切换为zhipu 向量计算 -1

app.py

@@ -21,6 +21,7 @@ from langchain.chains.combine_documents import create_stuff_documents_chain
 from langchain.chains import StuffDocumentsChain
 from langchain_core.prompts import PromptTemplate
 import hashlib
+from project.embeddings.zhipuai_embedding import ZhipuAIEmbeddings
 
 chain: Optional[Callable] = None
 
@@ -46,7 +47,8 @@ def web_loader(file, openai_key, puzhiai_key, zilliz_uri, user, password):
 
     text_splitter = CharacterTextSplitter(chunk_size=1024, chunk_overlap=0)
     docs = text_splitter.split_documents(docs)
-    embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", openai_api_key=openai_key)
+    #embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", openai_api_key=openai_key)
+    embeddings = ZhipuAIEmbeddings(zhipuai_api_key=puzhiai_key)
 
     if not embeddings:
         return "embeddings not"
@@ -69,6 +71,7 @@ def web_loader(file, openai_key, puzhiai_key, zilliz_uri, user, password):
             "password": password,
             "secure": True,
         },
+        collection_name="LangChainCollectionYin"
     )
 
     if not docsearch:
@@ -149,7 +152,7 @@ if __name__ == "__main__":
             """
         <h1><center>Langchain And Zilliz App</center></h1>
         
-        v.2.27.16.14
+        v.2.27.17.18
         
         """
         )

project/embeddings/zhipuai_embedding.py

@@ -0,0 +1,111 @@
+from __future__ import annotations
+
+import logging
+from typing import Any, Dict, List, Optional
+
+from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, root_validator
+from langchain.utils import get_from_dict_or_env
+
+logger = logging.getLogger(__name__)
+
+
+class ZhipuAIEmbeddings(BaseModel, Embeddings):
+    """`Zhipuai Embeddings` embedding models."""
+
+    zhipuai_api_key: Optional[str] = None
+    """Zhipuai application apikey"""
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """
+        Validate whether zhipuai_api_key in the environment variables or
+        configuration file are available or not.
+
+        Args:
+
+            values: a dictionary containing configuration information, must include the
+            fields of zhipuai_api_key
+        Returns:
+
+            a dictionary containing configuration information. If zhipuai_api_key
+            are not provided in the environment variables or configuration
+            file, the original values will be returned; otherwise, values containing
+            zhipuai_api_key will be returned.
+        Raises:
+
+            ValueError: zhipuai package not found, please install it with `pip install
+            zhipuai`
+        """
+        values["zhipuai_api_key"] = get_from_dict_or_env(
+            values,
+            "zhipuai_api_key",
+            "ZHIPUAI_API_KEY",
+        )
+
+        try:
+            from zhipuai import ZhipuAI
+            values["client"] = ZhipuAI(api_key=values["zhipuai_api_key"])
+
+        except ImportError:
+            raise ValueError(
+                "Zhipuai package not found, please install it with "
+                "`pip install zhipuai`"
+            )
+        return values
+
+    def _embed(self, texts: str) -> List[float]:
+        # send request
+        try:
+            resp = self.client.embeddings.create(
+                model="embedding-2",
+                input=texts
+            )
+        except Exception as e:
+            raise ValueError(f"Error raised by inference endpoint: {e}")
+
+        if resp["code"] != 200:
+            raise ValueError(
+                "Error raised by inference API HTTP code: %s, %s"
+                % (resp["code"], resp["msg"])
+            )
+        embeddings = resp["data"]["embedding"]
+        return embeddings
+
+    def embed_query(self, text: str) -> List[float]:
+        """
+        Embedding a text.
+
+        Args:
+
+            Text (str): A text to be embedded.
+
+        Return:
+
+            List [float]: An embedding list of input text, which is a list of floating-point values.
+        """
+        resp = self.embed_documents([text])
+        return resp[0]
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """
+        Embeds a list of text documents.
+
+        Args:
+            texts (List[str]): A list of text documents to embed.
+
+        Returns:
+            List[List[float]]: A list of embeddings for each document in the input list.
+                            Each embedding is represented as a list of float values.
+        """
+        return [self._embed(text) for text in texts]
+
+    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Asynchronous Embed search docs."""
+        raise NotImplementedError(
+            "Please use `embed_documents`. Official does not support asynchronous requests")
+
+    async def aembed_query(self, text: str) -> List[float]:
+        """Asynchronous Embed query text."""
+        raise NotImplementedError(
+            "Please use `aembed_query`. Official does not support asynchronous requests")