Merge branch 'bedrock-support'

env.example

@@ -123,7 +123,7 @@ MAX_PARALLEL_INSERT=2
 
 ###########################################################
 ### LLM Configuration
-### LLM_BINDING type: openai, ollama, lollms, azure_openai
+### LLM_BINDING type: openai, ollama, lollms, azure_openai, aws_bedrock
 ###########################################################
 ### LLM temperature setting for all llm binding (openai, azure_openai, ollama)
 # TEMPERATURE=1.0

lightrag/api/README-zh.md

@@ -40,6 +40,7 @@ LightRAG 需要同时集成 LLM（大型语言模型）和嵌入模型以有效
 * lollms
 * openai 或 openai 兼容
 * azure_openai
+* aws_bedrock
 
 建议使用环境变量来配置 LightRAG 服务器。项目根目录中有一个名为 `env.example` 的示例环境变量文件。请将此文件复制到启动目录并重命名为 `.env`。之后，您可以在 `.env` 文件中修改与 LLM 和嵌入模型相关的参数。需要注意的是，LightRAG 服务器每次启动时都会将 `.env` 中的环境变量加载到系统环境变量中。**LightRAG 服务器会优先使用系统环境变量中的设置**。
 
@@ -359,6 +360,7 @@ LightRAG 支持绑定到各种 LLM/嵌入后端：
 * openai 和 openai 兼容
 * azure_openai
 * lollms
+* aws_bedrock
 
 使用环境变量 `LLM_BINDING` 或 CLI 参数 `--llm-binding` 选择 LLM 后端类型。使用环境变量 `EMBEDDING_BINDING` 或 CLI 参数 `--embedding-binding` 选择嵌入后端类型。
 

lightrag/api/README.md

@@ -40,6 +40,7 @@ LightRAG necessitates the integration of both an LLM (Large Language Model) and
 * lollms
 * openai or openai compatible
 * azure_openai
+* aws_bedrock
 
 It is recommended to use environment variables to configure the LightRAG Server. There is an example environment variable file named `env.example` in the root directory of the project. Please copy this file to the startup directory and rename it to `.env`. After that, you can modify the parameters related to the LLM and Embedding models in the `.env` file. It is important to note that the LightRAG Server will load the environment variables from `.env` into the system environment variables each time it starts. **LightRAG Server will prioritize the settings in the system environment variables to .env file**.
 
@@ -362,6 +363,7 @@ LightRAG supports binding to various LLM/Embedding backends:
 * openai & openai compatible
 * azure_openai
 * lollms
+* aws_bedrock
 
 Use environment variables `LLM_BINDING` or CLI argument `--llm-binding` to select the LLM backend type. Use environment variables `EMBEDDING_BINDING` or CLI argument `--embedding-binding` to select the Embedding backend type.
 
@@ -461,8 +463,8 @@ You cannot change storage implementation selection after adding documents to Lig
 | --ssl-keyfile         | None          | Path to SSL private key file (required if --ssl is enabled)                                                                     |
 | --top-k               | 50            | Number of top-k items to retrieve; corresponds to entities in "local" mode and relationships in "global" mode.                  |
 | --cosine-threshold    | 0.4           | The cosine threshold for nodes and relation retrieval, works with top-k to control the retrieval of nodes and relations.        |
-| --llm-binding         | ollama        | LLM binding type (lollms, ollama, openai, openai-ollama, azure_openai)                                                          |
-| --embedding-binding   | ollama        | Embedding binding type (lollms, ollama, openai, azure_openai)                                                                   |
+| --llm-binding         | ollama        | LLM binding type (lollms, ollama, openai, openai-ollama, azure_openai, aws_bedrock)                                                          |
+| --embedding-binding   | ollama        | Embedding binding type (lollms, ollama, openai, azure_openai, aws_bedrock)                                                                   |
 | --auto-scan-at-startup| -             | Scan input directory for new files and start indexing                                                                           |
 
