改进 GPT 处理和回复

document_ai/run.py

@@ -1,4 +1,4 @@
-from threading import Thread
+from threading import Thread, Event
 
 import vector
 import server

document_ai/search.py

@@ -66,6 +66,7 @@ if len(res[0]) > 5:
 # document_chunk_ids = []
 # real_document = []
 plain_text = ""
+document_chunks = []
 
 for i in range(len(res[0])):
     _chunk_id = res[0][i].id
@@ -80,6 +81,7 @@ for i in range(len(res[0])):
 
         # _doc_content_full = _chunk_content.content
         # print("DOC OBJ:" + _doc_content_full)
+        document_chunks.append(_chunk_content.content)
         plain_text += "=== \n" + _chunk_content.content + " ===\n"
 
         # real_document.append(_doc_content)
@@ -116,7 +118,7 @@ messages = [
     {
         "role": "system",
         "content": f"""
-Context: {plain_text}
+{plain_text}
 """
     },
 {

document_ai/server.py

@@ -2,6 +2,7 @@ import os
 from concurrent import futures
 
 import langchain
+import openai
 
 import proto.document_query_pb2
 import proto.document_query_pb2_grpc
@@ -9,10 +10,10 @@ import grpc
 import proto.documents_pb2
 import init
 import doc_client
-from langchain.llms.openai import OpenAI
+# from langchain.llms.openai import OpenAI
-from langchain.schema.document import Document
+# from langchain.schema.document import Document
-from langchain.embeddings import OpenAIEmbeddings
+# from langchain.embeddings import OpenAIEmbeddings
-from langchain.chains.qa_with_sources import load_qa_with_sources_chain
+# from langchain.chains.qa_with_sources import load_qa_with_sources_chain
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.cache import InMemoryCache
 
@@ -20,6 +21,8 @@ langchain.llm_cache = InMemoryCache()
 
 CHUNK_SIZE = 500
 
+# openai.api_base = "https://api.openai.com/v1"
+# openai.api_key="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"
 
 class AIServer(proto.document_query_pb2_grpc.DocumentQuery):
     def Query(self, target,
@@ -32,30 +35,25 @@ class AIServer(proto.document_query_pb2_grpc.DocumentQuery):
               timeout=None,
               metadata=None):
 
-
         print("新的请求：" + target.question)
         vec = init.text_to_vector(target.question)
 
-        question = "Reply in spoken language:" + target.question
+        question = target.question
 
         search_param = {
             "data": [vec],
             "anns_field": "vector",
             "param": {"metric_type": "L2"},
             "limit": 5,
-            "expr": "user_id == " + str(target.user_id),
+            "expr": "user_id == " + str(target.user_id) + " && library_id == " + str(target.library_id),
-            "output_fields": ["document_id", "user_id"],
+            "output_fields": ["document_id", "user_id", "library_id"],
         }
 
         res = init.collection.search(**search_param)
 
-        # # 最多 5 个
+        document_text = ""
-        # if len(res[0]) > 5:
+        # real_document = []
-        #     res[0] = res[0][:5]
+        sources = []
-
-
-        # document_chunk_ids = []
-        real_document = []
 
         for i in range(len(res[0])):
             _chunk_id = res[0][i].id
@@ -66,27 +64,32 @@ class AIServer(proto.document_query_pb2_grpc.DocumentQuery):
                     id=_chunk_id
                 ))
 
-                _doc_content_full = _chunk_content.content
+                print(_chunk_content.document)
 
-                doc_obj = Document(page_content=_doc_content_full, metadata={"source": "chunked content"})
+                # _doc_content_full = _chunk_content.content
+                document_text += "\n" + _chunk_content.content + "\n"
 
-                real_document.append(doc_obj)
+                # append
+                sources.append({
+                    "text": _chunk_content.content,
+                    "document_id": _chunk_content.document.id
+                })
+
+                # doc_obj = Document(page_content=_doc_content_full, metadata={"source": "chunked content"})
+
+                # real_document.append(doc_obj)
 
             except Exception as e:
                 print(e)
 
-        print(real_document)
-
         print("正在调用 LLM...")
-        chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="map_reduce",
-                                           return_intermediate_steps=True,
-                                           verbose=True)
 
-        output = chain({"input_documents": real_document, "question": question}, return_only_outputs=False)
+        output = search(document_text, question)
-        print("回复：" + output["output_text"])
 
+        print("完成。")
         return proto.document_query_pb2.QueryResponse(
-            text=output["output_text"]
+            text=output,
+            sources=sources
         )
 
     def Chunk(self,
@@ -135,5 +138,35 @@ def serve():
     server.wait_for_termination()
 
