diff --git a/.github/workflows/check-broken-links.yml b/.github/workflows/check-broken-links.yml index 62eb34882bc..130a5097ec5 100644 --- a/.github/workflows/check-broken-links.yml +++ b/.github/workflows/check-broken-links.yml @@ -22,7 +22,3 @@ jobs: - name: Check broken links run: yarn check-broken-links working-directory: ./docs - - name: Check broken links for .mdx files - uses: gaurav-nelson/github-action-markdown-link-check@v1 - with: - file-extension: '.mdx' diff --git a/docs/docs/additional_resources/dependents.mdx b/docs/docs/additional_resources/dependents.mdx index b19e05a85a3..a09df5027ec 100644 --- a/docs/docs/additional_resources/dependents.mdx +++ b/docs/docs/additional_resources/dependents.mdx @@ -241,7 +241,6 @@ Dependents stats for `langchain-ai/langchain` |[alejandro-ao/langchain-ask-pdf](https://github.com/alejandro-ao/langchain-ask-pdf) | 514 | |[sajjadium/ctf-archives](https://github.com/sajjadium/ctf-archives) | 507 | |[continuum-llms/chatgpt-memory](https://github.com/continuum-llms/chatgpt-memory) | 502 | -|[llmOS/opencopilot](https://github.com/llmOS/opencopilot) | 495 | |[steamship-core/steamship-langchain](https://github.com/steamship-core/steamship-langchain) | 494 | |[mpaepper/content-chatbot](https://github.com/mpaepper/content-chatbot) | 493 | |[langchain-ai/langchain-aiplugin](https://github.com/langchain-ai/langchain-aiplugin) | 492 | @@ -455,7 +454,6 @@ Dependents stats for `langchain-ai/langchain` |[Teahouse-Studios/akari-bot](https://github.com/Teahouse-Studios/akari-bot) | 149 | |[realminchoi/babyagi-ui](https://github.com/realminchoi/babyagi-ui) | 148 | |[ssheng/BentoChain](https://github.com/ssheng/BentoChain) | 148 | -|[lmstudio-ai/examples](https://github.com/lmstudio-ai/examples) | 147 | |[solana-labs/chatgpt-plugin](https://github.com/solana-labs/chatgpt-plugin) | 147 | |[aurelio-labs/arxiv-bot](https://github.com/aurelio-labs/arxiv-bot) | 147 | |[Jaseci-Labs/jaseci](https://github.com/Jaseci-Labs/jaseci) | 146 | diff --git a/docs/docs/additional_resources/youtube.mdx b/docs/docs/additional_resources/youtube.mdx index 807f4521127..1fde4c30208 100644 --- a/docs/docs/additional_resources/youtube.mdx +++ b/docs/docs/additional_resources/youtube.mdx @@ -7,7 +7,7 @@ ### Introduction to LangChain with Harrison Chase, creator of LangChain - [Building the Future with LLMs, `LangChain`, & `Pinecone`](https://youtu.be/nMniwlGyX-c) by [Pinecone](https://www.youtube.com/@pinecone-io) - [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) -- [LangChain Demo + Q&A with Harrison Chase](https://youtu.be/zaYTXQFR0_s?t=788) by [Full Stack Deep Learning](https://www.youtube.com/@FullStackDeepLearning) +- [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) - [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) ## Videos (sorted by views) @@ -15,8 +15,8 @@ - [Using `ChatGPT` with YOUR OWN Data. This is magical. (LangChain OpenAI API)](https://youtu.be/9AXP7tCI9PI) by [TechLead](https://www.youtube.com/@TechLead) - [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) - [LangChain explained - The hottest new Python framework](https://youtu.be/RoR4XJw8wIc) by [AssemblyAI](https://www.youtube.com/@AssemblyAI) -- [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) -- [LangChain for LLMs is... basically just an Ansible playbook](https://youtu.be/X51N9C-OhlE) by [David Shapiro ~ AI](https://www.youtube.com/@DavidShapiroAutomator) +- [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) +- [LangChain for LLMs is... basically just an Ansible playbook](https://youtu.be/X51N9C-OhlE) by [David Shapiro ~ AI](https://www.youtube.com/@DaveShap) - [Build your own LLM Apps with LangChain & `GPT-Index`](https://youtu.be/-75p09zFUJY) by [1littlecoder](https://www.youtube.com/@1littlecoder) - [`BabyAGI` - New