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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,137 @@ | ||
| import boto3 | ||
| import json | ||
| import os | ||
|
|
||
| # Defaults | ||
| DEFAULT_MODEL_ID = os.environ.get("DEFAULT_MODEL_ID","anthropic.claude-instant-v1") | ||
| AWS_REGION = os.environ["AWS_REGION_OVERRIDE"] if "AWS_REGION_OVERRIDE" in os.environ else os.environ["AWS_REGION"] | ||
| ENDPOINT_URL = os.environ.get("ENDPOINT_URL", f'https://bedrock-runtime.{AWS_REGION}.amazonaws.com') | ||
| DEFAULT_MAX_TOKENS = 256 | ||
|
|
||
| # global variables - avoid creating a new client for every request | ||
| client = None | ||
|
|
||
| def get_client(): | ||
| print("Connecting to Bedrock Service: ", ENDPOINT_URL) | ||
| client = boto3.client(service_name='bedrock-runtime', region_name=AWS_REGION, endpoint_url=ENDPOINT_URL) | ||
| return client | ||
|
|
||
| def get_request_body(modelId, parameters, prompt): | ||
| provider = modelId.split(".")[0] | ||
| request_body = None | ||
| if provider == "anthropic": | ||
| request_body = { | ||
| "prompt": prompt, | ||
| "max_tokens_to_sample": DEFAULT_MAX_TOKENS | ||
| } | ||
| request_body.update(parameters) | ||
| elif provider == "ai21": | ||
| request_body = { | ||
| "prompt": prompt, | ||
| "maxTokens": DEFAULT_MAX_TOKENS | ||
| } | ||
| request_body.update(parameters) | ||
| elif provider == "amazon": | ||
| textGenerationConfig = { | ||
| "maxTokenCount": DEFAULT_MAX_TOKENS | ||
| } | ||
| textGenerationConfig.update(parameters) | ||
| request_body = { | ||
| "inputText": prompt, | ||
| "textGenerationConfig": textGenerationConfig | ||
| } | ||
| else: | ||
| raise Exception("Unsupported provider: ", provider) | ||
| return request_body | ||
|
|
||
| def get_generate_text(modelId, response): | ||
| provider = modelId.split(".")[0] | ||
| generated_text = None | ||
| if provider == "anthropic": | ||
| response_body = json.loads(response.get("body").read().decode()) | ||
| generated_text = response_body.get("completion") | ||
| elif provider == "ai21": | ||
| response_body = json.loads(response.get("body").read()) | ||
| generated_text = response_body.get("completions")[0].get("data").get("text") | ||
| elif provider == "amazon": | ||
| response_body = json.loads(response.get("body").read()) | ||
| generated_text = response_body.get("results")[0].get("outputText") | ||
| else: | ||
| raise Exception("Unsupported provider: ", provider) | ||
| return generated_text | ||
|
|
||
|
|
||
| def format_prompt(modelId, prompt): | ||
| # TODO - replace prompt template placeholders - eg query, input, chatHistory, session attributes, user info | ||
| provider = modelId.split(".")[0] | ||
| if provider == "anthropic": | ||
| print("Model provider is Anthropic. Checking prompt format.") | ||
| if not prompt.startswith("\n\nHuman:"): | ||
| prompt = "\n\nHuman: " + prompt | ||
| print("Prepended '\\n\\nHuman:'") | ||
| if not prompt.endswith("\n\nAssistant:"): | ||
| prompt = prompt + "\n\nAssistant:" | ||
| print("Appended '\\n\\nHuman:'") | ||
| print(f"Prompt: {json.dumps(prompt)}") | ||
| return prompt | ||
|
|
||
| def get_llm_response(parameters, prompt): | ||
| global client | ||
| modelId = parameters.pop("modelId", DEFAULT_MODEL_ID) | ||
| prompt = format_prompt(modelId, prompt) | ||
| body = get_request_body(modelId, parameters, prompt) | ||
| print("ModelId", modelId, "- Body: ", body) | ||
| if (client is None): | ||
| client = get_client() | ||
| response = client.invoke_model(body=json.dumps(body), modelId=modelId, accept='application/json', contentType='application/json') | ||
| generated_text = get_generate_text(modelId, response) | ||
| return generated_text | ||
|
|
||
| def get_args_from_lambdahook_args(event): | ||
| parameters = {} | ||
| lambdahook_args_list = event["res"]["result"].get("args",[]) | ||
| print("LambdaHook args: ", lambdahook_args_list) | ||
| if len(lambdahook_args_list): | ||
| try: | ||
| parameters = json.loads(lambdahook_args_list[0]) | ||
| except Exception as e: | ||
| print(f"Failed to parse JSON:", lambdahook_args_list[0], e) | ||
| print("..continuing") | ||
| return parameters | ||
|
|
||
| def format_response(event, llm_response, prefix): | ||
| # set plaintext, markdown, & ssml response | ||
| if prefix in ["None", "N/A", "Empty"]: | ||
| prefix = None | ||
| plainttext = llm_response | ||
| markdown = llm_response | ||
| ssml = llm_response | ||
| if prefix: | ||
| plainttext = f"{prefix}\n\n{plainttext}" | ||
| markdown = f"**{prefix}**\n\n{markdown}" | ||
| # add plaintext, markdown, and ssml fields to event.res | ||
| event["res"]["message"] = plainttext | ||
| event["res"]["session"]["appContext"] = { | ||
| "altMessages": { | ||
| "markdown": markdown, | ||
| "ssml": ssml | ||
| } | ||
| } | ||
| #TODO - can we determine when LLM has a good answer or not? | ||
| #For now, always assume it's a good answer. | ||
| #QnAbot sets session attribute qnabot_gotanswer True when got_hits > 0 | ||
| event["res"]["got_hits"] = 1 | ||
| return event | ||
|
|
||
| def lambda_handler(event, context): | ||
| print("Received event: %s" % json.dumps(event)) | ||
| # args = {"Prefix:"<Prefix|None>", "Model_params":{"modelId":"anthropic.claude-instant-v1", "max_tokens":256}, "Prompt":"<prompt>"} | ||
| args = get_args_from_lambdahook_args(event) | ||
| # prompt set from args, or from req.question if not specified in args. | ||
| prompt = args.get("Prompt", event["req"]["question"]) | ||
| model_params = args.get("Model_params",{}) | ||
| llm_response = get_llm_response(model_params, prompt) | ||
| prefix = args.get("Prefix","LLM Answer:") | ||
| event = format_response(event, llm_response, prefix) | ||
| print("Returning response: %s" % json.dumps(event)) | ||
| return event |
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