Embedding compatible with OpenAI API

comps/cores/proto/docarray.py

@@ -17,7 +17,7 @@ class TopologyInfo:
 
 
 class TextDoc(BaseDoc, TopologyInfo):
-    text: str = None
+    text: Union[str, List[str]] = None
 
 
 class Audio2text(BaseDoc, TopologyInfo):
@@ -93,15 +93,15 @@ class DocPath(BaseDoc):
 
 
 class EmbedDoc(BaseDoc):
-    text: str
-    embedding: conlist(float, min_length=0)
+    text: Union[str, List[str]]
+    embedding: Union[conlist(float, min_length=0), List[conlist(float, min_length=0)]]
     search_type: str = "similarity"
     k: int = 4
     distance_threshold: Optional[float] = None
     fetch_k: int = 20
     lambda_mult: float = 0.5
     score_threshold: float = 0.2
-    constraints: Optional[Union[Dict[str, Any], None]] = None
+    constraints: Optional[Union[Dict[str, Any], List[Dict[str, Any]], None]] = None
 
 
 class EmbedMultimodalDoc(EmbedDoc):

comps/embeddings/mosec/langchain/README.md

@@ -25,9 +25,34 @@ docker run -d --name="embedding-langchain-mosec-server" -e http_proxy=$http_prox
 
 ## run client test
 
-```
-curl localhost:6000/v1/embeddings \
-     -X POST \
-     -d '{"text":"Hello, world!"}' \
-     -H 'Content-Type: application/json'
+Use our basic API.
+
+```bash
+## query with single text
+curl http://localhost:6000/v1/embeddings\
+  -X POST \
+  -d '{"text":"Hello, world!"}' \
+  -H 'Content-Type: application/json'
+
+## query with multiple texts
+curl http://localhost:6000/v1/embeddings\
+  -X POST \
+  -d '{"text":["Hello, world!","How are you?"]}' \
+  -H 'Content-Type: application/json'
+```
+
+We are also compatible with [OpenAI API](https://platform.openai.com/docs/api-reference/embeddings).
+
+```bash
+## Input single text
+curl http://localhost:6000/v1/embeddings\
+  -X POST \
+  -d '{"input":"Hello, world!"}' \
+  -H 'Content-Type: application/json'
+
+## Input multiple texts with parameters
+curl http://localhost:6000/v1/embeddings\
+  -X POST \
+  -d '{"input":["Hello, world!","How are you?"], "dimensions":100}' \
+  -H 'Content-Type: application/json'
 ```

comps/embeddings/mosec/langchain/embedding_mosec.py

@@ -4,7 +4,7 @@
 import asyncio
 import os
 import time
-from typing import List, Optional
+from typing import List, Optional, Union
 
 from langchain_community.embeddings import OpenAIEmbeddings
 
@@ -18,6 +18,12 @@
     register_statistics,
     statistics_dict,
 )
+from comps.cores.proto.api_protocol import (
+    ChatCompletionRequest,
+    EmbeddingRequest,
+    EmbeddingResponse,
+    EmbeddingResponseData,
+)
 
 logger = CustomLogger("embedding_mosec")
 logflag = os.getenv("LOGFLAG", False)
@@ -62,18 +68,43 @@ async def get_embedding(e: Optional[List[float]]) -> List[float]:
     output_datatype=EmbedDoc,
 )
 @register_statistics(names=["opea_service@embedding_mosec"])
-async def embedding(input: TextDoc) -> EmbedDoc:
+async def embedding(
+    input: Union[TextDoc, EmbeddingRequest, ChatCompletionRequest]
+) -> Union[EmbedDoc, EmbeddingResponse, ChatCompletionRequest]:
     if logflag:
         logger.info(input)
     start = time.time()
-    embed_vector = await embeddings.aembed_query(input.text)
-    res = EmbedDoc(text=input.text, embedding=embed_vector)
+    if isinstance(input, TextDoc):
+        embed_vector = await get_embeddings(input.text)
+        embedding_res = embed_vector[0] if isinstance(input.text, str) else embed_vector
+        res = EmbedDoc(text=input.text, embedding=embedding_res)
+    else:
+        embed_vector = await get_embeddings(input.input)
+        if input.dimensions is not None:
+            embed_vector = [embed_vector[i][: input.dimensions] for i in range(len(embed_vector))]
+
+        # for standard openai embedding format
+        res = EmbeddingResponse(
+            data=[EmbeddingResponseData(index=i, embedding=embed_vector[i]) for i in range(len(embed_vector))]
+        )
+
+        if isinstance(input, ChatCompletionRequest):
+            input.embedding = res
+            # keep
+            res = input
+
     statistics_dict["opea_service@embedding_mosec"].append_latency(time.time() - start, None)
     if logflag:
         logger.info(res)
     return res
 
 
+async def get_embeddings(text: Union[str, List[str]]) -> List[List[float]]:
+    texts = [text] if isinstance(text, str) else text
+    embed_vector = await embeddings.aembed_documents(texts)
+    return embed_vector
+
+
 if __name__ == "__main__":
     MOSEC_EMBEDDING_ENDPOINT = os.environ.get("MOSEC_EMBEDDING_ENDPOINT", "http://127.0.0.1:8080")
     os.environ["OPENAI_API_BASE"] = MOSEC_EMBEDDING_ENDPOINT

comps/embeddings/multimodal_clip/README.md

@@ -44,9 +44,34 @@ curl http://localhost:6000/v1/health_check\
 
 ### 2.2 Consume Embedding Service
 
+Use our basic API.
+
+```bash
+## query with single text
+curl http://localhost:6000/v1/embeddings\
+  -X POST \
+  -d '{"text":"Hello, world!"}' \
+  -H 'Content-Type: application/json'
+
+## query with multiple texts
+curl http://localhost:6000/v1/embeddings\
+  -X POST \
+  -d '{"text":["Hello, world!","How are you?"]}' \
+  -H 'Content-Type: application/json'
+```
+
+We are also compatible with [OpenAI API](https://platform.openai.com/docs/api-reference/embeddings).
+
 ```bash
-curl http://localhost:6000/v1/embeddings \
-      -X POST   -d '{"text":"Sample text"}' \
-      -H 'Content-Type: application/json'
+## Input single text
+curl http://localhost:6000/v1/embeddings\
+  -X POST \
+  -d '{"input":"Hello, world!"}' \
+  -H 'Content-Type: application/json'
 
