Add Support for AWS Bedrock (#1408)

### What problem does this PR solve?

#308 

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

---------

Co-authored-by: KevinHuSh <kevinhu.sh@gmail.com>

rag/llm/__init__.py

@@ -31,7 +31,8 @@ EmbeddingModel = {
     "BaiChuan": BaiChuanEmbed,
     "Jina": JinaEmbed,
     "BAAI": DefaultEmbedding,
-    "Mistral": MistralEmbed
+    "Mistral": MistralEmbed,
+    "Bedrock": BedrockEmbed
 }
 
 
@@ -58,7 +59,8 @@ ChatModel = {
     "VolcEngine": VolcEngineChat,
     "BaiChuan": BaiChuanChat,
     "MiniMax": MiniMaxChat,
-    "Mistral": MistralChat
+    "Mistral": MistralChat,
+    "Bedrock": BedrockChat
 }
 
 

rag/llm/chat_model.py

@@ -533,3 +533,90 @@ class MistralChat(Base):
             yield ans + "\n**ERROR**: " + str(e)
 
         yield total_tokens
+
+
+class BedrockChat(Base):
+
+    def __init__(self, key, model_name, **kwargs):
+        import boto3
+        from botocore.exceptions import ClientError
+        self.bedrock_ak = eval(key).get('bedrock_ak', '')
+        self.bedrock_sk = eval(key).get('bedrock_sk', '')
+        self.bedrock_region = eval(key).get('bedrock_region', '')
+        self.model_name = model_name
+        self.client = boto3.client(service_name='bedrock-runtime', region_name=self.bedrock_region,
+                                   aws_access_key_id=self.bedrock_ak, aws_secret_access_key=self.bedrock_sk)
+
+    def chat(self, system, history, gen_conf):
+        if system:
+            history.insert(0, {"role": "system", "content": system})
+        for k in list(gen_conf.keys()):
+            if k not in ["temperature", "top_p", "max_tokens"]:
+                del gen_conf[k]
+        if "max_tokens" in gen_conf:
+            gen_conf["maxTokens"] = gen_conf["max_tokens"]
+            _ = gen_conf.pop("max_tokens")
+        if "top_p" in gen_conf:
+            gen_conf["topP"] = gen_conf["top_p"]
+            _ = gen_conf.pop("top_p")
+
+        try:
+            # Send the message to the model, using a basic inference configuration.
+            response = self.client.converse(
+                modelId=self.model_name,
+                messages=history,
+                inferenceConfig=gen_conf
+            )
+            
+            # Extract and print the response text.
+            ans = response["output"]["message"]["content"][0]["text"]
+            return ans, num_tokens_from_string(ans)
+
+        except (ClientError, Exception) as e:
+            return f"ERROR: Can't invoke '{self.model_name}'. Reason: {e}", 0
+
+    def chat_streamly(self, system, history, gen_conf):
+        if system:
+            history.insert(0, {"role": "system", "content": system})
+        for k in list(gen_conf.keys()):
+            if k not in ["temperature", "top_p", "max_tokens"]:
+                del gen_conf[k]
+        if "max_tokens" in gen_conf:
+            gen_conf["maxTokens"] = gen_conf["max_tokens"]
+            _ = gen_conf.pop("max_tokens")
+        if "top_p" in gen_conf:
+            gen_conf["topP"] = gen_conf["top_p"]
+            _ = gen_conf.pop("top_p")
+
+        if self.model_name.split('.')[0] == 'ai21':
+            try:
+                response = self.client.converse(
+                    modelId=self.model_name,
+                    messages=history,
+                    inferenceConfig=gen_conf
+                )
+                ans = response["output"]["message"]["content"][0]["text"]
+                return ans, num_tokens_from_string(ans)
+
+            except (ClientError, Exception) as e:
+                return f"ERROR: Can't invoke '{self.model_name}'. Reason: {e}", 0
+
+        ans = ""
+        try:
+            # Send the message to the model, using a basic inference configuration.
+            streaming_response = self.client.converse_stream(
+                modelId=self.model_name,
+                messages=history,
+                inferenceConfig=gen_conf
+            )
+
+            # Extract and print the streamed response text in real-time.
+            for resp in streaming_response["stream"]:
+                if "contentBlockDelta" in resp:
+                    ans += resp["contentBlockDelta"]["delta"]["text"]
+                    yield ans
+            
+        except (ClientError, Exception) as e:
+            yield ans + f"ERROR: Can't invoke '{self.model_name}'. Reason: {e}"
+
+        yield num_tokens_from_string(ans)

rag/llm/embedding_model.py

@@ -374,3 +374,48 @@ class MistralEmbed(Base):
         res = self.client.embeddings(input=[truncate(text, 8196)],
                                             model=self.model_name)
         return np.array(res.data[0].embedding), res.usage.total_tokens
+
+
+class BedrockEmbed(Base):
+    def __init__(self, key, model_name,
+                 **kwargs):
+        import boto3
+        self.bedrock_ak = eval(key).get('bedrock_ak', '')
+        self.bedrock_sk = eval(key).get('bedrock_sk', '')
+        self.bedrock_region = eval(key).get('bedrock_region', '')
+        self.model_name = model_name
+        self.client = boto3.client(service_name='bedrock-runtime', region_name=self.bedrock_region,
+                                   aws_access_key_id=self.bedrock_ak, aws_secret_access_key=self.bedrock_sk)
+
+    def encode(self, texts: list, batch_size=32):
+        texts = [truncate(t, 8196) for t in texts]
+        embeddings = []
+        token_count = 0
+        for text in texts:
+            if self.model_name.split('.')[0] == 'amazon':
+                body = {"inputText": text}
+            elif self.model_name.split('.')[0] == 'cohere':
+                body = {"texts": [text], "input_type": 'search_document'}
+
+            response = self.client.invoke_model(modelId=self.model_name, body=json.dumps(body))
+            model_response = json.loads(response["body"].read())
+            embeddings.extend([model_response["embedding"]])
+            token_count += num_tokens_from_string(text)
+
+        return np.array(embeddings), token_count
+
+    def encode_queries(self, text):
+
+        embeddings = []
+        token_count = num_tokens_from_string(text)
+        if self.model_name.split('.')[0] == 'amazon':
+            body = {"inputText": truncate(text, 8196)}
+        elif self.model_name.split('.')[0] == 'cohere':
+            body = {"texts": [truncate(text, 8196)], "input_type": 'search_query'}
+
+        response = self.client.invoke_model(modelId=self.model_name, body=json.dumps(body))
+        model_response = json.loads(response["body"].read())
+        embeddings.extend([model_response["embedding"]])
+
+        return np.array(embeddings), token_count
+