/azure-open-ai-embeddings-qna 358 101dotxyz/GPTeam 357 mtenenholtz/chat-twitter 354 amosjyng/langchain-visualizer 343 msoedov/langcorn 334 showlab/VLog 330 continuum-llms/chatgpt-memory 324 steamship-core/steamship-langchain 323

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324 steamship-core/steamship-langchain 323 daodao97/chatdoc 320 xuwenhao/geektime-ai-course 308 StevenGrove/GPT4Tools 301 logan-markewich/llama_index_starter_pack 300 andylokandy/gpt-4-search 299 Anil-matcha/ChatPDF 287 itamargol/openai 273 BlackHC/llm-strategy 267 momegas/megabots 259 bborn/howdoi.ai 238 Cheems-Seminar/grounded-segment-any-parts 232 ur-whitelab/exmol 227 sullivan-sean/chat-langchainjs 227 explosion/spacy-llm 226 recalign/RecAlign 218 jupyterlab/jupyter-ai 218 alvarosevilla95/autolang 215 conceptofmind/toolformer 213 MagnivOrg/prompt-layer-library 209 JohnSnowLabs/nlptest 208 airobotlab/KoChatGPT 197 langchain-ai/auto-evaluator 195 yvann-hub/Robby-chatbot 195 alejandro-ao/langchain-ask-pdf 192 daveebbelaar/langchain-experiments 189 NimbleBoxAI/ChainFury 187 kaleido-lab/dolphin 184 Anil-matcha/Website-to-Chatbot 183 plchld/InsightFlow 180 OpenBMB/AgentVerse 166 benthecoder/ClassGPT 166 jbrukh/gpt-jargon 161 hardbyte/qabot 160 shaman-ai/agent-actors 153 radi-cho/datasetGPT 153 poe-platform/poe-protocol 152

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radi-cho/datasetGPT 153 poe-platform/poe-protocol 152 paolorechia/learn-langchain 149 ajndkr/lanarky 149 fengyuli-dev/multimedia-gpt 147 yasyf/compress-gpt 144 homanp/superagent 143 realminchoi/babyagi-ui 141 ethanyanjiali/minChatGPT 141 ccurme/yolopandas 139 hwchase17/langchain-streamlit-template 138 Jaseci-Labs/jaseci 136 hirokidaichi/wanna 135 Haste171/langchain-chatbot 134 jmpaz/promptlib 130 Klingefjord/chatgpt-telegram 130 filip-michalsky/SalesGPT 128 handrew/browserpilot 128 shauryr/S2QA 127 steamship-core/vercel-examples 127 yasyf/summ 127 gia-guar/JARVIS-ChatGPT 126 jerlendds/osintbuddy 125 ibiscp/LLM-IMDB 124 Teahouse-Studios/akari-bot 124 hwchase17/chroma-langchain 124 menloparklab/langchain-cohere-qdrant-doc-retrieval 123 peterw/StoryStorm 123 chakkaradeep/pyCodeAGI 123 petehunt/langchain-github-bot 115 su77ungr/CASALIOY 113 eunomia-bpf/GPTtrace 113 zenml-io/zenml-projects 112 pablomarin/GPT-Azure-Search-Engine 111 shamspias/customizable-gpt-chatbot 109 WongSaang/chatgpt-ui-server

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109 WongSaang/chatgpt-ui-server 108 davila7/file-gpt 104 enhancedocs/enhancedocs 102 aurelio-labs/arxiv-bot 101 Generated by github-dependents-info [github-dependents-info –repo hwchase17/langchain –markdownfile dependents.md –minstars 100 –sort stars] previous Zilliz next Deployments By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/dependents.html

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.rst .pdf Integrations Contents Integrations by Module Dependencies All Integrations Integrations# LangChain integrates with many LLMs, systems, and products. Integrations by Module# Integrations grouped by the core LangChain module they map to: LLM Providers Chat Model Providers Text Embedding Model Providers Document Loader Integrations Text Splitter Integrations Vectorstore Providers Retriever Providers Tool Providers Toolkit Integrations Dependencies# LangChain depends on several hungered Python packages. All Integrations# A comprehensive list of LLMs, systems, and products integrated with LangChain: Tracing Walkthrough AI21 Labs Aim Airbyte Aleph Alpha AnalyticDB Anyscale Apify Arxiv AtlasDB AWS S3 Directory AZLyrics Azure Blob Storage Azure OpenAI Banana Beam Amazon Bedrock BiliBili Blackboard CerebriumAI Chroma ClearML Cohere College Confidential Comet Confluence C Transformers Databerry Databricks DeepInfra Deep Lake Diffbot Discord Docugami DuckDB EverNote Facebook Chat Figma ForefrontAI Git GitBook Google BigQuery Google Cloud Storage Google Drive Google Search Google Serper GooseAI GPT4All Graphsignal Gutenberg Hacker News Hazy Research Helicone Hugging Face iFixit IMSDb Jina LanceDB Llama.cpp MediaWikiDump Metal Microsoft OneDrive Microsoft PowerPoint Microsoft Word Milvus MLflow Modal Modern Treasury Momento MyScale NLPCloud Notion DB Obsidian

https://python.langchain.com/en/latest/integrations.html

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Modern Treasury Momento MyScale NLPCloud Notion DB Obsidian OpenAI OpenSearch OpenWeatherMap Petals PGVector Pinecone PipelineAI Prediction Guard PromptLayer Psychic Qdrant Rebuff Reddit Redis Replicate Runhouse RWKV-4 SageMaker Endpoint SearxNG Search API SerpAPI scikit-learn StochasticAI Tair Unstructured Vectara Weights & Biases Weaviate WhyLabs Wolfram Alpha Writer Yeager.ai Zilliz previous Experimental Modules next Tracing Walkthrough Contents Integrations by Module Dependencies All Integrations By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations.html

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.rst .pdf API References API References# Full documentation on all methods, classes, and APIs in LangChain. Models Prompts Indexes Memory Chains Agents Utilities Experimental Modules previous Installation next Models By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/reference.html

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Search Error Please activate JavaScript to enable the search functionality. Ctrl+K By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/search.html

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.md .pdf Weaviate Contents Installation and Setup Wrappers VectorStore Weaviate# This page covers how to use the Weaviate ecosystem within LangChain. What is Weaviate? Weaviate in a nutshell: Weaviate is an open-source ​database of the type ​vector search engine. Weaviate allows you to store JSON documents in a class property-like fashion while attaching machine learning vectors to these documents to represent them in vector space. Weaviate can be used stand-alone (aka bring your vectors) or with a variety of modules that can do the vectorization for you and extend the core capabilities. Weaviate has a GraphQL-API to access your data easily. We aim to bring your vector search set up to production to query in mere milliseconds (check our open source benchmarks to see if Weaviate fits your use case). Get to know Weaviate in the basics getting started guide in under five minutes. Weaviate in detail: Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), etc. Built from scratch in Go, Weaviate stores both objects and vectors, allowing for combining vector search with structured filtering and the fault tolerance of a cloud-native database. It is all accessible through GraphQL, REST, and various client-side programming languages. Installation and Setup# Install the Python SDK with pip install weaviate-client Wrappers# VectorStore# There exists a wrapper around Weaviate indexes, allowing you to use it as a vectorstore, whether for semantic search or example selection. To import this vectorstore: from langchain.vectorstores import Weaviate

https://python.langchain.com/en/latest/integrations/weaviate.html

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To import this vectorstore: from langchain.vectorstores import Weaviate For a more detailed walkthrough of the Weaviate wrapper, see this notebook previous Weights & Biases next WhyLabs Contents Installation and Setup Wrappers VectorStore By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/weaviate.html

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.md .pdf GitBook Contents Installation and Setup Document Loader GitBook# GitBook is a modern documentation platform where teams can document everything from products to internal knowledge bases and APIs. Installation and Setup# There isn’t any special setup for it. Document Loader# See a usage example. from langchain.document_loaders import GitbookLoader previous Git next Google BigQuery Contents Installation and Setup Document Loader By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/gitbook.html

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.md .pdf BiliBili Contents Installation and Setup Document Loader BiliBili# Bilibili is one of the most beloved long-form video sites in China. Installation and Setup# pip install bilibili-api-python Document Loader# See a usage example. from langchain.document_loaders import BiliBiliLoader previous Amazon Bedrock next Blackboard Contents Installation and Setup Document Loader By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/bilibili.html

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.md .pdf Hacker News Contents Installation and Setup Document Loader Hacker News# Hacker News (sometimes abbreviated as HN) is a social news website focusing on computer science and entrepreneurship. It is run by the investment fund and startup incubator Y Combinator. In general, content that can be submitted is defined as “anything that gratifies one’s intellectual curiosity.” Installation and Setup# There isn’t any special setup for it. Document Loader# See a usage example. from langchain.document_loaders import HNLoader previous Gutenberg next Hazy Research Contents Installation and Setup Document Loader By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/hacker_news.html

