add bedrock llama vision support + cohere / infinity rerank - 'return_documents' support (#8684)

* build(model_prices_and_context_window.json): mark bedrock llama as supporting vision based on docs

* Add price for Cerebras llama3.3-70b (#8676)

* docs(readme.md): fix contributing docs

point people to new mock directory testing structure s/o @vibhavbhat

* build: update contributing readme

* docs(readme.md): improve docs

* docs(readme.md): cleanup readme on tests/

* docs(README.md): cleanup doc

* feat(infinity/): support returning documents when return_documents=True

* test(test_rerank.py): add e2e testing for cohere rerank

* fix: fix linting errors

* fix(together_ai/): fix together ai transformation

* fix: fix linting error

* fix: fix linting errors

* fix: fix linting errors

* test: mark cohere as flaky

* build: fix model supports check

* test: fix test

* test: mark flaky test

* fix: fix test

* test: fix test

---------

Co-authored-by: Yury Koleda <fut.wrk@gmail.com>

README.md

@@ -343,25 +343,32 @@ curl 'http://0.0.0.0:4000/key/generate' \
 To contribute: Clone the repo locally -> Make a change -> Submit a PR with the change.
 
 Here's how to modify the repo locally:
+
 Step 1: Clone the repo
 
 ```
 git clone https://github.com/BerriAI/litellm.git
 ```
 
-Step 2: Navigate into the project, and install dependencies:
+Step 2: Install dependencies:
 
 ```
-cd litellm
-poetry install -E extra_proxy -E proxy
+pip install -r requirements.txt
 ```
 
 Step 3: Test your change:
 
+a. Add a pytest test within `tests/litellm/`
+
+This folder follows the same directory structure as `litellm/`.
+
+If a corresponding test file does not exist, create one.
+
+b. Run the test
+
 ```
-cd tests # pwd: Documents/litellm/litellm/tests
-poetry run flake8
-poetry run pytest .
+cd tests/litellm # pwd: Documents/litellm/litellm/tests/litellm
+pytest /path/to/test_file.py
 ```
 
 Step 4: Submit a PR with your changes! 🚀

litellm/llms/infinity/rerank/transformation.py

@@ -13,8 +13,14 @@ import litellm
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
 from litellm.llms.cohere.rerank.transformation import CohereRerankConfig
 from litellm.secret_managers.main import get_secret_str
-from litellm.types.rerank import RerankBilledUnits, RerankResponseMeta, RerankTokens
-from litellm.types.utils import RerankResponse
+from litellm.types.rerank import (
+    RerankBilledUnits,
+    RerankResponse,
+    RerankResponseDocument,
+    RerankResponseMeta,
+    RerankResponseResult,
+    RerankTokens,
+)
 
 from .common_utils import InfinityError
 
@@ -88,13 +94,23 @@ class InfinityRerankConfig(CohereRerankConfig):
         )
         rerank_meta = RerankResponseMeta(billed_units=_billed_units, tokens=_tokens)
 
-        _results: Optional[List[dict]] = raw_response_json.get("results")
-
-        if _results is None:
+        cohere_results: List[RerankResponseResult] = []
+        if raw_response_json.get("results"):
+            for result in raw_response_json.get("results"):
+                _rerank_response = RerankResponseResult(
+                    index=result.get("index"),
+                    relevance_score=result.get("relevance_score"),
+                )
+                if result.get("document"):
+                    _rerank_response["document"] = RerankResponseDocument(
+                        text=result.get("document")
+                    )
+                cohere_results.append(_rerank_response)
+        if cohere_results is None:
             raise ValueError(f"No results found in the response={raw_response_json}")
 
         return RerankResponse(
             id=raw_response_json.get("id") or str(uuid.uuid4()),
-            results=_results,  # type: ignore
+            results=cohere_results,
             meta=rerank_meta,
         )  # Return response

litellm/llms/together_ai/rerank/transformation.py

@@ -10,7 +10,9 @@ from typing import List, Optional
 from litellm.types.rerank import (
     RerankBilledUnits,
     RerankResponse,
+    RerankResponseDocument,
     RerankResponseMeta,
+    RerankResponseResult,
     RerankTokens,
 )
 
