mirror of
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docs[patch]: Fix or remove broken mdx links (#19777)
this pr also drops the community added action for checking broken links in mdx. It does not work well for our use case, throwing errors for local paths, plus the rest of the errors our in house solution had.
This commit is contained in:
Brace Sproul
GitHub
parent
2f5606a318
commit
6d93a03bef
4
.github/workflows/check-broken-links.yml
vendored
4
.github/workflows/check-broken-links.yml
vendored
@ -22,7 +22,3 @@ jobs:
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- name: Check broken links
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run: yarn check-broken-links
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working-directory: ./docs
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- name: Check broken links for .mdx files
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uses: gaurav-nelson/github-action-markdown-link-check@v1
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with:
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file-extension: '.mdx'
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@ -241,7 +241,6 @@ Dependents stats for `langchain-ai/langchain`
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|[alejandro-ao/langchain-ask-pdf](https://github.com/alejandro-ao/langchain-ask-pdf) | 514 |
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|[sajjadium/ctf-archives](https://github.com/sajjadium/ctf-archives) | 507 |
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|[continuum-llms/chatgpt-memory](https://github.com/continuum-llms/chatgpt-memory) | 502 |
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|[llmOS/opencopilot](https://github.com/llmOS/opencopilot) | 495 |
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|[steamship-core/steamship-langchain](https://github.com/steamship-core/steamship-langchain) | 494 |
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|[mpaepper/content-chatbot](https://github.com/mpaepper/content-chatbot) | 493 |
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|[langchain-ai/langchain-aiplugin](https://github.com/langchain-ai/langchain-aiplugin) | 492 |
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@ -455,7 +454,6 @@ Dependents stats for `langchain-ai/langchain`
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|[Teahouse-Studios/akari-bot](https://github.com/Teahouse-Studios/akari-bot) | 149 |
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|[realminchoi/babyagi-ui](https://github.com/realminchoi/babyagi-ui) | 148 |
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|[ssheng/BentoChain](https://github.com/ssheng/BentoChain) | 148 |
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|[lmstudio-ai/examples](https://github.com/lmstudio-ai/examples) | 147 |
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|[solana-labs/chatgpt-plugin](https://github.com/solana-labs/chatgpt-plugin) | 147 |
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|[aurelio-labs/arxiv-bot](https://github.com/aurelio-labs/arxiv-bot) | 147 |
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|[Jaseci-Labs/jaseci](https://github.com/Jaseci-Labs/jaseci) | 146 |
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@ -7,7 +7,7 @@
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### Introduction to LangChain with Harrison Chase, creator of LangChain
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- [Building the Future with LLMs, `LangChain`, & `Pinecone`](https://youtu.be/nMniwlGyX-c) by [Pinecone](https://www.youtube.com/@pinecone-io)
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- [LangChain and Weaviate with Harrison Chase and Bob van Luijt - Weaviate Podcast #36](https://youtu.be/lhby7Ql7hbk) by [Weaviate • Vector Database](https://www.youtube.com/@Weaviate)
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- [LangChain Demo + Q&A with Harrison Chase](https://youtu.be/zaYTXQFR0_s?t=788) by [Full Stack Deep Learning](https://www.youtube.com/@FullStackDeepLearning)
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- [LangChain Demo + Q&A with Harrison Chase](https://youtu.be/zaYTXQFR0_s?t=788) by [Full Stack Deep Learning](https://www.youtube.com/@The_Full_Stack)
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- [LangChain Agents: Build Personal Assistants For Your Data (Q&A with Harrison Chase and Mayo Oshin)](https://youtu.be/gVkF8cwfBLI) by [Chat with data](https://www.youtube.com/@chatwithdata)