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.md .pdf Beam Contents Installation and Setup Wrappers LLM Define your Beam app. Deploy your Beam app Call your Beam app Beam# This page covers how to use Beam within LangChain. It is broken into two parts: installation and setup, and then references to specific Beam wrappers. Installation and Setup# Create an account Install the Beam CLI with curl https://raw.githubusercontent.com/slai-labs/get-beam/main/get-beam.sh -sSfL | sh Register API keys with beam configure Set environment variables (BEAM_CLIENT_ID) and (BEAM_CLIENT_SECRET) Install the Beam SDK pip install beam-sdk Wrappers# LLM# There exists a Beam LLM wrapper, which you can access with from langchain.llms.beam import Beam Define your Beam app.# This is the environment you’ll be developing against once you start the app. It’s also used to define the maximum response length from the model. llm = Beam(model_name="gpt2", name="langchain-gpt2-test", cpu=8, memory="32Gi", gpu="A10G", python_version="python3.8", python_packages=[ "diffusers[torch]>=0.10", "transformers", "torch", "pillow", "accelerate", "safetensors", "xformers",], max_length="50", verbose=False) Deploy your Beam app# Once defined, you can deploy your Beam app by calling your model’s _deploy() method. llm._deploy() Call your Beam app# Once a beam model is deployed, it can be called by callying your model’s _call() method.

https://python.langchain.com/en/latest/integrations/beam.html

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This returns the GPT2 text response to your prompt. response = llm._call("Running machine learning on a remote GPU") An example script which deploys the model and calls it would be: from langchain.llms.beam import Beam import time llm = Beam(model_name="gpt2", name="langchain-gpt2-test", cpu=8, memory="32Gi", gpu="A10G", python_version="python3.8", python_packages=[ "diffusers[torch]>=0.10", "transformers", "torch", "pillow", "accelerate", "safetensors", "xformers",], max_length="50", verbose=False) llm._deploy() response = llm._call("Running machine learning on a remote GPU") print(response) previous Banana next Amazon Bedrock Contents Installation and Setup Wrappers LLM Define your Beam app. Deploy your Beam app Call your Beam app By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/beam.html

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.md .pdf Hugging Face Contents Installation and Setup Wrappers LLM Embeddings Tokenizer Datasets Hugging Face# This page covers how to use the Hugging Face ecosystem (including the Hugging Face Hub) within LangChain. It is broken into two parts: installation and setup, and then references to specific Hugging Face wrappers. Installation and Setup# If you want to work with the Hugging Face Hub: Install the Hub client library with pip install huggingface_hub Create a Hugging Face account (it’s free!) Create an access token and set it as an environment variable (HUGGINGFACEHUB_API_TOKEN) If you want work with the Hugging Face Python libraries: Install pip install transformers for working with models and tokenizers Install pip install datasets for working with datasets Wrappers# LLM# There exists two Hugging Face LLM wrappers, one for a local pipeline and one for a model hosted on Hugging Face Hub. Note that these wrappers only work for models that support the following tasks: text2text-generation, text-generation To use the local pipeline wrapper: from langchain.llms import HuggingFacePipeline To use a the wrapper for a model hosted on Hugging Face Hub: from langchain.llms import HuggingFaceHub For a more detailed walkthrough of the Hugging Face Hub wrapper, see this notebook Embeddings# There exists two Hugging Face Embeddings wrappers, one for a local model and one for a model hosted on Hugging Face Hub. Note that these wrappers only work for sentence-transformers models. To use the local pipeline wrapper: from langchain.embeddings import HuggingFaceEmbeddings To use a the wrapper for a model hosted on Hugging Face Hub: from langchain.embeddings import HuggingFaceHubEmbeddings

https://python.langchain.com/en/latest/integrations/huggingface.html

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from langchain.embeddings import HuggingFaceHubEmbeddings For a more detailed walkthrough of this, see this notebook Tokenizer# There are several places you can use tokenizers available through the transformers package. By default, it is used to count tokens for all LLMs. You can also use it to count tokens when splitting documents with from langchain.text_splitter import CharacterTextSplitter CharacterTextSplitter.from_huggingface_tokenizer(...) For a more detailed walkthrough of this, see this notebook Datasets# The Hugging Face Hub has lots of great datasets that can be used to evaluate your LLM chains. For a detailed walkthrough of how to use them to do so, see this notebook previous Helicone next iFixit Contents Installation and Setup Wrappers LLM Embeddings Tokenizer Datasets By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/huggingface.html

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.md .pdf Google Search Contents Installation and Setup Wrappers Utility Tool Google Search# This page covers how to use the Google Search API within LangChain. It is broken into two parts: installation and setup, and then references to the specific Google Search wrapper. Installation and Setup# Install requirements with pip install google-api-python-client Set up a Custom Search Engine, following these instructions Get an API Key and Custom Search Engine ID from the previous step, and set them as environment variables GOOGLE_API_KEY and GOOGLE_CSE_ID respectively Wrappers# Utility# There exists a GoogleSearchAPIWrapper utility which wraps this API. To import this utility: from langchain.utilities import GoogleSearchAPIWrapper For a more detailed walkthrough of this wrapper, see this notebook. Tool# You can also easily load this wrapper as a Tool (to use with an Agent). You can do this with: from langchain.agents import load_tools tools = load_tools(["google-search"]) For more information on this, see this page previous Google Drive next Google Serper Contents Installation and Setup Wrappers Utility Tool By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/google_search.html

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.md .pdf OpenAI Contents Installation and Setup LLM Text Embedding Model Tokenizer Chain Document Loader OpenAI# OpenAI is American artificial intelligence (AI) research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership. OpenAI conducts AI research with the declared intention of promoting and developing a friendly AI. OpenAI systems run on an Azure-based supercomputing platform from Microsoft. The OpenAI API is powered by a diverse set of models with different capabilities and price points. ChatGPT is the Artificial Intelligence (AI) chatbot developed by OpenAI. Installation and Setup# Install the Python SDK with pip install openai Get an OpenAI api key and set it as an environment variable (OPENAI_API_KEY) If you want to use OpenAI’s tokenizer (only available for Python 3.9+), install it pip install tiktoken LLM# from langchain.llms import OpenAI If you are using a model hosted on Azure, you should use different wrapper for that: from langchain.llms import AzureOpenAI For a more detailed walkthrough of the Azure wrapper, see this notebook Text Embedding Model# from langchain.embeddings import OpenAIEmbeddings For a more detailed walkthrough of this, see this notebook Tokenizer# There are several places you can use the tiktoken tokenizer. By default, it is used to count tokens for OpenAI LLMs. You can also use it to count tokens when splitting documents with from langchain.text_splitter import CharacterTextSplitter CharacterTextSplitter.from_tiktoken_encoder(...) For a more detailed walkthrough of this, see this notebook Chain# See a usage example. from langchain.chains import OpenAIModerationChain Document Loader# See a usage example.

https://python.langchain.com/en/latest/integrations/openai.html

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Document Loader# See a usage example. from langchain.document_loaders.chatgpt import ChatGPTLoader previous Obsidian next OpenSearch Contents Installation and Setup LLM Text Embedding Model Tokenizer Chain Document Loader By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/openai.html

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.ipynb .pdf ClearML Contents Installation and Setup Getting API Credentials Callbacks Scenario 1: Just an LLM Scenario 2: Creating an agent with tools Tips and Next Steps ClearML# ClearML is a ML/DL development and production suite, it contains 5 main modules: Experiment Manager - Automagical experiment tracking, environments and results MLOps - Orchestration, Automation & Pipelines solution for ML/DL jobs (K8s / Cloud / bare-metal) Data-Management - Fully differentiable data management & version control solution on top of object-storage (S3 / GS / Azure / NAS) Model-Serving - cloud-ready Scalable model serving solution! Deploy new model endpoints in under 5 minutes Includes optimized GPU serving support backed by Nvidia-Triton with out-of-the-box Model Monitoring Fire Reports - Create and share rich MarkDown documents supporting embeddable online content In order to properly keep track of your langchain experiments and their results, you can enable the ClearML integration. We use the ClearML Experiment Manager that neatly tracks and organizes all your experiment runs. Installation and Setup# !pip install clearml !pip install pandas !pip install textstat !pip install spacy !python -m spacy download en_core_web_sm Getting API Credentials# We’ll be using quite some APIs in this notebook, here is a list and where to get them: ClearML: https://app.clear.ml/settings/workspace-configuration OpenAI: https://platform.openai.com/account/api-keys SerpAPI (google search): https://serpapi.com/dashboard import os os.environ["CLEARML_API_ACCESS_KEY"] = "" os.environ["CLEARML_API_SECRET_KEY"] = "" os.environ["OPENAI_API_KEY"] = "" os.environ["SERPAPI_API_KEY"] = ""