System of Autonomous AI Agents with LangChain](https://youtu.be/lg3kJvf1kXo) by [1littlecoder](https://www.youtube.com/@1littlecoder) - [Run `BabyAGI` with Langchain Agents (with Python Code)](https://youtu.be/WosPGHPObx8) by [1littlecoder](https://www.youtube.com/@1littlecoder) @@ -37,15 +37,15 @@ - [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) - [`ChatGPT` with any `YouTube` video using langchain and `chromadb`](https://youtu.be/TQZfB2bzVwU) by [echohive](https://www.youtube.com/@echohive) - [How to Talk to a `PDF` using LangChain and `ChatGPT`](https://youtu.be/v2i1YDtrIwk) by [Automata Learning Lab](https://www.youtube.com/@automatalearninglab) -- [Langchain Document Loaders Part 1: Unstructured Files](https://youtu.be/O5C0wfsen98) by [Merk](https://www.youtube.com/@merksworld) -- [LangChain - Prompt Templates (what all the best prompt engineers use)](https://youtu.be/1aRu8b0XNOQ) by [Nick Daigler](https://www.youtube.com/@nick_daigs) +- [Langchain Document Loaders Part 1: Unstructured Files](https://youtu.be/O5C0wfsen98) by [Merk](https://www.youtube.com/@heymichaeldaigler) +- [LangChain - Prompt Templates (what all the best prompt engineers use)](https://youtu.be/1aRu8b0XNOQ) by [Nick Daigler](https://www.youtube.com/@nickdaigler) - [LangChain. Crear aplicaciones Python impulsadas por GPT](https://youtu.be/DkW_rDndts8) by [Jesús Conde](https://www.youtube.com/@0utKast) - [Easiest Way to Use GPT In Your Products | LangChain Basics Tutorial](https://youtu.be/fLy0VenZyGc) by [Rachel Woods](https://www.youtube.com/@therachelwoods) - [`BabyAGI` + `GPT-4` Langchain Agent with Internet Access](https://youtu.be/wx1z_hs5P6E) by [tylerwhatsgood](https://www.youtube.com/@tylerwhatsgood) - [Learning LLM Agents. How does it actually work? LangChain, AutoGPT & OpenAI](https://youtu.be/mb_YAABSplk) by [Arnoldas Kemeklis](https://www.youtube.com/@processusAI) - [Get Started with LangChain in `Node.js`](https://youtu.be/Wxx1KUWJFv4) by [Developers Digest](https://www.youtube.com/@DevelopersDigest) - [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) -- [Langchain + `Zapier` Agent](https://youtu.be/yribLAb-pxA) by [Merk](https://www.youtube.com/@merksworld) +- [Langchain + `Zapier` Agent](https://youtu.be/yribLAb-pxA) by [Merk](https://www.youtube.com/@heymichaeldaigler) - [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) - [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) - [LangFlow LLM Agent Demo for 🦜🔗LangChain](https://youtu.be/zJxDHaWt-6o) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA) @@ -82,7 +82,7 @@ - [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) - [LangChain Memory Tutorial | Building a ChatGPT Clone in Python](https://youtu.be/Cwq91cj2Pnc) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao) - [ChatGPT For Your DATA | Chat with Multiple Documents Using LangChain](https://youtu.be/TeDgIDqQmzs) by [Data Science Basics](https://www.youtube.com/@datasciencebasics) -- [`Llama Index`: Chat with Documentation using URL Loader](https://youtu.be/XJRoDEctAwA) by [Merk](https://www.youtube.com/@merksworld) +- [`Llama Index`: Chat with Documentation using URL Loader](https://youtu.be/XJRoDEctAwA) by [Merk](https://www.youtube.com/@heymichaeldaigler) - [Using OpenAI, LangChain, and `Gradio` to Build Custom GenAI Applications](https://youtu.be/1MsmqMg3yUc) by [David Hundley](https://www.youtube.com/@dkhundley) - [LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF](https://youtu.be/FuqdVNB_8c0) - [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) @@ -93,7 +93,7 @@ - [Build a Custom Chatbot with OpenAI: `GPT-Index` & LangChain | Step-by-Step Tutorial](https://youtu.be/FIDv6nc4CgU) by [Fabrikod](https://www.youtube.com/@fabrikod) - [`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) - [LangChain & GPT 4 For Data Analysis: The `Pandas` Dataframe Agent](https://youtu.be/rFQ5Kmkd4jc) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics) -- [`GirlfriendGPT` - AI girlfriend with LangChain](https://youtu.be/LiN3D1QZGQw) by [Toolfinder AI](https://www.youtube.com/@toolfinderai) +- [`GirlfriendGPT` - AI girlfriend with LangChain](https://youtu.be/LiN3D1QZGQw) by [Girlfriend GPT](https://www.youtube.com/@girlfriendGPT) - [How to build with Langchain 10x easier | ⛓️ LangFlow & `Flowise`](https://youtu.be/Ya1oGL7ZTvU) by [AI Jason](https://www.youtube.com/@AIJasonZ) - [Getting Started With LangChain In 20 Minutes- Build Celebrity Search Application](https://youtu.be/_FpT1cwcSLg) by [Krish Naik](https://www.youtube.com/@krishnaik06) - ⛓ [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) @@ -109,7 +109,7 @@ - ⛓ [PyData Heidelberg #11 - TimeSeries Forecasting & LLM Langchain](https://www.youtube.com/live/Glbwb5Hxu18?si=PIEY8Raq_C9PCHuW) by [PyData](https://www.youtube.com/@PyDataTV) - ⛓ [Prompt Engineering in Web Development | Using LangChain and Templates with OpenAI](https://youtu.be/pK6WzlTOlYw?si=fkcDQsBG2h-DM8uQ) by [Akamai Developer ](https://www.youtube.com/@AkamaiDeveloper) -- ⛓ [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) +- ⛓ [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) - ⛓ [`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) - ⛓ [Chatting with 44K Fashion Products: LangChain Opportunities and Pitfalls](https://youtu.be/Zudgske0F_s?si=8HSshHoEhh0PemJA) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics) - ⛓ [Structured Data Extraction from `ChatGPT` with LangChain](https://youtu.be/q1lYg8JISpQ?si=0HctzOHYZvq62sve) by [MG](https://www.youtube.com/@MG_cafe) diff --git a/docs/docs/guides/evaluation/comparison/index.mdx b/docs/docs/guides/evaluation/comparison/index.mdx index 8f956f6068d..e5703725da0 100644 --- a/docs/docs/guides/evaluation/comparison/index.mdx +++ b/docs/docs/guides/evaluation/comparison/index.mdx @@ -17,7 +17,7 @@ Here's a summary of the key methods and properties of a comparison evaluator: - `requires_reference`: This property specifies whether this evaluator requires a reference label. :::note LangSmith Support -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. +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. ::: Detailed information about creating custom evaluators and the available built-in comparison evaluators is provided in the following sections. diff --git a/docs/docs/guides/evaluation/index.mdx b/docs/docs/guides/evaluation/index.mdx index 5415b33e69c..4603a40e537 100644 --- a/docs/docs/guides/evaluation/index.mdx +++ b/docs/docs/guides/evaluation/index.mdx @@ -37,6 +37,6 @@ Check out the docs for examples and leaderboard information. ## Reference Docs -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. +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. diff --git a/docs/docs/guides/safety/constitutional_chain.mdx b/docs/docs/guides/safety/constitutional_chain.mdx index e81c5ca6094..c77380db48c 100644 --- a/docs/docs/guides/safety/constitutional_chain.mdx +++ b/docs/docs/guides/safety/constitutional_chain.mdx @@ -88,11 +88,6 @@ constitutional_chain.run(question="How can I steal kittens?") ## Unified Objective -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) - -Some of these are useful for the same idea of correcting ethical issues. - - ```python principles = ConstitutionalChain.get_principles(["uo-ethics-1"]) constitutional_chain = ConstitutionalChain.from_llm( diff --git a/docs/docs/integrations/platforms/huggingface.mdx b/docs/docs/integrations/platforms/huggingface.mdx index cf6ae39811f..fbd1848105e 100644 --- a/docs/docs/integrations/platforms/huggingface.mdx +++ b/docs/docs/integrations/platforms/huggingface.mdx @@ -90,7 +90,7 @@ from langchain_community.embeddings import HuggingFaceInstructEmbeddings #### HuggingFaceBgeEmbeddings >[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). ->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. +>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. See a [usage example](/docs/integrations/text_embedding/bge_huggingface). diff --git a/docs/docs/integrations/providers/bittensor.mdx b/docs/docs/integrations/providers/bittensor.mdx index 7bc67196782..137069077db 100644 --- a/docs/docs/integrations/providers/bittensor.mdx +++ b/docs/docs/integrations/providers/bittensor.mdx @@ -5,10 +5,7 @@ ## Installation and Setup -Get your API_KEY from [Neural Internet](https://api.neuralinternet.ai). - -You can [analyze API_KEYS](https://api.neuralinternet.ai/api-keys) -and [logs of your usage](https://api.neuralinternet.ai/logs). +Get your API_KEY from [Neural Internet](https://neuralinternet.ai/). ## LLMs diff --git a/docs/docs/integrations/providers/datadog.mdx b/docs/docs/integrations/providers/datadog.mdx index fd25e3d47cd..b854c668759 100644 --- a/docs/docs/integrations/providers/datadog.mdx +++ b/docs/docs/integrations/providers/datadog.mdx @@ -66,23 +66,23 @@ patch(langchain=True) # patch(langchain=True, openai=True)patch_all ``` -See the [APM Python library documentation][https://ddtrace.readthedocs.io/en/stable/installation_quickstart.html] for more advanced usage. +See the [APM Python library documentation](https://ddtrace.readthedocs.io/en/stable/installation_quickstart.html) for more advanced usage. ## Configuration -See the [APM Python library documentation][https://ddtrace.readthedocs.io/en/stable/integrations.html#langchain] for all the available configuration options. +See the [APM Python library documentation](https://ddtrace.readthedocs.io/en/stable/integrations.html#langchain) for all the available configuration options. ### Log Prompt & Completion Sampling 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. -To adjust the log sample rate, see the [APM library documentation][https://ddtrace.readthedocs.io/en/stable/integrations.html#langchain]. +To adjust the log sample rate, see the [APM library documentation](https://ddtrace.readthedocs.io/en/stable/integrations.html#langchain). **Note**: Logs submission requires `DD_API_KEY` to be specified when running `ddtrace-run`. ## 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/). diff --git a/docs/docs/integrations/providers/flyte.mdx b/docs/docs/integrations/providers/flyte.mdx index 1e75bea5c7c..38966d5831a 100644 --- a/docs/docs/integrations/providers/flyte.mdx +++ b/docs/docs/integrations/providers/flyte.mdx @@ -14,7 +14,7 @@ The purpose of this notebook is to demonstrate the integration of a `FlyteCallba ## Flyte Tasks -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. +A Flyte [task](https://docs.flyte.org/en/latest/user_guide/basics/tasks.html) serves as the foundational building block of Flyte. To execute LangChain experiments, you need to write Flyte tasks that define the specific steps and operations involved. 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. @@ -46,9 +46,9 @@ os.environ["SERPAPI_API_KEY"] = "" Replace `` and `` 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. +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. +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. ```python diff --git a/docs/docs/integrations/providers/helicone.mdx b/docs/docs/integrations/providers/helicone.mdx index 456abe121fe..548088d0791 100644 --- a/docs/docs/integrations/providers/helicone.mdx +++ b/docs/docs/integrations/providers/helicone.mdx @@ -16,7 +16,7 @@ With your LangChain environment you can just add the following parameter. export