Integration: Mistral

pip install mistral-haystack

import os
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.mistral import MistralChatGenerator

os.environ["MISTRAL_API_KEY"] = "YOUR_MISTRAL_API_KEY"
model = "mistral-medium"

client = MistralChatGenerator(model=model)


response = client.run(
    messages=[ChatMessage.from_user("What is the best French cheese?")]
)
print(response)

{'replies': [ChatMessage(content='The "best" French cheese is subjective and depends on personal taste...', role=<ChatRole.ASSISTANT: 'assistant'>, name=None, meta={'model': 'mistral-medium', 'index': 0, 'finish_reason': 'stop', 'usage': {'completion_tokens': 231, 'prompt_tokens': 16, 'total_tokens': 247}})]}

import os

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

os.environ["MISTRAL_API_KEY"] = "YOUR_MISTRAL_API_KEY"
model = "mistral-medium"

client = MistralChatGenerator(
    model=model,
    streaming_callback=print_streaming_chunk
)

response = client.run(
    messages=[ChatMessage.from_user("What is the best French cheese?")]
)
print(response)

import os
from haystack import Pipeline
from haystack.components.preprocessors import DocumentSplitter
from haystack.components.writers import DocumentWriter
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.converters.mistral import MistralOCRDocumentConverter
from haystack_integrations.components.embedders.mistral import MistralDocumentEmbedder
from mistralai.models import DocumentURLChunk

os.environ["MISTRAL_API_KEY"] = "YOUR_MISTRAL_API_KEY"

# Initialize document store
document_store = InMemoryDocumentStore()

# Create indexing pipeline
indexing_pipeline = Pipeline()
indexing_pipeline.add_component("converter", MistralOCRDocumentConverter())
indexing_pipeline.add_component(
    "splitter",
    DocumentSplitter(split_by="page", split_length=2, split_overlap=1)
)
indexing_pipeline.add_component("embedder", MistralDocumentEmbedder())
indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))

# Connect components
indexing_pipeline.connect("converter.documents", "splitter.documents")
indexing_pipeline.connect("splitter.documents", "embedder.documents")
indexing_pipeline.connect("embedder.documents", "writer.documents")

# Process documents
sources = [
    DocumentURLChunk(document_url="https://arxiv.org/pdf/1706.03762"),
    "./invoice.pdf",  # Local PDF file
]

result = indexing_pipeline.run({"converter": {"sources": sources}})

print(f"Indexed {len(document_store.filter_documents())} documents")

import os
from typing import List
from haystack_integrations.components.converters.mistral import MistralOCRDocumentConverter
from mistralai.models import DocumentURLChunk
from pydantic import BaseModel, Field

os.environ["MISTRAL_API_KEY"] = "YOUR_MISTRAL_API_KEY"

# Define schema for image annotations (applied to each image/bbox)
class ImageAnnotation(BaseModel):
    image_type: str = Field(..., description="Type of image (diagram, chart, photo, etc.)")
    description: str = Field(..., description="Brief description of the image content")

# Define schema for document-level annotations
class DocumentAnnotation(BaseModel):
    topics: List[str] = Field(..., description="Main topics covered")
    urls: List[str] = Field(..., description="URLs found in the document")

converter = MistralOCRDocumentConverter()

sources = ["./financial_report.pdf"]

result = converter.run(
    sources=sources,
    bbox_annotation_schema=ImageAnnotation,
    document_annotation_schema=DocumentAnnotation,
)

# Documents now include enriched content and metadata
doc = result["documents"][0]
print(doc.content)  # Markdown with image annotations inline
print(doc.meta["source_topics"])    # e.g., ["finance", "quarterly report", "revenue", "expenses", "performance"]
print(doc.meta["source_urls"])      # e.g., ["https://example.com", ...]

import os

from haystack import Document, Pipeline
from haystack.components.writers import DocumentWriter
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.embedders.mistral.document_embedder import MistralDocumentEmbedder

os.environ["MISTRAL_API_KEY"] = "YOUR_MISTRAL_API_KEY"

document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

documents = [Document(content="My name is Wolfgang and I live in Berlin"),
             Document(content="I saw a black horse running"),
             Document(content="Germany has many big cities")]

embedder = MistralDocumentEmbedder()
writer = DocumentWriter(document_store=document_store)

indexing_pipeline = Pipeline()
indexing_pipeline.add_component(name="embedder", instance=embedder)
indexing_pipeline.add_component(name="writer", instance=writer)

indexing_pipeline.connect("embedder", "writer")

indexing_pipeline.run(data={"embedder": {"documents": documents}})

import os

from haystack import Document, Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.embedders.mistral.document_embedder import MistralDocumentEmbedder
from haystack_integrations.components.embedders.mistral.text_embedder import MistralTextEmbedder
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
from haystack.components.builders import ChatPromptBuilder

os.environ["MISTRAL_API_KEY"] = "YOUR_MISTRAL_API_KEY"

document_store = InMemoryDocumentStore()

documents = [Document(content="My name is Wolfgang and I live in Berlin"),
             Document(content="I saw a black horse running"),
             Document(content="Germany has many big cities")]

document_embedder = MistralDocumentEmbedder()
documents_with_embeddings = document_embedder.run(documents)['documents']
document_store.write_documents(documents)

text_embedder = MistralTextEmbedder()
retriever = InMemoryEmbeddingRetriever(document_store=document_store)
prompt_builder = ChatPromptBuilder()
llm = MistralChatGenerator(streaming_callback=print_streaming_chunk)

messages = [ChatMessage.from_user("Here are some the documents: {{documents}} \\n Answer: {{query}}")]

rag_pipeline = Pipeline()
rag_pipeline.add_component("text_embedder", text_embedder)
rag_pipeline.add_component("retriever", retriever)
rag_pipeline.add_component("prompt_builder", prompt_builder)
rag_pipeline.add_component("llm", llm)


rag_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
rag_pipeline.connect("retriever.documents", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder.prompt", "llm.messages")

question = "Who lives in Berlin?"

result = rag_pipeline.run(
    {
        "text_embedder": {"text": question},
        "prompt_builder": {"template_variables": {"query": question}, "template": messages},
        "llm": {"generation_kwargs": {"max_tokens": 165}},
    }
)