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Copy file name to clipboardExpand all lines: learn/chat/conversational_search.mdx
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@@ -46,6 +46,10 @@ Meilisearch's chat completions API consolidates RAG creation into a single proce
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Follow the [chat completions tutorial](/learn/chat/getting_started_with_chat) for information on how to implement a RAG with Meilisearch.
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<Warning>
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Conversational search is still in early development. Conversational agents may occasionally hallucinate innacurate and misleading information, so it is important to closely monitor it in production environments.
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</Warning>
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### Model Context Protocol (MCP)
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An alternative method is using a Model Context Protocol (MCP) server. MCPs are designed for broader uses that go beyond answering questions, but can be useful in contexts where having up-to-date data is more important than comprehensive answers.
Copy file name to clipboardExpand all lines: learn/chat/getting_started_with_chat.mdx
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}'
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```
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<Warning>
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Conversational search is still in early development. Conversational agents may occasionally hallucinate innacurate and misleading information, so it is important to closely monitor it in production environments.
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</Warning>
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### Find your chat API key
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When Meilisearch runs with a master key on an instance created after v1.15.1, it automatically generates a "Default Chat API Key" with `chatCompletions` and `search` permissions on all indexes. Check if you have the key using:
Copy file name to clipboardExpand all lines: reference/api/chats.mdx
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```
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</Note>
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<Warning>
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Conversational search is still in early development. Conversational agents may occasionally hallucinate innacurate and misleading information, so it is important to closely monitor it in production environments.
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</Warning>
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## Authorization
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When working with a secure Meilisearch instance, Use an API key with access to both the `search` and `chatCompletions` actions, such as the default chat API key. You may also use tenant tokens instead of an API key, provided you generate the tokens with access to the required actions.
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When implementing conversational search, use an API key with access to both the `search` and `chatCompletions` actions such as the default chat API key. You may also use tenant tokens instead of an API key, provided you generate the tokens with access to the required actions.
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Chat queries only search indexes its API key can access. The default chat API key has access to all indexes. To limit chat access to specific indexes, you must either create a new key, or [generate a tenant token](/learn/security/generate_tenant_token_sdk) from the default chat API key.
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LLM credentials used to querying your AI provider are stored securely in the chat workspace settings.
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## Chat workspace object
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```json
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}
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```
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#### The prompts object
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#### `source`
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```json
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{
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"system": "Description of the general search context",
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"searchDescription": "Description of internal search processes",
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"searchQParam": "Description of expected input inside `q`",
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"searchIndexUidParam": "Description of content inside each accessible index"
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}
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```
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**Type**: String<br />
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**Default value**: N/A<br />
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**Description**: Name of the chosen embeddings provider. Must be one of: `"openAi"`, `"azureOpenAi"`, `"mistral"`, `"gemini"`, or `"vLlm"`
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#### `orgId`
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**Type**: String<br />
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**Default value**: N/A<br />
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**Description**: Organization ID used to access the LLM provider. Required for Azure OpenAI, incompatible with other sources
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#### `projectId`
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**Type**: String<br />
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**Default value**: N/A<br />
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**Description**: Project ID used to access the LLM provider. Required for Azure OpenAI, incompatible with other sources
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#### `apiVersion`
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**Type**: String<br />
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**Default value**: N/A<br />
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**Description**: API version used by the LLM provider. Required for Azure OpenAI, incompatible with other sources
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#### `deploymentId`
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**Type**: String<br />
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**Default value**: N/A<br />
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**Description**: Deployment ID used by the LLM provider. Required for Azure OpenAI, incompatible with other sources
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#### `baseUrl`
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**Type**: String<br />
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**Default value**: N/A<br />
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**Description**: Base URL Meilisearch should target when sending requests to the embeddings provider. Required for Azure OpenAI and vLLM
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#### `apiKey`
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**Type**: String<br />
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**Default value**: N/A<br />
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**Description**: API key to access the LLM provider. Optional for vLLM, mandatory for all other providers
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#### `prompts`
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**Type**: Object<br />
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**Default value**: N/A<br />
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**Description**: Prompts giving baseline context to the conversational agent.
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The prompts object accepts the following fields:
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-`prompts.system`: Default prompt giving the general usage context of the conversational search agent. Example: "You are a helpful bot answering questions on how to use Meilisearch"
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-`prompts.searchDescription`: An internal description of the Meilisearch chat tools. Use it to instruct the agent on how and when to use the configured tools. Example: "Tool for retrieving relevant documents. Use it when users ask for factual information, past records, or resources that might exist in indexed content."
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-`prompts.QParam`: Description of expected user input and the desired output. Example: "Users will ask about Meilisearch. Provide short and direct keyword-style queries."
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-`prompts.IndexUidParam`: Instructions describing each index the agent has access to and how to use them. Example: "If user asks about code or API or parameters, use the index called `documentation`."
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### Get chat workspace settings
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