Add Amazon Bedrock support

examples/using_amazon_bedrock.py

@@ -0,0 +1,19 @@
+from nano_graphrag import GraphRAG, QueryParam
+
+graph_func = GraphRAG(
+    working_dir="../bedrock_example",
+    using_amazon_bedrock=True,
+    best_model_id="us.anthropic.claude-3-sonnet-20240229-v1:0",
+    cheap_model_id="us.anthropic.claude-3-haiku-20240307-v1:0",
+)
+
+with open("../tests/mock_data.txt") as f:
+    graph_func.insert(f.read())
+
+prompt = "What are the top themes in this story?"
+
+# Perform global graphrag search
+print(graph_func.query(prompt, param=QueryParam(mode="global")))
+
+# Perform local graphrag search (I think is better and more scalable one)
+print(graph_func.query(prompt, param=QueryParam(mode="local")))

nano_graphrag/_llm.py

@@ -1,5 +1,8 @@
+import json
 import numpy as np
+from typing import Optional, List, Any, Callable
 
+import aioboto3
 from openai import AsyncOpenAI, AsyncAzureOpenAI, APIConnectionError, RateLimitError
 
 from tenacity import (
@@ -15,6 +18,7 @@
 
 global_openai_async_client = None
 global_azure_openai_async_client = None
+global_amazon_bedrock_async_client = None
 
 
 def get_openai_async_client_instance():
@@ -31,6 +35,13 @@ def get_azure_openai_async_client_instance():
     return global_azure_openai_async_client
 
 
+def get_amazon_bedrock_async_client_instance():
+    global global_amazon_bedrock_async_client
+    if global_amazon_bedrock_async_client is None:
+        global_amazon_bedrock_async_client = aioboto3.Session()
+    return global_amazon_bedrock_async_client
+
+
 @retry(
     stop=stop_after_attempt(5),
     wait=wait_exponential(multiplier=1, min=4, max=10),
@@ -64,6 +75,82 @@ async def openai_complete_if_cache(
     return response.choices[0].message.content
 
 
+@retry(
+    stop=stop_after_attempt(5),
+    wait=wait_exponential(multiplier=1, min=4, max=10),
+    retry=retry_if_exception_type((RateLimitError, APIConnectionError)),
+)
+async def amazon_bedrock_complete_if_cache(
+    model, prompt, system_prompt=None, history_messages=[], **kwargs
+) -> str:
+    amazon_bedrock_async_client = get_amazon_bedrock_async_client_instance()
+    hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
+    messages = []
+    messages.extend(history_messages)
+    messages.append({"role": "user", "content": [{"text": 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"]
+
+    inference_config = {
+        "temperature": 0,
+        "maxTokens": 4096 if "max_tokens" not in kwargs else kwargs["max_tokens"],
+    }
+
+    async with amazon_bedrock_async_client.client(
+        "bedrock-runtime",
+        region_name=os.getenv("AWS_REGION", "us-east-1")
+    ) as bedrock_runtime:
+        if system_prompt:
+            response = await bedrock_runtime.converse(
+                modelId=model, messages=messages, inferenceConfig=inference_config,
+                system=[{"text": system_prompt}]
+            )
+        else:
+            response = await bedrock_runtime.converse(
+                modelId=model, messages=messages, inferenceConfig=inference_config,
+            )
+
+    if hashing_kv is not None:
+        await hashing_kv.upsert(
+            {args_hash: {"return": response["output"]["message"]["content"][0]["text"], "model": model}}
+        )
+        await hashing_kv.index_done_callback()
+    return response["output"]["message"]["content"][0]["text"]
+
+
+def create_amazon_bedrock_complete_function(model_id: str) -> Callable:
+    """
+    Factory function to dynamically create completion functions for Amazon Bedrock
+
+    Args:
+        model_id (str): Amazon Bedrock model identifier (e.g., "us.anthropic.claude-3-sonnet-20240229-v1:0")
+
+    Returns:
+        Callable: Generated completion function
+    """
+    async def bedrock_complete(
+        prompt: str,
+        system_prompt: Optional[str] = None,
+        history_messages: List[Any] = [],
+        **kwargs
+    ) -> str:
+        return await amazon_bedrock_complete_if_cache(
+            model_id,
+            prompt,
+            system_prompt=system_prompt,
+            history_messages=history_messages,
+            **kwargs
+        )
+
+    # Set function name for easier debugging
+    bedrock_complete.__name__ = f"{model_id}_complete"
+
+    return bedrock_complete
+
+
 async def gpt_4o_complete(
     prompt, system_prompt=None, history_messages=[], **kwargs
 ) -> str:
@@ -88,6 +175,35 @@ async def gpt_4o_mini_complete(
     )
 
