flow established

app.py

@@ -1,106 +1,66 @@
-import json
 import logging
 
 import gradio as gr
 from mcp import StdioServerParameters
 from smolagents import InferenceClientModel, MCPClient
 
+from config import (
+    CHAT_HISTORY_TURNS_CUTOFF,
+    CHAT_HISTORY_WORD_CUTOFF,
+    GITHUB_TOOLSETS,
+    HF_TOKEN,
+)
 from config import GITHUB_PAT as GITHUB_TOKEN
-from config import GITHUB_TOOLSETS, HF_TOKEN
 from src.upgrade_advisor.agents.package import PackageDiscoveryAgent
-from src.upgrade_advisor.chat.chat import qn_rewriter, run_document_qa
+from src.upgrade_advisor.chat.chat import (
+    qn_rewriter,
+    run_document_qa,
+    summarize_chat_history,
+)
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
 logger.addHandler(logging.StreamHandler())
 
 
-def build_context_from_events(events):
-    # Build a compact textual context from structured tool outputs
-    lines = []
-    max_field_len = 500
-    for e in events:
-        # Only consider tool outputs here
-        if hasattr(e, "tool_call") and hasattr(e, "output"):
-            tool_call = e.tool_call
-            tool_name = getattr(tool_call, "name", None) if tool_call else None
-            out = getattr(e, "output", None)
-            # Convert Pydantic -> dict; else keep dict/list; else str
-            if hasattr(out, "model_dump"):
-                data = out.model_dump()
-            elif isinstance(out, (dict, list)):
-                data = out
-            else:
-                # best-effort parse
-                try:
-                    data = json.loads(str(out))
-                except Exception:
-                    data = {"text": str(out)}
-
-            # Redact long fields
-            def shorten(v):
-                if isinstance(v, str) and len(v) > max_field_len:
-                    return v[:max_field_len] + "..."
-                return v
-
-            def safe_kv_pairs(obj, prefix=None):
-                pairs = []
-                if isinstance(obj, dict):
-                    for k, v in obj.items():
-                        if isinstance(v, (dict, list)):
-                            pairs.extend(safe_kv_pairs(v, prefix=f"{k}."))
-                        else:
-                            pairs.append((f"{prefix or ''}{k}", shorten(v)))
-                elif isinstance(obj, list):
-                    for i, v in enumerate(obj[:20]):
-                        if not isinstance(v, (dict, list)):
-                            pairs.append((f"item[{i}]", shorten(v)))
-                return pairs
-
-            kv = safe_kv_pairs(data)
-            # Keep only key facts likely useful for QA
-            key_lines = []
-            for k, v in kv:
-                kl = k.lower()
-                if any(
-                    s in kl
-                    for s in [
-                        "name",
-                        "version",
-                        "summary",
-                        "home",
-                        "project_urls",
-                        "url",
-                        "owner",
-                        "repo",
-                        "releases",
-                    ]
-                ):
-                    key_lines.append(f"{k}: {v}")
-            if key_lines:
-                lines.append(f"[tool:{tool_name}]\n" + "\n".join(key_lines))
-    return "\n".join(lines)[:8000]  # cap context length
-
-
 async def chat_fn(message, history):
+    # parse incoming history is a list of dicts with 'role' and 'content' keys
+    if len(history) > 0:
+        summarized_history = await summarize_chat_history(
+            history,
+            turns_cutoff=CHAT_HISTORY_TURNS_CUTOFF,
+            word_cutoff=CHAT_HISTORY_WORD_CUTOFF,
+        )
+    else:
+        summarized_history = ""
+
     message = message.strip()
-    rewritten_qn = await qn_rewriter(message)
-    logger.info(f"Rewritten question: {rewritten_qn}")
+    rewritten_message, is_rewritten_good = await qn_rewriter(
+        message, summarized_history
+    )
+    if is_rewritten_good:
+        logger.info(f"Rewritten question: {rewritten_message}")
+    else:
+        logger.info(f"Using original question: {message}")
+        rewritten_message = None
     # Collect events from the agent run
-    events = list(package_agent.discover_package_info(rewritten_qn))
+    events = package_agent.discover_package_info(
+        user_input=message, reframed_question=rewritten_message
+    )
     # Build a concise context from tool outputs
-    context = build_context_from_events(events)
+    # context = build_context_from_events(events)
+    context = events
+    logger.info(f"Built context of length {len(context)}")
+    logger.info(f"Context content:\n{context}")
     # Run a document QA pass using the user's question
-    qa_answer = await run_document_qa(rewritten_qn, context, original_question=message)
-    # Also append a short bullet summary of key facts
-    lines = [qa_answer]
-    if context:
-        # Extract a few top lines as quick facts
-        facts = [ln for ln in context.splitlines() if ":" in ln][:10]
-        if facts:
-            lines.append("\nKey facts:")
-            lines.extend(f"- {f}" for f in facts[:10])
-    return "\n".join(lines)
+    qa_answer = await run_document_qa(
+        question=message, context=context, rewritten_question=rewritten_message
+    )
+    logger.info(f"QA answer: {qa_answer}")
+    return {
+        "role": "assistant",
+        "content": qa_answer,
+    }
 
 
 if __name__ == "__main__":
@@ -136,12 +96,8 @@ if __name__ == "__main__":
         )
 
         pypi_mcp_client = MCPClient(
-            server_parameters=[pypi_mcp_params], structured_output=True
-        )
-        gh_mcp_client = MCPClient(
-            server_parameters=[gh_mcp_params],
-            structured_output=False,  # explicitly set to silence
-            # FutureWarning; set True if you need structured outputs
+            server_parameters=[pypi_mcp_params, gh_mcp_params],
+            structured_output=True,
         )
 