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os.environ["SERPAPI_API_KEY"] = "" Callbacks# from langchain.callbacks import ClearMLCallbackHandler from datetime import datetime from langchain.callbacks import StdOutCallbackHandler from langchain.llms import OpenAI # Setup and use the ClearML Callback clearml_callback = ClearMLCallbackHandler( task_type="inference", project_name="langchain_callback_demo", task_name="llm", tags=["test"], # Change the following parameters based on the amount of detail you want tracked visualize=True, complexity_metrics=True, stream_logs=True ) callbacks = [StdOutCallbackHandler(), clearml_callback] # Get the OpenAI model ready to go llm = OpenAI(temperature=0, callbacks=callbacks) The clearml callback is currently in beta and is subject to change based on updates to `langchain`. Please report any issues to https://github.com/allegroai/clearml/issues with the tag `langchain`. Scenario 1: Just an LLM# First, let’s just run a single LLM a few times and capture the resulting prompt-answer conversation in ClearML # SCENARIO 1 - LLM llm_result = llm.generate(["Tell me a joke", "Tell me a poem"] * 3) # After every generation run, use flush to make sure all the metrics # prompts and other output are properly saved separately clearml_callback.flush_tracker(langchain_asset=llm, name="simple_sequential")

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

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clearml_callback.flush_tracker(langchain_asset=llm, name="simple_sequential") {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

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{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

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{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nQ: What did the fish say when it hit the wall?\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91}

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-5

{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17}

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-6

{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nQ: What did the fish say when it hit the wall?\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91}

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-7

{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17}

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-8

{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nQ: What did the fish say when it hit the wall?\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91}

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-9

{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17} {'action_records': action name step starts ends errors text_ctr chain_starts \

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-10

0 on_llm_start OpenAI 1 1 0 0 0 0 1 on_llm_start OpenAI 1 1 0 0 0 0 2 on_llm_start OpenAI 1 1 0 0 0 0 3 on_llm_start OpenAI 1 1 0 0 0 0 4 on_llm_start OpenAI 1 1 0 0 0 0 5 on_llm_start OpenAI 1 1 0 0 0 0 6 on_llm_end NaN 2 1 1 0 0 0 7 on_llm_end NaN 2 1 1 0 0 0 8 on_llm_end NaN 2 1 1 0 0 0 9 on_llm_end NaN 2 1 1 0 0 0 10 on_llm_end NaN 2 1 1 0 0 0 11 on_llm_end NaN 2 1 1 0 0 0 12 on_llm_start OpenAI 3 2 1 0 0 0 13 on_llm_start OpenAI 3 2 1 0 0 0

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-11

14 on_llm_start OpenAI 3 2 1 0 0 0 15 on_llm_start OpenAI 3 2 1 0 0 0 16 on_llm_start OpenAI 3 2 1 0 0 0 17 on_llm_start OpenAI 3 2 1 0 0 0 18 on_llm_end NaN 4 2 2 0 0 0 19 on_llm_end NaN 4 2 2 0 0 0 20 on_llm_end NaN 4 2 2 0 0 0 21 on_llm_end NaN 4 2 2 0 0 0 22 on_llm_end NaN 4 2 2 0 0 0 23 on_llm_end NaN 4 2 2 0 0 0 chain_ends llm_starts ... difficult_words linsear_write_formula \ 0 0 1 ... NaN NaN 1 0 1 ... NaN NaN 2 0 1 ... NaN NaN 3 0 1 ... NaN NaN 4 0 1 ... NaN NaN 5 0 1 ... NaN NaN

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-12

5 0 1 ... NaN NaN 6 0 1 ... 0.0 5.5 7 0 1 ... 2.0 6.5 8 0 1 ... 0.0 5.5 9 0 1 ... 2.0 6.5 10 0 1 ... 0.0 5.5 11 0 1 ... 2.0 6.5 12 0 2 ... NaN NaN 13 0 2 ... NaN NaN 14 0 2 ... NaN NaN 15 0 2 ... NaN NaN 16 0 2 ... NaN NaN 17 0 2 ... NaN NaN 18 0 2 ... 0.0 5.5 19 0 2 ... 2.0 6.5 20 0 2 ... 0.0 5.5 21 0 2 ... 2.0 6.5 22 0 2 ... 0.0 5.5 23 0 2 ... 2.0 6.5 gunning_fog text_standard fernandez_huerta szigriszt_pazos \ 0 NaN NaN NaN NaN

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-13

0 NaN NaN NaN NaN 1 NaN NaN NaN NaN 2 NaN NaN NaN NaN 3 NaN NaN NaN NaN 4 NaN NaN NaN NaN 5 NaN NaN NaN NaN 6 5.20 5th and 6th grade 133.58 131.54 7 8.28 6th and 7th grade 115.58 112.37 8 5.20 5th and 6th grade 133.58 131.54 9 8.28 6th and 7th grade 115.58 112.37 10 5.20 5th and 6th grade 133.58 131.54 11 8.28 6th and 7th grade 115.58 112.37 12 NaN NaN NaN NaN 13 NaN NaN NaN NaN 14 NaN NaN NaN NaN 15 NaN NaN NaN NaN 16 NaN NaN NaN NaN 17 NaN NaN NaN NaN 18 5.20 5th and 6th grade 133.58 131.54 19 8.28 6th and 7th grade 115.58 112.37 20 5.20 5th and 6th grade 133.58 131.54

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-14

21 8.28 6th and 7th grade 115.58 112.37 22 5.20 5th and 6th grade 133.58 131.54 23 8.28 6th and 7th grade 115.58 112.37 gutierrez_polini crawford gulpease_index osman 0 NaN NaN NaN NaN 1 NaN NaN NaN NaN 2 NaN NaN NaN NaN 3 NaN NaN NaN NaN 4 NaN NaN NaN NaN 5 NaN NaN NaN NaN 6 62.30 -0.2 79.8 116.91 7 54.83 1.4 72.1 100.17 8 62.30 -0.2 79.8 116.91 9 54.83 1.4 72.1 100.17 10 62.30 -0.2 79.8 116.91 11 54.83 1.4 72.1 100.17 12 NaN NaN NaN NaN 13 NaN NaN NaN NaN 14 NaN NaN NaN NaN 15 NaN NaN NaN NaN 16 NaN NaN NaN NaN 17 NaN NaN NaN NaN 18 62.30 -0.2 79.8 116.91

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-15

19 54.83 1.4 72.1 100.17 20 62.30 -0.2 79.8 116.91 21 54.83 1.4 72.1 100.17 22 62.30 -0.2 79.8 116.91 23 54.83 1.4 72.1 100.17 [24 rows x 39 columns], 'session_analysis': prompt_step prompts name output_step \ 0 1 Tell me a joke OpenAI 2 1 1 Tell me a poem OpenAI 2 2 1 Tell me a joke OpenAI 2 3 1 Tell me a poem OpenAI 2 4 1 Tell me a joke OpenAI 2 5 1 Tell me a poem OpenAI 2 6 3 Tell me a joke OpenAI 4 7 3 Tell me a poem OpenAI 4 8 3 Tell me a joke OpenAI 4 9 3 Tell me a poem OpenAI 4 10 3 Tell me a joke OpenAI 4 11 3 Tell me a poem OpenAI 4 output \ 0 \n\nQ: What did the fish say when it hit the w... 1 \n\nRoses are red,\nViolets are blue,\nSugar i...

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-16

2 \n\nQ: What did the fish say when it hit the w... 3 \n\nRoses are red,\nViolets are blue,\nSugar i... 4 \n\nQ: What did the fish say when it hit the w... 5 \n\nRoses are red,\nViolets are blue,\nSugar i... 6 \n\nQ: What did the fish say when it hit the w... 7 \n\nRoses are red,\nViolets are blue,\nSugar i... 8 \n\nQ: What did the fish say when it hit the w... 9 \n\nRoses are red,\nViolets are blue,\nSugar i... 10 \n\nQ: What did the fish say when it hit the w... 11 \n\nRoses are red,\nViolets are blue,\nSugar i... token_usage_total_tokens token_usage_prompt_tokens \ 0 162 24 1 162 24 2 162 24 3 162 24 4 162 24 5 162 24 6 162 24 7 162 24 8 162 24 9 162 24 10 162 24 11 162 24 token_usage_completion_tokens flesch_reading_ease flesch_kincaid_grade \ 0 138 109.04 1.3 1 138 83.66 4.8

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-17

1 138 83.66 4.8 2 138 109.04 1.3 3 138 83.66 4.8 4 138 109.04 1.3 5 138 83.66 4.8 6 138 109.04 1.3 7 138 83.66 4.8 8 138 109.04 1.3 9 138 83.66 4.8 10 138 109.04 1.3 11 138 83.66 4.8 ... difficult_words linsear_write_formula gunning_fog \ 0 ... 0 5.5 5.20 1 ... 2 6.5 8.28 2 ... 0 5.5 5.20 3 ... 2 6.5 8.28 4 ... 0 5.5 5.20 5 ... 2 6.5 8.28 6 ... 0 5.5 5.20 7 ... 2 6.5 8.28 8 ... 0 5.5 5.20 9 ... 2 6.5 8.28 10 ... 0 5.5 5.20

