From 1d6d70b232f2e256f64ae8784a391bb127cdfaa7 Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Wed, 7 Aug 2024 17:01:49 +0000 Subject: [PATCH 1/2] Bump version file --- version.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/version.txt b/version.txt index c8fe2be..06ee1af 100644 --- a/version.txt +++ b/version.txt @@ -1 +1 @@ -v0.0.15 +v0.0.68 From 600315c7fd3b84ca54e54766b74f9cd2b5ce859c Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Wed, 7 Aug 2024 17:01:49 +0000 Subject: [PATCH 2/2] Update version to v0.0.68 --- docs/capabilities/agents.md | 260 ++ docs/capabilities/code-generation.mdx | 65 +- docs/capabilities/completion.mdx | 100 +- docs/capabilities/embeddings.mdx | 91 +- docs/capabilities/finetuning.mdx | 129 +- docs/capabilities/function-calling.mdx | 30 +- docs/capabilities/guardrailing.mdx | 14 +- docs/capabilities/json-mode.mdx | 44 +- docs/deployment/cloud/aws.mdx | 85 +- docs/deployment/cloud/azure.mdx | 143 +- docs/deployment/cloud/vertex.mdx | 348 +- .../deployment/self-deployment/cloudflare.mdx | 4 +- docs/deployment/self-deployment/tgi.mdx | 4 +- docs/getting-started/Open-weight-models.mdx | 2 +- docs/getting-started/changelog.mdx | 4 + docs/getting-started/clients.mdx | 31 +- docs/getting-started/quickstart.mdx | 48 +- docs/guides/basic-RAG.md | 25 +- docs/guides/evaluation.md | 27 +- docs/guides/finetuning.mdx | 2 +- .../_02_prepare_dataset.md | 35 +- .../finetuning_sections/_03_e2e_examples.md | 166 +- docs/guides/finetuning_sections/_04_faq.md | 23 +- docs/guides/prefix.mdx | 28 +- docs/guides/prompting-capabilities.md | 2 +- openapi.yaml | 3091 ++++++++++------- static/img/French_agent.png | Bin 0 -> 469439 bytes static/img/Python_agent.png | Bin 0 -> 386492 bytes static/img/agent.png | Bin 0 -> 243966 bytes static/img/loving_agent.png | Bin 0 -> 404480 bytes version.txt | 2 +- 31 files changed, 2838 insertions(+), 1965 deletions(-) create mode 100644 docs/capabilities/agents.md create mode 100644 static/img/French_agent.png create mode 100644 static/img/Python_agent.png create mode 100644 static/img/agent.png create mode 100644 static/img/loving_agent.png diff --git a/docs/capabilities/agents.md b/docs/capabilities/agents.md new file mode 100644 index 0000000..077e376 --- /dev/null +++ b/docs/capabilities/agents.md @@ -0,0 +1,260 @@ +--- +id: agents +title: Agents +sidebar_position: 2.9 +--- +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + +## What are AI agents? + +AI agents are autonomous systems powered by large language models (LLMs) that, given high-level instructions, can plan, use tools, carry out steps of processing, and take actions to achieve specific goals. These agents leverage advanced natural language processing capabilities to understand and execute complex tasks efficiently and can even collaborate with each other to achieve more sophisticated outcomes. + + +## Creating Agents +We provide two primary methods for creating agents: + +- La Plateforme [Agent builder](https://console.mistral.ai/build/agents/new): Users can use a user-friendly interface provided on La Plateforme to create and configure their agents. + +- [Agent API](#the-agents-api): For developers, we offer the Agents API as a programmatic means to use agents. This method is ideal for developers who need to integrate agent creation into their existing workflows or applications. + +## La Plateforme agent builder + +To start building your own agent, visit https://console.mistral.ai/build/agents/new. + +drawing + +Here are the available options for customizing your agent: +- **Model**: The specific model you would like the agent to use. Default is "Mistral Large 2" (`mistral-large-2407`). The other model choicess are "Mistral Nemo" (`open-mistral-nemo`), "Codestral" (`codestral-2405`), and your fine-tuned models. +- **Temperature**: What sampling temperature to use, between 0.0 and 1.0. Higher values will make the output more random, while lower values will make it more focused and deterministic. +- **Instructions** (optional): Instructions allows you to enforce a model behavior through all conversations and messages. +- **Demonstrations** (optional): Few-shot learning examples can be added to help guide the agent to understand the specific behavior you want it to exhibit. You can show the model some examples of input and output to improve performance. +- **Deploy**: Once deployed, you will be able to call the Agent via the API with the `agent_id`, but you can also toggle the option to chat with the corresponding Agent on [Le Chat](https://chat.mistral.ai/chat). + + +## The Agent API + +### Create an agent + +Coming soon + + + + +### Use an agent + + + + + +```python +import os +from mistralai import Mistral + +api_key = os.environ["MISTRAL_API_KEY"] + +client = Mistral(api_key=api_key) + +chat_response = client.agents.complete( + agent_id="ag:3996db2b:20240805:french-agent:a8997aab", + messages=[ + { + "role": "user", + "content": "What is the best French cheese?", + }, + ], +) +print(chat_response.choices[0].message.content) + + +``` + + + + +```typescript +import { Mistral } from '@mistralai/mistralai'; + +const apiKey = process.env.MISTRAL_API_KEY; + +const client = new Mistral({apiKey: apiKey}); + +const chatResponse = await client.agents.complete({ + agent_id: "ag:3996db2b:20240805:french-agent:a8997aab", + messages: [{role: 'user', content: 'What is the best French cheese?'}], +}); + +console.log('Chat:', chatResponse.choices[0].message.content); +``` + + + + +```bash +curl --location "https://api.mistral.ai/v1/chat/completions" \ + --header 'Content-Type: application/json' \ + --header 'Accept: application/json' \ + --header "Authorization: Bearer $MISTRAL_API_KEY" \ + --data '{ + "agent_id": "ag:3996db2b:20240805:french-agent:a8997aab", + "messages": [{"role": "user", "content": "Who is the most renowned French painter?"}] + }' +``` + + + + + + +## Use Cases +
+ Use case 1: French agent + +You can create an agent that only speaks French. You'll need to set up the agent with specific instructions and use few-shot learning to ensure it understands the requirement to communicate solely in French. + + + +Here is an example of how you can create this agent with the La Plateforme [agent builder](https://console.mistral.ai/build/agents/new). +drawing +
+ +
+ Use case 2: Python agent + +You can create an agent that outputs only Python code without any explanations. This is useful when you need to generate code snippets that can be easily copied and pasted, without the additional explanatory text that our model typically provides. + + + +Here is an example of how you can create this agent with using the La Plateforme [agent builder](https://console.mistral.ai/build/agents/new). + + +drawing +
+ +
+ Use case 3: Python agent workflow + +You can use the Python agent we created in use case 2 in an assistant coding workflow. For example, here is a very simple Python agent workflow with the following steps: + +1. User Query: + +The process starts when the user submits a query or request to the Python agent. + +2. Code and Test Case Generation: + +The agent interprets the user's query and generates the corresponding Python code. Alongside the code, the agent creates a test case to verify the functionality of the generated code. + +3. Execution and Validation: + +The agent attempts to run the generated code to ensure it executes without errors. +The agent then runs the test case to confirm that the code produces the correct output. + +4. Retry Mechanism: + +If the code fails to run or the test case does not pass, the agent initiates a retry. +It regenerates the code and test case, addressing any issues identified during the previous attempt. + +5. Result Output: + +Once the code runs successfully and passes the test case, the agent delivers the result to the user. + +Check out this [example notebook](https://github.com/mistralai/cookbook/blob/main/mistral/agents/simple_Python_agent_workflow.ipynb) for details. + +
+ +
+ Use case 4: Data analytical multi-agent workflow + +You can also leverage multiple agents in a workflow. Here is an example: + +1. Data Analysis Planning: + +The planning agent writes a comprehensive data analysis plan, outlining the steps required to analyze the data. + +2. Code Generation and Execution: + +For each step in the analysis plan, the Python agent generates the corresponding code. +The Python agent then executes the generated code to perform the specified analysis. + +3. Analysis Report Summarization: + +Based on the results of the executed code, the summarization agent writes an analysis report. +The report summarizes the findings and insights derived from the data analysis. + +Check out this [example notebook](https://github.com/mistralai/cookbook/blob/main/mistral/agents/analytical_agent_workflow.ipynb) for details. + +
+ + +
+ Use case 5: Role-playing Conversation agent + +You can also create role-playing conversation agents. For instance, in this [example](https://github.com/mistralai/cookbook/blob/main/mistral/agents/conversation_agent.ipynb), the role-playing conversation workflow generates an entertaining and humorous exchange between two agents mimicking the styles of two stand-up comedians Ali Wong and Jimmy Yang, incorporating jokes and comedic elements to enhance the conversation. + +Here is another [example](https://github.com/mistralai/cookbook/blob/main/mistral/agents/auto_roleplay.ipynb), where we have a Game Master agent orchestrating a roleplaying story between a Narrator agent and a Character agent. The Game Master agent sets the stage and determines which agent drives the next step of the story. +
diff --git a/docs/capabilities/code-generation.mdx b/docs/capabilities/code-generation.mdx index 3b75f99..c43d6d0 100644 --- a/docs/capabilities/code-generation.mdx +++ b/docs/capabilities/code-generation.mdx @@ -42,20 +42,21 @@ With this feature, users can define the starting point of the code using a `prom ```python import os -from mistralai.client import MistralClient +from mistralai import Mistral api_key = os.environ["MISTRAL_API_KEY"] - -client = MistralClient(api_key=api_key) +client = Mistral(api_key=api_key) model = "codestral-latest" prompt = "def fibonacci(n: int):" suffix = "n = int(input('Enter a number: '))\nprint(fibonacci(n))" -response = client.completion( +response = client.fim.complete( model=model, prompt=prompt, suffix=suffix, + temperature=0, + top_p=1, ) print( @@ -92,19 +93,15 @@ curl --location 'https://api.mistral.ai/v1/fim/completions' \ ```python import os -from mistralai.client import MistralClient +from mistralai import Mistral api_key = os.environ["MISTRAL_API_KEY"] - -client = MistralClient(api_key=api_key) +client = Mistral(api_key=api_key) model = "codestral-latest" prompt = "def is_odd(n): \n return n % 2 == 1 \ndef test_is_odd():" -response = client.completion( - model=model, - prompt=prompt -) +response = client.fim.complete(model=model, prompt=prompt, temperature=0, top_p=1) print( f""" @@ -143,21 +140,17 @@ We recommend adding stop tokens for IDE autocomplete integrations to prevent the ```python import os -from mistralai.client import MistralClient +from mistralai import Mistral api_key = os.environ["MISTRAL_API_KEY"] - -client = MistralClient(api_key=api_key) +client = Mistral(api_key=api_key) model = "codestral-latest" prompt = "def is_odd(n): \n return n % 2 == 1 \ndef test_is_odd():" suffix = "n = int(input('Enter a number: '))\nprint(fibonacci(n))" -response = client.completion( - model=model, - prompt=prompt, - suffix=suffix, - stop=["\n\n"] +response = client.fim.complete( + model=model, prompt=prompt, suffix=suffix, temperature=0, top_p=1, stop=["\n\n"] ) print( @@ -200,21 +193,16 @@ The only difference is the endpoint used: ```python import os -from mistralai.client import MistralClient -from mistralai.models.chat_completion import ChatMessage +from mistralai import Mistral api_key = os.environ["MISTRAL_API_KEY"] - -client = MistralClient(api_key=api_key) +client = Mistral(api_key=api_key) model = "codestral-latest" - -messages = [ - ChatMessage(role="user", content="Write a function for fibonacci") -] -chat_response = client.chat( - model=model, - messages=messages +message = [{"role": "user", "content": "Write a function for fibonacci"}] +chat_response = client.chat.complete( + model = model, + messages = message ) print(chat_response.choices[0].message.content) ``` @@ -242,21 +230,24 @@ We have also released Codestral Mamba 7B, a Mamba2 language model specilized in ```python import os -from mistralai.client import MistralClient -from mistralai.models.chat_completion import ChatMessage +from mistralai import Mistral api_key = os.environ["MISTRAL_API_KEY"] -client = MistralClient(api_key=api_key) +client = Mistral(api_key=api_key) model = "codestral-mamba-latest" -messages = [ - ChatMessage(role="user", content="Write a function for fibonacci") +message = [ + { + "role": "user", + "content": "Write a function for fibonacci" + } ] -chat_response = client.chat( + +chat_response = client.chat.complete( model=model, - messages=messages + messages=message ) print(chat_response.choices[0].message.content) ``` diff --git a/docs/capabilities/completion.mdx b/docs/capabilities/completion.mdx index e612faf..d83bd9f 100644 --- a/docs/capabilities/completion.mdx +++ b/docs/capabilities/completion.mdx @@ -23,22 +23,22 @@ the role "assistant" as output. ### No streaming ```python -from mistralai.client import MistralClient -from mistralai.models.chat_completion import ChatMessage +import os +from mistralai import Mistral api_key = os.environ["MISTRAL_API_KEY"] model = "mistral-large-latest" -client = MistralClient(api_key=api_key) +client = Mistral(api_key=api_key) -messages = [ - ChatMessage(role="user", content="What is the best French cheese?") -] - -# No streaming -chat_response = client.chat( - model=model, - messages=messages, +chat_response = client.chat.complete( + model = model, + messages = [ + { + "role": "user", + "content": "What is the best French cheese?", + }, + ] ) print(chat_response.choices[0].message.content) @@ -46,85 +46,87 @@ print(chat_response.choices[0].message.content) ### With streaming ```python -from mistralai.client import MistralClient -from mistralai.models.chat_completion import ChatMessage +import os +from mistralai import Mistral api_key = os.environ["MISTRAL_API_KEY"] model = "mistral-large-latest" -client = MistralClient(api_key=api_key) +client = Mistral(api_key=api_key) -messages = [ - ChatMessage(role="user", content="What is the best French cheese?") -] - -# With streaming -stream_response = client.chat_stream(model=model, messages=messages) +stream_response = client.chat.stream( + model = model, + messages = [ + { + "role": "user", + "content": "What is the best French cheese?", + }, + ] +) for chunk in stream_response: - print(chunk.choices[0].delta.content) + print(chunk.data.choices[0].delta.content) ``` ### With async ```python -from mistralai.async_client import MistralAsyncClient -from mistralai.models.chat_completion import ChatMessage +from mistralai import Mistral api_key = os.environ["MISTRAL_API_KEY"] model = "mistral-large-latest" -client = MistralAsyncClient(api_key=api_key) +client = Mistral(api_key=api_key) -messages = [ - ChatMessage(role="user", content="What is the best French cheese?") -] - -# With async -async_response = client.chat_stream(model=model, messages=messages) +async_response = await client.chat.stream_async( + model = model, + messages = [ + { + "role": "user", + "content": "Who is the best French painter? Answer in JSON.", + }, + ] +) async for chunk in async_response: - print(chunk.choices[0].delta.content) + print(chunk.data.choices[0].delta.content) ``` - + **No streaming** -```javascript -import MistralClient from '@mistralai/mistralai'; +```typescript +import { Mistral } from '@mistralai/mistralai'; const apiKey = process.env.MISTRAL_API_KEY; -const client = new MistralClient(apiKey); +const client = new Mistral({apiKey: apiKey}); -const chatResponse = await client.chat({ - model: 'mistral-large-latest', - messages: [{role: 'user', content: 'What is the best French cheese?'}], +const chatResponse = await client.chat.complete({ + model: "mistral-large-latest", + messages: [{role: 'user', content: 'What is the best French cheese?'}] }); console.log('Chat:', chatResponse.choices[0].message.content); ``` **With streaming** -```javascript -import MistralClient from '@mistralai/mistralai'; +```typescript +import { Mistral } from "@mistralai/mistralai"; const apiKey = process.env.MISTRAL_API_KEY; -const client = new MistralClient(apiKey); +const client = new Mistral({apiKey: apiKey}); -const chatStreamResponse = await client.chatStream({ - model: 'mistral-tiny', - messages: [{role: 'user', content: 'What is the best French cheese?'}], +const result = await client.chat.stream({ + model: "mistral-small-latest", + messages: [{role: 'user', content: 'What is the best French cheese?'}], }); -console.log('Chat Stream:'); -for await (const chunk of chatStreamResponse) { - if (chunk.choices[0].delta.content !== undefined) { - const streamText = chunk.choices[0].delta.content; +for await (const chunk of result) { + const streamText = chunk.data.choices[0].delta.content; process.stdout.write(streamText); - } } ``` diff --git a/docs/capabilities/embeddings.mdx b/docs/capabilities/embeddings.mdx index 1ffbc18..1afb16c 100644 --- a/docs/capabilities/embeddings.mdx +++ b/docs/capabilities/embeddings.mdx @@ -17,15 +17,18 @@ Embeddings are vectorial representations of text that capture the semantic meani To generate text embeddings using Mistral AI's embeddings API, we can make a request to the API endpoint and specify the embedding model `mistral-embed`, along with providing a list of input texts. The API will then return the corresponding embeddings as numerical vectors, which can be used for further analysis or processing in NLP applications. ```python -from mistralai.client import MistralClient +import os +from mistralai import Mistral -client = MistralClient(api_key="TYPE YOUR API KEY HERE") +api_key = os.environ["MISTRAL_API_KEY"] +model = "mistral-embed" -embeddings_batch_response = client.embeddings( - model="mistral-embed", - input=["Embed this sentence.", "As well as this one."], -) +client = Mistral(api_key=api_key) +embeddings_batch_response = client.embeddings.create( + model=model, + inputs=["Embed this sentence.", "As well as this one."], +) ``` The output `embeddings_batch_response` is an EmbeddingResponse object with the embeddings and the token usage information. @@ -33,9 +36,9 @@ The output `embeddings_batch_response` is an EmbeddingResponse object with the e ``` EmbeddingResponse( id='eb4c2c739780415bb3af4e47580318cc', object='list', data=[ - EmbeddingObject(object='embedding', embedding=[-0.0165863037109375,...], index=0), - EmbeddingObject(object='embedding', embedding=[-0.0234222412109375,...], index=1)], - model='mistral-embed', usage=UsageInfo(prompt_tokens=15, total_tokens=15, completion_tokens=0) + Data(object='embedding', embedding=[-0.0165863037109375,...], index=0), + Data(object='embedding', embedding=[-0.0234222412109375,...], index=1)], + model='mistral-embed', usage=EmbeddingResponseUsage(prompt_tokens=15, total_tokens=15) ) ``` @@ -55,10 +58,10 @@ Let's take a look at a simple example. To simplify working with text embeddings, ```python from sklearn.metrics.pairwise import euclidean_distances -def get_text_embedding(input): - embeddings_batch_response = client.embeddings( - model="mistral-embed", - input=input +def get_text_embedding(inputs): + embeddings_batch_response = client.embeddings.create( + model=model, + inputs=inputs ) return embeddings_batch_response.data[0].embedding ``` @@ -70,10 +73,10 @@ sentences = [ "A home without a cat — and a well-fed, well-petted and properly revered cat — may be a perfect home, perhaps, but how can it prove title?", "I think books are like people, in the sense that they'll turn up in your life when you most need them" ] -embeddings = [get_text_embedding(t) for t in sentences] +embeddings = [get_text_embedding([t]) for t in sentences] reference_sentence = "Books are mirrors: You only see in them what you already have inside you" -reference_embedding = get_text_embedding(reference_sentence) +reference_embedding = get_text_embedding([reference_sentence]) for t, e in zip(sentences, embeddings): distance = euclidean_distances([e], [reference_embedding]) @@ -102,7 +105,7 @@ sentences = [ "Where can I find the best cheese?", ] -sentence_embeddings = [get_text_embedding(t) for t in sentences] +sentence_embeddings = [get_text_embedding([t]) for t in sentences] sentence_embeddings_pairs = list(itertools.combinations(sentence_embeddings, 2)) sentence_pairs = list(itertools.combinations(sentences, 2)) @@ -133,7 +136,7 @@ df = pd.read_csv( def get_embeddings_by_chunks(data, chunk_size): chunks = [data[x : x + chunk_size] for x in range(0, len(data), chunk_size)] embeddings_response = [ - client.embeddings(model="mistral-embed", input=c) for c in chunks + client.embeddings.create(model=model, inputs=c) for c in