Support SearchedDoc input type in LLM for No Rerank Pipeline

comps/llms/text-generation/tgi/README.md

@@ -16,9 +16,6 @@ pip install -r requirements.txt
 
 ```bash
 export HF_TOKEN=${your_hf_api_token}
-export LANGCHAIN_TRACING_V2=true
-export LANGCHAIN_API_KEY=${your_langchain_api_key}
-export LANGCHAIN_PROJECT="opea/gen-ai-comps:llms"
 docker run -p 8008:80 -v ./data:/data --name tgi_service --shm-size 1g ghcr.io/huggingface/text-generation-inference:2.1.0 --model-id ${your_hf_llm_model}
 ```
 
@@ -50,9 +47,6 @@ In order to start TGI and LLM services, you need to setup the following environm
 export HF_TOKEN=${your_hf_api_token}
 export TGI_LLM_ENDPOINT="http://${your_ip}:8008"
 export LLM_MODEL_ID=${your_hf_llm_model}
-export LANGCHAIN_TRACING_V2=true
-export LANGCHAIN_API_KEY=${your_langchain_api_key}
-export LANGCHAIN_PROJECT="opea/llms"
 ```
 
 ### 2.2 Build Docker Image
@@ -116,6 +110,12 @@ curl http://${your_ip}:9000/v1/chat/completions \
   -X POST \
   -d '{"query":"What is Deep Learning?","max_new_tokens":17,"top_k":10,"top_p":0.95,"typical_p":0.95,"temperature":0.01,"repetition_penalty":1.03,"streaming":true, "chat_template":"### You are a helpful, respectful and honest assistant to help the user with questions.\n### Context: {context}\n### Question: {question}\n### Answer:"}' \
   -H 'Content-Type: application/json'
+
+# consume with SearchedDoc
+curl http://${your_ip}:9000/v1/chat/completions \
+  -X POST \
+  -d '{"initial_query":"What is Deep Learning?","retrieved_docs":[{"text":"Deep Learning is a ..."},{"text":"Deep Learning is b ..."}]}' \
+  -H 'Content-Type: application/json'
 ```
 
 ### 4. Validated Model

comps/llms/text-generation/tgi/docker_compose_llm.yaml

@@ -12,6 +12,13 @@ services:
     volumes:
       - "./data:/data"
     shm_size: 1g
+    environment:
+      no_proxy: ${no_proxy}
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      HF_TOKEN: ${HF_TOKEN}
+      HF_HUB_DISABLE_PROGRESS_BARS: 1
+      HF_HUB_ENABLE_HF_TRANSFER: 0
     command: --model-id ${LLM_MODEL_ID}
   llm:
     image: opea/llm-tgi:latest
@@ -25,7 +32,6 @@ services:
       https_proxy: ${https_proxy}
       TGI_LLM_ENDPOINT: ${TGI_LLM_ENDPOINT}
       HF_TOKEN: ${HF_TOKEN}
-      LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY}
     restart: unless-stopped
 
 networks:

comps/llms/text-generation/tgi/llm.py

@@ -15,6 +15,7 @@
     CustomLogger,
     GeneratedDoc,
     LLMParamsDoc,
+    SearchedDoc,
     ServiceType,
     opea_microservices,
     register_microservice,
@@ -41,26 +42,76 @@
     port=9000,
 )
 @register_statistics(names=["opea_service@llm_tgi"])
-async def llm_generate(input: Union[LLMParamsDoc, ChatCompletionRequest]):
+async def llm_generate(input: Union[LLMParamsDoc, ChatCompletionRequest, SearchedDoc]):
     if logflag:
         logger.info(input)
     prompt_template = None
-    if input.chat_template:
+    if not isinstance(input, SearchedDoc) and input.chat_template:
         prompt_template = PromptTemplate.from_template(input.chat_template)
         input_variables = prompt_template.input_variables
 
     stream_gen_time = []
     start = time.time()
 