@@ -27,8 +29,35 @@ class TogetherAIRerankConfig:
         if _results is None:
             raise ValueError(f"No results found in the response={response}")
 
+        rerank_results: List[RerankResponseResult] = []
+
+        for result in _results:
+            # Validate required fields exist
+            if not all(key in result for key in ["index", "relevance_score"]):
+                raise ValueError(f"Missing required fields in the result={result}")
+
+            # Get document data if it exists
+            document_data = result.get("document", {})
+            document = (
+                RerankResponseDocument(text=str(document_data.get("text", "")))
+                if document_data
+                else None
+            )
+
+            # Create typed result
+            rerank_result = RerankResponseResult(
+                index=int(result["index"]),
+                relevance_score=float(result["relevance_score"]),
+            )
+
+            # Only add document if it exists
+            if document:
+                rerank_result["document"] = document
+
+            rerank_results.append(rerank_result)
+
         return RerankResponse(
             id=response.get("id") or str(uuid.uuid4()),
-            results=_results,  # type: ignore
+            results=rerank_results,
             meta=rerank_meta,
         )  # Return response

litellm/model_prices_and_context_window_backup.json

@@ -2643,6 +2643,17 @@
         "supports_function_calling": true,
         "supports_tool_choice": true
     },
+    "cerebras/llama3.3-70b": {
+        "max_tokens": 128000,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "input_cost_per_token": 0.00000085,
+        "output_cost_per_token": 0.0000012,
+        "litellm_provider": "cerebras",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_tool_choice": true
+    },
     "friendliai/meta-llama-3.1-8b-instruct": {
         "max_tokens": 8192,
         "max_input_tokens": 8192,
@@ -7450,7 +7461,8 @@
         "litellm_provider": "bedrock",
         "mode": "chat",
         "supports_function_calling": true, 
-        "supports_tool_choice": false
+        "supports_tool_choice": false,
+        "supports_vision": true
     },
     "us.meta.llama3-2-11b-instruct-v1:0": {
         "max_tokens": 128000,
@@ -7461,7 +7473,8 @@
         "litellm_provider": "bedrock",
         "mode": "chat",
         "supports_function_calling": true, 
-        "supports_tool_choice": false
+        "supports_tool_choice": false,
+        "supports_vision": true
     },
     "meta.llama3-2-90b-instruct-v1:0": {
         "max_tokens": 128000,
@@ -7472,7 +7485,8 @@
         "litellm_provider": "bedrock",
         "mode": "chat",
         "supports_function_calling": true, 
-        "supports_tool_choice": false
+        "supports_tool_choice": false,
+        "supports_vision": true
     },
     "us.meta.llama3-2-90b-instruct-v1:0": {
         "max_tokens": 128000,
@@ -7483,7 +7497,8 @@
         "litellm_provider": "bedrock",
         "mode": "chat",
         "supports_function_calling": true, 
-        "supports_tool_choice": false
+        "supports_tool_choice": false,
+        "supports_vision": true
     },
     "us.meta.llama3-3-70b-instruct-v1:0": {
         "max_tokens": 4096, 

litellm/types/llms/rerank.py

@@ -0,0 +1,19 @@
+import json
+from enum import Enum
+from typing import Any, Dict, List, Literal, Optional, Tuple, TypedDict, Union
+
+from typing_extensions import (
+    Protocol,
+    Required,
+    Self,
+    TypeGuard,
+    get_origin,
+    override,
+    runtime_checkable,
+)
+
+
+class InfinityRerankResult(TypedDict):
+    index: int
+    relevance_score: float
+    document: Optional[str]

litellm/types/rerank.py

@@ -7,7 +7,7 @@ https://docs.cohere.com/reference/rerank
 from typing import List, Optional, Union
 
 from pydantic import BaseModel, PrivateAttr
-from typing_extensions import TypedDict
+from typing_extensions import Required, TypedDict
 
 
 class RerankRequest(BaseModel):
@@ -45,9 +45,14 @@ class RerankResponseMeta(TypedDict, total=False):
     tokens: Optional[RerankTokens]
 
