refac

main.py

@@ -109,7 +109,7 @@ async def get_models():
 
 @app.post("/chat/completions")
 @app.post("/v1/chat/completions")
-async def generate_openai_chat_completion(form_data: OpenAIChatCompletionForm):
+def generate_openai_chat_completion(form_data: OpenAIChatCompletionForm):
     user_message = get_last_user_message(form_data.messages)
 
     if form_data.model not in PIPELINES:
@@ -119,7 +119,6 @@ async def generate_openai_chat_completion(form_data: OpenAIChatCompletionForm):
         )
 
     def job():
-
         get_response = PIPELINES[form_data.model]["module"].get_response
 
         if form_data.stream:
@@ -138,7 +137,7 @@ async def generate_openai_chat_completion(form_data: OpenAIChatCompletionForm):
                         yield f"data: {json.dumps(message)}\n\n"
 
                 finish_message = {
-                    "id": f"rag-{str(uuid.uuid4())}",
+                    "id": f"{form_data.model}-{str(uuid.uuid4())}",
                     "object": "chat.completion.chunk",
                     "created": int(time.time()),
                     "model": MODEL_ID,
@@ -168,7 +167,7 @@ async def generate_openai_chat_completion(form_data: OpenAIChatCompletionForm):
                     message = f"{message}{stream}"
 
             return {
-                "id": f"rag-{str(uuid.uuid4())}",
+                "id": f"{form_data.model}-{str(uuid.uuid4())}",
                 "object": "chat.completion",
                 "created": int(time.time()),
                 "model": MODEL_ID,
@@ -185,14 +184,7 @@ async def generate_openai_chat_completion(form_data: OpenAIChatCompletionForm):
                 ],
             }
 
-    try:
+    return job()
-        return await run_in_threadpool(job)
-    except Exception as e:
-        print(e)
-        raise HTTPException(
-            status_code=500,
-            detail="{e}",
-        )
 
 
 @app.get("/")

pipelines/examples/haystack_pipeline.py

@@ -2,76 +2,7 @@ from typing import List, Union, Generator
 from schemas import OpenAIChatMessage
 import os
 
-
+global basic_rag_pipeline
-os.environ["OPENAI_API_KEY"] = "your_openai_api_key_here"
-
-from haystack.components.embedders import SentenceTransformersDocumentEmbedder
-from haystack.components.embedders import SentenceTransformersTextEmbedder
-from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
-from haystack.components.builders import PromptBuilder
-from haystack.components.generators import OpenAIGenerator
-
-from haystack.document_stores.in_memory import InMemoryDocumentStore
-
-
-from datasets import load_dataset
-from haystack import Document
-from haystack import Pipeline
-
-
-document_store = InMemoryDocumentStore()
-
-dataset = load_dataset("bilgeyucel/seven-wonders", split="train")
-docs = [Document(content=doc["content"], meta=doc["meta"]) for doc in dataset]
-
-
-doc_embedder = SentenceTransformersDocumentEmbedder(
-    model="sentence-transformers/all-MiniLM-L6-v2"
-)
-doc_embedder.warm_up()
-
-
-docs_with_embeddings = doc_embedder.run(docs)
-document_store.write_documents(docs_with_embeddings["documents"])
-
-
-text_embedder = SentenceTransformersTextEmbedder(
-    model="sentence-transformers/all-MiniLM-L6-v2"
-)
-
-
-retriever = InMemoryEmbeddingRetriever(document_store)
-
-
-template = """
-Given the following information, answer the question.
-
-Context:
-{% for document in documents %}
-    {{ document.content }}
-{% endfor %}
-
-Question: {{question}}
-Answer:
-"""
-
-prompt_builder = PromptBuilder(template=template)
-
-
-generator = OpenAIGenerator(model="gpt-3.5-turbo")
-
-
-basic_rag_pipeline = Pipeline()
-# Add components to your pipeline
-basic_rag_pipeline.add_component("text_embedder", text_embedder)
-basic_rag_pipeline.add_component("retriever", retriever)
-basic_rag_pipeline.add_component("prompt_builder", prompt_builder)
-basic_rag_pipeline.add_component("llm", generator)
-
-# Now, connect the components to each other
-basic_rag_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
-basic_rag_pipeline.connect("retriever", "prompt_builder.documents")
-basic_rag_pipeline.connect("prompt_builder", "llm")
 