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-18

10 ... 0 5.5 5.20 11 ... 2 6.5 8.28 text_standard fernandez_huerta szigriszt_pazos gutierrez_polini \ 0 5th and 6th grade 133.58 131.54 62.30 1 6th and 7th grade 115.58 112.37 54.83 2 5th and 6th grade 133.58 131.54 62.30 3 6th and 7th grade 115.58 112.37 54.83 4 5th and 6th grade 133.58 131.54 62.30 5 6th and 7th grade 115.58 112.37 54.83 6 5th and 6th grade 133.58 131.54 62.30 7 6th and 7th grade 115.58 112.37 54.83 8 5th and 6th grade 133.58 131.54 62.30 9 6th and 7th grade 115.58 112.37 54.83 10 5th and 6th grade 133.58 131.54 62.30 11 6th and 7th grade 115.58 112.37 54.83 crawford gulpease_index osman

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-19

crawford gulpease_index osman 0 -0.2 79.8 116.91 1 1.4 72.1 100.17 2 -0.2 79.8 116.91 3 1.4 72.1 100.17 4 -0.2 79.8 116.91 5 1.4 72.1 100.17 6 -0.2 79.8 116.91 7 1.4 72.1 100.17 8 -0.2 79.8 116.91 9 1.4 72.1 100.17 10 -0.2 79.8 116.91 11 1.4 72.1 100.17 [12 rows x 24 columns]} 2023-03-29 14:00:25,948 - clearml.Task - INFO - Completed model upload to https://files.clear.ml/langchain_callback_demo/llm.988bd727b0e94a29a3ac0ee526813545/models/simple_sequential At this point you can already go to https://app.clear.ml and take a look at the resulting ClearML Task that was created. Among others, you should see that this notebook is saved along with any git information. The model JSON that contains the used parameters is saved as an artifact, there are also console logs and under the plots section, you’ll find tables that represent the flow of the chain. Finally, if you enabled visualizations, these are stored as HTML files under debug samples.

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-20

Finally, if you enabled visualizations, these are stored as HTML files under debug samples. Scenario 2: Creating an agent with tools# To show a more advanced workflow, let’s create an agent with access to tools. The way ClearML tracks the results is not different though, only the table will look slightly different as there are other types of actions taken when compared to the earlier, simpler example. You can now also see the use of the finish=True keyword, which will fully close the ClearML Task, instead of just resetting the parameters and prompts for a new conversation. from langchain.agents import initialize_agent, load_tools from langchain.agents import AgentType # SCENARIO 2 - Agent with Tools tools = load_tools(["serpapi", "llm-math"], llm=llm, callbacks=callbacks) agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, callbacks=callbacks, ) agent.run( "Who is the wife of the person who sang summer of 69?" ) clearml_callback.flush_tracker(langchain_asset=agent, name="Agent with Tools", finish=True) > Entering new AgentExecutor chain... {'action': 'on_chain_start', 'name': 'AgentExecutor', 'step': 1, 'starts': 1, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 0, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'input': 'Who is the wife of the person who sang summer of 69?'}

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-21

{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 2, 'starts': 2, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\n\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [Search, Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who is the wife of the person who sang summer of 69?\nThought:'}

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-22

{'action': 'on_llm_end', 'token_usage_prompt_tokens': 189, 'token_usage_completion_tokens': 34, 'token_usage_total_tokens': 223, 'model_name': 'text-davinci-003', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': ' I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 91.61, 'flesch_kincaid_grade': 3.8, 'smog_index': 0.0, 'coleman_liau_index': 3.41, 'automated_readability_index': 3.5, 'dale_chall_readability_score': 6.06, 'difficult_words': 2, 'linsear_write_formula': 5.75, 'gunning_fog': 5.4, 'text_standard': '3rd and 4th grade', 'fernandez_huerta': 121.07, 'szigriszt_pazos': 119.5, 'gutierrez_polini': 54.91, 'crawford': 0.9, 'gulpease_index': 72.7, 'osman': 92.16}

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-23

I need to find out who sang summer of 69 and then find out who their wife is. Action: Search Action Input: "Who sang summer of 69"{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who sang summer of 69', 'log': ' I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"', 'step': 4, 'starts': 3, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 1, 'tool_ends': 0, 'agent_ends': 0} {'action': 'on_tool_start', 'input_str': 'Who sang summer of 69', 'name': 'Search', 'description': 'A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', 'step': 5, 'starts': 4, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 0, 'agent_ends': 0} Observation: Bryan Adams - Summer Of 69 (Official Music Video).

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-24

Observation: Bryan Adams - Summer Of 69 (Official Music Video). Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams - Summer Of 69 (Official Music Video).', 'step': 6, 'starts': 4, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0}

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-25

{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 7, 'starts': 5, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\n\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [Search, Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who is the wife of the person who sang summer of 69?\nThought: I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\nThought:'}

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

b8d052aaafc7-26

{'action': 'on_llm_end', 'token_usage_prompt_tokens': 242, 'token_usage_completion_tokens': 28, 'token_usage_total_tokens': 270, 'model_name': 'text-davinci-003', 'step': 8, 'starts': 5, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0, 'text': ' I need to find out who Bryan Adams is married to.\nAction: Search\nAction Input: "Who is Bryan Adams married to"', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 94.66, 'flesch_kincaid_grade': 2.7, 'smog_index': 0.0, 'coleman_liau_index': 4.73, 'automated_readability_index': 4.0, 'dale_chall_readability_score': 7.16, 'difficult_words': 2, 'linsear_write_formula': 4.25, 'gunning_fog': 4.2, 'text_standard': '4th and 5th grade', 'fernandez_huerta': 124.13, 'szigriszt_pazos': 119.2, 'gutierrez_polini': 52.26, 'crawford': 0.7, 'gulpease_index': 74.7, 'osman': 84.2} I need to find out who Bryan Adams is married to. Action: Search

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

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I need to find out who Bryan Adams is married to. Action: Search Action Input: "Who is Bryan Adams married to"{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who is Bryan Adams married to', 'log': ' I need to find out who Bryan Adams is married to.\nAction: Search\nAction Input: "Who is Bryan Adams married to"', 'step': 9, 'starts': 6, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 3, 'tool_ends': 1, 'agent_ends': 0} {'action': 'on_tool_start', 'input_str': 'Who is Bryan Adams married to', 'name': 'Search', 'description': 'A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', 'step': 10, 'starts': 7, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 1, 'agent_ends': 0} Observation: Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

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Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...', 'step': 11, 'starts': 7, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0}

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

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{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 12, 'starts': 8, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\n\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [Search, Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who is the wife of the person who sang summer of 69?\nThought: I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\nThought: I need to find out who Bryan Adams is married to.\nAction: Search\nAction Input: "Who is Bryan Adams married to"\nObservation: Bryan Adams has never married. In the 1990s, he was in

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

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Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...\nThought:'}

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

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{'action': 'on_llm_end', 'token_usage_prompt_tokens': 314, 'token_usage_completion_tokens': 18, 'token_usage_total_tokens': 332, 'model_name': 'text-davinci-003', 'step': 13, 'starts': 8, 'ends': 5, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'text': ' I now know the final answer.\nFinal Answer: Bryan Adams has never been married.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 81.29, 'flesch_kincaid_grade': 3.7, 'smog_index': 0.0, 'coleman_liau_index': 5.75, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 7.37, 'difficult_words': 1, 'linsear_write_formula': 2.5, 'gunning_fog': 2.8, 'text_standard': '3rd and 4th grade', 'fernandez_huerta': 115.7, 'szigriszt_pazos': 110.84, 'gutierrez_polini': 49.79, 'crawford': 0.7, 'gulpease_index': 85.4, 'osman': 83.14} I now know the final answer. Final Answer: Bryan Adams has never been married.

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

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I now know the final answer. Final Answer: Bryan Adams has never been married. {'action': 'on_agent_finish', 'output': 'Bryan Adams has never been married.', 'log': ' I now know the final answer.\nFinal Answer: Bryan Adams has never been married.', 'step': 14, 'starts': 8, 'ends': 6, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 1} > Finished chain. {'action': 'on_chain_end', 'outputs': 'Bryan Adams has never been married.', 'step': 15, 'starts': 8, 'ends': 7, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 1, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 1} {'action_records': action name step starts ends errors text_ctr \ 0 on_llm_start OpenAI 1 1 0 0 0 1 on_llm_start OpenAI 1 1 0 0 0 2 on_llm_start OpenAI 1 1 0 0 0 3 on_llm_start OpenAI 1 1 0 0 0

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

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4 on_llm_start OpenAI 1 1 0 0 0 .. ... ... ... ... ... ... ... 66 on_tool_end NaN 11 7 4 0 0 67 on_llm_start OpenAI 12 8 4 0 0 68 on_llm_end NaN 13 8 5 0 0 69 on_agent_finish NaN 14 8 6 0 0 70 on_chain_end NaN 15 8 7 0 0 chain_starts chain_ends llm_starts ... gulpease_index osman input \ 0 0 0 1 ... NaN NaN NaN 1 0 0 1 ... NaN NaN NaN 2 0 0 1 ... NaN NaN NaN 3 0 0 1 ... NaN NaN NaN 4 0 0 1 ... NaN NaN NaN .. ... ... ... ... ... ... ... 66 1 0 2 ... NaN NaN NaN 67 1 0 3 ... NaN NaN NaN 68 1 0 3 ... 85.4 83.14 NaN 69 1 0 3 ... NaN NaN NaN

