ollama test

examples/lightrag_ollama_demo.py

@@ -0,0 +1,40 @@
+import os
+
+from lightrag import LightRAG, QueryParam
+from lightrag.llm import ollama_model_complete, ollama_embedding
+from lightrag.utils import EmbeddingFunc
+
+WORKING_DIR = "./dickens"
+
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=ollama_model_complete,  
+    llm_model_name='your_model_name',
+    embedding_func=EmbeddingFunc(
+        embedding_dim=768,
+        max_token_size=8192,
+        func=lambda texts: ollama_embedding(
+            texts, 
+            embed_model="nomic-embed-text"
+        )
+    ),
+)
+
+
+with open("./book.txt") as f:
+    rag.insert(f.read())
+
+# Perform naive search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="naive")))
+
+# Perform local search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="local")))
+
+# Perform global search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="global")))
+
+# Perform hybrid search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid")))

lightrag/__init__.py

@@ -1,5 +1,5 @@
 from .lightrag import LightRAG, QueryParam
 
-__version__ = "0.0.5"
+__version__ = "0.0.6"
 __author__ = "Zirui Guo"
 __url__ = "https://github.com/HKUDS/LightRAG"

lightrag/lightrag.py

@@ -6,7 +6,7 @@
 from typing import Type, cast, Any
 from transformers import AutoModel,AutoTokenizer, AutoModelForCausalLM
 
-from .llm import gpt_4o_complete, gpt_4o_mini_complete, openai_embedding,hf_model_complete,hf_embedding
+from .llm import gpt_4o_complete, gpt_4o_mini_complete, openai_embedding, hf_model_complete, hf_embedding
 from .operate import (
     chunking_by_token_size,
     extract_entities,

lightrag/llm.py

@@ -1,5 +1,6 @@
 import os
 import numpy as np
+import ollama
 from openai import AsyncOpenAI, APIConnectionError, RateLimitError, Timeout
 from tenacity import (
     retry,
@@ -92,6 +93,34 @@ async def hf_model_if_cache(
         )
     return response_text
 
+async def ollama_model_if_cache(
+    model, prompt, system_prompt=None, history_messages=[], **kwargs
+) -> str:
+    kwargs.pop("max_tokens", None)
+    kwargs.pop("response_format", None)
+
+    ollama_client = ollama.AsyncClient()
+    messages = []
+    if system_prompt:
+        messages.append({"role": "system", "content": system_prompt})
+
+    hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
+    messages.extend(history_messages)
+    messages.append({"role": "user", "content": prompt})
+    if hashing_kv is not None:
+        args_hash = compute_args_hash(model, messages)
+        if_cache_return = await hashing_kv.get_by_id(args_hash)
+        if if_cache_return is not None:
+            return if_cache_return["return"]
+
+    response = await ollama_client.chat(model=model, messages=messages, **kwargs)
+
+    result = response["message"]["content"]
+
+    if hashing_kv is not None:
+        await hashing_kv.upsert({args_hash: {"return": result, "model": model}})
+
+    return result
 
 async def gpt_4o_complete(
     prompt, system_prompt=None, history_messages=[], **kwargs
@@ -116,8 +145,6 @@ async def gpt_4o_mini_complete(
         **kwargs,
     )
 
-
-
 async def hf_model_complete(
     prompt, system_prompt=None, history_messages=[], **kwargs
 ) -> str:
@@ -130,6 +157,18 @@ async def hf_model_complete(
         **kwargs,
     )
 
+async def ollama_model_complete(
+    prompt, system_prompt=None, history_messages=[], **kwargs
+) -> str:
+    model_name = kwargs['hashing_kv'].global_config['llm_model_name']
+    return await ollama_model_if_cache(
+        model_name,
+        prompt,
+        system_prompt=system_prompt,
+        history_messages=history_messages,
+        **kwargs,
+    )
+
 @wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=8192)
 @retry(
     stop=stop_after_attempt(3),
@@ -154,6 +193,13 @@ async def hf_embedding(texts: list[str], tokenizer, embed_model) -> np.ndarray:
         embeddings = outputs.last_hidden_state.mean(dim=1)
     return embeddings.detach().numpy()
 
+async def ollama_embedding(texts: list[str], embed_model) -> np.ndarray:
+    embed_text = []
+    for text in texts:
+        data = ollama.embeddings(model=embed_model, prompt=text)
+        embed_text.append(data["embedding"])
+
+    return embed_text
 
 if __name__ == "__main__":
     import asyncio

requirements.txt

@@ -6,3 +6,6 @@ nano-vectordb
 hnswlib
 xxhash
 tenacity
+transformers
+torch
+ollama

setup.py

@@ -1,6 +1,6 @@
 import setuptools
 
-with open("README.md", "r") as fh:
+with open("README.md", "r", encoding="utf-8") as fh:
     long_description = fh.read()