 
 def get_response(
@@ -92,6 +23,68 @@ def get_response(
 
 
 async def on_startup():
+
+    os.environ["OPENAI_API_KEY"] = "your_openai_api_key_here"
+
+    from haystack.components.embedders import SentenceTransformersDocumentEmbedder
+    from haystack.components.embedders import SentenceTransformersTextEmbedder
+    from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+    from haystack.components.builders import PromptBuilder
+    from haystack.components.generators import OpenAIGenerator
+
+    from haystack.document_stores.in_memory import InMemoryDocumentStore
+
+    from datasets import load_dataset
+    from haystack import Document
+    from haystack import Pipeline
+
+    document_store = InMemoryDocumentStore()
+
+    dataset = load_dataset("bilgeyucel/seven-wonders", split="train")
+    docs = [Document(content=doc["content"], meta=doc["meta"]) for doc in dataset]
+
+    doc_embedder = SentenceTransformersDocumentEmbedder(
+        model="sentence-transformers/all-MiniLM-L6-v2"
+    )
+    doc_embedder.warm_up()
+
+    docs_with_embeddings = doc_embedder.run(docs)
+    document_store.write_documents(docs_with_embeddings["documents"])
+
+    text_embedder = SentenceTransformersTextEmbedder(
+        model="sentence-transformers/all-MiniLM-L6-v2"
+    )
+
+    retriever = InMemoryEmbeddingRetriever(document_store)
+
+    template = """
+    Given the following information, answer the question.
+
+    Context:
+    {% for document in documents %}
+        {{ document.content }}
+    {% endfor %}
+
+    Question: {{question}}
+    Answer:
+    """
+
+    prompt_builder = PromptBuilder(template=template)
+
+    generator = OpenAIGenerator(model="gpt-3.5-turbo")
+
+    basic_rag_pipeline = Pipeline()
+    # Add components to your pipeline
+    basic_rag_pipeline.add_component("text_embedder", text_embedder)
+    basic_rag_pipeline.add_component("retriever", retriever)
+    basic_rag_pipeline.add_component("prompt_builder", prompt_builder)
+    basic_rag_pipeline.add_component("llm", generator)
+
+    # Now, connect the components to each other
+    basic_rag_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+    basic_rag_pipeline.connect("retriever", "prompt_builder.documents")
+    basic_rag_pipeline.connect("prompt_builder", "llm")
+
     # This function is called when the server is started.
     pass
 

pipelines/examples/llamaindex_ollama_github_pipeline.py

@@ -3,18 +3,6 @@ from schemas import OpenAIChatMessage
 import os
 import asyncio
 
-from llama_index.embeddings.ollama import OllamaEmbedding
-from llama_index.llms.ollama import Ollama
-from llama_index.core import VectorStoreIndex, Settings
-from llama_index.readers.github import GithubRepositoryReader, GithubClient
-
-Settings.embed_model = OllamaEmbedding(
-    model_name="nomic-embed-text",
-    base_url="http://localhost:11434",
-)
-Settings.llm = Ollama(model="llama3")
-
-
 index = None
 documents = None
 
@@ -35,6 +23,18 @@ def get_response(
 
 
 async def on_startup():
+
+    from llama_index.embeddings.ollama import OllamaEmbedding
+    from llama_index.llms.ollama import Ollama
+    from llama_index.core import VectorStoreIndex, Settings
+    from llama_index.readers.github import GithubRepositoryReader, GithubClient
+
+    Settings.embed_model = OllamaEmbedding(
+        model_name="nomic-embed-text",
+        base_url="http://localhost:11434",
+    )
+    Settings.llm = Ollama(model="llama3")
+
     global index, documents
 
     github_token = os.environ.get("GITHUB_TOKEN")

pipelines/examples/llamaindex_ollama_pipeline.py

@@ -1,17 +1,6 @@
 from typing import List, Union, Generator
 from schemas import OpenAIChatMessage
 
-from llama_index.embeddings.ollama import OllamaEmbedding
-from llama_index.llms.ollama import Ollama
-from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader
-
-
-Settings.embed_model = OllamaEmbedding(
-    model_name="nomic-embed-text",
-    base_url="http://localhost:11434",
-)
-Settings.llm = Ollama(model="llama3")
-
 
 documents = None
 index = None
@@ -29,10 +18,22 @@ def get_response(
     query_engine = index.as_query_engine(streaming=True)
     response = query_engine.query(user_message)
 
+    print(response)
+
     return response.response_gen
 
 
 async def on_startup():
+    from llama_index.embeddings.ollama import OllamaEmbedding
+    from llama_index.llms.ollama import Ollama
+    from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader
+
+    Settings.embed_model = OllamaEmbedding(
+        model_name="nomic-embed-text",
+        base_url="http://localhost:11434",
+    )
+    Settings.llm = Ollama(model="llama3")
+
     # This function is called when the server is started.
     global documents, index
 

pipelines/examples/llamaindex_pipeline.py

@@ -1,15 +1,8 @@
 from typing import List, Union, Generator
 from schemas import OpenAIChatMessage
 
-import os
+documents = None
-
+index = None
-# Set the OpenAI API key
-os.environ["OPENAI_API_KEY"] = "your_openai_api_key_here"
-
-from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
-
-documents = SimpleDirectoryReader("./data").load_data()
-index = VectorStoreIndex.from_documents(documents)
 
 
 def get_response(
@@ -28,6 +21,16 @@ def get_response(
 
 
 async def on_startup():
+    global documents, index
+    import os
+
+    # Set the OpenAI API key
+    os.environ["OPENAI_API_KEY"] = "your_openai_api_key_here"
+
+    from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
+
+    documents = SimpleDirectoryReader("./data").load_data()
+    index = VectorStoreIndex.from_documents(documents)
     # This function is called when the server is started.
     pass
 

pipelines/examples/pipeline.py

@@ -16,12 +16,7 @@ def get_response(
 async def on_startup():
     # This function is called when the server is started.
     print(f"on_startup:{__name__}")
-
+    pass
-    # Optional: return pipeline metadata
-    # return {
-    #     "id": "pipeline_id",
-    #     "name": "pipeline_name",
-    # }
 
 
 async def on_shutdown():