two version of R2R are here HEAD master

1 files changed, 102 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/ollama/completion/handler.py b/.venv/lib/python3.12/site-packages/litellm/llms/ollama/completion/handler.py
new file mode 100644
index 00000000..208a9d81
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/ollama/completion/handler.py
@@ -0,0 +1,102 @@
+"""
+Ollama /chat/completion calls handled in llm_http_handler.py
+
+[TODO]: migrate embeddings to a base handler as well.
+"""
+
+import asyncio
+from typing import Any, Dict, List
+
+import litellm
+from litellm.types.utils import EmbeddingResponse
+
+# ollama wants plain base64 jpeg/png files as images.  strip any leading dataURI
+# and convert to jpeg if necessary.
+
+
+async def ollama_aembeddings(
+    api_base: str,
+    model: str,
+    prompts: List[str],
+    model_response: EmbeddingResponse,
+    optional_params: dict,
+    logging_obj: Any,
+    encoding: Any,
+):
+    if api_base.endswith("/api/embed"):
+        url = api_base
+    else:
+        url = f"{api_base}/api/embed"
+
+    ## 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: Dict[str, Any] = {"model": model, "input": prompts}
+    special_optional_params = ["truncate", "options", "keep_alive"]
+
+    for k, v in optional_params.items():
+        if k in special_optional_params:
+            data[k] = v
+        else:
+            # Ensure "options" is a dictionary before updating it
+            data.setdefault("options", {})
+            if isinstance(data["options"], dict):
+                data["options"].update({k: v})
+    total_input_tokens = 0
+    output_data = []
+
+    response = await litellm.module_level_aclient.post(url=url, json=data)
+
+    response_json = response.json()
+
+    embeddings: List[List[float]] = response_json["embeddings"]
+    for idx, emb in enumerate(embeddings):
+        output_data.append({"object": "embedding", "index": idx, "embedding": emb})
+
+    input_tokens = response_json.get("prompt_eval_count") or len(
+        encoding.encode("".join(prompt for prompt in prompts))
+    )
+    total_input_tokens += input_tokens
+
+    model_response.object = "list"
+    model_response.data = output_data
+    model_response.model = "ollama/" + model
+    setattr(
+        model_response,
+        "usage",
+        litellm.Usage(
+            prompt_tokens=total_input_tokens,
+            completion_tokens=total_input_tokens,
+            total_tokens=total_input_tokens,
+            prompt_tokens_details=None,
+            completion_tokens_details=None,
+        ),
+    )
+    return model_response
+
+
+def ollama_embeddings(
+    api_base: str,
+    model: str,
+    prompts: list,
+    optional_params: dict,
+    model_response: EmbeddingResponse,
+    logging_obj: Any,
+    encoding=None,
+):
+    return asyncio.run(
+        ollama_aembeddings(
+            api_base=api_base,
+            model=model,
+            prompts=prompts,
+            model_response=model_response,
+            optional_params=optional_params,
+            logging_obj=logging_obj,
+            encoding=encoding,
+        )
+    )