mirror of
https://github.com/arc53/DocsGPT.git
synced 2025-11-29 00:23:17 +00:00
(fix) avoid stringifying list
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@@ -324,7 +324,7 @@ class Stream(Resource):
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try:
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question = data["question"]
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history = str(limit_chat_history(json.loads(data.get("history", []))))
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history = limit_chat_history(json.loads(data.get("history", [])), gpt_model=gpt_model)
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conversation_id = data.get("conversation_id")
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prompt_id = data.get("prompt_id", "default")
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@@ -455,7 +455,7 @@ class Answer(Resource):
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try:
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question = data["question"]
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history = str(limit_chat_history(json.loads(data.get("history", []))))
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history = limit_chat_history(json.loads(data.get("history", [])), gpt_model=gpt_model)
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conversation_id = data.get("conversation_id")
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prompt_id = data.get("prompt_id", "default")
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chunks = int(data.get("chunks", 2))
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@@ -73,6 +73,7 @@ class BraveRetSearch(BaseRetriever):
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if len(self.chat_history) > 1:
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for i in self.chat_history:
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if "prompt" in i and "response" in i:
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messages_combine.append(
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{"role": "user", "content": i["prompt"]}
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)
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@@ -73,6 +73,7 @@ class ClassicRAG(BaseRetriever):
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if len(self.chat_history) > 1:
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for i in self.chat_history:
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if "prompt" in i and "response" in i:
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messages_combine.append(
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{"role": "user", "content": i["prompt"]}
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)
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@@ -80,7 +81,7 @@ class ClassicRAG(BaseRetriever):
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{"role": "system", "content": i["response"]}
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)
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messages_combine.append({"role": "user", "content": self.question})
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llm = LLMCreator.create_llm(
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settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=self.user_api_key
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)
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@@ -90,6 +90,7 @@ class DuckDuckSearch(BaseRetriever):
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if len(self.chat_history) > 1:
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for i in self.chat_history:
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if "prompt" in i and "response" in i:
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messages_combine.append(
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{"role": "user", "content": i["prompt"]}
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)
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@@ -54,13 +54,16 @@ def limit_chat_history(history, max_token_limit=None, gpt_model="docsgpt"):
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from application.core.settings import settings
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max_token_limit = (
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max_token_limit
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if max_token_limit
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and max_token_limit < settings.MODEL_TOKEN_LIMITS.get(
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gpt_model, settings.DEFAULT_MAX_HISTORY
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max_token_limit
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if max_token_limit and
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max_token_limit < settings.MODEL_TOKEN_LIMITS.get(
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gpt_model, settings.DEFAULT_MAX_HISTORY
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)
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else settings.MODEL_TOKEN_LIMITS.get(
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gpt_model, settings.DEFAULT_MAX_HISTORY
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)
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)
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else settings.MODEL_TOKEN_LIMITS.get(gpt_model, settings.DEFAULT_MAX_HISTORY)
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)
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if not history:
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return []
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@@ -78,5 +81,5 @@ def limit_chat_history(history, max_token_limit=None, gpt_model="docsgpt"):
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trimmed_history.insert(0, message)
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else:
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break
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return trimmed_history
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