 ### Additional Ollama Binding Options

lightrag/api/config.py

@@ -209,14 +209,21 @@ def parse_args() -> argparse.Namespace:
         "--llm-binding",
         type=str,
         default=get_env_value("LLM_BINDING", "ollama"),
-        choices=["lollms", "ollama", "openai", "openai-ollama", "azure_openai"],
+        choices=[
+            "lollms",
+            "ollama",
+            "openai",
+            "openai-ollama",
+            "azure_openai",
+            "aws_bedrock",
+        ],
         help="LLM binding type (default: from env or ollama)",
     )
     parser.add_argument(
         "--embedding-binding",
         type=str,
         default=get_env_value("EMBEDDING_BINDING", "ollama"),
-        choices=["lollms", "ollama", "openai", "azure_openai"],
+        choices=["lollms", "ollama", "openai", "azure_openai", "aws_bedrock", "jina"],
         help="Embedding binding type (default: from env or ollama)",
     )
 

lightrag/api/lightrag_server.py

@@ -104,8 +104,8 @@ def create_app(args):
         "lollms",
         "ollama",
         "openai",
-        "openai-ollama",
         "azure_openai",
+        "aws_bedrock",
     ]:
         raise Exception("llm binding not supported")
 
@@ -114,6 +114,7 @@ def create_app(args):
         "ollama",
         "openai",
         "azure_openai",
+        "aws_bedrock",
         "jina",
     ]:
         raise Exception("embedding binding not supported")
@@ -188,10 +189,12 @@ def create_app(args):
     # Initialize FastAPI
     app_kwargs = {
         "title": "LightRAG Server API",
-        "description": "Providing API for LightRAG core, Web UI and Ollama Model Emulation"
-        + "(With authentication)"
-        if api_key
-        else "",
+        "description": (
+            "Providing API for LightRAG core, Web UI and Ollama Model Emulation"
+            + "(With authentication)"
+            if api_key
+            else ""
+        ),
         "version": __api_version__,
         "openapi_url": "/openapi.json",  # Explicitly set OpenAPI schema URL
         "docs_url": "/docs",  # Explicitly set docs URL
@@ -244,9 +247,9 @@ def create_app(args):
             azure_openai_complete_if_cache,
             azure_openai_embed,
         )
-    if args.llm_binding_host == "openai-ollama" or args.embedding_binding == "ollama":
-        from lightrag.llm.openai import openai_complete_if_cache
-        from lightrag.llm.ollama import ollama_embed
+    if args.llm_binding == "aws_bedrock" or args.embedding_binding == "aws_bedrock":
+        from lightrag.llm.bedrock import bedrock_complete_if_cache, bedrock_embed
+    if args.embedding_binding == "ollama":
         from lightrag.llm.binding_options import OllamaEmbeddingOptions
     if args.embedding_binding == "jina":
         from lightrag.llm.jina import jina_embed
@@ -312,41 +315,80 @@ def create_app(args):
             **kwargs,
         )
 
