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Update application files and fix LLM models, create new retriever class

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This commit is contained in:
Alex
2024-04-09 14:02:33 +01:00
parent e07df29ab9
commit 391f686173
13 changed files with 202 additions and 185 deletions
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4
application/llm/anthropic.py
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@@ -10,7 +10,7 @@ class AnthropicLLM(BaseLLM):
self.HUMAN_PROMPT = HUMAN_PROMPT
self.AI_PROMPT = AI_PROMPT
def gen(self, model, messages, engine=None, max_tokens=300, stream=False, **kwargs):
def gen(self, model, messages, max_tokens=300, stream=False, **kwargs):
context = messages[0]['content']
user_question = messages[-1]['content']
prompt = f"### Context \n {context} \n ### Question \n {user_question}"
@@ -25,7 +25,7 @@ class AnthropicLLM(BaseLLM):
)
return completion.completion
def gen_stream(self, model, messages, engine=None, max_tokens=300, **kwargs):
def gen_stream(self, model, messages, max_tokens=300, **kwargs):
context = messages[0]['content']
user_question = messages[-1]['content']
prompt = f"### Context \n {context} \n ### Question \n {user_question}"

4
application/llm/docsgpt_provider.py
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@@ -8,7 +8,7 @@ class DocsGPTAPILLM(BaseLLM):
self.endpoint = "https://llm.docsgpt.co.uk"
def gen(self, model, engine, messages, stream=False, **kwargs):
def gen(self, model, messages, stream=False, **kwargs):
context = messages[0]['content']
user_question = messages[-1]['content']
prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
@@ -24,7 +24,7 @@ class DocsGPTAPILLM(BaseLLM):
return response_clean
def gen_stream(self, model, engine, messages, stream=True, **kwargs):
def gen_stream(self, model, messages, stream=True, **kwargs):
context = messages[0]['content']
user_question = messages[-1]['content']
prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"

4
application/llm/huggingface.py
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@@ -29,7 +29,7 @@ class HuggingFaceLLM(BaseLLM):
)
hf = HuggingFacePipeline(pipeline=pipe)
def gen(self, model, engine, messages, stream=False, **kwargs):
def gen(self, model, messages, stream=False, **kwargs):
context = messages[0]['content']
user_question = messages[-1]['content']
prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
@@ -38,7 +38,7 @@ class HuggingFaceLLM(BaseLLM):
return result.content
def gen_stream(self, model, engine, messages, stream=True, **kwargs):
def gen_stream(self, model, messages, stream=True, **kwargs):
raise NotImplementedError("HuggingFaceLLM Streaming is not implemented yet.")

4
application/llm/llama_cpp.py
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@@ -12,7 +12,7 @@ class LlamaCpp(BaseLLM):
llama = Llama(model_path=llm_name, n_ctx=2048)
def gen(self, model, engine, messages, stream=False, **kwargs):
def gen(self, model, messages, stream=False, **kwargs):
context = messages[0]['content']
user_question = messages[-1]['content']
prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
@@ -24,7 +24,7 @@ class LlamaCpp(BaseLLM):
return result['choices'][0]['text'].split('### Answer \n')[-1]
def gen_stream(self, model, engine, messages, stream=True, **kwargs):
def gen_stream(self, model, messages, stream=True, **kwargs):
context = messages[0]['content']
user_question = messages[-1]['content']
prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"

4
application/llm/openai.py
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@@ -18,7 +18,7 @@ class OpenAILLM(BaseLLM):
return openai
def gen(self, model, engine, messages, stream=False, **kwargs):
def gen(self, model, messages, stream=False, engine=settings.AZURE_DEPLOYMENT_NAME, **kwargs):
response = self.client.chat.completions.create(model=model,
messages=messages,
stream=stream,
@@ -26,7 +26,7 @@ class OpenAILLM(BaseLLM):
return response.choices[0].message.content
def gen_stream(self, model, engine, messages, stream=True, **kwargs):
def gen_stream(self, model, messages, stream=True, engine=settings.AZURE_DEPLOYMENT_NAME, **kwargs):
response = self.client.chat.completions.create(model=model,
messages=messages,
stream=stream,

4
application/llm/premai.py
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@@ -12,7 +12,7 @@ class PremAILLM(BaseLLM):
self.api_key = api_key
self.project_id = settings.PREMAI_PROJECT_ID
def gen(self, model, engine, messages, stream=False, **kwargs):
def gen(self, model, messages, stream=False, **kwargs):
response = self.client.chat.completions.create(model=model,
project_id=self.project_id,
messages=messages,
@@ -21,7 +21,7 @@ class PremAILLM(BaseLLM):
return response.choices[0].message["content"]
def gen_stream(self, model, engine, messages, stream=True, **kwargs):
def gen_stream(self, model, messages, stream=True, **kwargs):
response = self.client.chat.completions.create(model=model,
project_id=self.project_id,
messages=messages,

4
application/llm/sagemaker.py
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@@ -74,7 +74,7 @@ class SagemakerAPILLM(BaseLLM):
self.runtime = runtime
def gen(self, model, engine, messages, stream=False, **kwargs):
def gen(self, model, messages, stream=False, **kwargs):
context = messages[0]['content']
user_question = messages[-1]['content']
prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
@@ -103,7 +103,7 @@ class SagemakerAPILLM(BaseLLM):
print(result[0]['generated_text'], file=sys.stderr)
return result[0]['generated_text'][len(prompt):]
def gen_stream(self, model, engine, messages, stream=True, **kwargs):
def gen_stream(self, model, messages, stream=True, **kwargs):
context = messages[0]['content']
user_question = messages[-1]['content']
prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"