 
+@wrap_embedding_func_with_attrs(embedding_dim=1024, max_token_size=8192)
+@retry(
+    stop=stop_after_attempt(5),
+    wait=wait_exponential(multiplier=1, min=4, max=10),
+    retry=retry_if_exception_type((RateLimitError, APIConnectionError)),
+)
+async def amazon_bedrock_embedding(texts: list[str]) -> np.ndarray:
+    amazon_bedrock_async_client = get_amazon_bedrock_async_client_instance()
+
+    async with amazon_bedrock_async_client.client(
+        "bedrock-runtime",
+        region_name=os.getenv("AWS_REGION", "us-east-1")
+    ) as bedrock_runtime:
+        embeddings = []
+        for text in texts:
+            body = json.dumps(
+                {
+                    "inputText": text,
+                    "dimensions": 1024,
+                }
+            )
+            response = await bedrock_runtime.invoke_model(
+                modelId="amazon.titan-embed-text-v2:0", body=body,
+            )
+            response_body = await response.get("body").read()
+            embeddings.append(json.loads(response_body))
+    return np.array([dp["embedding"] for dp in embeddings])
+
+
 @wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=8192)
 @retry(
     stop=stop_after_attempt(5),

nano_graphrag/_op.py

@@ -293,6 +293,7 @@ async def extract_entities(
     knwoledge_graph_inst: BaseGraphStorage,
     entity_vdb: BaseVectorStorage,
     global_config: dict,
+    using_amazon_bedrock: bool=False,
 ) -> Union[BaseGraphStorage, None]:
     use_llm_func: callable = global_config["best_model_func"]
     entity_extract_max_gleaning = global_config["entity_extract_max_gleaning"]
@@ -320,12 +321,14 @@ async def _process_single_content(chunk_key_dp: tuple[str, TextChunkSchema]):
         content = chunk_dp["content"]
         hint_prompt = entity_extract_prompt.format(**context_base, input_text=content)
         final_result = await use_llm_func(hint_prompt)
+        if isinstance(final_result, list):
+            final_result = final_result[0]["text"]
 
-        history = pack_user_ass_to_openai_messages(hint_prompt, final_result)
+        history = pack_user_ass_to_openai_messages(hint_prompt, final_result, using_amazon_bedrock)
         for now_glean_index in range(entity_extract_max_gleaning):
             glean_result = await use_llm_func(continue_prompt, history_messages=history)
 
-            history += pack_user_ass_to_openai_messages(continue_prompt, glean_result)
+            history += pack_user_ass_to_openai_messages(continue_prompt, glean_result, using_amazon_bedrock)
             final_result += glean_result
             if now_glean_index == entity_extract_max_gleaning - 1:
                 break

nano_graphrag/_utils.py

@@ -162,11 +162,17 @@ def load_json(file_name):
 
 
 # it's dirty to type, so it's a good way to have fun
-def pack_user_ass_to_openai_messages(*args: str):
-    roles = ["user", "assistant"]
-    return [
-        {"role": roles[i % 2], "content": content} for i, content in enumerate(args)
-    ]
+def pack_user_ass_to_openai_messages(prompt: str, generated_content: str, using_amazon_bedrock: bool):
+    if using_amazon_bedrock:
+        return [
+            {"role": "user", "content": [{"text": prompt}]},
+            {"role": "assistant", "content": [{"text": generated_content}]},
+        ]
+    else:
+        return [
+            {"role": "user", "content": prompt},
+            {"role": "assistant", "content": generated_content},
+        ]
 
 
 def is_float_regex(value):

nano_graphrag/graphrag.py

@@ -9,6 +9,8 @@
 
 
 from ._llm import (
+    amazon_bedrock_embedding,
+    create_amazon_bedrock_complete_function,
     gpt_4o_complete,
     gpt_4o_mini_complete,
     openai_embedding,
@@ -107,6 +109,9 @@ class GraphRAG:
 
