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Deleted MCP (#1111)
* mcp mvp * precommit fix * docker added, thanks dwij * first gloss docs * added docker docs * deleted mcp (diff repo)
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docs/mint.json

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"anchors": {
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"topbarCtaButton": {
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"url": "https://agentops.ai/contact"
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}
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],
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"versions": [
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"v2",
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"v1",
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"v0"
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],
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"versions": ["v2", "v1", "v0"],
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"navigation": [
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"group": "",
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"pages": [
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"pages": ["v1/introduction"],
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"version": "v1"
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{
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"group": "Getting Started",
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"pages": [
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"pages": ["v1/quickstart", "v1/examples/examples"],
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"version": "v1"
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"pages": ["v2/quickstart", "v2/examples/examples"],
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"v2/usage/tracking-agents",
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docs/v2/examples/openai.mdx

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title: 'OpenAI'
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description: 'Load the dataset (ensure you're logged in with huggingface-cli if needed)'
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description: "Load the dataset (ensure you're logged in with huggingface-cli if needed)"
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{/* SOURCE_FILE: examples/openai/multi_tool_orchestration.ipynb */}
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### Multi-tool orchestration flow
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Now let us try to modify the input query and the system instructions to the responses API in order to follow a tool calling sequence and generate the output.
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Now let us try to modify the input query and the system instructions to the responses API in order to follow a tool calling sequence and generate the output.
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```python
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Here, we have seen how to utilize OpenAI's Responses API to implement a Retrieval-Augmented Generation (RAG) approach with multi-tool calling capabilities. It showcases an example where the model selects the appropriate tool based on the input query: general questions may be handled by built-in tools such as web-search, while specific medical inquiries related to internal knowledge are addressed by retrieving context from a vector database (such as Pinecone) via function calls. Additonally, we have showcased how multiple tool calls can be sequentially combined to generate a final response based on our instructions provided to responses API. Happy coding!
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Here, we have seen how to utilize OpenAI's Responses API to implement a Retrieval-Augmented Generation (RAG) approach with multi-tool calling capabilities. It showcases an example where the model selects the appropriate tool based on the input query: general questions may be handled by built-in tools such as web-search, while specific medical inquiries related to internal knowledge are addressed by retrieving context from a vector database (such as Pinecone) via function calls. Additonally, we have showcased how multiple tool calls can be sequentially combined to generate a final response based on our instructions provided to responses API. Happy coding!
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<script type="module" src="/scripts/github_stars.js"></script>
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<script type="module" src="/scripts/adjust_api_dynamically.js"></script>

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