 
+def search(summaries: str, question: str) -> str:
+    prompt = f"""
+使用以下文档回答问题，使用Markdown回答你得用“你”的身份指代用户。如果你不知道答案，你可以说你不知道，不要编造答案。总是使用中文回复。
+
+QUESTION: {question}
+
+===文档开始===
+{summaries}
+===文档结束===
+
+FINAL ANSWER:
+"""
+
+    messages = [
+        {
+            "role": "user",
+            "content": prompt
+        }
+    ]
+
+    print(prompt)
+
+    result = openai.ChatCompletion.create(
+        messages=messages, model="gpt-3.5-turbo", temperature=0
+    )
+    res = result["choices"][0]["message"].to_dict_recursive()
+    print(res)
+    return res["content"]
+
+
 if __name__ == '__main__':
     serve()

document_ai/server.py.bak

@@ -0,0 +1,166 @@
+import os
+from concurrent import futures
+
+import langchain
+import openai
+
+import proto.document_query_pb2
+import proto.document_query_pb2_grpc
+import grpc
+import proto.documents_pb2
+import init
+import doc_client
+from langchain.llms.openai import OpenAI
+from langchain.schema.document import Document
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.chains.qa_with_sources import load_qa_with_sources_chain
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.cache import InMemoryCache
+
+langchain.llm_cache = InMemoryCache()
+
+CHUNK_SIZE = 500
+
+
+class AIServer(proto.document_query_pb2_grpc.DocumentQuery):
+    def Query(self, target,
+              options=(),
+              channel_credentials=None,
+              call_credentials=None,
+              insecure=False,
+              compression=None,
+              wait_for_ready=None,
+              timeout=None,
+              metadata=None):
+
+
+        print("新的请求：" + target.question)
+        vec = init.text_to_vector(target.question)
+
+        question = "Reply in spoken language:" + target.question
+
+        search_param = {
+            "data": [vec],
+            "anns_field": "vector",
+            "param": {"metric_type": "L2"},
+            "limit": 5,
+            "expr": "user_id == " + str(target.user_id),
+            "output_fields": ["document_id", "user_id"],
+        }
+
+        res = init.collection.search(**search_param)
+
+        # # 最多 5 个
+        # if len(res[0]) > 5:
+        #     res[0] = res[0][:5]
+
+
+        # document_chunk_ids = []
+        real_document = []
+
+        for i in range(len(res[0])):
+            _chunk_id = res[0][i].id
+            print("正在获取分块 " + str(_chunk_id) + " 的内容...")
+
+            try:
+                _chunk_content = doc_client.stub.GetDocumentChunk(proto.documents_pb2.GetDocumentChunkByIdRequest(
+                    id=_chunk_id
+                ))
+
+                _doc_content_full = _chunk_content.content
+
+                doc_obj = Document(page_content=_doc_content_full, metadata={"source": "chunked content"})
+
+                real_document.append(doc_obj)
+
+            except Exception as e:
+                print(e)
+
+        print(real_document)
+
+        print("正在调用 LLM...")
+        chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="map_reduce",
+                                           return_intermediate_steps=True,
+                                           verbose=True)
+
+        output = chain({"input_documents": real_document, "question": question}, return_only_outputs=False)
+        print("回复：" + output["output_text"])
+
+        return proto.document_query_pb2.QueryResponse(
+            text=output["output_text"]
+        )
+
+    def Chunk(self,
+              target,
+              options=(),
+              channel_credentials=None,
+              call_credentials=None,
+              insecure=False,
+              compression=None,
+              wait_for_ready=None,
+              timeout=None,
+              metadata=None):
+
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=CHUNK_SIZE,
+            chunk_overlap=20,
+            length_function=len,
+            add_start_index=True,
+        )
+
+        page_contents = text_splitter.create_documents([
+            target.text
+        ])
+
+        texts = []
+
+        for page_content in page_contents:
+            texts.append(page_content.page_content)
+
+        return proto.document_query_pb2.ChunkResponse(
+            texts=texts
+        )
+
+
+def serve():
+    _ADDR = os.getenv("BIND")
+    if _ADDR is None:
+        _ADDR = "[::]:50051"
+    print("Listening on", _ADDR)
+
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
+    proto.document_query_pb2_grpc.add_DocumentQueryServicer_to_server(AIServer(), server)
+
+    server.add_insecure_port(_ADDR)
+    server.start()
+    server.wait_for_termination()
+
+
+def search(summaries: str, question: str) -> str:
+    prompt = f"""
+Given the following extracted parts of a long document and a question, create a final answer with references ("SOURCES").
+If you don't know the answer, just say that you don't know. Don't try to make up an answer. ALWAYS response with spoken language.
+ 
+QUESTION: {question}
+=========
+{summaries}
+=========
+FINAL ANSWER:
+"""
+
+    messages = [
+        {
+            "role": "user",
+            "content": prompt
+        }
+    ]
+
+    result = openai.ChatCompletion.create(
+        messages=messages, model="gpt-3.5-turbo", temperature=0
+    )
+    res = result["choices"][0]["message"].to_dict_recursive()
+    print(res)
+
+
+if __name__ == '__main__':
+    serve()