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

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69 1 0 3 ... NaN NaN NaN 70 1 1 3 ... NaN NaN NaN tool tool_input log \ 0 NaN NaN NaN 1 NaN NaN NaN 2 NaN NaN NaN 3 NaN NaN NaN 4 NaN NaN NaN .. ... ... ... 66 NaN NaN NaN 67 NaN NaN NaN 68 NaN NaN NaN 69 NaN NaN I now know the final answer.\nFinal Answer: B... 70 NaN NaN NaN input_str description output \ 0 NaN NaN NaN 1 NaN NaN NaN 2 NaN NaN NaN 3 NaN NaN NaN 4 NaN NaN NaN .. ... ... ... 66 NaN NaN Bryan Adams has never married. In the 1990s, h... 67 NaN NaN NaN 68 NaN NaN NaN 69 NaN NaN Bryan Adams has never been married. 70 NaN NaN NaN outputs 0 NaN 1 NaN 2 NaN 3 NaN 4 NaN .. ... 66 NaN 67 NaN 68 NaN 69 NaN 70 Bryan Adams has never been married. [71 rows x 47 columns], 'session_analysis': prompt_step prompts name \ 0 2 Answer the following questions as best you can... OpenAI

https://python.langchain.com/en/latest/integrations/clearml_tracking.html

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0 2 Answer the following questions as best you can... OpenAI 1 7 Answer the following questions as best you can... OpenAI 2 12 Answer the following questions as best you can... OpenAI output_step output \ 0 3 I need to find out who sang summer of 69 and ... 1 8 I need to find out who Bryan Adams is married... 2 13 I now know the final answer.\nFinal Answer: B... token_usage_total_tokens token_usage_prompt_tokens \ 0 223 189 1 270 242 2 332 314 token_usage_completion_tokens flesch_reading_ease flesch_kincaid_grade \ 0 34 91.61 3.8 1 28 94.66 2.7 2 18 81.29 3.7 ... difficult_words linsear_write_formula gunning_fog \ 0 ... 2 5.75 5.4 1 ... 2 4.25 4.2 2 ... 1 2.50 2.8 text_standard fernandez_huerta szigriszt_pazos gutierrez_polini \ 0 3rd and 4th grade 121.07 119.50 54.91 1 4th and 5th grade 124.13 119.20 52.26

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2 3rd and 4th grade 115.70 110.84 49.79 crawford gulpease_index osman 0 0.9 72.7 92.16 1 0.7 74.7 84.20 2 0.7 85.4 83.14 [3 rows x 24 columns]} Could not update last created model in Task 988bd727b0e94a29a3ac0ee526813545, Task status 'completed' cannot be updated Tips and Next Steps# Make sure you always use a unique name argument for the clearml_callback.flush_tracker function. If not, the model parameters used for a run will override the previous run! If you close the ClearML Callback using clearml_callback.flush_tracker(..., finish=True) the Callback cannot be used anymore. Make a new one if you want to keep logging. Check out the rest of the open source ClearML ecosystem, there is a data version manager, a remote execution agent, automated pipelines and much more! previous Chroma next Cohere Contents Installation and Setup Getting API Credentials Callbacks Scenario 1: Just an LLM Scenario 2: Creating an agent with tools Tips and Next Steps By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

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.md .pdf IMSDb Contents Installation and Setup Document Loader IMSDb# IMSDb is the Internet Movie Script Database. Installation and Setup# There isn’t any special setup for it. Document Loader# See a usage example. from langchain.document_loaders import IMSDbLoader previous iFixit next Jina Contents Installation and Setup Document Loader By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

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.md .pdf PGVector Contents Installation Setup Wrappers VectorStore Usage PGVector# This page covers how to use the Postgres PGVector ecosystem within LangChain It is broken into two parts: installation and setup, and then references to specific PGVector wrappers. Installation# Install the Python package with pip install pgvector Setup# The first step is to create a database with the pgvector extension installed. Follow the steps at PGVector Installation Steps to install the database and the extension. The docker image is the easiest way to get started. Wrappers# VectorStore# There exists a wrapper around Postgres vector databases, allowing you to use it as a vectorstore, whether for semantic search or example selection. To import this vectorstore: from langchain.vectorstores.pgvector import PGVector Usage# For a more detailed walkthrough of the PGVector Wrapper, see this notebook previous Petals next Pinecone Contents Installation Setup Wrappers VectorStore Usage By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

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.md .pdf Zilliz Contents Installation and Setup Wrappers VectorStore Zilliz# This page covers how to use the Zilliz Cloud ecosystem within LangChain. Zilliz uses the Milvus integration. It is broken into two parts: installation and setup, and then references to specific Milvus wrappers. Installation and Setup# Install the Python SDK with pip install pymilvus Wrappers# VectorStore# There exists a wrapper around Zilliz indexes, allowing you to use it as a vectorstore, whether for semantic search or example selection. To import this vectorstore: from langchain.vectorstores import Milvus For a more detailed walkthrough of the Miluvs wrapper, see this notebook previous Yeager.ai next Dependents Contents Installation and Setup Wrappers VectorStore By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/zilliz.html

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.md .pdf Prediction Guard Contents Installation and Setup LLM Wrapper Example usage Prediction Guard# This page covers how to use the Prediction Guard ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Prediction Guard wrappers. Installation and Setup# Install the Python SDK with pip install predictionguard Get an Prediction Guard access token (as described here) and set it as an environment variable (PREDICTIONGUARD_TOKEN) LLM Wrapper# There exists a Prediction Guard LLM wrapper, which you can access with from langchain.llms import PredictionGuard You can provide the name of the Prediction Guard model as an argument when initializing the LLM: pgllm = PredictionGuard(model="MPT-7B-Instruct") You can also provide your access token directly as an argument: pgllm = PredictionGuard(model="MPT-7B-Instruct", token="<your access token>") Finally, you can provide an “output” argument that is used to structure/ control the output of the LLM: pgllm = PredictionGuard(model="MPT-7B-Instruct", output={"type": "boolean"}) Example usage# Basic usage of the controlled or guarded LLM wrapper: import os import predictionguard as pg from langchain.llms import PredictionGuard from langchain import PromptTemplate, LLMChain # Your Prediction Guard API key. Get one at predictionguard.com os.environ["PREDICTIONGUARD_TOKEN"] = "<your Prediction Guard access token>" # Define a prompt template template = """Respond to the following query based on the context. Context: EVERY comment, DM + email suggestion has led us to this EXCITING announcement! 🎉 We have officially added TWO new candle subscription box options! 📦 Exclusive Candle Box - $80

https://python.langchain.com/en/latest/integrations/predictionguard.html

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Exclusive Candle Box - $80 Monthly Candle Box - $45 (NEW!) Scent of The Month Box - $28 (NEW!) Head to stories to get ALLL the deets on each box! 👆 BONUS: Save 50% on your first box with code 50OFF! 🎉 Query: {query} Result: """ prompt = PromptTemplate(template=template, input_variables=["query"]) # With "guarding" or controlling the output of the LLM. See the # Prediction Guard docs (https://docs.predictionguard.com) to learn how to # control the output with integer, float, boolean, JSON, and other types and # structures. pgllm = PredictionGuard(model="MPT-7B-Instruct", output={ "type": "categorical", "categories": [ "product announcement", "apology", "relational" ] }) pgllm(prompt.format(query="What kind of post is this?")) Basic LLM Chaining with the Prediction Guard wrapper: import os from langchain import PromptTemplate, LLMChain from langchain.llms import PredictionGuard # Optional, add your OpenAI API Key. This is optional, as Prediction Guard allows # you to access all the latest open access models (see https://docs.predictionguard.com) os.environ["OPENAI_API_KEY"] = "<your OpenAI api key>" # Your Prediction Guard API key. Get one at predictionguard.com os.environ["PREDICTIONGUARD_TOKEN"] = "<your Prediction Guard access token>" pgllm = PredictionGuard(model="OpenAI-text-davinci-003") template = """Question: {question} Answer: Let's think step by step."""

https://python.langchain.com/en/latest/integrations/predictionguard.html

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template = """Question: {question} Answer: Let's think step by step.""" prompt = PromptTemplate(template=template, input_variables=["question"]) llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True) question = "What NFL team won the Super Bowl in the year Justin Beiber was born?" llm_chain.predict(question=question) previous PipelineAI next PromptLayer Contents Installation and Setup LLM Wrapper Example usage By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/predictionguard.html

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.md .pdf Obsidian Contents Installation and Setup Document Loader Obsidian# Obsidian is a powerful and extensible knowledge base that works on top of your local folder of plain text files. Installation and Setup# All instructions are in examples below. Document Loader# See a usage example. from langchain.document_loaders import ObsidianLoader previous Notion DB next OpenAI Contents Installation and Setup Document Loader By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/obsidian.html