         with pypi_mcp_client as pypi_toolset:
@@ -156,6 +112,12 @@ if __name__ == "__main__":
                 fn=chat_fn,
                 title="Python Package Discovery Agent",
                 type="messages",
+                save_history=True,
+                examples=[
+                    ["Tell me about the 'requests' package."],
+                    ["What is the latest version of 'numpy'?"],
+                    ["Which version of 'pandas' is compatible with 'numpy' 2.0?"],
+                ],
             )
             demo.launch()
 

config.py

@@ -20,3 +20,6 @@ GITHUB_TOOLSETS = os.getenv("GITHUB_TOOLSETS", "repos")
 GRADIO_SERVER_NAME = os.getenv("GRADIO_SERVER_NAME", "0.0.0.0")
 GRADIO_SERVER_PORT = os.getenv("GRADIO_SERVER_PORT", "7860")
 GRADIO_SHARE = os.getenv("GRADIO_SHARE", "False")
+
+CHAT_HISTORY_TURNS_CUTOFF = int(os.getenv("CHAT_HISTORY_TURNS_CUTOFF", "10"))
+CHAT_HISTORY_WORD_CUTOFF = int(os.getenv("CHAT_HISTORY_WORD_CUTOFF", "100"))

poetry.lock

@@ -252,6 +252,47 @@ files = [
     {file = "brotli-1.2.0.tar.gz", hash = "sha256:e310f77e41941c13340a95976fe66a8a95b01e783d430eeaf7a2f87e0a57dd0a"},
 ]
 
+[[package]]
+name = "brotlicffi"
+version = "1.1.0.0"
+description = "Python CFFI bindings to the Brotli library"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+markers = "platform_python_implementation != \"CPython\""
+files = [
+    {file = "brotlicffi-1.1.0.0-cp37-abi3-macosx_10_9_x86_64.whl", hash = "sha256:9b7ae6bd1a3f0df532b6d67ff674099a96d22bc0948955cb338488c31bfb8851"},
+    {file = "brotlicffi-1.1.0.0-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:19ffc919fa4fc6ace69286e0a23b3789b4219058313cf9b45625016bf7ff996b"},
+    {file = "brotlicffi-1.1.0.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9feb210d932ffe7798ee62e6145d3a757eb6233aa9a4e7db78dd3690d7755814"},
+    {file = "brotlicffi-1.1.0.0-cp37-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:84763dbdef5dd5c24b75597a77e1b30c66604725707565188ba54bab4f114820"},
+    {file = "brotlicffi-1.1.0.0-cp37-abi3-win32.whl", hash = "sha256:1b12b50e07c3911e1efa3a8971543e7648100713d4e0971b13631cce22c587eb"},
+    {file = "brotlicffi-1.1.0.0-cp37-abi3-win_amd64.whl", hash = "sha256:994a4f0681bb6c6c3b0925530a1926b7a189d878e6e5e38fae8efa47c5d9c613"},
+    {file = "brotlicffi-1.1.0.0-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:2e4aeb0bd2540cb91b069dbdd54d458da8c4334ceaf2d25df2f4af576d6766ca"},
+    {file = "brotlicffi-1.1.0.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4b7b0033b0d37bb33009fb2fef73310e432e76f688af76c156b3594389d81391"},
+    {file = "brotlicffi-1.1.0.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:54a07bb2374a1eba8ebb52b6fafffa2afd3c4df85ddd38fcc0511f2bb387c2a8"},
+    {file = "brotlicffi-1.1.0.0-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7901a7dc4b88f1c1475de59ae9be59799db1007b7d059817948d8e4f12e24e35"},
+    {file = "brotlicffi-1.1.0.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:ce01c7316aebc7fce59da734286148b1d1b9455f89cf2c8a4dfce7d41db55c2d"},
+    {file = "brotlicffi-1.1.0.0-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:246f1d1a90279bb6069de3de8d75a8856e073b8ff0b09dcca18ccc14cec85979"},
+    {file = "brotlicffi-1.1.0.0-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cc4bc5d82bc56ebd8b514fb8350cfac4627d6b0743382e46d033976a5f80fab6"},
+    {file = "brotlicffi-1.1.0.0-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:37c26ecb14386a44b118ce36e546ce307f4810bc9598a6e6cb4f7fca725ae7e6"},
+    {file = "brotlicffi-1.1.0.0-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ca72968ae4eaf6470498d5c2887073f7efe3b1e7d7ec8be11a06a79cc810e990"},
+    {file = "brotlicffi-1.1.0.0-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:add0de5b9ad9e9aa293c3aa4e9deb2b61e99ad6c1634e01d01d98c03e6a354cc"},
+    {file = "brotlicffi-1.1.0.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:9b6068e0f3769992d6b622a1cd2e7835eae3cf8d9da123d7f51ca9c1e9c333e5"},
+    {file = "brotlicffi-1.1.0.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8557a8559509b61e65083f8782329188a250102372576093c88930c875a69838"},
+    {file = "brotlicffi-1.1.0.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2a7ae37e5d79c5bdfb5b4b99f2715a6035e6c5bf538c3746abc8e26694f92f33"},
+    {file = "brotlicffi-1.1.0.0-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:391151ec86bb1c683835980f4816272a87eaddc46bb91cbf44f62228b84d8cca"},
+    {file = "brotlicffi-1.1.0.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:2f3711be9290f0453de8eed5275d93d286abe26b08ab4a35d7452caa1fef532f"},
+    {file = "brotlicffi-1.1.0.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:1a807d760763e398bbf2c6394ae9da5815901aa93ee0a37bca5efe78d4ee3171"},
+    {file = "brotlicffi-1.1.0.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fa8ca0623b26c94fccc3a1fdd895be1743b838f3917300506d04aa3346fd2a14"},
+    {file = "brotlicffi-1.1.0.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3de0cf28a53a3238b252aca9fed1593e9d36c1d116748013339f0949bfc84112"},
+    {file = "brotlicffi-1.1.0.0-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6be5ec0e88a4925c91f3dea2bb0013b3a2accda6f77238f76a34a1ea532a1cb0"},
+    {file = "brotlicffi-1.1.0.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:d9eb71bb1085d996244439154387266fd23d6ad37161f6f52f1cd41dd95a3808"},
+    {file = "brotlicffi-1.1.0.0.tar.gz", hash = "sha256:b77827a689905143f87915310b93b273ab17888fd43ef350d4832c4a71083c13"},
+]
+
+[package.dependencies]
+cffi = ">=1.0.0"
+
 [[package]]
 name = "certifi"
 version = "2025.11.12"
@@ -271,7 +312,6 @@ description = "Foreign Function Interface for Python calling C code."
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "platform_python_implementation != \"PyPy\""
 files = [
     {file = "cffi-2.0.0-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:0cf2d91ecc3fcc0625c2c530fe004f82c110405f101548512cce44322fa8ac44"},
     {file = "cffi-2.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f73b96c41e3b2adedc34a7356e64c8eb96e03a3782b535e043a986276ce12a49"},
@@ -591,6 +631,28 @@ ssh = ["bcrypt (>=3.1.5)"]
 test = ["certifi (>=2024)", "cryptography-vectors (==46.0.3)", "pretend (>=0.7)", "pytest (>=7.4.0)", "pytest-benchmark (>=4.0)", "pytest-cov (>=2.10.1)", "pytest-xdist (>=3.5.0)"]
 test-randomorder = ["pytest-randomly"]
 
