Fix: Broken links in related_resources.md and README.md

articles/related_resources.md

@@ -6,19 +6,19 @@ People are writing great tools and papers for improving outputs from GPT. Here a
 
 - [Arthur Shield](https://www.arthur.ai/get-started): A paid product for detecting toxicity, hallucination, prompt injection, etc.
 - [Baserun](https://baserun.ai/): A paid product for testing, debugging, and monitoring LLM-based apps
-- [Chainlit](https://docs.chainlit.io/overview): A Python library for making chatbot interfaces.
+- [Chainlit](https://docs.chainlit.io/get-started/overview): A Python library for making chatbot interfaces.
 - [Embedchain](https://github.com/embedchain/embedchain): A Python library for managing and syncing unstructured data with LLMs.
 - [FLAML (A Fast Library for Automated Machine Learning & Tuning)](https://microsoft.github.io/FLAML/docs/Getting-Started/): A Python library for automating selection of models, hyperparameters, and other tunable choices.
-- [Guidance](https://github.com/microsoft/guidance): A handy looking Python library from Microsoft that uses Handlebars templating to interleave generation, prompting, and logical control.
+- [Guidance](https://github.com/guidance-ai/guidance): A handy looking Python library from Microsoft that uses Handlebars templating to interleave generation, prompting, and logical control.
 - [Haystack](https://github.com/deepset-ai/haystack): Open-source LLM orchestration framework to build customizable, production-ready LLM applications in Python.
 - [HoneyHive](https://honeyhive.ai): An enterprise platform to evaluate, debug, and monitor LLM apps.
-- [LangChain](https://github.com/hwchase17/langchain): A popular Python/JavaScript library for chaining sequences of language model prompts.
+- [LangChain](https://github.com/langchain-ai/langchain): A popular Python/JavaScript library for chaining sequences of language model prompts.
 - [LiteLLM](https://github.com/BerriAI/litellm): A minimal Python library for calling LLM APIs with a consistent format.
-- [LlamaIndex](https://github.com/jerryjliu/llama_index): A Python library for augmenting LLM apps with data.
+- [LlamaIndex](https://github.com/run-llama/llama_index): A Python library for augmenting LLM apps with data.
 - [LLMOps Database](https://www.reddit.com/r/LocalLLaMA/comments/1h4u7au/a_nobs_database_of_how_companies_actually_deploy/): Database of how companies actually deploy LLMs in production.
 - [LMQL](https://lmql.ai): A programming language for LLM interaction with support for typed prompting, control flow, constraints, and tools.
 - [OpenAI Evals](https://github.com/openai/evals): An open-source library for evaluating task performance of language models and prompts.
-- [Outlines](https://github.com/normal-computing/outlines): A Python library that provides a domain-specific language to simplify prompting and constrain generation.
+- [Outlines](https://github.com/dottxt-ai/outlines): A Python library that provides a domain-specific language to simplify prompting and constrain generation.
 - [Parea AI](https://www.parea.ai): A platform for debugging, testing, and monitoring LLM apps.
 - [Portkey](https://portkey.ai/): A platform for observability, model management, evals, and security for LLM apps.
 - [Promptify](https://github.com/promptslab/Promptify): A small Python library for using language models to perform NLP tasks.
@@ -27,7 +27,7 @@ People are writing great tools and papers for improving outputs from GPT. Here a
 - [Scale Spellbook](https://scale.com/spellbook): A paid product for building, comparing, and shipping language model apps.
 - [Semantic Kernel](https://github.com/microsoft/semantic-kernel): A Python/C#/Java library from Microsoft that supports prompt templating, function chaining, vectorized memory, and intelligent planning.
 - [Vellum](https://www.vellum.ai/): A paid AI product development platform to experiment with, evaluate, and deploy advanced LLM apps.
-- [Weights & Biases](https://wandb.ai/site/solutions/llmops): A paid product for tracking model training and prompt engineering experiments.
+- [Weights & Biases](https://wandb.ai/site/weave/): A paid product for tracking model training and prompt engineering experiments.
 - [YiVal](https://github.com/YiVal/YiVal): An open-source GenAI-Ops tool for tuning and evaluating prompts, retrieval configurations, and model parameters using customizable datasets, evaluation methods, and evolution strategies.
 
 ## Prompting guides

examples/vector_databases/README.md

@@ -7,20 +7,20 @@ Vector databases can be a great accompaniment for knowledge retrieval applicatio
 Each provider has their own named directory, with a standard notebook to introduce you to using our API with their product, and any supplementary notebooks they choose to add to showcase their functionality.
 
 ## Guides & deep dives
-- [AnalyticDB](https://www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/get-started-with-analyticdb-for-postgresql)
-- [Cassandra/Astra DB](https://docs.datastax.com/en/astra-serverless/docs/vector-search/qandasimsearch-quickstart.html)
+- [AnalyticDB](https://www.alibabacloud.com/help/en/analyticdb/analyticdb-for-postgresql/getting-started/overview-getting-started)
+- [Cassandra/Astra DB](https://docs.datastax.com/en/astra-serverless/docs/vector-search/quickstart.html)
 - [Azure AI Search](https://learn.microsoft.com/azure/search/search-get-started-vector)
-- [Azure SQL Database](https://learn.microsoft.com/azure/azure-sql/database/ai-artificial-intelligence-intelligent-applications?view=azuresql)
-- [Chroma](https://docs.trychroma.com/getting-started)
+- [Azure SQL Database](https://learn.microsoft.com/en-us/azure/azure-sql/database/ai-artificial-intelligence-intelligent-applications?view=azuresql)
+- [Chroma](https://docs.trychroma.com/docs/overview/getting-started)
 - [Elasticsearch](https://www.elastic.co/guide/en/elasticsearch/reference/current/knn-search.html)
-- [Hologres](https://www.alibabacloud.com/help/en/hologres/latest/procedure-to-use-hologres)
+- [Hologres](https://www.alibabacloud.com/help/en/hologres/getting-started/)
 - [Kusto](https://learn.microsoft.com/en-us/azure/data-explorer/web-query-data)
-- [Milvus](https://milvus.io/docs/example_code.md)
+- [Milvus](https://milvus.io/docs/get_started.md)
 - [MyScale](https://docs.myscale.com/en/quickstart/)
 - [MongoDB](https://www.mongodb.com/products/platform/atlas-vector-search)
 - [Neon Postgres](https://neon.tech/docs/ai/ai-intro)
-- [Pinecone](https://docs.pinecone.io/docs/quickstart)
-- [PolarDB](https://www.alibabacloud.com/help/en/polardb/latest/quick-start)
+- [Pinecone](https://docs.pinecone.io/guides/get-started/quickstart)
+- [PolarDB](https://www.alibabacloud.com/help/en/polardb/getting-started)
 - [Qdrant](https://qdrant.tech/documentation/quick-start/)
 - [Redis](https://github.com/RedisVentures/simple-vecsim-intro)
 - [SingleStoreDB](https://www.singlestore.com/blog/how-to-get-started-with-singlestore/)
@@ -29,4 +29,4 @@ Each provider has their own named directory, with a standard notebook to introdu
 - [Typesense](https://typesense.org/docs/guide/)
 - [Vespa AI](https://vespa.ai/)
 - [Weaviate](https://weaviate.io/developers/weaviate/quickstart)
-- [Zilliz](https://docs.zilliz.com/docs/quick-start-1)
+- [Zilliz](https://docs.zilliz.com/docs/quick-start)