 
-class RerankResponseResult(TypedDict):
-    index: int
-    relevance_score: float
+class RerankResponseDocument(TypedDict):
+    text: str
+
+
+class RerankResponseResult(TypedDict, total=False):
+    index: Required[int]
+    relevance_score: Required[float]
+    document: RerankResponseDocument
 
 
 class RerankResponse(BaseModel):

model_prices_and_context_window.json

@@ -2643,6 +2643,17 @@
         "supports_function_calling": true,
         "supports_tool_choice": true
     },
+    "cerebras/llama3.3-70b": {
+        "max_tokens": 128000,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "input_cost_per_token": 0.00000085,
+        "output_cost_per_token": 0.0000012,
+        "litellm_provider": "cerebras",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_tool_choice": true
+    },
     "friendliai/meta-llama-3.1-8b-instruct": {
         "max_tokens": 8192,
         "max_input_tokens": 8192,
@@ -7450,7 +7461,8 @@
         "litellm_provider": "bedrock",
         "mode": "chat",
         "supports_function_calling": true, 
-        "supports_tool_choice": false
+        "supports_tool_choice": false,
+        "supports_vision": true
     },
     "us.meta.llama3-2-11b-instruct-v1:0": {
         "max_tokens": 128000,
@@ -7461,7 +7473,8 @@
         "litellm_provider": "bedrock",
         "mode": "chat",
         "supports_function_calling": true, 
-        "supports_tool_choice": false
+        "supports_tool_choice": false,
+        "supports_vision": true
     },
     "meta.llama3-2-90b-instruct-v1:0": {
         "max_tokens": 128000,
@@ -7472,7 +7485,8 @@
         "litellm_provider": "bedrock",
         "mode": "chat",
         "supports_function_calling": true, 
-        "supports_tool_choice": false
+        "supports_tool_choice": false,
+        "supports_vision": true
     },
     "us.meta.llama3-2-90b-instruct-v1:0": {
         "max_tokens": 128000,
@@ -7483,7 +7497,8 @@
         "litellm_provider": "bedrock",
         "mode": "chat",
         "supports_function_calling": true, 
-        "supports_tool_choice": false
+        "supports_tool_choice": false,
+        "supports_vision": true
     },
     "us.meta.llama3-3-70b-instruct-v1:0": {
         "max_tokens": 4096, 

tests/README.MD

@@ -1 +1,9 @@
-**In total litellm runs 500+ tests** Most tests are in [/litellm/tests](https://github.com/BerriAI/litellm/tree/main/litellm/tests). These are just the tests for the proxy docker image, used for circle ci. 
+**In total litellm runs 1000+ tests** 
+
+[02/20/2025] Update:
+
+To make it easier to contribute and map what behavior is tested,
+
+we've started mapping the litellm directory in `tests/litellm` 
+
+This folder can only run mock tests.

tests/litellm_utils_tests/test_utils.py

@@ -1165,6 +1165,9 @@ def test_models_by_provider():
     """
     Make sure all providers from model map are in the valid providers list
     """
+    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
+    litellm.model_cost = litellm.get_model_cost_map(url="")
+
     from litellm import models_by_provider
 
     providers = set()

tests/llm_translation/test_infinity.py

@@ -87,6 +87,39 @@ async def test_infinity_rerank():
         assert_response_shape(response, custom_llm_provider="infinity")
 