     # LLM
     using_azure_openai: bool = False
+    using_amazon_bedrock: bool = False
+    best_model_id: str = "us.anthropic.claude-3-sonnet-20240229-v1:0"
+    cheap_model_id: str = "us.anthropic.claude-3-haiku-20240307-v1:0"
     best_model_func: callable = gpt_4o_complete
     best_model_max_token_size: int = 32768
     best_model_max_async: int = 16
@@ -145,6 +150,14 @@ def __post_init__(self):
                 "Switched the default openai funcs to Azure OpenAI if you didn't set any of it"
             )
 
+        if self.using_amazon_bedrock:
+            self.best_model_func = create_amazon_bedrock_complete_function(self.best_model_id)
+            self.cheap_model_func = create_amazon_bedrock_complete_function(self.cheap_model_id)
+            self.embedding_func = amazon_bedrock_embedding
+            logger.info(
+                "Switched the default openai funcs to Amazon Bedrock"
+            )
+
         if not os.path.exists(self.working_dir) and self.always_create_working_dir:
             logger.info(f"Creating working directory {self.working_dir}")
             os.makedirs(self.working_dir)
@@ -298,6 +311,7 @@ async def ainsert(self, string_or_strings):
                 knwoledge_graph_inst=self.chunk_entity_relation_graph,
                 entity_vdb=self.entities_vdb,
                 global_config=asdict(self),
+                using_amazon_bedrock=self.using_amazon_bedrock,
             )
             if maybe_new_kg is None:
                 logger.warning("No new entities found")

readme.md

@@ -73,6 +73,9 @@ pip install nano-graphrag
 > [!TIP]
 > If you're using Azure OpenAI API, refer to the [.env.example](./.env.example.azure) to set your azure openai. Then pass `GraphRAG(...,using_azure_openai=True,...)` to enable.
 
+> [!TIP]
+> If you're using Amazon Bedrock API, please ensure your credentials are properly set through commands like `aws configure`. Then enable it by configuring like this: `GraphRAG(...,using_amazon_bedrock=True, best_model_id="us.anthropic.claude-3-sonnet-20240229-v1:0", cheap_model_id="us.anthropic.claude-3-haiku-20240307-v1:0",...)`. Refer to an [example script](./examples/using_amazon_bedrock.py).
+
 > [!TIP]
 >
 > If you don't have any key, check out this [example](./examples/no_openai_key_at_all.py) that using `transformers` and `ollama` . If you like to use another LLM or Embedding Model, check [Advances](#Advances).
@@ -167,9 +170,11 @@ Below are the components you can use:
 | Type            |                             What                             |                       Where                       |
 | :-------------- | :----------------------------------------------------------: | :-----------------------------------------------: |
 | LLM             |                            OpenAI                            |                     Built-in                      |
+|                 |                        Amazon Bedrock                        |                     Built-in                      |
 |                 |                           DeepSeek                           |              [examples](./examples)               |
 |                 |                           `ollama`                           |              [examples](./examples)               |
 | Embedding       |                            OpenAI                            |                     Built-in                      |
+|                 |                        Amazon Bedrock                        |                     Built-in                      |
 |                 |                    Sentence-transformers                     |              [examples](./examples)               |
 | Vector DataBase | [`nano-vectordb`](https://github.com/gusye1234/nano-vectordb) |                     Built-in                      |
 |                 |        [`hnswlib`](https://github.com/nmslib/hnswlib)        |         Built-in, [examples](./examples)          |

requirements.txt

@@ -8,4 +8,5 @@ hnswlib
 xxhash
 tenacity
 dspy-ai
-neo4j
+neo4j
+aioboto3