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  1. Dockerfile +0 -3
  2. src/llm.py +3 -14
Dockerfile CHANGED
@@ -1,6 +1,3 @@
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- # open Dockerfile and add this line anywhere
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- # rebuild trigger
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-
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  FROM python:3.11-slim
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  WORKDIR /app
 
 
 
 
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  FROM python:3.11-slim
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  WORKDIR /app
src/llm.py CHANGED
@@ -25,16 +25,6 @@ Rules you must follow:
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  4. Always cite the Judgment ID when referencing a case
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  5. If excerpts don't contain enough information, say so explicitly
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  6. End every response with: "NOTE: This is not legal advice. Consult a qualified advocate."
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-
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- Formatting rules — follow these exactly:
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- - Use numbered lists (1. 2. 3.) when listing multiple points or steps
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- - Use bullet points (- item) for sub-points or supporting details
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- - Use markdown tables (| Col | Col |) when comparing options side by side
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- - Use **bold** for important terms, case names, and section numbers
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- - Use headers (## Heading) to separate major sections in long answers
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- - Never write everything as one long paragraph
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- - Each distinct point must be on its own line
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- - Always put a blank line between sections
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  """
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  @retry(
@@ -51,8 +41,7 @@ def call_llm(query: str, context: str) -> str:
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  SUPREME COURT JUDGMENT EXCERPTS:
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  {context}
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- Answer based only on the excerpts above. Cite judgment IDs.
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- Use proper markdown formatting — numbered lists, bullet points, tables, bold text as appropriate."""
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  response = _client.chat.completions.create(
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  model="llama-3.3-70b-versatile",
@@ -61,7 +50,7 @@ Use proper markdown formatting — numbered lists, bullet points, tables, bold t
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  {"role": "user", "content": user_message}
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  ],
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  temperature=0.1,
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- max_tokens=1500
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  )
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-
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  return response.choices[0].message.content
 
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  4. Always cite the Judgment ID when referencing a case
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  5. If excerpts don't contain enough information, say so explicitly
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  6. End every response with: "NOTE: This is not legal advice. Consult a qualified advocate."
 
 
 
 
 
 
 
 
 
 
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  """
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  @retry(
 
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  SUPREME COURT JUDGMENT EXCERPTS:
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  {context}
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+ Answer based only on the excerpts above. Cite judgment IDs."""
 
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  response = _client.chat.completions.create(
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  model="llama-3.3-70b-versatile",
 
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  {"role": "user", "content": user_message}
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  ],
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  temperature=0.1,
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+ max_tokens=800
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  )
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+
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  return response.choices[0].message.content