linting and spellcheck ignores

Signed-off-by: Ryan Cook <[email protected]>

Author: cooktheryan

.spellcheck-en-custom.txt

@@ -1,6 +1,7 @@
 # make spellcheck-sort
 # Please keep this file sorted:
 Abhishek
+ADR
 Akash
 AMDGPU
 Anil
@@ -35,6 +36,8 @@ CWD
 Cynefin
 dataset
 datasets
+dateutil
+DBs
 DCO
 Dependabot
 dev
@@ -70,25 +73,33 @@ Guang
 hacky
 hardcode
 hardcoded
+Hema
 hipBLAS
 ilab
+Ilya
 impactful
 Inferencing
 instantiation
 instructlab
 io
 ISA
 iters
+itertools
 Jie
 JIT
+jinja
 JSON
 Jupyter
 KAGGLE
 Kaggle
 Kaggle's
 Kai
+Kolchinsky
 Kubernetes
 Kumar
+Langchain
+langchain
+Langgraph
 lignment
 LLM
 llms
@@ -100,6 +111,7 @@ md
 Mergify
 Merlinite
 merlinite
+Milvus
 mimimum
 Miniforge
 Mixtral
@@ -108,14 +120,19 @@ MLX
 mlx
 MMLU
 Nakamura
+natively
+networkx
 num
+numpy
 NVidia
 Nvidia
 OCI
 Ollama
 ollama
 onboarding
 OpenStax
+openai
+OpenAI
 optimizers
 orchestrator
 ots
@@ -130,15 +147,20 @@ PNG
 POC
 Podman
 podman
+posthog
 pre
 preprint
+preprocessing
 prereqs
+productized
 PR's
+PSFL
 Pydantic
 pyenv
 PyPI
 pyproject
 PyTorch
+pyyaml
 qlora
 quantized
 Quantizing
@@ -173,6 +195,7 @@ specifiying
 src
 Srivastava
 Standup
+Staar
 subcommand
 subcommands
 subdirectory
@@ -187,6 +210,7 @@ th
 tl
 TODO
 tox
+tqdm
 traigers
 triager
 Triagers
@@ -197,7 +221,9 @@ ui
 unquantized
 USM
 UX
+vectordbs
 venv
+Veeradhi
 Vishnoi
 vllm
 watsonx

docs/retrieval-augmented-generation/rag-framework.md

@@ -36,7 +36,7 @@ Out of those, we propose to use Haystack for the following reasons:
 5. **Ease of use and documentation.** Being strictly focused on RAG as opposed to taking a generalist approach, the learning curve of Haystack is less steep than that of Langchain. At the same time, Haystack offers extensive documentation and tutorials which are more well-organized and easy to use than those of LlamaIndex.
 
 6. **Architecture.** Extending the previous point, Haystack can be seen as a middle ground between Langchain and LlamaIndex, sharing their benefits while only partially inheriting their drawbacks. Like the former, Haystack enables building custom flows and pipelines. Unlike Langchain though, Haystack does not try to be too abstract and general, strictly focusing on RAG and document search instead. As a result, Haystack is more straightforward to use, especially for users looking to implement custom and highly non-standard scenarios. On the other hand, like LlamaIndex, Haystack's performance is optimized towards data retrieval and indexing, but it offers a higher degree of flexibility and better interfaces for custom use cases.
-   
+
 7. **Actively maintained open source project under permissive license.** Haystack is very [actively](https://github.com/deepset-ai/haystack/pulse/monthly) [maintained](https://github.com/deepset-ai/haystack/issues?q=is%3Aissue+is%3Aclosed) and [supported](https://github.com/deepset-ai/haystack/discussions). [Tagged versions](https://github.com/deepset-ai/haystack/releases) are released on a regular basis and [trusted publishing automation](https://github.com/deepset-ai/haystack/actions/workflows/pypi_release.yml) is used. Haystack is licensed under Apache 2.0, and all of its dependencies (jinja2, lazy-imports, more-itertools, networkx, numpy, openai, pandas, posthog, python-dateutil, pyyaml, requests, tenacity, tqdm, typing-extensions) are licensed under Apache, MIT, BSD or PSFL.
 
 ## Goals
@@ -82,6 +82,7 @@ A downstream should be generated of the [https://github.com/deepset-ai/haystack]
 ## Risks
 
 Future versions of Haystack can potentially introduce new dependencies, that could be:
+
 1. Distributed under a non-permissive license (or not open source at all)
 2. Not regularly and/or properly maintained