+[[package]]
+name = "ddgs"
+version = "9.9.1"
+description = "Dux Distributed Global Search. A metasearch library that aggregates results from diverse web search services."
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "ddgs-9.9.1-py3-none-any.whl", hash = "sha256:bba5701462bc9c8eac69fdd2edf7e833e74741b2bae3593489e470e125e9601d"},
+    {file = "ddgs-9.9.1.tar.gz", hash = "sha256:d33529abb56aa5b98eb436565a6bfa48f07eb04dca18d323df723b83cb007e4f"},
+]
+
+[package.dependencies]
+click = ">=8.1.8"
+fake-useragent = ">=2.2.0"
+httpx = {version = ">=0.28.1", extras = ["brotli", "http2", "socks"]}
+lxml = ">=4.9.4"
+primp = ">=0.15.0"
+
+[package.extras]
+dev = ["lxml-stubs", "mypy (>=1.17.1)", "pre-commit", "pytest (>=8.4.1)", "pytest-dependency (>=0.6.0)", "ruff (>=0.13.0)", "types-Pygments", "types-pexpect"]
+
 [[package]]
 name = "exceptiongroup"
 version = "1.3.0"
@@ -610,6 +672,18 @@ typing-extensions = {version = ">=4.6.0", markers = "python_version < \"3.13\""}
 [package.extras]
 test = ["pytest (>=6)"]
 
+[[package]]
+name = "fake-useragent"
+version = "2.2.0"
+description = "Up-to-date simple useragent faker with real world database"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "fake_useragent-2.2.0-py3-none-any.whl", hash = "sha256:67f35ca4d847b0d298187443aaf020413746e56acd985a611908c73dba2daa24"},
+    {file = "fake_useragent-2.2.0.tar.gz", hash = "sha256:4e6ab6571e40cc086d788523cf9e018f618d07f9050f822ff409a4dfe17c16b2"},
+]
+
 [[package]]
 name = "fastapi"
 version = "0.121.2"
@@ -794,6 +868,22 @@ files = [
     {file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"},
 ]
 
+[[package]]
+name = "h2"
+version = "4.3.0"
+description = "Pure-Python HTTP/2 protocol implementation"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "h2-4.3.0-py3-none-any.whl", hash = "sha256:c438f029a25f7945c69e0ccf0fb951dc3f73a5f6412981daee861431b70e2bdd"},
+    {file = "h2-4.3.0.tar.gz", hash = "sha256:6c59efe4323fa18b47a632221a1888bd7fde6249819beda254aeca909f221bf1"},
+]
+
+[package.dependencies]
+hpack = ">=4.1,<5"
+hyperframe = ">=6.1,<7"
+
 [[package]]
 name = "hf-xet"
 version = "1.2.0"
@@ -830,6 +920,18 @@ files = [
 [package.extras]
 tests = ["pytest"]
 
+[[package]]
+name = "hpack"
+version = "4.1.0"
+description = "Pure-Python HPACK header encoding"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "hpack-4.1.0-py3-none-any.whl", hash = "sha256:157ac792668d995c657d93111f46b4535ed114f0c9c8d672271bbec7eae1b496"},
+    {file = "hpack-4.1.0.tar.gz", hash = "sha256:ec5eca154f7056aa06f196a557655c5b009b382873ac8d1e66e79e87535f1dca"},
+]
+
 [[package]]
 name = "httpcore"
 version = "1.0.9"
@@ -866,9 +968,13 @@ files = [
 
 [package.dependencies]
 anyio = "*"
+brotli = {version = "*", optional = true, markers = "platform_python_implementation == \"CPython\" and extra == \"brotli\""}
+brotlicffi = {version = "*", optional = true, markers = "platform_python_implementation != \"CPython\" and extra == \"brotli\""}
 certifi = "*"
+h2 = {version = ">=3,<5", optional = true, markers = "extra == \"http2\""}
 httpcore = "==1.*"
 idna = "*"
+socksio = {version = "==1.*", optional = true, markers = "extra == \"socks\""}
 