+    async def bedrock_model_complete(
+        prompt,
+        system_prompt=None,
+        history_messages=None,
+        keyword_extraction=False,
+        **kwargs,
+    ) -> str:
+        keyword_extraction = kwargs.pop("keyword_extraction", None)
+        if keyword_extraction:
+            kwargs["response_format"] = GPTKeywordExtractionFormat
+        if history_messages is None:
+            history_messages = []
+
+        # Use global temperature for Bedrock
+        kwargs["temperature"] = args.temperature
+
+        return await bedrock_complete_if_cache(
+            args.llm_model,
+            prompt,
+            system_prompt=system_prompt,
+            history_messages=history_messages,
+            **kwargs,
+        )
+
     embedding_func = EmbeddingFunc(
         embedding_dim=args.embedding_dim,
-        func=lambda texts: lollms_embed(
-            texts,
-            embed_model=args.embedding_model,
-            host=args.embedding_binding_host,
-            api_key=args.embedding_binding_api_key,
-        )
-        if args.embedding_binding == "lollms"
-        else ollama_embed(
-            texts,
-            embed_model=args.embedding_model,
-            host=args.embedding_binding_host,
-            api_key=args.embedding_binding_api_key,
-            options=OllamaEmbeddingOptions.options_dict(args),
-        )
-        if args.embedding_binding == "ollama"
-        else azure_openai_embed(
-            texts,
-            model=args.embedding_model,  # no host is used for openai,
-            api_key=args.embedding_binding_api_key,
-        )
-        if args.embedding_binding == "azure_openai"
-        else jina_embed(
-            texts,
-            dimensions=args.embedding_dim,
-            base_url=args.embedding_binding_host,
-            api_key=args.embedding_binding_api_key,
-        )
-        if args.embedding_binding == "jina"
-        else openai_embed(
-            texts,
-            model=args.embedding_model,
-            base_url=args.embedding_binding_host,
-            api_key=args.embedding_binding_api_key,
+        func=lambda texts: (
+            lollms_embed(
+                texts,
+                embed_model=args.embedding_model,
+                host=args.embedding_binding_host,
+                api_key=args.embedding_binding_api_key,
+            )
+            if args.embedding_binding == "lollms"
+            else (
+                ollama_embed(
+                    texts,
+                    embed_model=args.embedding_model,
+                    host=args.embedding_binding_host,
+                    api_key=args.embedding_binding_api_key,
+                    options=OllamaEmbeddingOptions.options_dict(args),
+                )
+                if args.embedding_binding == "ollama"
+                else (
+                    azure_openai_embed(
+                        texts,
+                        model=args.embedding_model,  # no host is used for openai,
+                        api_key=args.embedding_binding_api_key,
+                    )
+                    if args.embedding_binding == "azure_openai"
+                    else (
+                        bedrock_embed(
+                            texts,
+                            model=args.embedding_model,
+                        )
+                        if args.embedding_binding == "aws_bedrock"
+                        else (
+                            jina_embed(
+                                texts,
+                                dimensions=args.embedding_dim,
+                                base_url=args.embedding_binding_host,
+                                api_key=args.embedding_binding_api_key,
+                            )
+                            if args.embedding_binding == "jina"
+                            else openai_embed(
+                                texts,
+                                model=args.embedding_model,
+                                base_url=args.embedding_binding_host,
+                                api_key=args.embedding_binding_api_key,
+                            )
+                        )
+                    )
+                )
+            )
         ),
     )
 
@@ -386,28 +428,36 @@ def create_app(args):
     )
 
     # Initialize RAG
-    if args.llm_binding in ["lollms", "ollama", "openai"]:
+    if args.llm_binding in ["lollms", "ollama", "openai", "aws_bedrock"]:
         rag = LightRAG(
             working_dir=args.working_dir,
             workspace=args.workspace,
-            llm_model_func=lollms_model_complete
-            if args.llm_binding == "lollms"
-            else ollama_model_complete
-            if args.llm_binding == "ollama"
-            else openai_alike_model_complete,
+            llm_model_func=(
+                lollms_model_complete
+                if args.llm_binding == "lollms"
+                else (
+                    ollama_model_complete
+                    if args.llm_binding == "ollama"
+                    else bedrock_model_complete
+                    if args.llm_binding == "aws_bedrock"
+                    else openai_alike_model_complete
+                )
+            ),
             llm_model_name=args.llm_model,
             llm_model_max_async=args.max_async,
             summary_max_tokens=args.max_tokens,
             chunk_token_size=int(args.chunk_size),
             chunk_overlap_token_size=int(args.chunk_overlap_size),
-            llm_model_kwargs={
-                "host": args.llm_binding_host,
-                "timeout": args.timeout,
-                "options": OllamaLLMOptions.options_dict(args),
-                "api_key": args.llm_binding_api_key,
-            }
-            if args.llm_binding == "lollms" or args.llm_binding == "ollama"
-            else {},
+            llm_model_kwargs=(
+                {
+                    "host": args.llm_binding_host,
+                    "timeout": args.timeout,
+                    "options": OllamaLLMOptions.options_dict(args),
+                    "api_key": args.llm_binding_api_key,
+                }
+                if args.llm_binding == "lollms" or args.llm_binding == "ollama"
+                else {}
+            ),
             embedding_func=embedding_func,
             kv_storage=args.kv_storage,
             graph_storage=args.graph_storage,

lightrag/llm/bedrock.py

@@ -15,11 +15,25 @@ from tenacity import (
     retry_if_exception_type,
 )
 
+import sys
+
+if sys.version_info < (3, 9):
+    from typing import AsyncIterator
+else:
+    from collections.abc import AsyncIterator
+from typing import Union
+
 
 class BedrockError(Exception):
     """Generic error for issues related to Amazon Bedrock"""
 