+## Input multiple texts with parameters
+curl http://localhost:6000/v1/embeddings\
+  -X POST \
+  -d '{"input":["Hello, world!","How are you?"], "dimensions":100}' \
+  -H 'Content-Type: application/json'
 ```

comps/embeddings/multimodal_clip/embedding_multimodal.py

@@ -2,12 +2,15 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import datetime
+import os
 import time
+from typing import List, Optional, Union
 
 from dateparser.search import search_dates
 from embeddings_clip import vCLIP
 
 from comps import (
+    CustomLogger,
     EmbedDoc,
     ServiceType,
     TextDoc,
@@ -16,6 +19,15 @@
     register_statistics,
     statistics_dict,
 )
+from comps.cores.proto.api_protocol import (
+    ChatCompletionRequest,
+    EmbeddingRequest,
+    EmbeddingResponse,
+    EmbeddingResponseData,
+)
+
+logger = CustomLogger("embedding_multimodal")
+logflag = os.getenv("LOGFLAG", False)
 
 
 def filtler_dates(prompt):
@@ -64,21 +76,49 @@ def filtler_dates(prompt):
     output_datatype=EmbedDoc,
 )
 @register_statistics(names=["opea_service@embedding_multimodal"])
-def embedding(input: TextDoc) -> EmbedDoc:
+async def embedding(
+    input: Union[TextDoc, EmbeddingRequest, ChatCompletionRequest]
+) -> Union[EmbedDoc, EmbeddingResponse, ChatCompletionRequest]:
+    if logflag:
+        logger.info(input)
     start = time.time()
 
     if isinstance(input, TextDoc):
-        # Handle text input
-        embed_vector = embeddings.embed_query(input.text).tolist()[0]
-        res = EmbedDoc(text=input.text, embedding=embed_vector, constraints=filtler_dates(input.text))
-
+        embed_vector = await get_embeddings(input.text)
+        if isinstance(input.text, str):
+            embedding_res = embed_vector[0]
+            constraints_res = filtler_dates(input.text)
+        else:
+            embedding_res = embed_vector
+            constraints_res = [filtler_dates(input.text[i]) for i in range(len(input.text))]
+        res = EmbedDoc(text=input.text, embedding=embedding_res, constraints=constraints_res)
     else:
-        raise ValueError("Invalid input type")
+        embed_vector = await get_embeddings(input.input)
+        if input.dimensions is not None:
+            embed_vector = [embed_vector[i][: input.dimensions] for i in range(len(embed_vector))]
+
+        # for standard openai embedding format
+        res = EmbeddingResponse(
+            data=[EmbeddingResponseData(index=i, embedding=embed_vector[i]) for i in range(len(embed_vector))]
+        )
+
+        if isinstance(input, ChatCompletionRequest):
+            input.embedding = res
+            # keep
+            res = input
 
     statistics_dict["opea_service@embedding_multimodal"].append_latency(time.time() - start, None)
+    if logflag:
+        logger.info(res)
     return res
 
 
+async def get_embeddings(text: Union[str, List[str]]) -> List[List[float]]:
+    texts = [text] if isinstance(text, str) else text
+    embed_vector = embeddings.embed_query(texts).tolist()
+    return embed_vector
+
+
 if __name__ == "__main__":
     embeddings = vCLIP({"model_name": "openai/clip-vit-base-patch32", "num_frm": 4})
     opea_microservices["opea_service@embedding_multimodal"].start()

comps/embeddings/predictionguard/README.md

@@ -31,9 +31,34 @@ docker run -d --name="embedding-predictionguard" -p 6000:6000 -e PREDICTIONGUARD
 
 ## 🚀 Consume Embeddings Service
 
+Use our basic API.
+
 ```bash
-curl localhost:6000/v1/embeddings \
-     -X POST \
-     -d '{"text":"Hello, world!"}' \
-     -H 'Content-Type: application/json'
+## query with single text
+curl http://localhost:6000/v1/embeddings\
+  -X POST \
+  -d '{"text":"Hello, world!"}' \
