Update requirements, clean up cohere SDK code, add langchain to suppo…

…rt multi-needle

needlehaystack/providers/cohere.py

@@ -3,6 +3,8 @@
 
 from operator import itemgetter
 from typing import Optional
+from langchain.prompts import PromptTemplate
+from langchain_cohere import ChatCohere
 
 from cohere import Client, AsyncClient
 
@@ -30,23 +32,33 @@ def __init__(self,
 
         self.client = AsyncClient(api_key=self.api_key)
 
-    async def evaluate_model(self, prompt: str) -> str:
-        response = await self.client.chat(message=prompt[-1]["message"], chat_history=prompt[:-1], model=self.model_name, **self.model_kwargs)
+    async def evaluate_model(self, prompt: tuple[str, list[dict, str, str]]) -> str:
+        message, chat_history = prompt
+        response = await self.client.chat(message=message, chat_history=chat_history, model=self.model_name, **self.model_kwargs)
         return response.text
 
-    def generate_prompt(self, context: str, retrieval_question: str) -> str | list[dict[str, str]]:
-        return [{
+    def generate_prompt(self, context: str, retrieval_question: str) -> tuple[str, list[dict[str, str]]]:
+        '''
+        Prepares a chat-formatted prompt
+        Args:
+            context (str): The needle in a haystack context
+            retrieval_question (str): The needle retrieval question
+
+        Returns:
+            tuple[str, list[dict[str, str]]]: prompt encoded as last message, and chat history
+
+        '''
+        return (
+            f"{retrieval_question} Don't give information outside the document or repeat your findings", 
+            [{
                 "role": "System",
                 "message": "You are a helpful AI bot that answers questions for a user. Keep your response short and direct"
             },
             {
                 "role": "User",
                 "message": context
-            },
-            {
-                "role": "User",
-                "message": f"{retrieval_question} Don't give information outside the document or repeat your findings"
             }]
+        )
 
     def encode_text_to_tokens(self, text: str) -> list[int]:
         if not text: return []
@@ -55,3 +67,47 @@ def encode_text_to_tokens(self, text: str) -> list[int]:
     def decode_tokens(self, tokens: list[int], context_length: Optional[int] = None) -> str:
         # Assuming you have a different decoder for Anthropic
         return Client().detokenize(tokens=tokens[:context_length], model=self.model_name).text
+
+    def get_langchain_runnable(self, context: str):
+        """
+        Creates a LangChain runnable that constructs a prompt based on a given context and a question.
+
+        Args:
+            context (str): The context or background information relevant to the user's question. 
+            This context is provided to the model to aid in generating relevant and accurate responses.
+
+        Returns:
+            str: A LangChain runnable object that can be executed to obtain the model's response to a 
+            dynamically provided question. The runnable encapsulates the entire process from prompt 
+            generation to response retrieval.
+
+        Example:
+            To use the runnable:
+                - Define the context and question.
+                - Execute the runnable with these parameters to get the model's response.
+        """
+
+
+        template = """Human: You are a helpful AI bot that answers questions for a user. Keep your response short and direct" \n
+        <document_content>
+        {context} 
+        </document_content>
+        Here is the user question:
+        <question>
+         {question}
+        </question>
+        Don't give information outside the document or repeat your findings.
+        Assistant: Here is the most relevant information in the documents:"""
+
+        api_key = os.getenv('NIAH_MODEL_API_KEY')
+        model = ChatCohere(cohere_api_key=api_key, temperature=0.3, model=self.model_name)
+        prompt = PromptTemplate(
+            template=template,
+            input_variables=["context", "question"],
+        )
+        chain = ( {"context": lambda x: context,
+                  "question": itemgetter("question")} 
+                | prompt 
+                | model 
+                )
+        return chain

requirements.txt

@@ -6,7 +6,7 @@ anyio==3.7.1
 attrs==23.1.0
 certifi==2023.11.17
 charset-normalizer==3.3.2
-cohere>=5.0.0
+cohere>=5.1.2
 dataclasses-json==0.6.3
 distro==1.8.0
 filelock==3.13.1
@@ -22,10 +22,11 @@ jsonpatch==1.33
 jsonpointer==2.4
 langchain==0.1.9
 langchain-community>=0.0.24
-langchain-core==0.1.26
+langchain-core>=0.1.26
 langsmith>=0.1.8
 langchain_openai
 langchain_anthropic
+langchain_cohere
 marshmallow==3.20.1
 multidict==6.0.4
 mypy-extensions==1.0.0