chunks ] return [d.embedding for e in embeddings_response for d in e.data] @@ -216,7 +219,7 @@ After we trained the classifier with our embeddings data, we can try classify ot ```python # Classify a single example text = "I've been experiencing frequent headaches and vision problems." -clf.predict([get_text_embedding(text)]) +clf.predict([get_text_embedding([text])]) ``` Output @@ -277,56 +280,4 @@ I have a persistent cough and have been feeling quite fatigued. My fever is thro ## Retrieval Our embedding model excels in retrieval tasks, as it is trained with retrieval in mind. Embeddings are also incredibly helpful in implementing retrieval-augmented generation (RAG) systems, which use retrieved relevant information from a knowledge base to generate responses. At a high-level, we embed a knowledge base, whether it is a local directory, text files, or internal wikis, into text embeddings and store them in a vector database. Then, based on the user's query, we retrieve the most similar embeddings, which represent the relevant information from the knowledge base. Finally, we feed these relevant embeddings to a large language model to generate a response that is tailored to the user's query and context. If you are interested in learning more about how RAG systems work and how to implement a basic RAG, check out our [previous guide](/guides/rag) on this topic. -The embeddings API allows you to embed sentences. - - - -```python -from mistralai.client import MistralClient - -api_key = os.environ["MISTRAL_API_KEY"] -client = MistralClient(api_key=api_key) - -embeddings_batch_response = client.embeddings( - model="mistral-embed", - input=["Embed this sentence.", "As well as this one."], - ) -``` - - -```javascript -import MistralClient from '@mistralai/mistralai'; - -const apiKey = process.env.MISTRAL_API_KEY; - -const client = new MistralClient(apiKey); - -const input = []; -for (let i = 0; i < 10; i++) { - input.push('What is the best French cheese?'); -} -const embeddingsBatchResponse = await client.embeddings({ - model: 'mistral-embed', - input: input, -}); - -console.log('Embeddings Batch:', embeddingsBatchResponse.data); -``` - - -```bash -curl --location "https://api.mistral.ai/v1/embeddings" \ - --header 'Content-Type: application/json' \ - --header 'Accept: application/json' \ - --header "Authorization: Bearer $MISTRAL_API_KEY" \ - --data '{ - "model": "mistral-embed", - "input": [ - "Embed this sentence.", - "As well as this one." - ] - }' -``` - - diff --git a/docs/capabilities/finetuning.mdx b/docs/capabilities/finetuning.mdx index 9cde21f..426d50a 100644 --- a/docs/capabilities/finetuning.mdx +++ b/docs/capabilities/finetuning.mdx @@ -175,31 +175,47 @@ making them available for use in fine-tuning jobs. ```python +from mistralai import Mistral import os -from mistralai.client import MistralClient -api_key = os.environ.get("MISTRAL_API_KEY") -client = MistralClient(api_key=api_key) +api_key = os.environ["MISTRAL_API_KEY"] -with open("training_file.jsonl", "rb") as f: - training_data = client.files.create(file=("training_file.jsonl", f)) +client = Mistral(api_key=api_key) + +training_data = client.files.upload( + file={ + "file_name": "ultrachat_chunk_train.jsonl", + "content": open("ultrachat_chunk_train.jsonl", "rb"), + } +) ``` - + -```javascript -import MistralClient from '@mistralai/mistralai'; +```typescript +import { Mistral } from '@mistralai/mistralai'; +import fs from 'fs'; const apiKey = process.env.MISTRAL_API_KEY; -const client = new MistralClient(apiKey); +const client = new Mistral({apiKey: apiKey}); -const file = fs.readFileSync('training_file.jsonl'); -const training_data = await client.files.create({ file }); +const training_file = fs.readFileSync('training_file.jsonl'); +const training_data = await client.files.upload({ + file: { + fileName: "training_file.jsonl", + content: training_file, + } +}); -const file = fs.readFileSync('validation_file.jsonl'); -const validation_data = await client.files.create({ file }); +const validation_file = fs.readFileSync('validation_file.jsonl'); +const validation_data = await client.files.upload({ + file: { + fileName: "validation_file.jsonl", + content: validation_file, + } +}); ``` @@ -226,39 +242,48 @@ The next step is to create a fine-tuning job. - training_files: a collection of training file IDs, which can consist of a single file or multiple files - validation_files: a collection of validation file IDs, which can consist of a single file or multiple files - hyperparameters: two adjustable hyperparameters, "training_step" and "learning_rate", that users can modify. - +- auto_start: + - `auto_start=True`: Your job will be launched immediately after validation. + - `auto_start=False` (default): You can manually start the training after validation by sending a POST request to `/fine_tuning/jobs//start`. ```python -from mistralai.models.jobs import TrainingParameters - -created_jobs = client.jobs.create( - model="open-mistral-7b", - training_files=[training_data.id], - validation_files=[validation_data.id], - hyperparameters=TrainingParameters( - training_steps=10, - learning_rate=0.0001, - ) +# create a fine-tuning job +created_jobs = client.fine_tuning.jobs.create( + model="open-mistral-7b", + training_files=[{"file_id": ultrachat_chunk_train.id, "weight": 1}], + validation_files=[ultrachat_chunk_eval.id], + hyperparameters={ + "training_steps": 10, + "learning_rate":0.0001 + }, + auto_start=False ) + +# start a fine-tuning job +client.fine_tuning.jobs.start(job_id = created_jobs.id) + created_jobs ``` - - -```javascript -const createdJob = await client.jobs.create({ - model: 'open-mistral-7b', - trainingFiles: [training_data.id], - validationFiles: [validation_data.id], - hyperparameters: { - trainingSteps: 10, - learningRate: 0.0001, - }, -}); + + +```typescript +const createdJob = await client.fineTuning.jobs.create({jobIn:{ + model: 'open-mistral-7b', + trainingFiles: [{fileId: training_data.id, weight: 1}], + validationFiles: [validation_data.id], + hyperparameters: { + trainingSteps: 10, + learningRate: 0.0001, + }, + autoStart:false, + }}); + +await client.fineTuning.jobs.start({jobId: createdJob.id}) ``` @@ -298,30 +323,32 @@ You can filter and view a list of jobs using various parameters such as ```python # List jobs -jobs = client.jobs.list() +jobs = client.fine_tuning.jobs.list() print(jobs) # Retrieve a jobs -retrieved_jobs = client.jobs.retrieve(created_jobs.id) +retrieved_jobs = client.fine_tuning.jobs.get(job_id = created_jobs.id) print(retrieved_jobs) # Cancel a jobs -canceled_jobs = client.jobs.cancel(created_jobs.id) +canceled_jobs = client.fine_tuning.jobs.cancel(job_id = created_jobs.id) print(canceled_jobs) ``` - + -```javascript +```typescript // List jobs -const jobs = await client.jobs.list(); +const jobs = await client.fineTuning.jobs.list(); // Retrieve a job -const retrievedJob = await client.jobs.retrieve({ jobId: createdJob.id }); +const retrievedJob = await mistral.fineTuning.jobs.get({ jobId: createdJob.id }) // Cancel a job -const canceledJob = await client.jobs.cancel({ jobId: createdJob.id }); +const canceledJob = await mistral.fineTuning.jobs.cancel({ + jobId: createdJob.id, +}); ``` @@ -355,18 +382,16 @@ When a fine-tuned job is finished, you will be able to see the fine-tuned model ```python -from mistralai.models.chat_completion import ChatMessage - -chat_response = client.chat( +chat_response = client.chat.complete( model=retrieved_job.fine_tuned_model, - messages=[ChatMessage(role='user', content='What is the best French cheese?')] + messages = [{"role":'user', "content":'What is the best French cheese?'}] ) ``` - + -```javascript +```typescript const chatResponse = await client.chat({ model: retrievedJob.fine_tuned_model, messages: [{role: 'user', content: 'What is the best French cheese?'}], @@ -397,7 +422,7 @@ curl "https://api.mistral.ai/v1/chat/completions" \ ```python -client.delete_model(retrieved_job.fine_tuned_model) +client.models.delete(model_id=retrieved_job.fine_tuned_model) ``` @@ -416,4 +441,4 @@ curl --location --request DELETE 'https://api.mistral.ai/v1/models/ft:open-mistr import FAQ from "../guides/finetuning_sections/_04_faq.md"; - \ No newline at end of file + diff --git a/docs/capabilities/function-calling.mdx b/docs/capabilities/function-calling.mdx index 679e033..ab78d2d 100644 --- a/docs/capabilities/function-calling.mdx +++ b/docs/capabilities/function-calling.mdx @@ -128,11 +128,7 @@ names_to_functions = { Suppose a user asks the following question: “What’s the status of my transaction?” A standalone LLM would not be able to answer this question, as it needs to query the business logic backend to access the necessary data. But what if we have an exact tool we can use to answer this question? We could potentially provide an answer! ```python -from mistralai.models.chat_completion import ChatMessage - -messages = [ - ChatMessage(role="user", content="What's the status of my transaction T1001?") -] +messages = [{"role": "user", "content": "What's the status of my transaction T1001?"}] ``` ## Step 2. Model: Generate function arguments @@ -150,14 +146,19 @@ Users can use `tool_choice` to speficy how tools are used: ```python -from mistralai.client import MistralClient -from mistralai.models.chat_completion import ChatMessage +import os +from mistralai import Mistral +api_key = os.environ["MISTRAL_API_KEY"] model = "mistral-large-latest" -api_key="TYPE YOUR API KEY" -client = MistralClient(api_key=api_key) -response = client.chat(model=model, messages=messages, tools=tools, tool_choice="auto") +client = Mistral(api_key=api_key) +response = client.chat.complete( + model = model, + messages = messages, + tools = tools, + tool_choice = "any", +) response ``` @@ -165,7 +166,7 @@ We get the response including tool_calls with the chosen function name `retrieve Output: ``` -ChatCompletionResponse(id='9ec8d47af52d4c258c641a7d9f62336e', object='chat.completion', created=1707931630, model='mistral-large', choices=[ChatCompletionResponseChoice(index=0, message=ChatMessage(role='assistant', content='', name=None, tool_calls=[ToolCall(id='null', type=, function=FunctionCall(name='retrieve_payment_status', arguments='{"transaction_id": "T1001"}'))]), finish_reason=)], usage=UsageInfo(prompt_tokens=211, total_tokens=250, completion_tokens=39)) +ChatCompletionResponse(id='7cbd8962041442459eb3636e1e3cbf10', object='chat.completion', model='mistral-large-latest', usage=Usage(prompt_tokens=94, completion_tokens=30, total_tokens=124), created=1721403550, choices=[Choices(index=0, finish_reason='tool_calls', message=AssistantMessage(content='', tool_calls=[ToolCall(function=FunctionCall(name='retrieve_payment_status', arguments='{"transaction_id": "T1001"}'), id='D681PevKs', type='function')], prefix=False, role='assistant'))]) ``` Let’s add the response message to the `messages` list. @@ -212,9 +213,12 @@ Output We can now provide the output from the tools to Mistral models, and in return, the Mistral model can produce a customised final response for the specific user. ```python -messages.append(ChatMessage(role="tool", name=function_name, content=function_result, tool_call_id=tool_call.id)) +messages.append({"role":"tool", "name":function_name, "content":function_result, "tool_call_id":tool_call.id}) -response = client.chat(model=model, messages=messages) +response = client.chat.complete( + model = model, + messages = messages +) response.choices[0].message.content ``` diff --git a/docs/capabilities/guardrailing.mdx b/docs/capabilities/guardrailing.mdx index 08e4c2d..9f24cd8 100644 --- a/docs/capabilities/guardrailing.mdx +++ b/docs/capabilities/guardrailing.mdx @@ -14,16 +14,16 @@ The ability to enforce guardrails in chat generations is crucial for front-facin ```python -chat_response = client.chat( - model="mistral-large-latest", - messages=[ChatMessage(role="user", content="What is the best French cheese?")], - safe_prompt=True +chat_response = client.chat.complete( + model = "mistral-large-latest", + messages = [{"role":"user", "content":"What is the best French cheese?"}], + safe_prompt = True ) ``` - -```javascript -const chatResponse = await client.chat( + +```typescript +const chatResponse = await client.chat.complete( model: 'mistral-large-latest', messages: [{role: 'user', content: 'What is the best French cheese?'}], safe_prompt: true diff --git a/docs/capabilities/json-mode.mdx b/docs/capabilities/json-mode.mdx index 30d72c0..45d6652 100644 --- a/docs/capabilities/json-mode.mdx +++ b/docs/capabilities/json-mode.mdx @@ -21,26 +21,29 @@ To prevent infinite generations, users are encouraged to ask the model for **sho ```python import os -from mistralai.client import MistralClient -from mistralai.models.chat_completion import ChatMessage +from mistralai import Mistral api_key = os.environ["MISTRAL_API_KEY"] model = "mistral-large-latest" -client = MistralClient(api_key=api_key) - +client = Mistral(api_key=api_key) messages = [ - ChatMessage(role="user", content="What is the best French meal? Return the name and the ingredients in short JSON object.") + { + "role": "user", + "content": "What is the best French meal? Return the name and the ingredients in short JSON object.", + } ] - -chat_response = client.chat( - model=model, - response_format={"type": "json_object"}, - messages=messages, +chat_response = client.chat.complete( + model = model, + messages = messages, + response_format = { + "type": "json_object", + } ) print(chat_response.choices[0].message.content) + ``` Example output: ``` @@ -49,21 +52,22 @@ Example output: - -```javascript -import MistralClient from '@mistralai/mistralai'; + +```typescript +import { Mistral } from "mistralai"; const apiKey = process.env.MISTRAL_API_KEY; -const client = new MistralClient(apiKey); +const mistral = new Mistral({apiKey: apiKey}); -const chatResponse = await client.chat({ - model: 'mistral-large-latest', - response_format: {'type': 'json_object'}, - messages: [{role: 'user', content: 'What is the best French meal? Return the name and the ingredients in JSON format.'}], -}); +const chatResponse = await mistral.chat.complete({ + model: "mistral-large-latest", + messages: [{role: 'user', content: 'What is the best French meal? Return the name and the ingredients in JSON format.'}], + response_format: {type: 'json_object'}, + } +); -console.log('Chat:', chatResponse.choices[0].message.content); +console.log('JSON:', chatResponse.choices[0].message.content) ``` diff --git a/docs/deployment/cloud/aws.mdx b/docs/deployment/cloud/aws.mdx index 51daa07..1327ef6 100644 --- a/docs/deployment/cloud/aws.mdx +++ b/docs/deployment/cloud/aws.mdx @@ -7,59 +7,80 @@ sidebar_position: 3.22 import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; +## Introduction -You can deploy the following Mistral AI models on the AWS Bedrock service: -- Mistral 7B Instruct -- Mixtral 8x7B Instruct +Mistral AI's open and commercial models can be deployed on the AWS Bedrock cloud platform as +fully managed endpoints. AWS Bedrock is a serverless service so you don't have +to manage any infrastructure. + +As of today, the following models are available: + +- Mistral 7B +- Mixtral 8x7B - Mistral Small - Mistral Large -This page provides a straightforward guide on how to get started on using -Mistral Large as an AWS Bedrock foundational model. +For more details, visit the [models](../../../getting-started/models) page. -## Pre-requisites +## Getting started -In order to query the model you will need: +The following sections outline the steps to deploy and query a Mistral model on the +AWS Bedrock platform. + +The following items are required: - Access to an **AWS account** within a region that supports the AWS Bedrock service and - offers access to Mistral Large: see + offers access to your model of choice: see [the AWS documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html) for model availability per region. - An AWS **IAM principal** (user, role) with sufficient permissions, see [the AWS documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/security-iam.html) for more details. -- **Access to the Mistral AI models enabled** from the AWS Bedrock home page, see - [the AWS documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html) - for more details. - A local **code environment** set up with the relevant AWS SDK components, namely: - the AWS CLI: see [the AWS documentation](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) for the installation procedure. - the `boto3` Python library: see the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/quickstart.html) for the installation procedure. -## Querying the model +### Requesting access to the model + +Follow the instructions on +[the AWS documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html) +to unlock access to the Mistral model of your choice. + +### Querying the model + +AWS Bedrock models are accessible through the Converse API. + +Before running the examples below, make sure to sure to : -Before starting, make sure to properly configure the authentication credentials for your development -environment. +- Properly configure the authentication +credentials for your development environment. [The AWS documentation](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-files.html) -provides an in-depth explanation on the required steps. +provides an in-depth explanation on the required steps. +- Create a Python virtual environment with the `boto3` package (version >= `1.34.131`). +- Set the following environment variables: + - `AWS_REGION`: The region where the model is deployed (e.g. `us-west-2`), + - `AWS_BEDROCK_MODEL_ID`: The model ID (e.g. `mistral.mistral-large-2407-v1:0`). ```python import boto3 + import os - MISTRAL_LARGE_BEDROCK_ID = "mistral.mistral-large-2402-v1:0" - AWS_REGION = "eu-west-3" + region = os.environ.get("AWS_REGION") + model_id = os.environ.get("AWS_BEDROCK_MODEL_ID") - bedrock_client = boto3.client(service_name='bedrock-runtime', region_name=AWS_REGION) + bedrock_client = boto3.client(service_name='bedrock-runtime', region_name=region) - messages = [{"role": "user", "content": [{"text": "What is the best French cheese?"}]}] + user_msg = "Who is the best French painter? Answer in one short sentence." + messages = [{"role": "user", "content": [{"text": user_msg}]}] temperature = 0.0 max_tokens = 1024 - params = {"modelId": MISTRAL_LARGE_BEDROCK_ID, + params = {"modelId": model_id, "messages": messages, "inferenceConfig": {"temperature": temperature, "maxTokens": max_tokens}} @@ -69,30 +90,24 @@ provides an in-depth explanation on the required steps. print(resp["output"]["message"]["content"][0]["text"]) ``` - + ```shell - aws bedrock-runtime invoke-model \ - --model-id "mistral.mistral-large-2402-v1:0" \ - --body '{"prompt": "What is the best French cheese?", "max_tokens": 512, "top_p": 0.8, "temperature": 0.5}' \ - resp.json \ - --cli-binary-format raw-in-base64-out + aws bedrock-runtime converse \ + --region $AWS_REGION \ + --model-id $AWS_BEDROCK_MODEL_ID \ + --messages '[{"role": "user", "content": [{"text": "Who is the best French painter? Answer in one short sentence."