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.md .pdf Azure OpenAI Contents Installation and Setup LLM Text Embedding Models Chat Models Azure OpenAI# Microsoft Azure, often referred to as Azure is a cloud computing platform run by Microsoft, which offers access, management, and development of applications and services through global data centers. It provides a range of capabilities, including software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). Microsoft Azure supports many programming languages, tools, and frameworks, including Microsoft-specific and third-party software and systems. Azure OpenAI is an Azure service with powerful language models from OpenAI including the GPT-3, Codex and Embeddings model series for content generation, summarization, semantic search, and natural language to code translation. Installation and Setup# pip install openai pip install tiktoken Set the environment variables to get access to the Azure OpenAI service. import os os.environ["OPENAI_API_TYPE"] = "azure" os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/" os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key" os.environ["OPENAI_API_VERSION"] = "2023-03-15-preview" LLM# See a usage example. from langchain.llms import AzureOpenAI Text Embedding Models# See a usage example from langchain.embeddings import OpenAIEmbeddings Chat Models# See a usage example from langchain.chat_models import AzureChatOpenAI previous Azure Blob Storage next Banana Contents Installation and Setup LLM Text Embedding Models Chat Models By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/azure_openai.html

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.md .pdf Qdrant Contents Installation and Setup Wrappers VectorStore Qdrant# This page covers how to use the Qdrant ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Qdrant wrappers. Installation and Setup# Install the Python SDK with pip install qdrant-client Wrappers# VectorStore# There exists a wrapper around Qdrant indexes, allowing you to use it as a vectorstore, whether for semantic search or example selection. To import this vectorstore: from langchain.vectorstores import Qdrant For a more detailed walkthrough of the Qdrant wrapper, see this notebook previous Psychic next Rebuff Contents Installation and Setup Wrappers VectorStore By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/qdrant.html

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.md .pdf Notion DB Contents Installation and Setup Document Loader Notion DB# Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management. Installation and Setup# All instructions are in examples below. Document Loader# We have two different loaders: NotionDirectoryLoader and NotionDBLoader. See a usage example for the NotionDirectoryLoader. from langchain.document_loaders import NotionDirectoryLoader See a usage example for the NotionDBLoader. from langchain.document_loaders import NotionDBLoader previous NLPCloud next Obsidian Contents Installation and Setup Document Loader By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/notion.html

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.ipynb .pdf Databricks Contents Installation and Setup Connecting to Databricks Syntax Required Parameters Optional Parameters Examples SQL Chain example SQL Database Agent example Databricks# This notebook covers how to connect to the Databricks runtimes and Databricks SQL using the SQLDatabase wrapper of LangChain. It is broken into 3 parts: installation and setup, connecting to Databricks, and examples. Installation and Setup# !pip install databricks-sql-connector Connecting to Databricks# You can connect to Databricks runtimes and Databricks SQL using the SQLDatabase.from_databricks() method. Syntax# SQLDatabase.from_databricks( catalog: str, schema: str, host: Optional[str] = None, api_token: Optional[str] = None, warehouse_id: Optional[str] = None, cluster_id: Optional[str] = None, engine_args: Optional[dict] = None, **kwargs: Any) Required Parameters# catalog: The catalog name in the Databricks database. schema: The schema name in the catalog. Optional Parameters# There following parameters are optional. When executing the method in a Databricks notebook, you don’t need to provide them in most of the cases. host: The Databricks workspace hostname, excluding ‘https://’ part. Defaults to ‘DATABRICKS_HOST’ environment variable or current workspace if in a Databricks notebook. api_token: The Databricks personal access token for accessing the Databricks SQL warehouse or the cluster. Defaults to ‘DATABRICKS_API_TOKEN’ environment variable or a temporary one is generated if in a Databricks notebook. warehouse_id: The warehouse ID in the Databricks SQL.

https://python.langchain.com/en/latest/integrations/databricks.html

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warehouse_id: The warehouse ID in the Databricks SQL. cluster_id: The cluster ID in the Databricks Runtime. If running in a Databricks notebook and both ‘warehouse_id’ and ‘cluster_id’ are None, it uses the ID of the cluster the notebook is attached to. engine_args: The arguments to be used when connecting Databricks. **kwargs: Additional keyword arguments for the SQLDatabase.from_uri method. Examples# # Connecting to Databricks with SQLDatabase wrapper from langchain import SQLDatabase db = SQLDatabase.from_databricks(catalog='samples', schema='nyctaxi') # Creating a OpenAI Chat LLM wrapper from langchain.chat_models import ChatOpenAI llm = ChatOpenAI(temperature=0, model_name="gpt-4") SQL Chain example# This example demonstrates the use of the SQL Chain for answering a question over a Databricks database. from langchain import SQLDatabaseChain db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True) db_chain.run("What is the average duration of taxi rides that start between midnight and 6am?") > Entering new SQLDatabaseChain chain... What is the average duration of taxi rides that start between midnight and 6am? SQLQuery:SELECT AVG(UNIX_TIMESTAMP(tpep_dropoff_datetime) - UNIX_TIMESTAMP(tpep_pickup_datetime)) as avg_duration FROM trips WHERE HOUR(tpep_pickup_datetime) >= 0 AND HOUR(tpep_pickup_datetime) < 6 SQLResult: [(987.8122786304605,)] Answer:The average duration of taxi rides that start between midnight and 6am is 987.81 seconds. > Finished chain. 'The average duration of taxi rides that start between midnight and 6am is 987.81 seconds.'

https://python.langchain.com/en/latest/integrations/databricks.html

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SQL Database Agent example# This example demonstrates the use of the SQL Database Agent for answering questions over a Databricks database. from langchain.agents import create_sql_agent from langchain.agents.agent_toolkits import SQLDatabaseToolkit toolkit = SQLDatabaseToolkit(db=db, llm=llm) agent = create_sql_agent( llm=llm, toolkit=toolkit, verbose=True ) agent.run("What is the longest trip distance and how long did it take?") > Entering new AgentExecutor chain... Action: list_tables_sql_db Action Input: Observation: trips Thought:I should check the schema of the trips table to see if it has the necessary columns for trip distance and duration. Action: schema_sql_db Action Input: trips Observation: CREATE TABLE trips ( tpep_pickup_datetime TIMESTAMP, tpep_dropoff_datetime TIMESTAMP, trip_distance FLOAT, fare_amount FLOAT, pickup_zip INT, dropoff_zip INT ) USING DELTA /* 3 rows from trips table: tpep_pickup_datetime tpep_dropoff_datetime trip_distance fare_amount pickup_zip dropoff_zip 2016-02-14 16:52:13+00:00 2016-02-14 17:16:04+00:00 4.94 19.0 10282 10171 2016-02-04 18:44:19+00:00 2016-02-04 18:46:00+00:00 0.28 3.5 10110 10110

https://python.langchain.com/en/latest/integrations/databricks.html

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2016-02-17 17:13:57+00:00 2016-02-17 17:17:55+00:00 0.7 5.0 10103 10023 */ Thought:The trips table has the necessary columns for trip distance and duration. I will write a query to find the longest trip distance and its duration. Action: query_checker_sql_db Action Input: SELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1 Observation: SELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1 Thought:The query is correct. I will now execute it to find the longest trip distance and its duration. Action: query_sql_db Action Input: SELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1 Observation: [(30.6, '0 00:43:31.000000000')] Thought:I now know the final answer. Final Answer: The longest trip distance is 30.6 miles and it took 43 minutes and 31 seconds. > Finished chain. 'The longest trip distance is 30.6 miles and it took 43 minutes and 31 seconds.' previous Databerry next DeepInfra Contents Installation and Setup Connecting to Databricks Syntax Required Parameters Optional Parameters Examples SQL Chain example SQL Database Agent example By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

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.md .pdf Facebook Chat Contents Installation and Setup Document Loader Facebook Chat# Messenger is an American proprietary instant messaging app and platform developed by Meta Platforms. Originally developed as Facebook Chat in 2008, the company revamped its messaging service in 2010. Installation and Setup# First, you need to install pandas python package. pip install pandas Document Loader# See a usage example. from langchain.document_loaders import FacebookChatLoader previous EverNote next Figma Contents Installation and Setup Document Loader By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

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.md .pdf Modern Treasury Contents Installation and Setup Document Loader Modern Treasury# Modern Treasury simplifies complex payment operations. It is a unified platform to power products and processes that move money. Connect to banks and payment systems Track transactions and balances in real-time Automate payment operations for scale Installation and Setup# There isn’t any special setup for it. Document Loader# See a usage example. from langchain.document_loaders import ModernTreasuryLoader previous Modal next Momento Contents Installation and Setup Document Loader By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

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.md .pdf Gutenberg Contents Installation and Setup Document Loader Gutenberg# Project Gutenberg is an online library of free eBooks. Installation and Setup# There isn’t any special setup for it. Document Loader# See a usage example. from langchain.document_loaders import GutenbergLoader previous Graphsignal next Hacker News Contents Installation and Setup Document Loader By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/gutenberg.html