 
+def _set_env_if_present(key: str, value):
+    """Set environment variable only if a non-empty value is provided."""
+    if value is not None and value != "":
+        os.environ[key] = value
+
+
 @retry(
     stop=stop_after_attempt(5),
     wait=wait_exponential(multiplier=1, max=60),
@@ -34,17 +48,35 @@ async def bedrock_complete_if_cache(
     aws_secret_access_key=None,
     aws_session_token=None,
     **kwargs,
-) -> str:
-    os.environ["AWS_ACCESS_KEY_ID"] = os.environ.get(
-        "AWS_ACCESS_KEY_ID", aws_access_key_id
-    )
-    os.environ["AWS_SECRET_ACCESS_KEY"] = os.environ.get(
-        "AWS_SECRET_ACCESS_KEY", aws_secret_access_key
-    )
-    os.environ["AWS_SESSION_TOKEN"] = os.environ.get(
-        "AWS_SESSION_TOKEN", aws_session_token
-    )
+) -> Union[str, AsyncIterator[str]]:
+    # Respect existing env; only set if a non-empty value is available
+    access_key = os.environ.get("AWS_ACCESS_KEY_ID") or aws_access_key_id
+    secret_key = os.environ.get("AWS_SECRET_ACCESS_KEY") or aws_secret_access_key
+    session_token = os.environ.get("AWS_SESSION_TOKEN") or aws_session_token
+    _set_env_if_present("AWS_ACCESS_KEY_ID", access_key)
+    _set_env_if_present("AWS_SECRET_ACCESS_KEY", secret_key)
+    _set_env_if_present("AWS_SESSION_TOKEN", session_token)
+    # Region handling: prefer env, else kwarg (optional)
+    region = os.environ.get("AWS_REGION") or kwargs.pop("aws_region", None)
     kwargs.pop("hashing_kv", None)
+    # Capture stream flag (if provided) and remove from kwargs since it's not a Bedrock API parameter
+    # We'll use this to determine whether to call converse_stream or converse
+    stream = bool(kwargs.pop("stream", False))
+    # Remove unsupported args for Bedrock Converse API
+    for k in [
+        "response_format",
+        "tools",
+        "tool_choice",
+        "seed",
+        "presence_penalty",
+        "frequency_penalty",
+        "n",
+        "logprobs",
+        "top_logprobs",
+        "max_completion_tokens",
+        "response_format",
+    ]:
+        kwargs.pop(k, None)
     # Fix message history format
     messages = []
     for history_message in history_messages:
@@ -77,21 +109,131 @@ async def bedrock_complete_if_cache(
                 kwargs.pop(param)
             )
 
-    # Call model via Converse API
-    session = aioboto3.Session()
-    async with session.client("bedrock-runtime") as bedrock_async_client:
-        try:
-            response = await bedrock_async_client.converse(**args, **kwargs)
-        except Exception as e:
-            raise BedrockError(e)
+    # Import logging for error handling
+    import logging
 