OPENAI_API_BASE="https://oai.hconeai.com/v1" ``` -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. ![Interface for entering and managing OpenAI API keys in the Helicone dashboard.](/img/HeliconeKeys.png "Helicone API Key Input") diff --git a/docs/docs/integrations/providers/log10.mdx b/docs/docs/integrations/providers/log10.mdx index 38ef1fc7630..b9e3c580305 100644 --- a/docs/docs/integrations/providers/log10.mdx +++ b/docs/docs/integrations/providers/log10.mdx @@ -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) diff --git a/docs/docs/integrations/providers/mlflow.mdx b/docs/docs/integrations/providers/mlflow.mdx index 791b976f388..cb4d5aba840 100644 --- a/docs/docs/integrations/providers/mlflow.mdx +++ b/docs/docs/integrations/providers/mlflow.mdx @@ -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 diff --git a/docs/docs/integrations/providers/mlflow_ai_gateway.mdx b/docs/docs/integrations/providers/mlflow_ai_gateway.mdx index a18f4a28e68..fbe59c84650 100644 --- a/docs/docs/integrations/providers/mlflow_ai_gateway.mdx +++ b/docs/docs/integrations/providers/mlflow_ai_gateway.mdx @@ -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). diff --git a/docs/docs/integrations/providers/momento.mdx b/docs/docs/integrations/providers/momento.mdx index 159a0d53e4b..6d399998780 100644 --- a/docs/docs/integrations/providers/momento.mdx +++ b/docs/docs/integrations/providers/momento.mdx @@ -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 diff --git a/docs/docs/integrations/providers/psychic.mdx b/docs/docs/integrations/providers/psychic.mdx index c29fe6e3316..a415f8a5a48 100644 --- a/docs/docs/integrations/providers/psychic.mdx +++ b/docs/docs/integrations/providers/psychic.mdx @@ -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. diff --git a/docs/docs/integrations/providers/symblai_nebula.mdx b/docs/docs/integrations/providers/symblai_nebula.mdx index 57c27f6a249..a302bd81b55 100644 --- a/docs/docs/integrations/providers/symblai_nebula.mdx +++ b/docs/docs/integrations/providers/symblai_nebula.mdx @@ -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 diff --git a/docs/docs/integrations/providers/trulens.mdx b/docs/docs/integrations/providers/trulens.mdx index 97dc0c14a24..31b794b72f9 100644 --- a/docs/docs/integrations/providers/trulens.mdx +++ b/docs/docs/integrations/providers/trulens.mdx @@ -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 diff --git a/docs/docs/integrations/providers/typesense.mdx b/docs/docs/integrations/providers/typesense.mdx index 9036714e529..5bb2b3ca0e4 100644 --- a/docs/docs/integrations/providers/typesense.mdx +++ b/docs/docs/integrations/providers/typesense.mdx @@ -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. diff --git a/docs/docs/integrations/providers/unstructured.mdx b/docs/docs/integrations/providers/unstructured.mdx index 1c0ad91b09a..e23ce3c5029 100644 --- a/docs/docs/integrations/providers/unstructured.mdx +++ b/docs/docs/integrations/providers/unstructured.mdx @@ -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! diff --git a/docs/docs/integrations/text_embedding/bge_huggingface.ipynb b/docs/docs/integrations/text_embedding/bge_huggingface.ipynb index 50ffc161d77..a5032dcfe13 100644 --- a/docs/docs/integrations/text_embedding/bge_huggingface.ipynb +++ b/docs/docs/integrations/text_embedding/bge_huggingface.ipynb @@ -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`" ] diff --git a/docs/docs/modules/data_connection/document_transformers/index.mdx b/docs/docs/modules/data_connection/document_transformers/index.mdx index d350ea7e79e..c6d891a9606 100644 --- a/docs/docs/modules/data_connection/document_transformers/index.mdx +++ b/docs/docs/modules/data_connection/document_transformers/index.mdx @@ -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. |