-    if isinstance(input, LLMParamsDoc):
+    if isinstance(input, SearchedDoc):
+        if logflag:
+            logger.info("[ SearchedDoc ] input from retriever microservice")
+        prompt = input.initial_query
+        if input.retrieved_docs:
+            if logflag:
+                logger.info(f"[ SearchedDoc ] retrieved docs: {input.retrieved_docs}")
+                for doc in input.retrieved_docs:
+                    logger.info(f"[ SearchedDoc ] {doc}")
+            prompt = ChatTemplate.generate_rag_prompt(input.initial_query, input.retrieved_docs[0].text)
+        # use default llm parameters for inferencing
+        new_input = LLMParamsDoc(query=prompt)
+        if logflag:
+            logger.info(f"[ SearchedDoc ] final input: {new_input}")
+        text_generation = await llm.text_generation(
+            prompt=prompt,
+            stream=new_input.streaming,
+            max_new_tokens=new_input.max_new_tokens,
+            repetition_penalty=new_input.repetition_penalty,
+            temperature=new_input.temperature,
+            top_k=new_input.top_k,
+            top_p=new_input.top_p,
+        )
+        if new_input.streaming:
+
+            async def stream_generator():
+                chat_response = ""
+                async for text in text_generation:
+                    stream_gen_time.append(time.time() - start)
+                    chat_response += text
+                    chunk_repr = repr(text.encode("utf-8"))
+                    if logflag:
+                        logger.info(f"[ SearchedDoc ] chunk:{chunk_repr}")
+                    yield f"data: {chunk_repr}\n\n"
+                if logflag:
+                    logger.info(f"[ SearchedDoc ] stream response: {chat_response}")
+                statistics_dict["opea_service@llm_tgi"].append_latency(stream_gen_time[-1], stream_gen_time[0])
+                yield "data: [DONE]\n\n"
+
+            return StreamingResponse(stream_generator(), media_type="text/event-stream")
+        else:
+            statistics_dict["opea_service@llm_tgi"].append_latency(time.time() - start, None)
+            if logflag:
+                logger.info(text_generation)
+            return GeneratedDoc(text=text_generation, prompt=new_input.query)
+
+    elif isinstance(input, LLMParamsDoc):
+        if logflag:
+            logger.info("[ LLMParamsDoc ] input from rerank microservice")
         prompt = input.query
         if prompt_template:
             if sorted(input_variables) == ["context", "question"]:
                 prompt = prompt_template.format(question=input.query, context="\n".join(input.documents))
             elif input_variables == ["question"]:
                 prompt = prompt_template.format(question=input.query)
             else:
-                logger.info(f"{prompt_template} not used, we only support 2 input variables ['question', 'context']")
+                logger.info(
+                    f"[ LLMParamsDoc ] {prompt_template} not used, we only support 2 input variables ['question', 'context']"
+                )
         else:
             if input.documents:
                 # use rag default template
@@ -84,10 +135,10 @@ async def stream_generator():
                     chat_response += text
                     chunk_repr = repr(text.encode("utf-8"))
                     if logflag:
-                        logger.info(f"[llm - chat_stream] chunk:{chunk_repr}")
+                        logger.info(f"[ LLMParamsDoc ] chunk:{chunk_repr}")
                     yield f"data: {chunk_repr}\n\n"
                 if logflag:
-                    logger.info(f"[llm - chat_stream] stream response: {chat_response}")
+                    logger.info(f"[ LLMParamsDoc ] stream response: {chat_response}")
                 statistics_dict["opea_service@llm_tgi"].append_latency(stream_gen_time[-1], stream_gen_time[0])
                 yield "data: [DONE]\n\n"
 
@@ -99,6 +150,8 @@ async def stream_generator():
             return GeneratedDoc(text=text_generation, prompt=input.query)
 
     else:
+        if logflag:
+            logger.info("[ ChatCompletionRequest ] input in opea format")
         client = OpenAI(
             api_key="EMPTY",
             base_url=llm_endpoint + "/v1",
@@ -113,7 +166,7 @@ async def stream_generator():
                     prompt = prompt_template.format(question=input.messages)
                 else:
                     logger.info(
-                        f"{prompt_template} not used, we only support 2 input variables ['question', 'context']"
+                        f"[ ChatCompletionRequest ] {prompt_template} not used, we only support 2 input variables ['question', 'context']"
                     )
             else:
                 if input.documents:
@@ -152,7 +205,9 @@ async def stream_generator():
                     if input_variables == ["context"]:
                         system_prompt = prompt_template.format(context="\n".join(input.documents))
                     else:
-                        logger.info(f"{prompt_template} not used, only support 1 input variables ['context']")
+                        logger.info(
+                            f"[ ChatCompletionRequest ] {prompt_template} not used, only support 1 input variables ['context']"
+                        )
 
                     input.messages.insert(0, {"role": "system", "content": system_prompt})