-    return response["output"]["message"]["content"][0]["text"]
+    # For streaming responses, we need a different approach to keep the connection open
+    if stream:
+        # Create a session that will be used throughout the streaming process
+        session = aioboto3.Session()
+        client = None
+
+        # Define the generator function that will manage the client lifecycle
+        async def stream_generator():
+            nonlocal client
+
+            # Create the client outside the generator to ensure it stays open
+            client = await session.client(
+                "bedrock-runtime", region_name=region
+            ).__aenter__()
+            event_stream = None
+            iteration_started = False
+
+            try:
+                # Make the API call
+                response = await client.converse_stream(**args, **kwargs)
+                event_stream = response.get("stream")
+                iteration_started = True
+
+                # Process the stream
+                async for event in event_stream:
+                    # Validate event structure
+                    if not event or not isinstance(event, dict):
+                        continue
+
+                    if "contentBlockDelta" in event:
+                        delta = event["contentBlockDelta"].get("delta", {})
+                        text = delta.get("text")
+                        if text:
+                            yield text
+                    # Handle other event types that might indicate stream end
+                    elif "messageStop" in event:
+                        break
+
+            except Exception as e:
+                # Log the specific error for debugging
+                logging.error(f"Bedrock streaming error: {e}")
+
+                # Try to clean up resources if possible
+                if (
+                    iteration_started
+                    and event_stream
+                    and hasattr(event_stream, "aclose")
+                    and callable(getattr(event_stream, "aclose", None))
+                ):
+                    try:
+                        await event_stream.aclose()
+                    except Exception as close_error:
+                        logging.warning(
+                            f"Failed to close Bedrock event stream: {close_error}"
+                        )
+
+                raise BedrockError(f"Streaming error: {e}")
+
+            finally:
+                # Clean up the event stream
+                if (
+                    iteration_started
+                    and event_stream
+                    and hasattr(event_stream, "aclose")
+                    and callable(getattr(event_stream, "aclose", None))
+                ):
+                    try:
+                        await event_stream.aclose()
+                    except Exception as close_error:
+                        logging.warning(
+                            f"Failed to close Bedrock event stream in finally block: {close_error}"
+                        )
+
+                # Clean up the client
+                if client:
+                    try:
+                        await client.__aexit__(None, None, None)
+                    except Exception as client_close_error:
+                        logging.warning(
+                            f"Failed to close Bedrock client: {client_close_error}"
+                        )
+
+        # Return the generator that manages its own lifecycle
+        return stream_generator()
+
+    # For non-streaming responses, use the standard async context manager pattern
+    session = aioboto3.Session()
+    async with session.client(
+        "bedrock-runtime", region_name=region
+    ) as bedrock_async_client:
+        try:
+            # Use converse for non-streaming responses
+            response = await bedrock_async_client.converse(**args, **kwargs)
+
+            # Validate response structure
+            if (
+                not response
+                or "output" not in response
+                or "message" not in response["output"]
+                or "content" not in response["output"]["message"]
+                or not response["output"]["message"]["content"]
+            ):
+                raise BedrockError("Invalid response structure from Bedrock API")
+
+            content = response["output"]["message"]["content"][0]["text"]
+
+            if not content or content.strip() == "":
+                raise BedrockError("Received empty content from Bedrock API")
+
+            return content
+
+        except Exception as e:
+            if isinstance(e, BedrockError):
+                raise
+            else:
+                raise BedrockError(f"Bedrock API error: {e}")
 
 
 # Generic Bedrock completion function
 async def bedrock_complete(
     prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
-) -> str:
+) -> Union[str, AsyncIterator[str]]:
     kwargs.pop("keyword_extraction", None)
     model_name = kwargs["hashing_kv"].global_config["llm_model_name"]
     result = await bedrock_complete_if_cache(
@@ -117,18 +259,21 @@ async def bedrock_embed(
     aws_secret_access_key=None,
     aws_session_token=None,
 ) -> np.ndarray:
-    os.environ["AWS_ACCESS_KEY_ID"] = os.environ.get(
-        "AWS_ACCESS_KEY_ID", aws_access_key_id
-    )
-    os.environ["AWS_SECRET_ACCESS_KEY"] = os.environ.get(
-        "AWS_SECRET_ACCESS_KEY", aws_secret_access_key
-    )
-    os.environ["AWS_SESSION_TOKEN"] = os.environ.get(
-        "AWS_SESSION_TOKEN", aws_session_token
-    )
+    # Respect existing env; only set if a non-empty value is available
+    access_key = os.environ.get("AWS_ACCESS_KEY_ID") or aws_access_key_id
+    secret_key = os.environ.get("AWS_SECRET_ACCESS_KEY") or aws_secret_access_key
+    session_token = os.environ.get("AWS_SESSION_TOKEN") or aws_session_token
+    _set_env_if_present("AWS_ACCESS_KEY_ID", access_key)
+    _set_env_if_present("AWS_SECRET_ACCESS_KEY", secret_key)
+    _set_env_if_present("AWS_SESSION_TOKEN", session_token)
+
+    # Region handling: prefer env
+    region = os.environ.get("AWS_REGION")
 
     session = aioboto3.Session()
-    async with session.client("bedrock-runtime") as bedrock_async_client:
+    async with session.client(
+        "bedrock-runtime", region_name=region
+    ) as bedrock_async_client:
         if (model_provider := model.split(".")[0]) == "amazon":
             embed_texts = []
             for text in texts: