Merge pull request #5468 from pathwaycom/berke/pathway-sheets-app

Berke/pathway sheets app

GitOrigin-RevId: b8933ade9adfc31b295b39795813a9d0de2e3c5e

python/pathway/xpacks/llm/llms.py

@@ -178,6 +178,7 @@ async def __wrapped__(self, messages: list[dict] | pw.Json, **kwargs) -> str | N
 
         kwargs = {**self.kwargs, **kwargs}
         api_key = kwargs.pop("api_key", None)
+
         client = openai_mod.AsyncOpenAI(api_key=api_key)
         ret = await client.chat.completions.create(messages=messages_decoded, **kwargs)
         return ret.choices[0].message.content

python/pathway/xpacks/llm/parsers.py

@@ -60,6 +60,7 @@ def __init__(
             raise ValueError(
                 f"Got {mode} for `mode`, but should be one of `{_valid_modes}`"
             )
+
         self.kwargs = dict(
             mode=mode,
             post_processors=post_processors or [],

python/pathway/xpacks/llm/prompts.py

@@ -0,0 +1,143 @@
+import re
+
+import pathway as pw
+
+
+@pw.udf
+def prompt_citing_qa(query: str, docs: list[pw.Json]):
+    context_pieces = []
+
+    for i, doc in enumerate(docs, 1):
+        context_pieces.append(f"# Source {i}/n/n")
+        context_pieces.append(doc["text"])  # type: ignore
+        context_pieces.append("")
+    context_str = "\n".join(context_pieces)
+    prompt = (
+        "Please provide an answer based solely on the provided sources. "
+        "When referencing information from a source, "
+        "cite the appropriate source(s) using their corresponding numbers. "
+        "Every answer should include at least one source citation. "
+        "Only cite a source when you are explicitly referencing it. "
+        "If exists, mention specific article/section header you use at the beginning of answer, such as '4.a Client has rights to...'"  # noqa: E501
+        "Article headers may or may not be in docs, dont mention it if there is none."
+        # "If none of the sources are helpful, you should indicate that. "
+        # "For example:\n"
+        # "# Source 1:\n"
+        # "4.a The sky is red in the evening and blue in the morning.\n"
+        # "# Source 2:\n"
+        # "5.c Water is wet when the sky is red.\n"
+        # "Query: When is water wet?\n"
+        # "Answer: *5.c* Water will be wet when the sky is red [2], "
+        # "which occurs in the evening [1].\n"
+        # "If several citations are used, separate them with comma such as, '*5.c,4.a*'\n"
+        "If question cannot be inferred from documents SAY `No information found`"
+        "Now it's your turn. Below are several numbered sources of information:"
+        "\n------\n"
+        f"{context_str}"
+        "\n------\n"
+        f"Query: {query}\n"
+        "Answer: "
+    )
+    return prompt
+
+
+@pw.udf
+def prompt_short_qa(query: str, docs: list[pw.Json]):
+    context_pieces = []
+
+    for i, doc in enumerate(docs, 1):
+        context_pieces.append(doc["text"])
+        context_pieces.append("")  # type: ignore
+    context_str = "\n".join(context_pieces)  # type: ignore
+    prompt = (
+        "Please provide an answer based solely on the provided sources. "
+        "Keep your answer concise and accurate. Make sure that it starts with an expression in standardized format."
+        "Only respond without any explanation, for example questions asking for date should be answered in strictly date format: `05 January 2011`"  # noqa: E501
+        "Yes or No questions should be responded with simple `Yes` or `No` and so on."
+        "If question cannot be inferred from documents SAY `No information found`"
+        "Now it's your turn. Below are several sources of information:"
+        "\n------\n"
+        f"{context_str}"
+        "\n------\n"
+        f"Query: {query}\n"
+        "Answer: "
+    )
+    return prompt
+
+
+@pw.udf
+def prompt_summarize(text_list: list[str]):
+    text = "\n".join(text_list)
+    prompt = f"""Given a list of documents, summarize them in few sentences \
+        while preserving important points and entities.
+    Documents: {text}
+    Summary:"""
+
+    return prompt
+
+
+@pw.udf
+def prompt_query_rewrite_hyde(query: str) -> str:
+    prompt = f"""Write 4 responses to answer the given question with hypothetical data.
+    Try to include as many key details as possible.
+    Question: `{query}`.
+    Responses:"""
+    return prompt
+
+
+@pw.udf
+def prompt_query_rewrite(query: str, *additional_args: str) -> str:
+    prompt = f"""Given a question that will be used to retrieve similar documents for RAG application,.
+    Rewrite question to be better usable in retrieval search.
+    Your response should be three queries based on the question provided, separated by comma.
+    Question: `{query}`
+    """
+
+    if additional_args:
+        prompt += """If any of the provided sections are related to question, write section name in the query as well.
+        Here is additional info that you can include in search: """
+    for arg in additional_args:
+        prompt += f" `{arg}`\n"
+
+    prompt += "Rewritten query:"
+    return prompt
+
+
+@pw.udf
+def parse_cited_response(response_text, docs):
+    cited_docs = [
+        int(cite[1:-1]) - 1
+        for cite in set(re.findall("\[\d+\]", response_text))  # noqa: W605
+    ]
+    start_index = response_text.find("*") + 1
+    end_index = response_text.find("*", start_index)
+
+    citations = [docs[i] for i in cited_docs if i in cited_docs]
+    cleaned_citations = []
+
+    if (
+        start_index != -1 and end_index != -1
+    ):  # doing this for the GIF, we need a better way to do this, TODO: redo
+        cited = response_text[start_index:end_index]
+        response_text = response_text[end_index:].strip()
+        cited = (
+            cited.replace(" ", "")
+            .replace(",,", ",")
+            .replace(",", ",\n")
+            .replace(" ", "\n")
+        )
+
+        text_body = citations[0]["text"]
+        new_text = f"<b>{cited}</b>\n\n".replace("\n\n\n", "\n") + text_body
+
+        citations[0]["text"] = new_text
+
+        cleaned_citations.append(citations[0])
+
+    if len(citations) > 1:
+        for doc in citations[1:]:
+            text_body = doc["text"]  # TODO: unformat and clean the text
+            doc["text"] = text_body
+            cleaned_citations.append(doc)
+
+    return response_text, cleaned_citations