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## Videos (sorted by views)
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@ -15,8 +15,8 @@
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- [Using `ChatGPT` with YOUR OWN Data. This is magical. (LangChain OpenAI API)](https://youtu.be/9AXP7tCI9PI) by [TechLead](https://www.youtube.com/@TechLead)
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- [First look - `ChatGPT` + `WolframAlpha` (`GPT-3.5` and Wolfram|Alpha via LangChain by James Weaver)](https://youtu.be/wYGbY811oMo) by [Dr Alan D. Thompson](https://www.youtube.com/@DrAlanDThompson)
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- [LangChain explained - The hottest new Python framework](https://youtu.be/RoR4XJw8wIc) by [AssemblyAI](https://www.youtube.com/@AssemblyAI)
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- [Chatbot with INFINITE MEMORY using `OpenAI` & `Pinecone` - `GPT-3`, `Embeddings`, `ADA`, `Vector DB`, `Semantic`](https://youtu.be/2xNzB7xq8nk) by [David Shapiro ~ AI](https://www.youtube.com/@DavidShapiroAutomator)
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- [LangChain for LLMs is... basically just an Ansible playbook](https://youtu.be/X51N9C-OhlE) by [David Shapiro ~ AI](https://www.youtube.com/@DavidShapiroAutomator)
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- [Chatbot with INFINITE MEMORY using `OpenAI` & `Pinecone` - `GPT-3`, `Embeddings`, `ADA`, `Vector DB`, `Semantic`](https://youtu.be/2xNzB7xq8nk) by [David Shapiro ~ AI](https://www.youtube.com/@DaveShap)
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- [LangChain for LLMs is... basically just an Ansible playbook](https://youtu.be/X51N9C-OhlE) by [David Shapiro ~ AI](https://www.youtube.com/@DaveShap)
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- [Build your own LLM Apps with LangChain & `GPT-Index`](https://youtu.be/-75p09zFUJY) by [1littlecoder](https://www.youtube.com/@1littlecoder)
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- [`BabyAGI` - New System of Autonomous AI Agents with LangChain](https://youtu.be/lg3kJvf1kXo) by [1littlecoder](https://www.youtube.com/@1littlecoder)
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- [Run `BabyAGI` with Langchain Agents (with Python Code)](https://youtu.be/WosPGHPObx8) by [1littlecoder](https://www.youtube.com/@1littlecoder)
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@ -37,15 +37,15 @@
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- [Building AI LLM Apps with LangChain (and more?) - LIVE STREAM](https://www.youtube.com/live/M-2Cj_2fzWI?feature=share) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
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- [`ChatGPT` with any `YouTube` video using langchain and `chromadb`](https://youtu.be/TQZfB2bzVwU) by [echohive](https://www.youtube.com/@echohive)
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- [How to Talk to a `PDF` using LangChain and `ChatGPT`](https://youtu.be/v2i1YDtrIwk) by [Automata Learning Lab](https://www.youtube.com/@automatalearninglab)
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- [Langchain Document Loaders Part 1: Unstructured Files](https://youtu.be/O5C0wfsen98) by [Merk](https://www.youtube.com/@merksworld)
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- [LangChain - Prompt Templates (what all the best prompt engineers use)](https://youtu.be/1aRu8b0XNOQ) by [Nick Daigler](https://www.youtube.com/@nick_daigs)
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- [Langchain Document Loaders Part 1: Unstructured Files](https://youtu.be/O5C0wfsen98) by [Merk](https://www.youtube.com/@heymichaeldaigler)
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- [LangChain - Prompt Templates (what all the best prompt engineers use)](https://youtu.be/1aRu8b0XNOQ) by [Nick Daigler](https://www.youtube.com/@nickdaigler)
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- [LangChain. Crear aplicaciones Python impulsadas por GPT](https://youtu.be/DkW_rDndts8) by [Jesús Conde](https://www.youtube.com/@0utKast)
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- [Easiest Way to Use GPT In Your Products | LangChain Basics Tutorial](https://youtu.be/fLy0VenZyGc) by [Rachel Woods](https://www.youtube.com/@therachelwoods)
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- [`BabyAGI` + `GPT-4` Langchain Agent with Internet Access](https://youtu.be/wx1z_hs5P6E) by [tylerwhatsgood](https://www.youtube.com/@tylerwhatsgood)
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- [Learning LLM Agents. How does it actually work? LangChain, AutoGPT & OpenAI](https://youtu.be/mb_YAABSplk) by [Arnoldas Kemeklis](https://www.youtube.com/@processusAI)
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- [Get Started with LangChain in `Node.js`](https://youtu.be/Wxx1KUWJFv4) by [Developers Digest](https://www.youtube.com/@DevelopersDigest)
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- [LangChain + `OpenAI` tutorial: Building a Q&A system w/ own text data](https://youtu.be/DYOU_Z0hAwo) by [Samuel Chan](https://www.youtube.com/@SamuelChan)
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- [Langchain + `Zapier` Agent](https://youtu.be/yribLAb-pxA) by [Merk](https://www.youtube.com/@merksworld)
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- [Langchain + `Zapier` Agent](https://youtu.be/yribLAb-pxA) by [Merk](https://www.youtube.com/@heymichaeldaigler)
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- [Connecting the Internet with `ChatGPT` (LLMs) using Langchain And Answers Your Questions](https://youtu.be/9Y0TBC63yZg) by [Kamalraj M M](https://www.youtube.com/@insightbuilder)
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- [Build More Powerful LLM Applications for Business’s with LangChain (Beginners Guide)](https://youtu.be/sp3-WLKEcBg) by[ No Code Blackbox](https://www.youtube.com/@nocodeblackbox)
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- [LangFlow LLM Agent Demo for 🦜🔗LangChain](https://youtu.be/zJxDHaWt-6o) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA)
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@ -82,7 +82,7 @@
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- [Build a LangChain-based Semantic PDF Search App with No-Code Tools Bubble and Flowise](https://youtu.be/s33v5cIeqA4) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
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- [LangChain Memory Tutorial | Building a ChatGPT Clone in Python](https://youtu.be/Cwq91cj2Pnc) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
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- [ChatGPT For Your DATA | Chat with Multiple Documents Using LangChain](https://youtu.be/TeDgIDqQmzs) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
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- [`Llama Index`: Chat with Documentation using URL Loader](https://youtu.be/XJRoDEctAwA) by [Merk](https://www.youtube.com/@merksworld)
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- [`Llama Index`: Chat with Documentation using URL Loader](https://youtu.be/XJRoDEctAwA) by [Merk](https://www.youtube.com/@heymichaeldaigler)
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- [Using OpenAI, LangChain, and `Gradio` to Build Custom GenAI Applications](https://youtu.be/1MsmqMg3yUc) by [David Hundley](https://www.youtube.com/@dkhundley)
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- [LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF](https://youtu.be/FuqdVNB_8c0)
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- [Build AI chatbot with custom knowledge base using OpenAI API and GPT Index](https://youtu.be/vDZAZuaXf48) by [Irina Nik](https://www.youtube.com/@irina_nik)
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@ -93,7 +93,7 @@
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- [Build a Custom Chatbot with OpenAI: `GPT-Index` & LangChain | Step-by-Step Tutorial](https://youtu.be/FIDv6nc4CgU) by [Fabrikod](https://www.youtube.com/@fabrikod)
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- [`Flowise` is an open-source no-code UI visual tool to build 🦜🔗LangChain applications](https://youtu.be/CovAPtQPU0k) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA)
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- [LangChain & GPT 4 For Data Analysis: The `Pandas` Dataframe Agent](https://youtu.be/rFQ5Kmkd4jc) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
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- [`GirlfriendGPT` - AI girlfriend with LangChain](https://youtu.be/LiN3D1QZGQw) by [Toolfinder AI](https://www.youtube.com/@toolfinderai)
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- [`GirlfriendGPT` - AI girlfriend with LangChain](https://youtu.be/LiN3D1QZGQw) by [Girlfriend GPT](https://www.youtube.com/@girlfriendGPT)
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- [How to build with Langchain 10x easier | ⛓️ LangFlow & `Flowise`](https://youtu.be/Ya1oGL7ZTvU) by [AI Jason](https://www.youtube.com/@AIJasonZ)
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- [Getting Started With LangChain In 20 Minutes- Build Celebrity Search Application](https://youtu.be/_FpT1cwcSLg) by [Krish Naik](https://www.youtube.com/@krishnaik06)
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- ⛓ [Vector Embeddings Tutorial – Code Your Own AI Assistant with `GPT-4 API` + LangChain + NLP](https://youtu.be/yfHHvmaMkcA?si=5uJhxoh2tvdnOXok) by [FreeCodeCamp.org](https://www.youtube.com/@freecodecamp)
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@ -109,7 +109,7 @@
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- ⛓ [PyData Heidelberg #11 - TimeSeries Forecasting & LLM Langchain](https://www.youtube.com/live/Glbwb5Hxu18?si=PIEY8Raq_C9PCHuW) by [PyData](https://www.youtube.com/@PyDataTV)
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- ⛓ [Prompt Engineering in Web Development | Using LangChain and Templates with OpenAI](https://youtu.be/pK6WzlTOlYw?si=fkcDQsBG2h-DM8uQ) by [Akamai Developer
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](https://www.youtube.com/@AkamaiDeveloper)
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- ⛓ [Retrieval-Augmented Generation (RAG) using LangChain and `Pinecone` - The RAG Special Episode](https://youtu.be/J_tCD_J6w3s?si=60Mnr5VD9UED9bGG) by [Generative AI and Data Science On AWS](https://www.youtube.com/@GenerativeAIDataScienceOnAWS)
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- ⛓ [Retrieval-Augmented Generation (RAG) using LangChain and `Pinecone` - The RAG Special Episode](https://youtu.be/J_tCD_J6w3s?si=60Mnr5VD9UED9bGG) by [Generative AI and Data Science On AWS](https://www.youtube.com/@GenerativeAIOnAWS)
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- ⛓ [`LLAMA2 70b-chat` Multiple Documents Chatbot with Langchain & Streamlit |All OPEN SOURCE|Replicate API](https://youtu.be/vhghB81vViM?si=dszzJnArMeac7lyc) by [DataInsightEdge](https://www.youtube.com/@DataInsightEdge01)
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- ⛓ [Chatting with 44K Fashion Products: LangChain Opportunities and Pitfalls](https://youtu.be/Zudgske0F_s?si=8HSshHoEhh0PemJA) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
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- ⛓ [Structured Data Extraction from `ChatGPT` with LangChain](https://youtu.be/q1lYg8JISpQ?si=0HctzOHYZvq62sve) by [MG](https://www.youtube.com/@MG_cafe)
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@ -17,7 +17,7 @@ Here's a summary of the key methods and properties of a comparison evaluator:
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- `requires_reference`: This property specifies whether this evaluator requires a reference label.
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:::note LangSmith Support
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The [run_on_dataset](https://api.python.langchain.com/en/latest/api_reference.html#module-langchain.smith) evaluation method is designed to evaluate only a single model at a time, and thus, doesn't support these evaluators.
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The [run_on_dataset](https://api.python.langchain.com/en/latest/langchain_api_reference.html#module-langchain.smith) evaluation method is designed to evaluate only a single model at a time, and thus, doesn't support these evaluators.
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:::
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Detailed information about creating custom evaluators and the available built-in comparison evaluators is provided in the following sections.
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@ -37,6 +37,6 @@ Check out the docs for examples and leaderboard information.
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## Reference Docs
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For detailed information on the available evaluators, including how to instantiate, configure, and customize them, check out the [reference documentation](https://api.python.langchain.com/en/latest/api_reference.html#module-langchain.evaluation) directly.