 
+@pytest.mark.asyncio()
+async def test_infinity_rerank_with_return_documents():
+    mock_response = AsyncMock()
+
+    mock_response = AsyncMock()
+
+    def return_val():
+        return {
+            "id": "cmpl-mockid",
+            "results": [{"index": 0, "relevance_score": 0.95, "document": "hello"}],
+            "usage": {"prompt_tokens": 100, "total_tokens": 150},
+        }
+
+    mock_response.json = return_val
+    mock_response.headers = {"key": "value"}
+    mock_response.status_code = 200
+
+    with patch(
+        "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
+        return_value=mock_response,
+    ) as mock_post:
+        response = await litellm.arerank(
+            model="infinity/rerank-model",
+            query="hello",
+            documents=["hello", "world"],
+            top_n=3,
+            return_documents=True,
+            api_base="https://api.infinity.ai",
+        )
+        assert response.results[0]["document"] == {"text": "hello"}
+        assert_response_shape(response, custom_llm_provider="infinity")
+
+
 @pytest.mark.asyncio()
 async def test_infinity_rerank_with_env(monkeypatch):
     # Set up mock response

tests/llm_translation/test_rerank.py

@@ -9,6 +9,7 @@ from dotenv import load_dotenv
 load_dotenv()
 import io
 import os
+from typing import Optional, Dict
 
 sys.path.insert(
     0, os.path.abspath("../..")
@@ -29,7 +30,11 @@ from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
 def assert_response_shape(response, custom_llm_provider):
     expected_response_shape = {"id": str, "results": list, "meta": dict}
 
-    expected_results_shape = {"index": int, "relevance_score": float}
+    expected_results_shape = {
+        "index": int,
+        "relevance_score": float,
+        "document": Optional[Dict[str, str]],
+    }
 
     expected_meta_shape = {"api_version": dict, "billed_units": dict}
 
@@ -44,6 +49,9 @@ def assert_response_shape(response, custom_llm_provider):
         assert isinstance(
             result["relevance_score"], expected_results_shape["relevance_score"]
         )
+        if "document" in result:
+            assert isinstance(result["document"], Dict)
+            assert isinstance(result["document"]["text"], str)
     assert isinstance(response.meta, expected_response_shape["meta"])
 
     if custom_llm_provider == "cohere":
@@ -364,17 +372,15 @@ def test_rerank_response_assertions():
         **{
             "id": "ab0fcca0-b617-11ef-b292-0242ac110002",
             "results": [
-                {"index": 2, "relevance_score": 0.9958819150924683, "document": None},
-                {"index": 0, "relevance_score": 0.001293411129154265, "document": None},
+                {"index": 2, "relevance_score": 0.9958819150924683},
+                {"index": 0, "relevance_score": 0.001293411129154265},
                 {
                     "index": 1,
                     "relevance_score": 7.641685078851879e-05,
-                    "document": None,
                 },
                 {
                     "index": 3,
                     "relevance_score": 7.621097756782547e-05,
-                    "document": None,
                 },
             ],
             "meta": {
@@ -387,3 +393,19 @@ def test_rerank_response_assertions():
     )
 
     assert_response_shape(r, custom_llm_provider="custom")
+
+
+@pytest.mark.flaky(retries=3, delay=1)
+def test_rerank_cohere_api():
+    response = litellm.rerank(
+        model="cohere/rerank-english-v3.0",
+        query="hello",
+        documents=["hello", "world"],
+        return_documents=True,
+        top_n=3,
+    )
+    print("rerank response", response)
+    assert response.results[0]["document"] is not None
+    assert response.results[0]["document"]["text"] is not None
+    assert response.results[0]["document"]["text"] == "hello"
+    assert response.results[1]["document"]["text"] == "world"

tests/local_testing/test_completion_cost.py

@@ -2775,6 +2775,8 @@ def test_bedrock_cost_calc_with_region():
     os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
     litellm.model_cost = litellm.get_model_cost_map(url="")
 
+    litellm.add_known_models()
+
     hidden_params = {
         "custom_llm_provider": "bedrock",
         "region_name": "us-east-1",

tests/local_testing/test_embedding.py

@@ -961,6 +961,7 @@ async def test_gemini_embeddings(sync_mode, input):
 
 @pytest.mark.parametrize("sync_mode", [True, False])
 @pytest.mark.asyncio
+@pytest.mark.flaky(retries=3, delay=1)
 async def test_hf_embedddings_with_optional_params(sync_mode):
     litellm.set_verbose = True