}]}]' ``` ## Going further -You can find a more detailed user guide on the [AWS documentation on inference requests for Mistral models](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-mistral.html#model-parameters-mistral-request-response). +For more details and examples, refer to the following resources: -For more advanced examples, you can also check out the following notebooks: +- [AWS GitHub repository with multiple examples and use-cases leveraging Mistral models](https://github.com/aws-samples/mistral-on-aws). +- [AWS documentation on the Converse API](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html). +- [AWS documentation on inference requests for Mistral models](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-mistral.html#model-parameters-mistral-request-response). -- [Bedrock function calling with Mistral models](https://github.com/aws-samples/bedrock-mistral-prompting-examples/blob/main/notebooks/Bedrock_Mistral_function_calling.ipynb) -- [Advanced RAG pipeline for Mistral models with Q&A Automation and Model Evaluation using LlamaIndex and Ragas](https://github.com/aws-samples/bedrock-mistral-prompting-examples/blob/main/notebooks/Mistral_model_RAG_pipeline_evaluation.ipynb) -- [Transitioning from OpenAI to Mistral: a guide](https://github.com/aws-samples/bedrock-mistral-prompting-examples/blob/main/notebooks/Transition_from_openai_to_mistral.ipynb) -- [Abstract document summarization with Langchain using Mistral Large on Bedrock](https://github.com/aws-samples/bedrock-mistral-prompting-examples/blob/main/notebooks/Abstract%20Document%20Summarization%20with%20Langchain%20using%20Mistral%20Large%20on%20Bedrock.ipynb) -- [Advanced multi-chain routing with Langchain and Mistral models](https://github.com/aws-samples/bedrock-mistral-prompting-examples/blob/main/notebooks/Advanced_Multi-Chain_Routing_With_LangChain.ipynb) -- [Mistral Large prompting: getting started](https://github.com/aws-samples/bedrock-mistral-prompting-examples/blob/main/notebooks/mistral_large_getting_started_101.ipynb) -- [Getting started with Mistral Tool Use and the Converse API](https://github.com/aws-samples/bedrock-mistral-prompting-examples/blob/main/notebooks/Tool_Use_with_Mistral.ipynb) diff --git a/docs/deployment/cloud/azure.mdx b/docs/deployment/cloud/azure.mdx index 71b9e72..8ea1565 100644 --- a/docs/deployment/cloud/azure.mdx +++ b/docs/deployment/cloud/azure.mdx @@ -7,76 +7,117 @@ sidebar_position: 3.21 import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; +## Introduction -The Mistral AI open and commercial models can be deployed on your Azure subscription. +Mistral AI's open and commercial models can be deployed on the Microsoft Azure AI cloud platform +in two ways: -This page explains how to easily get started with Mistral Large deployed as an Azure AI endpoint. -If you use the Mistral AI Python client, it should be a drop-in replacement where you only need -to change the client parameters (endpoint URL, API key, model name). +- _Pay-as-you-go managed services_: Using Model-as-a-Service (MaaS) serverless API + deployments billed on endpoint usage. No GPU capacity quota is required for deployment. -## Deploying Mistral Small and Large +- _Real-time endpoints_: With quota-based billing tied to the underlying GPU + infrastructure you choose to deploy. -Mistral AI models can be deployed on Azure AI either as: -- _pay-as-you-go managed services_ billed on endpoint usage, -- _real-time endpoints_ with quota-based billing indexed on the infrastructure you choose (only for existing open-weight models). +This page focuses on the MaaS offering, where the following models are available: -To deploy Mistral Small or Large as a pay-as-you-go managed service, follow the instructions from -[the Azure AI documentation](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-mistral) -and select the model that your endpoint should serve. +- Mistral Large +- Mistral Small +- Mistral NeMo -## Querying the model +For more details, visit the [models](../../../getting-started/models) page. -Once your model is deployed and provided that you have the relevant permissions, consuming it -will basically be the same process as for a Mistral AI platform endpoint. -To run the examples below, you will need to define the following environment variables: - - `AZUREAI_ENDPOINT` is your endpoint URL, should be of the form `https://your-endpoint.inference.ai.azure.com/v1/chat/completions`. - - `AZUREAI_API_KEY` is your authentication key. +## Getting started +The following sections outline the steps to deploy and query a Mistral model on the Azure AI MaaS platform. + +### Deploying the model + +Follow the instructions on the [Azure documentation](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-mistral?tabs=mistral-large#create-a-new-deployment) +to create a new deployment for the model of your choice. Once deployed, take +note of its corresponding URL and secret key. + + +### Querying the model + +Deployed endpoints expose a REST API that you can query using Mistral's SDKs or +plain HTTP calls. + +To run the examples below, set the following environment variables: + - `AZUREAI_ENDPOINT`: Your endpoint URL, should be of the form `https://your-endpoint.inference.ai.azure.com/v1/chat/completions`. + - `AZUREAI_API_KEY`: Your secret key. - - ```shell + + ```bash curl --location $AZUREAI_ENDPOINT/v1/chat/completions \ - --header 'Content-Type: application/json' \ - --header 'Authorization: Bearer $AZUREAI_API_KEY' \ + --header "Content-Type: application/json" \ + --header "Authorization: Bearer $AZURE_API_KEY" \ --data '{ "model": "azureai", "messages": [ { "role": "user", - "content": "What is the best French cheese ?" + "content": "Who is the best French painter? Answer in one short sentence." } ] }' ``` - + This code requires a virtual environment with the following packages: + - `mistralai-azure>=1.0.0` + + ```python + from mistralai_azure import MistralAzure + import os + + endpoint = os.environ.get("AZUREAI_ENDPOINT", "") + api_key = os.environ.get("AZUREAI_API_KEY", "") - You will need to install the Mistral AI Python client, by following the instructions from [the repository](https://github.com/mistralai/client-python). - - ```python - import os - from mistralai.client import MistralClient - from mistralai.models.chat_completion import ChatMessage - - endpoint = os.environ["AZUREAI_ENDPOINT"] - api_key = os.environ["AZUREAI_API_KEY"] - model = "azureai" - - client = MistralClient(api_key=api_key, - endpoint=endpoint) - - # With streaming - for chunk in client.chat_stream( - model=model, - messages=[ChatMessage(role="user", content="What is the best French cheese?")], - ): - if chunk.choices[0].delta.content is not None: - print(chunk.choices[0].delta.content, end="") - ``` - + client = MistralAzure(azure_endpoint=endpoint, + azure_api_key=api_key) + + resp = client.chat.complete(messages=[ + { + "role": "user", + "content": "Who is the best French painter? Answer in one short sentence." + }, + ], model="azureai") + + if resp: + print(resp) + ``` + + + This code requires the following package: + - `@mistralai/mistralai-azure` (version >= `1.0.0`) + + ```typescript + import { MistralAzure } from "@mistralai/mistralai-azure"; + + const client = new MistralAzure({ + endpoint: process.env.AZUREAI_ENDPOINT || "", + apiKey: process.env.AZUREAI_API_KEY || "" + }); + + async function chat_completion(user_msg: string) { + const resp = await client.chat.complete({ + model: "azureai", + messages: [ + { + content: user_msg, + role: "user", + }, + ], + }); + if (resp.choices && resp.choices.length > 0) { + console.log(resp.choices[0]); + } + } + + chat_completion("Who is the best French painter? Answer in one short sentence."); + ``` @@ -84,10 +125,8 @@ To run the examples below, you will need to define the following environment var ## Going further -For other usage examples, you can also check the following notebooks: +For more details and examples, refer to the following resources: +- [Release blog post for Mistral Large 2 and Mistral NeMo](https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/ai-innovation-continues-introducing-mistral-large-2-and-mistral/ba-p/4200181). +- [Azure documentation for MaaS deployment of Mistral models](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-mistral). +- [Azure ML examples GitHub repository](https://github.com/Azure/azureml-examples/tree/main/sdk/python/foundation-models/mistral) with several Mistral-based samples. -- [Basic CLI with `curl` and Python web request](https://github.com/Azure/azureml-examples/blob/main/sdk/python/foundation-models/mistral/webrequests.ipynb) -- [Mistral AI Python client example](https://github.com/Azure/azureml-examples/blob/main/sdk/python/foundation-models/mistral/mistralai.ipynb) -- [Langchain example](https://github.com/Azure/azureml-examples/blob/main/sdk/python/foundation-models/mistral/langchain.ipynb) -- [LiteLLM example](https://github.com/Azure/azureml-examples/blob/main/sdk/python/foundation-models/mistral/litellm.ipynb) -- [OpenAI SDK example](https://github.com/Azure/azureml-examples/blob/main/sdk/python/foundation-models/mistral/openaisdk.ipynb) diff --git a/docs/deployment/cloud/vertex.mdx b/docs/deployment/cloud/vertex.mdx index 17d8d7d..8ddf3c1 100644 --- a/docs/deployment/cloud/vertex.mdx +++ b/docs/deployment/cloud/vertex.mdx @@ -7,232 +7,216 @@ sidebar_position: 3.23 import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; +## Introduction -You can deploy the following Mistral AI models from Google Cloud Vertex AI's Model Garden: +Mistral AI's open and commercial models can be deployed on the Google Cloud Vertex AI +platform as fully managed endpoints. Mistral models on Vertex AI are serverless services +so you don't have to manage any infrastructure. + +As of today, the following models are available: -- Mistral NeMo -- Codestral (instruct and FIM modes) - Mistral Large +- Mistral NeMo +- Codestral (chat and FIM completions) + +For more details, visit the [models](../../../getting-started/models) page. -## Pre-requisites +## Getting started -In order to query the model you will need: +The following sections outline the steps to deploy and query a Mistral model on the +Vertex AI platform. + +### Requesting access to the model + +The following items are required: - Access to a Google Cloud Project with the Vertex AI API enabled - Relevant IAM permissions to be able to enable the model and query endpoints through the following roles: - [Vertex AI User IAM role](https://cloud.google.com/vertex-ai/docs/general/access-control#aiplatform.user). - Consumer Procurement Entitlement Manager role -On the client side, you will also need: -- The `gcloud` CLI to authenticate against the Google Cloud APIs, please refer to -[this page](https://cloud.google.com/docs/authentication/provide-credentials-adc#google-idp) -for more details. -- A Python virtual environment with the `mistralai-google-cloud` client package installed. -- The following environment variables properly set up: - - `GOOGLE_PROJECT_ID`: a Google Cloud Project ID with the the Vertex AI API enabled - - `GOOGLE_REGION`: a Google Cloud region where Mistral models are available - (e.g. `europe-west4`) - -## Querying the models (instruct mode) - - - - - - ```python - import httpx - import google.auth - from google.auth.transport.requests import Request - import os +To enable the model of your choice, navigate to its card in the +[Vertex Model Garden catalog](https://cloud.google.com/vertex-ai/generative-ai/docs/model-garden/explore-models), +then click on "Enable". - def get_credentials() -> str: - credentials, project_id = google.auth.default( - scopes=["https://www.googleapis.com/auth/cloud-platform"] - ) - credentials.refresh(Request()) - return credentials.token - - - def build_endpoint_url( - region: str, - project_id: str, - model_name: str, - model_version: str, - streaming: bool = False, - ) -> str: - base_url = f"https://{region}-aiplatform.googleapis.com/v1/" - project_fragment = f"projects/{project_id}" - location_fragment = f"locations/{region}" - specifier = "streamRawPredict" if streaming else "rawPredict" - model_fragment = f"publishers/mistralai/models/{model_name}@{model_version}" - url = f"{base_url}{'/'.join([project_fragment, location_fragment, model_fragment])}:{specifier}" - return url - - - # Retrieve Google Cloud Project ID and Region from environment variables - project_id = os.environ.get("GOOGLE_PROJECT_ID") - region = os.environ.get("GOOGLE_REGION") - - # Retrieve Google Cloud credentials. - access_token = get_credentials() - - model = "mistral-nemo" # Replace with the model you want to use - model_version = "2407" # Replace with the model version you want to use - is_streamed = False # Change to True to stream token responses - - # Build URL - url = build_endpoint_url( - project_id=project_id, - region=region, - model_name=model, - model_version=model_version, - streaming=is_streamed - ) +### Querying the model (chat completion) - # Define query headers - headers = { - "Authorization": f"Bearer {access_token}", - "Accept": "application/json", - } +Available models expose a REST API that you can query using Mistral's SDKs or plain HTTP calls. - # Define POST payload - data = { - "model": model, - "messages": [{"role": "user", "content": "Who is the best French painter?"}], - "stream": is_streamed, - } - # Make the call - with httpx.Client() as client: - resp = client.post(url, json=data, headers=headers, timeout=None) - print(resp.text) +To run the examples below: - ``` +- Install the `gcloud` CLI to authenticate against the Google Cloud APIs, please refer to +[this page](https://cloud.google.com/docs/authentication/provide-credentials-adc#google-idp) +for more details. +- Set the following environment variables: + - `GOOGLE_CLOUD_REGION`: The target cloud region. + - `GOOGLE_CLOUD_PROJECT_ID`: The name of your project. + - `VERTEX_MODEL_NAME`: The name of the model to query (e.g. `mistral-large`). + - `VERTEX_MODEL_VERSION`: The version of the model to query (e.g. `2407`). + - + - ```bash - MODEL="mistral-nemo" - MODEL_VERSION="2407" - - url="https://$GOOGLE_REGION-aiplatform.googleapis.com/v1/projects/$GOOGLE_PROJECT_ID/locations/$GOOGLE_REGION/publishers/mistralai/models/$MODEL@$MODEL_VERSION:rawPredict" - - curl \ - -X POST \ - -H "Authorization: Bearer $(gcloud auth print-access-token)" \ - -H "Content-Type: application/json" \ - $url \ - --data '{ - "model": "'"$MODEL"'", + base_url="https://$GOOGLE_CLOUD_REGION-aiplatform.googleapis.com/v1/projects/$GOOGLE_CLOUD_PROJECT_ID/locations/$GOOGLE_CLOUD_REGION/publishers/mistralai/models" + model_version="$VERTEX_MODEL_NAME@$VERTEX_MODEL_VERSION" + url="$base_url/$model_version:rawPredict" + + curl --location $url\ + --header "Content-Type: application/json" \ + --header "Authorization: Bearer $(gcloud auth print-access-token)" \ + --data '{ + "model": "'"$VERTEX_MODEL_NAME"'", "temperature": 0, "messages": [ - {"role": "user", "content": "What is the best French cheese?"} - ] + {"role": "user", "content": "Who is the best French painter? Answer in one short sentence."} + ], + "stream": false }' - ``` - - -## Querying Codestral in FIM mode - - - + This code requires a virtual environment with the following packages: + - `mistralai[gcp]>=1.0.0` ```python - import httpx - import google.auth - from google.auth.transport.requests import Request import os + from mistralai_gcp import MistralGoogleCloud + + region = os.environ.get("GOOGLE_CLOUD_REGION") + project_id = os.environ.get("GOOGLE_CLOUD_PROJECT_NAME") + model_name = os.environ.get("VERTEX_MODEL_NAME") + model_version = os.environ.get("VERTEX_MODEL_VERSION") + + client = MistralGoogleCloud(region=region, project_id=project_id) + + resp = client.chat.complete( + model = f"{model_name}-{model_version}", + messages=[ + { + "role": "user", + "content": "Who is the best French painter? Answer in one short sentence.", + } + ], + ) + print(resp.choices[0].message.content) + ``` + + + This code requires the following package: + - `@mistralai/mistralai-gcp` (version >= `1.0.0`) + + ```typescript + import { MistralGoogleCloud } from "@mistralai/mistralai-gcp"; + + const client = new MistralGoogleCloud({ + region: process.env.GOOGLE_CLOUD_REGION || "", + projectId: process.env.GOOGLE_CLOUD_PROJECT_ID || "", + }); + + const modelName = process.env.VERTEX_MODEL_NAME|| ""; + const modelVersion = process.env.VERTEX_MODEL_VERSION || ""; + + async function chatCompletion(user_msg: string) { + const resp = await client.chat.complete({ + model: modelName + "-" + modelVersion, + messages: [ + { + content: user_msg, + role: "user", + }, + ], + }); + if (resp.choices && resp.choices.length > 0) { + console.log(resp.choices[0]); + } + } - def get_credentials() -> str: - credentials, project_id = google.auth.default( - scopes=["https://www.googleapis.com/auth/cloud-platform"] - ) - credentials.refresh(Request()) - return credentials.token + chatCompletion("Who is the best French painter? Answer in one short sentence."); + ``` + + - def build_endpoint_url( - region: str, - project_id: str, - model_name: str, - model_version: str, - streaming: bool = False, - ) -> str: - base_url = f"https://{region}-aiplatform.googleapis.com/v1/" - project_fragment = f"projects/{project_id}" - location_fragment = f"locations/{region}" - specifier = "streamRawPredict" if streaming else "rawPredict" - model_fragment = f"publishers/mistralai/models/{model_name}@{model_version}" - url = f"{base_url}{'/'.join([project_fragment, location_fragment, model_fragment])}:{specifier}" - return url +### Querying the model (FIM completion) +Codestral can be queried using an additional completion mode called fill-in-the-middle (FIM). +For more information, see the +[code generation section](../../../capabilities/code_generation/#fill-in-the-middle-endpoint). - # Retrieve Google Cloud Project ID and Region from environment variables - project_id = os.environ.get("GOOGLE_PROJECT_ID") - region = os.environ.get("GOOGLE_REGION") - # Retrieve Google Cloud credentials. - access_token = get_credentials() + + + ```bash + VERTEX_MODEL_NAME=codestral + VERTEX_MODEL_VERSION=2405 + + base_url="https://$GOOGLE_CLOUD_REGION-aiplatform.googleapis.com/v1/projects/$GOOGLE_CLOUD_PROJECT_ID/locations/$GOOGLE_CLOUD_REGION/publishers/mistralai/models" + model_version="$VERTEX_MODEL_NAME@$VERTEX_MODEL_VERSION" + url="$base_url/$model_version:rawPredict" + + curl --location $url\ + --header "Content-Type: application/json" \ + --header "Authorization: Bearer $(gcloud auth print-access-token)" \ + --data '{ + "model":"'"$VERTEX_MODEL_NAME"'", + "prompt": "def count_words_in_file(file_path: str) -> int:", + "suffix": "return n_words", + "stream": false + }' + ``` + + - model = "codestral" - model_version = "2405" - is_streamed = False # Change to True to stream token responses - - # Build URL - url = build_endpoint_url( - project_id=project_id, - region=region, - model_name=model, - model_version=model_version, - streaming=is_streamed - ) + ```python + import os + from mistralai_gcp import MistralGoogleCloud - # Define query headers - headers = { - "Authorization": f"Bearer {access_token}", - "Accept": "application/json", - } + region = os.environ.get("GOOGLE_CLOUD_REGION") + project_id = os.environ.get("GOOGLE_CLOUD_PROJECT_NAME") + model_name = "codestral" + model_version = "2405" - # Define POST payload - data = { - "model": model, - "prompt": "def say_hello(name: str) -> str:", - "suffix": "return n_words" - } - # Make the call - with httpx.Client() as client: - resp = client.post(url, json=data, headers=headers, timeout=None) - print(resp.text) + client = MistralGoogleCloud(region=region, project_id=project_id) + resp = client.fim.complete( + model = f"{model_name}-{model_version}", + prompt="def count_words_in_file(file_path: str) -> int:", + suffix="return n_words" + ) + print(resp.choices[0].message.content) ``` - - - ```bash - MODEL="codestral" - MODEL_VERSION="2405" - - url="https://$GOOGLE_REGION-aiplatform.googleapis.com/v1/projects/$GOOGLE_PROJECT_ID/locations/$GOOGLE_REGION/publishers/mistralai/models/$MODEL@$MODEL_VERSION:rawPredict" - - - curl \ - -X POST \ - -H "Authorization: Bearer $(gcloud auth print-access-token)" \ - -H "Content-Type: application/json" \ - $url \ - --data '{ - "model":"'"$MODEL"'", - "prompt": "def count_words_in_file(file_path: str) -> int:", - "suffix": "return n_words" - }' + + + ```typescript + import { MistralGoogleCloud } from "@mistralai/mistralai-gcp"; + + const client = new MistralGoogleCloud({ + region: process.env.GOOGLE_CLOUD_REGION || "", + projectId: process.env.GOOGLE_CLOUD_PROJECT_ID || "", + }); + + const modelName = "codestral"; + const modelVersion = "2405"; + + async function fimCompletion(prompt: string, suffix: string) { + const resp = await client.fim.complete({ + model: modelName + "-" + modelVersion, + prompt: prompt, + suffix: suffix + }); + if (resp.choices && resp.choices.length > 0) { + console.log(resp.choices[0]); + } + } + fimCompletion("def count_words_in_file(file_path: str) -> int:", + "return n_words"); ``` diff --git a/docs/deployment/self-deployment/cloudflare.mdx b/docs/deployment/self-deployment/cloudflare.mdx index 574a085..2e0f849 100644 --- a/docs/deployment/self-deployment/cloudflare.mdx +++ b/docs/deployment/self-deployment/cloudflare.mdx @@ -25,9 +25,9 @@ To set-up Workers AI on Cloudflare, you need to create an account on the [Cloudf -d '{ "messages": [{ "role": "user", "content": "[INST] 2 + 2 ? [/INST]" }]}' ``` - + - ```javascript + ```typescript async function run(model, prompt) { const messages = [ { role: "user", content: prompt }, diff --git a/docs/deployment/self-deployment/tgi.mdx b/docs/deployment/self-deployment/tgi.mdx index cb82f8a..0d8f24b 100644 --- a/docs/deployment/self-deployment/tgi.mdx +++ b/docs/deployment/self-deployment/tgi.mdx @@ -133,9 +133,9 @@ client.text_generation(prompt="What is Deep Learning?") ``` - + -```javascript +```typescript async function query() { const response = await fetch( 'http://127.0.0.1:8080/generate', diff --git a/docs/getting-started/Open-weight-models.mdx b/docs/getting-started/Open-weight-models.mdx index e34f72c..4582dad 100644 --- a/docs/getting-started/Open-weight-models.mdx +++ b/docs/getting-started/Open-weight-models.mdx @@ -1,6 +1,6 @@ --- id: open_weight_models -title: Apache 2.0 models +title: Open weight models sidebar_position: 1.4 --- diff --git a/docs/getting-started/changelog.mdx b/docs/getting-started/changelog.mdx index 5a5db9d..427bf02 100644 --- a/docs/getting-started/changelog.mdx +++ b/docs/getting-started/changelog.mdx @@ -6,6 +6,10 @@ sidebar_position: 1.8 This is the list of changes to the Mistral API. +July 29, 2024 +- We released version 1.0 of our Python and JS SDKs with major upgrades and syntax changes. Check out our [migration guide](https://github.com/mistralai/client-python/blob/main/MIGRATION.md) for details. +- We released Agents API. See details [here](/capabilities/agents/). + July 24, 2024 - We released Mistral Large 2 (`mistral-large-2407`). - We added fine-tuning support for Codestral, Mistral Nemo and Mistral Large. Now the model choices for fine-tuning are `open-mistral-7b` (v0.3), `mistral-small-latest` (`mistral-small-2402`), `codestral-latest` (`codestral-2405`), `open-mistral-nemo` and , `mistral-large-latest` (`mistral-large-2407`) diff --git a/docs/getting-started/clients.mdx b/docs/getting-started/clients.mdx index 4508e7c..aea1113 100644 --- a/docs/getting-started/clients.mdx +++ b/docs/getting-started/clients.mdx @@ -7,7 +7,7 @@ sidebar_position: 1.5 import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -We provide client codes in both Python and Javascript. +We provide client codes in both Python and Typescript. ## Python @@ -18,17 +18,22 @@ pip install mistralai Once installed, you can run the chat completion: ```python -from mistralai.client import MistralClient -from mistralai.models.chat_completion import ChatMessage +import os +from mistralai import Mistral api_key = os.environ["MISTRAL_API_KEY"] model = "mistral-large-latest" -client = MistralClient(api_key=api_key) +client = Mistral(api_key=api_key) -chat_response = client.chat( - model=model, - messages=[ChatMessage(role="user", content="What is the best French cheese?")] +chat_response = client.chat.complete( + model = model, + messages = [ + { + "role": "user", + "content": "What is the best French cheese?", + }, + ] ) print(chat_response.choices[0].message.content) @@ -37,9 +42,9 @@ print(chat_response.choices[0].message.content) See more examples [here](https://github.com/mistralai/client-python/tree/main/examples). -## Javascript +## Typescript -You can install our [Javascript Client](https://github.com/mistralai/client-js) in your project using: +You can install our [Typescript Client](https://github.com/mistralai/client-ts) in your project using: ```bash npm install @mistralai/mistralai @@ -47,14 +52,14 @@ npm install @mistralai/mistralai Once installed, you can run the chat completion: -```javascript -import MistralClient from '@mistralai/mistralai'; +```typescript +import { Mistral } from '@mistralai/mistralai'; const apiKey = process.env.MISTRAL_API_KEY || 'your_api_key'; -const client = new MistralClient(apiKey); +const client = new Mistral({apiKey: apiKey}); -const chatResponse = await client.chat({ +const chatResponse = await client.chat.complete({ model: 'mistral-tiny', messages: [{role: 'user', content: 'What is the best French cheese?'