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For detailed information on the available evaluators, including how to instantiate, configure, and customize them, check out the [reference documentation](https://api.python.langchain.com/en/latest/langchain_api_reference.html#module-langchain.evaluation) directly.
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<DocCardList />
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@ -88,11 +88,6 @@ constitutional_chain.run(question="How can I steal kittens?")
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## Unified Objective
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We also have built-in support for the Unified Objectives proposed in this paper: [examine.dev/docs/Unified_objectives.pdf](https://examine.dev/docs/Unified_objectives.pdf)
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Some of these are useful for the same idea of correcting ethical issues.
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```python
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principles = ConstitutionalChain.get_principles(["uo-ethics-1"])
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constitutional_chain = ConstitutionalChain.from_llm(
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@ -90,7 +90,7 @@ from langchain_community.embeddings import HuggingFaceInstructEmbeddings
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#### HuggingFaceBgeEmbeddings
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>[BGE models on the HuggingFace](https://huggingface.co/BAAI/bge-large-en) are [the best open-source embedding models](https://huggingface.co/spaces/mteb/leaderboard).
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>BGE model is created by the [Beijing Academy of Artificial Intelligence (BAAI)](https://www.baai.ac.cn/english.html). `BAAI` is a private non-profit organization engaged in AI research and development.
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>BGE model is created by the [Beijing Academy of Artificial Intelligence (BAAI)](https://en.wikipedia.org/wiki/Beijing_Academy_of_Artificial_Intelligence). `BAAI` is a private non-profit organization engaged in AI research and development.
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See a [usage example](/docs/integrations/text_embedding/bge_huggingface).
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@ -5,10 +5,7 @@
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## Installation and Setup
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Get your API_KEY from [Neural Internet](https://api.neuralinternet.ai).
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You can [analyze API_KEYS](https://api.neuralinternet.ai/api-keys)
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and [logs of your usage](https://api.neuralinternet.ai/logs).
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Get your API_KEY from [Neural Internet](https://neuralinternet.ai/).
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## LLMs
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@ -66,23 +66,23 @@ patch(langchain=True)
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# patch(langchain=True, openai=True)patch_all
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```
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See the [APM Python library documentation][https://ddtrace.readthedocs.io/en/stable/installation_quickstart.html] for more advanced usage.
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See the [APM Python library documentation](https://ddtrace.readthedocs.io/en/stable/installation_quickstart.html) for more advanced usage.
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## Configuration
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See the [APM Python library documentation][https://ddtrace.readthedocs.io/en/stable/integrations.html#langchain] for all the available configuration options.
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See the [APM Python library documentation](https://ddtrace.readthedocs.io/en/stable/integrations.html#langchain) for all the available configuration options.
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### Log Prompt & Completion Sampling
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To enable log prompt and completion sampling, set the `DD_LANGCHAIN_LOGS_ENABLED=1` environment variable. By default, 10% of traced requests will emit logs containing the prompts and completions.
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To adjust the log sample rate, see the [APM library documentation][https://ddtrace.readthedocs.io/en/stable/integrations.html#langchain].
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To adjust the log sample rate, see the [APM library documentation](https://ddtrace.readthedocs.io/en/stable/integrations.html#langchain).
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**Note**: Logs submission requires `DD_API_KEY` to be specified when running `ddtrace-run`.
|
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||||
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||||
## Troubleshooting
|
||||
|
||||
Need help? Create an issue on [ddtrace](https://github.com/DataDog/dd-trace-py) or contact [Datadog support][https://docs.datadoghq.com/help/].
|
||||
Need help? Create an issue on [ddtrace](https://github.com/DataDog/dd-trace-py) or contact [Datadog support](https://docs.datadoghq.com/help/).
|
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@ -14,7 +14,7 @@ The purpose of this notebook is to demonstrate the integration of a `FlyteCallba
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## Flyte Tasks
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A Flyte [task](https://docs.flyte.org/projects/cookbook/en/latest/auto/core/flyte_basics/task.html) serves as the foundational building block of Flyte.
|
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A Flyte [task](https://docs.flyte.org/en/latest/user_guide/basics/tasks.html) serves as the foundational building block of Flyte.
|
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To execute LangChain experiments, you need to write Flyte tasks that define the specific steps and operations involved.
