---
title: "MistralChatGenerator"
id: mistralchatgenerator
slug: "/mistralchatgenerator"
description: "This component enables chat completion using Mistral’s text generation models."
---

# MistralChatGenerator

This component enables chat completion using Mistral’s text generation models.

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [ChatPromptBuilder](../builders/chatpromptbuilder.mdx)  |
| **Mandatory init variables** | "api_key": The Mistral API key. Can be set with `MISTRAL_API_KEY` env var. |
| **Mandatory run variables** | “messages” A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx)  objects |
| **Output variables** | "replies": A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx)  objects  <br /> <br />”meta”: A list of dictionaries with the metadata associated with each reply, such as token count, finish reason, and so on |
| **API reference** | [Mistral](/reference/integrations-mistral) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/mistral |

## Overview

This integration supports Mistral’s models provided through the generative endpoint. For a full list of available models, check out the [Mistral documentation](https://docs.mistral.ai/platform/endpoints/#generative-endpoints).

`MistralChatGenerator` needs a Mistral API key to work. You can write this key in:

- The `api_key` init parameter using [Secret API](../../concepts/secret-management.mdx)
- The `MISTRAL_API_KEY` environment variable (recommended)

Currently, available models are:

- `mistral-tiny` (default)
- `mistral-small`
- `mistral-medium`(soon to be deprecated)
- `mistral-large-latest`
- `codestral-latest`

This component needs a list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx) objects to operate. `ChatMessage` is a data class that contains a message, a role (who generated the message, such as `user`, `assistant`, `system`, `function`), and optional metadata.

Refer to the [Mistral API documentation](https://docs.mistral.ai/api/#operation/createChatCompletion) for more details on the parameters supported by the Mistral API, which you can provide with `generation_kwargs` when running the component.

### Streaming

This Generator supports [streaming](guides-to-generators/choosing-the-right-generator.mdx#streaming-support) the tokens from the LLM directly in output. To do so, pass a function to the `streaming_callback` init parameter.

## Usage

Install the `mistral-haystack` package to use the  `MistralChatGenerator`:

```shell
pip install mistral-haystack
```

#### On its own

```python
from haystack_integrations.components.generators.mistral import MistralChatGenerator
from haystack.components.generators.utils import print_streaming_chunk
from haystack.dataclasses import ChatMessage
from haystack.utils import Secret

generator = MistralChatGenerator(
    api_key=Secret.from_env_var("MISTRAL_API_KEY"),
    streaming_callback=print_streaming_chunk,
)
message = ChatMessage.from_user("What's Natural Language Processing? Be brief.")
print(generator.run([message]))
```

#### In a Pipeline

Below is an example RAG Pipeline where we answer questions based on the URL contents. We add the contents of the URL into our `messages` in the `ChatPromptBuilder` and generate an answer with the `MistralChatGenerator`.

```python
from haystack import Document
from haystack import Pipeline
from haystack.components.builders import ChatPromptBuilder
from haystack.components.generators.utils import print_streaming_chunk
from haystack.components.fetchers import LinkContentFetcher
from haystack.components.converters import HTMLToDocument
from haystack.dataclasses import ChatMessage

from haystack_integrations.components.generators.mistral import MistralChatGenerator

fetcher = LinkContentFetcher()
converter = HTMLToDocument()
prompt_builder = ChatPromptBuilder(variables=["documents"])
llm = MistralChatGenerator(
    streaming_callback=print_streaming_chunk,
    model="mistral-small",
)

message_template = """Answer the following question based on the contents of the article: {{query}}\n
               Article: {{documents[0].content}} \n
           """
messages = [ChatMessage.from_user(message_template)]

rag_pipeline = Pipeline()
rag_pipeline.add_component(name="fetcher", instance=fetcher)
rag_pipeline.add_component(name="converter", instance=converter)
rag_pipeline.add_component("prompt_builder", prompt_builder)
rag_pipeline.add_component("llm", llm)

rag_pipeline.connect("fetcher.streams", "converter.sources")
rag_pipeline.connect("converter.documents", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder.prompt", "llm.messages")

question = "What are the capabilities of Mixtral?"

result = rag_pipeline.run(
    {
        "fetcher": {"urls": ["https://mistral.ai/news/mixtral-of-experts"]},
        "prompt_builder": {
            "template_variables": {"query": question},
            "template": messages,
        },
        "llm": {"generation_kwargs": {"max_tokens": 165}},
    },
)
```

## Additional References

🧑‍🍳 Cookbook: [Web QA with Mixtral-8x7B-Instruct-v0.1](https://haystack.deepset.ai/cookbook/mixtral-8x7b-for-web-qa)
