embedding兼容

model-providers/model_providers/bootstrap_web/openai_bootstrap_web.py

@@ -18,6 +18,7 @@ from typing import (
     cast,
 )
 
+import tiktoken
 import uvicorn
 from fastapi import APIRouter, FastAPI, HTTPException, Request, Response, status
 from fastapi.middleware.cors import CORSMiddleware
@@ -208,16 +209,39 @@ class RESTFulOpenAIBootstrapBaseWeb(OpenAIBootstrapBaseWeb):
         logger.info(
             f"Received create_embeddings request: {pprint.pformat(embeddings_request.dict())}"
         )
-        model_instance = self._provider_manager.get_model_instance(
+        try:
-            provider=provider,
+            model_instance = self._provider_manager.get_model_instance(
-            model_type=ModelType.TEXT_EMBEDDING,
+                provider=provider,
-            model=embeddings_request.model,
+                model_type=ModelType.TEXT_EMBEDDING,
-        )
+                model=embeddings_request.model,
-        texts = embeddings_request.input
+            )
-        if isinstance(texts, str):
+
-            texts = [texts]
+            # 判断embeddings_request.input是否为list
-        response = model_instance.invoke_text_embedding(texts=texts, user="abc-123")
+            input = ''
-        return await openai_embedding_text(response)
+            if isinstance(embeddings_request.input, list):
+                tokens = embeddings_request.input
+                try:
+                    encoding = tiktoken.encoding_for_model(embeddings_request.model)
+                except KeyError:
+                    logger.warning("Warning: model not found. Using cl100k_base encoding.")
+                    model = "cl100k_base"
+                    encoding = tiktoken.get_encoding(model)
+                for i, token in enumerate(tokens):
+                    text = encoding.decode(token)
+                    input += text
+
+            else:
+                input = embeddings_request.input
+
+            response = model_instance.invoke_text_embedding(texts=[input], user="abc-123")
+            return await openai_embedding_text(response)
+
+        except ValueError as e:
+            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=str(e))
+        except InvokeError as e:
+            raise HTTPException(
+                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e)
+            )
 
     async def create_chat_completion(
         self, provider: str, request: Request, chat_request: ChatCompletionRequest

model-providers/model_providers/core/model_runtime/model_providers/zhipuai/_common.py

@@ -25,7 +25,7 @@ class _CommonZhipuaiAI:
             if "zhipuai_api_key" in credentials
             else None,
             "api_base": credentials["api_base"]
-            if "api_key" in credentials
+            if "api_base" in credentials
             else credentials["zhipuai_api_base"]
             if "zhipuai_api_base" in credentials
             else None,

model-providers/tests/openai_providers_test/test_openai_service.py

@@ -1,6 +1,6 @@
 from langchain.chains import LLMChain
 from langchain_core.prompts import PromptTemplate
-from langchain_openai import ChatOpenAI
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
 import pytest
 import logging
 
@@ -20,3 +20,17 @@ def test_llm(init_server: str):
     logger.info("\033[1;32m" + f"llm_chain: {responses}" + "\033[0m")
 
 
+
+
+@pytest.mark.requires("openai")
+def test_embedding(init_server: str):
+
+    embeddings = OpenAIEmbeddings(model="text-embedding-3-large",
+                                  openai_api_key="YOUR_API_KEY",
+                                  openai_api_base=f"{init_server}/zhipuai/v1")
+
+    text = "你好"
+
+    query_result = embeddings.embed_query(text)
+
+    logger.info("\033[1;32m" + f"embeddings: {query_result}" + "\033[0m")

model-providers/tests/zhipuai_providers_test/model_providers.yaml

@@ -1,4 +1,4 @@
 zhipuai:
   provider_credential:
-    api_key: 'e6a98ef1c54484c2afeac1ae8cef93ef.1'
+    api_key: 'd4fa0690b6dfa205204cae2e12aa6fb6.1'
-    api_base: 'https://test.bigmodel.cn/stage-api/paas/v4'
+#    api_base: 'https://test.bigmodel.cn/stage-api/paas/v4'

model-providers/tests/zhipuai_providers_test/test_zhipuai_service.py

@@ -1,6 +1,6 @@
 from langchain.chains import LLMChain
 from langchain_core.prompts import PromptTemplate
-from langchain_openai import ChatOpenAI
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
 import pytest
 import logging
 
@@ -25,18 +25,15 @@ def test_llm(init_server: str):
 
 @pytest.mark.requires("zhipuai")
 def test_embedding(init_server: str):
-    llm = ChatOpenAI(
 
-        model_name="glm-4",
+    embeddings = OpenAIEmbeddings(model="text_embedding",
-        openai_api_key="YOUR_API_KEY", openai_api_base=f"{init_server}/zhipuai/v1")
+                                  openai_api_key="YOUR_API_KEY",
-    template = """Question: {question}
+                                  openai_api_base=f"{init_server}/zhipuai/v1")
 
-    Answer: Let's think step by step."""
+    text = "你好"
 
-    prompt = PromptTemplate.from_template(template)
+    query_result = embeddings.embed_query(text)
 
-    llm_chain = LLMChain(prompt=prompt, llm=llm)
+    logger.info("\033[1;32m" + f"embeddings: {query_result}" + "\033[0m")
-    responses = llm_chain.run("你好")
-    logger.info("\033[1;32m" + f"llm_chain: {responses}" + "\033[0m")