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Merge branch 'BerriAI:main' into ollama-image-handling

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frob 2024-05-09 20:25:30 +02:00 • committed by GitHub
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102 changed files with 1394 additions and 393 deletions
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44
litellm/llms/ollama.py
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@ -1,10 +1,10 @@
from itertools import chain
import requests, types, time
import requests, types, time # type: ignore
import json, uuid
import traceback
from typing import Optional
import litellm
import httpx, aiohttp, asyncio
import httpx, aiohttp, asyncio # type: ignore
from .prompt_templates.factory import prompt_factory, custom_prompt
@ -245,7 +245,10 @@ def get_ollama_response(
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
"function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
"function": {
"name": function_call["name"],
"arguments": json.dumps(function_call["arguments"]),
},
"type": "function",
}
],
@ -257,7 +260,9 @@ def get_ollama_response(
model_response["created"] = int(time.time())
model_response["model"] = "ollama/" + model
prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(prompt, disallowed_special=()))) # type: ignore
completion_tokens = response_json.get("eval_count", len(response_json.get("message",dict()).get("content", "")))
completion_tokens = response_json.get(
"eval_count", len(response_json.get("message", dict()).get("content", ""))
)
model_response["usage"] = litellm.Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
@ -298,7 +303,10 @@ def ollama_completion_stream(url, data, logging_obj):
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
"function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
"function": {
"name": function_call["name"],
"arguments": json.dumps(function_call["arguments"]),
},
"type": "function",
}
],
@ -339,9 +347,10 @@ async def ollama_async_streaming(url, data, model_response, encoding, logging_ob
first_chunk_content = first_chunk.choices[0].delta.content or ""
response_content = first_chunk_content + "".join(
[
chunk.choices[0].delta.content
async for chunk in streamwrapper
if chunk.choices[0].delta.content]
chunk.choices[0].delta.content
async for chunk in streamwrapper
if chunk.choices[0].delta.content
]
)
function_call = json.loads(response_content)
delta = litellm.utils.Delta(
@ -349,7 +358,10 @@ async def ollama_async_streaming(url, data, model_response, encoding, logging_ob
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
"function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
"function": {
"name": function_call["name"],
"arguments": json.dumps(function_call["arguments"]),
},
"type": "function",
}
],
@ -398,7 +410,10 @@ async def ollama_acompletion(url, data, model_response, encoding, logging_obj):
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
"function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
"function": {
"name": function_call["name"],
"arguments": json.dumps(function_call["arguments"]),
},
"type": "function",
}
],
@ -412,7 +427,10 @@ async def ollama_acompletion(url, data, model_response, encoding, logging_obj):
model_response["created"] = int(time.time())
model_response["model"] = "ollama/" + data["model"]
prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(data["prompt"], disallowed_special=()))) # type: ignore
completion_tokens = response_json.get("eval_count", len(response_json.get("message",dict()).get("content", "")))
completion_tokens = response_json.get(
"eval_count",
len(response_json.get("message", dict()).get("content", "")),
)
model_response["usage"] = litellm.Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
@ -500,6 +518,7 @@ async def ollama_aembeddings(
}
return model_response
def ollama_embeddings(
api_base: str,
model: str,
@ -517,5 +536,6 @@ def ollama_embeddings(
optional_params,
logging_obj,
model_response,
encoding)
encoding,
)
)