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.md .pdf Unstructured Contents Installation and Setup Wrappers Data Loaders Unstructured# The unstructured package from Unstructured.IO extracts clean text from raw source documents like PDFs and Word documents. This page covers how to use the unstructured ecosystem within LangChain. Installation and Setup# If you are using a loader that runs locally, use the following steps to get unstructured and its dependencies running locally. Install the Python SDK with pip install "unstructured[local-inference]" Install the following system dependencies if they are not already available on your system. Depending on what document types you’re parsing, you may not need all of these. libmagic-dev (filetype detection) poppler-utils (images and PDFs) tesseract-ocr(images and PDFs) libreoffice (MS Office docs) pandoc (EPUBs) If you want to get up and running with less set up, you can simply run pip install unstructured and use UnstructuredAPIFileLoader or UnstructuredAPIFileIOLoader. That will process your document using the hosted Unstructured API. Note that currently (as of 1 May 2023) the Unstructured API is open, but it will soon require an API. The Unstructured documentation page will have instructions on how to generate an API key once they’re available. Check out the instructions here if you’d like to self-host the Unstructured API or run it locally. Wrappers# Data Loaders# The primary unstructured wrappers within langchain are data loaders. The following shows how to use the most basic unstructured data loader. There are other file-specific data loaders available in the langchain.document_loaders module. from langchain.document_loaders import UnstructuredFileLoader

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from langchain.document_loaders import UnstructuredFileLoader loader = UnstructuredFileLoader("state_of_the_union.txt") loader.load() If you instantiate the loader with UnstructuredFileLoader(mode="elements"), the loader will track additional metadata like the page number and text type (i.e. title, narrative text) when that information is available. previous Tair next Vectara Contents Installation and Setup Wrappers Data Loaders By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/unstructured.html

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.md .pdf Hazy Research Contents Installation and Setup Wrappers LLM Hazy Research# This page covers how to use the Hazy Research ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Hazy Research wrappers. Installation and Setup# To use the manifest, install it with pip install manifest-ml Wrappers# LLM# There exists an LLM wrapper around Hazy Research’s manifest library. manifest is a python library which is itself a wrapper around many model providers, and adds in caching, history, and more. To use this wrapper: from langchain.llms.manifest import ManifestWrapper previous Hacker News next Helicone Contents Installation and Setup Wrappers LLM By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/hazy_research.html

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.md .pdf Diffbot Contents Installation and Setup Document Loader Diffbot# Diffbot is a service to read web pages. Unlike traditional web scraping tools, Diffbot doesn’t require any rules to read the content on a page. It starts with computer vision, which classifies a page into one of 20 possible types. Content is then interpreted by a machine learning model trained to identify the key attributes on a page based on its type. The result is a website transformed into clean-structured data (like JSON or CSV), ready for your application. Installation and Setup# Read instructions how to get the Diffbot API Token. Document Loader# See a usage example. from langchain.document_loaders import DiffbotLoader previous Deep Lake next Discord Contents Installation and Setup Document Loader By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/diffbot.html

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.md .pdf AZLyrics Contents Installation and Setup Document Loader AZLyrics# AZLyrics is a large, legal, every day growing collection of lyrics. Installation and Setup# There isn’t any special setup for it. Document Loader# See a usage example. from langchain.document_loaders import AZLyricsLoader previous AWS S3 Directory next Azure Blob Storage Contents Installation and Setup Document Loader By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/azlyrics.html

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.md .pdf Cohere Contents Installation and Setup Wrappers LLM Embeddings Cohere# This page covers how to use the Cohere ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Cohere wrappers. Installation and Setup# Install the Python SDK with pip install cohere Get an Cohere api key and set it as an environment variable (COHERE_API_KEY) Wrappers# LLM# There exists an Cohere LLM wrapper, which you can access with from langchain.llms import Cohere Embeddings# There exists an Cohere Embeddings wrapper, which you can access with from langchain.embeddings import CohereEmbeddings For a more detailed walkthrough of this, see this notebook previous ClearML next College Confidential Contents Installation and Setup Wrappers LLM Embeddings By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/cohere.html

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.md .pdf NLPCloud Contents Installation and Setup Wrappers LLM NLPCloud# This page covers how to use the NLPCloud ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific NLPCloud wrappers. Installation and Setup# Install the Python SDK with pip install nlpcloud Get an NLPCloud api key and set it as an environment variable (NLPCLOUD_API_KEY) Wrappers# LLM# There exists an NLPCloud LLM wrapper, which you can access with from langchain.llms import NLPCloud previous MyScale next Notion DB Contents Installation and Setup Wrappers LLM By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/nlpcloud.html

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.md .pdf MediaWikiDump Contents Installation and Setup Document Loader MediaWikiDump# MediaWiki XML Dumps contain the content of a wiki (wiki pages with all their revisions), without the site-related data. A XML dump does not create a full backup of the wiki database, the dump does not contain user accounts, images, edit logs, etc. Installation and Setup# We need to install several python packages. The mediawiki-utilities supports XML schema 0.11 in unmerged branches. pip install -qU git+https://github.com/mediawiki-utilities/python-mwtypes@updates_schema_0.11 The mediawiki-utilities mwxml has a bug, fix PR pending. pip install -qU git+https://github.com/gdedrouas/python-mwxml@xml_format_0.11 pip install -qU mwparserfromhell Document Loader# See a usage example. from langchain.document_loaders import MWDumpLoader previous Llama.cpp next Metal Contents Installation and Setup Document Loader By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/mediawikidump.html

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.md .pdf Vectara Contents Installation and Setup VectorStore Vectara# What is Vectara? Vectara Overview: Vectara is developer-first API platform for building conversational search applications To use Vectara - first sign up and create an account. Then create a corpus and an API key for indexing and searching. You can use Vectara’s indexing API to add documents into Vectara’s index You can use Vectara’s Search API to query Vectara’s index (which also supports Hybrid search implicitly). You can use Vectara’s integration with LangChain as a Vector store or using the Retriever abstraction. Installation and Setup# To use Vectara with LangChain no special installation steps are required. You just have to provide your customer_id, corpus ID, and an API key created within the Vectara console to enable indexing and searching. VectorStore# There exists a wrapper around the Vectara platform, allowing you to use it as a vectorstore, whether for semantic search or example selection. To import this vectorstore: from langchain.vectorstores import Vectara To create an instance of the Vectara vectorstore: vectara = Vectara( vectara_customer_id=customer_id, vectara_corpus_id=corpus_id, vectara_api_key=api_key ) The customer_id, corpus_id and api_key are optional, and if they are not supplied will be read from the environment variables VECTARA_CUSTOMER_ID, VECTARA_CORPUS_ID and VECTARA_API_KEY, respectively. For a more detailed walkthrough of the Vectara wrapper, see one of the two example notebooks: Chat Over Documents with Vectara Vectara Text Generation previous Unstructured next Weights & Biases Contents Installation and Setup VectorStore By Harrison Chase

https://python.langchain.com/en/latest/integrations/vectara.html

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Contents Installation and Setup VectorStore By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/vectara.html

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.md .pdf Yeager.ai Contents What is Yeager.ai? yAgents How to use? Creating and Executing Tools with yAgents Yeager.ai# This page covers how to use Yeager.ai to generate LangChain tools and agents. What is Yeager.ai?# Yeager.ai is an ecosystem designed to simplify the process of creating AI agents and tools. It features yAgents, a No-code LangChain Agent Builder, which enables users to build, test, and deploy AI solutions with ease. Leveraging the LangChain framework, yAgents allows seamless integration with various language models and resources, making it suitable for developers, researchers, and AI enthusiasts across diverse applications. yAgents# Low code generative agent designed to help you build, prototype, and deploy Langchain tools with ease. How to use?# pip install yeagerai-agent yeagerai-agent Go to http://127.0.0.1:7860 This will install the necessary dependencies and set up yAgents on your system. After the first run, yAgents will create a .env file where you can input your OpenAI API key. You can do the same directly from the Gradio interface under the tab “Settings”. OPENAI_API_KEY=<your_openai_api_key_here> We recommend using GPT-4,. However, the tool can also work with GPT-3 if the problem is broken down sufficiently. Creating and Executing Tools with yAgents# yAgents makes it easy to create and execute AI-powered tools. Here’s a brief overview of the process: Create a tool: To create a tool, provide a natural language prompt to yAgents. The prompt should clearly describe the tool’s purpose and functionality. For example: create a tool that returns the n-th prime number

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create a tool that returns the n-th prime number Load the tool into the toolkit: To load a tool into yAgents, simply provide a command to yAgents that says so. For example: load the tool that you just created it into your toolkit Execute the tool: To run a tool or agent, simply provide a command to yAgents that includes the name of the tool and any required parameters. For example: generate the 50th prime number You can see a video of how it works here. As you become more familiar with yAgents, you can create more advanced tools and agents to automate your work and enhance your productivity. For more information, see yAgents’ Github or our docs previous Writer next Zilliz Contents What is Yeager.ai? yAgents How to use? Creating and Executing Tools with yAgents By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/yeagerai.html

