(feat) ollama use /api/chat

litellm/llms/ollama_chat.py

@@ -132,10 +132,10 @@ def get_ollama_response(
     model_response=None,
     encoding=None,
 ):
-    if api_base.endswith("/api/generate"):
+    if api_base.endswith("/api/chat"):
         url = api_base
     else:
-        url = f"{api_base}/api/generate"
+        url = f"{api_base}/api/chat"
 
     ## Load Config
     config = litellm.OllamaConfig.get_config()
@@ -329,75 +329,3 @@ async def ollama_acompletion(url, data, model_response, encoding, logging_obj):
     except Exception as e:
         traceback.print_exc()
         raise e
-
-
-async def ollama_aembeddings(
-    api_base="http://localhost:11434",
-    model="llama2",
-    prompt="Why is the sky blue?",
-    optional_params=None,
-    logging_obj=None,
-    model_response=None,
-    encoding=None,
-):
-    if api_base.endswith("/api/embeddings"):
-        url = api_base
-    else:
-        url = f"{api_base}/api/embeddings"
-
-    ## Load Config
-    config = litellm.OllamaConfig.get_config()
-    for k, v in config.items():
-        if (
-            k not in optional_params
-        ):  # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in
-            optional_params[k] = v
-
-    data = {
-        "model": model,
-        "prompt": prompt,
-    }
-    ## LOGGING
-    logging_obj.pre_call(
-        input=None,
-        api_key=None,
-        additional_args={"api_base": url, "complete_input_dict": data, "headers": {}},
-    )
-    timeout = aiohttp.ClientTimeout(total=litellm.request_timeout)  # 10 minutes
-    async with aiohttp.ClientSession(timeout=timeout) as session:
-        response = await session.post(url, json=data)
-
-        if response.status != 200:
-            text = await response.text()
-            raise OllamaError(status_code=response.status, message=text)
-
-        ## LOGGING
-        logging_obj.post_call(
-            input=prompt,
-            api_key="",
-            original_response=response.text,
-            additional_args={
-                "headers": None,
-                "api_base": api_base,
-            },
-        )
-
-        response_json = await response.json()
-        embeddings = response_json["embedding"]
-        ## RESPONSE OBJECT
-        output_data = []
-        for idx, embedding in enumerate(embeddings):
-            output_data.append(
-                {"object": "embedding", "index": idx, "embedding": embedding}
-            )
-        model_response["object"] = "list"
-        model_response["data"] = output_data
-        model_response["model"] = model
-
-        input_tokens = len(encoding.encode(prompt))
-
-        model_response["usage"] = {
-            "prompt_tokens": input_tokens,
-            "total_tokens": input_tokens,
-        }
-        return model_response