 [package.extras]
 brotli = ["brotli ; platform_python_implementation == \"CPython\"", "brotlicffi ; platform_python_implementation != \"CPython\""]
@@ -928,6 +1034,18 @@ testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.
 torch = ["safetensors[torch]", "torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
+[[package]]
+name = "hyperframe"
+version = "6.1.0"
+description = "Pure-Python HTTP/2 framing"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "hyperframe-6.1.0-py3-none-any.whl", hash = "sha256:b03380493a519fce58ea5af42e4a42317bf9bd425596f7a0835ffce80f1a42e5"},
+    {file = "hyperframe-6.1.0.tar.gz", hash = "sha256:f630908a00854a7adeabd6382b43923a4c4cd4b821fcb527e6ab9e15382a3b08"},
+]
+
 [[package]]
 name = "idna"
 version = "3.11"
@@ -1022,6 +1140,162 @@ files = [
 [package.dependencies]
 referencing = ">=0.31.0"
 
+[[package]]
+name = "lxml"
+version = "6.0.2"
+description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "lxml-6.0.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e77dd455b9a16bbd2a5036a63ddbd479c19572af81b624e79ef422f929eef388"},
+    {file = "lxml-6.0.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5d444858b9f07cefff6455b983aea9a67f7462ba1f6cbe4a21e8bf6791bf2153"},
+    {file = "lxml-6.0.2-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f952dacaa552f3bb8834908dddd500ba7d508e6ea6eb8c52eb2d28f48ca06a31"},
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+    {file = "lxml-6.0.2-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:27220da5be049e936c3aca06f174e8827ca6445a4353a1995584311487fc4e3e"},
+    {file = "lxml-6.0.2.tar.gz", hash = "sha256:cd79f3367bd74b317dda655dc8fcfa304d9eb6e4fb06b7168c5cf27f96e0cd62"},
+]
+
+[package.extras]
+cssselect = ["cssselect (>=0.7)"]
+html-clean = ["lxml_html_clean"]
+html5 = ["html5lib"]
+htmlsoup = ["BeautifulSoup4"]
+
 [[package]]
 name = "markdown-it-py"
 version = "4.0.0"
@@ -1633,6 +1907,28 @@ files = [
 dev = ["pre-commit", "tox"]
 testing = ["coverage", "pytest", "pytest-benchmark"]
 
+[[package]]
+name = "primp"
+version = "0.15.0"
+description = "HTTP client that can impersonate web browsers, mimicking their headers and `TLS/JA3/JA4/HTTP2` fingerprints"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "primp-0.15.0-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:1b281f4ca41a0c6612d4c6e68b96e28acfe786d226a427cd944baa8d7acd644f"},
+    {file = "primp-0.15.0-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:489cbab55cd793ceb8f90bb7423c6ea64ebb53208ffcf7a044138e3c66d77299"},
+    {file = "primp-0.15.0-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c18b45c23f94016215f62d2334552224236217aaeb716871ce0e4dcfa08eb161"},
+    {file = "primp-0.15.0-cp38-abi3-manylinux_2_34_aarch64.whl", hash = "sha256:e985a9cba2e3f96a323722e5440aa9eccaac3178e74b884778e926b5249df080"},
+    {file = "primp-0.15.0-cp38-abi3-manylinux_2_34_armv7l.whl", hash = "sha256:6b84a6ffa083e34668ff0037221d399c24d939b5629cd38223af860de9e17a83"},
+    {file = "primp-0.15.0-cp38-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:592f6079646bdf5abbbfc3b0a28dac8de943f8907a250ce09398cda5eaebd260"},
+    {file = "primp-0.15.0-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:5a728e5a05f37db6189eb413d22c78bd143fa59dd6a8a26dacd43332b3971fe8"},
+    {file = "primp-0.15.0-cp38-abi3-win_amd64.whl", hash = "sha256:aeb6bd20b06dfc92cfe4436939c18de88a58c640752cf7f30d9e4ae893cdec32"},
+    {file = "primp-0.15.0.tar.gz", hash = "sha256:1af8ea4b15f57571ff7fc5e282a82c5eb69bc695e19b8ddeeda324397965b30a"},
+]
+
+[package.extras]
+dev = ["certifi", "mypy (>=1.14.1)", "pytest (>=8.1.1)", "pytest-asyncio (>=0.25.3)", "ruff (>=0.9.2)", "typing-extensions ; python_full_version < \"3.12.0\""]
+
 [[package]]
 name = "pycparser"
 version = "2.23"
@@ -1640,7 +1936,7 @@ description = "C parser in Python"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
-markers = "platform_python_implementation != \"PyPy\" and implementation_name != \"PyPy\""
+markers = "implementation_name != \"PyPy\""
 files = [
     {file = "pycparser-2.23-py3-none-any.whl", hash = "sha256:e5c6e8d3fbad53479cab09ac03729e0a9faf2bee3db8208a550daf5af81a5934"},
     {file = "pycparser-2.23.tar.gz", hash = "sha256:78816d4f24add8f10a06d6f05b4d424ad9e96cfebf68a4ddc99c65c0720d00c2"},
@@ -2501,18 +2797,18 @@ files = [
 
 [[package]]
 name = "smolagents"
-version = "1.22.0"
+version = "1.23.0"
 description = "🤗 smolagents: a barebones library for agents. Agents write python code to call tools or orchestrate other agents."
 optional = false
 python-versions = ">=3.10"
 groups = ["main"]
 files = [
-    {file = "smolagents-1.22.0-py3-none-any.whl", hash = "sha256:5334adb4e7e5814cd814f1d9ad7efa806ef57f53db40635a29d2bd727774c5f5"},
-    {file = "smolagents-1.22.0.tar.gz", hash = "sha256:5fb66f48e3b3ab5e8defcef577a89d5b6dfa8fcb55fc98a58e156cb3c59eb68f"},
+    {file = "smolagents-1.23.0-py3-none-any.whl", hash = "sha256:4851cb6a27e5adc1b916b61991d1d2a7cedf4947677d0ae92b3a9cc6d29c5528"},
+    {file = "smolagents-1.23.0.tar.gz", hash = "sha256:a8ead28a85a02a9f1bcf25016db6bdc3ba2cb456912e336fa7fabd9acf4203bc"},
 ]
 
 [package.dependencies]
-huggingface-hub = ">=0.31.2"
+huggingface-hub = ">=0.31.2,<1.0.0"
 jinja2 = ">=3.1.4"
 mcp = {version = "*", optional = true, markers = "extra == \"mcp\""}
 mcpadapt = {version = ">=0.1.13", optional = true, markers = "extra == \"mcp\""}
@@ -2522,9 +2818,10 @@ requests = ">=2.32.3"
 rich = ">=13.9.4"
 
 [package.extras]
-all = ["smolagents[audio,bedrock,docker,e2b,gradio,litellm,mcp,mlx-lm,modal,openai,telemetry,toolkit,transformers,vision]"]
+all = ["smolagents[audio,bedrock,blaxel,docker,e2b,gradio,litellm,mcp,mlx-lm,modal,openai,telemetry,toolkit,transformers,vision]"]
 audio = ["smolagents[torch]", "soundfile"]
 bedrock = ["boto3 (>=1.36.18)"]
+blaxel = ["blaxel (>=0.2.19)", "websocket-client"]
 dev = ["smolagents[quality,test]", "sqlalchemy"]
 docker = ["docker (>=7.1.0)", "websocket-client"]
 e2b = ["e2b-code-interpreter (>=1.0.3)", "python-dotenv (>=1.0.1)"]
@@ -2541,7 +2838,7 @@ toolkit = ["ddgs (>=9.0.0)", "markdownify (>=0.14.1)"]
 torch = ["numpy (>=1.21.2)", "torch", "torchvision"]
 transformers = ["accelerate", "smolagents[torch]", "transformers (>=4.0.0)"]
 vision = ["helium", "selenium"]
-vllm = ["torch", "vllm"]
+vllm = ["torch", "vllm (>=0.10.2)"]
 
 [[package]]
 name = "sniffio"
@@ -2555,6 +2852,18 @@ files = [
     {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
 ]
 
+[[package]]
+name = "socksio"
+version = "1.0.0"
+description = "Sans-I/O implementation of SOCKS4, SOCKS4A, and SOCKS5."
+optional = false
+python-versions = ">=3.6"
+groups = ["main"]
+files = [
+    {file = "socksio-1.0.0-py3-none-any.whl", hash = "sha256:95dc1f15f9b34e8d7b16f06d74b8ccf48f609af32ab33c608d08761c5dcbb1f3"},
+    {file = "socksio-1.0.0.tar.gz", hash = "sha256:f88beb3da5b5c38b9890469de67d0cb0f9d494b78b106ca1845f96c10b91c4ac"},
+]
+
 [[package]]
 name = "sse-starlette"
 version = "3.0.3"
@@ -2966,5 +3275,5 @@ files = [
 
 [metadata]
 lock-version = "2.1"
-python-versions = ">=3.10"
-content-hash = "f301d5722b2041165232c770f0524d74195b81151bf2841549067dd23ab77588"
+python-versions = ">=3.10,<3.15"
+content-hash = "cb3d331a7047ab0fb7e4a97e59ef8a75933ee86e4ac6e126fa8ec67299925aa5"

pyproject.toml

@@ -18,6 +18,7 @@ dependencies = [
     "transformers (>=4.57.1,<5.0.0)",
     "typer[all] (>=0.20.0,<0.21.0)",
     "gradio (>=5.49.1,<6.0.0)",
+    "ddgs (>=9.9.1,<10.0.0)",
 ]
 
 

src/upgrade_advisor/agents/package.py

@@ -1,14 +1,10 @@
-import ast
-import json
 import logging
-from typing import Any, Iterator, Optional
+from typing import Iterator, Optional
 
 from pydantic import BaseModel
-from smolagents import CodeAgent, ToolCall, ToolOutput
+from smolagents import CodeAgent
 from smolagents.agents import StreamEvent
 from smolagents.mcp_client import MCPClient
-from smolagents.memory import ActionStep, FinalAnswerStep, PlanningStep
-from smolagents.models import ChatMessageStreamDelta, ChatMessageToolCall
 
 from ..schema import (  # noqa
     GithubRepoSchema,
@@ -42,105 +38,6 @@ def map_tool_call_to_schema(tool_name: str) -> Optional[type[BaseModel]]:
 # See https://github.com/huggingface/smolagents/pull/1660
 