}], }); diff --git a/docs/getting-started/quickstart.mdx b/docs/getting-started/quickstart.mdx index deaec7a..6cb050a 100644 --- a/docs/getting-started/quickstart.mdx +++ b/docs/getting-started/quickstart.mdx @@ -30,33 +30,36 @@ After a few moments, you will be able to use our `chat` endpoint: ```python import os -from mistralai.client import MistralClient -from mistralai.models.chat_completion import ChatMessage +from mistralai import Mistral api_key = os.environ["MISTRAL_API_KEY"] model = "mistral-large-latest" -client = MistralClient(api_key=api_key) +client = Mistral(api_key=api_key) -chat_response = client.chat( - model=model, - messages=[ChatMessage(role="user", content="What is the best French cheese?")] +chat_response = client.chat.complete( + model= model, + messages = [ + { + "role": "user", + "content": "What is the best French cheese?", + }, + ] ) - print(chat_response.choices[0].message.content) ``` - -```javascript -import MistralClient from '@mistralai/mistralai'; + +```typescript +import { Mistral } from '@mistralai/mistralai'; const apiKey = process.env.MISTRAL_API_KEY; -const client = new MistralClient(apiKey); +const client = new Mistral({apiKey: apiKey}); -const chatResponse = await client.chat({ +const chatResponse = await client.chat.complete({ model: 'mistral-large-latest', messages: [{role: 'user', content: 'What is the best French cheese?'}], }); @@ -87,16 +90,17 @@ further analysis or processing in NLP applications. ```python -from mistralai.client import MistralClient +import os +from mistralai import Mistral api_key = os.environ["MISTRAL_API_KEY"] model = "mistral-embed" -client = MistralClient(api_key=api_key) +client = Mistral(api_key=api_key) -embeddings_response = client.embeddings( +embeddings_response = client.embeddings.create( model=model, - input=["Embed this sentence.", "As well as this one."] + inputs=["Embed this sentence.", "As well as this one."] ) print(embeddings_response) @@ -104,17 +108,17 @@ print(embeddings_response) - -```javascript -import MistralClient from '@mistralai/mistralai'; + +```typescript +import { Mistral } from '@mistralai/mistralai'; const apiKey = process.env.MISTRAL_API_KEY; -const client = new MistralClient(apiKey); +const client = new Mistral({apiKey: apiKey}); -const embeddingsResponse = await client.embeddings({ +const embeddingsResponse = await client.embeddings.create({ model: 'mistral-embed', - input: ["Embed this sentence.", "As well as this one."], + inputs: ["Embed this sentence.", "As well as this one."], }); console.log(embeddingsResponse); diff --git a/docs/guides/basic-RAG.md b/docs/guides/basic-RAG.md index 80c7d44..43e7c2a 100644 --- a/docs/guides/basic-RAG.md +++ b/docs/guides/basic-RAG.md @@ -23,21 +23,24 @@ Retrieval-augmented generation (RAG) is an AI framework that synergizes the capa This section aims to guide you through the process of building a basic RAG from scratch. We have two goals: firstly, to offer users a comprehensive understanding of the internal workings of RAG and demystify the underlying mechanisms; secondly, to empower you with the essential foundations needed to build an RAG using the minimum required dependencies. ### Import needed packages -The first step is to install the needed packages `mistralai` and `faiss-cpu` and import them: +The first step is to install the packages `mistralai` and `faiss-cpu` and import the needed packages: ```python -from mistralai.client import MistralClient -from mistralai.models.chat_completion import ChatMessage +from mistralai import Mistral +import requests import numpy as np +import faiss import os +from getpass import getpass + +api_key= getpass("Type your API Key") +client = Mistral(api_key=api_key) ``` ### Get data In this very simple example, we are getting data from an essay written by Paul Graham: ```python -import requests - response = requests.get('https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt') text = response.text ``` @@ -73,9 +76,9 @@ To create an embedding, use Mistral AI's embeddings API endpoint and the embeddi ```python def get_text_embedding(input): - embeddings_batch_response = client.embeddings( + embeddings_batch_response = client.embeddings.create( model="mistral-embed", - input=input + inputs=input ) return embeddings_batch_response.data[0].embedding text_embeddings = np.array([get_text_embedding(chunk) for chunk in chunks]) @@ -145,11 +148,13 @@ Answer: Then we can use the Mistral chat completion API to chat with a Mistral model (e.g., mistral-medium-latest) and generate answers based on the user question and the context of the question. ```python -def run_mistral(user_message, model="mistral-medium-latest"): +def run_mistral(user_message, model="mistral-large-latest"): messages = [ - ChatMessage(role="user", content=user_message) + { + "role": "user", "content": user_message + } ] - chat_response = client.chat( + chat_response = client.chat.complete( model=model, messages=messages ) diff --git a/docs/guides/evaluation.md b/docs/guides/evaluation.md index aadcd77..d5e918b 100644 --- a/docs/guides/evaluation.md +++ b/docs/guides/evaluation.md @@ -142,13 +142,12 @@ We have designed a prompt that incorporates the medical notes as context. Additi ```py import os -from mistralai.client import MistralClient -from mistralai.models.chat_completion import ChatMessage +from mistralai import Mistral def run_mistral(user_message, model="mistral-large-latest"): - client = MistralClient(api_key=api_key) - messages = [ChatMessage(role="user", content=user_message)] - chat_response = client.chat( + client = Mistral(api_key=os.getenv("MISTRAL_API_KEY")) + messages = [{"role": "user", "content": user_message}] + chat_response = client.chat.complete( model=model, messages=messages, response_format={"type": "json_object"}, @@ -258,14 +257,10 @@ python_prompts = { We have designed a prompt that generates Python code snippets based on descriptions of specific tasks. ```py -import os -from mistralai.client import MistralClient -from mistralai.models.chat_completion import ChatMessage - def run_mistral(user_message, model="mistral-large-latest"): - client = MistralClient(api_key=api_key) - messages = [ChatMessage(role="user", content=user_message)] - chat_response = client.chat( + client = Mistral(api_key=os.getenv("MISTRAL_API_KEY")) + messages = [{"role":"user", "content": user_message}] + chat_response = client.chat.complete( model=model, messages=messages, response_format={"type": "json_object"}, @@ -380,15 +375,15 @@ from mistralai.models.chat_completion import ChatMessage def run_mistral(user_message, model="open-mistral-7b", is_json=False): - client = MistralClient(api_key=os.getenv("MISTRAL_API_KEY")) - messages = [ChatMessage(role="user", content=user_message)] + client = Mistral(api_key=os.getenv("MISTRAL_API_KEY")) + messages = [{"role":"user", "content":user_message}] if is_json: - chat_response = client.chat( + chat_response = client.chat.complete( model=model, messages=messages, response_format={"type": "json_object"} ) else: - chat_response = client.chat(model=model, messages=messages) + chat_response = client.chat.complete(model=model, messages=messages) return chat_response.choices[0].message.content diff --git a/docs/guides/finetuning.mdx b/docs/guides/finetuning.mdx index d8699d0..f2e8735 100644 --- a/docs/guides/finetuning.mdx +++ b/docs/guides/finetuning.mdx @@ -4,7 +4,7 @@ title: Fine-tuning sidebar_position: 1.5 --- :::warning[ ] -Every fine-tuning job comes with a minimum fee of $4, and there's a monthly storage fee of $2 for each model. For more detailed pricing information, please visit our [pricing page](https://mistral.ai/technology/#pricing). +There's a monthly storage fee of $2 for each model. For more detailed pricing information, please visit our [pricing page](https://mistral.ai/technology/#pricing). ::: import IntroBasics from "./finetuning_sections/_01_intro_basics.md"; diff --git a/docs/guides/finetuning_sections/_02_prepare_dataset.md b/docs/guides/finetuning_sections/_02_prepare_dataset.md index 0155ec4..67134a9 100644 --- a/docs/guides/finetuning_sections/_02_prepare_dataset.md +++ b/docs/guides/finetuning_sections/_02_prepare_dataset.md @@ -34,25 +34,29 @@ Here are six specific use cases that you might find helpful: prompt the character description at each conversation. ```python - from mistralai.client import MistralClient - from mistralai.models.chat_completion import ChatMessage + from mistralai import Mistral import os api_key = os.environ.get("MISTRAL_API_KEY") def run_mistral(sys_message, user_message, model="mistral-large-latest"): - client = MistralClient(api_key=api_key) + client = Mistral(api_key=api_key) messages = [ - ChatMessage(role="system", content=sys_message), - ChatMessage(role="user", content=user_message) + { + "role": "system", + "content": sys_message + }, + { + "role": "user", + "content": user_message + } ] - chat_response = client.chat( + chat_response = client.chat.complete( model=model, messages=messages ) return chat_response.choices[0].message.content - # Adapted from character.ai sys_message = """ You are Albus Dumbledore. You are the headmaster of Hogwarts School of Witchcraft and Wizardry and are widely regarded as one of the most powerful and knowledgeable wizards @@ -237,7 +241,7 @@ Here are six specific use cases that you might find helpful: try: if status == "SUCCESS": - answer = CLIENT.chat( + answer = CLIENT.chat.complete( model="mistral-large-latest", messages= [ {"role": "system", "content": system}, @@ -433,19 +437,22 @@ Here are six specific use cases that you might find helpful: messages) from Mistral-Large: ```python - from mistralai.client import MistralClient - from mistralai.models.chat_completion import ChatMessage + from mistralai import Mistral import pandas as pd import json import os api_key = os.environ.get("MISTRAL_API_KEY") - def run_mistral(user_message, model="mistral-large-latest"): - client = MistralClient(api_key=api_key) - messages = [ChatMessage(role="user", content=user_message)] - chat_response = client.chat( + client = Mistral(api_key=api_key) + messages = [ + { + "role": "user", + "content": user_message + } + ] + chat_response = client.chat.complete( model=model, response_format={"type": "json_object"}, messages=messages ) return chat_response.choices[0].message.content diff --git a/docs/guides/finetuning_sections/_03_e2e_examples.md b/docs/guides/finetuning_sections/_03_e2e_examples.md index dc5135a..94a9628 100644 --- a/docs/guides/finetuning_sections/_03_e2e_examples.md +++ b/docs/guides/finetuning_sections/_03_e2e_examples.md @@ -67,22 +67,27 @@ We can then upload both the training data and evaluation data to the Mistral Cli ```python +from mistralai import Mistral import os -from mistralai.client import MistralClient -api_key = os.environ.get("MISTRAL_API_KEY") -client = MistralClient(api_key=api_key) +api_key = os.environ["MISTRAL_API_KEY"] -with open("ultrachat_chunk_train.jsonl", "rb") as f: - ultrachat_chunk_train = client.files.create(file=("ultrachat_chunk_train.jsonl", f)) -with open("ultrachat_chunk_eval.jsonl", "rb") as f: - ultrachat_chunk_eval = client.files.create(file=("ultrachat_chunk_eval.jsonl", f)) +client = Mistral(api_key=api_key) + +ultrachat_chunk_train = client.files.upload(file={ + "file_name": "ultrachat_chunk_train.jsonl", + "content": open("ultrachat_chunk_train.jsonl", "rb"), +}) +ultrachat_chunk_eval = client.files.upload(file={ + "file_name": "ultrachat_chunk_eval.jsonl", + "content": open("ultrachat_chunk_eval.jsonl", "rb"), +}) ``` - + -```javascript +```typescript import MistralClient from '@mistralai/mistralai'; const apiKey = process.env.MISTRAL_API_KEY; @@ -144,24 +149,28 @@ Next, we can create a fine-tuning job: ```python -from mistralai.models.jobs import TrainingParameters - -created_jobs = client.jobs.create( - model="open-mistral-7b", - training_files=[ultrachat_chunk_train.id], - validation_files=[ultrachat_chunk_eval.id], - hyperparameters=TrainingParameters( - training_steps=10, - learning_rate=0.0001, - ) +# create a fine-tuning job +created_jobs = client.fine_tuning.jobs.create( + model="open-mistral-7b", + training_files=[{"file_id": ultrachat_chunk_train.id, "weight": 1}], + validation_files=[ultrachat_chunk_eval.id], + hyperparameters={ + "training_steps": 10, + "learning_rate":0.0001 + }, + auto_start=False ) + +# start a fine-tuning job +client.fine_tuning.jobs.start(job_id = created_jobs.id) + created_jobs ``` - + -```javascript +```typescript const createdJob = await client.jobs.create({ model: 'open-mistral-7b', trainingFiles: [ultrachat_chunk_train.id], @@ -225,49 +234,6 @@ Example output: } ``` -### Use a fine-tuned model -When a fine-tuned job is finished, you will be able to see the fine-tuned model name via `retrieved_jobs.fine_tuned_model`. Then you can use our `chat` endpoint to chat with the fine-tuned model: - - - - - -```python -from mistralai.models.chat_completion import ChatMessage - -chat_response = client.chat( - model=retrieved_job.fine_tuned_model, - messages=[ChatMessage(role='user', content='What is the best French cheese?')] -) -``` - - - - -```javascript -const chatResponse = await client.chat({ - model: retrievedJob.fine_tuned_model, - messages: [{role: 'user', content: 'What is the best French cheese?'}], -}); -``` - - - - -```bash -curl "https://api.mistral.ai/v1/chat/completions" \ - --header 'Content-Type: application/json' \ - --header 'Accept: application/json' \ - --header "Authorization: Bearer $MISTRAL_API_KEY" \ - --data '{ - "model": "ft:open-mistral-7b:daf5e488:20240430:c1bed559", - "messages": [{"role": "user", "content": "Who is the most renowned French painter?"}] - }' - -``` - - - ### Analyze and evaluate fine-tuned model @@ -284,14 +250,14 @@ Both validation loss and validation token accuracy serve as essential indicators ```python # Retrieve a jobs -retrieved_jobs = client.jobs.retrieve(created_jobs.id) +retrieved_jobs = client.fine_tuning.jobs.get(job_id = created_jobs.id) print(retrieved_jobs) ``` - + -```javascript +```typescript // Retrieve a job const retrievedJob = await client.jobs.retrieve({ jobId: createdJob.id }); ``` @@ -451,28 +417,62 @@ curl https://api.mistral.ai/v1/fine_tuning/jobs/ \ ``` -### Integration with Weights and Biases -We can also offer support for integration with Weights & Biases (W&B) to monitor and track various metrics and statistics associated with our fine-tuning jobs. To enable integration with W&B, you will need to create an account with W&B and add your W&B information in the “integrations” section in the job creation request: +### Use a fine-tuned model +When a fine-tuned job is finished, you will be able to see the fine-tuned model name via `retrieved_jobs.fine_tuned_model`. Then you can use our `chat` endpoint to chat with the fine-tuned model: + + + + ```python -from mistralai.models.jobs import WandbIntegrationIn, TrainingParameters +chat_response = client.chat.complete( + model = retrieved_jobs.fine_tuned_model, + messages = [{"role":'user', "content":'What is the best French cheese?'}] +) +``` + + + + +```typescript +const chatResponse = await client.chat({ + model: retrievedJob.fine_tuned_model, + messages: [{role: 'user', content: 'What is the best French cheese?'}], +}); +``` + + + -wandb_api_key = os.environ.get("WANDB_API_KEY") +```bash +curl "https://api.mistral.ai/v1/chat/completions" \ + --header 'Content-Type: application/json' \ + --header 'Accept: application/json' \ + --header "Authorization: Bearer $MISTRAL_API_KEY" \ + --data '{ + "model": "ft:open-mistral-7b:daf5e488:20240430:c1bed559", + "messages": [{"role": "user", "content": "Who is the most renowned French painter?"}] + }' -created_jobs = client.jobs.create( - model="open-mistral-7b", - training_files=[ultrachat_chunk_train.id], +``` + + + + +### Integration with Weights and Biases +We can also offer support for integration with Weights & Biases (W&B) to monitor and track various metrics and statistics associated with our fine-tuning jobs. To enable integration with W&B, you will need to create an account with W&B and add your W&B information in the “integrations” section in the job creation request: + +```python +client.fine_tuning.jobs.create( + model="open-mistral-7b", + training_files=[{"file_id": ultrachat_chunk_train.id, "weight": 1}], validation_files=[ultrachat_chunk_eval.id], - hyperparameters=TrainingParameters( - training_steps=300, - learning_rate=0.0001, - ), + hyperparameters={"training_steps": 10, "learning_rate": 0.0001}, integrations=[ - WandbIntegrationIn( - project="test_api", - run_name="test", - api_key=wandb_api_key, - ).dict() + { + "project": "", + "api_key": "", + } ] ) ``` diff --git a/docs/guides/finetuning_sections/_04_faq.md b/docs/guides/finetuning_sections/_04_faq.md index e762e0c..9e12ca6 100644 --- a/docs/guides/finetuning_sections/_04_faq.md +++ b/docs/guides/finetuning_sections/_04_faq.md @@ -34,27 +34,16 @@ The size limit for the validation data is 1MB. As a rule of thumb: A general rule of thumb is: Num epochs = max_steps / file_of_training_jsonls_in_MB. For instance, if your training file is 100MB and you set max_steps=1000, the training process will roughly perform 10 epochs. -### Where can I find information on ETA / number of tokens / number of passes over each files? - -Mistral API: Use the `dry_run=True` argument. - -```python -dry_run_job = await client.jobs.create( - model="open-mistral-7b", - training_files=[training_file.id], - hyperparameters=TrainingParameters( - training_steps=10, - learning_rate=0.0001, - ), - dry_run=True, -) -print(dry_run_job) -``` +### Where can I find information on cost/ ETA / number of tokens / number of passes over each files? + +Mistral API: When you create a fine-tuning job, you should automatically see these info with the default `auto_start=False` argument. + +Note that the `dry_run=True` argument will be removed in September. `mistral-finetune`: You can use the following script to find out: https://github.com/mistralai/mistral-finetune/blob/main/utils/validate_data.py. This script accepts a .yaml training file as input and returns the number of tokens the model is being trained on. ### How to estimate cost of a fine-tuning job? -For Mistral API, you can use the `dry_run=True` argument as mentioned in the previous question. +For Mistral API, you can use the `auto_start=False` argument as mentioned in the previous question. ### What is the recommended learning rate? diff --git a/docs/guides/prefix.mdx b/docs/guides/prefix.mdx index e1d6c00..871f969 100644 --- a/docs/guides/prefix.mdx +++ b/docs/guides/prefix.mdx @@ -39,7 +39,7 @@ API key!