+  -H 'Content-Type: application/json'
+
+## query with multiple texts
+curl http://localhost:6000/v1/embeddings\
+  -X POST \
+  -d '{"text":["Hello, world!","How are you?"]}' \
+  -H 'Content-Type: application/json'
+```
+
+We are also compatible with [OpenAI API](https://platform.openai.com/docs/api-reference/embeddings).
+
+```bash
+## Input single text
+curl http://localhost:6000/v1/embeddings\
+  -X POST \
+  -d '{"input":"Hello, world!"}' \
+  -H 'Content-Type: application/json'
+
+## Input multiple texts with parameters
+curl http://localhost:6000/v1/embeddings\
+  -X POST \
+  -d '{"input":["Hello, world!","How are you?"], "dimensions":100}' \
+  -H 'Content-Type: application/json'
 ```

comps/embeddings/predictionguard/embedding_predictionguard.py

@@ -4,10 +4,12 @@
 
 import os
 import time
+from typing import List, Optional, Union
 
 from predictionguard import PredictionGuard
 
 from comps import (
+    CustomLogger,
     EmbedDoc,
     ServiceType,
     TextDoc,
@@ -16,6 +18,15 @@
     register_statistics,
     statistics_dict,
 )
+from comps.cores.proto.api_protocol import (
+    ChatCompletionRequest,
+    EmbeddingRequest,
+    EmbeddingResponse,
+    EmbeddingResponseData,
+)
+
+logger = CustomLogger("embedding_predictionguard")
+logflag = os.getenv("LOGFLAG", False)
 
 # Initialize Prediction Guard client
 client = PredictionGuard()
@@ -31,16 +42,46 @@
     output_datatype=EmbedDoc,
 )
 @register_statistics(names=["opea_service@embedding_predictionguard"])
-def embedding(input: TextDoc) -> EmbedDoc:
+async def embedding(
+    input: Union[TextDoc, EmbeddingRequest, ChatCompletionRequest]
+) -> Union[EmbedDoc, EmbeddingResponse, ChatCompletionRequest]:
+    if logflag:
+        logger.info(input)
     start = time.time()
-    response = client.embeddings.create(model=pg_embedding_model_name, input=[{"text": input.text}])
-    embed_vector = response["data"][0]["embedding"]
-    embed_vector = embed_vector[:512]  # Keep only the first 512 elements
-    res = EmbedDoc(text=input.text, embedding=embed_vector)
+
+    if isinstance(input, TextDoc):
+        embed_vector = await get_embeddings(input.text)
+        embedding_res = embed_vector[0] if isinstance(input.text, str) else embed_vector
+        res = EmbedDoc(text=input.text, embedding=embedding_res)
+    else:
+        embed_vector = await get_embeddings(input.input)
+        input.dimensions = input.dimensions if input.dimensions is not None else 512
+        embed_vector = [embed_vector[i][: input.dimensions] for i in range(len(embed_vector))]
+
+        # for standard openai embedding format
+        res = EmbeddingResponse(
+            data=[EmbeddingResponseData(index=i, embedding=embed_vector[i]) for i in range(len(embed_vector))]
+        )
+
+        if isinstance(input, ChatCompletionRequest):
+            input.embedding = res
+            # keep
+            res = input
+
     statistics_dict["opea_service@embedding_predictionguard"].append_latency(time.time() - start, None)
+    if logflag:
+        logger.info(res)
     return res
 
 
+async def get_embeddings(text: Union[str, List[str]]) -> List[List[float]]:
+    texts = [text] if isinstance(text, str) else text
+    texts = [{"text": texts[i]} for i in range(len(texts))]
+    response = client.embeddings.create(model=pg_embedding_model_name, input=texts)["data"]
+    embed_vector = [response[i]["embedding"] for i in range(len(response))]
+    return embed_vector
+
+
 if __name__ == "__main__":
     pg_embedding_model_name = os.getenv("PG_EMBEDDING_MODEL_NAME", "bridgetower-large-itm-mlm-itc")
     print("Prediction Guard Embedding initialized.")