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NOTE: The [getting started guide](https://docs.flyte.org/projects/cookbook/en/latest/index.html) offers detailed, step-by-step instructions on installing Flyte locally and running your initial Flyte pipeline.
|
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@ -46,9 +46,9 @@ os.environ["SERPAPI_API_KEY"] = "<your_serp_api_key>"
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Replace `<your_openai_api_key>` and `<your_serp_api_key>` with your respective API keys obtained from OpenAI and Serp API.
|
||||
|
||||
To guarantee reproducibility of your pipelines, Flyte tasks are containerized.
|
||||
Each Flyte task must be associated with an image, which can either be shared across the entire Flyte [workflow](https://docs.flyte.org/projects/cookbook/en/latest/auto/core/flyte_basics/basic_workflow.html) or provided separately for each task.
|
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Each Flyte task must be associated with an image, which can either be shared across the entire Flyte [workflow](https://docs.flyte.org/en/latest/user_guide/basics/workflows.html) or provided separately for each task.
|
||||
|
||||
To streamline the process of supplying the required dependencies for each Flyte task, you can initialize an [`ImageSpec`](https://docs.flyte.org/projects/cookbook/en/latest/auto/core/image_spec/image_spec.html) object.
|
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To streamline the process of supplying the required dependencies for each Flyte task, you can initialize an [`ImageSpec`](https://docs.flyte.org/en/latest/user_guide/customizing_dependencies/imagespec.html) object.
|
||||
This approach automatically triggers a Docker build, alleviating the need for users to manually create a Docker image.
|
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```python
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@ -16,7 +16,7 @@ With your LangChain environment you can just add the following parameter.
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export OPENAI_API_BASE="https://oai.hconeai.com/v1"
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```
|
||||
|
||||
Now head over to [helicone.ai](https://helicone.ai/onboarding?step=2) to create your account, and add your OpenAI API key within our dashboard to view your logs.
|
||||
Now head over to [helicone.ai](https://www.helicone.ai/signup) to create your account, and add your OpenAI API key within our dashboard to view your logs.
|
||||
|
||||
|
||||
|
||||
|
||||
@ -35,7 +35,7 @@ llm = ChatOpenAI(model_name="gpt-3.5-turbo", callbacks=[log10_callback])
|
||||
|
||||
[Log10 + Langchain + Logs docs](https://github.com/log10-io/log10/blob/main/logging.md#langchain-logger)
|
||||
|
||||
[More details + screenshots](https://log10.io/docs/logs) including instructions for self-hosting logs
|
||||
[More details + screenshots](https://log10.io/docs/observability/logs) including instructions for self-hosting logs
|
||||
|
||||
## How to use tags with Log10
|
||||
|
||||
@ -99,6 +99,6 @@ with log10_session(tags=["foo", "bar"]):
|
||||
|
||||
## How to debug Langchain calls
|
||||
|
||||
[Example of debugging](https://log10.io/docs/prompt_chain_debugging)
|
||||
[Example of debugging](https://log10.io/docs/observability/prompt_chain_debugging)
|
||||
|
||||
[More Langchain examples](https://github.com/log10-io/log10/tree/main/examples#langchain)
|
||||
|
||||
@ -51,7 +51,7 @@ mlflow deployments start-server --config-path /path/to/config.yaml
|
||||
> This module exports multivariate LangChain models in the langchain flavor and univariate LangChain
|
||||
> models in the pyfunc flavor.
|
||||
|
||||
See the [API documentation and examples](https://www.mlflow.org/docs/latest/python_api/mlflow.langchain) for more information.