 
-def to_event_schema(evt: Any) -> Optional[StreamEvent]:
-    """Convert various event types to standardized StreamEvent schemas."""
-    if isinstance(
-        evt,
-        (
-            ChatMessageStreamDelta,
-            PlanningStep,
-            ActionStep,
-            ToolCall,
-        ),
-    ):
-        return evt
-
-    # Tool output (result from a tool execution)
-    if isinstance(evt, ToolOutput):
-        tool_call = getattr(evt, "tool_call", None)
-        tool_name = getattr(tool_call, "name", None) if tool_call is not None else None
-        schema = map_tool_call_to_schema(tool_name) if tool_name else None
-
-        # Normalize output to a Python object first
-        raw_output = getattr(evt, "output", None)
-        normalized_output = raw_output
-        """
-        IMPORTANT:
-        Thing to note is that the MCP can
-        return outputs in different formats:
-        - JSON strings (e.g., '{"key": "value"}')
-        - Python-literal strings (e.g., "{'key': 'value'}")
-        - Direct Python objects (e.g., dicts, lists)
-        We need to handle these cases to convert them into a consistent Python object.
-        """
-        if isinstance(raw_output, str):
-            # Try JSON first
-            try:
-                normalized_output = json.loads(raw_output)
-            except Exception:
-                # Fallback to Python-literal parsing (single-quoted dicts)
-                try:
-                    normalized_output = ast.literal_eval(raw_output)
-                except Exception:
-                    logger.warning(
-                        f"Failed to parse tool output string for tool '{tool_name}'. Returning raw output."
-                    )
-                    normalized_output = raw_output  # keep as-is if not parseable
-
-        # if dict or list, keep as is
-        parsed_output = normalized_output
-        if schema is not None:
-            try:
-                # Pydantic v2: model_validate; fallback to parse_obj for v1
-                if hasattr(schema, "model_validate"):
-                    parsed_output = schema.model_validate(normalized_output)
-                else:
-                    parsed_output = schema.parse_obj(normalized_output)
-                logger.info(
-                    f"Successfully parsed tool output for tool '{tool_name}' into schema '{schema.__name__}'."
-                )
-            except Exception as e:
-                logger.warning(
-                    f"Failed to parse tool output for tool '{tool_name}' into schema '{schema.__name__}': {e}. Returning normalized output."
-                )
-
-        # Return a new ToolOutput with normalized output and original tool_call
-        return ToolOutput(
-            id=getattr(evt, "id", ""),
-            output=parsed_output,
-            is_final_answer=getattr(evt, "is_final_answer", False),
-            observation=getattr(evt, "observation", None),
-            tool_call=tool_call,
-        )
-
-    # Final answer
-    if isinstance(evt, FinalAnswerStep):
-        output = getattr(evt, "output", None)
-        return FinalAnswerStep(output=output)
-
-    # ChatMessageToolCall (non-streaming tool call object sometimes yielded by models)
-    if isinstance(evt, ChatMessageToolCall):
-        func = getattr(evt, "function", None)
-        name = getattr(func, "name", None)
-        arguments = getattr(func, "arguments", None)
-        if isinstance(arguments, str):
-            try:
-                arguments = json.loads(arguments)
-            except Exception:
-                logger.warning(
-                    f"Failed to parse arguments string for tool '{name}'. Returning raw arguments."
-                )
-                pass
-        return ToolCall(
-            id=getattr(evt, "id", None),
-            name=name,
-            arguments=arguments,
-        )
-
-    # Ignore unknown event types or return None
-    return None
-
-
 class PackageDiscoveryAgent:
     """Agent that discovers metadata about Python packages using MCP tools."""
 
@@ -154,6 +51,7 @@ class PackageDiscoveryAgent:
             tools=tools,
             model=model,
             max_steps=10,
+            add_base_tools=True,
             additional_authorized_imports=[
                 "json",
                 "datetime",
@@ -166,36 +64,39 @@ class PackageDiscoveryAgent:
         )
         logger.info(f"PackageDiscoveryAgent initialized with model and tools: {tools}.")
 
-    def _discover_package_info(self, user_input: str) -> Iterator[StreamEvent]:
+    def _discover_package_info(
+        self, user_input: str, reframed_question: str = None
+    ) -> Iterator[StreamEvent]:
         """Discover package information based on user input.
 
         Yields StreamEvent items as the agent runs (planning, action, tool calls,
         tool outputs, final answer, and streaming deltas). Each yielded event
         can be normalized further by the caller if needed.
         """
-        prompt = get_package_discovery_prompt(user_input)
+        prompt = get_package_discovery_prompt(user_input, reframed_question)
         logger.info(f"Running agent with max_steps: {self.agent.max_steps}.")
         try:
-            for event in self.agent.run(
-                prompt,
-                max_steps=self.agent.max_steps,
-                stream=True,
-            ):
-                normalized = to_event_schema(event)
-                if normalized is not None:
-                    yield normalized
+            result = self.agent.run(task=prompt, max_steps=self.agent.max_steps)
+            logger.info(
+                f"Package discovery completed successfully. The return type of result: {type(result)}"
+            )
+            return result
 
         except Exception as e:
             logger.error(f"Error discovering package info: {e}")
-            yield {
+            return {
                 "name": "unknown",
                 "version": "unknown",
                 "summary": "Error occurred: " + str(e),
             }
 
-    def discover_package_info(self, user_input: str):
+    def discover_package_info(
+        self, user_input: str, reframed_question: str = None
+    ) -> Iterator[StreamEvent]:
         """Public method to start package discovery."""
-        return self._discover_package_info(user_input)
+        return self._discover_package_info(
+            user_input, reframed_question=reframed_question
+        )
 
 
 if __name__ == "__main__":  # Example usage of PackageDiscoveryAgent

src/upgrade_advisor/agents/prompts.py

@@ -8,7 +8,17 @@ from ..schema import (
 )
 
 
-def get_package_discovery_prompt(user_input: str) -> str:
+def get_package_discovery_prompt(
+    original_question: str, reframed_question: str = None
+) -> str:
+    user_input = f"""
+    USER QUESTION:
+    {original_question}
+    """
+    if reframed_question:
+        user_input += f"\nREFRAMED QUESTION (LLM-generated):\n{reframed_question}\n"
+
+    # Add the rest of the prompt content here...
     return f"""
     You are a package discovery agent that discovers metadata about
     Python PyPI packages.
@@ -61,7 +71,6 @@ def get_package_discovery_prompt(user_input: str) -> str:
       if isinstance(d, dict) and "info" in d and isinstance(d["info"], dict):
           latest = d["info"].get("version") or _extract_version_fallback(str(d))
 