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.ipynb .pdf Comet Contents Install Comet and Dependencies Initialize Comet and Set your Credentials Set OpenAI and SerpAPI credentials Scenario 1: Using just an LLM Scenario 2: Using an LLM in a Chain Scenario 3: Using An Agent with Tools Scenario 4: Using Custom Evaluation Metrics Comet# In this guide we will demonstrate how to track your Langchain Experiments, Evaluation Metrics, and LLM Sessions with Comet. Example Project: Comet with LangChain Install Comet and Dependencies# %pip install comet_ml langchain openai google-search-results spacy textstat pandas import sys !{sys.executable} -m spacy download en_core_web_sm Initialize Comet and Set your Credentials# You can grab your Comet API Key here or click the link after initializing Comet import comet_ml comet_ml.init(project_name="comet-example-langchain") Set OpenAI and SerpAPI credentials# You will need an OpenAI API Key and a SerpAPI API Key to run the following examples import os os.environ["OPENAI_API_KEY"] = "..." #os.environ["OPENAI_ORGANIZATION"] = "..." os.environ["SERPAPI_API_KEY"] = "..." Scenario 1: Using just an LLM# from datetime import datetime from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler from langchain.llms import OpenAI comet_callback = CometCallbackHandler( project_name="comet-example-langchain", complexity_metrics=True, stream_logs=True, tags=["llm"], visualizations=["dep"], ) callbacks = [StdOutCallbackHandler(), comet_callback] llm = OpenAI(temperature=0.9, callbacks=callbacks, verbose=True)

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llm = OpenAI(temperature=0.9, callbacks=callbacks, verbose=True) llm_result = llm.generate(["Tell me a joke", "Tell me a poem", "Tell me a fact"] * 3) print("LLM result", llm_result) comet_callback.flush_tracker(llm, finish=True) Scenario 2: Using an LLM in a Chain# from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler from langchain.chains import LLMChain from langchain.llms import OpenAI from langchain.prompts import PromptTemplate comet_callback = CometCallbackHandler( complexity_metrics=True, project_name="comet-example-langchain", stream_logs=True, tags=["synopsis-chain"], ) callbacks = [StdOutCallbackHandler(), comet_callback] llm = OpenAI(temperature=0.9, callbacks=callbacks) template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title. Title: {title} Playwright: This is a synopsis for the above play:""" prompt_template = PromptTemplate(input_variables=["title"], template=template) synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks) test_prompts = [{"title": "Documentary about Bigfoot in Paris"}] print(synopsis_chain.apply(test_prompts)) comet_callback.flush_tracker(synopsis_chain, finish=True) Scenario 3: Using An Agent with Tools# from langchain.agents import initialize_agent, load_tools from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler from langchain.llms import OpenAI comet_callback = CometCallbackHandler( project_name="comet-example-langchain", complexity_metrics=True,

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project_name="comet-example-langchain", complexity_metrics=True, stream_logs=True, tags=["agent"], ) callbacks = [StdOutCallbackHandler(), comet_callback] llm = OpenAI(temperature=0.9, callbacks=callbacks) tools = load_tools(["serpapi", "llm-math"], llm=llm, callbacks=callbacks) agent = initialize_agent( tools, llm, agent="zero-shot-react-description", callbacks=callbacks, verbose=True, ) agent.run( "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?" ) comet_callback.flush_tracker(agent, finish=True) Scenario 4: Using Custom Evaluation Metrics# The CometCallbackManager also allows you to define and use Custom Evaluation Metrics to assess generated outputs from your model. Let’s take a look at how this works. In the snippet below, we will use the ROUGE metric to evaluate the quality of a generated summary of an input prompt. %pip install rouge-score from rouge_score import rouge_scorer from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler from langchain.chains import LLMChain from langchain.llms import OpenAI from langchain.prompts import PromptTemplate class Rouge: def __init__(self, reference): self.reference = reference self.scorer = rouge_scorer.RougeScorer(["rougeLsum"], use_stemmer=True) def compute_metric(self, generation, prompt_idx, gen_idx): prediction = generation.text results = self.scorer.score(target=self.reference, prediction=prediction) return {

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return { "rougeLsum_score": results["rougeLsum"].fmeasure, "reference": self.reference, } reference = """ The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building. It was the first structure to reach a height of 300 metres. It is now taller than the Chrysler Building in New York City by 5.2 metres (17 ft) Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France . """ rouge_score = Rouge(reference=reference) template = """Given the following article, it is your job to write a summary. Article: {article} Summary: This is the summary for the above article:""" prompt_template = PromptTemplate(input_variables=["article"], template=template) comet_callback = CometCallbackHandler( project_name="comet-example-langchain", complexity_metrics=False, stream_logs=True, tags=["custom_metrics"], custom_metrics=rouge_score.compute_metric, ) callbacks = [StdOutCallbackHandler(), comet_callback] llm = OpenAI(temperature=0.9) synopsis_chain = LLMChain(llm=llm, prompt=prompt_template) test_prompts = [ { "article": """ The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building

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a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct. """ } ] print(synopsis_chain.apply(test_prompts, callbacks=callbacks)) comet_callback.flush_tracker(synopsis_chain, finish=True) previous College Confidential next Confluence Contents Install Comet and Dependencies Initialize Comet and Set your Credentials Set OpenAI and SerpAPI credentials Scenario 1: Using just an LLM Scenario 2: Using an LLM in a Chain Scenario 3: Using An Agent with Tools Scenario 4: Using Custom Evaluation Metrics By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/comet_tracking.html

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.md .pdf Microsoft PowerPoint Contents Installation and Setup Document Loader Microsoft PowerPoint# Microsoft PowerPoint is a presentation program by Microsoft. Installation and Setup# There isn’t any special setup for it. Document Loader# See a usage example. from langchain.document_loaders import UnstructuredPowerPointLoader previous Microsoft OneDrive next Microsoft Word Contents Installation and Setup Document Loader By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/microsoft_powerpoint.html

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.md .pdf Milvus Contents Installation and Setup Wrappers VectorStore Milvus# This page covers how to use the Milvus ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Milvus wrappers. Installation and Setup# Install the Python SDK with pip install pymilvus Wrappers# VectorStore# There exists a wrapper around Milvus indexes, allowing you to use it as a vectorstore, whether for semantic search or example selection. To import this vectorstore: from langchain.vectorstores import Milvus For a more detailed walkthrough of the Miluvs wrapper, see this notebook previous Microsoft Word next MLflow Contents Installation and Setup Wrappers VectorStore By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/milvus.html

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.md .pdf DuckDB Contents Installation and Setup Document Loader DuckDB# DuckDB is an in-process SQL OLAP database management system. Installation and Setup# First, you need to install duckdb python package. pip install duckdb Document Loader# See a usage example. from langchain.document_loaders import DuckDBLoader previous Docugami next EverNote Contents Installation and Setup Document Loader By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/duckdb.html

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.md .pdf AI21 Labs Contents Installation and Setup Wrappers LLM AI21 Labs# This page covers how to use the AI21 ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific AI21 wrappers. Installation and Setup# Get an AI21 api key and set it as an environment variable (AI21_API_KEY) Wrappers# LLM# There exists an AI21 LLM wrapper, which you can access with from langchain.llms import AI21 previous Tracing Walkthrough next Aim Contents Installation and Setup Wrappers LLM By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

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.md .pdf OpenWeatherMap Contents Installation and Setup Wrappers Utility Tool OpenWeatherMap# OpenWeatherMap provides all essential weather data for a specific location: Current weather Minute forecast for 1 hour Hourly forecast for 48 hours Daily forecast for 8 days National weather alerts Historical weather data for 40+ years back This page covers how to use the OpenWeatherMap API within LangChain. Installation and Setup# Install requirements with pip install pyowm Go to OpenWeatherMap and sign up for an account to get your API key here Set your API key as OPENWEATHERMAP_API_KEY environment variable Wrappers# Utility# There exists a OpenWeatherMapAPIWrapper utility which wraps this API. To import this utility: from langchain.utilities.openweathermap import OpenWeatherMapAPIWrapper For a more detailed walkthrough of this wrapper, see this notebook. Tool# You can also easily load this wrapper as a Tool (to use with an Agent). You can do this with: from langchain.agents import load_tools tools = load_tools(["openweathermap-api"]) For more information on this, see this page previous OpenSearch next Petals Contents Installation and Setup Wrappers Utility Tool By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/openweathermap.html

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.md .pdf Google BigQuery Contents Installation and Setup Document Loader Google BigQuery# Google BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data. BigQuery is a part of the Google Cloud Platform. Installation and Setup# First, you need to install google-cloud-bigquery python package. pip install google-cloud-bigquery Document Loader# See a usage example. from langchain.document_loaders import BigQueryLoader previous GitBook next Google Cloud Storage Contents Installation and Setup Document Loader By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Jun 02, 2023.

https://python.langchain.com/en/latest/integrations/google_bigquery.html