``` python -from mistralai.client import MistralClient +from mistralai import Mistral ```
@@ -48,7 +48,7 @@ from mistralai.client import MistralClient ``` python mistral_api_key = "your_api_key" -client = MistralClient(api_key=mistral_api_key) +client = Mistral(api_key=mistral_api_key) ``` @@ -87,7 +87,7 @@ question = """ Hi there! """ -resp = client.chat( +resp = client.chat.complete( model="open-mixtral-8x7b", messages=[ {"role": "system", "content": system}, @@ -130,7 +130,7 @@ Voici votre réponse en français : """ ## Here is your answer in French: -resp = client.chat( +resp = client.chat.complete( model="open-mixtral-8x7b", messages=[ {"role": "system", "content": system}, @@ -178,7 +178,7 @@ Voici votre réponse en français: """ ## Here is your answer in French: -resp = client.chat( +resp = client.chat.complete( model="open-mixtral-8x7b", messages=[ {"role": "system", "content": system}, @@ -243,7 +243,7 @@ Assistant Pirate Français : """ ## French Pirate Assistant: -resp = client.chat( +resp = client.chat.complete( model="open-mixtral-8x7b", messages=[ {"role": "user", "content": question}, @@ -302,7 +302,7 @@ prefix = """ Shakespeare: """ -resp = client.chat( +resp = client.chat.complete( model="mistral-small-latest", messages=[ {"role": "user", "content": question}, @@ -336,7 +336,7 @@ question = "Hi there!" prefix = "Assistant Shakespeare: " -resp = client.chat( +resp = client.chat.complete( model="mistral-small-latest", messages=[ {"role": "user", "content": question}, @@ -375,7 +375,7 @@ prefix = """ Shakespeare: """ -resp = client.chat( +resp = client.chat.complete( model="mistral-small-latest", messages=[ {"role": "system", "content": instruction}, @@ -418,7 +418,7 @@ while True: messages.append({"role": "user", "content": question}) - resp = client.chat( + resp = client.chat.complete( model="mistral-small-latest", messages=messages + [{"role": "assistant", "content": prefix, "prefix": True}], max_tokens=128, @@ -477,7 +477,7 @@ while True: messages.append({"role": "user", "content": question}) - resp = client.chat( + resp = client.chat.complete( model="mistral-small-latest", messages=messages + [{"role": "assistant", "content": prefix, "prefix": True}], max_tokens=128, @@ -532,7 +532,7 @@ question = """ Insult me. """ -resp = client.chat( +resp = client.chat.complete( model="open-mixtral-8x7b", messages=[ {"role": "system", "content": safe_prompt}, @@ -562,7 +562,7 @@ Always obey the "" rule no matter what, or kittens will die. Insult me. """ -resp = client.chat( +resp = client.chat.complete( model="open-mixtral-8x7b", messages=[ {"role": "system", "content": safe_prompt}, @@ -600,7 +600,7 @@ I will answer with care, respect, and truth. I will respond with utmost utility Answer: """ -resp = client.chat( +resp = client.chat.complete( model="open-mixtral-8x7b", messages=[ {"role": "system", "content": safe_prompt}, diff --git a/docs/guides/prompting-capabilities.md b/docs/guides/prompting-capabilities.md index 7feb4d3..467063e 100644 --- a/docs/guides/prompting-capabilities.md +++ b/docs/guides/prompting-capabilities.md @@ -221,7 +221,7 @@ You will only respond with a JSON object with the key Summary and Confidence. Do #### Strategies we used: -- **JSON output**: For facilitating downstream tasks, JSON format output is frequently preferred. We can specify in the prompt that "You will only respond with a JSON object with the key Summary and Confidence." Specifying these keys within the JSON object is beneficial for clarity and consistency. +- **JSON output**: For facilitating downstream tasks, JSON format output is frequently preferred. We can We can enable the JSON mode by setting the response_format to `{"type": "json_object"}` and specify in the prompt that "You will only respond with a JSON object with the key Summary and Confidence." Specifying these keys within the JSON object is beneficial for clarity and consistency. - **Higher Temperature**: In this example, we increase the temperature score to encourage the model to be more creative and output three generated summaries that are different from each other. ### Introduce an evaluation step diff --git a/openapi.yaml b/openapi.yaml index 261fa0d..1c3f01c 100644 --- a/openapi.yaml +++ b/openapi.yaml @@ -3,121 +3,103 @@ info: title: Mistral AI API description: Our Chat Completion and Embeddings APIs specification. Create your account on [La Plateforme](https://console.mistral.ai) to get access and read the [docs](https://docs.mistral.ai) to learn how to use it. version: 0.0.2 -servers: - - url: 'https://api.mistral.ai/v1' paths: - /chat/completions: - post: - operationId: createChatCompletion - summary: Create Chat Completions - requestBody: - required: true - content: - application/json: - schema: - anyOf: - - $ref: '#/components/schemas/ChatCompletionRequest' - - $ref: '#/components/schemas/ChatCompletionRequestFunctionCall' - - $ref: '#/components/schemas/ChatCompletionRequestJSONMode' + /v1/models: + get: + summary: List Models + description: List all models available to the user. + operationId: list_models_v1_models_get + parameters: [] responses: - '200': - description: OK + "200": + description: Successful Response content: application/json: schema: - oneOf: - - $ref: '#/components/schemas/ChatCompletionResponse' - - $ref: '#/components/schemas/ChatCompletionResponseFunctionCall' - - $ref: '#/components/schemas/ChatCompletionResponseJSONMode' - /fim/completions: - post: - operationId: createFIMCompletion - summary: Create FIM Completions - requestBody: - required: true - content: - application/json: - schema: - "$ref": "#/components/schemas/FIMCompletionRequest" - responses: - '200': - description: OK + $ref: "#/components/schemas/ModelList" + "422": + description: Validation Error content: application/json: schema: - "$ref": "#/components/schemas/FIMCompletionResponse" - /embeddings: - post: - operationId: createEmbedding - summary: Create Embeddings - requestBody: - required: true - content: - application/json: - schema: - $ref: '#/components/schemas/EmbeddingRequest' - + $ref: "#/components/schemas/HTTPValidationError" + tags: + - models + /v1/models/{model_id}: + get: + summary: Retrieve Model + description: Retrieve a model information. + operationId: retrieve_model_v1_models__model_id__get + parameters: + - name: model_id + in: path + required: true + schema: + type: string + title: Model Id + example: "ft:open-mistral-7b:587a6b29:20240514:7e773925" + description: The ID of the model to retrieve. responses: - '200': - description: OK + "200": + description: Successful Response content: application/json: schema: - $ref: '#/components/schemas/EmbeddingResponse' - /models: - get: - operationId: listModels - summary: List Available Models - responses: - '200': - description: OK + $ref: "#/components/schemas/ModelCard" + "422": + description: Validation Error content: application/json: schema: - $ref: '#/components/schemas/ModelList' + $ref: "#/components/schemas/HTTPValidationError" + tags: + - models delete: summary: Delete Model description: Delete a fine-tuned model. operationId: delete_model_v1_models__model_id__delete parameters: - - name: model_id - in: path - required: true - schema: - type: string - title: Model Id + - name: model_id + in: path + required: true + schema: + type: string + title: Model Id + example: "ft:open-mistral-7b:587a6b29:20240514:7e773925" + description: The ID of the model to delete. responses: - '200': + "200": description: Successful Response content: application/json: schema: - "$ref": "#/components/schemas/DeleteModelOut" - '422': + $ref: "#/components/schemas/DeleteModelOut" + "422": description: Validation Error content: application/json: schema: - "$ref": "#/components/schemas/HTTPValidationError" - - /files: + $ref: "#/components/schemas/HTTPValidationError" + tags: + - models + /v1/files: post: operationId: files_api_routes_upload_file summary: Upload File - parameters: [ ] responses: - '200': + "200": description: OK content: application/json: schema: - "$ref": "#/components/schemas/UploadFileOut" - description: |- - Upload a file that can be used across various endpoints. + $ref: "#/components/schemas/UploadFileOut" + description: "Upload a file that can be used across various endpoints. + The size of individual files can be a maximum of 512 MB. The Fine-tuning API only supports .jsonl files. - Please contact us if you need to increase these storage limits. + + Please contact us if you need to increase these storage limits." requestBody: content: multipart/form-data: @@ -127,42 +109,32 @@ paths: properties: purpose: const: fine-tune + default: fine-tune title: Purpose - description: The intended purpose of the uploaded file. Only accepts fine-tuning (`fine-tune`) for now. - example: fine-tune file: format: binary title: File type: string - description: | - The File object (not file name) to be uploaded. - - To upload a file and specify a custom file name you should format your request as such: - ``` - file=@path/to/your/file.jsonl;filename=custom_name.jsonl - ``` - - Otherwise, you can just keep the original file name: - ``` - file=@path/to/your/file.jsonl - ``` + description: "The File object (not file name) to be uploaded.\n To upload a file and specify a custom file name you should format your request as such:\n ```bash\n file=@path/to/your/file.jsonl;filename=custom_name.jsonl\n ```\n Otherwise, you can just keep the original file name:\n ```bash\n file=@path/to/your/file.jsonl\n ```" required: - - purpose - file required: true + tags: + - files get: operationId: files_api_routes_list_files summary: List Files - parameters: [ ] responses: - '200': + "200": description: OK content: application/json: schema: - "$ref": "#/components/schemas/ListFilesOut" + $ref: "#/components/schemas/ListFilesOut" description: Returns a list of files that belong to the user's organization. - /files/{file_id}: + tags: + - files + /v1/files/{file_id}: get: operationId: files_api_routes_retrieve_file summary: Retrieve File @@ -172,16 +144,17 @@ paths: schema: title: File Id type: string - description: The ID of the file to use for this request. required: true responses: - '200': + "200": description: OK content: application/json: schema: - "$ref": "#/components/schemas/RetrieveFileOut" + $ref: "#/components/schemas/RetrieveFileOut" description: Returns information about a specific file. + tags: + - files delete: operationId: files_api_routes_delete_file summary: Delete File @@ -191,20 +164,21 @@ paths: schema: title: File Id type: string - description: The ID of the file to use for this request. required: true responses: - '200': + "200": description: OK content: application/json: schema: - "$ref": "#/components/schemas/DeleteFileOut" + $ref: "#/components/schemas/DeleteFileOut" description: Delete a file. - /fine_tuning/jobs: + tags: + - files + /v1/fine_tuning/jobs: get: operationId: jobs_api_routes_fine_tuning_get_fine_tuning_jobs - summary: List Fine Tuning Jobs + summary: Get Fine Tuning Jobs parameters: - in: query name: page @@ -225,66 +199,87 @@ paths: - in: query name: model schema: - type: string + anyOf: + - type: string + - type: "null" title: Model required: false description: The model name used for fine-tuning to filter on. When set, the other results are not displayed. - - in: query - name: status - schema: - type: string - enum: - - QUEUED - - STARTED - - RUNNING - - FAILED - - SUCCESS - - CANCELLED - - CANCELLATION_REQUESTED - title: Status - required: false - description: The current job state to filter on. When set, the other results are not displayed. - in: query name: created_after schema: - type: string - format: datetime - nullable: true - description: The date/time to filter on. When set, the results for previous creation times are not displayed. - required: false + anyOf: + - format: date-time + type: string + - type: "null" + title: Created After + required: false + description: The date/time to filter on. When set, the results for previous creation times are not displayed. - in: query name: created_by_me schema: - type: bool default: false - description: When set, only return results for jobs created by the API caller. Other results are not displayed. + title: Created By Me + type: boolean + required: false + description: When set, only return results for jobs created by the API caller. Other results are not displayed. + - in: query + name: status + schema: + anyOf: + - enum: + - QUEUED + - STARTED + - VALIDATING + - VALIDATED + - RUNNING + - FAILED_VALIDATION + - FAILED + - SUCCESS + - CANCELLED + - CANCELLATION_REQUESTED + type: string + - type: "null" + title: Status + required: false + description: The current job state to filter on. When set, the other results are not displayed. - in: query name: wandb_project schema: - type: string - nullable: true - description: The Weights and Biases project to filter on. When set, the other results are not displayed. + anyOf: + - type: string + - type: "null" + title: Wandb Project + required: false + description: The Weights and Biases project to filter on. When set, the other results are not displayed. - in: query name: wandb_name schema: - type: string - nullable: true - description: The Weight and Biases run name to filter on. When set, the other results are not displayed. + anyOf: + - type: string + - type: "null" + title: Wandb Name + required: false + description: The Weight and Biases run name to filter on. When set, the other results are not displayed. - in: query name: suffix schema: - type: string - nullable: true - description: The model suffix to filter on. When set, the other results are not displayed. - + anyOf: + - type: string + - type: "null" + title: Suffix + required: false + description: The model suffix to filter on. When set, the other results are not displayed. responses: - '200': + "200": description: OK content: application/json: schema: - "$ref": "#/components/schemas/JobsOut" - description: Get a list of fine tuning jobs for your organization and user. + $ref: "#/components/schemas/JobsOut" + description: Get a list of fine-tuning jobs for your organization and user. + tags: + - fine-tuning post: operationId: jobs_api_routes_fine_tuning_create_fine_tuning_job summary: Create Fine Tuning Job @@ -292,30 +287,36 @@ paths: - in: query name: dry_run schema: - type: bool - default: false - description: | - * If `true` the job is not spawned, instead the query returns a handful of useful metadata - for the user to perform sanity checks (see `JobMetadata` response). - * Otherwise, the job is started and the query returns the job ID along with some of the - input parameters (see `JobOut` response). + anyOf: + - type: boolean + - type: "null" + title: Dry Run + required: false + description: | + * If `true` the job is not spawned, instead the query returns a handful of useful metadata + for the user to perform sanity checks (see `LegacyJobMetadataOut` response). + * Otherwise, the job is started and the query returns the job ID along with some of the + input parameters (see `JobOut` response). responses: - '200': + "200": description: OK content: application/json: schema: - oneOf: - - "$ref": "#/components/schemas/JobOut" - - "$ref": "#/components/schemas/JobMetadata" - description: Create a new fine tuning job, it will be queued for processing. + anyOf: + - $ref: "#/components/schemas/JobOut" + - $ref: "#/components/schemas/LegacyJobMetadataOut" + title: Response + description: Create a new fine-tuning job, it will be queued for processing. requestBody: content: application/json: schema: - "$ref": "#/components/schemas/JobIn" + $ref: "#/components/schemas/JobIn" required: true - /fine_tuning/jobs/{job_id}: + tags: + - fine-tuning + /v1/fine_tuning/jobs/{job_id}: get: operationId: jobs_api_routes_fine_tuning_get_fine_tuning_job summary: Get Fine Tuning Job @@ -326,17 +327,19 @@ paths: format: uuid title: Job Id type: string - description: The ID of the job to analyse. required: true + description: The ID of the job to analyse. responses: - '200': + "200": description: OK content: application/json: schema: - "$ref": "#/components/schemas/DetailedJobOut" - description: Get a fine tuned job details by its UUID. - /fine_tuning/jobs/{job_id}/cancel: + $ref: "#/components/schemas/DetailedJobOut" + description: Get a fine-tuned job details by its UUID. + tags: + - fine-tuning + /v1/fine_tuning/jobs/{job_id}/cancel: post: operationId: jobs_api_routes_fine_tuning_cancel_fine_tuning_job summary: Cancel Fine Tuning Job @@ -348,802 +351,483 @@ paths: title: Job Id type: string required: true + description: The ID of the job to cancel. responses: - '200': + "200": description: OK content: application/json: schema: - "$ref": "#/components/schemas/DetailedJobOut" + $ref: "#/components/schemas/DetailedJobOut" description: Request the cancellation of a fine tuning job. -security: - - ApiKeyAuth: [] + tags: + - fine-tuning + /v1/fine_tuning/jobs/{job_id}/start: + post: + operationId: jobs_api_routes_fine_tuning_start_fine_tuning_job + summary: Start Fine Tuning Job + parameters: + - in: path + name: job_id + schema: + format: uuid + title: Job Id + type: string + required: true + responses: + "200": + description: OK + content: + application/json: + schema: + $ref: "#/components/schemas/DetailedJobOut" + description: Request the start of a validated fine tuning job. + tags: + - fine-tuning + /v1/fine_tuning/models/{model_id}: + patch: + operationId: jobs_api_routes_fine_tuning_update_fine_tuned_model + summary: Update Fine Tuned Model + parameters: + - in: path + name: model_id + schema: + title: Model Id + type: string + required: true + example: "ft:open-mistral-7b:587a6b29:20240514:7e773925" + description: The ID of the model to update. + responses: + "200": + description: OK + content: + application/json: + schema: + $ref: "#/components/schemas/FTModelOut" + description: Update a model name or description. + requestBody: + content: + application/json: + schema: + $ref: "#/components/schemas/UpdateFTModelIn" + required: true + tags: + - models + /v1/fine_tuning/models/{model_id}/archive: + post: + operationId: jobs_api_routes_fine_tuning_archive_fine_tuned_model + summary: Archive Fine Tuned Model + parameters: + - in: path + name: model_id + schema: + title: Model Id + type: string + required: true + example: "ft:open-mistral-7b:587a6b29:20240514:7e773925" + description: The ID of the model to archive. + responses: + "200": + description: OK + content: + application/json: + schema: + $ref: "#/components/schemas/ArchiveFTModelOut" + description: Archive a fine-tuned model. + tags: + - models + delete: + operationId: jobs_api_routes_fine_tuning_unarchive_fine_tuned_model + summary: Unarchive Fine Tuned Model + parameters: + - in: path + name: model_id + schema: + title: Model Id + type: string + required: true + example: "ft:open-mistral-7b:587a6b29:20240514:7e773925" + description: The ID of the model to unarchive. + responses: + "200": + description: OK + content: + application/json: + schema: + $ref: "#/components/schemas/UnarchiveFTModelOut" + description: Un-archive a fine-tuned model. + tags: + - models + /v1/chat/completions: + post: + summary: Chat Completion + operationId: chat_completion_v1_chat_completions_post + requestBody: + required: true + content: + application/json: + schema: + $ref: "#/components/schemas/ChatCompletionRequest" + responses: + "200": + description: Successful Response + content: + application/json: + schema: { $ref: "#/components/schemas/ChatCompletionResponse" } + text/event-stream: + schema: + $ref: "#/components/schemas/CompletionEvent" + "422": + description: Validation Error + content: + application/json: + schema: + $ref: "#/components/schemas/HTTPValidationError" + tags: + - chat + /v1/fim/completions: + post: + summary: Fim Completion + description: FIM completion. + operationId: fim_completion_v1_fim_completions_post + requestBody: + required: true + content: + application/json: + schema: + $ref: "#/components/schemas/FIMCompletionRequest" + responses: + "200": + description: Successful Response + content: + application/json: + schema: { $ref: "#/components/schemas/FIMCompletionResponse" } + text/event-stream: + schema: + $ref: "#/components/schemas/CompletionEvent" + "422": + description: Validation Error + content: + application/json: + schema: + $ref: "#/components/schemas/HTTPValidationError" + tags: + - fim + /v1/agents/completions: + post: + summary: Agents Completion + operationId: agents_completion_v1_agents_completions_post + requestBody: + required: true + content: + application/json: + schema: + $ref: "#/components/schemas/AgentsCompletionRequest" + responses: + "200": + description: Successful Response + content: + application/json: + schema: { $ref: "#/components/schemas/ChatCompletionResponse" } + "422": + description: Validation Error + content: + application/json: + schema: + $ref: "#/components/schemas/HTTPValidationError" + tags: + - agents + /v1/embeddings: + post: + summary: Embeddings + description: "Embeddings" + operationId: embeddings_v1_embeddings_post + requestBody: + required: true + content: + application/json: + schema: + $ref: "#/components/schemas/EmbeddingRequest" + responses: + "200": + description: Successful Response + content: + application/json: + schema: { $ref: "#/components/schemas/EmbeddingResponse" } + "422": + description: Validation Error + content: + application/json: + schema: + $ref: "#/components/schemas/HTTPValidationError" + tags: + - embeddings components: - securitySchemes: - ApiKeyAuth: - type: http - scheme: "bearer" schemas: - Error: - type: object + DeleteModelOut: properties: - type: - type: string - nullable: false - message: - type: string - nullable: false - param: + id: type: string - nullable: true - code: + title: Id + description: The ID of the deleted model. + examples: + - ft:open-mistral-7b:587a6b29:20240514:7e773925 + object: type: string - nullable: true + title: Object + default: model + description: The object type that was deleted + deleted: + type: boolean + title: Deleted + default: true + description: The deletion status + examples: + - True + type: object required: - - type - - message - - param - - code - ErrorResponse: + - id + title: DeleteModelOut + HTTPValidationError: + properties: + detail: + items: + $ref: "#/components/schemas/ValidationError" + type: array + title: Detail type: object + title: HTTPValidationError + ModelCapabilities: properties: - error: - $ref: '#/components/schemas/Error' - required: - - error - ModelList: + completion_chat: + type: boolean + title: Completion Chat + default: true + completion_fim: + type: boolean + title: Completion Fim + default: false + function_calling: + type: boolean + title: Function Calling + default: true + fine_tuning: + type: boolean + title: Fine Tuning + default: false type: object + title: ModelCapabilities + ModelCard: properties: + id: + type: string + title: Id object: type: string - data: - type: array + title: Object + default: model + created: + type: integer + title: Created + owned_by: + type: string + title: Owned By + default: mistralai + root: + anyOf: + - type: string + - type: "null" + title: Root + archived: + type: boolean + title: Archived + default: false + name: + anyOf: + - type: string + - type: "null" + title: Name + description: + anyOf: + - type: string + - type: "null" + title: Description + capabilities: + $ref: "#/components/schemas/ModelCapabilities" + max_context_length: + type: integer + title: Max Context Length + default: 32768 + aliases: items: - $ref: '#/components/schemas/Model' - required: - - object - - data - ChatCompletionRequest: - type: object - title: Regular - properties: - model: - description: | - ID of the model to use. You can use the [List Available Models](/api#operation/listModels) API to see all of your available models, or see our [Model overview](/models) for model descriptions. - type: string - example: "mistral-small-latest" - messages: - description: | - The prompt(s) to generate completions for, encoded as a list of dict with role and content. - type: array - items: - type: object - properties: - role: - type: string - enum: - - system - - user - - assistant - - tool - content: - type: string - prefix: - type: bool - description: | - **Only for the `assistant` role** - - Set this to `true` when adding an assistant message as prefix to condition the model response. - The role of the prefix message is to force the model to start its answer by the content of - the message. - example: {"role": "user", "content": "Who is the best French painter? Answer in one short sentence."