-    QUESTION:
     {user_input}
 
     SCHEMA DETAILS (use these exact shapes):
@@ -85,20 +94,12 @@ def get_package_discovery_prompt(user_input: str) -> str:
     - MCP tool outputs are often structured (Python dict/list). Use them directly.
     - If you get a string result, call _to_mapping(result) BEFORE indexing like result["info"].
     - Also be careful of the types. Some fields may be optional or missing. Some fields are ints/floats.
-    - For version numbers, use the `packaging.version` module.\
-    ```python
-    from packaging.version import parse
-    # 1. Numeric versions that break string comparison
-    print(parse("1.10") > parse("1.2"))
-    # 2. Release vs pre-release
-    print(parse("1.0") > parse("1.0rc1"))
-    # 3. Stable vs beta
-    print(parse("2.0") > parse("2.0b1"))
-    # 4. Different-length segments
-    print(parse("1.2") == parse("1.2.0"))
-    # 5. Dev releases
-    print(parse("3.0.dev2") < parse("3.0"))
-    ```
+    - Always prefer MCP tool data over web search data for package metadata.
+    - However, If you use the `web_search`, you must only rely on documentation
+    from the official package website, PyPI page, or official GitHub repo.
+    - If the `web_search` tool is used, ALWAYS validate the info with MCP tool data if possible.
+    - NEVER fabricate data. If you cannot find the info, say so.
+    - For parsing version numbers, use the `packaging.version` module. 
     - Never use ast/json modules outside the helpers; import them once at the top and only call _to_mapping / _extract_version_fallback.
     - When you have gathered the required info, call final_answer with the BEST structured object
       that answers the user query according to the appropriate schema.

src/upgrade_advisor/chat/chat.py

@@ -1,13 +1,23 @@
+import logging
 import os
 
 import requests
 from dotenv import load_dotenv
 
-from .prompts import query_rewriter_prompt, result_package_summary_prompt
+from .prompts import (
+    chat_summarizer_prompt,
+    query_rewriter_prompt,
+    result_package_summary_prompt,
+)
 
 load_dotenv()
 
 
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+logger.addHandler(logging.StreamHandler())
+
+
 async def query(payload):
     API_URL = "https://router.huggingface.co/v1/chat/completions"
     headers = {
@@ -27,7 +37,7 @@ def extract_answer_content(answer_content: str) -> str:
 
 
 async def run_document_qa(
-    question: str, context: str, original_question: str = ""
+    question: str, context: str, rewritten_question: str = None
 ) -> str:
     response = await query(
         {
@@ -36,8 +46,8 @@ async def run_document_qa(
                     "role": "user",
                     "content": result_package_summary_prompt(
                         context,
-                        question,
-                        original_question,
+                        original_question=question,
+                        rewritten_question=rewritten_question,
                     ),
                 }
             ],
@@ -50,13 +60,87 @@ async def run_document_qa(
     return extract_answer_content(answer["content"])
 
 
-async def qn_rewriter(original_question: str) -> str:
+async def qn_rewriter_judge(original_question: str, rewritten_question: str) -> str:
+    response = await query(
+        {
+            "messages": [
+                {
+                    "role": "user",
+                    "content": f"""
+                    You are a judge that evaluates whether a rewritten question
+                    captures the intent of the original question.
+                    Note that the rewritten question may include details from
+                    the chat history, but you should focus on whether the core
+                    intent of the original question is preserved. The
+                    additional history context will not change the user's intent, but
+                    may add clarifications.
+                    Return the word "YES" if it does, otherwise "NO". No additional
+                    explanation. Never return anything other than "YES" or "NO".
+
+                    EXAMPLE 1:
+                    `ORIGINAL QUESTION: latest version of nnumpy?`
+                    `REWRITTEN QUESTION: What is the latest version of numpy?`
+                    Answer: YES
+
+                    EXAMPLE 2:
+                    `ORIGINAL QUESTION: Show me the dev docu link of requests
+                    liberary.`
+                    `REWRITTEN QUESTION: Show me the user guide of the requests
+                    library.`
+                    Answer: NO
+
+                    EXAMPLE 3:
+                    `ORIGINAL QUESTION: How to install fapi?`
+                    `REWRITTEN QUESTION: What is the dependency list of fastapi?`
+                    Answer: NO
+
+                    EXAMPLE 4:
+                    `ORIGINAL QUESTION: The user had trouble with a version of
+                    pandas. User tried downgrading to 1.2.0 but it didn't help
+                    and has compatibility issues with other packages like numpy
+                    of version 1.19. What version should I use?`
+                    `REWRITTEN QUESTION: Which version of pandas is most stable
+                    with version 1.19 of numpy?`
+                    Answer: YES
+
+                    EXAMPLE 5:
+                    `ORIGINAL QUESTION: The user is talking about issues with
+                    version 2.0.0 of requests library. They mentioned that it
+                    broke their existing code that worked with version numpy 1.2.3. I
+                    am looking for a version that is compatible.`
+                    `REWRITTEN QUESTION: Which version of requests is compatible
+                    with version numpy 1.2.3?`
+                    Answer: YES
+
+
+                    ORIGINAL QUESTION: {original_question}\n
+                    REWRITTEN QUESTION: {rewritten_question}\n
+                    Answer:
+                    """,
+                }
+            ],
+            # "model": "Qwen/Qwen3-4B-Thinking-2507",
+            "model": "Qwen/Qwen2.5-Coder-32B-Instruct",
+        }
+    )
+    answer = response["choices"][0]["message"]
+    answer_text = extract_answer_content(answer["content"])
+    logger.info(f"Question Rewriter judge answer: {answer_text}")
+    if answer_text.strip().upper() == "YES":
+        return True
+    else:
+        return False
+
+
+async def qn_rewriter(original_question: str, summarized_history: str = "") -> str:
     response = await query(
         {
             "messages": [
                 {
                     "role": "user",
-                    "content": query_rewriter_prompt(original_question),
+                    "content": query_rewriter_prompt(
+                        original_question, summarized_history
+                    ),
                 }
             ],
             # "model": "Qwen/Qwen3-4B-Thinking-2507",
@@ -65,4 +149,47 @@ async def qn_rewriter(original_question: str) -> str:
     )
 