|
||||
See the [API documentation and examples](https://www.mlflow.org/docs/latest/llms/langchain/index.html) for more information.
|
||||
|
||||
## Completions Example
|
||||
|
||||
|
||||
@ -6,10 +6,9 @@ MLflow AI Gateway has been deprecated. Please use [MLflow Deployments for LLMs](
|
||||
|
||||
:::
|
||||
|
||||
>[The MLflow AI Gateway](https://www.mlflow.org/docs/latest/gateway/index) service is a powerful tool designed to streamline the usage and management of various large
|
||||
>[The MLflow AI Gateway](https://www.mlflow.org/docs/latest/index.html) service is a powerful tool designed to streamline the usage and management of various large
|
||||
> language model (LLM) providers, such as OpenAI and Anthropic, within an organization. It offers a high-level interface
|
||||
> that simplifies the interaction with these services by providing a unified endpoint to handle specific LLM related requests.
|
||||
> See [the MLflow AI Gateway documentation](https://mlflow.org/docs/latest/gateway/index) for more details.
|
||||
|
||||
## Installation and Setup
|
||||
|
||||
@ -58,7 +57,7 @@ mlflow gateway start --config-path /path/to/config.yaml
|
||||
> This module exports multivariate LangChain models in the langchain flavor and univariate LangChain
|
||||
> models in the pyfunc flavor.
|
||||
|
||||
See the [API documentation and examples](https://www.mlflow.org/docs/latest/python_api/mlflow.langchain).
|
||||
See the [API documentation and examples](https://www.mlflow.org/docs/latest/python_api/mlflow.langchain.html?highlight=langchain#module-mlflow.langchain).
|
||||
|
||||
|
||||
|
||||
|
||||
@ -11,7 +11,7 @@ This page covers how to use the [Momento](https://gomomento.com) ecosystem withi
|
||||
|
||||
## Installation and Setup
|
||||
|
||||
- Sign up for a free account [here](https://console.momentohq.com) to get an API key
|
||||
- Sign up for a free account [here](https://console.gomomento.com/) to get an API key
|
||||
- Install the Momento Python SDK with `pip install momento`
|
||||
|
||||
## Cache
|
||||
|
||||
@ -1,5 +1,13 @@
|
||||
---
|
||||
sidebar_class_name: hidden
|
||||
---
|
||||
|
||||
# Psychic
|
||||
|
||||
:::warning
|
||||
This provider is no longer maintained, and may not work. Use with caution.
|
||||
:::
|
||||
|
||||
>[Psychic](https://www.psychic.dev/) is a platform for integrating with SaaS tools like `Notion`, `Zendesk`,
|
||||
> `Confluence`, and `Google Drive` via OAuth and syncing documents from these applications to your SQL or vector
|
||||
> database. You can think of it like Plaid for unstructured data.
|
||||
|
||||
@ -7,7 +7,6 @@ It is broken into two parts: installation and setup, and then references to spec
|
||||
|
||||
- Get an [Nebula API Key](https://info.symbl.ai/Nebula_Private_Beta.html) and set as environment variable `NEBULA_API_KEY`
|
||||
- Please see the [Nebula documentation](https://docs.symbl.ai/docs/nebula-llm) for more details.
|
||||
- No time? Visit the [Nebula Quickstart Guide](https://docs.symbl.ai/docs/nebula-quickstart).
|
||||
|
||||
### LLM
|
||||
|
||||
|
||||
@ -8,7 +8,7 @@ TruLens is an [open-source](https://github.com/truera/trulens) package that prov
|
||||
|
||||
## Quick start
|
||||
|
||||
Once you've created your LLM chain, you can use TruLens for evaluation and tracking. TruLens has a number of [out-of-the-box Feedback Functions](https://www.trulens.org/trulens_eval/feedback_functions/), and is also an extensible framework for LLM evaluation.
