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docs: fix typos in documentation (#32201)

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## Summary
- Fixed redundant word "done" in SECURITY.md line 69  
- Fixed grammar errors in Fireworks README.md line 77: "how it fares
compares" → "how it compares" and "in terms just" → "in terms of"

## Test plan
- [x] Verified changes improve readability and correct grammar
- [x] No functional changes, documentation only

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Co-authored-by: Claude <claude@anthropic.com>
Co-authored-by: Claude <noreply@anthropic.com>
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Maxime Grenu 2025-07-23 16:43:25 +02:00 committed by GitHub
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SECURITY.md
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@ -66,7 +66,7 @@ All out of scope targets defined by huntr as well as:
for more details, but generally tools interact with the real world. Developers are
expected to understand the security implications of their code and are responsible
for the security of their tools.
- Code documented with security notices. This will be decided done on a case by
- Code documented with security notices. This will be decided on a case by
case basis, but likely will not be eligible for a bounty as the code is already
documented with guidelines for developers that should be followed for making their
application secure.

2
libs/partners/fireworks/README.md
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@ -74,7 +74,7 @@ print(output.generations)
### Tool use: LangChain Agent + Fireworks function calling model
Please checkout how to teach Fireworks function calling model to use a calculator [here](https://github.com/fw-ai/cookbook/blob/main/learn/function-calling/notebooks_langchain/fireworks_langchain_tool_usage.ipynb).
Fireworks focus on delivering the best experience for fast model inference as well as tool use. You can check out [our blog](https://fireworks.ai/blog/firefunction-v1-gpt-4-level-function-calling) for more details on how it fares compares to GPT-4, the punchline is that it is on par with GPT-4 in terms just function calling use cases, but it is way faster and much cheaper.
Fireworks focus on delivering the best experience for fast model inference as well as tool use. You can check out [our blog](https://fireworks.ai/blog/firefunction-v1-gpt-4-level-function-calling) for more details on how it compares to GPT-4, the punchline is that it is on par with GPT-4 in terms of function calling use cases, but it is way faster and much cheaper.
### RAG: LangChain agent + Fireworks function calling model + MongoDB + Nomic AI embeddings
Please check out the [cookbook here](https://github.com/fw-ai/cookbook/blob/main/integrations/MongoDB/project_rag_with_mongodb/mongodb_agent.ipynb) for an end to end flow