     answer = response["choices"][0]["message"]
-    return extract_answer_content(answer["content"])
+    rewritten_question = extract_answer_content(answer["content"])
+    is_good = await qn_rewriter_judge(original_question, rewritten_question)
+
+    return rewritten_question, is_good
+
+
+async def summarize_chat_history(
+    history: list[dict],
+    turns_cutoff=10,
+    word_cutoff=100,
+) -> str:
+    # history is a list of dicts with 'role' and 'content' keys
+    # [{"role": "user", "content": "..."}, {"role": "assistant", "content":
+    # "..."}]
+    logger.info(f"Summarizing chat history with {len(history)} turns.")
+    logger.info(f"Using last {turns_cutoff} turns with {word_cutoff} words each.")
+    chat_history_text = ""
+    for turn in history[-turns_cutoff:]:
+        # take only the last `cutoff` turns
+        role = turn["role"]
+        content = turn["content"]
+        if len(content.split()) > word_cutoff:
+            content = " ".join(content.split()[:word_cutoff]) + " [TRUNCATED]"
+        chat_history_text += f"{role.upper()}:\n{content}\n\n"
+
+    logger.info(
+        f"Chat history text for summarization ({len(chat_history_text.split())} words)"
+    )
+    response = await query(
+        {
+            "messages": [
+                {
+                    "role": "user",
+                    "content": chat_summarizer_prompt(chat_history_text),
+                }
+            ],
+            # "model": "Qwen/Qwen3-4B-Thinking-2507",
+            "model": "Qwen/Qwen2.5-Coder-32B-Instruct",
+        }
+    )
+
+    answer = response["choices"][0]["message"]
+    summary = extract_answer_content(answer["content"])
+    return summary

src/upgrade_advisor/chat/prompts.py

@@ -1,29 +1,48 @@
-def result_package_summary_prompt(context, question, original_question="") -> str:
+def result_package_summary_prompt(
+    context, original_question, rewritten_question=None
+) -> str:
+    user_input = f"""
+    DEVELOPER QUESTION:
+    {original_question}
+    """
+    if rewritten_question:
+        user_input += f"\nREWRITTEN QUESTION (LLM-generated):\n{rewritten_question}\n"
+
     return f"""Based on the following context from a package search, provide a
     concise summary of the key findings, issues, and recommendations. Focus on
-    the most critical points that would help a developer understand the
+    the main points that would help a developer understand the
     package and answers their question. Focus on the DEVELOPER QUESTION
-    provided. LLM REWRITTEN QUESTION is provided for clarity, but the original
-    intent should be prioritized.
+    provided. LLM REWRITTEN QUESTION is provided for clarity and to help you
+    better understand the intent, but always prioritize the original question.
+
     CONTEXT:
     {context}
-    DEVELOPER QUESTION:
-    {original_question}
-    LLM REWRITTEN QUESTION:
-    {question}
+
+    {user_input}
 
     SUMMARY:
     """
 
 
-def query_rewriter_prompt(original_question: str) -> str:
+def query_rewriter_prompt(original_question: str, summarized_history: str = "") -> str:
+    if not summarized_history:
+        summarized_history = "<NO PRIOR CHAT HISTORY>"
+    # construct the prompt
     return f"""
     You are a query rewriting agent that reformulates user questions
     about Python packages to be more specific and clear.
-    You also aim to remove any typos in the text. You focus the
+    You also aim to remove any typos in the text. 
+    You focus the
     typos made about the package metadata,
     versioning, repository or website URLs.
-    This will help downstream agents provide more accurate and relevant answers.
+    This will help downstream agents provide more accurate and relevant
+    answers.
+
+    ABOUT USING CHAT HISTORY:
+    Use the summarized chat history to provide context for rewriting.
+    Maybe the user has already asked related questions and you can use that
+    context to improve the question. If the history is not relevant, just focus
+    on improving the question itself.
 
     VERY IMPORTANT:
     - NEVER introduce new information that was not in the original question.
@@ -38,5 +57,30 @@ def query_rewriter_prompt(original_question: str) -> str:
     ORIGINAL QUESTION:
     {original_question}
 
+    SUMMARIZED CHAT HISTORY:
+    {summarized_history}
+
     REWRITTEN QUESTION:
     """
+
+
+def chat_summarizer_prompt(chat_history: str) -> str:
+    return f"""
+    You are a technical chat history summarization agent that condenses a conversation
+    between a user and an assistant about Python packages into a concise summary.
+    The summary should capture the main topics discussed, questions asked,
+    and any important context that would help understand the user's intent.
+
+    IMPORTANT GUIDELINES:
+    - Focus on specific packages, versions, issues discussed and any technical details.
+    - Emphasize any problems or challenges the user mentioned.
+    - Capture the user's goals or what they are trying to achieve.
+    - Omit any pleasantries or small talk; just focus on the technical content.
+    - Use bullet points or short sentences for clarity.
+    - Keep it brief and to the point, ideally under 100 words.
+
+    CHAT HISTORY:
+    {chat_history}
+
+    SUMMARY:
+    """