|
||||
Once you've created your LLM chain, you can use TruLens for evaluation and tracking. TruLens has a number of [out-of-the-box Feedback Functions](https://www.trulens.org/trulens_eval/evaluation/feedback_functions/), and is also an extensible framework for LLM evaluation.
|
||||
|
||||
```python
|
||||
# create a feedback function
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
# Typesense
|
||||
|
||||
> [Typesense](https://typesense.org) is an open-source, in-memory search engine, that you can either
|
||||
> [self-host](https://typesense.org/docs/guide/install-typesense#option-2-local-machine-self-hosting) or run
|
||||
> [self-host](https://typesense.org/docs/guide/install-typesense.html#option-2-local-machine-self-hosting) or run
|
||||
> on [Typesense Cloud](https://cloud.typesense.org/).
|
||||
> `Typesense` focuses on performance by storing the entire index in RAM (with a backup on disk) and also
|
||||
> focuses on providing an out-of-the-box developer experience by simplifying available options and setting good defaults.
|
||||
|
||||
@ -28,7 +28,7 @@ simply run `pip install unstructured` and use `UnstructuredAPIFileLoader` or
|
||||
|
||||
|
||||
The Unstructured API requires API keys to make requests.
|
||||
You can generate a free API key [here](https://www.unstructured.io/api-key) and start using it today!
|
||||
You can request an API key [here](https://unstructured.io/api-key-hosted) and start using it today!
|
||||
Checkout the README [here](https://github.com/Unstructured-IO/unstructured-api) here to get started making API calls.
|
||||
We'd love to hear your feedback, let us know how it goes in our [community slack](https://join.slack.com/t/unstructuredw-kbe4326/shared_invite/zt-1x7cgo0pg-PTptXWylzPQF9xZolzCnwQ).
|
||||
And stay tuned for improvements to both quality and performance!
|
||||
|
||||
@ -8,7 +8,7 @@
|
||||
"# BGE on Hugging Face\n",
|
||||
"\n",
|
||||
">[BGE models on the HuggingFace](https://huggingface.co/BAAI/bge-large-en) are [the best open-source embedding models](https://huggingface.co/spaces/mteb/leaderboard).\n",
|
||||
">BGE model is created by the [Beijing Academy of Artificial Intelligence (BAAI)](https://www.baai.ac.cn/english.html). `BAAI` is a private non-profit organization engaged in AI research and development.\n",
|
||||
">BGE model is created by the [Beijing Academy of Artificial Intelligence (BAAI)](https://en.wikipedia.org/wiki/Beijing_Academy_of_Artificial_Intelligence). `BAAI` is a private non-profit organization engaged in AI research and development.\n",
|
||||
"\n",
|
||||
"This notebook shows how to use `BGE Embeddings` through `Hugging Face`"
|
||||
]
|
||||
|
||||
@ -43,7 +43,7 @@ LangChain offers many different types of text splitters. These all live in the `
|
||||
| Code | Code (Python, JS) specific characters | | Splits text based on characters specific to coding languages. 15 different languages are available to choose from. |
|
||||
| Token | Tokens | | Splits text on tokens. There exist a few different ways to measure tokens. |
|
||||
| Character | A user defined character | | Splits text based on a user defined character. One of the simpler methods. |
|
||||
| [Experimental] Semantic Chunker | Sentences | | First splits on sentences. Then combines ones next to each other if they are semantically similar enough. Taken from [Greg Kamradt](https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/5_Levels_Of_Text_Splitting.ipynb) |
|
||||
| [Experimental] Semantic Chunker | Sentences | | First splits on sentences. Then combines ones next to each other if they are semantically similar enough. Taken from [Greg Kamradt](https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/tutorials/LevelsOfTextSplitting/5_Levels_Of_Text_Splitting.ipynb) |
|
||||
| [AI21 Semantic Text Splitter](/docs/integrations/document_transformers/ai21_semantic_text_splitter) | Semantics | ✅ | Identifies distinct topics that form coherent pieces of text and splits along those. |
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user