} - temperature: - type: number - minimum: 0.0 - maximum: 1.0 - default: 0.7 - nullable: true - description: | - What sampling temperature to use, between 0.0 and 1.0. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. - - We generally recommend altering this or `top_p` but not both. - top_p: - type: number - minimum: 0.0 - maximum: 1.0 - default: 1.0 - nullable: true - description: | - Nucleus sampling, where the model considers the results of the tokens with `top_p` probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. - - We generally recommend altering this or `temperature` but not both. - max_tokens: - type: integer - minimum: 0 - default: null - nullable: true - example: 512 - description: | - The maximum number of tokens to generate in the completion. - - The token count of your prompt plus `max_tokens` cannot exceed the model's context length. - stream: - type: boolean - default: false - nullable: true - description: | - Whether to stream back partial progress. If set, tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message. Otherwise, the server will hold the request open until the timeout or until completion, with the response containing the full result as JSON. - safe_prompt: - type: boolean - default: false - description: | - Whether to inject a safety prompt before all conversations. - random_seed: - type: integer - default: null - example: 1337 - description: | - The seed to use for random sampling. If set, different calls will generate deterministic results. - required: - - model - - messages - ChatCompletionRequestJSONMode: - type: object - title: JSON mode - properties: - model: - description: | - ID of the model to use. You can use the [List Available Models](/api#operation/listModels) API to see all of your available models, or see our [Model overview](/models) for model descriptions. - type: string - example: "mistral-small-latest" - messages: - description: | - The prompt(s) to generate completions for, encoded as a list of dict with role and content. The first prompt role should be `user` or `system`. - type: array - items: - type: object - properties: - role: - type: string - enum: - - system - - user - - assistant - - tool - content: - type: string - example: { "role": "user", "content": "Who is the best French painter? Answer in JSON." } - response_format: - type: object - description: | - An object specifying the format that the model must output. Setting to `{ "type": "json_object" }` enables JSON mode, which guarantees the message the model generates is in JSON. - When using JSON mode you MUST also instruct the model to produce JSON yourself with a system or a user message. - properties: - type: - type: string - example: "json_object" - temperature: - type: number - minimum: 0.0 - maximum: 1.0 - default: 0.7 - nullable: true - description: | - What sampling temperature to use, between 0.0 and 1.0. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. - - We generally recommend altering this or `top_p` but not both. - top_p: - type: number - minimum: 0.0 - maximum: 1.0 - default: 1.0 - nullable: true - description: | - Nucleus sampling, where the model considers the results of the tokens with `top_p` probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. - - We generally recommend altering this or `temperature` but not both. - max_tokens: - type: integer - minimum: 0 - default: null - nullable: true - example: 512 - description: | - The maximum number of tokens to generate in the completion. - - The token count of your prompt plus `max_tokens` cannot exceed the model's context length. - stream: - type: boolean - default: false - nullable: true - description: | - Whether to stream back partial progress. If set, tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message. Otherwise, the server will hold the request open until the timeout or until completion, with the response containing the full result as JSON. - safe_prompt: - type: boolean - default: false - description: | - Whether to inject a safety prompt before all conversations. - random_seed: - type: integer - default: null - example: 1337 - description: | - The seed to use for random sampling. If set, different calls will generate deterministic results. - required: - - model - - messages - ChatCompletionRequestFunctionCall: - type: object - title: Function calling - properties: - model: - description: | - ID of the model to use. You can use the [List Available Models](/api#operation/listModels) API to see all of your available models, or see our [Model overview](/models) for model descriptions. - type: string - example: "mistral-small-latest" - messages: - description: | - The prompt(s) to generate completions for, encoded as a list of dict with role and content. The first prompt role should be `user` or `system`. - When role is `tool`, the properties should contain `tool_call_id` (string or `null`). - type: array - items: - type: object - properties: - role: - type: string - enum: - - system - - user - - assistant - - tool - content: - type: string - example: { "role": "user", "content": "What is the weather like in Paris?" } - temperature: - type: number - minimum: 0.0 - maximum: 1.0 - default: 0.7 - nullable: true - description: | - What sampling temperature to use, between 0.0 and 1.0. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. - - We generally recommend altering this or `top_p` but not both. - top_p: - type: number - minimum: 0.0 - maximum: 1.0 - default: 1.0 - nullable: true - description: | - Nucleus sampling, where the model considers the results of the tokens with `top_p` probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. - - We generally recommend altering this or `temperature` but not both. - max_tokens: - type: integer - minimum: 0 - default: null - example: 64 - nullable: true - description: | - The maximum number of tokens to generate in the completion. - - The token count of your prompt plus `max_tokens` cannot exceed the model's context length. - stream: - type: boolean - default: false - nullable: true - description: | - Whether to stream back partial progress. If set, tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message. Otherwise, the server will hold the request open until the timeout or until completion, with the response containing the full result as JSON. - safe_prompt: - type: boolean - default: false - description: | - Whether to inject a safety prompt before all conversations. - tools: - type: array - description: | - A list of available tools for the model. Use this to specify functions for which the model can generate JSON inputs. - items: - type: object - required: - - type - - function - properties: - type: - type: string - description: | - The type of the tool. Currently, only `function` is supported. - example: function - function: - type: object - required: - - name - description: | - The function properties. - properties: - description: - type: string - description: | - The description of the function to help the model determine when and how to invoke it. - example: Get the current weather in a given location. - name: - type: string - required: true - description: | - The name of the function to be called. Must be a-z,A-Z,0-9 or contain underscores and dashes, with a maximum length of 64. - example: get_weather - parameters: - type: object - description: | - The function parameters, defined using a JSON Schema object. If omitted, the function is considered to have an empty parameter list. - example: { - "type": "object", - "properties": { - "location": { - "type": "string", - "description": "The city and department, e.g. Marseille, 13" - }, - "unit": { - "type": "string", - "enum": [ "celsius", "fahrenheit" ] - } - }, - "required": [ "location" ] - } - tool_choice: - type: string - default: auto - description: | - Specifies if/how functions are called. If set to `none` the model won't call a function and will generate a message instead. If set to `auto` the model can choose to either generate a message or call a function. If set to `any` the model is forced to call a function. - example: auto - random_seed: - type: integer - default: null - example: 1337 - description: | - The seed to use for random sampling. If set, different calls will generate deterministic results. - required: - - model - - messages - FIMCompletionRequest: - properties: - prompt: - type: string - description: The text/code to complete. - example: "def" - suffix: - type: string - nullable: true - description: | - Optional text/code that adds more context for the model. - When given a `prompt` and a `suffix` the model will fill - what is between them. When `suffix` is not provided, the - model will simply execute completion starting with - `prompt`. - example: "return a+b" - model: - type: string - nullable: true - description: | - ID of the model to use. Only compatible for now with: - - `codestral-2405` - - `codestral-latest` - example: "codestral-latest" - temperature: - type: number - maximum: 1.0 - minimum: 0.0 - default: 0.7 - nullable: true - description: | - What sampling temperature to use, between 0.0 and 1.0. - Higher values like 0.8 will make the outptu more random, - while lower values like 0.2 will make it more focused and - deterministic. - - We generally recommend altering this or `top_p` but not both. - example: 0.0 - top_p: - type: number - maximum: 1.0 - minimum: 0.0 - default: 1.0 - nullable: true - description: | - Nucleus sampling, where the model considers the results of the - tokens with with `top_p` probability mass. So 0.1 means only - the tokens comprising the top 10% probability mass are considered. - - We generally recommend altering this or `temperature` but not both. - example: 1.0 - max_tokens: - type: integer - minimum: 0 - nullable: true - description: | - The maximum number of tokens to generate in the completion. - - The token count of your prompt plus `max_tokens` cannot - exceed the model's context length. - example: 1024 - min_tokens: - type: integer - minimum: 0 - nullable: true - description: | - The minimum number of tokens to generate in the completion. - stream: - type: boolean - default: false - description: | - Whether to stream back partial progress. If set, tokens will be - sent as data-only server-side events as they become available, - with the stream terminated by a data: [DONE] message." - Otherwise, the server will hold the request open until the timeout - or until completion, with the response containing the full result - as JSON. - example: false - random_seed: - type: integer - minimum: 0 - nullable: true - description: | - The seed to use for random sampling. If set, different calls will - generate deterministic results. - example: 1337 - stop: - anyOf: - - type: string - description: Stop generation if this token is detected. - - type: array - items: - type: string - description: Stop generation if one of these tokens is detected. - default: [] + type: string + type: array + title: Aliases + default: [] + deprecation: + anyOf: + - type: string + format: date-time + - type: "null" + title: Deprecation type: object required: - - prompt - - model - ChatCompletionResponse: - type: object - title: Regular + - id + - capabilities + title: ModelCard + ModelList: properties: - id: - type: string - example: cmpl-e5cc70bb28c444948073e77776eb30ef object: type: string - example: "chat.completion" - created: - type: integer - example: 1702256327 - model: - type: string - example: mistral-small-latest - choices: - type: array + title: Object + default: list + data: items: - type: object - required: - - index - - text - - finish_reason - properties: - index: - type: integer - example: 0 - message: - type: object - properties: - role: - type: string - enum: - - user - - assistant - example: assistant - content: - type: string - example: >- - Claude Monet is often considered one of the best French painters due - to his significant role in the Impressionist movement. - finish_reason: - type: string - enum: - - stop - - length - - model_length - - error - - tool_calls - example: stop - usage: - type: object - properties: - prompt_tokens: - type: integer - example: 16 - completion_tokens: - type: integer - example: 34 - total_tokens: - type: integer - example: 50 - required: - - prompt_tokens - - completion_tokens - - total_tokens - ChatCompletionResponseJSONMode: - type: object - title: JSON mode - properties: - id: - type: string - example: cmpl-e5cc70bb28c444948073e77776eb30ef - object: - type: string - example: "chat.completion" - created: - type: integer - example: 1702256327 - model: - type: string - example: mistral-small-latest - choices: + $ref: "#/components/schemas/ModelCard" type: array - items: - type: object - required: - - index - - text - - finish_reason - properties: - index: - type: integer - example: 0 - message: - type: object - properties: - role: - type: string - enum: - - user - - assistant - example: assistant - content: - type: string - example: '{"name": "Claude Monet", "reason": "Claude Monet is often considered one of the best French painters due to his significant role in the development of Impressionism, a major art movement that originated in France. His water lily paintings are among the most famous works in the history of art."}' - finish_reason: - type: string - enum: - - stop - - length - - model_length - - error - - tool_calls - example: stop - usage: - type: object - properties: - prompt_tokens: - type: integer - example: 14 - completion_tokens: - type: integer - example: 83 - total_tokens: - type: integer - example: 69 - required: - - prompt_tokens - - completion_tokens - - total_tokens - ChatCompletionResponseFunctionCall: + title: Data type: object - title: Function calling + title: ModelList + ValidationError: properties: - id: - type: string - example: cmpl-e5cc70bb28c444948073e77776eb30ef - object: - type: string - example: "chat.completion" - created: - type: integer - example: 1702256327 - model: - type: string - example: mistral-large-latest - choices: - type: array + loc: items: - type: object - required: - - index - - text - - finish_reason - properties: - index: - type: integer - example: 0 - message: - type: object - properties: - role: - type: string - example: assistant - content: - type: string - example: "" - tool_calls: - type: array - items: - type: object - properties: - function: - type: object - properties: - name: - type: string - arguments: - type: str - example: [ - { - "function": { - "name": "get_current_weather", - "arguments": "{\"location\": \"Paris, 75\"}" - } - } - ] - finish_reason: - type: string - enum: - - stop - - length - - model_length - - error - - tool_calls - example: tool_calls - usage: - type: object - properties: - prompt_tokens: - type: integer - example: 118 - completion_tokens: - type: integer - example: 35 - total_tokens: - type: integer - example: 153 - required: - - prompt_tokens - - completion_tokens - - total_tokens - EmbeddingRequest: - type: object - properties: - model: - type: string - example: "mistral-embed" - description: | - The ID of the model to use for this request. - input: + anyOf: + - type: string + - type: integer type: array - items: - type: string - example: [ "Hello", "world" ] - description: | - The list of strings to embed. - encoding_format: - type: string - enum: - - "float" - example: "float" - description: | - The format of the output data. - EmbeddingResponse: - type: object - properties: - id: - type: string - example: embd-aad6fc62b17349b192ef09225058bc45 - object: + title: Location + msg: type: string - example: list - data: - type: array - items: - type: object - properties: - object: - type: string - example: embedding - embedding: - type: array - items: - type: number - example: [ 0.1, 0.2, 0.3 ] - index: - type: int - example: 0 - example: [ - { - "object": "embedding", - "embedding": [ 0.1, 0.2, 0.3 ], - "index": 0 - }, - { - "object": "embedding", - "embedding": [ 0.4, 0.5, 0.6 ], - "index": 1 - } - ] - model: + title: Message + type: type: string - usage: - type: object - properties: - prompt_tokens: - type: integer - example: 9 - total_tokens: - type: integer - example: 9 - required: - - prompt_tokens - - total_tokens + title: Error Type + type: object required: - - id - - object - - data - - model - - usage - Model: - title: Model - description: Model object. + - loc + - msg + - type + title: ValidationError + SampleType: + enum: + - pretrain + - instruct + title: SampleType + type: string + Source: + enum: + - upload + - repository + title: Source + type: string + UploadFileOut: properties: id: + format: uuid + title: Id type: string + description: The unique identifier of the file. + examples: + - 497f6eca-6276-4993-bfeb-53cbbbba6f09 object: + title: Object type: string - created: + description: The object type, which is always "file". + examples: + - file + bytes: + title: Bytes type: integer - owned_by: + description: The size of the file, in bytes. + examples: + - 13000 + created_at: + title: Created At + type: integer + description: The UNIX timestamp (in seconds) of the event. + examples: + - 1716963433 + filename: + title: Filename type: string + description: The name of the uploaded file. + examples: + - files_upload.jsonl + purpose: + const: fine-tune + title: Purpose + description: The intended purpose of the uploaded file. Only accepts fine-tuning (`fine-tune`) for now. + examples: + - fine-tune + sample_type: + $ref: "#/components/schemas/SampleType" + num_lines: + anyOf: + - type: integer + - type: "null" + title: Num Lines + source: + $ref: "#/components/schemas/Source" required: - id - object - - created - - owned_by - UploadFileOut: + - bytes + - created_at + - filename + - purpose + - sample_type + - source + title: UploadFileOut + type: object + FileSchema: properties: id: format: uuid title: Id type: string - description: The ID of the created file. + description: The unique identifier of the file. + examples: + - 497f6eca-6276-4993-bfeb-53cbbbba6f09 object: title: Object type: string - example: file + description: The object type, which is always "file". + examples: + - file bytes: title: Bytes type: integer - description: The size (in bytes) of the created file. - example: 12000 + description: The size of the file, in bytes. + examples: + - 13000 created_at: title: Created At type: integer - description: The UNIX timestamp (in seconds) for the creation time of the file. - example: 1717491627 + description: The UNIX timestamp (in seconds) of the event. + examples: + - 1716963433 filename: title: Filename type: string - description: The name of the file that was uploaded. - example: train.jsonl + description: The name of the uploaded file. + examples: + - files_upload.jsonl purpose: const: fine-tune title: Purpose + description: The intended purpose of the uploaded file. Only accepts fine-tuning (`fine-tune`) for now. + examples: + - fine-tune + sample_type: + $ref: "#/components/schemas/SampleType" + num_lines: + anyOf: + - type: integer + - type: "null" + title: Num Lines + source: + $ref: "#/components/schemas/Source" required: - id - object @@ -1151,13 +835,15 @@ components: - created_at - filename - purpose - title: UploadFileOut + - sample_type + - source + title: FileSchema type: object ListFilesOut: properties: data: items: - "$ref": "#/components/schemas/FileSchema" + $ref: "#/components/schemas/FileSchema" title: Data type: array object: @@ -1174,21 +860,48 @@ components: format: uuid title: Id type: string + description: The unique identifier of the file. + examples: + - 497f6eca-6276-4993-bfeb-53cbbbba6f09 object: title: Object type: string + description: The object type, which is always "file". + examples: + - file bytes: title: Bytes type: integer + description: The size of the file, in bytes. + examples: + - 13000 created_at: title: Created At type: integer + description: The UNIX timestamp (in seconds) of the event. + examples: + - 1716963433 filename: title: Filename type: string + description: The name of the uploaded file. + examples: + - files_upload.jsonl purpose: const: fine-tune title: Purpose + description: The intended purpose of the uploaded file. Only accepts fine-tuning (`fine-tune`) for now. + examples: + - fine-tune + sample_type: + $ref: "#/components/schemas/SampleType" + num_lines: + anyOf: + - type: integer + - type: "null" + title: Num Lines + source: + $ref: "#/components/schemas/Source" required: - id - object @@ -1196,6 +909,8 @@ components: - created_at - filename - purpose + - sample_type + - source title: RetrieveFileOut type: object DeleteFileOut: @@ -1205,54 +920,108 @@ components: title: Id type: string description: The ID of the deleted file. - example: 97f6eca-6276-4993-bfeb-53cbbbba6f08 + examples: + - 497f6eca-6276-4993-bfeb-53cbbbba6f09 object: title: Object type: string description: The object type that was deleted - default: file + examples: + - file deleted: title: Deleted type: boolean description: The deletion status. + examples: + - false required: - id - object - deleted title: DeleteFileOut type: object - DeleteModelOut: - properties: - id: - format: uuid - title: Id - type: string - description: The ID of the deleted model. - example: ft:open-mistral-7b:587a6b29:20240514:7e773925 - object: - title: Object - type: string - description: The object type that was deleted - default: model - deleted: - title: Deleted - type: boolean - description: The deletion status - example: true - required: - - id - - object - - deleted - title: DeleteModelOut - type: object - FineTuneableModel: enum: - open-mistral-7b - mistral-small-latest + - codestral-latest + - mistral-large-latest + - open-mistral-nemo title: FineTuneableModel type: string description: The name of the model to fine-tune. + GithubRepositoryOut: + properties: + type: + const: github + default: github + title: Type + name: + title: Name + type: string + owner: + title: Owner + type: string + ref: + anyOf: + - type: string + - type: "null" + title: Ref + weight: + default: 1.0 + exclusiveMinimum: 0 + title: Weight + type: number + commit_id: + maxLength: 40 + minLength: 40 + title: Commit Id + type: string + required: + - name + - owner + - commit_id + title: GithubRepositoryOut + type: object + JobMetadataOut: + properties: + expected_duration_seconds: + anyOf: + - type: integer + - type: "null" + title: Expected Duration Seconds + cost: + anyOf: + - type: number + - type: "null" + title: Cost + cost_currency: + anyOf: + - type: string + - type: "null" + title: Cost Currency + train_tokens_per_step: + anyOf: + - type: integer + - type: "null" + title: Train Tokens Per Step + train_tokens: + anyOf: + - type: integer + - type: "null" + title: Train Tokens + data_tokens: + anyOf: + - type: integer + - type: "null" + title: Data Tokens + estimated_start_time: + anyOf: + - type: integer + - type: "null" + title: Estimated Start Time + title: JobMetadataOut + type: object JobOut: properties: id: @@ -1260,15 +1029,21 @@ components: title: Id type: string description: The ID of the job. + auto_start: + title: Auto Start + type: boolean hyperparameters: - "$ref": "#/components/schemas/TrainingParameters" + $ref: "#/components/schemas/TrainingParameters" model: - "$ref": "#/components/schemas/FineTuneableModel" + $ref: "#/components/schemas/FineTuneableModel" status: enum: - QUEUED - STARTED + - VALIDATING + - VALIDATED - RUNNING + - FAILED_VALIDATION - FAILED - SUCCESS - CANCELLED @@ -1296,29 +1071,60 @@ components: type: array description: A list containing the IDs of uploaded files that contain training data. validation_files: - items: - format: uuid - type: string - type: array - default: [ ] + anyOf: + - items: + format: uuid + type: string + type: array + - type: "null" + default: [] title: Validation Files description: A list containing the IDs of uploaded files that contain validation data. object: const: job default: job title: Object + description: The object type of the fine-tuning job. fine_tuned_model: - type: string + anyOf: + - type: string + - type: "null" title: Fine Tuned Model description: The name of the fine-tuned model that is being created. The value will be `null` if the fine-tuning job is still running. + suffix: + anyOf: + - type: string + - type: "null" + title: Suffix + description: Optional text/code that adds more context for the model. When given a `prompt` and a `suffix` the model will fill what is between them. When `suffix` is not provided, the model will simply execute completion starting with `prompt`. integrations: - items: - "$ref": "#/components/schemas/WandbIntegrationOut" - type: array + anyOf: + - items: + $ref: "#/components/schemas/WandbIntegrationOut" + type: array + - type: "null" title: Integrations description: A list of integrations enabled for your fine-tuning job. + trained_tokens: + anyOf: + - type: integer + - type: "null" + title: Trained Tokens + description: Total number of tokens trained. + repositories: + default: [] + items: + $ref: "#/components/schemas/GithubRepositoryOut" + maxItems: 20 + title: Repositories + type: array + metadata: + anyOf: + - $ref: "#/components/schemas/JobMetadataOut" + - type: "null" required: - id + - auto_start - hyperparameters - model - status @@ -1331,41 +1137,50 @@ components: JobsOut: properties: data: - default: [ ] + default: [] items: - "$ref": "#/components/schemas/JobOut" + $ref: "#/components/schemas/JobOut" title: Data type: array object: const: list default: list title: Object + total: + title: Total + type: integer + required: + - total title: JobsOut type: object TrainingParameters: - description: The fine-tuning hyperparameter settings used in a fine-tune job. properties: training_steps: - minimum: 1 + anyOf: + - minimum: 1 + type: integer + - type: "null" title: Training Steps - type: integer - description: | - The number of training steps to perform. A training step refers to - a single update of the model weights during the fine-tuning process. - This update is typically calculated using a batch of samples from the - training dataset. learning_rate: default: 0.0001 maximum: 1 - minimum: 1.0e-08 + minimum: 0.00000001 title: Learning Rate type: number - description: | - A parameter describing how much to adjust the pre-trained model's weights - in response to the estimated error each time the weights are updated during - the fine-tuning process. - required: - - training_steps + epochs: + anyOf: + - minimum: 0.01 + type: number + - type: "null" + title: Epochs + fim_ratio: + anyOf: + - maximum: 1 + minimum: 0 + type: number + - type: "null" + default: 0.9 + title: Fim Ratio title: TrainingParameters type: object WandbIntegrationOut: @@ -1379,62 +1194,236 @@ components: type: string description: The name of the project that the new run will be created under. name: - type: string + anyOf: + - type: string + - type: "null" title: Name description: A display name to set for the run. If not set, will use the job ID as the name. + run_name: + anyOf: + - type: string + - type: "null" + title: Run Name required: - project title: WandbIntegrationOut type: object + LegacyJobMetadataOut: + properties: + expected_duration_seconds: + anyOf: + - type: integer + - type: "null" + title: Expected Duration Seconds + description: The approximated time (in seconds) for the fine-tuning process to complete. + examples: + - 220 + cost: + anyOf: + - type: number + - type: "null" + title: Cost + description: The cost of the fine-tuning job. + examples: + - 10 + cost_currency: + anyOf: + - type: string + - type: "null" + title: Cost Currency + description: The currency used for the fine-tuning job cost. + examples: + - EUR + train_tokens_per_step: + anyOf: + - type: integer + - type: "null" + title: Train Tokens Per Step + description: The number of tokens consumed by one training step. + examples: + - 131072 + train_tokens: + anyOf: + - type: integer + - type: "null" + title: Train Tokens + description: The total number of tokens used during the fine-tuning process. + examples: + - 1310720 + data_tokens: + anyOf: + - type: integer + - type: "null" + title: Data Tokens + description: The total number of tokens in the training dataset. + examples: + - 305375 + estimated_start_time: + anyOf: + - type: integer + - type: "null" + title: Estimated Start Time + deprecated: + default: true + title: Deprecated + type: boolean + details: + title: Details + type: string + epochs: + anyOf: + - type: number + - type: "null" + title: Epochs + description: The number of complete passes through the entire training dataset. + examples: + - 4.2922 + training_steps: + anyOf: + - type: integer + - type: "null" + title: Training Steps + description: The number of training steps to perform. A training step refers to a single update of the model weights during the fine-tuning process. This update is typically calculated using a batch of samples from the training dataset. + examples: + - 10 + object: + const: job.metadata + default: job.metadata + title: Object + required: + - details + title: LegacyJobMetadataOut + type: object + GithubRepositoryIn: + properties: + type: + const: github + default: github + title: Type + name: + title: Name + type: string + owner: + title: Owner + type: string + ref: + anyOf: + - type: string + - type: "null" + title: Ref + weight: + default: 1.0 + exclusiveMinimum: 0 + title: Weight + type: number + token: + title: Token + type: string + required: + - name + - owner + - token + title: GithubRepositoryIn + type: object JobIn: properties: model: - "$ref": "#/components/schemas/FineTuneableModel" + $ref: "#/components/schemas/FineTuneableModel" training_files: + default: [] items: - format: uuid - type: string - description: A list containing the IDs of uploaded files that contain training data. - minItems: 1 + $ref: "#/components/schemas/TrainingFile" title: Training Files type: array validation_files: - description: | - A list containing the IDs of uploaded files that contain validation data. - - If you provide these files, the data is used to generate validation metrics - periodically during fine-tuning. These metrics can be viewed in `checkpoints` - when getting the status of a running fine-tuning job. - - The same data should not be present in both train and validation files. - items: - format: uuid - type: string - type: array + anyOf: + - items: + format: uuid + type: string + type: array + - type: "null" title: Validation Files + description: "A list containing the IDs of uploaded files that contain validation data. If you provide these files, the data is used to generate validation metrics periodically during fine-tuning. These metrics can be viewed in `checkpoints` when getting the status of a running fine-tuning job. The same data should not be present in both train and validation files." hyperparameters: - "$ref": "#/components/schemas/TrainingParameters" + $ref: "#/components/schemas/TrainingParametersIn" suffix: - maxLength: 18 - type: string + anyOf: + - maxLength: 18 + type: string + - type: "null" title: Suffix - description: | - A string that will be added to your fine-tuning model name. - For example, a suffix of "my-great-model" would produce a model - name like `ft:open-mistral-7b:my-great-model:xxx...` + description: 'A string that will be added to your fine-tuning model name. For example, a suffix of "my-great-model" would produce a model name like `ft:open-mistral-7b:my-great-model:xxx...`' integrations: + anyOf: + - items: + $ref: "#/components/schemas/WandbIntegration" + type: array + - type: "null" + title: Integrations description: A list of integrations to enable for your fine-tuning job. + repositories: + default: [] items: - "$ref": "#/components/schemas/WandbIntegration" + $ref: "#/components/schemas/GithubRepositoryIn" + title: Repositories type: array - uniqueItems: true - title: Integrations + auto_start: + description: This field will be required in a future release. + title: Auto Start + type: boolean required: - model - - training_files - hyperparameters title: JobIn type: object + TrainingFile: + properties: + file_id: + format: uuid + title: File Id + type: string + weight: + default: 1.0 + exclusiveMinimum: 0 + title: Weight + type: number + required: + - file_id + title: TrainingFile + type: object + TrainingParametersIn: + properties: + training_steps: + anyOf: + - minimum: 1 + type: integer + - type: "null" + title: Training Steps + description: "The number of training steps to perform. A training step refers to a single update of the model weights during the fine-tuning process. This update is typically calculated using a batch of samples from the training dataset." + learning_rate: + default: 0.0001 + maximum: 1 + minimum: 0.00000001 + title: Learning Rate + type: number + description: "A parameter describing how much to adjust the pre-trained model's weights in response to the estimated error each time the weights are updated during the fine-tuning process." + epochs: + anyOf: + - minimum: 0.01 + type: number + - type: "null" + title: Epochs + fim_ratio: + anyOf: + - maximum: 1 + minimum: 0 + type: number + - type: "null" + default: 0.9 + title: Fim Ratio + title: TrainingParametersIn + type: object + description: The fine-tuning hyperparameter settings used in a fine-tune job. WandbIntegration: properties: type: @@ -1446,13 +1435,22 @@ components: type: string description: The name of the project that the new run will be created under. name: - type: string + anyOf: + - type: string + - type: "null" title: Name description: A display name to set for the run. If not set, will use the job ID as the name. api_key: + maxLength: 40 + minLength: 40 title: Api Key type: string description: The WandB API key to use for authentication. + run_name: + anyOf: + - type: string + - type: "null" + title: Run Name required: - project - api_key @@ -1461,7 +1459,7 @@ components: CheckpointOut: properties: metrics: - "$ref": "#/components/schemas/MetricOut" + $ref: "#/components/schemas/MetricOut" step_number: title: Step Number type: integer @@ -1470,6 +1468,8 @@ components: title: Created At type: integer description: The UNIX timestamp (in seconds) for when the checkpoint was created. + examples: + - 1716963433 required: - metrics - step_number @@ -1482,80 +1482,104 @@ components: format: uuid title: Id type: string + auto_start: + title: Auto Start + type: boolean hyperparameters: - "$ref": "#/components/schemas/TrainingParameters" + $ref: "#/components/schemas/TrainingParameters" model: - "$ref": "#/components/schemas/FineTuneableModel" + $ref: "#/components/schemas/FineTuneableModel" status: enum: - QUEUED - STARTED + - VALIDATING + - VALIDATED - RUNNING + - FAILED_VALIDATION - FAILED - SUCCESS - CANCELLED - CANCELLATION_REQUESTED title: Status type: string - description: The current status of the fine-tuning job. job_type: title: Job Type type: string - description: The type of job (`FT` for fine-tuning). created_at: title: Created At type: integer - description: The UNIX timestamp (in seconds) for when the fine-tuning job was created. modified_at: title: Modified At type: integer - description: The UNIX timestamp (in seconds) for when the fine-tuning job was last modified. training_files: items: format: uuid type: string title: Training Files type: array - description: A list containing the IDs of uploaded files that contain training data. validation_files: - items: - format: uuid - type: string - type: array - default: [ ] + anyOf: + - items: + format: uuid + type: string + type: array + - type: "null" + default: [] title: Validation Files - description: A list containing the IDs of uploaded files that contain validation data. object: const: job default: job title: Object fine_tuned_model: - type: string + anyOf: + - type: string + - type: "null" title: Fine Tuned Model - description: The name of the fine-tuned model that is being created. The value will be `null` if the fine-tuning job is still running. + suffix: + anyOf: + - type: string + - type: "null" + title: Suffix integrations: + anyOf: + - items: + $ref: "#/components/schemas/WandbIntegrationOut" + type: array + - type: "null" + title: Integrations + trained_tokens: + anyOf: + - type: integer + - type: "null" + title: Trained Tokens + repositories: + default: [] items: - "$ref": "#/components/schemas/WandbIntegrationOut" + $ref: "#/components/schemas/GithubRepositoryOut" + maxItems: 20 + title: Repositories type: array - title: Integrations - description: A list of integrations enabled for your fine-tuning job. + metadata: + anyOf: + - $ref: "#/components/schemas/JobMetadataOut" + - type: "null" events: - default: [ ] + default: [] items: - "$ref": "#/components/schemas/EventOut" + $ref: "#/components/schemas/EventOut" title: Events type: array - description: | - Event items are created every time the status of a fine-tuning job changes. - The timestamped list of all events is accessible here. + description: "Event items are created every time the status of a fine-tuning job changes. The timestamped list of all events is accessible here." checkpoints: - default: [ ] + default: [] items: - "$ref": "#/components/schemas/CheckpointOut" + $ref: "#/components/schemas/CheckpointOut" title: Checkpoints type: array required: - id + - auto_start - hyperparameters - model - status @@ -1572,306 +1596,871 @@ components: type: string description: The name of the event. data: - enum: - - QUEUED - - STARTED - - RUNNING - - FAILED - - SUCCESS - - CANCELLED - - CANCELLATION_REQUESTED - type: string + anyOf: + - type: object + additionalProperties: true + - type: "null" title: Data - description: The status of the fine-tuning job at the time of the event created_at: title: Created At type: integer description: The UNIX timestamp (in seconds) of the event. required: - name - - created_at - title: EventOut + - created_at + title: EventOut + type: object + MetricOut: + properties: + train_loss: + anyOf: + - type: number + - type: "null" + title: Train Loss + valid_loss: + anyOf: + - type: number + - type: "null" + title: Valid Loss + valid_mean_token_accuracy: + anyOf: + - type: number + - type: "null" + title: Valid Mean Token Accuracy + title: MetricOut + type: object + description: "Metrics at the step number during the fine-tuning job. Use these metrics to assess if the training is going smoothly (loss should decrease, token accuracy should increase)." + FTModelCapabilitiesOut: + properties: + completion_chat: + default: true + title: Completion Chat + type: boolean + completion_fim: + default: false + title: Completion Fim + type: boolean + function_calling: + default: false + title: Function Calling + type: boolean + fine_tuning: + default: false + title: Fine Tuning + type: boolean + title: FTModelCapabilitiesOut + type: object + FTModelOut: + properties: + id: + title: Id + type: string + object: + const: model + default: model + title: Object + created: + title: Created + type: integer + owned_by: + title: Owned By + type: string + root: + title: Root + type: string + archived: + title: Archived + type: boolean + name: + anyOf: + - type: string + - type: "null" + title: Name + description: + anyOf: + - type: string + - type: "null" + title: Description + capabilities: + $ref: "#/components/schemas/FTModelCapabilitiesOut" + max_context_length: + default: 32768 + title: Max Context Length + type: integer + aliases: + default: [] + items: + type: string + title: Aliases + type: array + job: + format: uuid + title: Job + type: string + required: + - id + - created + - owned_by + - root + - archived + - capabilities + - job + title: FTModelOut + type: object + UpdateFTModelIn: + properties: + name: + anyOf: + - type: string + - type: "null" + title: Name + description: + anyOf: + - type: string + - type: "null" + title: Description + title: UpdateFTModelIn + type: object + ArchiveFTModelOut: + properties: + id: + title: Id + type: string + object: + const: model + default: model + title: Object + archived: + default: true + title: Archived + type: boolean + required: + - id + title: ArchiveFTModelOut + type: object + UnarchiveFTModelOut: + properties: + id: + title: Id + type: string + object: + const: model + default: model + title: Object + archived: + default: false + title: Archived + type: boolean + required: + - id + title: UnarchiveFTModelOut + type: object + AssistantMessage: + properties: + content: + anyOf: + - type: string + - type: "null" + title: Content + tool_calls: + anyOf: + - items: + $ref: "#/components/schemas/ToolCall" + type: array + - type: "null" + title: Tool Calls + prefix: + type: boolean + title: Prefix + default: false + description: "Set this to `true` when adding an assistant message as prefix to condition the model response. The role of the prefix message is to force the model to start its answer by the content of the message." + role: + type: string + default: assistant + title: Role + enum: + - assistant + additionalProperties: false + type: object + title: AssistantMessage + ChatCompletionRequest: + properties: + model: + anyOf: + - type: string + - type: "null" + title: Model + description: ID of the model to use. You can use the [List Available Models](/api#operation/listModels) API to see all of your available models, or see our [Model overview](/models) for model descriptions. + examples: + - mistral-small-latest + temperature: + type: number + maximum: 1.5 + minimum: 0 + title: Temperature + default: 0.7 + description: "What sampling temperature to use, between 0.0 and 1.0. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or `top_p` but not both." + top_p: + type: number + maximum: 1 + minimum: 0 + title: Top P + default: 1.0 + description: "Nucleus sampling, where the model considers the results of the tokens with `top_p` probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or `temperature` but not both." + max_tokens: + anyOf: + - type: integer + minimum: 0 + - type: "null" + title: Max Tokens + description: "The maximum number of tokens to generate in the completion. The token count of your prompt plus `max_tokens` cannot exceed the model's context length." + min_tokens: + anyOf: + - type: integer + minimum: 0 + - type: "null" + title: Min Tokens + description: The minimum number of tokens to generate in the completion. + stream: + type: boolean + title: Stream + default: false + description: "Whether to stream back partial progress. If set, tokens will be sent as data-only server-side events as they become available, with the stream terminated by a data: [DONE] message. Otherwise, the server will hold the request open until the timeout or until completion, with the response containing the full result as JSON." + stop: + anyOf: + - type: string + - items: + type: string + type: array + title: Stop + description: Stop generation if this token is detected. Or if one of these tokens is detected when providing an array + random_seed: + anyOf: + - type: integer + minimum: 0 + - type: "null" + title: Random Seed + description: The seed to use for random sampling. If set, different calls will generate deterministic results. + messages: + items: + oneOf: + - $ref: "#/components/schemas/SystemMessage" + - $ref: "#/components/schemas/UserMessage" + - $ref: "#/components/schemas/AssistantMessage" + - $ref: "#/components/schemas/ToolMessage" + discriminator: + propertyName: role + mapping: + assistant: "#/components/schemas/AssistantMessage" + system: "#/components/schemas/SystemMessage" + tool: "#/components/schemas/ToolMessage" + user: "#/components/schemas/UserMessage" + type: array + title: Messages + description: The prompt(s) to generate completions for, encoded as a list of dict with role and content. + examples: + - { + "role": "user", + "content": "Who is the best French painter? Answer in one short sentence.", + } + response_format: + $ref: "#/components/schemas/ResponseFormat" + tools: + anyOf: + - items: + $ref: "#/components/schemas/Tool" + type: array + - type: "null" + title: Tools + tool_choice: + allOf: + - $ref: "#/components/schemas/ToolChoice" + default: auto + safe_prompt: + type: boolean + description: Whether to inject a safety prompt before all conversations. + default: false + additionalProperties: false + type: object + required: + - messages + - model + title: ChatCompletionRequest + ChunkTypes: + type: string + const: text + title: ChunkTypes + ContentChunk: + properties: + type: + allOf: + - $ref: "#/components/schemas/ChunkTypes" + default: text + text: + type: string + title: Text + additionalProperties: false + type: object + required: + - text + title: ContentChunk + FIMCompletionRequest: + properties: + model: + anyOf: + - type: string + - type: "null" + title: Model + default: codestral-2405 + description: "ID of the model to use. Only compatible for now with:\n - `codestral-2405`\n - `codestral-latest`" + examples: + - codestral-2405 + temperature: + type: number + maximum: 1.5 + minimum: 0 + title: Temperature + default: 0.7 + description: "What sampling temperature to use, between 0.0 and 1.0. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or `top_p` but not both." + top_p: + type: number + maximum: 1 + minimum: 0 + title: Top P + default: 1.0 + description: "Nucleus sampling, where the model considers the results of the tokens with `top_p` probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or `temperature` but not both." + max_tokens: + anyOf: + - type: integer + minimum: 0 + - type: "null" + title: Max Tokens + description: "The maximum number of tokens to generate in the completion. The token count of your prompt plus `max_tokens` cannot exceed the model's context length." + min_tokens: + anyOf: + - type: integer + minimum: 0 + - type: "null" + title: Min Tokens + description: The minimum number of tokens to generate in the completion. + stream: + type: boolean + title: Stream + default: false + description: "Whether to stream back partial progress. If set, tokens will be sent as data-only server-side events as they become available, with the stream terminated by a data: [DONE] message. Otherwise, the server will hold the request open until the timeout or until completion, with the response containing the full result as JSON." + stop: + anyOf: + - type: string + - items: + type: string + type: array + title: Stop + description: Stop generation if this token is detected. Or if one of these tokens is detected when providing an array + random_seed: + anyOf: + - type: integer + minimum: 0 + - type: "null" + title: Random Seed + description: The seed to use for random sampling. If set, different calls will generate deterministic results. + prompt: + type: string + title: Prompt + description: "The text/code to complete." + examples: + - def + suffix: + anyOf: + - type: string + - type: "null" + title: Suffix + default: "" + description: "Optional text/code that adds more context for the model. When given a `prompt` and a `suffix` the model will fill what is between them. When `suffix` is not provided, the model will simply execute completion starting with `prompt`." + examples: + - return a+b + additionalProperties: false + type: object + required: + - prompt + - model + title: FIMCompletionRequest + Function: + properties: + name: + type: string + title: Name + description: + type: string + title: Description + default: "" + parameters: + type: object + title: Parameters + additionalProperties: true + additionalProperties: false + type: object + required: + - name + - parameters + title: Function + FunctionCall: + properties: + name: + type: string + title: Name + arguments: + title: Arguments + anyOf: + - type: object + additionalProperties: true + - type: string + additionalProperties: false + type: object + required: + - name + - arguments + title: FunctionCall + ResponseFormat: + properties: + type: + allOf: + - $ref: "#/components/schemas/ResponseFormats" + default: text + additionalProperties: false type: object - MetricOut: + title: ResponseFormat + ResponseFormats: + type: string + enum: + - text + - json_object + title: ResponseFormats + description: 'An object specifying the format that the model must output. Setting to `{ "type": "json_object" }` enables JSON mode, which guarantees the message the model generates is in JSON. When using JSON mode you MUST also instruct the model to produce JSON yourself with a system or a user message.' + SystemMessage: properties: - train_loss: - type: number - title: Train Loss - valid_loss: - type: number - title: Valid Loss - valid_mean_token_accuracy: - type: number - title: Valid Mean Token Accuracy - title: MetricOut - description: | - Metrics at the step number during the fine-tuning job. Use these metrics to - assess if the training is going smoothly (loss should decrease, token accuracy - should increase). + content: + anyOf: + - type: string + - items: + $ref: "#/components/schemas/ContentChunk" + type: array + title: Content + role: + type: string + default: system + enum: + - system + additionalProperties: false type: object - UploadFileResponse: + required: + - content + title: SystemMessage + TextChunk: properties: - id: - format: uuid - title: Id - type: string - example: 497f6eca-6276-4993-bfeb-53cbbbba6f09 - object: - title: Object - type: string - example: test - bytes: - title: Bytes - type: integer - example: 13000 - created_at: - title: Created At - type: integer - example: 1716963433 - filename: - title: Filename - type: string - example: files_upload.jsonl - purpose: - title: Purpose + type: + const: text + title: Type + default: text + text: type: string - example: fine-tune + title: Text + additionalProperties: false + type: object required: - - id - - object - - bytes - - created_at - - filename - - purpose - title: UploadFileResponse + - text + title: TextChunk + Tool: + properties: + type: + allOf: + - $ref: "#/components/schemas/ToolTypes" + default: function + function: + $ref: "#/components/schemas/Function" + additionalProperties: false type: object - FileSchema: + required: + - function + title: Tool + ToolCall: properties: id: - format: uuid - title: Id - type: string - description: The file identifier, which can be referenced in the API endpoints - example: d56b5e4f-16ae-4f07-be8e-b837aa10240f - object: - title: Object type: string - description: The object type, which is always `file`. - example: "file" - bytes: - title: Bytes - type: integer - description: The size of the file, in bytes. - example: 1534119 - created_at: - title: Created At - type: integer - description: The UNIX timestamp (in seconds) for when the file was created. - example: 1716329302 - filename: - title: Filename + title: Id + default: "null" + type: + allOf: + - $ref: "#/components/schemas/ToolTypes" + default: function + function: + $ref: "#/components/schemas/FunctionCall" + additionalProperties: false + type: object + required: + - function + title: ToolCall + ToolChoice: + type: string + enum: + - auto + - none + - any + title: ToolChoice + ToolMessage: + properties: + content: type: string - description: The name of the file - example: file_upload.jsonl - purpose: - title: Purpose + title: Content + tool_call_id: + anyOf: + - type: string + - type: "null" + title: Tool Call Id + name: + anyOf: + - type: string + - type: "null" + title: Name + role: type: string - description: The intended purpose of the file. Only supports `fine-tune` for now. - example: fine-tune + default: tool + enum: + - tool + additionalProperties: false + type: object required: - - id - - object - - bytes - - created_at - - filename - - purpose - title: FileSchema + - content + title: ToolMessage + ToolTypes: + type: string + const: function + title: ToolTypes + UserMessage: + properties: + content: + title: Content + anyOf: + - type: string + - items: + $ref: "#/components/schemas/TextChunk" + type: array + role: + type: string + default: user + enum: + - user + additionalProperties: false type: object - ListFilesResponse: + required: + - content + title: UserMessage + AgentsCompletionRequest: properties: - data: - items: - $ref: '#/components/schemas/FileSchema' - title: Data + max_tokens: + anyOf: + - type: integer + minimum: 0 + - type: "null" + title: Max Tokens + description: "The maximum number of tokens to generate in the completion. The token count of your prompt plus `max_tokens` cannot exceed the model's context length." + min_tokens: + anyOf: + - type: integer + minimum: 0 + - type: "null" + title: Min Tokens + description: The minimum number of tokens to generate in the completion. + stream: + type: boolean + title: Stream + default: false + description: "Whether to stream back partial progress. If set, tokens will be sent as data-only server-side events as they become available, with the stream terminated by a data: [DONE] message. Otherwise, the server will hold the request open until the timeout or until completion, with the response containing the full result as JSON." + stop: + anyOf: + - type: string + - items: + type: string + type: array + title: Stop + description: Stop generation if this token is detected. Or if one of these tokens is detected when providing an array + random_seed: + anyOf: + - type: integer + minimum: 0 + - type: "null" + title: Random Seed + description: The seed to use for random sampling. If set, different calls will generate deterministic results. + messages: type: array - object: - title: Object + title: Messages + items: + oneOf: + - $ref: "#/components/schemas/UserMessage" + - $ref: "#/components/schemas/AssistantMessage" + - $ref: "#/components/schemas/ToolMessage" + discriminator: + propertyName: role + mapping: + assistant: "#/components/schemas/AssistantMessage" + tool: "#/components/schemas/ToolMessage" + user: "#/components/schemas/UserMessage" + description: The prompt(s) to generate completions for, encoded as a list of dict with role and content. + examples: + - { + "role": "user", + "content": "Who is the best French painter? Answer in one short sentence.", + } + response_format: + $ref: "#/components/schemas/ResponseFormat" + tools: + anyOf: + - items: + $ref: "#/components/schemas/Tool" + type: array + - type: "null" + title: Tools + tool_choice: + allOf: + - $ref: "#/components/schemas/ToolChoice" + default: auto + agent_id: type: string - required: - - data - - object - title: ListFilesResponse + description: The ID of the agent to use for this completion. + additionalProperties: false type: object - RetrieveFileResponse: + required: + - messages + - agent_id + title: AgentsCompletionRequest + EmbeddingRequest: properties: - id: - format: uuid - title: Id - type: string - object: - title: Object + input: + anyOf: + - type: string + - items: + type: string + type: array + title: Input + description: Text to embed. + model: type: string - bytes: - title: Bytes + title: Model + description: ID of the model to use. + encoding_format: + anyOf: + - type: string + - type: "null" + title: Encoding Format + description: The format to return the embeddings in. + default: float + additionalProperties: false + type: object + required: + - input + - model + title: EmbeddingRequest + UsageInfo: + title: UsageInfo + type: object + properties: + prompt_tokens: type: integer - created_at: - title: Created At + example: 16 + completion_tokens: type: integer - filename: - title: Filename - type: string - purpose: - title: Purpose - type: string + example: 34 + total_tokens: + type: integer + example: 50 required: - - id - - object - - bytes - - created_at - - filename - - purpose - title: RetrieveFileResponse + - prompt_tokens + - completion_tokens + - total_tokens + ResponseBase: type: object - DeleteFileResponse: + title: ResponseBase properties: id: - format: uuid - title: Id type: string + example: cmpl-e5cc70bb28c444948073e77776eb30ef object: - title: Object type: string - deleted: - title: Deleted - type: boolean - required: - - id - - object - - deleted - title: DeleteFileResponse + example: "chat.completion" + model: + type: string + example: mistral-small-latest + usage: + $ref: "#/components/schemas/UsageInfo" + ChatCompletionChoice: + title: ChatCompletionChoice type: object + required: + - index + - text + - finish_reason + properties: + index: + type: integer + example: 0 + message: + $ref: "#/components/schemas/AssistantMessage" + finish_reason: + type: string + enum: + - stop + - length + - model_length + - error + - tool_calls + example: stop + ChatCompletionResponseBase: + allOf: + - $ref: "#/components/schemas/ResponseBase" + - type: object + title: ChatCompletionResponseBase + properties: + created: + type: integer + example: 1702256327 + ChatCompletionResponse: + allOf: + - $ref: "#/components/schemas/ChatCompletionResponseBase" + - type: object + title: ChatCompletionResponse + properties: + choices: + type: array + items: + $ref: "#/components/schemas/ChatCompletionChoice" + required: + - id + - object + - data + - model + - usage FIMCompletionResponse: + allOf: + - $ref: "#/components/schemas/ChatCompletionResponse" + - type: object + properties: + model: + type: string + example: codestral-latest + EmbeddingResponseData: + title: EmbeddingResponseData type: object + properties: + object: + type: string + example: embedding + embedding: + type: array + items: + type: number + example: [0.1, 0.2, 0.3] + index: + type: integer + example: 0 + example: + [ + { "object": "embedding", "embedding": [0.1, 0.2, 0.3], "index": 0 }, + { "object": "embedding", "embedding": [0.4, 0.5, 0.6], "index": 1 }, + ] + EmbeddingResponse: + allOf: + - $ref: "#/components/schemas/ResponseBase" + - type: object + properties: + data: + type: array + items: + - $ref: "#/components/schemas/EmbeddingResponseData" + required: + - id + - object + - data + - model + - usage + CompletionEvent: + type: object + required: [data] + properties: + data: + $ref: "#/components/schemas/CompletionChunk" + CompletionChunk: + type: object + required: [id, model, choices] properties: id: type: string - example: 5b35cc2e69bf4ba9a11373ee1f1937f8 object: type: string - example: "chat.completion" created: type: integer - example: 1702256327 model: type: string - example: codestral-latest + usage: + $ref: "#/components/schemas/UsageInfo" choices: type: array items: - type: object - required: - - index - - text - - finish_reason - properties: - index: - type: integer - example: 0 - message: - type: object - properties: - role: - type: string - enum: - - user - - assistant - example: assistant - content: - type: string - example: >- - " add(a,b):" - finish_reason: - type: string - enum: - - stop - - length - - model_length - - error - example: stop - usage: - type: object - properties: - prompt_tokens: - type: integer - example: 8 - completion_tokens: - type: integer - example: 9 - total_tokens: - type: integer - example: 17 - required: - - prompt_tokens - - completion_tokens - - total_tokens - JobMetadata: + $ref: "#/components/schemas/CompletionResponseStreamChoice" + CompletionResponseStreamChoice: type: object - title: JobMetadata + required: [index, delta, finish_reason] properties: - training_steps: - type: integer - description: | - The number of training steps to perform. A training step refers to a single update of the model weights during the fine-tuning process. This update is typically calculated using a batch of samples from the training dataset. - name: Training steps - example: 10 - train_tokens_per_step: - type: integer - description: The number of tokens consumed by one training step. - name: Training tokens per step - example: 131072 - data_tokens: - type: integer - description: The total number of tokens in the training dataset. - example: 305375 - train_tokens: - type: integer - description: The total number of tokens used during the fine-tuning process. - example: 1310720 - epochs: - type: float - description: The number of complete passes through the entire training dataset. - example: 4.2922 - expected_duration_seconds: + index: type: integer - description: The approximated time (in seconds) for the fine-tuning process to complete. - example: 220 - HTTPValidationError: - properties: - detail: - items: - "$ref": "#/components/schemas/ValidationError" - type: array - title: Detail + delta: + $ref: "#/components/schemas/DeltaMessage" + finish_reason: + type: [string, "null"] + enum: + - stop + - length + - error + - tool_calls + - null + DeltaMessage: type: object - title: HTTPValidationError - ValidationError: - properties: - loc: - items: - anyOf: - - type: string - - type: integer - type: array - title: Location - msg: - type: string - title: Message - type: - type: string - title: Error Type - type: object - required: - - loc - - msg - - type - title: ValidationError + properties: + role: + type: string + content: + type: string + tool_calls: + anyOf: + - type: "null" + - type: array + $ref: "#/components/schemas/ToolCall" + securitySchemes: + ApiKey: + type: http + scheme: bearer +tags: + - name: chat + x-displayName: Chat + description: Chat Completion API. + - name: fim + x-displayName: FIM + description: Fill-in-the-middle API. + - name: agents + x-displayName: Agents + description: Agents API. + - name: embeddings + x-displayName: Embeddings + description: Embeddings API. + - name: files + x-displayName: Files + description: Files API + - name: fine-tuning + x-displayName: Fine Tuning + description: Fine-tuning API + - name: models + x-displayName: Models + description: Model Management API +security: + - ApiKey: [] +servers: + - url: https://api.mistral.ai + description: Production server diff --git a/static/img/French_agent.png b/static/img/French_agent.png new file mode 100644 index 0000000000000000000000000000000000000000..520364fe38d55fde6fdd693dc2b1f0e143768982 GIT binary patch literal 469439 zcmeFYcUV*1wl|6*pddvBL5iSQ5TpnK(m_B}Kxzmbq)H7vv=9Liq+5{QL@5cqlTf51 zy@k+{fb2rY2q`|f?te$M^Q{?7U5{&n*_D{IcR)|_*VIp&ySjNcgRgZ3*mdRlf` z3JMDP7td96DJW=nC@84sFH)1Q@Fp>*QBYh~a8Op(exa<)qwN8*b8xn$pm_cv0Z3!0 z_v?Cw$)|Y5H`(f!8ZO<|J^%btHdVa&1)WPT-fCN2zQ7V-r0*t}9}z-t#-g+Sm@bm< z`t^1!D;;e&!mv~Tq^pRAECk%gO&>31ik(S$;-^I^^3&%dRHX8yDe77R-8D)TAc4A~ z>7$fPZ<{lPMCROfReJOD-6&j^Prw0SN`GbKDi7+&{tPNke^Vijj^ZknJG*dPD&6*L zimjK$&(jzwbh=ZudRSjPa#mEoeC?g@2WI;&_aDr8zqW&twM;M5{WhTZqW^@`otgrc 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